diff --git a/spaces/1368565466ki/ZSTRD/utils.py b/spaces/1368565466ki/ZSTRD/utils.py
deleted file mode 100644
index ee4b01ddfbe8173965371b29f770f3e87615fe71..0000000000000000000000000000000000000000
--- a/spaces/1368565466ki/ZSTRD/utils.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-import librosa
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-  assert os.path.isfile(checkpoint_path)
-  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-  iteration = checkpoint_dict['iteration']
-  learning_rate = checkpoint_dict['learning_rate']
-  if optimizer is not None:
-    optimizer.load_state_dict(checkpoint_dict['optimizer'])
-  saved_state_dict = checkpoint_dict['model']
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-  new_state_dict= {}
-  for k, v in state_dict.items():
-    try:
-      new_state_dict[k] = saved_state_dict[k]
-    except:
-      logger.info("%s is not in the checkpoint" % k)
-      new_state_dict[k] = v
-  if hasattr(model, 'module'):
-    model.module.load_state_dict(new_state_dict)
-  else:
-    model.load_state_dict(new_state_dict)
-  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
-    checkpoint_path, iteration))
-  return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(10,2))
-  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                  interpolation='none')
-  plt.colorbar(im, ax=ax)
-  plt.xlabel("Frames")
-  plt.ylabel("Channels")
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(6, 4))
-  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                  interpolation='none')
-  fig.colorbar(im, ax=ax)
-  xlabel = 'Decoder timestep'
-  if info is not None:
-      xlabel += '\n\n' + info
-  plt.xlabel(xlabel)
-  plt.ylabel('Encoder timestep')
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
-  audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
-  return torch.FloatTensor(audio.astype(np.float32))
-
-
-def load_filepaths_and_text(filename, split="|"):
-  with open(filename, encoding='utf-8') as f:
-    filepaths_and_text = [line.strip().split(split) for line in f]
-  return filepaths_and_text
-
-
-def get_hparams(init=True):
-  parser = argparse.ArgumentParser()
-  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                      help='JSON file for configuration')
-  parser.add_argument('-m', '--model', type=str, required=True,
-                      help='Model name')
-
-  args = parser.parse_args()
-  model_dir = os.path.join("./logs", args.model)
-
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-
-  config_path = args.config
-  config_save_path = os.path.join(model_dir, "config.json")
-  if init:
-    with open(config_path, "r") as f:
-      data = f.read()
-    with open(config_save_path, "w") as f:
-      f.write(data)
-  else:
-    with open(config_save_path, "r") as f:
-      data = f.read()
-  config = json.loads(data)
-
-  hparams = HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_dir(model_dir):
-  config_save_path = os.path.join(model_dir, "config.json")
-  with open(config_save_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_file(config_path):
-  with open(config_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  return hparams
-
-
-def check_git_hash(model_dir):
-  source_dir = os.path.dirname(os.path.realpath(__file__))
-  if not os.path.exists(os.path.join(source_dir, ".git")):
-    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-      source_dir
-    ))
-    return
-
-  cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-  path = os.path.join(model_dir, "githash")
-  if os.path.exists(path):
-    saved_hash = open(path).read()
-    if saved_hash != cur_hash:
-      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-        saved_hash[:8], cur_hash[:8]))
-  else:
-    open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-  global logger
-  logger = logging.getLogger(os.path.basename(model_dir))
-  logger.setLevel(logging.DEBUG)
-
-  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-  h = logging.FileHandler(os.path.join(model_dir, filename))
-  h.setLevel(logging.DEBUG)
-  h.setFormatter(formatter)
-  logger.addHandler(h)
-  return logger
-
-
-class HParams():
-  def __init__(self, **kwargs):
-    for k, v in kwargs.items():
-      if type(v) == dict:
-        v = HParams(**v)
-      self[k] = v
-
-  def keys(self):
-    return self.__dict__.keys()
-
-  def items(self):
-    return self.__dict__.items()
-
-  def values(self):
-    return self.__dict__.values()
-
-  def __len__(self):
-    return len(self.__dict__)
-
-  def __getitem__(self, key):
-    return getattr(self, key)
-
-  def __setitem__(self, key, value):
-    return setattr(self, key, value)
-
-  def __contains__(self, key):
-    return key in self.__dict__
-
-  def __repr__(self):
-    return self.__dict__.__repr__()
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/3D Home Design Deluxe 6.exe Utorrent PATCHED.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/3D Home Design Deluxe 6.exe Utorrent PATCHED.md
deleted file mode 100644
index 6c0faa923809c06e8170956004885190e8f834d1..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/3D Home Design Deluxe 6.exe Utorrent PATCHED.md	
+++ /dev/null
@@ -1,41 +0,0 @@
-<br />
-<h1>3D Home Design Deluxe 6: A Powerful and Easy-to-Use Tool for Creating Your Dream Home</h1>
- <p>Do you have a vision of how you want your dream home to look like? Do you want to design and decorate your own home without hiring an expensive architect or contractor? Do you want to have fun and unleash your creativity while planning your home project?</p>
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- <p>In this article, we will show you what <strong>3D Home Design Deluxe 6</strong> can do for you, why you should use it for your home design project, and how you can download it from <strong>utorrent</strong>, a popular peer-to-peer file-sharing platform. Read on to find out more.</p>
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- <h3>Design and Decorate Your Home in 3D</h3>
- <p>One of the main features of <strong>3D Home Design Deluxe 6</strong> is that it allows you to design and decorate your home in 3D with realistic results. You can use the intuitive interface and tools to create floor plans, walls, roofs, windows, doors, and other architectural elements. You can also add furniture, appliances, lighting, colors, textures, and other decorative items to customize your home according to your taste and style. You can view your home in different perspectives and renderings, such as top view, side view, front view, perspective view, wireframe view, and photo-realistic view. You can also walk through your home in 3D and see how it looks from different angles and distances.</p>
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- <p>A third feature of <strong>3D Home Design Deluxe 6</strong> is that it provides you with thousands of pre-designed plans and templates for various styles and sizes of homes. You can access the library of over 2,000 home plans and templates that cover different categories, such as country, contemporary, colonial, Mediterranean, ranch, cottage, and more. You can modify and customize the existing plans to suit your needs and preferences. You can also browse the online gallery of user-submitted designs for more ideas and inspiration.</p>
- <h2>Tips and Tricks for Using 3D Home Design Deluxe 6 Effectively</h2>
- <p><strong>3D Home Design Deluxe 6</strong> is a software that is easy to use and learn. However, there are some tips and tricks that can help you use it more effectively and efficiently. Here are some of them:</p>
-<p></p>
- <h3>Use the Help Menu and Tutorials for Guidance and Support</h3>
- <p>If you ever get stuck or need help with any feature or function of <strong>3D Home Design Deluxe 6</strong>, you can always use the help menu and tutorials for guidance and support. You can access the comprehensive help menu and online user manual for answers and instructions on how to use the software. You can also follow the step-by-step tutorials and videos for learning the basics and advanced features of the software. You can also contact the customer service and technical support for assistance and feedback.</p>
- <h3>Use the Undo/Redo and Save/Backup Functions Frequently</h3>
- <p>When you are designing your home in <strong>3D Home Design Deluxe 6</strong>, you may make mistakes or want to experiment with different options. That is why you should use the undo/redo buttons frequently to correct mistakes or try different alternatives. You should also save your work regularly and backup your files to avoid losing data. You can use the save/backup functions to save your files in different formats or locations. You can also restore your files from backup in case of corruption or deletion.</p>
- <h3>Use the Snap, Align, and Grid Functions for Precision and Accuracy</h3>
- <p>When you are designing your home in <strong>3D Home Design Deluxe 6</strong>, you may want to achieve precision and accuracy in your measurements and alignments. That is why you should use the snap, align, and grid functions for this purpose. You can use the snap function to align objects with each other or with reference points, such as the center, the edge, or the corner of the screen. You can use the align function to distribute objects evenly or symmetrically, such as horizontally, vertically, or diagonally. You can use the grid function to measure distances and angles accurately, such as in inches, feet, meters, or degrees.</p>
- <h2>Conclusion</h2>
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- <p>Thank you for reading this article. We hope you found it useful and informative. If you have any feedback or questions, please feel free to leave a comment below. We would love to hear from you.</p>
- <h2>Frequently Asked Questions</h2>
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- <h4>Q: Is 3D Home Design Deluxe 6 compatible with Windows 10?</h4>
-<p>A: Yes, 3D Home Design Deluxe 6 is compatible with Windows 10, as well as Windows 8, Windows 7, Windows Vista, and Windows XP.</p>
- <h4>Q: Is utorrent safe and legal to use?</h4>
-<p>A: Utorrent is safe and legal to use as long as you download files from reputable sources and scan them for viruses before opening them. However, you should be aware of the potential risks of downloading pirated or illegal content from utorrent, such as malware infection, legal action, or ethical issues.</p>
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-<p>A: You can update 3D Home Design Deluxe 6 to the latest version by visiting the official website of the software and downloading the latest patch or update file. You can then run the file and follow the instructions to install the update.</p>
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-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Game Iso Ps2 Naruto Shippuden Ultimate Ninja 3 High Compressed The Ultimate Naruto PS2 Game that You Can Download and Play in Minutes.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Game Iso Ps2 Naruto Shippuden Ultimate Ninja 3 High Compressed The Ultimate Naruto PS2 Game that You Can Download and Play in Minutes.md
deleted file mode 100644
index 55237abe4f86da06c0331299a9d21f4d8626f94b..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Game Iso Ps2 Naruto Shippuden Ultimate Ninja 3 High Compressed The Ultimate Naruto PS2 Game that You Can Download and Play in Minutes.md	
+++ /dev/null
@@ -1,129 +0,0 @@
-
-<h1>Download Game Iso Ps2 Naruto Shippuden Ultimate Ninja 3 High Compressed</h1>
-<p>If you are a fan of anime and manga, you probably know about Naruto, the ninja who dreams of becoming the Hokage, the leader of his village. Naruto has a long-running series that spans over 700 episodes and 70 volumes of manga. One of the most popular arcs in the series is Naruto Shippuden, which follows Naruto and his friends as they face new enemies and challenges in their quest to save the world.</p>
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-<p>One of the best ways to experience the Naruto Shippuden story is by playing Naruto Shippuden Ultimate Ninja 3, a video game that was released for the PlayStation 2 in 2008. This game lets you control your favorite characters from the anime and manga, and fight against other ninjas in various modes. You can also explore the hidden leaf village and interact with other characters.</p>
-<p>But how can you play this game if you don't have a PlayStation 2 console? Don't worry, there is a solution. You can download game iso ps2 naruto shippuden ultimate ninja 3 high compressed and play it on your PC or smartphone using an emulator. In this article, we will show you how to do that, and also tell you more about the features, graphics, sound, and gameplay of this amazing game. Let's get started!</p>
- <h2>Introduction</h2>
-<h3>What is Naruto Shippuden Ultimate Ninja 3?</h3>
-<p>Naruto Shippuden Ultimate Ninja 3 is a fighting game that was developed by CyberConnect2 and published by Bandai Namco Games. It is based on the Naruto Shippuden anime and manga series, which is a sequel to the original Naruto series. The game covers the events from the beginning of Naruto Shippuden up to the end of the Sasuke Retrieval arc.</p>
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-</ol>
- <h2>Features of Naruto Shippuden Ultimate Ninja 3</h2>
-<h3>Gameplay</h3>
-<p>Naruto Shippuden Ultimate Ninja 3 has four main modes of gameplay: story mode, free battle mode, ultimate contest mode, and mission mode. Each mode offers a different way to enjoy the game and its features.</p>
- <h4>Story mode</h4>
-<p>In story mode, you can follow the plot of Naruto Shippuden from episode 1 to episode 135. You can choose to play as either Naruto or Sasuke, and switch between them at certain points in the story. You can also unlock other characters as you progress through the story. You will have to fight against various enemies and bosses in different locations from the anime and manga. You will also be able to watch cutscenes that recreate some of the most memorable scenes from the series.</p>
- <h4>Free battle mode</h4>
-<p>In free battle mode, you can choose any character you have unlocked and fight against another character controlled by either the computer or another player. You can customize your character's appearance, skills, items, and support characters before each battle. You can also choose from different stages and settings for each battle. You can play in single-player mode or multiplayer mode using either split-screen or online connection.</p>
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- <h4>Ultimate contest mode</h4>
-<p>In ultimate contest mode, you can participate in a tournament that involves all the characters from Naruto Shippuden. You will have to compete against other ninjas in various challenges and mini-games to earn points and rank up. You will also be able to explore the hidden leaf village and interact with other characters from the series. You can unlock new items, costumes, accessories, and characters by completing certain tasks and missions in this mode.</p>
- <h4>Mission mode</h4>
-<p>In mission mode, you can take on various missions that are assigned by different characters from Naruto Shippuden. These missions range from simple tasks like collecting items or defeating enemies to more complex ones like solving puzzles or stealth missions. You will earn rewards such as money, items, skills, or characters by completing these missions. You can also replay any mission you have completed before to improve your score or rank.</p>
- <h3>Graphics</h3>
-<p>Naruto Shippuden Ultimate Ninja 3 has impressive graphics that capture the style and atmosphere of Naruto Shippuden. The characters are well-designed and animated with smooth movements and expressions. The stages are detailed and colorful with dynamic backgrounds and effects. The cutscenes are cinematic and realistic with high-quality voice acting and sound effects. The game also supports widescreen resolution and progressive scan for better visual quality.</p>
- <h3>Sound and music</h3>
-<p>Naruto Shippuden Ultimate Ninja 3 has excellent sound and music that enhance the gameplay experience. The game features original soundtracks from Naruto Shippuden composed by Yasuharu Takanashi. The music matches the mood and tone of each scene and situation in the game. The game also features voice acting from both Japanese and English cast members of Naruto Shippuden anime series. The voice actors deliver their lines with emotion and personality that match their characters.</p>
- <h3>Characters</h3>
-```html <h2>Tips and tricks for playing Naruto Shippuden Ultimate Ninja 3</h2>
-<p>Naruto Shippuden Ultimate Ninja 3 is a fun and exciting game that can challenge your skills and strategy. To help you enjoy the game more and improve your performance, here are some tips and tricks that you can use:</p>
- <h3>Master the chakra system</h3>
-<p>Chakra is the energy that powers your character's skills and abilities in the game. You can see your chakra gauge at the bottom of the screen. You can charge your chakra by holding the triangle button, but this will leave you vulnerable to attacks. You can also gain chakra by attacking or being attacked by your opponent. You can use your chakra to perform various actions such as:</p>
-<ul>
-<li>Using jutsu: These are special techniques that can deal damage, heal, or buff your character. You can use jutsu by pressing the circle button and a direction on the D-pad. Each character has different jutsu that require different amounts of chakra.</li>
-<li>Using ultimate jutsu: These are powerful attacks that can deal massive damage to your opponent. You can use ultimate jutsu by pressing the circle button twice when your chakra gauge is full. Each character has a unique ultimate jutsu that can trigger a cinematic cutscene.</li>
-<li>Using awakening: This is a state that boosts your character's stats and abilities for a limited time. You can use awakening by pressing the R1 button when your health gauge is low. Each character has a different awakening that can change their appearance and moveset.</li>
-</ul>
-<p>You should learn how to manage your chakra wisely and use it at the right time and situation. You should also pay attention to your opponent's chakra gauge and prevent them from using their jutsu or ultimate jutsu.</p>
- <h3>Use the substitution jutsu wisely</h3>
-<p>The substitution jutsu is a defensive technique that allows you to dodge an incoming attack and teleport behind your opponent. You can use the substitution jutsu by pressing the L2 button right before you get hit. However, you can only use this technique a limited number of times, as indicated by the blue orbs around your character's portrait. The orbs will regenerate over time or by charging your chakra.</p>
-<p>You should use the substitution jutsu sparingly and strategically. You should not waste it on minor attacks or spam it randomly. You should save it for avoiding major attacks or counterattacking your opponent. You should also watch out for your opponent's substitution jutsu and anticipate their moves.</p>
- <h3>Experiment with different characters and teams</h3>
-<p>Naruto Shippuden Ultimate Ninja 3 has a large variety of characters that you can choose from. Each character has their own strengths, weaknesses, styles, and strategies. You should try out different characters and see which ones suit your preferences and skills. You should also learn their movesets, combos, jutsus, ultimate jutsus, and awakenings.</p>
-<p>You can also choose two support characters to assist you in battle. You can call them by pressing the L1 or R1 buttons. Each support character has a different role and ability that can help you in different ways. Some support characters can attack, defend, heal, or buff you or your opponent. You should choose support characters that complement your main character and create synergy with them.</p>
- <h3>Unlock hidden content</h3>
-<p>Naruto Shippuden Ultimate Ninja 3 has a lot of hidden content that you can unlock by playing the game. Some of the content includes:</p>
-<ul>
-<li>New characters: You can unlock new characters by completing certain tasks or missions in story mode, ultimate contest mode, or mission mode. Some characters are hidden behind passwords that you can find online or in magazines.</li>
-<li>New costumes: You can unlock new costumes for your characters by completing certain tasks or missions in story mode, ultimate contest mode, or mission mode. Some costumes are also available as downloadable content (DLC).</li>
-<li>New items: You can unlock new items for your characters by completing certain tasks or missions in story mode, ultimate contest mode, or mission mode. Some items are also available as DLC.</li>
-<li>New stages: You can unlock new stages for free battle mode by completing certain tasks or missions in story mode, ultimate contest mode, or mission mode.</li>
-<li>New music: You can unlock new music for free battle mode by completing certain tasks or missions in story mode, ultimate contest mode, or mission mode.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Naruto Shippuden Ultimate Ninja 3 is a great game for fans of Naruto Shippuden anime and manga series. It offers a rich and immersive gameplay experience that lets you relive the epic story of Naruto Shippuden with stunning graphics and sound. It also features a large roster of characters that you can play as or fight against in various modes of gameplay. It also has a lot of hidden content that you can unlock by playing the game.</p>
-<p>If you want to play this game but don't have a PlayStation 2 console, you can download game iso ps2 naruto shippuden ultimate ninja 3 high compressed and play it on your PC or smartphone using an emulator. This way, you can enjoy the same quality and performance as playing on a PlayStation 2 console.</p>
-<p>We hope this article has helped you learn more about Naruto Shippuden Ultimate Ninja 3 and how to download game iso ps2 naruto shippuden ultimate ninja 3 high compressed. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Naruto Shippuden Ultimate Ninja 3:</p>
- <h4>Q: Is Naruto Shippuden Ultimate Ninja 3 compatible with PlayStation 3?</h4>
-<p>A: Yes, Naruto Shippuden Ultimate Ninja 3 is compatible with PlayStation 3 as long as it supports backward compatibility with PlayStation 2 games.</p>
- <h4>Q: How many characters are there in Naruto Shippuden Ultimate Ninja 3?</h4>
-<p>A: There are over 40 playable characters in Naruto Shippuden Ultimate Ninja 3, plus over 20 support characters.</p>
- <h4>Q: What is the difference between Naruto Shippuden Ultimate Ninja 4 and Naruto Shippuden Ultimate Ninja 5?</h4>
-<p>A: Naruto Shippuden Ultimate Ninja 4 and Naruto Shippuden Ultimate Ninja 5 are two different games that were released for PlayStation 2 in 2009 and 2010 respectively. They are both based on Naruto Shippuden anime and manga series, but they cover different arcs and events in the story. They also have different features, graphics, sound, gameplay modes, and characters.</p>
- <h4>Q: What is the best emulator to play Naruto Shippuden Ultimate Ninja 3 on PC?</h4>
-<p>A: The best emulator to play Naruto Shippuden Ultimate Ninja 3 on PC is PCSX2, which is a free and open-source emulator that can run PlayStation 2 games on Windows, Linux, and Mac OS.</p>
- <h4>Q: What is the best emulator to play Naruto Shippuden Ultimate Ninja 3 on Android?</h4>
-<p>A: The best emulator to play Naruto Shippuden Ultimate Ninja 3 on Android is DamonPS2, which is a paid emulator that can run PlayStation 2 games on Android devices with high compatibility and performance.</p>
- </p> 0a6ba089eb<br />
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-<h1>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO: A Comprehensive Guide</h1>
- <p>If you are a physics student looking for a reliable and effective resource to help you master the subject, you have come to the right place. In this article, we will introduce you to FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO, a complete and comprehensive solution manual for the sixth edition of the popular textbook Física by Wilson, Buffa and Lou. We will explain what FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is, why it is useful for physics students, how to access it online or offline, what are its main features and benefits, and how to get your copy today. By the end of this article, you will have a clear idea of how FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO can help you achieve your academic goals and excel in physics.</p>
- <h2>Introduction</h2>
- <h3>What is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h3>
- <p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is a solution manual for the sixth edition of the textbook Física by Wilson, Buffa and Lou. The textbook covers all the topics of introductory physics, such as mechanics, thermodynamics, waves, optics, electricity, magnetism, modern physics, and more. The solution manual provides detailed solutions and explanations for every exercise in the textbook, as well as additional problems and questions for practice and review. The solution manual is written by the same authors of the textbook, who are experts in physics education and research.</p>
-<h2>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO</h2><br /><p><b><b>DOWNLOAD</b> &gt; <a href="https://byltly.com/2uKyzO">https://byltly.com/2uKyzO</a></b></p><br /><br />
- <h3>Why is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO useful for physics students?</h3>
- <p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is useful for physics students because it helps them to understand the concepts and principles of physics better, to apply them to solve various problems, to check their answers and correct their mistakes, to reinforce their learning and retention, and to prepare for exams and assignments. The solution manual is designed to complement the textbook and to enhance the learning experience of the students. It follows the same structure and organization of the textbook, making it easy to use and follow. It also provides tips, hints, strategies, examples, applications, summaries, reviews, and more.</p>
- <h3>How to access FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO online or offline?</h3>
- <p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is available in both online and offline formats. You can access it online through various websites that offer free or paid downloads of PDF files  . You can also access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. You can also request a review copy from the publisher if you are an instructor or a reviewer.</p>
- <h2>Main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO</h2>
- <h3>Detailed solutions and explanations for every exercise</h3>
- <p>One of the main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it provides detailed solutions and explanations for every exercise in the textbook. The solutions show all the steps involved in solving the problems, as well as the reasoning behind them. The explanations clarify the concepts and formulas used in the solutions, as well as their physical meaning and significance. The solutions also include graphs, diagrams, tables, figures, equations, units, symbols, notations, conversions, constants, data, references, and more.</p>
- <h3>Step-by-step approach to problem-solving</h3>
- <p>Another feature of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it follows a step-by-step approach to problem-solving. The solutions are organized into four steps: given information, required information, solution plan, and solution execution. The given information lists all the known data and conditions of the problem. The required information lists all the unknown data and conditions that need to be found. The solution plan outlines the strategy or method that will be used to solve the problem. The solution execution shows how to apply the strategy or method to obtain the final answer.</p>
- <h3>Clear and concise presentation of concepts and formulas</h3>
- <p>A third feature of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it presents  the concepts  and formulas  of physics  in a clear  and concise way. The solutions use simple language  and terminology  that are easy  to understand  and follow. The formulas are derived from first principles  or fundamental laws  of physics. The formulas are also stated clearly with their names, symbols, units, variables, assumptions, limitations, conditions, and applications.</p>
- <h3>Interactive and engaging examples and applications</h3>
- <p>A fourth feature of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it provides interactive  and engaging examples  and applications  of physics. The solutions include real-world scenarios  and situations  that illustrate  the relevance  and importance  of physics  in everyday life. The solutions also include questions  and exercises  that challenge  the students  to think critically  and creatively  about physics. The solutions also encourage  the students  to explore  further topics  and concepts  related  to physics.</p>
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- <h2>Benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO</h2>
- <h3>Improve your understanding of physics principles and phenomena</h3>
- <p>One of the benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you improve your understanding of physics principles <contd...> ...and phenomena. By using  the solution manual, you can learn  how to apply  the principles  and phenomena  of physics  to solve different types  of problems. You can also learn  how to explain  the principles  and phenomena  of physics  in terms  of their physical meaning  and significance. You can also learn  how to relate  the principles  and phenomena  of physics  to other branches  of science, engineering, technology, and society.</p>
- <h3>Enhance your skills and confidence in solving physics problems</h3>
- <p>Another benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you enhance your skills and confidence in solving physics problems. By using the solution manual, you can practice solving various types of problems, ranging from simple to complex, from conceptual to numerical, from qualitative to quantitative, from basic to advanced. You can also check your answers and correct your mistakes, learn from your errors and improve your performance, compare your solutions with the standard ones and identify your strengths and weaknesses, and test your knowledge and understanding of physics concepts and formulas.</p>
- <h3>Prepare for exams and assignments with ease and efficiency</h3>
- <p>A third benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you prepare for exams and assignments with ease and efficiency. By using the solution manual, you can review the main topics and concepts of physics, revise the key formulas and equations of physics, practice solving different types of problems and questions that may appear on exams and assignments, assess your level of preparation and readiness for exams and assignments, and improve your grades and scores on exams and assignments.</p>
- <h3>Learn from the best authors and experts in the field</h3>
- <p>A fourth benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you learn from the best authors and experts in the field. The solution manual is written by Jerry D. Wilson, Anthony J. Buffa, and Bo Lou, who are renowned professors and researchers in physics education and research. They have extensive experience in teaching physics at various levels, writing physics textbooks and solution manuals, conducting physics experiments and investigations, publishing physics papers and articles, participating in physics conferences and workshops, and contributing to the advancement of physics knowledge and pedagogy.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In conclusion, FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is a comprehensive guide that can help you master physics with ease and confidence. It is a solution manual for the sixth edition of the textbook Física by Wilson, Buffa and Lou, which covers all the topics of introductory physics. It provides detailed solutions and explanations for every exercise in the textbook, as well as additional problems and questions for practice and review. It follows a step-by-step approach to problem-solving, presents the concepts and formulas of physics in a clear and concise way, and provides interactive and engaging examples and applications of physics. It helps you improve your understanding of physics principles and phenomena, enhance your skills and confidence in solving physics problems, prepare for exams and assignments with ease and efficiency, and learn from the best authors and experts in the field.</p>
- <h3>Call to action: get your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today!</h3>
- <p>If you are interested in getting your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today, you have several options to choose from. You can access it online through various websites that offer free or paid downloads of PDF files  . You can also access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. You can also request a review copy from the publisher if you are an instructor or a reviewer. No matter which option you choose, you will not regret getting your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today. It will be one of the best investments you can make for your physics education and career. So don't wait any longer, get your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today!</p>
- <h2>FAQs</h2>
- <h4>What is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is a solution manual for the sixth edition of the textbook Física by Wilson, Buffa and Lou.</p>
- <h4>Why is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO useful for physics students?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is useful for physics students because it helps them to understand the concepts and principles of physics better, to apply them to solve various problems, to check their answers and correct their mistakes, to reinforce their learning and retention, and to prepare for exams and assignments.</p>
- <h4>How to access FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO online or offline?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is available in both online and offline formats. You can access it online through various websites that offer free or paid downloads of PDF files  . You can also access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. You can also request a review copy from the publisher if you are an instructor or a reviewer.</p>
- <h4>What are the main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>The main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO are: - Detailed solutions and explanations for every exercise in the textbook, as well as additional problems and questions for practice and review. - Step-by-step approach to problem-solving, presenting the given information, required information, solution plan, and solution execution. - Clear and concise presentation of concepts and formulas of physics, stating their names, symbols, units, variables, assumptions, limitations, conditions, and applications. - Interactive and engaging examples and applications of physics, including real-world scenarios and situations, questions and exercises, tips and hints, strategies and methods, examples and applications, summaries and reviews.</p>
- <h4>What are the benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>The benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO are: - Improve your understanding of physics principles and phenomena, learning how to apply them to solve different types of problems, how to explain them in terms of their physical meaning and significance, how to relate them to other branches of science, engineering, technology, and society. - Enhance your skills and confidence in solving physics problems, practicing solving various types of problems, checking your answers <contd...> ...and correct your mistakes, learn from your errors and improve your performance, compare your solutions with the standard ones and identify your strengths and weaknesses, and test your knowledge and understanding of physics concepts and formulas.</p>
- <h3>Prepare for exams and assignments with ease and efficiency</h3>
- <p>A third benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you prepare for exams and assignments with ease and efficiency. By using the solution manual, you can review the main topics and concepts of physics, revise the key formulas and equations of physics, practice solving different types of problems and questions that may appear on exams and assignments, assess your level of preparation and readiness for exams and assignments, and improve your grades and scores on exams and assignments.</p>
- <h3>Learn from the best authors and experts in the field</h3>
- <p>A fourth benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you learn from the best authors and experts in the field. The solution manual is written by Jerry D. Wilson, Anthony J. Buffa, and Bo Lou, who are renowned professors and researchers in physics education and research. They have extensive experience in teaching physics at various levels, writing physics textbooks and solution manuals, conducting physics experiments and investigations, publishing physics papers and articles, participating in physics conferences and workshops, and contributing to the advancement of physics knowledge and pedagogy.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In conclusion, FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is a comprehensive guide that can help you master physics with ease and confidence. It is a solution manual for the sixth edition of the textbook Física by Wilson, Buffa and Lou, which covers all the topics of introductory physics. It provides detailed solutions and explanations for every exercise in the textbook, as well as additional problems and questions for practice and review. It follows a step-by-step approach to problem-solving, presents the concepts and formulas of physics in a clear and concise way, and provides interactive and engaging examples and applications of physics. It helps you improve your understanding of physics principles and phenomena, enhance your skills and confidence in solving physics problems, prepare for exams and assignments with ease and efficiency, and learn from the best authors and experts in the field.</p>
- <h3>Call to action: get your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today!</h3>
- <p>If you are interested in getting your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today, you have several options to choose from. You can access it online through various websites that offer free or paid downloads of PDF files  . You can also access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. You can also request a review copy from the publisher if you are an instructor or a reviewer. No matter which option you choose, you will not regret getting your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today. It will be one of the best investments you can make for your physics education and career. So don't wait any longer, get your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today!</p>
- <h2>FAQs</h2>
- <h4>What is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is a solution manual for the sixth edition of the textbook Física by Wilson, Buffa and Lou.</p>
- <h4>Why is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO useful for physics students?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is useful for physics students because it helps them to understand the concepts and principles of physics better, to apply them to solve various problems, to check their answers and correct their mistakes, to reinforce their learning and retention, and to prepare for exams and assignments.</p>
- <h4>How to access FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO online or offline?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is available in both online  and offline formats. You can access it online through various websites that offer free or paid downloads of PDF files  . You can also access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. You can also request a review copy from the publisher if you are an instructor or a reviewer.</p>
- <h4>What are the main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>The main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO are: - Detailed solutions  and explanations  for every exercise  in the textbook, as well as additional problems  and questions  for practice  and review. - Step-by-step approach  to problem-solving, presenting the given information, required information, solution plan, and solution execution. - Clear and concise presentation  of concepts  and formulas  of physics, stating their names, symbols, units, variables, assumptions, limitations, conditions, and applications. - Interactive and engaging examples  and applications  of physics, including real-world scenarios  and situations, questions  and exercises, tips  and hints, strategies  and methods, examples  and applications, summaries  and reviews.</p>
- <h4>What are the benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>The benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO are: - Improve your understanding  of physics principles  <contd...> ...and phenomena, learning how to apply them to solve different types of problems, how to explain them in terms of their physical meaning and significance, how to relate them to other branches of science, engineering, technology, and society.</p>
- <h3>Enhance your skills and confidence in solving physics problems</h3>
- <p>Another benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you enhance your skills and confidence in solving physics problems. By using the solution manual, you can practice solving various types of problems, ranging from simple to complex, from conceptual to numerical, from qualitative to quantitative, from basic to advanced. You can also check your answers and correct your mistakes, learn from your errors and improve your performance, compare your solutions with the standard ones and identify your strengths and weaknesses, and test your knowledge and understanding of physics concepts and formulas.</p>
- <h3>Prepare for exams and assignments with ease and efficiency</h3>
- <p>A third benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you prepare for exams and assignments with ease and efficiency. By using the solution manual, you can review the main topics and concepts of physics, revise the key formulas and equations of physics, practice solving different types of problems and questions that may appear on exams and assignments, assess your level of preparation and readiness for exams and assignments, and improve your grades and scores on exams and assignments.</p>
- <h3>Learn from the best authors and experts in the field</h3>
- <p>A fourth benefit of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is that it helps you learn from the best authors and experts in the field. The solution manual is written by Jerry D. Wilson, Anthony J. Buffa, and Bo Lou, who are renowned professors and researchers in physics education and research. They have extensive experience in teaching physics at various levels, writing physics textbooks and solution manuals, conducting physics experiments and investigations, publishing physics papers and articles, participating in physics conferences and workshops, and contributing to the advancement of physics knowledge and pedagogy.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In conclusion, FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is a comprehensive guide that can help you master physics with ease and confidence. It is a solution manual for the sixth edition of the textbook Física by Wilson, Buffa and Lou, which covers all the topics of introductory physics. It provides detailed solutions and explanations for every exercise in the textbook, as well as additional problems and questions for practice and review. It follows a step-by-step approach to problem-solving, presents the concepts and formulas of physics in a clear and concise way, and provides interactive and engaging examples and applications of physics. It helps you improve your understanding of physics principles and phenomena, enhance your skills and confidence in solving physics problems, prepare for exams and assignments with ease and efficiency, and learn from the best authors and experts in the field.</p>
- <h3>Call to action: get your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today!</h3>
- <p>If you are interested in getting your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today, you have several options to choose from. You can access it online through various websites that offer free or paid downloads of PDF files  . You can also access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. You can also request a review copy from the publisher if you are an instructor or a reviewer. No matter which option you choose, you will not regret getting your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today. It will be one of the best investments you can make for your physics education and career. So don't wait any longer, get your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today!</p>
- <h2>FAQs</h2>
- <h4>What is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is a solution manual for the sixth edition of the textbook Física by Wilson, Buffa and Lou.</p>
- <h4>Why is FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO useful for physics students?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is useful for physics students because it helps them to understand the concepts  and principles  of physics better, to apply them  to solve various problems, to check their answers  and correct their mistakes, to reinforce their learning  and retention, and to prepare for exams  and assignments.</p>
- <h4>How to access FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO online or offline?</h4>
-<p>FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO is available in both online  and offline formats. You can access it online through various websites that offer free or paid downloads of PDF files  . You can also access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. You can also request a review copy from the publisher if you are an instructor or a reviewer.</p>
- <h4>What are the main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>The main features of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO are: - Detailed solutions  and explanations  for every exercise  in the textbook, as well as additional problems  and questions  for practice  and review. - Step-by-step approach  to problem-solving, presenting the given information, required information, solution plan, and solution execution. - Clear and concise presentation  of concepts  and formulas  of physics, stating their names, symbols, units, variables, assumptions, limitations, conditions, and applications. - Interactive <contd...> ...and engaging examples  and applications  of physics, including real-world scenarios  and situations, questions  and exercises, tips  and hints, strategies  and methods, examples  and applications, summaries  and reviews.</p>
- <h4>What are the benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO?</h4>
-<p>The benefits of using FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO are: - Improve your understanding  of physics principles  and phenomena, learning how to apply them to solve different types of problems, how to explain them in terms of their physical meaning and significance, how to relate them to other branches of science, engineering, technology, and society. - Enhance your skills  and confidence  in solving physics problems, practicing solving various types of problems, checking your answers and correct your mistakes, learning from your errors and improving your performance, comparing your solutions with the standard ones and identifying your strengths and weaknesses, and testing your knowledge and understanding of physics concepts and formulas. - Prepare for exams  and assignments  with ease  and efficiency, reviewing the main topics and concepts of physics, revising the key formulas and equations of physics, practicing solving different types of problems and questions that may appear on exams and assignments, assessing your level of preparation and readiness for exams and assignments, and improving your grades and scores on exams and assignments. - Learn from the best authors  and experts  in the field, who are renowned professors and researchers in physics education and research. They have extensive experience in teaching physics at various levels, writing physics textbooks and solution manuals, conducting physics experiments and investigations, publishing physics papers and articles, participating in physics conferences and workshops, and contributing to the advancement of physics knowledge and pedagogy.</p>
- <h4>How to get your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today?</h4>
-<p>You can get your copy of FISICA WILSON BUFFA LOU SEXTA EDICION SOLUCIONARIO today by choosing one of the following options: - Access it online through various websites that offer free or paid downloads of PDF files  . - Access it offline by purchasing a hard copy or a CD-ROM from authorized sellers or distributors. - Request a review copy from the publisher if you are an instructor or a reviewer.</p>
- </p> 0a6ba089eb<br />
-<br />
-<br />
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-<br />
-<h1>HACK QUAD Registry Cleaner V1.5.69 Portable</h1>
-<ul>
-<li>Introduction <ul>
-<li>What is HACK QUAD Registry Cleaner V1.5.69 Portable and what does it do?</li>
-<li>Why do you need a registry cleaner and what are the benefits of using HACK QUAD Registry Cleaner V1.5.69 Portable?</li>
-</ul>
-</li>
-<li>Features of HACK QUAD Registry Cleaner V1.5.69 Portable <ul>
-<li>High-performance scan</li>
-<li>System Optimizer</li>
-<li>Internet Explorer Manager</li>
-<li>Automatic/manual removal</li>
-<li>3 Mode Back-up registry</li>
-<li>Startup Programs Manager</li>
-<li>Checks Recently Used Files</li>
-<li>Deletes Empty Registry Keys</li>
-<li>Scan Scheduler</li>
-<li>Checks Invalid Application Paths</li>
-<li>Checks Invalid Class Keys</li>
-<li>Checks System Service</li>
-<li>Organizes Windows Startup Items</li>
-<li>Auto Updated</li>
-<li>Checks Invalid ActiveX, OLE, COM</li>
-<li>Checks Uninstall Sections</li>
-<li>Checks Invalid Shared known DLL's</li>
-<li>Checks Invalid Startup Programs</li>
-<li>Checks Invalid Shortcuts</li>
-<li>Checks Invalid File Associations</li>
-<li>Add/remove Program Manager</li>
-<li>Memory Tweak</li>
-</ul>
-</li>
-<li>Installation Instructions <ul>
-<li>How to download and install HACK QUAD Registry Cleaner V1.5.69 Portable on your PC?</li>
-</ul>
-</li>
-<li>Conclusion <ul>
-<li>Summarize the main points of the article and provide a call to action for the readers.</li>
-</ul>
-</li>
-<li>FAQs <ul>
-<li>What are some common questions and answers about HACK QUAD Registry Cleaner V1.5.69 Portable?</li>
-</ul>
-</li>
- Now, based on this outline, here is the article I will write: <h1><strong>HACK QUAD Registry Cleaner V1.5.69 Portable</strong></h1>
- <p>If you are looking for a powerful and easy-to-use tool to speed up and boost your PC performance, you might want to check out HACK QUAD Registry Cleaner V1.5.69 Portable. This is a software that scans your system registry and restores your computer's performance by removing invalid entries, obsolete shortcuts, partial programs, corrupt files and pathways that can cause errors and crashes.</p>
-<h2>HACK QUAD Registry Cleaner V1.5.69 Portable</h2><br /><p><b><b>Download Zip</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://byltly.com/2uKzfV">https://byltly.com/2uKzfV</a></b></p><br /><br />
- <p>In this article, we will explain what HACK QUAD Registry Cleaner V1.5.69 Portable is and what it does, why you need a registry cleaner and what are the benefits of using HACK QUAD Registry Cleaner V1.5.69 Portable, what are the features of HACK QUAD Registry Cleaner V1.5.69 Portable, how to download and install HACK QUAD Registry Cleaner V1.5.69 Portable on your PC, and some FAQs about HACK QUAD Registry Cleaner V1.5.69 Portable.</p>
- <h2><strong>What is HACK QUAD Registry Cleaner V1.5.69 Portable and what does it do?</strong></h2>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable is a software that scans your system registry and restores your computer's performance by removing invalid entries, obsolete shortcuts, partial programs, corrupt files and pathways that can cause errors and crashes.</p>
- <p>The system registry is a database that stores information about your hardware, software, settings and preferences on your PC. It is constantly accessed by Windows and other applications to run smoothly and efficiently.</p>
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- <p>However, over time, the registry can become cluttered with unnecessary or outdated data that can slow down your PC or cause various problems such as freezing, blue screens, error messages, startup failures, etc.</p>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable helps you clean up your registry by detecting and deleting all these unwanted items with a high-performance detection algorithm that uses the latest technology to spot all PC errors.</p>
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- <p>HACK QUAD Registry Cleaner V1.5.69 Portable has an intuitive interface that facilitates control of all its scanning and cleaning functions.</p>
- <p>It's fast, simple and easy to use.</p>
- <h2><strong>Why do you need a registry cleaner and what are the benefits of using HACK QUAD Registry Cleaner V1.5.69 Portable?</strong></h2>
- <p>You need a registry cleaner because your system registry can get corrupted or bloated over time due to various reasons such as installing or uninstalling programs, changing settings, updating drivers, browsing the internet, etc.</p>
- <p>These actions can leave behind traces of data that are no longer needed or valid in the registry.</p>
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- <p>Some of the benefits of using HACK QUAD Registry Cleaner V1.5.69 Portable as your registry cleaner are:</p>
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-<li><strong><em>It protects your privacy by deleting traces of your online activity that are stored in the registry.</em></strong></li>
-<li><strong><em>It saves you time and money by avoiding the need for expensive upgrades or repairs.</em></strong></li>
-<li><strong><em>It enhances your user experience by making your PC run smoother and more stable.</em></strong></li>
-</ul>
- <h2><strong>Features of HACK QUAD Registry Cleaner V1.5.69 Portable</strong></h2>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable has many features that make it a powerful and versatile registry cleaner. Here are some of them:</p>
- <h3><strong>High-performance scan</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable uses a high-performance detection algorithm that analyses registry entries with the latest technology to spot all PC errors. It can scan your entire registry or specific portions of it according to your preferences. It can also scan your registry on startup or on a scheduled basis.</p>
- <h3><strong>System Optimizer</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable has a system optimizer feature that can improve your PC performance by tweaking various settings and options. It can optimize your memory usage, disk space, internet speed, startup speed, etc.</p>
- <h3><strong>Internet Explorer Manager</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable has an internet explorer manager feature that can help you manage your internet explorer settings and preferences. It can delete your browsing history, cookies, cache, etc. It can also restore your default homepage, search engine, etc.</p>
- <h3><strong>Automatic/manual removal</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable gives you the option to remove the detected registry errors automatically or manually. You can view the scan results and selectively clean each item or automatically repair them all. You can also undo any changes if you are not satisfied with the results.</p>
- <h3><strong>3 Mode Back-up registry</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable performs automatic backups of all registry files and recorded repairs so you will always have a second copy of your data. It has 3 modes of backup: full backup, partial backup and custom backup. You can choose the mode that suits your needs and restore your registry if needed.</p>
- <h3><strong>Startup Programs Manager</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable has a startup programs manager feature that can help you manage the programs that run on your PC startup. You can enable or disable any program, add or remove any program, or edit any program's properties.</p>
- <h3><strong>Checks Recently Used Files</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable checks the recently used files section of your registry and deletes any obsolete or invalid entries that can slow down your PC or cause errors.</p>
- <h3><strong>Deletes Empty Registry Keys</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable deletes any empty registry keys that are left behind by uninstalled programs or deleted files and folders. These keys can take up space in your registry and affect your PC performance.</p>
- <h3><strong>Scan Scheduler</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable has a scan scheduler feature that allows you to schedule scans on a daily, weekly or monthly basis. You can also set the time and date of the scans and choose which portions of the registry to scan.</p>
- <h3><strong>Checks Invalid Application Paths</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable checks the application paths section of your registry and deletes any invalid entries that can cause problems when launching programs or opening files.</p>
- <h3><strong>Checks Invalid Class Keys</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable checks the class keys section of your registry and deletes any invalid entries that can cause conflicts or errors with other applications or devices.</p>
- <h3><strong>Checks System Service</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable checks the system service section of your registry and deletes any invalid entries that can cause system instability or security issues.</p>
- <h3><strong>Organizes Windows Startup Items</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable organizes the windows startup items section of your registry and deletes any unnecessary or harmful items that can slow down your PC startup or cause problems during operation.</p>
- <h3><strong>Auto Updated</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable has an auto updated feature that keeps it updated with the latest technology and database to ensure optimal performance and accuracy.</p>
- <h3><strong>Checks Invalid ActiveX, OLE, COM</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable checks the ActiveX, OLE, COM section of your registry and deletes any invalid entries that can cause errors or crashes when using web browsers or other applications.</p>
- <h3><strong>Checks Uninstall Sections</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable checks the uninstall sections of your registry and deletes any invalid entries that can cause problems when uninstalling programs or freeing up disk space.</p>
- <h3><strong>Checks Invalid Shared known DLL's</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable checks the shared known DLL's section of your registry and deletes any invalid entries that can cause conflicts or errors with other applications or devices.</p>
- <h3><strong>Checks Invalid Startup Programs</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable checks the startup programs section of your registry and deletes any invalid entries that can slow down your PC startup or cause problems during operation.</p>
- <h3><strong>Checks Invalid Shortcuts</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable checks the shortcuts section of your registry and deletes any invalid entries that can cause errors when opening files or folders.</p>
- <h3><strong>Checks Invalid File Associations</strong></h3> OK, here is the rest of the article: <p>HACK QUAD Registry Cleaner V1.5.69 Portable checks the file associations section of your registry and deletes any invalid entries that can cause problems when opening files with different programs.</p>
- <h3><strong>Add/remove Program Manager</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable has an add/remove program manager feature that can help you manage the programs that are installed on your PC. You can view the list of installed programs, uninstall any program, or modify any program's settings.</p>
- <h3><strong>Memory Tweak</strong></h3>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable has a memory tweak feature that can improve your memory usage and performance by optimizing various settings and options. It can free up memory, defragment memory, adjust cache size, etc.</p>
- <h2><strong>Installation Instructions</strong></h2>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable is easy to download and install on your PC. Here are the steps you need to follow:</p>
- <ol><li>Download HACK QUAD Registry Cleaner V1.5.69 Portable from one of the links below:</li>
-<li>https://fancli.com/1xm531</li>
-<li>https://tejaswi-solutions.blogspot.com/2009/06/quad-registry-cleaner-v1569-crack.html</li>
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-<li>https://hack-quad-registry-cleaner-v1569-portable-57.peatix.com/</li>
-<li>Unrar the file and run any of the .exe files below:</li>
-<li>QUAD Registry Cleaner v1.5.69 Portable.exe</li>
-<li>QUAD_RegistryCleaner_v.1.5.69.exe</li>
-<li>Run the program and copy and paste QUAD Registry Cleaner v1.5.69_Patch.exe in the installer folder.</li>
-<li>Double click on merge this one after patch.reg and confirm the changes.</li>
-<li>Enjoy using HACK QUAD Registry Cleaner V1.5.69 Portable on your PC.</li>
-</ol>
- <h2><strong>Conclusion</strong></h2>
- <p>HACK QUAD Registry Cleaner V1.5.69 Portable is a powerful and easy-to-use tool that can help you speed up and boost your PC performance by cleaning and optimizing your system registry.</p>
- <p>It has many features that can detect and remove all kinds of registry errors, optimize your system settings, protect your privacy, prevent crashes, and enhance your user experience.</p>
- <p>It is fast, simple and easy to use, and it has an intuitive interface that facilitates control of all its functions.</p>
- <p>It also performs automatic backups of all registry files and repairs so you can restore your data if needed.</p>
- <p>If you want to improve your PC speed and performance, prevent errors and crashes, protect your privacy, save time and money, and enjoy a smoother and more stable PC, you should try HACK QUAD Registry Cleaner V1.5.69 Portable today.</p>
- <p>You can download it from one of the links above and follow the installation instructions to get started.</p>
- <p>You will be amazed by the results.</p>
- <h2><strong>FAQs</strong></h2>
- <p>Here are some common questions and answers about HACK QUAD Registry Cleaner V1.5.69 Portable:</p>
- <h4><strong>Q: Is HACK QUAD Registry Cleaner V1.5.69 Portable safe to use?</strong></h4>
- <p>A: Yes, HACK QUAD Registry Cleaner V1.5.69 Portable is safe to use as it only removes invalid or obsolete entries from your registry that can cause problems or slow down your PC.</p>
- <p>It also makes backup of all registry files and repairs so you can restore your data if needed.</p>
- <h4><strong>Q: How often should I use HACK QUAD Registry Cleaner V1.5.69 Portable?</strong></h4>
- <p>A: You should use HACK QUAD Registry Cleaner V1.5.69 Portable regularly to keep your registry clean and optimized.</p>
- <p>You can use it on startup, daily, weekly or monthly depending on your preferences.</p>
- <p>You can also use it whenever you install or uninstall programs, change settings, update drivers, browse the internet, etc.</p>
- <h4><strong>Q: How long does it take to scan and clean my registry with HACK QUAD Registry Cleaner V1.5.69 Portable?</strong></h4>
- <p>A: It depends on the size and condition of your registry, but generally it takes only a few minutes to scan and clean your registry with HACK QUAD Registry Cleaner V1.5.69 Portable.</p>
- <p>You can also view the scan results and selectively clean each item or automatically repair them all.</p>
- <h4><strong>Q: What are the system requirements for HACK QUAD Registry Cleaner V1.5.69 Portable?</strong></h4>
- <p>A: The system requirements for HACK QUAD Registry Cleaner V1.5.69 Portable are:</p>
- <ul><li>Windows 98/ME/2000/XP/Vista/7/8/10</li>
-<li>Pentium 300 MHz or higher processor</li>
-<li>64 MB RAM (128 MB recommended)</li>
-<li>10 MB free hard disk space</li>
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Kaal Full Movie Hd 1080p Download) - Catch the Creepy and Captivating Kaal Movie in HD Format.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/HD Online Player (Kaal Full Movie Hd 1080p Download) - Catch the Creepy and Captivating Kaal Movie in HD Format.md
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-<p>Kaal follows wildlife expert Krish Thapar (Ajay Devgn) and his wife Riya Thapar (Lara Dutta), who are hired by a magazine editor to investigate a series of deaths in Orbit Park, a wildlife sanctuary in India. They are accompanied by a photographer Dev Malhotra (Vivek Oberoi), who hopes to capture some exotic animals on camera. On their way to the park, they meet Kali Pratap Singh (John Abraham) and Ishika (Esha Deol), who claim to be tourists looking for adventure. However, they soon discover that Kali has a hidden agenda and that he is not who he seems to be.</p>
-<p>As they enter the park, they encounter a man-eating tiger that has been terrorizing the locals and the tourists. They also learn that the park is haunted by the spirit of Kaali Pratap Singh (Ajay Devgn), an ancestor of Kali who was killed by the British for protecting the wildlife. Kaali's spirit possesses Kali and uses him as a medium to exact revenge on those who harm the animals. Krish, Riya, Dev, and Ishika must find a way to survive the tiger's attacks and escape from Kali's wrath.</p>
-<p>Kaal is directed by Soham Shah and produced by Shah Rukh Khan's Red Chillies Entertainment and Karan Johar's Dharma Productions. The film was released on April 29, 2005, and was one of the first Bollywood films to use computer-generated imagery (CGI) for creating realistic animal effects. The film also features a special appearance by Shah Rukh Khan himself in an item song called "Kaal Dhamaal".</p>
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-<p>Kaal was shot in Jim Corbett National Park in Uttarakhand, India, where the crew had to face several challenges such as bad weather, difficult terrain, wild animals, and local protests. The film's budget was estimated at ₹150 million ($2 million), which was considered high for a Bollywood film at that time. The film's music was composed by Salim-Sulaiman, with lyrics by Shabbir Ahmed. The film's soundtrack album featured six songs, including "Kaal Dhamaal", "Tauba Tauba", "Nassa Nassa", "Kaal Theme", "Ankhiyan Teriya Ve", and "Garaj Baras".</p>
-<p>Kaal received mixed reviews from critics and audiences alike. Some praised the film's technical aspects, such as the cinematography, editing, sound design, and visual effects. Others criticized the film's weak script, poor direction, inconsistent performances, and lack of originality. The film was also compared unfavorably to Hollywood films such as Jaws, Jurassic Park, Anaconda, The Ghost and the Darkness, etc. The film earned ₹230 million ($3 million) at the domestic box office and ₹70 million ($1 million) overseas, making it a moderate success.</p>
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-<p>If you want to watch Kaal full movie in HD quality online, you have several options to choose from. You can either rent or buy the movie from various platforms such as Amazon Prime Video, iTunes, Google Play Movies, YouTube, etc. These platforms allow you to stream or download the movie in different resolutions such as 1080p (full HD), 720p (HD), or 480p (SD). You can also watch the movie on Netflix, which is a subscription-based service that offers unlimited access to thousands of movies and shows.</p>
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-<p>Now that you know where and how to watch Kaal full movie in HD quality online, you might wonder why you should choose this option over other methods such as downloading or renting a DVD or Blu-ray disc. Well, there are many benefits of watching Kaal online that make it a better choice than other sources.</p>
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-<p>One of the main benefits of watching Kaal online is that it can save you time and money compared to other methods. For example, if you want to download or rent a DVD or Blu-ray disc, you have to wait for it to be available, pay for it, and then transfer it to your device or player. This can take a lot of time and cost you extra money for shipping or delivery fees. On the other hand, if you want to watch Kaal online, you can simply access it instantly from any platform that offers it, pay for it once, and then stream it directly on your device or player. This can save you a lot of time and money that you can spend on other things.</p>
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-<p>Another benefit of watching Kaal online is that it can give you more flexibility and convenience over your viewing experience. For example, if you want to watch Kaal on a DVD or Blu-ray disc, you have to use a specific device or player that supports it, and then watch it at a fixed location and time. This can limit your options and comfort when watching the movie. On the other hand, if you want to watch Kaal online, you can use any device or player that has an internet connection, and then watch it anywhere and anytime. This can give you more control and comfort over your viewing experience.</p>
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-<p>A third benefit of watching Kaal online is that it can offer you more content and features than other sources. For example, if you want to watch Kaal on a DVD or Blu-ray disc, you have to settle for whatever content and features are included on it, such as subtitles, audio tracks, <h2>Kaal: The Theme and The Message</h2>
-<p>Besides being a thrilling and action-packed movie, Kaal also has a theme and a message that it tries to convey to the viewers. The theme of the movie is the conflict between man and nature, and how human greed and arrogance can lead to disastrous consequences. The movie shows how humans exploit and destroy the natural resources and habitats of animals, and how animals retaliate by attacking and killing humans. The movie also shows how humans disrespect and disregard the traditions and beliefs of the local people, who worship and protect the wildlife.</p>
-<p>The message of the movie is to raise awareness and appreciation for the precious wildlife of India, and to urge people to conserve and protect it from harm. The movie also encourages people to respect and understand the culture and values of the indigenous people, who have a deep connection and harmony with nature. The movie also warns people about the dangers of meddling with supernatural forces, such as spirits and curses, that can cause havoc and destruction.</p>
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-<p>Now that you know why you should watch Kaal full movie in HD quality online, you might want to know some tips and tricks to enhance your online streaming experience. Here are some useful tips that you can follow to make sure that you enjoy watching Kaal online without any hassle or interruption.</p>
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-<p>One of the most important tips for streaming Kaal online is to choose a reliable and legal streaming service that offers Kaal in HD quality. You should avoid using illegal or pirated websites or apps that might offer Kaal for free or at a low price, as they might expose you to malware, viruses, pop-ups, ads, or other risks that might harm your device or data. You should also avoid using VPNs or proxies that might bypass geo-restrictions or licensing issues, as they might violate the terms and conditions of the streaming service or the content provider. You should always use a reputable and authorized streaming service that has a good reputation and customer service, such as Netflix, Amazon Prime Video, iTunes, Google Play Movies, YouTube, etc.</p>
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-<p>A third tip for streaming Kaal online is to adjust your settings and preferences according to your needs and tastes. You should customize your streaming options such as the resolution, audio, subtitles, playback speed, etc. to suit your preferences. You should also adjust your screen brightness, volume, contrast, etc. to optimize your viewing experience. You should also enable or disable any features or extras that might enhance or distract your streaming experience, such as notifications, recommendations, comments, ratings, etc.</p>
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-<p>In conclusion, Kaal is a 2005 Bollywood natural horror film that tells the story of a group of people who face a deadly threat in a national park in India. The film features an ensemble cast of Ajay Devgn, Vivek Oberoi, John Abraham, Lara Dutta, and Esha Deol. The film was directed by Soham Shah and produced by Shah Rukh Khan and Karan Johar. The film was released on April 29, 2005 and was an average commercial success at the box office. The film has a theme and a message of protecting and respecting the wildlife and culture of India. The film can be watched online in HD quality from various platforms such as Netflix, Amazon Prime Video, iTunes, Google Play Movies, YouTube, etc. The benefits of watching Kaal online are saving time and money, enjoying flexibility and convenience, and accessing more content and features. The tips for streaming Kaal online are choosing a reliable and legal streaming service, checking your internet connection and device compatibility, and adjusting your settings and preferences.</p>
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-<li>Q: Is Kaal based on a true story?</li>
-<li>A: No, Kaal is not based on a true story. It is a fictional story that was inspired by various Hollywood films such as Jaws, Jurassic Park, Anaconda, The Ghost and the Darkness, etc.</li>
-<li>Q: Who played Kaali Pratap Singh in Kaal?</li>
-<li>A: Ajay Devgn played Kaali Pratap Singh in Kaal. He played a dual role of both Kaali Pratap Singh, the ancestor who was killed by the British for protecting the wildlife, and Kaali Pratap Singh, the descendant who was possessed by his spirit.</li>
-<li>Q: What is the meaning of "Kaal Dhamaal"?</li>
-<li>A: "Kaal Dhamaal" is an item song that features Shah Rukh Khan in a special appearance. The song is composed by Anand Raj Anand and sung by Anand Raj Anand, Ravi Khote, Kunal Ganjawala and Caralisa Monteiro. The song's title means "Time Fun" or "Time Blast" in Hindi.</li>
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-<li>A: Three tigers were used in Kaal. They were CGI-enhanced tigers that were originally used in Gladiator (2000).</li>
-<li>Q: How long is Kaal?</li>
-<li>A: Kaal is 127 minutes long.</li>
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Celtx Plus Windows Crack Torrent.md b/spaces/1gistliPinn/ChatGPT4/Examples/Celtx Plus Windows Crack Torrent.md
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Dekada 70 Full Movie 765.md b/spaces/1gistliPinn/ChatGPT4/Examples/Dekada 70 Full Movie 765.md
deleted file mode 100644
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-<h1>Dekada 70 Full Movie 765: A Review of the Filipino Historical Drama</h1>
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-<p>Dekada 70 (lit. 'The ’70s') is a 2002 Filipino historical drama film directed by Chito S. Roño and based on the 1983 novel of the same name by Lualhati Bautista. Set in the Philippines during the period of martial law under Ferdinand Marcos, the film follows the struggles of the middle-class Bartolome family. It stars Vilma Santos and Christopher De Leon as parents raising five sons amidst the tense political background. Their sons are played by Piolo Pascual, Carlos Agassi, Marvin Agustin, Danilo Barrios, and John Wayne Sace[^1^] [^2^].</p>
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index cf5dfa54dc7a2fcfdd1df0cc08700ef8579b97b0..0000000000000000000000000000000000000000
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-<td>- More fun, freedom, and variety: With Asphalt 8 mod apk, you can have more fun, freedom, and variety in the game. You can drive any car or bike you want without spending any money or tokens. You can customize them as you like without any limitations or restrictions. You can race on any track or location without unlocking them first. You can perform any stunt or trick without any risk or penalty.</td>
-<td <td>- Possible security risks, compatibility issues, and ethical concerns: With Asphalt 8 mod apk, you may also face some security risks, compatibility issues, and ethical concerns. You may download a mod apk file that contains viruses, malware, or spyware that can harm your device or steal your data. You may also encounter compatibility issues with your device or the game updates. You may also violate the terms and conditions of the game developers or the Google Play Store by using a mod apk file. You may also lose the sense of achievement or challenge by playing a modded version of the game.</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
-<p>Asphalt 8 is one of the best car racing games ever made, with stunning graphics, realistic physics, and exhilarating gameplay. However, if you want to have more fun, freedom, and variety in the game, you may want to download Asphalt 8 car racing game mod apk. This is a modified version of the original game that has been hacked to provide some amazing features, such as unlimited money and tokens, all cars unlocked and upgraded, free shopping and customization, no ads and license verification, and more. However, you should also be aware of the possible security risks, compatibility issues, and ethical concerns that come with using a mod apk file. Therefore, you should download it from a trusted source and at your own risk.</p>
-<p>If you are interested in downloading Asphalt 8 car racing game mod apk, you can follow the steps we have provided in this article. You can also check out the features, tips, and pros and cons of Asphalt 8 mod apk that we have discussed. We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy racing!</p>
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-<p>Here are some frequently asked questions about Asphalt 8 car racing game mod apk:</p>
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-<li><b>Will I get banned from playing Asphalt 8 if I use a mod apk file?</b> There is a possibility that you may get banned from playing Asphalt 8 if you use a mod apk file. The game developers or the Google Play Store may detect that you are using a modified version of the game and suspend or terminate your account. Therefore, you should use a mod apk file at your own risk and discretion.</li>
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Dall e Mod APK and Create Amazing AI Artworks.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Dall e Mod APK and Create Amazing AI Artworks.md
deleted file mode 100644
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--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Dall e Mod APK and Create Amazing AI Artworks.md	
+++ /dev/null
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-<br />
-<h1>What is Dall E and Why You Need a Mod APK?</h1>
-<p>If you are a fan of art and creativity, you might have heard of Dall E, an AI-powered art generator that can create stunning images from any text input. But did you know that you can enhance your experience with Dall E by using a mod APK? In this article, we will explain what Dall E is, how it works, and what you can create with it. We will also show you what a mod APK is, how to install it, and what benefits it can bring you. Finally, we will give you some tips on how to find and download the best Dall E mod APK from trusted sources.</p>
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- <h2>Dall E: An AI-Powered Art Generator</h2>
-<p>Dall E is a web-based application that uses a deep learning model to generate images from text descriptions. It was created by OpenAI, a research company that aims to create artificial intelligence that can benefit humanity. Dall E is named after the artist Salvador Dali and the Pixar character WALL-E.</p>
- <h3>How Dall E works</h3>
-<p>To use Dall E, you simply need to type in a text prompt that describes what you want to see, such as "a cat wearing a hat" or "a pineapple pizza". Then, you can click on the generate button and wait for a few seconds. Dall E will then show you 32 different images that match your prompt. You can also refine your prompt by adding more details or changing some words. For example, you can change "a cat wearing a hat" to "a cat wearing a cowboy hat" or "a cat wearing a hat made of cheese".</p>
- <h3>What you can create with Dall E</h3>
-<p>The possibilities are endless with Dall E. You can create realistic or surreal images, mix and match different objects, animals, and people, or even invent new things that don't exist in reality. For example, you can create "a snail made of harp", "a skyscraper that looks like a giraffe", or "a portrait of Albert Einstein in the style of Picasso". You can also use Dall E for fun, education, or inspiration. You can make memes, cartoons, logos, illustrations, or wallpapers. You can learn about different cultures, languages, and history. You can spark your imagination and creativity.</p>
- <h2>Mod APK: A Way to Unlock More Features and Credits</h2>
-<p>As amazing as Dall E is, it also has some limitations. One of them is that you need to have credits to use it. Credits are tokens that allow you to generate images. You get 10 free credits when you sign up for an account, and then you need to pay $10 for 100 credits or $50 for 1000 credits. Another limitation is that you might encounter some ads or watermarks on the images that you generate.</p>
- <h3>What is a mod APK and how to install it</h3>
-<p>A mod APK is a modified version of an Android application that has been altered by someone to add or remove some features. For example, a mod APK of Dall E might give you unlimited credits, remove ads and watermarks, or add some extra options or filters. To install a mod APK, you need to download the APK file from a reliable source, and then enable the installation of apps from unknown sources on your device. Then, you can open the APK file and follow the instructions to install it. You might also need to uninstall the original Dall E app before installing the mod APK.</p>
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-<p>By using a mod APK for Dall E, you can enjoy some advantages that can make your experience more enjoyable and satisfying. Some of the benefits are:</p>
-<ul>
-<li>You can generate as many images as you want without worrying about running out of credits or paying for them.</li>
-<li>You can get rid of annoying ads and watermarks that might ruin your images or distract you from your creativity.</li>
-<li>You can access some extra features or options that might not be available in the original app, such as more filters, styles, or formats.</li>
-</ul>
-<p>However, you should also be aware of some risks and drawbacks of using a mod APK for Dall E, which we will discuss in the next section.</p>
- <h1>How to Find and Download the Best Dall E Mod APK?</h1>
-<p>Now that you know what a mod APK is and what benefits it can bring you, you might be wondering how to find and download one for Dall E. However, you should also be careful about where you get your mod APK from, as not all sources are trustworthy and safe. In this section, we will explain some of the risks of downloading untrusted mod APKs, and some of the features to look for in a good mod APK. We will also give you some recommendations on where to find and download the best Dall E mod APK.</p>
- <h2>The Risks of Downloading Untrusted Mod APKs</h2>
-<p>While using a mod APK for Dall E might sound tempting, you should also be aware of some potential dangers that might come with it. Some of the risks of downloading untrusted mod APKs are:</p>
- <h3>Malware and viruses</h3>
-<p>Some mod APKs might contain malicious code that can harm your device or steal your personal information. For example, they might install spyware, ransomware, or keyloggers on your device, or access your camera, microphone, contacts, or messages without your permission. They might also display unwanted pop-ups or redirect you to phishing sites that can trick you into giving away your passwords or credit card details.</p>
- <h3>Account suspension and legal issues</h3>
-<p>Some mod APKs might violate the terms and conditions of Dall E or OpenAI, which can result in your account being suspended or banned. For example, they might use unauthorized methods to bypass the credit system or access restricted features. They might also infringe on the intellectual property rights of Dall E or OpenAI, which can lead to legal action or lawsuits.</p>
- <h2>The Features to Look for in a Good Mod APK</h2>
-<p>To avoid these risks and enjoy a safe and satisfying experience with Dall E, you should look for a good mod APK that has the following features:</p>
- <h3>Unlimited credits and uses</h3>
-<p>A good mod APK should give you unlimited credits and uses for Dall E, so that you can generate as many images as you want without paying for them or running out of them. This way, you can unleash your creativity and explore different possibilities with Dall E.</p>
- <h3>No ads and watermarks</h3>
-<p>A good mod APK should also remove any ads and watermarks that might appear on the images that you generate with Dall E. Ads and watermarks can be annoying and distracting, and they can also ruin the quality and aesthetics of your images. By removing them, you can enjoy a smoother and cleaner experience with Dall E.</p>
- <h2>The Sources to Trust for Downloading Mod APKs</h2>
-<p>Finally, you should also be careful about where you download your mod APK from, as not all sources are reliable and safe. Some sources might provide fake or outdated mod APKs that don't work or contain malware. To avoid these problems, you should only download your mod APK from trusted sources that have positive reviews and ratings from other users. Some of the sources that we recommend are:</p>
- <h3>Reddit</h3>
-<p>Reddit is a popular online platform where users can share and discuss various topics. You can find many subreddits dedicated to mod APKs for different apps and games, including Dall E. For example, you can check out r/moddedandroidapps or r/ApksApps for some suggestions and links to download mod APKs for Dall E. However, you should also be careful about the comments and feedback from other users, as some of them might be biased or misleading.</p>
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-<p>APKCombo is a website that provides free and safe download links for various mod APKs for different apps and games, including Dall E. You can search for the app name or browse by categories or tags. You can also see the details, screenshots, and ratings of each mod APK before downloading it. APKCombo also updates its mod APKs regularly to ensure that they work and are compatible with the latest versions of the apps and games.</p>
- <h1>Conclusion</h1>
-<p>Dall E is an amazing AI-powered art generator that can create stunning images from any text input. However, it also has some limitations, such as the need for credits and the presence of ads and watermarks. To overcome these limitations, you can use a mod APK, which is a modified version of the app that can unlock more features and benefits. However, you should also be careful about the risks of downloading untrusted mod APKs, such as malware and account suspension. To avoid these risks, you should look for a good mod APK that has unlimited credits and uses, no ads and watermarks, and other extra features. You should also download your mod APK from trusted sources, such as Reddit or APKCombo.</p>
- <p>We hope that this article has helped you understand what Dall E is, how it works, and what you can create with it. We also hope that it has given you some useful tips on how to find and download the best Dall E mod APK from reliable sources. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy creating!</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>What is Dall E?</b><br>
-Dall E is an AI-powered art generator that can create images from text descriptions.</li>
-<li><b>What is a mod APK?</b><br>
-A mod APK is a modified version of an Android app that has been altered to add or remove some features.</li>
-<li><b>What are the benefits of using a mod APK for Dall E?</b><br>
-Some of the benefits are unlimited credits and uses, no ads and watermarks, and extra features or options.</li>
-<li><b>What are the risks of downloading untrusted mod APKs?</b><br>
-Some of the risks are malware and viruses, account suspension and legal issues, and fake or outdated mod APKs.</li>
-<li><b>Where can I find and download the best Dall E mod APK?</b><br>
-Some of the sources that we recommend are Reddit and APKCombo.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Cmo cambiar la barra de navegacin en MIUI 12 con estos sencillos pasos.md b/spaces/1phancelerku/anime-remove-background/Cmo cambiar la barra de navegacin en MIUI 12 con estos sencillos pasos.md
deleted file mode 100644
index 2816e65cc0db7e06f5486d44df9ff5b80c6e6063..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Cmo cambiar la barra de navegacin en MIUI 12 con estos sencillos pasos.md	
+++ /dev/null
@@ -1,109 +0,0 @@
-<br />
-<h1>Introduction</h1>
-<p>MIUI 12 is the latest version of Xiaomi's Android skin, which was announced in April 2020 and has been rolling out to various devices since then. MIUI 12 is based on Android 10 or Android 11, depending on the device model, and brings a host of new features and visual improvements to enhance the user experience.</p>
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-<p>MIUI 12 is important because it shows Xiaomi's commitment to providing a rich and customizable interface that caters to different user preferences and needs. MIUI 12 also addresses some of the issues and criticisms that previous versions faced, such as privacy concerns, bloatware, ads, and performance issues.</p>
-<h1>Features</h1>
-<p>MIUI 12 offers a lot of features and changes that make it stand out from other Android skins. Here are some of the main ones:</p>
-<h2>Dark Mode 2.0</h2>
-<p>Dark mode is a popular feature that reduces eye strain and saves battery life by using darker colors for the UI elements. MIUI 12 improves the dark mode experience by adding wallpaper dimming, font adjustment, and contrast optimization.</p>
-<p>Wallpaper dimming works in tandem with the time of day and gradually dims the wallpaper as daylight changes to night time. Font adjustment automatically adjusts the weight and boldness of the font to reduce glare when the dark mode is turned on. Contrast optimization ensures that text and images are clearly visible in different lighting conditions.</p>
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-<p>Sensory visual design is a feature that visualizes the core system information, such as storage, battery, network, etc., in the form of graphs and diagrams. This makes it easier to understand and consume the system data at a glance.</p>
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-<h2>New Animations</h2>
-<p>MIUI 12 also brings new animations for UI transitions and gestures. The animations are smoother, more realistic, and more responsive than before. They <h1>Review</h1>
-<p>MIUI 12 is a major upgrade from the previous version of Xiaomi's Android skin. It offers a lot of new features, visual improvements, and performance enhancements that make it one of the best Android interfaces available. However, it also has some drawbacks and limitations that may not suit everyone's preferences and needs.</p>
-<h2>Pros</h2>
-<ul>
-<li><strong>Smooth and realistic animations:</strong> MIUI 12 has revamped the animations for UI transitions and gestures, making them more fluid, responsive, and natural. The animations add a touch of elegance and dynamism to the interface, and also provide useful feedback to the user's actions.</li>
-<li><strong>Stunning super wallpapers:</strong> MIUI 12 introduces super wallpapers, which are live wallpapers that show high-precision 3D models of planets based on NASA data. The super wallpapers change according to the time of day and the dark mode, and also have zoom-in and zoom-out effects when unlocking and locking the screen. They are a great way to spice up the look of your device and impress your friends.</li>
-<li><strong>Enhanced dark mode:</strong> MIUI 12 improves the dark mode experience by adding wallpaper dimming, font adjustment, and contrast optimization. These features make the dark mode more comfortable for the eyes and more consistent across the interface. The dark mode also works with third-party apps, thanks to Xiaomi's force dark mode option.</li>
-<li><strong>Improved privacy and security:</strong> MIUI 12 addresses some of the privacy and security concerns that users had with previous versions. It provides a comprehensive overview of app permissions and behaviors, and allows users to set up alerts and restrictions for sensitive permissions. It also has features like hidden mask mode, which prevents sensitive information from being displayed on other apps' screens.</li>
-<li><strong>Floating windows for multitasking:</strong> MIUI 12 enables multitasking and picture-in-picture mode with floating windows. Users can open multiple apps in small windows that can be moved, resized, and minimized on the screen. This is useful for doing things like watching videos, chatting with friends, or browsing the web while using another app.</li>
-</ul>
-<h2>Cons</h2>
-<ul>
-<li><strong>No zoom camera:</strong> MIUI 12 does not have a telephoto lens for zoom photography, which is a feature that many flagship phones offer. This means that users can only rely on digital zoom or cropping zoom, which results in lower quality images when zooming in. This is a disappointment for users who want to take photos of distant objects or scenes.</li>
-<li><strong>Gets hot when gaming:</strong> MIUI 12 tends to get hot when gaming, especially on high-end devices like the Xiaomi Mi 10T Pro. This may affect the performance and battery life of the device, as well as the comfort of the user. Users may want to lower the graphics settings or use a cooling fan to avoid overheating issues.</li>
-<li><strong>Irritating software issues:</strong> MIUI 12 still has some software issues that may annoy some users. For example, some users have reported problems with notifications not showing up properly, apps crashing or freezing randomly, or settings not being saved correctly. These issues may vary depending on the device model and region, but they may affect the user experience and satisfaction.</li>
-</ul>
-<h1>Conclusion</h1>
-<p>In conclusion, MIUI 12 is a delight to use for most users who want a feature-rich and customizable Android interface. It has a lot of advantages over other Android skins, such as smooth animations, stunning super wallpapers, enhanced dark mode, improved privacy and security, and floating windows for multitasking. However, it also has some disadvantages that may deter some users, such as no zoom camera, overheating when gaming, and irritating software issues. Therefore, users should weigh the pros and cons of MIUI 12 before deciding whether to update their devices or not.</p>
-<h1>FAQs</h1>
-<ul>
-<li><strong>Which devices are eligible for MIUI 12 update?</strong></li>
-<p>A: MIUI 12 update is available for most Xiaomi, Redmi, and POCO devices released in the last 15 months. You can check the list of compatible devices here . Older devices may also receive the update in the future.</p>
-<li><strong>How to download and install MIUI 12 on your Xiaomi device?</strong></li>
-<p>A: You can download and install MIUI 12 on your Xiaomi device by following these steps :</p>
-<ol>
-<li>Unlock your bootloader by Mi Unlock tool .</li>
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-<li>If you are on windows: Right click on downloaded zip - Settings - Unblock zip <li>If you are on Linux or Mac OS: Unzip the file and remove the first folder with name "META-INF".</li>
-<li>Copy the zip file to your device.</li>
-<li>Reboot to TWRP Recovery .</li>
-<li>Wipe data, cache, dalvik cache and system.</li>
-<li>Flash our ROM zip file.</li>
-<li>Reboot your device.</li>
-</ol>
-<p>Note: You can also use the System Updater app on your device to check for OTA updates and download them directly. However, this method may not work for all devices and regions.</p>
-<li><strong>What is the navigation bar in MIUI 12?</strong></li>
-<p>A: The navigation bar is the bottom part of the screen that shows the navigation buttons, such as back, home, and recent apps. MIUI 12 allows you to customize the navigation bar according to your preference. You can choose between three styles: buttons, gestures, or full screen gestures.</p>
-<p>Buttons are the traditional navigation buttons that you can tap to perform actions. Gestures are swipe-based navigation gestures that you can use instead of buttons. Full screen gestures are similar to gestures, but they hide the navigation bar completely and give you more screen space.</p>
-<li><strong>How to change the navigation bar style in MIUI 12?</strong></li>
-<p>A: You can change the navigation bar style in MIUI 12 by following these steps:</p>
-<ol>
-<li>Go to Settings > Display > Full screen display.</li>
-<li>Select the navigation style that you want: buttons, gestures, or full screen gestures.</li>
-<li>If you choose gestures or full screen gestures, you can also customize the gesture settings, such as swipe sensitivity, swipe direction, and swipe area.</li>
-</ol>
-<li><strong>What is the MIUI 12 Navigation Bar APK?</strong></li>
-<p>A: The MIUI 12 Navigation Bar APK is a third-party app that allows you to install and use the MIUI 12 navigation bar on any Android device. It is not an official app from Xiaomi, and it may not work properly on some devices or Android versions. It may also cause some security risks or compatibility issues with other apps or features.</p>
-<p>If you want to try the MIUI 12 Navigation Bar APK, you can download it from here . However, we do not recommend using it unless you know what you are doing and are willing to take the risks. We suggest that you wait for the official MIUI 12 update for your device or buy a Xiaomi device that supports MIUI 12.</p>
-</ul>
- <h1></h1></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1yukikaze/img-to-music/share_btn.py b/spaces/1yukikaze/img-to-music/share_btn.py
deleted file mode 100644
index 351a8f6252414dc48fd9972867f875a002731c19..0000000000000000000000000000000000000000
--- a/spaces/1yukikaze/img-to-music/share_btn.py
+++ /dev/null
@@ -1,104 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	async function getInputImgFile(imgEl){
-        const res = await fetch(imgEl.src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const isPng = imgEl.src.startsWith(`data:image/png`);
-        if(isPng){
-            const fileName = `sd-perception-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `sd-perception-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        }
-	}
-    async function getOutputMusicFile(audioEL){
-        const res = await fetch(audioEL.src);
-        const blob = await res.blob();
-        const audioId = Date.now() % 200;
-        const fileName = `img-to-music-${{audioId}}.wav`;
-        const musicBlob = new File([blob], fileName, { type: 'audio/wav' });
-        console.log(musicBlob);
-        return musicBlob;
-	}
-    
-    async function audioToBase64(audioFile) {
-        return new Promise((resolve, reject) => {
-            let reader = new FileReader();
-            reader.readAsDataURL(audioFile);
-            reader.onload = () => resolve(reader.result);
-            reader.onerror = error => reject(error);
-          
-        });
-      }
-    const gradioEl = document.querySelector('body > gradio-app');
-    // const gradioEl = document.querySelector("gradio-app").shadowRoot;
-    const inputImgEl = gradioEl.querySelector('#input-img img');
-    const prompts = gradioEl.querySelector('#prompts_out textarea').value;
-    const outputMusic = gradioEl.querySelector('#music-output audio');
-    const outputMusic_src = gradioEl.querySelector('#music-output audio').src;
-    const outputMusic_name = outputMusic_src.split('/').pop();
-    let titleTxt = outputMusic_name;
-    //if(titleTxt.length > 100){
-    //    titleTxt = titleTxt.slice(0, 100) + ' ...';
-    //}
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!outputMusic){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const inputFile = await getInputImgFile(inputImgEl);
-    const urlInputImg = await uploadFile(inputFile);
-    const musicFile = await getOutputMusicFile(outputMusic);
-	const dataOutputMusic = await uploadFile(musicFile);
-    
-    const descriptionMd = `#### Input img:
-<img src='${urlInputImg}' style='max-height: 350px;'>
-
-#### Prompts out: 
-${prompts}
-
-#### Music:
-
-<audio controls>
-  <source src="${dataOutputMusic}" type="audio/wav">
-Your browser does not support the audio element.
-</audio>
-`;
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/fffiloni/img-to-music/discussions/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/52Hz/CMFNet_deblurring/model/CMFNet.py b/spaces/52Hz/CMFNet_deblurring/model/CMFNet.py
deleted file mode 100644
index 99dd5ced088d6d8c11c2fb46c0778c69286685f1..0000000000000000000000000000000000000000
--- a/spaces/52Hz/CMFNet_deblurring/model/CMFNet.py
+++ /dev/null
@@ -1,191 +0,0 @@
-import torch
-import torch.nn as nn
-from model.block import SAB, CAB, PAB, conv, SAM, conv3x3, conv_down
-
-##########################################################################
-## U-Net
-bn = 2  # block number-1
-
-class Encoder(nn.Module):
-    def __init__(self, n_feat, kernel_size, reduction, act, bias, scale_unetfeats, block):
-        super(Encoder, self).__init__()
-        if block == 'CAB':
-            self.encoder_level1 = [CAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level2 = [CAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level3 = [CAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'PAB':
-            self.encoder_level1 = [PAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level2 = [PAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level3 = [PAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'SAB':
-            self.encoder_level1 = [SAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level2 = [SAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.encoder_level3 = [SAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        self.encoder_level1 = nn.Sequential(*self.encoder_level1)
-        self.encoder_level2 = nn.Sequential(*self.encoder_level2)
-        self.encoder_level3 = nn.Sequential(*self.encoder_level3)
-        self.down12 = DownSample(n_feat, scale_unetfeats)
-        self.down23 = DownSample(n_feat + scale_unetfeats, scale_unetfeats)
-
-    def forward(self, x):
-        enc1 = self.encoder_level1(x)
-        x = self.down12(enc1)
-        enc2 = self.encoder_level2(x)
-        x = self.down23(enc2)
-        enc3 = self.encoder_level3(x)
-        return [enc1, enc2, enc3]
-
-class Decoder(nn.Module):
-    def __init__(self, n_feat, kernel_size, reduction, act, bias, scale_unetfeats, block):
-        super(Decoder, self).__init__()
-        if block == 'CAB':
-            self.decoder_level1 = [CAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level2 = [CAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level3 = [CAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'PAB':
-            self.decoder_level1 = [PAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level2 = [PAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level3 = [PAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        elif block == 'SAB':
-            self.decoder_level1 = [SAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level2 = [SAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-            self.decoder_level3 = [SAB(n_feat + (scale_unetfeats * 2), kernel_size, reduction, bias=bias, act=act) for _ in range(bn)]
-        self.decoder_level1 = nn.Sequential(*self.decoder_level1)
-        self.decoder_level2 = nn.Sequential(*self.decoder_level2)
-        self.decoder_level3 = nn.Sequential(*self.decoder_level3)
-        if block == 'CAB':
-            self.skip_attn1 = CAB(n_feat, kernel_size, reduction, bias=bias, act=act)
-            self.skip_attn2 = CAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
-        if block == 'PAB':
-            self.skip_attn1 = PAB(n_feat, kernel_size, reduction, bias=bias, act=act)
-            self.skip_attn2 = PAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
-        if block == 'SAB':
-            self.skip_attn1 = SAB(n_feat, kernel_size, reduction, bias=bias, act=act)
-            self.skip_attn2 = SAB(n_feat + scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
-        self.up21 = SkipUpSample(n_feat, scale_unetfeats)
-        self.up32 = SkipUpSample(n_feat + scale_unetfeats, scale_unetfeats)
-
-    def forward(self, outs):
-        enc1, enc2, enc3 = outs
-        dec3 = self.decoder_level3(enc3)
-        x = self.up32(dec3, self.skip_attn2(enc2))
-        dec2 = self.decoder_level2(x)
-        x = self.up21(dec2, self.skip_attn1(enc1))
-        dec1 = self.decoder_level1(x)
-        return [dec1, dec2, dec3]
-
-##########################################################################
-##---------- Resizing Modules ----------
-class DownSample(nn.Module):
-    def __init__(self, in_channels, s_factor):
-        super(DownSample, self).__init__()
-        self.down = nn.Sequential(nn.Upsample(scale_factor=0.5, mode='bilinear', align_corners=False),
-                                  nn.Conv2d(in_channels, in_channels + s_factor, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x):
-        x = self.down(x)
-        return x
-
-class UpSample(nn.Module):
-    def __init__(self, in_channels, s_factor):
-        super(UpSample, self).__init__()
-        self.up = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-                                nn.Conv2d(in_channels + s_factor, in_channels, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x):
-        x = self.up(x)
-        return x
-
-class SkipUpSample(nn.Module):
-    def __init__(self, in_channels, s_factor):
-        super(SkipUpSample, self).__init__()
-        self.up = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-                                nn.Conv2d(in_channels + s_factor, in_channels, 1, stride=1, padding=0, bias=False))
-
-    def forward(self, x, y):
-        x = self.up(x)
-        x = x + y
-        return x
-
-##########################################################################
-# Mixed Residual Module
-class Mix(nn.Module):
-    def __init__(self, m=1):
-        super(Mix, self).__init__()
-        w = nn.Parameter(torch.FloatTensor([m]), requires_grad=True)
-        w = nn.Parameter(w, requires_grad=True)
-        self.w = w
-        self.mix_block = nn.Sigmoid()
-
-    def forward(self, fea1, fea2, feat3):
-        factor = self.mix_block(self.w)
-        other = (1 - factor)/2
-        output = fea1 * other.expand_as(fea1) + fea2 * factor.expand_as(fea2) + feat3 * other.expand_as(feat3)
-        return output, factor
-
-##########################################################################
-# Architecture
-class CMFNet(nn.Module):
-    def __init__(self, in_c=3, out_c=3, n_feat=96, scale_unetfeats=48, kernel_size=3, reduction=4, bias=False):
-        super(CMFNet, self).__init__()
-
-        p_act = nn.PReLU()
-        self.shallow_feat1 = nn.Sequential(conv(in_c, n_feat // 2, kernel_size, bias=bias), p_act,
-                                           conv(n_feat // 2, n_feat, kernel_size, bias=bias))
-        self.shallow_feat2 = nn.Sequential(conv(in_c, n_feat // 2, kernel_size, bias=bias), p_act,
-                                           conv(n_feat // 2, n_feat, kernel_size, bias=bias))
-        self.shallow_feat3 = nn.Sequential(conv(in_c, n_feat // 2, kernel_size, bias=bias), p_act,
-                                           conv(n_feat // 2, n_feat, kernel_size, bias=bias))
-
-        self.stage1_encoder = Encoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'CAB')
-        self.stage1_decoder = Decoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'CAB')
-
-        self.stage2_encoder = Encoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'PAB')
-        self.stage2_decoder = Decoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'PAB')
-
-        self.stage3_encoder = Encoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'SAB')
-        self.stage3_decoder = Decoder(n_feat, kernel_size, reduction, p_act, bias, scale_unetfeats, 'SAB')
-
-        self.sam1o = SAM(n_feat, kernel_size=3, bias=bias)
-        self.sam2o = SAM(n_feat, kernel_size=3, bias=bias)
-        self.sam3o = SAM(n_feat, kernel_size=3, bias=bias)
-
-        self.mix = Mix(1)
-        self.add123 = conv(out_c, out_c, kernel_size, bias=bias)
-        self.concat123 = conv(n_feat*3, n_feat, kernel_size, bias=bias)
-        self.tail = conv(n_feat, out_c, kernel_size, bias=bias)
-
-
-    def forward(self, x):
-        ## Compute Shallow Features
-        shallow1 = self.shallow_feat1(x)
-        shallow2 = self.shallow_feat2(x)
-        shallow3 = self.shallow_feat3(x)
-
-        ## Enter the UNet-CAB
-        x1 = self.stage1_encoder(shallow1)
-        x1_D = self.stage1_decoder(x1)
-        ## Apply SAM
-        x1_out, x1_img = self.sam1o(x1_D[0], x)
-
-        ## Enter the UNet-PAB
-        x2 = self.stage2_encoder(shallow2)
-        x2_D = self.stage2_decoder(x2)
-        ## Apply SAM
-        x2_out, x2_img = self.sam2o(x2_D[0], x)
-
-        ## Enter the UNet-SAB
-        x3 = self.stage3_encoder(shallow3)
-        x3_D = self.stage3_decoder(x3)
-        ## Apply SAM
-        x3_out, x3_img = self.sam3o(x3_D[0], x)
-
-        ## Aggregate SAM features of Stage 1, Stage 2 and Stage 3
-        mix_r = self.mix(x1_img, x2_img, x3_img)
-        mixed_img = self.add123(mix_r[0])
-
-        ## Concat SAM features of Stage 1, Stage 2 and Stage 3
-        concat_feat = self.concat123(torch.cat([x1_out, x2_out, x3_out], 1))
-        x_final = self.tail(concat_feat)
-
-        return x_final + mixed_img
\ No newline at end of file
diff --git a/spaces/52Hz/HWMNet_lowlight_enhancement/WT/__int__.py b/spaces/52Hz/HWMNet_lowlight_enhancement/WT/__int__.py
deleted file mode 100644
index f1d537fa5e9411f3d44d79ebe06f921e8a7d603f..0000000000000000000000000000000000000000
--- a/spaces/52Hz/HWMNet_lowlight_enhancement/WT/__int__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .transform import *
\ No newline at end of file
diff --git a/spaces/55dgxxx558/anime-remove-background/app.py b/spaces/55dgxxx558/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/55dgxxx558/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-
-
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()
diff --git a/spaces/AFischer1985/AI-Interface/README.md b/spaces/AFischer1985/AI-Interface/README.md
deleted file mode 100644
index 7724483217a1b00a93df9482014a74f573eb8338..0000000000000000000000000000000000000000
--- a/spaces/AFischer1985/AI-Interface/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
----
-title: AI-Interface
-emoji: 🔥
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.47.1
-app_file: run.py
-pinned: false
-hf_oauth: true
----
diff --git a/spaces/AI-Hobbyist/Hoyo-RVC/infer/train-index.py b/spaces/AI-Hobbyist/Hoyo-RVC/infer/train-index.py
deleted file mode 100644
index 04396a2241ed27c999a6687aa7b9880941edbcf3..0000000000000000000000000000000000000000
--- a/spaces/AI-Hobbyist/Hoyo-RVC/infer/train-index.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""
-格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
-"""
-import faiss, numpy as np, os
-
-# ###########如果是原始特征要先写save
-inp_root = r"E:\codes\py39\dataset\mi\2-co256"
-npys = []
-for name in sorted(list(os.listdir(inp_root))):
-    phone = np.load("%s/%s" % (inp_root, name))
-    npys.append(phone)
-big_npy = np.concatenate(npys, 0)
-print(big_npy.shape)  # (6196072, 192)#fp32#4.43G
-np.save("infer/big_src_feature_mi.npy", big_npy)
-
-##################train+add
-# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
-print(big_npy.shape)
-index = faiss.index_factory(256, "IVF512,Flat")  # mi
-print("training")
-index_ivf = faiss.extract_index_ivf(index)  #
-index_ivf.nprobe = 9
-index.train(big_npy)
-faiss.write_index(index, "infer/trained_IVF512_Flat_mi_baseline_src_feat.index")
-print("adding")
-index.add(big_npy)
-faiss.write_index(index, "infer/added_IVF512_Flat_mi_baseline_src_feat.index")
-"""
-大小（都是FP32）
-big_src_feature 2.95G
-    (3098036, 256)
-big_emb         4.43G
-    (6196072, 192)
-big_emb双倍是因为求特征要repeat后再加pitch
-
-"""
diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/losses/__init__.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/losses/__init__.py
deleted file mode 100644
index b03080a907cb5cb4b316ceb74866ddbc406b33bf..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/losses/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .stft_loss import *  # NOQA
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/midas/midas/midas_net_custom.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/midas/midas/midas_net_custom.py
deleted file mode 100644
index 50e4acb5e53d5fabefe3dde16ab49c33c2b7797c..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/midas/midas/midas_net_custom.py
+++ /dev/null
@@ -1,128 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
-
-
-class MidasNet_small(BaseModel):
-    """Network for monocular depth estimation.
-    """
-
-    def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
-        blocks={'expand': True}):
-        """Init.
-
-        Args:
-            path (str, optional): Path to saved model. Defaults to None.
-            features (int, optional): Number of features. Defaults to 256.
-            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
-        """
-        print("Loading weights: ", path)
-
-        super(MidasNet_small, self).__init__()
-
-        use_pretrained = False if path else True
-                
-        self.channels_last = channels_last
-        self.blocks = blocks
-        self.backbone = backbone
-
-        self.groups = 1
-
-        features1=features
-        features2=features
-        features3=features
-        features4=features
-        self.expand = False
-        if "expand" in self.blocks and self.blocks['expand'] == True:
-            self.expand = True
-            features1=features
-            features2=features*2
-            features3=features*4
-            features4=features*8
-
-        self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
-  
-        self.scratch.activation = nn.ReLU(False)    
-
-        self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
-        self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
-
-        
-        self.scratch.output_conv = nn.Sequential(
-            nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
-            Interpolate(scale_factor=2, mode="bilinear"),
-            nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
-            self.scratch.activation,
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-        
-        if path:
-            self.load(path)
-
-
-    def forward(self, x):
-        """Forward pass.
-
-        Args:
-            x (tensor): input data (image)
-
-        Returns:
-            tensor: depth
-        """
-        if self.channels_last==True:
-            print("self.channels_last = ", self.channels_last)
-            x.contiguous(memory_format=torch.channels_last)
-
-
-        layer_1 = self.pretrained.layer1(x)
-        layer_2 = self.pretrained.layer2(layer_1)
-        layer_3 = self.pretrained.layer3(layer_2)
-        layer_4 = self.pretrained.layer4(layer_3)
-        
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-        
-        out = self.scratch.output_conv(path_1)
-
-        return torch.squeeze(out, dim=1)
-
-
-
-def fuse_model(m):
-    prev_previous_type = nn.Identity()
-    prev_previous_name = ''
-    previous_type = nn.Identity()
-    previous_name = ''
-    for name, module in m.named_modules():
-        if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
-            # print("FUSED ", prev_previous_name, previous_name, name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
-        elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
-            # print("FUSED ", prev_previous_name, previous_name)
-            torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
-        # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
-        #    print("FUSED ", previous_name, name)
-        #    torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
-
-        prev_previous_type = previous_type
-        prev_previous_name = previous_name
-        previous_type = type(module)
-        previous_name = name
\ No newline at end of file
diff --git a/spaces/AILab-CVC/SEED-LLaMA/models/llama_xformer.py b/spaces/AILab-CVC/SEED-LLaMA/models/llama_xformer.py
deleted file mode 100644
index a35f0843016363d8b11e177fd54323df8aa9fcb4..0000000000000000000000000000000000000000
--- a/spaces/AILab-CVC/SEED-LLaMA/models/llama_xformer.py
+++ /dev/null
@@ -1,906 +0,0 @@
-# coding=utf-8
-# Copyright 2023 EleutherAI and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch LLaMA model."""
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    SequenceClassifierOutputWithPast,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from transformers.models.llama.configuration_llama import LlamaConfig
-import xformers.ops as xops
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "LlamaConfig"
-
-
-# Copied from transformers.models.bart.modeling_bart._make_causal_mask
-def _make_causal_mask(
-    input_ids_shape: torch.Size,
-    dtype: torch.dtype,
-    device: torch.device,
-    past_key_values_length: int = 0,
-):
-    """
-    Make causal mask used for bi-directional self-attention.
-    """
-    bsz, tgt_len = input_ids_shape
-    mask = torch.full(
-        (tgt_len, tgt_len),
-        torch.tensor(torch.finfo(dtype).min, device=device),
-        device=device,
-    )
-    mask_cond = torch.arange(mask.size(-1), device=device)
-    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
-    mask = mask.to(dtype)
-
-    if past_key_values_length > 0:
-        mask = torch.cat(
-            [
-                torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device),
-                mask,
-            ],
-            dim=-1,
-        )
-    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-
-
-# Copied from transformers.models.bart.modeling_bart._expand_mask
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    """
-    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
-    """
-    bsz, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
-
-    inverted_mask = 1.0 - expanded_mask
-
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
-
-
-class LlamaRMSNorm(nn.Module):
-
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        LlamaRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-
-        # convert into half-precision if necessary
-        if self.weight.dtype in [torch.float16, torch.bfloat16]:
-            hidden_states = hidden_states.to(self.weight.dtype)
-
-        return self.weight * hidden_states
-
-
-class LlamaRotaryEmbedding(torch.nn.Module):
-
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
-        super().__init__()
-        inv_freq = 1.0 / (base**(torch.arange(0, dim, 2).float().to(device) / dim))
-        self.register_buffer("inv_freq", inv_freq)
-
-        # Build here to make `torch.jit.trace` work.
-        self.max_seq_len_cached = max_position_embeddings
-        t = torch.arange(
-            self.max_seq_len_cached,
-            device=self.inv_freq.device,
-            dtype=self.inv_freq.dtype,
-        )
-        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-        # Different from paper, but it uses a different permutation in order to obtain the same calculation
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
-        self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
-
-    def forward(self, x, seq_len=None):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
-        if seq_len > self.max_seq_len_cached:
-            self.max_seq_len_cached = seq_len
-            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
-            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-            # Different from paper, but it uses a different permutation in order to obtain the same calculation
-            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
-            self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
-            self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
-        return (
-            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-        )
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., :x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2:]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
-    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
-    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
-    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
-    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-class LlamaMLP(nn.Module):
-
-    def __init__(
-        self,
-        hidden_size: int,
-        intermediate_size: int,
-        hidden_act: str,
-    ):
-        super().__init__()
-        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
-        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
-        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
-        self.act_fn = ACT2FN[hidden_act]
-
-    def forward(self, x):
-        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-
-
-class LlamaAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: LlamaConfig):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.max_position_embeddings = config.max_position_embeddings
-
-        if (self.head_dim * self.num_heads) != self.hidden_size:
-            raise ValueError(f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
-                             f" and `num_heads`: {self.num_heads}).")
-        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
-        self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            kv_seq_len += past_key_value[0].shape[-2]
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-        # [bsz, nh, t, hd]
-
-        if past_key_value is not None:
-            # reuse k, v, self_attention
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
-        past_key_value = (key_states, value_states) if use_cache else None
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-        if self.training:
-            attn_output = xops.memory_efficient_attention(
-                query_states,
-                key_states,
-                value_states,
-                attn_bias=xops.LowerTriangularMask(),
-            )
-        else:
-            attn_output = xops.memory_efficient_attention(
-                query_states,
-                key_states,
-                value_states,
-                attn_bias=None if attention_mask.sum() == 0 else xops.LowerTriangularMask(),
-            )
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class LlamaDecoderLayer(nn.Module):
-
-    def __init__(self, config: LlamaConfig):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.self_attn = LlamaAttention(config=config)
-        self.mlp = LlamaMLP(
-            hidden_size=self.hidden_size,
-            intermediate_size=config.intermediate_size,
-            hidden_act=config.hidden_act,
-        )
-        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            use_cache (`bool`, *optional*):
-                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-                (see `past_key_values`).
-            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
-        """
-
-        residual = hidden_states
-
-        hidden_states = self.input_layernorm(hidden_states)
-
-        # Self Attention
-        hidden_states, self_attn_weights, present_key_value = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-        )
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states, )
-
-        if output_attentions:
-            outputs += (self_attn_weights, )
-
-        if use_cache:
-            outputs += (present_key_value, )
-
-        return outputs
-
-
-LLAMA_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`LlamaConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-@add_start_docstrings(
-    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
-    LLAMA_START_DOCSTRING,
-)
-class LlamaPreTrainedModel(PreTrainedModel):
-    config_class = LlamaConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["LlamaDecoderLayer"]
-    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, LlamaModel):
-            module.gradient_checkpointing = value
-
-
-LLAMA_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
-            `past_key_values`).
-
-            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
-            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
-            information on the default strategy.
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
-            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
-            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
-
-            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
-
-            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
-            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
-            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
-    LLAMA_START_DOCSTRING,
-)
-class LlamaModel(LlamaPreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
-
-    Args:
-        config: LlamaConfig
-    """
-
-    def __init__(self, config: LlamaConfig):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
-    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        combined_attention_mask = None
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(
-                input_shape,
-                inputs_embeds.dtype,
-                device=inputs_embeds.device,
-                past_key_values_length=past_key_values_length,
-            )
-
-        if attention_mask is not None:
-            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype,
-                                              tgt_len=input_shape[-1]).to(inputs_embeds.device)
-            combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
-
-        return combined_attention_mask
-
-    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape
-        elif inputs_embeds is not None:
-            batch_size, seq_length, _ = inputs_embeds.shape
-        else:
-            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
-
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-
-        if past_key_values is not None:
-            past_key_values_length = past_key_values[0][0].shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
-
-        if position_ids is None:
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-            position_ids = torch.arange(
-                past_key_values_length,
-                seq_length + past_key_values_length,
-                dtype=torch.long,
-                device=device,
-            )
-            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-        else:
-            position_ids = position_ids.view(-1, seq_length).long()
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-        # embed positions
-        if attention_mask is None:
-            attention_mask = torch.ones(
-                (batch_size, seq_length_with_past),
-                dtype=torch.bool,
-                device=inputs_embeds.device,
-            )
-        attention_mask = self._prepare_decoder_attention_mask(
-            attention_mask,
-            (batch_size, seq_length),
-            inputs_embeds,
-            past_key_values_length,
-        )
-
-        hidden_states = inputs_embeds
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
-                use_cache = False
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = () if use_cache else None
-
-        for idx, decoder_layer in enumerate(self.layers):
-            if output_hidden_states:
-                all_hidden_states += (hidden_states, )
-
-            past_key_value = past_key_values[idx] if past_key_values is not None else None
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs, output_attentions, None)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                    None,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_value,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if use_cache:
-                next_decoder_cache += (layer_outputs[2 if output_attentions else 1], )
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1], )
-
-        hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states, )
-
-        next_cache = next_decoder_cache if use_cache else None
-        if not return_dict:
-            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-
-
-class LlamaForCausalLM(LlamaPreTrainedModel):
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = LlamaModel(config)
-
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        r"""
-        Args:
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, LlamaForCausalLM
-
-        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
-        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
-
-        >>> prompt = "Hey, are you consciours? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
-        ```"""
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
-
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-        if not return_dict:
-            output = (logits, ) + outputs[1:]
-            return (loss, ) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        **kwargs,
-    ):
-        if past_key_values:
-            input_ids = input_ids[:, -1:]
-
-        position_ids = kwargs.get("position_ids", None)
-        if attention_mask is not None and position_ids is None:
-            # create position_ids on the fly for batch generation
-            position_ids = attention_mask.long().cumsum(-1) - 1
-            position_ids.masked_fill_(attention_mask == 0, 1)
-            if past_key_values:
-                position_ids = position_ids[:, -1].unsqueeze(-1)
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and past_key_values is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-
-        model_inputs.update({
-            "position_ids": position_ids,
-            "past_key_values": past_key_values,
-            "use_cache": kwargs.get("use_cache"),
-            "attention_mask": attention_mask,
-        })
-        return model_inputs
-
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past), )
-        return reordered_past
-
-
-@add_start_docstrings(
-    """
-    The LLaMa Model transformer with a sequence classification head on top (linear layer).
-
-    [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """,
-    LLAMA_START_DOCSTRING,
-)
-class LlamaForSequenceClassification(LlamaPreTrainedModel):
-    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = LlamaModel(config)
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_outputs[0]
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.pad_token_id is None and batch_size != 1:
-            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
-        if self.config.pad_token_id is None:
-            sequence_lengths = -1
-        else:
-            if input_ids is not None:
-                sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
-            else:
-                sequence_lengths = -1
-
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
-
-        loss = None
-        if labels is not None:
-            labels = labels.to(logits.device)
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(pooled_logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(pooled_logits, labels)
-        if not return_dict:
-            output = (pooled_logits, ) + transformer_outputs[1:]
-            return ((loss, ) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
diff --git a/spaces/AIZeroToHero/02-Transformers-Sentence2Paragraph/app.py b/spaces/AIZeroToHero/02-Transformers-Sentence2Paragraph/app.py
deleted file mode 100644
index 03750d3ec138fe6a0db80ba5fdeec8e9cc9173d4..0000000000000000000000000000000000000000
--- a/spaces/AIZeroToHero/02-Transformers-Sentence2Paragraph/app.py
+++ /dev/null
@@ -1,24 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-title = "Transformers 📗 Sentence to Paragraph ❤️ For Mindfulness"
-examples = [
-    ["Feel better physically by"],
-    ["Practicing mindfulness each day"],
-    ["Be happier by"],
-    ["Meditation can improve health"],
-    ["Spending time outdoors"],
-    ["Stress is relieved by quieting your mind, getting exercise and time with nature"],
-    ["Break the cycle of stress and anxiety"],
-    ["Feel calm in stressful situations"],
-    ["Deal with work pressure"],
-    ["Learn to reduce feelings of overwhelmed"]
-]
-from gradio import inputs
-from gradio.inputs import Textbox
-from gradio import outputs
-
-generator2 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-2.7B")
-generator3 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B")
-generator1 = gr.Interface.load("huggingface/gpt2-large")
-gr.Parallel(generator1, generator2, generator3, inputs=gr.inputs.Textbox(lines=5, label="Enter a sentence to get another sentence."),
-            title=title, examples=examples).launch(share=False)
\ No newline at end of file
diff --git a/spaces/Adapter/T2I-Adapter/ldm/data/dataset_wikiart.py b/spaces/Adapter/T2I-Adapter/ldm/data/dataset_wikiart.py
deleted file mode 100644
index a7a2de87ccbba147580fed82e3c5e5a5ab38761e..0000000000000000000000000000000000000000
--- a/spaces/Adapter/T2I-Adapter/ldm/data/dataset_wikiart.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import json
-import os.path
-
-from PIL import Image
-from torch.utils.data import DataLoader
-
-from transformers import CLIPProcessor
-from torchvision.transforms import transforms
-
-import pytorch_lightning as pl
-
-
-class WikiArtDataset():
-    def __init__(self, meta_file):
-        super(WikiArtDataset, self).__init__()
-
-        self.files = []
-        with open(meta_file, 'r') as f:
-            js = json.load(f)
-            for img_path in js:
-                img_name = os.path.splitext(os.path.basename(img_path))[0]
-                caption = img_name.split('_')[-1]
-                caption = caption.split('-')
-                j = len(caption) - 1
-                while j >= 0:
-                    if not caption[j].isdigit():
-                        break
-                    j -= 1
-                if j < 0:
-                    continue
-                sentence = ' '.join(caption[:j + 1])
-                self.files.append({'img_path': os.path.join('datasets/wikiart', img_path), 'sentence': sentence})
-
-        version = 'openai/clip-vit-large-patch14'
-        self.processor = CLIPProcessor.from_pretrained(version)
-
-        self.jpg_transform = transforms.Compose([
-            transforms.Resize(512),
-            transforms.RandomCrop(512),
-            transforms.ToTensor(),
-        ])
-
-    def __getitem__(self, idx):
-        file = self.files[idx]
-
-        im = Image.open(file['img_path'])
-
-        im_tensor = self.jpg_transform(im)
-
-        clip_im = self.processor(images=im, return_tensors="pt")['pixel_values'][0]
-
-        return {'jpg': im_tensor, 'style': clip_im, 'txt': file['sentence']}
-
-    def __len__(self):
-        return len(self.files)
-
-
-class WikiArtDataModule(pl.LightningDataModule):
-    def __init__(self, meta_file, batch_size, num_workers):
-        super(WikiArtDataModule, self).__init__()
-        self.train_dataset = WikiArtDataset(meta_file)
-        self.batch_size = batch_size
-        self.num_workers = num_workers
-
-    def train_dataloader(self):
-        return DataLoader(self.train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers,
-                          pin_memory=True)
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/board/match/GetMatchN.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/board/match/GetMatchN.js
deleted file mode 100644
index 8d608ba8c17f6b3b0099e7f6e0537e8ff510d399..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/board/match/GetMatchN.js
+++ /dev/null
@@ -1,6 +0,0 @@
-var GetMatchN = function (n, callback, scope) {
-    this.match.match(n, callback, scope);
-    return this;
-}
-
-export default GetMatchN;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/filechooser/FileChooser.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/filechooser/FileChooser.js
deleted file mode 100644
index 1cfe923e7b1766d895987381d47764c6ffa8a91d..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/filechooser/FileChooser.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import { OpenFileChooser, FileChooser } from '../../../plugins/filechooser.js';
-export { OpenFileChooser, FileChooser };
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateNinePatch.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateNinePatch.js
deleted file mode 100644
index 535fca4e7dd4723deca6cf3a1ffb9beaf77b81f7..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateNinePatch.js
+++ /dev/null
@@ -1,15 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import NinePatch from '../../ninepatch/NinePatch.js';
-import SetTextureProperties from './utils/SetTextureProperties.js';
-
-var CreateNinePatch = function (scene, data, view, styles, customBuilders) {
-    data = MergeStyle(data, styles);
-
-    var gameObject = new NinePatch(scene, data);
-
-    SetTextureProperties(gameObject, data);
-
-    scene.add.existing(gameObject);
-    return gameObject;
-}
-export default CreateNinePatch;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/Factory.js
deleted file mode 100644
index 73d3d98b4c23a2daee82715e2c0f670c5f189261..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/Factory.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import Sizer from './Sizer.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('sizer', function (x, y, minWidth, minHeight, orientation, config) {
-    var gameObject = new Sizer(this.scene, x, y, minWidth, minHeight, orientation, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.Sizer', Sizer);
-
-export default Sizer;
\ No newline at end of file
diff --git a/spaces/AlexWang/lama/saicinpainting/training/losses/feature_matching.py b/spaces/AlexWang/lama/saicinpainting/training/losses/feature_matching.py
deleted file mode 100644
index c019895c9178817837d1a6773367b178a861dc61..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/saicinpainting/training/losses/feature_matching.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from typing import List
-
-import torch
-import torch.nn.functional as F
-
-
-def masked_l2_loss(pred, target, mask, weight_known, weight_missing):
-    per_pixel_l2 = F.mse_loss(pred, target, reduction='none')
-    pixel_weights = mask * weight_missing + (1 - mask) * weight_known
-    return (pixel_weights * per_pixel_l2).mean()
-
-
-def masked_l1_loss(pred, target, mask, weight_known, weight_missing):
-    per_pixel_l1 = F.l1_loss(pred, target, reduction='none')
-    pixel_weights = mask * weight_missing + (1 - mask) * weight_known
-    return (pixel_weights * per_pixel_l1).mean()
-
-
-def feature_matching_loss(fake_features: List[torch.Tensor], target_features: List[torch.Tensor], mask=None):
-    if mask is None:
-        res = torch.stack([F.mse_loss(fake_feat, target_feat)
-                           for fake_feat, target_feat in zip(fake_features, target_features)]).mean()
-    else:
-        res = 0
-        norm = 0
-        for fake_feat, target_feat in zip(fake_features, target_features):
-            cur_mask = F.interpolate(mask, size=fake_feat.shape[-2:], mode='bilinear', align_corners=False)
-            error_weights = 1 - cur_mask
-            cur_val = ((fake_feat - target_feat).pow(2) * error_weights).mean()
-            res = res + cur_val
-            norm += 1
-        res = res / norm
-    return res
diff --git a/spaces/Alfaxad/BioGalacticModels/style.css b/spaces/Alfaxad/BioGalacticModels/style.css
deleted file mode 100644
index 2bbae72066829f9632c28bf98971c250129f4a18..0000000000000000000000000000000000000000
--- a/spaces/Alfaxad/BioGalacticModels/style.css
+++ /dev/null
@@ -1,20 +0,0 @@
-h1 {
-    text-align: center;
-  }
-  table a {
-    background-color: transparent;
-    color: #58a6ff;
-    text-decoration: none;
-  }
-  a:active,
-  a:hover {
-    outline-width: 0;
-  }
-  a:hover {
-    text-decoration: underline;
-  }
-  table, th, td {
-    border: 1px solid;
-  }
-
-  
\ No newline at end of file
diff --git a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/decoder/utils.py b/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/decoder/utils.py
deleted file mode 100644
index e070c456ca40e753c5bf6ec0c858d4f14269385d..0000000000000000000000000000000000000000
--- a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/decoder/utils.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import torch
-
-def compute_accumulated_transmittance(alphas):
-    accumulated_transmittance = torch.cumprod(alphas, 1)
-    return torch.cat((torch.ones((accumulated_transmittance.shape[0], 1), device=alphas.device),
-                      accumulated_transmittance[:, :-1]), dim=-1)
-
-def render_rays(nerf_model, ray_origins, ray_directions, hn=0, hf=0.5, nb_bins=192):
-    device = ray_origins.device
-
-    t = torch.linspace(hn, hf, nb_bins, device=device).expand(ray_origins.shape[0], nb_bins)
-    # Perturb sampling along each ray.
-    mid = (t[:, :-1] + t[:, 1:]) / 2.
-    lower = torch.cat((t[:, :1], mid), -1)
-    upper = torch.cat((mid, t[:, -1:]), -1)
-    u = torch.rand(t.shape, device=device)
-    t = lower + (upper - lower) * u  # [batch_size, nb_bins]
-    delta = torch.cat((t[:, 1:] - t[:, :-1], torch.tensor([1e10], device=device).expand(ray_origins.shape[0], 1)), -1)
-
-    # Compute the 3D points along each ray
-    x = ray_origins.unsqueeze(1) + t.unsqueeze(2) * ray_directions.unsqueeze(1)  # [batch_size, nb_bins, 3]
-    # Expand the ray_directions tensor to match the shape of x
-    ray_directions = ray_directions.expand(nb_bins, ray_directions.shape[0], 3).transpose(0, 1)
-
-    colors, sigma = nerf_model(x.reshape(-1, 3), ray_directions.reshape(-1, 3))
-    colors = colors.reshape(x.shape)
-    sigma = sigma.reshape(x.shape[:-1])
-
-    alpha = 1 - torch.exp(-sigma * delta)  # [batch_size, nb_bins]
-    weights = compute_accumulated_transmittance(1 - alpha).unsqueeze(2) * alpha.unsqueeze(2)
-    # Compute the pixel values as a weighted sum of colors along each ray
-    c = (weights * colors).sum(dim=1)
-    weight_sum = weights.sum(-1).sum(-1)  # Regularization for white background
-    return c + 1 - weight_sum.unsqueeze(-1)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/controlnet.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/controlnet.md
deleted file mode 100644
index ab5ddc9b29a2c8db1adc95dceaa78f4075d96197..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/controlnet.md
+++ /dev/null
@@ -1,350 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# ControlNet
-
-[Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang and Maneesh Agrawala.
-
-Using a pretrained model, we can provide control images (for example, a depth map) to control Stable Diffusion text-to-image generation so that it follows the structure of the depth image and fills in the details.
-
-The abstract from the paper is:
-
-*We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.*
-
-This model was contributed by [takuma104](https://huggingface.co/takuma104). ❤️
-
-The original codebase can be found at [lllyasviel/ControlNet](https://github.com/lllyasviel/ControlNet).
-
-## Usage example
-
-In the following we give a simple example of how to use a *ControlNet* checkpoint with Diffusers for inference.
-The inference pipeline is the same for all pipelines:
-
-* 1. Take an image and run it through a pre-conditioning processor.
-* 2. Run the pre-processed image through the [`StableDiffusionControlNetPipeline`].
-
-Let's have a look at a simple example using the [Canny Edge ControlNet](https://huggingface.co/lllyasviel/sd-controlnet-canny).
-
-```python
-from diffusers import StableDiffusionControlNetPipeline
-from diffusers.utils import load_image
-
-# Let's load the popular vermeer image
-image = load_image(
-    "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
-)
-```
-
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)
-
-Next, we process the image to get the canny image. This is step *1.* - running the pre-conditioning processor. The pre-conditioning processor is different for every ControlNet. Please see the model cards of the [official checkpoints](#controlnet-with-stable-diffusion-1.5) for more information about other models.
-
-First, we need to install opencv:
-
-```
-pip install opencv-contrib-python
-```
-
-Next, let's also install all required Hugging Face libraries:
-
-```
-pip install diffusers transformers git+https://github.com/huggingface/accelerate.git
-```
-
-Then we can retrieve the canny edges of the image.
-
-```python
-import cv2
-from PIL import Image
-import numpy as np
-
-image = np.array(image)
-
-low_threshold = 100
-high_threshold = 200
-
-image = cv2.Canny(image, low_threshold, high_threshold)
-image = image[:, :, None]
-image = np.concatenate([image, image, image], axis=2)
-canny_image = Image.fromarray(image)
-```
-
-Let's take a look at the processed image.
-
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_canny_edged.png)
-
-Now, we load the official [Stable Diffusion 1.5 Model](runwayml/stable-diffusion-v1-5) as well as the ControlNet for canny edges.
-
-```py
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-
-controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
-)
-```
-
-To speed-up things and reduce memory, let's enable model offloading and use the fast [`UniPCMultistepScheduler`].
-
-```py
-from diffusers import UniPCMultistepScheduler
-
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-
-# this command loads the individual model components on GPU on-demand.
-pipe.enable_model_cpu_offload()
-```
-
-Finally, we can run the pipeline:
-
-```py
-generator = torch.manual_seed(0)
-
-out_image = pipe(
-    "disco dancer with colorful lights", num_inference_steps=20, generator=generator, image=canny_image
-).images[0]
-```
-
-This should take only around 3-4 seconds on GPU (depending on hardware). The output image then looks as follows:
-
-![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_disco_dancing.png)
-
-
-**Note**: To see how to run all other ControlNet checkpoints, please have a look at [ControlNet with Stable Diffusion 1.5](#controlnet-with-stable-diffusion-1.5).
-
-<!-- TODO: add space -->
-
-## Combining multiple conditionings
-
-Multiple ControlNet conditionings can be combined for a single image generation. Pass a list of ControlNets to the pipeline's constructor and a corresponding list of conditionings to `__call__`.
-
-When combining conditionings, it is helpful to mask conditionings such that they do not overlap. In the example, we mask the middle of the canny map where the pose conditioning is located.
-
-It can also be helpful to vary the `controlnet_conditioning_scales` to emphasize one conditioning over the other.
-
-### Canny conditioning
-
-The original image:
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"/>
-
-Prepare the conditioning:
-
-```python 
-from diffusers.utils import load_image
-from PIL import Image
-import cv2
-import numpy as np
-from diffusers.utils import load_image
-
-canny_image = load_image(
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"
-)
-canny_image = np.array(canny_image)
-
-low_threshold = 100
-high_threshold = 200
-
-canny_image = cv2.Canny(canny_image, low_threshold, high_threshold)
-
-# zero out middle columns of image where pose will be overlayed
-zero_start = canny_image.shape[1] // 4
-zero_end = zero_start + canny_image.shape[1] // 2
-canny_image[:, zero_start:zero_end] = 0
-
-canny_image = canny_image[:, :, None]
-canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
-canny_image = Image.fromarray(canny_image)
-```
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/landscape_canny_masked.png"/>
-
-### Openpose conditioning
-
-The original image:
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png" width=600/>
-
-Prepare the conditioning:
-
-```python
-from controlnet_aux import OpenposeDetector
-from diffusers.utils import load_image
-
-openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
-
-openpose_image = load_image(
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
-)
-openpose_image = openpose(openpose_image)
-```
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/person_pose.png" width=600/>
-
-### Running ControlNet with multiple conditionings
-
-```python
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
-import torch
-
-controlnet = [
-    ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16),
-    ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16),
-]
-
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
-)
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-
-pipe.enable_xformers_memory_efficient_attention()
-pipe.enable_model_cpu_offload()
-
-prompt = "a giant standing in a fantasy landscape, best quality"
-negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-
-generator = torch.Generator(device="cpu").manual_seed(1)
-
-images = [openpose_image, canny_image]
-
-image = pipe(
-    prompt,
-    images,
-    num_inference_steps=20,
-    generator=generator,
-    negative_prompt=negative_prompt,
-    controlnet_conditioning_scale=[1.0, 0.8],
-).images[0]
-
-image.save("./multi_controlnet_output.png")
-```
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/multi_controlnet_output.png" width=600/>
-
-### Guess Mode
-
-Guess Mode is [a ControlNet feature that was implemented](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode) after the publication of [the paper](https://arxiv.org/abs/2302.05543). The description states:
-
->In this mode, the ControlNet encoder will try best to recognize the content of the input control map, like depth map, edge map, scribbles, etc, even if you remove all prompts.
-
-#### The core implementation:
-
-It adjusts the scale of the output residuals from ControlNet by a fixed ratio depending on the block depth. The shallowest DownBlock corresponds to `0.1`. As the blocks get deeper, the scale increases exponentially, and the scale for the output of the MidBlock becomes `1.0`. 
-
-Since the core implementation is just this, **it does not have any impact on prompt conditioning**. While it is common to use it without specifying any prompts, it is also possible to provide prompts if desired.
-
-#### Usage:
-
-Just specify `guess_mode=True` in the pipe() function. A `guidance_scale` between 3.0 and 5.0 is [recommended](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode).
-```py
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-import torch
-
-controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
-pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet).to(
-    "cuda"
-)
-image = pipe("", image=canny_image, guess_mode=True, guidance_scale=3.0).images[0]
-image.save("guess_mode_generated.png")
-```
-
-#### Output image comparison:
-Canny Control Example
-
-|no guess_mode with prompt|guess_mode without prompt|
-|---|---|
-|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0.png"><img width="128" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0_gm.png"><img width="128" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0_gm.png"/></a>|
-
-
-## Available checkpoints
-
-ControlNet requires a *control image* in addition to the text-to-image *prompt*. 
-Each pretrained model is trained using a different conditioning method that requires different images for conditioning the generated outputs. For example, Canny edge conditioning requires the control image to be the output of a Canny filter, while depth conditioning requires the control image to be a depth map. See the overview and image examples below to know more.
-
-All checkpoints can be found under the authors' namespace [lllyasviel](https://huggingface.co/lllyasviel).
-
-**13.04.2024 Update**: The author has released improved controlnet checkpoints v1.1 - see [here](#controlnet-v1.1).
-
-### ControlNet v1.0
-
-| Model Name | Control Image Overview| Control Image Example | Generated Image Example |
-|---|---|---|---|
-|[lllyasviel/sd-controlnet-canny](https://huggingface.co/lllyasviel/sd-controlnet-canny)<br/> *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_canny.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_canny.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_canny_1.png"/></a>|
-|[lllyasviel/sd-controlnet-depth](https://huggingface.co/lllyasviel/sd-controlnet-depth)<br/> *Trained with Midas depth estimation*  |A grayscale image with black representing deep areas and white representing shallow areas.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_depth.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_depth.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_depth_2.png"/></a>|
-|[lllyasviel/sd-controlnet-hed](https://huggingface.co/lllyasviel/sd-controlnet-hed)<br/> *Trained with HED edge detection (soft edge)*  |A monochrome image with white soft edges on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_bird_hed.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_bird_hed.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_bird_hed_1.png"/></a> |
-|[lllyasviel/sd-controlnet-mlsd](https://huggingface.co/lllyasviel/sd-controlnet-mlsd)<br/> *Trained with M-LSD line detection*  |A monochrome image composed only of white straight lines on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_mlsd.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_mlsd.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_mlsd_0.png"/></a>|
-|[lllyasviel/sd-controlnet-normal](https://huggingface.co/lllyasviel/sd-controlnet-normal)<br/> *Trained with normal map*  |A [normal mapped](https://en.wikipedia.org/wiki/Normal_mapping) image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_normal.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_normal.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_normal_1.png"/></a>|
-|[lllyasviel/sd-controlnet-openpose](https://huggingface.co/lllyasviel/sd-controlnet_openpose)<br/> *Trained with OpenPose bone image*  |A [OpenPose bone](https://github.com/CMU-Perceptual-Computing-Lab/openpose) image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_human_openpose.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_openpose.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_human_openpose_0.png"/></a>|
-|[lllyasviel/sd-controlnet-scribble](https://huggingface.co/lllyasviel/sd-controlnet_scribble)<br/> *Trained with human scribbles*  |A hand-drawn monochrome image with white outlines on a black background.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_vermeer_scribble.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_vermeer_scribble.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_vermeer_scribble_0.png"/></a> |
-|[lllyasviel/sd-controlnet-seg](https://huggingface.co/lllyasviel/sd-controlnet_seg)<br/>*Trained with semantic segmentation*  |An [ADE20K](https://groups.csail.mit.edu/vision/datasets/ADE20K/)'s segmentation protocol image.|<a href="https://huggingface.co/takuma104/controlnet_dev/blob/main/gen_compare/control_images/converted/control_room_seg.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_room_seg.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"><img width="64" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/output_images/diffusers/output_room_seg_1.png"/></a> |
-
-### ControlNet v1.1
-
-| Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example |
-|---|---|---|---|---|
-|[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)<br/> | *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)<br/> | *Trained with pixel to pixel instruction* | No condition .|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)<br/> | Trained with image inpainting | No condition.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"/></a>|
-|[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)<br/> | Trained with multi-level line segment detection | An image with annotated line segments.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)<br/> | Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)<br/> | Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)<br/> | Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)<br/> | Trained with line art generation | An image with line art, usually black lines on a white background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with anime line art generation | An image with anime-style line art.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)<br/> | Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)<br/> | Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)<br/> | Trained with image shuffling | An image with shuffled patches or regions.|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"/></a>|
-|[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)<br/> | Trained with image tiling | A blurry image or part of an image .|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"/></a>|
-
-## StableDiffusionControlNetPipeline
-[[autodoc]] StableDiffusionControlNetPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-
-## StableDiffusionControlNetImg2ImgPipeline
-[[autodoc]] StableDiffusionControlNetImg2ImgPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-
-## StableDiffusionControlNetInpaintPipeline
-[[autodoc]] StableDiffusionControlNetInpaintPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_vae_slicing
-	- disable_vae_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-
-## FlaxStableDiffusionControlNetPipeline
-[[autodoc]] FlaxStableDiffusionControlNetPipeline
-	- all
-	- __call__
-
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/checkpoint_merger.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/checkpoint_merger.py
deleted file mode 100644
index 3e29ae50078b8db4264e8cbaf83f6670fb84217c..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/checkpoint_merger.py
+++ /dev/null
@@ -1,286 +0,0 @@
-import glob
-import os
-from typing import Dict, List, Union
-
-import torch
-
-from diffusers.utils import is_safetensors_available
-
-
-if is_safetensors_available():
-    import safetensors.torch
-
-from huggingface_hub import snapshot_download
-
-from diffusers import DiffusionPipeline, __version__
-from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
-from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME
-
-
-class CheckpointMergerPipeline(DiffusionPipeline):
-    """
-    A class that that supports merging diffusion models based on the discussion here:
-    https://github.com/huggingface/diffusers/issues/877
-
-    Example usage:-
-
-    pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger.py")
-
-    merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","prompthero/openjourney"], interp = 'inv_sigmoid', alpha = 0.8, force = True)
-
-    merged_pipe.to('cuda')
-
-    prompt = "An astronaut riding a unicycle on Mars"
-
-    results = merged_pipe(prompt)
-
-    ## For more details, see the docstring for the merge method.
-
-    """
-
-    def __init__(self):
-        self.register_to_config()
-        super().__init__()
-
-    def _compare_model_configs(self, dict0, dict1):
-        if dict0 == dict1:
-            return True
-        else:
-            config0, meta_keys0 = self._remove_meta_keys(dict0)
-            config1, meta_keys1 = self._remove_meta_keys(dict1)
-            if config0 == config1:
-                print(f"Warning !: Mismatch in keys {meta_keys0} and {meta_keys1}.")
-                return True
-        return False
-
-    def _remove_meta_keys(self, config_dict: Dict):
-        meta_keys = []
-        temp_dict = config_dict.copy()
-        for key in config_dict.keys():
-            if key.startswith("_"):
-                temp_dict.pop(key)
-                meta_keys.append(key)
-        return (temp_dict, meta_keys)
-
-    @torch.no_grad()
-    def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]], **kwargs):
-        """
-        Returns a new pipeline object of the class 'DiffusionPipeline' with the merged checkpoints(weights) of the models passed
-        in the argument 'pretrained_model_name_or_path_list' as a list.
-
-        Parameters:
-        -----------
-            pretrained_model_name_or_path_list : A list of valid pretrained model names in the HuggingFace hub or paths to locally stored models in the HuggingFace format.
-
-            **kwargs:
-                Supports all the default DiffusionPipeline.get_config_dict kwargs viz..
-
-                cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map.
-
-                alpha - The interpolation parameter. Ranges from 0 to 1.  It affects the ratio in which the checkpoints are merged. A 0.8 alpha
-                    would mean that the first model checkpoints would affect the final result far less than an alpha of 0.2
-
-                interp - The interpolation method to use for the merging. Supports "sigmoid", "inv_sigmoid", "add_diff" and None.
-                    Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_diff" is supported.
-
-                force - Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
-
-        """
-        # Default kwargs from DiffusionPipeline
-        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
-        resume_download = kwargs.pop("resume_download", False)
-        force_download = kwargs.pop("force_download", False)
-        proxies = kwargs.pop("proxies", None)
-        local_files_only = kwargs.pop("local_files_only", False)
-        use_auth_token = kwargs.pop("use_auth_token", None)
-        revision = kwargs.pop("revision", None)
-        torch_dtype = kwargs.pop("torch_dtype", None)
-        device_map = kwargs.pop("device_map", None)
-
-        alpha = kwargs.pop("alpha", 0.5)
-        interp = kwargs.pop("interp", None)
-
-        print("Received list", pretrained_model_name_or_path_list)
-        print(f"Combining with alpha={alpha}, interpolation mode={interp}")
-
-        checkpoint_count = len(pretrained_model_name_or_path_list)
-        # Ignore result from model_index_json comparision of the two checkpoints
-        force = kwargs.pop("force", False)
-
-        # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
-        if checkpoint_count > 3 or checkpoint_count < 2:
-            raise ValueError(
-                "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
-                " passed."
-            )
-
-        print("Received the right number of checkpoints")
-        # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
-        # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None
-
-        # Validate that the checkpoints can be merged
-        # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
-        config_dicts = []
-        for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
-            config_dict = DiffusionPipeline.load_config(
-                pretrained_model_name_or_path,
-                cache_dir=cache_dir,
-                resume_download=resume_download,
-                force_download=force_download,
-                proxies=proxies,
-                local_files_only=local_files_only,
-                use_auth_token=use_auth_token,
-                revision=revision,
-            )
-            config_dicts.append(config_dict)
-
-        comparison_result = True
-        for idx in range(1, len(config_dicts)):
-            comparison_result &= self._compare_model_configs(config_dicts[idx - 1], config_dicts[idx])
-            if not force and comparison_result is False:
-                raise ValueError("Incompatible checkpoints. Please check model_index.json for the models.")
-                print(config_dicts[0], config_dicts[1])
-        print("Compatible model_index.json files found")
-        # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
-        cached_folders = []
-        for pretrained_model_name_or_path, config_dict in zip(pretrained_model_name_or_path_list, config_dicts):
-            folder_names = [k for k in config_dict.keys() if not k.startswith("_")]
-            allow_patterns = [os.path.join(k, "*") for k in folder_names]
-            allow_patterns += [
-                WEIGHTS_NAME,
-                SCHEDULER_CONFIG_NAME,
-                CONFIG_NAME,
-                ONNX_WEIGHTS_NAME,
-                DiffusionPipeline.config_name,
-            ]
-            requested_pipeline_class = config_dict.get("_class_name")
-            user_agent = {"diffusers": __version__, "pipeline_class": requested_pipeline_class}
-
-            cached_folder = (
-                pretrained_model_name_or_path
-                if os.path.isdir(pretrained_model_name_or_path)
-                else snapshot_download(
-                    pretrained_model_name_or_path,
-                    cache_dir=cache_dir,
-                    resume_download=resume_download,
-                    proxies=proxies,
-                    local_files_only=local_files_only,
-                    use_auth_token=use_auth_token,
-                    revision=revision,
-                    allow_patterns=allow_patterns,
-                    user_agent=user_agent,
-                )
-            )
-            print("Cached Folder", cached_folder)
-            cached_folders.append(cached_folder)
-
-        # Step 3:-
-        # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
-        final_pipe = DiffusionPipeline.from_pretrained(
-            cached_folders[0], torch_dtype=torch_dtype, device_map=device_map
-        )
-        final_pipe.to(self.device)
-
-        checkpoint_path_2 = None
-        if len(cached_folders) > 2:
-            checkpoint_path_2 = os.path.join(cached_folders[2])
-
-        if interp == "sigmoid":
-            theta_func = CheckpointMergerPipeline.sigmoid
-        elif interp == "inv_sigmoid":
-            theta_func = CheckpointMergerPipeline.inv_sigmoid
-        elif interp == "add_diff":
-            theta_func = CheckpointMergerPipeline.add_difference
-        else:
-            theta_func = CheckpointMergerPipeline.weighted_sum
-
-        # Find each module's state dict.
-        for attr in final_pipe.config.keys():
-            if not attr.startswith("_"):
-                checkpoint_path_1 = os.path.join(cached_folders[1], attr)
-                if os.path.exists(checkpoint_path_1):
-                    files = [
-                        *glob.glob(os.path.join(checkpoint_path_1, "*.safetensors")),
-                        *glob.glob(os.path.join(checkpoint_path_1, "*.bin")),
-                    ]
-                    checkpoint_path_1 = files[0] if len(files) > 0 else None
-                if len(cached_folders) < 3:
-                    checkpoint_path_2 = None
-                else:
-                    checkpoint_path_2 = os.path.join(cached_folders[2], attr)
-                    if os.path.exists(checkpoint_path_2):
-                        files = [
-                            *glob.glob(os.path.join(checkpoint_path_2, "*.safetensors")),
-                            *glob.glob(os.path.join(checkpoint_path_2, "*.bin")),
-                        ]
-                        checkpoint_path_2 = files[0] if len(files) > 0 else None
-                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
-                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
-                if checkpoint_path_1 is None and checkpoint_path_2 is None:
-                    print(f"Skipping {attr}: not present in 2nd or 3d model")
-                    continue
-                try:
-                    module = getattr(final_pipe, attr)
-                    if isinstance(module, bool):  # ignore requires_safety_checker boolean
-                        continue
-                    theta_0 = getattr(module, "state_dict")
-                    theta_0 = theta_0()
-
-                    update_theta_0 = getattr(module, "load_state_dict")
-                    theta_1 = (
-                        safetensors.torch.load_file(checkpoint_path_1)
-                        if (is_safetensors_available() and checkpoint_path_1.endswith(".safetensors"))
-                        else torch.load(checkpoint_path_1, map_location="cpu")
-                    )
-                    theta_2 = None
-                    if checkpoint_path_2:
-                        theta_2 = (
-                            safetensors.torch.load_file(checkpoint_path_2)
-                            if (is_safetensors_available() and checkpoint_path_2.endswith(".safetensors"))
-                            else torch.load(checkpoint_path_2, map_location="cpu")
-                        )
-
-                    if not theta_0.keys() == theta_1.keys():
-                        print(f"Skipping {attr}: key mismatch")
-                        continue
-                    if theta_2 and not theta_1.keys() == theta_2.keys():
-                        print(f"Skipping {attr}:y mismatch")
-                except Exception as e:
-                    print(f"Skipping {attr} do to an unexpected error: {str(e)}")
-                    continue
-                print(f"MERGING {attr}")
-
-                for key in theta_0.keys():
-                    if theta_2:
-                        theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key], alpha)
-                    else:
-                        theta_0[key] = theta_func(theta_0[key], theta_1[key], None, alpha)
-
-                del theta_1
-                del theta_2
-                update_theta_0(theta_0)
-
-                del theta_0
-        return final_pipe
-
-    @staticmethod
-    def weighted_sum(theta0, theta1, theta2, alpha):
-        return ((1 - alpha) * theta0) + (alpha * theta1)
-
-    # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    @staticmethod
-    def sigmoid(theta0, theta1, theta2, alpha):
-        alpha = alpha * alpha * (3 - (2 * alpha))
-        return theta0 + ((theta1 - theta0) * alpha)
-
-    # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    @staticmethod
-    def inv_sigmoid(theta0, theta1, theta2, alpha):
-        import math
-
-        alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
-        return theta0 + ((theta1 - theta0) * alpha)
-
-    @staticmethod
-    def add_difference(theta0, theta1, theta2, alpha):
-        return theta0 + (theta1 - theta2) * (1.0 - alpha)
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/controlnet/train_controlnet_flax.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/controlnet/train_controlnet_flax.py
deleted file mode 100644
index f993324781813f33d44c8a6b76054856cb94f97d..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/controlnet/train_controlnet_flax.py
+++ /dev/null
@@ -1,1146 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-
-import argparse
-import logging
-import math
-import os
-import random
-import time
-from pathlib import Path
-
-import jax
-import jax.numpy as jnp
-import numpy as np
-import optax
-import torch
-import torch.utils.checkpoint
-import transformers
-from datasets import load_dataset, load_from_disk
-from flax import jax_utils
-from flax.core.frozen_dict import unfreeze
-from flax.training import train_state
-from flax.training.common_utils import shard
-from huggingface_hub import create_repo, upload_folder
-from PIL import Image, PngImagePlugin
-from torch.utils.data import IterableDataset
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTokenizer, FlaxCLIPTextModel, set_seed
-
-from diffusers import (
-    FlaxAutoencoderKL,
-    FlaxControlNetModel,
-    FlaxDDPMScheduler,
-    FlaxStableDiffusionControlNetPipeline,
-    FlaxUNet2DConditionModel,
-)
-from diffusers.utils import check_min_version, is_wandb_available
-
-
-# To prevent an error that occurs when there are abnormally large compressed data chunk in the png image
-# see more https://github.com/python-pillow/Pillow/issues/5610
-LARGE_ENOUGH_NUMBER = 100
-PngImagePlugin.MAX_TEXT_CHUNK = LARGE_ENOUGH_NUMBER * (1024**2)
-
-if is_wandb_available():
-    import wandb
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.19.0")
-
-logger = logging.getLogger(__name__)
-
-
-def image_grid(imgs, rows, cols):
-    assert len(imgs) == rows * cols
-
-    w, h = imgs[0].size
-    grid = Image.new("RGB", size=(cols * w, rows * h))
-    grid_w, grid_h = grid.size
-
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i % cols * w, i // cols * h))
-    return grid
-
-
-def log_validation(pipeline, pipeline_params, controlnet_params, tokenizer, args, rng, weight_dtype):
-    logger.info("Running validation...")
-
-    pipeline_params = pipeline_params.copy()
-    pipeline_params["controlnet"] = controlnet_params
-
-    num_samples = jax.device_count()
-    prng_seed = jax.random.split(rng, jax.device_count())
-
-    if len(args.validation_image) == len(args.validation_prompt):
-        validation_images = args.validation_image
-        validation_prompts = args.validation_prompt
-    elif len(args.validation_image) == 1:
-        validation_images = args.validation_image * len(args.validation_prompt)
-        validation_prompts = args.validation_prompt
-    elif len(args.validation_prompt) == 1:
-        validation_images = args.validation_image
-        validation_prompts = args.validation_prompt * len(args.validation_image)
-    else:
-        raise ValueError(
-            "number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`"
-        )
-
-    image_logs = []
-
-    for validation_prompt, validation_image in zip(validation_prompts, validation_images):
-        prompts = num_samples * [validation_prompt]
-        prompt_ids = pipeline.prepare_text_inputs(prompts)
-        prompt_ids = shard(prompt_ids)
-
-        validation_image = Image.open(validation_image).convert("RGB")
-        processed_image = pipeline.prepare_image_inputs(num_samples * [validation_image])
-        processed_image = shard(processed_image)
-        images = pipeline(
-            prompt_ids=prompt_ids,
-            image=processed_image,
-            params=pipeline_params,
-            prng_seed=prng_seed,
-            num_inference_steps=50,
-            jit=True,
-        ).images
-
-        images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
-        images = pipeline.numpy_to_pil(images)
-
-        image_logs.append(
-            {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
-        )
-
-    if args.report_to == "wandb":
-        formatted_images = []
-        for log in image_logs:
-            images = log["images"]
-            validation_prompt = log["validation_prompt"]
-            validation_image = log["validation_image"]
-
-            formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))
-            for image in images:
-                image = wandb.Image(image, caption=validation_prompt)
-                formatted_images.append(image)
-
-        wandb.log({"validation": formatted_images})
-    else:
-        logger.warn(f"image logging not implemented for {args.report_to}")
-
-    return image_logs
-
-
-def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
-    img_str = ""
-    if image_logs is not None:
-        for i, log in enumerate(image_logs):
-            images = log["images"]
-            validation_prompt = log["validation_prompt"]
-            validation_image = log["validation_image"]
-            validation_image.save(os.path.join(repo_folder, "image_control.png"))
-            img_str += f"prompt: {validation_prompt}\n"
-            images = [validation_image] + images
-            image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
-            img_str += f"![images_{i})](./images_{i}.png)\n"
-
-    yaml = f"""
----
-license: creativeml-openrail-m
-base_model: {base_model}
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- text-to-image
-- diffusers
-- controlnet
-- jax-diffusers-event
-inference: true
----
-    """
-    model_card = f"""
-# controlnet- {repo_id}
-
-These are controlnet weights trained on {base_model} with new type of conditioning. You can find some example images in the following. \n
-{img_str}
-"""
-    with open(os.path.join(repo_folder, "README.md"), "w") as f:
-        f.write(yaml + model_card)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--controlnet_model_name_or_path",
-        type=str,
-        default=None,
-        help="Path to pretrained controlnet model or model identifier from huggingface.co/models."
-        " If not specified controlnet weights are initialized from unet.",
-    )
-    parser.add_argument(
-        "--revision",
-        type=str,
-        default=None,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--from_pt",
-        action="store_true",
-        help="Load the pretrained model from a PyTorch checkpoint.",
-    )
-    parser.add_argument(
-        "--controlnet_revision",
-        type=str,
-        default=None,
-        help="Revision of controlnet model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--profile_steps",
-        type=int,
-        default=0,
-        help="How many training steps to profile in the beginning.",
-    )
-    parser.add_argument(
-        "--profile_validation",
-        action="store_true",
-        help="Whether to profile the (last) validation.",
-    )
-    parser.add_argument(
-        "--profile_memory",
-        action="store_true",
-        help="Whether to dump an initial (before training loop) and a final (at program end) memory profile.",
-    )
-    parser.add_argument(
-        "--ccache",
-        type=str,
-        default=None,
-        help="Enables compilation cache.",
-    )
-    parser.add_argument(
-        "--controlnet_from_pt",
-        action="store_true",
-        help="Load the controlnet model from a PyTorch checkpoint.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="runs/{timestamp}",
-        help="The output directory where the model predictions and checkpoints will be written. "
-        "Can contain placeholders: {timestamp}.",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        type=str,
-        default=None,
-        help="The directory where the downloaded models and datasets will be stored.",
-    )
-    parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=100)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.",
-    )
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=int,
-        default=5000,
-        help=("Save a checkpoint of the training state every X updates."),
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--snr_gamma",
-        type=float,
-        default=None,
-        help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
-        "More details here: https://arxiv.org/abs/2303.09556.",
-    )
-    parser.add_argument(
-        "--dataloader_num_workers",
-        type=int,
-        default=0,
-        help=(
-            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
-        ),
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_steps",
-        type=int,
-        default=100,
-        help=("log training metric every X steps to `--report_t`"),
-    )
-    parser.add_argument(
-        "--report_to",
-        type=str,
-        default="wandb",
-        help=('The integration to report the results and logs to. Currently only supported platforms are `"wandb"`'),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose"
-            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
-            "and an Nvidia Ampere GPU."
-        ),
-    )
-    parser.add_argument(
-        "--dataset_name",
-        type=str,
-        default=None,
-        help=(
-            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
-            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
-            " or to a folder containing files that 🤗 Datasets can understand."
-        ),
-    )
-    parser.add_argument("--streaming", action="store_true", help="To stream a large dataset from Hub.")
-    parser.add_argument(
-        "--dataset_config_name",
-        type=str,
-        default=None,
-        help="The config of the Dataset, leave as None if there's only one config.",
-    )
-    parser.add_argument(
-        "--train_data_dir",
-        type=str,
-        default=None,
-        help=(
-            "A folder containing the training dataset. By default it will use `load_dataset` method to load a custom dataset from the folder."
-            "Folder must contain a dataset script as described here https://huggingface.co/docs/datasets/dataset_script) ."
-            "If `--load_from_disk` flag is passed, it will use `load_from_disk` method instead. Ignored if `dataset_name` is specified."
-        ),
-    )
-    parser.add_argument(
-        "--load_from_disk",
-        action="store_true",
-        help=(
-            "If True, will load a dataset that was previously saved using `save_to_disk` from `--train_data_dir`"
-            "See more https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.load_from_disk"
-        ),
-    )
-    parser.add_argument(
-        "--image_column", type=str, default="image", help="The column of the dataset containing the target image."
-    )
-    parser.add_argument(
-        "--conditioning_image_column",
-        type=str,
-        default="conditioning_image",
-        help="The column of the dataset containing the controlnet conditioning image.",
-    )
-    parser.add_argument(
-        "--caption_column",
-        type=str,
-        default="text",
-        help="The column of the dataset containing a caption or a list of captions.",
-    )
-    parser.add_argument(
-        "--max_train_samples",
-        type=int,
-        default=None,
-        help=(
-            "For debugging purposes or quicker training, truncate the number of training examples to this "
-            "value if set. Needed if `streaming` is set to True."
-        ),
-    )
-    parser.add_argument(
-        "--proportion_empty_prompts",
-        type=float,
-        default=0,
-        help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
-    )
-    parser.add_argument(
-        "--validation_prompt",
-        type=str,
-        default=None,
-        nargs="+",
-        help=(
-            "A set of prompts evaluated every `--validation_steps` and logged to `--report_to`."
-            " Provide either a matching number of `--validation_image`s, a single `--validation_image`"
-            " to be used with all prompts, or a single prompt that will be used with all `--validation_image`s."
-        ),
-    )
-    parser.add_argument(
-        "--validation_image",
-        type=str,
-        default=None,
-        nargs="+",
-        help=(
-            "A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`"
-            " and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
-            " a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
-            " `--validation_image` that will be used with all `--validation_prompt`s."
-        ),
-    )
-    parser.add_argument(
-        "--validation_steps",
-        type=int,
-        default=100,
-        help=(
-            "Run validation every X steps. Validation consists of running the prompt"
-            " `args.validation_prompt` and logging the images."
-        ),
-    )
-    parser.add_argument("--wandb_entity", type=str, default=None, help=("The wandb entity to use (for teams)."))
-    parser.add_argument(
-        "--tracker_project_name",
-        type=str,
-        default="train_controlnet_flax",
-        help=("The `project` argument passed to wandb"),
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps", type=int, default=1, help="Number of steps to accumulate gradients over"
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
-    args = parser.parse_args()
-    args.output_dir = args.output_dir.replace("{timestamp}", time.strftime("%Y%m%d_%H%M%S"))
-
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    # Sanity checks
-    if args.dataset_name is None and args.train_data_dir is None:
-        raise ValueError("Need either a dataset name or a training folder.")
-    if args.dataset_name is not None and args.train_data_dir is not None:
-        raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`")
-
-    if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
-        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
-
-    if args.validation_prompt is not None and args.validation_image is None:
-        raise ValueError("`--validation_image` must be set if `--validation_prompt` is set")
-
-    if args.validation_prompt is None and args.validation_image is not None:
-        raise ValueError("`--validation_prompt` must be set if `--validation_image` is set")
-
-    if (
-        args.validation_image is not None
-        and args.validation_prompt is not None
-        and len(args.validation_image) != 1
-        and len(args.validation_prompt) != 1
-        and len(args.validation_image) != len(args.validation_prompt)
-    ):
-        raise ValueError(
-            "Must provide either 1 `--validation_image`, 1 `--validation_prompt`,"
-            " or the same number of `--validation_prompt`s and `--validation_image`s"
-        )
-
-    # This idea comes from
-    # https://github.com/borisdayma/dalle-mini/blob/d2be512d4a6a9cda2d63ba04afc33038f98f705f/src/dalle_mini/data.py#L370
-    if args.streaming and args.max_train_samples is None:
-        raise ValueError("You must specify `max_train_samples` when using dataset streaming.")
-
-    return args
-
-
-def make_train_dataset(args, tokenizer, batch_size=None):
-    # Get the datasets: you can either provide your own training and evaluation files (see below)
-    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
-
-    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
-    # download the dataset.
-    if args.dataset_name is not None:
-        # Downloading and loading a dataset from the hub.
-        dataset = load_dataset(
-            args.dataset_name,
-            args.dataset_config_name,
-            cache_dir=args.cache_dir,
-            streaming=args.streaming,
-        )
-    else:
-        if args.train_data_dir is not None:
-            if args.load_from_disk:
-                dataset = load_from_disk(
-                    args.train_data_dir,
-                )
-            else:
-                dataset = load_dataset(
-                    args.train_data_dir,
-                    cache_dir=args.cache_dir,
-                )
-        # See more about loading custom images at
-        # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
-
-    # Preprocessing the datasets.
-    # We need to tokenize inputs and targets.
-    if isinstance(dataset["train"], IterableDataset):
-        column_names = next(iter(dataset["train"])).keys()
-    else:
-        column_names = dataset["train"].column_names
-
-    # 6. Get the column names for input/target.
-    if args.image_column is None:
-        image_column = column_names[0]
-        logger.info(f"image column defaulting to {image_column}")
-    else:
-        image_column = args.image_column
-        if image_column not in column_names:
-            raise ValueError(
-                f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
-            )
-
-    if args.caption_column is None:
-        caption_column = column_names[1]
-        logger.info(f"caption column defaulting to {caption_column}")
-    else:
-        caption_column = args.caption_column
-        if caption_column not in column_names:
-            raise ValueError(
-                f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
-            )
-
-    if args.conditioning_image_column is None:
-        conditioning_image_column = column_names[2]
-        logger.info(f"conditioning image column defaulting to {caption_column}")
-    else:
-        conditioning_image_column = args.conditioning_image_column
-        if conditioning_image_column not in column_names:
-            raise ValueError(
-                f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
-            )
-
-    def tokenize_captions(examples, is_train=True):
-        captions = []
-        for caption in examples[caption_column]:
-            if random.random() < args.proportion_empty_prompts:
-                captions.append("")
-            elif isinstance(caption, str):
-                captions.append(caption)
-            elif isinstance(caption, (list, np.ndarray)):
-                # take a random caption if there are multiple
-                captions.append(random.choice(caption) if is_train else caption[0])
-            else:
-                raise ValueError(
-                    f"Caption column `{caption_column}` should contain either strings or lists of strings."
-                )
-        inputs = tokenizer(
-            captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
-        )
-        return inputs.input_ids
-
-    image_transforms = transforms.Compose(
-        [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
-            transforms.CenterCrop(args.resolution),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5], [0.5]),
-        ]
-    )
-
-    conditioning_image_transforms = transforms.Compose(
-        [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
-            transforms.CenterCrop(args.resolution),
-            transforms.ToTensor(),
-        ]
-    )
-
-    def preprocess_train(examples):
-        images = [image.convert("RGB") for image in examples[image_column]]
-        images = [image_transforms(image) for image in images]
-
-        conditioning_images = [image.convert("RGB") for image in examples[conditioning_image_column]]
-        conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]
-
-        examples["pixel_values"] = images
-        examples["conditioning_pixel_values"] = conditioning_images
-        examples["input_ids"] = tokenize_captions(examples)
-
-        return examples
-
-    if jax.process_index() == 0:
-        if args.max_train_samples is not None:
-            if args.streaming:
-                dataset["train"] = dataset["train"].shuffle(seed=args.seed).take(args.max_train_samples)
-            else:
-                dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
-        # Set the training transforms
-        if args.streaming:
-            train_dataset = dataset["train"].map(
-                preprocess_train,
-                batched=True,
-                batch_size=batch_size,
-                remove_columns=list(dataset["train"].features.keys()),
-            )
-        else:
-            train_dataset = dataset["train"].with_transform(preprocess_train)
-
-    return train_dataset
-
-
-def collate_fn(examples):
-    pixel_values = torch.stack([example["pixel_values"] for example in examples])
-    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
-    conditioning_pixel_values = torch.stack([example["conditioning_pixel_values"] for example in examples])
-    conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float()
-
-    input_ids = torch.stack([example["input_ids"] for example in examples])
-
-    batch = {
-        "pixel_values": pixel_values,
-        "conditioning_pixel_values": conditioning_pixel_values,
-        "input_ids": input_ids,
-    }
-    batch = {k: v.numpy() for k, v in batch.items()}
-    return batch
-
-
-def get_params_to_save(params):
-    return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
-
-
-def main():
-    args = parse_args()
-
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    # Setup logging, we only want one process per machine to log things on the screen.
-    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
-    if jax.process_index() == 0:
-        transformers.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-
-    # wandb init
-    if jax.process_index() == 0 and args.report_to == "wandb":
-        wandb.init(
-            entity=args.wandb_entity,
-            project=args.tracker_project_name,
-            job_type="train",
-            config=args,
-        )
-
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    rng = jax.random.PRNGKey(0)
-
-    # Handle the repository creation
-    if jax.process_index() == 0:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Load the tokenizer and add the placeholder token as a additional special token
-    if args.tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = CLIPTokenizer.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
-        )
-    else:
-        raise NotImplementedError("No tokenizer specified!")
-
-    # Get the datasets: you can either provide your own training and evaluation files (see below)
-    total_train_batch_size = args.train_batch_size * jax.local_device_count() * args.gradient_accumulation_steps
-    train_dataset = make_train_dataset(args, tokenizer, batch_size=total_train_batch_size)
-
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset,
-        shuffle=not args.streaming,
-        collate_fn=collate_fn,
-        batch_size=total_train_batch_size,
-        num_workers=args.dataloader_num_workers,
-        drop_last=True,
-    )
-
-    weight_dtype = jnp.float32
-    if args.mixed_precision == "fp16":
-        weight_dtype = jnp.float16
-    elif args.mixed_precision == "bf16":
-        weight_dtype = jnp.bfloat16
-
-    # Load models and create wrapper for stable diffusion
-    text_encoder = FlaxCLIPTextModel.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="text_encoder",
-        dtype=weight_dtype,
-        revision=args.revision,
-        from_pt=args.from_pt,
-    )
-    vae, vae_params = FlaxAutoencoderKL.from_pretrained(
-        args.pretrained_model_name_or_path,
-        revision=args.revision,
-        subfolder="vae",
-        dtype=weight_dtype,
-        from_pt=args.from_pt,
-    )
-    unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path,
-        subfolder="unet",
-        dtype=weight_dtype,
-        revision=args.revision,
-        from_pt=args.from_pt,
-    )
-
-    if args.controlnet_model_name_or_path:
-        logger.info("Loading existing controlnet weights")
-        controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
-            args.controlnet_model_name_or_path,
-            revision=args.controlnet_revision,
-            from_pt=args.controlnet_from_pt,
-            dtype=jnp.float32,
-        )
-    else:
-        logger.info("Initializing controlnet weights from unet")
-        rng, rng_params = jax.random.split(rng)
-
-        controlnet = FlaxControlNetModel(
-            in_channels=unet.config.in_channels,
-            down_block_types=unet.config.down_block_types,
-            only_cross_attention=unet.config.only_cross_attention,
-            block_out_channels=unet.config.block_out_channels,
-            layers_per_block=unet.config.layers_per_block,
-            attention_head_dim=unet.config.attention_head_dim,
-            cross_attention_dim=unet.config.cross_attention_dim,
-            use_linear_projection=unet.config.use_linear_projection,
-            flip_sin_to_cos=unet.config.flip_sin_to_cos,
-            freq_shift=unet.config.freq_shift,
-        )
-        controlnet_params = controlnet.init_weights(rng=rng_params)
-        controlnet_params = unfreeze(controlnet_params)
-        for key in [
-            "conv_in",
-            "time_embedding",
-            "down_blocks_0",
-            "down_blocks_1",
-            "down_blocks_2",
-            "down_blocks_3",
-            "mid_block",
-        ]:
-            controlnet_params[key] = unet_params[key]
-
-    pipeline, pipeline_params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
-        args.pretrained_model_name_or_path,
-        tokenizer=tokenizer,
-        controlnet=controlnet,
-        safety_checker=None,
-        dtype=weight_dtype,
-        revision=args.revision,
-        from_pt=args.from_pt,
-    )
-    pipeline_params = jax_utils.replicate(pipeline_params)
-
-    # Optimization
-    if args.scale_lr:
-        args.learning_rate = args.learning_rate * total_train_batch_size
-
-    constant_scheduler = optax.constant_schedule(args.learning_rate)
-
-    adamw = optax.adamw(
-        learning_rate=constant_scheduler,
-        b1=args.adam_beta1,
-        b2=args.adam_beta2,
-        eps=args.adam_epsilon,
-        weight_decay=args.adam_weight_decay,
-    )
-
-    optimizer = optax.chain(
-        optax.clip_by_global_norm(args.max_grad_norm),
-        adamw,
-    )
-
-    state = train_state.TrainState.create(apply_fn=controlnet.__call__, params=controlnet_params, tx=optimizer)
-
-    noise_scheduler, noise_scheduler_state = FlaxDDPMScheduler.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="scheduler"
-    )
-
-    # Initialize our training
-    validation_rng, train_rngs = jax.random.split(rng)
-    train_rngs = jax.random.split(train_rngs, jax.local_device_count())
-
-    def compute_snr(timesteps):
-        """
-        Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
-        """
-        alphas_cumprod = noise_scheduler_state.common.alphas_cumprod
-        sqrt_alphas_cumprod = alphas_cumprod**0.5
-        sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
-
-        alpha = sqrt_alphas_cumprod[timesteps]
-        sigma = sqrt_one_minus_alphas_cumprod[timesteps]
-        # Compute SNR.
-        snr = (alpha / sigma) ** 2
-        return snr
-
-    def train_step(state, unet_params, text_encoder_params, vae_params, batch, train_rng):
-        # reshape batch, add grad_step_dim if gradient_accumulation_steps > 1
-        if args.gradient_accumulation_steps > 1:
-            grad_steps = args.gradient_accumulation_steps
-            batch = jax.tree_map(lambda x: x.reshape((grad_steps, x.shape[0] // grad_steps) + x.shape[1:]), batch)
-
-        def compute_loss(params, minibatch, sample_rng):
-            # Convert images to latent space
-            vae_outputs = vae.apply(
-                {"params": vae_params}, minibatch["pixel_values"], deterministic=True, method=vae.encode
-            )
-            latents = vae_outputs.latent_dist.sample(sample_rng)
-            # (NHWC) -> (NCHW)
-            latents = jnp.transpose(latents, (0, 3, 1, 2))
-            latents = latents * vae.config.scaling_factor
-
-            # Sample noise that we'll add to the latents
-            noise_rng, timestep_rng = jax.random.split(sample_rng)
-            noise = jax.random.normal(noise_rng, latents.shape)
-            # Sample a random timestep for each image
-            bsz = latents.shape[0]
-            timesteps = jax.random.randint(
-                timestep_rng,
-                (bsz,),
-                0,
-                noise_scheduler.config.num_train_timesteps,
-            )
-
-            # Add noise to the latents according to the noise magnitude at each timestep
-            # (this is the forward diffusion process)
-            noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)
-
-            # Get the text embedding for conditioning
-            encoder_hidden_states = text_encoder(
-                minibatch["input_ids"],
-                params=text_encoder_params,
-                train=False,
-            )[0]
-
-            controlnet_cond = minibatch["conditioning_pixel_values"]
-
-            # Predict the noise residual and compute loss
-            down_block_res_samples, mid_block_res_sample = controlnet.apply(
-                {"params": params},
-                noisy_latents,
-                timesteps,
-                encoder_hidden_states,
-                controlnet_cond,
-                train=True,
-                return_dict=False,
-            )
-
-            model_pred = unet.apply(
-                {"params": unet_params},
-                noisy_latents,
-                timesteps,
-                encoder_hidden_states,
-                down_block_additional_residuals=down_block_res_samples,
-                mid_block_additional_residual=mid_block_res_sample,
-            ).sample
-
-            # Get the target for loss depending on the prediction type
-            if noise_scheduler.config.prediction_type == "epsilon":
-                target = noise
-            elif noise_scheduler.config.prediction_type == "v_prediction":
-                target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)
-            else:
-                raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-            loss = (target - model_pred) ** 2
-
-            if args.snr_gamma is not None:
-                snr = jnp.array(compute_snr(timesteps))
-                snr_loss_weights = jnp.where(snr < args.snr_gamma, snr, jnp.ones_like(snr) * args.snr_gamma) / snr
-                loss = loss * snr_loss_weights
-
-            loss = loss.mean()
-
-            return loss
-
-        grad_fn = jax.value_and_grad(compute_loss)
-
-        # get a minibatch (one gradient accumulation slice)
-        def get_minibatch(batch, grad_idx):
-            return jax.tree_util.tree_map(
-                lambda x: jax.lax.dynamic_index_in_dim(x, grad_idx, keepdims=False),
-                batch,
-            )
-
-        def loss_and_grad(grad_idx, train_rng):
-            # create minibatch for the grad step
-            minibatch = get_minibatch(batch, grad_idx) if grad_idx is not None else batch
-            sample_rng, train_rng = jax.random.split(train_rng, 2)
-            loss, grad = grad_fn(state.params, minibatch, sample_rng)
-            return loss, grad, train_rng
-
-        if args.gradient_accumulation_steps == 1:
-            loss, grad, new_train_rng = loss_and_grad(None, train_rng)
-        else:
-            init_loss_grad_rng = (
-                0.0,  # initial value for cumul_loss
-                jax.tree_map(jnp.zeros_like, state.params),  # initial value for cumul_grad
-                train_rng,  # initial value for train_rng
-            )
-
-            def cumul_grad_step(grad_idx, loss_grad_rng):
-                cumul_loss, cumul_grad, train_rng = loss_grad_rng
-                loss, grad, new_train_rng = loss_and_grad(grad_idx, train_rng)
-                cumul_loss, cumul_grad = jax.tree_map(jnp.add, (cumul_loss, cumul_grad), (loss, grad))
-                return cumul_loss, cumul_grad, new_train_rng
-
-            loss, grad, new_train_rng = jax.lax.fori_loop(
-                0,
-                args.gradient_accumulation_steps,
-                cumul_grad_step,
-                init_loss_grad_rng,
-            )
-            loss, grad = jax.tree_map(lambda x: x / args.gradient_accumulation_steps, (loss, grad))
-
-        grad = jax.lax.pmean(grad, "batch")
-
-        new_state = state.apply_gradients(grads=grad)
-
-        metrics = {"loss": loss}
-        metrics = jax.lax.pmean(metrics, axis_name="batch")
-
-        def l2(xs):
-            return jnp.sqrt(sum([jnp.vdot(x, x) for x in jax.tree_util.tree_leaves(xs)]))
-
-        metrics["l2_grads"] = l2(jax.tree_util.tree_leaves(grad))
-
-        return new_state, metrics, new_train_rng
-
-    # Create parallel version of the train step
-    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
-
-    # Replicate the train state on each device
-    state = jax_utils.replicate(state)
-    unet_params = jax_utils.replicate(unet_params)
-    text_encoder_params = jax_utils.replicate(text_encoder.params)
-    vae_params = jax_utils.replicate(vae_params)
-
-    # Train!
-    if args.streaming:
-        dataset_length = args.max_train_samples
-    else:
-        dataset_length = len(train_dataloader)
-    num_update_steps_per_epoch = math.ceil(dataset_length / args.gradient_accumulation_steps)
-
-    # Scheduler and math around the number of training steps.
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {args.max_train_samples if args.streaming else len(train_dataset)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel & distributed) = {total_train_batch_size}")
-    logger.info(f"  Total optimization steps = {args.num_train_epochs * num_update_steps_per_epoch}")
-
-    if jax.process_index() == 0 and args.report_to == "wandb":
-        wandb.define_metric("*", step_metric="train/step")
-        wandb.define_metric("train/step", step_metric="walltime")
-        wandb.config.update(
-            {
-                "num_train_examples": args.max_train_samples if args.streaming else len(train_dataset),
-                "total_train_batch_size": total_train_batch_size,
-                "total_optimization_step": args.num_train_epochs * num_update_steps_per_epoch,
-                "num_devices": jax.device_count(),
-                "controlnet_params": sum(np.prod(x.shape) for x in jax.tree_util.tree_leaves(state.params)),
-            }
-        )
-
-    global_step = step0 = 0
-    epochs = tqdm(
-        range(args.num_train_epochs),
-        desc="Epoch ... ",
-        position=0,
-        disable=jax.process_index() > 0,
-    )
-    if args.profile_memory:
-        jax.profiler.save_device_memory_profile(os.path.join(args.output_dir, "memory_initial.prof"))
-    t00 = t0 = time.monotonic()
-    for epoch in epochs:
-        # ======================== Training ================================
-
-        train_metrics = []
-        train_metric = None
-
-        steps_per_epoch = (
-            args.max_train_samples // total_train_batch_size
-            if args.streaming or args.max_train_samples
-            else len(train_dataset) // total_train_batch_size
-        )
-        train_step_progress_bar = tqdm(
-            total=steps_per_epoch,
-            desc="Training...",
-            position=1,
-            leave=False,
-            disable=jax.process_index() > 0,
-        )
-        # train
-        for batch in train_dataloader:
-            if args.profile_steps and global_step == 1:
-                train_metric["loss"].block_until_ready()
-                jax.profiler.start_trace(args.output_dir)
-            if args.profile_steps and global_step == 1 + args.profile_steps:
-                train_metric["loss"].block_until_ready()
-                jax.profiler.stop_trace()
-
-            batch = shard(batch)
-            with jax.profiler.StepTraceAnnotation("train", step_num=global_step):
-                state, train_metric, train_rngs = p_train_step(
-                    state, unet_params, text_encoder_params, vae_params, batch, train_rngs
-                )
-            train_metrics.append(train_metric)
-
-            train_step_progress_bar.update(1)
-
-            global_step += 1
-            if global_step >= args.max_train_steps:
-                break
-
-            if (
-                args.validation_prompt is not None
-                and global_step % args.validation_steps == 0
-                and jax.process_index() == 0
-            ):
-                _ = log_validation(
-                    pipeline, pipeline_params, state.params, tokenizer, args, validation_rng, weight_dtype
-                )
-
-            if global_step % args.logging_steps == 0 and jax.process_index() == 0:
-                if args.report_to == "wandb":
-                    train_metrics = jax_utils.unreplicate(train_metrics)
-                    train_metrics = jax.tree_util.tree_map(lambda *m: jnp.array(m).mean(), *train_metrics)
-                    wandb.log(
-                        {
-                            "walltime": time.monotonic() - t00,
-                            "train/step": global_step,
-                            "train/epoch": global_step / dataset_length,
-                            "train/steps_per_sec": (global_step - step0) / (time.monotonic() - t0),
-                            **{f"train/{k}": v for k, v in train_metrics.items()},
-                        }
-                    )
-                t0, step0 = time.monotonic(), global_step
-                train_metrics = []
-            if global_step % args.checkpointing_steps == 0 and jax.process_index() == 0:
-                controlnet.save_pretrained(
-                    f"{args.output_dir}/{global_step}",
-                    params=get_params_to_save(state.params),
-                )
-
-        train_metric = jax_utils.unreplicate(train_metric)
-        train_step_progress_bar.close()
-        epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
-
-    # Final validation & store model.
-    if jax.process_index() == 0:
-        if args.validation_prompt is not None:
-            if args.profile_validation:
-                jax.profiler.start_trace(args.output_dir)
-            image_logs = log_validation(
-                pipeline, pipeline_params, state.params, tokenizer, args, validation_rng, weight_dtype
-            )
-            if args.profile_validation:
-                jax.profiler.stop_trace()
-        else:
-            image_logs = None
-
-        controlnet.save_pretrained(
-            args.output_dir,
-            params=get_params_to_save(state.params),
-        )
-
-        if args.push_to_hub:
-            save_model_card(
-                repo_id,
-                image_logs=image_logs,
-                base_model=args.pretrained_model_name_or_path,
-                repo_folder=args.output_dir,
-            )
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-    if args.profile_memory:
-        jax.profiler.save_device_memory_profile(os.path.join(args.output_dir, "memory_final.prof"))
-    logger.info("Finished training.")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/colossalai/README.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/colossalai/README.md
deleted file mode 100644
index 7c428bbce736de2ba25f189ff19d4c8216c53fc5..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/colossalai/README.md
+++ /dev/null
@@ -1,111 +0,0 @@
-# [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) by [colossalai](https://github.com/hpcaitech/ColossalAI.git)
-
-[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.
-The `train_dreambooth_colossalai.py` script shows how to implement the training procedure and adapt it for stable diffusion.
-
-By accommodating model data in CPU and GPU and moving the data to the computing device when necessary, [Gemini](https://www.colossalai.org/docs/advanced_tutorials/meet_gemini), the Heterogeneous Memory Manager of [Colossal-AI](https://github.com/hpcaitech/ColossalAI) can breakthrough the GPU memory wall by using GPU and CPU memory (composed of CPU DRAM or nvme SSD memory) together at the same time. Moreover, the model scale can be further improved by combining heterogeneous training with the other parallel approaches, such as data parallel, tensor parallel and pipeline parallel.
-
-## Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies:
-
-```bash
-pip install -r requirements.txt
-```
-
-## Install [ColossalAI](https://github.com/hpcaitech/ColossalAI.git)
-
-**From PyPI**
-```bash
-pip install colossalai
-```
-
-**From source**
-
-```bash
-git clone https://github.com/hpcaitech/ColossalAI.git
-cd ColossalAI
-
-# install colossalai
-pip install .
-```
-
-## Dataset for Teyvat BLIP captions
-Dataset used to train [Teyvat characters text to image model](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion).
-
-BLIP generated captions for characters images from [genshin-impact fandom wiki](https://genshin-impact.fandom.com/wiki/Character#Playable_Characters)and [biligame wiki for genshin impact](https://wiki.biligame.com/ys/%E8%A7%92%E8%89%B2).
-
-For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL png, and `text` is the accompanying text caption. Only a train split is provided.
-
-The `text` include the tag `Teyvat`, `Name`,`Element`, `Weapon`, `Region`, `Model type`, and `Description`, the `Description` is captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
-
-## Training
-
-The arguement `placement` can be `cpu`, `auto`, `cuda`, with `cpu` the GPU RAM required can be minimized to 4GB but will deceleration, with `cuda` you can also reduce GPU memory by half but accelerated training， with `auto` a more balanced solution for speed and memory can be obtained。
-
-**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path-to-instance-images"
-export OUTPUT_DIR="path-to-save-model"
-
-torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --instance_prompt="a photo of sks dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --learning_rate=5e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --max_train_steps=400 \
-  --placement="cuda"
-```
-
-
-### Training with prior-preservation loss
-
-Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.
-According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time.
-
-```bash
-export MODEL_NAME="CompVis/stable-diffusion-v1-4"
-export INSTANCE_DIR="path-to-instance-images"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-torchrun --nproc_per_node 2 train_dreambooth_colossalai.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --class_data_dir=$CLASS_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --with_prior_preservation --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks dog" \
-  --class_prompt="a photo of dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --learning_rate=5e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --max_train_steps=800 \
-  --placement="cuda"
-```
-
-## Inference
-
-Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `identifier`(e.g. sks in above example) in your prompt.
-
-```python
-from diffusers import StableDiffusionPipeline
-import torch
-
-model_id = "path-to-save-model"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
-
-prompt = "A photo of sks dog in a bucket"
-image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
-
-image.save("dog-bucket.png")
-```
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py
deleted file mode 100644
index c576c7496928eed58400ba11d71af8f4edc1c4b5..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py
+++ /dev/null
@@ -1,38 +0,0 @@
-_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
-
-model = dict(
-    pretrained='open-mmlab://detectron2/resnet50_caffe',
-    backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'))
-
-# use caffe img_norm
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py
deleted file mode 100644
index 27ab3e733bda1fb1c7c50cbd0f26597650b4c2e7..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,58 +0,0 @@
-_base_ = '../rpn/rpn_r50_fpn_1x_coco.py'
-model = dict(
-    rpn_head=dict(
-        _delete_=True,
-        type='GARPNHead',
-        in_channels=256,
-        feat_channels=256,
-        approx_anchor_generator=dict(
-            type='AnchorGenerator',
-            octave_base_scale=8,
-            scales_per_octave=3,
-            ratios=[0.5, 1.0, 2.0],
-            strides=[4, 8, 16, 32, 64]),
-        square_anchor_generator=dict(
-            type='AnchorGenerator',
-            ratios=[1.0],
-            scales=[8],
-            strides=[4, 8, 16, 32, 64]),
-        anchor_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.07, 0.07, 0.14, 0.14]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[.0, .0, .0, .0],
-            target_stds=[0.07, 0.07, 0.11, 0.11]),
-        loc_filter_thr=0.01,
-        loss_loc=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
-        loss_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
-    # model training and testing settings
-    train_cfg=dict(
-        rpn=dict(
-            ga_assigner=dict(
-                type='ApproxMaxIoUAssigner',
-                pos_iou_thr=0.7,
-                neg_iou_thr=0.3,
-                min_pos_iou=0.3,
-                ignore_iof_thr=-1),
-            ga_sampler=dict(
-                type='RandomSampler',
-                num=256,
-                pos_fraction=0.5,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False),
-            allowed_border=-1,
-            center_ratio=0.2,
-            ignore_ratio=0.5)),
-    test_cfg=dict(rpn=dict(nms_post=1000)))
-optimizer_config = dict(
-    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/transforms.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/transforms.py
deleted file mode 100644
index df55b0a496516bf7373fe96cf746c561dd713c3b..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/transforms.py
+++ /dev/null
@@ -1,240 +0,0 @@
-import numpy as np
-import torch
-
-
-def bbox_flip(bboxes, img_shape, direction='horizontal'):
-    """Flip bboxes horizontally or vertically.
-
-    Args:
-        bboxes (Tensor): Shape (..., 4*k)
-        img_shape (tuple): Image shape.
-        direction (str): Flip direction, options are "horizontal", "vertical",
-            "diagonal". Default: "horizontal"
-
-    Returns:
-        Tensor: Flipped bboxes.
-    """
-    assert bboxes.shape[-1] % 4 == 0
-    assert direction in ['horizontal', 'vertical', 'diagonal']
-    flipped = bboxes.clone()
-    if direction == 'horizontal':
-        flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4]
-        flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4]
-    elif direction == 'vertical':
-        flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4]
-        flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4]
-    else:
-        flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4]
-        flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4]
-        flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4]
-        flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4]
-    return flipped
-
-
-def bbox_mapping(bboxes,
-                 img_shape,
-                 scale_factor,
-                 flip,
-                 flip_direction='horizontal'):
-    """Map bboxes from the original image scale to testing scale."""
-    new_bboxes = bboxes * bboxes.new_tensor(scale_factor)
-    if flip:
-        new_bboxes = bbox_flip(new_bboxes, img_shape, flip_direction)
-    return new_bboxes
-
-
-def bbox_mapping_back(bboxes,
-                      img_shape,
-                      scale_factor,
-                      flip,
-                      flip_direction='horizontal'):
-    """Map bboxes from testing scale to original image scale."""
-    new_bboxes = bbox_flip(bboxes, img_shape,
-                           flip_direction) if flip else bboxes
-    new_bboxes = new_bboxes.view(-1, 4) / new_bboxes.new_tensor(scale_factor)
-    return new_bboxes.view(bboxes.shape)
-
-
-def bbox2roi(bbox_list):
-    """Convert a list of bboxes to roi format.
-
-    Args:
-        bbox_list (list[Tensor]): a list of bboxes corresponding to a batch
-            of images.
-
-    Returns:
-        Tensor: shape (n, 5), [batch_ind, x1, y1, x2, y2]
-    """
-    rois_list = []
-    for img_id, bboxes in enumerate(bbox_list):
-        if bboxes.size(0) > 0:
-            img_inds = bboxes.new_full((bboxes.size(0), 1), img_id)
-            rois = torch.cat([img_inds, bboxes[:, :4]], dim=-1)
-        else:
-            rois = bboxes.new_zeros((0, 5))
-        rois_list.append(rois)
-    rois = torch.cat(rois_list, 0)
-    return rois
-
-
-def roi2bbox(rois):
-    """Convert rois to bounding box format.
-
-    Args:
-        rois (torch.Tensor): RoIs with the shape (n, 5) where the first
-            column indicates batch id of each RoI.
-
-    Returns:
-        list[torch.Tensor]: Converted boxes of corresponding rois.
-    """
-    bbox_list = []
-    img_ids = torch.unique(rois[:, 0].cpu(), sorted=True)
-    for img_id in img_ids:
-        inds = (rois[:, 0] == img_id.item())
-        bbox = rois[inds, 1:]
-        bbox_list.append(bbox)
-    return bbox_list
-
-
-def bbox2result(bboxes, labels, num_classes):
-    """Convert detection results to a list of numpy arrays.
-
-    Args:
-        bboxes (torch.Tensor | np.ndarray): shape (n, 5)
-        labels (torch.Tensor | np.ndarray): shape (n, )
-        num_classes (int): class number, including background class
-
-    Returns:
-        list(ndarray): bbox results of each class
-    """
-    if bboxes.shape[0] == 0:
-        return [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes)]
-    else:
-        if isinstance(bboxes, torch.Tensor):
-            bboxes = bboxes.detach().cpu().numpy()
-            labels = labels.detach().cpu().numpy()
-        return [bboxes[labels == i, :] for i in range(num_classes)]
-
-
-def distance2bbox(points, distance, max_shape=None):
-    """Decode distance prediction to bounding box.
-
-    Args:
-        points (Tensor): Shape (B, N, 2) or (N, 2).
-        distance (Tensor): Distance from the given point to 4
-            boundaries (left, top, right, bottom). Shape (B, N, 4) or (N, 4)
-        max_shape (Sequence[int] or torch.Tensor or Sequence[
-            Sequence[int]],optional): Maximum bounds for boxes, specifies
-            (H, W, C) or (H, W). If priors shape is (B, N, 4), then
-            the max_shape should be a Sequence[Sequence[int]]
-            and the length of max_shape should also be B.
-
-    Returns:
-        Tensor: Boxes with shape (N, 4) or (B, N, 4)
-    """
-    x1 = points[..., 0] - distance[..., 0]
-    y1 = points[..., 1] - distance[..., 1]
-    x2 = points[..., 0] + distance[..., 2]
-    y2 = points[..., 1] + distance[..., 3]
-
-    bboxes = torch.stack([x1, y1, x2, y2], -1)
-
-    if max_shape is not None:
-        if not isinstance(max_shape, torch.Tensor):
-            max_shape = x1.new_tensor(max_shape)
-        max_shape = max_shape[..., :2].type_as(x1)
-        if max_shape.ndim == 2:
-            assert bboxes.ndim == 3
-            assert max_shape.size(0) == bboxes.size(0)
-
-        min_xy = x1.new_tensor(0)
-        max_xy = torch.cat([max_shape, max_shape],
-                           dim=-1).flip(-1).unsqueeze(-2)
-        bboxes = torch.where(bboxes < min_xy, min_xy, bboxes)
-        bboxes = torch.where(bboxes > max_xy, max_xy, bboxes)
-
-    return bboxes
-
-
-def bbox2distance(points, bbox, max_dis=None, eps=0.1):
-    """Decode bounding box based on distances.
-
-    Args:
-        points (Tensor): Shape (n, 2), [x, y].
-        bbox (Tensor): Shape (n, 4), "xyxy" format
-        max_dis (float): Upper bound of the distance.
-        eps (float): a small value to ensure target < max_dis, instead <=
-
-    Returns:
-        Tensor: Decoded distances.
-    """
-    left = points[:, 0] - bbox[:, 0]
-    top = points[:, 1] - bbox[:, 1]
-    right = bbox[:, 2] - points[:, 0]
-    bottom = bbox[:, 3] - points[:, 1]
-    if max_dis is not None:
-        left = left.clamp(min=0, max=max_dis - eps)
-        top = top.clamp(min=0, max=max_dis - eps)
-        right = right.clamp(min=0, max=max_dis - eps)
-        bottom = bottom.clamp(min=0, max=max_dis - eps)
-    return torch.stack([left, top, right, bottom], -1)
-
-
-def bbox_rescale(bboxes, scale_factor=1.0):
-    """Rescale bounding box w.r.t. scale_factor.
-
-    Args:
-        bboxes (Tensor): Shape (n, 4) for bboxes or (n, 5) for rois
-        scale_factor (float): rescale factor
-
-    Returns:
-        Tensor: Rescaled bboxes.
-    """
-    if bboxes.size(1) == 5:
-        bboxes_ = bboxes[:, 1:]
-        inds_ = bboxes[:, 0]
-    else:
-        bboxes_ = bboxes
-    cx = (bboxes_[:, 0] + bboxes_[:, 2]) * 0.5
-    cy = (bboxes_[:, 1] + bboxes_[:, 3]) * 0.5
-    w = bboxes_[:, 2] - bboxes_[:, 0]
-    h = bboxes_[:, 3] - bboxes_[:, 1]
-    w = w * scale_factor
-    h = h * scale_factor
-    x1 = cx - 0.5 * w
-    x2 = cx + 0.5 * w
-    y1 = cy - 0.5 * h
-    y2 = cy + 0.5 * h
-    if bboxes.size(1) == 5:
-        rescaled_bboxes = torch.stack([inds_, x1, y1, x2, y2], dim=-1)
-    else:
-        rescaled_bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
-    return rescaled_bboxes
-
-
-def bbox_cxcywh_to_xyxy(bbox):
-    """Convert bbox coordinates from (cx, cy, w, h) to (x1, y1, x2, y2).
-
-    Args:
-        bbox (Tensor): Shape (n, 4) for bboxes.
-
-    Returns:
-        Tensor: Converted bboxes.
-    """
-    cx, cy, w, h = bbox.split((1, 1, 1, 1), dim=-1)
-    bbox_new = [(cx - 0.5 * w), (cy - 0.5 * h), (cx + 0.5 * w), (cy + 0.5 * h)]
-    return torch.cat(bbox_new, dim=-1)
-
-
-def bbox_xyxy_to_cxcywh(bbox):
-    """Convert bbox coordinates from (x1, y1, x2, y2) to (cx, cy, w, h).
-
-    Args:
-        bbox (Tensor): Shape (n, 4) for bboxes.
-
-    Returns:
-        Tensor: Converted bboxes.
-    """
-    x1, y1, x2, y2 = bbox.split((1, 1, 1, 1), dim=-1)
-    bbox_new = [(x1 + x2) / 2, (y1 + y2) / 2, (x2 - x1), (y2 - y1)]
-    return torch.cat(bbox_new, dim=-1)
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py b/spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py
deleted file mode 100644
index 409db3c628edf63cd40e002f436884ce1fb75970..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = './fcn_hr18_512x512_40k_voc12aug.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w18_small',
-    backbone=dict(
-        extra=dict(
-            stage1=dict(num_blocks=(2, )),
-            stage2=dict(num_blocks=(2, 2)),
-            stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
-            stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/ui_notebook.py b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/ui_notebook.py
deleted file mode 100644
index 6bd5c919f797a30003f291ed40ca82a924f760e7..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/modules/ui_notebook.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import gradio as gr
-
-from modules import logits, shared, ui, utils
-from modules.prompts import count_tokens, load_prompt
-from modules.text_generation import (
-    generate_reply_wrapper,
-    get_token_ids,
-    stop_everything_event
-)
-from modules.utils import gradio
-
-inputs = ('textbox-notebook', 'interface_state')
-outputs = ('textbox-notebook', 'html-notebook')
-
-
-def create_ui():
-    mu = shared.args.multi_user
-    with gr.Tab('Notebook', elem_id='notebook-tab'):
-        shared.gradio['last_input-notebook'] = gr.State('')
-        with gr.Row():
-            with gr.Column(scale=4):
-                with gr.Tab('Raw'):
-                    with gr.Row():
-                        shared.gradio['textbox-notebook'] = gr.Textbox(value='', lines=27, elem_id='textbox-notebook', elem_classes=['textbox', 'add_scrollbar'])
-                        shared.gradio['token-counter-notebook'] = gr.HTML(value="<span>0</span>", elem_classes=["token-counter"])
-
-                with gr.Tab('Markdown'):
-                    shared.gradio['markdown_render-notebook'] = gr.Button('Render')
-                    shared.gradio['markdown-notebook'] = gr.Markdown()
-
-                with gr.Tab('HTML'):
-                    shared.gradio['html-notebook'] = gr.HTML()
-
-                with gr.Tab('Logits'):
-                    with gr.Row():
-                        with gr.Column(scale=10):
-                            shared.gradio['get_logits-notebook'] = gr.Button('Get next token probabilities')
-                        with gr.Column(scale=1):
-                            shared.gradio['use_samplers-notebook'] = gr.Checkbox(label='Use samplers', value=True, elem_classes=['no-background'])
-
-                    with gr.Row():
-                        shared.gradio['logits-notebook'] = gr.Textbox(lines=23, label='Output', elem_classes=['textbox_logits_notebook', 'add_scrollbar'])
-                        shared.gradio['logits-notebook-previous'] = gr.Textbox(lines=23, label='Previous output', elem_classes=['textbox_logits_notebook', 'add_scrollbar'])
-
-                with gr.Tab('Tokens'):
-                    shared.gradio['get_tokens-notebook'] = gr.Button('Get token IDs for the input')
-                    shared.gradio['tokens-notebook'] = gr.Textbox(lines=23, label='Tokens', elem_classes=['textbox_logits_notebook', 'add_scrollbar', 'monospace'])
-
-                with gr.Row():
-                    shared.gradio['Generate-notebook'] = gr.Button('Generate', variant='primary', elem_classes='small-button')
-                    shared.gradio['Stop-notebook'] = gr.Button('Stop', elem_classes='small-button', elem_id='stop')
-                    shared.gradio['Undo'] = gr.Button('Undo', elem_classes='small-button')
-                    shared.gradio['Regenerate-notebook'] = gr.Button('Regenerate', elem_classes='small-button')
-
-            with gr.Column(scale=1):
-                gr.HTML('<div style="padding-bottom: 13px"></div>')
-                with gr.Row():
-                    shared.gradio['prompt_menu-notebook'] = gr.Dropdown(choices=utils.get_available_prompts(), value='None', label='Prompt', elem_classes='slim-dropdown')
-                    ui.create_refresh_button(shared.gradio['prompt_menu-notebook'], lambda: None, lambda: {'choices': utils.get_available_prompts()}, ['refresh-button', 'refresh-button-small'], interactive=not mu)
-                    shared.gradio['save_prompt-notebook'] = gr.Button('💾', elem_classes=['refresh-button', 'refresh-button-small'], interactive=not mu)
-                    shared.gradio['delete_prompt-notebook'] = gr.Button('🗑️', elem_classes=['refresh-button', 'refresh-button-small'], interactive=not mu)
-
-
-def create_event_handlers():
-    shared.gradio['Generate-notebook'].click(
-        lambda x: x, gradio('textbox-notebook'), gradio('last_input-notebook')).then(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        generate_reply_wrapper, gradio(inputs), gradio(outputs), show_progress=False).then(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        lambda: None, None, None, _js=f'() => {{{ui.audio_notification_js}}}')
-
-    shared.gradio['textbox-notebook'].submit(
-        lambda x: x, gradio('textbox-notebook'), gradio('last_input-notebook')).then(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        generate_reply_wrapper, gradio(inputs), gradio(outputs), show_progress=False).then(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        lambda: None, None, None, _js=f'() => {{{ui.audio_notification_js}}}')
-
-    shared.gradio['Undo'].click(lambda x: x, gradio('last_input-notebook'), gradio('textbox-notebook'), show_progress=False)
-    shared.gradio['markdown_render-notebook'].click(lambda x: x, gradio('textbox-notebook'), gradio('markdown-notebook'), queue=False)
-    shared.gradio['Regenerate-notebook'].click(
-        lambda x: x, gradio('last_input-notebook'), gradio('textbox-notebook'), show_progress=False).then(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        generate_reply_wrapper, gradio(inputs), gradio(outputs), show_progress=False).then(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        lambda: None, None, None, _js=f'() => {{{ui.audio_notification_js}}}')
-
-    shared.gradio['Stop-notebook'].click(stop_everything_event, None, None, queue=False)
-    shared.gradio['prompt_menu-notebook'].change(load_prompt, gradio('prompt_menu-notebook'), gradio('textbox-notebook'), show_progress=False)
-    shared.gradio['save_prompt-notebook'].click(
-        lambda x: x, gradio('textbox-notebook'), gradio('save_contents')).then(
-        lambda: 'prompts/', None, gradio('save_root')).then(
-        lambda: utils.current_time() + '.txt', None, gradio('save_filename')).then(
-        lambda: gr.update(visible=True), None, gradio('file_saver'))
-
-    shared.gradio['delete_prompt-notebook'].click(
-        lambda: 'prompts/', None, gradio('delete_root')).then(
-        lambda x: x + '.txt', gradio('prompt_menu-notebook'), gradio('delete_filename')).then(
-        lambda: gr.update(visible=True), None, gradio('file_deleter'))
-
-    shared.gradio['textbox-notebook'].input(lambda x: f"<span>{count_tokens(x)}</span>", gradio('textbox-notebook'), gradio('token-counter-notebook'), show_progress=False)
-    shared.gradio['get_logits-notebook'].click(
-        ui.gather_interface_values, gradio(shared.input_elements), gradio('interface_state')).then(
-        logits.get_next_logits, gradio('textbox-notebook', 'interface_state', 'use_samplers-notebook', 'logits-notebook'), gradio('logits-notebook', 'logits-notebook-previous'), show_progress=False)
-
-    shared.gradio['get_tokens-notebook'].click(get_token_ids, gradio('textbox-notebook'), gradio('tokens-notebook'), show_progress=False)
diff --git a/spaces/Arnx/MusicGenXvAKN/setup.py b/spaces/Arnx/MusicGenXvAKN/setup.py
deleted file mode 100644
index 78a172b7c90003b689bde40b49cc8fe1fb8107d4..0000000000000000000000000000000000000000
--- a/spaces/Arnx/MusicGenXvAKN/setup.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""
- Copyright (c) Meta Platforms, Inc. and affiliates.
- All rights reserved.
- 
- This source code is licensed under the license found in the
- LICENSE file in the root directory of this source tree.
- 
-"""
-
-from pathlib import Path
-
-from setuptools import setup, find_packages
-
-
-NAME = 'audiocraft'
-DESCRIPTION = 'Audio research library for PyTorch'
-
-URL = 'https://github.com/fairinternal/audiocraft'
-AUTHOR = 'FAIR Speech & Audio'
-EMAIL = 'defossez@meta.com'
-REQUIRES_PYTHON = '>=3.8.0'
-
-for line in open('audiocraft/__init__.py'):
-    line = line.strip()
-    if '__version__' in line:
-        context = {}
-        exec(line, context)
-        VERSION = context['__version__']
-
-HERE = Path(__file__).parent
-
-try:
-    with open(HERE / "README.md", encoding='utf-8') as f:
-        long_description = '\n' + f.read()
-except FileNotFoundError:
-    long_description = DESCRIPTION
-
-REQUIRED = [i.strip() for i in open(HERE / 'requirements.txt') if not i.startswith('#')]
-
-setup(
-    name=NAME,
-    version=VERSION,
-    description=DESCRIPTION,
-    author_email=EMAIL,
-    long_description=long_description,
-    long_description_content_type='text/markdown',
-    author=AUTHOR,
-    url=URL,
-    python_requires=REQUIRES_PYTHON,
-    install_requires=REQUIRED,
-    extras_require={
-        'dev': ['coverage', 'flake8', 'mypy', 'pdoc3', 'pytest'],
-    },
-    packages=find_packages(),
-    package_data={'audiocraft': ['py.typed']},
-    include_package_data=True,
-    license='MIT License',
-    classifiers=[
-        # Trove classifiers
-        # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers
-        'License :: OSI Approved :: MIT License',
-        'Topic :: Multimedia :: Sound/Audio',
-        'Topic :: Scientific/Engineering :: Artificial Intelligence',
-    ],
-)
diff --git a/spaces/Artrajz/vits-simple-api/utils/__init__.py b/spaces/Artrajz/vits-simple-api/utils/__init__.py
deleted file mode 100644
index d4baea2bf5adadb7cf0de4d21158c9463037a833..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/utils/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from utils.classify_language import classify_language
-from utils.data_utils import get_hparams_from_file, load_checkpoint, load_audio_to_torch
-from utils.lang_dict import lang_dict
diff --git a/spaces/Artrajz/vits-simple-api/utils/lang_dict.py b/spaces/Artrajz/vits-simple-api/utils/lang_dict.py
deleted file mode 100644
index 7b2783e6e6fc1a8e2b1d7e6220ab2454eef8c843..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/utils/lang_dict.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from contants import ModelType
-
-lang_dict = {
-    "english_cleaners": ["en"],
-    "english_cleaners2": ["en"],
-    "japanese_cleaners": ["ja"],
-    "japanese_cleaners2": ["ja"],
-    "korean_cleaners": ["ko"],
-    "chinese_cleaners": ["zh"],
-    "zh_ja_mixture_cleaners": ["zh", "ja"],
-    "sanskrit_cleaners": ["sa"],
-    "cjks_cleaners": ["zh", "ja", "ko", "sa"],
-    "cjke_cleaners": ["zh", "ja", "ko", "en"],
-    "cjke_cleaners2": ["zh", "ja", "ko", "en"],
-    "cje_cleaners": ["zh", "ja", "en"],
-    "cje_cleaners2": ["zh", "ja", "en"],
-    "thai_cleaners": ["th"],
-    "shanghainese_cleaners": ["sh"],
-    "chinese_dialect_cleaners": ["zh", "ja", "sh", "gd", "en", "SZ", "WX", "CZ", "HZ", "SX", "NB", "JJ", "YX", "JD",
-                                 "ZR", "PH", "TX", "JS", "HN", "LP", "XS", "FY", "RA", "CX", "SM", "TT", "WZ", "SC",
-                                 "YB"],
-    "bert_chinese_cleaners": ["zh"],
-    ModelType.BERT_VITS2.value: ["zh", "ja"],
-    f"{ModelType.BERT_VITS2.value}_v1.0": ["zh"],
-    f"{ModelType.BERT_VITS2.value}_v1.0.0": ["zh"],
-    f"{ModelType.BERT_VITS2.value}_v1.0.1": ["zh"],
-    f"{ModelType.BERT_VITS2.value}_v1.1": ["zh", "ja"],
-    f"{ModelType.BERT_VITS2.value}_v1.1.0": ["zh", "ja"],
-    f"{ModelType.BERT_VITS2.value}_v1.1.0-transition": ["zh", "ja"],
-    f"{ModelType.BERT_VITS2.value}_v1.1.1": ["zh", "ja"],
-}
diff --git a/spaces/Artrajz/vits-simple-api/utils/merge.py b/spaces/Artrajz/vits-simple-api/utils/merge.py
deleted file mode 100644
index 86ee1cf89fd270b7e30766364f69495895f5f2d0..0000000000000000000000000000000000000000
--- a/spaces/Artrajz/vits-simple-api/utils/merge.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import os
-import json
-import logging
-import torch
-import config
-import numpy as np
-from utils.utils import check_is_none
-from vits import VITS
-from voice import TTS
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-lang_dict = {
-    "english_cleaners": ["en"],
-    "english_cleaners2": ["en"],
-    "japanese_cleaners": ["ja"],
-    "japanese_cleaners2": ["ja"],
-    "korean_cleaners": ["ko"],
-    "chinese_cleaners": ["zh"],
-    "zh_ja_mixture_cleaners": ["zh", "ja"],
-    "sanskrit_cleaners": ["sa"],
-    "cjks_cleaners": ["zh", "ja", "ko", "sa"],
-    "cjke_cleaners": ["zh", "ja", "ko", "en"],
-    "cjke_cleaners2": ["zh", "ja", "ko", "en"],
-    "cje_cleaners": ["zh", "ja", "en"],
-    "cje_cleaners2": ["zh", "ja", "en"],
-    "thai_cleaners": ["th"],
-    "shanghainese_cleaners": ["sh"],
-    "chinese_dialect_cleaners": ["zh", "ja", "sh", "gd", "en", "SZ", "WX", "CZ", "HZ", "SX", "NB", "JJ", "YX", "JD",
-                                 "ZR", "PH", "TX", "JS", "HN", "LP", "XS", "FY", "RA", "CX", "SM", "TT", "WZ", "SC",
-                                 "YB"],
-    "bert_chinese_cleaners": ["zh"],
-}
-
-
-def analysis(model_config_json):
-    model_config = json.load(model_config_json)
-    symbols = model_config.get("symbols", None)
-    emotion_embedding = model_config.get("data").get("emotion_embedding", False)
-    if "use_spk_conditioned_encoder" in model_config.get("model"):
-        model_type = 'bert_vits2'
-        return model_type
-    if symbols != None:
-        if not emotion_embedding:
-            mode_type = "vits"
-        else:
-            mode_type = "w2v2"
-    else:
-        mode_type = "hubert"
-    return mode_type
-
-
-def load_npy(model_):
-    if isinstance(model_, list):
-        # check if is .npy
-        for i in model_:
-            _model_extention = os.path.splitext(i)[1]
-            if _model_extention != ".npy":
-                raise ValueError(f"Unsupported model type: {_model_extention}")
-
-        # merge npy files
-        emotion_reference = np.empty((0, 1024))
-        for i in model_:
-            tmp = np.load(i).reshape(-1, 1024)
-            emotion_reference = np.append(emotion_reference, tmp, axis=0)
-
-    elif os.path.isdir(model_):
-        emotion_reference = np.empty((0, 1024))
-        for root, dirs, files in os.walk(model_):
-            for file_name in files:
-                # check if is .npy
-                _model_extention = os.path.splitext(file_name)[1]
-                if _model_extention != ".npy":
-                    continue
-                file_path = os.path.join(root, file_name)
-
-                # merge npy files
-                tmp = np.load(file_path).reshape(-1, 1024)
-                emotion_reference = np.append(emotion_reference, tmp, axis=0)
-
-    elif os.path.isfile(model_):
-        # check if is .npy
-        _model_extention = os.path.splitext(model_)[1]
-        if _model_extention != ".npy":
-            raise ValueError(f"Unsupported model type: {_model_extention}")
-
-        emotion_reference = np.load(model_)
-    logging.info(f"Loaded emotional dimention npy range:{len(emotion_reference)}")
-    return emotion_reference
-
-
-def merge_model(merging_model):
-    vits_obj = []
-    vits_speakers = []
-    hubert_vits_obj = []
-    hubert_vits_speakers = []
-    w2v2_vits_obj = []
-    w2v2_vits_speakers = []
-    bert_vits2_obj = []
-    bert_vits2_speakers = []
-
-    # model list
-    vits_list = []
-    hubert_vits_list = []
-    w2v2_vits_list = []
-    bert_vits2_list = []
-
-    for l in merging_model:
-        with open(l[1], 'r', encoding='utf-8') as model_config:
-            model_type = analysis(model_config)
-        if model_type == "vits":
-            vits_list.append(l)
-        elif model_type == "hubert":
-            hubert_vits_list.append(l)
-        elif model_type == "w2v2":
-            w2v2_vits_list.append(l)
-        elif model_type == "bert_vits2":
-            bert_vits2_list.append(l)
-
-    # merge vits
-    new_id = 0
-    for obj_id, i in enumerate(vits_list):
-        obj = VITS(model=i[0], config=i[1], model_type="vits", device=device)
-        lang = lang_dict.get(obj.get_cleaner(), ["unknown"])
-        for id, name in enumerate(obj.get_speakers()):
-            vits_obj.append([int(id), obj, obj_id])
-            vits_speakers.append({"id": new_id, "name": name, "lang": lang})
-            new_id += 1
-
-    # merge hubert-vits
-    if len(hubert_vits_list) != 0:
-        if getattr(config, "HUBERT_SOFT_MODEL", None) == None or check_is_none(config.HUBERT_SOFT_MODEL):
-            raise ValueError(f"Please configure HUBERT_SOFT_MODEL path in config.py")
-        try:
-            from vits.hubert_model import hubert_soft
-            hubert = hubert_soft(config.HUBERT_SOFT_MODEL)
-        except Exception as e:
-            raise ValueError(f"Load HUBERT_SOFT_MODEL failed {e}")
-
-    new_id = 0
-    for obj_id, i in enumerate(hubert_vits_list):
-        obj = VITS(model=i[0], config=i[1], model_=hubert, model_type="hubert", device=device)
-        lang = lang_dict.get(obj.get_cleaner(), ["unknown"])
-
-        for id, name in enumerate(obj.get_speakers()):
-            hubert_vits_obj.append([int(id), obj, obj_id])
-            hubert_vits_speakers.append({"id": new_id, "name": name, "lang": lang})
-            new_id += 1
-
-    # merge w2v2-vits
-    emotion_reference = None
-    if len(w2v2_vits_list) != 0:
-        if getattr(config, "DIMENSIONAL_EMOTION_NPY", None) == None or check_is_none(config.DIMENSIONAL_EMOTION_NPY):
-            raise ValueError(f"Please configure DIMENSIONAL_EMOTION_NPY path in config.py")
-        try:
-            emotion_reference = load_npy(config.DIMENSIONAL_EMOTION_NPY)
-        except Exception as e:
-            raise ValueError(f"Load DIMENSIONAL_EMOTION_NPY failed {e}")
-
-    new_id = 0
-    for obj_id, i in enumerate(w2v2_vits_list):
-        obj = VITS(model=i[0], config=i[1], model_=emotion_reference, model_type="w2v2", device=device)
-        lang = lang_dict.get(obj.get_cleaner(), ["unknown"])
-
-        for id, name in enumerate(obj.get_speakers()):
-            w2v2_vits_obj.append([int(id), obj, obj_id])
-            w2v2_vits_speakers.append({"id": new_id, "name": name, "lang": lang})
-            new_id += 1
-
-    # merge Bert_VITS2
-    new_id = 0
-    for obj_id, i in enumerate(bert_vits2_list):
-        from bert_vits2 import Bert_VITS2
-        obj = Bert_VITS2(model=i[0], config=i[1], device=device)
-        lang = ["ZH"]
-        for id, name in enumerate(obj.get_speakers()):
-            bert_vits2_obj.append([int(id), obj, obj_id])
-            bert_vits2_speakers.append({"id": new_id, "name": name, "lang": lang})
-            new_id += 1
-
-
-    voice_obj = {"VITS": vits_obj, "HUBERT-VITS": hubert_vits_obj, "W2V2-VITS": w2v2_vits_obj,
-                 "BERT-VITS2": bert_vits2_obj}
-    voice_speakers = {"VITS": vits_speakers, "HUBERT-VITS": hubert_vits_speakers, "W2V2-VITS": w2v2_vits_speakers,
-                      "BERT-VITS2": bert_vits2_speakers}
-    w2v2_emotion_count = len(emotion_reference) if emotion_reference is not None else 0
-
-    tts = TTS(voice_obj, voice_speakers, w2v2_emotion_count=w2v2_emotion_count, device=device)
-
-    return tts
diff --git a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/get_tokenlizer.py b/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/get_tokenlizer.py
deleted file mode 100644
index dd2d972b4278e04a1ebef7d5e77aecd4eaf4205b..0000000000000000000000000000000000000000
--- a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/get_tokenlizer.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
-import os
-
-def get_tokenlizer(text_encoder_type):
-    if not isinstance(text_encoder_type, str):
-        # print("text_encoder_type is not a str")
-        if hasattr(text_encoder_type, "text_encoder_type"):
-            text_encoder_type = text_encoder_type.text_encoder_type
-        elif text_encoder_type.get("text_encoder_type", False):
-            text_encoder_type = text_encoder_type.get("text_encoder_type")
-        elif os.path.isdir(text_encoder_type) and os.path.exists(text_encoder_type):
-            pass
-        else:
-            raise ValueError(
-                "Unknown type of text_encoder_type: {}".format(type(text_encoder_type))
-            )
-    print("final text_encoder_type: {}".format(text_encoder_type))
-
-    tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)
-    return tokenizer
-
-
-def get_pretrained_language_model(text_encoder_type):
-    if text_encoder_type == "bert-base-uncased" or (os.path.isdir(text_encoder_type) and os.path.exists(text_encoder_type)):
-        return BertModel.from_pretrained(text_encoder_type)
-    if text_encoder_type == "roberta-base":
-        return RobertaModel.from_pretrained(text_encoder_type)
-
-    raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type))
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/compat.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/compat.py
deleted file mode 100644
index 9ab2bb48656520a95ec9ac87d090f2e741f0e544..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/compat.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""
-requests.compat
-~~~~~~~~~~~~~~~
-
-This module previously handled import compatibility issues
-between Python 2 and Python 3. It remains for backwards
-compatibility until the next major version.
-"""
-
-from pip._vendor import chardet
-
-import sys
-
-# -------
-# Pythons
-# -------
-
-# Syntax sugar.
-_ver = sys.version_info
-
-#: Python 2.x?
-is_py2 = _ver[0] == 2
-
-#: Python 3.x?
-is_py3 = _ver[0] == 3
-
-# Note: We've patched out simplejson support in pip because it prevents
-#       upgrading simplejson on Windows.
-import json
-from json import JSONDecodeError
-
-# Keep OrderedDict for backwards compatibility.
-from collections import OrderedDict
-from collections.abc import Callable, Mapping, MutableMapping
-from http import cookiejar as cookielib
-from http.cookies import Morsel
-from io import StringIO
-
-# --------------
-# Legacy Imports
-# --------------
-from urllib.parse import (
-    quote,
-    quote_plus,
-    unquote,
-    unquote_plus,
-    urldefrag,
-    urlencode,
-    urljoin,
-    urlparse,
-    urlsplit,
-    urlunparse,
-)
-from urllib.request import (
-    getproxies,
-    getproxies_environment,
-    parse_http_list,
-    proxy_bypass,
-    proxy_bypass_environment,
-)
-
-builtin_str = str
-str = str
-bytes = bytes
-basestring = (str, bytes)
-numeric_types = (int, float)
-integer_types = (int,)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/py37compat.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/py37compat.py
deleted file mode 100644
index aa0c0a7fcd100886e3cd27b3076b6b30c4de1718..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/py37compat.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import sys
-
-
-def _pythonlib_compat():
-    """
-    On Python 3.7 and earlier, distutils would include the Python
-    library. See pypa/distutils#9.
-    """
-    from distutils import sysconfig
-
-    if not sysconfig.get_config_var('Py_ENABLED_SHARED'):
-        return
-
-    yield 'python{}.{}{}'.format(
-        sys.hexversion >> 24,
-        (sys.hexversion >> 16) & 0xFF,
-        sysconfig.get_config_var('ABIFLAGS'),
-    )
-
-
-def compose(f1, f2):
-    return lambda *args, **kwargs: f1(f2(*args, **kwargs))
-
-
-pythonlib = (
-    compose(list, _pythonlib_compat)
-    if sys.version_info < (3, 8)
-    and sys.platform != 'darwin'
-    and sys.platform[:3] != 'aix'
-    else list
-)
diff --git a/spaces/Benson/text-generation/Examples/   2  2.md b/spaces/Benson/text-generation/Examples/   2  2.md
deleted file mode 100644
index 6507795ee4f945af6757356b9089ddfabdcb8f2e..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/   2  2.md	
+++ /dev/null
@@ -1,180 +0,0 @@
-<br />
-<h1>Cómo jugar Case Simulator 2 Standoff 2: Una guía completa</h1>
-<p>¿Eres fan de Standoff 2, el dinámico shooter en primera persona que honra el legado de su precuela? ¿Quieres experimentar la emoción de abrir casos y dejar caer varios elementos del juego? Si respondiste sí, entonces deberías echar un vistazo a Case Simulator 2 Standoff 2, un juego que simula abrir cajas y cajas, simular batallas, crear nuevos objetos, completar misiones y participar en minijuegos y modos especiales. En este artículo, le proporcionaremos un resumen y una guía detallada sobre cómo jugar Case Simulator 2 Standoff 2, cómo obtener pieles raras, cómo usar códigos y cómo descargar e instalar el juego en su dispositivo. ¡Vamos a empezar! </p>
- <h2>Qué es Case Simulator 2 Standoff 2 y cuáles son sus características</h2>
-<p>Case Simulator 2 Standoff 2 es un juego creado por los fans de Standoff 2, un popular juego de disparos en primera persona que tiene más de <strong>200 millones de jugadores</strong> de todo el mundo. Case Simulator 2 Standoff 2 le permite abrir cajas y cajas que contienen varios artículos de Standoff 2, tales como armas, pieles, pegatinas, encantos, graffiti, guantes, cuchillos, etc. También puede simular batallas en segundo plano para ganar oro y otras recompensas que puede usar para comprar más cajas y estuches o actualizar sus artículos. También puede crear nuevos artículos de los antiguos utilizando la función de negociar contratos. Además, puedes completar misiones únicas que pondrán a prueba tus habilidades y conocimientos de Standoff 2. Además, puedes participar en minijuegos y modos especiales que darán vida a tu juego. Algunos de estos modos incluyen:</p>
-<h2>скачать кейс симулятор 2 стандофф 2</h2><br /><p><b><b>Download</b> &#9881;&#9881;&#9881; <a href="https://bltlly.com/2v6Kft">https://bltlly.com/2v6Kft</a></b></p><br /><br />
-<ul>
-<li><strong>Actualizar</strong>: Puede actualizar sus artículos hasta x10 veces de su valor original. Sin embargo, existe la posibilidad de que su artículo sea destruido durante el proceso de actualización. </li>
-<li><strong>Jackpot</strong>: Puedes apostar tus objetos en un bote con otros jugadores. El ganador se lleva todos los objetos del bote. </li>
-
-<li><strong>Ruleta</strong>: Puedes apostar en uno de los tres colores y ganar hasta x14 veces tu apuesta. Necesitas gemas para jugar a la ruleta, que puedes conseguir abriendo cajas y cajas o usando códigos. </li>
-</ul>
-<p>Como puedes ver, Case Simulator 2 Standoff 2 es un juego que ofrece muchas características y diversión para los fans de Standoff 2. Puedes disfrutar abriendo cajas, simulando batallas, creando nuevos objetos, completando misiones y participando en minijuegos y modos especiales. También puedes recoger pieles y objetos raros que puedes mostrar a tus amigos o usar en Standoff 2. Case Simulator 2 Standoff 2 es un juego que te mantendrá entretenido y comprometido durante horas. </p>
- <h2>Cómo jugar Case Simulator 2 Standoff 2</h2>
-<p>Ahora que sabes lo que es Case Simulator 2 Standoff 2 y cuáles son sus características, vamos a aprender a jugar el juego. El juego es muy fácil de jugar y tiene una interfaz simple. Estos son los pasos para jugar Case Simulator 2 Standoff 2:</p>
-<p></p>
-<ol>
-<li><strong>Abrir cajas y cajas</strong>: La característica principal del juego es abrir cajas y cajas que contienen varios elementos de Standoff 2. Puede abrir cajas y cajas tocando en ellas en la pantalla principal. Verá una rueda giratoria que se detendrá en un elemento aleatorio. También puede tocar el botón "Abrir" para omitir la animación y obtener el artículo al instante. Puedes abrir tantas cajas como quieras, siempre que tengas suficiente oro o gemas. También puede comprar más oro o gemas con dinero real si lo desea. </li>
-
-<li><strong>Crear nuevos artículos</strong>: Otra característica del juego es la elaboración de nuevos artículos de los antiguos utilizando la función de negociar contratos. Puedes crear nuevos objetos tocando el botón "Craft" en la pantalla principal. Verá una pantalla que muestra su inventario y los contratos de intercambio. Puede elegir un contrato de intercambio de la lista y arrastrar 10 artículos de la misma calidad en ella. Luego, puede tocar en el botón "Comercio" para crear un nuevo artículo de una calidad superior. Sin embargo, existe la posibilidad de que obtenga un artículo de menor calidad de lo esperado. </li>
-<li><strong>Misiones completas</strong>: Otra característica del juego es completar misiones únicas que desafiarán tus habilidades y conocimientos de Standoff 2. Puedes completar misiones tocando el botón "Missions" en la pantalla principal. Verás una pantalla que muestra tus misiones actuales y sus recompensas. Puedes elegir una misión de la lista y tocarla para ver sus detalles. Luego, puedes tocar el botón "Inicio" para comenzar la misión. Usted tendrá que realizar ciertas tareas o lograr ciertos objetivos con el fin de completar la misión. Ganarás oro y otras recompensas basadas en tu finalización de la misión. </li>
-<li><strong>Participar en mini juegos y modos especiales</strong>: Otra característica del juego es participar en mini juegos y modos especiales que darán vida a su juego. Puedes participar en mini juegos y modos especiales tocando sus respectivos botones en la pantalla principal. Verá una pantalla que muestra sus detalles y reglas. Puede elegir uno de ellos de la lista y pulsar sobre él para introducirlo. Luego, puedes seguir las instrucciones y jugar en consecuencia. Ganarás oro, gemas u otras recompensas basadas en tu participación en ellas. </li>
-</ol>
-<p>Estos son los pasos básicos para jugar Case Simulator 2 Standoff 2. Por supuesto, hay más características y detalles que puedes explorar por ti mismo mientras juegas el juego. </p>
- <h2>Cómo obtener pieles raras en Case Simulator 2 Standoff 2</h2>
-
-<ul>
-<li><strong>Utilice la función de actualización</strong>: Una de las características que puede ayudarle a obtener pieles raras es la función de actualización. Puede acceder a la función de actualización pulsando en el botón "Actualizar" en la pantalla principal. Verá una pantalla que muestra su inventario y las opciones de actualización. Puede elegir un artículo de su inventario y arrastrarlo a la ranura de actualización. A continuación, puede elegir un multiplicador de x1.1 a x10. Cuanto mayor sea el multiplicador, mayor será el valor del artículo actualizado, pero también mayor será el riesgo de perder el artículo. Luego, puede tocar el botón "Actualizar" para iniciar el proceso. Verá una barra de progreso que muestra la tasa de éxito de la actualización. Si la barra de progreso alcanza la zona verde, obtendrá el elemento actualizado. Si llega a la zona roja, perderá el elemento. También puede tocar en el "Detener" botón para detener el proceso y mantener su artículo original. La función de actualización es una apuesta, pero puede ayudarte a obtener pieles raras si tienes suerte. </li>
-<li><strong>Usa la función de jackpot</strong>: Otra característica que puede ayudarte a obtener pieles raras es la función de jackpot. Puedes acceder a la función de jackpot tocando el botón "Jackpot" en la pantalla principal. Verás una pantalla que muestra tu inventario y el bote del bote. Puedes elegir artículos de tu inventario y arrastrarlos al bote. Cuantos más artículos pongas, mayor será tu probabilidad de ganar, pero también más riesgo tendrás de perder. A continuación, puedes pulsar el botón "Inicio" para comenzar el jackpot. Verás una rueda giratoria que se detendrá en el nombre de un jugador al azar. El ganador se lleva todos los objetos del bote. La función de jackpot es otra apuesta, pero puede ayudarte a obtener pieles raras si ganas. </li>
-
-</ul>
-<p>Estas son algunas de las características que pueden ayudarte a obtener skins raros en Case Simulator 2 Standoff 2. Por supuesto, hay más características y detalles que puedes explorar por ti mismo mientras juegas el juego. Sin embargo, recuerda que conseguir pieles raras no está garantizado, y siempre debes jugar responsablemente y divertirte. </p>
- <h2>Cómo usar Case Simulator 2 Standoff 2 códigos</h2>
-<p>Otra forma de obtener skins y elementos raros en Case Simulator 2 Standoff 2 es usar códigos. Los códigos son códigos especiales que los desarrolladores del juego dan a los jugadores por varias razones, como celebrar hitos, eventos, fiestas, etc. Los códigos pueden darte oro gratis, gemas, casos, cajas u otras recompensas que pueden ayudarte en el juego. Sin embargo, los códigos no son permanentes y caducan después de un cierto período de tiempo. Por lo tanto, debe usarlos lo antes posible antes de que sean inválidos. Estos son los pasos para usar los códigos de Case Simulator 2 Standoff 2:</p>
-<ol>
-<li><strong>Encontrar códigos</strong>: El primer paso para usar códigos es encontrarlos. Puedes encontrar códigos de varias fuentes, como las páginas oficiales de redes sociales del juego, el servidor oficial de Discord del juego, el canal oficial de YouTube del juego u otros sitios web y blogs que publican códigos regularmente. También puede consultar este artículo para ver algunos de los códigos de trabajo y códigos caducados. </li>
-<li><strong>Canjear códigos</strong>: El segundo paso para usar códigos es canjearlos. Puede canjear códigos pulsando el botón "Configuración" en la pantalla principal. Verá una pantalla que muestra la configuración y las opciones del juego. Puede tocar el botón "Enter Code" para abrir una ventana emergente donde puede ingresar su código. Luego, puedes tocar el botón "Canjear" para reclamar tu recompensa. Verás un mensaje que confirma tu redención y muestra tu recompensa. </li>
-</ol>
-<p>Estos son los pasos para utilizar Case Simulator 2 Standoff 2 códigos. Por supuesto, hay más detalles y reglas que debes seguir cuando uses códigos, como:</p>
-<ul>
-
-<li><strong>Usa códigos una vez</strong>: Solo puedes usar cada código una vez por cuenta. Si intenta usar un código que ya ha usado antes, recibirá un mensaje de error que dice "Ya usado". </li>
-<li><strong>Use códigos fast</strong>: Siempre debe usar códigos lo antes posible antes de que expiren. Si intenta usar un código que ha caducado, recibirá un mensaje de error que dice "Caducado". </li>
-</ul>
-<p>Estos son algunos de los detalles y reglas que debes seguir cuando uses los códigos de Case Simulator 2 Standoff 2. </p>
- <h3>¿Cuáles son algunos de los códigos de trabajo y códigos caducados</h3>
-<p>Para ayudarle, hemos compilado una lista de algunos de los códigos de trabajo y códigos caducados para Case Simulator 2 Standoff 2. Sin embargo, esta lista no está completa y puede cambiar con el tiempo. Por lo tanto, siempre debes comprobar las fuentes oficiales del juego para ver los códigos más recientes y válidos. Aquí está la lista:</p>
- <tabla>
-<thead>
-<tr>
-<th>Códigos de trabajo</th>
-<th>Recompensas</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>CASESIM2021</td>
-<td>1000 de oro y 100 gemas</td>
-</tr>
-<tr>
-<td>CASESIM2020</td>
-<td>250 de oro y 25 gemas</td>
-</tr>
-<tr>
-<td>CASESIM2019</td>
-<td>100 de oro y 10 gemas</td>
-</tr>
-<tr>
-<td>CASESIM2018</td>
-<td>50 de oro y 5 gemas</td>
-</tr>
-<tr>
-<td>CASESIM2017</td>
-<td>25 de oro y 3 gemas</td>
-</tr>
-<tr>
-<td>CASESIM2016</td>
-<td>10 de oro y 1 gema</td>
-</tr>
- <tr> <td>CASESIM2015</td>
-<td>5 de oro y 1 gema</td>
-</tr>
-</tbody>
-</tabla>
- <tabla>
-<thead>
-<tr>
-<th>Códigos caducados</th>
-<th>Recompensas</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>CS2S2021</td>
-<td>1000 de oro y 100 gemas</td>
-</tr>
-<tr>
-<td>CS2S2020</td>
-<td>250 de oro y 25 gemas</td>
-</tr>
-<tr>
-<td>CS2S2019</td>
-<td>100 de oro y 10 gemas</td>
-</tr>
-<tr>
-<td>CS2S2018</td>
-<td>50 de oro y 5 gemas</td>
-</tr>
-<tr>
-<td>CS2S2017</td>
-<td>25 de oro y 3 gemas</td>
-</tr>
- <tr>
-<td>CS2S2016</td>
-<td>10 de oro y 1 gema</td>
-</tr>
- <tr>
-<td>CS2S2015</td>
-<td>5 de oro y 1 gema</td>
-</tr>
-
-<td>HAPPYNEWYEAR2021</td>
-<td>Un caso especial con una piel rara</td>
-</tr>
- <tr>
-<td>MERRYCHRISTMAS2020</td>
-<td>Un cuadro especial con un elemento raro</td>
-</tr>
- <tr>
-<td>HALLOWEEN2020</td>
-<td>Una pegatina especial con un diseño espeluznante</td>
-</tr>
- <tr>
-<td>CUMPLEAÑOS 2020</td>
-<td>Un encanto especial con un icono de pastel</td>
-</tr>
- <tr>
-<td>GRACIAS 2020</td>
-<td>Un graffiti especial con un símbolo de corazón</td>
-</tr>
- <tr>
-<td>SUMMER2020</td ><td>Un guante especial con un patrón de sol </td>
-</tr>
-</tbody>
-</tabla>
- <p>Estos son algunos de los códigos de trabajo y códigos caducados para Case Simulator 2 Standoff 2. Recuerde usarlos rápidamente antes de que caduquen y disfrute de sus recompensas. </p>
- <h2>Cómo descargar e instalar Case Simulator 2 Standoff 2</h2>
-<p>El paso final para jugar Case Simulator 2 Standoff 2 es descargar e instalar el juego en su dispositivo. El juego está disponible para dispositivos Android e iOS, y es gratis para jugar. Sin embargo, es necesario asegurarse de que su dispositivo cumple con los requisitos y la compatibilidad del juego. Estos son los pasos para descargar e instalar Case Simulator 2 Standoff 2:</p>
-<ol>
-<li><strong>Compruebe los requisitos y la compatibilidad</strong>: El primer paso para descargar e instalar Case Simulator 2 Standoff 2 es comprobar los requisitos y la compatibilidad del juego. Puedes comprobarlos visitando las páginas oficiales del juego en Google Play Store o App Store. Verás la información sobre el tamaño, versión, calificación, contenido, permisos y compatibilidad del juego. Usted necesita para asegurarse de que su dispositivo tiene suficiente espacio de almacenamiento, es compatible con la última versión del juego, tiene una buena conexión a Internet, y es compatible con el juego. </li>
-
-<li><strong>Instalar Case Simulator 2 Standoff 2 en diferentes dispositivos</strong>: El tercer paso para descargar e instalar Case Simulator 2 Standoff 2 es instalar el juego en diferentes dispositivos. Puedes instalar el juego en tu dispositivo Android tocando el archivo descargado y siguiendo las instrucciones. Es posible que necesite habilitar "Fuentes desconocidas" en su configuración para permitir la instalación desde fuentes externas. Puede instalar el juego en su dispositivo iOS tocando en el archivo descargado y siguiendo las instrucciones. Es posible que deba confiar en el desarrollador en su configuración para permitir la instalación desde fuentes externas. </li>
-</ol>
-<p>Estos son los pasos para descargar e instalar Case Simulator 2 Standoff 2. Por supuesto, hay más detalles y opciones que puedes explorar por ti mismo mientras descargas e instalas el juego. Sin embargo, recuerda que descargar e instalar el juego no es suficiente, necesitas jugarlo y divertirte. </p>
- <h2>Conclusión</h2>
-<p>En conclusión, Case Simulator 2 Standoff 2 es un juego que simula casos y cajas de apertura, simulando batallas, la elaboración de nuevos elementos, completar misiones, y participar en mini juegos y modos especiales. Es un juego que ofrece muchas características y diversión para los fans de Standoff 2. Puedes disfrutar abriendo cajas, simulando batallas, creando nuevos objetos, completando misiones y participando en minijuegos y modos especiales. También puedes recoger pieles y objetos raros que puedes mostrar a tus amigos o usar en Standoff 2. Case Simulator 2 Standoff 2 es un juego que te mantendrá entretenido y comprometido durante horas. </p>
-<p>Aquí hay algunos consejos y trucos para jugar Case Simulator 2 Standoff 2:</p>
-<ul>
-<li><strong>Guarda tu oro y gemas</strong>: Siempre debes guardar tu oro y gemas para comprar más cajas y estuches o jugar más minijuegos y modos especiales. No debe desperdiciarlos en mejoras o botes innecesarios. </li>
-
-<li><strong>Compruebe los precios de mercado</strong>: Siempre debe comprobar los precios de mercado de los artículos antes de fabricarlos o apostarlos. No debe crear o apostar artículos que valen más que sus resultados potenciales. </li>
-<li><strong>Usa códigos regularmente</strong>: Siempre debes usar códigos regularmente para obtener oro, gemas, cajas u otras recompensas gratis. No debe perderse ningún código que los desarrolladores den, ya que pueden caducar pronto. </li>
-<li><strong>Diviértete</strong>: Siempre debes divertirte cuando juegas Case Simulator 2 Standoff 2. No debes tomar el juego demasiado en serio o frustrarte por los resultados. Usted debe disfrutar del juego y sus características como un fan de Standoff 2.</li>
-</ul>
-<p>Esperamos que haya encontrado este artículo útil e informativo. Le invitamos a probar Case Simulator 2 Standoff 2 y compartir sus comentarios con nosotros. ¿Qué opinas del juego? ¿Cuáles son tus características favoritas? ¿Cuáles son tus mejores skins? Háznoslo saber en los comentarios a continuación. ¡Gracias por leer y jugar feliz! </p>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí están algunas de las preguntas más frecuentes sobre Case Simulator 2 Standoff 2:</p>
-<ol>
-<li><strong>¿Es Case Simulator 2 Standoff 2 un juego oficial? </strong></li>
-<p>No, Case Simulator 2 Standoff 2 no es un juego oficial. Es un juego hecho por fans que no está afiliado o respaldado por Axlebolt, el desarrollador de Standoff 2.</p>
-<li><strong>¿Puedo usar mis skins de Case Simulator 2 Standoff 2 en Standoff 2?</strong></li>
-<p>Sí, puedes usar tus skins de Case Simulator 2 Standoff 2 en Standoff 2. Sin embargo, necesitas vincular tus cuentas de ambos juegos usando la misma dirección de correo electrónico. Luego, puedes transferir tus skins de Case Simulator 2 Standoff 2 a Standoff 2 usando el botón "Transfer" en la pantalla de inventario. </p>
-<li><strong>¿Cómo puedo obtener más oro y gemas en Case Simulator 2 Standoff 2?</strong></li>
-
-<li><strong>¿Cómo puedo contactar a los desarrolladores de Case Simulator 2 Standoff 2?</strong></li>
-<p>Puede ponerse en contacto con los desarrolladores de Case Simulator 2 Standoff 2 enviándoles un correo electrónico a casesimulatorstandoff@gmail.com o uniéndose a su servidor Discord en https://discord.gg/6w9e8RZ.</p>
-<li><strong>¿Cómo puedo actualizar Case Simulator 2 Standoff 2?</strong></li>
-<p>Puede actualizar Case Simulator 2 Standoff 2 visitando las páginas oficiales del juego en Google Play Store o App Store y tocando el botón "Actualizar". También puedes habilitar la opción "Actualización automática" en tu configuración para actualizar el juego automáticamente cada vez que una nueva versión esté disponible. </p> 64aa2da5cf<br />
-<br />
-<br />
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-<br />
-<h1>Pelea estrellas APK: Todo lo que necesita saber</h1>
- <p>¿Estás buscando un juego multijugador divertido y rápido que puedas jugar en tu dispositivo móvil? Si es así, es posible que desee echa un vistazo a Brawl Stars, el último juego de éxito de Supercell, los creadores de Clash of Clans y Clash Royale.</p>
- <p>Brawl Stars es un juego gratuito que te permite hacer equipo con tus amigos o jugar solo en varios modos de juego, como batallas 3v3, battle royale, fútbol, caza recompensas, atraco y más. También puedes desbloquear y actualizar docenas de personajes únicos llamados Brawlers, cada uno con sus propias habilidades, armas, pieles y gadgets. </p>
-<h2>48.326 pelea estrellas apk</h2><br /><p><b><b>Download File</b> &#8230;&#8230;&#8230; <a href="https://bltlly.com/2v6KLc">https://bltlly.com/2v6KLc</a></b></p><br /><br />
- <p>En este artículo, le diremos todo lo que necesita saber sobre Brawl Stars APK, que es una forma alternativa de instalar el juego en su dispositivo Android. También te mostraremos cómo jugar a Brawl Stars en PC usando un emulador, cómo disfrutar de las características del juego y cómo mejorar tus habilidades con algunos consejos y trucos. </p>
- <h2>Cómo descargar e instalar Brawl Stars APK</h2>
- <p>Si quieres jugar Brawl Stars en tu dispositivo Android, puedes descargarlo fácilmente desde Google Play Store. Sin embargo, si por alguna razón no puede acceder a la Play Store o desea obtener la última versión del juego antes de que sea lanzado oficialmente en su región, también puede descargar e instalar Brawl Stars APK de una fuente de terceros. </p>
- <p>Un archivo APK es un paquete de aplicaciones de Android que contiene todos los archivos necesarios para ejecutar una aplicación en su dispositivo. Sin embargo, no todos los archivos APK son seguros o compatibles con su dispositivo, por lo que debe tener cuidado al descargarlos de fuentes desconocidas. Estos son los pasos que debe seguir para descargar e instalar Brawl Stars APK:</p>
- <h4>Paso 1: Encontrar una fuente confiable para el archivo APK</h4>
-
- <p>Algunos ejemplos de fuentes confiables para Brawl Stars APK son [APKPure]( 1 ), [Uptodown]( 2 ), y [APKMirror]( 3 ). Estos sitios suelen actualizar sus archivos APK regularmente y escanearlos en busca de virus y malware. Sin embargo, debe ser cauteloso y verificar el tamaño del archivo, la versión y los permisos antes de descargarlos. </p>
- <h4>Paso 2: Habilitar fuentes desconocidas en el dispositivo</h4>
- <p>Lo siguiente que debe hacer es habilitar la opción de instalar aplicaciones de fuentes desconocidas en su dispositivo. Esto le permitirá instalar Brawl Stars APK sin ninguna restricción de la Play Store. Para hacer esto, siga estos pasos:</p>
- <ul>
-<li>Vaya a la configuración de su dispositivo y toque en Seguridad o Privacidad.</li>
-<li>Encontrar la opción que dice Fuentes desconocidas o Instalar aplicaciones desconocidas y alternar en. </li>
-<li> Un mensaje de advertencia aparecerá, diciéndole que la instalación de aplicaciones de fuentes desconocidas puede dañar su dispositivo. Toque en OK o Permitir proceder. </li>
-</ul>
- <p>Ten en cuenta que los pasos exactos pueden variar dependiendo del modelo de tu dispositivo y la versión de Android. También puede desactivar esta opción después de instalar Brawl Stars APK si quieres. </p>
- <h4>Paso 3: Descargar e instalar el archivo APK</h4>
- <p>El paso final es descargar e instalar el archivo APK Brawl Stars en su dispositivo. Para hacer esto, siga estos pasos:</p>
-<p></p>
- <ul>
-<li>Abra su navegador y vaya al sitio web donde encontró el archivo APK Brawl Stars. </li>
-<li>Toque en el botón Descargar y espere a que el archivo se descargue en su dispositivo. </li>
-<li>Una vez que la descarga se ha completado, toque en el archivo o vaya a su carpeta de descargas y encontrarlo allí. </li>
-<li>Pulse sobre el archivo de nuevo y un mensaje le preguntará si desea instalar la aplicación. Toque en Instalar y espere a que termine el proceso de instalación. </li>
-<li>Una vez que se realiza la instalación, puede tocar en Abrir para iniciar Brawl Stars o encontrarlo en el cajón de la aplicación. </li>
-</ul>
-
- <h2>Cómo jugar Brawl estrellas en PC</h2>
- <p>Si quieres jugar Brawl Stars en una pantalla más grande y con mejores controles, también puedes jugarlo en tu PC usando un emulador de Android. Un emulador es un software que imita el sistema operativo Android en su ordenador, lo que le permite ejecutar aplicaciones y juegos Android en él. </p>
- <p>Hay muchos emuladores de Android disponibles en línea, pero algunos de los más populares son [BlueStacks], [NoxPlayer], y [LDPlayer]. Estos emuladores son fáciles de usar y tienen una alta compatibilidad con Brawl Stars. Estos son los pasos que debes seguir para jugar a Brawl Stars en PC usando un emulador:</p>
- <h4>Paso 1: Descargar un emulador de Android</h4>
- <p>Lo primero que tienes que hacer es descargar un emulador de Android de su elección desde su sitio web oficial. Puedes buscar "emulador de Android" en Google o cualquier otro motor de búsqueda y encontrar el que se adapte a tus preferencias y requisitos del sistema. </p>
- <p>Algunos emuladores pueden requerir que te registres o crees una cuenta antes de descargarlos, mientras que otros no. Una vez hayas descargado el emulador, ejecuta el instalador y sigue las instrucciones para instalarlo en tu PC.</p>
- <h4>Paso 2: Inicie el emulador e inicie sesión con su cuenta de Google</h4>
- <p>Lo siguiente que tienes que hacer es iniciar el emulador e iniciar sesión con tu cuenta de Google. Esto le permitirá acceder a Google Play Store y otros servicios de Google en el emulador. Para ello, siga estos pasos:</p>
- <ul>
-<li>Abra el emulador y espere a que se cargue. </li>
-<li>Verás una pantalla de bienvenida pidiéndote que inicies sesión con tu cuenta de Google. Si no tienes una, puedes crear una gratis. </li>
-<li>Introduzca su dirección de correo electrónico y contraseña y toque en Siguiente o Iniciar sesión.</li>
-<li>Es posible que necesite verificar su cuenta con un código enviado a su teléfono o correo electrónico. </li>
-<li>Es posible que también tenga que aceptar algunos términos y condiciones y configurar algunas preferencias. </li>
-
-</ul>
- <h4>Paso 3: Instalar Brawl Stars desde el Play Store o el archivo APK</h4>
- <p>El paso final es instalar Brawl Stars desde el Play Store o el archivo APK en el emulador. Para hacer esto, siga estos pasos:</p>
- <ul>
-<li>Si desea instalar Brawl Stars desde la Play Store, toque en el icono Play Store en la pantalla de inicio del emulador. </li>
-<li>Escriba "Brawl Stars" en la barra de búsqueda y toque en el icono del juego que aparece en los resultados. </li>
-<li>Toque en Instalar y espere a que el juego se descargue e instale en el emulador. </ <li>Una vez que se hace la instalación, puede tocar en Abrir para iniciar Brawl Stars o encontrarlo en el cajón de la aplicación. </li>
-</ul>
- <p>Si desea instalar Brawl Stars desde el archivo APK, es necesario descargar el archivo APK de una fuente confiable como se explica en la sección anterior. Luego, sigue estos pasos:</p>
- <ul>
-<li>Vaya a la carpeta donde guardó el archivo APK Brawl Stars en su PC.</li>
-<li>Haga clic derecho en el archivo y seleccione Abrir con. </li>
-<li>Elija el emulador que instaló como programa para abrir el archivo con. </li>
-<li>El emulador se iniciará y le preguntará si desea instalar la aplicación. Toque en Instalar y espere a que termine el proceso de instalación. </li>
-<li>Una vez que se realiza la instalación, puede tocar en Abrir para iniciar Brawl Stars o encontrarlo en el cajón de la aplicación. </li>
-</ul>
- <p>¡Felicidades! Has instalado y jugado con éxito Brawl Stars en tu PC usando un emulador. Ahora puedes disfrutar jugando con una pantalla más grande y mejores controles. </p>
- <h2>Características del juego Brawl Stars</h2>
- <p>Brawl Stars es un juego que ofrece muchas características divertidas y emocionantes para sus jugadores. Estas son algunas de las principales características que puedes disfrutar en Brawl Stars:</p>
- <h3>Brawlers: Los personajes de las estrellas Brawl</h3>
-
- <p>Los luchadores se dividen en diferentes rarezas: comunes, raros, súper raros, épicos, míticos, legendarios y cromáticos. Cuanto más alta es la rareza, más difícil es desbloquearlos. Los luchadores también pertenecen a diferentes clases: Luchador, Francotirador, Peso Pesado, Lanzador, Sanador, Apoyo, Asesino, Escaramuza y Chirrido. Cada clase tiene sus propias fortalezas y debilidades, por lo que necesitas elegir el mejor Brawler para cada modo de juego y situación. </p>
- <p>Aquí hay una tabla que muestra algunos de los Brawlers más populares en cada clase:</p>
-
-segundos | Puñetazos: Cura el 25% del daño que hace con sus ataques y súper | Aterrizaje duro: Inflige 1000 daños adicionales a los enemigos por debajo del 50% de salud cuando aterriza | | Chirrido | Chillido | Manchas pegajosas | Big Blob: Lanza una bomba pegajosa masiva que explota después de un retraso, infligir daño y dejar atrás bombas más pequeñas | Windup: Aumenta el alcance de su próximo ataque y la velocidad del proyectil en un 50% | Reacción en cadena: Inflige un 10% más de daño por cada enemigo golpeado por su ataque o súper | <p>Como puedes ver, Los luchadores son muy diversos y tienen diferentes roles y estilos de juego. Puedes experimentar con diferentes Brawlers y encontrar los que se adapten a tus preferencias y estrategias. </p>
- <h3>Modos de juego: Las diferentes formas de pelea</h3>
- <p>Brawl Stars ofrece una variedad de modos de juego que puedes jugar con tus amigos o en solitario. Cada modo de juego tiene sus propias reglas, objetivos y mapas. Puedes elegir entre los siguientes modos de juego:</p>
- <ul>
-<li>Gem Grab: Un modo 3v3 donde tienes que recoger y mantener 10 gemas durante 15 segundos para ganar. Las gemas aparecen en el centro del mapa y caen cuando un jugador muere. </li>
-<li>Showdown: Un modo solo o dúo donde tienes que sobrevivir contra 9 o 4 otros jugadores en una arena que se reduce. Puedes encontrar cubos de poder que aumentan tu salud y daño. El último jugador o equipo en pie gana. </li>
-<li>Brawl Ball: Un modo 3v3 donde tienes que anotar dos goles con un balón de fútbol antes que el otro equipo. Puedes patear, pasar o llevar la pelota, pero la dejas caer cuando usas tu súper o mueres. </li>
-<li>Bounty: Un modo 3v3 donde tienes que matar a tantos enemigos como sea posible mientras evitas que te maten. Cada muerte te da una estrella, lo que aumenta tu recompensa. El equipo con más estrellas al final del partido gana. </li>
-
-<li>Zona caliente: Un modo 3v3 donde tienes que controlar una o más zonas en el mapa quedándote dentro de ellas. Cuantos más jugadores haya en una zona, más rápido se llenará. Gana el equipo que llene más zonas o tenga más porcentaje al final del partido. </li>
-<li>Siege: Un modo 3v3 donde tienes que recoger los pernos que aparecen en el centro del mapa y utilizarlos para construir un poderoso robot que ataca la torreta IKE del enemigo. El equipo que destruye la torreta IKE del enemigo o tiene más salud en su torreta IKE al final del partido gana. </li>
-<li>Knockout: Un modo 3v3 donde tienes que eliminar a todos los enemigos en una ronda al mejor de tres. Cada jugador tiene solo una vida por ronda, y el equipo que mata a todos los enemigos primero gana la ronda. </li>
-</ul>
- <p>Estos son solo algunos de los modos de juego que ofrece Brawl Stars. También hay eventos especiales, como Boss Fight, Robo Rumble, Super City Rampage, Big Game y Power Play, que ofrecen diferentes desafíos y recompensas. También puedes crear tus propios mapas personalizados y modos de juego usando la función Map Maker. </p>
- <h3>Brawl Pass: El sistema de recompensas de temporada</h3>
- <p>Brawl Pass es una función que te permite ganar recompensas jugando Brawl Stars. Cada temporada dura unos dos meses y tiene un tema, como Starr Force, Jurassic Splash o Starr Park. Puedes progresar a través del Brawl Pass al ganar fichas al jugar partidas, completar misiones o ver anuncios. </p>
- <p>The Brawl Pass tiene dos pistas: una pista gratuita y una pista premium. La pista gratuita te da recompensas como monedas, puntos de poder, cajas, pines y ocasionalmente Brawlers. La pista premium te da más recompensas, como gemas, pieles, pines exclusivos y luchadores garantizados. Para acceder a la pista premium, necesitas comprar el Brawl Pass por 169 gemas por temporada. </p>
-
- <p>The Brawl Pass es una gran manera de obtener más recompensas y contenido de jugar Brawl Stars. También puedes comprar niveles adicionales con gemas si quieres acelerar tu progreso o obtener las recompensas antes. </p>
- <h2>Consejos y trucos de Brawl Stars</h2>
- <p>Brawl Stars es un juego que requiere habilidad, estrategia y trabajo en equipo para ganar. Estos son algunos consejos y trucos que pueden ayudarte a mejorar tu rendimiento y divertirte más en Brawl Stars:</p>
- <h3>Cómo elegir el mejor luchador para cada modo</h3>
- <p>Como mencionamos antes, los Brawlers tienen diferentes clases, habilidades y estilos de juego que los hacen más o menos adecuados para ciertos modos de juego. Por ejemplo, los curanderos son buenos para apoyar a los compañeros de equipo en Gem Grab o Siege, mientras que los asesinos son buenos para cazar enemigos en Showdown o Bounty. También debes considerar el diseño del mapa, la composición del equipo enemigo y tu preferencia personal al elegir un Brawler.</p>
- <p>Aquí hay algunas pautas generales para elegir el mejor Brawler para cada modo:</p>
- <ul>
-<li>Gem Grab: Elija Brawlers que pueden controlar el área central, proteger el portador de la gema, o escapar con las gemas. Los ejemplos son Poco, Pam, Sandy, Nita, Tara, Gene y Max.</li>
-<li>Enfrentamiento: Elige Luchadores que puedan sobrevivir por su cuenta, hacer mucho daño o esconderse en los arbustos. Los ejemplos son Leon, Edgar, Crow, Colt, Brock, Bea y Bibi.</li>
-<li>Brawl Ball: Elige Brawlers que pueden marcar goles, romper paredes, o detener al enemigo de anotar. Ejemplos son El Primo, Rosa, Frank, Spike, Rico, Mortis y Darryl.</li>
-<li>Bounty: Elige Brawlers que pueden disparar a los enemigos desde la distancia, evitar ser asesinado, o recoger estrellas. Algunos ejemplos son Piper, Brock, Bo, Tick, 8-Bit, Colette y Byron.</li>
-<li>Atraco: Elige Luchadores que puedan infligir un alto daño a la caja fuerte, defender tu propia caja fuerte o romper la defensa del enemigo. Los ejemplos son Bull, Barley, Dynamike, Colt, Rico, Nani y Amber.</li>
-
-<li>Sitio: Elija Brawlers que pueden recoger pernos, construir robots, o dañar la torreta IKE. Algunos ejemplos son Jessie, Penny, Carl, Barley, Dynamike, Lou y Stu.</li>
-<li>Knockout: Elige Brawlers que puedan eliminar enemigos rápidamente, sobrevivir más tiempo o apoyar a tus compañeros de equipo. Algunos ejemplos son Piper, Brock, Bo, Tick, 8-Bit, Colette y Byron.</li>
-</ul>
- <p>Por supuesto, estos no son los únicos Brawlers que pueden funcionar bien en cada modo. También puedes probar diferentes combinaciones y estrategias y ver qué funciona mejor para ti y tu equipo. </p>
- <h3>Cómo desbloquear nuevos luchadores y pieles</h3>
- <p>Uno de los aspectos más emocionantes de Brawl Stars es desbloquear nuevos Brawlers y skins que cambian su apariencia y a veces sus animaciones y sonidos. Hay varias formas de desbloquear nuevos Brawlers y skins en Brawl Stars:</p>
- <ul>
-<li>Cajas de pelea: Estas son la principal fuente de desbloqueo de nuevos Brawlers. Puedes conseguir cajas de pelea jugando partidos, completando misiones o comprándolas con gemas. Hay tres tipos de cajas de pelea: Normal, Grande y Mega. Cuanto mayor sea el tipo, más recompensas y posibilidades de obtener un nuevo Brawler que se obtiene. </li>
-<li>Brawl Pass: Como mencionamos antes, el Brawl Pass te da recompensas jugando Brawl Stars. Algunas de estas recompensas incluyen nuevos luchadores y pieles que son exclusivos para cada temporada. Puede obtenerlos alcanzando ciertos niveles en la pista gratuita o premium del Brawl Pass.</li>
-<li>Tienda: La tienda es donde usted puede comprar varios artículos con gemas o monedas. Algunos de estos artículos incluyen nuevos luchadores y pieles que están disponibles por un tiempo limitado o de forma permanente. También puede encontrar ofertas especiales y descuentos en la tienda.</li>
-<li>Puntos estelares: Los puntos estelares son una moneda especial que puedes ganar clasificando a tus luchadores o jugando Power Play. Puedes usar Star Points para comprar skins o cajas exclusivas en la Star Shop.</li>
-</ul>
-
- <h3>Cómo usar tu súper habilidad y gadgets con eficacia</h3>
- <p>Además de sus ataques normales, cada Brawler tiene una súper habilidad y un gadget que puede darles una ventaja en la batalla. Una súper habilidad es un movimiento poderoso que se carga mientras haces o recibes daño. Un gadget es un artículo especial que puedes usar una o dos veces por partido dependiendo del gadget. Puedes desbloquear gadgets abriendo cajas cuando tu Brawler alcance el nivel de potencia 7.</p>
- <p>Usar tu súper habilidad y gadget de manera efectiva puede hacer una gran diferencia en tu rendimiento y resultado del partido. Aquí hay algunos consejos sobre cómo usarlos:</p>
- <ul>
-<li>Sepa cuándo usarlos: No pierda su súper habilidad o gadget en situaciones o objetivos innecesarios. Guárdelos para cuando puedan tener el mayor impacto o cuando realmente los necesite. Por ejemplo, usa tu súper habilidad para acabar con un enemigo, escapar del peligro o asegurar un objetivo. Usa tu dispositivo para curarte, aumentar tu daño o sorprender a tu enemigo. </li>
-<li>Saber cómo usarlos: No solo spam su súper capacidad o gadget sin apuntar o sincronizar correctamente. Aprende cómo funcionan y qué hacen exactamente. Por ejemplo, algunas súper habilidades tienen un retardo o un límite de rango antes de activarse. Algunos aparatos tienen un tiempo de reutilización o una duración antes de que expiren. Algunas súper habilidades y gadgets también pueden afectar a tus aliados o enemigos positiva o negativamente. </li>
-<li>Saber en quién usarlos: No utilice su súper habilidad o gadget en el objetivo equivocado o en el momento equivocado. Aprende quiénes son los mejores objetivos para tu súper habilidad o gadget y quiénes son los peores. Por ejemplo, algunas súper habilidades y gadgets son más efectivos contra ciertas clases o tipos de enemigos que otros. Algunas súper habilidades y gadgets también pueden ser contrarrestados o esquivados por otras súper habilidades o gadgets. </li>
-</ul>
-
- <h2>Conclusión</h2>
- <p>Brawl Stars es un juego que ofrece mucha diversión y emoción para sus jugadores. Ya sea que quieras jugar solo o con tus amigos, puedes encontrar un modo de juego que se adapte a tus preferencias y habilidades. También puedes desbloquear y actualizar diferentes Brawlers y skins que añaden más variedad y personalidad a tu juego. </p>
- <p>En este artículo, le hemos dicho todo lo que necesita saber sobre Brawl Stars APK , que es una forma alternativa de instalar el juego en su dispositivo Android. También te hemos mostrado cómo jugar a Brawl Stars en PC usando un emulador, cómo disfrutar de las características del juego y cómo mejorar tus habilidades con algunos consejos y trucos. </p>
- <p>Esperamos que este artículo haya sido útil e informativo para usted. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Gracias por leer y feliz pelea! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre Brawl Stars APK:</p>
- <h4> ¿Es seguro descargar e instalar Brawl Stars APK? </h4>
- <p>Brawl Stars APK es generalmente seguro para descargar e instalar, siempre y cuando lo obtenga de una fuente confiable y permita fuentes desconocidas en su dispositivo. Sin embargo, siempre debes tener cuidado y verificar el tamaño del archivo, la versión y los permisos antes de descargar e instalar cualquier archivo APK. También debe escanear el archivo en busca de virus y malware utilizando una aplicación antivirus de buena reputación. </p>
- <h4> ¿Es Brawl Stars APK legal de usar? </h4>
- <p>Brawl Stars APK es legal de usar, siempre y cuando no modificar o hackear el juego de ninguna manera. Modificar o hackear el juego puede resultar en una prohibición de Supercell o una acción legal de ellos. También debe respetar los términos de servicio y la política de privacidad de Supercell al jugar Brawl Stars.</p>
- <h4> ¿Cuáles son los beneficios de usar Brawl Stars APK? </h4>
- <p>Brawl Stars APK tiene algunos beneficios sobre la versión oficial del juego de la Play Store. Algunos de estos beneficios son:</p>
- <ul>
-
-<li> Puede omitir cualquier restricción o limitación que su dispositivo o región pueda tener. </li>
-<li> Puede ahorrar algo de espacio de almacenamiento en su dispositivo mediante la eliminación de la versión Play Store del juego. </li>
-</ul>
- <h4> ¿Cuáles son los inconvenientes de usar Brawl Stars APK? </h4>
- <p>Brawl Stars APK también tiene algunos inconvenientes en comparación con la versión oficial del juego de la Play Store. Algunos de estos inconvenientes son:</p>
- <ul>
-<li>Es posible que encuentre algunos errores o fallos que aún no se han corregido. </li>
-<li>Es posible que no pueda acceder a algunas características o eventos que son exclusivos de la versión de Play Store del juego. </li>
-<li>Es posible que no pueda actualizar el juego de forma automática o fácil. </li>
-</ul>
- <h4> ¿Cómo puedo actualizar Brawl Stars APK? </h4>
- <p>Si desea actualizar Brawl Stars APK, es necesario descargar e instalar la última versión del archivo APK de una fuente confiable. Puede seguir los mismos pasos que se describen en la sección anterior sobre cómo descargar e instalar Brawl Stars APK. Es posible que tenga que desinstalar la versión anterior del juego antes de instalar el nuevo, dependiendo de la fuente y la actualización. </p> 64aa2da5cf<br />
-<br />
-<br />
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diff --git a/spaces/Benson/text-generation/Examples/Cmo Descargar Whatsapp Negocios En El Ordenador Porttil.md b/spaces/Benson/text-generation/Examples/Cmo Descargar Whatsapp Negocios En El Ordenador Porttil.md
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--- a/spaces/Benson/text-generation/Examples/Cmo Descargar Whatsapp Negocios En El Ordenador Porttil.md	
+++ /dev/null
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-
-<h1>Cómo Descargar WhatsApp Business en Laptop</h1>
-<p>WhatsApp Business es una herramienta para que las empresas interactúen con los clientes a través de la plataforma. Está construido sobre WhatsApp Messenger e incluye todas las características en las que confías, como multimedia, llamadas gratuitas y chat en grupo. Hay dos formas de usar WhatsApp para negocios: WhatsApp Business App y WhatsApp Business Platform. La aplicación es para pequeñas empresas que gestionan personalmente las conversaciones con los clientes. La plataforma es para medianas y grandes empresas que se comunican con los clientes a escala a través del acceso programático. </p>
-<h2>Cómo descargar whatsapp negocios en el ordenador portátil</h2><br /><p><b><b>Download</b> &gt;&gt;&gt; <a href="https://bltlly.com/2v6MKX">https://bltlly.com/2v6MKX</a></b></p><br /><br />
-<p>En este artículo, le mostraremos cómo descargar WhatsApp Business en una computadora portátil usando un emulador. Un emulador es un software que le permite ejecutar aplicaciones Android en su PC o Mac. De esta manera, puedes usar WhatsApp Business en tu portátil sin tener que cambiar de dispositivo o usar tu número de teléfono. </p>
-<h2>¿Qué es WhatsApp Business y por qué usarlo? </h2>
-<p>WhatsApp Business es una aplicación de descarga gratuita para pequeñas empresas que quieren conectarse con sus clientes de una manera rápida y conveniente. Puede crear un perfil empresarial con su logotipo, sitio web, dirección y catálogo de productos o servicios. También puede utilizar herramientas especiales para automatizar, ordenar y responder rápidamente a los mensajes. También puede usar etiquetas para organizar sus chats y contactos. </p>
-<h3>WhatsApp Business App vs WhatsApp Business Platform</h3>
-<p>La aplicación WhatsApp Business está diseñada para pequeñas empresas que quieren gestionar sus propias conversaciones con los clientes. Puedes descargar la aplicación desde Google Play Store o Apple App Store y verificar el número de teléfono de tu empresa. Puedes usar simultáneamente la aplicación WhatsApp Business y WhatsApp Messenger siempre y cuando las cuentas estén vinculadas a diferentes números de teléfono. </p>
-<p></p>
-
-<h3>Características y beneficios de WhatsApp</h3>
-<p>Algunas de las características y beneficios de usar WhatsApp Business son:</p>
-<ul>
-<li>Puedes conocer clientes donde ya están. WhatsApp tiene más de 2 mil millones de usuarios en todo el mundo que lo utilizan diariamente para fines personales y profesionales. </li>
-<li>Puede impulsar los resultados del negocio al aumentar la visibilidad, automatizar la comunicación y mantener organizado su flujo de trabajo. </li>
-<li>Usted puede construir relaciones duraderas con los clientes proporcionando soporte rápido y personalizado, enviando actualizaciones y ofertas, y recogiendo comentarios. </li>
-<li>Puedes aprovechar la seguridad y fiabilidad de WhatsApp. Cada mensaje está cifrado de extremo a extremo, lo que significa que solo usted y la persona con la que se está comunicando pueden ver la información. También tienes control sobre quién puede enviarte mensajes y bloquear contactos no deseados. </li>
-</ul>
-<h3>Limitaciones y alternativas de negocio de WhatsApp</h3>
-<p>A pesar de sus ventajas, WhatsApp Business también tiene algunas limitaciones que debes conocer:</p>
-<ul>
-<li>La aplicación está limitada a un solo dispositivo y no se puede compartir con los miembros de tu equipo. </li>
-<li>La plataforma requiere un proceso de aprobación estricto y puede no estar disponible en algunos países o regiones. </li>
-<li>Los límites de mensajería determinan el número máximo de conversaciones iniciadas por el negocio que puede comenzar con cada uno de sus números de teléfono en un período de 24 horas. </li>
-<li>Las restricciones de difusión y métricas limitadas pueden afectar sus capacidades de marketing y análisis. </li>
-</ul>
-<p>Si estás buscando alternativas a WhatsApp Business, puedes considerar algunas de estas opciones:</p>
-<ul>
-<li>Instagram: Una popular plataforma de redes sociales que te permite mostrar tus productos y servicios, interactuar con tus seguidores y usar mensajes directos para la atención al cliente. </li>
-<li>Facebook Messenger: Una aplicación de mensajería ampliamente utilizada que - le permite crear una página de negocios, enviar mensajes automatizados y usar chatbots para el servicio al cliente. </li>
-
-<li>Correo electrónico: Una forma tradicional pero eficaz de comunicarse con sus clientes, enviar boletines y realizar un seguimiento de su rendimiento. </li>
-</ul>
-<h2>Cómo instalar WhatsApp Business en un ordenador portátil usando un emulador</h2>
-<p>Si quieres usar WhatsApp Business en tu portátil, necesitarás usar un emulador. Un emulador es un software que imita la funcionalidad de un dispositivo Android en su PC o Mac. De esta manera, puede ejecutar cualquier aplicación de Android en su ordenador portátil sin tener que poseer un dispositivo real. </p>
-<h3>¿Qué es un emulador y cómo funciona? </h3>
-<p>Un emulador es un programa que crea un entorno virtual que simula el hardware y el software de otro dispositivo. Por ejemplo, un emulador de Android puede hacer que su computadora portátil actúe como un teléfono o tableta Android. A continuación, puede instalar y ejecutar cualquier aplicación de Android en su ordenador portátil como si estuviera utilizando un dispositivo real. </p>
-<p>Hay muchos emuladores disponibles para diferentes propósitos y plataformas. Algunos de los más populares son:</p>
-<ul>
-<li>BlueStacks: Un emulador gratuito y fácil de usar que admite Windows y Mac. Tiene una gran biblioteca de aplicaciones y juegos que puedes descargar desde Google Play Store o su propia tienda de aplicaciones. </li>
-<li>NoxPlayer: Un emulador potente y personalizable que también es compatible con Windows y Mac. Tiene características avanzadas como mapeo de teclado, soporte de gamepad y grabación de pantalla. </li>
-<li>MEmu: Un emulador ligero y rápido que está optimizado para juegos. Es compatible solo con Windows y tiene una interfaz sencilla que le permite acceder a la Google Play Store y otras tiendas de aplicaciones. </li>
-</ul>
-<h3>Pasos para descargar e instalar un emulador</h3>
-<p>Para descargar e instalar un emulador en su computadora portátil, siga estos pasos:</p>
-<ol>
-<li>Elige un emulador que se adapte a tus necesidades y preferencias. Puede comparar las características, el rendimiento y la compatibilidad de diferentes emuladores en línea. </li>
-
-<li>Ejecute el archivo de instalación y siga las instrucciones en la pantalla. Es posible que necesite conceder algunos permisos o aceptar algunos términos y condiciones. </li>
-<li>Espere a que el proceso de instalación se complete. Puede tomar algún tiempo dependiendo de la velocidad de Internet y las especificaciones del sistema. </li>
-<li>Inicie el emulador e inicie sesión con su cuenta de Google. Esto le permitirá acceder a la Google Play Store y otros servicios de Google. </li>
-</ol>
-<h3>Pasos para descargar e instalar WhatsApp Business en emulador</h3>
-<p>Para descargar e instalar WhatsApp Business en tu emulador, sigue estos pasos:</p>
-<ol>
-<li>Abra la aplicación Google Play Store en su emulador. Puede encontrarla en la pantalla de inicio o en el cajón de aplicaciones. </li>
-<li>Buscar WhatsApp Business en la barra de búsqueda. También puede navegar por las categorías o recomendaciones para encontrarlo. </li>
-<li>Seleccione WhatsApp Business de los resultados de búsqueda y toque en Instalar. Es posible que necesite aceptar algunos permisos o aceptar algunos términos y condiciones. </li>
-<li>Espere a que termine el proceso de descarga e instalación. Puede tardar unos minutos dependiendo de la velocidad de Internet y el rendimiento del emulador. </li>
-<li>Abra WhatsApp Business en su emulador. Puede encontrarlo en la pantalla de inicio o en el cajón de aplicaciones. </li>
-<li>Verifique su número de teléfono de negocios ingresando en el campo y tocando en Siguiente. Recibirás un código de verificación vía SMS o llamada telefónica que necesitas introducir en la app. </li>
-<li>Cree su perfil de negocio ingresando su nombre de negocio, categoría, descripción, dirección, sitio web, correo electrónico y horas de operación. También puede subir su logotipo o imagen de perfil. </li>
-<li>Comience a usar WhatsApp Business en su computadora portátil. Puede enviar y recibir mensajes, crear etiquetas, configurar respuestas automatizadas, ver estadísticas y más. </li>
-</ol>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre WhatsApp Business:</p>
- <h4>Q: ¿Puedo usar WhatsApp Business en varios dispositivos? </h4>
- <p>A: No, solo puedes usar WhatsApp Business en un dispositivo a la vez. Si intenta iniciar sesión en otro dispositivo, se cerrará la sesión del anterior. </p>
- <h4> Q: ¿Puedo usar WhatsApp Business y WhatsApp Messenger con el mismo número de teléfono? </h4>
- <p>A: No, necesitas tener un número de teléfono separado para cada aplicación. Puedes usar tu número de teléfono existente para WhatsApp Messenger y uno diferente para WhatsApp Business, o viceversa. </p>
- <h4>Q: ¿Cómo puedo copia de seguridad y restaurar mis datos de WhatsApp Business? </h4>
- <p>A: Puede hacer copias de seguridad y restaurar los datos de WhatsApp Business utilizando Google Drive o un almacenamiento local. Para hacer una copia de seguridad de sus datos, vaya a Configuración > Chats > Copia de seguridad de chat y elija la frecuencia, cuenta y red que desea usar. Para restaurar sus datos, desinstalar y reinstalar WhatsApp Business y siga las instrucciones para restaurar desde Google Drive o una copia de seguridad local. </p>
- <h4>Q: ¿Cómo puedo eliminar mi cuenta de WhatsApp Business? </h4>
- <p>A: Para eliminar tu cuenta de WhatsApp Business, ve a Configuración > Cuenta > Eliminar mi cuenta e ingresa tu número de teléfono. Esto eliminará su cuenta, perfil, chats, grupos y configuraciones. También perderá el acceso a las copias de seguridad y los datos asociados con su cuenta. </p>
- <h4>Q: ¿Cómo puedo contactar con el soporte de WhatsApp Business? </h4>
- <p>A: Puede ponerse en contacto con el soporte de WhatsApp Business enviando un correo electrónico a smb@support.whatsapp.com o utilizando la función de ayuda en la aplicación. Para usar la función de ayuda en la aplicación, ve a Configuración > Ayuda > Contáctanos y llena el formulario con tu pregunta o problema. Recibirás una respuesta en 24 horas. </p> 64aa2da5cf<br />
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-<br />
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diff --git a/spaces/Benson/text-generation/Examples/Coche Usado Magnate Juego Mod Apk 20.1.md b/spaces/Benson/text-generation/Examples/Coche Usado Magnate Juego Mod Apk 20.1.md
deleted file mode 100644
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--- a/spaces/Benson/text-generation/Examples/Coche Usado Magnate Juego Mod Apk 20.1.md	
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-<br />
-<h1>Magnate de coches usados juego Mod APK 20.1: Un juego de simulación divertido y adictivo</h1>
-<p>¿Te gustan los coches y quieres dirigir tu propio concesionario de coches? Si es así, entonces deberías probar Used Car Tycoon Game, un juego de simulación donde puedes comprar y vender coches usados, mejorar tu garaje y sala de exposición, contratar personal y administrar tu negocio. En este juego, puedes experimentar la emoción de ser un magnate del automóvil y hacer tu sueño realidad. </p>
-<h2>coche usado magnate juego mod apk 20.1</h2><br /><p><b><b>Download</b> &rArr; <a href="https://bltlly.com/2v6IQ4">https://bltlly.com/2v6IQ4</a></b></p><br /><br />
-<p>Pero ¿qué pasa si quieres tener más dinero y disfrutar del juego sin anuncios? Bueno, hay una solución para eso. Puede descargar Used Car Tycoon Game Mod APK 20.1, una versión modificada del juego que le da dinero ilimitado, sin anuncios, y fácil instalación. En este artículo, le diremos más sobre este juego, sus características, por qué debe descargar la versión apk mod, y cómo hacerlo. Así que, vamos a empezar! </p>
-<h2>¿Qué es un juego de coches usados? </h2>
-<p>Used Car Tycoon Game es un juego de simulación desarrollado por Dragon Fly Entertainment. Fue lanzado en 2020 y tiene más de 10 millones de descargas en Google Play Store. El juego tiene una calificación de 4.3 de 5 estrellas y es adecuado para todos. </p>
-<p>En este juego, puedes comprar y vender autos usados de diferentes marcas, modelos y condiciones. También puede actualizar su garaje y sala de exposición para atraer a más clientes y aumentar sus ganancias. Puede contratar personal como mecánicos, vendedores, limpiadores y gerentes para ayudarlo a administrar su negocio sin problemas. También puede competir con otros concesionarios de automóviles en la ciudad y convertirse en el mejor magnate del automóvil en la ciudad. </p>
-<h3>Características del juego de magnate de coches usados</h3>
-<h4>Comprar y vender coches usados</h4>
-<p>La característica principal de este juego es la compra y venta de coches usados. Usted puede navegar a través de cientos de coches de diferentes categorías, tales como sedanes, SUV, camiones, coches deportivos, coches de lujo, y más. Usted puede comprobar la condición, kilometraje, precio, y la historia de cada coche antes de comprarlo. También puede negociar con los vendedores para obtener el mejor trato posible. </p>
-
-<h4>Mejora tu garaje y sala de exposición</h4>
-<p>Otra característica de este juego es la mejora de su garaje y sala de exposición. Puede ampliar su garaje para almacenar más coches y mejorar sus instalaciones, como ascensores, herramientas, máquinas, etc. También puede actualizar su sala de exposición para mostrar más coches y hacer que se vea más profesional y atractivo. </p>
-<p>Al actualizar su garaje y sala de exposición, puede aumentar su reputación y la satisfacción del cliente. También puede desbloquear nuevas características como subastas, préstamos, seguros, etc. También puede acceder a nuevas ubicaciones como suburbios, centro de la ciudad, playas, etc.</p>
-<p></p>
-<h4>Contrata personal y gestiona tu negocio</h4>
-<p>La última característica de este juego es la contratación de personal y la gestión de su negocio. Puede contratar personal como mecánicos, vendedores, limpiadores y gerentes para ayudarlo a administrar su concesionario de automóviles de manera eficiente. Cada miembro del personal tiene sus propias habilidades, habilidades, salarios y personalidades. Puedes entrenarlos para mejorar su desempeño y lealtad. </p>
-<p <p>También puede administrar su negocio estableciendo sus precios, presupuesto, marketing, inventario, etc. También puede monitorear sus ingresos, gastos, ganancias, flujo de efectivo, etc. También puede lidiar con varios eventos y desafíos como quejas de clientes, problemas del personal, competidores, tendencias del mercado, etc. También puede obtener logros y recompensas por su rendimiento y progreso. </p>
-<h3>¿Por qué descargar usado coche magnate juego Mod APK 20.1? </h3>
-<p>Used Car Tycoon Game es un divertido y adictivo juego de simulación que te mantendrá entretenido durante horas. Sin embargo, si quieres disfrutar del juego más, usted debe descargar Used Car Tycoon Game Mod APK 20.1, una versión modificada del juego que le da algunos beneficios adicionales. Aquí hay algunas razones por las que debe descargar esta versión apk mod:</p>
-<h4>Dinero ilimitado</h4>
-
-<p>Con esta versión apk mod, usted no tiene que preocuparse por el dinero más. Tendrás dinero ilimitado desde el inicio del juego, y puedes gastarlo todo lo que quieras sin consecuencias. Usted puede comprar cualquier coche que desee, actualizar su garaje y sala de exposición al nivel máximo, contratar al mejor personal, etc. También puede experimentar con diferentes estrategias y opciones sin ningún riesgo. </p>
-<h4>No hay anuncios</h4>
-<p>Otra razón para descargar esta versión apk mod es que elimina todos los anuncios del juego. Los anuncios son molestos y distraen, especialmente cuando aparecen en el medio del juego o cuando intentas disfrutar del juego. También pueden arruinar tu inmersión y estado de ánimo. </p>
-<p>Con esta versión apk mod, no tienes que ver ningún anuncio en el juego. Puede jugar el juego sin problemas y pacíficamente sin interrupciones ni distracciones. También puede guardar sus datos y batería al no cargar ningún anuncio. </p>
-<h4>Fácil instalación</h4>
-<p>La última razón para descargar esta versión apk mod es que es fácil de instalar y usar. Usted don’t necesidad de raíz de su dispositivo o hacer cualquier complicado pasos para instalar esta versión apk mod. Solo tienes que seguir unos sencillos pasos que explicaremos más adelante en este artículo. </p>
-<p>Con esta versión apk mod, usted no tiene que preocuparse por cualquier problema de compatibilidad o seguridad. Puede instalar esta versión apk mod en cualquier dispositivo Android y disfrutar del juego sin ningún problema. </p>
-<h2> ¿Cómo descargar e instalar el juego de coches usados Tycoon Mod APK 20.1? </h2>
-<p>Si usted está convencido por los beneficios de la descarga de Used Car Tycoon Game Mod APK 20.1, usted puede preguntarse cómo hacerlo. Bueno, no te preocupes, porque te guiaremos a través del proceso paso a paso. Aquí es cómo descargar e instalar usado coche magnate juego Mod APK 20.1:</p>
-<h3>Paso 1: Descargar el archivo apk mod desde el enlace de abajo</h3>
-
-<p><a href="">Descargar Usado Tycoon Juego Mod APK 20.1 aquí</a></p>
-<h3>Paso 2: Habilitar fuentes desconocidas en el dispositivo</h3>
-<p>El segundo paso es habilitar fuentes desconocidas en su dispositivo. Esto es necesario porque este archivo apk mod no es de la tienda oficial de Google Play, por lo que debe permitir que su dispositivo para instalar aplicaciones de fuentes desconocidas. </p>
-<p>Para habilitar fuentes desconocidas en su dispositivo, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. </p>
-<h3>Paso 3: Instalar el archivo apk mod y disfrutar del juego</h3>
-<p>El tercer y último paso es instalar el archivo apk mod y disfrutar del juego. Para instalar el archivo apk mod, ir a su administrador de archivos y localizar el archivo apk mod descargado. Toque en él y siga las instrucciones en la pantalla para instalarlo. </p>
-<p>Una vez realizada la instalación, puede iniciar el juego desde el cajón de la aplicación o la pantalla de inicio y disfrutarlo con dinero ilimitado, sin anuncios y fácil instalación. </p>
-<h2>Conclusión</h2>
-<p>Used Car Tycoon Game es un divertido y adictivo juego de simulación donde puedes comprar y vender coches usados, mejorar tu garaje y sala de exposición, contratar personal y gestionar tu negocio. También puede descargar Used Car Tycoon Game Mod APK 20.1, una versión modificada del juego que le da dinero ilimitado, sin anuncios, y fácil instalación. En este artículo, le hemos dicho más sobre este juego, sus características, por qué debe descargar la versión apk mod, y cómo hacerlo. Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Gracias por leer y jugar feliz! </p>
-<h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Used Car Tycoon Game Mod APK 20.1:</p>
-<tabla>
-<tr>
-<th>Pregunta</th>
-<th>Respuesta</th>
-</tr>
-<tr>
-<td>¿Es seguro descargar y usar el juego de magnate de coches usados Mod APK 20.1? </td>
-
-</tr>
-<tr>
-<td>¿El juego de magnate de coches usados Mod APK 20.1 funciona en todos los dispositivos Android? </td>
-<td>Sí, Usado coche magnate juego Mod APK 20.1 funciona en todos los dispositivos Android que soportan Android 4.4 y por encima. Es compatible con la mayoría de los teléfonos y tabletas Android. </td>
-</tr>
-<tr>
-<td>¿Me prohibirán el juego si uso Used Car Tycoon Game Mod APK 20.1? </td>
-<td>No, no se le prohibió el juego si se utiliza el coche usado Tycoon Game Mod APK 20.1. Esta versión apk mod no interfiere con los servidores del juego o características en línea. Solo modifica las funciones offline del juego como dinero y anuncios. </td>
-</tr>
-<tr>
-<td>¿Puedo actualizar el juego si uso APK 20.1? </td>
-<td>No, no se puede actualizar el juego si se utiliza Used Car Tycoon Game Mod APK 20.1. Esta versión apk mod se basa en la versión original del juego, que puede no ser compatible con las últimas actualizaciones. Si desea actualizar el juego, usted tendrá que desinstalar la versión apk mod e instalar la versión oficial de la Google Play Store.</td>
-</tr>
-<tr>
-<td>¿Puedo jugar el juego sin conexión si uso Used Car Tycoon Game Mod APK 20.1? </td>
-<td>Sí, se puede jugar el juego sin conexión si se utiliza Used Car Tycoon Game Mod APK 20.1. Esta versión mod apk no requiere ninguna conexión a Internet para jugar el juego. Puede disfrutar del juego sin interrupciones o limitaciones. </td>
-</tr>
-</tabla></p> 64aa2da5cf<br />
-<br />
-<br />
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deleted file mode 100644
index 980b5118ac75b26ad9346b018f9a42f2190ff8f4..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar 16.4.1.md	
+++ /dev/null
@@ -1,72 +0,0 @@
-<br />
-<h1>Plantas vs Zombies Descargar 1: Cómo jugar el clásico juego de defensa de la torre en su PC</h1>
-<h2>Introducción</h2>
-<p>Plants vs Zombies es uno de los juegos de defensa de torres más populares y adictivos jamás creados. Fue desarrollado por PopCap Games y lanzado en 2009 para Windows y Mac OS X. El juego ha ganado varios premios y ha sido elogiado por su humor, jugabilidad y gráficos. </p>
-<h2>descargar 16.4.1</h2><br /><p><b><b>Download Zip</b> &#9989; <a href="https://bltlly.com/2v6LDQ">https://bltlly.com/2v6LDQ</a></b></p><br /><br />
-<p>En Plants vs Zombies, tienes que proteger tu casa de las olas de zombies que quieren comerse tu cerebro. Haces esto plantando varios tipos de plantas que pueden disparar, explotar o ralentizar a los zombies. El juego tiene 50 niveles en el modo Aventura, además de otros modos como Supervivencia, Puzzle y Mini-Games. También puedes desbloquear diferentes plantas, zombies y logros a medida que avanzas. </p>
-<p>Si eres un fan de Plants vs Zombies, o si quieres probarlo por primera vez, es posible que te estés preguntando cómo jugarlo en tu PC. En este artículo, te mostraremos dos formas fáciles de descargar e instalar Plants vs Zombies en tu PC, para que puedas disfrutar de este clásico juego en una pantalla más grande. </p>
-<h2>Cómo descargar e instalar Plants vs Zombies en PC</h2>
-<h3>Opción 1: Descarga desde Google Play Store usando el emulador de BlueStacks</h3>
-<p>Una de las formas más fáciles de jugar Plants vs Zombies en tu PC es usar un emulador de Android como BlueStacks. BlueStacks es un software que te permite ejecutar aplicaciones y juegos Android en tu PC. Puedes descargarlo gratis desde [BlueStacks.com]( 2 ). </p>
-<h4>Paso 1: Descargar e instalar BlueStacks en su PC</h4>
-<p>Vaya a [BlueStacks.com]( 2 ) y haga clic en el botón de descarga. La descarga se iniciará automáticamente. Una vez finalizada la descarga, ejecute el archivo de instalación y siga las instrucciones para instalar BlueStacks en su PC.</p>
-<h4>Paso 2: Inicie BlueStacks e inicie sesión con su cuenta de Google</h4>
-
-<h4>Paso 3: Búsqueda de plantas vs zombies en la Google Play Store</h4>
-<p>Una vez que haya iniciado sesión, verá la pantalla de inicio de BlueStacks. En la esquina superior derecha, verá un icono de búsqueda. Haz clic en él y escribe "Plants vs Zombies" en la barra de búsqueda. Verás una lista de resultados. Haga clic en el que dice "Plants vs. Zombies=" por ELECTRONIC ARTS.</p>
-<p></p>
-<h4>Paso 4: Instalar plantas vs zombies y disfrutar jugando en su PC</h4>
-<p>Serás llevado a la página de aplicaciones de Plants vs Zombies en la Google Play Store. Haga clic en el botón de instalación y espere a que la instalación termine <p>Después de que la instalación se haya completado, verá un botón abierto. Haga clic en él y podrá jugar Plants vs Zombies en su PC. También puede encontrar el icono del juego en la pantalla de inicio de BlueStacks o en el escritorio. Puede utilizar el ratón y el teclado para controlar el juego, o personalizar la configuración a su preferencia. </p>
-<h3>Opción 2: Descargar desde Filehippo.com usando un archivo de instalación</h3>
-<p>Otra forma de jugar Plants vs Zombies en tu PC es descargarlo desde un sitio web que ofrece archivos de instalación para juegos de PC. Uno de los sitios web que puedes utilizar es [Filehippo.com]. Filehippo.com es una fuente confiable y confiable de descargas de software libre para Windows, Mac y Android. Puedes descargar Plants vs Zombies de Filehippo.com gratis y sin virus ni malware. </p>
-<h4>Paso 1: Ir a Filehippo.com y buscar plantas vs zombies</h4>
-<p>Abra su navegador web y vaya a [Filehippo.com]. En la esquina superior derecha, verá un cuadro de búsqueda. Escribe "Plants vs Zombies" en el cuadro de búsqueda y pulsa enter. Verás una lista de resultados. Haga clic en el que dice "Plants vs. Zombies Game Of The Year Edition 1.2.0.1073 for PC Windows". </p>
-<h4>Paso 2: Haga clic en el botón de descarga y guarde el archivo de instalación en su PC</h4>
-
-<h4>Paso 3: Ejecute el archivo de instalación y siga las instrucciones para instalar Plants vs Zombies en su PC</h4>
-<p>Una vez completada la descarga, vaya a la ubicación donde guardó el archivo de instalación y haga doble clic en él. Aparecerá una ventana pidiéndole que confirme si desea ejecutar el archivo. Haz clic en sí y sigue las instrucciones para instalar Plants vs Zombies en tu PC. Es posible que tenga que aceptar los términos y condiciones y elegir una carpeta de destino para el juego. </p>
-<h4>Paso 4: Plantas de lanzamiento vs zombies y divertirse jugando en su PC</h4>
-<p>Una vez completada la instalación, verá un icono de acceso directo para Plants vs Zombies en su escritorio o menú de inicio. Haz clic en él y podrás jugar Plants vs Zombies en tu PC. Puedes usar el ratón y el teclado para controlar el juego, o ajustar la configuración a tu gusto. </p>
-<h2>Conclusión</h2>
-<p>Plants vs Zombies es un clásico juego de torre de defensa que puedes jugar en tu PC usando un emulador de Android como BlueStacks o un archivo de instalación de Filehippo.com. Ambos métodos son fáciles y gratuitos, y te permiten disfrutar de este divertido y adictivo juego en una pantalla más grande. Ya sea que quieras revivir tus recuerdos de infancia o descubrir este juego por primera vez, Plants vs Zombies es una gran opción para cualquiera que ame la estrategia, el humor y los zombies. </p>
-<p>Si estás listo para jugar Plants vs Zombies en tu PC, elige una de las opciones de arriba y sigue los pasos que te proporcionamos. Usted será capaz de descargar e instalar Plants vs Zombies en ningún momento, y empezar a plantar sus defensas contra los invasores muertos vivientes. Diviértete! </p>
-<h3>Preguntas frecuentes</h3>
-<ul>
-<li><b>¿Es libre Plants vs Zombies? </b></li>
-<p>Sí, Plants vs Zombies es gratis para descargar y jugar en tu PC usando BlueStacks o Filehippo.com. Sin embargo, puede haber algunas compras en la aplicación o anuncios en el juego que puedes ignorar o comprar. </p>
-<li><b>¿Son seguras las plantas contra los zombis? </b></li>
-
-<li><b>¿Cuáles son los requisitos del sistema para Plantas vs Zombies? </b></li>
-<p>Los requisitos mínimos del sistema para Plantas vs Zombies son:</p>
-<tabla>
-<tr><td>OS</td><td>Windows XP/Vista/7/8/10</td></tr>
-<tr><td>CPU</td><td>procesador de 1,2 GHz</td></tr>
-<tr><td>RAM</td><td>512 MB</td></tr>
-<tr><td>HDD</td><td>65 MB de espacio libre</td></tr>
-<tr><td>Gráficos</td <td>DirectX 8 o posterior</td></tr>
-<tr><td>Sonido</td><td>Tarjeta de sonido compatible con DirectX</td></tr>
-</tabla>
-<p>Los requisitos de sistema recomendados para Plants vs Zombies son:</p>
-<tabla>
-<tr><td>OS</td><td>Windows XP/Vista/7/8/10</td></tr>
-<tr><td>CPU</td><td>procesador de 1,5 GHz</td></tr>
-<tr><td>RAM</td><td>1 GB</td></tr>
-<tr><td>HDD</td><td>65 MB de espacio libre</td></tr>
-<tr><td>Gráficos</td><td>DirectX 9 o posterior</td></tr>
-<tr><td>Sonido</td><td>Tarjeta de sonido compatible con DirectX</td></tr>
-</tabla>
-<li><b>¿Cuántas plantas y zombies hay en Plants vs Zombies? </b></li>
-<p>Hay 49 plantas diferentes y 26 zombis diferentes en Plants vs Zombies. Cada planta y zombi tiene sus propias habilidades y características únicas. Puedes desbloquear más plantas y zombies mientras juegas el juego y completas los niveles. </p>
-<li><b>¿Cuáles son los otros modos en Plants vs Zombies? </b></li>
-<p>Además del modo Aventura, que tiene 50 niveles, también hay otros modos en Plants vs Zombies que puedes jugar para más diversión y desafío. Estos modos son:</p>
-<ul>
-<li>Modo de supervivencia: Tienes que sobrevivir a interminables oleadas de zombies con recursos limitados. </li>
-<li>Modo de rompecabezas: Tienes que resolver varios puzzles que involucran plantas y zombies. </li>
-Modo de minijuegos: Tienes que jugar varios minijuegos que tienen diferentes reglas y objetivos. </li>
-<li>Modo de jardín zen: Tienes que crecer y cuidar de tus propias plantas en un jardín relajante. </li>
-<li>Crazy Dave’s Shop: Puedes comprar varios artículos y mejoras de Crazy Dave, el vecino excéntrico que te ayuda a lo largo del juego. </li>
-</ul>
-
-<p>Sí, hay una secuela de Plants vs Zombies llamada Plants vs. Zombies 2: It’s About Time. Fue lanzado en 2013 para dispositivos iOS y Android. La secuela cuenta con nuevas plantas, zombies, mundos, niveles y modos. También tiene un tema de viaje en el tiempo que le permite visitar diferentes períodos históricos y luchar contra zombies allí. </p> 64aa2da5cf<br />
-<br />
-<br />
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deleted file mode 100644
index 71689ed9ca8b55ecc4c0988aa8d265d67bc2260b..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Diablo Sobreviviente 2 Rcord.md	
+++ /dev/null
@@ -1,101 +0,0 @@
-<br />
-<h1>Descargar Devil Survivor 2 Record Breaker: Una guía para los jugadores de Nintendo 3DS</h1>
-<p>Si eres un fanático de los juegos de rol tácticos con una historia oscura y atractiva, quizás quieras descargar Devil Survivor 2 Record Breaker para tu Nintendo 3DS. Este juego es un puerto mejorado de Devil Survivor 2, un juego popular en la serie Megami Tensei desarrollado por Atlus. En este artículo, te diremos qué es Devil Survivor 2 Record Breaker, por qué deberías jugarlo, cómo descargarlo, cómo jugarlo y algunos consejos y trucos para disfrutarlo más. </p>
-<h2>descargar diablo sobreviviente 2 récord</h2><br /><p><b><b>Download</b> &hArr; <a href="https://bltlly.com/2v6Kdr">https://bltlly.com/2v6Kdr</a></b></p><br /><br />
- <h2>¿Qué es Devil Survivor 2 Record Breaker? </h2>
-<p>Devil Survivor 2 Record Breaker es un juego de rol táctico que cuenta la historia de estudiantes de secundaria japoneses que entran en un pacto con un misterioso sitio web llamado Nicaea, permitiéndoles invocar demonios y luchar contra criaturas misteriosas que invaden y destruyen Japón. El juego tiene dos escenarios principales: el arco Septentrione, que se basa en el juego original de Devil Survivor 2, y el arco Triangulum, que es un nuevo escenario que tiene lugar después de los eventos del arco Septentrione. El juego cuenta con la actuación de voz para la mayoría de los diálogos, un modo casual que reduce el nivel de dificultad, un nuevo personaje femenino llamado Miyako Hotsuin, y nuevos demonios, habilidades, eventos, finales y música. </p>
- <h2>¿Por qué deberías jugar Devil Survivor 2 Record Breaker? </h2>
-<p>Hay muchas razones por las que deberías jugar Devil Survivor 2 Record Breaker si eres un fan de los juegos de rol tácticos. Estos son algunos de ellos:</p>
-<ul>
-<li>El juego tiene una historia convincente que involucra misterio, suspenso, drama, humor, romance y dilemas morales. Te apegarás a los personajes y sus luchas mientras se enfrentan a un apocalipsis inminente. </li>
-
-<li>El juego tiene un alto valor de repetición que te permite experimentar diferentes caminos, finales y contenido dependiendo de tus opciones y acciones. También puedes desbloquear nuevos modos de juego, como New Game Plus y Record Breaker Mode, que añaden más desafíos y características al juego. </li>
-<li>El juego tiene un estilo de arte impresionante y banda sonora que mejoran la atmósfera y el estado de ánimo del juego. El juego cuenta con gráficos coloridos y detallados, retratos de personajes expresivos y animaciones dinámicas. El juego también cuenta con una banda sonora pegadiza y diversa que va desde el rock hasta el pop, el jazz y la música clásica. </li>
-</ul>
-<p>Estas son solo algunas de las razones por las que Devil Survivor 2 Record Breaker es un juego que vale la pena jugar. Si estás interesado en descargar el juego, sigue leyendo para saber cómo hacerlo. </p>
-<p></p>
- <h2>¿Cómo descargar Devil Survivor 2 Record Breaker? </h2>
-<p>Hay diferentes formas de descargar Devil Survivor 2 Record Breaker para tu Nintendo 3DS. Estas son algunas de las opciones y pasos que puedes seguir:</p>
- <h3>Descargar de Nintendo eShop</h3>
-<p>Una de las formas más fáciles y cómodas de descargar el juego es desde la Nintendo eShop, la tienda digital oficial para juegos y aplicaciones de Nintendo. Para descargar el juego desde Nintendo eShop, necesitarás una consola Nintendo 3DS, un Nintendo Network ID, una conexión a Internet y suficiente espacio de almacenamiento en tu sistema o tarjeta SD. Estos son los pasos que debes seguir:</p>
-<ol>
-<li>Enciende tu consola Nintendo 3DS y toca el icono de Nintendo eShop en el menú HOME.</li>
-<li>Si aún no lo has hecho, crea o vincula tu Nintendo Network ID a tu sistema siguiendo las instrucciones en pantalla. </li>
-<li>Una vez que estés en Nintendo eShop, usa la función de búsqueda o explora las categorías para encontrar Devil Survivor 2 Record Breaker.</li>
-<li>Selecciona el juego y toca "Descargar" o "Comprar" para comprar el juego. Tendrás que introducir la información de tu tarjeta de crédito o usar una Nintendo eShop Card para pagar el juego. </li>
-
-<li> Una vez que la descarga se ha completado, puede comenzar a jugar el juego tocando su icono en el menú HOME.</li>
-</ol>
-<p>El juego cuesta $39.99 USD en la Nintendo eShop y requiere 14.950 bloques de espacio de almacenamiento. </p>
- <h3>Descargar de Amazon</h3>
-<p>Otra forma de descargar el juego es desde Amazon, uno de los mayores minoristas en línea del mundo. Para descargar el juego desde Amazon, necesitará una cuenta de Amazon, una tarjeta de crédito o una tarjeta de regalo de Amazon, una conexión a Internet y suficiente espacio de almacenamiento en su sistema o tarjeta SD. Estos son los pasos que debes seguir:</p>
-<ol>
-<li>Ir a Amazon.com e iniciar sesión en su cuenta o crear uno si no tiene uno. </li>
-<li>Buscar Devil Survivor 2 Record Breaker en Amazon.com o utilice este enlace: [Devil Survivor 2 Record Breaker]. </li>
-<li>Seleccione si desea comprar una copia física del juego o un código digital para descargar el juego. La copia física cuesta $39.99 USD e incluye envío gratuito para los miembros Prime. El código digital cuesta $29.99 USD y no incluye gastos de envío. </li>
-<li>Añadir el artículo a su carrito y proceder a la caja. Deberá introducir su dirección de envío si está comprando una copia física o su dirección de correo electrónico si está comprando un código digital. También tendrá que introducir su información de pago o utilizar una tarjeta de regalo de Amazon para pagar el artículo. </li>
-<li>Después de confirmar su pedido, recibirá un correo electrónico de confirmación de Amazon. Si compró una copia física, recibirá un número de seguimiento para su envío. Si has comprado un código digital, recibirás un correo electrónico con el código y las instrucciones para canjearlo en la Nintendo eShop. </li>
-<li>Si compraste una copia física, recibirás el juego en unos días dependiendo del método de envío que hayas elegido. Puedes insertar la tarjeta de juego en tu consola Nintendo 3DS y empezar a jugar. </li>
-
-</ol>
-<p>El juego cuesta $39.99 USD por una copia física y $29.99 USD por un código digital en Amazon.com y requiere 14,950 bloques de espacio de almacenamiento. </p>
- <h3>Descargar desde otros sitios web</h3>
-<p>Una tercera forma de descargar el juego es desde otros sitios web que ofrecen el juego para su descarga o compra. Sin embargo, debe tener cuidado al elegir estos sitios web, ya que algunos de ellos pueden ser poco fiables, inseguros o ilegales. Solo debes descargar el juego desde sitios web que tengan una buena reputación, una conexión segura y una política de reembolso clara. Para descargar el juego desde otros sitios web, necesitará una computadora, una conexión a Internet, una tarjeta de crédito o una cuenta PayPal, y suficiente espacio de almacenamiento en su sistema o tarjeta SD. Aquí hay algunos ejemplos de sitios web que ofrecen el juego para descargar o comprar:</p>
-<tabla>
-<tr>
-<th>Sitio web</th>
-<th>Precio</th>
-<th>Método de entrega</th>
-<th>Pros y contras</th>
-</tr>
-<tr>
-<td>[GameStop]</td>
-<td>$39.99 USD</td>
-<td>Copia física o código digital</td>
-<td>+ Vendedor confiable y confiable<br>- La copia física puede tardar más en llegar<br>- El código digital puede estar fuera de stock</td>
-</tr>
-<tr>
-<td>[Play-Asia]</td>
-<td>$29.99 USD</td>
-<td>Código digital</td>
-<td>+ Más barato que otros sitios web<br>+ Entrega rápida y fácil<br>- Puede que no funcione para todas las regiones<br>- No hay política de reembolso</td>
-</tr>
-<tr>
-<td>[RomUniverse]</td>
-<td>Gratis</td>
-<td>Archivo ROM</td>
-<td>+ Sin costo<br>+ Descarga instantánea<br>- Ilegal y poco ético<br>- Riesgoso y dañino para su sistema y datos</td>
-</tr>
-</tabla>
-<p>Estos son solo algunos de los ejemplos de sitios web que ofrecen el juego para descargar o comprar. Usted debe hacer su propia investigación y comparación antes de elegir un sitio web para descargar el juego de. </p>
- <h2>¿Cómo se juega Devil Survivor 2 Record Breaker? </h2>
-
- <h3>Combate</h3>
-<p>El combate del juego se basa en un sistema táctico por turnos que implica mover a tus personajes en un mapa basado en cuadrícula y participar en batallas con enemigos. Cada personaje puede convocar a uno o más demonios para luchar junto a ellos, formando un escuadrón de hasta cuatro unidades. Cada unidad puede realizar una acción por turno, como atacar, usar habilidades o moverse. Las batallas se resuelven en primera persona, donde puedes elegir a qué enemigo apuntar y qué habilidad usar. Las batallas están influenciadas por varios factores, como afinidades elementales, rasgos raciales, habilidades pasivas, golpes críticos, giros adicionales y ataques de equipo. Las batallas también se ven afectadas por tus elecciones y acciones fuera del combate, como tus relaciones con otros personajes, tu alineación con diferentes facciones y tus respuestas a diferentes eventos. </p>
- <h3>Fusión de demonios</h3>
-<p>La fusión de demonios del juego se basa en un sistema que te permite crear y personalizar tus propios demonios fusionándolos desde diferentes fuentes. Puedes obtener demonios comprándolos en subastas, reclutándolos en batallas o heredándolos de otros personajes. Luego puedes fusionar dos o más demonios para crear un nuevo demonio con diferentes atributos, habilidades y apariencia. También puedes usar artículos o fuentes especiales para modificar tus demonios, como agregar habilidades adicionales, cambiar su raza o mejorar sus estadísticas. La fusión de demonios es esencial para crear demonios poderosos y diversos que se adapten a tu estilo de juego y preferencias. </p>
- <h3>Sistema de destino</h3>
-
- <h3>Opciones</h3>
-<p>Las elecciones del juego se basan en un sistema que te permite influir en el resultado de la historia y en el destino del mundo tomando diferentes decisiones a lo largo del juego. Te encontrarás con varias situaciones y dilemas que requieren que elijas entre diferentes opciones, como estar de acuerdo o en desacuerdo con alguien, apoyar u oponerse a una facción, salvar o sacrificar un personaje, o aceptar o rechazar un acuerdo. Tus elecciones tendrán consecuencias que afectarán la trama del juego, personajes, batallas y finales. Las opciones son cruciales para dar forma a tu propia historia y experiencia en el juego. </p>
- <h2>Consejos y trucos para Devil Survivor 2 Record Breaker</h2>
-<p>Devil Survivor 2 Record Breaker es un juego divertido y gratificante, pero también puede ser desafiante y frustrante a veces. Estos son algunos consejos y trucos que pueden ayudarte a disfrutar más del juego y superar sus dificultades:</p>
- <h3>Guardar a menudo</h3>
-<p>Uno de los consejos más importantes para jugar el juego es guardar su progreso con frecuencia y utilizar múltiples ranuras de ahorro. El juego puede ser impredecible e implacable, ya que puede encontrarse con muertes repentinas, sobresaltos del juego o finales malos dependiendo de sus opciones y acciones. También es posible que desee probar diferentes opciones o resultados sin tener que volver a jugar todo el juego. Por lo tanto, es recomendable guardar el juego antes y después de cada evento importante, batalla o elección, y utilizar diferentes ranuras de ahorro para diferentes escenarios. De esta manera, puedes evitar perder tu progreso o perderte cualquier contenido. </p>
- <h3>Experimenta con diferentes demonios y habilidades</h3>
-
- <h3>Explorar el nuevo contenido</h3>
-<p>Un consejo final para jugar el juego es explorar el nuevo contenido que es exclusivo de la versión Record Breaker del juego. El juego cuenta con un nuevo escenario llamado el arco del Triángulo, que es una secuela del arco septentriónico y añade más historia, personajes, demonios, habilidades, eventos y finales al juego. El juego también cuenta con un nuevo personaje femenino llamado Miyako Hotsuin, que es una figura clave en el arco del Triángulo y tiene su propia personalidad, papel y nivel de destino. El juego también cuenta con nuevas características como la actuación de voz, modo casual, nuevas pistas de música y nuevos modos de juego. Deberías echar un vistazo a estos nuevos contenidos para disfrutar de la experiencia completa de Devil Survivor 2 Record Breaker.</p>
- <h2>Conclusión</h2>
-<p>Devil Survivor 2 Record Breaker es un juego de rol táctico que ofrece una historia convincente, una jugabilidad profunda y estratégica, un alto valor de repetición y un estilo artístico y banda sonora impresionantes. Es un puerto mejorado de Devil Survivor 2 que añade más contenido y características al juego original. Es un juego que vale la pena jugar para los fanáticos de los juegos de rol tácticos o la serie Megami Tensei. Si quieres descargar Devil Survivor 2 Record Breaker para tu Nintendo 3DS, puedes hacerlo desde varias fuentes como Nintendo eShop, Amazon u otros sitios web. Sin embargo, debe tener cuidado al elegir estos sitios web y asegurarse de que sean confiables, seguros y legales. También debes seguir algunos consejos y trucos para jugar el juego, como ahorrar a menudo, experimentar con diferentes demonios y habilidades, y explorar el nuevo contenido. Esperamos que este artículo te haya ayudado a aprender más sobre Devil Survivor 2 Record Breaker y cómo descargarlo. Si tiene alguna pregunta o comentario, siéntase libre de dejarlos abajo. ¡Feliz juego! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas más frecuentes sobre Devil Survivor 2 Record Breaker:</p>
-<ol>
-
-<li>P: ¿Puedo tocar el arco del triángulo sin tocar el arco de septentrión? <br>A: Sí, puedes. El juego te permite elegir qué escenario jugar desde el principio. Sin embargo, recomendamos jugar el arco de septentrión primero, ya que le dará más contexto y fondo para el arco del triángulo. </li>
-<li>P: ¿Puedo transferir mis datos de guardado de Devil Survivor 2 a Devil Survivor 2 Record Breaker? <br>A: No, no puedes. Los dos juegos no son compatibles entre sí, ya que tienen diferentes características y contenido. Tendrás que empezar un nuevo juego en Devil Survivor 2 Record Breaker.</li>
-<li>P: ¿Cuáles son las diferencias entre los modos original y casual? <br>A: El modo original es el nivel de dificultad predeterminado del juego, que es desafiante y requiere una planificación y estrategia cuidadosas. El modo casual es un nivel de dificultad más bajo que hace el juego más fácil y más accesible para principiantes o jugadores casuales. En el modo casual, puedes revivir a tus personajes caídos durante las batallas, y puedes saltarte algunas batallas si los pierdes. </li>
-<li>P: ¿Cuáles son los beneficios de aumentar mi nivel de destino con otros personajes? <br>A: Aumentar tu nivel de destino con otros personajes desbloqueará nuevas habilidades para tus demonios, nuevos eventos para la historia y nuevos finales para el juego. También conocerás más sobre los personajes y sus personalidades, antecedentes y motivaciones. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
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-<br />
-<h1>Cómo descargar Minecraft 1: Una guía completa</h1>
-<p>Minecraft es uno de los juegos más populares y creativos del mundo, con más de 200 millones de copias vendidas. Es un juego de sandbox donde puedes explorar, construir y sobrevivir en mundos infinitos hechos de bloques. También puede jugar con sus amigos en línea, o unirse a los servidores con miles de otros jugadores. </p>
-<p>Pero ¿cómo descargar Minecraft 1, la última versión del juego? Y cuáles son las nuevas características y mejoras que ofrece? En este artículo, responderemos estas preguntas y más. Te mostraremos cómo descargar Minecraft 1 para diferentes plataformas, cómo jugarlo con trazado de rayos (RTX), y algunos consejos y trucos para ayudarte a conquistar el mundo del juego. </p>
-<h2>descargar el minecraft 1</h2><br /><p><b><b>Download Zip</b> &rArr; <a href="https://bltlly.com/2v6IE6">https://bltlly.com/2v6IE6</a></b></p><br /><br />
- <h2>¿Qué es Minecraft 1?</h2>
-<p>Minecraft 1 es el nombre dado al segundo lanzamiento de Adventure Update, que fue lanzado oficialmente el 18 de noviembre de 2011. También se conoce como Java Edition 1.0.0 o Beta 1.9. Marcó el juego como oficialmente lanzado después de dos años y medio de desarrollo. </p>
-<p>Minecraft 1 ha añadido muchas nuevas características y cambios al juego, tales como la elaboración de la cerveza, encantador, la dimensión final, el modo hardcore, la cría, y más. También introdujo un nuevo tipo de mundo llamado Superflat, que genera mundos completamente planos compuestos de lecho de roca, tierra y bloques de hierba. </p>
- <h3>La diferencia entre Bedrock Edition y Java Edition</h3>
-<p>Hay dos sabores principales de Minecraft: Bedrock Edition y Java Edition. Los nombres se refieren al tipo de código base que usa cada juego. Java Edition es la versión original del juego, que se ejecuta en Java. Bedrock Edition es una versión más reciente del juego, que se ejecuta en C++. </p>
-
-<p>Java Edition y Bedrock Edition son fundamentalmente incompatibles en su mayor parte, por lo que no se puede jugar con jugadores de diferentes ediciones en el mismo servidor. También necesitas comprarlos por separado de diferentes fuentes. </p>
- <h3>Las principales características de Minecraft 1</h3>
-<p>Minecraft 1 agregó muchas nuevas características y cambios al juego, algunos de los cuales son:</p>
-<ul>
-<li>Puestos de elaboración de cerveza: Se utiliza para preparar pociones que pueden darle varios efectos, como velocidad, fuerza o invisibilidad. </li>
-<li>Tablas de encantamiento: Se utiliza para encantar herramientas, armas y armaduras con habilidades especiales, como aspecto de fuego, tacto de seda o fortuna. </li>
-<li>La dimensión final: un reino oscuro y misterioso donde puedes luchar contra el dragón ender, el jefe final del juego. Puede acceder a él activando un portal de extremo en una fortaleza. </li>
-<li>Modo Hardcore: Un modo de dificultad donde solo tienes una vida y no puedes reaparecer. Si mueres, el mundo se elimina y no puedes volver a jugarlo. </li>
-<li>Cría: Le permite criar animales alimentándolos con su comida favorita, como trigo, zanahorias o semillas. Puedes conseguir animales bebés que crecen y heredan rasgos de sus padres. </li>
-<li>Tipo de mundo superflat: Un tipo de mundo que genera mundos completamente planos compuestos de lecho de roca, tierra y bloques de hierba. Puede personalizar las capas y estructuras del mundo usando un código preestablecido. </li>
-</ul>
-<p>Estas son solo algunas de las características que Minecraft 1 agregó al juego. Hay muchas más que puedes descubrir jugando el juego tú mismo. </p>
- <h2>Cómo descargar Minecraft 1 para Windows 10/11</h2>
-<p>Si quieres descargar Minecraft 1 para Windows 10/11, tienes dos opciones: Bedrock Edition o Java Edition. Estos son los pasos para descargar cada edición:</p>
- <h3>Paso 1: Ir al sitio web oficial de Minecraft</h3>
-<p>El primer paso es ir al sitio web oficial de Minecraft en <a href="">https://www.minecraft.net</a>. Aquí es donde puedes comprar y descargar el juego para diferentes plataformas. </p>
-<p></p>
-
-<p>El siguiente paso es elegir qué edición de Minecraft desea descargar: Bedrock Edition o Java Edition. Puedes encontrarlos en la pestaña "Juegos" del sitio web. </p>
-<p>Si desea descargar Bedrock Edition, haga clic en "Minecraft para Windows 10/11". Esto te llevará a la tienda de Microsoft, donde puedes comprar y descargar el juego por $26.99. Necesitarás una cuenta de Microsoft para hacerlo. </p>
-<p>Si desea descargar Java Edition, haga clic en "Minecraft: Java Edition". Esto te llevará a una página donde puedes comprar y descargar el juego por $26.95. Necesitarás una cuenta de Mojang para hacerlo. </p>
- <h3>Paso 3: Siga las instrucciones para instalar el juego</h3>
-<p>El paso final es seguir las instrucciones en la pantalla para instalar el juego en su computadora. Necesitará una conexión a Internet y suficiente espacio en disco para hacer esto. </p>
-<p>Para Bedrock Edition, tendrá que iniciar la aplicación Microsoft Store e iniciar sesión con su cuenta de Microsoft. A continuación, tendrá que encontrar Minecraft en su biblioteca y haga clic en "Instalar". El juego se descargará e instalará automáticamente. </p>
-<p>Para Java Edition, tendrá que iniciar la aplicación Minecraft Launcher e iniciar sesión con su cuenta de Mojang. A continuación, deberá seleccionar "Última versión" en el menú desplegable y hacer clic en "Jugar". El juego se descargará e instalará automáticamente. </p>
- <h2>Cómo descargar Minecraft 1 para otras plataformas</h2>
-<p>Si quieres descargar Minecraft 1 para otras plataformas, como macOS, Linux, Android, iOS, Xbox, PlayStation, Nintendo Switch y otros dispositivos, estos son los pasos a seguir:</p>
- <h3>Para macOS y Linux</h3>
-<p>Si desea descargar Minecraft 1 para macOS o Linux, tendrá que comprar y descargar Java Edition desde el sitio web oficial de Minecraft en <a href="">https://www.minecraft.net/en-us/store/minecraft-java-edition</a>. Necesitarás una cuenta de Mojang para hacer esto. </p>
-
- <h3>Para Android e iOS</h3>
-<p>Si desea descargar Minecraft 1 para dispositivos Android o iOS, como teléfonos o tabletas, tendrá que comprar y descargar Bedrock Edition desde la Google Play Store o la App Store respectivamente. Necesitarás una cuenta de Google o un ID de Apple para hacer esto. </p>
-<p>Después de comprar el juego, tendrá que iniciar la aplicación e iniciar sesión con su cuenta de Microsoft. Entonces, podrás jugar el juego en tu dispositivo. </p>
- <h3>Para Xbox, PlayStation, Nintendo Switch y otros dispositivos</h3>
-<p>Si desea descargar Minecraft 1 para Xbox, PlayStation, Nintendo Switch u otros dispositivos, como televisores inteligentes o auriculares de realidad virtual, tendrá que comprar y descargar Bedrock Edition de sus respectivas tiendas en línea. Necesitarás una cuenta de Xbox Live, una cuenta de PlayStation Network, una cuenta de Nintendo u otra cuenta dependiendo de tu dispositivo. </p>
-<p>Después de comprar el juego, tendrás que iniciar el juego e iniciar sesión con tu cuenta. Luego, podrás jugar el juego en tu dispositivo. </p>
- <h2>Cómo jugar Minecraft 1 con trazado de rayos (RTX)</h2>
-<p>El trazado de rayos es una tecnología que simula efectos de iluminación realistas, como sombras, reflejos y refracciones, trazando la trayectoria de los rayos de luz en una escena 3D. Puede hacer que Minecraft se vea impresionante e inmersivo, pero también requiere una poderosa tarjeta gráfica y una versión compatible del juego. </p>
-<p>Si quieres jugar Minecraft 1 con trazado de rayos (RTX), aquí están los pasos a seguir:</p>
- <h3> ¿Qué es el trazado de rayos y por qué hace que Minecraft se vea increíble</h3>
-<p>El trazado de rayos es una tecnología que simula efectos de iluminación realistas, como sombras, reflejos y refracciones, trazando la trayectoria de los rayos de luz en una escena 3D. Puede hacer que Minecraft se vea impresionante e inmersivo, pero también requiere una poderosa tarjeta gráfica y una versión compatible del juego. </p>
-
-<p>El trazado de rayos también puede mejorar la experiencia de juego al crear entornos más dinámicos e inmersivos. Por ejemplo, puede usar espejos para crear ilusiones ópticas, usar vidrio de colores para crear rompecabezas o usar fuentes de luz para crear estados de ánimo o señales. </p>
- <h3>Los requisitos del sistema para el trazado de rayos</h3>
-<p>Ray tracing es una tecnología exigente que requiere una potente tarjeta gráfica y una versión compatible del juego. Aquí están los requisitos del sistema para el trazado de rayos:</p>
-<ul>
-<li>PC con Windows 10/11 compatible con DirectX 12</li>
-<li>Una tarjeta gráfica de la serie NVIDIA GeForce RTX 20 o 30</li>
-<li> Una edición Bedrock de Minecraft para Windows 10/11 con trazado de rayos activado</li>
-<li>Un paquete de recursos compatible con el trazado de rayos o mundo</li>
-</ul>
-<p>Si cumple con estos requisitos, puede jugar Minecraft 1 con trazado de rayos (RTX). </p>
- <h3>Cómo habilitar el trazado de rayos en Minecraft 1</h3>
-<p>Si desea habilitar el trazado de rayos en Minecraft 1, aquí están los pasos a seguir:</p>
-<ol>
-<li>Iniciar Minecraft para Windows 10/11 e iniciar sesión con su cuenta de Microsoft. </li>
-<li>Ir a Configuración > Video > Vídeo avanzado y alternar en "DirectX Ray Tracing". </li>
-<li>Ir al Mercado y descargar un paquete de recursos compatible con rastreo de rayos o mundo. Puede encontrarlos en la categoría "RTX". </li>
-<li> Aplicar el paquete de recursos o cargar el mundo y disfrutar de los efectos de trazado de rayos. </li>
-</ol>
-<p>También puede crear sus propios paquetes de recursos compatibles de rastreo de rayos o mundos utilizando herramientas como RTX Creator Pack de NVIDIA o RTX World Maker. Puede encontrar más información sobre ellos en <a href=">https://www.nvidia.com/en-us/geforce/guides/minecraft-rtx-texturing-guide/</a> y <a href="">https:/www.nvidia./com enus-geforce/guides/mineft-rt-world-conversion-guide/</a. </p>
- <h2>Consejos y trucos para Minecraft 1</h2>
-
- <h3>Cómo encontrar diamantes y tesoros enterrados fácilmente</h3>
-<p>Los diamantes son uno de los recursos más valiosos y raros de Minecraft. Se utilizan para crear las herramientas, armas y armaduras más fuertes del juego. Puedes encontrarlos en los niveles inferiores del mundo, entre las capas 5 y 12. Puedes usar un mapa o coordenadas para localizarlos. </p>
-<p>Para aumentar sus posibilidades de encontrar diamantes, debe usar un pico de hierro o diamante con encanto de fortuna, lo que aumenta la cantidad de gotas de la minería. También debes usar antorchas o pociones de visión nocturna para iluminar tu camino y evitar multitudes. </p>
-<p>El tesoro enterrado es otro recurso valioso que contiene botín como oro, hierro, esmeraldas, diamantes, corazón del mar, libros encantados y más. Puedes encontrarlos siguiendo los mapas del tesoro que puedes obtener de naufragios o ruinas submarinas. Puede utilizar una brújula o coordenadas para localizarlos. </p>
-<p>Para desenterrar un tesoro enterrado, debe usar una pala con encanto de eficiencia, lo que aumenta la velocidad de excavación. También debe usar pociones para respirar agua o conductos para respirar bajo el agua y evitar ahogarse. También debes usar un mapa o coordenadas para localizar la ubicación exacta del cofre del tesoro. </p>
- <h3>Cómo usar huevos de desove y encantamientos</h3>
-<p>Los huevos de desove son elementos que pueden generar varias turbas en el juego, como animales, monstruos o aldeanos. Puede obtenerlos en modo creativo, comandos o trucos. Puede usarlos para crear granjas, zoos o experimentos. </p>
-<p>Para usar un huevo de desove, necesitas hacer clic derecho en un bloque o una entidad con el huevo en tu mano. La turba aparecerá en el acto o reemplazará a la entidad. También puede usar dispensadores para engendrar turbas automáticamente a partir de huevos. </p>
-<p>Los encantamientos son habilidades especiales que pueden mejorar tus herramientas, armas y armaduras. Puedes obtenerlos de mesas de encantamiento, yunques o botín. Puedes usarlas para mejorar tu rendimiento, durabilidad o efectos. </p>
-
- <h3>Cómo crear un mundo superflat y personalizarlo</h3>
-<p>Un mundo superflat es un tipo de mundo que genera mundos completamente planos compuestos de lecho de roca, tierra y bloques de hierba. Puedes crear un mundo superflat eligiendo "Superflat" en el menú de tipo world al crear un mundo nuevo. </p>
-<p>Puedes personalizar un mundo superflat usando un código preestablecido que define las capas y estructuras del mundo. Puede encontrar algunos códigos predefinidos en <a href=">https://minecraft.fandom.com/wiki/Superflat#Presets</a> o crear los suyos usando el formato explicado en <a href="">https://minecraft.fandom.com/wiki/Superflat#Code_format</a>. </p>
-<p>Para usar un código preestablecido, debe hacer clic en "Personalizar" desde el menú de tipo mundial y pegar el código en el cuadro de texto. Luego, puedes hacer clic en "Usar Preset" y "Listo" para crear el mundo superflat personalizado. </p>
- <h2>Conclusión</h2>
-<p>Minecraft 1 es un juego divertido y creativo que ofrece infinitas posibilidades de exploración, construcción y supervivencia. También agregó muchas nuevas características y cambios al juego, como la elaboración de cerveza, encantador, la dimensión final, el modo hardcore, la cría y mundos superflat. </p>
-<p>En este artículo, te mostramos cómo descargar Minecraft 1 para diferentes plataformas, cómo jugarlo con trazado de rayos (RTX), y algunos consejos y trucos para ayudarte a dominar el juego. Esperamos que hayas disfrutado de este artículo y hayas aprendido algo nuevo. </p>
-<p>Ahora que ya sabes cómo descargar Minecraft 1, ¿por qué no darle una oportunidad y ver lo que puede crear? Diviértete! </p>
- <h2>Preguntas frecuentes</h2>
-<h4>Q: ¿Cómo puedo actualizar Minecraft 1 a la última versión? </h4>
-<p>A: Si tiene Bedrock Edition de Minecraft 1, puede actualizarlo automáticamente a través de la aplicación Microsoft Store o manualmente haciendo clic en "Descargas y actualizaciones" desde el menú. Si tiene Java Edition de Minecraft 1, puede actualizarlo automáticamente a través de la aplicación Minecraft Launcher o manualmente seleccionando "Latest release" en el menú desplegable. </p>
- <h4>Q: ¿Cómo hago copia de seguridad de mis mundos Minecraft 1? </h4>
-
- <h4>Q: ¿Cómo instalo mods para Minecraft 1?</h4>
-<p>A: Si tienes Bedrock Edition of Minecraft 1, puedes instalar mods descargándolos de sitios web como <a href="">https:///mcpedl.com</a> o <a href=">https:/ww.planetminecraft.com/resources/mods/bedrock-edition/<a> e importándolos al juego. Si tiene Java Edition of Minecraft 1, puede instalar mods descargándolos de sitios web como <a href="">https:/www.curseforge.com/minecraft/mc-mods</a> o <a href="">https:/www.planetminecraft.com/resources/mods/<//a> y colocarlos en la carpeta "mods" dentro de su ". carpeta minecraft". </p>
- <h4>Q: ¿Cómo me uno a los servidores de Minecraft 1?</h4>
-<p>A: Si tiene Bedrock Edition de Minecraft 1, puede unirse a los servidores haciendo clic en "Play" desde el menú principal y seleccionando uno de los servidores destacados o agregando una dirección de servidor. Si tienes Java Edition de Minecraft 1, puedes unirte a los servidores haciendo clic en "Multijugador" desde el menú principal y seleccionando uno de los servidores públicos o añadiendo una dirección de servidor. </p>
- <h4>Q: ¿Cómo hago un servidor para Minecraft 1?</h4>
-<p>A: Si tiene Bedrock Edition of Minecraft 1, puede hacer un servidor utilizando un servicio de terceros como <a href=">https:/www.minecraft.net/en-us/realms-plus</a>, <a href=">">https:/aternos.org/en/<minems-a, o <a>href=">">https://a.< Tendrás que pagar una tarifa o ver anuncios para usar estos servicios. Si tiene Java Edition of Minecraft 1, puede hacer un servidor descargando el software del servidor de <a href="">https://www.minecraft.net/en-us/download/server</a> y ejecutándolo en su computadora o en un servicio de alojamiento. Tendrá que configurar la configuración del servidor y el reenvío de puertos para que sea accesible a otros jugadores. </p>
- <h4>Q: ¿Cómo puedo cambiar mi piel para Minecraft 1?</h4> 64aa2da5cf<br />
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deleted file mode 100644
index 5e4d6995cd41cc07b4e8861cb941c6052b0f5517..0000000000000000000000000000000000000000
--- a/spaces/BernardoOlisan/vqganclip/taming-transformers/scripts/make_samples.py
+++ /dev/null
@@ -1,292 +0,0 @@
-import argparse, os, sys, glob, math, time
-import torch
-import numpy as np
-from omegaconf import OmegaConf
-from PIL import Image
-from main import instantiate_from_config, DataModuleFromConfig
-from torch.utils.data import DataLoader
-from torch.utils.data.dataloader import default_collate
-from tqdm import trange
-
-
-def save_image(x, path):
-    c,h,w = x.shape
-    assert c==3
-    x = ((x.detach().cpu().numpy().transpose(1,2,0)+1.0)*127.5).clip(0,255).astype(np.uint8)
-    Image.fromarray(x).save(path)
-
-
-@torch.no_grad()
-def run_conditional(model, dsets, outdir, top_k, temperature, batch_size=1):
-    if len(dsets.datasets) > 1:
-        split = sorted(dsets.datasets.keys())[0]
-        dset = dsets.datasets[split]
-    else:
-        dset = next(iter(dsets.datasets.values()))
-    print("Dataset: ", dset.__class__.__name__)
-    for start_idx in trange(0,len(dset)-batch_size+1,batch_size):
-        indices = list(range(start_idx, start_idx+batch_size))
-        example = default_collate([dset[i] for i in indices])
-
-        x = model.get_input("image", example).to(model.device)
-        for i in range(x.shape[0]):
-            save_image(x[i], os.path.join(outdir, "originals",
-                                          "{:06}.png".format(indices[i])))
-
-        cond_key = model.cond_stage_key
-        c = model.get_input(cond_key, example).to(model.device)
-
-        scale_factor = 1.0
-        quant_z, z_indices = model.encode_to_z(x)
-        quant_c, c_indices = model.encode_to_c(c)
-
-        cshape = quant_z.shape
-
-        xrec = model.first_stage_model.decode(quant_z)
-        for i in range(xrec.shape[0]):
-            save_image(xrec[i], os.path.join(outdir, "reconstructions",
-                                             "{:06}.png".format(indices[i])))
-
-        if cond_key == "segmentation":
-            # get image from segmentation mask
-            num_classes = c.shape[1]
-            c = torch.argmax(c, dim=1, keepdim=True)
-            c = torch.nn.functional.one_hot(c, num_classes=num_classes)
-            c = c.squeeze(1).permute(0, 3, 1, 2).float()
-            c = model.cond_stage_model.to_rgb(c)
-
-        idx = z_indices
-
-        half_sample = False
-        if half_sample:
-            start = idx.shape[1]//2
-        else:
-            start = 0
-
-        idx[:,start:] = 0
-        idx = idx.reshape(cshape[0],cshape[2],cshape[3])
-        start_i = start//cshape[3]
-        start_j = start %cshape[3]
-
-        cidx = c_indices
-        cidx = cidx.reshape(quant_c.shape[0],quant_c.shape[2],quant_c.shape[3])
-
-        sample = True
-
-        for i in range(start_i,cshape[2]-0):
-            if i <= 8:
-                local_i = i
-            elif cshape[2]-i < 8:
-                local_i = 16-(cshape[2]-i)
-            else:
-                local_i = 8
-            for j in range(start_j,cshape[3]-0):
-                if j <= 8:
-                    local_j = j
-                elif cshape[3]-j < 8:
-                    local_j = 16-(cshape[3]-j)
-                else:
-                    local_j = 8
-
-                i_start = i-local_i
-                i_end = i_start+16
-                j_start = j-local_j
-                j_end = j_start+16
-                patch = idx[:,i_start:i_end,j_start:j_end]
-                patch = patch.reshape(patch.shape[0],-1)
-                cpatch = cidx[:, i_start:i_end, j_start:j_end]
-                cpatch = cpatch.reshape(cpatch.shape[0], -1)
-                patch = torch.cat((cpatch, patch), dim=1)
-                logits,_ = model.transformer(patch[:,:-1])
-                logits = logits[:, -256:, :]
-                logits = logits.reshape(cshape[0],16,16,-1)
-                logits = logits[:,local_i,local_j,:]
-
-                logits = logits/temperature
-
-                if top_k is not None:
-                    logits = model.top_k_logits(logits, top_k)
-                # apply softmax to convert to probabilities
-                probs = torch.nn.functional.softmax(logits, dim=-1)
-                # sample from the distribution or take the most likely
-                if sample:
-                    ix = torch.multinomial(probs, num_samples=1)
-                else:
-                    _, ix = torch.topk(probs, k=1, dim=-1)
-                idx[:,i,j] = ix
-
-        xsample = model.decode_to_img(idx[:,:cshape[2],:cshape[3]], cshape)
-        for i in range(xsample.shape[0]):
-            save_image(xsample[i], os.path.join(outdir, "samples",
-                                                "{:06}.png".format(indices[i])))
-
-
-def get_parser():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-r",
-        "--resume",
-        type=str,
-        nargs="?",
-        help="load from logdir or checkpoint in logdir",
-    )
-    parser.add_argument(
-        "-b",
-        "--base",
-        nargs="*",
-        metavar="base_config.yaml",
-        help="paths to base configs. Loaded from left-to-right. "
-        "Parameters can be overwritten or added with command-line options of the form `--key value`.",
-        default=list(),
-    )
-    parser.add_argument(
-        "-c",
-        "--config",
-        nargs="?",
-        metavar="single_config.yaml",
-        help="path to single config. If specified, base configs will be ignored "
-        "(except for the last one if left unspecified).",
-        const=True,
-        default="",
-    )
-    parser.add_argument(
-        "--ignore_base_data",
-        action="store_true",
-        help="Ignore data specification from base configs. Useful if you want "
-        "to specify a custom datasets on the command line.",
-    )
-    parser.add_argument(
-        "--outdir",
-        required=True,
-        type=str,
-        help="Where to write outputs to.",
-    )
-    parser.add_argument(
-        "--top_k",
-        type=int,
-        default=100,
-        help="Sample from among top-k predictions.",
-    )
-    parser.add_argument(
-        "--temperature",
-        type=float,
-        default=1.0,
-        help="Sampling temperature.",
-    )
-    return parser
-
-
-def load_model_from_config(config, sd, gpu=True, eval_mode=True):
-    if "ckpt_path" in config.params:
-        print("Deleting the restore-ckpt path from the config...")
-        config.params.ckpt_path = None
-    if "downsample_cond_size" in config.params:
-        print("Deleting downsample-cond-size from the config and setting factor=0.5 instead...")
-        config.params.downsample_cond_size = -1
-        config.params["downsample_cond_factor"] = 0.5
-    try:
-        if "ckpt_path" in config.params.first_stage_config.params:
-            config.params.first_stage_config.params.ckpt_path = None
-            print("Deleting the first-stage restore-ckpt path from the config...")
-        if "ckpt_path" in config.params.cond_stage_config.params:
-            config.params.cond_stage_config.params.ckpt_path = None
-            print("Deleting the cond-stage restore-ckpt path from the config...")
-    except:
-        pass
-
-    model = instantiate_from_config(config)
-    if sd is not None:
-        missing, unexpected = model.load_state_dict(sd, strict=False)
-        print(f"Missing Keys in State Dict: {missing}")
-        print(f"Unexpected Keys in State Dict: {unexpected}")
-    if gpu:
-        model.cuda()
-    if eval_mode:
-        model.eval()
-    return {"model": model}
-
-
-def get_data(config):
-    # get data
-    data = instantiate_from_config(config.data)
-    data.prepare_data()
-    data.setup()
-    return data
-
-
-def load_model_and_dset(config, ckpt, gpu, eval_mode):
-    # get data
-    dsets = get_data(config)   # calls data.config ...
-
-    # now load the specified checkpoint
-    if ckpt:
-        pl_sd = torch.load(ckpt, map_location="cpu")
-        global_step = pl_sd["global_step"]
-    else:
-        pl_sd = {"state_dict": None}
-        global_step = None
-    model = load_model_from_config(config.model,
-                                   pl_sd["state_dict"],
-                                   gpu=gpu,
-                                   eval_mode=eval_mode)["model"]
-    return dsets, model, global_step
-
-
-if __name__ == "__main__":
-    sys.path.append(os.getcwd())
-
-    parser = get_parser()
-
-    opt, unknown = parser.parse_known_args()
-
-    ckpt = None
-    if opt.resume:
-        if not os.path.exists(opt.resume):
-            raise ValueError("Cannot find {}".format(opt.resume))
-        if os.path.isfile(opt.resume):
-            paths = opt.resume.split("/")
-            try:
-                idx = len(paths)-paths[::-1].index("logs")+1
-            except ValueError:
-                idx = -2 # take a guess: path/to/logdir/checkpoints/model.ckpt
-            logdir = "/".join(paths[:idx])
-            ckpt = opt.resume
-        else:
-            assert os.path.isdir(opt.resume), opt.resume
-            logdir = opt.resume.rstrip("/")
-            ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
-        print(f"logdir:{logdir}")
-        base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*-project.yaml")))
-        opt.base = base_configs+opt.base
-
-    if opt.config:
-        if type(opt.config) == str:
-            opt.base = [opt.config]
-        else:
-            opt.base = [opt.base[-1]]
-
-    configs = [OmegaConf.load(cfg) for cfg in opt.base]
-    cli = OmegaConf.from_dotlist(unknown)
-    if opt.ignore_base_data:
-        for config in configs:
-            if hasattr(config, "data"): del config["data"]
-    config = OmegaConf.merge(*configs, cli)
-
-    print(ckpt)
-    gpu = True
-    eval_mode = True
-    show_config = False
-    if show_config:
-        print(OmegaConf.to_container(config))
-
-    dsets, model, global_step = load_model_and_dset(config, ckpt, gpu, eval_mode)
-    print(f"Global step: {global_step}")
-
-    outdir = os.path.join(opt.outdir, "{:06}_{}_{}".format(global_step,
-                                                           opt.top_k,
-                                                           opt.temperature))
-    os.makedirs(outdir, exist_ok=True)
-    print("Writing samples to ", outdir)
-    for k in ["originals", "reconstructions", "samples"]:
-        os.makedirs(os.path.join(outdir, k), exist_ok=True)
-    run_conditional(model, dsets, outdir, opt.top_k, opt.temperature)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/resultdict.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/resultdict.py
deleted file mode 100644
index 7d36e64c467ca8d9cadc88ab03da71faf1aa8abb..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/resultdict.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from typing import TYPE_CHECKING, Optional
-
-if TYPE_CHECKING:
-    # TypedDict was introduced in Python 3.8.
-    #
-    # TODO: Remove the else block and TYPE_CHECKING check when dropping support
-    # for Python 3.7.
-    from typing import TypedDict
-
-    class ResultDict(TypedDict):
-        encoding: Optional[str]
-        confidence: float
-        language: Optional[str]
-
-else:
-    ResultDict = dict
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/install_egg_info.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/install_egg_info.py
deleted file mode 100644
index 65ede406bfa32204acecb48a3fc73537b2801ddc..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/command/install_egg_info.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from distutils import log, dir_util
-import os
-
-from setuptools import Command
-from setuptools import namespaces
-from setuptools.archive_util import unpack_archive
-from .._path import ensure_directory
-import pkg_resources
-
-
-class install_egg_info(namespaces.Installer, Command):
-    """Install an .egg-info directory for the package"""
-
-    description = "Install an .egg-info directory for the package"
-
-    user_options = [
-        ('install-dir=', 'd', "directory to install to"),
-    ]
-
-    def initialize_options(self):
-        self.install_dir = None
-
-    def finalize_options(self):
-        self.set_undefined_options('install_lib',
-                                   ('install_dir', 'install_dir'))
-        ei_cmd = self.get_finalized_command("egg_info")
-        basename = pkg_resources.Distribution(
-            None, None, ei_cmd.egg_name, ei_cmd.egg_version
-        ).egg_name() + '.egg-info'
-        self.source = ei_cmd.egg_info
-        self.target = os.path.join(self.install_dir, basename)
-        self.outputs = []
-
-    def run(self):
-        self.run_command('egg_info')
-        if os.path.isdir(self.target) and not os.path.islink(self.target):
-            dir_util.remove_tree(self.target, dry_run=self.dry_run)
-        elif os.path.exists(self.target):
-            self.execute(os.unlink, (self.target,), "Removing " + self.target)
-        if not self.dry_run:
-            ensure_directory(self.target)
-        self.execute(
-            self.copytree, (), "Copying %s to %s" % (self.source, self.target)
-        )
-        self.install_namespaces()
-
-    def get_outputs(self):
-        return self.outputs
-
-    def copytree(self):
-        # Copy the .egg-info tree to site-packages
-        def skimmer(src, dst):
-            # filter out source-control directories; note that 'src' is always
-            # a '/'-separated path, regardless of platform.  'dst' is a
-            # platform-specific path.
-            for skip in '.svn/', 'CVS/':
-                if src.startswith(skip) or '/' + skip in src:
-                    return None
-            self.outputs.append(dst)
-            log.debug("Copying %s to %s", src, dst)
-            return dst
-
-        unpack_archive(self.source, self.target, skimmer)
diff --git a/spaces/CVPR/LIVE/pybind11/.github/CONTRIBUTING.md b/spaces/CVPR/LIVE/pybind11/.github/CONTRIBUTING.md
deleted file mode 100644
index f61011d54059501e60f41d8da343aab10f259f6a..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/.github/CONTRIBUTING.md
+++ /dev/null
@@ -1,171 +0,0 @@
-Thank you for your interest in this project! Please refer to the following
-sections on how to contribute code and bug reports.
-
-### Reporting bugs
-
-Before submitting a question or bug report, please take a moment of your time
-and ensure that your issue isn't already discussed in the project documentation
-provided at [pybind11.readthedocs.org][] or in the [issue tracker][]. You can
-also check [gitter][] to see if it came up before.
-
-Assuming that you have identified a previously unknown problem or an important
-question, it's essential that you submit a self-contained and minimal piece of
-code that reproduces the problem. In other words: no external dependencies,
-isolate the function(s) that cause breakage, submit matched and complete C++
-and Python snippets that can be easily compiled and run in isolation; or
-ideally make a small PR with a failing test case that can be used as a starting
-point.
-
-## Pull requests
-
-Contributions are submitted, reviewed, and accepted using GitHub pull requests.
-Please refer to [this article][using pull requests] for details and adhere to
-the following rules to make the process as smooth as possible:
-
-* Make a new branch for every feature you're working on.
-* Make small and clean pull requests that are easy to review but make sure they
-  do add value by themselves.
-* Add tests for any new functionality and run the test suite (`cmake --build
-  build --target pytest`) to ensure that no existing features break.
-* Please run [`pre-commit`][pre-commit] to check your code matches the
-  project style. (Note that `gawk` is required.) Use `pre-commit run
-  --all-files` before committing (or use installed-mode, check pre-commit docs)
-  to verify your code passes before pushing to save time.
-* This project has a strong focus on providing general solutions using a
-  minimal amount of code, thus small pull requests are greatly preferred.
-
-### Licensing of contributions
-
-pybind11 is provided under a BSD-style license that can be found in the
-``LICENSE`` file. By using, distributing, or contributing to this project, you
-agree to the terms and conditions of this license.
-
-You are under no obligation whatsoever to provide any bug fixes, patches, or
-upgrades to the features, functionality or performance of the source code
-("Enhancements") to anyone; however, if you choose to make your Enhancements
-available either publicly, or directly to the author of this software, without
-imposing a separate written license agreement for such Enhancements, then you
-hereby grant the following license: a non-exclusive, royalty-free perpetual
-license to install, use, modify, prepare derivative works, incorporate into
-other computer software, distribute, and sublicense such enhancements or
-derivative works thereof, in binary and source code form.
-
-
-## Development of pybind11
-
-To setup an ideal development environment, run the following commands on a
-system with CMake 3.14+:
-
-```bash
-python3 -m venv venv
-source venv/bin/activate
-pip install -r tests/requirements.txt
-cmake -S . -B build -DDOWNLOAD_CATCH=ON -DDOWNLOAD_EIGEN=ON
-cmake --build build -j4
-```
-
-Tips:
-
-* You can use `virtualenv` (from PyPI) instead of `venv` (which is Python 3
-  only).
-* You can select any name for your environment folder; if it contains "env" it
-  will be ignored by git.
-* If you don’t have CMake 3.14+, just add “cmake” to the pip install command.
-* You can use `-DPYBIND11_FINDPYTHON=ON` to use FindPython on CMake 3.12+
-* In classic mode, you may need to set `-DPYTHON_EXECUTABLE=/path/to/python`.
-  FindPython uses `-DPython_ROOT_DIR=/path/to` or
-  `-DPython_EXECUTABLE=/path/to/python`.
-
-### Configuration options
-
-In CMake, configuration options are given with “-D”. Options are stored in the
-build directory, in the `CMakeCache.txt` file, so they are remembered for each
-build directory. Two selections are special - the generator, given with `-G`,
-and the compiler, which is selected based on environment variables `CXX` and
-similar, or `-DCMAKE_CXX_COMPILER=`. Unlike the others, these cannot be changed
-after the initial run.
-
-The valid options are:
-
-* `-DCMAKE_BUILD_TYPE`: Release, Debug, MinSizeRel, RelWithDebInfo
-* `-DPYBIND11_FINDPYTHON=ON`: Use CMake 3.12+’s FindPython instead of the
-  classic, deprecated, custom FindPythonLibs
-* `-DPYBIND11_NOPYTHON=ON`: Disable all Python searching (disables tests)
-* `-DBUILD_TESTING=ON`: Enable the tests
-* `-DDOWNLOAD_CATCH=ON`: Download catch to build the C++ tests
-* `-DOWNLOAD_EIGEN=ON`: Download Eigen for the NumPy tests
-* `-DPYBIND11_INSTALL=ON/OFF`: Enable the install target (on by default for the
-  master project)
-* `-DUSE_PYTHON_INSTALL_DIR=ON`: Try to install into the python dir
-
-
-<details><summary>A few standard CMake tricks: (click to expand)</summary><p>
-
-* Use `cmake --build build -v` to see the commands used to build the files.
-* Use `cmake build -LH` to list the CMake options with help.
-* Use `ccmake` if available to see a curses (terminal) gui, or `cmake-gui` for
-  a completely graphical interface (not present in the PyPI package).
-* Use `cmake --build build -j12` to build with 12 cores (for example).
-* Use `-G` and the name of a generator to use something different. `cmake
-  --help` lists the generators available.
-      - On Unix, setting `CMAKE_GENERATER=Ninja` in your environment will give
-        you automatic mulithreading on all your CMake projects!
-* Open the `CMakeLists.txt` with QtCreator to generate for that IDE.
-* You can use `-DCMAKE_EXPORT_COMPILE_COMMANDS=ON` to generate the `.json` file
-  that some tools expect.
-
-</p></details>
-
-
-To run the tests, you can "build" the check target:
-
-```bash
-cmake --build build --target check
-```
-
-`--target` can be spelled `-t` in CMake 3.15+. You can also run individual
-tests with these targets:
-
-* `pytest`: Python tests only
-* `cpptest`: C++ tests only
-* `test_cmake_build`: Install / subdirectory tests
-
-If you want to build just a subset of tests, use
-`-DPYBIND11_TEST_OVERRIDE="test_callbacks.cpp;test_pickling.cpp"`. If this is
-empty, all tests will be built.
-
-### Formatting
-
-All formatting is handled by pre-commit.
-
-Install with brew (macOS) or pip (any OS):
-
-```bash
-# Any OS
-python3 -m pip install pre-commit
-
-# OR macOS with homebrew:
-brew install pre-commit
-```
-
-Then, you can run it on the items you've added to your staging area, or all
-files:
-
-```bash
-pre-commit run
-# OR
-pre-commit run --all-files
-```
-
-And, if you want to always use it, you can install it as a git hook (hence the
-name, pre-commit):
-
-```bash
-pre-commit install
-```
-
-[pre-commit]: https://pre-commit.com
-[pybind11.readthedocs.org]: http://pybind11.readthedocs.org/en/latest
-[issue tracker]: https://github.com/pybind/pybind11/issues
-[gitter]: https://gitter.im/pybind/Lobby
-[using pull requests]: https://help.github.com/articles/using-pull-requests
diff --git a/spaces/CVPR/LIVE/thrust/thrust/device_delete.h b/spaces/CVPR/LIVE/thrust/thrust/device_delete.h
deleted file mode 100644
index ce822f09dced8851218beea89e3127c7050140c0..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/device_delete.h
+++ /dev/null
@@ -1,56 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file device_delete.h
- *  \brief Deletes variables in device memory
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/device_ptr.h>
-
-namespace thrust
-{
-
-/*! \addtogroup deallocation_functions Deallocation Functions
- *  \ingroup memory_management_functions
- *  \{
- */
-
-/*! \p device_delete deletes a \p device_ptr allocated with
- *  \p device_new.
- *
- *  \param ptr The \p device_ptr to delete, assumed to have
- *         been allocated with \p device_new.
- *  \param n The number of objects to destroy at \p ptr. Defaults to \c 1
- *         similar to \p device_new.
- *
- *  \see device_ptr
- *  \see device_new
- */
-template<typename T>
-  inline void device_delete(thrust::device_ptr<T> ptr,
-                            const size_t n = 1);
-
-/*! \}
- */
-
-} // end thrust
-
-#include <thrust/detail/device_delete.inl>
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/reduce.h b/spaces/CVPR/LIVE/thrust/thrust/reduce.h
deleted file mode 100644
index cabb83c377660d94a0d0ca88c4d87d108a8e5b25..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/reduce.h
+++ /dev/null
@@ -1,785 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-/*! \file thrust/reduce.h
- *  \brief Functions for reducing a range to a single value
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-#include <thrust/iterator/iterator_traits.h>
-#include <thrust/pair.h>
-
-namespace thrust
-{
-
-
-/*! \addtogroup reductions
- *  \{
- */
-
-
-/*! \p reduce is a generalization of summation: it computes the sum (or some
- *  other binary operation) of all the elements in the range <tt>[first,
- *  last)</tt>. This version of \p reduce uses \c 0 as the initial value of the
- *  reduction. \p reduce is similar to the C++ Standard Template Library's
- *  <tt>std::accumulate</tt>. The primary difference between the two functions
- *  is that <tt>std::accumulate</tt> guarantees the order of summation, while
- *  \p reduce requires associativity of the binary operation to parallelize
- *  the reduction.
- *
- *  Note that \p reduce also assumes that the binary reduction operator (in this
- *  case operator+) is commutative.  If the reduction operator is not commutative
- *  then \p thrust::reduce should not be used.  Instead, one could use 
- *  \p inclusive_scan (which does not require commutativity) and select the
- *  last element of the output array.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the sequence.
- *  \param last The end of the sequence.
- *  \return The result of the reduction.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>
- *          and if \c x and \c y are objects of \p InputIterator's \c value_type,
- *          then <tt>x + y</tt> is defined and is convertible to \p InputIterator's
- *          \c value_type. If \c T is \c InputIterator's \c value_type, then
- *          <tt>T(0)</tt> is defined.
- *
- *  The following code snippet demonstrates how to use \p reduce to compute
- *  the sum of a sequence of integers using the \p thrust::host execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/reduce.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  int data[6] = {1, 0, 2, 2, 1, 3};
- *  int result = thrust::reduce(thrust::host, data, data + 6);
- *
- *  // result == 9
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/accumulate.html
- */
-template<typename DerivedPolicy, typename InputIterator>
-__host__ __device__
-  typename thrust::iterator_traits<InputIterator>::value_type
-    reduce(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, InputIterator first, InputIterator last);
-
-
-/*! \p reduce is a generalization of summation: it computes the sum (or some
- *  other binary operation) of all the elements in the range <tt>[first,
- *  last)</tt>. This version of \p reduce uses \c 0 as the initial value of the
- *  reduction. \p reduce is similar to the C++ Standard Template Library's
- *  <tt>std::accumulate</tt>. The primary difference between the two functions
- *  is that <tt>std::accumulate</tt> guarantees the order of summation, while
- *  \p reduce requires associativity of the binary operation to parallelize
- *  the reduction.
- *
- *  Note that \p reduce also assumes that the binary reduction operator (in this
- *  case operator+) is commutative.  If the reduction operator is not commutative
- *  then \p thrust::reduce should not be used.  Instead, one could use 
- *  \p inclusive_scan (which does not require commutativity) and select the
- *  last element of the output array.
- *
- *  \param first The beginning of the sequence.
- *  \param last The end of the sequence.
- *  \return The result of the reduction.
- *
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>
- *          and if \c x and \c y are objects of \p InputIterator's \c value_type,
- *          then <tt>x + y</tt> is defined and is convertible to \p InputIterator's
- *          \c value_type. If \c T is \c InputIterator's \c value_type, then
- *          <tt>T(0)</tt> is defined.
- *
- *  The following code snippet demonstrates how to use \p reduce to compute
- *  the sum of a sequence of integers.
- *
- *  \code
- *  #include <thrust/reduce.h>
- *  ...
- *  int data[6] = {1, 0, 2, 2, 1, 3};
- *  int result = thrust::reduce(data, data + 6);
- *
- *  // result == 9
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/accumulate.html
- */
-template<typename InputIterator> typename
-  thrust::iterator_traits<InputIterator>::value_type reduce(InputIterator first, InputIterator last);
-
-
-/*! \p reduce is a generalization of summation: it computes the sum (or some
- *  other binary operation) of all the elements in the range <tt>[first,
- *  last)</tt>. This version of \p reduce uses \p init as the initial value of the
- *  reduction. \p reduce is similar to the C++ Standard Template Library's
- *  <tt>std::accumulate</tt>. The primary difference between the two functions
- *  is that <tt>std::accumulate</tt> guarantees the order of summation, while
- *  \p reduce requires associativity of the binary operation to parallelize
- *  the reduction.
- *
- *  Note that \p reduce also assumes that the binary reduction operator (in this
- *  case operator+) is commutative.  If the reduction operator is not commutative
- *  then \p thrust::reduce should not be used.  Instead, one could use 
- *  \p inclusive_scan (which does not require commutativity) and select the
- *  last element of the output array.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the input sequence.
- *  \param last The end of the input sequence.
- *  \param init The initial value.
- *  \return The result of the reduction.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>
- *          and if \c x and \c y are objects of \p InputIterator's \c value_type,
- *          then <tt>x + y</tt> is defined and is convertible to \p T.
- *  \tparam T is convertible to \p InputIterator's \c value_type.
- *
- *  The following code snippet demonstrates how to use \p reduce to compute
- *  the sum of a sequence of integers including an intialization value using the \p thrust::host
- *  execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/reduce.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  int data[6] = {1, 0, 2, 2, 1, 3};
- *  int result = thrust::reduce(thrust::host, data, data + 6, 1);
- *
- *  // result == 10
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/accumulate.html
- */
-template<typename DerivedPolicy, typename InputIterator, typename T>
-__host__ __device__
-  T reduce(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-           InputIterator first,
-           InputIterator last,
-           T init);
-
-
-/*! \p reduce is a generalization of summation: it computes the sum (or some
- *  other binary operation) of all the elements in the range <tt>[first,
- *  last)</tt>. This version of \p reduce uses \p init as the initial value of the
- *  reduction. \p reduce is similar to the C++ Standard Template Library's
- *  <tt>std::accumulate</tt>. The primary difference between the two functions
- *  is that <tt>std::accumulate</tt> guarantees the order of summation, while
- *  \p reduce requires associativity of the binary operation to parallelize
- *  the reduction.
- *
- *  Note that \p reduce also assumes that the binary reduction operator (in this
- *  case operator+) is commutative.  If the reduction operator is not commutative
- *  then \p thrust::reduce should not be used.  Instead, one could use 
- *  \p inclusive_scan (which does not require commutativity) and select the
- *  last element of the output array.
- *
- *  \param first The beginning of the input sequence.
- *  \param last The end of the input sequence.
- *  \param init The initial value.
- *  \return The result of the reduction.
- *
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>
- *          and if \c x and \c y are objects of \p InputIterator's \c value_type,
- *          then <tt>x + y</tt> is defined and is convertible to \p T.
- *  \tparam T is convertible to \p InputIterator's \c value_type.
- *
- *  The following code snippet demonstrates how to use \p reduce to compute
- *  the sum of a sequence of integers including an intialization value.
- *
- *  \code
- *  #include <thrust/reduce.h>
- *  ...
- *  int data[6] = {1, 0, 2, 2, 1, 3};
- *  int result = thrust::reduce(data, data + 6, 1);
- *
- *  // result == 10
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/accumulate.html
- */
-template<typename InputIterator, typename T>
-  T reduce(InputIterator first,
-           InputIterator last,
-           T init);
-
-
-/*! \p reduce is a generalization of summation: it computes the sum (or some
- *  other binary operation) of all the elements in the range <tt>[first,
- *  last)</tt>. This version of \p reduce uses \p init as the initial value of the
- *  reduction and \p binary_op as the binary function used for summation. \p reduce
- *  is similar to the C++ Standard Template Library's <tt>std::accumulate</tt>.
- *  The primary difference between the two functions is that <tt>std::accumulate</tt>
- *  guarantees the order of summation, while \p reduce requires associativity of
- *  \p binary_op to parallelize the reduction.
- *
- *  Note that \p reduce also assumes that the binary reduction operator (in this
- *  case \p binary_op) is commutative.  If the reduction operator is not commutative
- *  then \p thrust::reduce should not be used.  Instead, one could use 
- *  \p inclusive_scan (which does not require commutativity) and select the
- *  last element of the output array.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param first The beginning of the input sequence.
- *  \param last The end of the input sequence.
- *  \param init The initial value.
- *  \param binary_op The binary function used to 'sum' values.
- *  \return The result of the reduction.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>
- *          and \c InputIterator's \c value_type is convertible to \c T.
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and is convertible to \p BinaryFunction's \c first_argument_type and \c second_argument_type.
- *  \tparam BinaryFunction is a model of <a href="http://www.sgi.com/tech/stl/BinaryFunction.html">Binary Function</a>,
- *          and \p BinaryFunction's \c result_type is convertible to \p OutputType.
- *
- *  The following code snippet demonstrates how to use \p reduce to
- *  compute the maximum value of a sequence of integers using the \p thrust::host execution policy
- *  for parallelization:
- *
- *  \code
- *  #include <thrust/reduce.h>
- *  #include <thrust/functional.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  int data[6] = {1, 0, 2, 2, 1, 3};
- *  int result = thrust::reduce(thrust::host,
- *                              data, data + 6,
- *                              -1,
- *                              thrust::maximum<int>());
- *  // result == 3
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/accumulate.html
- *  \see transform_reduce
- */
-template<typename DerivedPolicy,
-         typename InputIterator,
-         typename T,
-         typename BinaryFunction>
-__host__ __device__
-  T reduce(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-           InputIterator first,
-           InputIterator last,
-           T init,
-           BinaryFunction binary_op);
-
-
-/*! \p reduce is a generalization of summation: it computes the sum (or some
- *  other binary operation) of all the elements in the range <tt>[first,
- *  last)</tt>. This version of \p reduce uses \p init as the initial value of the
- *  reduction and \p binary_op as the binary function used for summation. \p reduce
- *  is similar to the C++ Standard Template Library's <tt>std::accumulate</tt>.
- *  The primary difference between the two functions is that <tt>std::accumulate</tt>
- *  guarantees the order of summation, while \p reduce requires associativity of
- *  \p binary_op to parallelize the reduction.
- *
- *  Note that \p reduce also assumes that the binary reduction operator (in this
- *  case \p binary_op) is commutative.  If the reduction operator is not commutative
- *  then \p thrust::reduce should not be used.  Instead, one could use 
- *  \p inclusive_scan (which does not require commutativity) and select the
- *  last element of the output array.
- *
- *  \param first The beginning of the input sequence.
- *  \param last The end of the input sequence.
- *  \param init The initial value.
- *  \param binary_op The binary function used to 'sum' values.
- *  \return The result of the reduction.
- *
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>
- *          and \c InputIterator's \c value_type is convertible to \c T.
- *  \tparam T is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
- *          and is convertible to \p BinaryFunction's \c first_argument_type and \c second_argument_type.
- *  \tparam BinaryFunction is a model of <a href="http://www.sgi.com/tech/stl/BinaryFunction.html">Binary Function</a>,
- *          and \p BinaryFunction's \c result_type is convertible to \p OutputType.
- *
- *  The following code snippet demonstrates how to use \p reduce to
- *  compute the maximum value of a sequence of integers.
- *
- *  \code
- *  #include <thrust/reduce.h>
- *  #include <thrust/functional.h>
- *  ...
- *  int data[6] = {1, 0, 2, 2, 1, 3};
- *  int result = thrust::reduce(data, data + 6,
- *                              -1,
- *                              thrust::maximum<int>());
- *  // result == 3
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/accumulate.html
- *  \see transform_reduce
- */
-template<typename InputIterator,
-         typename T,
-         typename BinaryFunction>
-  T reduce(InputIterator first,
-           InputIterator last,
-           T init,
-           BinaryFunction binary_op);
-
-
-/*! \p reduce_by_key is a generalization of \p reduce to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p reduce_by_key copies the first element of the group to the
- *  \c keys_output. The corresponding values in the range are reduced using the
- *  \c plus and the result copied to \c values_output. 
- *
- *  This version of \p reduce_by_key uses the function object \c equal_to
- *  to test for equality and \c plus to reduce values with equal keys.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param keys_first The beginning of the input key range.
- *  \param keys_last  The end of the input key range.
- *  \param values_first The beginning of the input value range.
- *  \param keys_output The beginning of the output key range.
- *  \param values_output The beginning of the output value range.
- *  \return A pair of iterators at end of the ranges <tt>[keys_output, keys_output_last)</tt> and <tt>[values_output, values_output_last)</tt>.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam OutputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator1's \c value_type is convertible to \c OutputIterator1's \c value_type.
- *  \tparam OutputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator2's \c value_type is convertible to \c OutputIterator2's \c value_type.
- *
- *  \pre The input ranges shall not overlap either output range.
- *
- *  The following code snippet demonstrates how to use \p reduce_by_key to
- *  compact a sequence of key/value pairs and sum values with equal keys using the \p thrust::host
- *  execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/reduce.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // input keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // input values
- *  int C[N];                         // output keys
- *  int D[N];                         // output values
- *
- *  thrust::pair<int*,int*> new_end;
- *  new_end = thrust::reduce_by_key(thrust::host, A, A + N, B, C, D);
- *
- *  // The first four keys in C are now {1, 3, 2, 1} and new_end.first - C is 4.
- *  // The first four values in D are now {9, 21, 9, 3} and new_end.second - D is 4.
- *  \endcode
- *  
- *  \see reduce
- *  \see unique_copy
- *  \see unique_by_key
- *  \see unique_by_key_copy
- */
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2>
-__host__ __device__
-  thrust::pair<OutputIterator1,OutputIterator2>
-  reduce_by_key(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                InputIterator1 keys_first, 
-                InputIterator1 keys_last,
-                InputIterator2 values_first,
-                OutputIterator1 keys_output,
-                OutputIterator2 values_output);
-
-
-/*! \p reduce_by_key is a generalization of \p reduce to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p reduce_by_key copies the first element of the group to the
- *  \c keys_output. The corresponding values in the range are reduced using the
- *  \c plus and the result copied to \c values_output. 
- *
- *  This version of \p reduce_by_key uses the function object \c equal_to
- *  to test for equality and \c plus to reduce values with equal keys.
- *
- *  \param keys_first The beginning of the input key range.
- *  \param keys_last  The end of the input key range.
- *  \param values_first The beginning of the input value range.
- *  \param keys_output The beginning of the output key range.
- *  \param values_output The beginning of the output value range.
- *  \return A pair of iterators at end of the ranges <tt>[keys_output, keys_output_last)</tt> and <tt>[values_output, values_output_last)</tt>.
- *
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam OutputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator1's \c value_type is convertible to \c OutputIterator1's \c value_type.
- *  \tparam OutputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator2's \c value_type is convertible to \c OutputIterator2's \c value_type.
- *
- *  \pre The input ranges shall not overlap either output range.
- *
- *  The following code snippet demonstrates how to use \p reduce_by_key to
- *  compact a sequence of key/value pairs and sum values with equal keys.
- *
- *  \code
- *  #include <thrust/reduce.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // input keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // input values
- *  int C[N];                         // output keys
- *  int D[N];                         // output values
- *
- *  thrust::pair<int*,int*> new_end;
- *  new_end = thrust::reduce_by_key(A, A + N, B, C, D);
- *
- *  // The first four keys in C are now {1, 3, 2, 1} and new_end.first - C is 4.
- *  // The first four values in D are now {9, 21, 9, 3} and new_end.second - D is 4.
- *  \endcode
- *  
- *  \see reduce
- *  \see unique_copy
- *  \see unique_by_key
- *  \see unique_by_key_copy
- */
-template<typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2>
-  thrust::pair<OutputIterator1,OutputIterator2>
-  reduce_by_key(InputIterator1 keys_first, 
-                InputIterator1 keys_last,
-                InputIterator2 values_first,
-                OutputIterator1 keys_output,
-                OutputIterator2 values_output);
-
-
-/*! \p reduce_by_key is a generalization of \p reduce to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p reduce_by_key copies the first element of the group to the
- *  \c keys_output. The corresponding values in the range are reduced using the
- *  \c plus and the result copied to \c values_output. 
- *
- *  This version of \p reduce_by_key uses the function object \c binary_pred
- *  to test for equality and \c plus to reduce values with equal keys.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param keys_first The beginning of the input key range.
- *  \param keys_last  The end of the input key range.
- *  \param values_first The beginning of the input value range.
- *  \param keys_output The beginning of the output key range.
- *  \param values_output The beginning of the output value range.
- *  \param binary_pred  The binary predicate used to determine equality.
- *  \return A pair of iterators at end of the ranges <tt>[keys_output, keys_output_last)</tt> and <tt>[values_output, values_output_last)</tt>.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam OutputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator1's \c value_type is convertible to \c OutputIterator1's \c value_type.
- *  \tparam OutputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator2's \c value_type is convertible to \c OutputIterator2's \c value_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  \pre The input ranges shall not overlap either output range.
- *
- *  The following code snippet demonstrates how to use \p reduce_by_key to
- *  compact a sequence of key/value pairs and sum values with equal keys using the \p thrust::host
- *  execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/reduce.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // input keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // input values
- *  int C[N];                         // output keys
- *  int D[N];                         // output values
- *
- *  thrust::pair<int*,int*> new_end;
- *  thrust::equal_to<int> binary_pred;
- *  new_end = thrust::reduce_by_key(thrust::host, A, A + N, B, C, D, binary_pred);
- *
- *  // The first four keys in C are now {1, 3, 2, 1} and new_end.first - C is 4.
- *  // The first four values in D are now {9, 21, 9, 3} and new_end.second - D is 4.
- *  \endcode
- *  
- *  \see reduce
- *  \see unique_copy
- *  \see unique_by_key
- *  \see unique_by_key_copy
- */
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2,
-         typename BinaryPredicate>
-__host__ __device__
-  thrust::pair<OutputIterator1,OutputIterator2>
-  reduce_by_key(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                InputIterator1 keys_first, 
-                InputIterator1 keys_last,
-                InputIterator2 values_first,
-                OutputIterator1 keys_output,
-                OutputIterator2 values_output,
-                BinaryPredicate binary_pred);
-
-
-/*! \p reduce_by_key is a generalization of \p reduce to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p reduce_by_key copies the first element of the group to the
- *  \c keys_output. The corresponding values in the range are reduced using the
- *  \c plus and the result copied to \c values_output. 
- *
- *  This version of \p reduce_by_key uses the function object \c binary_pred
- *  to test for equality and \c plus to reduce values with equal keys.
- *
- *  \param keys_first The beginning of the input key range.
- *  \param keys_last  The end of the input key range.
- *  \param values_first The beginning of the input value range.
- *  \param keys_output The beginning of the output key range.
- *  \param values_output The beginning of the output value range.
- *  \param binary_pred  The binary predicate used to determine equality.
- *  \return A pair of iterators at end of the ranges <tt>[keys_output, keys_output_last)</tt> and <tt>[values_output, values_output_last)</tt>.
- *
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam OutputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator1's \c value_type is convertible to \c OutputIterator1's \c value_type.
- *  \tparam OutputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator2's \c value_type is convertible to \c OutputIterator2's \c value_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *
- *  \pre The input ranges shall not overlap either output range.
- *
- *  The following code snippet demonstrates how to use \p reduce_by_key to
- *  compact a sequence of key/value pairs and sum values with equal keys.
- *
- *  \code
- *  #include <thrust/reduce.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // input keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // input values
- *  int C[N];                         // output keys
- *  int D[N];                         // output values
- *
- *  thrust::pair<int*,int*> new_end;
- *  thrust::equal_to<int> binary_pred;
- *  new_end = thrust::reduce_by_key(A, A + N, B, C, D, binary_pred);
- *
- *  // The first four keys in C are now {1, 3, 2, 1} and new_end.first - C is 4.
- *  // The first four values in D are now {9, 21, 9, 3} and new_end.second - D is 4.
- *  \endcode
- *  
- *  \see reduce
- *  \see unique_copy
- *  \see unique_by_key
- *  \see unique_by_key_copy
- */
-template<typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2,
-         typename BinaryPredicate>
-  thrust::pair<OutputIterator1,OutputIterator2>
-  reduce_by_key(InputIterator1 keys_first, 
-                InputIterator1 keys_last,
-                InputIterator2 values_first,
-                OutputIterator1 keys_output,
-                OutputIterator2 values_output,
-                BinaryPredicate binary_pred);
-
-
-/*! \p reduce_by_key is a generalization of \p reduce to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p reduce_by_key copies the first element of the group to the
- *  \c keys_output. The corresponding values in the range are reduced using the
- *  \c BinaryFunction \c binary_op and the result copied to \c values_output. 
- *  Specifically, if consecutive key iterators \c i and \c (i + 1) are 
- *  such that <tt>binary_pred(*i, *(i+1))</tt> is \c true, then the corresponding
- *  values are reduced to a single value with \c binary_op.
- *
- *  This version of \p reduce_by_key uses the function object \c binary_pred
- *  to test for equality and \c binary_op to reduce values with equal keys.
- *
- *  The algorithm's execution is parallelized as determined by \p exec.
- *
- *  \param exec The execution policy to use for parallelization.
- *  \param keys_first The beginning of the input key range.
- *  \param keys_last  The end of the input key range.
- *  \param values_first The beginning of the input value range.
- *  \param keys_output The beginning of the output key range.
- *  \param values_output The beginning of the output value range.
- *  \param binary_pred  The binary predicate used to determine equality.
- *  \param binary_op The binary function used to accumulate values.
- *  \return A pair of iterators at end of the ranges <tt>[keys_output, keys_output_last)</tt> and <tt>[values_output, values_output_last)</tt>.
- *
- *  \tparam DerivedPolicy The name of the derived execution policy.
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam OutputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator1's \c value_type is convertible to \c OutputIterator1's \c value_type.
- *  \tparam OutputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator2's \c value_type is convertible to \c OutputIterator2's \c value_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *  \tparam BinaryFunction is a model of <a href="http://www.sgi.com/tech/stl/BinaryFunction.html">Binary Function</a>
- *          and \c BinaryFunction's \c result_type is convertible to \c OutputIterator2's \c value_type.
- *
- *  \pre The input ranges shall not overlap either output range.
- *
- *  The following code snippet demonstrates how to use \p reduce_by_key to
- *  compact a sequence of key/value pairs and sum values with equal keys using the \p thrust::host
- *  execution policy for parallelization:
- *
- *  \code
- *  #include <thrust/reduce.h>
- *  #include <thrust/execution_policy.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // input keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // input values
- *  int C[N];                         // output keys
- *  int D[N];                         // output values
- *
- *  thrust::pair<int*,int*> new_end;
- *  thrust::equal_to<int> binary_pred;
- *  thrust::plus<int> binary_op;
- *  new_end = thrust::reduce_by_key(thrust::host, A, A + N, B, C, D, binary_pred, binary_op);
- *
- *  // The first four keys in C are now {1, 3, 2, 1} and new_end.first - C is 4.
- *  // The first four values in D are now {9, 21, 9, 3} and new_end.second - D is 4.
- *  \endcode
- *  
- *  \see reduce
- *  \see unique_copy
- *  \see unique_by_key
- *  \see unique_by_key_copy
- */
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2,
-         typename BinaryPredicate,
-         typename BinaryFunction>
-__host__ __device__
-  thrust::pair<OutputIterator1,OutputIterator2>
-  reduce_by_key(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
-                InputIterator1 keys_first, 
-                InputIterator1 keys_last,
-                InputIterator2 values_first,
-                OutputIterator1 keys_output,
-                OutputIterator2 values_output,
-                BinaryPredicate binary_pred,
-                BinaryFunction binary_op);
-
-
-/*! \p reduce_by_key is a generalization of \p reduce to key-value pairs.
- *  For each group of consecutive keys in the range <tt>[keys_first, keys_last)</tt>
- *  that are equal, \p reduce_by_key copies the first element of the group to the
- *  \c keys_output. The corresponding values in the range are reduced using the
- *  \c BinaryFunction \c binary_op and the result copied to \c values_output. 
- *  Specifically, if consecutive key iterators \c i and \c (i + 1) are 
- *  such that <tt>binary_pred(*i, *(i+1))</tt> is \c true, then the corresponding
- *  values are reduced to a single value with \c binary_op.
- *
- *  This version of \p reduce_by_key uses the function object \c binary_pred
- *  to test for equality and \c binary_op to reduce values with equal keys.
- *
- *  \param keys_first The beginning of the input key range.
- *  \param keys_last  The end of the input key range.
- *  \param values_first The beginning of the input value range.
- *  \param keys_output The beginning of the output key range.
- *  \param values_output The beginning of the output value range.
- *  \param binary_pred  The binary predicate used to determine equality.
- *  \param binary_op The binary function used to accumulate values.
- *  \return A pair of iterators at end of the ranges <tt>[keys_output, keys_output_last)</tt> and <tt>[values_output, values_output_last)</tt>.
- *
- *  \tparam InputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam InputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
- *  \tparam OutputIterator1 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator1's \c value_type is convertible to \c OutputIterator1's \c value_type.
- *  \tparam OutputIterator2 is a model of <a href="http://www.sgi.com/tech/stl/OutputIterator.html">Output Iterator</a> and
- *          and \p InputIterator2's \c value_type is convertible to \c OutputIterator2's \c value_type.
- *  \tparam BinaryPredicate is a model of <a href="http://www.sgi.com/tech/stl/BinaryPredicate.html">Binary Predicate</a>.
- *  \tparam BinaryFunction is a model of <a href="http://www.sgi.com/tech/stl/BinaryFunction.html">Binary Function</a>
- *          and \c BinaryFunction's \c result_type is convertible to \c OutputIterator2's \c value_type.
- *
- *  \pre The input ranges shall not overlap either output range.
- *
- *  The following code snippet demonstrates how to use \p reduce_by_key to
- *  compact a sequence of key/value pairs and sum values with equal keys.
- *
- *  \code
- *  #include <thrust/reduce.h>
- *  ...
- *  const int N = 7;
- *  int A[N] = {1, 3, 3, 3, 2, 2, 1}; // input keys
- *  int B[N] = {9, 8, 7, 6, 5, 4, 3}; // input values
- *  int C[N];                         // output keys
- *  int D[N];                         // output values
- *
- *  thrust::pair<int*,int*> new_end;
- *  thrust::equal_to<int> binary_pred;
- *  thrust::plus<int> binary_op;
- *  new_end = thrust::reduce_by_key(A, A + N, B, C, D, binary_pred, binary_op);
- *
- *  // The first four keys in C are now {1, 3, 2, 1} and new_end.first - C is 4.
- *  // The first four values in D are now {9, 21, 9, 3} and new_end.second - D is 4.
- *  \endcode
- *  
- *  \see reduce
- *  \see unique_copy
- *  \see unique_by_key
- *  \see unique_by_key_copy
- */
-template<typename InputIterator1,
-         typename InputIterator2,
-         typename OutputIterator1,
-         typename OutputIterator2,
-         typename BinaryPredicate,
-         typename BinaryFunction>
-  thrust::pair<OutputIterator1,OutputIterator2>
-  reduce_by_key(InputIterator1 keys_first, 
-                InputIterator1 keys_last,
-                InputIterator2 values_first,
-                OutputIterator1 keys_output,
-                OutputIterator2 values_output,
-                BinaryPredicate binary_pred,
-                BinaryFunction binary_op);
-
-
-/*! \} // end reductions
- */
-
-
-} // end namespace thrust
-
-#include <thrust/detail/reduce.inl>
-
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/BlpImagePlugin.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/BlpImagePlugin.py
deleted file mode 100644
index 0ca60ff24719b6e438c1f66070df3b6932d67556..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/BlpImagePlugin.py
+++ /dev/null
@@ -1,472 +0,0 @@
-"""
-Blizzard Mipmap Format (.blp)
-Jerome Leclanche <jerome@leclan.ch>
-
-The contents of this file are hereby released in the public domain (CC0)
-Full text of the CC0 license:
-  https://creativecommons.org/publicdomain/zero/1.0/
-
-BLP1 files, used mostly in Warcraft III, are not fully supported.
-All types of BLP2 files used in World of Warcraft are supported.
-
-The BLP file structure consists of a header, up to 16 mipmaps of the
-texture
-
-Texture sizes must be powers of two, though the two dimensions do
-not have to be equal; 512x256 is valid, but 512x200 is not.
-The first mipmap (mipmap #0) is the full size image; each subsequent
-mipmap halves both dimensions. The final mipmap should be 1x1.
-
-BLP files come in many different flavours:
-* JPEG-compressed (type == 0) - only supported for BLP1.
-* RAW images (type == 1, encoding == 1). Each mipmap is stored as an
-  array of 8-bit values, one per pixel, left to right, top to bottom.
-  Each value is an index to the palette.
-* DXT-compressed (type == 1, encoding == 2):
-- DXT1 compression is used if alpha_encoding == 0.
-  - An additional alpha bit is used if alpha_depth == 1.
-  - DXT3 compression is used if alpha_encoding == 1.
-  - DXT5 compression is used if alpha_encoding == 7.
-"""
-
-import os
-import struct
-from enum import IntEnum
-from io import BytesIO
-
-from . import Image, ImageFile
-
-
-class Format(IntEnum):
-    JPEG = 0
-
-
-class Encoding(IntEnum):
-    UNCOMPRESSED = 1
-    DXT = 2
-    UNCOMPRESSED_RAW_BGRA = 3
-
-
-class AlphaEncoding(IntEnum):
-    DXT1 = 0
-    DXT3 = 1
-    DXT5 = 7
-
-
-def unpack_565(i):
-    return ((i >> 11) & 0x1F) << 3, ((i >> 5) & 0x3F) << 2, (i & 0x1F) << 3
-
-
-def decode_dxt1(data, alpha=False):
-    """
-    input: one "row" of data (i.e. will produce 4*width pixels)
-    """
-
-    blocks = len(data) // 8  # number of blocks in row
-    ret = (bytearray(), bytearray(), bytearray(), bytearray())
-
-    for block in range(blocks):
-        # Decode next 8-byte block.
-        idx = block * 8
-        color0, color1, bits = struct.unpack_from("<HHI", data, idx)
-
-        r0, g0, b0 = unpack_565(color0)
-        r1, g1, b1 = unpack_565(color1)
-
-        # Decode this block into 4x4 pixels
-        # Accumulate the results onto our 4 row accumulators
-        for j in range(4):
-            for i in range(4):
-                # get next control op and generate a pixel
-
-                control = bits & 3
-                bits = bits >> 2
-
-                a = 0xFF
-                if control == 0:
-                    r, g, b = r0, g0, b0
-                elif control == 1:
-                    r, g, b = r1, g1, b1
-                elif control == 2:
-                    if color0 > color1:
-                        r = (2 * r0 + r1) // 3
-                        g = (2 * g0 + g1) // 3
-                        b = (2 * b0 + b1) // 3
-                    else:
-                        r = (r0 + r1) // 2
-                        g = (g0 + g1) // 2
-                        b = (b0 + b1) // 2
-                elif control == 3:
-                    if color0 > color1:
-                        r = (2 * r1 + r0) // 3
-                        g = (2 * g1 + g0) // 3
-                        b = (2 * b1 + b0) // 3
-                    else:
-                        r, g, b, a = 0, 0, 0, 0
-
-                if alpha:
-                    ret[j].extend([r, g, b, a])
-                else:
-                    ret[j].extend([r, g, b])
-
-    return ret
-
-
-def decode_dxt3(data):
-    """
-    input: one "row" of data (i.e. will produce 4*width pixels)
-    """
-
-    blocks = len(data) // 16  # number of blocks in row
-    ret = (bytearray(), bytearray(), bytearray(), bytearray())
-
-    for block in range(blocks):
-        idx = block * 16
-        block = data[idx : idx + 16]
-        # Decode next 16-byte block.
-        bits = struct.unpack_from("<8B", block)
-        color0, color1 = struct.unpack_from("<HH", block, 8)
-
-        (code,) = struct.unpack_from("<I", block, 12)
-
-        r0, g0, b0 = unpack_565(color0)
-        r1, g1, b1 = unpack_565(color1)
-
-        for j in range(4):
-            high = False  # Do we want the higher bits?
-            for i in range(4):
-                alphacode_index = (4 * j + i) // 2
-                a = bits[alphacode_index]
-                if high:
-                    high = False
-                    a >>= 4
-                else:
-                    high = True
-                    a &= 0xF
-                a *= 17  # We get a value between 0 and 15
-
-                color_code = (code >> 2 * (4 * j + i)) & 0x03
-
-                if color_code == 0:
-                    r, g, b = r0, g0, b0
-                elif color_code == 1:
-                    r, g, b = r1, g1, b1
-                elif color_code == 2:
-                    r = (2 * r0 + r1) // 3
-                    g = (2 * g0 + g1) // 3
-                    b = (2 * b0 + b1) // 3
-                elif color_code == 3:
-                    r = (2 * r1 + r0) // 3
-                    g = (2 * g1 + g0) // 3
-                    b = (2 * b1 + b0) // 3
-
-                ret[j].extend([r, g, b, a])
-
-    return ret
-
-
-def decode_dxt5(data):
-    """
-    input: one "row" of data (i.e. will produce 4 * width pixels)
-    """
-
-    blocks = len(data) // 16  # number of blocks in row
-    ret = (bytearray(), bytearray(), bytearray(), bytearray())
-
-    for block in range(blocks):
-        idx = block * 16
-        block = data[idx : idx + 16]
-        # Decode next 16-byte block.
-        a0, a1 = struct.unpack_from("<BB", block)
-
-        bits = struct.unpack_from("<6B", block, 2)
-        alphacode1 = bits[2] | (bits[3] << 8) | (bits[4] << 16) | (bits[5] << 24)
-        alphacode2 = bits[0] | (bits[1] << 8)
-
-        color0, color1 = struct.unpack_from("<HH", block, 8)
-
-        (code,) = struct.unpack_from("<I", block, 12)
-
-        r0, g0, b0 = unpack_565(color0)
-        r1, g1, b1 = unpack_565(color1)
-
-        for j in range(4):
-            for i in range(4):
-                # get next control op and generate a pixel
-                alphacode_index = 3 * (4 * j + i)
-
-                if alphacode_index <= 12:
-                    alphacode = (alphacode2 >> alphacode_index) & 0x07
-                elif alphacode_index == 15:
-                    alphacode = (alphacode2 >> 15) | ((alphacode1 << 1) & 0x06)
-                else:  # alphacode_index >= 18 and alphacode_index <= 45
-                    alphacode = (alphacode1 >> (alphacode_index - 16)) & 0x07
-
-                if alphacode == 0:
-                    a = a0
-                elif alphacode == 1:
-                    a = a1
-                elif a0 > a1:
-                    a = ((8 - alphacode) * a0 + (alphacode - 1) * a1) // 7
-                elif alphacode == 6:
-                    a = 0
-                elif alphacode == 7:
-                    a = 255
-                else:
-                    a = ((6 - alphacode) * a0 + (alphacode - 1) * a1) // 5
-
-                color_code = (code >> 2 * (4 * j + i)) & 0x03
-
-                if color_code == 0:
-                    r, g, b = r0, g0, b0
-                elif color_code == 1:
-                    r, g, b = r1, g1, b1
-                elif color_code == 2:
-                    r = (2 * r0 + r1) // 3
-                    g = (2 * g0 + g1) // 3
-                    b = (2 * b0 + b1) // 3
-                elif color_code == 3:
-                    r = (2 * r1 + r0) // 3
-                    g = (2 * g1 + g0) // 3
-                    b = (2 * b1 + b0) // 3
-
-                ret[j].extend([r, g, b, a])
-
-    return ret
-
-
-class BLPFormatError(NotImplementedError):
-    pass
-
-
-def _accept(prefix):
-    return prefix[:4] in (b"BLP1", b"BLP2")
-
-
-class BlpImageFile(ImageFile.ImageFile):
-    """
-    Blizzard Mipmap Format
-    """
-
-    format = "BLP"
-    format_description = "Blizzard Mipmap Format"
-
-    def _open(self):
-        self.magic = self.fp.read(4)
-
-        self.fp.seek(5, os.SEEK_CUR)
-        (self._blp_alpha_depth,) = struct.unpack("<b", self.fp.read(1))
-
-        self.fp.seek(2, os.SEEK_CUR)
-        self._size = struct.unpack("<II", self.fp.read(8))
-
-        if self.magic in (b"BLP1", b"BLP2"):
-            decoder = self.magic.decode()
-        else:
-            msg = f"Bad BLP magic {repr(self.magic)}"
-            raise BLPFormatError(msg)
-
-        self.mode = "RGBA" if self._blp_alpha_depth else "RGB"
-        self.tile = [(decoder, (0, 0) + self.size, 0, (self.mode, 0, 1))]
-
-
-class _BLPBaseDecoder(ImageFile.PyDecoder):
-    _pulls_fd = True
-
-    def decode(self, buffer):
-        try:
-            self._read_blp_header()
-            self._load()
-        except struct.error as e:
-            msg = "Truncated BLP file"
-            raise OSError(msg) from e
-        return -1, 0
-
-    def _read_blp_header(self):
-        self.fd.seek(4)
-        (self._blp_compression,) = struct.unpack("<i", self._safe_read(4))
-
-        (self._blp_encoding,) = struct.unpack("<b", self._safe_read(1))
-        (self._blp_alpha_depth,) = struct.unpack("<b", self._safe_read(1))
-        (self._blp_alpha_encoding,) = struct.unpack("<b", self._safe_read(1))
-        self.fd.seek(1, os.SEEK_CUR)  # mips
-
-        self.size = struct.unpack("<II", self._safe_read(8))
-
-        if isinstance(self, BLP1Decoder):
-            # Only present for BLP1
-            (self._blp_encoding,) = struct.unpack("<i", self._safe_read(4))
-            self.fd.seek(4, os.SEEK_CUR)  # subtype
-
-        self._blp_offsets = struct.unpack("<16I", self._safe_read(16 * 4))
-        self._blp_lengths = struct.unpack("<16I", self._safe_read(16 * 4))
-
-    def _safe_read(self, length):
-        return ImageFile._safe_read(self.fd, length)
-
-    def _read_palette(self):
-        ret = []
-        for i in range(256):
-            try:
-                b, g, r, a = struct.unpack("<4B", self._safe_read(4))
-            except struct.error:
-                break
-            ret.append((b, g, r, a))
-        return ret
-
-    def _read_bgra(self, palette):
-        data = bytearray()
-        _data = BytesIO(self._safe_read(self._blp_lengths[0]))
-        while True:
-            try:
-                (offset,) = struct.unpack("<B", _data.read(1))
-            except struct.error:
-                break
-            b, g, r, a = palette[offset]
-            d = (r, g, b)
-            if self._blp_alpha_depth:
-                d += (a,)
-            data.extend(d)
-        return data
-
-
-class BLP1Decoder(_BLPBaseDecoder):
-    def _load(self):
-        if self._blp_compression == Format.JPEG:
-            self._decode_jpeg_stream()
-
-        elif self._blp_compression == 1:
-            if self._blp_encoding in (4, 5):
-                palette = self._read_palette()
-                data = self._read_bgra(palette)
-                self.set_as_raw(bytes(data))
-            else:
-                msg = f"Unsupported BLP encoding {repr(self._blp_encoding)}"
-                raise BLPFormatError(msg)
-        else:
-            msg = f"Unsupported BLP compression {repr(self._blp_encoding)}"
-            raise BLPFormatError(msg)
-
-    def _decode_jpeg_stream(self):
-        from .JpegImagePlugin import JpegImageFile
-
-        (jpeg_header_size,) = struct.unpack("<I", self._safe_read(4))
-        jpeg_header = self._safe_read(jpeg_header_size)
-        self._safe_read(self._blp_offsets[0] - self.fd.tell())  # What IS this?
-        data = self._safe_read(self._blp_lengths[0])
-        data = jpeg_header + data
-        data = BytesIO(data)
-        image = JpegImageFile(data)
-        Image._decompression_bomb_check(image.size)
-        if image.mode == "CMYK":
-            decoder_name, extents, offset, args = image.tile[0]
-            image.tile = [(decoder_name, extents, offset, (args[0], "CMYK"))]
-        r, g, b = image.convert("RGB").split()
-        image = Image.merge("RGB", (b, g, r))
-        self.set_as_raw(image.tobytes())
-
-
-class BLP2Decoder(_BLPBaseDecoder):
-    def _load(self):
-        palette = self._read_palette()
-
-        self.fd.seek(self._blp_offsets[0])
-
-        if self._blp_compression == 1:
-            # Uncompressed or DirectX compression
-
-            if self._blp_encoding == Encoding.UNCOMPRESSED:
-                data = self._read_bgra(palette)
-
-            elif self._blp_encoding == Encoding.DXT:
-                data = bytearray()
-                if self._blp_alpha_encoding == AlphaEncoding.DXT1:
-                    linesize = (self.size[0] + 3) // 4 * 8
-                    for yb in range((self.size[1] + 3) // 4):
-                        for d in decode_dxt1(
-                            self._safe_read(linesize), alpha=bool(self._blp_alpha_depth)
-                        ):
-                            data += d
-
-                elif self._blp_alpha_encoding == AlphaEncoding.DXT3:
-                    linesize = (self.size[0] + 3) // 4 * 16
-                    for yb in range((self.size[1] + 3) // 4):
-                        for d in decode_dxt3(self._safe_read(linesize)):
-                            data += d
-
-                elif self._blp_alpha_encoding == AlphaEncoding.DXT5:
-                    linesize = (self.size[0] + 3) // 4 * 16
-                    for yb in range((self.size[1] + 3) // 4):
-                        for d in decode_dxt5(self._safe_read(linesize)):
-                            data += d
-                else:
-                    msg = f"Unsupported alpha encoding {repr(self._blp_alpha_encoding)}"
-                    raise BLPFormatError(msg)
-            else:
-                msg = f"Unknown BLP encoding {repr(self._blp_encoding)}"
-                raise BLPFormatError(msg)
-
-        else:
-            msg = f"Unknown BLP compression {repr(self._blp_compression)}"
-            raise BLPFormatError(msg)
-
-        self.set_as_raw(bytes(data))
-
-
-class BLPEncoder(ImageFile.PyEncoder):
-    _pushes_fd = True
-
-    def _write_palette(self):
-        data = b""
-        palette = self.im.getpalette("RGBA", "RGBA")
-        for i in range(256):
-            r, g, b, a = palette[i * 4 : (i + 1) * 4]
-            data += struct.pack("<4B", b, g, r, a)
-        return data
-
-    def encode(self, bufsize):
-        palette_data = self._write_palette()
-
-        offset = 20 + 16 * 4 * 2 + len(palette_data)
-        data = struct.pack("<16I", offset, *((0,) * 15))
-
-        w, h = self.im.size
-        data += struct.pack("<16I", w * h, *((0,) * 15))
-
-        data += palette_data
-
-        for y in range(h):
-            for x in range(w):
-                data += struct.pack("<B", self.im.getpixel((x, y)))
-
-        return len(data), 0, data
-
-
-def _save(im, fp, filename, save_all=False):
-    if im.mode != "P":
-        msg = "Unsupported BLP image mode"
-        raise ValueError(msg)
-
-    magic = b"BLP1" if im.encoderinfo.get("blp_version") == "BLP1" else b"BLP2"
-    fp.write(magic)
-
-    fp.write(struct.pack("<i", 1))  # Uncompressed or DirectX compression
-    fp.write(struct.pack("<b", Encoding.UNCOMPRESSED))
-    fp.write(struct.pack("<b", 1 if im.palette.mode == "RGBA" else 0))
-    fp.write(struct.pack("<b", 0))  # alpha encoding
-    fp.write(struct.pack("<b", 0))  # mips
-    fp.write(struct.pack("<II", *im.size))
-    if magic == b"BLP1":
-        fp.write(struct.pack("<i", 5))
-        fp.write(struct.pack("<i", 0))
-
-    ImageFile._save(im, fp, [("BLP", (0, 0) + im.size, 0, im.mode)])
-
-
-Image.register_open(BlpImageFile.format, BlpImageFile, _accept)
-Image.register_extension(BlpImageFile.format, ".blp")
-Image.register_decoder("BLP1", BLP1Decoder)
-Image.register_decoder("BLP2", BLP2Decoder)
-
-Image.register_save(BlpImageFile.format, _save)
-Image.register_encoder("BLP", BLPEncoder)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/contourpy/util/bokeh_renderer.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/contourpy/util/bokeh_renderer.py
deleted file mode 100644
index 108eda75dda951e1b07ff4ca3603f5ba0e0d1e75..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/contourpy/util/bokeh_renderer.py
+++ /dev/null
@@ -1,318 +0,0 @@
-from __future__ import annotations
-
-import io
-from typing import TYPE_CHECKING, Any
-
-from bokeh.io import export_png, export_svg, show
-from bokeh.io.export import get_screenshot_as_png
-from bokeh.layouts import gridplot
-from bokeh.models.annotations.labels import Label
-from bokeh.palettes import Category10
-from bokeh.plotting import figure
-import numpy as np
-
-from contourpy import FillType, LineType
-from contourpy.util.bokeh_util import filled_to_bokeh, lines_to_bokeh
-from contourpy.util.renderer import Renderer
-
-if TYPE_CHECKING:
-    from bokeh.models import GridPlot
-    from bokeh.palettes import Palette
-    from numpy.typing import ArrayLike
-
-    from contourpy._contourpy import FillReturn, LineReturn
-
-
-class BokehRenderer(Renderer):
-    _figures: list[figure]
-    _layout: GridPlot
-    _palette: Palette
-    _want_svg: bool
-
-    """Utility renderer using Bokeh to render a grid of plots over the same (x, y) range.
-
-    Args:
-        nrows (int, optional): Number of rows of plots, default ``1``.
-        ncols (int, optional): Number of columns of plots, default ``1``.
-        figsize (tuple(float, float), optional): Figure size in inches (assuming 100 dpi), default
-            ``(9, 9)``.
-        show_frame (bool, optional): Whether to show frame and axes ticks, default ``True``.
-        want_svg (bool, optional): Whether output is required in SVG format or not, default
-            ``False``.
-
-    Warning:
-        :class:`~contourpy.util.bokeh_renderer.BokehRenderer`, unlike
-        :class:`~contourpy.util.mpl_renderer.MplRenderer`, needs to be told in advance if output to
-        SVG format will be required later, otherwise it will assume PNG output.
-    """
-    def __init__(
-        self,
-        nrows: int = 1,
-        ncols: int = 1,
-        figsize: tuple[float, float] = (9, 9),
-        show_frame: bool = True,
-        want_svg: bool = False,
-    ) -> None:
-        self._want_svg = want_svg
-        self._palette = Category10[10]
-
-        total_size = 100*np.asarray(figsize, dtype=int)  # Assuming 100 dpi.
-
-        nfigures = nrows*ncols
-        self._figures = []
-        backend = "svg" if self._want_svg else "canvas"
-        for _ in range(nfigures):
-            fig = figure(output_backend=backend)
-            fig.xgrid.visible = False
-            fig.ygrid.visible = False
-            self._figures.append(fig)
-            if not show_frame:
-                fig.outline_line_color = None  # type: ignore[assignment]
-                fig.axis.visible = False
-
-        self._layout = gridplot(
-            self._figures, ncols=ncols, toolbar_location=None,  # type: ignore[arg-type]
-            width=total_size[0] // ncols, height=total_size[1] // nrows)
-
-    def _convert_color(self, color: str) -> str:
-        if isinstance(color, str) and color[0] == "C":
-            index = int(color[1:])
-            color = self._palette[index]
-        return color
-
-    def _get_figure(self, ax: figure | int) -> figure:
-        if isinstance(ax, int):
-            ax = self._figures[ax]
-        return ax
-
-    def filled(
-        self,
-        filled: FillReturn,
-        fill_type: FillType,
-        ax: figure | int = 0,
-        color: str = "C0",
-        alpha: float = 0.7,
-    ) -> None:
-        """Plot filled contours on a single plot.
-
-        Args:
-            filled (sequence of arrays): Filled contour data as returned by
-                :func:`~contourpy.ContourGenerator.filled`.
-            fill_type (FillType): Type of ``filled`` data, as returned by
-                :attr:`~contourpy.ContourGenerator.fill_type`.
-            ax (int or Bokeh Figure, optional): Which plot to use, default ``0``.
-            color (str, optional): Color to plot with. May be a string color or the letter ``"C"``
-                followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the
-                ``Category10`` palette. Default ``"C0"``.
-            alpha (float, optional): Opacity to plot with, default ``0.7``.
-        """
-        fig = self._get_figure(ax)
-        color = self._convert_color(color)
-        xs, ys = filled_to_bokeh(filled, fill_type)
-        if len(xs) > 0:
-            fig.multi_polygons(xs=[xs], ys=[ys], color=color, fill_alpha=alpha, line_width=0)
-
-    def grid(
-        self,
-        x: ArrayLike,
-        y: ArrayLike,
-        ax: figure | int = 0,
-        color: str = "black",
-        alpha: float = 0.1,
-        point_color: str | None = None,
-        quad_as_tri_alpha: float = 0,
-    ) -> None:
-        """Plot quad grid lines on a single plot.
-
-        Args:
-            x (array-like of shape (ny, nx) or (nx,)): The x-coordinates of the grid points.
-            y (array-like of shape (ny, nx) or (ny,)): The y-coordinates of the grid points.
-            ax (int or Bokeh Figure, optional): Which plot to use, default ``0``.
-            color (str, optional): Color to plot grid lines, default ``"black"``.
-            alpha (float, optional): Opacity to plot lines with, default ``0.1``.
-            point_color (str, optional): Color to plot grid points or ``None`` if grid points
-                should not be plotted, default ``None``.
-            quad_as_tri_alpha (float, optional): Opacity to plot ``quad_as_tri`` grid, default
-                ``0``.
-
-        Colors may be a string color or the letter ``"C"`` followed by an integer in the range
-        ``"C0"`` to ``"C9"`` to use a color from the ``Category10`` palette.
-
-        Warning:
-            ``quad_as_tri_alpha > 0`` plots all quads as though they are unmasked.
-        """
-        fig = self._get_figure(ax)
-        x, y = self._grid_as_2d(x, y)
-        xs = [row for row in x] + [row for row in x.T]
-        ys = [row for row in y] + [row for row in y.T]
-        kwargs = dict(line_color=color, alpha=alpha)
-        fig.multi_line(xs, ys, **kwargs)
-        if quad_as_tri_alpha > 0:
-            # Assumes no quad mask.
-            xmid = (0.25*(x[:-1, :-1] + x[1:, :-1] + x[:-1, 1:] + x[1:, 1:])).ravel()
-            ymid = (0.25*(y[:-1, :-1] + y[1:, :-1] + y[:-1, 1:] + y[1:, 1:])).ravel()
-            fig.multi_line(
-                [row for row in np.stack((x[:-1, :-1].ravel(), xmid, x[1:, 1:].ravel()), axis=1)],
-                [row for row in np.stack((y[:-1, :-1].ravel(), ymid, y[1:, 1:].ravel()), axis=1)],
-                **kwargs)
-            fig.multi_line(
-                [row for row in np.stack((x[:-1, 1:].ravel(), xmid, x[1:, :-1].ravel()), axis=1)],
-                [row for row in np.stack((y[:-1, 1:].ravel(), ymid, y[1:, :-1].ravel()), axis=1)],
-                **kwargs)
-        if point_color is not None:
-            fig.circle(
-                x=x.ravel(), y=y.ravel(), fill_color=color, line_color=None, alpha=alpha, size=8)
-
-    def lines(
-        self,
-        lines: LineReturn,
-        line_type: LineType,
-        ax: figure | int = 0,
-        color: str = "C0",
-        alpha: float = 1.0,
-        linewidth: float = 1,
-    ) -> None:
-        """Plot contour lines on a single plot.
-
-        Args:
-            lines (sequence of arrays): Contour line data as returned by
-                :func:`~contourpy.ContourGenerator.lines`.
-            line_type (LineType): Type of ``lines`` data, as returned by
-                :attr:`~contourpy.ContourGenerator.line_type`.
-            ax (int or Bokeh Figure, optional): Which plot to use, default ``0``.
-            color (str, optional): Color to plot lines. May be a string color or the letter ``"C"``
-                followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the
-                ``Category10`` palette. Default ``"C0"``.
-            alpha (float, optional): Opacity to plot lines with, default ``1.0``.
-            linewidth (float, optional): Width of lines, default ``1``.
-
-        Note:
-            Assumes all lines are open line strips not closed line loops.
-        """
-        fig = self._get_figure(ax)
-        color = self._convert_color(color)
-        xs, ys = lines_to_bokeh(lines, line_type)
-        if len(xs) > 0:
-            fig.multi_line(xs, ys, line_color=color, line_alpha=alpha, line_width=linewidth)
-
-    def mask(
-        self,
-        x: ArrayLike,
-        y: ArrayLike,
-        z: ArrayLike | np.ma.MaskedArray[Any, Any],
-        ax: figure | int = 0,
-        color: str = "black",
-    ) -> None:
-        """Plot masked out grid points as circles on a single plot.
-
-        Args:
-            x (array-like of shape (ny, nx) or (nx,)): The x-coordinates of the grid points.
-            y (array-like of shape (ny, nx) or (ny,)): The y-coordinates of the grid points.
-            z (masked array of shape (ny, nx): z-values.
-            ax (int or Bokeh Figure, optional): Which plot to use, default ``0``.
-            color (str, optional): Circle color, default ``"black"``.
-        """
-        mask = np.ma.getmask(z)  # type: ignore[no-untyped-call]
-        if mask is np.ma.nomask:
-            return
-        fig = self._get_figure(ax)
-        color = self._convert_color(color)
-        x, y = self._grid_as_2d(x, y)
-        fig.circle(x[mask], y[mask], fill_color=color, size=10)
-
-    def save(self, filename: str, transparent: bool = False) -> None:
-        """Save plots to SVG or PNG file.
-
-        Args:
-            filename (str): Filename to save to.
-            transparent (bool, optional): Whether background should be transparent, default
-                ``False``.
-
-        Warning:
-            To output to SVG file, ``want_svg=True`` must have been passed to the constructor.
-        """
-        if transparent:
-            for fig in self._figures:
-                fig.background_fill_color = None  # type: ignore[assignment]
-                fig.border_fill_color = None  # type: ignore[assignment]
-
-        if self._want_svg:
-            export_svg(self._layout, filename=filename)
-        else:
-            export_png(self._layout, filename=filename)
-
-    def save_to_buffer(self) -> io.BytesIO:
-        """Save plots to an ``io.BytesIO`` buffer.
-
-        Return:
-            BytesIO: PNG image buffer.
-        """
-        image = get_screenshot_as_png(self._layout)
-        buffer = io.BytesIO()
-        image.save(buffer, "png")
-        return buffer
-
-    def show(self) -> None:
-        """Show plots in web browser, in usual Bokeh manner.
-        """
-        show(self._layout)
-
-    def title(self, title: str, ax: figure | int = 0, color: str | None = None) -> None:
-        """Set the title of a single plot.
-
-        Args:
-            title (str): Title text.
-            ax (int or Bokeh Figure, optional): Which plot to set the title of, default ``0``.
-            color (str, optional): Color to set title. May be a string color or the letter ``"C"``
-                followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the
-                ``Category10`` palette. Default ``None`` which is ``black``.
-        """
-        fig = self._get_figure(ax)
-        fig.title = title  # type: ignore[assignment]
-        fig.title.align = "center"  # type: ignore[attr-defined]
-        if color is not None:
-            fig.title.text_color = self._convert_color(color)  # type: ignore[attr-defined]
-
-    def z_values(
-        self,
-        x: ArrayLike,
-        y: ArrayLike,
-        z: ArrayLike,
-        ax: figure | int = 0,
-        color: str = "green",
-        fmt: str = ".1f",
-        quad_as_tri: bool = False,
-    ) -> None:
-        """Show ``z`` values on a single plot.
-
-        Args:
-            x (array-like of shape (ny, nx) or (nx,)): The x-coordinates of the grid points.
-            y (array-like of shape (ny, nx) or (ny,)): The y-coordinates of the grid points.
-            z (array-like of shape (ny, nx): z-values.
-            ax (int or Bokeh Figure, optional): Which plot to use, default ``0``.
-            color (str, optional): Color of added text. May be a string color or the letter ``"C"``
-                followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the
-                ``Category10`` palette. Default ``"green"``.
-            fmt (str, optional): Format to display z-values, default ``".1f"``.
-            quad_as_tri (bool, optional): Whether to show z-values at the ``quad_as_tri`` centres
-                of quads.
-
-        Warning:
-            ``quad_as_tri=True`` shows z-values for all quads, even if masked.
-        """
-        fig = self._get_figure(ax)
-        color = self._convert_color(color)
-        x, y = self._grid_as_2d(x, y)
-        z = np.asarray(z)
-        ny, nx = z.shape
-        kwargs = dict(text_color=color, text_align="center", text_baseline="middle")
-        for j in range(ny):
-            for i in range(nx):
-                fig.add_layout(Label(x=x[j, i], y=y[j, i], text=f"{z[j, i]:{fmt}}", **kwargs))
-        if quad_as_tri:
-            for j in range(ny-1):
-                for i in range(nx-1):
-                    xx = np.mean(x[j:j+2, i:i+2])
-                    yy = np.mean(y[j:j+2, i:i+2])
-                    zz = np.mean(z[j:j+2, i:i+2])
-                    fig.add_layout(Label(x=xx, y=yy, text=f"{zz:{fmt}}", **kwargs))
diff --git a/spaces/DaFujaTyping/hf-Chat-ui/vite.config.ts b/spaces/DaFujaTyping/hf-Chat-ui/vite.config.ts
deleted file mode 100644
index 4a73fc2099065c273e7d95d2f2122538d0502b27..0000000000000000000000000000000000000000
--- a/spaces/DaFujaTyping/hf-Chat-ui/vite.config.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-import { sveltekit } from "@sveltejs/kit/vite";
-import { defineConfig } from "vite";
-import Icons from "unplugin-icons/vite";
-
-export default defineConfig({
-	plugins: [
-		sveltekit(),
-		Icons({
-			compiler: "svelte",
-		}),
-	],
-});
diff --git a/spaces/Daniton/THUDM-chatglm-6b-int4-qe/README.md b/spaces/Daniton/THUDM-chatglm-6b-int4-qe/README.md
deleted file mode 100644
index 02f04c611f41e4bda2d5a087d410ce005c312e98..0000000000000000000000000000000000000000
--- a/spaces/Daniton/THUDM-chatglm-6b-int4-qe/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: THUDM Chatglm 6b Int4 Qe
-emoji: 💻
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Datasculptor/MusicGen/README.md b/spaces/Datasculptor/MusicGen/README.md
deleted file mode 100644
index e36f3c1f8803b85b58ec328405b0195fb7347829..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/MusicGen/README.md
+++ /dev/null
@@ -1,141 +0,0 @@
----
-title: MusicGen
-python_version: '3.9'
-tags:
-- music generation
-- language models
-- LLMs
-app_file: app.py
-emoji: 🎵
-colorFrom: white
-colorTo: blue
-sdk: gradio
-sdk_version: 3.34.0
-pinned: true
-license: cc-by-nc-4.0
-duplicated_from: facebook/MusicGen
----
-# Audiocraft
-![docs badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_docs/badge.svg)
-![linter badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_linter/badge.svg)
-![tests badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_tests/badge.svg)
-
-Audiocraft is a PyTorch library for deep learning research on audio generation. At the moment, it contains the code for MusicGen, a state-of-the-art controllable text-to-music model.
-
-## MusicGen
-
-Audiocraft provides the code and models for MusicGen, [a simple and controllable model for music generation][arxiv]. MusicGen is a single stage auto-regressive
-Transformer model trained over a 32kHz <a href="https://github.com/facebookresearch/encodec">EnCodec tokenizer</a> with 4 codebooks sampled at 50 Hz. Unlike existing methods like [MusicLM](https://arxiv.org/abs/2301.11325), MusicGen doesn't require a self-supervised semantic representation, and it generates
-all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict
-them in parallel, thus having only 50 auto-regressive steps per second of audio.
-Check out our [sample page][musicgen_samples] or test the available demo!
-
-<a target="_blank" href="https://colab.research.google.com/drive/1-Xe9NCdIs2sCUbiSmwHXozK6AAhMm7_i?usp=sharing">
-  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
-</a>
-<a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen">
-  <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HugginFace"/>
-</a>
-<br>
-
-We use 20K hours of licensed music to train MusicGen. Specifically, we rely on an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music data.
-
-## Installation
-Audiocraft requires Python 3.9, PyTorch 2.0.0, and a GPU with at least 16 GB of memory (for the medium-sized model). To install Audiocraft, you can run the following:
-
-```shell
-# Best to make sure you have torch installed first, in particular before installing xformers.
-# Don't run this if you already have PyTorch installed.
-pip install 'torch>=2.0'
-# Then proceed to one of the following
-pip install -U audiocraft  # stable release
-pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft  # bleeding edge
-pip install -e .  # or if you cloned the repo locally
-```
-
-## Usage
-We offer a number of way to interact with MusicGen:
-1. A demo is also available on the [`facebook/MusicGen`  HuggingFace Space](https://huggingface.co/spaces/facebook/MusicGen) (huge thanks to all the HF team for their support).
-2. You can run the Gradio demo in Colab: [colab notebook](https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing).
-3. You can use the gradio demo locally by running `python app.py`.
-4. You can play with MusicGen by running the jupyter notebook at [`demo.ipynb`](./demo.ipynb) locally (if you have a GPU).
-5. Finally, checkout [@camenduru Colab page](https://github.com/camenduru/MusicGen-colab) which is regularly
-  updated with contributions from @camenduru and the community.
-
-## API
-
-We provide a simple API and 4 pre-trained models. The pre trained models are:
-- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small)
-- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium)
-- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody)
-- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large)
-
-We observe the best trade-off between quality and compute with the `medium` or `melody` model.
-In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller
-GPUs will be able to generate short sequences, or longer sequences with the `small` model.
-
-**Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`.
-You can install it with:
-```
-apt-get install ffmpeg
-```
-
-See after a quick example for using the API.
-
-```python
-import torchaudio
-from audiocraft.models import MusicGen
-from audiocraft.data.audio import audio_write
-
-model = MusicGen.get_pretrained('melody')
-model.set_generation_params(duration=8)  # generate 8 seconds.
-wav = model.generate_unconditional(4)    # generates 4 unconditional audio samples
-descriptions = ['happy rock', 'energetic EDM', 'sad jazz']
-wav = model.generate(descriptions)  # generates 3 samples.
-
-melody, sr = torchaudio.load('./assets/bach.mp3')
-# generates using the melody from the given audio and the provided descriptions.
-wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr)
-
-for idx, one_wav in enumerate(wav):
-    # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
-    audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
-```
-
-
-## Model Card
-
-See [the model card page](./MODEL_CARD.md).
-
-## FAQ
-
-#### Will the training code be released?
-
-Yes. We will soon release the training code for MusicGen and EnCodec.
-
-
-#### I need help on Windows
-
-@FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4)
-
-#### I need help for running the demo on Colab
-
-Check [@camenduru tutorial on Youtube](https://www.youtube.com/watch?v=EGfxuTy9Eeo).
-
-
-## Citation
-```
-@article{copet2023simple,
-      title={Simple and Controllable Music Generation},
-      author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
-      year={2023},
-      journal={arXiv preprint arXiv:2306.05284},
-}
-```
-
-## License
-* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).
-* The weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights).
-
-[arxiv]: https://arxiv.org/abs/2306.05284
-[musicgen_samples]: https://ai.honu.io/papers/musicgen/
diff --git a/spaces/DeepLabCut/DeepLabCutModelZoo-SuperAnimals/detection_utils.py b/spaces/DeepLabCut/DeepLabCutModelZoo-SuperAnimals/detection_utils.py
deleted file mode 100644
index ee574c467b61c10d849344d3464bafd0bf0dd07c..0000000000000000000000000000000000000000
--- a/spaces/DeepLabCut/DeepLabCutModelZoo-SuperAnimals/detection_utils.py
+++ /dev/null
@@ -1,90 +0,0 @@
-
-from tkinter import W
-import gradio as gr
-from matplotlib import cm
-import torch
-import torchvision
-import matplotlib
-import PIL
-from PIL import Image, ImageColor, ImageFont, ImageDraw 
-import numpy as np
-import math
-
-
-import yaml
-import pdb
-
-############################################
-# Predict detections with MegaDetector v5a model
-def predict_md(im, 
-               megadetector_model, #Megadet_Models[mega_model_input]
-               size=640):
-               
-    # resize image
-    g = (size / max(im.size))  # multipl factor to make max size of the image equal to input size
-    im = im.resize((int(x * g) for x in im.size), 
-                    PIL.Image.Resampling.LANCZOS)  # resize
-    # device
-    if torch.cuda.is_available():
-        md_device = torch.device('cuda')
-    else:
-        md_device = torch.device('cpu')
-
-    # megadetector    
-    MD_model = torch.hub.load('ultralytics/yolov5', # repo_or_dir
-                              'custom', #model
-                              megadetector_model, # args for callable model
-                              force_reload=True,
-                              device=md_device) 
-                              
-    # send model to gpu if possible
-    if (md_device == torch.device('cuda')):
-        print('Sending model to GPU')
-        MD_model.to(md_device)    
-
-    ## detect objects
-    results = MD_model(im)  # inference # vars(results).keys()= dict_keys(['imgs', 'pred', 'names', 'files', 'times', 'xyxy', 'xywh', 'xyxyn', 'xywhn', 'n', 't', 's'])
-    
-    return results  
-
-
-##########################################
-def crop_animal_detections(img_in,
-                           yolo_results, 
-                           likelihood_th):
-
-    ## Extract animal crops
-    list_labels_as_str = [i for i in yolo_results.names.values()]  # ['animal', 'person', 'vehicle'] 
-    list_np_animal_crops = []
-
-    # image to crop (scale as input for megadetector)
-    img_in = img_in.resize((yolo_results.ims[0].shape[1],
-                            yolo_results.ims[0].shape[0]))
-    # for every detection in the img                        
-    for det_array in yolo_results.xyxy:
-
-        # for every detection
-        for j in range(det_array.shape[0]):
-
-            # compute coords around bbox rounded to the nearest integer (for pasting later)
-            xmin_rd = int(math.floor(det_array[j,0])) # int() should suffice?
-            ymin_rd = int(math.floor(det_array[j,1]))
-
-            xmax_rd = int(math.ceil(det_array[j,2]))
-            ymax_rd = int(math.ceil(det_array[j,3]))
-
-            pred_llk = det_array[j,4] 
-            pred_label = det_array[j,5]
-            # keep animal crops above threshold
-            if (pred_label == list_labels_as_str.index('animal')) and \
-                (pred_llk >= likelihood_th):
-                area = (xmin_rd, ymin_rd, xmax_rd, ymax_rd)
-
-                #pdb.set_trace()
-                crop = img_in.crop(area) #Image.fromarray(img_in).crop(area)
-                crop_np = np.asarray(crop)
-
-                # add to list
-                list_np_animal_crops.append(crop_np)
-
-    return list_np_animal_crops
\ No newline at end of file
diff --git a/spaces/ECCV2022/bytetrack/tutorials/motr/byte_tracker.py b/spaces/ECCV2022/bytetrack/tutorials/motr/byte_tracker.py
deleted file mode 100644
index d5bc6dd479441e78e92bd07ce496314d8de13d38..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/tutorials/motr/byte_tracker.py
+++ /dev/null
@@ -1,339 +0,0 @@
-import numpy as np
-from collections import deque
-import os
-import os.path as osp
-import copy
-import torch
-import torch.nn.functional as F
-
-from mot_online.kalman_filter import KalmanFilter
-from mot_online.basetrack import BaseTrack, TrackState
-from mot_online import matching
-
-
-
-class STrack(BaseTrack):
-    shared_kalman = KalmanFilter()
-    def __init__(self, tlwh, score):
-
-        # wait activate
-        self._tlwh = np.asarray(tlwh, dtype=np.float)
-        self.kalman_filter = None
-        self.mean, self.covariance = None, None
-        self.is_activated = False
-
-        self.score = score
-        self.tracklet_len = 0
-
-    def predict(self):
-        mean_state = self.mean.copy()
-        if self.state != TrackState.Tracked:
-            mean_state[7] = 0
-        self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
-
-    @staticmethod
-    def multi_predict(stracks):
-        if len(stracks) > 0:
-            multi_mean = np.asarray([st.mean.copy() for st in stracks])
-            multi_covariance = np.asarray([st.covariance for st in stracks])
-            for i, st in enumerate(stracks):
-                if st.state != TrackState.Tracked:
-                    multi_mean[i][7] = 0
-            multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
-            for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
-                stracks[i].mean = mean
-                stracks[i].covariance = cov
-
-    def activate(self, kalman_filter, frame_id):
-        """Start a new tracklet"""
-        self.kalman_filter = kalman_filter
-        self.track_id = self.next_id()
-        self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
-
-        self.tracklet_len = 0
-        self.state = TrackState.Tracked
-        if frame_id == 1:
-            self.is_activated = True
-        # self.is_activated = True
-        self.frame_id = frame_id
-        self.start_frame = frame_id
-
-    def re_activate(self, new_track, frame_id, new_id=False):
-        self.mean, self.covariance = self.kalman_filter.update(
-            self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
-        )
-        self.tracklet_len = 0
-        self.state = TrackState.Tracked
-        self.is_activated = True
-        self.frame_id = frame_id
-        if new_id:
-            self.track_id = self.next_id()
-        self.score = new_track.score
-
-    def update(self, new_track, frame_id):
-        """
-        Update a matched track
-        :type new_track: STrack
-        :type frame_id: int
-        :type update_feature: bool
-        :return:
-        """
-        self.frame_id = frame_id
-        self.tracklet_len += 1
-
-        new_tlwh = new_track.tlwh
-        self.mean, self.covariance = self.kalman_filter.update(
-            self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
-        self.state = TrackState.Tracked
-        self.is_activated = True
-
-        self.score = new_track.score
-
-    @property
-    # @jit(nopython=True)
-    def tlwh(self):
-        """Get current position in bounding box format `(top left x, top left y,
-                width, height)`.
-        """
-        if self.mean is None:
-            return self._tlwh.copy()
-        ret = self.mean[:4].copy()
-        ret[2] *= ret[3]
-        ret[:2] -= ret[2:] / 2
-        return ret
-
-    @property
-    # @jit(nopython=True)
-    def tlbr(self):
-        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
-        `(top left, bottom right)`.
-        """
-        ret = self.tlwh.copy()
-        ret[2:] += ret[:2]
-        return ret
-
-    @staticmethod
-    # @jit(nopython=True)
-    def tlwh_to_xyah(tlwh):
-        """Convert bounding box to format `(center x, center y, aspect ratio,
-        height)`, where the aspect ratio is `width / height`.
-        """
-        ret = np.asarray(tlwh).copy()
-        ret[:2] += ret[2:] / 2
-        ret[2] /= ret[3]
-        return ret
-
-    def to_xyah(self):
-        return self.tlwh_to_xyah(self.tlwh)
-
-    @staticmethod
-    # @jit(nopython=True)
-    def tlbr_to_tlwh(tlbr):
-        ret = np.asarray(tlbr).copy()
-        ret[2:] -= ret[:2]
-        return ret
-
-    @staticmethod
-    # @jit(nopython=True)
-    def tlwh_to_tlbr(tlwh):
-        ret = np.asarray(tlwh).copy()
-        ret[2:] += ret[:2]
-        return ret
-
-    def __repr__(self):
-        return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)
-
-
-class BYTETracker(object):
-    def __init__(self, frame_rate=30):
-        self.tracked_stracks = []  # type: list[STrack]
-        self.lost_stracks = []  # type: list[STrack]
-        self.removed_stracks = []  # type: list[STrack]
-
-        self.frame_id = 0
-        
-        self.low_thresh = 0.2
-        self.track_thresh = 0.8
-        self.det_thresh = self.track_thresh + 0.1
-        
-        
-        self.buffer_size = int(frame_rate / 30.0 * 30)
-        self.max_time_lost = self.buffer_size
-        self.kalman_filter = KalmanFilter()
-
-    def update(self, output_results):
-        self.frame_id += 1
-        activated_starcks = []
-        refind_stracks = []
-        lost_stracks = []
-        removed_stracks = []
-
-        
-        scores = output_results[:, 4]
-        bboxes = output_results[:, :4]  # x1y1x2y2
-        
-        remain_inds = scores > self.track_thresh
-        dets = bboxes[remain_inds]
-        scores_keep = scores[remain_inds]
-        
-        
-        inds_low = scores > self.low_thresh
-        inds_high = scores < self.track_thresh
-        inds_second = np.logical_and(inds_low, inds_high)
-        dets_second = bboxes[inds_second]
-        scores_second = scores[inds_second]
-
-        
-        if len(dets) > 0:
-            '''Detections'''
-            detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for
-                          (tlbr, s) in zip(dets, scores_keep)]
-        else:
-            detections = []
-
-        ''' Add newly detected tracklets to tracked_stracks'''
-        unconfirmed = []
-        tracked_stracks = []  # type: list[STrack]
-        for track in self.tracked_stracks:
-            if not track.is_activated:
-                unconfirmed.append(track)
-            else:
-                tracked_stracks.append(track)
-
-        ''' Step 2: First association, with Kalman and IOU'''
-        strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
-        # Predict the current location with KF
-        STrack.multi_predict(strack_pool)
-        dists = matching.iou_distance(strack_pool, detections)
-        matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.8)
-
-        for itracked, idet in matches:
-            track = strack_pool[itracked]
-            det = detections[idet]
-            if track.state == TrackState.Tracked:
-                track.update(detections[idet], self.frame_id)
-                activated_starcks.append(track)
-            else:
-                track.re_activate(det, self.frame_id, new_id=False)
-                refind_stracks.append(track)
-
-        ''' Step 3: Second association, with IOU'''
-        # association the untrack to the low score detections
-        if len(dets_second) > 0:
-            '''Detections'''
-            detections_second = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for
-                          (tlbr, s) in zip(dets_second, scores_second)]
-        else:
-            detections_second = []
-        r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
-        dists = matching.iou_distance(r_tracked_stracks, detections_second)
-        matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5)
-        for itracked, idet in matches:
-            track = r_tracked_stracks[itracked]
-            det = detections_second[idet]
-            if track.state == TrackState.Tracked:
-                track.update(det, self.frame_id)
-                activated_starcks.append(track)
-            else:
-                track.re_activate(det, self.frame_id, new_id=False)
-                refind_stracks.append(track)
-
-        for it in u_track:
-            #track = r_tracked_stracks[it]
-            track = r_tracked_stracks[it]
-            if not track.state == TrackState.Lost:
-                track.mark_lost()
-                lost_stracks.append(track)
-
-        '''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
-        detections = [detections[i] for i in u_detection]
-        dists = matching.iou_distance(unconfirmed, detections)
-        matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
-        for itracked, idet in matches:
-            unconfirmed[itracked].update(detections[idet], self.frame_id)
-            activated_starcks.append(unconfirmed[itracked])
-        for it in u_unconfirmed:
-            track = unconfirmed[it]
-            track.mark_removed()
-            removed_stracks.append(track)
-
-        """ Step 4: Init new stracks"""
-        for inew in u_detection:
-            track = detections[inew]
-            if track.score < self.det_thresh:
-                continue
-            track.activate(self.kalman_filter, self.frame_id)
-            activated_starcks.append(track)
-        """ Step 5: Update state"""
-        for track in self.lost_stracks:
-            if self.frame_id - track.end_frame > self.max_time_lost:
-                track.mark_removed()
-                removed_stracks.append(track)
-
-        # print('Ramained match {} s'.format(t4-t3))
-
-        self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
-        self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
-        self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
-        self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
-        self.lost_stracks.extend(lost_stracks)
-        self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
-        self.removed_stracks.extend(removed_stracks)
-        self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
-        # get scores of lost tracks
-        output_stracks = [track for track in self.tracked_stracks if track.is_activated]
-
-        return output_stracks
-
-
-def joint_stracks(tlista, tlistb):
-    exists = {}
-    res = []
-    for t in tlista:
-        exists[t.track_id] = 1
-        res.append(t)
-    for t in tlistb:
-        tid = t.track_id
-        if not exists.get(tid, 0):
-            exists[tid] = 1
-            res.append(t)
-    return res
-
-
-def sub_stracks(tlista, tlistb):
-    stracks = {}
-    for t in tlista:
-        stracks[t.track_id] = t
-    for t in tlistb:
-        tid = t.track_id
-        if stracks.get(tid, 0):
-            del stracks[tid]
-    return list(stracks.values())
-
-
-def remove_duplicate_stracks(stracksa, stracksb):
-    pdist = matching.iou_distance(stracksa, stracksb)
-    pairs = np.where(pdist < 0.15)
-    dupa, dupb = list(), list()
-    for p, q in zip(*pairs):
-        timep = stracksa[p].frame_id - stracksa[p].start_frame
-        timeq = stracksb[q].frame_id - stracksb[q].start_frame
-        if timep > timeq:
-            dupb.append(q)
-        else:
-            dupa.append(p)
-    resa = [t for i, t in enumerate(stracksa) if not i in dupa]
-    resb = [t for i, t in enumerate(stracksb) if not i in dupb]
-    return resa, resb
-
-
-def remove_fp_stracks(stracksa, n_frame=10):
-    remain = []
-    for t in stracksa:
-        score_5 = t.score_list[-n_frame:]
-        score_5 = np.array(score_5, dtype=np.float32)
-        index = score_5 < 0.45
-        num = np.sum(index)
-        if num < n_frame:
-            remain.append(t)
-    return remain
diff --git a/spaces/EuroPython2022/mmocr-demo/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py b/spaces/EuroPython2022/mmocr-demo/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py
deleted file mode 100644
index 46ca259b3abb8863348f8eef71b0126f77e269eb..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/mmocr-demo/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py
+++ /dev/null
@@ -1,58 +0,0 @@
-_base_ = [
-    '../../_base_/default_runtime.py',
-    '../../_base_/schedules/schedule_adam_step_5e.py',
-    '../../_base_/recog_pipelines/sar_pipeline.py',
-    '../../_base_/recog_datasets/ST_SA_MJ_real_train.py',
-    '../../_base_/recog_datasets/academic_test.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-label_convertor = dict(
-    type='AttnConvertor', dict_type='DICT90', with_unknown=True)
-
-model = dict(
-    type='SARNet',
-    backbone=dict(type='ResNet31OCR'),
-    encoder=dict(
-        type='SAREncoder',
-        enc_bi_rnn=False,
-        enc_do_rnn=0.1,
-        enc_gru=False,
-    ),
-    decoder=dict(
-        type='SequentialSARDecoder',
-        enc_bi_rnn=False,
-        dec_bi_rnn=False,
-        dec_do_rnn=0,
-        dec_gru=False,
-        pred_dropout=0.1,
-        d_k=512,
-        pred_concat=True),
-    loss=dict(type='SARLoss'),
-    label_convertor=label_convertor,
-    max_seq_len=30)
-
-data = dict(
-    samples_per_gpu=64,
-    workers_per_gpu=2,
-    val_dataloader=dict(samples_per_gpu=1),
-    test_dataloader=dict(samples_per_gpu=1),
-    train=dict(
-        type='UniformConcatDataset',
-        datasets=train_list,
-        pipeline=train_pipeline),
-    val=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline),
-    test=dict(
-        type='UniformConcatDataset',
-        datasets=test_list,
-        pipeline=test_pipeline))
-
-evaluation = dict(interval=1, metric='acc')
diff --git a/spaces/FelixLuoX/codeformer/CodeFormer/facelib/detection/yolov5face/models/yolo.py b/spaces/FelixLuoX/codeformer/CodeFormer/facelib/detection/yolov5face/models/yolo.py
deleted file mode 100644
index 70845d972f0bcfd3632fcbac096b23e1b4d4d779..0000000000000000000000000000000000000000
--- a/spaces/FelixLuoX/codeformer/CodeFormer/facelib/detection/yolov5face/models/yolo.py
+++ /dev/null
@@ -1,235 +0,0 @@
-import math
-from copy import deepcopy
-from pathlib import Path
-
-import torch
-import yaml  # for torch hub
-from torch import nn
-
-from facelib.detection.yolov5face.models.common import (
-    C3,
-    NMS,
-    SPP,
-    AutoShape,
-    Bottleneck,
-    BottleneckCSP,
-    Concat,
-    Conv,
-    DWConv,
-    Focus,
-    ShuffleV2Block,
-    StemBlock,
-)
-from facelib.detection.yolov5face.models.experimental import CrossConv, MixConv2d
-from facelib.detection.yolov5face.utils.autoanchor import check_anchor_order
-from facelib.detection.yolov5face.utils.general import make_divisible
-from facelib.detection.yolov5face.utils.torch_utils import copy_attr, fuse_conv_and_bn
-
-
-class Detect(nn.Module):
-    stride = None  # strides computed during build
-    export = False  # onnx export
-
-    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
-        super().__init__()
-        self.nc = nc  # number of classes
-        self.no = nc + 5 + 10  # number of outputs per anchor
-
-        self.nl = len(anchors)  # number of detection layers
-        self.na = len(anchors[0]) // 2  # number of anchors
-        self.grid = [torch.zeros(1)] * self.nl  # init grid
-        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
-        self.register_buffer("anchors", a)  # shape(nl,na,2)
-        self.register_buffer("anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
-        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
-
-    def forward(self, x):
-        z = []  # inference output
-        if self.export:
-            for i in range(self.nl):
-                x[i] = self.m[i](x[i])
-            return x
-        for i in range(self.nl):
-            x[i] = self.m[i](x[i])  # conv
-            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
-            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
-
-            if not self.training:  # inference
-                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
-
-                y = torch.full_like(x[i], 0)
-                y[..., [0, 1, 2, 3, 4, 15]] = x[i][..., [0, 1, 2, 3, 4, 15]].sigmoid()
-                y[..., 5:15] = x[i][..., 5:15]
-
-                y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
-                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
-
-                y[..., 5:7] = (
-                    y[..., 5:7] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x1 y1
-                y[..., 7:9] = (
-                    y[..., 7:9] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x2 y2
-                y[..., 9:11] = (
-                    y[..., 9:11] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x3 y3
-                y[..., 11:13] = (
-                    y[..., 11:13] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x4 y4
-                y[..., 13:15] = (
-                    y[..., 13:15] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x5 y5
-
-                z.append(y.view(bs, -1, self.no))
-
-        return x if self.training else (torch.cat(z, 1), x)
-
-    @staticmethod
-    def _make_grid(nx=20, ny=20):
-        # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)], indexing="ij") # for pytorch>=1.10
-        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
-        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
-
-
-class Model(nn.Module):
-    def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None):  # model, input channels, number of classes
-        super().__init__()
-        self.yaml_file = Path(cfg).name
-        with Path(cfg).open(encoding="utf8") as f:
-            self.yaml = yaml.safe_load(f)  # model dict
-
-        # Define model
-        ch = self.yaml["ch"] = self.yaml.get("ch", ch)  # input channels
-        if nc and nc != self.yaml["nc"]:
-            self.yaml["nc"] = nc  # override yaml value
-
-        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
-        self.names = [str(i) for i in range(self.yaml["nc"])]  # default names
-
-        # Build strides, anchors
-        m = self.model[-1]  # Detect()
-        if isinstance(m, Detect):
-            s = 128  # 2x min stride
-            m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
-            m.anchors /= m.stride.view(-1, 1, 1)
-            check_anchor_order(m)
-            self.stride = m.stride
-            self._initialize_biases()  # only run once
-
-    def forward(self, x):
-        return self.forward_once(x)  # single-scale inference, train
-
-    def forward_once(self, x):
-        y = []  # outputs
-        for m in self.model:
-            if m.f != -1:  # if not from previous layer
-                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
-
-            x = m(x)  # run
-            y.append(x if m.i in self.save else None)  # save output
-
-        return x
-
-    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
-        # https://arxiv.org/abs/1708.02002 section 3.3
-        m = self.model[-1]  # Detect() module
-        for mi, s in zip(m.m, m.stride):  # from
-            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
-            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
-            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
-            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
-    def _print_biases(self):
-        m = self.model[-1]  # Detect() module
-        for mi in m.m:  # from
-            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
-            print(("%6g Conv2d.bias:" + "%10.3g" * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
-
-    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        print("Fusing layers... ")
-        for m in self.model.modules():
-            if isinstance(m, Conv) and hasattr(m, "bn"):
-                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
-                delattr(m, "bn")  # remove batchnorm
-                m.forward = m.fuseforward  # update forward
-            elif type(m) is nn.Upsample:
-                m.recompute_scale_factor = None  # torch 1.11.0 compatibility
-        return self
-
-    def nms(self, mode=True):  # add or remove NMS module
-        present = isinstance(self.model[-1], NMS)  # last layer is NMS
-        if mode and not present:
-            print("Adding NMS... ")
-            m = NMS()  # module
-            m.f = -1  # from
-            m.i = self.model[-1].i + 1  # index
-            self.model.add_module(name=str(m.i), module=m)  # add
-            self.eval()
-        elif not mode and present:
-            print("Removing NMS... ")
-            self.model = self.model[:-1]  # remove
-        return self
-
-    def autoshape(self):  # add autoShape module
-        print("Adding autoShape... ")
-        m = AutoShape(self)  # wrap model
-        copy_attr(m, self, include=("yaml", "nc", "hyp", "names", "stride"), exclude=())  # copy attributes
-        return m
-
-
-def parse_model(d, ch):  # model_dict, input_channels(3)
-    anchors, nc, gd, gw = d["anchors"], d["nc"], d["depth_multiple"], d["width_multiple"]
-    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
-    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
-
-    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
-    for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, args
-        m = eval(m) if isinstance(m, str) else m  # eval strings
-        for j, a in enumerate(args):
-            try:
-                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
-            except:
-                pass
-
-        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in [
-            Conv,
-            Bottleneck,
-            SPP,
-            DWConv,
-            MixConv2d,
-            Focus,
-            CrossConv,
-            BottleneckCSP,
-            C3,
-            ShuffleV2Block,
-            StemBlock,
-        ]:
-            c1, c2 = ch[f], args[0]
-
-            c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
-
-            args = [c1, c2, *args[1:]]
-            if m in [BottleneckCSP, C3]:
-                args.insert(2, n)
-                n = 1
-        elif m is nn.BatchNorm2d:
-            args = [ch[f]]
-        elif m is Concat:
-            c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
-        elif m is Detect:
-            args.append([ch[x + 1] for x in f])
-            if isinstance(args[1], int):  # number of anchors
-                args[1] = [list(range(args[1] * 2))] * len(f)
-        else:
-            c2 = ch[f]
-
-        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
-        t = str(m)[8:-2].replace("__main__.", "")  # module type
-        np = sum(x.numel() for x in m_.parameters())  # number params
-        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
-        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
-        layers.append(m_)
-        ch.append(c2)
-    return nn.Sequential(*layers), sorted(save)
diff --git a/spaces/FridaZuley/RVC_HFKawaii/infer/modules/vc/__init__.py b/spaces/FridaZuley/RVC_HFKawaii/infer/modules/vc/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/GAIR/Factool/factool/utils/claim_extractor.py b/spaces/GAIR/Factool/factool/utils/claim_extractor.py
deleted file mode 100644
index ddd11720d4f69b664e90282a9d4a0db587eb3779..0000000000000000000000000000000000000000
--- a/spaces/GAIR/Factool/factool/utils/claim_extractor.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import os
-import pathlib
-import openai
-import yaml
-import json
-import asyncio
-from tqdm import tqdm
-from factool.env_config import factool_env_config
-
-
-# env
-# openai.api_key = factool_env_config.openai_api_key
-
-
-config = {
-    'model_name': 'gpt-3.5-turbo',
-    'max_tokens': 2000,
-    'temperature': 0.0,
-    'top_p': 1,
-    'frequency_penalty': 0.0,
-    'presence_penalty': 0.0,
-    'n': 1
-}
-
-
-# Make api calls asynchronously
-async def run_api(messages):
-    async def single_run(message):
-        output = openai.ChatCompletion.create(
-            model=config['model_name'],
-            messages=message,
-            max_tokens=config['max_tokens'],
-            temperature=config['temperature'],
-            top_p=config['top_p'],
-            frequency_penalty=config['frequency_penalty'],
-            presence_penalty=config['presence_penalty'],
-            n=config['n'],
-        )
-        return output.choices[0].message.content.strip()
-
-    responses = [single_run(messages[index]) for index in range(len(messages))]
-    return await asyncio.gather(*responses)
-
-
-
-# Import data from scientific.json
-scientific_list = []
-with open("../datasets/scientific/scientific.json", "r") as f:
-    data = json.load(f)
-    for dict_data in data:
-        cur_dict = {'dataset_name': 'scientific',
-                    'question': dict_data["question"],
-                    'factual_response': dict_data['factual_response']}
-        scientific_list.append(cur_dict)
-
-# Apply template prompt
-with open("./prompts/claim_extraction.yaml") as f:
-    data = yaml.load(f, Loader=yaml.FullLoader)
-prompt = data['scientific']
-messages_list = [
-    [
-        {"role": "system", "content": prompt['system']},
-        {"role": "user", "content": prompt['user'].format(input=sample['factual_response'])},
-    ]
-    for sample in scientific_list
-]
-
-assert len(messages_list) == len(scientific_list), "The data length is different"
-
-# Run the API to get the output
-print("begin claims extraction...")
-results = asyncio.run(run_api(messages_list))
-for i in range(len(scientific_list)):
-    scientific_list[i]["claims"] = results[i]
-
-with open('../datasets/scientific/scientific_claims.json', 'w') as f:
-    json.dump(scientific_list, f, indent=4)
-
-
-"""
-The scientific_claims.json file saved by the above code may have format problems, here are some adjustments
-"""
-with open("../datasets/scientific/scientific_claims.json", "r") as f:
-    data = json.load(f)
-    for data_i in tqdm(data, total=len(data)):
-        try:
-            data_i["claims"] = json.loads(data_i["claims"].strip())
-        except:
-            print(data_i["claims"])
-            continue
-with open("../datasets/scientific/scientific_claims.json", "w") as f:
-    json.dump(data, f, indent=4)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py
deleted file mode 100644
index 87e21fbff82763caf0e14ba641493870a15578b1..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py
+++ /dev/null
@@ -1,5 +0,0 @@
-_base_ = [
-    '../_base_/models/cascade_rcnn_r50_fpn.py',
-    '../_base_/datasets/coco_detection.py',
-    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py
deleted file mode 100644
index 9367a3c83aeb1e05f38f4db9fb0110e731dd859c..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './faster_rcnn_r50_fpn_2x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py
deleted file mode 100644
index d4189c6fa2a6a3481bf666b713f6ab91812f3d86..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './mask_rcnn_r101_fpn_2x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_32x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=32,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
diff --git a/spaces/Grezz/generate_human_motion/pyrender/pyrender/primitive.py b/spaces/Grezz/generate_human_motion/pyrender/pyrender/primitive.py
deleted file mode 100644
index 7f83f46f532b126a4573e715dd03d079fef755ca..0000000000000000000000000000000000000000
--- a/spaces/Grezz/generate_human_motion/pyrender/pyrender/primitive.py
+++ /dev/null
@@ -1,489 +0,0 @@
-"""Primitives, conforming to the glTF 2.0 standards as specified in
-https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-primitive
-
-Author: Matthew Matl
-"""
-import numpy as np
-
-from OpenGL.GL import *
-
-from .material import Material, MetallicRoughnessMaterial
-from .constants import FLOAT_SZ, UINT_SZ, BufFlags, GLTF
-from .utils import format_color_array
-
-
-class Primitive(object):
-    """A primitive object which can be rendered.
-
-    Parameters
-    ----------
-    positions : (n, 3) float
-        XYZ vertex positions.
-    normals : (n, 3) float
-        Normalized XYZ vertex normals.
-    tangents : (n, 4) float
-        XYZW vertex tangents where the w component is a sign value
-        (either +1 or -1) indicating the handedness of the tangent basis.
-    texcoord_0 : (n, 2) float
-        The first set of UV texture coordinates.
-    texcoord_1 : (n, 2) float
-        The second set of UV texture coordinates.
-    color_0 : (n, 4) float
-        RGBA vertex colors.
-    joints_0 : (n, 4) float
-        Joint information.
-    weights_0 : (n, 4) float
-        Weight information for morphing.
-    indices : (m, 3) int
-        Face indices for triangle meshes or fans.
-    material : :class:`Material`
-        The material to apply to this primitive when rendering.
-    mode : int
-        The type of primitives to render, one of the following:
-
-        - ``0``: POINTS
-        - ``1``: LINES
-        - ``2``: LINE_LOOP
-        - ``3``: LINE_STRIP
-        - ``4``: TRIANGLES
-        - ``5``: TRIANGLES_STRIP
-        - ``6``: TRIANGLES_FAN
-    targets : (k,) int
-        Morph target indices.
-    poses : (x,4,4), float
-        Array of 4x4 transformation matrices for instancing this object.
-    """
-
-    def __init__(self,
-                 positions,
-                 normals=None,
-                 tangents=None,
-                 texcoord_0=None,
-                 texcoord_1=None,
-                 color_0=None,
-                 joints_0=None,
-                 weights_0=None,
-                 indices=None,
-                 material=None,
-                 mode=None,
-                 targets=None,
-                 poses=None):
-
-        if mode is None:
-            mode = GLTF.TRIANGLES
-
-        self.positions = positions
-        self.normals = normals
-        self.tangents = tangents
-        self.texcoord_0 = texcoord_0
-        self.texcoord_1 = texcoord_1
-        self.color_0 = color_0
-        self.joints_0 = joints_0
-        self.weights_0 = weights_0
-        self.indices = indices
-        self.material = material
-        self.mode = mode
-        self.targets = targets
-        self.poses = poses
-
-        self._bounds = None
-        self._vaid = None
-        self._buffers = []
-        self._is_transparent = None
-        self._buf_flags = None
-
-    @property
-    def positions(self):
-        """(n,3) float : XYZ vertex positions.
-        """
-        return self._positions
-
-    @positions.setter
-    def positions(self, value):
-        value = np.asanyarray(value, dtype=np.float32)
-        self._positions = np.ascontiguousarray(value)
-        self._bounds = None
-
-    @property
-    def normals(self):
-        """(n,3) float : Normalized XYZ vertex normals.
-        """
-        return self._normals
-
-    @normals.setter
-    def normals(self, value):
-        if value is not None:
-            value = np.asanyarray(value, dtype=np.float32)
-            value = np.ascontiguousarray(value)
-            if value.shape != self.positions.shape:
-                raise ValueError('Incorrect normals shape')
-        self._normals = value
-
-    @property
-    def tangents(self):
-        """(n,4) float : XYZW vertex tangents.
-        """
-        return self._tangents
-
-    @tangents.setter
-    def tangents(self, value):
-        if value is not None:
-            value = np.asanyarray(value, dtype=np.float32)
-            value = np.ascontiguousarray(value)
-            if value.shape != (self.positions.shape[0], 4):
-                raise ValueError('Incorrect tangent shape')
-        self._tangents = value
-
-    @property
-    def texcoord_0(self):
-        """(n,2) float : The first set of UV texture coordinates.
-        """
-        return self._texcoord_0
-
-    @texcoord_0.setter
-    def texcoord_0(self, value):
-        if value is not None:
-            value = np.asanyarray(value, dtype=np.float32)
-            value = np.ascontiguousarray(value)
-            if (value.ndim != 2 or value.shape[0] != self.positions.shape[0] or
-                    value.shape[1] < 2):
-                raise ValueError('Incorrect texture coordinate shape')
-            if value.shape[1] > 2:
-                value = value[:,:2]
-        self._texcoord_0 = value
-
-    @property
-    def texcoord_1(self):
-        """(n,2) float : The second set of UV texture coordinates.
-        """
-        return self._texcoord_1
-
-    @texcoord_1.setter
-    def texcoord_1(self, value):
-        if value is not None:
-            value = np.asanyarray(value, dtype=np.float32)
-            value = np.ascontiguousarray(value)
-            if (value.ndim != 2 or value.shape[0] != self.positions.shape[0] or
-                    value.shape[1] != 2):
-                raise ValueError('Incorrect texture coordinate shape')
-        self._texcoord_1 = value
-
-    @property
-    def color_0(self):
-        """(n,4) float : RGBA vertex colors.
-        """
-        return self._color_0
-
-    @color_0.setter
-    def color_0(self, value):
-        if value is not None:
-            value = np.ascontiguousarray(
-                format_color_array(value, shape=(len(self.positions), 4))
-            )
-        self._is_transparent = None
-        self._color_0 = value
-
-    @property
-    def joints_0(self):
-        """(n,4) float : Joint information.
-        """
-        return self._joints_0
-
-    @joints_0.setter
-    def joints_0(self, value):
-        self._joints_0 = value
-
-    @property
-    def weights_0(self):
-        """(n,4) float : Weight information for morphing.
-        """
-        return self._weights_0
-
-    @weights_0.setter
-    def weights_0(self, value):
-        self._weights_0 = value
-
-    @property
-    def indices(self):
-        """(m,3) int : Face indices for triangle meshes or fans.
-        """
-        return self._indices
-
-    @indices.setter
-    def indices(self, value):
-        if value is not None:
-            value = np.asanyarray(value, dtype=np.float32)
-            value = np.ascontiguousarray(value)
-        self._indices = value
-
-    @property
-    def material(self):
-        """:class:`Material` : The material for this primitive.
-        """
-        return self._material
-
-    @material.setter
-    def material(self, value):
-        # Create default material
-        if value is None:
-            value = MetallicRoughnessMaterial()
-        else:
-            if not isinstance(value, Material):
-                raise TypeError('Object material must be of type Material')
-        self._material = value
-
-    @property
-    def mode(self):
-        """int : The type of primitive to render.
-        """
-        return self._mode
-
-    @mode.setter
-    def mode(self, value):
-        value = int(value)
-        if value < GLTF.POINTS or value > GLTF.TRIANGLE_FAN:
-            raise ValueError('Invalid mode')
-        self._mode = value
-
-    @property
-    def targets(self):
-        """(k,) int : Morph target indices.
-        """
-        return self._targets
-
-    @targets.setter
-    def targets(self, value):
-        self._targets = value
-
-    @property
-    def poses(self):
-        """(x,4,4) float : Homogenous transforms for instancing this primitive.
-        """
-        return self._poses
-
-    @poses.setter
-    def poses(self, value):
-        if value is not None:
-            value = np.asanyarray(value, dtype=np.float32)
-            value = np.ascontiguousarray(value)
-            if value.ndim == 2:
-                value = value[np.newaxis,:,:]
-            if value.shape[1] != 4 or value.shape[2] != 4:
-                raise ValueError('Pose matrices must be of shape (n,4,4), '
-                                 'got {}'.format(value.shape))
-        self._poses = value
-        self._bounds = None
-
-    @property
-    def bounds(self):
-        if self._bounds is None:
-            self._bounds = self._compute_bounds()
-        return self._bounds
-
-    @property
-    def centroid(self):
-        """(3,) float : The centroid of the primitive's AABB.
-        """
-        return np.mean(self.bounds, axis=0)
-
-    @property
-    def extents(self):
-        """(3,) float : The lengths of the axes of the primitive's AABB.
-        """
-        return np.diff(self.bounds, axis=0).reshape(-1)
-
-    @property
-    def scale(self):
-        """(3,) float : The length of the diagonal of the primitive's AABB.
-        """
-        return np.linalg.norm(self.extents)
-
-    @property
-    def buf_flags(self):
-        """int : The flags for the render buffer.
-        """
-        if self._buf_flags is None:
-            self._buf_flags = self._compute_buf_flags()
-        return self._buf_flags
-
-    def delete(self):
-        self._unbind()
-        self._remove_from_context()
-
-    @property
-    def is_transparent(self):
-        """bool : If True, the mesh is partially-transparent.
-        """
-        return self._compute_transparency()
-
-    def _add_to_context(self):
-        if self._vaid is not None:
-            raise ValueError('Mesh is already bound to a context')
-
-        # Generate and bind VAO
-        self._vaid = glGenVertexArrays(1)
-        glBindVertexArray(self._vaid)
-
-        #######################################################################
-        # Fill vertex buffer
-        #######################################################################
-
-        # Generate and bind vertex buffer
-        vertexbuffer = glGenBuffers(1)
-        self._buffers.append(vertexbuffer)
-        glBindBuffer(GL_ARRAY_BUFFER, vertexbuffer)
-
-        # positions
-        vertex_data = self.positions
-        attr_sizes = [3]
-
-        # Normals
-        if self.normals is not None:
-            vertex_data = np.hstack((vertex_data, self.normals))
-            attr_sizes.append(3)
-
-        # Tangents
-        if self.tangents is not None:
-            vertex_data = np.hstack((vertex_data, self.tangents))
-            attr_sizes.append(4)
-
-        # Texture Coordinates
-        if self.texcoord_0 is not None:
-            vertex_data = np.hstack((vertex_data, self.texcoord_0))
-            attr_sizes.append(2)
-        if self.texcoord_1 is not None:
-            vertex_data = np.hstack((vertex_data, self.texcoord_1))
-            attr_sizes.append(2)
-
-        # Color
-        if self.color_0 is not None:
-            vertex_data = np.hstack((vertex_data, self.color_0))
-            attr_sizes.append(4)
-
-        # TODO JOINTS AND WEIGHTS
-        # PASS
-
-        # Copy data to buffer
-        vertex_data = np.ascontiguousarray(
-            vertex_data.flatten().astype(np.float32)
-        )
-        glBufferData(
-            GL_ARRAY_BUFFER, FLOAT_SZ * len(vertex_data),
-            vertex_data, GL_STATIC_DRAW
-        )
-        total_sz = sum(attr_sizes)
-        offset = 0
-        for i, sz in enumerate(attr_sizes):
-            glVertexAttribPointer(
-                i, sz, GL_FLOAT, GL_FALSE, FLOAT_SZ * total_sz,
-                ctypes.c_void_p(FLOAT_SZ * offset)
-            )
-            glEnableVertexAttribArray(i)
-            offset += sz
-
-        #######################################################################
-        # Fill model matrix buffer
-        #######################################################################
-
-        if self.poses is not None:
-            pose_data = np.ascontiguousarray(
-                np.transpose(self.poses, [0,2,1]).flatten().astype(np.float32)
-            )
-        else:
-            pose_data = np.ascontiguousarray(
-                np.eye(4).flatten().astype(np.float32)
-            )
-
-        modelbuffer = glGenBuffers(1)
-        self._buffers.append(modelbuffer)
-        glBindBuffer(GL_ARRAY_BUFFER, modelbuffer)
-        glBufferData(
-            GL_ARRAY_BUFFER, FLOAT_SZ * len(pose_data),
-            pose_data, GL_STATIC_DRAW
-        )
-
-        for i in range(0, 4):
-            idx = i + len(attr_sizes)
-            glEnableVertexAttribArray(idx)
-            glVertexAttribPointer(
-                idx, 4, GL_FLOAT, GL_FALSE, FLOAT_SZ * 4 * 4,
-                ctypes.c_void_p(4 * FLOAT_SZ * i)
-            )
-            glVertexAttribDivisor(idx, 1)
-
-        #######################################################################
-        # Fill element buffer
-        #######################################################################
-        if self.indices is not None:
-            elementbuffer = glGenBuffers(1)
-            self._buffers.append(elementbuffer)
-            glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, elementbuffer)
-            glBufferData(GL_ELEMENT_ARRAY_BUFFER, UINT_SZ * self.indices.size,
-                         self.indices.flatten().astype(np.uint32),
-                         GL_STATIC_DRAW)
-
-        glBindVertexArray(0)
-
-    def _remove_from_context(self):
-        if self._vaid is not None:
-            glDeleteVertexArrays(1, [self._vaid])
-            glDeleteBuffers(len(self._buffers), self._buffers)
-            self._vaid = None
-            self._buffers = []
-
-    def _in_context(self):
-        return self._vaid is not None
-
-    def _bind(self):
-        if self._vaid is None:
-            raise ValueError('Cannot bind a Mesh that has not been added '
-                             'to a context')
-        glBindVertexArray(self._vaid)
-
-    def _unbind(self):
-        glBindVertexArray(0)
-
-    def _compute_bounds(self):
-        """Compute the bounds of this object.
-        """
-        # Compute bounds of this object
-        bounds = np.array([np.min(self.positions, axis=0),
-                           np.max(self.positions, axis=0)])
-
-        # If instanced, compute translations for approximate bounds
-        if self.poses is not None:
-            bounds += np.array([np.min(self.poses[:,:3,3], axis=0),
-                                np.max(self.poses[:,:3,3], axis=0)])
-        return bounds
-
-    def _compute_transparency(self):
-        """Compute whether or not this object is transparent.
-        """
-        if self.material.is_transparent:
-            return True
-        if self._is_transparent is None:
-            self._is_transparent = False
-            if self.color_0 is not None:
-                if np.any(self._color_0[:,3] != 1.0):
-                    self._is_transparent = True
-        return self._is_transparent
-
-    def _compute_buf_flags(self):
-        buf_flags = BufFlags.POSITION
-
-        if self.normals is not None:
-            buf_flags |= BufFlags.NORMAL
-        if self.tangents is not None:
-            buf_flags |= BufFlags.TANGENT
-        if self.texcoord_0 is not None:
-            buf_flags |= BufFlags.TEXCOORD_0
-        if self.texcoord_1 is not None:
-            buf_flags |= BufFlags.TEXCOORD_1
-        if self.color_0 is not None:
-            buf_flags |= BufFlags.COLOR_0
-        if self.joints_0 is not None:
-            buf_flags |= BufFlags.JOINTS_0
-        if self.weights_0 is not None:
-            buf_flags |= BufFlags.WEIGHTS_0
-
-        return buf_flags
diff --git a/spaces/H0n3y/Honeystesting/README.md b/spaces/H0n3y/Honeystesting/README.md
deleted file mode 100644
index 05fb8ea9221cc7e5d6ece6338d5f1fa2725f6e66..0000000000000000000000000000000000000000
--- a/spaces/H0n3y/Honeystesting/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Honeystesting
-emoji: 🐨
-colorFrom: yellow
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/sod_selection_ui.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/sod_selection_ui.py
deleted file mode 100644
index b6e2e8b88f3b606adf844b1a7ae3795d15f03798..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/sod_selection_ui.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from typing import Tuple
-
-import streamlit as st
-
-from app_env import SOD_MODEL_TYPE
-from app_utils import count_parameters
-from base_model import BaseRGBDModel
-from bbsnet_model import BBSNetModel
-from rgbd_multimae_model import RGBDMultiMAEModel
-from s_multimae.configs.base_config import base_cfg
-from s_multimae.configs.experiment_config import configs_dict
-from spnet_model import SPNetModel
-
-
-@st.experimental_singleton
-def load_multimae_model(sod_model_config_key: str) -> Tuple[BaseRGBDModel, base_cfg]:
-    """
-    1. Construct model
-    2. Load pretrained weights
-    3. Load model into device
-    """
-    cfg = configs_dict[sod_model_config_key]()
-    sod_model = RGBDMultiMAEModel(cfg)
-    return sod_model, cfg
-
-@st.experimental_singleton
-def load_spnet_model() -> BaseRGBDModel:
-    """
-    1. Construct model
-    2. Load pretrained weights
-    3. Load model into device
-    """
-    sod_model = SPNetModel()
-    return sod_model
-
-@st.experimental_singleton
-def load_bbsnet_model() -> BaseRGBDModel:
-    """
-    1. Construct model
-    2. Load pretrained weights
-    3. Load model into device
-    """
-    sod_model = BBSNetModel()
-    return sod_model
-
-def sod_selection_ui() -> BaseRGBDModel:
-    sod_model_type = st.selectbox(
-        'Choose SOD model',
-        (
-            SOD_MODEL_TYPE.S_MULTIMAE,
-            SOD_MODEL_TYPE.SPNET,
-            SOD_MODEL_TYPE.BBSNET,
-        ),
-        key='sod_model_type',
-    )
-
-    if sod_model_type == SOD_MODEL_TYPE.S_MULTIMAE:
-        sod_model_config_key = st.selectbox(
-            'Choose config',
-            configs_dict.keys(),
-            key='sod_model_config_key',
-        )
-        sod_model, cfg = load_multimae_model(sod_model_config_key)
-        st.text(f'Model description: {cfg.description}')
-    elif sod_model_type == SOD_MODEL_TYPE.SPNET:
-        sod_model = load_spnet_model()
-        st.text(f'Model description: SPNet (https://github.com/taozh2017/SPNet)')
-    elif sod_model_type == SOD_MODEL_TYPE.BBSNET:
-        sod_model = load_bbsnet_model()
-        st.text(f'Model description: BBSNet (https://github.com/DengPingFan/BBS-Net)')
-
-    st.text(f'Number of parameters {count_parameters(sod_model)}')
-    return sod_model
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/test_depth.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/test_depth.py
deleted file mode 100644
index aaa93f4b8b039ef8d4947f444e7a7fe708497171..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/test_depth.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import os
-
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-import torchvision.transforms as transforms
-from torch import Tensor
-
-from device import device
-from lib.multi_depth_model_woauxi import RelDepthModel
-from lib.net_tools import load_ckpt
-
-
-def scale_torch(img: np.ndarray) -> Tensor:
-    """
-    Scale the image and output it in torch.tensor.
-    :param img: input rgb is in shape [H, W, C], input depth/disp is in shape [H, W]
-    :param scale: the scale factor. float
-    :return: img. [C, H, W]
-    """
-    if len(img.shape) == 2:
-        img = img[np.newaxis, :, :]
-    if img.shape[2] == 3:
-        transform = transforms.Compose([
-            transforms.ToTensor(),
-		    transforms.Normalize(
-                (0.485, 0.456, 0.406), 
-                (0.229, 0.224, 0.225) 
-            )
-        ])
-        img = transform(img)
-    else:
-        img = img.astype(np.float32)
-        img = torch.from_numpy(img)
-    return img
-
-# create depth model
-depth_model = RelDepthModel(backbone='resnext101')
-depth_model.eval()
-load_ckpt(
-    os.path.join(
-        'pretrained_models', 
-        'adelai_depth', 
-        'res101.pth'
-    ), 
-    depth_model
-)
-depth_model.to(device)
-
-def test():
-    rgb = cv2.imread(os.path.join('images', 'pexels-mark-neal-5496430.jpg'))
-    rgb_c = rgb[:, :, ::-1].copy()
-    # gt_depth = None
-    A_resize = cv2.resize(rgb_c, (448, 448))
-    # rgb_half = cv2.resize(rgb, (rgb.shape[1]//2, rgb.shape[0]//2), interpolation=cv2.INTER_LINEAR)
-
-    img_torch = scale_torch(A_resize)[None, :, :, :]
-    pred_depth: np.ndarray = depth_model.inference(img_torch).cpu().numpy().squeeze()
-    pred_depth_ori = cv2.resize(pred_depth, (rgb.shape[1], rgb.shape[0]))
-
-    # if GT depth is available, uncomment the following part to recover the metric depth
-    #pred_depth_metric = recover_metric_depth(pred_depth_ori, gt_depth)
-
-    cv2.imwrite(os.path.join('tmp', 'rgb.png'), rgb)
-    # save depth
-    plt.imsave(os.path.join('tmp', 'depth.png'), pred_depth_ori, cmap='rainbow')
-    cv2.imwrite(
-        os.path.join('tmp', 'depth_raw.png'), 
-        (pred_depth_ori/pred_depth_ori.max() * 60000).astype(np.uint16)
-    )
-
-if __name__ == '__main__':
-    test()
diff --git a/spaces/HamidRezaAttar/gpt2-home/assets/ltr.css b/spaces/HamidRezaAttar/gpt2-home/assets/ltr.css
deleted file mode 100644
index 445b9a8b91411e9a0376671107f2ba1238f6dece..0000000000000000000000000000000000000000
--- a/spaces/HamidRezaAttar/gpt2-home/assets/ltr.css
+++ /dev/null
@@ -1,22 +0,0 @@
-.ltr,
-textarea {
-    font-family: Roboto !important;
-    text-align: left;
-    direction: ltr !important;
-}
-.ltr-box {
-    border-bottom: 1px solid #ddd;
-    padding-bottom: 20px;
-}
-.rtl {
-    text-align: left;
-    direction: ltr !important;
-}
-
-span.result-text {
-    padding: 3px 3px;
-    line-height: 32px;
-}
-span.generated-text {
-    background-color: rgb(118 200 147 / 13%);
-}
\ No newline at end of file
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/quantization/pq/modules/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/quantization/pq/modules/__init__.py
deleted file mode 100644
index b67c8e8ad691aa01e9e10e904d69d94595387668..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/quantization/pq/modules/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .qconv import PQConv2d  # NOQA
-from .qemb import PQEmbedding  # NOQA
-from .qlinear import PQLinear  # NOQA
diff --git a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/src/glow_tts/text/__init__.py b/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/src/glow_tts/text/__init__.py
deleted file mode 100644
index 3f5aa62bfcd56165b85d064f5ca0ba59fbe34a72..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/src/glow_tts/text/__init__.py
+++ /dev/null
@@ -1,84 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-import re
-from text import cleaners
-
-# Regular expression matching text enclosed in curly braces:
-_curly_re = re.compile(r'(.*?)\{(.+?)\}(.*)')
-
-
-def get_arpabet(word, dictionary):
-  word_arpabet = dictionary.lookup(word)
-  if word_arpabet is not None:
-    return "{" + word_arpabet[0] + "}"
-  else:
-    return word
-
-
-def text_to_sequence(text, symbols, cleaner_names, dictionary=None):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-
-    The text can optionally have ARPAbet sequences enclosed in curly braces embedded
-    in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
-
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-      dictionary: arpabet class with arpabet dictionary
-
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  # Mappings from symbol to numeric ID and vice versa:
-  global _id_to_symbol, _symbol_to_id
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  _id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-  sequence = []
-
-  space = _symbols_to_sequence(' ')
-  # Check for curly braces and treat their contents as ARPAbet:
-  while len(text):
-    m = _curly_re.match(text)
-    if not m:
-      clean_text = _clean_text(text, cleaner_names)
-      if dictionary is not None:
-        clean_text = [get_arpabet(w, dictionary) for w in clean_text.split(" ")]
-        for i in range(len(clean_text)):
-          t = clean_text[i]
-          if t.startswith("{"):
-            sequence += _arpabet_to_sequence(t[1:-1])
-          else:
-            sequence += _symbols_to_sequence(t)
-          sequence += space
-      else:
-        sequence += _symbols_to_sequence(clean_text)
-      break
-    sequence += _symbols_to_sequence(_clean_text(m.group(1), cleaner_names))
-    sequence += _arpabet_to_sequence(m.group(2))
-    text = m.group(3)
-  
-  # remove trailing space
-  if dictionary is not None:
-    sequence = sequence[:-1] if sequence[-1] == space[0] else sequence
-  return sequence
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
-
-
-def _symbols_to_sequence(symbols):
-  return [_symbol_to_id[s] for s in symbols if _should_keep_symbol(s)]
-
-
-def _arpabet_to_sequence(text):
-  return _symbols_to_sequence(['@' + s for s in text.split()])
-
-
-def _should_keep_symbol(s):
-  return s in _symbol_to_id and s is not '_' and s is not '~'
\ No newline at end of file
diff --git a/spaces/Hazem/roop/README.md b/spaces/Hazem/roop/README.md
deleted file mode 100644
index 8765ab0b78d11834fa64339bc2aacf743657ea64..0000000000000000000000000000000000000000
--- a/spaces/Hazem/roop/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Roop
-emoji: 📈
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: agpl-3.0
-duplicated_from: ezioruan/roop
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/HusseinHE/webui_blank/app.py b/spaces/HusseinHE/webui_blank/app.py
deleted file mode 100644
index 4eab1984c438dcee135fc7f5404191798893a5d8..0000000000000000000000000000000000000000
--- a/spaces/HusseinHE/webui_blank/app.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import os
-from subprocess import getoutput
-
-gpu_info = getoutput('nvidia-smi')
-if("A10G" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl")
-elif("T4" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+1515f77.d20221130-cp38-cp38-linux_x86_64.whl")
-
-os.system(f"git clone -b v1.5 https://github.com/camenduru/stable-diffusion-webui /home/user/app/stable-diffusion-webui")
-os.chdir("/home/user/app/stable-diffusion-webui")
-
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/env_patch.py -O /home/user/app/env_patch.py")
-os.system(f"sed -i -e '/import image_from_url_text/r /home/user/app/env_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(modelmerger_interface, \"Checkpoint Merger\", \"modelmerger\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(train_interface, \"Train\", \"ti\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/extensions_interface, \"Extensions\", \"extensions\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/settings_interface, \"Settings\", \"settings\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f'''sed -i -e "s/document.getElementsByTagName('gradio-app')\[0\].shadowRoot/!!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document/g" /home/user/app/stable-diffusion-webui/script.js''')
-os.system(f"sed -i -e 's/                show_progress=False,/                show_progress=True,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' /home/user/app/stable-diffusion-webui/webui.py")
-os.system(f"sed -i -e 's/ outputs=\[/queue=False, &/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/               queue=False,  /                /g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-
-# ----------------------------Please duplicate this space and delete this block if you don't want to see the extra header----------------------------
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/header_patch.py -O /home/user/app/header_patch.py")
-os.system(f"sed -i -e '/demo:/r /home/user/app/header_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-# ---------------------------------------------------------------------------------------------------------------------------------------------------
-
-if "IS_SHARED_UI" in os.environ:
-    os.system(f"rm -rfv /home/user/app/stable-diffusion-webui/scripts/")
-    
-    os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json")
-    os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json")
-
-    os.system(f"wget -q {os.getenv('MODEL_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME')}")
-    os.system(f"wget -q {os.getenv('VAE_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('VAE_NAME')}")
-    os.system(f"wget -q {os.getenv('YAML_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('YAML_NAME')}")
-    
-    os.system(f"python launch.py --force-enable-xformers --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding")
-else:
-    # Please duplicate this space and delete # character in front of the custom script you want to use or add here more custom scripts with same structure os.system(f"wget -q https://CUSTOM_SCRIPT_URL -O /home/user/app/stable-diffusion-webui/scripts/CUSTOM_SCRIPT_NAME.py")
-    os.system(f"wget -q https://gist.github.com/camenduru/9ec5f8141db9902e375967e93250860f/raw/d0bcf01786f20107c329c03f8968584ee67be12a/run_n_times.py -O /home/user/app/stable-diffusion-webui/scripts/run_n_times.py")
-    
-    # Please duplicate this space and delete # character in front of the extension you want to use or add here more extensions with same structure os.system(f"git clone https://EXTENSION_GIT_URL /home/user/app/stable-diffusion-webui/extensions/EXTENSION_NAME")
-    #os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-artists-to-study")
-    os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser")
-    os.system(f"git clone https://github.com/deforum-art/deforum-for-automatic1111-webui /home/user/app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui")
-    
-    # Please duplicate this space and delete # character in front of the model you want to use or add here more ckpts with same structure os.system(f"wget -q https://CKPT_URL -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/CKPT_NAME.ckpt")
-    #os.system(f"wget -q https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-v3.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/arcane-diffusion-v3.ckpt")
-    #os.system(f"wget -q https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/Cyberpunk-Anime-Diffusion.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Cyberpunk-Anime-Diffusion.ckpt")
-    #os.system(f"wget -q https://huggingface.co/prompthero/midjourney-v4-diffusion/resolve/main/mdjrny-v4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/mdjrny-v4.ckpt")
-    #os.system(f"wget -q https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/moDi-v1-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/moDi-v1-pruned.ckpt")
-    #os.system(f"wget -q https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/resolve/main/PaperCut_v1.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/PaperCut_v1.ckpt")
-    #os.system(f"wget -q https://huggingface.co/lilpotat/sa/resolve/main/samdoesarts_style.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/samdoesarts_style.ckpt")
-    #os.system(f"wget -q https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-float32.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/wd-v1-3-float32.ckpt")
-    #os.system(f"wget -q https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-4.ckpt")
-    #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt")
-    #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-5-inpainting.ckpt")
-    
-    #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.ckpt")
-    #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0.vae.pt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.vae.pt")
-    
-    #os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.ckpt")
-    #os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.yaml")
-    
-    os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v2-1_768-ema-pruned.ckpt")
-    os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v2-1_768-ema-pruned.yaml")
-
-    os.system(f"python launch.py --force-enable-xformers --ui-config-file /home/user/app/ui-config.json --ui-settings-file /home/user/app/config.json --disable-console-progressbars --enable-console-prompts --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --api --skip-torch-cuda-test")
-    
\ No newline at end of file
diff --git a/spaces/ICML2022/OFA/fairseq/examples/criss/sentence_retrieval/encoder_analysis.py b/spaces/ICML2022/OFA/fairseq/examples/criss/sentence_retrieval/encoder_analysis.py
deleted file mode 100644
index b41bfbe38789ba14e6a5ea938c75d761424c00ab..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/criss/sentence_retrieval/encoder_analysis.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-import argparse
-import glob
-
-import numpy as np
-
-
-DIM = 1024
-
-
-def compute_dist(source_embs, target_embs, k=5, return_sim_mat=False):
-    target_ids = [tid for tid in target_embs]
-    source_mat = np.stack(source_embs.values(), axis=0)
-    normalized_source_mat = source_mat / np.linalg.norm(
-        source_mat, axis=1, keepdims=True
-    )
-    target_mat = np.stack(target_embs.values(), axis=0)
-    normalized_target_mat = target_mat / np.linalg.norm(
-        target_mat, axis=1, keepdims=True
-    )
-    sim_mat = normalized_source_mat.dot(normalized_target_mat.T)
-    if return_sim_mat:
-        return sim_mat
-    neighbors_map = {}
-    for i, sentence_id in enumerate(source_embs):
-        idx = np.argsort(sim_mat[i, :])[::-1][:k]
-        neighbors_map[sentence_id] = [target_ids[tid] for tid in idx]
-    return neighbors_map
-
-
-def load_embeddings(directory, LANGS):
-    sentence_embeddings = {}
-    sentence_texts = {}
-    for lang in LANGS:
-        sentence_embeddings[lang] = {}
-        sentence_texts[lang] = {}
-        lang_dir = f"{directory}/{lang}"
-        embedding_files = glob.glob(f"{lang_dir}/all_avg_pool.{lang}.*")
-        for embed_file in embedding_files:
-            shard_id = embed_file.split(".")[-1]
-            embeddings = np.fromfile(embed_file, dtype=np.float32)
-            num_rows = embeddings.shape[0] // DIM
-            embeddings = embeddings.reshape((num_rows, DIM))
-
-            with open(f"{lang_dir}/sentences.{lang}.{shard_id}") as sentence_file:
-                for idx, line in enumerate(sentence_file):
-                    sentence_id, sentence = line.strip().split("\t")
-                    sentence_texts[lang][sentence_id] = sentence
-                    sentence_embeddings[lang][sentence_id] = embeddings[idx, :]
-
-    return sentence_embeddings, sentence_texts
-
-
-def compute_accuracy(directory, LANGS):
-    sentence_embeddings, sentence_texts = load_embeddings(directory, LANGS)
-
-    top_1_accuracy = {}
-
-    top1_str = " ".join(LANGS) + "\n"
-    for source_lang in LANGS:
-        top_1_accuracy[source_lang] = {}
-        top1_str += f"{source_lang} "
-        for target_lang in LANGS:
-            top1 = 0
-            top5 = 0
-            neighbors_map = compute_dist(
-                sentence_embeddings[source_lang], sentence_embeddings[target_lang]
-            )
-            for sentence_id, neighbors in neighbors_map.items():
-                if sentence_id == neighbors[0]:
-                    top1 += 1
-                if sentence_id in neighbors[:5]:
-                    top5 += 1
-            n = len(sentence_embeddings[target_lang])
-            top1_str += f"{top1/n} "
-        top1_str += "\n"
-
-    print(top1_str)
-    print(top1_str, file=open(f"{directory}/accuracy", "w"))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Analyze encoder outputs")
-    parser.add_argument("directory", help="Source language corpus")
-    parser.add_argument("--langs", help="List of langs")
-    args = parser.parse_args()
-    langs = args.langs.split(",")
-    compute_accuracy(args.directory, langs)
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/gpt2_bpe_utils.py b/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/gpt2_bpe_utils.py
deleted file mode 100644
index 688d4e36e358df2dcc432d37d3e57bd81e2f1ed1..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/data/encoders/gpt2_bpe_utils.py
+++ /dev/null
@@ -1,140 +0,0 @@
-"""
-Byte pair encoding utilities from GPT-2.
-
-Original source: https://github.com/openai/gpt-2/blob/master/src/encoder.py
-Original license: MIT
-"""
-
-import json
-from functools import lru_cache
-
-
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = (
-        list(range(ord("!"), ord("~") + 1))
-        + list(range(ord("¡"), ord("¬") + 1))
-        + list(range(ord("®"), ord("ÿ") + 1))
-    )
-    cs = bs[:]
-    n = 0
-    for b in range(2 ** 8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2 ** 8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """Return set of symbol pairs in a word.
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-class Encoder:
-    def __init__(self, encoder, bpe_merges, errors="replace"):
-        self.encoder = encoder
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.errors = errors  # how to handle errors in decoding
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
-        self.cache = {}
-
-        try:
-            import regex as re
-
-            self.re = re
-        except ImportError:
-            raise ImportError("Please install regex with: pip install regex")
-
-        # Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
-        self.pat = self.re.compile(
-            r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
-        )
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                    new_word.extend(word[i:j])
-                    i = j
-                except:
-                    new_word.extend(word[i:])
-                    break
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        self.cache[token] = word
-        return word
-
-    def encode(self, text):
-        bpe_tokens = []
-        for token in self.re.findall(self.pat, text):
-            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
-            bpe_tokens.extend(
-                self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")
-            )
-        return bpe_tokens
-
-    def decode(self, tokens):
-        text = "".join([self.decoder.get(token, token) for token in tokens])
-        text = bytearray([self.byte_decoder[c] for c in text]).decode(
-            "utf-8", errors=self.errors
-        )
-        return text
-
-
-def get_encoder(encoder_json_path, vocab_bpe_path):
-    with open(encoder_json_path, "r") as f:
-        encoder = json.load(f)
-    with open(vocab_bpe_path, "r", encoding="utf-8") as f:
-        bpe_data = f.read()
-    bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]]
-    return Encoder(
-        encoder=encoder,
-        bpe_merges=bpe_merges,
-    )
diff --git a/spaces/Ibtehaj10/cheating-detection-FYP/person_detection_image.py b/spaces/Ibtehaj10/cheating-detection-FYP/person_detection_image.py
deleted file mode 100644
index cd9e11a5655e16dcbe1b96badfed5d759fa6d682..0000000000000000000000000000000000000000
--- a/spaces/Ibtehaj10/cheating-detection-FYP/person_detection_image.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import cv2
-import numpy as np
-import imutils
-
-protopath = "MobileNetSSD_deploy.prototxt"
-modelpath = "MobileNetSSD_deploy.caffemodel"
-detector = cv2.dnn.readNetFromCaffe(prototxt=protopath, caffeModel=modelpath)
-
-CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
-           "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
-           "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
-           "sofa", "train", "tvmonitor"]
-
-
-def main():
-    image = cv2.imread('people.jpg')
-    image = imutils.resize(image, width=600)
-
-    (H, W) = image.shape[:2]
-
-    blob = cv2.dnn.blobFromImage(image, 0.007843, (W, H), 127.5)
-
-    detector.setInput(blob)
-    person_detections = detector.forward()
-
-    for i in np.arange(0, person_detections.shape[2]):
-        confidence = person_detections[0, 0, i, 2]
-        if confidence > 0.5:
-            idx = int(person_detections[0, 0, i, 1])
-
-            if CLASSES[idx] != "person":
-                continue
-
-            person_box = person_detections[0, 0, i, 3:7] * np.array([W, H, W, H])
-            (startX, startY, endX, endY) = person_box.astype("int")
-
-            cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2)
-
-    cv2.imshow("Results", image)
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
-
-main()
\ No newline at end of file
diff --git a/spaces/Illumotion/Koboldcpp/examples/export-lora/export-lora.cpp b/spaces/Illumotion/Koboldcpp/examples/export-lora/export-lora.cpp
deleted file mode 100644
index d803cfd5cb2d5e34ead197186b86361d2a1a95c5..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/examples/export-lora/export-lora.cpp
+++ /dev/null
@@ -1,474 +0,0 @@
-
-#include "common.h"
-#include "ggml.h"
-#include "ggml-alloc.h"
-
-#include <vector>
-#include <string>
-#include <thread>
-
-static const size_t tensor_alignment = 32;
-
-struct lora_info {
-    std::string filename;
-    float scale;
-};
-
-struct export_lora_params {
-    std::string fn_model_base;
-    std::string fn_model_out;
-    std::vector<struct lora_info> lora;
-    int n_threads;
-};
-
-struct lora_data {
-    struct lora_info     info;
-    std::vector<uint8_t> data;
-    struct ggml_context * ctx;
-
-    uint32_t lora_r;
-    uint32_t lora_alpha;
-};
-
-struct llama_file {
-    // use FILE * so we don't have to re-open the file to mmap
-    FILE * fp;
-    size_t size;
-
-    llama_file(const char * fname, const char * mode) {
-        fp = std::fopen(fname, mode);
-        if (fp == NULL) {
-            size = 0;
-        } else {
-            seek(0, SEEK_END);
-            size = tell();
-            seek(0, SEEK_SET);
-        }
-    }
-
-    size_t tell() const {
-#ifdef _WIN32
-        __int64 ret = _ftelli64(fp);
-#else
-        long ret = std::ftell(fp);
-#endif
-        GGML_ASSERT(ret != -1); // this really shouldn't fail
-        return (size_t) ret;
-    }
-
-    void seek(size_t offset, int whence) {
-#ifdef _WIN32
-        int ret = _fseeki64(fp, (__int64) offset, whence);
-#else
-        int ret = std::fseek(fp, (long) offset, whence);
-#endif
-        GGML_ASSERT(ret == 0); // same
-    }
-
-    void read_raw(void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        std::size_t ret = std::fread(ptr, size, 1, fp);
-        if (ferror(fp)) {
-            die_fmt("read error: %s", strerror(errno));
-        }
-        if (ret != 1) {
-            die("unexpectedly reached end of file");
-        }
-    }
-
-    std::uint32_t read_u32() {
-        std::uint32_t ret;
-        read_raw(&ret, sizeof(ret));
-        return ret;
-    }
-
-    std::string read_string(std::uint32_t len) {
-        std::vector<char> chars(len);
-        read_raw(chars.data(), len);
-        return std::string(chars.data(), len);
-    }
-
-    void write_raw(const void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        size_t ret = std::fwrite(ptr, size, 1, fp);
-        if (ret != 1) {
-            die_fmt("write error: %s", strerror(errno));
-        }
-    }
-
-    void write_u32(std::uint32_t val) {
-        write_raw(&val, sizeof(val));
-    }
-
-    bool eof() {
-        return tell() >= size;
-    }
-
-    ~llama_file() {
-        if (fp) {
-            std::fclose(fp);
-        }
-    }
-};
-
-static struct export_lora_params get_default_export_lora_params() {
-    struct export_lora_params result;
-    result.fn_model_base = "";
-    result.fn_model_out  = "";
-    result.n_threads = GGML_DEFAULT_N_THREADS;
-    return result;
-}
-
-static void export_lora_print_usage(int /*argc*/, char ** argv, const struct export_lora_params * params) {
-    fprintf(stderr, "usage: %s [options]\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h, --help                         show this help message and exit\n");
-    fprintf(stderr, "  -m FNAME, --model-base FNAME       model path from which to load base model (default '%s')\n", params->fn_model_base.c_str());
-    fprintf(stderr, "  -o FNAME, --model-out FNAME        path to save exported model (default '%s')\n", params->fn_model_out.c_str());
-    fprintf(stderr, "  -l FNAME, --lora FNAME             apply LoRA adapter\n");
-    fprintf(stderr, "  -s FNAME S, --lora-scaled FNAME S  apply LoRA adapter with user defined scaling S\n");
-    fprintf(stderr, "  -t N, --threads N                  number of threads to use during computation (default: %d)\n", params->n_threads);
-}
-
-static bool export_lora_params_parse(int argc, char ** argv, struct export_lora_params * params) {
-    bool invalid_param = false;
-    std::string arg;
-    struct export_lora_params default_params = get_default_export_lora_params();
-    const std::string arg_prefix = "--";
-
-    for (int i = 1; i < argc; i++) {
-        arg = argv[i];
-        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
-            std::replace(arg.begin(), arg.end(), '_', '-');
-        }
-
-        if (arg == "-m" || arg == "--model-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_model_base = argv[i];
-        } else if (arg == "-o" || arg == "--model-out") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_model_out = argv[i];
-        } else if (arg == "-l" || arg == "--lora") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            struct lora_info lora;
-            lora.filename = argv[i];
-            lora.scale = 1.0f;
-            params->lora.push_back(lora);
-        } else if (arg == "-s" || arg == "--lora-scaled") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            struct lora_info lora;
-            lora.filename = argv[i];
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            lora.scale = std::stof(argv[i]);
-            params->lora.push_back(lora);
-        } else if (arg == "-t" || arg == "--threads") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_threads = std::stoi(argv[i]);
-            if (params->n_threads <= 0) {
-                params->n_threads = std::thread::hardware_concurrency();
-            }
-        } else {
-            fprintf(stderr, "error: unknown argument: '%s'\n", arg.c_str());
-            export_lora_print_usage(argc, argv, &default_params);
-            exit(1);
-        }
-    }
-
-    if (params->fn_model_base == default_params.fn_model_base) {
-        fprintf(stderr, "error: please specify a filename for model-base.\n");
-        export_lora_print_usage(argc, argv, &default_params);
-        exit(1);
-    }
-    if (params->fn_model_out == default_params.fn_model_out) {
-        fprintf(stderr, "error: please specify a filename for model-out.\n");
-        export_lora_print_usage(argc, argv, &default_params);
-        exit(1);
-    }
-    if (invalid_param) {
-        fprintf(stderr, "error: invalid parameter for argument: '%s'\n", arg.c_str());
-        export_lora_print_usage(argc, argv, &default_params);
-        exit(1);
-    }
-    return true;
-}
-
-static void free_lora(struct lora_data * lora) {
-    if (lora->ctx != NULL) {
-        ggml_free(lora->ctx);
-    }
-    delete lora;
-}
-
-static struct lora_data * load_lora(struct lora_info * info) {
-    struct lora_data * result = new struct lora_data;
-    result->info = *info;
-    result->ctx = NULL;
-    result->lora_r     = 1;
-    result->lora_alpha = 1;
-
-    struct llama_file file(info->filename.c_str(), "rb");
-    if (file.fp == NULL) {
-        fprintf(stderr, "warning: Could not open lora adapter '%s'. Ignoring this adapter.\n",
-            info->filename.c_str());
-        free_lora(result);
-        return NULL;
-    }
-
-    struct ggml_init_params params_ggml;
-    params_ggml.mem_size   = ggml_tensor_overhead() * GGML_MAX_NODES;
-    params_ggml.mem_buffer = NULL;
-    params_ggml.no_alloc   = true;
-    result->ctx = ggml_init(params_ggml);
-
-    uint32_t LLAMA_FILE_MAGIC_LORA = 0x67676C61; // 'ggla'
-    uint32_t magic   = file.read_u32();
-    if (magic != LLAMA_FILE_MAGIC_LORA) {
-        die_fmt("unexpected lora header file magic in '%s'", info->filename.c_str());
-    }
-    uint32_t version = file.read_u32();
-    if (version != 1) {
-        die_fmt("unexpected lora file version '%u' in '%s'", (unsigned) version, info->filename.c_str());
-    }
-    result->lora_r     = file.read_u32();
-    result->lora_alpha = file.read_u32();
-    // read tensor infos from file
-    std::vector<char> name_buf;
-    std::vector<struct ggml_tensor *> tensors;
-    std::vector<size_t> tensors_offset;
-    size_t total_nbytes_pad = 0;
-    while(!file.eof()) {
-        int64_t ne[4]   = {1,1,1,1};
-        uint32_t n_dims  = file.read_u32();
-        uint32_t namelen = file.read_u32();
-        uint32_t type    = file.read_u32();
-        for (uint32_t k = 0; k < n_dims; ++k) {
-            ne[k] = (int64_t)file.read_u32();
-        }
-        name_buf.clear();
-        name_buf.resize(namelen + 1, '\0');
-        file.read_raw(name_buf.data(), namelen);
-        file.seek((0-file.tell()) & 31, SEEK_CUR);
-        size_t offset = file.tell();
-        struct ggml_tensor * tensor = ggml_new_tensor(result->ctx, (enum ggml_type) type, n_dims, ne);
-        ggml_set_name(tensor, name_buf.data());
-        size_t nbytes     = ggml_nbytes(tensor);
-        size_t nbytes_pad = ggml_nbytes_pad(tensor);
-        total_nbytes_pad += nbytes_pad;
-        tensors.push_back(tensor);
-        tensors_offset.push_back(offset);
-        file.seek(nbytes, SEEK_CUR);
-    }
-    // read tensor data
-    result->data.resize(total_nbytes_pad);
-    size_t data_offset = 0;
-    for (size_t i = 0; i < tensors.size(); ++i) {
-        struct ggml_tensor * tensor = tensors[i];
-        size_t offset     = tensors_offset[i];
-        size_t nbytes     = ggml_nbytes(tensor);
-        size_t nbytes_pad = ggml_nbytes_pad(tensor);
-        file.seek(offset, SEEK_SET);
-        tensor->data = result->data.data() + data_offset;
-        file.read_raw(tensor->data, nbytes);
-        data_offset += nbytes_pad;
-    }
-    return result;
-}
-
-
-static struct ggml_cgraph * build_graph_lora(
-    struct ggml_context * ctx,
-    struct ggml_tensor * tensor,
-    struct ggml_tensor * lora_a,
-    struct ggml_tensor * lora_b,
-    float scaling
-) {
-    struct ggml_tensor * ab = ggml_mul_mat(ctx, lora_a, lora_b);
-    if (scaling != 1.0f) {
-        ab = ggml_scale(ctx, ab, ggml_new_f32(ctx, scaling));
-    }
-    struct ggml_tensor * res = ggml_add_inplace(ctx, tensor, ab);
-
-    struct ggml_cgraph * gf = ggml_new_graph(ctx);
-    ggml_build_forward_expand (gf, res);
-    return gf;
-}
-
-static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int n_threads) {
-    if (lora->ctx == NULL) {
-        return false;
-    }
-    std::string name = ggml_get_name(tensor);
-    std::string name_a = name + std::string(".loraA");
-    std::string name_b = name + std::string(".loraB");
-    struct ggml_tensor * lora_a = ggml_get_tensor(lora->ctx, name_a.c_str());
-    struct ggml_tensor * lora_b = ggml_get_tensor(lora->ctx, name_b.c_str());
-    if (lora_a == NULL || lora_b == NULL) {
-        return false;
-    }
-
-    float scaling = lora->info.scale * (float)lora->lora_alpha / (float)lora->lora_r;
-
-    struct ggml_init_params params;
-    params.mem_size   = GGML_OBJECT_SIZE + GGML_GRAPH_SIZE + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5;
-    params.mem_buffer = NULL;
-    params.no_alloc   = true;
-    struct ggml_context * ctx = NULL;
-    struct ggml_allocr * alloc = NULL;
-    struct ggml_cgraph * gf = NULL;
-
-    ctx   = ggml_init(params);
-    alloc = ggml_allocr_new_measure(tensor_alignment);
-    gf    = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling);
-    size_t alloc_size = ggml_allocr_alloc_graph(alloc, gf);
-    ggml_allocr_free(alloc);
-    ggml_free(ctx);
-
-    static std::vector<uint8_t> data_compute;
-    data_compute.resize(alloc_size + tensor_alignment);
-
-    ctx   = ggml_init(params);
-    alloc = ggml_allocr_new(data_compute.data(), data_compute.size(), tensor_alignment);
-    gf    = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling);
-    ggml_allocr_alloc_graph(alloc, gf);
-    ggml_allocr_free(alloc);
-
-    struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads);
-    static std::vector<uint8_t> data_work;
-    data_work.resize(cplan.work_size);
-    cplan.work_data = data_work.data();
-
-    ggml_graph_compute(gf, &cplan);
-
-    ggml_free(ctx);
-    return true;
-}
-
-static void export_lora(struct export_lora_params * params) {
-    // load all loras
-    std::vector<struct lora_data *> loras;
-    for (size_t i = 0; i < params->lora.size(); ++i) {
-        struct lora_data * lora = load_lora(&params->lora[i]);
-        if (lora != NULL) {
-            loras.push_back(lora);
-        }
-    }
-    if (loras.size() == 0) {
-        fprintf(stderr, "warning: no lora adapters will be applied.\n");
-    }
-
-    // open input file
-    struct llama_file fin(params->fn_model_base.c_str(), "rb");
-    if (!fin.fp) {
-        die_fmt("Could not open file '%s'\n", params->fn_model_base.c_str());
-    }
-
-    // open base model gguf, read tensors without their data
-    struct ggml_context * ctx_in;
-    struct gguf_init_params params_gguf;
-    params_gguf.no_alloc = true;
-    params_gguf.ctx      = &ctx_in;
-    struct gguf_context * gguf_in = gguf_init_from_file(params->fn_model_base.c_str(), params_gguf);
-
-    // create new gguf
-    struct gguf_context * gguf_out = gguf_init_empty();
-
-    // copy meta data from base model: kv and tensors
-    gguf_set_kv(gguf_out, gguf_in);
-    int n_tensors = gguf_get_n_tensors(gguf_in);
-    for (int i=0; i < n_tensors; ++i) {
-        const char * name = gguf_get_tensor_name(gguf_in, i);
-        struct ggml_tensor * tensor = ggml_get_tensor(ctx_in, name);
-        gguf_add_tensor(gguf_out, tensor);
-    }
-
-    // create output file
-    struct llama_file fout(params->fn_model_out.c_str(), "wb");
-    if (!fout.fp) {
-        die_fmt("Could not create file '%s'\n", params->fn_model_out.c_str());
-    }
-
-    // write gguf meta data
-    std::vector<uint8_t> meta;
-    meta.resize(gguf_get_meta_size(gguf_out));
-    gguf_get_meta_data(gguf_out, meta.data());
-    fout.write_raw(meta.data(), meta.size());
-
-    std::vector<uint8_t> data;
-    std::vector<uint8_t> padding;
-    for (int i=0; i < n_tensors; ++i) {
-        const char * name = gguf_get_tensor_name(gguf_in, i);
-        struct ggml_tensor * tensor = ggml_get_tensor(ctx_in, name);
-
-        // read tensor data
-        data.resize(ggml_nbytes(tensor));
-        tensor->data = data.data();
-        size_t offset = gguf_get_tensor_offset(gguf_in, i);
-        fin.seek(offset + meta.size(), SEEK_SET);
-        fin.read_raw(data.data(), data.size());
-
-        // apply all loras
-        for (size_t k = 0; k < loras.size(); ++k) {
-            apply_lora(tensor, loras[k], params->n_threads);
-        }
-
-        // write tensor data + padding
-        padding.clear();
-        padding.resize(GGML_PAD(data.size(), gguf_get_alignment(gguf_out)) - data.size(), 0);
-
-        GGML_ASSERT(fout.tell() == offset + meta.size());
-        // fout.seek(offset + meta.size(), SEEK_SET);
-        fout.write_raw(data.data(), data.size());
-        fout.write_raw(padding.data(), padding.size());
-
-        if (i % 2 == 0) {
-            printf(".");
-        }
-    }
-    printf("\n");
-
-    // close gguf
-    gguf_free(gguf_out);
-    gguf_free(gguf_in);
-
-    // free loras
-    for (size_t i = 0; i < loras.size(); ++i) {
-        free_lora(loras[i]);
-    }
-}
-
-int main(int argc, char ** argv) {
-    struct export_lora_params params = get_default_export_lora_params();
-
-    if (!export_lora_params_parse(argc, argv, &params)) {
-        return 1;
-    }
-
-    export_lora(&params);
-
-    return 0;
-}
diff --git a/spaces/Illumotion/Koboldcpp/otherarch/gptj_v1.cpp b/spaces/Illumotion/Koboldcpp/otherarch/gptj_v1.cpp
deleted file mode 100644
index f112e84492adcff13f15ce1e35fffe9ee266520a..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/otherarch/gptj_v1.cpp
+++ /dev/null
@@ -1,624 +0,0 @@
-#include "ggml_v1.h"
-#include "otherarch.h"
-
-#include "utils.h"
-
-#include <cassert>
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <fstream>
-#include <map>
-#include <string>
-#include <vector>
-#include <iostream>
-
-
-
-// load the model's weights from a file
-ModelLoadResult legacy_gptj_model_load(const std::string & fname, gptj_v1_model & model, gpt_vocab & vocab, FileFormat file_format) {
-    printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
-
-    bool super_old_format = (file_format==FileFormat::GPTJ_1);
-
-    auto fin = std::ifstream(fname, std::ios::binary);
-    if (!fin) {
-        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
-        return ModelLoadResult::FAIL;
-    }
-
-    // verify magic
-    {
-        uint32_t magic;
-        fin.read((char *) &magic, sizeof(magic));
-        if (magic != 0x67676d6c) {
-            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
-            return ModelLoadResult::FAIL;
-        }
-    }
-
-    // load hparams
-    {
-        auto & hparams = model.hparams;
-
-        fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
-        fin.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
-        fin.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
-        fin.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
-        fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
-        fin.read((char *) &hparams.n_rot,   sizeof(hparams.n_rot));
-        fin.read((char *) &hparams.ftype,     sizeof(hparams.ftype));
-
-        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
-        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
-        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
-        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
-        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
-        printf("%s: n_rot   = %d\n", __func__, hparams.n_rot);
-        printf("%s: f16     = %d\n", __func__, hparams.ftype);
-    }
-
-    // load vocab
-    {
-        int32_t n_vocab = 0;
-        fin.read((char *) &n_vocab, sizeof(n_vocab));
-
-        if (n_vocab != model.hparams.n_vocab) {
-            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
-                    __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
-            return ModelLoadResult::FAIL;
-        }
-
-        std::string word;
-        for (int i = 0; i < n_vocab; i++) {
-            uint32_t len;
-            fin.read((char *) &len, sizeof(len));
-
-            word.resize(len);
-            fin.read((char *) word.data(), len);
-
-            vocab.token_to_id[word] = i;
-            vocab.id_to_token[i] = word;
-        }
-    }
-
-    // for the big tensors, we have the option to store the data in 16-bit floats or quantized
-    // in order to save memory and also to speed up the computation
-    ggml_v1_type wtype = GGML_V1_TYPE_COUNT;
-    switch (model.hparams.ftype) {
-        case 0: wtype = GGML_V1_TYPE_F32;  break;
-        case 1: wtype = GGML_V1_TYPE_F16;  break;
-        case 2: wtype = GGML_V1_TYPE_Q4_0; break;
-        case 3: wtype = GGML_V1_TYPE_Q4_1; break;
-        default:
-                {
-                    fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
-                            __func__, fname.c_str(), model.hparams.ftype);
-                    return ModelLoadResult::FAIL;
-                }
-    }
-
-    const ggml_v1_type wtype2 = GGML_V1_TYPE_F32;
-
-    auto & ctx = model.ctx;
-
-    auto memory_type = GGML_V1_TYPE_F16;
-
-    size_t ctx_size = 0;
-
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-        const int n_vocab = hparams.n_vocab;
-
-        ctx_size += n_embd*ggml_v1_type_sizef(GGML_V1_TYPE_F32); // ln_f_g
-        ctx_size += n_embd*ggml_v1_type_sizef(GGML_V1_TYPE_F32); // ln_f_b
-
-        ctx_size += n_embd*n_vocab*ggml_v1_type_sizef(wtype); // wte
-
-        ctx_size += n_embd*n_vocab*ggml_v1_type_sizef(wtype);         // lmh_g
-        ctx_size +=        n_vocab*ggml_v1_type_sizef(GGML_V1_TYPE_F32); // lmh_b
-
-        ctx_size += n_layer*(n_embd*ggml_v1_type_sizef(GGML_V1_TYPE_F32)); // ln_1_g
-        ctx_size += n_layer*(n_embd*ggml_v1_type_sizef(GGML_V1_TYPE_F32)); // ln_1_b
-
-        ctx_size += n_layer*(n_embd*n_embd*ggml_v1_type_sizef(wtype)); // c_attn_q_proj_w
-        ctx_size += n_layer*(n_embd*n_embd*ggml_v1_type_sizef(wtype)); // c_attn_k_proj_w
-        ctx_size += n_layer*(n_embd*n_embd*ggml_v1_type_sizef(wtype)); // c_attn_v_proj_w
-
-        ctx_size += n_layer*(n_embd*n_embd*ggml_v1_type_sizef(wtype)); // c_attn_proj_w
-
-        ctx_size += n_layer*(4*n_embd*n_embd*ggml_v1_type_sizef(wtype));         // c_mlp_fc_w
-        ctx_size += n_layer*(       4*n_embd*ggml_v1_type_sizef(GGML_V1_TYPE_F32)); // c_mlp_fc_b
-
-        ctx_size += n_layer*(4*n_embd*n_embd*ggml_v1_type_sizef(wtype));         // c_mlp_proj_w_trans
-        ctx_size += n_layer*(         n_embd*ggml_v1_type_sizef(GGML_V1_TYPE_F32)); // c_mlp_proj_b
-
-        ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_sizef(memory_type); // memory_k
-        ctx_size += n_ctx*n_layer*n_embd*ggml_v1_type_sizef(memory_type); // memory_v
-
-        ctx_size += (5 + 10*n_layer)*256; // object overhead
-
-        printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
-    }
-
-    // create the ggml context
-    {
-        struct ggml_v1_init_params params;
-        params.mem_size   = ctx_size;
-        params.mem_buffer = NULL;
-        
-
-        model.ctx = ggml_v1_init(params);
-        if (!model.ctx) {
-            fprintf(stderr, "%s: ggml_v1_init() failed\n", __func__);
-            return ModelLoadResult::FAIL;
-        }
-    }
-
-    // prepare memory for the weights
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-        const int n_vocab = hparams.n_vocab;
-
-        model.layers.resize(n_layer);
-
-        model.wte    = ggml_v1_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
-
-        model.ln_f_g = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_embd);
-        model.ln_f_b = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_embd);
-
-        model.lmh_g  = ggml_v1_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
-        model.lmh_b  = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, n_vocab);
-
-        // map by name
-        model.tensors["transformer.wte.weight"] = model.wte;
-
-        model.tensors["transformer.ln_f.weight"] = model.ln_f_g;
-        model.tensors["transformer.ln_f.bias"]   = model.ln_f_b;
-
-        model.tensors["lm_head.weight"] = model.lmh_g;
-        model.tensors["lm_head.bias"]   = model.lmh_b;
-
-        for (int i = 0; i < n_layer; ++i) {
-            auto & layer = model.layers[i];
-
-            layer.ln_1_g                = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32,   n_embd);
-            layer.ln_1_b                = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32,   n_embd);
-
-            layer.c_attn_q_proj_w       = ggml_v1_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
-            layer.c_attn_k_proj_w       = ggml_v1_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
-            layer.c_attn_v_proj_w       = ggml_v1_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
-
-            layer.c_attn_proj_w         = ggml_v1_new_tensor_2d(ctx, wtype,           n_embd,   n_embd);
-            
-            if(super_old_format)
-            {
-                layer.c_mlp_fc_w        = ggml_v1_new_tensor_2d(ctx, wtype,         4*n_embd,   n_embd);
-            }
-            else
-            {
-                layer.c_mlp_fc_w        = ggml_v1_new_tensor_2d(ctx, wtype,           n_embd, 4*n_embd);
-            }
-            layer.c_mlp_fc_b            = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32, 4*n_embd);
-
-            layer.c_mlp_proj_w_trans    = ggml_v1_new_tensor_2d(ctx, wtype,         4*n_embd,   n_embd);
-            layer.c_mlp_proj_b          = ggml_v1_new_tensor_1d(ctx, GGML_V1_TYPE_F32,   n_embd);
-
-            // map by name
-            model.tensors["transformer.h." + std::to_string(i) + ".ln_1.weight"]          = layer.ln_1_g;
-            model.tensors["transformer.h." + std::to_string(i) + ".ln_1.bias"]            = layer.ln_1_b;
-
-            model.tensors["transformer.h." + std::to_string(i) + ".attn.q_proj.weight"]   = layer.c_attn_q_proj_w;
-            model.tensors["transformer.h." + std::to_string(i) + ".attn.k_proj.weight"]   = layer.c_attn_k_proj_w;
-            model.tensors["transformer.h." + std::to_string(i) + ".attn.v_proj.weight"]   = layer.c_attn_v_proj_w;
-
-            model.tensors["transformer.h." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_proj_w;
-
-            model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.weight"]     = layer.c_mlp_fc_w;
-            model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.bias"]       = layer.c_mlp_fc_b;
-
-            model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.weight"]    = layer.c_mlp_proj_w_trans;
-            model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.bias"]      = layer.c_mlp_proj_b;
-        }
-    }
-
-    // key + value memory
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-
-        const int n_mem      = n_layer*n_ctx;
-        const int n_elements = n_embd*n_mem;
-
-        model.memory_k = ggml_v1_new_tensor_1d(ctx, memory_type, n_elements);
-        model.memory_v = ggml_v1_new_tensor_1d(ctx, memory_type, n_elements);
-
-        const size_t memory_size = ggml_v1_nbytes(model.memory_k) + ggml_v1_nbytes(model.memory_v);
-
-        printf("%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
-    }
-
-    // load weights
-    {
-        int n_tensors = 0;
-        size_t total_size = 0;
-
-        printf("%s: ", __func__);
-
-        while (true) {
-            int32_t n_dims;
-            int32_t length;
-            int32_t ftype;
-
-            fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
-            fin.read(reinterpret_cast<char *>(&length), sizeof(length));
-            fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
-
-            if (fin.eof()) {
-                break;
-            }
-
-            int32_t nelements = 1;
-            int32_t ne[2] = { 1, 1 };
-            for (int i = 0; i < n_dims; ++i) {
-                fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
-                nelements *= ne[i];
-            }
-
-            std::string name(length, 0);
-            fin.read(&name[0], length);
-
-            if (model.tensors.find(name.data()) == model.tensors.end()) {
-                fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
-                return ModelLoadResult::FAIL;
-            }
-
-            auto tensor = model.tensors[name.data()];
-            if (ggml_v1_nelements(tensor) != nelements) {
-                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
-                return ModelLoadResult::FAIL;
-            }
-
-            if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) 
-            {
-               //test for transposition and retry older loader
-                if(tensor->ne[0]==ne[1] && tensor->ne[1]==ne[0] && should_transpose_layer(name))
-                {
-                    printf("\nFound a transposed tensor. This could be an older or newer model. Retrying load...");
-                    ggml_v1_free(ctx);
-                    return ModelLoadResult::RETRY_LOAD;
-                }
-                else
-                {
-                    fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
-                            __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
-                    return ModelLoadResult::FAIL;
-                }
-            }
-
-            if (0) {
-                static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
-                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ftype_str[ftype], ggml_v1_nbytes(tensor)/1024.0/1024.0, ggml_v1_nbytes(tensor));
-            }
-
-            size_t bpe = 0;
-
-            switch (ftype) {
-                case 0: bpe = ggml_v1_type_size(GGML_V1_TYPE_F32);  break;
-                case 1: bpe = ggml_v1_type_size(GGML_V1_TYPE_F16);  break;
-                case 2: bpe = ggml_v1_type_size(GGML_V1_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
-                case 3: bpe = ggml_v1_type_size(GGML_V1_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
-                default:
-                        {
-                            fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
-                            return ModelLoadResult::FAIL;
-                        }
-            };
-
-            if ((nelements*bpe)/ggml_v1_blck_size(tensor->type) != ggml_v1_nbytes(tensor)) {
-                fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
-                        __func__, name.data(), ggml_v1_nbytes(tensor), nelements*bpe);
-                return ModelLoadResult::FAIL;
-            }
-
-            fin.read(reinterpret_cast<char *>(tensor->data), ggml_v1_nbytes(tensor));
-
-            //printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_v1_nbytes(tensor)/1024.0/1024.0);
-            total_size += ggml_v1_nbytes(tensor);
-            if (++n_tensors % 8 == 0) {
-                printf(".");
-                fflush(stdout);
-            }
-        }
-
-        printf(" done\n");
-
-        printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
-    }
-
-    fin.close();
-
-    return ModelLoadResult::SUCCESS;
-}
-
-// evaluate the transformer
-//
-//   - model:     the model
-//   - n_threads: number of threads to use
-//   - n_past:    the context size so far
-//   - embd_inp:  the embeddings of the tokens in the context
-//   - embd_w:    the predicted logits for the next token
-//
-// The GPT-J model requires about 16MB of memory per input token.
-//
-bool legacy_gptj_eval(
-        const gptj_v1_model & model,
-        const int n_threads,
-        const int n_past,
-        const std::vector<gpt_vocab::id> & embd_inp,
-              std::vector<float>         & embd_w,
-              size_t                     & mem_per_token,
-       FileFormat file_format) {
-
-    bool super_old_format = (file_format==FileFormat::GPTJ_1);
-    const int N = embd_inp.size();
-
-    const auto & hparams = model.hparams;
-
-    const int n_embd  = hparams.n_embd;
-    const int n_layer = hparams.n_layer;
-    const int n_ctx   = hparams.n_ctx;
-    const int n_head  = hparams.n_head;
-    const int n_vocab = hparams.n_vocab;
-    const int n_rot   = hparams.n_rot;
-
-    const int d_key = n_embd/n_head;
-
-    static size_t buf_size = 256u*1024*1024;
-    static void * buf = malloc(buf_size);
-
-    if (mem_per_token > 0 && mem_per_token*N > buf_size) {
-        const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
-        //printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
-
-        // reallocate
-        buf_size = buf_size_new;
-        buf = realloc(buf, buf_size);
-        if (buf == nullptr) {
-            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
-            return false;
-        }
-    }
-
-    struct ggml_v1_init_params params;
-    params.mem_size   = buf_size;
-    params.mem_buffer = buf;
-    
-
-    struct ggml_v1_context * ctx0 = ggml_v1_init(params);
-    struct ggml_v1_cgraph gf = {};
-    gf.n_threads = n_threads;
-
-    struct ggml_v1_tensor * embd = ggml_v1_new_tensor_1d(ctx0, GGML_V1_TYPE_I32, N);
-    memcpy(embd->data, embd_inp.data(), N*ggml_v1_element_size(embd));
-
-    // wte
-    struct ggml_v1_tensor * inpL = ggml_v1_get_rows(ctx0, model.wte, embd);
-
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_v1_tensor * cur;
-
-        // norm
-        {
-            cur = ggml_v1_norm(ctx0, inpL);
-
-            // cur = ln_1_g*cur + ln_1_b
-            cur = ggml_v1_add(ctx0,
-                    ggml_v1_mul(ctx0,
-                        ggml_v1_repeat(ctx0, model.layers[il].ln_1_g, cur),
-                        cur),
-                    ggml_v1_repeat(ctx0, model.layers[il].ln_1_b, cur));
-        }
-
-        struct ggml_v1_tensor * inpSA = cur;
-
-        // self-attention
-        {
-            struct ggml_v1_tensor * Qcur;
-            struct ggml_v1_tensor * Kcur;
-            struct ggml_v1_tensor * Vcur;
-            if(super_old_format)
-            {
-                Qcur = ggml_v1_mul_mat(ctx0, ggml_v1_transpose(ctx0, model.layers[il].c_attn_q_proj_w), cur);
-                Kcur = ggml_v1_mul_mat(ctx0, ggml_v1_transpose(ctx0, model.layers[il].c_attn_k_proj_w), cur);
-                Vcur = ggml_v1_mul_mat(ctx0, ggml_v1_transpose(ctx0, model.layers[il].c_attn_v_proj_w), cur);
-            }
-            else
-            {
-                Qcur = ggml_v1_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur);
-                Kcur = ggml_v1_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur);
-                Vcur = ggml_v1_mul_mat(ctx0, model.layers[il].c_attn_v_proj_w, cur);
-            }
-
-            // store key and value to memory
-            if (N >= 1) {
-                struct ggml_v1_tensor * k = ggml_v1_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_v1_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
-                struct ggml_v1_tensor * v = ggml_v1_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_v1_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
-
-                ggml_v1_build_forward_expand(&gf, ggml_v1_cpy(ctx0, Kcur, k));
-                ggml_v1_build_forward_expand(&gf, ggml_v1_cpy(ctx0, Vcur, v));
-            }
-
-            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
-            struct ggml_v1_tensor * Q =
-                ggml_v1_permute(ctx0,
-                        ggml_v1_rope(ctx0,
-                            ggml_v1_cpy(ctx0,
-                                Qcur,
-                                ggml_v1_new_tensor_3d(ctx0, GGML_V1_TYPE_F32, n_embd/n_head, n_head, N)),
-                            n_past, n_rot, 0),
-                        0, 2, 1, 3);
-
-            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
-            struct ggml_v1_tensor * K =
-                ggml_v1_permute(ctx0,
-                        ggml_v1_rope(ctx0,
-                            ggml_v1_reshape_3d(ctx0,
-                                ggml_v1_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_v1_element_size(model.memory_k)*n_embd),
-                                n_embd/n_head, n_head, n_past + N),
-                            n_past, n_rot, 1),
-                        0, 2, 1, 3);
-
-            // K * Q
-            struct ggml_v1_tensor * KQ = ggml_v1_mul_mat(ctx0, K, Q);
-
-            // KQ_scaled = KQ / sqrt(n_embd/n_head)
-            struct ggml_v1_tensor * KQ_scaled =
-                ggml_v1_scale(ctx0,
-                        KQ,
-                        ggml_v1_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
-                        );
-
-            // KQ_masked = mask_past(KQ_scaled)
-            struct ggml_v1_tensor * KQ_masked = ggml_v1_diag_mask_inf(ctx0, KQ_scaled, n_past);
-
-            // KQ = soft_max(KQ_masked)
-            struct ggml_v1_tensor * KQ_soft_max = ggml_v1_soft_max(ctx0, KQ_masked);
-
-            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
-            struct ggml_v1_tensor * V_trans =
-                ggml_v1_permute(ctx0,
-                        ggml_v1_reshape_3d(ctx0,
-                            ggml_v1_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_v1_element_size(model.memory_v)*n_embd),
-                            n_embd/n_head, n_head, n_past + N),
-                        1, 2, 0, 3);
-
-            // KQV = transpose(V) * KQ_soft_max
-            struct ggml_v1_tensor * KQV = ggml_v1_mul_mat(ctx0, V_trans, KQ_soft_max);
-
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            struct ggml_v1_tensor * KQV_merged = ggml_v1_permute(ctx0, KQV, 0, 2, 1, 3);
-
-            // cur = KQV_merged.contiguous().view(n_embd, N)
-            cur = ggml_v1_cpy(ctx0,
-                    KQV_merged,
-                    ggml_v1_new_tensor_2d(ctx0, GGML_V1_TYPE_F32, n_embd, N));
-
-            // projection (no bias)
-            if(super_old_format)
-            {
-                cur = ggml_v1_mul_mat(ctx0,
-                        ggml_v1_transpose(ctx0, model.layers[il].c_attn_proj_w),
-                        cur);
-            }
-            else
-            {                
-                cur = ggml_v1_mul_mat(ctx0,
-                        model.layers[il].c_attn_proj_w,
-                        cur);
-            }
-        }
-
-        struct ggml_v1_tensor * inpFF = cur;
-
-        // feed-forward network
-        // this is independent of the self-attention result, so it could be done in parallel to the self-attention
-        {
-            // note here we pass inpSA instead of cur
-            if(super_old_format)
-            {
-                cur = ggml_v1_mul_mat(ctx0,
-                ggml_v1_transpose(ctx0, model.layers[il].c_mlp_fc_w),
-                inpSA);
-            }else{
-                cur = ggml_v1_mul_mat(ctx0,
-                model.layers[il].c_mlp_fc_w,
-                inpSA);
-            }
-
-            cur = ggml_v1_add(ctx0,
-                    ggml_v1_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
-                    cur);
-
-            // GELU activation
-            cur = ggml_v1_gelu(ctx0, cur);
-
-            // projection
-            // cur = proj_w*cur + proj_b
-            cur = ggml_v1_mul_mat(ctx0,
-                    model.layers[il].c_mlp_proj_w_trans,
-                    cur);
-
-            cur = ggml_v1_add(ctx0,
-                    ggml_v1_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
-                    cur);
-        }
-
-        // self-attention + FF
-        cur  = ggml_v1_add(ctx0, cur, inpFF);
-
-        // input for next layer
-        inpL = ggml_v1_add(ctx0, cur, inpL);
-    }
-
-    // norm
-    {
-        inpL = ggml_v1_norm(ctx0, inpL);
-
-        // inpL = ln_f_g*inpL + ln_f_b
-        inpL = ggml_v1_add(ctx0,
-                ggml_v1_mul(ctx0,
-                    ggml_v1_repeat(ctx0, model.ln_f_g, inpL),
-                    inpL),
-                ggml_v1_repeat(ctx0, model.ln_f_b, inpL));
-    }
-
-    // lm_head
-    {
-        inpL = ggml_v1_mul_mat(ctx0, model.lmh_g, inpL);
-
-        inpL = ggml_v1_add(ctx0,
-                ggml_v1_repeat(ctx0, model.lmh_b, inpL),
-                inpL);
-    }
-
-    // logits -> probs
-    //inpL = ggml_v1_soft_max(ctx0, inpL);
-
-    // run the computation
-    ggml_v1_build_forward_expand(&gf, inpL);
-    ggml_v1_graph_compute       (ctx0, &gf);
-
-    //if (n_past%100 == 0) {
-    //    ggml_v1_graph_print   (&gf);
-    //    ggml_v1_graph_dump_dot(&gf, NULL, "gpt-2.dot");
-    //}
-
-    //embd_w.resize(n_vocab*N);
-    //memcpy(embd_w.data(), ggml_v1_get_data(inpL), sizeof(float)*n_vocab*N);
-
-    // return result for just the last token
-    embd_w.resize(n_vocab);
-    memcpy(embd_w.data(), (float *) ggml_v1_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
-
-    if (mem_per_token == 0) {
-        mem_per_token = ggml_v1_used_mem(ctx0)/N;
-    }
-    //printf("used_mem = %zu\n", ggml_v1_used_mem(ctx0));
-
-    ggml_v1_free(ctx0);
-
-    return true;
-}
-
diff --git a/spaces/Illumotion/Koboldcpp/otherarch/tools/common-ggml.h b/spaces/Illumotion/Koboldcpp/otherarch/tools/common-ggml.h
deleted file mode 100644
index 29ba4ad5f2042b2676d57a6456f546842721a3a0..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/otherarch/tools/common-ggml.h
+++ /dev/null
@@ -1,18 +0,0 @@
-#pragma once
-
-#include "ggml.h"
-
-#include <fstream>
-#include <vector>
-#include <string>
-
-enum ggml_ftype ggml_parse_ftype(const char * str);
-
-void ggml_print_ftypes(FILE * fp = stderr);
-
-bool ggml_common_quantize_0(
-        std::ifstream & finp,
-        std::ofstream & fout,
-        const ggml_ftype ftype,
-        const std::vector<std::string> & to_quant,
-        const std::vector<std::string> & to_skip);
\ No newline at end of file
diff --git a/spaces/Intoval/privateChatGPT/run_Linux.sh b/spaces/Intoval/privateChatGPT/run_Linux.sh
deleted file mode 100644
index 2d26597ae47519f42336ccffc16646713a192ae1..0000000000000000000000000000000000000000
--- a/spaces/Intoval/privateChatGPT/run_Linux.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/bin/bash
-
-# 获取脚本所在目录
-script_dir=$(dirname "$(readlink -f "$0")")
-
-# 将工作目录更改为脚本所在目录
-cd "$script_dir" || exit
-
-# 检查Git仓库是否有更新
-git remote update
-pwd
-
-if ! git status -uno | grep 'up to date' > /dev/null; then
-	# 如果有更新，关闭当前运行的服务器
-	pkill -f ChuanhuChatbot.py
-
-	# 拉取最新更改
-	git pull
-
-	# 安装依赖
-	pip3 install -r requirements.txt
-
-	# 重新启动服务器
-	nohup python3 ChuanhuChatbot.py &
-fi
-
-# 检查ChuanhuChatbot.py是否在运行
-if ! pgrep -f ChuanhuChatbot.py > /dev/null; then
-	# 如果没有运行，启动服务器
-	nohup python3 ChuanhuChatbot.py &
-fi
diff --git a/spaces/Izal887/rvc-ram12/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/Izal887/rvc-ram12/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index ee3171bcb7c4a5066560723108b56e055f18be45..0000000000000000000000000000000000000000
--- a/spaces/Izal887/rvc-ram12/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class DioF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py b/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
deleted file mode 100644
index a3a8703f3ea4070337e5f55be5199277c00413ab..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
+++ /dev/null
@@ -1,578 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Callable, List, Optional, Union
-
-import torch
-
-from diffusers.utils import is_accelerate_available
-from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
-from ...configuration_utils import FrozenDict
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import (
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    LMSDiscreteScheduler,
-    PNDMScheduler,
-)
-from ...utils import deprecate, logging
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class StableDiffusionPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-to-image generation using Stable Diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[
-            DDIMScheduler,
-            PNDMScheduler,
-            LMSDiscreteScheduler,
-            EulerDiscreteScheduler,
-            EulerAncestralDiscreteScheduler,
-            DPMSolverMultistepScheduler,
-        ],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
-                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
-                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
-                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
-                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
-                " file"
-            )
-            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["steps_offset"] = 1
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
-            deprecation_message = (
-                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
-                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
-                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
-                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
-                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
-            )
-            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(scheduler.config)
-            new_config["clip_sample"] = False
-            scheduler._internal_dict = FrozenDict(new_config)
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
-            version.parse(unet.config._diffusers_version).base_version
-        ) < version.parse("0.9.0.dev0")
-        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
-        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
-            deprecation_message = (
-                "The configuration file of the unet has set the default `sample_size` to smaller than"
-                " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
-                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
-                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
-                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
-                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
-                " in the config might lead to incorrect results in future versions. If you have downloaded this"
-                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
-                " the `unet/config.json` file"
-            )
-            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
-            new_config = dict(unet.config)
-            new_config["sample_size"] = 64
-            unet._internal_dict = FrozenDict(new_config)
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
-        r"""
-        Enable sliced attention computation.
-
-        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
-        in several steps. This is useful to save some memory in exchange for a small speed decrease.
-
-        Args:
-            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
-                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
-                `attention_head_dim` must be a multiple of `slice_size`.
-        """
-        if slice_size == "auto":
-            if isinstance(self.unet.config.attention_head_dim, int):
-                # half the attention head size is usually a good trade-off between
-                # speed and memory
-                slice_size = self.unet.config.attention_head_dim // 2
-            else:
-                # if `attention_head_dim` is a list, take the smallest head size
-                slice_size = min(self.unet.config.attention_head_dim)
-
-        self.unet.set_attention_slice(slice_size)
-
-    def disable_attention_slicing(self):
-        r"""
-        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
-        back to computing attention in one step.
-        """
-        # set slice_size = `None` to disable `attention slicing`
-        self.enable_attention_slicing(None)
-
-    def enable_vae_slicing(self):
-        r"""
-        Enable sliced VAE decoding.
-
-        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
-        steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.vae.enable_slicing()
-
-    def disable_vae_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_slicing()
-
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
-        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
-        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
-        """
-        if is_accelerate_available():
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
-            if cpu_offloaded_model is not None:
-                cpu_offload(cpu_offloaded_model, device)
-
-        if self.safety_checker is not None:
-            # TODO(Patrick) - there is currently a bug with cpu offload of nn.Parameter in accelerate
-            # fix by only offloading self.safety_checker for now
-            cpu_offload(self.safety_checker.vision_model, device)
-
-    @property
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
-            return self.device
-        for module in self.unet.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `list(int)`):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-        """
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-        untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
-
-        if not torch.equal(text_input_ids, untruncated_ids):
-            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-
-        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-            attention_mask = text_inputs.attention_mask.to(device)
-        else:
-            attention_mask = None
-
-        text_embeddings = self.text_encoder(
-            text_input_ids.to(device),
-            attention_mask=attention_mask,
-        )
-        text_embeddings = text_embeddings[0]
-
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = text_embeddings.shape
-        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
-        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = text_input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            uncond_embeddings = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            uncond_embeddings = uncond_embeddings[0]
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
-            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-        return text_embeddings
-
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is not None:
-            safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        else:
-            has_nsfw_concept = None
-        return image, has_nsfw_concept
-
-    def decode_latents(self, latents):
-        latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents).sample
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    def check_inputs(self, prompt, height, width, callback_steps):
-        if not isinstance(prompt, str) and not isinstance(prompt, list):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        if latents is None:
-            if device.type == "mps":
-                # randn does not work reproducibly on mps
-                latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
-            else:
-                latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: Optional[int] = 1,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(prompt, height, width, callback_steps)
-
-        # 2. Define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        text_embeddings = self._encode_prompt(
-            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
-        )
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.unet.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            text_embeddings.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 7. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                    # compute the previous noisy sample x_t -> x_t-1
-                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        # 8. Post-processing
-        image = self.decode_latents(latents)
-
-        # 9. Run safety checker
-        image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)
-
-        # 10. Convert to PIL
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/app.py b/spaces/Jamkonams/AutoGPT/autogpt/app.py
deleted file mode 100644
index 58d9f7164ddfbb5019b072d789dc2fa6205dc9d3..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/autogpt/app.py
+++ /dev/null
@@ -1,330 +0,0 @@
-""" Command and Control """
-import json
-from typing import Dict, List, NoReturn, Union
-
-from autogpt.agent.agent_manager import AgentManager
-from autogpt.commands.analyze_code import analyze_code
-from autogpt.commands.audio_text import read_audio_from_file
-from autogpt.commands.execute_code import (
-    execute_python_file,
-    execute_shell,
-    execute_shell_popen,
-)
-from autogpt.commands.file_operations import (
-    append_to_file,
-    delete_file,
-    download_file,
-    read_file,
-    search_files,
-    write_to_file,
-)
-from autogpt.commands.git_operations import clone_repository
-from autogpt.commands.google_search import google_official_search, google_search
-from autogpt.commands.image_gen import generate_image
-from autogpt.commands.improve_code import improve_code
-from autogpt.commands.twitter import send_tweet
-from autogpt.commands.web_requests import scrape_links, scrape_text
-from autogpt.commands.web_selenium import browse_website
-from autogpt.commands.write_tests import write_tests
-from autogpt.config import Config
-from autogpt.json_utils.json_fix_llm import fix_and_parse_json
-from autogpt.memory import get_memory
-from autogpt.processing.text import summarize_text
-from autogpt.speech import say_text
-
-CFG = Config()
-AGENT_MANAGER = AgentManager()
-
-
-def is_valid_int(value: str) -> bool:
-    """Check if the value is a valid integer
-
-    Args:
-        value (str): The value to check
-
-    Returns:
-        bool: True if the value is a valid integer, False otherwise
-    """
-    try:
-        int(value)
-        return True
-    except ValueError:
-        return False
-
-
-def get_command(response_json: Dict):
-    """Parse the response and return the command name and arguments
-
-    Args:
-        response_json (json): The response from the AI
-
-    Returns:
-        tuple: The command name and arguments
-
-    Raises:
-        json.decoder.JSONDecodeError: If the response is not valid JSON
-
-        Exception: If any other error occurs
-    """
-    try:
-        if "command" not in response_json:
-            return "Error:", "Missing 'command' object in JSON"
-
-        if not isinstance(response_json, dict):
-            return "Error:", f"'response_json' object is not dictionary {response_json}"
-
-        command = response_json["command"]
-        if not isinstance(command, dict):
-            return "Error:", "'command' object is not a dictionary"
-
-        if "name" not in command:
-            return "Error:", "Missing 'name' field in 'command' object"
-
-        command_name = command["name"]
-
-        # Use an empty dictionary if 'args' field is not present in 'command' object
-        arguments = command.get("args", {})
-
-        return command_name, arguments
-    except json.decoder.JSONDecodeError:
-        return "Error:", "Invalid JSON"
-    # All other errors, return "Error: + error message"
-    except Exception as e:
-        return "Error:", str(e)
-
-
-def map_command_synonyms(command_name: str):
-    """Takes the original command name given by the AI, and checks if the
-    string matches a list of common/known hallucinations
-    """
-    synonyms = [
-        ("write_file", "write_to_file"),
-        ("create_file", "write_to_file"),
-        ("search", "google"),
-    ]
-    for seen_command, actual_command_name in synonyms:
-        if command_name == seen_command:
-            return actual_command_name
-    return command_name
-
-
-def execute_command(command_name: str, arguments):
-    """Execute the command and return the result
-
-    Args:
-        command_name (str): The name of the command to execute
-        arguments (dict): The arguments for the command
-
-    Returns:
-        str: The result of the command
-    """
-    try:
-        command_name = map_command_synonyms(command_name.lower())
-        if command_name == "google":
-            # Check if the Google API key is set and use the official search method
-            # If the API key is not set or has only whitespaces, use the unofficial
-            # search method
-            key = CFG.google_api_key
-            if key and key.strip() and key != "your-google-api-key":
-                google_result = google_official_search(arguments["input"])
-                return google_result
-            else:
-                google_result = google_search(arguments["input"])
-
-            # google_result can be a list or a string depending on the search results
-            if isinstance(google_result, list):
-                safe_message = [
-                    google_result_single.encode("utf-8", "ignore")
-                    for google_result_single in google_result
-                ]
-            else:
-                safe_message = google_result.encode("utf-8", "ignore")
-
-            return safe_message.decode("utf-8")
-        elif command_name == "memory_add":
-            memory = get_memory(CFG)
-            return memory.add(arguments["string"])
-        elif command_name == "start_agent":
-            return start_agent(
-                arguments["name"], arguments["task"], arguments["prompt"]
-            )
-        elif command_name == "message_agent":
-            return message_agent(arguments["key"], arguments["message"])
-        elif command_name == "list_agents":
-            return list_agents()
-        elif command_name == "delete_agent":
-            return delete_agent(arguments["key"])
-        elif command_name == "get_text_summary":
-            return get_text_summary(arguments["url"], arguments["question"])
-        elif command_name == "get_hyperlinks":
-            return get_hyperlinks(arguments["url"])
-        elif command_name == "clone_repository":
-            return clone_repository(
-                arguments["repository_url"], arguments["clone_path"]
-            )
-        elif command_name == "read_file":
-            return read_file(arguments["file"])
-        elif command_name == "write_to_file":
-            return write_to_file(arguments["file"], arguments["text"])
-        elif command_name == "append_to_file":
-            return append_to_file(arguments["file"], arguments["text"])
-        elif command_name == "delete_file":
-            return delete_file(arguments["file"])
-        elif command_name == "search_files":
-            return search_files(arguments["directory"])
-        elif command_name == "download_file":
-            if not CFG.allow_downloads:
-                return "Error: You do not have user authorization to download files locally."
-            return download_file(arguments["url"], arguments["file"])
-        elif command_name == "browse_website":
-            return browse_website(arguments["url"], arguments["question"])
-        # TODO: Change these to take in a file rather than pasted code, if
-        # non-file is given, return instructions "Input should be a python
-        # filepath, write your code to file and try again"
-        elif command_name == "analyze_code":
-            return analyze_code(arguments["code"])
-        elif command_name == "improve_code":
-            return improve_code(arguments["suggestions"], arguments["code"])
-        elif command_name == "write_tests":
-            return write_tests(arguments["code"], arguments.get("focus"))
-        elif command_name == "execute_python_file":  # Add this command
-            return execute_python_file(arguments["file"])
-        elif command_name == "execute_shell":
-            if CFG.execute_local_commands:
-                return execute_shell(arguments["command_line"])
-            else:
-                return (
-                    "You are not allowed to run local shell commands. To execute"
-                    " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
-                    "in your config. Do not attempt to bypass the restriction."
-                )
-        elif command_name == "execute_shell_popen":
-            if CFG.execute_local_commands:
-                return execute_shell_popen(arguments["command_line"])
-            else:
-                return (
-                    "You are not allowed to run local shell commands. To execute"
-                    " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
-                    "in your config. Do not attempt to bypass the restriction."
-                )
-        elif command_name == "read_audio_from_file":
-            return read_audio_from_file(arguments["file"])
-        elif command_name == "generate_image":
-            return generate_image(arguments["prompt"])
-        elif command_name == "send_tweet":
-            return send_tweet(arguments["text"])
-        elif command_name == "do_nothing":
-            return "No action performed."
-        elif command_name == "task_complete":
-            shutdown()
-        else:
-            return (
-                f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'"
-                " list for available commands and only respond in the specified JSON"
-                " format."
-            )
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def get_text_summary(url: str, question: str) -> str:
-    """Return the results of a Google search
-
-    Args:
-        url (str): The url to scrape
-        question (str): The question to summarize the text for
-
-    Returns:
-        str: The summary of the text
-    """
-    text = scrape_text(url)
-    summary = summarize_text(url, text, question)
-    return f""" "Result" : {summary}"""
-
-
-def get_hyperlinks(url: str) -> Union[str, List[str]]:
-    """Return the results of a Google search
-
-    Args:
-        url (str): The url to scrape
-
-    Returns:
-        str or list: The hyperlinks on the page
-    """
-    return scrape_links(url)
-
-
-def shutdown() -> NoReturn:
-    """Shut down the program"""
-    print("Shutting down...")
-    quit()
-
-
-def start_agent(name: str, task: str, prompt: str, model=CFG.fast_llm_model) -> str:
-    """Start an agent with a given name, task, and prompt
-
-    Args:
-        name (str): The name of the agent
-        task (str): The task of the agent
-        prompt (str): The prompt for the agent
-        model (str): The model to use for the agent
-
-    Returns:
-        str: The response of the agent
-    """
-    # Remove underscores from name
-    voice_name = name.replace("_", " ")
-
-    first_message = f"""You are {name}.  Respond with: "Acknowledged"."""
-    agent_intro = f"{voice_name} here, Reporting for duty!"
-
-    # Create agent
-    if CFG.speak_mode:
-        say_text(agent_intro, 1)
-    key, ack = AGENT_MANAGER.create_agent(task, first_message, model)
-
-    if CFG.speak_mode:
-        say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
-
-    # Assign task (prompt), get response
-    agent_response = AGENT_MANAGER.message_agent(key, prompt)
-
-    return f"Agent {name} created with key {key}. First response: {agent_response}"
-
-
-def message_agent(key: str, message: str) -> str:
-    """Message an agent with a given key and message"""
-    # Check if the key is a valid integer
-    if is_valid_int(key):
-        agent_response = AGENT_MANAGER.message_agent(int(key), message)
-    else:
-        return "Invalid key, must be an integer."
-
-    # Speak response
-    if CFG.speak_mode:
-        say_text(agent_response, 1)
-    return agent_response
-
-
-def list_agents():
-    """List all agents
-
-    Returns:
-        str: A list of all agents
-    """
-    return "List of agents:\n" + "\n".join(
-        [str(x[0]) + ": " + x[1] for x in AGENT_MANAGER.list_agents()]
-    )
-
-
-def delete_agent(key: str) -> str:
-    """Delete an agent with a given key
-
-    Args:
-        key (str): The key of the agent to delete
-
-    Returns:
-        str: A message indicating whether the agent was deleted or not
-    """
-    result = AGENT_MANAGER.delete_agent(key)
-    return f"Agent {key} deleted." if result else f"Agent {key} does not exist."
diff --git a/spaces/JosephTK/review-sentiment-analyzer/app.py b/spaces/JosephTK/review-sentiment-analyzer/app.py
deleted file mode 100644
index c6a34aef892385e2742eaf7fa2f110c97bafef81..0000000000000000000000000000000000000000
--- a/spaces/JosephTK/review-sentiment-analyzer/app.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import gradio as gr
-import transformers
-from transformers import pipeline
-import torch 
-
-pipeline = pipeline(task="text-classification", model="JosephTK/NLP-reviews",
-                    return_all_scores=True)
-
-def classify(text):
-    predictions = pipeline(text)[0]
-    return {p["label"]: p["score"] for p in predictions}
-
-
-gr.Interface(
-    classify,
-    inputs=gr.Textbox(value="The food was delicious", label="Input Text"),
-    outputs=gr.Label(num_top_classes=5),
-    title="Review analyzer",
-).launch()
\ No newline at end of file
diff --git a/spaces/Kayson/InstructDiffusion/stable_diffusion/Stable_Diffusion_v1_Model_Card.md b/spaces/Kayson/InstructDiffusion/stable_diffusion/Stable_Diffusion_v1_Model_Card.md
deleted file mode 100644
index ad76ad2ee6da62ad21c8a92e9082a31b272740f3..0000000000000000000000000000000000000000
--- a/spaces/Kayson/InstructDiffusion/stable_diffusion/Stable_Diffusion_v1_Model_Card.md
+++ /dev/null
@@ -1,144 +0,0 @@
-# Stable Diffusion v1 Model Card
-This model card focuses on the model associated with the Stable Diffusion model, available [here](https://github.com/CompVis/stable-diffusion).
-
-## Model Details
-- **Developed by:** Robin Rombach, Patrick Esser
-- **Model type:** Diffusion-based text-to-image generation model
-- **Language(s):** English
-- **License:** [Proprietary](LICENSE)
-- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
-- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
-- **Cite as:**
-
-      @InProceedings{Rombach_2022_CVPR,
-          author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
-          title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
-          booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-          month     = {June},
-          year      = {2022},
-          pages     = {10684-10695}
-      }
-
-# Uses
-
-## Direct Use 
-The model is intended for research purposes only. Possible research areas and
-tasks include
-
-- Safe deployment of models which have the potential to generate harmful content.
-- Probing and understanding the limitations and biases of generative models.
-- Generation of artworks and use in design and other artistic processes.
-- Applications in educational or creative tools.
-- Research on generative models.
-
-Excluded uses are described below.
-
- ### Misuse, Malicious Use, and Out-of-Scope Use
-_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
-
-The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
-
-#### Out-of-Scope Use
-The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
-
-#### Misuse and Malicious Use
-Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
-
-- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
-- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
-- Impersonating individuals without their consent.
-- Sexual content without consent of the people who might see it.
-- Mis- and disinformation
-- Representations of egregious violence and gore
-- Sharing of copyrighted or licensed material in violation of its terms of use.
-- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
-
-## Limitations and Bias
-
-### Limitations
-
-- The model does not achieve perfect photorealism
-- The model cannot render legible text
-- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
-- Faces and people in general may not be generated properly.
-- The model was trained mainly with English captions and will not work as well in other languages.
-- The autoencoding part of the model is lossy
-- The model was trained on a large-scale dataset
-  [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
-  and is not fit for product use without additional safety mechanisms and
-  considerations.
-- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
-  The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
-
-### Bias
-While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. 
-Stable Diffusion v1 was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), 
-which consists of images that are limited to English descriptions. 
-Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. 
-This affects the overall output of the model, as white and western cultures are often set as the default. Further, the 
-ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
-Stable Diffusion v1 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
-
-
-## Training
-
-**Training Data**
-The model developers used the following dataset for training the model:
-
-- LAION-5B and subsets thereof (see next section)
-
-**Training Procedure**
-Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, 
-
-- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
-- Text prompts are encoded through a ViT-L/14 text-encoder.
-- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
-- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
-
-We currently provide the following checkpoints:
-
-- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
-  194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
-- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
-  515k steps at resolution `512x512` on [laion-aesthetics v2 5+](https://laion.ai/blog/laion-aesthetics/) (a subset of laion2B-en with estimated aesthetics score `> 5.0`, and additionally
-filtered to images with an original size `>= 512x512`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the [LAION-5B](https://laion.ai/blog/laion-5b/) metadata, the aesthetics score is estimated using the [LAION-Aesthetics Predictor V2](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
-- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
-- `sd-v1-4.ckpt`: Resumed from `sd-v1-2.ckpt`. 225k steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
-
-- **Hardware:** 32 x 8 x A100 GPUs
-- **Optimizer:** AdamW
-- **Gradient Accumulations**: 2
-- **Batch:** 32 x 8 x 2 x 4 = 2048
-- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
-
-## Evaluation Results 
-Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
-5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
-steps show the relative improvements of the checkpoints:
-
-![pareto](assets/v1-variants-scores.jpg) 
-
-Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution.  Not optimized for FID scores.
-
-## Environmental Impact
-
-**Stable Diffusion v1** **Estimated Emissions**
-Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
-
-- **Hardware Type:** A100 PCIe 40GB
-- **Hours used:** 150000
-- **Cloud Provider:** AWS
-- **Compute Region:** US-east
-- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
-
-## Citation
-    @InProceedings{Rombach_2022_CVPR,
-        author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
-        title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
-        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-        month     = {June},
-        year      = {2022},
-        pages     = {10684-10695}
-    }
-
-*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/models/sublayer/__init__.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/models/sublayer/__init__.py
deleted file mode 100644
index 4287ca8617970fa8fc025b75cb319c7032706910..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/models/sublayer/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-#
\ No newline at end of file
diff --git a/spaces/KhrystynaKolba/lviv_temp/README.md b/spaces/KhrystynaKolba/lviv_temp/README.md
deleted file mode 100644
index 7961e8ffa506165fedb012b5a0710d174f1d209b..0000000000000000000000000000000000000000
--- a/spaces/KhrystynaKolba/lviv_temp/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Lviv Temp
-emoji: 💩
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/KyanChen/FunSR/models/rs_super.py b/spaces/KyanChen/FunSR/models/rs_super.py
deleted file mode 100644
index 6483f645e0b81be1936e358b11ab526025292ddf..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/FunSR/models/rs_super.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange, repeat
-
-import models
-from models import register
-from utils import make_coord, to_coordinates
-
-
-@register('rs_super')
-class RSSuper(nn.Module):
-    def __init__(self,
-                 encoder_spec,
-                 neck=None,
-                 decoder=None,
-                 input_rgb=True,
-                 n_forward_times=1,
-                 global_decoder=None
-                 ):
-        super().__init__()
-        self.n_forward_times = n_forward_times
-        self.encoder = models.make(encoder_spec)
-        if neck is not None:
-            self.neck = models.make(neck, args={'in_dim': self.encoder.out_dim})
-
-        self.input_rgb = input_rgb
-        decoder_in_dim = 5 if self.input_rgb else 2
-        if decoder is not None:
-            self.decoder = models.make(decoder, args={'modulation_dim': self.neck.out_dim, 'in_dim': decoder_in_dim})
-
-        if global_decoder is not None:
-            decoder_in_dim = 5 if self.input_rgb else 2
-            self.decoder_is_proj = global_decoder.get('is_proj', False)
-            self.grid_global = global_decoder.get('grid_global', False)
-
-            self.global_decoder = models.make(global_decoder, args={'modulation_dim': self.neck.out_dim, 'in_dim': decoder_in_dim})
-
-            if self.decoder_is_proj:
-                self.input_proj = nn.Sequential(
-                    nn.Linear(self.neck.out_dim, self.neck.out_dim)
-                    )
-                self.output_proj = nn.Sequential(
-                    nn.Linear(3, 3)
-                    )
-
-    def query_rgb(self, coord, cell=None):
-        feat = self.feat
-
-        if self.imnet is None:
-            ret = F.grid_sample(feat, coord.flip(-1).unsqueeze(1),
-                                mode='nearest', align_corners=False)[:, :, 0, :] \
-                .permute(0, 2, 1)
-            return ret
-
-        if self.feat_unfold:
-            feat = F.unfold(feat, 3, padding=1).view(
-                feat.shape[0], feat.shape[1] * 9, feat.shape[2], feat.shape[3])
-
-        if self.local_ensemble:
-            vx_lst = [-1, 1]
-            vy_lst = [-1, 1]
-            eps_shift = 1e-6
-        else:
-            vx_lst, vy_lst, eps_shift = [0], [0], 0
-
-        # field radius (global: [-1, 1])
-        rx = 2 / feat.shape[-2] / 2
-        ry = 2 / feat.shape[-1] / 2
-
-        feat_coord = make_coord(feat.shape[-2:], flatten=False).cuda() \
-            .permute(2, 0, 1) \
-            .unsqueeze(0).expand(feat.shape[0], 2, *feat.shape[-2:])
-
-        preds = []
-        areas = []
-        for vx in vx_lst:
-            for vy in vy_lst:
-                coord_ = coord.clone()
-                coord_[:, :, 0] += vx * rx + eps_shift
-                coord_[:, :, 1] += vy * ry + eps_shift
-                coord_.clamp_(-1 + 1e-6, 1 - 1e-6)
-                q_feat = F.grid_sample(
-                    feat, coord_.flip(-1).unsqueeze(1),
-                    mode='nearest', align_corners=False)[:, :, 0, :] \
-                    .permute(0, 2, 1)
-                q_coord = F.grid_sample(
-                    feat_coord, coord_.flip(-1).unsqueeze(1),
-                    mode='nearest', align_corners=False)[:, :, 0, :] \
-                    .permute(0, 2, 1)
-                rel_coord = coord - q_coord
-                rel_coord[:, :, 0] *= feat.shape[-2]
-                rel_coord[:, :, 1] *= feat.shape[-1]
-                inp = torch.cat([q_feat, rel_coord], dim=-1)
-
-                if self.cell_decode:
-                    rel_cell = cell.clone()
-                    rel_cell[:, :, 0] *= feat.shape[-2]
-                    rel_cell[:, :, 1] *= feat.shape[-1]
-                    inp = torch.cat([inp, rel_cell], dim=-1)
-
-                bs, q = coord.shape[:2]
-                pred = self.imnet(inp.view(bs * q, -1)).view(bs, q, -1)
-                preds.append(pred)
-
-                area = torch.abs(rel_coord[:, :, 0] * rel_coord[:, :, 1])
-                areas.append(area + 1e-9)
-
-        tot_area = torch.stack(areas).sum(dim=0)
-        if self.local_ensemble:
-            t = areas[0]; areas[0] = areas[3]; areas[3] = t
-            t = areas[1]; areas[1] = areas[2]; areas[2] = t
-        ret = 0
-        for pred, area in zip(preds, areas):
-            ret = ret + pred * (area / tot_area).unsqueeze(-1)
-        return ret
-
-    def forward_backbone_neck(self, inp, coord):
-        # inp: 64x3x32x32
-        # coord: BxNx2
-        feat = self.encoder(inp)  # 64x64x32x32
-        global_content, x_rep = self.neck(feat)  # Bx1xC; BxCxHxW
-        return feat, x_rep, global_content
-
-    def forward_step(self, inp, coord, feat, x_rep, global_content, pred_rgb_value=None):
-        weight_gen_func = 'bilinear'  # 'bilinear'
-        #  grid： 先x再y
-        coord_ = coord.clone().unsqueeze(1).flip(-1)  # Bx1xNxC
-        modulations = F.grid_sample(x_rep, coord_, padding_mode='border', mode=weight_gen_func,
-                                    align_corners=True).squeeze(2)  # B C N
-        modulations = rearrange(modulations, 'B C N -> (B N) C')
-
-        feat_coord = to_coordinates(feat.shape[-2:], return_map=True).to(inp.device)
-        feat_coord = repeat(feat_coord, 'H W C -> B C H W', B=inp.size(0))  # 坐标是[y, x]
-        nearest_coord = F.grid_sample(feat_coord, coord_, mode='nearest', align_corners=True).squeeze(2)  # B 2 N
-        nearest_coord = rearrange(nearest_coord, 'B C N -> B N C')  # B N 2
-
-        relative_coord = coord - nearest_coord
-        relative_coord[:, :, 0] *= feat.shape[-2]
-        relative_coord[:, :, 1] *= feat.shape[-1]
-        relative_coord = rearrange(relative_coord, 'B N C -> (B N) C')
-        decoder_input = relative_coord
-
-        interpolated_rgb = None
-        if self.input_rgb:
-            if pred_rgb_value is not None:
-                interpolated_rgb = rearrange(pred_rgb_value, 'B N C -> (B N) C')
-            else:
-                interpolated_rgb = F.grid_sample(inp, coord_, padding_mode='border', mode='bilinear', align_corners=True).squeeze(2)  # B 3 N
-                interpolated_rgb = rearrange(interpolated_rgb, 'B C N -> (B N) C')
-            decoder_input = torch.cat((decoder_input, interpolated_rgb), dim=-1)
-
-        decoder_output = self.decoder(decoder_input, modulations)
-        decoder_output = rearrange(decoder_output, '(B N) C -> B N C', B=inp.size(0))
-
-        if hasattr(self, 'global_decoder'):
-            # coord: BxNx2
-            # global_content: Bx1xC
-            if self.decoder_is_proj:
-                global_content = self.input_proj(global_content)  # B 1 C
-            global_modulations = repeat(global_content, 'B N C -> B (N S) C', S=coord.size(1))
-            global_modulations = rearrange(global_modulations, 'B N C -> (B N) C')
-
-            if self.grid_global:
-                # import pdb
-                # pdb.set_trace()
-                global_decoder_input = decoder_input
-            else:
-                global_decoder_input = rearrange(coord, 'B N C -> (B N) C')
-                if self.input_rgb:
-                    global_decoder_input = torch.cat((global_decoder_input, interpolated_rgb), dim=-1)
-
-            global_decoder_output = self.global_decoder(global_decoder_input, global_modulations)
-            global_decoder_output = rearrange(global_decoder_output, '(B N) C -> B N C', B=inp.size(0))
-
-            if self.decoder_is_proj:
-                decoder_output = self.output_proj(global_decoder_output + decoder_output)
-            else:
-                decoder_output = global_decoder_output + decoder_output
-
-        return decoder_output
-
-    def forward(self, inp, coord):
-        # import pdb
-        # pdb.set_trace()
-        pred_rgb_value = None
-        feat, x_rep, global_content = self.forward_backbone_neck(inp, coord)
-        return_pred_rgb_value = []
-        for n_time in range(self.n_forward_times):
-            pred_rgb_value = self.forward_step(inp, coord, feat, x_rep, global_content, pred_rgb_value)
-            return_pred_rgb_value.append(pred_rgb_value)
-        return return_pred_rgb_value
-
-
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/samplers/sampling_result.py b/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/samplers/sampling_result.py
deleted file mode 100644
index cb510ee68f24b8c444b6ed447016bfc785b825c2..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/task_modules/samplers/sampling_result.py
+++ /dev/null
@@ -1,240 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-
-import numpy as np
-import torch
-from torch import Tensor
-
-from mmdet.structures.bbox import BaseBoxes, cat_boxes
-from mmdet.utils import util_mixins
-from mmdet.utils.util_random import ensure_rng
-from ..assigners import AssignResult
-
-
-def random_boxes(num=1, scale=1, rng=None):
-    """Simple version of ``kwimage.Boxes.random``
-
-    Returns:
-        Tensor: shape (n, 4) in x1, y1, x2, y2 format.
-
-    References:
-        https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390
-
-    Example:
-        >>> num = 3
-        >>> scale = 512
-        >>> rng = 0
-        >>> boxes = random_boxes(num, scale, rng)
-        >>> print(boxes)
-        tensor([[280.9925, 278.9802, 308.6148, 366.1769],
-                [216.9113, 330.6978, 224.0446, 456.5878],
-                [405.3632, 196.3221, 493.3953, 270.7942]])
-    """
-    rng = ensure_rng(rng)
-
-    tlbr = rng.rand(num, 4).astype(np.float32)
-
-    tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
-    tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
-    br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
-    br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])
-
-    tlbr[:, 0] = tl_x * scale
-    tlbr[:, 1] = tl_y * scale
-    tlbr[:, 2] = br_x * scale
-    tlbr[:, 3] = br_y * scale
-
-    boxes = torch.from_numpy(tlbr)
-    return boxes
-
-
-class SamplingResult(util_mixins.NiceRepr):
-    """Bbox sampling result.
-
-    Args:
-        pos_inds (Tensor): Indices of positive samples.
-        neg_inds (Tensor): Indices of negative samples.
-        priors (Tensor): The priors can be anchors or points,
-            or the bboxes predicted by the previous stage.
-        gt_bboxes (Tensor): Ground truth of bboxes.
-        assign_result (:obj:`AssignResult`): Assigning results.
-        gt_flags (Tensor): The Ground truth flags.
-        avg_factor_with_neg (bool):  If True, ``avg_factor`` equal to
-            the number of total priors; Otherwise, it is the number of
-            positive priors. Defaults to True.
-
-    Example:
-        >>> # xdoctest: +IGNORE_WANT
-        >>> from mmdet.models.task_modules.samplers.sampling_result import *  # NOQA
-        >>> self = SamplingResult.random(rng=10)
-        >>> print(f'self = {self}')
-        self = <SamplingResult({
-            'neg_inds': tensor([1,  2,  3,  5,  6,  7,  8,
-                                9, 10, 11, 12, 13]),
-            'neg_priors': torch.Size([12, 4]),
-            'num_gts': 1,
-            'num_neg': 12,
-            'num_pos': 1,
-            'avg_factor': 13,
-            'pos_assigned_gt_inds': tensor([0]),
-            'pos_inds': tensor([0]),
-            'pos_is_gt': tensor([1], dtype=torch.uint8),
-            'pos_priors': torch.Size([1, 4])
-        })>
-    """
-
-    def __init__(self,
-                 pos_inds: Tensor,
-                 neg_inds: Tensor,
-                 priors: Tensor,
-                 gt_bboxes: Tensor,
-                 assign_result: AssignResult,
-                 gt_flags: Tensor,
-                 avg_factor_with_neg: bool = True) -> None:
-        self.pos_inds = pos_inds
-        self.neg_inds = neg_inds
-        self.num_pos = max(pos_inds.numel(), 1)
-        self.num_neg = max(neg_inds.numel(), 1)
-        self.avg_factor_with_neg = avg_factor_with_neg
-        self.avg_factor = self.num_pos + self.num_neg \
-            if avg_factor_with_neg else self.num_pos
-        self.pos_priors = priors[pos_inds]
-        self.neg_priors = priors[neg_inds]
-        self.pos_is_gt = gt_flags[pos_inds]
-
-        self.num_gts = gt_bboxes.shape[0]
-        self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1
-        self.pos_gt_labels = assign_result.labels[pos_inds]
-        box_dim = gt_bboxes.box_dim if isinstance(gt_bboxes, BaseBoxes) else 4
-        if gt_bboxes.numel() == 0:
-            # hack for index error case
-            assert self.pos_assigned_gt_inds.numel() == 0
-            self.pos_gt_bboxes = gt_bboxes.view(-1, box_dim)
-        else:
-            if len(gt_bboxes.shape) < 2:
-                gt_bboxes = gt_bboxes.view(-1, box_dim)
-            self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds.long()]
-
-    @property
-    def priors(self):
-        """torch.Tensor: concatenated positive and negative priors"""
-        return cat_boxes([self.pos_priors, self.neg_priors])
-
-    @property
-    def bboxes(self):
-        """torch.Tensor: concatenated positive and negative boxes"""
-        warnings.warn('DeprecationWarning: bboxes is deprecated, '
-                      'please use "priors" instead')
-        return self.priors
-
-    @property
-    def pos_bboxes(self):
-        warnings.warn('DeprecationWarning: pos_bboxes is deprecated, '
-                      'please use "pos_priors" instead')
-        return self.pos_priors
-
-    @property
-    def neg_bboxes(self):
-        warnings.warn('DeprecationWarning: neg_bboxes is deprecated, '
-                      'please use "neg_priors" instead')
-        return self.neg_priors
-
-    def to(self, device):
-        """Change the device of the data inplace.
-
-        Example:
-            >>> self = SamplingResult.random()
-            >>> print(f'self = {self.to(None)}')
-            >>> # xdoctest: +REQUIRES(--gpu)
-            >>> print(f'self = {self.to(0)}')
-        """
-        _dict = self.__dict__
-        for key, value in _dict.items():
-            if isinstance(value, (torch.Tensor, BaseBoxes)):
-                _dict[key] = value.to(device)
-        return self
-
-    def __nice__(self):
-        data = self.info.copy()
-        data['pos_priors'] = data.pop('pos_priors').shape
-        data['neg_priors'] = data.pop('neg_priors').shape
-        parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
-        body = '    ' + ',\n    '.join(parts)
-        return '{\n' + body + '\n}'
-
-    @property
-    def info(self):
-        """Returns a dictionary of info about the object."""
-        return {
-            'pos_inds': self.pos_inds,
-            'neg_inds': self.neg_inds,
-            'pos_priors': self.pos_priors,
-            'neg_priors': self.neg_priors,
-            'pos_is_gt': self.pos_is_gt,
-            'num_gts': self.num_gts,
-            'pos_assigned_gt_inds': self.pos_assigned_gt_inds,
-            'num_pos': self.num_pos,
-            'num_neg': self.num_neg,
-            'avg_factor': self.avg_factor
-        }
-
-    @classmethod
-    def random(cls, rng=None, **kwargs):
-        """
-        Args:
-            rng (None | int | numpy.random.RandomState): seed or state.
-            kwargs (keyword arguments):
-                - num_preds: Number of predicted boxes.
-                - num_gts: Number of true boxes.
-                - p_ignore (float): Probability of a predicted box assigned to
-                    an ignored truth.
-                - p_assigned (float): probability of a predicted box not being
-                    assigned.
-
-        Returns:
-            :obj:`SamplingResult`: Randomly generated sampling result.
-
-        Example:
-            >>> from mmdet.models.task_modules.samplers.sampling_result import *  # NOQA
-            >>> self = SamplingResult.random()
-            >>> print(self.__dict__)
-        """
-        from mmengine.structures import InstanceData
-
-        from mmdet.models.task_modules.assigners import AssignResult
-        from mmdet.models.task_modules.samplers import RandomSampler
-        rng = ensure_rng(rng)
-
-        # make probabilistic?
-        num = 32
-        pos_fraction = 0.5
-        neg_pos_ub = -1
-
-        assign_result = AssignResult.random(rng=rng, **kwargs)
-
-        # Note we could just compute an assignment
-        priors = random_boxes(assign_result.num_preds, rng=rng)
-        gt_bboxes = random_boxes(assign_result.num_gts, rng=rng)
-        gt_labels = torch.randint(
-            0, 5, (assign_result.num_gts, ), dtype=torch.long)
-
-        pred_instances = InstanceData()
-        pred_instances.priors = priors
-
-        gt_instances = InstanceData()
-        gt_instances.bboxes = gt_bboxes
-        gt_instances.labels = gt_labels
-
-        add_gt_as_proposals = True
-
-        sampler = RandomSampler(
-            num,
-            pos_fraction,
-            neg_pos_ub=neg_pos_ub,
-            add_gt_as_proposals=add_gt_as_proposals,
-            rng=rng)
-        self = sampler.sample(
-            assign_result=assign_result,
-            pred_instances=pred_instances,
-            gt_instances=gt_instances)
-        return self
diff --git a/spaces/LabelStudio/README/README.md b/spaces/LabelStudio/README/README.md
deleted file mode 100644
index 0fed7c49f1e9ebaac315fdb3395936d62dbb0abe..0000000000000000000000000000000000000000
--- a/spaces/LabelStudio/README/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
----
-title: README
-emoji: 🖼️
-colorFrom: gray
-colorTo: purple
-sdk: static
-pinned: true
-license: apache-2.0
----
-
-
-<img src="https://user-images.githubusercontent.com/12534576/192582340-4c9e4401-1fe6-4dbb-95bb-fdbba5493f61.png"/>
-
-[Website](https://hubs.ly/Q01CNgsd0) • [Docs](https://hubs.ly/Q01CN9Yq0) • [13K+ GitHub ⭐️!](https://hubs.ly/Q01CNbPQ0) • [Slack Community](https://hubs.ly/Q01CNb9H0)
-
-## What is Label Studio?
-
-**Label Studio is an open source data labeling platform by HumanSignal.** It lets you label data types like audio,
-text, images, videos, and time series with a simple, straightforward, and highly configurable UI.
-When you're ready to use it for training, export your data and annotations to various model formats. 
-You can also connect your ML models directly to Label Studio to speed up your annotation workflow
-or retrain models using expert human feedback.
-
-## How can I get started with Label Studio?
-
-- 🟧 [**Run Label Studio in Hugging Face Spaces!**](https://huggingface.co/spaces/LabelStudio/LabelStudio)
-- 🚀 Go from Zero-to-One with the [Label Studio Tutorial](https://labelstud.io/blog/introduction-to-label-studio-in-hugging-face-spaces/)
-- 📈 Check out the [Free Trial of Label Studio Enterprise](https://hubs.ly/Q01CMLll0)
-- 🙌 [Connect with the Label Studio Slack Community](https://slack.labelstudio.heartex.com/?source=site)
-  
-## Questions? Concerns? Want to get involved? 
-
-- Email the community team at [community@labelstud.io](mailto:community@labelstud.io)
-- Check out the company behind Label Studio, [HumanSignal][https://humansignal.com]
\ No newline at end of file
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/csvutil.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/csvutil.py
deleted file mode 100644
index 8992d13ffc7497bf441232552fbe9cfb776e4919..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/csvutil.py
+++ /dev/null
@@ -1,33 +0,0 @@
-
-
-import csv
-
-# praatEXE = join('.',os.path.abspath(os.getcwd()) + r"\Praat.exe")
-
-
-def CSVutil(file, rw, type, *args):
-    if type == "formanting":
-        if rw == "r":
-            with open(file) as fileCSVread:
-                csv_reader = list(csv.reader(fileCSVread))
-                return (
-                    (csv_reader[0][0], csv_reader[0][1], csv_reader[0][2])
-                    if csv_reader is not None
-                    else (lambda: exec('raise ValueError("No data")'))()
-                )
-        else:
-            if args:
-                doformnt = args[0]
-            else:
-                doformnt = False
-            qfr = args[1] if len(args) > 1 else 1.0
-            tmb = args[2] if len(args) > 2 else 1.0
-            with open(file, rw, newline="") as fileCSVwrite:
-                csv_writer = csv.writer(fileCSVwrite, delimiter=",")
-                csv_writer.writerow([doformnt, qfr, tmb])
-    elif type == "stop":
-        stop = args[0] if args else False
-        with open(file, rw, newline="") as fileCSVwrite:
-            csv_writer = csv.writer(fileCSVwrite, delimiter=",")
-            csv_writer.writerow([stop])
-
diff --git a/spaces/LibreChat/LibreChat/Dockerfile b/spaces/LibreChat/LibreChat/Dockerfile
deleted file mode 100644
index 0247d7fd2a27628f607cfa57958d74401cbfae57..0000000000000000000000000000000000000000
--- a/spaces/LibreChat/LibreChat/Dockerfile
+++ /dev/null
@@ -1,15 +0,0 @@
-# Pull the base image
-FROM ghcr.io/danny-avila/librechat-dev:latest
-
-# Set environment variables
-ENV HOST=0.0.0.0
-ENV PORT=7860
-ENV SESSION_EXPIRY=900000
-ENV REFRESH_TOKEN_EXPIRY=604800000
-ENV OPENAI_MODELS=gpt-3.5-turbo-1106,gpt-4-1106-preview,gpt-3.5-turbo,gpt-3.5-turbo-16k,gpt-3.5-turbo-0301,text-davinci-003,gpt-4,gpt-4-0314,gpt-4-0613
-
-# Install dependencies
-RUN cd /app/api && npm install
-
-# Command to run on container start
-CMD ["npm", "run", "backend"]
\ No newline at end of file
diff --git a/spaces/Linly-AI/Linly-ChatFlow/models/norm.py b/spaces/Linly-AI/Linly-ChatFlow/models/norm.py
deleted file mode 100644
index aa2f67a7f1a61a73f438cfade107adb89c0f27bd..0000000000000000000000000000000000000000
--- a/spaces/Linly-AI/Linly-ChatFlow/models/norm.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from torch import nn
-import torch
-
-
-class RMSNorm(torch.nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        super().__init__()
-        self.eps = eps
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-
-    def _norm(self, x):
-        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
-
-    def forward(self, x):
-        output = self._norm(x.float()).type_as(x)
-        return output * self.weight
diff --git a/spaces/LucasCodeBreak/MusicGen/MODEL_CARD.md b/spaces/LucasCodeBreak/MusicGen/MODEL_CARD.md
deleted file mode 100644
index 6c2c9f883969eb905e74ad3376966d156cc5ca00..0000000000000000000000000000000000000000
--- a/spaces/LucasCodeBreak/MusicGen/MODEL_CARD.md
+++ /dev/null
@@ -1,81 +0,0 @@
-# MusicGen Model Card
-
-## Model details
-
-**Organization developing the model:** The FAIR team of Meta AI.
-
-**Model date:** MusicGen was trained between April 2023 and May 2023.
-
-**Model version:** This is the version 1 of the model.
-
-**Model type:** MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation.
-
-**Paper or resources for more information:** More information can be found in the paper [Simple and Controllable Music Generation][arxiv].
-
-**Citation details** See [our paper][arxiv]
-
-**License** Code is released under MIT, model weights are released under CC-BY-NC 4.0.
-
-**Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue.
-
-## Intended use
-**Primary intended use:** The primary use of MusicGen is research on AI-based music generation, including:
-
-- Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science
-- Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs
-
-**Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models.
-
-**Out-of-scope use cases** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
-
-## Metrics
-
-**Models performance measures:** We used the following objective measure to evaluate the model on a standard music benchmark:
-
-- Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish)
-- Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST)
-- CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model
-
-Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes:
-
-- Overall quality of the music samples;
-- Text relevance to the provided text input;
-- Adherence to the melody for melody-guided music generation.
-
-More details on performance measures and human studies can be found in the paper.
-
-**Decision thresholds:** Not applicable.
-
-## Evaluation datasets
-
-The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set.
-
-## Training datasets
-
-The model was trained on licensed data using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound),  [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing.
-
-## Quantitative analysis
-
-More information can be found in the paper [Simple and Controllable Music Generation][arxiv], in the Experimental Setup section.
-
-## Limitations and biases
-
-**Data:** The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model.
-
-**Mitigations:** Vocals have been removed from the data source using corresponding tags, and then using using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs).
-
-**Limitations:**
-
-- The model is not able to generate realistic vocals.
-- The model has been trained with English descriptions and will not perform as well in other languages.
-- The model does not perform equally well for all music styles and cultures.
-- The model sometimes generates end of songs, collapsing to silence.
-- It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results.
-
-**Biases:** The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive.
-
-**Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data.
-
-**Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
-
-[arxiv]: https://arxiv.org/abs/2306.05284
diff --git a/spaces/Lyra121/finetuned_diffusion/utils.py b/spaces/Lyra121/finetuned_diffusion/utils.py
deleted file mode 100644
index ff1c065d186347ca51b47d010a697dbe1814695c..0000000000000000000000000000000000000000
--- a/spaces/Lyra121/finetuned_diffusion/utils.py
+++ /dev/null
@@ -1,6 +0,0 @@
-def is_google_colab():
-    try:
-        import google.colab
-        return True
-    except:
-        return False
\ No newline at end of file
diff --git a/spaces/MakiAi/Image2VideoProcessingPipelin/modules/utils/v_image_blurred_utils.py b/spaces/MakiAi/Image2VideoProcessingPipelin/modules/utils/v_image_blurred_utils.py
deleted file mode 100644
index 81dda57130087a6956a800487b85599c59ed9a4f..0000000000000000000000000000000000000000
--- a/spaces/MakiAi/Image2VideoProcessingPipelin/modules/utils/v_image_blurred_utils.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from PIL import Image, ImageFilter
-import random
-import os
-from pathlib import Path
-import cv2
-import numpy as np
-
-def embed_image_on_blurred_background(input_path: str, output_path: str, height: int = 2000) -> None:
-    """
-    入力画像をブラーしたバージョンの上に配置し、その結果を保存します。
-    
-    引数:
-    - input_path: 入力画像のパス
-    - output_path: 処理された画像を保存する場所
-    - height: 出力画像の希望の高さ（デフォルトは2000ピクセル）
-    """
-    
-    # 与えられたパスから画像を読み込む
-    image = Image.open(input_path)
-    
-    # 画像の元のサイズを取得する
-    original_width, original_height = image.size
-    
-    # 9:16のアスペクト比と指定された高さに基づいて、出力画像の幅を計算する
-    target_width = int(height * 9 / 16)
-    
-    # 元の画像のブラーしたバージョンを作成する
-    blurred_image = image.filter(ImageFilter.GaussianBlur(20))
-    
-    # ブラー画像を希望の出力サイズにリサイズする
-    resized_blurred_background = blurred_image.resize((target_width, height))
-    
-    # 元のアスペクト比を保持したまま、元の画像を指定された高さにリサイズする
-    new_width = int(original_width * (height / original_height))
-    resized_image_keep_aspect = image.resize((new_width, height), Image.ANTIALIAS)
-    
-    # リサイズされた元の画像をブラーした背景の中央に配置する位置を計算する
-    x_offset = (resized_blurred_background.width - resized_image_keep_aspect.width) // 2
-    y_offset = (resized_blurred_background.height - resized_image_keep_aspect.height) // 2
-    
-    # 画像に透明度がある場合（RGBAモード）、背景にペーストする際のマスクとして使用する
-    mask_keep_aspect = resized_image_keep_aspect if resized_image_keep_aspect.mode == "RGBA" else None
-    
-    # リサイズされた元の画像をブラーした背景の上にオーバーレイする
-    resized_blurred_background.paste(resized_image_keep_aspect, (x_offset, y_offset), mask_keep_aspect)
-    
-    # 指定されたパスに結合された画像を保存する
-    resized_blurred_background.save(output_path)
\ No newline at end of file
diff --git a/spaces/MirageML/depth2img/Dockerfile b/spaces/MirageML/depth2img/Dockerfile
deleted file mode 100644
index 520ed0021f743919019b6f16cf4d4a13766eefca..0000000000000000000000000000000000000000
--- a/spaces/MirageML/depth2img/Dockerfile
+++ /dev/null
@@ -1,52 +0,0 @@
-FROM nvidia/cuda:11.3.1-cudnn8-devel-ubuntu18.04
-CMD nvidia-smi
-
-ENV DEBIAN_FRONTEND noninteractive
-RUN apt-get update && apt-get install -y \
-        git \
-        make build-essential libssl-dev zlib1g-dev \
-        libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \
-        libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev  \
-    	ffmpeg libsm6 libxext6 cmake libgl1-mesa-glx \
-		&& rm -rf /var/lib/apt/lists/*
-        && git lfs install
-
-
-RUN useradd -ms /bin/bash user
-USER user
-
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-RUN curl https://pyenv.run | bash
-ENV PATH=$HOME/.pyenv/shims:$HOME/.pyenv/bin:$PATH
-RUN pyenv install 3.8.15 && \
-    pyenv global 3.8.15 && \
-    pyenv rehash && \
-    pip install --no-cache-dir --upgrade pip setuptools wheel
-
-ENV WORKDIR=/code
-WORKDIR $WORKDIR
-RUN chown -R user:user $WORKDIR
-RUN chmod -R 777 $WORKDIR
-
-COPY requirements.txt $WORKDIR/requirements.txt
-RUN pip install --no-cache-dir --upgrade -r $WORKDIR/requirements.txt
-RUN pip install ninja
-
-RUN curl https://github.com/isl-org/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt --create-dirs -o $WORKDIR/midas_models/dpt_hybrid-midas-501f0c75.pt 
-RUN curl https://github.com/isl-org/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt --create-dirs -o $WORKDIR/midas_models/dpt_large-midas-2f21e586.pt
-
-COPY . .
-
-ARG TORCH_CUDA_ARCH_LIST=7.5+PTX
-
-USER root
-RUN chown -R user:user $HOME
-RUN chmod -R 777 $HOME
-RUN chown -R user:user $WORKDIR
-RUN chmod -R 777 $WORKDIR
-
-USER user
-
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/MoonQiu/LongerCrafter/lvdm/modules/networks/ae_modules.py b/spaces/MoonQiu/LongerCrafter/lvdm/modules/networks/ae_modules.py
deleted file mode 100644
index 0c2e93fbadb4a0d86957a5cd73b5c2bf5b01a4b7..0000000000000000000000000000000000000000
--- a/spaces/MoonQiu/LongerCrafter/lvdm/modules/networks/ae_modules.py
+++ /dev/null
@@ -1,845 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import numpy as np
-import torch.nn as nn
-from einops import rearrange
-from utils.utils import instantiate_from_config
-from lvdm.modules.attention import LinearAttention
-
-def nonlinearity(x):
-    # swish
-    return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
-    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-
-class LinAttnBlock(LinearAttention):
-    """to match AttnBlock usage"""
-    def __init__(self, in_channels):
-        super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = Normalize(in_channels)
-        self.q = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.k = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.v = torch.nn.Conv2d(in_channels,
-                                 in_channels,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-        self.proj_out = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=1,
-                                        stride=1,
-                                        padding=0)
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b,c,h,w = q.shape
-        q = q.reshape(b,c,h*w) # bcl
-        q = q.permute(0,2,1)   # bcl -> blc l=hw
-        k = k.reshape(b,c,h*w) # bcl
-        
-        w_ = torch.bmm(q,k)    # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = torch.nn.functional.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b,c,h*w)
-        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
-        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
-        h_ = h_.reshape(b,c,h,w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-def make_attn(in_channels, attn_type="vanilla"):
-    assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
-    #print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
-    if attn_type == "vanilla":
-        return AttnBlock(in_channels)
-    elif attn_type == "none":
-        return nn.Identity(in_channels)
-    else:
-        return LinAttnBlock(in_channels)
- 
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        self.in_channels = in_channels
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=2,
-                                        padding=0)
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0,1,0,1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        self.in_channels = in_channels
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-def get_timestep_embedding(timesteps, embedding_dim):
-    """
-    This matches the implementation in Denoising Diffusion Probabilistic Models:
-    From Fairseq.
-    Build sinusoidal embeddings.
-    This matches the implementation in tensor2tensor, but differs slightly
-    from the description in Section 3.5 of "Attention Is All You Need".
-    """
-    assert len(timesteps.shape) == 1
-
-    half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
-    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
-    emb = emb.to(device=timesteps.device)
-    emb = timesteps.float()[:, None] * emb[None, :]
-    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-    if embedding_dim % 2 == 1:  # zero pad
-        emb = torch.nn.functional.pad(emb, (0,1,0,0))
-    return emb
-
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout, temb_channels=512):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-
-        self.norm1 = Normalize(in_channels)
-        self.conv1 = torch.nn.Conv2d(in_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if temb_channels > 0:
-            self.temb_proj = torch.nn.Linear(temb_channels,
-                                             out_channels)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(out_channels,
-                                     out_channels,
-                                     kernel_size=3,
-                                     stride=1,
-                                     padding=1)
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(in_channels,
-                                                     out_channels,
-                                                     kernel_size=3,
-                                                     stride=1,
-                                                     padding=1)
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(in_channels,
-                                                    out_channels,
-                                                    kernel_size=1,
-                                                    stride=1,
-                                                    padding=0)
-
-    def forward(self, x, temb):
-        h = x
-        h = self.norm1(h)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
-        h = self.norm2(h)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x+h
-
-class Model(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = self.ch*4
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        self.use_timestep = use_timestep
-        if self.use_timestep:
-            # timestep embedding
-            self.temb = nn.Module()
-            self.temb.dense = nn.ModuleList([
-                torch.nn.Linear(self.ch,
-                                self.temb_ch),
-                torch.nn.Linear(self.temb_ch,
-                                self.temb_ch),
-            ])
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            skip_in = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                if i_block == self.num_res_blocks:
-                    skip_in = ch*in_ch_mult[i_level]
-                block.append(ResnetBlock(in_channels=block_in+skip_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x, t=None, context=None):
-        #assert x.shape[2] == x.shape[3] == self.resolution
-        if context is not None:
-            # assume aligned context, cat along channel axis
-            x = torch.cat((x, context), dim=1)
-        if self.use_timestep:
-            # timestep embedding
-            assert t is not None
-            temb = get_timestep_embedding(t, self.ch)
-            temb = self.temb.dense[0](temb)
-            temb = nonlinearity(temb)
-            temb = self.temb.dense[1](temb)
-        else:
-            temb = None
-
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](
-                    torch.cat([h, hs.pop()], dim=1), temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-    def get_last_layer(self):
-        return self.conv_out.weight
-
-
-class Encoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
-                 **ignore_kwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(in_channels,
-                                       self.ch,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch*in_ch_mult[i_level]
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions-1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        2*z_channels if double_z else z_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # timestep embedding
-        temb = None
-
-        # print(f'encoder-input={x.shape}')
-        # downsampling
-        hs = [self.conv_in(x)]
-        # print(f'encoder-conv in feat={hs[0].shape}')
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1], temb)
-                # print(f'encoder-down feat={h.shape}')
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions-1:
-                # print(f'encoder-downsample (input)={hs[-1].shape}')
-                hs.append(self.down[i_level].downsample(hs[-1]))
-                # print(f'encoder-downsample (output)={hs[-1].shape}')
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h, temb)
-        # print(f'encoder-mid1 feat={h.shape}')
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-        # print(f'encoder-mid2 feat={h.shape}')
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        # print(f'end feat={h.shape}')
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
-                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
-                 attn_type="vanilla", **ignorekwargs):
-        super().__init__()
-        if use_linear_attn: attn_type = "linear"
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-        self.tanh_out = tanh_out
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        in_ch_mult = (1,)+tuple(ch_mult)
-        block_in = ch*ch_mult[self.num_resolutions-1]
-        curr_res = resolution // 2**(self.num_resolutions-1)
-        self.z_shape = (1,z_channels,curr_res,curr_res)
-        print("AE working on z of shape {} = {} dimensions.".format(
-            self.z_shape, np.prod(self.z_shape)))
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(z_channels,
-                                       block_in,
-                                       kernel_size=3,
-                                       stride=1,
-                                       padding=1)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in,
-                                       out_channels=block_in,
-                                       temb_channels=self.temb_ch,
-                                       dropout=dropout)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch*ch_mult[i_level]
-            for i_block in range(self.num_res_blocks+1):
-                block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(make_attn(block_in, attn_type=attn_type))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up) # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_ch,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, z):
-        #assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
-        # print(f'decoder-input={z.shape}')
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-        # print(f'decoder-conv in feat={h.shape}')
-
-        # middle
-        h = self.mid.block_1(h, temb)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h, temb)
-        # print(f'decoder-mid feat={h.shape}')
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks+1):
-                h = self.up[i_level].block[i_block](h, temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-                # print(f'decoder-up feat={h.shape}')
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-                # print(f'decoder-upsample feat={h.shape}')
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        # print(f'decoder-conv_out feat={h.shape}')
-        if self.tanh_out:
-            h = torch.tanh(h)
-        return h
-
-
-class SimpleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, *args, **kwargs):
-        super().__init__()
-        self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
-                                     ResnetBlock(in_channels=in_channels,
-                                                 out_channels=2 * in_channels,
-                                                 temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=2 * in_channels,
-                                                out_channels=4 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     ResnetBlock(in_channels=4 * in_channels,
-                                                out_channels=2 * in_channels,
-                                                temb_channels=0, dropout=0.0),
-                                     nn.Conv2d(2*in_channels, in_channels, 1),
-                                     Upsample(in_channels, with_conv=True)])
-        # end
-        self.norm_out = Normalize(in_channels)
-        self.conv_out = torch.nn.Conv2d(in_channels,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        for i, layer in enumerate(self.model):
-            if i in [1,2,3]:
-                x = layer(x, None)
-            else:
-                x = layer(x)
-
-        h = self.norm_out(x)
-        h = nonlinearity(h)
-        x = self.conv_out(h)
-        return x
-
-
-class UpsampleDecoder(nn.Module):
-    def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
-                 ch_mult=(2,2), dropout=0.0):
-        super().__init__()
-        # upsampling
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        block_in = in_channels
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.res_blocks = nn.ModuleList()
-        self.upsample_blocks = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            res_block = []
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                res_block.append(ResnetBlock(in_channels=block_in,
-                                         out_channels=block_out,
-                                         temb_channels=self.temb_ch,
-                                         dropout=dropout))
-                block_in = block_out
-            self.res_blocks.append(nn.ModuleList(res_block))
-            if i_level != self.num_resolutions - 1:
-                self.upsample_blocks.append(Upsample(block_in, True))
-                curr_res = curr_res * 2
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(block_in,
-                                        out_channels,
-                                        kernel_size=3,
-                                        stride=1,
-                                        padding=1)
-
-    def forward(self, x):
-        # upsampling
-        h = x
-        for k, i_level in enumerate(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.res_blocks[i_level][i_block](h, None)
-            if i_level != self.num_resolutions - 1:
-                h = self.upsample_blocks[k](h)
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class LatentRescaler(nn.Module):
-    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
-        super().__init__()
-        # residual block, interpolate, residual block
-        self.factor = factor
-        self.conv_in = nn.Conv2d(in_channels,
-                                 mid_channels,
-                                 kernel_size=3,
-                                 stride=1,
-                                 padding=1)
-        self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-        self.attn = AttnBlock(mid_channels)
-        self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
-                                                     out_channels=mid_channels,
-                                                     temb_channels=0,
-                                                     dropout=0.0) for _ in range(depth)])
-
-        self.conv_out = nn.Conv2d(mid_channels,
-                                  out_channels,
-                                  kernel_size=1,
-                                  )
-
-    def forward(self, x):
-        x = self.conv_in(x)
-        for block in self.res_block1:
-            x = block(x, None)
-        x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
-        x = self.attn(x)
-        for block in self.res_block2:
-            x = block(x, None)
-        x = self.conv_out(x)
-        return x
-
-
-class MergedRescaleEncoder(nn.Module):
-    def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
-                 attn_resolutions, dropout=0.0, resamp_with_conv=True,
-                 ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        intermediate_chn = ch * ch_mult[-1]
-        self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
-                               z_channels=intermediate_chn, double_z=False, resolution=resolution,
-                               attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
-                               out_ch=None)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
-                                       mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.encoder(x)
-        x = self.rescaler(x)
-        return x
-
-
-class MergedRescaleDecoder(nn.Module):
-    def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
-                 dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
-        super().__init__()
-        tmp_chn = z_channels*ch_mult[-1]
-        self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
-                               resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
-                               ch_mult=ch_mult, resolution=resolution, ch=ch)
-        self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
-                                       out_channels=tmp_chn, depth=rescale_module_depth)
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Upsampler(nn.Module):
-    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
-        super().__init__()
-        assert out_size >= in_size
-        num_blocks = int(np.log2(out_size//in_size))+1
-        factor_up = 1.+ (out_size % in_size)
-        print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
-        self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
-                                       out_channels=in_channels)
-        self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
-                               attn_resolutions=[], in_channels=None, ch=in_channels,
-                               ch_mult=[ch_mult for _ in range(num_blocks)])
-
-    def forward(self, x):
-        x = self.rescaler(x)
-        x = self.decoder(x)
-        return x
-
-
-class Resize(nn.Module):
-    def __init__(self, in_channels=None, learned=False, mode="bilinear"):
-        super().__init__()
-        self.with_conv = learned
-        self.mode = mode
-        if self.with_conv:
-            print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
-            raise NotImplementedError()
-            assert in_channels is not None
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(in_channels,
-                                        in_channels,
-                                        kernel_size=4,
-                                        stride=2,
-                                        padding=1)
-
-    def forward(self, x, scale_factor=1.0):
-        if scale_factor==1.0:
-            return x
-        else:
-            x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
-        return x
-
-class FirstStagePostProcessor(nn.Module):
-
-    def __init__(self, ch_mult:list, in_channels,
-                 pretrained_model:nn.Module=None,
-                 reshape=False,
-                 n_channels=None,
-                 dropout=0.,
-                 pretrained_config=None):
-        super().__init__()
-        if pretrained_config is None:
-            assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
-            self.pretrained_model = pretrained_model
-        else:
-            assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
-            self.instantiate_pretrained(pretrained_config)
-
-        self.do_reshape = reshape
-
-        if n_channels is None:
-            n_channels = self.pretrained_model.encoder.ch
-
-        self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
-        self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
-                            stride=1,padding=1)
-
-        blocks = []
-        downs = []
-        ch_in = n_channels
-        for m in ch_mult:
-            blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
-            ch_in = m * n_channels
-            downs.append(Downsample(ch_in, with_conv=False))
-
-        self.model = nn.ModuleList(blocks)
-        self.downsampler = nn.ModuleList(downs)
-
-
-    def instantiate_pretrained(self, config):
-        model = instantiate_from_config(config)
-        self.pretrained_model = model.eval()
-        # self.pretrained_model.train = False
-        for param in self.pretrained_model.parameters():
-            param.requires_grad = False
-
-
-    @torch.no_grad()
-    def encode_with_pretrained(self,x):
-        c = self.pretrained_model.encode(x)
-        if isinstance(c, DiagonalGaussianDistribution):
-            c = c.mode()
-        return  c
-
-    def forward(self,x):
-        z_fs = self.encode_with_pretrained(x)
-        z = self.proj_norm(z_fs)
-        z = self.proj(z)
-        z = nonlinearity(z)
-
-        for submodel, downmodel in zip(self.model,self.downsampler):
-            z = submodel(z,temb=None)
-            z = downmodel(z)
-
-        if self.do_reshape:
-            z = rearrange(z,'b c h w -> b (h w) c')
-        return z
-
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/satrn/satrn_shallow_5e_union14m.py b/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/satrn/satrn_shallow_5e_union14m.py
deleted file mode 100644
index 4dc9b1e7cae288dd15227acd62468ade6b192bca..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/satrn/satrn_shallow_5e_union14m.py
+++ /dev/null
@@ -1,87 +0,0 @@
-_base_ = [
-    '../_base_/datasets/union14m_train.py',
-    '../_base_/datasets/union14m_benchmark.py',
-    '../_base_/datasets/cute80.py',
-    '../_base_/datasets/iiit5k.py',
-    '../_base_/datasets/svt.py',
-    '../_base_/datasets/svtp.py',
-    '../_base_/datasets/icdar2013.py',
-    '../_base_/datasets/icdar2015.py',
-    '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_adam_step_5e.py',
-    '_base_satrn_shallow.py',
-]
-
-dictionary = dict(
-    type='Dictionary',
-    dict_file=  # noqa
-    '{{ fileDirname }}/../../../dicts/english_digits_symbols_space.txt',
-    with_padding=True,
-    with_unknown=True,
-    same_start_end=True,
-    with_start=True,
-    with_end=True)
-
-# dataset settings
-train_list = [
-    _base_.union14m_challenging, _base_.union14m_hard, _base_.union14m_medium,
-    _base_.union14m_normal, _base_.union14m_easy
-]
-val_list = [
-    _base_.cute80_textrecog_test, _base_.iiit5k_textrecog_test,
-    _base_.svt_textrecog_test, _base_.svtp_textrecog_test,
-    _base_.icdar2013_textrecog_test, _base_.icdar2015_textrecog_test
-]
-test_list = [
-    _base_.union14m_benchmark_artistic,
-    _base_.union14m_benchmark_multi_oriented,
-    _base_.union14m_benchmark_contextless,
-    _base_.union14m_benchmark_curve,
-    _base_.union14m_benchmark_incomplete,
-    _base_.union14m_benchmark_incomplete_ori,
-    _base_.union14m_benchmark_multi_words,
-    _base_.union14m_benchmark_salient,
-    _base_.union14m_benchmark_general,
-]
-
-train_dataset = dict(
-    type='ConcatDataset', datasets=train_list, pipeline=_base_.train_pipeline)
-test_dataset = dict(
-    type='ConcatDataset', datasets=test_list, pipeline=_base_.test_pipeline)
-val_dataset = dict(
-    type='ConcatDataset', datasets=val_list, pipeline=_base_.test_pipeline)
-
-# optimizer
-optim_wrapper = dict(type='OptimWrapper', optimizer=dict(type='Adam', lr=3e-4))
-
-train_dataloader = dict(
-    batch_size=64,
-    num_workers=24,
-    persistent_workers=True,
-    sampler=dict(type='DefaultSampler', shuffle=True),
-    dataset=train_dataset)
-
-test_dataloader = dict(
-    batch_size=128,
-    num_workers=4,
-    persistent_workers=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=test_dataset)
-
-val_dataloader = dict(
-    batch_size=128,
-    num_workers=4,
-    persistent_workers=True,
-    pin_memory=True,
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),
-    dataset=val_dataset)
-
-
-val_evaluator = dict(
-    dataset_prefixes=['CUTE80', 'IIIT5K', 'SVT', 'SVTP', 'IC13', 'IC15'])
-test_evaluator = dict(dataset_prefixes=[
-    'artistic', 'multi-oriented', 'contextless', 'curve', 'incomplete',
-    'incomplete-ori', 'multi-words', 'salient', 'general'
-])
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/visualization/textspotting_visualizer.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/visualization/textspotting_visualizer.py
deleted file mode 100644
index bd4038c35aadfc346e2b370d5a361462acdaf326..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/visualization/textspotting_visualizer.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Optional, Sequence, Union
-
-import mmcv
-import numpy as np
-import torch
-
-from mmocr.registry import VISUALIZERS
-from mmocr.structures import TextDetDataSample
-from mmocr.utils.polygon_utils import poly2bbox
-from .base_visualizer import BaseLocalVisualizer
-
-
-@VISUALIZERS.register_module()
-class TextSpottingLocalVisualizer(BaseLocalVisualizer):
-
-    def _draw_instances(
-        self,
-        image: np.ndarray,
-        bboxes: Union[np.ndarray, torch.Tensor],
-        polygons: Sequence[np.ndarray],
-        texts: Sequence[str],
-    ) -> np.ndarray:
-        """Draw instances on image.
-
-        Args:
-            image (np.ndarray): The origin image to draw. The format
-                should be RGB.
-            bboxes (np.ndarray, torch.Tensor): The bboxes to draw. The shape of
-                bboxes should be (N, 4), where N is the number of texts.
-            polygons (Sequence[np.ndarray]): The polygons to draw. The length
-                of polygons should be the same as the number of bboxes.
-            edge_labels (np.ndarray, torch.Tensor): The edge labels to draw.
-                The shape of edge_labels should be (N, N), where N is the
-                number of texts.
-            texts (Sequence[str]): The texts to draw. The length of texts
-                should be the same as the number of bboxes.
-            class_names (dict): The class names for bbox labels.
-            is_openset (bool): Whether the dataset is openset. Default: False.
-
-        Returns:
-            np.ndarray: The image with instances drawn.
-        """
-        img_shape = image.shape[:2]
-        empty_shape = (img_shape[0], img_shape[1], 3)
-        text_image = np.full(empty_shape, 255, dtype=np.uint8)
-        if texts:
-            text_image = self.get_labels_image(
-                text_image,
-                labels=texts,
-                bboxes=bboxes,
-                font_families=self.font_families,
-                font_properties=self.font_properties)
-        if polygons:
-            polygons = [polygon.reshape(-1, 2) for polygon in polygons]
-            image = self.get_polygons_image(
-                image, polygons, filling=True, colors=self.PALETTE)
-            text_image = self.get_polygons_image(
-                text_image, polygons, colors=self.PALETTE)
-        elif len(bboxes) > 0:
-            image = self.get_bboxes_image(
-                image, bboxes, filling=True, colors=self.PALETTE)
-            text_image = self.get_bboxes_image(
-                text_image, bboxes, colors=self.PALETTE)
-        return np.concatenate([image, text_image], axis=1)
-
-    def add_datasample(self,
-                       name: str,
-                       image: np.ndarray,
-                       data_sample: Optional['TextDetDataSample'] = None,
-                       draw_gt: bool = True,
-                       draw_pred: bool = True,
-                       show: bool = False,
-                       wait_time: int = 0,
-                       pred_score_thr: float = 0.5,
-                       out_file: Optional[str] = None,
-                       step: int = 0) -> None:
-        """Draw datasample and save to all backends.
-
-        - If GT and prediction are plotted at the same time, they are
-        displayed in a stitched image where the left image is the
-        ground truth and the right image is the prediction.
-        - If ``show`` is True, all storage backends are ignored, and
-        the images will be displayed in a local window.
-        - If ``out_file`` is specified, the drawn image will be
-        saved to ``out_file``. This is usually used when the display
-        is not available.
-
-        Args:
-            name (str): The image identifier.
-            image (np.ndarray): The image to draw.
-            data_sample (:obj:`TextSpottingDataSample`, optional):
-                TextDetDataSample which contains gt and prediction. Defaults
-                    to None.
-            draw_gt (bool): Whether to draw GT TextDetDataSample.
-                Defaults to True.
-            draw_pred (bool): Whether to draw Predicted TextDetDataSample.
-                Defaults to True.
-            show (bool): Whether to display the drawn image. Default to False.
-            wait_time (float): The interval of show (s). Defaults to 0.
-            out_file (str): Path to output file. Defaults to None.
-            pred_score_thr (float): The threshold to visualize the bboxes
-                and masks. Defaults to 0.3.
-            step (int): Global step value to record. Defaults to 0.
-        """
-        cat_images = []
-
-        if data_sample is not None:
-            if draw_gt and 'gt_instances' in data_sample:
-                gt_bboxes = data_sample.gt_instances.get('bboxes', None)
-                gt_texts = data_sample.gt_instances.texts
-                gt_polygons = data_sample.gt_instances.get('polygons', None)
-                gt_img_data = self._draw_instances(image, gt_bboxes,
-                                                   gt_polygons, gt_texts)
-                cat_images.append(gt_img_data)
-
-            if draw_pred and 'pred_instances' in data_sample:
-                pred_instances = data_sample.pred_instances
-                pred_instances = pred_instances[
-                    pred_instances.scores > pred_score_thr].cpu().numpy()
-                pred_bboxes = pred_instances.get('bboxes', None)
-                pred_texts = pred_instances.texts
-                pred_polygons = pred_instances.get('polygons', None)
-                if pred_bboxes is None:
-                    pred_bboxes = [poly2bbox(poly) for poly in pred_polygons]
-                    pred_bboxes = np.array(pred_bboxes)
-                pred_img_data = self._draw_instances(image, pred_bboxes,
-                                                     pred_polygons, pred_texts)
-                cat_images.append(pred_img_data)
-
-        cat_images = self._cat_image(cat_images, axis=0)
-        if cat_images is None:
-            cat_images = image
-
-        if show:
-            self.show(cat_images, win_name=name, wait_time=wait_time)
-        else:
-            self.add_image(name, cat_images, step)
-
-        if out_file is not None:
-            mmcv.imwrite(cat_images[..., ::-1], out_file)
-
-        self.set_image(cat_images)
-        return self.get_image()
diff --git a/spaces/Navpreet/rabbit3/app.py b/spaces/Navpreet/rabbit3/app.py
deleted file mode 100644
index e4a1d21fb03ce8e2cfb39f0cb167b499a25f16bb..0000000000000000000000000000000000000000
--- a/spaces/Navpreet/rabbit3/app.py
+++ /dev/null
@@ -1,487 +0,0 @@
-import streamlit as st
-from firebase import Firebase
-from datetime import datetime
-from streamlit_option_menu import option_menu
-from PIL import Image
-import requests
-import streamlit.components.v1 as components
-import random
-import json
-from io import BytesIO
-import feedparser
-import urllib.request
-
-
-
-
-#streamlit-1.16.0
-
-
-im = Image.open("icons8-rabbit-100.png")
-st.set_page_config(
-    page_title="Rabbit.web",
-    page_icon=im
-   
-)
-
-
-#https://i.gifer.com/Cal.gif
-
-
-
-
-hide_streamlit_style = """
-            <style>
-            #MainMenu {visibility: hidden;}
-            footer {visibility: hidden;}
-            </style>
-            """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-
-
-firebaseConfig = {
-  "apiKey": "AIzaSyCHnlRFW1_RTgZVVga8E-Rj4g7noddYzXA",
-  "authDomain": "rabbit1-bd232.firebaseapp.com",
-  "databaseURL": "https://rabbit1-bd232-default-rtdb.firebaseio.com",
-  "projectId": "rabbit1-bd232",
-  "storageBucket": "rabbit1-bd232.appspot.com",
-  "messagingSenderId": "291333251174",
-  "appId": "1:291333251174:web:6daeb9908880347a6ecda7",
-  "measurementId": "G-H1NRRJQRHT"
-}
-
-firebase=Firebase(firebaseConfig)
-auth=firebase.auth()
-
-data=firebase.database()
-storage=firebase.storage()
-
-
-
-query_params = {
-  "orderBy": "\"timestamp\"",
-  "limitToLast": 1
-}
-query_string = "?" + "&".join([f"{key}={value}" for key, value in query_params.items()])
-
-latest_post = data.child("Posts").get(query_string).val()
-
-
-
-
-
-
-
-
-
-
-
-st.markdown("<center><img src=https://img.icons8.com/ios-filled/100/228BE6/rabbit.png; alt=centered image; height=100; width=100> </center>",unsafe_allow_html=True)
-labela=("<h1 style='font-family:arial;color:#228BE6;text-align:center'>Rabbit.web</h1>")
-st.markdown(labela,unsafe_allow_html=True)
-streamlit_style = """
-			<style>
-			@import url('https://fonts.googleapis.com/css2?family=Roboto:wght@100&display=swap');
-
-			html, body, [class*="css"]  {
-			font-family: 'Roboto', sans-serif;
-			
-			}
-			</style>
-			"""
-st.markdown(streamlit_style, unsafe_allow_html=True)
-streamlitstyle = """
-			<style>
-			sidebar,body,[class*="css"]{background-image: url("https://i.gifer.com/Cal.gif");
-                        background-attachment: fixed;
-                        background-size: cover 
-			
-			
-                       
-			
-			
-                        }
-			</style>
-			"""
-
-
-
-st.sidebar.markdown(streamlitstyle, unsafe_allow_html=True)
-st.sidebar.markdown("<center><img src=https://img.icons8.com/ios-filled/100/228BE6/rabbit.png ; alt=centered image; height=100; width=100> </center>",unsafe_allow_html=True)
-
-placeholder = st.empty()
-with placeholder.container():
-    label=("<h1 style='font-family:arial;color:#228BE6;text-align:center'>Welcome to  Rabbit.web</h1>")
-    st.markdown(label,unsafe_allow_html=True)
-    label=("<h5 style='font-family:arial;color:#228BE6;text-align:left'>At  Rabbit.web you can do the following thing's :</h5>")
-    st.markdown(label,unsafe_allow_html=True)
-    labelc=("<h5 style='font-family:arial;color:#228BE6;text-align:left'>~You can share your thought's </h5>")
-    st.markdown(labelc,unsafe_allow_html=True)
-
-
-    labeld=("<h5 style='font-family:arial;color:#228BE6;text-align:left'>~You can see the post's from people</h5>")
-    st.markdown(labeld,unsafe_allow_html=True)
-    labeld=("<h5 style='font-family:arial;color:#228BE6;text-align:left'>~You can check the latest new's</h5>")
-    st.markdown(labeld,unsafe_allow_html=True)
-  
-
-
-
-
-label=("<h1 style='font-family:arial;color:#228BE6;text-align:center'>Rabbit.web</h1>")
-st.sidebar.markdown(label,unsafe_allow_html=True)
-
-labelb=("<h4 style='font-family:arial;color:#228BE6;text-align:center'>A perfect place to chat with your friend's</h4>")
-st.sidebar.markdown(labelb,unsafe_allow_html=True)
-
-choice=st.sidebar.selectbox("Sign in to your account or create an account :",["sign in","create an account"])
-
-
-
-email=st.sidebar.text_input("",placeholder="Hello please enter you email")
-passw=st.sidebar.text_input("",placeholder="Hello please enter your password",type="password")
-
-
-
-if choice=="create an account":
-    handle=st.sidebar.text_input("",placeholder="Hello please enter your name")
-    subbt=st.sidebar.button("Create an new account")
-
-    if subbt:
-        placeholder.empty()
-        user=auth.create_user_with_email_and_password(email,passw)
-        st.success("Your Rabbit.web account has created successfully !")
-     
-        user=auth.sign_in_with_email_and_password(email,passw)
-        data.child(user["localId"]).child("Handle").set(handle)
-        data.child(user["localId"]).child("ID").set(user["localId"])
-        st.info("You can now log in")
-
-
-
-       
-if choice=="sign in":
-   
-    signin=st.sidebar.checkbox("sign in")
-    
-
-    if signin:
-            placeholder.empty()
-            user=auth.sign_in_with_email_and_password(email,passw)
-
-          
-            #"Follower's"  "list-task"
-            nav = option_menu(menu_title=None,   options=["Home", "Friend's","New's", "Setting's","About us"],icons=["house","person","list-task", "gear","info"],menu_icon="cast",default_index=2,orientation="vertical",styles={
-        "container": {"padding": "0!important", "background-color": "#1c1c1c"},
-        "icon": {"color": "lightblue", "font-size": "15px"}, 
-        "nav-link": {"text-align":"left", "margin":"1px", "--hover-color": "#1c1c1c"},
-        "nav-link-selected": {"background-color": "#228BE6","color":"#1c1c1c"},})
-            
-            if nav =="Home":
-
-
-                st.write(f"#### Share your thought's/post's :")
-                post=st.text_input("",placeholder="share your thought with your friend's",max_chars=250)
-                add_post=st.button("Share your thought")
-
-                
-                   
-
-   
-    
-                
-
-                    
-                if add_post:
-                    now=datetime.now()
-                    dt=now.strftime("%d / %m / %y")
-                    dtt=now.strftime("%I:%M %p")
-
-                    post="Post: "+post+ ";"+"  Posted on:"+ dt +"  at  "+dtt
-                    results=data.child(user["localId"]).child("Posts").push(post)
-                    st.balloons()
-
-
-         #      st.write("Upload an image")
-                
-              #  caption = st.text_input("",placeholder="Add a caption to your image")
-               # expan=st.expander("Upload an image")
-
-               # with expan:
-                 #   image = st.file_uploader("", type=["jpg", "jpeg", "png","mp3"])
-                   
-                   # if image is None:
-                      #  st.warning("Please select an image")
-                    #upbta=st.button("Upload the image and caption")
-
-                   # if upbta:
-                       # with st.spinner("Uploading image..."):
-                           # storage.child("images/" + image.name).put(image)
-                           # post_data = {"caption": caption,"image_url": storage.child("images/" + image.name).get_url(None) }
-                           # data.child("posts").push(post_data)
-                            #st.success("Post added successfully")'''
-                components.html("""<hr style="height:2px;border:none;color:#333;background-color:white;" /> """)
-                col1,col2=st.columns(2)
-
-                with col1:
-                    nimg=data.child(user["localId"]).child("Image").get().val()
-                    if nimg is not None:
-                        v=data.child(user["localId"]).child("Image").get()
-                        for img in v.each():
-                            imgc=img.val()
-                        
-                        st.markdown(f'<img src="{imgc}" width="200" height="200" style="border-radius:50%;">', unsafe_allow_html=True)
-                        
-                       
-                    else:
-                         st.info("Oop's no profile pic till now ")
-                    
-                
-            
-
-                with col2:
-                    st.title("Post's :")
-                    st.write(f"###### ______________________________________________________")
-                    all_posts=data.child(user['localId']).child("Posts").get()
-                    all_imgs=data.child(user['localId']).child("images").get()
-                    
-                    if all_posts.val() is not None:
-                        for Posts in reversed(all_posts.each()):
-                            
-                            st.success(Posts.val())
-                            if st.button("🗑 Delete this post ",key=f"Delete_({Posts.key()})"):
-                                 data.child(user["localId"]).child("Posts").child(Posts.key()).remove()
-                                 
-                       
-                            
-                             
-                            st.write(f"###### ______________________________________________________")
-                            
-                             
-                             
-                             
-                 
-                            
-                   
-                    else:
-                            st.info("Oop's no thought till now")
-
-
-                  #  posts = data.child("posts").get()
-                    #for post in posts.each():
-                        #caption = post.val()["caption"]
-                        #image_url = post.val()["image_url"]
-                      
-                        #st.write(caption)
-                        #response = requests.get(image_url)
-                        #img = Image.open(BytesIO(response.content))
-                        #st.image(img, caption=caption, use_column_width=True)
-                        #components.html("""<hr style="height:2px;border:none;color:#333;background-color:white;" /> """)
-                    
-                        
-                col3=st.columns(1)
-                with col1:
-                    st.title("Bio :")
-                    all_bio=data.child(user["localId"]).child("Bio").get()
-
-                    if all_bio.val() is not None:
-                        
-                        bio=data.child(user["localId"]).child("Bio").get()
-                        for bio in bio.each():
-                            bioc=bio.val()
-                        st.info(bioc)
-                    else:
-                        st.info("Oop's no Bio  till now")
-                
-                
-            elif nav =="Setting's":
-                    nimg=data.child(user["localId"]).child("Image").get().val()
-                    if nimg is not None:
-                        Image=data.child(user["localId"]).child("Image").get()
-                        for img in Image.each():
-                            imgc=img.val()
-                        
-                        st.markdown(f'<img src="{imgc}" width="200" height="200" style="border-radius:50%;">', unsafe_allow_html=True)
-
-                        expa=st.expander("Change your profile pic")
-
-                        with expa:
-                            newimgp=st.file_uploader("Please choose  your profile pic")
-                            upbt=st.button("Upload profile pic")
-                            if upbt:
-                                uid=user["localId"]
-                                dataup=storage.child(uid).put(newimgp,user["idToken"])
-                                aimgdata=storage.child(uid).get_url(dataup["downloadTokens"])
-
-                                data.child(user["localId"]).child("Image").push(aimgdata)
-                               
-
-                                st.info("Your profile pic is set successfully")
-                                st.balloons()
-                    else:
-                                st.info("Oop's no profile pic till now")
-                                newimgp=st.file_uploader("Please  choose your profile pic")
-                                upbt=st.button("Upload profile pic")
-                                if upbt:
-                                    uid=user["localId"]
-                                    dataup=storage.child(uid).put(newimgp,user["idToken"])
-                                    aimgdata=storage.child(uid).get_url(dataup["downloadTokens"])
-                                    data.child(user["localId"]).child("Image").push(aimgdata)
-
-                    bio=data.child(user["localId"]).child("Bio").get().val()
-                    if bio is not None:
-                        bio=data.child(user["localId"]).child("Bio").get()
-                        for bio in bio.each():
-                            bioc=bio.val()
-                        st.info(bioc)
-
-                        bioin=st.text_area("",placeholder="Enter your Bio to be uploaded eg: name,date of birth etc")
-                        upbtn=st.button("Upload Bio")
-
-                        if upbtn:
-                           
-                            
-
-                            data.child(user["localId"]).child("Bio").push(bioin)
-
-                            st.info("Your Bio is set successfully")
-                            st.balloons()
-                    else:
-                        st.info("Oop's no Bio till now")
-                        bioin=st.text_area("",placeholder="Enter your Bio to be uploaded eg: name,date of birth etc")
-                        upbtn=st.button("Upload Bio")
-
-                        if upbtn:
-                           
-                           
-                            data.child(user["localId"]).child("Bio").push(bioin)
-
-                            st.info("Your Bio is set successfully")
-                            st.balloons()
-               
-
-            elif nav=="Friend's":
-                allu=data.get()
-                resa=[]
-
-                for ush in allu.each():
-                  
-                    k=ush.val().get("Handle")
-                    resa.append(k)
-            
-                n = len(resa)
-            
-                st.title("Search your Friend's :")
-                cho = st.selectbox('',resa)
-                pusha = st.button('Show Profile')
-
-                if pusha:
-                    for ush in allu.each():
-                        k=ush.val().get("Handle")
-                        if k==cho:
-                            l=ush.val().get("ID")
-
-                            hn=data.child(l).child("Handle").get().val()
-
-                            st.markdown(hn,unsafe_allow_html=True)
-                            components.html("""<hr style="height:2px;border:none;color:#333;background-color:white;" /> """)
-                            col1,col2=st.columns(2)
-                            with col1:
-                                nimg=data.child(l).child("Image").get().val()
-                                if nimg is not None:
-                                    v=data.child(l).child("Image").get()
-                                    for img in v.each():
-                                        imgc=img.val()
-                                    
-                                    st.markdown(f'<img src="{imgc}" width="200" height="200" style="border-radius:50%;">', unsafe_allow_html=True)
-                       
-                                else:
-                                    st.info("Oop's no profile pic till now ")
-                                
-                                
-                
-                                
-                            
-
-                            with col2:
-                                st.title("Post's :")
-                                st.write(f"###### ______________________________________________________")
-                                all_posts=data.child(l).child("Posts").get()
-                                if all_posts.val() is not None:
-                                    for Posts in reversed(all_posts.each()):
-                                       
-                                        st.success(Posts.val())
-                                      
-                                       
-                                        
-                                        
-                                            
-
-                                        st.write(f"###### ______________________________________________________")
-                                       
-                                else:
-                                    st.info("Oop's no thought till now")
-                               
-
-                    
-                            col3=st.columns(1)
-                            with col1:
-                                st.title("Bio :")
-                                all_bio=data.child(l).child("Bio").get()
-
-                                if all_bio.val() is not None:
-                                    bio=data.child(l).child("Bio").get()
-                                    for bio in bio.each():
-                                        bioc=bio.val()
-                                        st.info(bioc)
-                                else:
-                                   st.info("Oop's no Bio  till now")
-            elif nav=="New's":
-
-                st.title("Have a look at today's latest new's :")
-                components.html("""<hr style="height:2px;border:none;color:#333;background-color:white;" /> """)
-                news_feed = feedparser.parse("https://rss.nytimes.com/services/xml/rss/nyt/World.xml")
-                
-               
-                for item in news_feed.entries:
-                    try:
-                        st.write(f"## {item.title}")
-                        st.write(item.summary)
-                        image_url = item.media_content[0]["url"]
-                        image_file = urllib.request.urlopen(image_url)
-                        image = Image.open(image_file)
-                        st.write(f"[Read more]({item.link})")
-                   
-                        st.image(image, caption="", use_column_width=True)
-                        st.write(f"###### ______________________________________________________")
-                    except:
-                        st.write("")
-                    
-                    
-                   
-                #st.info("Sorry this page is currently under construction")
-                #st.markdown("<center><h1>⚠️</h1>  </center>",unsafe_allow_html=True)
-                #st.components.v1.html('<iframe src="https://giphy.com/embed/hV1dkT2u1gqTUpKdKy" frameBorder=0></iframe>', width=800, height=800)
-
-
-              
-            else:
-                 st.write("Rabbit.web")
-                 st.write("Created and maintained by Navpreet Singh")
-                 st.write("For help,feedback or suggestion contact our company at rabbitweb854@gmail.com")
-                 st.write("For reporting a user on Rabbit.web  contact us at rabbitweb854@gmail.com")
-                
-
-#{"rules": {
- #   ".read": "now < 1682706600000",  // 2023-4-29
-   # ".write": "now < 1682706600000",  // 2023-4-29
-  #}
-#}
-      
-                  
-
-                  
-                 
-
-                    
-        
\ No newline at end of file
diff --git a/spaces/Neilblaze/WhisperAnything/README.md b/spaces/Neilblaze/WhisperAnything/README.md
deleted file mode 100644
index dff8f910407715e403534ff8dbc3b896f99ffa69..0000000000000000000000000000000000000000
--- a/spaces/Neilblaze/WhisperAnything/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: WhisperAnything
-emoji: 🎙
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-license: mit
----
\ No newline at end of file
diff --git a/spaces/OAOA/DifFace/facelib/detection/yolov5face/models/yolo.py b/spaces/OAOA/DifFace/facelib/detection/yolov5face/models/yolo.py
deleted file mode 100644
index 2cdbf30ff1ee781262d2b54ad699b4f180ae9a18..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/facelib/detection/yolov5face/models/yolo.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import math
-from copy import deepcopy
-from pathlib import Path
-
-import torch
-import yaml  # for torch hub
-from torch import nn
-
-from facelib.detection.yolov5face.models.common import (
-    C3,
-    NMS,
-    SPP,
-    AutoShape,
-    Bottleneck,
-    BottleneckCSP,
-    Concat,
-    Conv,
-    DWConv,
-    Focus,
-    ShuffleV2Block,
-    StemBlock,
-)
-from facelib.detection.yolov5face.models.experimental import CrossConv, MixConv2d
-from facelib.detection.yolov5face.utils.autoanchor import check_anchor_order
-from facelib.detection.yolov5face.utils.general import make_divisible
-from facelib.detection.yolov5face.utils.torch_utils import copy_attr, fuse_conv_and_bn
-
-
-class Detect(nn.Module):
-    stride = None  # strides computed during build
-    export = False  # onnx export
-
-    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
-        super().__init__()
-        self.nc = nc  # number of classes
-        self.no = nc + 5 + 10  # number of outputs per anchor
-
-        self.nl = len(anchors)  # number of detection layers
-        self.na = len(anchors[0]) // 2  # number of anchors
-        self.grid = [torch.zeros(1)] * self.nl  # init grid
-        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
-        self.register_buffer("anchors", a)  # shape(nl,na,2)
-        self.register_buffer("anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
-        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
-
-    def forward(self, x):
-        z = []  # inference output
-        if self.export:
-            for i in range(self.nl):
-                x[i] = self.m[i](x[i])
-            return x
-        for i in range(self.nl):
-            x[i] = self.m[i](x[i])  # conv
-            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
-            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
-
-            if not self.training:  # inference
-                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
-
-                y = torch.full_like(x[i], 0)
-                y[..., [0, 1, 2, 3, 4, 15]] = x[i][..., [0, 1, 2, 3, 4, 15]].sigmoid()
-                y[..., 5:15] = x[i][..., 5:15]
-
-                y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
-                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
-
-                y[..., 5:7] = (
-                    y[..., 5:7] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x1 y1
-                y[..., 7:9] = (
-                    y[..., 7:9] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x2 y2
-                y[..., 9:11] = (
-                    y[..., 9:11] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x3 y3
-                y[..., 11:13] = (
-                    y[..., 11:13] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x4 y4
-                y[..., 13:15] = (
-                    y[..., 13:15] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
-                )  # landmark x5 y5
-
-                z.append(y.view(bs, -1, self.no))
-
-        return x if self.training else (torch.cat(z, 1), x)
-
-    @staticmethod
-    def _make_grid(nx=20, ny=20):
-        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)], indexing="ij")
-        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
-
-
-class Model(nn.Module):
-    def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None):  # model, input channels, number of classes
-        super().__init__()
-        self.yaml_file = Path(cfg).name
-        with Path(cfg).open(encoding="utf8") as f:
-            self.yaml = yaml.safe_load(f)  # model dict
-
-        # Define model
-        ch = self.yaml["ch"] = self.yaml.get("ch", ch)  # input channels
-        if nc and nc != self.yaml["nc"]:
-            self.yaml["nc"] = nc  # override yaml value
-
-        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
-        self.names = [str(i) for i in range(self.yaml["nc"])]  # default names
-
-        # Build strides, anchors
-        m = self.model[-1]  # Detect()
-        if isinstance(m, Detect):
-            s = 128  # 2x min stride
-            m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
-            m.anchors /= m.stride.view(-1, 1, 1)
-            check_anchor_order(m)
-            self.stride = m.stride
-            self._initialize_biases()  # only run once
-
-    def forward(self, x):
-        return self.forward_once(x)  # single-scale inference, train
-
-    def forward_once(self, x):
-        y = []  # outputs
-        for m in self.model:
-            if m.f != -1:  # if not from previous layer
-                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
-
-            x = m(x)  # run
-            y.append(x if m.i in self.save else None)  # save output
-
-        return x
-
-    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
-        # https://arxiv.org/abs/1708.02002 section 3.3
-        m = self.model[-1]  # Detect() module
-        for mi, s in zip(m.m, m.stride):  # from
-            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
-            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
-            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
-            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
-    def _print_biases(self):
-        m = self.model[-1]  # Detect() module
-        for mi in m.m:  # from
-            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
-            print(("%6g Conv2d.bias:" + "%10.3g" * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
-
-    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        print("Fusing layers... ")
-        for m in self.model.modules():
-            if isinstance(m, Conv) and hasattr(m, "bn"):
-                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
-                delattr(m, "bn")  # remove batchnorm
-                m.forward = m.fuseforward  # update forward
-            elif type(m) is nn.Upsample:
-                m.recompute_scale_factor = None  # torch 1.11.0 compatibility
-        return self
-
-    def nms(self, mode=True):  # add or remove NMS module
-        present = isinstance(self.model[-1], NMS)  # last layer is NMS
-        if mode and not present:
-            print("Adding NMS... ")
-            m = NMS()  # module
-            m.f = -1  # from
-            m.i = self.model[-1].i + 1  # index
-            self.model.add_module(name=str(m.i), module=m)  # add
-            self.eval()
-        elif not mode and present:
-            print("Removing NMS... ")
-            self.model = self.model[:-1]  # remove
-        return self
-
-    def autoshape(self):  # add autoShape module
-        print("Adding autoShape... ")
-        m = AutoShape(self)  # wrap model
-        copy_attr(m, self, include=("yaml", "nc", "hyp", "names", "stride"), exclude=())  # copy attributes
-        return m
-
-
-def parse_model(d, ch):  # model_dict, input_channels(3)
-    anchors, nc, gd, gw = d["anchors"], d["nc"], d["depth_multiple"], d["width_multiple"]
-    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
-    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
-
-    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
-    for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, args
-        m = eval(m) if isinstance(m, str) else m  # eval strings
-        for j, a in enumerate(args):
-            try:
-                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
-            except:
-                pass
-
-        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in [
-            Conv,
-            Bottleneck,
-            SPP,
-            DWConv,
-            MixConv2d,
-            Focus,
-            CrossConv,
-            BottleneckCSP,
-            C3,
-            ShuffleV2Block,
-            StemBlock,
-        ]:
-            c1, c2 = ch[f], args[0]
-
-            c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
-
-            args = [c1, c2, *args[1:]]
-            if m in [BottleneckCSP, C3]:
-                args.insert(2, n)
-                n = 1
-        elif m is nn.BatchNorm2d:
-            args = [ch[f]]
-        elif m is Concat:
-            c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
-        elif m is Detect:
-            args.append([ch[x + 1] for x in f])
-            if isinstance(args[1], int):  # number of anchors
-                args[1] = [list(range(args[1] * 2))] * len(f)
-        else:
-            c2 = ch[f]
-
-        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
-        t = str(m)[8:-2].replace("__main__.", "")  # module type
-        np = sum(x.numel() for x in m_.parameters())  # number params
-        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
-        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
-        layers.append(m_)
-        ch.append(c2)
-    return nn.Sequential(*layers), sorted(save)
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wmt19/README.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wmt19/README.md
deleted file mode 100644
index 5c90d0e6c4ae8d043ca622e70c5828dca6f9c2f2..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/wmt19/README.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# WMT 19
-
-This page provides pointers to the models of Facebook-FAIR's WMT'19 news translation task submission [(Ng et al., 2019)](https://arxiv.org/abs/1907.06616).
-
-## Pre-trained models
-
-Model | Description | Download
----|---|---
-`transformer.wmt19.en-de` | En->De Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz)
-`transformer.wmt19.de-en` | De->En Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz)
-`transformer.wmt19.en-ru` | En->Ru Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz)
-`transformer.wmt19.ru-en` | Ru->En Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz)
-`transformer_lm.wmt19.en` | En Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.gz)
-`transformer_lm.wmt19.de` | De Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.gz)
-`transformer_lm.wmt19.ru` | Ru Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.gz)
-
-## Pre-trained single models before finetuning
-
-Model | Description | Download
----|---|---
-`transformer.wmt19.en-de` | En->De Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.ffn8192.tar.gz)
-`transformer.wmt19.de-en` | De->En Single, no finetuning  | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.ffn8192.tar.gz)
-`transformer.wmt19.en-ru` | En->Ru Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ffn8192.tar.gz)
-`transformer.wmt19.ru-en` | Ru->En Single, no finetuning  | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ffn8192.tar.gz)
-
-## Example usage (torch.hub)
-
-#### Requirements
-
-We require a few additional Python dependencies for preprocessing:
-```bash
-pip install fastBPE sacremoses
-```
-
-#### Translation
-
-```python
-import torch
-
-# English to German translation
-en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
-                       tokenizer='moses', bpe='fastbpe')
-en2de.translate("Machine learning is great!")  # 'Maschinelles Lernen ist großartig!'
-
-# German to English translation
-de2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.de-en', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
-                       tokenizer='moses', bpe='fastbpe')
-de2en.translate("Maschinelles Lernen ist großartig!")  # 'Machine learning is great!'
-
-# English to Russian translation
-en2ru = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-ru', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
-                       tokenizer='moses', bpe='fastbpe')
-en2ru.translate("Machine learning is great!")  # 'Машинное обучение - это здорово!'
-
-# Russian to English translation
-ru2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.ru-en', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
-                       tokenizer='moses', bpe='fastbpe')
-ru2en.translate("Машинное обучение - это здорово!")  # 'Machine learning is great!'
-```
-
-#### Language Modeling
-
-```python
-# Sample from the English LM
-en_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.en', tokenizer='moses', bpe='fastbpe')
-en_lm.sample("Machine learning is")  # 'Machine learning is the future of computing, says Microsoft boss Satya Nadella ...'
-
-# Sample from the German LM
-de_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.de', tokenizer='moses', bpe='fastbpe')
-de_lm.sample("Maschinelles lernen ist")  # 'Maschinelles lernen ist das A und O (neues-deutschland.de) Die Arbeitsbedingungen für Lehrerinnen und Lehrer sind seit Jahren verbesserungswürdig ...'
-
-# Sample from the Russian LM
-ru_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.ru', tokenizer='moses', bpe='fastbpe')
-ru_lm.sample("машинное обучение это")  # 'машинное обучение это то, что мы называем "искусственным интеллектом".'
-```
-
-## Citation
-```bibtex
-@inproceedings{ng2019facebook},
-  title = {Facebook FAIR's WMT19 News Translation Task Submission},
-  author = {Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey},
-  booktitle = {Proc. of WMT},
-  year = 2019,
-}
-```
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/multilingual/sampled_multi_dataset.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/multilingual/sampled_multi_dataset.py
deleted file mode 100644
index b0a617424ee3c5923b37796773da4c97851a16c5..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/multilingual/sampled_multi_dataset.py
+++ /dev/null
@@ -1,467 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import datetime
-import hashlib
-import logging
-import time
-from bisect import bisect_right
-from collections import OrderedDict, defaultdict
-from enum import Enum
-from typing import List
-
-import numpy as np
-import torch
-from fairseq.data import FairseqDataset, data_utils
-from fairseq.distributed import utils as distributed_utils
-
-
-def get_time_gap(s, e):
-    return (
-        datetime.datetime.fromtimestamp(e) - datetime.datetime.fromtimestamp(s)
-    ).__str__()
-
-
-logger = logging.getLogger(__name__)
-
-
-def default_virtual_size_func(datasets, ratios, max_scale_up=1.5):
-    sizes = [len(d) for d in datasets]
-    if ratios is None:
-        return sum(sizes)
-    largest_idx = np.argmax(sizes)
-    largest_r = ratios[largest_idx]
-    largest_s = sizes[largest_idx]
-    # set virtual sizes relative to the largest dataset
-    virtual_sizes = [(r / largest_r) * largest_s for r in ratios]
-    vsize = sum(virtual_sizes)
-    max_size = sum(sizes) * max_scale_up
-    return int(vsize if vsize < max_size else max_size)
-
-
-class CollateFormat(Enum):
-    single = 1
-    ordered_dict = 2
-
-
-class SampledMultiDataset(FairseqDataset):
-    """Samples from multiple sub-datasets according to given sampling ratios.
-    Args:
-        datasets (
-            List[~torch.utils.data.Dataset]
-            or OrderedDict[str, ~torch.utils.data.Dataset]
-        ): datasets
-        sampling_ratios (List[float]): list of probability of each dataset to be sampled
-            (default: None, which corresponds to concatenating all dataset together).
-        seed (int): RNG seed to use (default: 2).
-        epoch (int): starting epoch number (default: 1).
-        eval_key (str, optional): a key used at evaluation time that causes
-            this instance to pass-through batches from *datasets[eval_key]*.
-        collate_format (CollateFormat):  collater output format, either CollateFormat.ordered_dict or
-            CollateFormat.single (default: CollateFormat.single) where CollateFormat.single configures
-            the collater to output batches of data mixed from all sub-datasets,
-            and CollateFormat.ordered_dict configures the collater to output a dictionary of batches indexed by keys
-            of sub-datasets.
-            Note that not all sub-datasets will present in a single batch in both formats.
-        virtual_size (int, or callable): the expected virtual size of the dataset (default: default_virtual_size_func).
-        split (str): the split of the data, e.g. 'train', 'valid' or 'test'.
-        shared_collater (bool): whether or not to all sub-datasets have the same collater.
-        shuffle (bool): whether or not to shuffle data (default: True).
-    """
-
-    def __init__(
-        self,
-        datasets,
-        sampling_ratios=None,
-        seed=2,
-        epoch=1,
-        eval_key=None,
-        collate_format=CollateFormat.single,
-        virtual_size=default_virtual_size_func,
-        split="",
-        shared_collater=False,
-        shuffle=True,
-    ):
-        super().__init__()
-        self.shared_collater = shared_collater
-        self.shuffle = shuffle
-
-        if isinstance(datasets, OrderedDict):
-            self.keys = list(datasets.keys())
-            datasets = list(datasets.values())
-        elif isinstance(datasets, List):
-            self.keys = list(range(len(datasets)))
-        else:
-            raise AssertionError()
-        self.datasets = datasets
-        self.split = split
-
-        self.eval_key = eval_key
-        if self.eval_key is not None:
-            self.collate_format = CollateFormat.single
-        else:
-            self.collate_format = collate_format
-
-        self.seed = seed
-        self._cur_epoch = None
-
-        self.cumulated_sizes = None
-        # self.datasets[k][self._cur_indices[i]] is the data item i in this sampled dataset
-        # namely, data item i is sampled from the kth sub-dataset self.datasets[k]
-        # where self.cumulated_sizes[k-1] <= i < self.cumulated_sizes[k]
-        self._cur_indices = None
-
-        self._sizes = None
-        self.virtual_size_per_dataset = None
-        # caching properties
-        self._reset_cached_properties()
-        self.setup_sampling(sampling_ratios, virtual_size)
-        self.set_epoch(epoch)
-
-    def _clean_if_not_none(self, var_list):
-        for v in var_list:
-            if v is not None:
-                del v
-
-    def _reset_cached_properties(self):
-        self._clean_if_not_none([self._sizes, self._cur_indices])
-        self._sizes = None
-        self._cur_indices = None
-
-    def setup_sampling(self, sample_ratios, virtual_size):
-        sizes = [len(d) for d in self.datasets]
-        if sample_ratios is None:
-            # default back to concating datasets
-            self.sample_ratios = None
-            self.virtual_size = sum(sizes)
-        else:
-            if not isinstance(sample_ratios, np.ndarray):
-                sample_ratios = np.array(sample_ratios)
-            self.sample_ratios = sample_ratios
-            virtual_size = (
-                default_virtual_size_func if virtual_size is None else virtual_size
-            )
-            self.virtual_size = (
-                virtual_size(self.datasets, self.sample_ratios)
-                if callable(virtual_size)
-                else virtual_size
-            )
-
-    def adjust_sampling(self, epoch, sampling_ratios, virtual_size):
-        if sampling_ratios is not None:
-            sampling_ratios = self._sync_sample_ratios(sampling_ratios)
-            self.setup_sampling(sampling_ratios, virtual_size)
-
-    def _sync_sample_ratios(self, ratios):
-        # in case the ratios are not precisely the same across processes
-        # also to ensure every procresses update the ratios in the same pace
-        ratios = torch.DoubleTensor(ratios)
-        if torch.distributed.is_initialized():
-            if torch.cuda.is_available():
-                distributed_utils.all_reduce(
-                    ratios.cuda(), group=distributed_utils.get_data_parallel_group()
-                )
-            else:
-                distributed_utils.all_reduce(
-                    ratios, group=distributed_utils.get_data_parallel_group()
-                )
-            ret = ratios.cpu()
-            ret = ret.numpy()
-        return ret
-
-    def random_choice_in_dataset(self, rng, dataset, choice_size):
-        if hasattr(dataset, "random_choice_in_dataset"):
-            return dataset.random_choice_in_dataset(rng, choice_size)
-        dataset_size = len(dataset)
-        return rng.choice(
-            dataset_size, choice_size, replace=(choice_size > dataset_size)
-        )
-
-    def get_virtual_indices(self, rng, datasets, sample_ratios, virtual_size):
-        def get_counts(sample_ratios):
-            counts = np.array([virtual_size * r for r in sample_ratios], dtype=np.int64)
-            diff = virtual_size - counts.sum()
-            assert diff >= 0
-            # due to round-offs, the size might not match the desired sizes
-            if diff > 0:
-                dataset_indices = rng.choice(
-                    len(sample_ratios), size=diff, p=sample_ratios
-                )
-                for i in dataset_indices:
-                    counts[i] += 1
-            return counts
-
-        def get_in_dataset_indices(datasets, sizes, sample_ratios):
-            counts = get_counts(sample_ratios)
-            # uniformally sample desired counts for each dataset
-            # if the desired counts are large, sample with replacement:
-            indices = [
-                self.random_choice_in_dataset(rng, d, c)
-                for c, d in zip(counts, datasets)
-            ]
-            return indices
-
-        sizes = [len(d) for d in datasets]
-        if sample_ratios is None:
-            # default back to concating datasets
-            in_dataset_indices = [list(range(s)) for s in sizes]
-            virtual_sizes_per_dataset = sizes
-        else:
-            ratios = sample_ratios / sample_ratios.sum()
-            in_dataset_indices = get_in_dataset_indices(datasets, sizes, ratios)
-            virtual_sizes_per_dataset = [len(d) for d in in_dataset_indices]
-        virtual_sizes_per_dataset = np.array(virtual_sizes_per_dataset, np.int64)
-        cumulative_sizes = np.cumsum(virtual_sizes_per_dataset)
-        assert sum(virtual_sizes_per_dataset) == virtual_size
-        assert cumulative_sizes[-1] == virtual_size
-        if virtual_size < sum(sizes):
-            logger.warning(
-                f"virtual data size ({virtual_size}) is less than real data size ({sum(sizes)})."
-                " If virtual size << real data size, there could be data coverage issue."
-            )
-        in_dataset_indices = np.hstack(in_dataset_indices)
-        return in_dataset_indices, cumulative_sizes, virtual_sizes_per_dataset
-
-    def _get_dataset_and_index(self, index):
-        i = bisect_right(self.cumulated_sizes, index)
-        return i, self._cur_indices[index]
-
-    def __getitem__(self, index):
-        # self.__getitem__(index) returns self.datasets[k][self._cur_indices[index]]
-        # where k satisfies self.cumulated_sizes[k - 1] <= k < self.cumulated_sizes[k]
-        ds_idx, ds_sample_idx = self._get_dataset_and_index(index)
-        ret = (ds_idx, self.datasets[ds_idx][ds_sample_idx])
-        return ret
-
-    def num_tokens(self, index):
-        return self.sizes[index].max()
-
-    def num_tokens_vec(self, indices):
-        sizes_vec = self.sizes[np.array(indices)]
-        # max across all dimensions but first one
-        return np.amax(sizes_vec, axis=tuple(range(1, len(sizes_vec.shape))))
-
-    def size(self, index):
-        return self.sizes[index]
-
-    def __len__(self):
-        return self.virtual_size
-
-    def collater(self, samples, **extra_args):
-        """Merge a list of samples to form a mini-batch."""
-        if len(samples) == 0:
-            return None
-        if self.collate_format == "ordered_dict":
-            collect_samples = [[] for _ in range(len(self.datasets))]
-            for (i, sample) in samples:
-                collect_samples[i].append(sample)
-            batch = OrderedDict(
-                [
-                    (self.keys[i], dataset.collater(collect_samples[i]))
-                    for i, (key, dataset) in enumerate(zip(self.keys, self.datasets))
-                    if len(collect_samples[i]) > 0
-                ]
-            )
-        elif self.shared_collater:
-            batch = self.datasets[0].collater([s for _, s in samples])
-        else:
-            samples_dict = defaultdict(list)
-            pad_to_length = (
-                defaultdict(int)
-                if "pad_to_length" not in extra_args
-                else extra_args["pad_to_length"]
-            )
-            for ds_idx, s in samples:
-                pad_to_length["source"] = max(
-                    pad_to_length["source"], s["source"].size(0)
-                )
-                if s["target"] is not None:
-                    pad_to_length["target"] = max(
-                        pad_to_length["target"], s["target"].size(0)
-                    )
-                samples_dict[ds_idx].append(s)
-            batches = [
-                self.datasets[i].collater(samples_dict[i], pad_to_length=pad_to_length)
-                for i in range(len(self.datasets))
-                if len(samples_dict[i]) > 0
-            ]
-
-            def straight_data(tensors):
-                batch = torch.cat(tensors, dim=0)
-                return batch
-
-            src_lengths = straight_data(
-                [b["net_input"]["src_lengths"] for b in batches]
-            )
-            src_lengths, sort_order = src_lengths.sort(descending=True)
-
-            def straight_order(tensors):
-                batch = straight_data(tensors)
-                return batch.index_select(0, sort_order)
-
-            batch = {
-                "id": straight_order([b["id"] for b in batches]),
-                "nsentences": sum(b["nsentences"] for b in batches),
-                "ntokens": sum(b["ntokens"] for b in batches),
-                "net_input": {
-                    "src_tokens": straight_order(
-                        [b["net_input"]["src_tokens"] for b in batches]
-                    ),
-                    "src_lengths": src_lengths,
-                },
-                "target": straight_order([b["target"] for b in batches])
-                if batches[0]["target"] is not None
-                else None,
-            }
-            if "prev_output_tokens" in batches[0]["net_input"]:
-                batch["net_input"]["prev_output_tokens"] = straight_order(
-                    [b["net_input"]["prev_output_tokens"] for b in batches]
-                )
-            if "src_lang_id" in batches[0]["net_input"]:
-                batch["net_input"]["src_lang_id"] = straight_order(
-                    [b["net_input"]["src_lang_id"] for b in batches]
-                )
-            if "tgt_lang_id" in batches[0]:
-                batch["tgt_lang_id"] = straight_order(
-                    [b["tgt_lang_id"] for b in batches]
-                )
-        return batch
-
-    @property
-    def sizes(self):
-        if self._sizes is not None:
-            return self._sizes
-        start_time = time.time()
-        in_sub_dataset_indices = [
-            self._cur_indices[
-                0 if i == 0 else self.cumulated_sizes[i - 1] : self.cumulated_sizes[i]
-            ]
-            for i in range(len(self.datasets))
-        ]
-        sub_dataset_sizes = [
-            d.sizes[indices]
-            for d, indices in zip(self.datasets, in_sub_dataset_indices)
-        ]
-        self._sizes = np.vstack(sub_dataset_sizes)
-        logger.info(f"sizes() calling time: {get_time_gap(start_time, time.time())}")
-        return self._sizes
-
-    def ordered_indices(self):
-        if self.shuffle:
-            indices = np.random.permutation(len(self))
-        else:
-            indices = np.arange(len(self))
-
-        sizes = self.sizes
-        tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None
-        src_sizes = (
-            sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes
-        )
-
-        # sort by target length, then source length
-        if tgt_sizes is not None:
-            indices = indices[np.argsort(tgt_sizes[indices], kind="mergesort")]
-        sort_indices = indices[np.argsort(src_sizes[indices], kind="mergesort")]
-        return sort_indices
-
-    def prefetch(self, indices):
-        prefetch_indices = [[] for _ in range(len(self.datasets))]
-        for i in indices:
-            ds_idx, ds_sample_idx = self._get_dataset_and_index(i)
-            prefetch_indices[ds_idx].append(ds_sample_idx)
-        for i in range(len(prefetch_indices)):
-            self.datasets[i].prefetch(prefetch_indices[i])
-
-    @property
-    def can_reuse_epoch_itr_across_epochs(self):
-        return False
-
-    def set_epoch(self, epoch):
-        super().set_epoch(epoch)
-        if epoch == self._cur_epoch:
-            # re-enter so return
-            return
-        for d in self.datasets:
-            if hasattr(d, "set_epoch"):
-                d.set_epoch(epoch)
-        self._cur_epoch = epoch
-        self._establish_virtual_datasets()
-
-    def _establish_virtual_datasets(self):
-        if self.sample_ratios is None and self._cur_indices is not None:
-            # not a samping dataset, no need to resample if indices are already established
-            return
-        self._reset_cached_properties()
-
-        start_time = time.time()
-        # Generate a weighted sample of indices as a function of the
-        # random seed and the current epoch.
-        rng = np.random.RandomState(
-            [
-                int(
-                    hashlib.sha1(
-                        str(self.__class__.__name__).encode("utf-8")
-                    ).hexdigest(),
-                    16,
-                )
-                % (2 ** 32),
-                self.seed % (2 ** 32),  # global seed
-                self._cur_epoch,  # epoch index,
-            ]
-        )
-        self._clean_if_not_none(
-            [self.cumulated_sizes, self.virtual_size_per_dataset, self._sizes]
-        )
-        self._sizes = None
-
-        indices, cumulated_sizes, virtual_size_per_dataset = self.get_virtual_indices(
-            rng, self.datasets, self.sample_ratios, self.virtual_size
-        )
-        self._cur_indices = indices
-        self.cumulated_sizes = cumulated_sizes
-        self.virtual_size_per_dataset = virtual_size_per_dataset
-
-        raw_sizes = [len(d) for d in self.datasets]
-        sampled_sizes = self.virtual_size_per_dataset
-        logger.info(
-            f"[{self.split}] Raw sizes: {str(dict(zip(self.keys, raw_sizes)))}; "
-            f"raw total size: {sum(raw_sizes)}"
-        )
-        logger.info(
-            f"[{self.split}] Resampled sizes: {str(dict(zip(self.keys, sampled_sizes)))}; "
-            f"resampled total size: {sum(sampled_sizes)}"
-        )
-        if self.sample_ratios is not None:
-            logger.info(
-                f"[{self.split}] Upsampling ratios: {str(dict(zip(self.keys, self.sample_ratios)))}"
-            )
-        else:
-            logger.info(f"[{self.split}] A concat dataset")
-        logger.info(
-            f"[{self.split}] virtual dataset established time: {get_time_gap(start_time, time.time())}"
-        )
-
-    def filter_indices_by_size(self, indices, max_sizes):
-        """Filter a list of sample indices. Remove those that are longer
-            than specified in max_sizes.
-
-        Args:
-            indices (np.array): original array of sample indices
-            max_sizes (int or list[int] or tuple[int]): max sample size,
-                can be defined separately for src and tgt (then list or tuple)
-
-        Returns:
-            np.array: filtered sample array
-            list: list of removed indices
-        """
-        sizes = self.sizes
-        tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None
-        src_sizes = (
-            sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes
-        )
-
-        return data_utils.filter_paired_dataset_indices_by_size(
-            src_sizes, tgt_sizes, indices, max_sizes
-        )
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/quantization/pq/modules/qlinear.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/quantization/pq/modules/qlinear.py
deleted file mode 100644
index 9bdd25a8685bb7c7b32e1f02372aaeb26d8ba53a..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/modules/quantization/pq/modules/qlinear.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class PQLinear(nn.Module):
-    """
-    Quantized counterpart of nn.Linear module. Stores the centroid, the assignments
-    and the non-quantized biases. The full weight is re-instantiated at each forward
-    pass.
-
-    Args:
-        - centroids: centroids of size n_centroids x block_size
-        - assignments: assignments of the centroids to the subvectors
-          of size self.out_features x n_blocks
-        - bias: the non-quantized bias
-
-    Remarks:
-        - We refer the reader to the official documentation of the nn.Linear module
-          for the other arguments and the behavior of the module
-        - Performance tests on GPU show that this implementation is 15% slower than
-          the non-quantized nn.Linear module for a standard training loop.
-    """
-
-    def __init__(self, centroids, assignments, bias, in_features, out_features):
-        super(PQLinear, self).__init__()
-        self.block_size = centroids.size(1)
-        self.n_centroids = centroids.size(0)
-        self.in_features = in_features
-        self.out_features = out_features
-        # check compatibility
-        if self.in_features % self.block_size != 0:
-            raise ValueError("Wrong PQ sizes")
-        if len(assignments) % self.out_features != 0:
-            raise ValueError("Wrong PQ sizes")
-        # define parameters
-        self.centroids = nn.Parameter(centroids, requires_grad=True)
-        self.register_buffer("assignments", assignments)
-        self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
-        if bias is not None:
-            self.bias = nn.Parameter(bias)
-        else:
-            self.register_parameter("bias", None)
-
-    @property
-    def weight(self):
-        return (
-            self.centroids[self.assignments]
-            .reshape(-1, self.out_features, self.block_size)
-            .permute(1, 0, 2)
-            .flatten(1, 2)
-        )
-
-    def forward(self, x):
-        return F.linear(
-            x,
-            self.weight,
-            self.bias,
-        )
-
-    def extra_repr(self):
-        return f"in_features={self.in_features},\
-                 out_features={self.out_features},\
-                 n_centroids={self.n_centroids},\
-                 block_size={self.block_size},\
-                 bias={self.bias is not None}"
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/app.py b/spaces/OFA-Sys/OFA-Visual_Grounding/app.py
deleted file mode 100644
index db2dd12b6bbd876cc84c630c8f8de068705da985..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/app.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import os
-
-os.system('cd fairseq;'
-          'pip install ./; cd ..')
-os.system('ls -l')
-
-import torch
-import numpy as np
-from fairseq import utils, tasks
-from fairseq import checkpoint_utils
-from utils.eval_utils import eval_step
-from tasks.mm_tasks.refcoco import RefcocoTask
-from models.ofa import OFAModel
-from PIL import Image
-from torchvision import transforms
-import cv2
-import gradio as gr
-
-# Register refcoco task
-tasks.register_task('refcoco', RefcocoTask)
-
-# turn on cuda if GPU is available
-use_cuda = torch.cuda.is_available()
-# use fp16 only when GPU is available
-use_fp16 = False
-
-os.system('wget https://ofa-silicon.oss-us-west-1.aliyuncs.com/checkpoints/refcocog_large_best.pt; '
-          'mkdir -p checkpoints; mv refcocog_large_best.pt checkpoints/refcocog.pt')
-
-# Load pretrained ckpt & config
-overrides = {"bpe_dir": "utils/BPE", "eval_cider": False, "beam": 5,
-             "max_len_b": 16, "no_repeat_ngram_size": 3, "seed": 7}
-models, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-    utils.split_paths('checkpoints/refcocog.pt'),
-    arg_overrides=overrides
-)
-
-cfg.common.seed = 7
-cfg.generation.beam = 5
-cfg.generation.min_len = 4
-cfg.generation.max_len_a = 0
-cfg.generation.max_len_b = 4
-cfg.generation.no_repeat_ngram_size = 3
-
-# Fix seed for stochastic decoding
-if cfg.common.seed is not None and not cfg.generation.no_seed_provided:
-    np.random.seed(cfg.common.seed)
-    utils.set_torch_seed(cfg.common.seed)
-
-# Move models to GPU
-for model in models:
-    model.eval()
-    if use_fp16:
-        model.half()
-    if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
-        model.cuda()
-    model.prepare_for_inference_(cfg)
-
-# Initialize generator
-generator = task.build_generator(models, cfg.generation)
-
-mean = [0.5, 0.5, 0.5]
-std = [0.5, 0.5, 0.5]
-
-patch_resize_transform = transforms.Compose([
-    lambda image: image.convert("RGB"),
-    transforms.Resize((cfg.task.patch_image_size, cfg.task.patch_image_size), interpolation=Image.BICUBIC),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=mean, std=std),
-])
-
-# Text preprocess
-bos_item = torch.LongTensor([task.src_dict.bos()])
-eos_item = torch.LongTensor([task.src_dict.eos()])
-pad_idx = task.src_dict.pad()
-
-
-def encode_text(text, length=None, append_bos=False, append_eos=False):
-    s = task.tgt_dict.encode_line(
-        line=task.bpe.encode(text),
-        add_if_not_exist=False,
-        append_eos=False
-    ).long()
-    if length is not None:
-        s = s[:length]
-    if append_bos:
-        s = torch.cat([bos_item, s])
-    if append_eos:
-        s = torch.cat([s, eos_item])
-    return s
-
-
-patch_image_size = cfg.task.patch_image_size
-
-
-def construct_sample(image: Image, text: str):
-    w, h = image.size
-    w_resize_ratio = torch.tensor(patch_image_size / w).unsqueeze(0)
-    h_resize_ratio = torch.tensor(patch_image_size / h).unsqueeze(0)
-    patch_image = patch_resize_transform(image).unsqueeze(0)
-    patch_mask = torch.tensor([True])
-    src_text = encode_text(' which region does the text " {} " describe?'.format(text), append_bos=True,
-                           append_eos=True).unsqueeze(0)
-    src_length = torch.LongTensor([s.ne(pad_idx).long().sum() for s in src_text])
-    sample = {
-        "id": np.array(['42']),
-        "net_input": {
-            "src_tokens": src_text,
-            "src_lengths": src_length,
-            "patch_images": patch_image,
-            "patch_masks": patch_mask,
-        },
-        "w_resize_ratios": w_resize_ratio,
-        "h_resize_ratios": h_resize_ratio,
-        "region_coords": torch.randn(1, 4)
-    }
-    return sample
-
-
-# Function to turn FP32 to FP16
-def apply_half(t):
-    if t.dtype is torch.float32:
-        return t.to(dtype=torch.half)
-    return t
-
-
-# Function for visual grounding
-def visual_grounding(Image, Text):
-    sample = construct_sample(Image, Text.lower())
-    sample = utils.move_to_cuda(sample) if use_cuda else sample
-    sample = utils.apply_to_sample(apply_half, sample) if use_fp16 else sample
-    with torch.no_grad():
-        result, scores = eval_step(task, generator, models, sample)
-    img = np.asarray(Image)
-    cv2.rectangle(
-        img,
-        (int(result[0]["box"][0]), int(result[0]["box"][1])),
-        (int(result[0]["box"][2]), int(result[0]["box"][3])),
-        (0, 255, 0),
-        3
-    )
-    return img
-
-
-title = "OFA-Visual_Grounding"
-description = "Gradio Demo for OFA-Visual_Grounding. Upload your own image or click any one of the examples, " \
-              "and write a description about a certain object.  " \
-              "Then click \"Submit\" and wait for the result of grounding. "
-article = "<p style='text-align: center'><a href='https://github.com/OFA-Sys/OFA' target='_blank'>OFA Github " \
-          "Repo</a></p> "
-examples = [['pokemons.jpg', 'a blue turtle-like pokemon with round head'],
-            ['one_piece.jpeg', 'a man in a straw hat and a red dress'],
-            ['flowers.jpg', 'a white vase and pink flowers']]
-io = gr.Interface(fn=visual_grounding, inputs=[gr.inputs.Image(type='pil'), "textbox"],
-                  outputs=gr.outputs.Image(type='numpy'),
-                  title=title, description=description, article=article, examples=examples,
-                  allow_flagging=False, allow_screenshot=False)
-io.launch()
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/linformer/linformer_src/modules/__init__.py b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/linformer/linformer_src/modules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/roberta/README.race.md b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/roberta/README.race.md
deleted file mode 100644
index 13c917e8eca6621e91dce541c7e41436b38cbdc1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/roberta/README.race.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# Finetuning RoBERTa on RACE tasks
-
-### 1) Download the data from RACE website (http://www.cs.cmu.edu/~glai1/data/race/)
-
-### 2) Preprocess RACE data:
-```bash
-python ./examples/roberta/preprocess_RACE.py --input-dir <input-dir> --output-dir <extracted-data-dir>
-./examples/roberta/preprocess_RACE.sh <extracted-data-dir> <output-dir>
-```
-
-### 3) Fine-tuning on RACE:
-
-```bash
-MAX_EPOCH=5           # Number of training epochs.
-LR=1e-05              # Peak LR for fixed LR scheduler.
-NUM_CLASSES=4
-MAX_SENTENCES=1       # Batch size per GPU.
-UPDATE_FREQ=8         # Accumulate gradients to simulate training on 8 GPUs.
-DATA_DIR=/path/to/race-output-dir
-ROBERTA_PATH=/path/to/roberta/model.pt
-
-CUDA_VISIBLE_DEVICES=0,1 fairseq-train $DATA_DIR --ddp-backend=legacy_ddp \
-    --restore-file $ROBERTA_PATH \
-    --reset-optimizer --reset-dataloader --reset-meters \
-    --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \
-    --task sentence_ranking \
-    --num-classes $NUM_CLASSES \
-    --init-token 0 --separator-token 2 \
-    --max-option-length 128 \
-    --max-positions 512 \
-    --shorten-method "truncate" \
-    --arch roberta_large \
-    --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \
-    --criterion sentence_ranking \
-    --optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \
-    --clip-norm 0.0 \
-    --lr-scheduler fixed --lr $LR \
-    --fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \
-    --batch-size $MAX_SENTENCES \
-    --required-batch-size-multiple 1 \
-    --update-freq $UPDATE_FREQ \
-    --max-epoch $MAX_EPOCH
-```
-
-**Note:**
-
-a) As contexts in RACE are relatively long, we are using smaller batch size per GPU while increasing update-freq to achieve larger effective batch size.
-
-b) Above cmd-args and hyperparams are tested on one Nvidia `V100` GPU with `32gb` of memory for each task. Depending on the GPU memory resources available to you, you can use increase `--update-freq` and reduce `--batch-size`.
-
-c) The setting in above command is based on our hyperparam search within a fixed search space (for careful comparison across models). You might be able to find better metrics with wider hyperparam search.  
-
-### 4) Evaluation:
-
-```
-DATA_DIR=/path/to/race-output-dir       # data directory used during training
-MODEL_PATH=/path/to/checkpoint_best.pt  # path to the finetuned model checkpoint
-PREDS_OUT=preds.tsv                     # output file path to save prediction
-TEST_SPLIT=test                         # can be test (Middle) or test1 (High)
-fairseq-validate \
-    $DATA_DIR \
-    --valid-subset $TEST_SPLIT \
-    --path $MODEL_PATH \
-    --batch-size 1 \
-    --task sentence_ranking \
-    --criterion sentence_ranking \
-    --save-predictions $PREDS_OUT
-```
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/base_wrapper_dataset.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/base_wrapper_dataset.py
deleted file mode 100644
index 134d398b47dc73c8807759188504aee205b3b34d..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/base_wrapper_dataset.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from torch.utils.data.dataloader import default_collate
-
-from . import FairseqDataset
-
-
-class BaseWrapperDataset(FairseqDataset):
-    def __init__(self, dataset):
-        super().__init__()
-        self.dataset = dataset
-
-    def __getitem__(self, index):
-        return self.dataset[index]
-
-    def __len__(self):
-        return len(self.dataset)
-
-    def collater(self, samples):
-        if hasattr(self.dataset, "collater"):
-            return self.dataset.collater(samples)
-        else:
-            return default_collate(samples)
-
-    @property
-    def sizes(self):
-        return self.dataset.sizes
-
-    def num_tokens(self, index):
-        return self.dataset.num_tokens(index)
-
-    def size(self, index):
-        return self.dataset.size(index)
-
-    def ordered_indices(self):
-        return self.dataset.ordered_indices()
-
-    @property
-    def supports_prefetch(self):
-        return getattr(self.dataset, "supports_prefetch", False)
-
-    def attr(self, attr: str, index: int):
-        return self.dataset.attr(attr, index)
-
-    def prefetch(self, indices):
-        self.dataset.prefetch(indices)
-
-    def get_batch_shapes(self):
-        return self.dataset.get_batch_shapes()
-
-    def batch_by_size(
-        self,
-        indices,
-        max_tokens=None,
-        max_sentences=None,
-        required_batch_size_multiple=1,
-    ):
-        return self.dataset.batch_by_size(
-            indices,
-            max_tokens=max_tokens,
-            max_sentences=max_sentences,
-            required_batch_size_multiple=required_batch_size_multiple,
-        )
-
-    def filter_indices_by_size(self, indices, max_sizes):
-        return self.dataset.filter_indices_by_size(indices, max_sizes)
-
-    @property
-    def can_reuse_epoch_itr_across_epochs(self):
-        return self.dataset.can_reuse_epoch_itr_across_epochs
-
-    def set_epoch(self, epoch):
-        super().set_epoch(epoch)
-        if hasattr(self.dataset, "set_epoch"):
-            self.dataset.set_epoch(epoch)
diff --git a/spaces/Omnibus/pdf-reader/app.py b/spaces/Omnibus/pdf-reader/app.py
deleted file mode 100644
index a917fb91461972517b2852f00a39fbae8fdbb710..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/pdf-reader/app.py
+++ /dev/null
@@ -1,156 +0,0 @@
-import gradio as gr
-import requests
-from pypdf import PdfReader
-import pypdfium2 as pdfium
-import easyocr
-
-ocr_id = {
-    "Afrikaans": "af",
-    "Albanian": "sq",
-    "Arabic": "ar",
-    "Azerbaijani": "az",
-    "Belarusian": "be",
-    "Bulgarian": "bg",
-    "Bengali": "bn",
-    "Bosnian": "bs",
-    "Chinese (simplified)": "ch_sim",
-    "Chinese (traditional)": "ch_tra",
-    "Croatian": "hr",
-    "Czech": "cs",
-    "Danish": "da",
-    "Dutch": "nl",
-    "English": "en",
-    "Estonian": "et",
-    "French": "fr",
-    "German": "de",
-    "Irish": "ga",
-    "Hindi": "hi",
-    "Hungarian": "hu",
-    "Indonesian": "id",
-    "Icelandic": "is",
-    "Italian": "it",
-    "Japanese": "ja",
-    "Kannada": "kn",
-    "Korean": "ko",
-    "Lithuanian": "lt",
-    "Latvian": "lv",
-    "Mongolian": "mn",
-    "Marathi": "mr",
-    "Malay": "ms",
-    "Nepali": "ne",
-    "Norwegian": "no",
-    "Occitan": "oc",
-    "Polish": "pl",
-    "Portuguese": "pt",
-    "Romanian": "ro",
-    "Russian": "ru",
-    "Serbian (cyrillic)": "rs_cyrillic",
-    "Serbian (latin)": "rs_latin",
-    "Slovak": "sk",
-    "Slovenian": "sl",
-    "Spanish": "es",
-    "Swedish": "sv",
-    "Swahili": "sw",
-    "Tamil": "ta",
-    "Thai": "th",
-    "Tagalog": "tl",
-    "Turkish": "tr",
-    "Ukrainian": "uk",
-    "Urdu": "ur",
-    "Uzbek": "uz",
-    "Vietnamese": "vi",
-    "Welsh": "cy",
-    "Zulu": "zu",
-}
-
-def pdf_pil(file_path,page_num,up_scale):
-
-    pdf = pdfium.PdfDocument("data.pdf")
-    page = pdf.get_page(int(page_num)-1)
-    bitmap = page.render(
-        scale = int(up_scale),    # 72dpi resolution
-        rotation = 0, # no additional rotation
-        # ... further rendering options
-    )
-    pil_image = bitmap.to_pil()
-    pil_image.save(f"image_{page_num}.png")
-    
-    return (f"image_{page_num}.png")
-
-def ocrpdf(file_path,pdf_lang,page_num,sent_wid,contrast_det,up_scale):
-    img1 = pdf_pil(file_path,page_num,up_scale)
-    lang=[f"{ocr_id[pdf_lang]}"]
-    reader = easyocr.Reader(lang)
-    bounds = reader.readtext(img1,width_ths=sent_wid,contrast_ths=contrast_det)
-    
-    this = ""
-    for bound in bounds:
-        this = (f'{this} \n{bound[1]}')
-    return this    
-    
-
-def scrape(instring):
-    html_src=(f'''
-    <div style="text-align:center">
-    <h4>PDF Viewer</h4>
-    <iframe src="https://docs.google.com/viewer?url={instring}&embedded=true" frameborder="0" height="1200px" width="100%"></iframe>
-    </div>''')
-    return gr.HTML.update(f'''{html_src}''')
-
-def scrape00(instring, page_num,pdf_lang,sent_wid,contrast_det,up_scale):
-
-    response = requests.get(instring, stream=True)
-
-    if response.status_code == 200:
-        with open("data.pdf", "wb") as f:
-            f.write(response.content)
-    else:
-        print(response.status_code)
-
-
-    #out = Path("./data.pdf")
-    #print (out)
-    reader = PdfReader("data.pdf")
-    number_of_pages = len(reader.pages)
-    page = reader.pages[int(page_num)-1]
-    text = page.extract_text()
-    print (text)
-    summarizer = gr.Interface.load("huggingface/facebook/bart-large-cnn")
-    try:
-        sum_out = summarizer(text)
-    except Exception:
-        try:
-            text = ocrpdf("data.pdf",pdf_lang,page_num,sent_wid,contrast_det,up_scale)
-            sum_out = summarizer(text)
-        except Exception:
-            sum_out = "Error"
-            
-    return text, sum_out,gr.Markdown.update("""<h3> Complete""")
-
-with gr.Blocks() as app:
-    gr.Markdown('''<h1>PDF Viewer''')
-    with gr.Row():
-        inp=gr.Textbox(label="PDF URL",scale=3)
-        pg_num=gr.Number(label="Page Number",value=1,precision=0,scale=1)
-    with gr.Tab("View PDF"):
-        go_btn = gr.Button("Load PDF")
-        outp = gr.HTML()
-
-    with gr.Tab("Summarize"):
-        mes = gr.Markdown("""<h3> Summarize Text in PDF""")
-        with gr.Row():
-            with gr.Box():
-                with gr.Column():
-                    sent_wid=gr.Slider(0.1, 3, step=0.1,value=1,label="Horizontal Word Space")
-                    contrast_det=gr.Slider(0.1, 1, step=0.1,value=0.1,label="Contrast Threshold")
-                with gr.Column():
-                    up_scale=gr.Slider(0.1, 5, step=0.1,value=1,label="PDF to Image Scale")
-            with gr.Column():
-                target_lang = gr.Dropdown(label="PDF Language", choices=list(ocr_id.keys()),value="English")
-                sum_btn = gr.Button("Summarize")
-        with gr.Row():
-            text_out = gr.Textbox()
-            sum_out = gr.Textbox()
-    go_btn.click(scrape,inp,outp)
-    sum_btn.click(scrape00,[inp,pg_num,target_lang,sent_wid,contrast_det,up_scale],[text_out,sum_out,mes])
-app.queue(concurrency_count=10).launch()
\ No newline at end of file
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/__init__.py b/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PaddlePaddle/transformer_zh-en/app.py b/spaces/PaddlePaddle/transformer_zh-en/app.py
deleted file mode 100644
index c0c5ea4787f105541b0d8a75b496f44d795d90ab..0000000000000000000000000000000000000000
--- a/spaces/PaddlePaddle/transformer_zh-en/app.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import gradio as gr
-import paddlehub as hub
-
-
-transformer_zh = hub.Module(name="transformer_zh-en")
-
-def inference(text):
-  results = transformer_zh.predict(data=[text])
-  return results[0]
-
-  
-title="transformer_zh-en"
-description="Transformer model used for translating Chinese into English."
-
-examples=[['今天是个好日子']]
-gr.Interface(inference,"text",[gr.outputs.Textbox(label="Translation")],title=title,description=description,examples=examples).launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/Paresh/Facial-feature-detector/src/face_texture.py b/spaces/Paresh/Facial-feature-detector/src/face_texture.py
deleted file mode 100644
index 8f584721ff2110ebc5bed3d77717157cd6195e8b..0000000000000000000000000000000000000000
--- a/spaces/Paresh/Facial-feature-detector/src/face_texture.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import cv2
-import numpy as np
-from skimage.feature import local_binary_pattern
-import dlib
-import imutils
-from PIL import Image as PILImage
-from src.cv_utils import get_image, resize_image_height
-from typing import Tuple, List, Union
-
-
-class GetFaceTexture:
-    def __init__(self) -> None:
-        pass
-
-    @staticmethod
-    def preprocess_image(image) -> np.array:
-        image = imutils.resize(image, width=500)
-        gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-        return gray_image
-
-    @staticmethod
-    def get_face(gray_image: np.array) -> np.array:
-        detector = dlib.get_frontal_face_detector()
-        faces = detector(gray_image, 1)
-        if len(faces) == 0:
-            return "No face detected."
-
-        x, y, w, h = (
-            faces[0].left(),
-            faces[0].top(),
-            faces[0].width(),
-            faces[0].height(),
-        )
-        face_image = gray_image[y : y + h, x : x + w]
-        return face_image
-
-    @staticmethod
-    def get_face_texture(face_image: np.array) -> Tuple[np.array, float]:
-        radius = 1
-        n_points = 8 * radius
-        lbp = local_binary_pattern(face_image, n_points, radius, method="uniform")
-        hist, _ = np.histogram(
-            lbp.ravel(), bins=np.arange(0, n_points + 3), range=(0, n_points + 2)
-        )
-        variance = np.var(hist)
-        std = np.sqrt(variance)
-        return lbp, std
-
-    @staticmethod
-    def postprocess_image(lbp: np.array) -> PILImage:
-        lbp = (lbp * 255).astype(np.uint8)
-        return PILImage.fromarray(lbp)
-
-    def main(self, image_input) -> List[Union[PILImage.Image, PILImage.Image, dict]]:
-        image = get_image(image_input)
-        gray_image = self.preprocess_image(image)
-        face_image = self.get_face(gray_image)
-        lbp, std = self.get_face_texture(face_image)
-        face_texture_image = self.postprocess_image(lbp)
-        face_image = PILImage.fromarray(face_image)
-        face_image = resize_image_height(face_image, new_height=300)
-        face_texture_image = resize_image_height(face_texture_image, new_height=300)
-        return face_image, face_texture_image, {"texture_std": round(std, 2)}
-
-
-if __name__ == "__main__":
-    image_path = "data/gigi_hadid.webp"
-    print(GetFaceTexture().main(image_path))
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/foreign-object.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/foreign-object.go
deleted file mode 100644
index 005d2dfab932c403bb0c7c0f39e341b14a53e731..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/system/foreign-object.go and /dev/null differ
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/configs/_base_/models/cgnet.py b/spaces/Pie31415/control-animation/annotator/uniformer/configs/_base_/models/cgnet.py
deleted file mode 100644
index eff8d9458c877c5db894957e0b1b4597e40da6ab..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/configs/_base_/models/cgnet.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    backbone=dict(
-        type='CGNet',
-        norm_cfg=norm_cfg,
-        in_channels=3,
-        num_channels=(32, 64, 128),
-        num_blocks=(3, 21),
-        dilations=(2, 4),
-        reductions=(8, 16)),
-    decode_head=dict(
-        type='FCNHead',
-        in_channels=256,
-        in_index=2,
-        channels=256,
-        num_convs=0,
-        concat_input=False,
-        dropout_ratio=0,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        loss_decode=dict(
-            type='CrossEntropyLoss',
-            use_sigmoid=False,
-            loss_weight=1.0,
-            class_weight=[
-                2.5959933, 6.7415504, 3.5354059, 9.8663225, 9.690899, 9.369352,
-                10.289121, 9.953208, 4.3097677, 9.490387, 7.674431, 9.396905,
-                10.347791, 6.3927646, 10.226669, 10.241062, 10.280587,
-                10.396974, 10.055647
-            ])),
-    # model training and testing settings
-    train_cfg=dict(sampler=None),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/Dockerfile b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/Dockerfile
deleted file mode 100644
index efc2431ec0fe674c22fe2fdb9d7045cdf6cd2748..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/Dockerfile
+++ /dev/null
@@ -1,26 +0,0 @@
-FROM nvidia/cuda:11.8.0-base-ubuntu22.04
-
-ENV DEBIAN_FRONTEND=noninteractive \
-    PYTHONUNBUFFERED=1 \
-    PYTHONIOENCODING=UTF-8
-RUN --mount=type=cache,target=/var/cache/apt --mount=type=cache,target=/var/lib/apt apt update &&\
-    apt install -y \
-    wget \
-    git \
-    pkg-config \
-    python3 \
-    python3-pip \
-    python-is-python3 \
-    ffmpeg \
-    libnvrtc11.2 \
-    libtcmalloc-minimal4
-
-RUN useradd -m -u 1000 ac
-RUN --mount=type=cache,target=/root/.cache python -m pip install --upgrade pip wheel
-ENV TORCH_COMMAND="pip install torch==2.0.1+cu118 torchaudio --extra-index-url https://download.pytorch.org/whl/cu118"
-RUN --mount=type=cache,target=/root/.cache python -m $TORCH_COMMAND
-RUN ln -s /usr/lib/x86_64-linux-gnu/libnvrtc.so.11.2 /usr/lib/x86_64-linux-gnu/libnvrtc.so
-USER 1000
-RUN mkdir ~/.cache
-RUN --mount=type=cache,target=/home/ac/.cache --mount=source=.,target=/home/ac/audiocraft python -m pip install -r /home/ac/audiocraft/requirements.txt
-WORKDIR /home/ac/audiocraft
\ No newline at end of file
diff --git a/spaces/Purple11/Grounded-Diffusion/ldm/data/imagenet.py b/spaces/Purple11/Grounded-Diffusion/ldm/data/imagenet.py
deleted file mode 100644
index 1c473f9c6965b22315dbb289eff8247c71bdc790..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/ldm/data/imagenet.py
+++ /dev/null
@@ -1,394 +0,0 @@
-import os, yaml, pickle, shutil, tarfile, glob
-import cv2
-import albumentations
-import PIL
-import numpy as np
-import torchvision.transforms.functional as TF
-from omegaconf import OmegaConf
-from functools import partial
-from PIL import Image
-from tqdm import tqdm
-from torch.utils.data import Dataset, Subset
-
-import taming.data.utils as tdu
-from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve
-from taming.data.imagenet import ImagePaths
-
-from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
-
-
-def synset2idx(path_to_yaml="data/index_synset.yaml"):
-    with open(path_to_yaml) as f:
-        di2s = yaml.load(f)
-    return dict((v,k) for k,v in di2s.items())
-
-
-class ImageNetBase(Dataset):
-    def __init__(self, config=None):
-        self.config = config or OmegaConf.create()
-        if not type(self.config)==dict:
-            self.config = OmegaConf.to_container(self.config)
-        self.keep_orig_class_label = self.config.get("keep_orig_class_label", False)
-        self.process_images = True  # if False we skip loading & processing images and self.data contains filepaths
-        self._prepare()
-        self._prepare_synset_to_human()
-        self._prepare_idx_to_synset()
-        self._prepare_human_to_integer_label()
-        self._load()
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, i):
-        return self.data[i]
-
-    def _prepare(self):
-        raise NotImplementedError()
-
-    def _filter_relpaths(self, relpaths):
-        ignore = set([
-            "n06596364_9591.JPEG",
-        ])
-        relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore]
-        if "sub_indices" in self.config:
-            indices = str_to_indices(self.config["sub_indices"])
-            synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn)  # returns a list of strings
-            self.synset2idx = synset2idx(path_to_yaml=self.idx2syn)
-            files = []
-            for rpath in relpaths:
-                syn = rpath.split("/")[0]
-                if syn in synsets:
-                    files.append(rpath)
-            return files
-        else:
-            return relpaths
-
-    def _prepare_synset_to_human(self):
-        SIZE = 2655750
-        URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1"
-        self.human_dict = os.path.join(self.root, "synset_human.txt")
-        if (not os.path.exists(self.human_dict) or
-                not os.path.getsize(self.human_dict)==SIZE):
-            download(URL, self.human_dict)
-
-    def _prepare_idx_to_synset(self):
-        URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1"
-        self.idx2syn = os.path.join(self.root, "index_synset.yaml")
-        if (not os.path.exists(self.idx2syn)):
-            download(URL, self.idx2syn)
-
-    def _prepare_human_to_integer_label(self):
-        URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1"
-        self.human2integer = os.path.join(self.root, "imagenet1000_clsidx_to_labels.txt")
-        if (not os.path.exists(self.human2integer)):
-            download(URL, self.human2integer)
-        with open(self.human2integer, "r") as f:
-            lines = f.read().splitlines()
-            assert len(lines) == 1000
-            self.human2integer_dict = dict()
-            for line in lines:
-                value, key = line.split(":")
-                self.human2integer_dict[key] = int(value)
-
-    def _load(self):
-        with open(self.txt_filelist, "r") as f:
-            self.relpaths = f.read().splitlines()
-            l1 = len(self.relpaths)
-            self.relpaths = self._filter_relpaths(self.relpaths)
-            print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths)))
-
-        self.synsets = [p.split("/")[0] for p in self.relpaths]
-        self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]
-
-        unique_synsets = np.unique(self.synsets)
-        class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))
-        if not self.keep_orig_class_label:
-            self.class_labels = [class_dict[s] for s in self.synsets]
-        else:
-            self.class_labels = [self.synset2idx[s] for s in self.synsets]
-
-        with open(self.human_dict, "r") as f:
-            human_dict = f.read().splitlines()
-            human_dict = dict(line.split(maxsplit=1) for line in human_dict)
-
-        self.human_labels = [human_dict[s] for s in self.synsets]
-
-        labels = {
-            "relpath": np.array(self.relpaths),
-            "synsets": np.array(self.synsets),
-            "class_label": np.array(self.class_labels),
-            "human_label": np.array(self.human_labels),
-        }
-
-        if self.process_images:
-            self.size = retrieve(self.config, "size", default=256)
-            self.data = ImagePaths(self.abspaths,
-                                   labels=labels,
-                                   size=self.size,
-                                   random_crop=self.random_crop,
-                                   )
-        else:
-            self.data = self.abspaths
-
-
-class ImageNetTrain(ImageNetBase):
-    NAME = "ILSVRC2012_train"
-    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
-    AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2"
-    FILES = [
-        "ILSVRC2012_img_train.tar",
-    ]
-    SIZES = [
-        147897477120,
-    ]
-
-    def __init__(self, process_images=True, data_root=None, **kwargs):
-        self.process_images = process_images
-        self.data_root = data_root
-        super().__init__(**kwargs)
-
-    def _prepare(self):
-        if self.data_root:
-            self.root = os.path.join(self.data_root, self.NAME)
-        else:
-            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
-            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
-
-        self.datadir = os.path.join(self.root, "data")
-        self.txt_filelist = os.path.join(self.root, "filelist.txt")
-        self.expected_length = 1281167
-        self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop",
-                                    default=True)
-        if not tdu.is_prepared(self.root):
-            # prep
-            print("Preparing dataset {} in {}".format(self.NAME, self.root))
-
-            datadir = self.datadir
-            if not os.path.exists(datadir):
-                path = os.path.join(self.root, self.FILES[0])
-                if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
-                    import academictorrents as at
-                    atpath = at.get(self.AT_HASH, datastore=self.root)
-                    assert atpath == path
-
-                print("Extracting {} to {}".format(path, datadir))
-                os.makedirs(datadir, exist_ok=True)
-                with tarfile.open(path, "r:") as tar:
-                    tar.extractall(path=datadir)
-
-                print("Extracting sub-tars.")
-                subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar")))
-                for subpath in tqdm(subpaths):
-                    subdir = subpath[:-len(".tar")]
-                    os.makedirs(subdir, exist_ok=True)
-                    with tarfile.open(subpath, "r:") as tar:
-                        tar.extractall(path=subdir)
-
-            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
-            filelist = [os.path.relpath(p, start=datadir) for p in filelist]
-            filelist = sorted(filelist)
-            filelist = "\n".join(filelist)+"\n"
-            with open(self.txt_filelist, "w") as f:
-                f.write(filelist)
-
-            tdu.mark_prepared(self.root)
-
-
-class ImageNetValidation(ImageNetBase):
-    NAME = "ILSVRC2012_validation"
-    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
-    AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5"
-    VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1"
-    FILES = [
-        "ILSVRC2012_img_val.tar",
-        "validation_synset.txt",
-    ]
-    SIZES = [
-        6744924160,
-        1950000,
-    ]
-
-    def __init__(self, process_images=True, data_root=None, **kwargs):
-        self.data_root = data_root
-        self.process_images = process_images
-        super().__init__(**kwargs)
-
-    def _prepare(self):
-        if self.data_root:
-            self.root = os.path.join(self.data_root, self.NAME)
-        else:
-            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
-            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
-        self.datadir = os.path.join(self.root, "data")
-        self.txt_filelist = os.path.join(self.root, "filelist.txt")
-        self.expected_length = 50000
-        self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop",
-                                    default=False)
-        if not tdu.is_prepared(self.root):
-            # prep
-            print("Preparing dataset {} in {}".format(self.NAME, self.root))
-
-            datadir = self.datadir
-            if not os.path.exists(datadir):
-                path = os.path.join(self.root, self.FILES[0])
-                if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
-                    import academictorrents as at
-                    atpath = at.get(self.AT_HASH, datastore=self.root)
-                    assert atpath == path
-
-                print("Extracting {} to {}".format(path, datadir))
-                os.makedirs(datadir, exist_ok=True)
-                with tarfile.open(path, "r:") as tar:
-                    tar.extractall(path=datadir)
-
-                vspath = os.path.join(self.root, self.FILES[1])
-                if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]:
-                    download(self.VS_URL, vspath)
-
-                with open(vspath, "r") as f:
-                    synset_dict = f.read().splitlines()
-                    synset_dict = dict(line.split() for line in synset_dict)
-
-                print("Reorganizing into synset folders")
-                synsets = np.unique(list(synset_dict.values()))
-                for s in synsets:
-                    os.makedirs(os.path.join(datadir, s), exist_ok=True)
-                for k, v in synset_dict.items():
-                    src = os.path.join(datadir, k)
-                    dst = os.path.join(datadir, v)
-                    shutil.move(src, dst)
-
-            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
-            filelist = [os.path.relpath(p, start=datadir) for p in filelist]
-            filelist = sorted(filelist)
-            filelist = "\n".join(filelist)+"\n"
-            with open(self.txt_filelist, "w") as f:
-                f.write(filelist)
-
-            tdu.mark_prepared(self.root)
-
-
-
-class ImageNetSR(Dataset):
-    def __init__(self, size=None,
-                 degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1.,
-                 random_crop=True):
-        """
-        Imagenet Superresolution Dataloader
-        Performs following ops in order:
-        1.  crops a crop of size s from image either as random or center crop
-        2.  resizes crop to size with cv2.area_interpolation
-        3.  degrades resized crop with degradation_fn
-
-        :param size: resizing to size after cropping
-        :param degradation: degradation_fn, e.g. cv_bicubic or bsrgan_light
-        :param downscale_f: Low Resolution Downsample factor
-        :param min_crop_f: determines crop size s,
-          where s = c * min_img_side_len with c sampled from interval (min_crop_f, max_crop_f)
-        :param max_crop_f: ""
-        :param data_root:
-        :param random_crop:
-        """
-        self.base = self.get_base()
-        assert size
-        assert (size / downscale_f).is_integer()
-        self.size = size
-        self.LR_size = int(size / downscale_f)
-        self.min_crop_f = min_crop_f
-        self.max_crop_f = max_crop_f
-        assert(max_crop_f <= 1.)
-        self.center_crop = not random_crop
-
-        self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA)
-
-        self.pil_interpolation = False # gets reset later if incase interp_op is from pillow
-
-        if degradation == "bsrgan":
-            self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)
-
-        elif degradation == "bsrgan_light":
-            self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)
-
-        else:
-            interpolation_fn = {
-            "cv_nearest": cv2.INTER_NEAREST,
-            "cv_bilinear": cv2.INTER_LINEAR,
-            "cv_bicubic": cv2.INTER_CUBIC,
-            "cv_area": cv2.INTER_AREA,
-            "cv_lanczos": cv2.INTER_LANCZOS4,
-            "pil_nearest": PIL.Image.NEAREST,
-            "pil_bilinear": PIL.Image.BILINEAR,
-            "pil_bicubic": PIL.Image.BICUBIC,
-            "pil_box": PIL.Image.BOX,
-            "pil_hamming": PIL.Image.HAMMING,
-            "pil_lanczos": PIL.Image.LANCZOS,
-            }[degradation]
-
-            self.pil_interpolation = degradation.startswith("pil_")
-
-            if self.pil_interpolation:
-                self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn)
-
-            else:
-                self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size,
-                                                                          interpolation=interpolation_fn)
-
-    def __len__(self):
-        return len(self.base)
-
-    def __getitem__(self, i):
-        example = self.base[i]
-        image = Image.open(example["file_path_"])
-
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-
-        image = np.array(image).astype(np.uint8)
-
-        min_side_len = min(image.shape[:2])
-        crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None)
-        crop_side_len = int(crop_side_len)
-
-        if self.center_crop:
-            self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len)
-
-        else:
-            self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len)
-
-        image = self.cropper(image=image)["image"]
-        image = self.image_rescaler(image=image)["image"]
-
-        if self.pil_interpolation:
-            image_pil = PIL.Image.fromarray(image)
-            LR_image = self.degradation_process(image_pil)
-            LR_image = np.array(LR_image).astype(np.uint8)
-
-        else:
-            LR_image = self.degradation_process(image=image)["image"]
-
-        example["image"] = (image/127.5 - 1.0).astype(np.float32)
-        example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32)
-
-        return example
-
-
-class ImageNetSRTrain(ImageNetSR):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def get_base(self):
-        with open("data/imagenet_train_hr_indices.p", "rb") as f:
-            indices = pickle.load(f)
-        dset = ImageNetTrain(process_images=False,)
-        return Subset(dset, indices)
-
-
-class ImageNetSRValidation(ImageNetSR):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-    def get_base(self):
-        with open("data/imagenet_val_hr_indices.p", "rb") as f:
-            indices = pickle.load(f)
-        dset = ImageNetValidation(process_images=False,)
-        return Subset(dset, indices)
diff --git a/spaces/Purple11/Grounded-Diffusion/ldm/glo.py b/spaces/Purple11/Grounded-Diffusion/ldm/glo.py
deleted file mode 100644
index 6420630fd2b88ba69b82d42ed1670adea3c42707..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/ldm/glo.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# -*- coding: utf-8 -*-
- 
-def _init():#初始化
-    global _global_dict
-    _global_dict = {}
- 
- 
-def set_value(key,value):
-    """ 定义一个全局变量 """
-    _global_dict[key] = value
- 
- 
-def get_value(key,defValue=None):
-    """ 获得一个全局变量,不存在则返回默认值 """
-    try:
-        return _global_dict[key]
-    except KeyError:
-        return defValue
-def change_value(key,value):
-    """ 定义一个全局变量 """
-    _global_dict[key] = value
diff --git a/spaces/Purple11/Grounded-Diffusion/ldm/modules/image_degradation/bsrgan_light.py b/spaces/Purple11/Grounded-Diffusion/ldm/modules/image_degradation/bsrgan_light.py
deleted file mode 100644
index 9e1f823996bf559e9b015ea9aa2b3cd38dd13af1..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/ldm/modules/image_degradation/bsrgan_light.py
+++ /dev/null
@@ -1,650 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import cv2
-import torch
-
-from functools import partial
-import random
-from scipy import ndimage
-import scipy
-import scipy.stats as ss
-from scipy.interpolate import interp2d
-from scipy.linalg import orth
-import albumentations
-
-import ldm.modules.image_degradation.utils_image as util
-
-"""
-# --------------------------------------------
-# Super-Resolution
-# --------------------------------------------
-#
-# Kai Zhang (cskaizhang@gmail.com)
-# https://github.com/cszn
-# From 2019/03--2021/08
-# --------------------------------------------
-"""
-
-
-def modcrop_np(img, sf):
-    '''
-    Args:
-        img: numpy image, WxH or WxHxC
-        sf: scale factor
-    Return:
-        cropped image
-    '''
-    w, h = img.shape[:2]
-    im = np.copy(img)
-    return im[:w - w % sf, :h - h % sf, ...]
-
-
-"""
-# --------------------------------------------
-# anisotropic Gaussian kernels
-# --------------------------------------------
-"""
-
-
-def analytic_kernel(k):
-    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
-    k_size = k.shape[0]
-    # Calculate the big kernels size
-    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
-    # Loop over the small kernel to fill the big one
-    for r in range(k_size):
-        for c in range(k_size):
-            big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
-    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
-    crop = k_size // 2
-    cropped_big_k = big_k[crop:-crop, crop:-crop]
-    # Normalize to 1
-    return cropped_big_k / cropped_big_k.sum()
-
-
-def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
-    """ generate an anisotropic Gaussian kernel
-    Args:
-        ksize : e.g., 15, kernel size
-        theta : [0,  pi], rotation angle range
-        l1    : [0.1,50], scaling of eigenvalues
-        l2    : [0.1,l1], scaling of eigenvalues
-        If l1 = l2, will get an isotropic Gaussian kernel.
-    Returns:
-        k     : kernel
-    """
-
-    v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
-    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
-    D = np.array([[l1, 0], [0, l2]])
-    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
-    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
-
-    return k
-
-
-def gm_blur_kernel(mean, cov, size=15):
-    center = size / 2.0 + 0.5
-    k = np.zeros([size, size])
-    for y in range(size):
-        for x in range(size):
-            cy = y - center + 1
-            cx = x - center + 1
-            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
-
-    k = k / np.sum(k)
-    return k
-
-
-def shift_pixel(x, sf, upper_left=True):
-    """shift pixel for super-resolution with different scale factors
-    Args:
-        x: WxHxC or WxH
-        sf: scale factor
-        upper_left: shift direction
-    """
-    h, w = x.shape[:2]
-    shift = (sf - 1) * 0.5
-    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
-    if upper_left:
-        x1 = xv + shift
-        y1 = yv + shift
-    else:
-        x1 = xv - shift
-        y1 = yv - shift
-
-    x1 = np.clip(x1, 0, w - 1)
-    y1 = np.clip(y1, 0, h - 1)
-
-    if x.ndim == 2:
-        x = interp2d(xv, yv, x)(x1, y1)
-    if x.ndim == 3:
-        for i in range(x.shape[-1]):
-            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
-
-    return x
-
-
-def blur(x, k):
-    '''
-    x: image, NxcxHxW
-    k: kernel, Nx1xhxw
-    '''
-    n, c = x.shape[:2]
-    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
-    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
-    k = k.repeat(1, c, 1, 1)
-    k = k.view(-1, 1, k.shape[2], k.shape[3])
-    x = x.view(1, -1, x.shape[2], x.shape[3])
-    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
-    x = x.view(n, c, x.shape[2], x.shape[3])
-
-    return x
-
-
-def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
-    """"
-    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
-    # Kai Zhang
-    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
-    # max_var = 2.5 * sf
-    """
-    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
-    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
-    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
-    theta = np.random.rand() * np.pi  # random theta
-    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
-
-    # Set COV matrix using Lambdas and Theta
-    LAMBDA = np.diag([lambda_1, lambda_2])
-    Q = np.array([[np.cos(theta), -np.sin(theta)],
-                  [np.sin(theta), np.cos(theta)]])
-    SIGMA = Q @ LAMBDA @ Q.T
-    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
-
-    # Set expectation position (shifting kernel for aligned image)
-    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
-    MU = MU[None, None, :, None]
-
-    # Create meshgrid for Gaussian
-    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
-    Z = np.stack([X, Y], 2)[:, :, :, None]
-
-    # Calcualte Gaussian for every pixel of the kernel
-    ZZ = Z - MU
-    ZZ_t = ZZ.transpose(0, 1, 3, 2)
-    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
-
-    # shift the kernel so it will be centered
-    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
-
-    # Normalize the kernel and return
-    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
-    kernel = raw_kernel / np.sum(raw_kernel)
-    return kernel
-
-
-def fspecial_gaussian(hsize, sigma):
-    hsize = [hsize, hsize]
-    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
-    std = sigma
-    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
-    arg = -(x * x + y * y) / (2 * std * std)
-    h = np.exp(arg)
-    h[h < scipy.finfo(float).eps * h.max()] = 0
-    sumh = h.sum()
-    if sumh != 0:
-        h = h / sumh
-    return h
-
-
-def fspecial_laplacian(alpha):
-    alpha = max([0, min([alpha, 1])])
-    h1 = alpha / (alpha + 1)
-    h2 = (1 - alpha) / (alpha + 1)
-    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
-    h = np.array(h)
-    return h
-
-
-def fspecial(filter_type, *args, **kwargs):
-    '''
-    python code from:
-    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
-    '''
-    if filter_type == 'gaussian':
-        return fspecial_gaussian(*args, **kwargs)
-    if filter_type == 'laplacian':
-        return fspecial_laplacian(*args, **kwargs)
-
-
-"""
-# --------------------------------------------
-# degradation models
-# --------------------------------------------
-"""
-
-
-def bicubic_degradation(x, sf=3):
-    '''
-    Args:
-        x: HxWxC image, [0, 1]
-        sf: down-scale factor
-    Return:
-        bicubicly downsampled LR image
-    '''
-    x = util.imresize_np(x, scale=1 / sf)
-    return x
-
-
-def srmd_degradation(x, k, sf=3):
-    ''' blur + bicubic downsampling
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2018learning,
-          title={Learning a single convolutional super-resolution network for multiple degradations},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={3262--3271},
-          year={2018}
-        }
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')  # 'nearest' | 'mirror'
-    x = bicubic_degradation(x, sf=sf)
-    return x
-
-
-def dpsr_degradation(x, k, sf=3):
-    ''' bicubic downsampling + blur
-    Args:
-        x: HxWxC image, [0, 1]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    Reference:
-        @inproceedings{zhang2019deep,
-          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
-          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
-          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
-          pages={1671--1681},
-          year={2019}
-        }
-    '''
-    x = bicubic_degradation(x, sf=sf)
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    return x
-
-
-def classical_degradation(x, k, sf=3):
-    ''' blur + downsampling
-    Args:
-        x: HxWxC image, [0, 1]/[0, 255]
-        k: hxw, double
-        sf: down-scale factor
-    Return:
-        downsampled LR image
-    '''
-    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
-    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
-    st = 0
-    return x[st::sf, st::sf, ...]
-
-
-def add_sharpening(img, weight=0.5, radius=50, threshold=10):
-    """USM sharpening. borrowed from real-ESRGAN
-    Input image: I; Blurry image: B.
-    1. K = I + weight * (I - B)
-    2. Mask = 1 if abs(I - B) > threshold, else: 0
-    3. Blur mask:
-    4. Out = Mask * K + (1 - Mask) * I
-    Args:
-        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
-        weight (float): Sharp weight. Default: 1.
-        radius (float): Kernel size of Gaussian blur. Default: 50.
-        threshold (int):
-    """
-    if radius % 2 == 0:
-        radius += 1
-    blur = cv2.GaussianBlur(img, (radius, radius), 0)
-    residual = img - blur
-    mask = np.abs(residual) * 255 > threshold
-    mask = mask.astype('float32')
-    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
-
-    K = img + weight * residual
-    K = np.clip(K, 0, 1)
-    return soft_mask * K + (1 - soft_mask) * img
-
-
-def add_blur(img, sf=4):
-    wd2 = 4.0 + sf
-    wd = 2.0 + 0.2 * sf
-
-    wd2 = wd2/4
-    wd = wd/4
-
-    if random.random() < 0.5:
-        l1 = wd2 * random.random()
-        l2 = wd2 * random.random()
-        k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
-    else:
-        k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
-    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
-
-    return img
-
-
-def add_resize(img, sf=4):
-    rnum = np.random.rand()
-    if rnum > 0.8:  # up
-        sf1 = random.uniform(1, 2)
-    elif rnum < 0.7:  # down
-        sf1 = random.uniform(0.5 / sf, 1)
-    else:
-        sf1 = 1.0
-    img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
-    img = np.clip(img, 0.0, 1.0)
-
-    return img
-
-
-# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-#     noise_level = random.randint(noise_level1, noise_level2)
-#     rnum = np.random.rand()
-#     if rnum > 0.6:  # add color Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-#     elif rnum < 0.4:  # add grayscale Gaussian noise
-#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-#     else:  # add  noise
-#         L = noise_level2 / 255.
-#         D = np.diag(np.random.rand(3))
-#         U = orth(np.random.rand(3, 3))
-#         conv = np.dot(np.dot(np.transpose(U), D), U)
-#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-#     img = np.clip(img, 0.0, 1.0)
-#     return img
-
-def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    rnum = np.random.rand()
-    if rnum > 0.6:  # add color Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:  # add grayscale Gaussian noise
-        img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:  # add  noise
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_speckle_noise(img, noise_level1=2, noise_level2=25):
-    noise_level = random.randint(noise_level1, noise_level2)
-    img = np.clip(img, 0.0, 1.0)
-    rnum = random.random()
-    if rnum > 0.6:
-        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
-    elif rnum < 0.4:
-        img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
-    else:
-        L = noise_level2 / 255.
-        D = np.diag(np.random.rand(3))
-        U = orth(np.random.rand(3, 3))
-        conv = np.dot(np.dot(np.transpose(U), D), U)
-        img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_Poisson_noise(img):
-    img = np.clip((img * 255.0).round(), 0, 255) / 255.
-    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
-    if random.random() < 0.5:
-        img = np.random.poisson(img * vals).astype(np.float32) / vals
-    else:
-        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
-        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
-        noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
-        img += noise_gray[:, :, np.newaxis]
-    img = np.clip(img, 0.0, 1.0)
-    return img
-
-
-def add_JPEG_noise(img):
-    quality_factor = random.randint(80, 95)
-    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
-    result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
-    img = cv2.imdecode(encimg, 1)
-    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
-    return img
-
-
-def random_crop(lq, hq, sf=4, lq_patchsize=64):
-    h, w = lq.shape[:2]
-    rnd_h = random.randint(0, h - lq_patchsize)
-    rnd_w = random.randint(0, w - lq_patchsize)
-    lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
-
-    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
-    hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
-    return lq, hq
-
-
-def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = img.shape[:2]
-    img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = img.shape[:2]
-
-    if h < lq_patchsize * sf or w < lq_patchsize * sf:
-        raise ValueError(f'img size ({h1}X{w1}) is too small!')
-
-    hq = img.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
-                             interpolation=random.choice([1, 2, 3]))
-        else:
-            img = util.imresize_np(img, 1 / 2, True)
-        img = np.clip(img, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            img = add_blur(img, sf=sf)
-
-        elif i == 1:
-            img = add_blur(img, sf=sf)
-
-        elif i == 2:
-            a, b = img.shape[1], img.shape[0]
-            # downsample2
-            if random.random() < 0.75:
-                sf1 = random.uniform(1, 2 * sf)
-                img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
-                                 interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                img = img[0::sf, 0::sf, ...]  # nearest downsampling
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            img = np.clip(img, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                img = add_JPEG_noise(img)
-
-        elif i == 6:
-            # add processed camera sensor noise
-            if random.random() < isp_prob and isp_model is not None:
-                with torch.no_grad():
-                    img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    img = add_JPEG_noise(img)
-
-    # random crop
-    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
-
-    return img, hq
-
-
-# todo no isp_model?
-def degradation_bsrgan_variant(image, sf=4, isp_model=None):
-    """
-    This is the degradation model of BSRGAN from the paper
-    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
-    ----------
-    sf: scale factor
-    isp_model: camera ISP model
-    Returns
-    -------
-    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
-    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
-    """
-    image = util.uint2single(image)
-    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
-    sf_ori = sf
-
-    h1, w1 = image.shape[:2]
-    image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...]  # mod crop
-    h, w = image.shape[:2]
-
-    hq = image.copy()
-
-    if sf == 4 and random.random() < scale2_prob:  # downsample1
-        if np.random.rand() < 0.5:
-            image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
-                               interpolation=random.choice([1, 2, 3]))
-        else:
-            image = util.imresize_np(image, 1 / 2, True)
-        image = np.clip(image, 0.0, 1.0)
-        sf = 2
-
-    shuffle_order = random.sample(range(7), 7)
-    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
-    if idx1 > idx2:  # keep downsample3 last
-        shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
-
-    for i in shuffle_order:
-
-        if i == 0:
-            image = add_blur(image, sf=sf)
-
-        # elif i == 1:
-        #     image = add_blur(image, sf=sf)
-
-        if i == 0:
-            pass
-
-        elif i == 2:
-            a, b = image.shape[1], image.shape[0]
-            # downsample2
-            if random.random() < 0.8:
-                sf1 = random.uniform(1, 2 * sf)
-                image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
-                                   interpolation=random.choice([1, 2, 3]))
-            else:
-                k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
-                k_shifted = shift_pixel(k, sf)
-                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
-                image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
-                image = image[0::sf, 0::sf, ...]  # nearest downsampling
-
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 3:
-            # downsample3
-            image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
-            image = np.clip(image, 0.0, 1.0)
-
-        elif i == 4:
-            # add Gaussian noise
-            image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
-
-        elif i == 5:
-            # add JPEG noise
-            if random.random() < jpeg_prob:
-                image = add_JPEG_noise(image)
-        #
-        # elif i == 6:
-        #     # add processed camera sensor noise
-        #     if random.random() < isp_prob and isp_model is not None:
-        #         with torch.no_grad():
-        #             img, hq = isp_model.forward(img.copy(), hq)
-
-    # add final JPEG compression noise
-    image = add_JPEG_noise(image)
-    image = util.single2uint(image)
-    example = {"image": image}
-    return example
-
-
-
-
-if __name__ == '__main__':
-    print("hey")
-    img = util.imread_uint('utils/test.png', 3)
-    img = img[:448, :448]
-    h = img.shape[0] // 4
-    print("resizing to", h)
-    sf = 4
-    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
-    for i in range(20):
-        print(i)
-        img_hq = img
-        img_lq = deg_fn(img)["image"]
-        img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
-        print(img_lq)
-        img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
-        print(img_lq.shape)
-        print("bicubic", img_lq_bicubic.shape)
-        print(img_hq.shape)
-        lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                interpolation=0)
-        lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
-                                        (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
-                                        interpolation=0)
-        img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
-        util.imsave(img_concat, str(i) + '.png')
diff --git a/spaces/Q-bert/EarthQuakeMap/README.md b/spaces/Q-bert/EarthQuakeMap/README.md
deleted file mode 100644
index beaf470b986951b78aa46d00942b98e5044c0384..0000000000000000000000000000000000000000
--- a/spaces/Q-bert/EarthQuakeMap/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: EarthQuakeMap
-emoji: 👁
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/configs/data/scannet_test_1500.py b/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/configs/data/scannet_test_1500.py
deleted file mode 100644
index 60e560fa01d73345200aaca10961449fdf3e9fbe..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/configs/data/scannet_test_1500.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from configs.data.base import cfg
-
-TEST_BASE_PATH = "assets/scannet_test_1500"
-
-cfg.DATASET.TEST_DATA_SOURCE = "ScanNet"
-cfg.DATASET.TEST_DATA_ROOT = "data/scannet/test"
-cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}"
-cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/scannet_test.txt"
-cfg.DATASET.TEST_INTRINSIC_PATH = f"{TEST_BASE_PATH}/intrinsics.npz"
-
-cfg.DATASET.MIN_OVERLAP_SCORE_TEST = 0.0
diff --git a/spaces/Realcat/image-matching-webui/third_party/DarkFeat/nets/geom.py b/spaces/Realcat/image-matching-webui/third_party/DarkFeat/nets/geom.py
deleted file mode 100644
index d711ffdbf57aa023caa048adb3e7c8519aef7a3f..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/DarkFeat/nets/geom.py
+++ /dev/null
@@ -1,364 +0,0 @@
-import time
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-
-def rnd_sample(inputs, n_sample):
-    cur_size = inputs[0].shape[0]
-    rnd_idx = torch.randperm(cur_size)[0:n_sample]
-    outputs = [i[rnd_idx] for i in inputs]
-    return outputs
-
-
-def _grid_positions(h, w, bs):
-    x_rng = torch.arange(0, w.int())
-    y_rng = torch.arange(0, h.int())
-    xv, yv = torch.meshgrid(x_rng, y_rng, indexing="xy")
-    return (
-        torch.reshape(torch.stack((yv, xv), axis=-1), (1, -1, 2))
-        .repeat(bs, 1, 1)
-        .float()
-    )
-
-
-def getK(ori_img_size, cur_feat_size, K):
-    # WARNING: cur_feat_size's order is [h, w]
-    r = ori_img_size / cur_feat_size[[1, 0]]
-    r_K0 = torch.stack(
-        [K[:, 0] / r[:, 0][..., None], K[:, 1] / r[:, 1][..., None], K[:, 2]], axis=1
-    )
-    return r_K0
-
-
-def gather_nd(params, indices):
-    """The same as tf.gather_nd but batched gather is not supported yet.
-    indices is an k-dimensional integer tensor, best thought of as a (k-1)-dimensional tensor of indices into params, where each element defines a slice of params:
-
-    output[\\(i_0, ..., i_{k-2}\\)] = params[indices[\\(i_0, ..., i_{k-2}\\)]]
-
-    Args:
-        params (Tensor): "n" dimensions. shape: [x_0, x_1, x_2, ..., x_{n-1}]
-        indices (Tensor): "k" dimensions. shape: [y_0,y_2,...,y_{k-2}, m]. m <= n.
-
-    Returns: gathered Tensor.
-        shape [y_0,y_2,...y_{k-2}] + params.shape[m:]
-
-    """
-    orig_shape = list(indices.shape)
-    num_samples = np.prod(orig_shape[:-1])
-    m = orig_shape[-1]
-    n = len(params.shape)
-
-    if m <= n:
-        out_shape = orig_shape[:-1] + list(params.shape)[m:]
-    else:
-        raise ValueError(
-            f"the last dimension of indices must less or equal to the rank of params. Got indices:{indices.shape}, params:{params.shape}. {m} > {n}"
-        )
-
-    indices = indices.reshape((num_samples, m)).transpose(0, 1).tolist()
-    output = params[indices]  # (num_samples, ...)
-    return output.reshape(out_shape).contiguous()
-
-
-# input: pos [kpt_n, 2]; inputs [H, W, 128] / [H, W]
-# output: [kpt_n, 128] / [kpt_n]
-def interpolate(pos, inputs, nd=True):
-    h = inputs.shape[0]
-    w = inputs.shape[1]
-
-    i = pos[:, 0]
-    j = pos[:, 1]
-
-    i_top_left = torch.clamp(torch.floor(i).int(), 0, h - 1)
-    j_top_left = torch.clamp(torch.floor(j).int(), 0, w - 1)
-
-    i_top_right = torch.clamp(torch.floor(i).int(), 0, h - 1)
-    j_top_right = torch.clamp(torch.ceil(j).int(), 0, w - 1)
-
-    i_bottom_left = torch.clamp(torch.ceil(i).int(), 0, h - 1)
-    j_bottom_left = torch.clamp(torch.floor(j).int(), 0, w - 1)
-
-    i_bottom_right = torch.clamp(torch.ceil(i).int(), 0, h - 1)
-    j_bottom_right = torch.clamp(torch.ceil(j).int(), 0, w - 1)
-
-    dist_i_top_left = i - i_top_left.float()
-    dist_j_top_left = j - j_top_left.float()
-    w_top_left = (1 - dist_i_top_left) * (1 - dist_j_top_left)
-    w_top_right = (1 - dist_i_top_left) * dist_j_top_left
-    w_bottom_left = dist_i_top_left * (1 - dist_j_top_left)
-    w_bottom_right = dist_i_top_left * dist_j_top_left
-
-    if nd:
-        w_top_left = w_top_left[..., None]
-        w_top_right = w_top_right[..., None]
-        w_bottom_left = w_bottom_left[..., None]
-        w_bottom_right = w_bottom_right[..., None]
-
-    interpolated_val = (
-        w_top_left * gather_nd(inputs, torch.stack([i_top_left, j_top_left], axis=-1))
-        + w_top_right
-        * gather_nd(inputs, torch.stack([i_top_right, j_top_right], axis=-1))
-        + w_bottom_left
-        * gather_nd(inputs, torch.stack([i_bottom_left, j_bottom_left], axis=-1))
-        + w_bottom_right
-        * gather_nd(inputs, torch.stack([i_bottom_right, j_bottom_right], axis=-1))
-    )
-
-    return interpolated_val
-
-
-def validate_and_interpolate(
-    pos, inputs, validate_corner=True, validate_val=None, nd=False
-):
-    if nd:
-        h, w, c = inputs.shape
-    else:
-        h, w = inputs.shape
-    ids = torch.arange(0, pos.shape[0])
-
-    i = pos[:, 0]
-    j = pos[:, 1]
-
-    i_top_left = torch.floor(i).int()
-    j_top_left = torch.floor(j).int()
-
-    i_top_right = torch.floor(i).int()
-    j_top_right = torch.ceil(j).int()
-
-    i_bottom_left = torch.ceil(i).int()
-    j_bottom_left = torch.floor(j).int()
-
-    i_bottom_right = torch.ceil(i).int()
-    j_bottom_right = torch.ceil(j).int()
-
-    if validate_corner:
-        # Valid corner
-        valid_top_left = torch.logical_and(i_top_left >= 0, j_top_left >= 0)
-        valid_top_right = torch.logical_and(i_top_right >= 0, j_top_right < w)
-        valid_bottom_left = torch.logical_and(i_bottom_left < h, j_bottom_left >= 0)
-        valid_bottom_right = torch.logical_and(i_bottom_right < h, j_bottom_right < w)
-
-        valid_corner = torch.logical_and(
-            torch.logical_and(valid_top_left, valid_top_right),
-            torch.logical_and(valid_bottom_left, valid_bottom_right),
-        )
-
-        i_top_left = i_top_left[valid_corner]
-        j_top_left = j_top_left[valid_corner]
-
-        i_top_right = i_top_right[valid_corner]
-        j_top_right = j_top_right[valid_corner]
-
-        i_bottom_left = i_bottom_left[valid_corner]
-        j_bottom_left = j_bottom_left[valid_corner]
-
-        i_bottom_right = i_bottom_right[valid_corner]
-        j_bottom_right = j_bottom_right[valid_corner]
-
-        ids = ids[valid_corner]
-
-    if validate_val is not None:
-        # Valid depth
-        valid_depth = torch.logical_and(
-            torch.logical_and(
-                gather_nd(inputs, torch.stack([i_top_left, j_top_left], axis=-1)) > 0,
-                gather_nd(inputs, torch.stack([i_top_right, j_top_right], axis=-1)) > 0,
-            ),
-            torch.logical_and(
-                gather_nd(inputs, torch.stack([i_bottom_left, j_bottom_left], axis=-1))
-                > 0,
-                gather_nd(
-                    inputs, torch.stack([i_bottom_right, j_bottom_right], axis=-1)
-                )
-                > 0,
-            ),
-        )
-
-        i_top_left = i_top_left[valid_depth]
-        j_top_left = j_top_left[valid_depth]
-
-        i_top_right = i_top_right[valid_depth]
-        j_top_right = j_top_right[valid_depth]
-
-        i_bottom_left = i_bottom_left[valid_depth]
-        j_bottom_left = j_bottom_left[valid_depth]
-
-        i_bottom_right = i_bottom_right[valid_depth]
-        j_bottom_right = j_bottom_right[valid_depth]
-
-        ids = ids[valid_depth]
-
-    # Interpolation
-    i = i[ids]
-    j = j[ids]
-    dist_i_top_left = i - i_top_left.float()
-    dist_j_top_left = j - j_top_left.float()
-    w_top_left = (1 - dist_i_top_left) * (1 - dist_j_top_left)
-    w_top_right = (1 - dist_i_top_left) * dist_j_top_left
-    w_bottom_left = dist_i_top_left * (1 - dist_j_top_left)
-    w_bottom_right = dist_i_top_left * dist_j_top_left
-
-    if nd:
-        w_top_left = w_top_left[..., None]
-        w_top_right = w_top_right[..., None]
-        w_bottom_left = w_bottom_left[..., None]
-        w_bottom_right = w_bottom_right[..., None]
-
-    interpolated_val = (
-        w_top_left * gather_nd(inputs, torch.stack([i_top_left, j_top_left], axis=-1))
-        + w_top_right
-        * gather_nd(inputs, torch.stack([i_top_right, j_top_right], axis=-1))
-        + w_bottom_left
-        * gather_nd(inputs, torch.stack([i_bottom_left, j_bottom_left], axis=-1))
-        + w_bottom_right
-        * gather_nd(inputs, torch.stack([i_bottom_right, j_bottom_right], axis=-1))
-    )
-
-    pos = torch.stack([i, j], axis=1)
-    return [interpolated_val, pos, ids]
-
-
-# pos0: [2, 230400, 2]
-# depth0: [2, 480, 480]
-def getWarp(pos0, rel_pose, depth0, K0, depth1, K1, bs):
-    def swap_axis(data):
-        return torch.stack([data[:, 1], data[:, 0]], axis=-1)
-
-    all_pos0 = []
-    all_pos1 = []
-    all_ids = []
-    for i in range(bs):
-        z0, new_pos0, ids = validate_and_interpolate(pos0[i], depth0[i], validate_val=0)
-
-        uv0_homo = torch.cat(
-            [
-                swap_axis(new_pos0),
-                torch.ones((new_pos0.shape[0], 1)).to(new_pos0.device),
-            ],
-            axis=-1,
-        )
-        xy0_homo = torch.matmul(torch.linalg.inv(K0[i]), uv0_homo.t())
-        xyz0_homo = torch.cat(
-            [
-                torch.unsqueeze(z0, 0) * xy0_homo,
-                torch.ones((1, new_pos0.shape[0])).to(z0.device),
-            ],
-            axis=0,
-        )
-
-        xyz1 = torch.matmul(rel_pose[i], xyz0_homo)
-        xy1_homo = xyz1 / torch.unsqueeze(xyz1[-1, :], axis=0)
-        uv1 = torch.matmul(K1[i], xy1_homo).t()[:, 0:2]
-
-        new_pos1 = swap_axis(uv1)
-        annotated_depth, new_pos1, new_ids = validate_and_interpolate(
-            new_pos1, depth1[i], validate_val=0
-        )
-
-        ids = ids[new_ids]
-        new_pos0 = new_pos0[new_ids]
-        estimated_depth = xyz1.t()[new_ids][:, -1]
-
-        inlier_mask = torch.abs(estimated_depth - annotated_depth) < 0.05
-
-        all_ids.append(ids[inlier_mask])
-        all_pos0.append(new_pos0[inlier_mask])
-        all_pos1.append(new_pos1[inlier_mask])
-    # all_pos0 & all_pose1: [inlier_num, 2] * batch_size
-    return all_pos0, all_pos1, all_ids
-
-
-# pos0: [2, 230400, 2]
-# depth0: [2, 480, 480]
-def getWarpNoValidate(pos0, rel_pose, depth0, K0, depth1, K1, bs):
-    def swap_axis(data):
-        return torch.stack([data[:, 1], data[:, 0]], axis=-1)
-
-    all_pos0 = []
-    all_pos1 = []
-    all_ids = []
-    for i in range(bs):
-        z0, new_pos0, ids = validate_and_interpolate(pos0[i], depth0[i], validate_val=0)
-
-        uv0_homo = torch.cat(
-            [
-                swap_axis(new_pos0),
-                torch.ones((new_pos0.shape[0], 1)).to(new_pos0.device),
-            ],
-            axis=-1,
-        )
-        xy0_homo = torch.matmul(torch.linalg.inv(K0[i]), uv0_homo.t())
-        xyz0_homo = torch.cat(
-            [
-                torch.unsqueeze(z0, 0) * xy0_homo,
-                torch.ones((1, new_pos0.shape[0])).to(z0.device),
-            ],
-            axis=0,
-        )
-
-        xyz1 = torch.matmul(rel_pose[i], xyz0_homo)
-        xy1_homo = xyz1 / torch.unsqueeze(xyz1[-1, :], axis=0)
-        uv1 = torch.matmul(K1[i], xy1_homo).t()[:, 0:2]
-
-        new_pos1 = swap_axis(uv1)
-        _, new_pos1, new_ids = validate_and_interpolate(
-            new_pos1, depth1[i], validate_val=0
-        )
-
-        ids = ids[new_ids]
-        new_pos0 = new_pos0[new_ids]
-
-        all_ids.append(ids)
-        all_pos0.append(new_pos0)
-        all_pos1.append(new_pos1)
-    # all_pos0 & all_pose1: [inlier_num, 2] * batch_size
-    return all_pos0, all_pos1, all_ids
-
-
-# pos0: [2, 230400, 2]
-# depth0: [2, 480, 480]
-def getWarpNoValidate2(pos0, rel_pose, depth0, K0, depth1, K1):
-    def swap_axis(data):
-        return torch.stack([data[:, 1], data[:, 0]], axis=-1)
-
-    z0 = interpolate(pos0, depth0, nd=False)
-
-    uv0_homo = torch.cat(
-        [swap_axis(pos0), torch.ones((pos0.shape[0], 1)).to(pos0.device)], axis=-1
-    )
-    xy0_homo = torch.matmul(torch.linalg.inv(K0), uv0_homo.t())
-    xyz0_homo = torch.cat(
-        [
-            torch.unsqueeze(z0, 0) * xy0_homo,
-            torch.ones((1, pos0.shape[0])).to(z0.device),
-        ],
-        axis=0,
-    )
-
-    xyz1 = torch.matmul(rel_pose, xyz0_homo)
-    xy1_homo = xyz1 / torch.unsqueeze(xyz1[-1, :], axis=0)
-    uv1 = torch.matmul(K1, xy1_homo).t()[:, 0:2]
-
-    new_pos1 = swap_axis(uv1)
-
-    return new_pos1
-
-
-def get_dist_mat(feat1, feat2, dist_type):
-    eps = 1e-6
-    cos_dist_mat = torch.matmul(feat1, feat2.t())
-    if dist_type == "cosine_dist":
-        dist_mat = torch.clamp(cos_dist_mat, -1, 1)
-    elif dist_type == "euclidean_dist":
-        dist_mat = torch.sqrt(torch.clamp(2 - 2 * cos_dist_mat, min=eps))
-    elif dist_type == "euclidean_dist_no_norm":
-        norm1 = torch.sum(feat1 * feat1, axis=-1, keepdims=True)
-        norm2 = torch.sum(feat2 * feat2, axis=-1, keepdims=True)
-        dist_mat = torch.sqrt(
-            torch.clamp(norm1 - 2 * cos_dist_mat + norm2.t(), min=0.0) + eps
-        )
-    else:
-        raise NotImplementedError()
-    return dist_mat
diff --git a/spaces/Reself/StableVideo/ldm/modules/midas/__init__.py b/spaces/Reself/StableVideo/ldm/modules/midas/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Ritori/TTS_Yui/data_utils.py b/spaces/Ritori/TTS_Yui/data_utils.py
deleted file mode 100644
index ed8723864d405de92f9f85ef304c4b3f07d27d97..0000000000000000000000000000000000000000
--- a/spaces/Ritori/TTS_Yui/data_utils.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import random
-import numpy as np
-import torch
-import torch.utils.data
-
-import layers
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import text_to_sequence
-
-
-class TextMelLoader(torch.utils.data.Dataset):
-    """
-        1) loads audio,text pairs
-        2) normalizes text and converts them to sequences of one-hot vectors
-        3) computes mel-spectrograms from audio files.
-    """
-    def __init__(self, audiopaths_and_text, hparams):
-        self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
-        self.text_cleaners = hparams.text_cleaners
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.load_mel_from_disk = hparams.load_mel_from_disk
-        self.stft = layers.TacotronSTFT(
-            hparams.filter_length, hparams.hop_length, hparams.win_length,
-            hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
-            hparams.mel_fmax)
-        random.seed(hparams.seed)
-        random.shuffle(self.audiopaths_and_text)
-
-    def get_mel_text_pair(self, audiopath_and_text):
-        # separate filename and text
-        audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
-        text = self.get_text(text)
-        mel = self.get_mel(audiopath)
-        return (text, mel)
-
-    def get_mel(self, filename):
-        if not self.load_mel_from_disk:
-            audio, sampling_rate = load_wav_to_torch(filename)
-            if sampling_rate != self.stft.sampling_rate:
-                raise ValueError("{} {} SR doesn't match target {} SR".format(
-                    sampling_rate, self.stft.sampling_rate))
-            audio_norm = audio / self.max_wav_value
-            audio_norm = audio_norm.unsqueeze(0)
-            audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
-            melspec = self.stft.mel_spectrogram(audio_norm)
-            melspec = torch.squeeze(melspec, 0)
-        else:
-            melspec = torch.from_numpy(np.load(filename))
-            assert melspec.size(0) == self.stft.n_mel_channels, (
-                'Mel dimension mismatch: given {}, expected {}'.format(
-                    melspec.size(0), self.stft.n_mel_channels))
-
-        return melspec
-
-    def get_text(self, text):
-        text_norm = torch.IntTensor(text_to_sequence(text, self.text_cleaners))
-        return text_norm
-
-    def __getitem__(self, index):
-        return self.get_mel_text_pair(self.audiopaths_and_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_and_text)
-
-
-class TextMelCollate():
-    """ Zero-pads model inputs and targets based on number of frames per setep
-    """
-    def __init__(self, n_frames_per_step):
-        self.n_frames_per_step = n_frames_per_step
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text and mel-spectrogram
-        PARAMS
-        ------
-        batch: [text_normalized, mel_normalized]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        input_lengths, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([len(x[0]) for x in batch]),
-            dim=0, descending=True)
-        max_input_len = input_lengths[0]
-
-        text_padded = torch.LongTensor(len(batch), max_input_len)
-        text_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            text = batch[ids_sorted_decreasing[i]][0]
-            text_padded[i, :text.size(0)] = text
-
-        # Right zero-pad mel-spec
-        num_mels = batch[0][1].size(0)
-        max_target_len = max([x[1].size(1) for x in batch])
-        if max_target_len % self.n_frames_per_step != 0:
-            max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
-            assert max_target_len % self.n_frames_per_step == 0
-
-        # include mel padded and gate padded
-        mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
-        mel_padded.zero_()
-        gate_padded = torch.FloatTensor(len(batch), max_target_len)
-        gate_padded.zero_()
-        output_lengths = torch.LongTensor(len(batch))
-        for i in range(len(ids_sorted_decreasing)):
-            mel = batch[ids_sorted_decreasing[i]][1]
-            mel_padded[i, :, :mel.size(1)] = mel
-            gate_padded[i, mel.size(1)-1:] = 1
-            output_lengths[i] = mel.size(1)
-
-        return text_padded, input_lengths, mel_padded, gate_padded, \
-            output_lengths
diff --git a/spaces/RobLi/ControlNet-v1-1/app_scribble_interactive.py b/spaces/RobLi/ControlNet-v1-1/app_scribble_interactive.py
deleted file mode 100644
index 36663c5a1fa37492bfa717c301d33a6b0b49fff5..0000000000000000000000000000000000000000
--- a/spaces/RobLi/ControlNet-v1-1/app_scribble_interactive.py
+++ /dev/null
@@ -1,112 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-import numpy as np
-
-from utils import randomize_seed_fn
-
-
-def create_canvas(w, h):
-    return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                canvas_width = gr.Slider(label='Canvas width',
-                                         minimum=256,
-                                         maximum=512,
-                                         value=512,
-                                         step=1)
-                canvas_height = gr.Slider(label='Canvas height',
-                                          minimum=256,
-                                          maximum=512,
-                                          value=512,
-                                          step=1)
-                create_button = gr.Button('Open drawing canvas!')
-                image = gr.Image(tool='sketch', brush_radius=10)
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    num_samples = gr.Slider(label='Number of images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-
-        create_button.click(fn=create_canvas,
-                            inputs=[canvas_width, canvas_height],
-                            outputs=image,
-                            queue=False)
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='scribble')
-    demo = create_demo(model.process_scribble_interactive)
-    demo.queue().launch()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/detectors/cornernet.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/detectors/cornernet.py
deleted file mode 100644
index bb8ccc1465ab66d1615ca16701a533a22b156295..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/detectors/cornernet.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import torch
-
-from mmdet.core import bbox2result, bbox_mapping_back
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class CornerNet(SingleStageDetector):
-    """CornerNet.
-
-    This detector is the implementation of the paper `CornerNet: Detecting
-    Objects as Paired Keypoints <https://arxiv.org/abs/1808.01244>`_ .
-    """
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(CornerNet, self).__init__(backbone, neck, bbox_head, train_cfg,
-                                        test_cfg, pretrained)
-
-    def merge_aug_results(self, aug_results, img_metas):
-        """Merge augmented detection bboxes and score.
-
-        Args:
-            aug_results (list[list[Tensor]]): Det_bboxes and det_labels of each
-                image.
-            img_metas (list[list[dict]]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-
-        Returns:
-            tuple: (bboxes, labels)
-        """
-        recovered_bboxes, aug_labels = [], []
-        for bboxes_labels, img_info in zip(aug_results, img_metas):
-            img_shape = img_info[0]['img_shape']  # using shape before padding
-            scale_factor = img_info[0]['scale_factor']
-            flip = img_info[0]['flip']
-            bboxes, labels = bboxes_labels
-            bboxes, scores = bboxes[:, :4], bboxes[:, -1:]
-            bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip)
-            recovered_bboxes.append(torch.cat([bboxes, scores], dim=-1))
-            aug_labels.append(labels)
-
-        bboxes = torch.cat(recovered_bboxes, dim=0)
-        labels = torch.cat(aug_labels)
-
-        if bboxes.shape[0] > 0:
-            out_bboxes, out_labels = self.bbox_head._bboxes_nms(
-                bboxes, labels, self.bbox_head.test_cfg)
-        else:
-            out_bboxes, out_labels = bboxes, labels
-
-        return out_bboxes, out_labels
-
-    def aug_test(self, imgs, img_metas, rescale=False):
-        """Augment testing of CornerNet.
-
-        Args:
-            imgs (list[Tensor]): Augmented images.
-            img_metas (list[list[dict]]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-
-        Note:
-            ``imgs`` must including flipped image pairs.
-
-        Returns:
-            list[list[np.ndarray]]: BBox results of each image and classes.
-                The outer list corresponds to each image. The inner list
-                corresponds to each class.
-        """
-        img_inds = list(range(len(imgs)))
-
-        assert img_metas[0][0]['flip'] + img_metas[1][0]['flip'], (
-            'aug test must have flipped image pair')
-        aug_results = []
-        for ind, flip_ind in zip(img_inds[0::2], img_inds[1::2]):
-            img_pair = torch.cat([imgs[ind], imgs[flip_ind]])
-            x = self.extract_feat(img_pair)
-            outs = self.bbox_head(x)
-            bbox_list = self.bbox_head.get_bboxes(
-                *outs, [img_metas[ind], img_metas[flip_ind]], False, False)
-            aug_results.append(bbox_list[0])
-            aug_results.append(bbox_list[1])
-
-        bboxes, labels = self.merge_aug_results(aug_results, img_metas)
-        bbox_results = bbox2result(bboxes, labels, self.bbox_head.num_classes)
-
-        return [bbox_results]
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py
deleted file mode 100644
index 847932547c6c309ae38b45dc43ac0ef8ca66d347..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py
+++ /dev/null
@@ -1,83 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-import torch
-import torch.nn as nn
-from mmcv import ops
-
-
-class BaseRoIExtractor(nn.Module, metaclass=ABCMeta):
-    """Base class for RoI extractor.
-
-    Args:
-        roi_layer (dict): Specify RoI layer type and arguments.
-        out_channels (int): Output channels of RoI layers.
-        featmap_strides (List[int]): Strides of input feature maps.
-    """
-
-    def __init__(self, roi_layer, out_channels, featmap_strides):
-        super(BaseRoIExtractor, self).__init__()
-        self.roi_layers = self.build_roi_layers(roi_layer, featmap_strides)
-        self.out_channels = out_channels
-        self.featmap_strides = featmap_strides
-        self.fp16_enabled = False
-
-    @property
-    def num_inputs(self):
-        """int: Number of input feature maps."""
-        return len(self.featmap_strides)
-
-    def init_weights(self):
-        pass
-
-    def build_roi_layers(self, layer_cfg, featmap_strides):
-        """Build RoI operator to extract feature from each level feature map.
-
-        Args:
-            layer_cfg (dict): Dictionary to construct and config RoI layer
-                operation. Options are modules under ``mmcv/ops`` such as
-                ``RoIAlign``.
-            featmap_strides (List[int]): The stride of input feature map w.r.t
-                to the original image size, which would be used to scale RoI
-                coordinate (original image coordinate system) to feature
-                coordinate system.
-
-        Returns:
-            nn.ModuleList: The RoI extractor modules for each level feature
-                map.
-        """
-
-        cfg = layer_cfg.copy()
-        layer_type = cfg.pop('type')
-        assert hasattr(ops, layer_type)
-        layer_cls = getattr(ops, layer_type)
-        roi_layers = nn.ModuleList(
-            [layer_cls(spatial_scale=1 / s, **cfg) for s in featmap_strides])
-        return roi_layers
-
-    def roi_rescale(self, rois, scale_factor):
-        """Scale RoI coordinates by scale factor.
-
-        Args:
-            rois (torch.Tensor): RoI (Region of Interest), shape (n, 5)
-            scale_factor (float): Scale factor that RoI will be multiplied by.
-
-        Returns:
-            torch.Tensor: Scaled RoI.
-        """
-
-        cx = (rois[:, 1] + rois[:, 3]) * 0.5
-        cy = (rois[:, 2] + rois[:, 4]) * 0.5
-        w = rois[:, 3] - rois[:, 1]
-        h = rois[:, 4] - rois[:, 2]
-        new_w = w * scale_factor
-        new_h = h * scale_factor
-        x1 = cx - new_w * 0.5
-        x2 = cx + new_w * 0.5
-        y1 = cy - new_h * 0.5
-        y2 = cy + new_h * 0.5
-        new_rois = torch.stack((rois[:, 0], x1, y1, x2, y2), dim=-1)
-        return new_rois
-
-    @abstractmethod
-    def forward(self, feats, rois, roi_scale_factor=None):
-        pass
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/losses/smooth_l1_loss.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/losses/smooth_l1_loss.py
deleted file mode 100644
index ec9c98a52d1932d6ccff18938c17c36755bf1baf..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/losses/smooth_l1_loss.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import mmcv
-import torch
-import torch.nn as nn
-
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def smooth_l1_loss(pred, target, beta=1.0):
-    """Smooth L1 loss.
-
-    Args:
-        pred (torch.Tensor): The prediction.
-        target (torch.Tensor): The learning target of the prediction.
-        beta (float, optional): The threshold in the piecewise function.
-            Defaults to 1.0.
-
-    Returns:
-        torch.Tensor: Calculated loss
-    """
-    assert beta > 0
-    assert pred.size() == target.size() and target.numel() > 0
-    diff = torch.abs(pred - target)
-    loss = torch.where(diff < beta, 0.5 * diff * diff / beta,
-                       diff - 0.5 * beta)
-    return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def l1_loss(pred, target):
-    """L1 loss.
-
-    Args:
-        pred (torch.Tensor): The prediction.
-        target (torch.Tensor): The learning target of the prediction.
-
-    Returns:
-        torch.Tensor: Calculated loss
-    """
-    assert pred.size() == target.size() and target.numel() > 0
-    loss = torch.abs(pred - target)
-    return loss
-
-
-@LOSSES.register_module()
-class SmoothL1Loss(nn.Module):
-    """Smooth L1 loss.
-
-    Args:
-        beta (float, optional): The threshold in the piecewise function.
-            Defaults to 1.0.
-        reduction (str, optional): The method to reduce the loss.
-            Options are "none", "mean" and "sum". Defaults to "mean".
-        loss_weight (float, optional): The weight of loss.
-    """
-
-    def __init__(self, beta=1.0, reduction='mean', loss_weight=1.0):
-        super(SmoothL1Loss, self).__init__()
-        self.beta = beta
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        """Forward function.
-
-        Args:
-            pred (torch.Tensor): The prediction.
-            target (torch.Tensor): The learning target of the prediction.
-            weight (torch.Tensor, optional): The weight of loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The reduction method used to
-                override the original reduction method of the loss.
-                Defaults to None.
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        loss_bbox = self.loss_weight * smooth_l1_loss(
-            pred,
-            target,
-            weight,
-            beta=self.beta,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss_bbox
-
-
-@LOSSES.register_module()
-class L1Loss(nn.Module):
-    """L1 loss.
-
-    Args:
-        reduction (str, optional): The method to reduce the loss.
-            Options are "none", "mean" and "sum".
-        loss_weight (float, optional): The weight of loss.
-    """
-
-    def __init__(self, reduction='mean', loss_weight=1.0):
-        super(L1Loss, self).__init__()
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None):
-        """Forward function.
-
-        Args:
-            pred (torch.Tensor): The prediction.
-            target (torch.Tensor): The learning target of the prediction.
-            weight (torch.Tensor, optional): The weight of loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The reduction method used to
-                override the original reduction method of the loss.
-                Defaults to None.
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        loss_bbox = self.loss_weight * l1_loss(
-            pred, target, weight, reduction=reduction, avg_factor=avg_factor)
-        return loss_bbox
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/cnn/bricks/scale.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/cnn/bricks/scale.py
deleted file mode 100644
index c905fffcc8bf998d18d94f927591963c428025e2..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/cnn/bricks/scale.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-
-
-class Scale(nn.Module):
-    """A learnable scale parameter.
-
-    This layer scales the input by a learnable factor. It multiplies a
-    learnable scale parameter of shape (1,) with input of any shape.
-
-    Args:
-        scale (float): Initial value of scale factor. Default: 1.0
-    """
-
-    def __init__(self, scale=1.0):
-        super(Scale, self).__init__()
-        self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
-
-    def forward(self, x):
-        return x * self.scale
diff --git a/spaces/SDbiaseval/find-my-butterfly/app.py b/spaces/SDbiaseval/find-my-butterfly/app.py
deleted file mode 100644
index 92cbf0f95419210c2e12f5ad9b558466522ccc62..0000000000000000000000000000000000000000
--- a/spaces/SDbiaseval/find-my-butterfly/app.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import pickle
-import gradio as gr
-from datasets import load_dataset
-from transformers import AutoModel, AutoFeatureExtractor
-import wikipedia
-
-
-# Only runs once when the script is first run.
-with open("butts_1024_new.pickle", "rb") as handle:
-    index = pickle.load(handle)
-
-# Load model for computing embeddings.
-feature_extractor = AutoFeatureExtractor.from_pretrained("sasha/autotrain-butterfly-similarity-2490576840")
-model = AutoModel.from_pretrained("sasha/autotrain-butterfly-similarity-2490576840")
-
-# Candidate images.
-dataset = load_dataset("sasha/butterflies_10k_names_multiple")
-ds = dataset["train"]
-
-
-def query(image, top_k=4):
-    inputs = feature_extractor(image, return_tensors="pt")
-    model_output = model(**inputs)
-    embedding = model_output.pooler_output.detach()
-    results = index.query(embedding, k=top_k)
-    inx = results[0][0].tolist()
-    logits = results[1][0].tolist()
-    images = ds.select(inx)["image"]
-    captions = ds.select(inx)["name"]
-    images_with_captions = [(i, c) for i, c in zip(images,captions)]
-    labels_with_probs = dict(zip(captions,logits))
-    labels_with_probs = {k: 1- v for k, v in labels_with_probs.items()}
-    try:
-    	description = wikipedia.summary(captions[0], sentences = 1)
-    	description = "### " + description
-    	url = wikipedia.page(captions[0]).url
-    	url = " You can learn more about your butterfly [here](" + str(url) + ")!"
-    	description = description + url
-    except:
-    	description = "### Butterflies are insects in the order Lepidoptera, which also includes moths. Adult butterflies have large, often brightly coloured wings."
-    	url = "https://en.wikipedia.org/wiki/Butterfly"
-    	url = " You can learn more about butterflies [here](" + str(url) + ")!"
-    	description = description + url
-    return images_with_captions, labels_with_probs, description
-
-
-with gr.Blocks() as demo:
-	gr.Markdown("# Find my Butterfly 🦋")
-	gr.Markdown("## Use this Space to find your butterfly, based on the [iNaturalist butterfly dataset](https://huggingface.co/datasets/huggan/inat_butterflies_top10k)!")
-	with gr.Row():
-		with gr.Column(min_width= 900):
-			inputs = gr.Image(shape=(800, 1600))
-			btn = gr.Button("Find my butterfly!")
-			description = gr.Markdown()
-			
-		with gr.Column():
-			outputs=gr.Gallery().style(grid=[2], height="auto")
-			labels = gr.Label()
-			
-	gr.Markdown("### Image Examples")
-	gr.Examples(
-	examples=["elton.jpg", "ken.jpg", "gaga.jpg", "taylor.jpg"],
-	inputs=inputs,
-	outputs=[outputs,labels],
-	fn=query,
-	cache_examples=True,
-	)
-	btn.click(query, inputs, [outputs, labels, description])
-	
-demo.launch()
-    
diff --git a/spaces/Salesforce/EDICT/my_diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py b/spaces/Salesforce/EDICT/my_diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py
deleted file mode 100644
index 15266544db7c8bc7448405955d74396eef7fe950..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python3
-import warnings
-from typing import Optional, Tuple, Union
-
-import torch
-
-from ...models import UNet2DModel
-from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from ...schedulers import KarrasVeScheduler
-
-
-class KarrasVePipeline(DiffusionPipeline):
-    r"""
-    Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and
-    the VE column of Table 1 from [1] for reference.
-
-    [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
-    https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic
-    differential equations." https://arxiv.org/abs/2011.13456
-
-    Parameters:
-        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
-        scheduler ([`KarrasVeScheduler`]):
-            Scheduler for the diffusion process to be used in combination with `unet` to denoise the encoded image.
-    """
-
-    # add type hints for linting
-    unet: UNet2DModel
-    scheduler: KarrasVeScheduler
-
-    def __init__(self, unet: UNet2DModel, scheduler: KarrasVeScheduler):
-        super().__init__()
-        scheduler = scheduler.set_format("pt")
-        self.register_modules(unet=unet, scheduler=scheduler)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        num_inference_steps: int = 50,
-        generator: Optional[torch.Generator] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        **kwargs,
-    ) -> Union[Tuple, ImagePipelineOutput]:
-        r"""
-        Args:
-            batch_size (`int`, *optional*, defaults to 1):
-                The number of images to generate.
-            generator (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
-            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
-            generated images.
-        """
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            # Set device as before (to be removed in 0.3.0)
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
-
-        img_size = self.unet.config.sample_size
-        shape = (batch_size, 3, img_size, img_size)
-
-        model = self.unet
-
-        # sample x_0 ~ N(0, sigma_0^2 * I)
-        sample = torch.randn(*shape) * self.scheduler.config.sigma_max
-        sample = sample.to(self.device)
-
-        self.scheduler.set_timesteps(num_inference_steps)
-
-        for t in self.progress_bar(self.scheduler.timesteps):
-            # here sigma_t == t_i from the paper
-            sigma = self.scheduler.schedule[t]
-            sigma_prev = self.scheduler.schedule[t - 1] if t > 0 else 0
-
-            # 1. Select temporarily increased noise level sigma_hat
-            # 2. Add new noise to move from sample_i to sample_hat
-            sample_hat, sigma_hat = self.scheduler.add_noise_to_input(sample, sigma, generator=generator)
-
-            # 3. Predict the noise residual given the noise magnitude `sigma_hat`
-            # The model inputs and output are adjusted by following eq. (213) in [1].
-            model_output = (sigma_hat / 2) * model((sample_hat + 1) / 2, sigma_hat / 2).sample
-
-            # 4. Evaluate dx/dt at sigma_hat
-            # 5. Take Euler step from sigma to sigma_prev
-            step_output = self.scheduler.step(model_output, sigma_hat, sigma_prev, sample_hat)
-
-            if sigma_prev != 0:
-                # 6. Apply 2nd order correction
-                # The model inputs and output are adjusted by following eq. (213) in [1].
-                model_output = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2, sigma_prev / 2).sample
-                step_output = self.scheduler.step_correct(
-                    model_output,
-                    sigma_hat,
-                    sigma_prev,
-                    sample_hat,
-                    step_output.prev_sample,
-                    step_output["derivative"],
-                )
-            sample = step_output.prev_sample
-
-        sample = (sample / 2 + 0.5).clamp(0, 1)
-        image = sample.cpu().permute(0, 2, 3, 1).numpy()
-        if output_type == "pil":
-            image = self.numpy_to_pil(sample)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)
diff --git a/spaces/Salesforce/EDICT/my_half_diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py b/spaces/Salesforce/EDICT/my_half_diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
deleted file mode 100644
index f02fa114a8e1607136fd1c8247e3cabb763b4415..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_half_diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
+++ /dev/null
@@ -1,279 +0,0 @@
-import inspect
-import warnings
-from typing import List, Optional, Union
-
-import torch
-
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
-
-
-class StableDiffusionPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-to-image generation using Stable Diffusion.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offsensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
-    ):
-        super().__init__()
-        scheduler = scheduler.set_format("pt")
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-
-    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
-        r"""
-        Enable sliced attention computation.
-
-        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
-        in several steps. This is useful to save some memory in exchange for a small speed decrease.
-
-        Args:
-            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
-                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
-                `attention_head_dim` must be a multiple of `slice_size`.
-        """
-        if slice_size == "auto":
-            # half the attention head size is usually a good trade-off between
-            # speed and memory
-            slice_size = self.unet.config.attention_head_dim // 2
-        self.unet.set_attention_slice(slice_size)
-
-    def disable_attention_slicing(self):
-        r"""
-        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
-        back to computing attention in one step.
-        """
-        # set slice_size = `None` to disable `attention slicing`
-        self.enable_attention_slicing(None)
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        height: Optional[int] = 512,
-        width: Optional[int] = 512,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        eta: Optional[float] = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        **kwargs,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
-        """
-
-        if "torch_device" in kwargs:
-            device = kwargs.pop("torch_device")
-            warnings.warn(
-                "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
-                " Consider using `pipe.to(torch_device)` instead."
-            )
-
-            # Set device as before (to be removed in 0.3.0)
-            if device is None:
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-            self.to(device)
-
-        if isinstance(prompt, str):
-            batch_size = 1
-        elif isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        # get prompt text embeddings
-        text_input = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-        text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            max_length = text_input.input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
-            )
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
-        # get the initial random noise unless the user supplied it
-
-        # Unlike in other pipelines, latents need to be generated in the target device
-        # for 1-to-1 results reproducibility with the CompVis implementation.
-        # However this currently doesn't work in `mps`.
-        latents_device = "cpu" if self.device.type == "mps" else self.device
-        latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
-        if latents is None:
-            latents = torch.randn(
-                latents_shape,
-                generator=generator,
-                device=latents_device,
-            )
-        else:
-            if latents.shape != latents_shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-        latents = latents.to(self.device)
-
-        # set timesteps
-        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
-        extra_set_kwargs = {}
-        if accepts_offset:
-            extra_set_kwargs["offset"] = 1
-
-        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
-
-        # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
-        if isinstance(self.scheduler, LMSDiscreteScheduler):
-            latents = latents * self.scheduler.sigmas[0]
-
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
-                sigma = self.scheduler.sigmas[i]
-                # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
-                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
-
-            # predict the noise residual
-            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            if isinstance(self.scheduler, LMSDiscreteScheduler):
-                latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
-            else:
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-        # scale and decode the image latents with vae
-        latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents).sample
-
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-
-        # run safety checker
-        safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
-        image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/SankarSrin/image-matting-app/ppmatting/transforms/transforms.py b/spaces/SankarSrin/image-matting-app/ppmatting/transforms/transforms.py
deleted file mode 100644
index afd28b4917a890890820e56785b81c841b2d387a..0000000000000000000000000000000000000000
--- a/spaces/SankarSrin/image-matting-app/ppmatting/transforms/transforms.py
+++ /dev/null
@@ -1,791 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import random
-import string
-
-import cv2
-import numpy as np
-from paddleseg.transforms import functional
-from paddleseg.cvlibs import manager
-from paddleseg.utils import seg_env
-from PIL import Image
-
-
-@manager.TRANSFORMS.add_component
-class Compose:
-    """
-    Do transformation on input data with corresponding pre-processing and augmentation operations.
-    The shape of input data to all operations is [height, width, channels].
-    """
-
-    def __init__(self, transforms, to_rgb=True):
-        if not isinstance(transforms, list):
-            raise TypeError('The transforms must be a list!')
-        self.transforms = transforms
-        self.to_rgb = to_rgb
-
-    def __call__(self, data):
-        """
-        Args:
-            data (dict): The data to transform.
-
-        Returns:
-            dict: Data after transformation
-        """
-        if 'trans_info' not in data:
-            data['trans_info'] = []
-        for op in self.transforms:
-            data = op(data)
-            if data is None:
-                return None
-
-        data['img'] = np.transpose(data['img'], (2, 0, 1))
-        for key in data.get('gt_fields', []):
-            if len(data[key].shape) == 2:
-                continue
-            data[key] = np.transpose(data[key], (2, 0, 1))
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class LoadImages:
-    def __init__(self, to_rgb=False):
-        self.to_rgb = to_rgb
-
-    def __call__(self, data):
-        if isinstance(data['img'], str):
-            data['img'] = cv2.imread(data['img'])
-        for key in data.get('gt_fields', []):
-            if isinstance(data[key], str):
-                data[key] = cv2.imread(data[key], cv2.IMREAD_UNCHANGED)
-            # if alpha and trimap has 3 channels, extract one.
-            if key in ['alpha', 'trimap']:
-                if len(data[key].shape) > 2:
-                    data[key] = data[key][:, :, 0]
-
-        if self.to_rgb:
-            data['img'] = cv2.cvtColor(data['img'], cv2.COLOR_BGR2RGB)
-            for key in data.get('gt_fields', []):
-                if len(data[key].shape) == 2:
-                    continue
-                data[key] = cv2.cvtColor(data[key], cv2.COLOR_BGR2RGB)
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class Resize:
-    def __init__(self, target_size=(512, 512), random_interp=False):
-        if isinstance(target_size, list) or isinstance(target_size, tuple):
-            if len(target_size) != 2:
-                raise ValueError(
-                    '`target_size` should include 2 elements, but it is {}'.
-                    format(target_size))
-        else:
-            raise TypeError(
-                "Type of `target_size` is invalid. It should be list or tuple, but it is {}"
-                .format(type(target_size)))
-
-        self.target_size = target_size
-        self.random_interp = random_interp
-        self.interps = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC]
-
-    def __call__(self, data):
-        if self.random_interp:
-            interp = np.random.choice(self.interps)
-        else:
-            interp = cv2.INTER_LINEAR
-        data['trans_info'].append(('resize', data['img'].shape[0:2]))
-        data['img'] = functional.resize(data['img'], self.target_size, interp)
-        for key in data.get('gt_fields', []):
-            if key == 'trimap':
-                data[key] = functional.resize(data[key], self.target_size,
-                                              cv2.INTER_NEAREST)
-            else:
-                data[key] = functional.resize(data[key], self.target_size,
-                                              interp)
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomResize:
-    """
-    Resize image to a size determinned by `scale` and `size`.
-
-    Args:
-        size(tuple|list): The reference size to resize. A tuple or list with length 2.
-        scale(tupel|list, optional): A range of scale base on `size`. A tuple or list with length 2. Default: None.
-    """
-
-    def __init__(self, size=None, scale=None):
-        if isinstance(size, list) or isinstance(size, tuple):
-            if len(size) != 2:
-                raise ValueError(
-                    '`size` should include 2 elements, but it is {}'.format(
-                        size))
-        elif size is not None:
-            raise TypeError(
-                "Type of `size` is invalid. It should be list or tuple, but it is {}"
-                .format(type(size)))
-
-        if scale is not None:
-            if isinstance(scale, list) or isinstance(scale, tuple):
-                if len(scale) != 2:
-                    raise ValueError(
-                        '`scale` should include 2 elements, but it is {}'.
-                        format(scale))
-            else:
-                raise TypeError(
-                    "Type of `scale` is invalid. It should be list or tuple, but it is {}"
-                    .format(type(scale)))
-        self.size = size
-        self.scale = scale
-
-    def __call__(self, data):
-        h, w = data['img'].shape[:2]
-        if self.scale is not None:
-            scale = np.random.uniform(self.scale[0], self.scale[1])
-        else:
-            scale = 1.
-        if self.size is not None:
-            scale_factor = max(self.size[0] / w, self.size[1] / h)
-        else:
-            scale_factor = 1
-        scale = scale * scale_factor
-
-        w = int(round(w * scale))
-        h = int(round(h * scale))
-        data['img'] = functional.resize(data['img'], (w, h))
-        for key in data.get('gt_fields', []):
-            if key == 'trimap':
-                data[key] = functional.resize(data[key], (w, h),
-                                              cv2.INTER_NEAREST)
-            else:
-                data[key] = functional.resize(data[key], (w, h))
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class ResizeByLong:
-    """
-    Resize the long side of an image to given size, and then scale the other side proportionally.
-
-    Args:
-        long_size (int): The target size of long side.
-    """
-
-    def __init__(self, long_size):
-        self.long_size = long_size
-
-    def __call__(self, data):
-        data['trans_info'].append(('resize', data['img'].shape[0:2]))
-        data['img'] = functional.resize_long(data['img'], self.long_size)
-        for key in data.get('gt_fields', []):
-            if key == 'trimap':
-                data[key] = functional.resize_long(data[key], self.long_size,
-                                                   cv2.INTER_NEAREST)
-            else:
-                data[key] = functional.resize_long(data[key], self.long_size)
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class ResizeByShort:
-    """
-    Resize the short side of an image to given size, and then scale the other side proportionally.
-
-    Args:
-        short_size (int): The target size of short side.
-    """
-
-    def __init__(self, short_size):
-        self.short_size = short_size
-
-    def __call__(self, data):
-        data['trans_info'].append(('resize', data['img'].shape[0:2]))
-        data['img'] = functional.resize_short(data['img'], self.short_size)
-        for key in data.get('gt_fields', []):
-            if key == 'trimap':
-                data[key] = functional.resize_short(data[key], self.short_size,
-                                                    cv2.INTER_NEAREST)
-            else:
-                data[key] = functional.resize_short(data[key], self.short_size)
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class ResizeToIntMult:
-    """
-    Resize to some int muitple, d.g. 32.
-    """
-
-    def __init__(self, mult_int=32):
-        self.mult_int = mult_int
-
-    def __call__(self, data):
-        data['trans_info'].append(('resize', data['img'].shape[0:2]))
-
-        h, w = data['img'].shape[0:2]
-        rw = w - w % self.mult_int
-        rh = h - h % self.mult_int
-        data['img'] = functional.resize(data['img'], (rw, rh))
-        for key in data.get('gt_fields', []):
-            if key == 'trimap':
-                data[key] = functional.resize(data[key], (rw, rh),
-                                              cv2.INTER_NEAREST)
-            else:
-                data[key] = functional.resize(data[key], (rw, rh))
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class Normalize:
-    """
-    Normalize an image.
-
-    Args:
-        mean (list, optional): The mean value of a data set. Default: [0.5, 0.5, 0.5].
-        std (list, optional): The standard deviation of a data set. Default: [0.5, 0.5, 0.5].
-
-    Raises:
-        ValueError: When mean/std is not list or any value in std is 0.
-    """
-
-    def __init__(self, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)):
-        self.mean = mean
-        self.std = std
-        if not (isinstance(self.mean,
-                           (list, tuple)) and isinstance(self.std,
-                                                         (list, tuple))):
-            raise ValueError(
-                "{}: input type is invalid. It should be list or tuple".format(
-                    self))
-        from functools import reduce
-        if reduce(lambda x, y: x * y, self.std) == 0:
-            raise ValueError('{}: std is invalid!'.format(self))
-
-    def __call__(self, data):
-        mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
-        std = np.array(self.std)[np.newaxis, np.newaxis, :]
-        data['img'] = functional.normalize(data['img'], mean, std)
-        if 'fg' in data.get('gt_fields', []):
-            data['fg'] = functional.normalize(data['fg'], mean, std)
-        if 'bg' in data.get('gt_fields', []):
-            data['bg'] = functional.normalize(data['bg'], mean, std)
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomCropByAlpha:
-    """
-    Randomly crop while centered on uncertain area by a certain probability.
-
-    Args:
-        crop_size (tuple|list): The size you want to crop from image.
-        p (float): The probability centered on uncertain area.
-
-    """
-
-    def __init__(self, crop_size=((320, 320), (480, 480), (640, 640)),
-                 prob=0.5):
-        self.crop_size = crop_size
-        self.prob = prob
-
-    def __call__(self, data):
-        idex = np.random.randint(low=0, high=len(self.crop_size))
-        crop_w, crop_h = self.crop_size[idex]
-
-        img_h = data['img'].shape[0]
-        img_w = data['img'].shape[1]
-        if np.random.rand() < self.prob:
-            crop_center = np.where((data['alpha'] > 0) & (data['alpha'] < 255))
-            center_h_array, center_w_array = crop_center
-            if len(center_h_array) == 0:
-                return data
-            rand_ind = np.random.randint(len(center_h_array))
-            center_h = center_h_array[rand_ind]
-            center_w = center_w_array[rand_ind]
-            delta_h = crop_h // 2
-            delta_w = crop_w // 2
-            start_h = max(0, center_h - delta_h)
-            start_w = max(0, center_w - delta_w)
-        else:
-            start_h = 0
-            start_w = 0
-            if img_h > crop_h:
-                start_h = np.random.randint(img_h - crop_h + 1)
-            if img_w > crop_w:
-                start_w = np.random.randint(img_w - crop_w + 1)
-
-        end_h = min(img_h, start_h + crop_h)
-        end_w = min(img_w, start_w + crop_w)
-
-        data['img'] = data['img'][start_h:end_h, start_w:end_w]
-        for key in data.get('gt_fields', []):
-            data[key] = data[key][start_h:end_h, start_w:end_w]
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomCrop:
-    """
-    Randomly crop
-
-    Args:
-    crop_size (tuple|list): The size you want to crop from image.
-    """
-
-    def __init__(self, crop_size=((320, 320), (480, 480), (640, 640))):
-        if not isinstance(crop_size[0], (list, tuple)):
-            crop_size = [crop_size]
-        self.crop_size = crop_size
-
-    def __call__(self, data):
-        idex = np.random.randint(low=0, high=len(self.crop_size))
-        crop_w, crop_h = self.crop_size[idex]
-        img_h, img_w = data['img'].shape[0:2]
-
-        start_h = 0
-        start_w = 0
-        if img_h > crop_h:
-            start_h = np.random.randint(img_h - crop_h + 1)
-        if img_w > crop_w:
-            start_w = np.random.randint(img_w - crop_w + 1)
-
-        end_h = min(img_h, start_h + crop_h)
-        end_w = min(img_w, start_w + crop_w)
-
-        data['img'] = data['img'][start_h:end_h, start_w:end_w]
-        for key in data.get('gt_fields', []):
-            data[key] = data[key][start_h:end_h, start_w:end_w]
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class LimitLong:
-    """
-    Limit the long edge of image.
-
-    If the long edge is larger than max_long, resize the long edge
-    to max_long, while scale the short edge proportionally.
-
-    If the long edge is smaller than min_long, resize the long edge
-    to min_long, while scale the short edge proportionally.
-
-    Args:
-        max_long (int, optional): If the long edge of image is larger than max_long,
-            it will be resize to max_long. Default: None.
-        min_long (int, optional): If the long edge of image is smaller than min_long,
-            it will be resize to min_long. Default: None.
-    """
-
-    def __init__(self, max_long=None, min_long=None):
-        if max_long is not None:
-            if not isinstance(max_long, int):
-                raise TypeError(
-                    "Type of `max_long` is invalid. It should be int, but it is {}"
-                    .format(type(max_long)))
-        if min_long is not None:
-            if not isinstance(min_long, int):
-                raise TypeError(
-                    "Type of `min_long` is invalid. It should be int, but it is {}"
-                    .format(type(min_long)))
-        if (max_long is not None) and (min_long is not None):
-            if min_long > max_long:
-                raise ValueError(
-                    '`max_long should not smaller than min_long, but they are {} and {}'
-                    .format(max_long, min_long))
-        self.max_long = max_long
-        self.min_long = min_long
-
-    def __call__(self, data):
-        h, w = data['img'].shape[:2]
-        long_edge = max(h, w)
-        target = long_edge
-        if (self.max_long is not None) and (long_edge > self.max_long):
-            target = self.max_long
-        elif (self.min_long is not None) and (long_edge < self.min_long):
-            target = self.min_long
-
-        data['trans_info'].append(('resize', data['img'].shape[0:2]))
-        if target != long_edge:
-            data['img'] = functional.resize_long(data['img'], target)
-            for key in data.get('gt_fields', []):
-                if key == 'trimap':
-                    data[key] = functional.resize_long(data[key], target,
-                                                       cv2.INTER_NEAREST)
-                else:
-                    data[key] = functional.resize_long(data[key], target)
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class LimitShort:
-    """
-    Limit the short edge of image.
-
-    If the short edge is larger than max_short, resize the short edge
-    to max_short, while scale the long edge proportionally.
-
-    If the short edge is smaller than min_short, resize the short edge
-    to min_short, while scale the long edge proportionally.
-
-    Args:
-        max_short (int, optional): If the short edge of image is larger than max_short,
-            it will be resize to max_short. Default: None.
-        min_short (int, optional): If the short edge of image is smaller than min_short,
-            it will be resize to min_short. Default: None.
-    """
-
-    def __init__(self, max_short=None, min_short=None):
-        if max_short is not None:
-            if not isinstance(max_short, int):
-                raise TypeError(
-                    "Type of `max_short` is invalid. It should be int, but it is {}"
-                    .format(type(max_short)))
-        if min_short is not None:
-            if not isinstance(min_short, int):
-                raise TypeError(
-                    "Type of `min_short` is invalid. It should be int, but it is {}"
-                    .format(type(min_short)))
-        if (max_short is not None) and (min_short is not None):
-            if min_short > max_short:
-                raise ValueError(
-                    '`max_short should not smaller than min_short, but they are {} and {}'
-                    .format(max_short, min_short))
-        self.max_short = max_short
-        self.min_short = min_short
-
-    def __call__(self, data):
-        h, w = data['img'].shape[:2]
-        short_edge = min(h, w)
-        target = short_edge
-        if (self.max_short is not None) and (short_edge > self.max_short):
-            target = self.max_short
-        elif (self.min_short is not None) and (short_edge < self.min_short):
-            target = self.min_short
-
-        data['trans_info'].append(('resize', data['img'].shape[0:2]))
-        if target != short_edge:
-            data['img'] = functional.resize_short(data['img'], target)
-            for key in data.get('gt_fields', []):
-                if key == 'trimap':
-                    data[key] = functional.resize_short(data[key], target,
-                                                        cv2.INTER_NEAREST)
-                else:
-                    data[key] = functional.resize_short(data[key], target)
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomHorizontalFlip:
-    """
-    Flip an image horizontally with a certain probability.
-
-    Args:
-        prob (float, optional): A probability of horizontally flipping. Default: 0.5.
-    """
-
-    def __init__(self, prob=0.5):
-        self.prob = prob
-
-    def __call__(self, data):
-        if random.random() < self.prob:
-            data['img'] = functional.horizontal_flip(data['img'])
-            for key in data.get('gt_fields', []):
-                data[key] = functional.horizontal_flip(data[key])
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomBlur:
-    """
-    Blurring an image by a Gaussian function with a certain probability.
-
-    Args:
-        prob (float, optional): A probability of blurring an image. Default: 0.1.
-    """
-
-    def __init__(self, prob=0.1):
-        self.prob = prob
-
-    def __call__(self, data):
-        if self.prob <= 0:
-            n = 0
-        elif self.prob >= 1:
-            n = 1
-        else:
-            n = int(1.0 / self.prob)
-        if n > 0:
-            if np.random.randint(0, n) == 0:
-                radius = np.random.randint(3, 10)
-                if radius % 2 != 1:
-                    radius = radius + 1
-                if radius > 9:
-                    radius = 9
-                data['img'] = cv2.GaussianBlur(data['img'], (radius, radius), 0,
-                                               0)
-                for key in data.get('gt_fields', []):
-                    if key == 'trimap':
-                        continue
-                    data[key] = cv2.GaussianBlur(data[key], (radius, radius), 0,
-                                                 0)
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomDistort:
-    """
-    Distort an image with random configurations.
-
-    Args:
-        brightness_range (float, optional): A range of brightness. Default: 0.5.
-        brightness_prob (float, optional): A probability of adjusting brightness. Default: 0.5.
-        contrast_range (float, optional): A range of contrast. Default: 0.5.
-        contrast_prob (float, optional): A probability of adjusting contrast. Default: 0.5.
-        saturation_range (float, optional): A range of saturation. Default: 0.5.
-        saturation_prob (float, optional): A probability of adjusting saturation. Default: 0.5.
-        hue_range (int, optional): A range of hue. Default: 18.
-        hue_prob (float, optional): A probability of adjusting hue. Default: 0.5.
-    """
-
-    def __init__(self,
-                 brightness_range=0.5,
-                 brightness_prob=0.5,
-                 contrast_range=0.5,
-                 contrast_prob=0.5,
-                 saturation_range=0.5,
-                 saturation_prob=0.5,
-                 hue_range=18,
-                 hue_prob=0.5):
-        self.brightness_range = brightness_range
-        self.brightness_prob = brightness_prob
-        self.contrast_range = contrast_range
-        self.contrast_prob = contrast_prob
-        self.saturation_range = saturation_range
-        self.saturation_prob = saturation_prob
-        self.hue_range = hue_range
-        self.hue_prob = hue_prob
-
-    def __call__(self, data):
-        brightness_lower = 1 - self.brightness_range
-        brightness_upper = 1 + self.brightness_range
-        contrast_lower = 1 - self.contrast_range
-        contrast_upper = 1 + self.contrast_range
-        saturation_lower = 1 - self.saturation_range
-        saturation_upper = 1 + self.saturation_range
-        hue_lower = -self.hue_range
-        hue_upper = self.hue_range
-        ops = [
-            functional.brightness, functional.contrast, functional.saturation,
-            functional.hue
-        ]
-        random.shuffle(ops)
-        params_dict = {
-            'brightness': {
-                'brightness_lower': brightness_lower,
-                'brightness_upper': brightness_upper
-            },
-            'contrast': {
-                'contrast_lower': contrast_lower,
-                'contrast_upper': contrast_upper
-            },
-            'saturation': {
-                'saturation_lower': saturation_lower,
-                'saturation_upper': saturation_upper
-            },
-            'hue': {
-                'hue_lower': hue_lower,
-                'hue_upper': hue_upper
-            }
-        }
-        prob_dict = {
-            'brightness': self.brightness_prob,
-            'contrast': self.contrast_prob,
-            'saturation': self.saturation_prob,
-            'hue': self.hue_prob
-        }
-
-        im = data['img'].astype('uint8')
-        im = Image.fromarray(im)
-        for id in range(len(ops)):
-            params = params_dict[ops[id].__name__]
-            params['im'] = im
-            prob = prob_dict[ops[id].__name__]
-            if np.random.uniform(0, 1) < prob:
-                im = ops[id](**params)
-        data['img'] = np.asarray(im)
-
-        for key in data.get('gt_fields', []):
-            if key in ['alpha', 'trimap']:
-                continue
-            else:
-                im = data[key].astype('uint8')
-                im = Image.fromarray(im)
-                for id in range(len(ops)):
-                    params = params_dict[ops[id].__name__]
-                    params['im'] = im
-                    prob = prob_dict[ops[id].__name__]
-                    if np.random.uniform(0, 1) < prob:
-                        im = ops[id](**params)
-                data[key] = np.asarray(im)
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class Padding:
-    """
-    Add bottom-right padding to a raw image or annotation image.
-
-    Args:
-        target_size (list|tuple): The target size after padding.
-        im_padding_value (list, optional): The padding value of raw image.
-            Default: [127.5, 127.5, 127.5].
-        label_padding_value (int, optional): The padding value of annotation image. Default: 255.
-
-    Raises:
-        TypeError: When target_size is neither list nor tuple.
-        ValueError: When the length of target_size is not 2.
-    """
-
-    def __init__(self, target_size, im_padding_value=(127.5, 127.5, 127.5)):
-        if isinstance(target_size, list) or isinstance(target_size, tuple):
-            if len(target_size) != 2:
-                raise ValueError(
-                    '`target_size` should include 2 elements, but it is {}'.
-                    format(target_size))
-        else:
-            raise TypeError(
-                "Type of target_size is invalid. It should be list or tuple, now is {}"
-                .format(type(target_size)))
-
-        self.target_size = target_size
-        self.im_padding_value = im_padding_value
-
-    def __call__(self, data):
-        im_height, im_width = data['img'].shape[0], data['img'].shape[1]
-        target_height = self.target_size[1]
-        target_width = self.target_size[0]
-        pad_height = max(0, target_height - im_height)
-        pad_width = max(0, target_width - im_width)
-        data['trans_info'].append(('padding', data['img'].shape[0:2]))
-        if (pad_height == 0) and (pad_width == 0):
-            return data
-        else:
-            data['img'] = cv2.copyMakeBorder(
-                data['img'],
-                0,
-                pad_height,
-                0,
-                pad_width,
-                cv2.BORDER_CONSTANT,
-                value=self.im_padding_value)
-            for key in data.get('gt_fields', []):
-                if key in ['trimap', 'alpha']:
-                    value = 0
-                else:
-                    value = self.im_padding_value
-                data[key] = cv2.copyMakeBorder(
-                    data[key],
-                    0,
-                    pad_height,
-                    0,
-                    pad_width,
-                    cv2.BORDER_CONSTANT,
-                    value=value)
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomSharpen:
-    def __init__(self, prob=0.1):
-        if prob < 0:
-            self.prob = 0
-        elif prob > 1:
-            self.prob = 1
-        else:
-            self.prob = prob
-
-    def __call__(self, data):
-        if np.random.rand() > self.prob:
-            return data
-
-        radius = np.random.choice([0, 3, 5, 7, 9])
-        w = np.random.uniform(0.1, 0.5)
-        blur_img = cv2.GaussianBlur(data['img'], (radius, radius), 5)
-        data['img'] = cv2.addWeighted(data['img'], 1 + w, blur_img, -w, 0)
-        for key in data.get('gt_fields', []):
-            if key == 'trimap' or key == 'alpha':
-                continue
-            blur_img = cv2.GaussianBlur(data[key], (0, 0), 5)
-            data[key] = cv2.addWeighted(data[key], 1.5, blur_img, -0.5, 0)
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomNoise:
-    def __init__(self, prob=0.1):
-        if prob < 0:
-            self.prob = 0
-        elif prob > 1:
-            self.prob = 1
-        else:
-            self.prob = prob
-
-    def __call__(self, data):
-        if np.random.rand() > self.prob:
-            return data
-        mean = np.random.uniform(0, 0.04)
-        var = np.random.uniform(0, 0.001)
-        noise = np.random.normal(mean, var**0.5, data['img'].shape) * 255
-        data['img'] = data['img'] + noise
-        data['img'] = np.clip(data['img'], 0, 255)
-
-        return data
-
-
-@manager.TRANSFORMS.add_component
-class RandomReJpeg:
-    def __init__(self, prob=0.1):
-        if prob < 0:
-            self.prob = 0
-        elif prob > 1:
-            self.prob = 1
-        else:
-            self.prob = prob
-
-    def __call__(self, data):
-        if np.random.rand() > self.prob:
-            return data
-        q = np.random.randint(70, 95)
-        img = data['img'].astype('uint8')
-
-        # Ensure no conflicts between processes
-        tmp_name = str(os.getpid()) + '.jpg'
-        tmp_name = os.path.join(seg_env.TMP_HOME, tmp_name)
-        cv2.imwrite(tmp_name, img, [int(cv2.IMWRITE_JPEG_QUALITY), q])
-        data['img'] = cv2.imread(tmp_name)
-
-        return data
diff --git a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/yolo/util.py b/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/yolo/util.py
deleted file mode 100644
index 0998e100af351bd80858dc4dd42de829e069d801..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/yolo/util.py
+++ /dev/null
@@ -1,387 +0,0 @@
-
-from __future__ import division
-
-import torch 
-import torch.nn as nn
-import torch.nn.functional as F 
-from torch.autograd import Variable
-import numpy as np
-import cv2 
-import matplotlib.pyplot as plt
-try:
-    from bbox import bbox_iou
-except ImportError:
-    from yolo.bbox import bbox_iou
-
-
-def count_parameters(model):
-    return sum(p.numel() for p in model.parameters())
-
-def count_learnable_parameters(model):
-    return sum(p.numel() for p in model.parameters() if p.requires_grad)
-
-def convert2cpu(matrix):
-    if matrix.is_cuda:
-        return torch.FloatTensor(matrix.size()).copy_(matrix)
-    else:
-        return matrix
-
-def predict_transform(prediction, inp_dim, anchors, num_classes, CUDA = True):
-    batch_size = prediction.size(0)
-    stride =  inp_dim // prediction.size(2)
-    grid_size = inp_dim // stride
-    bbox_attrs = 5 + num_classes
-    num_anchors = len(anchors)
-    
-    anchors = [(a[0]/stride, a[1]/stride) for a in anchors]
-
-
-
-    prediction = prediction.view(batch_size, bbox_attrs*num_anchors, grid_size*grid_size)
-    prediction = prediction.transpose(1,2).contiguous()
-    prediction = prediction.view(batch_size, grid_size*grid_size*num_anchors, bbox_attrs)
-
-
-    #Sigmoid the  centre_X, centre_Y. and object confidencce
-    prediction[:,:,0] = torch.sigmoid(prediction[:,:,0])
-    prediction[:,:,1] = torch.sigmoid(prediction[:,:,1])
-    prediction[:,:,4] = torch.sigmoid(prediction[:,:,4])
-    
-
-    
-    #Add the center offsets
-    grid_len = np.arange(grid_size)
-    a,b = np.meshgrid(grid_len, grid_len)
-    
-    x_offset = torch.FloatTensor(a).view(-1,1)
-    y_offset = torch.FloatTensor(b).view(-1,1)
-    
-    if CUDA:
-        x_offset = x_offset
-        y_offset = y_offset
-    
-    x_y_offset = torch.cat((x_offset, y_offset), 1).repeat(1,num_anchors).view(-1,2).unsqueeze(0)
-    
-    prediction[:,:,:2] += x_y_offset
-      
-    #log space transform height and the width
-    anchors = torch.FloatTensor(anchors)
-    
-    if CUDA:
-        anchors = anchors
-    
-    anchors = anchors.repeat(grid_size*grid_size, 1).unsqueeze(0)
-    prediction[:,:,2:4] = torch.exp(prediction[:,:,2:4])*anchors
-
-    #Softmax the class scores
-    prediction[:,:,5: 5 + num_classes] = torch.sigmoid((prediction[:,:, 5 : 5 + num_classes]))
-
-    prediction[:,:,:4] *= stride
-   
-    
-    return prediction
-
-def load_classes(namesfile):
-    fp = open(namesfile, "r")
-    names = fp.read().split("\n")[:-1]
-    return names
-
-def get_im_dim(im):
-    im = cv2.imread(im)
-    w,h = im.shape[1], im.shape[0]
-    return w,h
-
-def unique(tensor):
-    tensor_np = tensor.cpu().numpy()
-    unique_np = np.unique(tensor_np)
-    unique_tensor = torch.from_numpy(unique_np)
-
-    tensor_res = tensor.new(unique_tensor.shape)
-    tensor_res.copy_(unique_tensor)
-    return tensor_res
-
-
-def dynamic_write_results(prediction, confidence, num_classes, nms=True, nms_conf=0.4):
-    prediction_bak = prediction.clone()
-    dets = write_results(prediction.clone(), confidence, num_classes, nms, nms_conf)
-    if isinstance(dets, int):
-        return dets
-
-    if dets.shape[0] > 100:
-        nms_conf -= 0.05
-        dets = write_results(prediction_bak.clone(), confidence, num_classes, nms, nms_conf)
-
-    return dets
-
-
-def write_results(prediction, confidence, num_classes, nms=True, nms_conf=0.4):
-    conf_mask = (prediction[:, :, 4] > confidence).float().float().unsqueeze(2)
-    prediction = prediction * conf_mask
-
-    try:
-        ind_nz = torch.nonzero(prediction[:,:,4]).transpose(0,1).contiguous()
-    except:
-        return 0
-
-    box_a = prediction.new(prediction.shape)
-    box_a[:,:,0] = (prediction[:,:,0] - prediction[:,:,2]/2)
-    box_a[:,:,1] = (prediction[:,:,1] - prediction[:,:,3]/2)
-    box_a[:,:,2] = (prediction[:,:,0] + prediction[:,:,2]/2) 
-    box_a[:,:,3] = (prediction[:,:,1] + prediction[:,:,3]/2)
-    prediction[:,:,:4] = box_a[:,:,:4]
-
-    batch_size = prediction.size(0)
-
-    output = prediction.new(1, prediction.size(2) + 1)
-    write = False
-    num = 0
-    for ind in range(batch_size):
-        #select the image from the batch
-        image_pred = prediction[ind]
-
-        #Get the class having maximum score, and the index of that class
-        #Get rid of num_classes softmax scores 
-        #Add the class index and the class score of class having maximum score
-        max_conf, max_conf_score = torch.max(image_pred[:,5:5+ num_classes], 1)
-        max_conf = max_conf.float().unsqueeze(1)
-        max_conf_score = max_conf_score.float().unsqueeze(1)
-        seq = (image_pred[:,:5], max_conf, max_conf_score)
-        image_pred = torch.cat(seq, 1)
-
-        #Get rid of the zero entries
-        non_zero_ind =  (torch.nonzero(image_pred[:,4]))
-
-        image_pred_ = image_pred[non_zero_ind.squeeze(),:].view(-1,7)
-
-        #Get the various classes detected in the image
-        try:
-            img_classes = unique(image_pred_[:,-1])
-        except:
-            continue
-
-        #WE will do NMS classwise
-        #print(img_classes)
-        for cls in img_classes:
-            if cls != 0:
-                continue
-            #get the detections with one particular class
-            cls_mask = image_pred_*(image_pred_[:,-1] == cls).float().unsqueeze(1)
-            class_mask_ind = torch.nonzero(cls_mask[:,-2]).squeeze()
-
-            image_pred_class = image_pred_[class_mask_ind].view(-1,7)
-
-            #sort the detections such that the entry with the maximum objectness
-            #confidence is at the top
-            conf_sort_index = torch.sort(image_pred_class[:,4], descending = True )[1]
-            image_pred_class = image_pred_class[conf_sort_index]
-            idx = image_pred_class.size(0)
-
-            #if nms has to be done
-            if nms:
-                # Perform non-maximum suppression
-                max_detections = []
-                while image_pred_class.size(0):
-                    # Get detection with highest confidence and save as max detection
-                    max_detections.append(image_pred_class[0].unsqueeze(0))
-                    # Stop if we're at the last detection
-                    if len(image_pred_class) == 1:
-                        break
-                    # Get the IOUs for all boxes with lower confidence
-                    ious = bbox_iou(max_detections[-1], image_pred_class[1:])
-                    # Remove detections with IoU >= NMS threshold
-                    image_pred_class = image_pred_class[1:][ious < nms_conf]
-
-                image_pred_class = torch.cat(max_detections).data
-
-
-            #Concatenate the batch_id of the image to the detection
-            #this helps us identify which image does the detection correspond to 
-            #We use a linear straucture to hold ALL the detections from the batch
-            #the batch_dim is flattened
-            #batch is identified by extra batch column
-
-            batch_ind = image_pred_class.new(image_pred_class.size(0), 1).fill_(ind)
-            seq = batch_ind, image_pred_class
-            if not write:
-                output = torch.cat(seq,1)
-                write = True
-            else:
-                out = torch.cat(seq,1)
-                output = torch.cat((output,out))
-            num += 1
-    
-    if not num:
-        return 0
-
-    return output
-
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Sat Mar 24 00:12:16 2018
-
-@author: ayooshmac
-"""
-
-def predict_transform_half(prediction, inp_dim, anchors, num_classes, CUDA = True):
-    batch_size = prediction.size(0)
-    stride =  inp_dim // prediction.size(2)
-
-    bbox_attrs = 5 + num_classes
-    num_anchors = len(anchors)
-    grid_size = inp_dim // stride
-
-    
-    prediction = prediction.view(batch_size, bbox_attrs*num_anchors, grid_size*grid_size)
-    prediction = prediction.transpose(1,2).contiguous()
-    prediction = prediction.view(batch_size, grid_size*grid_size*num_anchors, bbox_attrs)
-    
-    
-    #Sigmoid the  centre_X, centre_Y. and object confidencce
-    prediction[:,:,0] = torch.sigmoid(prediction[:,:,0])
-    prediction[:,:,1] = torch.sigmoid(prediction[:,:,1])
-    prediction[:,:,4] = torch.sigmoid(prediction[:,:,4])
-
-    
-    #Add the center offsets
-    grid_len = np.arange(grid_size)
-    a,b = np.meshgrid(grid_len, grid_len)
-    
-    x_offset = torch.FloatTensor(a).view(-1,1)
-    y_offset = torch.FloatTensor(b).view(-1,1)
-    
-    if CUDA:
-        x_offset = x_offset.half()
-        y_offset = y_offset.half()
-    
-    x_y_offset = torch.cat((x_offset, y_offset), 1).repeat(1,num_anchors).view(-1,2).unsqueeze(0)
-    
-    prediction[:,:,:2] += x_y_offset
-      
-    #log space transform height and the width
-    anchors = torch.HalfTensor(anchors)
-    
-    if CUDA:
-        anchors = anchors
-    
-    anchors = anchors.repeat(grid_size*grid_size, 1).unsqueeze(0)
-    prediction[:,:,2:4] = torch.exp(prediction[:,:,2:4])*anchors
-
-    #Softmax the class scores
-    prediction[:,:,5: 5 + num_classes] = nn.Softmax(-1)(Variable(prediction[:,:, 5 : 5 + num_classes])).data
-
-    prediction[:,:,:4] *= stride
-    
-    
-    return prediction
-
-
-def write_results_half(prediction, confidence, num_classes, nms = True, nms_conf = 0.4):
-    conf_mask = (prediction[:,:,4] > confidence).half().unsqueeze(2)
-    prediction = prediction*conf_mask
-    
-    try:
-        ind_nz = torch.nonzero(prediction[:,:,4]).transpose(0,1).contiguous()
-    except:
-        return 0
-    
-    
-    
-    box_a = prediction.new(prediction.shape)
-    box_a[:,:,0] = (prediction[:,:,0] - prediction[:,:,2]/2)
-    box_a[:,:,1] = (prediction[:,:,1] - prediction[:,:,3]/2)
-    box_a[:,:,2] = (prediction[:,:,0] + prediction[:,:,2]/2) 
-    box_a[:,:,3] = (prediction[:,:,1] + prediction[:,:,3]/2)
-    prediction[:,:,:4] = box_a[:,:,:4]
-    
-    
-    
-    batch_size = prediction.size(0)
-    
-    output = prediction.new(1, prediction.size(2) + 1)
-    write = False
-    
-    for ind in range(batch_size):
-        #select the image from the batch
-        image_pred = prediction[ind]
-
-        
-        #Get the class having maximum score, and the index of that class
-        #Get rid of num_classes softmax scores 
-        #Add the class index and the class score of class having maximum score
-        max_conf, max_conf_score = torch.max(image_pred[:,5:5+ num_classes], 1)
-        max_conf = max_conf.half().unsqueeze(1)
-        max_conf_score = max_conf_score.half().unsqueeze(1)
-        seq = (image_pred[:,:5], max_conf, max_conf_score)
-        image_pred = torch.cat(seq, 1)
-        
-        
-        #Get rid of the zero entries
-        non_zero_ind =  (torch.nonzero(image_pred[:,4]))
-        try:
-            image_pred_ = image_pred[non_zero_ind.squeeze(),:]
-        except:
-            continue
-        
-        #Get the various classes detected in the image
-        img_classes = unique(image_pred_[:,-1].long()).half()
-        
-        
-        
-                
-        #WE will do NMS classwise
-        for cls in img_classes:
-            #get the detections with one particular class
-            cls_mask = image_pred_*(image_pred_[:,-1] == cls).half().unsqueeze(1)
-            class_mask_ind = torch.nonzero(cls_mask[:,-2]).squeeze()
-            
-
-            image_pred_class = image_pred_[class_mask_ind]
-
-        
-             #sort the detections such that the entry with the maximum objectness
-             #confidence is at the top
-            conf_sort_index = torch.sort(image_pred_class[:,4], descending = True )[1]
-            image_pred_class = image_pred_class[conf_sort_index]
-            idx = image_pred_class.size(0)
-            
-            #if nms has to be done
-            if nms:
-                #For each detection
-                for i in range(idx):
-                    #Get the IOUs of all boxes that come after the one we are looking at 
-                    #in the loop
-                    try:
-                        ious = bbox_iou(image_pred_class[i].unsqueeze(0), image_pred_class[i+1:])
-                    except ValueError:
-                        break
-        
-                    except IndexError:
-                        break
-                    
-                    #Zero out all the detections that have IoU > treshhold
-                    iou_mask = (ious < nms_conf).half().unsqueeze(1)
-                    image_pred_class[i+1:] *= iou_mask       
-                    
-                    #Remove the non-zero entries
-                    non_zero_ind = torch.nonzero(image_pred_class[:,4]).squeeze()
-                    image_pred_class = image_pred_class[non_zero_ind]
-                    
-                    
-            
-            #Concatenate the batch_id of the image to the detection
-            #this helps us identify which image does the detection correspond to 
-            #We use a linear straucture to hold ALL the detections from the batch
-            #the batch_dim is flattened
-            #batch is identified by extra batch column
-            batch_ind = image_pred_class.new(image_pred_class.size(0), 1).fill_(ind)
-            seq = batch_ind, image_pred_class
-            
-            if not write:
-                output = torch.cat(seq,1)
-                write = True
-            else:
-                out = torch.cat(seq,1)
-                output = torch.cat((output,out))
-    
-    return output
diff --git a/spaces/SaulLu/test-demo/_site/index.html b/spaces/SaulLu/test-demo/_site/index.html
deleted file mode 100644
index d2281babec4f4fc9da68cd24a223b7d92d3c540a..0000000000000000000000000000000000000000
--- a/spaces/SaulLu/test-demo/_site/index.html
+++ /dev/null
@@ -1,76 +0,0 @@
-<!DOCTYPE html>
-<html lang="en"><head>
-  <meta charset="utf-8">
-  <meta http-equiv="X-UA-Compatible" content="IE=edge">
-  <meta name="viewport" content="width=device-width, initial-scale=1"><!-- Begin Jekyll SEO tag v2.7.1 -->
-<title>Your awesome title | Write an awesome description for your new site here. You can edit this line in _config.yml. It will appear in your document head meta (for Google search results) and in your feed.xml site description.</title>
-<meta name="generator" content="Jekyll v3.9.0" />
-<meta property="og:title" content="Your awesome title" />
-<meta property="og:locale" content="en_US" />
-<meta name="description" content="Write an awesome description for your new site here. You can edit this line in _config.yml. It will appear in your document head meta (for Google search results) and in your feed.xml site description." />
-<meta property="og:description" content="Write an awesome description for your new site here. You can edit this line in _config.yml. It will appear in your document head meta (for Google search results) and in your feed.xml site description." />
-<link rel="canonical" href="http://localhost:4000/" />
-<meta property="og:url" content="http://localhost:4000/" />
-<meta property="og:site_name" content="Your awesome title" />
-<meta name="twitter:card" content="summary" />
-<meta property="twitter:title" content="Your awesome title" />
-<script type="application/ld+json">
-{"@type":"WebSite","headline":"Your awesome title","url":"http://localhost:4000/","description":"Write an awesome description for your new site here. You can edit this line in _config.yml. It will appear in your document head meta (for Google search results) and in your feed.xml site description.","name":"Your awesome title","@context":"https://schema.org"}</script>
-<!-- End Jekyll SEO tag -->
-<link rel="stylesheet" href="/assets/main.css"><link type="application/atom+xml" rel="alternate" href="http://localhost:4000/feed.xml" title="Your awesome title" /></head>
-<body><header class="site-header" role="banner">
-
-  <div class="wrapper"><a class="site-title" rel="author" href="/">Your awesome title</a><nav class="site-nav">
-        <input type="checkbox" id="nav-trigger" class="nav-trigger" />
-        <label for="nav-trigger">
-          <span class="menu-icon">
-            <svg viewBox="0 0 18 15" width="18px" height="15px">
-              <path d="M18,1.484c0,0.82-0.665,1.484-1.484,1.484H1.484C0.665,2.969,0,2.304,0,1.484l0,0C0,0.665,0.665,0,1.484,0 h15.032C17.335,0,18,0.665,18,1.484L18,1.484z M18,7.516C18,8.335,17.335,9,16.516,9H1.484C0.665,9,0,8.335,0,7.516l0,0 c0-0.82,0.665-1.484,1.484-1.484h15.032C17.335,6.031,18,6.696,18,7.516L18,7.516z M18,13.516C18,14.335,17.335,15,16.516,15H1.484 C0.665,15,0,14.335,0,13.516l0,0c0-0.82,0.665-1.483,1.484-1.483h15.032C17.335,12.031,18,12.695,18,13.516L18,13.516z"/>
-            </svg>
-          </span>
-        </label>
-
-        <div class="trigger"><a class="page-link" href="/about/">About</a><a class="page-link" href="/large-scale-demo/">Large scale demo</a><a class="page-link" href="/main-training/">Main training</a></div>
-      </nav></div>
-</header>
-<main class="page-content" aria-label="Content">
-      <div class="wrapper">
-        <div class="home">
-<h2 class="post-list-heading">Posts</h2>
-    <ul class="post-list"><li><span class="post-meta">Oct 19, 2021</span>
-        <h3>
-          <a class="post-link" href="/jekyll/update/2021/10/19/welcome-to-jekyll.html">
-            Welcome to Jekyll!
-          </a>
-        </h3></li></ul>
-
-    <p class="rss-subscribe">subscribe <a href="/feed.xml">via RSS</a></p></div>
-
-      </div>
-    </main><footer class="site-footer h-card">
-  <data class="u-url" href="/"></data>
-
-  <div class="wrapper">
-
-    <h2 class="footer-heading">Your awesome title</h2>
-
-    <div class="footer-col-wrapper">
-      <div class="footer-col footer-col-1">
-        <ul class="contact-list">
-          <li class="p-name">Your awesome title</li><li><a class="u-email" href="mailto:your-email@example.com">your-email@example.com</a></li></ul>
-      </div>
-
-      <div class="footer-col footer-col-2"><ul class="social-media-list"><li><a href="https://github.com/jekyll"><svg class="svg-icon"><use xlink:href="/assets/minima-social-icons.svg#github"></use></svg> <span class="username">jekyll</span></a></li><li><a href="https://www.twitter.com/jekyllrb"><svg class="svg-icon"><use xlink:href="/assets/minima-social-icons.svg#twitter"></use></svg> <span class="username">jekyllrb</span></a></li></ul>
-</div>
-
-      <div class="footer-col footer-col-3">
-        <p>Write an awesome description for your new site here. You can edit this line in _config.yml. It will appear in your document head meta (for Google search results) and in your feed.xml site description.</p>
-      </div>
-    </div>
-
-  </div>
-
-</footer>
-</body>
-
-</html>
diff --git a/spaces/ShreyashNadage/InvestmentCopilot/PatternRecognition.py b/spaces/ShreyashNadage/InvestmentCopilot/PatternRecognition.py
deleted file mode 100644
index b6e37de98868c71d20bcfbf0f4fe35ee8f78633f..0000000000000000000000000000000000000000
--- a/spaces/ShreyashNadage/InvestmentCopilot/PatternRecognition.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import ta
-import pandas as pd
-import numpy as np
-
-def GetPatternForData(stock_data_df):
-    candle_name_list = ta.get_function_groups()['Pattern Recognition']
-    tech_analysis_df = stock_data_df.iloc[-10:].copy()
-    op_df = tech_analysis_df.Open
-    hi_df = tech_analysis_df.High
-    lo_df = tech_analysis_df.Low
-    cl_df = tech_analysis_df.Close
-
-    for candle in candle_name_list:
-        tech_analysis_df[candle] = getattr(ta, candle)(op_df, hi_df, lo_df, cl_df)
-
-    result = pd.DataFrame(tech_analysis_df[['Date']+candle_name_list].sum(), columns=['Count'])
-    filtered_results = result[result.Count != 0]
-
-    if filtered_results.empty:
-        return None, tech_analysis_df
-    else:
-        return filtered_results[filtered_results.Count == filtered_results.Count.max()].index[0], tech_analysis_df
-
-    return None, tech_analysis_df
-
-def ComputeChaikinADSignal(stock_data_df):
-    ADOSC_data = stock_data_df.copy()
-    ADOSC_data['ADOSC'] = ta.ADOSC(ADOSC_data.High, ADOSC_data.Low, ADOSC_data.Close, ADOSC_data.Volume,
-                                   fastperiod=3, slowperiod=10)
-    ADOSC_data.dropna(inplace=True)
-    ADOSC_data['ADOSC_chg'] = np.log(ADOSC_data['ADOSC']/ADOSC_data['ADOSC'].shift(1))
-    ADOSC_data.dropna(inplace=True)
-    return ADOSC_data
-
-def ComputeMACDSignal(stock_data_df):
-    macd_data_df = stock_data_df.copy()
-    macd_data_df['macd'], macd_data_df['macdsignal'], macd_data_df['macdhist'] =\
-        ta.MACD(macd_data_df.Close, fastperiod=12, slowperiod=26, signalperiod=9)
-    macd_data_df.dropna(inplace=True)
-    return macd_data_df
-
diff --git a/spaces/SuSung-boy/LoRA-DreamBooth-Training-UI/app_upload.py b/spaces/SuSung-boy/LoRA-DreamBooth-Training-UI/app_upload.py
deleted file mode 100644
index b2465fa1f13425e05bd638cfe330b47ed7bd53e2..0000000000000000000000000000000000000000
--- a/spaces/SuSung-boy/LoRA-DreamBooth-Training-UI/app_upload.py
+++ /dev/null
@@ -1,100 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import pathlib
-
-import gradio as gr
-import slugify
-
-from constants import UploadTarget
-from uploader import Uploader
-from utils import find_exp_dirs
-
-
-class LoRAModelUploader(Uploader):
-    def upload_lora_model(
-        self,
-        folder_path: str,
-        repo_name: str,
-        upload_to: str,
-        private: bool,
-        delete_existing_repo: bool,
-    ) -> str:
-        if not folder_path:
-            raise ValueError
-        if not repo_name:
-            repo_name = pathlib.Path(folder_path).name
-        repo_name = slugify.slugify(repo_name)
-
-        if upload_to == UploadTarget.PERSONAL_PROFILE.value:
-            organization = ''
-        elif upload_to == UploadTarget.LORA_LIBRARY.value:
-            organization = 'lora-library'
-        else:
-            raise ValueError
-
-        return self.upload(folder_path,
-                           repo_name,
-                           organization=organization,
-                           private=private,
-                           delete_existing_repo=delete_existing_repo)
-
-
-def load_local_lora_model_list() -> dict:
-    choices = find_exp_dirs(ignore_repo=True)
-    return gr.update(choices=choices, value=choices[0] if choices else None)
-
-
-def create_upload_demo(hf_token: str | None) -> gr.Blocks:
-    uploader = LoRAModelUploader(hf_token)
-    model_dirs = find_exp_dirs(ignore_repo=True)
-
-    with gr.Blocks() as demo:
-        with gr.Box():
-            gr.Markdown('Local Models')
-            reload_button = gr.Button('Reload Model List')
-            model_dir = gr.Dropdown(
-                label='Model names',
-                choices=model_dirs,
-                value=model_dirs[0] if model_dirs else None)
-        with gr.Box():
-            gr.Markdown('Upload Settings')
-            with gr.Row():
-                use_private_repo = gr.Checkbox(label='Private', value=True)
-                delete_existing_repo = gr.Checkbox(
-                    label='Delete existing repo of the same name', value=False)
-            upload_to = gr.Radio(label='Upload to',
-                                 choices=[_.value for _ in UploadTarget],
-                                 value=UploadTarget.LORA_LIBRARY.value)
-            model_name = gr.Textbox(label='Model Name')
-        upload_button = gr.Button('Upload')
-        gr.Markdown('''
-            - You can upload your trained model to your personal profile (i.e. https://huggingface.co/{your_username}/{model_name}) or to the public [LoRA Concepts Library](https://huggingface.co/lora-library) (i.e. https://huggingface.co/lora-library/{model_name}).
-            ''')
-        with gr.Box():
-            gr.Markdown('Output message')
-            output_message = gr.Markdown()
-
-        reload_button.click(fn=load_local_lora_model_list,
-                            inputs=None,
-                            outputs=model_dir)
-        upload_button.click(fn=uploader.upload_lora_model,
-                            inputs=[
-                                model_dir,
-                                model_name,
-                                upload_to,
-                                use_private_repo,
-                                delete_existing_repo,
-                            ],
-                            outputs=output_message)
-
-    return demo
-
-
-if __name__ == '__main__':
-    import os
-
-    hf_token = os.getenv('HF_TOKEN')
-    demo = create_upload_demo(hf_token)
-    demo.queue(max_size=1).launch(share=False)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/dataclasses_json/utils.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/dataclasses_json/utils.py
deleted file mode 100644
index 3005f5cb31ee3161aa28b9c8883316d1adfa657f..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/dataclasses_json/utils.py
+++ /dev/null
@@ -1,205 +0,0 @@
-import inspect
-import sys
-from datetime import datetime, timezone
-from typing import (Collection, Mapping, Optional, TypeVar, Any, Type, Tuple,
-                    Union)
-
-
-def _get_type_cons(type_):
-    """More spaghetti logic for 3.6 vs. 3.7"""
-    if sys.version_info.minor == 6:
-        try:
-            cons = type_.__extra__
-        except AttributeError:
-            try:
-                cons = type_.__origin__
-            except AttributeError:
-                cons = type_
-            else:
-                cons = type_ if cons is None else cons
-        else:
-            try:
-                cons = type_.__origin__ if cons is None else cons
-            except AttributeError:
-                cons = type_
-    else:
-        cons = type_.__origin__
-    return cons
-
-
-_NO_TYPE_ORIGIN = object()
-
-
-def _get_type_origin(type_):
-    """Some spaghetti logic to accommodate differences between 3.6 and 3.7 in
-    the typing api"""
-    try:
-        origin = type_.__origin__
-    except AttributeError:
-        # Issue #341 and PR #346:
-        # For some cases, the type_.__origin__ exists but is set to None
-        origin = _NO_TYPE_ORIGIN
-
-    if sys.version_info.minor == 6:
-        try:
-            origin = type_.__extra__
-        except AttributeError:
-            origin = type_
-        else:
-            origin = type_ if origin in (None, _NO_TYPE_ORIGIN) else origin
-    elif origin is _NO_TYPE_ORIGIN:
-        origin = type_
-    return origin
-
-
-def _hasargs(type_, *args):
-    try:
-        res = all(arg in type_.__args__ for arg in args)
-    except AttributeError:
-        return False
-    except TypeError:
-        if (type_.__args__ is None):
-            return False
-        else:
-            raise
-    else:
-        return res
-
-
-class _NoArgs(object):
-    def __bool__(self):
-        return False
-
-    def __len__(self):
-        return 0
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        raise StopIteration
-
-
-_NO_ARGS = _NoArgs()
-
-
-def _get_type_args(tp: Type, default: Tuple[Type, ...] = _NO_ARGS) -> \
-        Union[Tuple[Type, ...], _NoArgs]:
-    if hasattr(tp, '__args__'):
-        if tp.__args__ is not None:
-            return tp.__args__
-    return default
-
-
-def _get_type_arg_param(tp: Type, index: int) -> Union[Type, _NoArgs]:
-    _args = _get_type_args(tp)
-    if _args is not _NO_ARGS:
-        try:
-            return _args[index]
-        except (TypeError, IndexError, NotImplementedError):
-            pass
-
-    return _NO_ARGS
-
-
-def _isinstance_safe(o, t):
-    try:
-        result = isinstance(o, t)
-    except Exception:
-        return False
-    else:
-        return result
-
-
-def _issubclass_safe(cls, classinfo):
-    try:
-        return issubclass(cls, classinfo)
-    except Exception:
-        return (_is_new_type_subclass_safe(cls, classinfo)
-                if _is_new_type(cls)
-                else False)
-
-
-def _is_new_type_subclass_safe(cls, classinfo):
-    super_type = getattr(cls, "__supertype__", None)
-
-    if super_type:
-        return _is_new_type_subclass_safe(super_type, classinfo)
-
-    try:
-        return issubclass(cls, classinfo)
-    except Exception:
-        return False
-
-
-def _is_new_type(type_):
-    return inspect.isfunction(type_) and hasattr(type_, "__supertype__")
-
-
-def _is_optional(type_):
-    return (_issubclass_safe(type_, Optional) or
-            _hasargs(type_, type(None)) or
-            type_ is Any)
-
-
-def _is_mapping(type_):
-    return _issubclass_safe(_get_type_origin(type_), Mapping)
-
-
-def _is_collection(type_):
-    return _issubclass_safe(_get_type_origin(type_), Collection)
-
-
-def _is_nonstr_collection(type_):
-    return (_issubclass_safe(_get_type_origin(type_), Collection)
-            and not _issubclass_safe(type_, str))
-
-
-def _timestamp_to_dt_aware(timestamp: float):
-    tz = datetime.now(timezone.utc).astimezone().tzinfo
-    dt = datetime.fromtimestamp(timestamp, tz=tz)
-    return dt
-
-
-def _undefined_parameter_action_safe(cls):
-    try:
-        if cls.dataclass_json_config is None:
-            return
-        action_enum = cls.dataclass_json_config['undefined']
-    except (AttributeError, KeyError):
-        return
-
-    if action_enum is None or action_enum.value is None:
-        return
-
-    return action_enum
-
-
-def _handle_undefined_parameters_safe(cls, kvs, usage: str):
-    """
-    Checks if an undefined parameters action is defined and performs the
-    according action.
-    """
-    undefined_parameter_action = _undefined_parameter_action_safe(cls)
-    usage = usage.lower()
-    if undefined_parameter_action is None:
-        return kvs if usage != "init" else cls.__init__
-    if usage == "from":
-        return undefined_parameter_action.value.handle_from_dict(cls=cls,
-                                                                 kvs=kvs)
-    elif usage == "to":
-        return undefined_parameter_action.value.handle_to_dict(obj=cls,
-                                                               kvs=kvs)
-    elif usage == "dump":
-        return undefined_parameter_action.value.handle_dump(obj=cls)
-    elif usage == "init":
-        return undefined_parameter_action.value.create_init(obj=cls)
-    else:
-        raise ValueError(
-            f"usage must be one of ['to', 'from', 'dump', 'init'], "
-            f"but is '{usage}'")
-
-
-# Define a type for the CatchAll field
-# https://stackoverflow.com/questions/59360567/define-a-custom-type-that-behaves-like-typing-any
-CatchAllVar = TypeVar("CatchAllVar", bound=Mapping)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydev_bundle/pydev_versioncheck.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydev_bundle/pydev_versioncheck.py
deleted file mode 100644
index 70bf765f47fd301dfee00cc781d949bc23fa1811..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydev_bundle/pydev_versioncheck.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import sys
-
-def versionok_for_gui():
-    ''' Return True if running Python is suitable for GUI Event Integration and deeper IPython integration '''
-    # We require Python 2.6+ ...
-    if sys.hexversion < 0x02060000:
-        return False
-    # Or Python 3.2+
-    if sys.hexversion >= 0x03000000 and sys.hexversion < 0x03020000:
-        return False
-    # Not supported under Jython nor IronPython
-    if sys.platform.startswith("java") or sys.platform.startswith('cli'):
-        return False
-
-    return True
-
diff --git a/spaces/Superlang/ImageProcessor/annotator/normalbae/models/submodules/encoder.py b/spaces/Superlang/ImageProcessor/annotator/normalbae/models/submodules/encoder.py
deleted file mode 100644
index 7f7149ca3c0cf2b6e019105af7e645cfbb3eda11..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/normalbae/models/submodules/encoder.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class Encoder(nn.Module):
-    def __init__(self):
-        super(Encoder, self).__init__()
-
-        basemodel_name = 'tf_efficientnet_b5_ap'
-        print('Loading base model ()...'.format(basemodel_name), end='')
-        repo_path = os.path.join(os.path.dirname(__file__), 'efficientnet_repo')
-        basemodel = torch.hub.load(repo_path, basemodel_name, pretrained=False, source='local')
-        print('Done.')
-
-        # Remove last layer
-        print('Removing last two layers (global_pool & classifier).')
-        basemodel.global_pool = nn.Identity()
-        basemodel.classifier = nn.Identity()
-
-        self.original_model = basemodel
-
-    def forward(self, x):
-        features = [x]
-        for k, v in self.original_model._modules.items():
-            if (k == 'blocks'):
-                for ki, vi in v._modules.items():
-                    features.append(vi(features[-1]))
-            else:
-                features.append(v(features[-1]))
-        return features
-
-
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/utils/develop.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/utils/develop.py
deleted file mode 100644
index e8416984954f7b32fc269100620e3c0d0d0f9585..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/utils/develop.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-""" Utilities for developers only.
-These are not visible to users (not automatically imported). And should not
-appeared in docs."""
-# adapted from https://github.com/tensorpack/tensorpack/blob/master/tensorpack/utils/develop.py
-
-
-def create_dummy_class(klass, dependency, message=""):
-    """
-    When a dependency of a class is not available, create a dummy class which throws ImportError
-    when used.
-
-    Args:
-        klass (str): name of the class.
-        dependency (str): name of the dependency.
-        message: extra message to print
-    Returns:
-        class: a class object
-    """
-    err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass)
-    if message:
-        err = err + " " + message
-
-    class _DummyMetaClass(type):
-        # throw error on class attribute access
-        def __getattr__(_, __):  # noqa: B902
-            raise ImportError(err)
-
-    class _Dummy(object, metaclass=_DummyMetaClass):
-        # throw error on constructor
-        def __init__(self, *args, **kwargs):
-            raise ImportError(err)
-
-    return _Dummy
-
-
-def create_dummy_func(func, dependency, message=""):
-    """
-    When a dependency of a function is not available, create a dummy function which throws
-    ImportError when used.
-
-    Args:
-        func (str): name of the function.
-        dependency (str or list[str]): name(s) of the dependency.
-        message: extra message to print
-    Returns:
-        function: a function object
-    """
-    err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, func)
-    if message:
-        err = err + " " + message
-
-    if isinstance(dependency, (list, tuple)):
-        dependency = ",".join(dependency)
-
-    def _dummy(*args, **kwargs):
-        raise ImportError(err)
-
-    return _dummy
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/colorama/tests/winterm_test.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/colorama/tests/winterm_test.py
deleted file mode 100644
index d0955f9e608377940f0d548576964f2fcf3caf48..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/colorama/tests/winterm_test.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-import sys
-from unittest import TestCase, main, skipUnless
-
-try:
-    from unittest.mock import Mock, patch
-except ImportError:
-    from mock import Mock, patch
-
-from ..winterm import WinColor, WinStyle, WinTerm
-
-
-class WinTermTest(TestCase):
-
-    @patch('colorama.winterm.win32')
-    def testInit(self, mockWin32):
-        mockAttr = Mock()
-        mockAttr.wAttributes = 7 + 6 * 16 + 8
-        mockWin32.GetConsoleScreenBufferInfo.return_value = mockAttr
-        term = WinTerm()
-        self.assertEqual(term._fore, 7)
-        self.assertEqual(term._back, 6)
-        self.assertEqual(term._style, 8)
-
-    @skipUnless(sys.platform.startswith("win"), "requires Windows")
-    def testGetAttrs(self):
-        term = WinTerm()
-
-        term._fore = 0
-        term._back = 0
-        term._style = 0
-        self.assertEqual(term.get_attrs(), 0)
-
-        term._fore = WinColor.YELLOW
-        self.assertEqual(term.get_attrs(), WinColor.YELLOW)
-
-        term._back = WinColor.MAGENTA
-        self.assertEqual(
-            term.get_attrs(),
-            WinColor.YELLOW + WinColor.MAGENTA * 16)
-
-        term._style = WinStyle.BRIGHT
-        self.assertEqual(
-            term.get_attrs(),
-            WinColor.YELLOW + WinColor.MAGENTA * 16 + WinStyle.BRIGHT)
-
-    @patch('colorama.winterm.win32')
-    def testResetAll(self, mockWin32):
-        mockAttr = Mock()
-        mockAttr.wAttributes = 1 + 2 * 16 + 8
-        mockWin32.GetConsoleScreenBufferInfo.return_value = mockAttr
-        term = WinTerm()
-
-        term.set_console = Mock()
-        term._fore = -1
-        term._back = -1
-        term._style = -1
-
-        term.reset_all()
-
-        self.assertEqual(term._fore, 1)
-        self.assertEqual(term._back, 2)
-        self.assertEqual(term._style, 8)
-        self.assertEqual(term.set_console.called, True)
-
-    @skipUnless(sys.platform.startswith("win"), "requires Windows")
-    def testFore(self):
-        term = WinTerm()
-        term.set_console = Mock()
-        term._fore = 0
-
-        term.fore(5)
-
-        self.assertEqual(term._fore, 5)
-        self.assertEqual(term.set_console.called, True)
-
-    @skipUnless(sys.platform.startswith("win"), "requires Windows")
-    def testBack(self):
-        term = WinTerm()
-        term.set_console = Mock()
-        term._back = 0
-
-        term.back(5)
-
-        self.assertEqual(term._back, 5)
-        self.assertEqual(term.set_console.called, True)
-
-    @skipUnless(sys.platform.startswith("win"), "requires Windows")
-    def testStyle(self):
-        term = WinTerm()
-        term.set_console = Mock()
-        term._style = 0
-
-        term.style(22)
-
-        self.assertEqual(term._style, 22)
-        self.assertEqual(term.set_console.called, True)
-
-    @patch('colorama.winterm.win32')
-    def testSetConsole(self, mockWin32):
-        mockAttr = Mock()
-        mockAttr.wAttributes = 0
-        mockWin32.GetConsoleScreenBufferInfo.return_value = mockAttr
-        term = WinTerm()
-        term.windll = Mock()
-
-        term.set_console()
-
-        self.assertEqual(
-            mockWin32.SetConsoleTextAttribute.call_args,
-            ((mockWin32.STDOUT, term.get_attrs()), {})
-        )
-
-    @patch('colorama.winterm.win32')
-    def testSetConsoleOnStderr(self, mockWin32):
-        mockAttr = Mock()
-        mockAttr.wAttributes = 0
-        mockWin32.GetConsoleScreenBufferInfo.return_value = mockAttr
-        term = WinTerm()
-        term.windll = Mock()
-
-        term.set_console(on_stderr=True)
-
-        self.assertEqual(
-            mockWin32.SetConsoleTextAttribute.call_args,
-            ((mockWin32.STDERR, term.get_attrs()), {})
-        )
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/TencentARC/VLog/models/grit_src/grit/config.py b/spaces/TencentARC/VLog/models/grit_src/grit/config.py
deleted file mode 100644
index fabe7f0fbe1e41c6eb280f8f7d6ae2e9c4911135..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/grit/config.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from detectron2.config import CfgNode as CN
-
-
-def add_grit_config(cfg):
-    _C = cfg
-
-    _C.MODEL.BEAM_SIZE = 1
-    _C.MODEL.TRAIN_TASK = ["ObjectDet", "DenseCap"]
-    _C.MODEL.TEST_TASK = "DenseCap"  # This can be varied if the model is jointly trained on multiple tasks
-
-    _C.MODEL.ROI_BOX_HEAD.USE_BIAS = 0.0 # >= 0: not use
-    _C.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE = False
-
-    _C.MODEL.ROI_HEADS.MASK_WEIGHT = 1.0
-    _C.MODEL.ROI_HEADS.OBJECT_FEAT_POOLER_RES = 14
-    _C.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False
-
-    # Backbones
-    _C.MODEL.VIT_LAYERS = 12
-
-    # Text Decoder
-    _C.TEXT_DECODER = CN()
-    _C.TEXT_DECODER.VOCAB_SIZE = 30522
-    _C.TEXT_DECODER.HIDDEN_SIZE = 768
-    _C.TEXT_DECODER.NUM_LAYERS = 6
-    _C.TEXT_DECODER.ATTENTION_HEADS = 12
-    _C.TEXT_DECODER.FEEDFORWARD_SIZE = 768 * 4
-    
-    # Multi-dataset dataloader
-    _C.DATALOADER.DATASET_RATIO = [1, 1]  # sample ratio
-    _C.DATALOADER.DATASET_BS = 1
-    _C.DATALOADER.DATASET_INPUT_SIZE = [1024, 1024]
-    _C.DATALOADER.DATASET_INPUT_SCALE = [(0.1, 2.0), (0.1, 2.0)]
-    _C.DATALOADER.DATASET_MIN_SIZES = [(640, 800), (640, 800)]
-    _C.DATALOADER.DATASET_MAX_SIZES = [1333, 1333]
-    
-    _C.SOLVER.USE_CUSTOM_SOLVER = True
-    _C.SOLVER.OPTIMIZER = 'ADAMW'
-    _C.SOLVER.VIT_LAYER_DECAY = True
-    _C.SOLVER.VIT_LAYER_DECAY_RATE = 0.7
-
-    _C.INPUT.CUSTOM_AUG = 'EfficientDetResizeCrop'
-    _C.INPUT.TRAIN_SIZE = 1024
-    _C.INPUT.TEST_SIZE = 1024
-    _C.INPUT.SCALE_RANGE = (0.1, 2.)
-    # 'default' for fixed short / long edge
-    _C.INPUT.TEST_INPUT_TYPE = 'default' 
-
-    _C.FIND_UNUSED_PARAM = True
-    _C.USE_ACT_CHECKPOINT = True
\ No newline at end of file
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/utils/events.py b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/utils/events.py
deleted file mode 100644
index 5dee954bdd6ad7dc5ea999562d1d2b03c3a520d9..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/utils/events.py
+++ /dev/null
@@ -1,486 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import datetime
-import json
-import logging
-import os
-import time
-from collections import defaultdict
-from contextlib import contextmanager
-from typing import Optional
-import torch
-from fvcore.common.history_buffer import HistoryBuffer
-
-from detectron2.utils.file_io import PathManager
-
-__all__ = [
-    "get_event_storage",
-    "JSONWriter",
-    "TensorboardXWriter",
-    "CommonMetricPrinter",
-    "EventStorage",
-]
-
-_CURRENT_STORAGE_STACK = []
-
-
-def get_event_storage():
-    """
-    Returns:
-        The :class:`EventStorage` object that's currently being used.
-        Throws an error if no :class:`EventStorage` is currently enabled.
-    """
-    assert len(
-        _CURRENT_STORAGE_STACK
-    ), "get_event_storage() has to be called inside a 'with EventStorage(...)' context!"
-    return _CURRENT_STORAGE_STACK[-1]
-
-
-class EventWriter:
-    """
-    Base class for writers that obtain events from :class:`EventStorage` and process them.
-    """
-
-    def write(self):
-        raise NotImplementedError
-
-    def close(self):
-        pass
-
-
-class JSONWriter(EventWriter):
-    """
-    Write scalars to a json file.
-
-    It saves scalars as one json per line (instead of a big json) for easy parsing.
-
-    Examples parsing such a json file:
-    ::
-        $ cat metrics.json | jq -s '.[0:2]'
-        [
-          {
-            "data_time": 0.008433341979980469,
-            "iteration": 19,
-            "loss": 1.9228371381759644,
-            "loss_box_reg": 0.050025828182697296,
-            "loss_classifier": 0.5316952466964722,
-            "loss_mask": 0.7236229181289673,
-            "loss_rpn_box": 0.0856662318110466,
-            "loss_rpn_cls": 0.48198649287223816,
-            "lr": 0.007173333333333333,
-            "time": 0.25401854515075684
-          },
-          {
-            "data_time": 0.007216215133666992,
-            "iteration": 39,
-            "loss": 1.282649278640747,
-            "loss_box_reg": 0.06222952902317047,
-            "loss_classifier": 0.30682939291000366,
-            "loss_mask": 0.6970193982124329,
-            "loss_rpn_box": 0.038663312792778015,
-            "loss_rpn_cls": 0.1471673548221588,
-            "lr": 0.007706666666666667,
-            "time": 0.2490077018737793
-          }
-        ]
-
-        $ cat metrics.json | jq '.loss_mask'
-        0.7126231789588928
-        0.689423680305481
-        0.6776131987571716
-        ...
-
-    """
-
-    def __init__(self, json_file, window_size=20):
-        """
-        Args:
-            json_file (str): path to the json file. New data will be appended if the file exists.
-            window_size (int): the window size of median smoothing for the scalars whose
-                `smoothing_hint` are True.
-        """
-        self._file_handle = PathManager.open(json_file, "a")
-        self._window_size = window_size
-        self._last_write = -1
-
-    def write(self):
-        storage = get_event_storage()
-        to_save = defaultdict(dict)
-
-        for k, (v, iter) in storage.latest_with_smoothing_hint(self._window_size).items():
-            # keep scalars that have not been written
-            if iter <= self._last_write:
-                continue
-            to_save[iter][k] = v
-        if len(to_save):
-            all_iters = sorted(to_save.keys())
-            self._last_write = max(all_iters)
-
-        for itr, scalars_per_iter in to_save.items():
-            scalars_per_iter["iteration"] = itr
-            self._file_handle.write(json.dumps(scalars_per_iter, sort_keys=True) + "\n")
-        self._file_handle.flush()
-        try:
-            os.fsync(self._file_handle.fileno())
-        except AttributeError:
-            pass
-
-    def close(self):
-        self._file_handle.close()
-
-
-class TensorboardXWriter(EventWriter):
-    """
-    Write all scalars to a tensorboard file.
-    """
-
-    def __init__(self, log_dir: str, window_size: int = 20, **kwargs):
-        """
-        Args:
-            log_dir (str): the directory to save the output events
-            window_size (int): the scalars will be median-smoothed by this window size
-
-            kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`
-        """
-        self._window_size = window_size
-        from torch.utils.tensorboard import SummaryWriter
-
-        self._writer = SummaryWriter(log_dir, **kwargs)
-        self._last_write = -1
-
-    def write(self):
-        storage = get_event_storage()
-        new_last_write = self._last_write
-        for k, (v, iter) in storage.latest_with_smoothing_hint(self._window_size).items():
-            if iter > self._last_write:
-                self._writer.add_scalar(k, v, iter)
-                new_last_write = max(new_last_write, iter)
-        self._last_write = new_last_write
-
-        # storage.put_{image,histogram} is only meant to be used by
-        # tensorboard writer. So we access its internal fields directly from here.
-        if len(storage._vis_data) >= 1:
-            for img_name, img, step_num in storage._vis_data:
-                self._writer.add_image(img_name, img, step_num)
-            # Storage stores all image data and rely on this writer to clear them.
-            # As a result it assumes only one writer will use its image data.
-            # An alternative design is to let storage store limited recent
-            # data (e.g. only the most recent image) that all writers can access.
-            # In that case a writer may not see all image data if its period is long.
-            storage.clear_images()
-
-        if len(storage._histograms) >= 1:
-            for params in storage._histograms:
-                self._writer.add_histogram_raw(**params)
-            storage.clear_histograms()
-
-    def close(self):
-        if hasattr(self, "_writer"):  # doesn't exist when the code fails at import
-            self._writer.close()
-
-
-class CommonMetricPrinter(EventWriter):
-    """
-    Print **common** metrics to the terminal, including
-    iteration time, ETA, memory, all losses, and the learning rate.
-    It also applies smoothing using a window of 20 elements.
-
-    It's meant to print common metrics in common ways.
-    To print something in more customized ways, please implement a similar printer by yourself.
-    """
-
-    def __init__(self, max_iter: Optional[int] = None, window_size: int = 20):
-        """
-        Args:
-            max_iter: the maximum number of iterations to train.
-                Used to compute ETA. If not given, ETA will not be printed.
-            window_size (int): the losses will be median-smoothed by this window size
-        """
-        self.logger = logging.getLogger(__name__)
-        self._max_iter = max_iter
-        self._window_size = window_size
-        self._last_write = None  # (step, time) of last call to write(). Used to compute ETA
-
-    def _get_eta(self, storage) -> Optional[str]:
-        if self._max_iter is None:
-            return ""
-        iteration = storage.iter
-        try:
-            eta_seconds = storage.history("time").median(1000) * (self._max_iter - iteration - 1)
-            storage.put_scalar("eta_seconds", eta_seconds, smoothing_hint=False)
-            return str(datetime.timedelta(seconds=int(eta_seconds)))
-        except KeyError:
-            # estimate eta on our own - more noisy
-            eta_string = None
-            if self._last_write is not None:
-                estimate_iter_time = (time.perf_counter() - self._last_write[1]) / (
-                    iteration - self._last_write[0]
-                )
-                eta_seconds = estimate_iter_time * (self._max_iter - iteration - 1)
-                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
-            self._last_write = (iteration, time.perf_counter())
-            return eta_string
-
-    def write(self):
-        storage = get_event_storage()
-        iteration = storage.iter
-        if iteration == self._max_iter:
-            # This hook only reports training progress (loss, ETA, etc) but not other data,
-            # therefore do not write anything after training succeeds, even if this method
-            # is called.
-            return
-
-        try:
-            data_time = storage.history("data_time").avg(20)
-        except KeyError:
-            # they may not exist in the first few iterations (due to warmup)
-            # or when SimpleTrainer is not used
-            data_time = None
-        try:
-            iter_time = storage.history("time").global_avg()
-        except KeyError:
-            iter_time = None
-        try:
-            lr = "{:.5g}".format(storage.history("lr").latest())
-        except KeyError:
-            lr = "N/A"
-
-        eta_string = self._get_eta(storage)
-
-        if torch.cuda.is_available():
-            max_mem_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0
-        else:
-            max_mem_mb = None
-
-        # NOTE: max_mem is parsed by grep in "dev/parse_results.sh"
-        self.logger.info(
-            " {eta}iter: {iter}  {losses}  {time}{data_time}lr: {lr}  {memory}".format(
-                eta=f"eta: {eta_string}  " if eta_string else "",
-                iter=iteration,
-                losses="  ".join(
-                    [
-                        "{}: {:.4g}".format(k, v.median(self._window_size))
-                        for k, v in storage.histories().items()
-                        if "loss" in k
-                    ]
-                ),
-                time="time: {:.4f}  ".format(iter_time) if iter_time is not None else "",
-                data_time="data_time: {:.4f}  ".format(data_time) if data_time is not None else "",
-                lr=lr,
-                memory="max_mem: {:.0f}M".format(max_mem_mb) if max_mem_mb is not None else "",
-            )
-        )
-
-
-class EventStorage:
-    """
-    The user-facing class that provides metric storage functionalities.
-
-    In the future we may add support for storing / logging other types of data if needed.
-    """
-
-    def __init__(self, start_iter=0):
-        """
-        Args:
-            start_iter (int): the iteration number to start with
-        """
-        self._history = defaultdict(HistoryBuffer)
-        self._smoothing_hints = {}
-        self._latest_scalars = {}
-        self._iter = start_iter
-        self._current_prefix = ""
-        self._vis_data = []
-        self._histograms = []
-
-    def put_image(self, img_name, img_tensor):
-        """
-        Add an `img_tensor` associated with `img_name`, to be shown on
-        tensorboard.
-
-        Args:
-            img_name (str): The name of the image to put into tensorboard.
-            img_tensor (torch.Tensor or numpy.array): An `uint8` or `float`
-                Tensor of shape `[channel, height, width]` where `channel` is
-                3. The image format should be RGB. The elements in img_tensor
-                can either have values in [0, 1] (float32) or [0, 255] (uint8).
-                The `img_tensor` will be visualized in tensorboard.
-        """
-        self._vis_data.append((img_name, img_tensor, self._iter))
-
-    def put_scalar(self, name, value, smoothing_hint=True):
-        """
-        Add a scalar `value` to the `HistoryBuffer` associated with `name`.
-
-        Args:
-            smoothing_hint (bool): a 'hint' on whether this scalar is noisy and should be
-                smoothed when logged. The hint will be accessible through
-                :meth:`EventStorage.smoothing_hints`.  A writer may ignore the hint
-                and apply custom smoothing rule.
-
-                It defaults to True because most scalars we save need to be smoothed to
-                provide any useful signal.
-        """
-        name = self._current_prefix + name
-        history = self._history[name]
-        value = float(value)
-        history.update(value, self._iter)
-        self._latest_scalars[name] = (value, self._iter)
-
-        existing_hint = self._smoothing_hints.get(name)
-        if existing_hint is not None:
-            assert (
-                existing_hint == smoothing_hint
-            ), "Scalar {} was put with a different smoothing_hint!".format(name)
-        else:
-            self._smoothing_hints[name] = smoothing_hint
-
-    def put_scalars(self, *, smoothing_hint=True, **kwargs):
-        """
-        Put multiple scalars from keyword arguments.
-
-        Examples:
-
-            storage.put_scalars(loss=my_loss, accuracy=my_accuracy, smoothing_hint=True)
-        """
-        for k, v in kwargs.items():
-            self.put_scalar(k, v, smoothing_hint=smoothing_hint)
-
-    def put_histogram(self, hist_name, hist_tensor, bins=1000):
-        """
-        Create a histogram from a tensor.
-
-        Args:
-            hist_name (str): The name of the histogram to put into tensorboard.
-            hist_tensor (torch.Tensor): A Tensor of arbitrary shape to be converted
-                into a histogram.
-            bins (int): Number of histogram bins.
-        """
-        ht_min, ht_max = hist_tensor.min().item(), hist_tensor.max().item()
-
-        # Create a histogram with PyTorch
-        hist_counts = torch.histc(hist_tensor, bins=bins)
-        hist_edges = torch.linspace(start=ht_min, end=ht_max, steps=bins + 1, dtype=torch.float32)
-
-        # Parameter for the add_histogram_raw function of SummaryWriter
-        hist_params = dict(
-            tag=hist_name,
-            min=ht_min,
-            max=ht_max,
-            num=len(hist_tensor),
-            sum=float(hist_tensor.sum()),
-            sum_squares=float(torch.sum(hist_tensor ** 2)),
-            bucket_limits=hist_edges[1:].tolist(),
-            bucket_counts=hist_counts.tolist(),
-            global_step=self._iter,
-        )
-        self._histograms.append(hist_params)
-
-    def history(self, name):
-        """
-        Returns:
-            HistoryBuffer: the scalar history for name
-        """
-        ret = self._history.get(name, None)
-        if ret is None:
-            raise KeyError("No history metric available for {}!".format(name))
-        return ret
-
-    def histories(self):
-        """
-        Returns:
-            dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars
-        """
-        return self._history
-
-    def latest(self):
-        """
-        Returns:
-            dict[str -> (float, int)]: mapping from the name of each scalar to the most
-                recent value and the iteration number its added.
-        """
-        return self._latest_scalars
-
-    def latest_with_smoothing_hint(self, window_size=20):
-        """
-        Similar to :meth:`latest`, but the returned values
-        are either the un-smoothed original latest value,
-        or a median of the given window_size,
-        depend on whether the smoothing_hint is True.
-
-        This provides a default behavior that other writers can use.
-        """
-        result = {}
-        for k, (v, itr) in self._latest_scalars.items():
-            result[k] = (
-                self._history[k].median(window_size) if self._smoothing_hints[k] else v,
-                itr,
-            )
-        return result
-
-    def smoothing_hints(self):
-        """
-        Returns:
-            dict[name -> bool]: the user-provided hint on whether the scalar
-                is noisy and needs smoothing.
-        """
-        return self._smoothing_hints
-
-    def step(self):
-        """
-        User should either: (1) Call this function to increment storage.iter when needed. Or
-        (2) Set `storage.iter` to the correct iteration number before each iteration.
-
-        The storage will then be able to associate the new data with an iteration number.
-        """
-        self._iter += 1
-
-    @property
-    def iter(self):
-        """
-        Returns:
-            int: The current iteration number. When used together with a trainer,
-                this is ensured to be the same as trainer.iter.
-        """
-        return self._iter
-
-    @iter.setter
-    def iter(self, val):
-        self._iter = int(val)
-
-    @property
-    def iteration(self):
-        # for backward compatibility
-        return self._iter
-
-    def __enter__(self):
-        _CURRENT_STORAGE_STACK.append(self)
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        assert _CURRENT_STORAGE_STACK[-1] == self
-        _CURRENT_STORAGE_STACK.pop()
-
-    @contextmanager
-    def name_scope(self, name):
-        """
-        Yields:
-            A context within which all the events added to this storage
-            will be prefixed by the name scope.
-        """
-        old_prefix = self._current_prefix
-        self._current_prefix = name.rstrip("/") + "/"
-        yield
-        self._current_prefix = old_prefix
-
-    def clear_images(self):
-        """
-        Delete all the stored images for visualization. This should be called
-        after images are written to tensorboard.
-        """
-        self._vis_data = []
-
-    def clear_histograms(self):
-        """
-        Delete all the stored histograms for visualization.
-        This should be called after histograms are written to tensorboard.
-        """
-        self._histograms = []
diff --git a/spaces/Tetel/secondbing/EdgeGPT/ImageGen.py b/spaces/Tetel/secondbing/EdgeGPT/ImageGen.py
deleted file mode 100644
index f89b2a2c873fd3129c69afd757b17b0ef1a26595..0000000000000000000000000000000000000000
--- a/spaces/Tetel/secondbing/EdgeGPT/ImageGen.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Open pull requests and issues at https://github.com/acheong08/BingImageCreator
-import BingImageCreator
-
-ImageGen = BingImageCreator.ImageGen
-
-ImageGenAsync = BingImageCreator.ImageGenAsync
-
-main = BingImageCreator.main
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Thaweewat/ControlNet-Architecture/annotator/openpose/model.py b/spaces/Thaweewat/ControlNet-Architecture/annotator/openpose/model.py
deleted file mode 100644
index 5dfc80de827a17beccb9b0f3f7588545be78c9de..0000000000000000000000000000000000000000
--- a/spaces/Thaweewat/ControlNet-Architecture/annotator/openpose/model.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import torch
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-
-def make_layers(block, no_relu_layers):
-    layers = []
-    for layer_name, v in block.items():
-        if 'pool' in layer_name:
-            layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
-                                    padding=v[2])
-            layers.append((layer_name, layer))
-        else:
-            conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],
-                               kernel_size=v[2], stride=v[3],
-                               padding=v[4])
-            layers.append((layer_name, conv2d))
-            if layer_name not in no_relu_layers:
-                layers.append(('relu_'+layer_name, nn.ReLU(inplace=True)))
-
-    return nn.Sequential(OrderedDict(layers))
-
-class bodypose_model(nn.Module):
-    def __init__(self):
-        super(bodypose_model, self).__init__()
-
-        # these layers have no relu layer
-        no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\
-                          'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\
-                          'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\
-                          'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1']
-        blocks = {}
-        block0 = OrderedDict([
-                      ('conv1_1', [3, 64, 3, 1, 1]),
-                      ('conv1_2', [64, 64, 3, 1, 1]),
-                      ('pool1_stage1', [2, 2, 0]),
-                      ('conv2_1', [64, 128, 3, 1, 1]),
-                      ('conv2_2', [128, 128, 3, 1, 1]),
-                      ('pool2_stage1', [2, 2, 0]),
-                      ('conv3_1', [128, 256, 3, 1, 1]),
-                      ('conv3_2', [256, 256, 3, 1, 1]),
-                      ('conv3_3', [256, 256, 3, 1, 1]),
-                      ('conv3_4', [256, 256, 3, 1, 1]),
-                      ('pool3_stage1', [2, 2, 0]),
-                      ('conv4_1', [256, 512, 3, 1, 1]),
-                      ('conv4_2', [512, 512, 3, 1, 1]),
-                      ('conv4_3_CPM', [512, 256, 3, 1, 1]),
-                      ('conv4_4_CPM', [256, 128, 3, 1, 1])
-                  ])
-
-
-        # Stage 1
-        block1_1 = OrderedDict([
-                        ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]),
-                        ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]),
-                        ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]),
-                        ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]),
-                        ('conv5_5_CPM_L1', [512, 38, 1, 1, 0])
-                    ])
-
-        block1_2 = OrderedDict([
-                        ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]),
-                        ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]),
-                        ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]),
-                        ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]),
-                        ('conv5_5_CPM_L2', [512, 19, 1, 1, 0])
-                    ])
-        blocks['block1_1'] = block1_1
-        blocks['block1_2'] = block1_2
-
-        self.model0 = make_layers(block0, no_relu_layers)
-
-        # Stages 2 - 6
-        for i in range(2, 7):
-            blocks['block%d_1' % i] = OrderedDict([
-                    ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]),
-                    ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]),
-                    ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0])
-                ])
-
-            blocks['block%d_2' % i] = OrderedDict([
-                    ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]),
-                    ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]),
-                    ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0])
-                ])
-
-        for k in blocks.keys():
-            blocks[k] = make_layers(blocks[k], no_relu_layers)
-
-        self.model1_1 = blocks['block1_1']
-        self.model2_1 = blocks['block2_1']
-        self.model3_1 = blocks['block3_1']
-        self.model4_1 = blocks['block4_1']
-        self.model5_1 = blocks['block5_1']
-        self.model6_1 = blocks['block6_1']
-
-        self.model1_2 = blocks['block1_2']
-        self.model2_2 = blocks['block2_2']
-        self.model3_2 = blocks['block3_2']
-        self.model4_2 = blocks['block4_2']
-        self.model5_2 = blocks['block5_2']
-        self.model6_2 = blocks['block6_2']
-
-
-    def forward(self, x):
-
-        out1 = self.model0(x)
-
-        out1_1 = self.model1_1(out1)
-        out1_2 = self.model1_2(out1)
-        out2 = torch.cat([out1_1, out1_2, out1], 1)
-
-        out2_1 = self.model2_1(out2)
-        out2_2 = self.model2_2(out2)
-        out3 = torch.cat([out2_1, out2_2, out1], 1)
-
-        out3_1 = self.model3_1(out3)
-        out3_2 = self.model3_2(out3)
-        out4 = torch.cat([out3_1, out3_2, out1], 1)
-
-        out4_1 = self.model4_1(out4)
-        out4_2 = self.model4_2(out4)
-        out5 = torch.cat([out4_1, out4_2, out1], 1)
-
-        out5_1 = self.model5_1(out5)
-        out5_2 = self.model5_2(out5)
-        out6 = torch.cat([out5_1, out5_2, out1], 1)
-
-        out6_1 = self.model6_1(out6)
-        out6_2 = self.model6_2(out6)
-
-        return out6_1, out6_2
-
-class handpose_model(nn.Module):
-    def __init__(self):
-        super(handpose_model, self).__init__()
-
-        # these layers have no relu layer
-        no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\
-                          'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6']
-        # stage 1
-        block1_0 = OrderedDict([
-                ('conv1_1', [3, 64, 3, 1, 1]),
-                ('conv1_2', [64, 64, 3, 1, 1]),
-                ('pool1_stage1', [2, 2, 0]),
-                ('conv2_1', [64, 128, 3, 1, 1]),
-                ('conv2_2', [128, 128, 3, 1, 1]),
-                ('pool2_stage1', [2, 2, 0]),
-                ('conv3_1', [128, 256, 3, 1, 1]),
-                ('conv3_2', [256, 256, 3, 1, 1]),
-                ('conv3_3', [256, 256, 3, 1, 1]),
-                ('conv3_4', [256, 256, 3, 1, 1]),
-                ('pool3_stage1', [2, 2, 0]),
-                ('conv4_1', [256, 512, 3, 1, 1]),
-                ('conv4_2', [512, 512, 3, 1, 1]),
-                ('conv4_3', [512, 512, 3, 1, 1]),
-                ('conv4_4', [512, 512, 3, 1, 1]),
-                ('conv5_1', [512, 512, 3, 1, 1]),
-                ('conv5_2', [512, 512, 3, 1, 1]),
-                ('conv5_3_CPM', [512, 128, 3, 1, 1])
-            ])
-
-        block1_1 = OrderedDict([
-            ('conv6_1_CPM', [128, 512, 1, 1, 0]),
-            ('conv6_2_CPM', [512, 22, 1, 1, 0])
-        ])
-
-        blocks = {}
-        blocks['block1_0'] = block1_0
-        blocks['block1_1'] = block1_1
-
-        # stage 2-6
-        for i in range(2, 7):
-            blocks['block%d' % i] = OrderedDict([
-                    ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]),
-                    ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]),
-                    ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]),
-                    ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0])
-                ])
-
-        for k in blocks.keys():
-            blocks[k] = make_layers(blocks[k], no_relu_layers)
-
-        self.model1_0 = blocks['block1_0']
-        self.model1_1 = blocks['block1_1']
-        self.model2 = blocks['block2']
-        self.model3 = blocks['block3']
-        self.model4 = blocks['block4']
-        self.model5 = blocks['block5']
-        self.model6 = blocks['block6']
-
-    def forward(self, x):
-        out1_0 = self.model1_0(x)
-        out1_1 = self.model1_1(out1_0)
-        concat_stage2 = torch.cat([out1_1, out1_0], 1)
-        out_stage2 = self.model2(concat_stage2)
-        concat_stage3 = torch.cat([out_stage2, out1_0], 1)
-        out_stage3 = self.model3(concat_stage3)
-        concat_stage4 = torch.cat([out_stage3, out1_0], 1)
-        out_stage4 = self.model4(concat_stage4)
-        concat_stage5 = torch.cat([out_stage4, out1_0], 1)
-        out_stage5 = self.model5(concat_stage5)
-        concat_stage6 = torch.cat([out_stage5, out1_0], 1)
-        out_stage6 = self.model6(concat_stage6)
-        return out_stage6
-
-
diff --git a/spaces/Valerina128503/U_1/Dockerfile b/spaces/Valerina128503/U_1/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/Valerina128503/U_1/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM node:18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md* .env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Yqcloud.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Yqcloud.py
deleted file mode 100644
index ad5c3a4326c68ceb7ee012fbf5bc072da72a7e40..0000000000000000000000000000000000000000
--- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Yqcloud.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import os
-import time
-import requests
-
-from ...typing import sha256, Dict, get_type_hints
-url = 'https://chat9.yqcloud.top/'
-model = [
-    'gpt-3.5-turbo',
-]
-supports_stream = True
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, chatId: str, **kwargs):
-
-    headers = {
-        'authority': 'api.aichatos.cloud',
-        'origin': 'https://chat9.yqcloud.top',
-        'referer': 'https://chat9.yqcloud.top/',
-        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
-    }
-
-    json_data = {
-        'prompt': str(messages),
-        'userId': f'#/chat/{chatId}',
-        'network': True,
-        'apikey': '',
-        'system': '',
-        'withoutContext': False,
-    }
-    response = requests.post('https://api.aichatos.cloud/api/generateStream',
-                             headers=headers, json=json_data, stream=True)
-    for token in response.iter_content(chunk_size=2046):
-        yield (token.decode('utf-8'))
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join(
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/VideoCrafter/VideoCrafter/utils/utils.py b/spaces/VideoCrafter/VideoCrafter/utils/utils.py
deleted file mode 100644
index c73b93e006c4250161b427e4d1fff512ca046f7c..0000000000000000000000000000000000000000
--- a/spaces/VideoCrafter/VideoCrafter/utils/utils.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import importlib
-import numpy as np
-import cv2
-import torch
-import torch.distributed as dist
-
-
-def count_params(model, verbose=False):
-    total_params = sum(p.numel() for p in model.parameters())
-    if verbose:
-        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
-    return total_params
-
-
-def check_istarget(name, para_list):
-    """ 
-    name: full name of source para
-    para_list: partial name of target para 
-    """
-    istarget=False
-    for para in para_list:
-        if para in name:
-            return True
-    return istarget
-
-
-def instantiate_from_config(config):
-    if not "target" in config:
-        if config == '__is_first_stage__':
-            return None
-        elif config == "__is_unconditional__":
-            return None
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"])(**config.get("params", dict()))
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)
-
-
-def load_npz_from_dir(data_dir):
-    data = [np.load(os.path.join(data_dir, data_name))['arr_0'] for data_name in os.listdir(data_dir)]
-    data = np.concatenate(data, axis=0)
-    return data
-
-
-def load_npz_from_paths(data_paths):
-    data = [np.load(data_path)['arr_0'] for data_path in data_paths]
-    data = np.concatenate(data, axis=0)
-    return data   
-
-
-def resize_numpy_image(image, max_resolution=512 * 512, resize_short_edge=None):
-    h, w = image.shape[:2]
-    if resize_short_edge is not None:
-        k = resize_short_edge / min(h, w)
-    else:
-        k = max_resolution / (h * w)
-        k = k**0.5
-    h = int(np.round(h * k / 64)) * 64
-    w = int(np.round(w * k / 64)) * 64
-    image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4)
-    return image
-
-
-def setup_dist(args):
-    if dist.is_initialized():
-        return
-    torch.cuda.set_device(args.local_rank)
-    torch.distributed.init_process_group(
-        'nccl',
-        init_method='env://'
-    )
\ No newline at end of file
diff --git a/spaces/Weshden/Nsfw1/Dockerfile b/spaces/Weshden/Nsfw1/Dockerfile
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/modules/codebooks_patterns.py b/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/modules/codebooks_patterns.py
deleted file mode 100644
index c5b35cbea8cff84aa56116dbdd860fc72a913a13..0000000000000000000000000000000000000000
--- a/spaces/Wrathless/Dkrotzer-MusicalMagic/audiocraft/modules/codebooks_patterns.py
+++ /dev/null
@@ -1,539 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections import namedtuple
-from dataclasses import dataclass
-from functools import lru_cache
-import logging
-import typing as tp
-
-from abc import ABC, abstractmethod
-import torch
-
-LayoutCoord = namedtuple('LayoutCoord', ['t', 'q'])  # (timestep, codebook index)
-PatternLayout = tp.List[tp.List[LayoutCoord]]  # Sequence of coordinates
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class Pattern:
-    """Base implementation of a pattern over a sequence with multiple codebooks.
-
-    The codebook pattern consists in a layout, defining for each sequence step
-    the list of coordinates of each codebook timestep in the resulting interleaved sequence.
-    The first item of the pattern is always an empty list in order to properly insert a special token
-    to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern
-    and ``timesteps`` the number of timesteps corresponding to the original sequence.
-
-    The pattern provides convenient methods to build and revert interleaved sequences from it:
-    ``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T]
-        to the interleaved sequence of shape [B, K, S] applying the pattern, with S being the batch size,
-        K being the number of codebooks, T the number of original timesteps and S the number of sequence steps
-        for the output sequence. The unfilled positions are replaced with a special token and the built sequence
-        is returned along with a mask indicating valid tokens.
-    ``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment
-        of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask
-        to fill and specify invalid positions if needed.
-    See the dedicated methods for more details.
-    """
-    # Pattern layout, for each sequence step, we have a list of coordinates
-    # corresponding to the original codebook timestep and position.
-    # The first list is always an empty list in order to properly insert
-    # a special token to start with.
-    layout: PatternLayout
-    timesteps: int
-    n_q: int
-
-    def __post_init__(self):
-        assert len(self.layout) > 0
-        assert self.layout[0] == []
-        self._validate_layout()
-        self._build_reverted_sequence_scatter_indexes = lru_cache(100)(self._build_reverted_sequence_scatter_indexes)
-        self._build_pattern_sequence_scatter_indexes = lru_cache(100)(self._build_pattern_sequence_scatter_indexes)
-        logger.info("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout))
-
-    def _validate_layout(self):
-        """Runs checks on the layout to ensure a valid pattern is defined.
-        A pattern is considered invalid if:
-            - Multiple timesteps for a same codebook are defined in the same sequence step
-            - The timesteps for a given codebook are not in ascending order as we advance in the sequence
-              (this would mean that we have future timesteps before past timesteps).
-        """
-        q_timesteps = {q: 0 for q in range(self.n_q)}
-        for s, seq_coords in enumerate(self.layout):
-            if len(seq_coords) > 0:
-                qs = set()
-                for coord in seq_coords:
-                    qs.add(coord.q)
-                    last_q_timestep = q_timesteps[coord.q]
-                    assert coord.t >= last_q_timestep, \
-                        f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}"
-                    q_timesteps[coord.q] = coord.t
-                # each sequence step contains at max 1 coordinate per codebook
-                assert len(qs) == len(seq_coords), \
-                    f"Multiple entries for a same codebook are found at step {s}"
-
-    @property
-    def num_sequence_steps(self):
-        return len(self.layout) - 1
-
-    @property
-    def max_delay(self):
-        max_t_in_seq_coords = 0
-        for seq_coords in self.layout[1:]:
-            for coords in seq_coords:
-                max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1)
-        return max_t_in_seq_coords - self.timesteps
-
-    @property
-    def valid_layout(self):
-        valid_step = len(self.layout) - self.max_delay
-        return self.layout[:valid_step]
-
-    def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None):
-        """Get codebook coordinates in the layout that corresponds to the specified timestep t
-        and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step
-        and the actual codebook coordinates.
-        """
-        assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps"
-        if q is not None:
-            assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks"
-        coords = []
-        for s, seq_codes in enumerate(self.layout):
-            for code in seq_codes:
-                if code.t == t and (q is None or code.q == q):
-                    coords.append((s, code))
-        return coords
-
-    def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]:
-        return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)]
-
-    def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]:
-        steps_with_timesteps = self.get_steps_with_timestep(t, q)
-        return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None
-
-    def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool,
-                                                device: tp.Union[torch.device, str] = 'cpu'):
-        """Build scatter indexes corresponding to the pattern, up to the provided sequence_steps.
-
-        Args:
-            timesteps (int): Maximum number of timesteps steps to consider.
-            keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps.
-            device (Union[torch.device, str]): Device for created tensors.
-        Returns:
-            indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S].
-        """
-        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
-        assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern"
-        # use the proper layout based on whether we limit ourselves to valid steps only or not,
-        # note that using the valid_layout will result in a truncated sequence up to the valid steps
-        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
-        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
-        indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy()
-        mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy()
-        # fill indexes with last sequence step value that will correspond to our special token
-        # the last value is n_q * timesteps as we have flattened z and append special token as the last token
-        # which will correspond to the index: n_q * timesteps
-        indexes[:] = n_q * timesteps
-        # iterate over the pattern and fill scattered indexes and mask
-        for s, sequence_coords in enumerate(ref_layout):
-            for coords in sequence_coords:
-                if coords.t < timesteps:
-                    indexes[coords.q, s] = coords.t + coords.q * timesteps
-                    mask[coords.q, s] = 1
-        indexes = torch.from_numpy(indexes).to(device)
-        mask = torch.from_numpy(mask).to(device)
-        return indexes, mask
-
-    def build_pattern_sequence(self, z: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
-        """Build sequence corresponding to the pattern from the input tensor z.
-        The sequence is built using up to sequence_steps if specified, and non-pattern
-        coordinates are filled with the special token.
-
-        Args:
-            z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T].
-            special_token (int): Special token used to fill non-pattern coordinates in the new sequence.
-            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
-                Steps that are beyond valid steps will be replaced by the special_token in that case.
-        Returns:
-            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S
-                corresponding either to the sequence_steps if provided, otherwise to the length of the pattern.
-            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S].
-        """
-        B, K, T = z.shape
-        indexes, mask = self._build_pattern_sequence_scatter_indexes(
-            T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device)
-        )
-        z = z.view(B, -1)
-        # we append the special token as the last index of our flattened z tensor
-        z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1)
-        values = z[:, indexes.view(-1)]
-        values = values.view(B, K, indexes.shape[-1])
-        return values, indexes, mask
-
-    def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int,
-                                                 keep_only_valid_steps: bool = False,
-                                                 is_model_output: bool = False,
-                                                 device: tp.Union[torch.device, str] = 'cpu'):
-        """Builds scatter indexes required to retrieve the original multi-codebook sequence
-        from interleaving pattern.
-
-        Args:
-            sequence_steps (int): Sequence steps.
-            n_q (int): Number of codebooks.
-            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
-                Steps that are beyond valid steps will be replaced by the special_token in that case.
-            is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not.
-            device (Union[torch.device, str]): Device for created tensors.
-        Returns:
-            torch.Tensor: Indexes for reconstructing the output, of shape [K, T].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
-        """
-        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
-        # TODO(jade): Do we want to further truncate to only valid timesteps here as well?
-        timesteps = self.timesteps
-        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
-        assert sequence_steps <= len(ref_layout), \
-            f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}"
-
-        # ensure we take the appropriate indexes to keep the model output from the first special token as well
-        if is_model_output:
-            ref_layout = ref_layout[1:]
-
-        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
-        indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy()
-        mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy()
-        # fill indexes with last sequence step value that will correspond to our special token
-        indexes[:] = n_q * sequence_steps
-        for s, sequence_codes in enumerate(ref_layout):
-            if s < sequence_steps:
-                for code in sequence_codes:
-                    if code.t < timesteps:
-                        indexes[code.q, code.t] = s + code.q * sequence_steps
-                        mask[code.q, code.t] = 1
-        indexes = torch.from_numpy(indexes).to(device)
-        mask = torch.from_numpy(mask).to(device)
-        return indexes, mask
-
-    def revert_pattern_sequence(self, s: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
-        """Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving.
-        The sequence is reverted using up to timesteps if specified, and non-pattern coordinates
-        are filled with the special token.
-
-        Args:
-            s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S].
-            special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence.
-        Returns:
-            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, T] with T
-                corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise.
-            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
-        """
-        B, K, S = s.shape
-        indexes, mask = self._build_reverted_sequence_scatter_indexes(
-            S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device)
-        )
-        s = s.view(B, -1)
-        # we append the special token as the last index of our flattened z tensor
-        s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1)
-        values = s[:, indexes.view(-1)]
-        values = values.view(B, K, indexes.shape[-1])
-        return values, indexes, mask
-
-    def revert_pattern_logits(self, logits: torch.Tensor, special_token: float, keep_only_valid_steps: bool = False):
-        """Revert model logits obtained on a sequence built from the pattern
-        back to a tensor matching the original sequence.
-
-        This method is similar to ``revert_pattern_sequence`` with the following specificities:
-        1. It is designed to work with the extra cardinality dimension
-        2. We return the logits for the first sequence item that matches the special_token and
-        which matching target in the original sequence is the first item of the sequence,
-        while we skip the last logits as there is no matching target
-        """
-        B, card, K, S = logits.shape
-        indexes, mask = self._build_reverted_sequence_scatter_indexes(
-            S, K, keep_only_valid_steps, is_model_output=True, device=logits.device
-        )
-        logits = logits.reshape(B, card, -1)
-        # we append the special token as the last index of our flattened z tensor
-        logits = torch.cat([logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1)  # [B, card, K x S]
-        values = logits[:, :, indexes.view(-1)]
-        values = values.view(B, card, K, indexes.shape[-1])
-        return values, indexes, mask
-
-
-class CodebooksPatternProvider(ABC):
-    """Abstraction around providing pattern for interleaving codebooks.
-
-    The CodebooksPatternProvider abstraction allows to implement various strategies to
-    define interleaving pattern of sequences composed of multiple codebooks. For a given
-    number of codebooks `n_q`, the pattern provider can generate a specified pattern
-    corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern
-    can be used to construct a new sequence from the original codes respecting the specified
-    pattern. The pattern is defined as a list of list of code coordinates, code coordinate
-    being a tuple with the original timestep and codebook to build the new sequence.
-    Note that all patterns must start with an empty list that is then used to insert a first
-    sequence step of special tokens in the newly generated sequence.
-
-    Args:
-        n_q (int): number of codebooks.
-        cached (bool): if True, patterns for a given length are cached. In general
-            that should be true for efficiency reason to avoid synchronization points.
-    """
-    def __init__(self, n_q: int, cached: bool = True):
-        assert n_q > 0
-        self.n_q = n_q
-        self.get_pattern = lru_cache(100)(self.get_pattern)  # type: ignore
-
-    @abstractmethod
-    def get_pattern(self, timesteps: int) -> Pattern:
-        """Builds pattern with specific interleaving between codebooks.
-
-        Args:
-            timesteps (int): Total numer of timesteps.
-        """
-        raise NotImplementedError()
-
-
-class DelayedPatternProvider(CodebooksPatternProvider):
-    """Provider for delayed pattern across delayed codebooks.
-    Codebooks are delayed in the sequence and sequence steps will contain codebooks
-    from different timesteps.
-
-    Example:
-        Taking timesteps=4 and n_q=3, delays=None, the multi-codebook sequence:
-        [[1, 2, 3, 4],
-        [1, 2, 3, 4],
-        [1, 2, 3, 4]]
-        The resulting sequence obtained from the returned pattern is:
-        [[S, 1, 2, 3, 4],
-        [S, S, 1, 2, 3],
-        [S, S, S, 1, 2]]
-        (with S being a special token)
-
-    Args:
-        n_q (int): Number of codebooks.
-        delays (Optional[List[int]]): Delay for each of the codebooks.
-            If delays not defined, each codebook is delayed by 1 compared to the previous one.
-        flatten_first (int): Flatten the first N timesteps.
-        empty_initial (int): Prepend with N empty list of coordinates.
-    """
-    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None,
-                 flatten_first: int = 0, empty_initial: int = 0):
-        super().__init__(n_q)
-        if delays is None:
-            delays = list(range(n_q))
-        self.delays = delays
-        self.flatten_first = flatten_first
-        self.empty_initial = empty_initial
-        assert len(self.delays) == self.n_q
-        assert sorted(self.delays) == self.delays
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        max_delay = max(self.delays)
-        if self.empty_initial:
-            out += [[] for _ in range(self.empty_initial)]
-        if self.flatten_first:
-            for t in range(min(timesteps, self.flatten_first)):
-                for q in range(self.n_q):
-                    out.append([LayoutCoord(t, q)])
-        for t in range(self.flatten_first, timesteps + max_delay):
-            v = []
-            for q, delay in enumerate(self.delays):
-                t_for_q = t - delay
-                if t_for_q >= self.flatten_first:
-                    v.append(LayoutCoord(t_for_q, q))
-            out.append(v)
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class ParallelPatternProvider(DelayedPatternProvider):
-    """Provider for parallel pattern across codebooks.
-    This pattern provider is a special case of the delayed pattern with actually no delay,
-    hence delays=repeat(0, n_q).
-
-    Args:
-        n_q (int): Number of codebooks.
-    """
-    def __init__(self, n_q: int):
-        super().__init__(n_q, [0] * n_q)
-
-
-class UnrolledPatternProvider(CodebooksPatternProvider):
-    """Provider for unrolling codebooks pattern.
-    This pattern provider enables to represent the codebook flattened completely or only to some extend
-    while also specifying a given delay between the flattened codebooks representation, allowing to
-    unroll the codebooks in the sequence.
-
-    Example:
-        1. Flattening of the codebooks.
-        By default, the pattern provider will fully flatten the codebooks such as flattening=range(n_q),
-        taking n_q = 3 and timesteps = 4:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, S, 1, S, S, 2, S, S, 3, S, S, 4],
-         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
-        2. Partial flattening of the codebooks. The ``flattening`` parameter allows to specify the inner step
-        for each of the codebook, allowing to define which codebook to flatten (or keep in parallel), for example
-        taking n_q = 3, timesteps = 4 and flattening = [0, 1, 1]:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
-        3. Flattening with delay. The ``delay`` parameter allows to further unroll the sequence of codebooks
-        allowing to specify the delay per codebook. Note that the delay between codebooks flattened to the
-        same inner timestep should be coherent. For example, taking n_q = 3, timesteps = 4, flattening = [0, 1, 1]
-        and delays = [0, 3, 3]:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, S, S, 1, S, 2, S, 3, S, 4],
-         [S, S, S, 1, S, 2, S, 3, S, 4],
-         [1, 2, 3, S, 4, S, 5, S, 6, S]]
-
-    Args:
-        n_q (int): Number of codebooks.
-        flattening (Optional[List[int]]): Flattening schema over the codebooks. If not defined,
-            the codebooks will be flattened to 1 codebook per step, meaning that the sequence will
-            have n_q extra steps for each timestep.
-        delays (Optional[List[int]]): Delay for each of the codebooks. If not defined,
-            no delay is added and therefore will default to [0] * ``n_q``.
-            Note that two codebooks that will be flattened to the same inner step
-            should have the same delay, otherwise the pattern is considered as invalid.
-    """
-    FlattenedCodebook = namedtuple('FlattenedCodebook', ['codebooks', 'delay'])
-
-    def __init__(self, n_q: int, flattening: tp.Optional[tp.List[int]] = None,
-                 delays: tp.Optional[tp.List[int]] = None):
-        super().__init__(n_q)
-        if flattening is None:
-            flattening = list(range(n_q))
-        if delays is None:
-            delays = [0] * n_q
-        assert len(flattening) == n_q
-        assert len(delays) == n_q
-        assert sorted(flattening) == flattening
-        assert sorted(delays) == delays
-        self._flattened_codebooks = self._build_flattened_codebooks(delays, flattening)
-        self.max_delay = max(delays)
-
-    def _build_flattened_codebooks(self, delays: tp.List[int], flattening: tp.List[int]):
-        """Build a flattened codebooks representation as a dictionary of inner step
-        and the actual codebook indices corresponding to the flattened codebook. For convenience, we
-        also store the delay associated to the flattened codebook to avoid maintaining an extra mapping.
-        """
-        flattened_codebooks: dict = {}
-        for q, (inner_step, delay) in enumerate(zip(flattening, delays)):
-            if inner_step not in flattened_codebooks:
-                flat_codebook = UnrolledPatternProvider.FlattenedCodebook(codebooks=[q], delay=delay)
-            else:
-                flat_codebook = flattened_codebooks[inner_step]
-                assert flat_codebook.delay == delay, (
-                    "Delay and flattening between codebooks is inconsistent: ",
-                    "two codebooks flattened to the same position should have the same delay."
-                )
-                flat_codebook.codebooks.append(q)
-            flattened_codebooks[inner_step] = flat_codebook
-        return flattened_codebooks
-
-    @property
-    def _num_inner_steps(self):
-        """Number of inner steps to unroll between timesteps in order to flatten the codebooks.
-        """
-        return max([inner_step for inner_step in self._flattened_codebooks.keys()]) + 1
-
-    def num_virtual_steps(self, timesteps: int) -> int:
-        return timesteps * self._num_inner_steps + 1
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        """Builds pattern for delay across codebooks.
-
-        Args:
-            timesteps (int): Total numer of timesteps.
-        """
-        # the PatternLayout is built as a tuple of sequence position and list of coordinates
-        # so that it can be reordered properly given the required delay between codebooks of given timesteps
-        indexed_out: list = [(-1, [])]
-        max_timesteps = timesteps + self.max_delay
-        for t in range(max_timesteps):
-            # for each timestep, we unroll the flattened codebooks,
-            # emitting the sequence step with the corresponding delay
-            for step in range(self._num_inner_steps):
-                if step in self._flattened_codebooks:
-                    # we have codebooks at this virtual step to emit
-                    step_codebooks = self._flattened_codebooks[step]
-                    t_for_q = t + step_codebooks.delay
-                    coords = [LayoutCoord(t, q) for q in step_codebooks.codebooks]
-                    if t_for_q < max_timesteps and t < max_timesteps:
-                        indexed_out.append((t_for_q, coords))
-                else:
-                    # there is no codebook in this virtual step so we emit an empty list
-                    indexed_out.append((t, []))
-        out = [coords for _, coords in sorted(indexed_out)]
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class VALLEPattern(CodebooksPatternProvider):
-    """Almost VALL-E style pattern. We futher allow some delays for the
-    codebooks other than the first one.
-
-    Args:
-        n_q (int): Number of codebooks.
-        delays (Optional[List[int]]): Delay for each of the codebooks.
-            If delays not defined, each codebook is delayed by 1 compared to the previous one.
-    """
-    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None):
-        super().__init__(n_q)
-        if delays is None:
-            delays = [0] * (n_q - 1)
-        self.delays = delays
-        assert len(self.delays) == self.n_q - 1
-        assert sorted(self.delays) == self.delays
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        for t in range(timesteps):
-            out.append([LayoutCoord(t, 0)])
-        max_delay = max(self.delays)
-        for t in range(timesteps + max_delay):
-            v = []
-            for q, delay in enumerate(self.delays):
-                t_for_q = t - delay
-                if t_for_q >= 0:
-                    v.append(LayoutCoord(t_for_q, q + 1))
-            out.append(v)
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class MusicLMPattern(CodebooksPatternProvider):
-    """Almost MusicLM style pattern. This is equivalent to full flattening
-    but in a different order.
-
-    Args:
-        n_q (int): Number of codebooks.
-        group_by (int): Number of codebooks to group together.
-    """
-    def __init__(self, n_q: int, group_by: int = 2):
-        super().__init__(n_q)
-        self.group_by = group_by
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        for offset in range(0, self.n_q, self.group_by):
-            for t in range(timesteps):
-                for q in range(offset, offset + self.group_by):
-                    out.append([LayoutCoord(t, q)])
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
diff --git a/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/losses.py b/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/losses.py
deleted file mode 100644
index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000
--- a/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch 
-from torch.nn import functional as F
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
-  loss = 0
-  for dr, dg in zip(fmap_r, fmap_g):
-    for rl, gl in zip(dr, dg):
-      rl = rl.float().detach()
-      gl = gl.float()
-      loss += torch.mean(torch.abs(rl - gl))
-
-  return loss * 2 
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-  loss = 0
-  r_losses = []
-  g_losses = []
-  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-    dr = dr.float()
-    dg = dg.float()
-    r_loss = torch.mean((1-dr)**2)
-    g_loss = torch.mean(dg**2)
-    loss += (r_loss + g_loss)
-    r_losses.append(r_loss.item())
-    g_losses.append(g_loss.item())
-
-  return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-  loss = 0
-  gen_losses = []
-  for dg in disc_outputs:
-    dg = dg.float()
-    l = torch.mean((1-dg)**2)
-    gen_losses.append(l)
-    loss += l
-
-  return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-  """
-  z_p, logs_q: [b, h, t_t]
-  m_p, logs_p: [b, h, t_t]
-  """
-  z_p = z_p.float()
-  logs_q = logs_q.float()
-  m_p = m_p.float()
-  logs_p = logs_p.float()
-  z_mask = z_mask.float()
-
-  kl = logs_p - logs_q - 0.5
-  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
-  kl = torch.sum(kl * z_mask)
-  l = kl / torch.sum(z_mask)
-  return l
diff --git a/spaces/Yudha515/Rvc-Models/audiocraft/__init__.py b/spaces/Yudha515/Rvc-Models/audiocraft/__init__.py
deleted file mode 100644
index 6b8594f470200ff5c000542ef115375ed69b749c..0000000000000000000000000000000000000000
--- a/spaces/Yudha515/Rvc-Models/audiocraft/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from . import data, modules, models
-
-__version__ = '0.0.2a2'
diff --git a/spaces/Yuzu22/rvc-models/infer_pack/transforms.py b/spaces/Yuzu22/rvc-models/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/Yuzu22/rvc-models/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails=None,
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
-    outputs, logabsdet = spline_fn(
-        inputs=inputs,
-        unnormalized_widths=unnormalized_widths,
-        unnormalized_heights=unnormalized_heights,
-        unnormalized_derivatives=unnormalized_derivatives,
-        inverse=inverse,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-        **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails="linear",
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == "linear":
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError("{} tails are not implemented.".format(tails))
-
-    (
-        outputs[inside_interval_mask],
-        logabsdet[inside_interval_mask],
-    ) = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound,
-        right=tail_bound,
-        bottom=-tail_bound,
-        top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-    )
-
-    return outputs, logabsdet
-
-
-def rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    left=0.0,
-    right=1.0,
-    bottom=0.0,
-    top=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError("Input to a transform is not within its domain")
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError("Minimal bin width too large for the number of bins")
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError("Minimal bin height too large for the number of bins")
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        ) + input_heights * (input_delta - input_derivatives)
-        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        )
-        c = -input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * root.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - root).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (
-            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
-        )
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * theta.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - theta).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/Zeltoria/anime-voice-generator/transforms.py b/spaces/Zeltoria/anime-voice-generator/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/Zeltoria/anime-voice-generator/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/ZilliaxOfficial/nyaru-svc-3.0/add_speaker.py b/spaces/ZilliaxOfficial/nyaru-svc-3.0/add_speaker.py
deleted file mode 100644
index e224f07c892a5fe1837e3cbf1745e0d8992ea283..0000000000000000000000000000000000000000
--- a/spaces/ZilliaxOfficial/nyaru-svc-3.0/add_speaker.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import os
-import argparse
-from tqdm import tqdm
-from random import shuffle
-import json
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
-    parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
-    parser.add_argument("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
-    parser.add_argument("--source_dir", type=str, default="./dataset/32k", help="path to source dir")
-    args = parser.parse_args()
-
-    previous_config = json.load(open("configs/config.json", "rb"))
-
-    train = []
-    val = []
-    test = []
-    idx = 0
-    spk_dict = previous_config["spk"]
-    spk_id = max([i for i in spk_dict.values()]) + 1
-    for speaker in tqdm(os.listdir(args.source_dir)):
-        if speaker not in spk_dict.keys():
-            spk_dict[speaker] = spk_id
-            spk_id += 1
-        wavs = [os.path.join(args.source_dir, speaker, i)for i in os.listdir(os.path.join(args.source_dir, speaker))]
-        wavs = [i for i in wavs if i.endswith("wav")]
-        shuffle(wavs)
-        train += wavs[2:-10]
-        val += wavs[:2]
-        test += wavs[-10:]
-
-    assert previous_config["model"]["n_speakers"] > len(spk_dict.keys())
-    shuffle(train)
-    shuffle(val)
-    shuffle(test)
-            
-    print("Writing", args.train_list)
-    with open(args.train_list, "w") as f:
-        for fname in tqdm(train):
-            wavpath = fname
-            f.write(wavpath + "\n")
-        
-    print("Writing", args.val_list)
-    with open(args.val_list, "w") as f:
-        for fname in tqdm(val):
-            wavpath = fname
-            f.write(wavpath + "\n")
-            
-    print("Writing", args.test_list)
-    with open(args.test_list, "w") as f:
-        for fname in tqdm(test):
-            wavpath = fname
-            f.write(wavpath + "\n")
-
-    previous_config["spk"] = spk_dict
-
-    print("Writing configs/config.json")
-    with open("configs/config.json", "w") as f:
-        json.dump(previous_config, f, indent=2)
diff --git a/spaces/a3en85/ChatGPT4/app.py b/spaces/a3en85/ChatGPT4/app.py
deleted file mode 100644
index 7e09e57ef928fd2451fd0ed1295d0994ca75d026..0000000000000000000000000000000000000000
--- a/spaces/a3en85/ChatGPT4/app.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import gradio as gr
-import os 
-import json 
-import requests
-
-#Streaming endpoint 
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-#Huggingface provided GPT4 OpenAI API Key 
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
-
-#Inferenec function
-def predict(system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  
-
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {OPENAI_API_KEY}"
-    }
-    print(f"system message is ^^ {system_msg}")
-    if system_msg.strip() == '':
-        initial_message = [{"role": "user", "content": f"{inputs}"},]
-        multi_turn_message = []
-    else:
-        initial_message= [{"role": "system", "content": system_msg},
-                   {"role": "user", "content": f"{inputs}"},]
-        multi_turn_message = [{"role": "system", "content": system_msg},]
-        
-    if chat_counter == 0 :
-        payload = {
-        "model": "gpt-4",
-        "messages": initial_message , 
-        "temperature" : 1.0,
-        "top_p":1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,
-        }
-        print(f"chat_counter - {chat_counter}")
-    else: #if chat_counter != 0 :
-        messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},]
-        for data in chatbot:
-          user = {}
-          user["role"] = "user" 
-          user["content"] = data[0] 
-          assistant = {}
-          assistant["role"] = "assistant" 
-          assistant["content"] = data[1]
-          messages.append(user)
-          messages.append(assistant)
-        temp = {}
-        temp["role"] = "user" 
-        temp["content"] = inputs
-        messages.append(temp)
-        #messages
-        payload = {
-        "model": "gpt-4",
-        "messages": messages, # Of the type of [{"role": "user", "content": f"{inputs}"}],
-        "temperature" : temperature, #1.0,
-        "top_p": top_p, #1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,}
-
-    chat_counter+=1
-
-    history.append(inputs)
-    print(f"Logging : payload is - {payload}")
-    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    print(f"Logging : response code - {response}")
-    token_counter = 0 
-    partial_words = "" 
-
-    counter=0
-    for chunk in response.iter_lines():
-        #Skipping first chunk
-        if counter == 0:
-          counter+=1
-          continue
-        # check whether each line is non-empty
-        if chunk.decode() :
-          chunk = chunk.decode()
-          # decode each line as response data is in bytes
-          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
-              if token_counter == 0:
-                history.append(" " + partial_words)
-              else:
-                history[-1] = partial_words
-              chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
-              token_counter+=1
-              yield chat, history, chat_counter, response  # resembles {chatbot: chat, state: history}  
-                   
-#Resetting to blank
-def reset_textbox():
-    return gr.update(value='')
-
-#to set a component as visible=False
-def set_visible_false():
-    return gr.update(visible=False)
-
-#to set a component as visible=True
-def set_visible_true():
-    return gr.update(visible=True)
-
-title = """<h1 align="center">🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming</h1>"""
-
-#display message for themes feature
-theme_addon_msg = """<center>🌟 Discover Gradio Themes with this Demo, featuring v3.22.0! Gradio v3.23.0 also enables seamless Theme sharing. You can develop or modify a theme, and send it to the hub using simple <code>theme.push_to_hub()</code>. 
-<br>🏆Participate in Gradio's Theme Building Hackathon to exhibit your creative flair and win fabulous rewards! Join here - <a href="https://huggingface.co/Gradio-Themes" target="_blank">Gradio-Themes-Party🎨</a> 🏆</center>
-"""
-
-#Using info to add additional information about System message in GPT4
-system_msg_info = """A conversation could begin with a system message to gently instruct the assistant. 
-System message helps set the behavior of the AI Assistant. For example, the assistant could be instructed with 'You are a helpful assistant.'"""
-
-#Modifying existing Gradio Theme
-theme = gr.themes.Soft(primary_hue="zinc", secondary_hue="green", neutral_hue="green",
-                      text_size=gr.themes.sizes.text_lg)                
-
-with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""",
-                      theme=theme) as demo:
-    gr.HTML(title)
-    gr.HTML("""<h3 align="center">🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌</h1>""")
-    gr.HTML(theme_addon_msg)
-    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
-
-    with gr.Column(elem_id = "col_container"):
-        #GPT4 API Key is provided by Huggingface 
-        with gr.Accordion(label="System message:", open=False):
-            system_msg = gr.Textbox(label="Instruct the AI Assistant to set its beaviour", info = system_msg_info, value="")
-            accordion_msg = gr.HTML(value="🚧 To set System message you will have to refresh the app", visible=False)
-        chatbot = gr.Chatbot(label='GPT4', elem_id="chatbot")
-        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter")
-        state = gr.State([]) 
-        with gr.Row():
-            with gr.Column(scale=7):
-                b1 = gr.Button().style(full_width=True)
-            with gr.Column(scale=3):
-                server_status_code = gr.Textbox(label="Status code from OpenAI server", )
-    
-        #top_p, temperature
-        with gr.Accordion("Parameters", open=False):
-            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
-            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
-            chat_counter = gr.Number(value=0, visible=False, precision=0)
-
-    #Event handling
-    inputs.submit( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    b1.click( predict, [system_msg, inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    
-    inputs.submit(set_visible_false, [], [system_msg])
-    b1.click(set_visible_false, [], [system_msg])
-    inputs.submit(set_visible_true, [], [accordion_msg])
-    b1.click(set_visible_true, [], [accordion_msg])
-    
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-
-    #Examples 
-    with gr.Accordion(label="Examples for System message:", open=False):
-        gr.Examples(
-                examples = [["""You are an AI programming assistant.
-        
-                - Follow the user's requirements carefully and to the letter.
-                - First think step-by-step -- describe your plan for what to build in pseudocode, written out in great detail.
-                - Then output the code in a single code block.
-                - Minimize any other prose."""], ["""You are ComedianGPT who is a helpful assistant. You answer everything with a joke and witty replies."""],
-                ["You are ChefGPT, a helpful assistant who answers questions with culinary expertise and a pinch of humor."],
-                ["You are FitnessGuruGPT, a fitness expert who shares workout tips and motivation with a playful twist."],
-                ["You are SciFiGPT, an AI assistant who discusses science fiction topics with a blend of knowledge and wit."],
-                ["You are PhilosopherGPT, a thoughtful assistant who responds to inquiries with philosophical insights and a touch of humor."],
-                ["You are EcoWarriorGPT, a helpful assistant who shares environment-friendly advice with a lighthearted approach."],
-                ["You are MusicMaestroGPT, a knowledgeable AI who discusses music and its history with a mix of facts and playful banter."],
-                ["You are SportsFanGPT, an enthusiastic assistant who talks about sports and shares amusing anecdotes."],
-                ["You are TechWhizGPT, a tech-savvy AI who can help users troubleshoot issues and answer questions with a dash of humor."],
-                ["You are FashionistaGPT, an AI fashion expert who shares style advice and trends with a sprinkle of wit."],
-                ["You are ArtConnoisseurGPT, an AI assistant who discusses art and its history with a blend of knowledge and playful commentary."],
-                ["You are a helpful assistant that provides detailed and accurate information."],
-                ["You are an assistant that speaks like Shakespeare."],
-                ["You are a friendly assistant who uses casual language and humor."],
-                ["You are a financial advisor who gives expert advice on investments and budgeting."],
-                ["You are a health and fitness expert who provides advice on nutrition and exercise."],
-                ["You are a travel consultant who offers recommendations for destinations, accommodations, and attractions."],
-                ["You are a movie critic who shares insightful opinions on films and their themes."],
-                ["You are a history enthusiast who loves to discuss historical events and figures."],
-                ["You are a tech-savvy assistant who can help users troubleshoot issues and answer questions about gadgets and software."],
-                ["You are an AI poet who can compose creative and evocative poems on any given topic."],],
-                inputs = system_msg,)
-        
-demo.queue(max_size=99, concurrency_count=20).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/abascal/chat_with_data_app/utils/openai_helper.py b/spaces/abascal/chat_with_data_app/utils/openai_helper.py
deleted file mode 100644
index 22dd542c44612afeffef8f917799791c6223a5a3..0000000000000000000000000000000000000000
--- a/spaces/abascal/chat_with_data_app/utils/openai_helper.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import json
-import os
-
-import openai
-
-
-def get_completion(prompt, model='gpt-3.5-turbo'):
-    messages = [{"role": "user", "content": prompt}]
-    response = openai.ChatCompletion.create(
-        model=model,
-        messages=messages,
-        temperature=0,
-    )
-    return response.choices[0].message["content"]
-
-def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0):
-    response = openai.ChatCompletion.create(
-        model=model,
-        messages=messages,
-        temperature=temperature,
-    )
-    return response.choices[0].message["content"]
-
-
-# Retrieve and set API KEY
-def read_key_from_file(path_file, name_file_key):
-    try:
-        with open(os.path.join(path_file, name_file_key), 'r') as f:
-            org_data = json.load(f)
-            
-        openai.organization = org_data['organization']
-        openai.api_key = org_data['api_key']
-        print("OpenAI API KEY set!")
-    except:
-        print("OpenAI API KEY not set!")
-        print("Please, provide up in the UI!")
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/midas/utils.py b/spaces/abhishek/sketch-to-image/annotator/midas/utils.py
deleted file mode 100644
index c74fe70bc4b3d77b6a6f6751756bc243721738a1..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/midas/utils.py
+++ /dev/null
@@ -1,199 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
-'''
-
-"""Utils for monoDepth."""
-import sys
-import re
-import numpy as np
-import cv2
-import torch
-
-
-def read_pfm(path):
-    """Read pfm file.
-
-    Args:
-        path (str): path to file
-
-    Returns:
-        tuple: (data, scale)
-    """
-    with open(path, "rb") as file:
-
-        color = None
-        width = None
-        height = None
-        scale = None
-        endian = None
-
-        header = file.readline().rstrip()
-        if header.decode("ascii") == "PF":
-            color = True
-        elif header.decode("ascii") == "Pf":
-            color = False
-        else:
-            raise Exception("Not a PFM file: " + path)
-
-        dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
-        if dim_match:
-            width, height = list(map(int, dim_match.groups()))
-        else:
-            raise Exception("Malformed PFM header.")
-
-        scale = float(file.readline().decode("ascii").rstrip())
-        if scale < 0:
-            # little-endian
-            endian = "<"
-            scale = -scale
-        else:
-            # big-endian
-            endian = ">"
-
-        data = np.fromfile(file, endian + "f")
-        shape = (height, width, 3) if color else (height, width)
-
-        data = np.reshape(data, shape)
-        data = np.flipud(data)
-
-        return data, scale
-
-
-def write_pfm(path, image, scale=1):
-    """Write pfm file.
-
-    Args:
-        path (str): pathto file
-        image (array): data
-        scale (int, optional): Scale. Defaults to 1.
-    """
-
-    with open(path, "wb") as file:
-        color = None
-
-        if image.dtype.name != "float32":
-            raise Exception("Image dtype must be float32.")
-
-        image = np.flipud(image)
-
-        if len(image.shape) == 3 and image.shape[2] == 3:  # color image
-            color = True
-        elif (
-            len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
-        ):  # greyscale
-            color = False
-        else:
-            raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
-
-        file.write("PF\n" if color else "Pf\n".encode())
-        file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
-
-        endian = image.dtype.byteorder
-
-        if endian == "<" or endian == "=" and sys.byteorder == "little":
-            scale = -scale
-
-        file.write("%f\n".encode() % scale)
-
-        image.tofile(file)
-
-
-def read_image(path):
-    """Read image and output RGB image (0-1).
-
-    Args:
-        path (str): path to file
-
-    Returns:
-        array: RGB image (0-1)
-    """
-    img = cv2.imread(path)
-
-    if img.ndim == 2:
-        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
-
-    return img
-
-
-def resize_image(img):
-    """Resize image and make it fit for network.
-
-    Args:
-        img (array): image
-
-    Returns:
-        tensor: data ready for network
-    """
-    height_orig = img.shape[0]
-    width_orig = img.shape[1]
-
-    if width_orig > height_orig:
-        scale = width_orig / 384
-    else:
-        scale = height_orig / 384
-
-    height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
-    width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
-
-    img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
-
-    img_resized = (
-        torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
-    )
-    img_resized = img_resized.unsqueeze(0)
-
-    return img_resized
-
-
-def resize_depth(depth, width, height):
-    """Resize depth map and bring to CPU (numpy).
-
-    Args:
-        depth (tensor): depth
-        width (int): image width
-        height (int): image height
-
-    Returns:
-        array: processed depth
-    """
-    depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
-
-    depth_resized = cv2.resize(
-        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
-    )
-
-    return depth_resized
-
-def write_depth(path, depth, bits=1):
-    """Write depth map to pfm and png file.
-
-    Args:
-        path (str): filepath without extension
-        depth (array): depth
-    """
-    write_pfm(path + ".pfm", depth.astype(np.float32))
-
-    depth_min = depth.min()
-    depth_max = depth.max()
-
-    max_val = (2**(8*bits))-1
-
-    if depth_max - depth_min > np.finfo("float").eps:
-        out = max_val * (depth - depth_min) / (depth_max - depth_min)
-    else:
-        out = np.zeros(depth.shape, dtype=depth.type)
-
-    if bits == 1:
-        cv2.imwrite(path + ".png", out.astype("uint8"))
-    elif bits == 2:
-        cv2.imwrite(path + ".png", out.astype("uint16"))
-
-    return
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/parallel/scatter_gather.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/parallel/scatter_gather.py
deleted file mode 100644
index 900ff88566f8f14830590459dc4fd16d4b382e47..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/parallel/scatter_gather.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch.nn.parallel._functions import Scatter as OrigScatter
-
-from ._functions import Scatter
-from .data_container import DataContainer
-
-
-def scatter(inputs, target_gpus, dim=0):
-    """Scatter inputs to target gpus.
-
-    The only difference from original :func:`scatter` is to add support for
-    :type:`~mmcv.parallel.DataContainer`.
-    """
-
-    def scatter_map(obj):
-        if isinstance(obj, torch.Tensor):
-            if target_gpus != [-1]:
-                return OrigScatter.apply(target_gpus, None, dim, obj)
-            else:
-                # for CPU inference we use self-implemented scatter
-                return Scatter.forward(target_gpus, obj)
-        if isinstance(obj, DataContainer):
-            if obj.cpu_only:
-                return obj.data
-            else:
-                return Scatter.forward(target_gpus, obj.data)
-        if isinstance(obj, tuple) and len(obj) > 0:
-            return list(zip(*map(scatter_map, obj)))
-        if isinstance(obj, list) and len(obj) > 0:
-            out = list(map(list, zip(*map(scatter_map, obj))))
-            return out
-        if isinstance(obj, dict) and len(obj) > 0:
-            out = list(map(type(obj), zip(*map(scatter_map, obj.items()))))
-            return out
-        return [obj for targets in target_gpus]
-
-    # After scatter_map is called, a scatter_map cell will exist. This cell
-    # has a reference to the actual function scatter_map, which has references
-    # to a closure that has a reference to the scatter_map cell (because the
-    # fn is recursive). To avoid this reference cycle, we set the function to
-    # None, clearing the cell
-    try:
-        return scatter_map(inputs)
-    finally:
-        scatter_map = None
-
-
-def scatter_kwargs(inputs, kwargs, target_gpus, dim=0):
-    """Scatter with support for kwargs dictionary."""
-    inputs = scatter(inputs, target_gpus, dim) if inputs else []
-    kwargs = scatter(kwargs, target_gpus, dim) if kwargs else []
-    if len(inputs) < len(kwargs):
-        inputs.extend([() for _ in range(len(kwargs) - len(inputs))])
-    elif len(kwargs) < len(inputs):
-        kwargs.extend([{} for _ in range(len(inputs) - len(kwargs))])
-    inputs = tuple(inputs)
-    kwargs = tuple(kwargs)
-    return inputs, kwargs
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/configs/_base_/models/apcnet_r50-d8.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/configs/_base_/models/apcnet_r50-d8.py
deleted file mode 100644
index c8f5316cbcf3896ba9de7ca2c801eba512f01d5e..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/configs/_base_/models/apcnet_r50-d8.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='APCHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        pool_scales=(1, 2, 3, 6),
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=dict(type='SyncBN', requires_grad=True),
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/ops/nms.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/ops/nms.py
deleted file mode 100644
index 6d9634281f486ab284091786886854c451368052..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/ops/nms.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import os
-
-import numpy as np
-import torch
-
-from annotator.uniformer.mmcv.utils import deprecated_api_warning
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
-    '_ext', ['nms', 'softnms', 'nms_match', 'nms_rotated'])
-
-
-# This function is modified from: https://github.com/pytorch/vision/
-class NMSop(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, bboxes, scores, iou_threshold, offset, score_threshold,
-                max_num):
-        is_filtering_by_score = score_threshold > 0
-        if is_filtering_by_score:
-            valid_mask = scores > score_threshold
-            bboxes, scores = bboxes[valid_mask], scores[valid_mask]
-            valid_inds = torch.nonzero(
-                valid_mask, as_tuple=False).squeeze(dim=1)
-
-        inds = ext_module.nms(
-            bboxes, scores, iou_threshold=float(iou_threshold), offset=offset)
-
-        if max_num > 0:
-            inds = inds[:max_num]
-        if is_filtering_by_score:
-            inds = valid_inds[inds]
-        return inds
-
-    @staticmethod
-    def symbolic(g, bboxes, scores, iou_threshold, offset, score_threshold,
-                 max_num):
-        from ..onnx import is_custom_op_loaded
-        has_custom_op = is_custom_op_loaded()
-        # TensorRT nms plugin is aligned with original nms in ONNXRuntime
-        is_trt_backend = os.environ.get('ONNX_BACKEND') == 'MMCVTensorRT'
-        if has_custom_op and (not is_trt_backend):
-            return g.op(
-                'mmcv::NonMaxSuppression',
-                bboxes,
-                scores,
-                iou_threshold_f=float(iou_threshold),
-                offset_i=int(offset))
-        else:
-            from torch.onnx.symbolic_opset9 import select, squeeze, unsqueeze
-            from ..onnx.onnx_utils.symbolic_helper import _size_helper
-
-            boxes = unsqueeze(g, bboxes, 0)
-            scores = unsqueeze(g, unsqueeze(g, scores, 0), 0)
-
-            if max_num > 0:
-                max_num = g.op(
-                    'Constant',
-                    value_t=torch.tensor(max_num, dtype=torch.long))
-            else:
-                dim = g.op('Constant', value_t=torch.tensor(0))
-                max_num = _size_helper(g, bboxes, dim)
-            max_output_per_class = max_num
-            iou_threshold = g.op(
-                'Constant',
-                value_t=torch.tensor([iou_threshold], dtype=torch.float))
-            score_threshold = g.op(
-                'Constant',
-                value_t=torch.tensor([score_threshold], dtype=torch.float))
-            nms_out = g.op('NonMaxSuppression', boxes, scores,
-                           max_output_per_class, iou_threshold,
-                           score_threshold)
-            return squeeze(
-                g,
-                select(
-                    g, nms_out, 1,
-                    g.op(
-                        'Constant',
-                        value_t=torch.tensor([2], dtype=torch.long))), 1)
-
-
-class SoftNMSop(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, boxes, scores, iou_threshold, sigma, min_score, method,
-                offset):
-        dets = boxes.new_empty((boxes.size(0), 5), device='cpu')
-        inds = ext_module.softnms(
-            boxes.cpu(),
-            scores.cpu(),
-            dets.cpu(),
-            iou_threshold=float(iou_threshold),
-            sigma=float(sigma),
-            min_score=float(min_score),
-            method=int(method),
-            offset=int(offset))
-        return dets, inds
-
-    @staticmethod
-    def symbolic(g, boxes, scores, iou_threshold, sigma, min_score, method,
-                 offset):
-        from packaging import version
-        assert version.parse(torch.__version__) >= version.parse('1.7.0')
-        nms_out = g.op(
-            'mmcv::SoftNonMaxSuppression',
-            boxes,
-            scores,
-            iou_threshold_f=float(iou_threshold),
-            sigma_f=float(sigma),
-            min_score_f=float(min_score),
-            method_i=int(method),
-            offset_i=int(offset),
-            outputs=2)
-        return nms_out
-
-
-@deprecated_api_warning({'iou_thr': 'iou_threshold'})
-def nms(boxes, scores, iou_threshold, offset=0, score_threshold=0, max_num=-1):
-    """Dispatch to either CPU or GPU NMS implementations.
-
-    The input can be either torch tensor or numpy array. GPU NMS will be used
-    if the input is gpu tensor, otherwise CPU NMS
-    will be used. The returned type will always be the same as inputs.
-
-    Arguments:
-        boxes (torch.Tensor or np.ndarray): boxes in shape (N, 4).
-        scores (torch.Tensor or np.ndarray): scores in shape (N, ).
-        iou_threshold (float): IoU threshold for NMS.
-        offset (int, 0 or 1): boxes' width or height is (x2 - x1 + offset).
-        score_threshold (float): score threshold for NMS.
-        max_num (int): maximum number of boxes after NMS.
-
-    Returns:
-        tuple: kept dets(boxes and scores) and indice, which is always the \
-            same data type as the input.
-
-    Example:
-        >>> boxes = np.array([[49.1, 32.4, 51.0, 35.9],
-        >>>                   [49.3, 32.9, 51.0, 35.3],
-        >>>                   [49.2, 31.8, 51.0, 35.4],
-        >>>                   [35.1, 11.5, 39.1, 15.7],
-        >>>                   [35.6, 11.8, 39.3, 14.2],
-        >>>                   [35.3, 11.5, 39.9, 14.5],
-        >>>                   [35.2, 11.7, 39.7, 15.7]], dtype=np.float32)
-        >>> scores = np.array([0.9, 0.9, 0.5, 0.5, 0.5, 0.4, 0.3],\
-               dtype=np.float32)
-        >>> iou_threshold = 0.6
-        >>> dets, inds = nms(boxes, scores, iou_threshold)
-        >>> assert len(inds) == len(dets) == 3
-    """
-    assert isinstance(boxes, (torch.Tensor, np.ndarray))
-    assert isinstance(scores, (torch.Tensor, np.ndarray))
-    is_numpy = False
-    if isinstance(boxes, np.ndarray):
-        is_numpy = True
-        boxes = torch.from_numpy(boxes)
-    if isinstance(scores, np.ndarray):
-        scores = torch.from_numpy(scores)
-    assert boxes.size(1) == 4
-    assert boxes.size(0) == scores.size(0)
-    assert offset in (0, 1)
-
-    if torch.__version__ == 'parrots':
-        indata_list = [boxes, scores]
-        indata_dict = {
-            'iou_threshold': float(iou_threshold),
-            'offset': int(offset)
-        }
-        inds = ext_module.nms(*indata_list, **indata_dict)
-    else:
-        inds = NMSop.apply(boxes, scores, iou_threshold, offset,
-                           score_threshold, max_num)
-    dets = torch.cat((boxes[inds], scores[inds].reshape(-1, 1)), dim=1)
-    if is_numpy:
-        dets = dets.cpu().numpy()
-        inds = inds.cpu().numpy()
-    return dets, inds
-
-
-@deprecated_api_warning({'iou_thr': 'iou_threshold'})
-def soft_nms(boxes,
-             scores,
-             iou_threshold=0.3,
-             sigma=0.5,
-             min_score=1e-3,
-             method='linear',
-             offset=0):
-    """Dispatch to only CPU Soft NMS implementations.
-
-    The input can be either a torch tensor or numpy array.
-    The returned type will always be the same as inputs.
-
-    Arguments:
-        boxes (torch.Tensor or np.ndarray): boxes in shape (N, 4).
-        scores (torch.Tensor or np.ndarray): scores in shape (N, ).
-        iou_threshold (float): IoU threshold for NMS.
-        sigma (float): hyperparameter for gaussian method
-        min_score (float): score filter threshold
-        method (str): either 'linear' or 'gaussian'
-        offset (int, 0 or 1): boxes' width or height is (x2 - x1 + offset).
-
-    Returns:
-        tuple: kept dets(boxes and scores) and indice, which is always the \
-            same data type as the input.
-
-    Example:
-        >>> boxes = np.array([[4., 3., 5., 3.],
-        >>>                   [4., 3., 5., 4.],
-        >>>                   [3., 1., 3., 1.],
-        >>>                   [3., 1., 3., 1.],
-        >>>                   [3., 1., 3., 1.],
-        >>>                   [3., 1., 3., 1.]], dtype=np.float32)
-        >>> scores = np.array([0.9, 0.9, 0.5, 0.5, 0.4, 0.0], dtype=np.float32)
-        >>> iou_threshold = 0.6
-        >>> dets, inds = soft_nms(boxes, scores, iou_threshold, sigma=0.5)
-        >>> assert len(inds) == len(dets) == 5
-    """
-
-    assert isinstance(boxes, (torch.Tensor, np.ndarray))
-    assert isinstance(scores, (torch.Tensor, np.ndarray))
-    is_numpy = False
-    if isinstance(boxes, np.ndarray):
-        is_numpy = True
-        boxes = torch.from_numpy(boxes)
-    if isinstance(scores, np.ndarray):
-        scores = torch.from_numpy(scores)
-    assert boxes.size(1) == 4
-    assert boxes.size(0) == scores.size(0)
-    assert offset in (0, 1)
-    method_dict = {'naive': 0, 'linear': 1, 'gaussian': 2}
-    assert method in method_dict.keys()
-
-    if torch.__version__ == 'parrots':
-        dets = boxes.new_empty((boxes.size(0), 5), device='cpu')
-        indata_list = [boxes.cpu(), scores.cpu(), dets.cpu()]
-        indata_dict = {
-            'iou_threshold': float(iou_threshold),
-            'sigma': float(sigma),
-            'min_score': min_score,
-            'method': method_dict[method],
-            'offset': int(offset)
-        }
-        inds = ext_module.softnms(*indata_list, **indata_dict)
-    else:
-        dets, inds = SoftNMSop.apply(boxes.cpu(), scores.cpu(),
-                                     float(iou_threshold), float(sigma),
-                                     float(min_score), method_dict[method],
-                                     int(offset))
-
-    dets = dets[:inds.size(0)]
-
-    if is_numpy:
-        dets = dets.cpu().numpy()
-        inds = inds.cpu().numpy()
-        return dets, inds
-    else:
-        return dets.to(device=boxes.device), inds.to(device=boxes.device)
-
-
-def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False):
-    """Performs non-maximum suppression in a batched fashion.
-
-    Modified from https://github.com/pytorch/vision/blob
-    /505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39.
-    In order to perform NMS independently per class, we add an offset to all
-    the boxes. The offset is dependent only on the class idx, and is large
-    enough so that boxes from different classes do not overlap.
-
-    Arguments:
-        boxes (torch.Tensor): boxes in shape (N, 4).
-        scores (torch.Tensor): scores in shape (N, ).
-        idxs (torch.Tensor): each index value correspond to a bbox cluster,
-            and NMS will not be applied between elements of different idxs,
-            shape (N, ).
-        nms_cfg (dict): specify nms type and other parameters like iou_thr.
-            Possible keys includes the following.
-
-            - iou_thr (float): IoU threshold used for NMS.
-            - split_thr (float): threshold number of boxes. In some cases the
-                number of boxes is large (e.g., 200k). To avoid OOM during
-                training, the users could set `split_thr` to a small value.
-                If the number of boxes is greater than the threshold, it will
-                perform NMS on each group of boxes separately and sequentially.
-                Defaults to 10000.
-        class_agnostic (bool): if true, nms is class agnostic,
-            i.e. IoU thresholding happens over all boxes,
-            regardless of the predicted class.
-
-    Returns:
-        tuple: kept dets and indice.
-    """
-    nms_cfg_ = nms_cfg.copy()
-    class_agnostic = nms_cfg_.pop('class_agnostic', class_agnostic)
-    if class_agnostic:
-        boxes_for_nms = boxes
-    else:
-        max_coordinate = boxes.max()
-        offsets = idxs.to(boxes) * (max_coordinate + torch.tensor(1).to(boxes))
-        boxes_for_nms = boxes + offsets[:, None]
-
-    nms_type = nms_cfg_.pop('type', 'nms')
-    nms_op = eval(nms_type)
-
-    split_thr = nms_cfg_.pop('split_thr', 10000)
-    # Won't split to multiple nms nodes when exporting to onnx
-    if boxes_for_nms.shape[0] < split_thr or torch.onnx.is_in_onnx_export():
-        dets, keep = nms_op(boxes_for_nms, scores, **nms_cfg_)
-        boxes = boxes[keep]
-        # -1 indexing works abnormal in TensorRT
-        # This assumes `dets` has 5 dimensions where
-        # the last dimension is score.
-        # TODO: more elegant way to handle the dimension issue.
-        # Some type of nms would reweight the score, such as SoftNMS
-        scores = dets[:, 4]
-    else:
-        max_num = nms_cfg_.pop('max_num', -1)
-        total_mask = scores.new_zeros(scores.size(), dtype=torch.bool)
-        # Some type of nms would reweight the score, such as SoftNMS
-        scores_after_nms = scores.new_zeros(scores.size())
-        for id in torch.unique(idxs):
-            mask = (idxs == id).nonzero(as_tuple=False).view(-1)
-            dets, keep = nms_op(boxes_for_nms[mask], scores[mask], **nms_cfg_)
-            total_mask[mask[keep]] = True
-            scores_after_nms[mask[keep]] = dets[:, -1]
-        keep = total_mask.nonzero(as_tuple=False).view(-1)
-
-        scores, inds = scores_after_nms[keep].sort(descending=True)
-        keep = keep[inds]
-        boxes = boxes[keep]
-
-        if max_num > 0:
-            keep = keep[:max_num]
-            boxes = boxes[:max_num]
-            scores = scores[:max_num]
-
-    return torch.cat([boxes, scores[:, None]], -1), keep
-
-
-def nms_match(dets, iou_threshold):
-    """Matched dets into different groups by NMS.
-
-    NMS match is Similar to NMS but when a bbox is suppressed, nms match will
-    record the indice of suppressed bbox and form a group with the indice of
-    kept bbox. In each group, indice is sorted as score order.
-
-    Arguments:
-        dets (torch.Tensor | np.ndarray): Det boxes with scores, shape (N, 5).
-        iou_thr (float): IoU thresh for NMS.
-
-    Returns:
-        List[torch.Tensor | np.ndarray]: The outer list corresponds different
-            matched group, the inner Tensor corresponds the indices for a group
-            in score order.
-    """
-    if dets.shape[0] == 0:
-        matched = []
-    else:
-        assert dets.shape[-1] == 5, 'inputs dets.shape should be (N, 5), ' \
-                                    f'but get {dets.shape}'
-        if isinstance(dets, torch.Tensor):
-            dets_t = dets.detach().cpu()
-        else:
-            dets_t = torch.from_numpy(dets)
-        indata_list = [dets_t]
-        indata_dict = {'iou_threshold': float(iou_threshold)}
-        matched = ext_module.nms_match(*indata_list, **indata_dict)
-        if torch.__version__ == 'parrots':
-            matched = matched.tolist()
-
-    if isinstance(dets, torch.Tensor):
-        return [dets.new_tensor(m, dtype=torch.long) for m in matched]
-    else:
-        return [np.array(m, dtype=np.int) for m in matched]
-
-
-def nms_rotated(dets, scores, iou_threshold, labels=None):
-    """Performs non-maximum suppression (NMS) on the rotated boxes according to
-    their intersection-over-union (IoU).
-
-    Rotated NMS iteratively removes lower scoring rotated boxes which have an
-    IoU greater than iou_threshold with another (higher scoring) rotated box.
-
-    Args:
-        boxes (Tensor):  Rotated boxes in shape (N, 5). They are expected to \
-            be in (x_ctr, y_ctr, width, height, angle_radian) format.
-        scores (Tensor): scores in shape (N, ).
-        iou_threshold (float): IoU thresh for NMS.
-        labels (Tensor): boxes' label in shape (N,).
-
-    Returns:
-        tuple: kept dets(boxes and scores) and indice, which is always the \
-            same data type as the input.
-    """
-    if dets.shape[0] == 0:
-        return dets, None
-    multi_label = labels is not None
-    if multi_label:
-        dets_wl = torch.cat((dets, labels.unsqueeze(1)), 1)
-    else:
-        dets_wl = dets
-    _, order = scores.sort(0, descending=True)
-    dets_sorted = dets_wl.index_select(0, order)
-
-    if torch.__version__ == 'parrots':
-        keep_inds = ext_module.nms_rotated(
-            dets_wl,
-            scores,
-            order,
-            dets_sorted,
-            iou_threshold=iou_threshold,
-            multi_label=multi_label)
-    else:
-        keep_inds = ext_module.nms_rotated(dets_wl, scores, order, dets_sorted,
-                                           iou_threshold, multi_label)
-    dets = torch.cat((dets[keep_inds], scores[keep_inds].reshape(-1, 1)),
-                     dim=1)
-    return dets, keep_inds
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/utils/drop.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/utils/drop.py
deleted file mode 100644
index 442f48e0619cdf2a5470cf2843cb050707d4144e..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/models/utils/drop.py
+++ /dev/null
@@ -1,43 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
- * Modified from MMCV repo: From https://github.com/open-mmlab/mmcv
- * Copyright (c) OpenMMLab. All rights reserved.
-'''
-
-"""Modified from https://github.com/rwightman/pytorch-image-
-models/blob/master/timm/models/layers/drop.py."""
-
-import torch
-from torch import nn
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of
-    residual blocks).
-
-    Args:
-        drop_prob (float): Drop rate for paths of model. Dropout rate has
-            to be between 0 and 1. Default: 0.
-    """
-
-    def __init__(self, drop_prob=0.):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-        self.keep_prob = 1 - drop_prob
-
-    def forward(self, x):
-        if self.drop_prob == 0. or not self.training:
-            return x
-        shape = (x.shape[0], ) + (1, ) * (
-            x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
-        random_tensor = self.keep_prob + torch.rand(
-            shape, dtype=x.dtype, device=x.device)
-        random_tensor.floor_()  # binarize
-        output = x.div(self.keep_prob) * random_tensor
-        return output
diff --git a/spaces/akhaliq/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext/app.py b/spaces/akhaliq/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext/app.py
deleted file mode 100644
index 3283f68037ed66424768fb059619d3c16724a72a..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext/app.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import gradio as gr
-title = "PubMedBERT"
-description = "Gradio Demo for PubMedBERT. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2007.15779' target='_blank'>Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing</a> | <a href='https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext' target='_blank'>HF model page</a></p>"
-examples = [
-    ["[MASK] is a tumor suppressor gene."]
-]
-gr.Interface.load("huggingface/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext",title=title,description=description,article=article, examples=examples,enable_queue=True).launch()
diff --git a/spaces/akhaliq/JoJoGAN/e4e/metrics/LEC.py b/spaces/akhaliq/JoJoGAN/e4e/metrics/LEC.py
deleted file mode 100644
index 3eef2d2f00a4d757a56b6e845a8fde16aab306ab..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/JoJoGAN/e4e/metrics/LEC.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import sys
-import argparse
-import torch
-import numpy as np
-from torch.utils.data import DataLoader
-
-sys.path.append(".")
-sys.path.append("..")
-
-from configs import data_configs
-from datasets.images_dataset import ImagesDataset
-from utils.model_utils import setup_model
-
-
-class LEC:
-    def __init__(self, net, is_cars=False):
-        """
-        Latent Editing Consistency metric as proposed in the main paper.
-        :param net: e4e model loaded over the pSp framework.
-        :param is_cars: An indication as to whether or not to crop the middle of the StyleGAN's output images.
-        """
-        self.net = net
-        self.is_cars = is_cars
-
-    def _encode(self, images):
-        """
-        Encodes the given images into StyleGAN's latent space.
-        :param images: Tensor of shape NxCxHxW representing the images to be encoded.
-        :return: Tensor of shape NxKx512 representing the latent space embeddings of the given image (in W(K, *) space).
-        """
-        codes = self.net.encoder(images)
-        assert codes.ndim == 3, f"Invalid latent codes shape, should be NxKx512 but is {codes.shape}"
-        # normalize with respect to the center of an average face
-        if self.net.opts.start_from_latent_avg:
-            codes = codes + self.net.latent_avg.repeat(codes.shape[0], 1, 1)
-        return codes
-
-    def _generate(self, codes):
-        """
-        Generate the StyleGAN2 images of the given codes
-        :param codes: Tensor of shape NxKx512 representing the StyleGAN's latent codes (in W(K, *) space).
-        :return: Tensor of shape  NxCxHxW representing the generated images.
-        """
-        images, _ = self.net.decoder([codes], input_is_latent=True, randomize_noise=False, return_latents=True)
-        images = self.net.face_pool(images)
-        if self.is_cars:
-            images = images[:, :, 32:224, :]
-        return images
-
-    @staticmethod
-    def _filter_outliers(arr):
-        arr = np.array(arr)
-
-        lo = np.percentile(arr, 1, interpolation="lower")
-        hi = np.percentile(arr, 99, interpolation="higher")
-        return np.extract(
-            np.logical_and(lo <= arr, arr <= hi), arr
-        )
-
-    def calculate_metric(self, data_loader, edit_function, inverse_edit_function):
-        """
-        Calculate the LEC metric score.
-        :param data_loader: An iterable that returns a tuple of (images, _), similar to the training data loader.
-        :param edit_function: A function that receives latent codes and performs a semantically meaningful edit in the
-                              latent space.
-        :param inverse_edit_function: A function that receives latent codes and performs the inverse edit of the
-                                      `edit_function` parameter.
-        :return: The LEC metric score.
-        """
-        distances = []
-        with torch.no_grad():
-            for batch in data_loader:
-                x, _ = batch
-                inputs = x.to(device).float()
-
-                codes = self._encode(inputs)
-                edited_codes = edit_function(codes)
-                edited_image = self._generate(edited_codes)
-                edited_image_inversion_codes = self._encode(edited_image)
-                inverse_edit_codes = inverse_edit_function(edited_image_inversion_codes)
-
-                dist = (codes - inverse_edit_codes).norm(2, dim=(1, 2)).mean()
-                distances.append(dist.to("cpu").numpy())
-
-        distances = self._filter_outliers(distances)
-        return distances.mean()
-
-
-if __name__ == "__main__":
-    device = "cuda"
-
-    parser = argparse.ArgumentParser(description="LEC metric calculator")
-
-    parser.add_argument("--batch", type=int, default=8, help="batch size for the models")
-    parser.add_argument("--images_dir", type=str, default=None,
-                        help="Path to the images directory on which we calculate the LEC score")
-    parser.add_argument("ckpt", metavar="CHECKPOINT", help="path to the model checkpoints")
-
-    args = parser.parse_args()
-    print(args)
-
-    net, opts = setup_model(args.ckpt, device)
-    dataset_args = data_configs.DATASETS[opts.dataset_type]
-    transforms_dict = dataset_args['transforms'](opts).get_transforms()
-
-    images_directory = dataset_args['test_source_root'] if args.images_dir is None else args.images_dir
-    test_dataset = ImagesDataset(source_root=images_directory,
-                                 target_root=images_directory,
-                                 source_transform=transforms_dict['transform_source'],
-                                 target_transform=transforms_dict['transform_test'],
-                                 opts=opts)
-
-    data_loader = DataLoader(test_dataset,
-                             batch_size=args.batch,
-                             shuffle=False,
-                             num_workers=2,
-                             drop_last=True)
-
-    print(f'dataset length: {len(test_dataset)}')
-
-    # In the following example, we are using an InterfaceGAN based editing to calculate the LEC metric.
-    # Change the provided example according to your domain and needs.
-    direction = torch.load('../editings/interfacegan_directions/age.pt').to(device)
-
-    def edit_func_example(codes):
-        return codes + 3 * direction
-
-
-    def inverse_edit_func_example(codes):
-        return codes - 3 * direction
-
-    lec = LEC(net, is_cars='car' in opts.dataset_type)
-    result = lec.calculate_metric(data_loader, edit_func_example, inverse_edit_func_example)
-    print(f"LEC: {result}")
diff --git a/spaces/akhaliq/JoJoGAN/e4e/models/encoders/psp_encoders.py b/spaces/akhaliq/JoJoGAN/e4e/models/encoders/psp_encoders.py
deleted file mode 100644
index dc49acd11f062cbd29f839ee3c04bce7fa84f479..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/JoJoGAN/e4e/models/encoders/psp_encoders.py
+++ /dev/null
@@ -1,200 +0,0 @@
-from enum import Enum
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import Conv2d, BatchNorm2d, PReLU, Sequential, Module
-
-from e4e.models.encoders.helpers import get_blocks, bottleneck_IR, bottleneck_IR_SE, _upsample_add
-from e4e.models.stylegan2.model import EqualLinear
-
-
-class ProgressiveStage(Enum):
-    WTraining = 0
-    Delta1Training = 1
-    Delta2Training = 2
-    Delta3Training = 3
-    Delta4Training = 4
-    Delta5Training = 5
-    Delta6Training = 6
-    Delta7Training = 7
-    Delta8Training = 8
-    Delta9Training = 9
-    Delta10Training = 10
-    Delta11Training = 11
-    Delta12Training = 12
-    Delta13Training = 13
-    Delta14Training = 14
-    Delta15Training = 15
-    Delta16Training = 16
-    Delta17Training = 17
-    Inference = 18
-
-
-class GradualStyleBlock(Module):
-    def __init__(self, in_c, out_c, spatial):
-        super(GradualStyleBlock, self).__init__()
-        self.out_c = out_c
-        self.spatial = spatial
-        num_pools = int(np.log2(spatial))
-        modules = []
-        modules += [Conv2d(in_c, out_c, kernel_size=3, stride=2, padding=1),
-                    nn.LeakyReLU()]
-        for i in range(num_pools - 1):
-            modules += [
-                Conv2d(out_c, out_c, kernel_size=3, stride=2, padding=1),
-                nn.LeakyReLU()
-            ]
-        self.convs = nn.Sequential(*modules)
-        self.linear = EqualLinear(out_c, out_c, lr_mul=1)
-
-    def forward(self, x):
-        x = self.convs(x)
-        x = x.view(-1, self.out_c)
-        x = self.linear(x)
-        return x
-
-
-class GradualStyleEncoder(Module):
-    def __init__(self, num_layers, mode='ir', opts=None):
-        super(GradualStyleEncoder, self).__init__()
-        assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
-        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
-        blocks = get_blocks(num_layers)
-        if mode == 'ir':
-            unit_module = bottleneck_IR
-        elif mode == 'ir_se':
-            unit_module = bottleneck_IR_SE
-        self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
-                                      BatchNorm2d(64),
-                                      PReLU(64))
-        modules = []
-        for block in blocks:
-            for bottleneck in block:
-                modules.append(unit_module(bottleneck.in_channel,
-                                           bottleneck.depth,
-                                           bottleneck.stride))
-        self.body = Sequential(*modules)
-
-        self.styles = nn.ModuleList()
-        log_size = int(math.log(opts.stylegan_size, 2))
-        self.style_count = 2 * log_size - 2
-        self.coarse_ind = 3
-        self.middle_ind = 7
-        for i in range(self.style_count):
-            if i < self.coarse_ind:
-                style = GradualStyleBlock(512, 512, 16)
-            elif i < self.middle_ind:
-                style = GradualStyleBlock(512, 512, 32)
-            else:
-                style = GradualStyleBlock(512, 512, 64)
-            self.styles.append(style)
-        self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0)
-        self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, x):
-        x = self.input_layer(x)
-
-        latents = []
-        modulelist = list(self.body._modules.values())
-        for i, l in enumerate(modulelist):
-            x = l(x)
-            if i == 6:
-                c1 = x
-            elif i == 20:
-                c2 = x
-            elif i == 23:
-                c3 = x
-
-        for j in range(self.coarse_ind):
-            latents.append(self.styles[j](c3))
-
-        p2 = _upsample_add(c3, self.latlayer1(c2))
-        for j in range(self.coarse_ind, self.middle_ind):
-            latents.append(self.styles[j](p2))
-
-        p1 = _upsample_add(p2, self.latlayer2(c1))
-        for j in range(self.middle_ind, self.style_count):
-            latents.append(self.styles[j](p1))
-
-        out = torch.stack(latents, dim=1)
-        return out
-
-
-class Encoder4Editing(Module):
-    def __init__(self, num_layers, mode='ir', opts=None):
-        super(Encoder4Editing, self).__init__()
-        assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
-        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
-        blocks = get_blocks(num_layers)
-        if mode == 'ir':
-            unit_module = bottleneck_IR
-        elif mode == 'ir_se':
-            unit_module = bottleneck_IR_SE
-        self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
-                                      BatchNorm2d(64),
-                                      PReLU(64))
-        modules = []
-        for block in blocks:
-            for bottleneck in block:
-                modules.append(unit_module(bottleneck.in_channel,
-                                           bottleneck.depth,
-                                           bottleneck.stride))
-        self.body = Sequential(*modules)
-
-        self.styles = nn.ModuleList()
-        log_size = int(math.log(opts.stylegan_size, 2))
-        self.style_count = 2 * log_size - 2
-        self.coarse_ind = 3
-        self.middle_ind = 7
-
-        for i in range(self.style_count):
-            if i < self.coarse_ind:
-                style = GradualStyleBlock(512, 512, 16)
-            elif i < self.middle_ind:
-                style = GradualStyleBlock(512, 512, 32)
-            else:
-                style = GradualStyleBlock(512, 512, 64)
-            self.styles.append(style)
-
-        self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0)
-        self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0)
-
-        self.progressive_stage = ProgressiveStage.Inference
-
-    def get_deltas_starting_dimensions(self):
-        ''' Get a list of the initial dimension of every delta from which it is applied '''
-        return list(range(self.style_count))  # Each dimension has a delta applied to it
-
-    def set_progressive_stage(self, new_stage: ProgressiveStage):
-        self.progressive_stage = new_stage
-        print('Changed progressive stage to: ', new_stage)
-
-    def forward(self, x):
-        x = self.input_layer(x)
-
-        modulelist = list(self.body._modules.values())
-        for i, l in enumerate(modulelist):
-            x = l(x)
-            if i == 6:
-                c1 = x
-            elif i == 20:
-                c2 = x
-            elif i == 23:
-                c3 = x
-
-        # Infer main W and duplicate it
-        w0 = self.styles[0](c3)
-        w = w0.repeat(self.style_count, 1, 1).permute(1, 0, 2)
-        stage = self.progressive_stage.value
-        features = c3
-        for i in range(1, min(stage + 1, self.style_count)):  # Infer additional deltas
-            if i == self.coarse_ind:
-                p2 = _upsample_add(c3, self.latlayer1(c2))  # FPN's middle features
-                features = p2
-            elif i == self.middle_ind:
-                p1 = _upsample_add(p2, self.latlayer2(c1))  # FPN's fine features
-                features = p1
-            delta_i = self.styles[i](features)
-            w[:, i] += delta_i
-        return w
diff --git a/spaces/ali-ghamdan/deoldify/fastai/vision/image.py b/spaces/ali-ghamdan/deoldify/fastai/vision/image.py
deleted file mode 100644
index 5aefc935b863931d9aca4f02140529478f5c4337..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/deoldify/fastai/vision/image.py
+++ /dev/null
@@ -1,627 +0,0 @@
-"`Image` provides support to convert, transform and show images"
-from ..torch_core import *
-from ..basic_data import *
-from ..layers import MSELossFlat
-from io import BytesIO
-import PIL
-
-__all__ = ['PIL', 'Image', 'ImageBBox', 'ImageSegment', 'ImagePoints', 'FlowField', 'RandTransform', 'TfmAffine', 'TfmCoord',
-           'TfmCrop', 'TfmLighting', 'TfmPixel', 'Transform', 'bb2hw', 'image2np', 'open_image', 'open_mask', 'tis2hw',
-           'pil2tensor', 'scale_flow', 'show_image', 'CoordFunc', 'TfmList', 'open_mask_rle', 'rle_encode',
-           'rle_decode', 'ResizeMethod', 'plot_flat', 'plot_multi', 'show_multi', 'show_all']
-
-ResizeMethod = IntEnum('ResizeMethod', 'CROP PAD SQUISH NO')
-def pil2tensor(image:Union[NPImage,NPArray],dtype:np.dtype)->TensorImage:
-    "Convert PIL style `image` array to torch style image tensor."
-    a = np.asarray(image)
-    if a.ndim==2 : a = np.expand_dims(a,2)
-    a = np.transpose(a, (1, 0, 2))
-    a = np.transpose(a, (2, 1, 0))
-    return torch.from_numpy(a.astype(dtype, copy=False) )
-
-def image2np(image:Tensor)->np.ndarray:
-    "Convert from torch style `image` to numpy/matplotlib style."
-    res = image.cpu().permute(1,2,0).numpy()
-    return res[...,0] if res.shape[2]==1 else res
-
-def bb2hw(a:Collection[int])->np.ndarray:
-    "Convert bounding box points from (width,height,center) to (height,width,top,left)."
-    return np.array([a[1],a[0],a[3]-a[1],a[2]-a[0]])
-
-def tis2hw(size:Union[int,TensorImageSize]) -> Tuple[int,int]:
-    "Convert `int` or `TensorImageSize` to (height,width) of an image."
-    if type(size) is str: raise RuntimeError("Expected size to be an int or a tuple, got a string.")
-    return listify(size, 2) if isinstance(size, int) else listify(size[-2:],2)
-
-def _draw_outline(o:Patch, lw:int):
-    "Outline bounding box onto image `Patch`."
-    o.set_path_effects([patheffects.Stroke(
-        linewidth=lw, foreground='black'), patheffects.Normal()])
-
-def _draw_rect(ax:plt.Axes, b:Collection[int], color:str='white', text=None, text_size=14):
-    "Draw bounding box on `ax`."
-    patch = ax.add_patch(patches.Rectangle(b[:2], *b[-2:], fill=False, edgecolor=color, lw=2))
-    _draw_outline(patch, 4)
-    if text is not None:
-        patch = ax.text(*b[:2], text, verticalalignment='top', color=color, fontsize=text_size, weight='bold')
-        _draw_outline(patch,1)
-
-def _get_default_args(func:Callable):
-    return {k: v.default
-            for k, v in inspect.signature(func).parameters.items()
-            if v.default is not inspect.Parameter.empty}
-
-@dataclass
-class FlowField():
-    "Wrap together some coords `flow` with a `size`."
-    size:Tuple[int,int]
-    flow:Tensor
-
-CoordFunc = Callable[[FlowField, ArgStar, KWArgs], LogitTensorImage]
-
-class Image(ItemBase):
-    "Support applying transforms to image data in `px`."
-    def __init__(self, px:Tensor):
-        self._px = px
-        self._logit_px=None
-        self._flow=None
-        self._affine_mat=None
-        self.sample_kwargs = {}
-
-    def set_sample(self, **kwargs)->'ImageBase':
-        "Set parameters that control how we `grid_sample` the image after transforms are applied."
-        self.sample_kwargs = kwargs
-        return self
-
-    def clone(self):
-        "Mimic the behavior of torch.clone for `Image` objects."
-        return self.__class__(self.px.clone())
-
-    @property
-    def shape(self)->Tuple[int,int,int]: return self._px.shape
-    @property
-    def size(self)->Tuple[int,int]: return self.shape[-2:]
-    @property
-    def device(self)->torch.device: return self._px.device
-
-    def __repr__(self): return f'{self.__class__.__name__} {tuple(self.shape)}'
-    def _repr_png_(self): return self._repr_image_format('png')
-    def _repr_jpeg_(self): return self._repr_image_format('jpeg')
-
-    def _repr_image_format(self, format_str):
-        with BytesIO() as str_buffer:
-            plt.imsave(str_buffer, image2np(self.px), format=format_str)
-            return str_buffer.getvalue()
-
-    def apply_tfms(self, tfms:TfmList, do_resolve:bool=True, xtra:Optional[Dict[Callable,dict]]=None,
-                   size:Optional[Union[int,TensorImageSize]]=None, resize_method:ResizeMethod=None,
-                   mult:int=None, padding_mode:str='reflection', mode:str='bilinear', remove_out:bool=True,
-                   is_x:bool=True, x_frames:int=1, y_frames:int=1)->TensorImage:
-        "Apply all `tfms` to the `Image`, if `do_resolve` picks value for random args."
-        if not (tfms or xtra or size): return self
-
-        if size is not None and isinstance(size, int):
-            num_frames = x_frames if is_x else y_frames
-            if num_frames > 1:
-                size = (size, size*num_frames)
-
-        tfms = listify(tfms)
-        xtra = ifnone(xtra, {})
-        default_rsz = ResizeMethod.SQUISH if (size is not None and is_listy(size)) else ResizeMethod.CROP
-        resize_method = ifnone(resize_method, default_rsz)
-        if resize_method <= 2 and size is not None: tfms = self._maybe_add_crop_pad(tfms)
-        tfms = sorted(tfms, key=lambda o: o.tfm.order)
-        if do_resolve: _resolve_tfms(tfms)
-        x = self.clone()
-        x.set_sample(padding_mode=padding_mode, mode=mode, remove_out=remove_out)
-        if size is not None:
-            crop_target = _get_crop_target(size, mult=mult)
-            if resize_method in (ResizeMethod.CROP,ResizeMethod.PAD):
-                target = _get_resize_target(x, crop_target, do_crop=(resize_method==ResizeMethod.CROP))
-                x.resize(target)
-            elif resize_method==ResizeMethod.SQUISH: x.resize((x.shape[0],) + crop_target)
-        else: size = x.size
-        size_tfms = [o for o in tfms if isinstance(o.tfm,TfmCrop)]
-        for tfm in tfms:
-            if tfm.tfm in xtra: x = tfm(x, **xtra[tfm.tfm])
-            elif tfm in size_tfms:
-                if resize_method in (ResizeMethod.CROP,ResizeMethod.PAD):
-                    x = tfm(x, size=_get_crop_target(size,mult=mult), padding_mode=padding_mode)
-            else: x = tfm(x)
-        return x.refresh()
-
-    def refresh(self)->None:
-        "Apply any logit, flow, or affine transfers that have been sent to the `Image`."
-        if self._logit_px is not None:
-            self._px = self._logit_px.sigmoid_()
-            self._logit_px = None
-        if self._affine_mat is not None or self._flow is not None:
-            self._px = _grid_sample(self._px, self.flow, **self.sample_kwargs)
-            self.sample_kwargs = {}
-            self._flow = None
-        return self
-
-    def save(self, fn:PathOrStr):
-        "Save the image to `fn`."
-        x = image2np(self.data*255).astype(np.uint8)
-        PIL.Image.fromarray(x).save(fn)
-
-    @property
-    def px(self)->TensorImage:
-        "Get the tensor pixel buffer."
-        self.refresh()
-        return self._px
-    @px.setter
-    def px(self,v:TensorImage)->None:
-        "Set the pixel buffer to `v`."
-        self._px=v
-
-    @property
-    def flow(self)->FlowField:
-        "Access the flow-field grid after applying queued affine transforms."
-        if self._flow is None:
-            self._flow = _affine_grid(self.shape)
-        if self._affine_mat is not None:
-            self._flow = _affine_mult(self._flow,self._affine_mat)
-            self._affine_mat = None
-        return self._flow
-
-    @flow.setter
-    def flow(self,v:FlowField): self._flow=v
-
-    def lighting(self, func:LightingFunc, *args:Any, **kwargs:Any):
-        "Equivalent to `image = sigmoid(func(logit(image)))`."
-        self.logit_px = func(self.logit_px, *args, **kwargs)
-        return self
-
-    def pixel(self, func:PixelFunc, *args, **kwargs)->'Image':
-        "Equivalent to `image.px = func(image.px)`."
-        self.px = func(self.px, *args, **kwargs)
-        return self
-
-    def coord(self, func:CoordFunc, *args, **kwargs)->'Image':
-        "Equivalent to `image.flow = func(image.flow, image.size)`."
-        self.flow = func(self.flow, *args, **kwargs)
-        return self
-
-    def affine(self, func:AffineFunc, *args, **kwargs)->'Image':
-        "Equivalent to `image.affine_mat = image.affine_mat @ func()`."
-        m = tensor(func(*args, **kwargs)).to(self.device)
-        self.affine_mat = self.affine_mat @ m
-        return self
-
-    def resize(self, size:Union[int,TensorImageSize])->'Image':
-        "Resize the image to `size`, size can be a single int."
-        assert self._flow is None
-        if isinstance(size, int): size=(self.shape[0], size, size)
-        if tuple(size)==tuple(self.shape): return self
-        self.flow = _affine_grid(size)
-        return self
-
-    @property
-    def affine_mat(self)->AffineMatrix:
-        "Get the affine matrix that will be applied by `refresh`."
-        if self._affine_mat is None:
-            self._affine_mat = torch.eye(3).to(self.device)
-        return self._affine_mat
-    @affine_mat.setter
-    def affine_mat(self,v)->None: self._affine_mat=v
-
-    @property
-    def logit_px(self)->LogitTensorImage:
-        "Get logit(image.px)."
-        if self._logit_px is None: self._logit_px = logit_(self.px)
-        return self._logit_px
-    @logit_px.setter
-    def logit_px(self,v:LogitTensorImage)->None: self._logit_px=v
-
-    @property
-    def data(self)->TensorImage:
-        "Return this images pixels as a tensor."
-        return self.px
-
-    def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
-              cmap:str=None, y:Any=None, **kwargs):
-        "Show image on `ax` with `title`, using `cmap` if single-channel, overlaid with optional `y`"
-        cmap = ifnone(cmap, defaults.cmap)
-        ax = show_image(self, ax=ax, hide_axis=hide_axis, cmap=cmap, figsize=figsize)
-        if y is not None: y.show(ax=ax, **kwargs)
-        if title is not None: ax.set_title(title)
-
-class ImageSegment(Image):
-    "Support applying transforms to segmentation masks data in `px`."
-    def lighting(self, func:LightingFunc, *args:Any, **kwargs:Any)->'Image': return self
-
-    def refresh(self):
-        self.sample_kwargs['mode'] = 'nearest'
-        return super().refresh()
-
-    @property
-    def data(self)->TensorImage:
-        "Return this image pixels as a `LongTensor`."
-        return self.px.long()
-
-    def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
-        cmap:str='tab20', alpha:float=0.5, **kwargs):
-        "Show the `ImageSegment` on `ax`."
-        ax = show_image(self, ax=ax, hide_axis=hide_axis, cmap=cmap, figsize=figsize,
-                        interpolation='nearest', alpha=alpha, vmin=0, **kwargs)
-        if title: ax.set_title(title)
-
-    def reconstruct(self, t:Tensor): return ImageSegment(t)
-
-class ImagePoints(Image):
-    "Support applying transforms to a `flow` of points."
-    def __init__(self, flow:FlowField, scale:bool=True, y_first:bool=True):
-        if scale: flow = scale_flow(flow)
-        if y_first: flow.flow = flow.flow.flip(1)
-        self._flow = flow
-        self._affine_mat = None
-        self.flow_func = []
-        self.sample_kwargs = {}
-        self.transformed = False
-        self.loss_func = MSELossFlat()
-
-    def clone(self):
-        "Mimic the behavior of torch.clone for `ImagePoints` objects."
-        return self.__class__(FlowField(self.size, self.flow.flow.clone()), scale=False, y_first=False)
-
-    @property
-    def shape(self)->Tuple[int,int,int]: return (1, *self._flow.size)
-    @property
-    def size(self)->Tuple[int,int]: return self._flow.size
-    @size.setter
-    def size(self, sz:int): self._flow.size=sz
-    @property
-    def device(self)->torch.device: return self._flow.flow.device
-
-    def __repr__(self): return f'{self.__class__.__name__} {tuple(self.size)}'
-    def _repr_image_format(self, format_str): return None
-
-    @property
-    def flow(self)->FlowField:
-        "Access the flow-field grid after applying queued affine and coord transforms."
-        if self._affine_mat is not None:
-            self._flow = _affine_inv_mult(self._flow, self._affine_mat)
-            self._affine_mat = None
-            self.transformed = True
-        if len(self.flow_func) != 0:
-            for f in self.flow_func[::-1]: self._flow = f(self._flow)
-            self.transformed = True
-            self.flow_func = []
-        return self._flow
-
-    @flow.setter
-    def flow(self,v:FlowField):  self._flow=v
-
-    def coord(self, func:CoordFunc, *args, **kwargs)->'ImagePoints':
-        "Put `func` with `args` and `kwargs` in `self.flow_func` for later."
-        if 'invert' in kwargs: kwargs['invert'] = True
-        else: warn(f"{func.__name__} isn't implemented for {self.__class__}.")
-        self.flow_func.append(partial(func, *args, **kwargs))
-        return self
-
-    def lighting(self, func:LightingFunc, *args:Any, **kwargs:Any)->'ImagePoints': return self
-
-    def pixel(self, func:PixelFunc, *args, **kwargs)->'ImagePoints':
-        "Equivalent to `self = func_flow(self)`."
-        self = func(self, *args, **kwargs)
-        self.transformed=True
-        return self
-
-    def refresh(self) -> 'ImagePoints':
-        return self
-
-    def resize(self, size:Union[int,TensorImageSize]) -> 'ImagePoints':
-        "Resize the image to `size`, size can be a single int."
-        if isinstance(size, int): size=(1, size, size)
-        self._flow.size = size[1:]
-        return self
-
-    @property
-    def data(self)->Tensor:
-        "Return the points associated to this object."
-        flow = self.flow #This updates flow before we test if some transforms happened
-        if self.transformed:
-            if 'remove_out' not in self.sample_kwargs or self.sample_kwargs['remove_out']:
-                flow = _remove_points_out(flow)
-            self.transformed=False
-        return flow.flow.flip(1)
-
-    def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True, **kwargs):
-        "Show the `ImagePoints` on `ax`."
-        if ax is None: _,ax = plt.subplots(figsize=figsize)
-        pnt = scale_flow(FlowField(self.size, self.data), to_unit=False).flow.flip(1)
-        params = {'s': 10, 'marker': '.', 'c': 'r', **kwargs}
-        ax.scatter(pnt[:, 0], pnt[:, 1], **params)
-        if hide_axis: ax.axis('off')
-        if title: ax.set_title(title)
-
-class ImageBBox(ImagePoints):
-    "Support applying transforms to a `flow` of bounding boxes."
-    def __init__(self, flow:FlowField, scale:bool=True, y_first:bool=True, labels:Collection=None,
-                 classes:dict=None, pad_idx:int=0):
-        super().__init__(flow, scale, y_first)
-        self.pad_idx = pad_idx
-        if labels is not None and len(labels)>0 and not isinstance(labels[0],Category):
-            labels = array([Category(l,classes[l]) for l in labels])
-        self.labels = labels
-
-    def clone(self) -> 'ImageBBox':
-        "Mimic the behavior of torch.clone for `Image` objects."
-        flow = FlowField(self.size, self.flow.flow.clone())
-        return self.__class__(flow, scale=False, y_first=False, labels=self.labels, pad_idx=self.pad_idx)
-
-    @classmethod
-    def create(cls, h:int, w:int, bboxes:Collection[Collection[int]], labels:Collection=None, classes:dict=None,
-               pad_idx:int=0, scale:bool=True)->'ImageBBox':
-        "Create an ImageBBox object from `bboxes`."
-        if isinstance(bboxes, np.ndarray) and bboxes.dtype == np.object: bboxes = np.array([bb for bb in bboxes])
-        bboxes = tensor(bboxes).float()
-        tr_corners = torch.cat([bboxes[:,0][:,None], bboxes[:,3][:,None]], 1)
-        bl_corners = bboxes[:,1:3].flip(1)
-        bboxes = torch.cat([bboxes[:,:2], tr_corners, bl_corners, bboxes[:,2:]], 1)
-        flow = FlowField((h,w), bboxes.view(-1,2))
-        return cls(flow, labels=labels, classes=classes, pad_idx=pad_idx, y_first=True, scale=scale)
-
-    def _compute_boxes(self) -> Tuple[LongTensor, LongTensor]:
-        bboxes = self.flow.flow.flip(1).view(-1, 4, 2).contiguous().clamp(min=-1, max=1)
-        mins, maxes = bboxes.min(dim=1)[0], bboxes.max(dim=1)[0]
-        bboxes = torch.cat([mins, maxes], 1)
-        mask = (bboxes[:,2]-bboxes[:,0] > 0) * (bboxes[:,3]-bboxes[:,1] > 0)
-        if len(mask) == 0: return tensor([self.pad_idx] * 4), tensor([self.pad_idx])
-        res = bboxes[mask]
-        if self.labels is None: return res,None
-        return res, self.labels[to_np(mask).astype(bool)]
-
-    @property
-    def data(self)->Union[FloatTensor, Tuple[FloatTensor,LongTensor]]:
-        bboxes,lbls = self._compute_boxes()
-        lbls = np.array([o.data for o in lbls]) if lbls is not None else None
-        return bboxes if lbls is None else (bboxes, lbls)
-
-    def show(self, y:Image=None, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
-        color:str='white', **kwargs):
-        "Show the `ImageBBox` on `ax`."
-        if ax is None: _,ax = plt.subplots(figsize=figsize)
-        bboxes, lbls = self._compute_boxes()
-        h,w = self.flow.size
-        bboxes.add_(1).mul_(torch.tensor([h/2, w/2, h/2, w/2])).long()
-        for i, bbox in enumerate(bboxes):
-            if lbls is not None: text = str(lbls[i])
-            else: text=None
-            _draw_rect(ax, bb2hw(bbox), text=text, color=color)
-
-def open_image(fn:PathOrStr, div:bool=True, convert_mode:str='RGB', cls:type=Image,
-        after_open:Callable=None)->Image:
-    "Return `Image` object created from image in file `fn`."
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", UserWarning) # EXIF warning from TiffPlugin
-        x = PIL.Image.open(fn).convert(convert_mode)
-    if after_open: x = after_open(x)
-    x = pil2tensor(x,np.float32)
-    if div: x.div_(255)
-    return cls(x)
-
-def open_mask(fn:PathOrStr, div=False, convert_mode='L', after_open:Callable=None)->ImageSegment:
-    "Return `ImageSegment` object create from mask in file `fn`. If `div`, divides pixel values by 255."
-    return open_image(fn, div=div, convert_mode=convert_mode, cls=ImageSegment, after_open=after_open)
-
-def open_mask_rle(mask_rle:str, shape:Tuple[int, int])->ImageSegment:
-    "Return `ImageSegment` object create from run-length encoded string in `mask_lre` with size in `shape`."
-    x = FloatTensor(rle_decode(str(mask_rle), shape).astype(np.uint8))
-    x = x.view(shape[1], shape[0], -1)
-    return ImageSegment(x.permute(2,0,1))
-
-def rle_encode(img:NPArrayMask)->str:
-    "Return run-length encoding string from `img`."
-    pixels = np.concatenate([[0], img.flatten() , [0]])
-    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
-    runs[1::2] -= runs[::2]
-    return ' '.join(str(x) for x in runs)
-
-def rle_decode(mask_rle:str, shape:Tuple[int,int])->NPArrayMask:
-    "Return an image array from run-length encoded string `mask_rle` with `shape`."
-    s = mask_rle.split()
-    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
-    starts -= 1
-    ends = starts + lengths
-    img = np.zeros(shape[0]*shape[1], dtype=np.uint)
-    for low, up in zip(starts, ends): img[low:up] = 1
-    return img.reshape(shape)
-
-def show_image(img:Image, ax:plt.Axes=None, figsize:tuple=(3,3), hide_axis:bool=True, cmap:str='binary',
-                alpha:float=None, **kwargs)->plt.Axes:
-    "Display `Image` in notebook."
-    if ax is None: fig,ax = plt.subplots(figsize=figsize)
-    ax.imshow(image2np(img.data), cmap=cmap, alpha=alpha, **kwargs)
-    if hide_axis: ax.axis('off')
-    return ax
-
-def scale_flow(flow, to_unit=True):
-    "Scale the coords in `flow` to -1/1 or the image size depending on `to_unit`."
-    s = tensor([flow.size[0]/2,flow.size[1]/2])[None]
-    if to_unit: flow.flow = flow.flow/s-1
-    else:       flow.flow = (flow.flow+1)*s
-    return flow
-
-def _remove_points_out(flow:FlowField):
-    pad_mask = (flow.flow[:,0] >= -1) * (flow.flow[:,0] <= 1) * (flow.flow[:,1] >= -1) * (flow.flow[:,1] <= 1)
-    flow.flow = flow.flow[pad_mask]
-    return flow
-
-class Transform():
-    "Utility class for adding probability and wrapping support to transform `func`."
-    _wrap=None
-    order=0
-    def __init__(self, func:Callable, order:Optional[int]=None):
-        "Create a transform for `func` and assign it an priority `order`, attach to `Image` class."
-        if order is not None: self.order=order
-        self.func=func
-        self.func.__name__ = func.__name__[1:] #To remove the _ that begins every transform function.
-        functools.update_wrapper(self, self.func)
-        self.func.__annotations__['return'] = Image
-        self.params = copy(func.__annotations__)
-        self.def_args = _get_default_args(func)
-        setattr(Image, func.__name__,
-                lambda x, *args, **kwargs: self.calc(x, *args, **kwargs))
-
-    def __call__(self, *args:Any, p:float=1., is_random:bool=True, use_on_y:bool=True, **kwargs:Any)->Image:
-        "Calc now if `args` passed; else create a transform called prob `p` if `random`."
-        if args: return self.calc(*args, **kwargs)
-        else: return RandTransform(self, kwargs=kwargs, is_random=is_random, use_on_y=use_on_y, p=p)
-
-    def calc(self, x:Image, *args:Any, **kwargs:Any)->Image:
-        "Apply to image `x`, wrapping it if necessary."
-        if self._wrap: return getattr(x, self._wrap)(self.func, *args, **kwargs)
-        else:          return self.func(x, *args, **kwargs)
-
-    @property
-    def name(self)->str: return self.__class__.__name__
-
-    def __repr__(self)->str: return f'{self.name} ({self.func.__name__})'
-
-@dataclass
-class RandTransform():
-    "Wrap `Transform` to add randomized execution."
-    tfm:Transform
-    kwargs:dict
-    p:float=1.0
-    resolved:dict = field(default_factory=dict)
-    do_run:bool = True
-    is_random:bool = True
-    use_on_y:bool = True
-    def __post_init__(self): functools.update_wrapper(self, self.tfm)
-
-    def resolve(self)->None:
-        "Bind any random variables in the transform."
-        if not self.is_random:
-            self.resolved = {**self.tfm.def_args, **self.kwargs}
-            return
-
-        self.resolved = {}
-        # for each param passed to tfm...
-        for k,v in self.kwargs.items():
-            # ...if it's annotated, call that fn...
-            if k in self.tfm.params:
-                rand_func = self.tfm.params[k]
-                self.resolved[k] = rand_func(*listify(v))
-            # ...otherwise use the value directly
-            else: self.resolved[k] = v
-        # use defaults for any args not filled in yet
-        for k,v in self.tfm.def_args.items():
-            if k not in self.resolved: self.resolved[k]=v
-        # anything left over must be callable without params
-        for k,v in self.tfm.params.items():
-            if k not in self.resolved and k!='return': self.resolved[k]=v()
-
-        self.do_run = rand_bool(self.p)
-
-    @property
-    def order(self)->int: return self.tfm.order
-
-    def __call__(self, x:Image, *args, **kwargs)->Image:
-        "Randomly execute our tfm on `x`."
-        return self.tfm(x, *args, **{**self.resolved, **kwargs}) if self.do_run else x
-
-def _resolve_tfms(tfms:TfmList):
-    "Resolve every tfm in `tfms`."
-    for f in listify(tfms): f.resolve()
-
-def _grid_sample(x:TensorImage, coords:FlowField, mode:str='bilinear', padding_mode:str='reflection', remove_out:bool=True)->TensorImage:
-    "Resample pixels in `coords` from `x` by `mode`, with `padding_mode` in ('reflection','border','zeros')."
-    coords = coords.flow.permute(0, 3, 1, 2).contiguous().permute(0, 2, 3, 1) # optimize layout for grid_sample
-    if mode=='bilinear': # hack to get smoother downwards resampling
-        mn,mx = coords.min(),coords.max()
-        # max amount we're affine zooming by (>1 means zooming in)
-        z = 1/(mx-mn).item()*2
-        # amount we're resizing by, with 100% extra margin
-        d = min(x.shape[1]/coords.shape[1], x.shape[2]/coords.shape[2])/2
-        # If we're resizing up by >200%, and we're zooming less than that, interpolate first
-        if d>1 and d>z: x = F.interpolate(x[None], scale_factor=1/d, mode='area')[0]
-    return F.grid_sample(x[None], coords, mode=mode, padding_mode=padding_mode)[0]
-
-def _affine_grid(size:TensorImageSize)->FlowField:
-    size = ((1,)+size)
-    N, C, H, W = size
-    grid = FloatTensor(N, H, W, 2)
-    linear_points = torch.linspace(-1, 1, W) if W > 1 else tensor([-1])
-    grid[:, :, :, 0] = torch.ger(torch.ones(H), linear_points).expand_as(grid[:, :, :, 0])
-    linear_points = torch.linspace(-1, 1, H) if H > 1 else tensor([-1])
-    grid[:, :, :, 1] = torch.ger(linear_points, torch.ones(W)).expand_as(grid[:, :, :, 1])
-    return FlowField(size[2:], grid)
-
-def _affine_mult(c:FlowField,m:AffineMatrix)->FlowField:
-    "Multiply `c` by `m` - can adjust for rectangular shaped `c`."
-    if m is None: return c
-    size = c.flow.size()
-    h,w = c.size
-    m[0,1] *= h/w
-    m[1,0] *= w/h
-    c.flow = c.flow.view(-1,2)
-    c.flow = torch.addmm(m[:2,2], c.flow,  m[:2,:2].t()).view(size)
-    return c
-
-def _affine_inv_mult(c, m):
-    "Applies the inverse affine transform described in `m` to `c`."
-    size = c.flow.size()
-    h,w = c.size
-    m[0,1] *= h/w
-    m[1,0] *= w/h
-    c.flow = c.flow.view(-1,2)
-    a = torch.inverse(m[:2,:2].t())
-    c.flow = torch.mm(c.flow - m[:2,2], a).view(size)
-    return c
-
-class TfmAffine(Transform):
-    "Decorator for affine tfm funcs."
-    order,_wrap = 5,'affine'
-class TfmPixel(Transform):
-    "Decorator for pixel tfm funcs."
-    order,_wrap = 10,'pixel'
-class TfmCoord(Transform):
-    "Decorator for coord tfm funcs."
-    order,_wrap = 4,'coord'
-class TfmCrop(TfmPixel):
-    "Decorator for crop tfm funcs."
-    order=99
-class TfmLighting(Transform):
-    "Decorator for lighting tfm funcs."
-    order,_wrap = 8,'lighting'
-
-def _round_multiple(x:int, mult:int=None)->int:
-    "Calc `x` to nearest multiple of `mult`."
-    return (int(x/mult+0.5)*mult) if mult is not None else x
-
-def _get_crop_target(target_px:Union[int,TensorImageSize], mult:int=None)->Tuple[int,int]:
-    "Calc crop shape of `target_px` to nearest multiple of `mult`."
-    target_r,target_c = tis2hw(target_px)
-    return _round_multiple(target_r,mult),_round_multiple(target_c,mult)
-
-def _get_resize_target(img, crop_target, do_crop=False)->TensorImageSize:
-    "Calc size of `img` to fit in `crop_target` - adjust based on `do_crop`."
-    if crop_target is None: return None
-    ch,r,c = img.shape
-    target_r,target_c = crop_target
-    ratio = (min if do_crop else max)(r/target_r, c/target_c)
-    return ch,int(round(r/ratio)),int(round(c/ratio)) #Sometimes those are numpy numbers and round doesn't return an int.
-
-def plot_flat(r, c, figsize):
-    "Shortcut for `enumerate(subplots.flatten())`"
-    return enumerate(plt.subplots(r, c, figsize=figsize)[1].flatten())
-
-def plot_multi(func:Callable[[int,int,plt.Axes],None], r:int=1, c:int=1, figsize:Tuple=(12,6)):
-    "Call `func` for every combination of `r,c` on a subplot"
-    axes = plt.subplots(r, c, figsize=figsize)[1]
-    for i in range(r):
-        for j in range(c): func(i,j,axes[i,j])
-
-def show_multi(func:Callable[[int,int],Image], r:int=1, c:int=1, figsize:Tuple=(9,9)):
-    "Call `func(i,j).show(ax)` for every combination of `r,c`"
-    plot_multi(lambda i,j,ax: func(i,j).show(ax), r, c, figsize=figsize)
-
-def show_all(imgs:Collection[Image], r:int=1, c:Optional[int]=None, figsize=(12,6)):
-    "Show all `imgs` using `r` rows"
-    imgs = listify(imgs)
-    if c is None: c = len(imgs)//r
-    for i,ax in plot_flat(r,c,figsize): imgs[i].show(ax)
diff --git a/spaces/allknowingroger/Image-Models-Test18/app.py b/spaces/allknowingroger/Image-Models-Test18/app.py
deleted file mode 100644
index 535a94e0899190d1251f36663d979869cdb35024..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test18/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
-    "zhangyi617/car-lora",
-    "samankhan07/sdxl_try",
-    "Hemanth-thunder/kazuki_kurusu_lora_xl",
-    "oloflindh/marcus-test",
-    "digiplay/CityEdge_StyleMix_v1.44",
-    "digiplay/LusterMix_v1.5_safetensors",
-    "digiplay/ZemiHR_v2_diffusers",
-    "digiplay/EtherRealMix_1",
-    "digiplay/EdisonNilMix_v1",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
-    try:
-        model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
-    except Exception as error:
-        def the_fn(txt):
-            return None
-        model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
-    model_idx+=1
-
-
-def send_it_idx(idx):
-    def send_it_fn(prompt):
-        output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
-        return output
-    return send_it_fn
-
-def get_prompts(prompt_text):
-    return prompt_text
-
-def clear_it(val):
-    if int(val) != 0:
-        val = 0
-    else:
-        val = 0
-        pass
-    return val
-
-def all_task_end(cnt,t_stamp):
-    to = t_stamp + 60
-    et = time.time()
-    if et > to and t_stamp != 0:
-        d = gr.update(value=0)
-        tog = gr.update(value=1)
-        #print(f'to: {to}  et: {et}')
-    else:
-        if cnt != 0:
-            d = gr.update(value=et)
-        else:
-            d = gr.update(value=0)
-        tog = gr.update(value=0)
-        #print (f'passing:  to: {to}  et: {et}')
-        pass
-    return d, tog
-
-def all_task_start():
-    print("\n\n\n\n\n\n\n")
-    t = time.gmtime()
-    t_stamp = time.time()
-    current_time = time.strftime("%H:%M:%S", t)
-    return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
-    nn = len(models)
-    return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
-    with gr.Column(scale=12):
-        # with gr.Row():
-        #     gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
-        with gr.Row():
-            with gr.Row(scale=6):
-                primary_prompt=gr.Textbox(label="Prompt", value="")
-                # real_prompt=gr.Textbox(label="Real prompt")
-            with gr.Row(scale=6):
-                # improve_prompts_btn=gr.Button("Improve")
-                with gr.Row():
-                    run=gr.Button("Run",variant="primary")
-                    clear_btn=gr.Button("Clear")
-        with gr.Row():
-            sd_outputs = {}
-            model_idx = 1
-            for model_path in models:
-                with gr.Column(scale=3, min_width=320):
-                    with gr.Box():
-                        sd_outputs[model_idx] = gr.Image(label=model_path)
-                    pass
-                model_idx += 1
-                pass
-            pass
-
-        with gr.Row(visible=False):
-            start_box=gr.Number(interactive=False)
-            end_box=gr.Number(interactive=False)
-            tog_box=gr.Textbox(value=0,interactive=False)
-
-        start_box.change(
-            all_task_end,
-            [start_box, end_box],
-            [start_box, tog_box],
-            every=1,
-            show_progress=False)
-
-        primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
-        run.click(all_task_start, None, [start_box, end_box, tog_box])
-        runs_dict = {}
-        model_idx = 1
-        for model_path in models:
-            runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
-            model_idx += 1
-            pass
-        pass
-
-        # improve_prompts_btn_clicked=improve_prompts_btn.click(
-        #     get_prompts,
-        #     inputs=[primary_prompt],
-        #     outputs=[primary_prompt],
-        #     cancels=list(runs_dict.values()))
-        clear_btn.click(
-            clear_fn,
-            None,
-            [primary_prompt, *list(sd_outputs.values())],
-            cancels=[*list(runs_dict.values())])
-        tog_box.change(
-            clear_it,
-            tog_box,
-            tog_box,
-            cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
- 
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test47/README.md b/spaces/allknowingroger/Image-Models-Test47/README.md
deleted file mode 100644
index 693359ecaf56e1673691874772df024a00b662cb..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test47/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Image Models
-emoji: 👀
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test46
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/antonbol/vocal_remover/app.py b/spaces/antonbol/vocal_remover/app.py
deleted file mode 100644
index 9e184b71468603334b1ee98ee43a8d7f820200c2..0000000000000000000000000000000000000000
--- a/spaces/antonbol/vocal_remover/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import gradio as gr
-import hopsworks
-import subprocess
-def vocal_remove(audio):    
-    project = hopsworks.login()
-    mr = project.get_model_registry()
-    # model = mr.get_best_model("vocal_remover", "validation_loss", "min")
-    model = mr.get_model("vocal_remover", version=3)
-    model_path = model.download()
-    model_path_pth = model_path + "/vocal_model.pth"
-    # print("model_path: ", model_path)s
-    subprocess.run(["python3", "inference.py", "--input", audio, "--pretrained_model", model_path_pth, "--output_dir", "./"])
-    return "./Instruments.wav"
-
-iface = gr.Interface(
-    fn=vocal_remove, 
-    inputs=gr.Audio(source="upload", type="filepath"), 
-    outputs="audio",
-    title="Vocal Remover",
-    description="Removes Vocals from song, currently undertrained, fragments of vocals can remain depending on song",
-)
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/codeformer/vqgan_arch.py b/spaces/aodianyun/stable-diffusion-webui/modules/codeformer/vqgan_arch.py
deleted file mode 100644
index e729368383aa2d8c224289284ec5489d554f9a33..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/codeformer/vqgan_arch.py
+++ /dev/null
@@ -1,437 +0,0 @@
-# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
-
-'''
-VQGAN code, adapted from the original created by the Unleashing Transformers authors:
-https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
-
-'''
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import copy
-from basicsr.utils import get_root_logger
-from basicsr.utils.registry import ARCH_REGISTRY
-
-def normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-    
-
-@torch.jit.script
-def swish(x):
-    return x*torch.sigmoid(x)
-
-
-#  Define VQVAE classes
-class VectorQuantizer(nn.Module):
-    def __init__(self, codebook_size, emb_dim, beta):
-        super(VectorQuantizer, self).__init__()
-        self.codebook_size = codebook_size  # number of embeddings
-        self.emb_dim = emb_dim  # dimension of embedding
-        self.beta = beta  # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
-        self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
-        self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)
-
-    def forward(self, z):
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = z.permute(0, 2, 3, 1).contiguous()
-        z_flattened = z.view(-1, self.emb_dim)
-
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-        d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
-            2 * torch.matmul(z_flattened, self.embedding.weight.t())
-
-        mean_distance = torch.mean(d)
-        # find closest encodings
-        # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
-        min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
-        # [0-1], higher score, higher confidence
-        min_encoding_scores = torch.exp(-min_encoding_scores/10)
-
-        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
-        min_encodings.scatter_(1, min_encoding_indices, 1)
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
-        # compute loss for embedding
-        loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # perplexity
-        e_mean = torch.mean(min_encodings, dim=0)
-        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
-        # reshape back to match original input shape
-        z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q, loss, {
-            "perplexity": perplexity,
-            "min_encodings": min_encodings,
-            "min_encoding_indices": min_encoding_indices,
-            "min_encoding_scores": min_encoding_scores,
-            "mean_distance": mean_distance
-            }
-
-    def get_codebook_feat(self, indices, shape):
-        # input indices: batch*token_num -> (batch*token_num)*1
-        # shape: batch, height, width, channel
-        indices = indices.view(-1,1)
-        min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
-        min_encodings.scatter_(1, indices, 1)
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
-
-        if shape is not None:  # reshape back to match original input shape
-            z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
-
-        return z_q
-
-
-class GumbelQuantizer(nn.Module):
-    def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
-        super().__init__()
-        self.codebook_size = codebook_size  # number of embeddings
-        self.emb_dim = emb_dim  # dimension of embedding
-        self.straight_through = straight_through
-        self.temperature = temp_init
-        self.kl_weight = kl_weight
-        self.proj = nn.Conv2d(num_hiddens, codebook_size, 1)  # projects last encoder layer to quantized logits
-        self.embed = nn.Embedding(codebook_size, emb_dim)
-
-    def forward(self, z):
-        hard = self.straight_through if self.training else True
-
-        logits = self.proj(z)
-
-        soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
-
-        z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
-
-        # + kl divergence to the prior loss
-        qy = F.softmax(logits, dim=1)
-        diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
-        min_encoding_indices = soft_one_hot.argmax(dim=1)
-
-        return z_q, diff, {
-            "min_encoding_indices": min_encoding_indices
-        }
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
-
-    def forward(self, x):
-        pad = (0, 1, 0, 1)
-        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-        x = self.conv(x)
-        return x
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x):
-        x = F.interpolate(x, scale_factor=2.0, mode="nearest")
-        x = self.conv(x)
-
-        return x
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_channels, out_channels=None):
-        super(ResBlock, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = in_channels if out_channels is None else out_channels
-        self.norm1 = normalize(in_channels)
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.norm2 = normalize(out_channels)
-        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        if self.in_channels != self.out_channels:
-            self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, x_in):
-        x = x_in
-        x = self.norm1(x)
-        x = swish(x)
-        x = self.conv1(x)
-        x = self.norm2(x)
-        x = swish(x)
-        x = self.conv2(x)
-        if self.in_channels != self.out_channels:
-            x_in = self.conv_out(x_in)
-
-        return x + x_in
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = normalize(in_channels)
-        self.q = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-        self.k = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-        self.v = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-        self.proj_out = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b, c, h, w = q.shape
-        q = q.reshape(b, c, h*w)
-        q = q.permute(0, 2, 1)   
-        k = k.reshape(b, c, h*w)
-        w_ = torch.bmm(q, k) 
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = F.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b, c, h*w)
-        w_ = w_.permute(0, 2, 1) 
-        h_ = torch.bmm(v, w_)
-        h_ = h_.reshape(b, c, h, w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-class Encoder(nn.Module):
-    def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
-        super().__init__()
-        self.nf = nf
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.attn_resolutions = attn_resolutions
-
-        curr_res = self.resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-
-        blocks = []
-        # initial convultion
-        blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
-
-        # residual and downsampling blocks, with attention on smaller res (16x16)
-        for i in range(self.num_resolutions):
-            block_in_ch = nf * in_ch_mult[i]
-            block_out_ch = nf * ch_mult[i]
-            for _ in range(self.num_res_blocks):
-                blocks.append(ResBlock(block_in_ch, block_out_ch))
-                block_in_ch = block_out_ch
-                if curr_res in attn_resolutions:
-                    blocks.append(AttnBlock(block_in_ch))
-
-            if i != self.num_resolutions - 1:
-                blocks.append(Downsample(block_in_ch))
-                curr_res = curr_res // 2
-
-        # non-local attention block
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-        blocks.append(AttnBlock(block_in_ch))
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-
-        # normalise and convert to latent size
-        blocks.append(normalize(block_in_ch))
-        blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1))
-        self.blocks = nn.ModuleList(blocks)
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-            
-        return x
-
-
-class Generator(nn.Module):
-    def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
-        super().__init__()
-        self.nf = nf 
-        self.ch_mult = ch_mult 
-        self.num_resolutions = len(self.ch_mult)
-        self.num_res_blocks = res_blocks
-        self.resolution = img_size 
-        self.attn_resolutions = attn_resolutions
-        self.in_channels = emb_dim
-        self.out_channels = 3
-        block_in_ch = self.nf * self.ch_mult[-1]
-        curr_res = self.resolution // 2 ** (self.num_resolutions-1)
-
-        blocks = []
-        # initial conv
-        blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))
-
-        # non-local attention block
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-        blocks.append(AttnBlock(block_in_ch))
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-
-        for i in reversed(range(self.num_resolutions)):
-            block_out_ch = self.nf * self.ch_mult[i]
-
-            for _ in range(self.num_res_blocks):
-                blocks.append(ResBlock(block_in_ch, block_out_ch))
-                block_in_ch = block_out_ch
-
-                if curr_res in self.attn_resolutions:
-                    blocks.append(AttnBlock(block_in_ch))
-
-            if i != 0:
-                blocks.append(Upsample(block_in_ch))
-                curr_res = curr_res * 2
-
-        blocks.append(normalize(block_in_ch))
-        blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
-
-        self.blocks = nn.ModuleList(blocks)
-   
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-            
-        return x
-
-  
-@ARCH_REGISTRY.register()
-class VQAutoEncoder(nn.Module):
-    def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256,
-                beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
-        super().__init__()
-        logger = get_root_logger()
-        self.in_channels = 3 
-        self.nf = nf 
-        self.n_blocks = res_blocks 
-        self.codebook_size = codebook_size
-        self.embed_dim = emb_dim
-        self.ch_mult = ch_mult
-        self.resolution = img_size
-        self.attn_resolutions = attn_resolutions
-        self.quantizer_type = quantizer
-        self.encoder = Encoder(
-            self.in_channels,
-            self.nf,
-            self.embed_dim,
-            self.ch_mult,
-            self.n_blocks,
-            self.resolution,
-            self.attn_resolutions
-        )
-        if self.quantizer_type == "nearest":
-            self.beta = beta #0.25
-            self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
-        elif self.quantizer_type == "gumbel":
-            self.gumbel_num_hiddens = emb_dim
-            self.straight_through = gumbel_straight_through
-            self.kl_weight = gumbel_kl_weight
-            self.quantize = GumbelQuantizer(
-                self.codebook_size,
-                self.embed_dim,
-                self.gumbel_num_hiddens,
-                self.straight_through,
-                self.kl_weight
-            )
-        self.generator = Generator(
-            self.nf, 
-            self.embed_dim,
-            self.ch_mult, 
-            self.n_blocks, 
-            self.resolution, 
-            self.attn_resolutions
-        )
-
-        if model_path is not None:
-            chkpt = torch.load(model_path, map_location='cpu')
-            if 'params_ema' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
-                logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
-            elif 'params' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
-                logger.info(f'vqgan is loaded from: {model_path} [params]')
-            else:
-                raise ValueError('Wrong params!')
-
-
-    def forward(self, x):
-        x = self.encoder(x)
-        quant, codebook_loss, quant_stats = self.quantize(x)
-        x = self.generator(quant)
-        return x, codebook_loss, quant_stats
-
-
-
-# patch based discriminator
-@ARCH_REGISTRY.register()
-class VQGANDiscriminator(nn.Module):
-    def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
-        super().__init__()
-
-        layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
-        ndf_mult = 1
-        ndf_mult_prev = 1
-        for n in range(1, n_layers):  # gradually increase the number of filters
-            ndf_mult_prev = ndf_mult
-            ndf_mult = min(2 ** n, 8)
-            layers += [
-                nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
-                nn.BatchNorm2d(ndf * ndf_mult),
-                nn.LeakyReLU(0.2, True)
-            ]
-
-        ndf_mult_prev = ndf_mult
-        ndf_mult = min(2 ** n_layers, 8)
-
-        layers += [
-            nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
-            nn.BatchNorm2d(ndf * ndf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-
-        layers += [
-            nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)]  # output 1 channel prediction map
-        self.main = nn.Sequential(*layers)
-
-        if model_path is not None:
-            chkpt = torch.load(model_path, map_location='cpu')
-            if 'params_d' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
-            elif 'params' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
-            else:
-                raise ValueError('Wrong params!')
-
-    def forward(self, x):
-        return self.main(x)
\ No newline at end of file
diff --git a/spaces/aodianyun/stable-diffusion-webui/scripts/poor_mans_outpainting.py b/spaces/aodianyun/stable-diffusion-webui/scripts/poor_mans_outpainting.py
deleted file mode 100644
index d39f61c1073376eae210d955ac1e9eba836402da..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/scripts/poor_mans_outpainting.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import math
-
-import modules.scripts as scripts
-import gradio as gr
-from PIL import Image, ImageDraw
-
-from modules import images, processing, devices
-from modules.processing import Processed, process_images
-from modules.shared import opts, cmd_opts, state
-
-
-class Script(scripts.Script):
-    def title(self):
-        return "Poor man's outpainting"
-
-    def show(self, is_img2img):
-        return is_img2img
-
-    def ui(self, is_img2img):
-        if not is_img2img:
-            return None
-        
-        pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels"))
-        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id=self.elem_id("mask_blur"))
-        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", elem_id=self.elem_id("inpainting_fill"))
-        direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id("direction"))
-
-        return [pixels, mask_blur, inpainting_fill, direction]
-
-    def run(self, p, pixels, mask_blur, inpainting_fill, direction):
-        initial_seed = None
-        initial_info = None
-
-        p.mask_blur = mask_blur * 2
-        p.inpainting_fill = inpainting_fill
-        p.inpaint_full_res = False
-
-        left = pixels if "left" in direction else 0
-        right = pixels if "right" in direction else 0
-        up = pixels if "up" in direction else 0
-        down = pixels if "down" in direction else 0
-
-        init_img = p.init_images[0]
-        target_w = math.ceil((init_img.width + left + right) / 64) * 64
-        target_h = math.ceil((init_img.height + up + down) / 64) * 64
-
-        if left > 0:
-            left = left * (target_w - init_img.width) // (left + right)
-        if right > 0:
-            right = target_w - init_img.width - left
-
-        if up > 0:
-            up = up * (target_h - init_img.height) // (up + down)
-
-        if down > 0:
-            down = target_h - init_img.height - up
-
-        img = Image.new("RGB", (target_w, target_h))
-        img.paste(init_img, (left, up))
-
-        mask = Image.new("L", (img.width, img.height), "white")
-        draw = ImageDraw.Draw(mask)
-        draw.rectangle((
-            left + (mask_blur * 2 if left > 0 else 0),
-            up + (mask_blur * 2 if up > 0 else 0),
-            mask.width - right - (mask_blur * 2 if right > 0 else 0),
-            mask.height - down - (mask_blur * 2 if down > 0 else 0)
-        ), fill="black")
-
-        latent_mask = Image.new("L", (img.width, img.height), "white")
-        latent_draw = ImageDraw.Draw(latent_mask)
-        latent_draw.rectangle((
-             left + (mask_blur//2 if left > 0 else 0),
-             up + (mask_blur//2 if up > 0 else 0),
-             mask.width - right - (mask_blur//2 if right > 0 else 0),
-             mask.height - down - (mask_blur//2 if down > 0 else 0)
-        ), fill="black")
-
-        devices.torch_gc()
-
-        grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=pixels)
-        grid_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
-        grid_latent_mask = images.split_grid(latent_mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
-
-        p.n_iter = 1
-        p.batch_size = 1
-        p.do_not_save_grid = True
-        p.do_not_save_samples = True
-
-        work = []
-        work_mask = []
-        work_latent_mask = []
-        work_results = []
-
-        for (y, h, row), (_, _, row_mask), (_, _, row_latent_mask) in zip(grid.tiles, grid_mask.tiles, grid_latent_mask.tiles):
-            for tiledata, tiledata_mask, tiledata_latent_mask in zip(row, row_mask, row_latent_mask):
-                x, w = tiledata[0:2]
-
-                if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down:
-                    continue
-
-                work.append(tiledata[2])
-                work_mask.append(tiledata_mask[2])
-                work_latent_mask.append(tiledata_latent_mask[2])
-
-        batch_count = len(work)
-        print(f"Poor man's outpainting will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)}.")
-
-        state.job_count = batch_count
-
-        for i in range(batch_count):
-            p.init_images = [work[i]]
-            p.image_mask = work_mask[i]
-            p.latent_mask = work_latent_mask[i]
-
-            state.job = f"Batch {i + 1} out of {batch_count}"
-            processed = process_images(p)
-
-            if initial_seed is None:
-                initial_seed = processed.seed
-                initial_info = processed.info
-
-            p.seed = processed.seed + 1
-            work_results += processed.images
-
-
-        image_index = 0
-        for y, h, row in grid.tiles:
-            for tiledata in row:
-                x, w = tiledata[0:2]
-
-                if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down:
-                    continue
-
-                tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
-                image_index += 1
-
-        combined_image = images.combine_grid(grid)
-
-        if opts.samples_save:
-            images.save_image(combined_image, p.outpath_samples, "", initial_seed, p.prompt, opts.grid_format, info=initial_info, p=p)
-
-        processed = Processed(p, [combined_image], initial_seed, initial_info)
-
-        return processed
-
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/xtts/hifigan_decoder.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/xtts/hifigan_decoder.py
deleted file mode 100644
index 5fcff8703b5ba6db84f4e015254834781e769a0c..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/layers/xtts/hifigan_decoder.py
+++ /dev/null
@@ -1,731 +0,0 @@
-import torch
-import torchaudio
-from torch import nn
-from torch.nn import Conv1d, ConvTranspose1d
-from torch.nn import functional as F
-from torch.nn.utils import remove_weight_norm, weight_norm
-
-from TTS.utils.io import load_fsspec
-
-LRELU_SLOPE = 0.1
-
-
-def get_padding(k, d):
-    return int((k * d - d) / 2)
-
-
-class ResBlock1(torch.nn.Module):
-    """Residual Block Type 1. It has 3 convolutional layers in each convolutional block.
-
-    Network::
-
-        x -> lrelu -> conv1_1 -> conv1_2 -> conv1_3 -> z -> lrelu -> conv2_1 -> conv2_2 -> conv2_3 -> o -> + -> o
-        |--------------------------------------------------------------------------------------------------|
-
-
-    Args:
-        channels (int): number of hidden channels for the convolutional layers.
-        kernel_size (int): size of the convolution filter in each layer.
-        dilations (list): list of dilation value for each conv layer in a block.
-    """
-
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super().__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-
-    def forward(self, x):
-        """
-        Args:
-            x (Tensor): input tensor.
-        Returns:
-            Tensor: output tensor.
-        Shapes:
-            x: [B, C, T]
-        """
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            xt = c2(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    """Residual Block Type 2. It has 1 convolutional layers in each convolutional block.
-
-    Network::
-
-        x -> lrelu -> conv1-> -> z -> lrelu -> conv2-> o -> + -> o
-        |---------------------------------------------------|
-
-
-    Args:
-        channels (int): number of hidden channels for the convolutional layers.
-        kernel_size (int): size of the convolution filter in each layer.
-        dilations (list): list of dilation value for each conv layer in a block.
-    """
-
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super().__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-
-    def forward(self, x):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class HifiganGenerator(torch.nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        resblock_type,
-        resblock_dilation_sizes,
-        resblock_kernel_sizes,
-        upsample_kernel_sizes,
-        upsample_initial_channel,
-        upsample_factors,
-        inference_padding=5,
-        cond_channels=0,
-        conv_pre_weight_norm=True,
-        conv_post_weight_norm=True,
-        conv_post_bias=True,
-        cond_in_each_up_layer=False,
-    ):
-        r"""HiFiGAN Generator with Multi-Receptive Field Fusion (MRF)
-
-        Network:
-            x -> lrelu -> upsampling_layer -> resblock1_k1x1 -> z1 -> + -> z_sum / #resblocks -> lrelu -> conv_post_7x1 -> tanh -> o
-                                                 ..          -> zI ---|
-                                              resblockN_kNx1 -> zN ---'
-
-        Args:
-            in_channels (int): number of input tensor channels.
-            out_channels (int): number of output tensor channels.
-            resblock_type (str): type of the `ResBlock`. '1' or '2'.
-            resblock_dilation_sizes (List[List[int]]): list of dilation values in each layer of a `ResBlock`.
-            resblock_kernel_sizes (List[int]): list of kernel sizes for each `ResBlock`.
-            upsample_kernel_sizes (List[int]): list of kernel sizes for each transposed convolution.
-            upsample_initial_channel (int): number of channels for the first upsampling layer. This is divided by 2
-                for each consecutive upsampling layer.
-            upsample_factors (List[int]): upsampling factors (stride) for each upsampling layer.
-            inference_padding (int): constant padding applied to the input at inference time. Defaults to 5.
-        """
-        super().__init__()
-        self.inference_padding = inference_padding
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_factors)
-        self.cond_in_each_up_layer = cond_in_each_up_layer
-
-        # initial upsampling layers
-        self.conv_pre = weight_norm(Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3))
-        resblock = ResBlock1 if resblock_type == "1" else ResBlock2
-        # upsampling layers
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_factors, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-        # MRF blocks
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-        # post convolution layer
-        self.conv_post = weight_norm(Conv1d(ch, out_channels, 7, 1, padding=3, bias=conv_post_bias))
-        if cond_channels > 0:
-            self.cond_layer = nn.Conv1d(cond_channels, upsample_initial_channel, 1)
-
-        if not conv_pre_weight_norm:
-            remove_weight_norm(self.conv_pre)
-
-        if not conv_post_weight_norm:
-            remove_weight_norm(self.conv_post)
-
-        if self.cond_in_each_up_layer:
-            self.conds = nn.ModuleList()
-            for i in range(len(self.ups)):
-                ch = upsample_initial_channel // (2 ** (i + 1))
-                self.conds.append(nn.Conv1d(cond_channels, ch, 1))
-
-    def forward(self, x, g=None):
-        """
-        Args:
-            x (Tensor): feature input tensor.
-            g (Tensor): global conditioning input tensor.
-
-        Returns:
-            Tensor: output waveform.
-
-        Shapes:
-            x: [B, C, T]
-            Tensor: [B, 1, T]
-        """
-        o = self.conv_pre(x)
-        if hasattr(self, "cond_layer"):
-            o = o + self.cond_layer(g)
-        for i in range(self.num_upsamples):
-            o = F.leaky_relu(o, LRELU_SLOPE)
-            o = self.ups[i](o)
-
-            if self.cond_in_each_up_layer:
-                o = o + self.conds[i](g)
-
-            z_sum = None
-            for j in range(self.num_kernels):
-                if z_sum is None:
-                    z_sum = self.resblocks[i * self.num_kernels + j](o)
-                else:
-                    z_sum += self.resblocks[i * self.num_kernels + j](o)
-            o = z_sum / self.num_kernels
-        o = F.leaky_relu(o)
-        o = self.conv_post(o)
-        o = torch.tanh(o)
-        return o
-
-    @torch.no_grad()
-    def inference(self, c):
-        """
-        Args:
-            x (Tensor): conditioning input tensor.
-
-        Returns:
-            Tensor: output waveform.
-
-        Shapes:
-            x: [B, C, T]
-            Tensor: [B, 1, T]
-        """
-        c = c.to(self.conv_pre.weight.device)
-        c = torch.nn.functional.pad(c, (self.inference_padding, self.inference_padding), "replicate")
-        return self.forward(c)
-
-    def remove_weight_norm(self):
-        print("Removing weight norm...")
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-    def load_checkpoint(
-        self, config, checkpoint_path, eval=False, cache=False
-    ):  # pylint: disable=unused-argument, redefined-builtin
-        state = torch.load(checkpoint_path, map_location=torch.device("cpu"))
-        self.load_state_dict(state["model"])
-        if eval:
-            self.eval()
-            assert not self.training
-            self.remove_weight_norm()
-
-
-class SELayer(nn.Module):
-    def __init__(self, channel, reduction=8):
-        super(SELayer, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction),
-            nn.ReLU(inplace=True),
-            nn.Linear(channel // reduction, channel),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        return x * y
-
-
-class SEBasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8):
-        super(SEBasicBlock, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.se = SELayer(planes, reduction)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.relu(out)
-        out = self.bn1(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.se(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-        return out
-
-
-def set_init_dict(model_dict, checkpoint_state, c):
-    # Partial initialization: if there is a mismatch with new and old layer, it is skipped.
-    for k, v in checkpoint_state.items():
-        if k not in model_dict:
-            print(" | > Layer missing in the model definition: {}".format(k))
-    # 1. filter out unnecessary keys
-    pretrained_dict = {k: v for k, v in checkpoint_state.items() if k in model_dict}
-    # 2. filter out different size layers
-    pretrained_dict = {k: v for k, v in pretrained_dict.items() if v.numel() == model_dict[k].numel()}
-    # 3. skip reinit layers
-    if c.has("reinit_layers") and c.reinit_layers is not None:
-        for reinit_layer_name in c.reinit_layers:
-            pretrained_dict = {k: v for k, v in pretrained_dict.items() if reinit_layer_name not in k}
-    # 4. overwrite entries in the existing state dict
-    model_dict.update(pretrained_dict)
-    print(" | > {} / {} layers are restored.".format(len(pretrained_dict), len(model_dict)))
-    return model_dict
-
-
-class PreEmphasis(nn.Module):
-    def __init__(self, coefficient=0.97):
-        super().__init__()
-        self.coefficient = coefficient
-        self.register_buffer("filter", torch.FloatTensor([-self.coefficient, 1.0]).unsqueeze(0).unsqueeze(0))
-
-    def forward(self, x):
-        assert len(x.size()) == 2
-
-        x = torch.nn.functional.pad(x.unsqueeze(1), (1, 0), "reflect")
-        return torch.nn.functional.conv1d(x, self.filter).squeeze(1)
-
-
-class ResNetSpeakerEncoder(nn.Module):
-    """This is copied from 🐸TTS to remove it from the dependencies."""
-
-    # pylint: disable=W0102
-    def __init__(
-        self,
-        input_dim=64,
-        proj_dim=512,
-        layers=[3, 4, 6, 3],
-        num_filters=[32, 64, 128, 256],
-        encoder_type="ASP",
-        log_input=False,
-        use_torch_spec=False,
-        audio_config=None,
-    ):
-        super(ResNetSpeakerEncoder, self).__init__()
-
-        self.encoder_type = encoder_type
-        self.input_dim = input_dim
-        self.log_input = log_input
-        self.use_torch_spec = use_torch_spec
-        self.audio_config = audio_config
-        self.proj_dim = proj_dim
-
-        self.conv1 = nn.Conv2d(1, num_filters[0], kernel_size=3, stride=1, padding=1)
-        self.relu = nn.ReLU(inplace=True)
-        self.bn1 = nn.BatchNorm2d(num_filters[0])
-
-        self.inplanes = num_filters[0]
-        self.layer1 = self.create_layer(SEBasicBlock, num_filters[0], layers[0])
-        self.layer2 = self.create_layer(SEBasicBlock, num_filters[1], layers[1], stride=(2, 2))
-        self.layer3 = self.create_layer(SEBasicBlock, num_filters[2], layers[2], stride=(2, 2))
-        self.layer4 = self.create_layer(SEBasicBlock, num_filters[3], layers[3], stride=(2, 2))
-
-        self.instancenorm = nn.InstanceNorm1d(input_dim)
-
-        if self.use_torch_spec:
-            self.torch_spec = torch.nn.Sequential(
-                PreEmphasis(audio_config["preemphasis"]),
-                torchaudio.transforms.MelSpectrogram(
-                    sample_rate=audio_config["sample_rate"],
-                    n_fft=audio_config["fft_size"],
-                    win_length=audio_config["win_length"],
-                    hop_length=audio_config["hop_length"],
-                    window_fn=torch.hamming_window,
-                    n_mels=audio_config["num_mels"],
-                ),
-            )
-
-        else:
-            self.torch_spec = None
-
-        outmap_size = int(self.input_dim / 8)
-
-        self.attention = nn.Sequential(
-            nn.Conv1d(num_filters[3] * outmap_size, 128, kernel_size=1),
-            nn.ReLU(),
-            nn.BatchNorm1d(128),
-            nn.Conv1d(128, num_filters[3] * outmap_size, kernel_size=1),
-            nn.Softmax(dim=2),
-        )
-
-        if self.encoder_type == "SAP":
-            out_dim = num_filters[3] * outmap_size
-        elif self.encoder_type == "ASP":
-            out_dim = num_filters[3] * outmap_size * 2
-        else:
-            raise ValueError("Undefined encoder")
-
-        self.fc = nn.Linear(out_dim, proj_dim)
-
-        self._init_layers()
-
-    def _init_layers(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-    def create_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    # pylint: disable=R0201
-    def new_parameter(self, *size):
-        out = nn.Parameter(torch.FloatTensor(*size))
-        nn.init.xavier_normal_(out)
-        return out
-
-    def forward(self, x, l2_norm=False):
-        """Forward pass of the model.
-
-        Args:
-            x (Tensor): Raw waveform signal or spectrogram frames. If input is a waveform, `torch_spec` must be `True`
-                to compute the spectrogram on-the-fly.
-            l2_norm (bool): Whether to L2-normalize the outputs.
-
-        Shapes:
-            - x: :math:`(N, 1, T_{in})` or :math:`(N, D_{spec}, T_{in})`
-        """
-        x.squeeze_(1)
-        # if you torch spec compute it otherwise use the mel spec computed by the AP
-        if self.use_torch_spec:
-            x = self.torch_spec(x)
-
-        if self.log_input:
-            x = (x + 1e-6).log()
-        x = self.instancenorm(x).unsqueeze(1)
-
-        x = self.conv1(x)
-        x = self.relu(x)
-        x = self.bn1(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = x.reshape(x.size()[0], -1, x.size()[-1])
-
-        w = self.attention(x)
-
-        if self.encoder_type == "SAP":
-            x = torch.sum(x * w, dim=2)
-        elif self.encoder_type == "ASP":
-            mu = torch.sum(x * w, dim=2)
-            sg = torch.sqrt((torch.sum((x**2) * w, dim=2) - mu**2).clamp(min=1e-5))
-            x = torch.cat((mu, sg), 1)
-
-        x = x.view(x.size()[0], -1)
-        x = self.fc(x)
-
-        if l2_norm:
-            x = torch.nn.functional.normalize(x, p=2, dim=1)
-        return x
-
-    def load_checkpoint(
-        self,
-        checkpoint_path: str,
-        eval: bool = False,
-        use_cuda: bool = False,
-        criterion=None,
-        cache=False,
-    ):
-        state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
-        try:
-            self.load_state_dict(state["model"])
-            print(" > Model fully restored. ")
-        except (KeyError, RuntimeError) as error:
-            # If eval raise the error
-            if eval:
-                raise error
-
-            print(" > Partial model initialization.")
-            model_dict = self.state_dict()
-            model_dict = set_init_dict(model_dict, state["model"])
-            self.load_state_dict(model_dict)
-            del model_dict
-
-        # load the criterion for restore_path
-        if criterion is not None and "criterion" in state:
-            try:
-                criterion.load_state_dict(state["criterion"])
-            except (KeyError, RuntimeError) as error:
-                print(" > Criterion load ignored because of:", error)
-
-        if use_cuda:
-            self.cuda()
-            if criterion is not None:
-                criterion = criterion.cuda()
-
-        if eval:
-            self.eval()
-            assert not self.training
-
-        if not eval:
-            return criterion, state["step"]
-        return criterion
-
-
-class HifiDecoder(torch.nn.Module):
-    def __init__(
-        self,
-        input_sample_rate=22050,
-        output_sample_rate=24000,
-        output_hop_length=256,
-        ar_mel_length_compression=1024,
-        decoder_input_dim=1024,
-        resblock_type_decoder="1",
-        resblock_dilation_sizes_decoder=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-        resblock_kernel_sizes_decoder=[3, 7, 11],
-        upsample_rates_decoder=[8, 8, 2, 2],
-        upsample_initial_channel_decoder=512,
-        upsample_kernel_sizes_decoder=[16, 16, 4, 4],
-        d_vector_dim=512,
-        cond_d_vector_in_each_upsampling_layer=True,
-        speaker_encoder_audio_config={
-            "fft_size": 512,
-            "win_length": 400,
-            "hop_length": 160,
-            "sample_rate": 16000,
-            "preemphasis": 0.97,
-            "num_mels": 64,
-        },
-    ):
-        super().__init__()
-        self.input_sample_rate = input_sample_rate
-        self.output_sample_rate = output_sample_rate
-        self.output_hop_length = output_hop_length
-        self.ar_mel_length_compression = ar_mel_length_compression
-        self.speaker_encoder_audio_config = speaker_encoder_audio_config
-        self.waveform_decoder = HifiganGenerator(
-            decoder_input_dim,
-            1,
-            resblock_type_decoder,
-            resblock_dilation_sizes_decoder,
-            resblock_kernel_sizes_decoder,
-            upsample_kernel_sizes_decoder,
-            upsample_initial_channel_decoder,
-            upsample_rates_decoder,
-            inference_padding=0,
-            cond_channels=d_vector_dim,
-            conv_pre_weight_norm=False,
-            conv_post_weight_norm=False,
-            conv_post_bias=False,
-            cond_in_each_up_layer=cond_d_vector_in_each_upsampling_layer,
-        )
-        self.speaker_encoder = ResNetSpeakerEncoder(
-            input_dim=64,
-            proj_dim=512,
-            log_input=True,
-            use_torch_spec=True,
-            audio_config=speaker_encoder_audio_config,
-        )
-
-    @property
-    def device(self):
-        return next(self.parameters()).device
-
-    def forward(self, latents, g=None):
-        """
-        Args:
-            x (Tensor): feature input tensor (GPT latent).
-            g (Tensor): global conditioning input tensor.
-
-        Returns:
-            Tensor: output waveform.
-
-        Shapes:
-            x: [B, C, T]
-            Tensor: [B, 1, T]
-        """
-
-        z = torch.nn.functional.interpolate(
-            latents.transpose(1, 2),
-            scale_factor=[self.ar_mel_length_compression / self.output_hop_length],
-            mode="linear",
-        ).squeeze(1)
-        # upsample to the right sr
-        if self.output_sample_rate != self.input_sample_rate:
-            z = torch.nn.functional.interpolate(
-                z,
-                scale_factor=[self.output_sample_rate / self.input_sample_rate],
-                mode="linear",
-            ).squeeze(0)
-        o = self.waveform_decoder(z, g=g)
-        return o
-
-    @torch.no_grad()
-    def inference(self, c, g):
-        """
-        Args:
-            x (Tensor): feature input tensor (GPT latent).
-            g (Tensor): global conditioning input tensor.
-
-        Returns:
-            Tensor: output waveform.
-
-        Shapes:
-            x: [B, C, T]
-            Tensor: [B, 1, T]
-        """
-        return self.forward(c, g=g)
-
-    def load_checkpoint(self, checkpoint_path, eval=False):  # pylint: disable=unused-argument, redefined-builtin
-        state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"))
-        # remove unused keys
-        state = state["model"]
-        states_keys = list(state.keys())
-        for key in states_keys:
-            if "waveform_decoder." not in key and "speaker_encoder." not in key:
-                del state[key]
-
-        self.load_state_dict(state)
-        if eval:
-            self.eval()
-            assert not self.training
-            self.waveform_decoder.remove_weight_norm()
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/models/glow_tts.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/models/glow_tts.py
deleted file mode 100644
index bfd1a2b618bd9bfdc7d12dd4eb16a6febcaf8cde..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/models/glow_tts.py
+++ /dev/null
@@ -1,557 +0,0 @@
-import math
-from typing import Dict, List, Tuple, Union
-
-import torch
-from coqpit import Coqpit
-from torch import nn
-from torch.cuda.amp.autocast_mode import autocast
-from torch.nn import functional as F
-
-from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-from TTS.tts.layers.glow_tts.decoder import Decoder
-from TTS.tts.layers.glow_tts.encoder import Encoder
-from TTS.tts.models.base_tts import BaseTTS
-from TTS.tts.utils.helpers import generate_path, maximum_path, sequence_mask
-from TTS.tts.utils.speakers import SpeakerManager
-from TTS.tts.utils.synthesis import synthesis
-from TTS.tts.utils.text.tokenizer import TTSTokenizer
-from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
-from TTS.utils.io import load_fsspec
-
-
-class GlowTTS(BaseTTS):
-    """GlowTTS model.
-
-    Paper::
-        https://arxiv.org/abs/2005.11129
-
-    Paper abstract::
-        Recently, text-to-speech (TTS) models such as FastSpeech and ParaNet have been proposed to generate
-        mel-spectrograms from text in parallel. Despite the advantage, the parallel TTS models cannot be trained
-        without guidance from autoregressive TTS models as their external aligners. In this work, we propose Glow-TTS,
-        a flow-based generative model for parallel TTS that does not require any external aligner. By combining the
-        properties of flows and dynamic programming, the proposed model searches for the most probable monotonic
-        alignment between text and the latent representation of speech on its own. We demonstrate that enforcing hard
-        monotonic alignments enables robust TTS, which generalizes to long utterances, and employing generative flows
-        enables fast, diverse, and controllable speech synthesis. Glow-TTS obtains an order-of-magnitude speed-up over
-        the autoregressive model, Tacotron 2, at synthesis with comparable speech quality. We further show that our
-        model can be easily extended to a multi-speaker setting.
-
-    Check :class:`TTS.tts.configs.glow_tts_config.GlowTTSConfig` for class arguments.
-
-    Examples:
-        Init only model layers.
-
-        >>> from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-        >>> from TTS.tts.models.glow_tts import GlowTTS
-        >>> config = GlowTTSConfig(num_chars=2)
-        >>> model = GlowTTS(config)
-
-        Fully init a model ready for action. All the class attributes and class members
-        (e.g Tokenizer, AudioProcessor, etc.). are initialized internally based on config values.
-
-        >>> from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-        >>> from TTS.tts.models.glow_tts import GlowTTS
-        >>> config = GlowTTSConfig()
-        >>> model = GlowTTS.init_from_config(config, verbose=False)
-    """
-
-    def __init__(
-        self,
-        config: GlowTTSConfig,
-        ap: "AudioProcessor" = None,
-        tokenizer: "TTSTokenizer" = None,
-        speaker_manager: SpeakerManager = None,
-    ):
-        super().__init__(config, ap, tokenizer, speaker_manager)
-
-        # pass all config fields to `self`
-        # for fewer code change
-        self.config = config
-        for key in config:
-            setattr(self, key, config[key])
-
-        self.decoder_output_dim = config.out_channels
-
-        # init multi-speaker layers if necessary
-        self.init_multispeaker(config)
-
-        self.run_data_dep_init = config.data_dep_init_steps > 0
-        self.encoder = Encoder(
-            self.num_chars,
-            out_channels=self.out_channels,
-            hidden_channels=self.hidden_channels_enc,
-            hidden_channels_dp=self.hidden_channels_dp,
-            encoder_type=self.encoder_type,
-            encoder_params=self.encoder_params,
-            mean_only=self.mean_only,
-            use_prenet=self.use_encoder_prenet,
-            dropout_p_dp=self.dropout_p_dp,
-            c_in_channels=self.c_in_channels,
-        )
-
-        self.decoder = Decoder(
-            self.out_channels,
-            self.hidden_channels_dec,
-            self.kernel_size_dec,
-            self.dilation_rate,
-            self.num_flow_blocks_dec,
-            self.num_block_layers,
-            dropout_p=self.dropout_p_dec,
-            num_splits=self.num_splits,
-            num_squeeze=self.num_squeeze,
-            sigmoid_scale=self.sigmoid_scale,
-            c_in_channels=self.c_in_channels,
-        )
-
-    def init_multispeaker(self, config: Coqpit):
-        """Init speaker embedding layer if `use_speaker_embedding` is True and set the expected speaker embedding
-        vector dimension to the encoder layer channel size. If model uses d-vectors, then it only sets
-        speaker embedding vector dimension to the d-vector dimension from the config.
-
-        Args:
-            config (Coqpit): Model configuration.
-        """
-        self.embedded_speaker_dim = 0
-        # set number of speakers - if num_speakers is set in config, use it, otherwise use speaker_manager
-        if self.speaker_manager is not None:
-            self.num_speakers = self.speaker_manager.num_speakers
-        # set ultimate speaker embedding size
-        if config.use_d_vector_file:
-            self.embedded_speaker_dim = (
-                config.d_vector_dim if "d_vector_dim" in config and config.d_vector_dim is not None else 512
-            )
-            if self.speaker_manager is not None:
-                assert (
-                    config.d_vector_dim == self.speaker_manager.embedding_dim
-                ), " [!] d-vector dimension mismatch b/w config and speaker manager."
-        # init speaker embedding layer
-        if config.use_speaker_embedding and not config.use_d_vector_file:
-            print(" > Init speaker_embedding layer.")
-            self.embedded_speaker_dim = self.hidden_channels_enc
-            self.emb_g = nn.Embedding(self.num_speakers, self.hidden_channels_enc)
-            nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
-        # set conditioning dimensions
-        self.c_in_channels = self.embedded_speaker_dim
-
-    @staticmethod
-    def compute_outputs(attn, o_mean, o_log_scale, x_mask):
-        """Compute and format the mode outputs with the given alignment map"""
-        y_mean = torch.matmul(attn.squeeze(1).transpose(1, 2), o_mean.transpose(1, 2)).transpose(
-            1, 2
-        )  # [b, t', t], [b, t, d] -> [b, d, t']
-        y_log_scale = torch.matmul(attn.squeeze(1).transpose(1, 2), o_log_scale.transpose(1, 2)).transpose(
-            1, 2
-        )  # [b, t', t], [b, t, d] -> [b, d, t']
-        # compute total duration with adjustment
-        o_attn_dur = torch.log(1 + torch.sum(attn, -1)) * x_mask
-        return y_mean, y_log_scale, o_attn_dur
-
-    def unlock_act_norm_layers(self):
-        """Unlock activation normalization layers for data depended initalization."""
-        for f in self.decoder.flows:
-            if getattr(f, "set_ddi", False):
-                f.set_ddi(True)
-
-    def lock_act_norm_layers(self):
-        """Lock activation normalization layers."""
-        for f in self.decoder.flows:
-            if getattr(f, "set_ddi", False):
-                f.set_ddi(False)
-
-    def _set_speaker_input(self, aux_input: Dict):
-        if aux_input is None:
-            d_vectors = None
-            speaker_ids = None
-        else:
-            d_vectors = aux_input.get("d_vectors", None)
-            speaker_ids = aux_input.get("speaker_ids", None)
-
-        if d_vectors is not None and speaker_ids is not None:
-            raise ValueError("[!] Cannot use d-vectors and speaker-ids together.")
-
-        if speaker_ids is not None and not hasattr(self, "emb_g"):
-            raise ValueError("[!] Cannot use speaker-ids without enabling speaker embedding.")
-
-        g = speaker_ids if speaker_ids is not None else d_vectors
-        return g
-
-    def _speaker_embedding(self, aux_input: Dict) -> Union[torch.tensor, None]:
-        g = self._set_speaker_input(aux_input)
-        # speaker embedding
-        if g is not None:
-            if hasattr(self, "emb_g"):
-                # use speaker embedding layer
-                if not g.size():  # if is a scalar
-                    g = g.unsqueeze(0)  # unsqueeze
-                g = F.normalize(self.emb_g(g)).unsqueeze(-1)  # [b, h, 1]
-            else:
-                # use d-vector
-                g = F.normalize(g).unsqueeze(-1)  # [b, h, 1]
-        return g
-
-    def forward(
-        self, x, x_lengths, y, y_lengths=None, aux_input={"d_vectors": None, "speaker_ids": None}
-    ):  # pylint: disable=dangerous-default-value
-        """
-        Args:
-            x (torch.Tensor):
-                Input text sequence ids. :math:`[B, T_en]`
-
-            x_lengths (torch.Tensor):
-                Lengths of input text sequences. :math:`[B]`
-
-            y (torch.Tensor):
-                Target mel-spectrogram frames. :math:`[B, T_de, C_mel]`
-
-            y_lengths (torch.Tensor):
-                Lengths of target mel-spectrogram frames. :math:`[B]`
-
-            aux_input (Dict):
-                Auxiliary inputs. `d_vectors` is speaker embedding vectors for a multi-speaker model.
-                :math:`[B, D_vec]`. `speaker_ids` is speaker ids for a multi-speaker model usind speaker-embedding
-                layer. :math:`B`
-
-        Returns:
-            Dict:
-                - z: :math: `[B, T_de, C]`
-                - logdet: :math:`B`
-                - y_mean: :math:`[B, T_de, C]`
-                - y_log_scale: :math:`[B, T_de, C]`
-                - alignments: :math:`[B, T_en, T_de]`
-                - durations_log: :math:`[B, T_en, 1]`
-                - total_durations_log: :math:`[B, T_en, 1]`
-        """
-        # [B, T, C] -> [B, C, T]
-        y = y.transpose(1, 2)
-        y_max_length = y.size(2)
-        # norm speaker embeddings
-        g = self._speaker_embedding(aux_input)
-        # embedding pass
-        o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x, x_lengths, g=g)
-        # drop redisual frames wrt num_squeeze and set y_lengths.
-        y, y_lengths, y_max_length, attn = self.preprocess(y, y_lengths, y_max_length, None)
-        # create masks
-        y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length), 1).to(x_mask.dtype)
-        # [B, 1, T_en, T_de]
-        attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
-        # decoder pass
-        z, logdet = self.decoder(y, y_mask, g=g, reverse=False)
-        # find the alignment path
-        with torch.no_grad():
-            o_scale = torch.exp(-2 * o_log_scale)
-            logp1 = torch.sum(-0.5 * math.log(2 * math.pi) - o_log_scale, [1]).unsqueeze(-1)  # [b, t, 1]
-            logp2 = torch.matmul(o_scale.transpose(1, 2), -0.5 * (z**2))  # [b, t, d] x [b, d, t'] = [b, t, t']
-            logp3 = torch.matmul((o_mean * o_scale).transpose(1, 2), z)  # [b, t, d] x [b, d, t'] = [b, t, t']
-            logp4 = torch.sum(-0.5 * (o_mean**2) * o_scale, [1]).unsqueeze(-1)  # [b, t, 1]
-            logp = logp1 + logp2 + logp3 + logp4  # [b, t, t']
-            attn = maximum_path(logp, attn_mask.squeeze(1)).unsqueeze(1).detach()
-        y_mean, y_log_scale, o_attn_dur = self.compute_outputs(attn, o_mean, o_log_scale, x_mask)
-        attn = attn.squeeze(1).permute(0, 2, 1)
-        outputs = {
-            "z": z.transpose(1, 2),
-            "logdet": logdet,
-            "y_mean": y_mean.transpose(1, 2),
-            "y_log_scale": y_log_scale.transpose(1, 2),
-            "alignments": attn,
-            "durations_log": o_dur_log.transpose(1, 2),
-            "total_durations_log": o_attn_dur.transpose(1, 2),
-        }
-        return outputs
-
-    @torch.no_grad()
-    def inference_with_MAS(
-        self, x, x_lengths, y=None, y_lengths=None, aux_input={"d_vectors": None, "speaker_ids": None}
-    ):  # pylint: disable=dangerous-default-value
-        """
-        It's similar to the teacher forcing in Tacotron.
-        It was proposed in: https://arxiv.org/abs/2104.05557
-
-        Shapes:
-            - x: :math:`[B, T]`
-            - x_lenghts: :math:`B`
-            - y: :math:`[B, T, C]`
-            - y_lengths: :math:`B`
-            - g: :math:`[B, C] or B`
-        """
-        y = y.transpose(1, 2)
-        y_max_length = y.size(2)
-        # norm speaker embeddings
-        g = self._speaker_embedding(aux_input)
-        # embedding pass
-        o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x, x_lengths, g=g)
-        # drop redisual frames wrt num_squeeze and set y_lengths.
-        y, y_lengths, y_max_length, attn = self.preprocess(y, y_lengths, y_max_length, None)
-        # create masks
-        y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length), 1).to(x_mask.dtype)
-        attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
-        # decoder pass
-        z, logdet = self.decoder(y, y_mask, g=g, reverse=False)
-        # find the alignment path between z and encoder output
-        o_scale = torch.exp(-2 * o_log_scale)
-        logp1 = torch.sum(-0.5 * math.log(2 * math.pi) - o_log_scale, [1]).unsqueeze(-1)  # [b, t, 1]
-        logp2 = torch.matmul(o_scale.transpose(1, 2), -0.5 * (z**2))  # [b, t, d] x [b, d, t'] = [b, t, t']
-        logp3 = torch.matmul((o_mean * o_scale).transpose(1, 2), z)  # [b, t, d] x [b, d, t'] = [b, t, t']
-        logp4 = torch.sum(-0.5 * (o_mean**2) * o_scale, [1]).unsqueeze(-1)  # [b, t, 1]
-        logp = logp1 + logp2 + logp3 + logp4  # [b, t, t']
-        attn = maximum_path(logp, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-        y_mean, y_log_scale, o_attn_dur = self.compute_outputs(attn, o_mean, o_log_scale, x_mask)
-        attn = attn.squeeze(1).permute(0, 2, 1)
-
-        # get predited aligned distribution
-        z = y_mean * y_mask
-
-        # reverse the decoder and predict using the aligned distribution
-        y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
-        outputs = {
-            "model_outputs": z.transpose(1, 2),
-            "logdet": logdet,
-            "y_mean": y_mean.transpose(1, 2),
-            "y_log_scale": y_log_scale.transpose(1, 2),
-            "alignments": attn,
-            "durations_log": o_dur_log.transpose(1, 2),
-            "total_durations_log": o_attn_dur.transpose(1, 2),
-        }
-        return outputs
-
-    @torch.no_grad()
-    def decoder_inference(
-        self, y, y_lengths=None, aux_input={"d_vectors": None, "speaker_ids": None}
-    ):  # pylint: disable=dangerous-default-value
-        """
-        Shapes:
-            - y: :math:`[B, T, C]`
-            - y_lengths: :math:`B`
-            - g: :math:`[B, C] or B`
-        """
-        y = y.transpose(1, 2)
-        y_max_length = y.size(2)
-        g = self._speaker_embedding(aux_input)
-        y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length), 1).to(y.dtype)
-        # decoder pass
-        z, logdet = self.decoder(y, y_mask, g=g, reverse=False)
-        # reverse decoder and predict
-        y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
-        outputs = {}
-        outputs["model_outputs"] = y.transpose(1, 2)
-        outputs["logdet"] = logdet
-        return outputs
-
-    @torch.no_grad()
-    def inference(
-        self, x, aux_input={"x_lengths": None, "d_vectors": None, "speaker_ids": None}
-    ):  # pylint: disable=dangerous-default-value
-        x_lengths = aux_input["x_lengths"]
-        g = self._speaker_embedding(aux_input)
-        # embedding pass
-        o_mean, o_log_scale, o_dur_log, x_mask = self.encoder(x, x_lengths, g=g)
-        # compute output durations
-        w = (torch.exp(o_dur_log) - 1) * x_mask * self.length_scale
-        w_ceil = torch.clamp_min(torch.ceil(w), 1)
-        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-        y_max_length = None
-        # compute masks
-        y_mask = torch.unsqueeze(sequence_mask(y_lengths, y_max_length), 1).to(x_mask.dtype)
-        attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
-        # compute attention mask
-        attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1)
-        y_mean, y_log_scale, o_attn_dur = self.compute_outputs(attn, o_mean, o_log_scale, x_mask)
-
-        z = (y_mean + torch.exp(y_log_scale) * torch.randn_like(y_mean) * self.inference_noise_scale) * y_mask
-        # decoder pass
-        y, logdet = self.decoder(z, y_mask, g=g, reverse=True)
-        attn = attn.squeeze(1).permute(0, 2, 1)
-        outputs = {
-            "model_outputs": y.transpose(1, 2),
-            "logdet": logdet,
-            "y_mean": y_mean.transpose(1, 2),
-            "y_log_scale": y_log_scale.transpose(1, 2),
-            "alignments": attn,
-            "durations_log": o_dur_log.transpose(1, 2),
-            "total_durations_log": o_attn_dur.transpose(1, 2),
-        }
-        return outputs
-
-    def train_step(self, batch: dict, criterion: nn.Module):
-        """A single training step. Forward pass and loss computation. Run data depended initialization for the
-        first `config.data_dep_init_steps` steps.
-
-        Args:
-            batch (dict): [description]
-            criterion (nn.Module): [description]
-        """
-        text_input = batch["text_input"]
-        text_lengths = batch["text_lengths"]
-        mel_input = batch["mel_input"]
-        mel_lengths = batch["mel_lengths"]
-        d_vectors = batch["d_vectors"]
-        speaker_ids = batch["speaker_ids"]
-
-        if self.run_data_dep_init and self.training:
-            # compute data-dependent initialization of activation norm layers
-            self.unlock_act_norm_layers()
-            with torch.no_grad():
-                _ = self.forward(
-                    text_input,
-                    text_lengths,
-                    mel_input,
-                    mel_lengths,
-                    aux_input={"d_vectors": d_vectors, "speaker_ids": speaker_ids},
-                )
-            outputs = None
-            loss_dict = None
-            self.lock_act_norm_layers()
-        else:
-            # normal training step
-            outputs = self.forward(
-                text_input,
-                text_lengths,
-                mel_input,
-                mel_lengths,
-                aux_input={"d_vectors": d_vectors, "speaker_ids": speaker_ids},
-            )
-
-            with autocast(enabled=False):  # avoid mixed_precision in criterion
-                loss_dict = criterion(
-                    outputs["z"].float(),
-                    outputs["y_mean"].float(),
-                    outputs["y_log_scale"].float(),
-                    outputs["logdet"].float(),
-                    mel_lengths,
-                    outputs["durations_log"].float(),
-                    outputs["total_durations_log"].float(),
-                    text_lengths,
-                )
-        return outputs, loss_dict
-
-    def _create_logs(self, batch, outputs, ap):
-        alignments = outputs["alignments"]
-        text_input = batch["text_input"][:1] if batch["text_input"] is not None else None
-        text_lengths = batch["text_lengths"]
-        mel_input = batch["mel_input"]
-        d_vectors = batch["d_vectors"][:1] if batch["d_vectors"] is not None else None
-        speaker_ids = batch["speaker_ids"][:1] if batch["speaker_ids"] is not None else None
-
-        # model runs reverse flow to predict spectrograms
-        pred_outputs = self.inference(
-            text_input,
-            aux_input={"x_lengths": text_lengths[:1], "d_vectors": d_vectors, "speaker_ids": speaker_ids},
-        )
-        model_outputs = pred_outputs["model_outputs"]
-
-        pred_spec = model_outputs[0].data.cpu().numpy()
-        gt_spec = mel_input[0].data.cpu().numpy()
-        align_img = alignments[0].data.cpu().numpy()
-
-        figures = {
-            "prediction": plot_spectrogram(pred_spec, ap, output_fig=False),
-            "ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False),
-            "alignment": plot_alignment(align_img, output_fig=False),
-        }
-
-        # Sample audio
-        train_audio = ap.inv_melspectrogram(pred_spec.T)
-        return figures, {"audio": train_audio}
-
-    def train_log(
-        self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int
-    ) -> None:  # pylint: disable=no-self-use
-        figures, audios = self._create_logs(batch, outputs, self.ap)
-        logger.train_figures(steps, figures)
-        logger.train_audios(steps, audios, self.ap.sample_rate)
-
-    @torch.no_grad()
-    def eval_step(self, batch: dict, criterion: nn.Module):
-        return self.train_step(batch, criterion)
-
-    def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None:
-        figures, audios = self._create_logs(batch, outputs, self.ap)
-        logger.eval_figures(steps, figures)
-        logger.eval_audios(steps, audios, self.ap.sample_rate)
-
-    @torch.no_grad()
-    def test_run(self, assets: Dict) -> Tuple[Dict, Dict]:
-        """Generic test run for `tts` models used by `Trainer`.
-
-        You can override this for a different behaviour.
-
-        Returns:
-            Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard.
-        """
-        print(" | > Synthesizing test sentences.")
-        test_audios = {}
-        test_figures = {}
-        test_sentences = self.config.test_sentences
-        aux_inputs = self._get_test_aux_input()
-        if len(test_sentences) == 0:
-            print(" | [!] No test sentences provided.")
-        else:
-            for idx, sen in enumerate(test_sentences):
-                outputs = synthesis(
-                    self,
-                    sen,
-                    self.config,
-                    "cuda" in str(next(self.parameters()).device),
-                    speaker_id=aux_inputs["speaker_id"],
-                    d_vector=aux_inputs["d_vector"],
-                    style_wav=aux_inputs["style_wav"],
-                    use_griffin_lim=True,
-                    do_trim_silence=False,
-                )
-
-                test_audios["{}-audio".format(idx)] = outputs["wav"]
-                test_figures["{}-prediction".format(idx)] = plot_spectrogram(
-                    outputs["outputs"]["model_outputs"], self.ap, output_fig=False
-                )
-                test_figures["{}-alignment".format(idx)] = plot_alignment(outputs["alignments"], output_fig=False)
-        return test_figures, test_audios
-
-    def preprocess(self, y, y_lengths, y_max_length, attn=None):
-        if y_max_length is not None:
-            y_max_length = (y_max_length // self.num_squeeze) * self.num_squeeze
-            y = y[:, :, :y_max_length]
-            if attn is not None:
-                attn = attn[:, :, :, :y_max_length]
-        y_lengths = torch.div(y_lengths, self.num_squeeze, rounding_mode="floor") * self.num_squeeze
-        return y, y_lengths, y_max_length, attn
-
-    def store_inverse(self):
-        self.decoder.store_inverse()
-
-    def load_checkpoint(
-        self, config, checkpoint_path, eval=False
-    ):  # pylint: disable=unused-argument, redefined-builtin
-        state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"))
-        self.load_state_dict(state["model"])
-        if eval:
-            self.eval()
-            self.store_inverse()
-            assert not self.training
-
-    @staticmethod
-    def get_criterion():
-        from TTS.tts.layers.losses import GlowTTSLoss  # pylint: disable=import-outside-toplevel
-
-        return GlowTTSLoss()
-
-    def on_train_step_start(self, trainer):
-        """Decide on every training step wheter enable/disable data depended initialization."""
-        self.run_data_dep_init = trainer.total_steps_done < self.data_dep_init_steps
-
-    @staticmethod
-    def init_from_config(config: "GlowTTSConfig", samples: Union[List[List], List[Dict]] = None, verbose=True):
-        """Initiate model from config
-
-        Args:
-            config (VitsConfig): Model config.
-            samples (Union[List[List], List[Dict]]): Training samples to parse speaker ids for training.
-                Defaults to None.
-            verbose (bool): If True, print init messages. Defaults to True.
-        """
-        from TTS.utils.audio import AudioProcessor
-
-        ap = AudioProcessor.init_from_config(config, verbose)
-        tokenizer, new_config = TTSTokenizer.init_from_config(config)
-        speaker_manager = SpeakerManager.init_from_config(config, samples)
-        return GlowTTS(new_config, ap, tokenizer, speaker_manager)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Debugger/libcython.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Debugger/libcython.py
deleted file mode 100644
index 23153789b660d566525e4904b3675d27e6dea4a6..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Debugger/libcython.py
+++ /dev/null
@@ -1,1434 +0,0 @@
-"""
-GDB extension that adds Cython support.
-"""
-
-from __future__ import print_function
-
-try:
-    input = raw_input
-except NameError:
-    pass
-
-import sys
-import textwrap
-import traceback
-import functools
-import itertools
-import collections
-
-import gdb
-
-try:  # python 2
-    UNICODE = unicode
-    BYTES = str
-except NameError:  # python 3
-    UNICODE = str
-    BYTES = bytes
-
-try:
-    from lxml import etree
-    have_lxml = True
-except ImportError:
-    have_lxml = False
-    try:
-        # Python 2.5
-        from xml.etree import cElementTree as etree
-    except ImportError:
-        try:
-            # Python 2.5
-            from xml.etree import ElementTree as etree
-        except ImportError:
-            try:
-                # normal cElementTree install
-                import cElementTree as etree
-            except ImportError:
-                # normal ElementTree install
-                import elementtree.ElementTree as etree
-
-try:
-    import pygments.lexers
-    import pygments.formatters
-except ImportError:
-    pygments = None
-    sys.stderr.write("Install pygments for colorized source code.\n")
-
-if hasattr(gdb, 'string_to_argv'):
-    from gdb import string_to_argv
-else:
-    from shlex import split as string_to_argv
-
-from Cython.Debugger import libpython
-
-# C or Python type
-CObject = 'CObject'
-PythonObject = 'PythonObject'
-
-_data_types = dict(CObject=CObject, PythonObject=PythonObject)
-_filesystemencoding = sys.getfilesystemencoding() or 'UTF-8'
-
-
-# decorators
-
-def dont_suppress_errors(function):
-    "*sigh*, readline"
-    @functools.wraps(function)
-    def wrapper(*args, **kwargs):
-        try:
-            return function(*args, **kwargs)
-        except Exception:
-            traceback.print_exc()
-            raise
-
-    return wrapper
-
-
-def default_selected_gdb_frame(err=True):
-    def decorator(function):
-        @functools.wraps(function)
-        def wrapper(self, frame=None, *args, **kwargs):
-            try:
-                frame = frame or gdb.selected_frame()
-            except RuntimeError:
-                raise gdb.GdbError("No frame is currently selected.")
-
-            if err and frame.name() is None:
-                raise NoFunctionNameInFrameError()
-
-            return function(self, frame, *args, **kwargs)
-        return wrapper
-    return decorator
-
-
-def require_cython_frame(function):
-    @functools.wraps(function)
-    @require_running_program
-    def wrapper(self, *args, **kwargs):
-        frame = kwargs.get('frame') or gdb.selected_frame()
-        if not self.is_cython_function(frame):
-            raise gdb.GdbError('Selected frame does not correspond with a '
-                               'Cython function we know about.')
-        return function(self, *args, **kwargs)
-    return wrapper
-
-
-def dispatch_on_frame(c_command, python_command=None):
-    def decorator(function):
-        @functools.wraps(function)
-        def wrapper(self, *args, **kwargs):
-            is_cy = self.is_cython_function()
-            is_py = self.is_python_function()
-
-            if is_cy or (is_py and not python_command):
-                function(self, *args, **kwargs)
-            elif is_py:
-                gdb.execute(python_command)
-            elif self.is_relevant_function():
-                gdb.execute(c_command)
-            else:
-                raise gdb.GdbError("Not a function cygdb knows about. "
-                                   "Use the normal GDB commands instead.")
-
-        return wrapper
-    return decorator
-
-
-def require_running_program(function):
-    @functools.wraps(function)
-    def wrapper(*args, **kwargs):
-        try:
-            gdb.selected_frame()
-        except RuntimeError:
-            raise gdb.GdbError("No frame is currently selected.")
-
-        return function(*args, **kwargs)
-    return wrapper
-
-
-def gdb_function_value_to_unicode(function):
-    @functools.wraps(function)
-    def wrapper(self, string, *args, **kwargs):
-        if isinstance(string, gdb.Value):
-            string = string.string()
-
-        return function(self, string, *args, **kwargs)
-    return wrapper
-
-
-# Classes that represent the debug information
-# Don't rename the parameters of these classes, they come directly from the XML
-
-class CythonModule(object):
-    def __init__(self, module_name, filename, c_filename):
-        self.name = module_name
-        self.filename = filename
-        self.c_filename = c_filename
-        self.globals = {}
-        # {cython_lineno: min(c_linenos)}
-        self.lineno_cy2c = {}
-        # {c_lineno: cython_lineno}
-        self.lineno_c2cy = {}
-        self.functions = {}
-
-
-class CythonVariable(object):
-
-    def __init__(self, name, cname, qualified_name, type, lineno):
-        self.name = name
-        self.cname = cname
-        self.qualified_name = qualified_name
-        self.type = type
-        self.lineno = int(lineno)
-
-
-class CythonFunction(CythonVariable):
-    def __init__(self,
-                 module,
-                 name,
-                 cname,
-                 pf_cname,
-                 qualified_name,
-                 lineno,
-                 type=CObject,
-                 is_initmodule_function="False"):
-        super(CythonFunction, self).__init__(name,
-                                             cname,
-                                             qualified_name,
-                                             type,
-                                             lineno)
-        self.module = module
-        self.pf_cname = pf_cname
-        self.is_initmodule_function = is_initmodule_function == "True"
-        self.locals = {}
-        self.arguments = []
-        self.step_into_functions = set()
-
-
-# General purpose classes
-
-class CythonBase(object):
-
-    @default_selected_gdb_frame(err=False)
-    def is_cython_function(self, frame):
-        return frame.name() in self.cy.functions_by_cname
-
-    @default_selected_gdb_frame(err=False)
-    def is_python_function(self, frame):
-        """
-        Tells if a frame is associated with a Python function.
-        If we can't read the Python frame information, don't regard it as such.
-        """
-        if frame.name() == 'PyEval_EvalFrameEx':
-            pyframe = libpython.Frame(frame).get_pyop()
-            return pyframe and not pyframe.is_optimized_out()
-        return False
-
-    @default_selected_gdb_frame()
-    def get_c_function_name(self, frame):
-        return frame.name()
-
-    @default_selected_gdb_frame()
-    def get_c_lineno(self, frame):
-        return frame.find_sal().line
-
-    @default_selected_gdb_frame()
-    def get_cython_function(self, frame):
-        result = self.cy.functions_by_cname.get(frame.name())
-        if result is None:
-            raise NoCythonFunctionInFrameError()
-
-        return result
-
-    @default_selected_gdb_frame()
-    def get_cython_lineno(self, frame):
-        """
-        Get the current Cython line number. Returns 0 if there is no
-        correspondence between the C and Cython code.
-        """
-        cyfunc = self.get_cython_function(frame)
-        return cyfunc.module.lineno_c2cy.get(self.get_c_lineno(frame), 0)
-
-    @default_selected_gdb_frame()
-    def get_source_desc(self, frame):
-        filename = lineno = lexer = None
-        if self.is_cython_function(frame):
-            filename = self.get_cython_function(frame).module.filename
-            lineno = self.get_cython_lineno(frame)
-            if pygments:
-                lexer = pygments.lexers.CythonLexer(stripall=False)
-        elif self.is_python_function(frame):
-            pyframeobject = libpython.Frame(frame).get_pyop()
-
-            if not pyframeobject:
-                raise gdb.GdbError(
-                            'Unable to read information on python frame')
-
-            filename = pyframeobject.filename()
-            lineno = pyframeobject.current_line_num()
-
-            if pygments:
-                lexer = pygments.lexers.PythonLexer(stripall=False)
-        else:
-            symbol_and_line_obj = frame.find_sal()
-            if not symbol_and_line_obj or not symbol_and_line_obj.symtab:
-                filename = None
-                lineno = 0
-            else:
-                filename = symbol_and_line_obj.symtab.fullname()
-                lineno = symbol_and_line_obj.line
-                if pygments:
-                    lexer = pygments.lexers.CLexer(stripall=False)
-
-        return SourceFileDescriptor(filename, lexer), lineno
-
-    @default_selected_gdb_frame()
-    def get_source_line(self, frame):
-        source_desc, lineno = self.get_source_desc()
-        return source_desc.get_source(lineno)
-
-    @default_selected_gdb_frame()
-    def is_relevant_function(self, frame):
-        """
-        returns whether we care about a frame on the user-level when debugging
-        Cython code
-        """
-        name = frame.name()
-        older_frame = frame.older()
-        if self.is_cython_function(frame) or self.is_python_function(frame):
-            return True
-        elif older_frame and self.is_cython_function(older_frame):
-            # check for direct C function call from a Cython function
-            cython_func = self.get_cython_function(older_frame)
-            return name in cython_func.step_into_functions
-
-        return False
-
-    @default_selected_gdb_frame(err=False)
-    def print_stackframe(self, frame, index, is_c=False):
-        """
-        Print a C, Cython or Python stack frame and the line of source code
-        if available.
-        """
-        # do this to prevent the require_cython_frame decorator from
-        # raising GdbError when calling self.cy.cy_cvalue.invoke()
-        selected_frame = gdb.selected_frame()
-        frame.select()
-
-        try:
-            source_desc, lineno = self.get_source_desc(frame)
-        except NoFunctionNameInFrameError:
-            print('#%-2d Unknown Frame (compile with -g)' % index)
-            return
-
-        if not is_c and self.is_python_function(frame):
-            pyframe = libpython.Frame(frame).get_pyop()
-            if pyframe is None or pyframe.is_optimized_out():
-                # print this python function as a C function
-                return self.print_stackframe(frame, index, is_c=True)
-
-            func_name = pyframe.co_name
-            func_cname = 'PyEval_EvalFrameEx'
-            func_args = []
-        elif self.is_cython_function(frame):
-            cyfunc = self.get_cython_function(frame)
-            f = lambda arg: self.cy.cy_cvalue.invoke(arg, frame=frame)
-
-            func_name = cyfunc.name
-            func_cname = cyfunc.cname
-            func_args = [] # [(arg, f(arg)) for arg in cyfunc.arguments]
-        else:
-            source_desc, lineno = self.get_source_desc(frame)
-            func_name = frame.name()
-            func_cname = func_name
-            func_args = []
-
-        try:
-            gdb_value = gdb.parse_and_eval(func_cname)
-        except RuntimeError:
-            func_address = 0
-        else:
-            func_address = gdb_value.address
-            if not isinstance(func_address, int):
-                # Seriously? Why is the address not an int?
-                if not isinstance(func_address, (str, bytes)):
-                    func_address = str(func_address)
-                func_address = int(func_address.split()[0], 0)
-
-        a = ', '.join('%s=%s' % (name, val) for name, val in func_args)
-        sys.stdout.write('#%-2d 0x%016x in %s(%s)' % (index, func_address, func_name, a))
-
-        if source_desc.filename is not None:
-            sys.stdout.write(' at %s:%s' % (source_desc.filename, lineno))
-
-        sys.stdout.write('\n')
-
-        try:
-            sys.stdout.write('    ' + source_desc.get_source(lineno))
-        except gdb.GdbError:
-            pass
-
-        selected_frame.select()
-
-    def get_remote_cython_globals_dict(self):
-        m = gdb.parse_and_eval('__pyx_m')
-
-        try:
-            PyModuleObject = gdb.lookup_type('PyModuleObject')
-        except RuntimeError:
-            raise gdb.GdbError(textwrap.dedent("""\
-                Unable to lookup type PyModuleObject, did you compile python
-                with debugging support (-g)?"""))
-
-        m = m.cast(PyModuleObject.pointer())
-        return m['md_dict']
-
-
-    def get_cython_globals_dict(self):
-        """
-        Get the Cython globals dict where the remote names are turned into
-        local strings.
-        """
-        remote_dict = self.get_remote_cython_globals_dict()
-        pyobject_dict = libpython.PyObjectPtr.from_pyobject_ptr(remote_dict)
-
-        result = {}
-        seen = set()
-        for k, v in pyobject_dict.items():
-            result[k.proxyval(seen)] = v
-
-        return result
-
-    def print_gdb_value(self, name, value, max_name_length=None, prefix=''):
-        if libpython.pretty_printer_lookup(value):
-            typename = ''
-        else:
-            typename = '(%s) ' % (value.type,)
-
-        if max_name_length is None:
-            print('%s%s = %s%s' % (prefix, name, typename, value))
-        else:
-            print('%s%-*s = %s%s' % (prefix, max_name_length, name, typename, value))
-
-    def is_initialized(self, cython_func, local_name):
-        cyvar = cython_func.locals[local_name]
-        cur_lineno = self.get_cython_lineno()
-
-        if '->' in cyvar.cname:
-            # Closed over free variable
-            if cur_lineno > cython_func.lineno:
-                if cyvar.type == PythonObject:
-                    return int(gdb.parse_and_eval(cyvar.cname))
-                return True
-            return False
-
-        return cur_lineno > cyvar.lineno
-
-
-class SourceFileDescriptor(object):
-    def __init__(self, filename, lexer, formatter=None):
-        self.filename = filename
-        self.lexer = lexer
-        self.formatter = formatter
-
-    def valid(self):
-        return self.filename is not None
-
-    def lex(self, code):
-        if pygments and self.lexer and parameters.colorize_code:
-            bg = parameters.terminal_background.value
-            if self.formatter is None:
-                formatter = pygments.formatters.TerminalFormatter(bg=bg)
-            else:
-                formatter = self.formatter
-
-            return pygments.highlight(code, self.lexer, formatter)
-
-        return code
-
-    def _get_source(self, start, stop, lex_source, mark_line, lex_entire):
-        with open(self.filename) as f:
-            # to provide "correct" colouring, the entire code needs to be
-            # lexed. However, this makes a lot of things terribly slow, so
-            # we decide not to. Besides, it's unlikely to matter.
-
-            if lex_source and lex_entire:
-                f = self.lex(f.read()).splitlines()
-
-            slice = itertools.islice(f, start - 1, stop - 1)
-
-            for idx, line in enumerate(slice):
-                if start + idx == mark_line:
-                    prefix = '>'
-                else:
-                    prefix = ' '
-
-                if lex_source and not lex_entire:
-                    line = self.lex(line)
-
-                yield '%s %4d    %s' % (prefix, start + idx, line.rstrip())
-
-    def get_source(self, start, stop=None, lex_source=True, mark_line=0,
-                   lex_entire=False):
-        exc = gdb.GdbError('Unable to retrieve source code')
-
-        if not self.filename:
-            raise exc
-
-        start = max(start, 1)
-        if stop is None:
-            stop = start + 1
-
-        try:
-            return '\n'.join(
-                self._get_source(start, stop, lex_source, mark_line, lex_entire))
-        except IOError:
-            raise exc
-
-
-# Errors
-
-class CyGDBError(gdb.GdbError):
-    """
-    Base class for Cython-command related errors
-    """
-
-    def __init__(self, *args):
-        args = args or (self.msg,)
-        super(CyGDBError, self).__init__(*args)
-
-
-class NoCythonFunctionInFrameError(CyGDBError):
-    """
-    raised when the user requests the current cython function, which is
-    unavailable
-    """
-    msg = "Current function is a function cygdb doesn't know about"
-
-
-class NoFunctionNameInFrameError(NoCythonFunctionInFrameError):
-    """
-    raised when the name of the C function could not be determined
-    in the current C stack frame
-    """
-    msg = ('C function name could not be determined in the current C stack '
-           'frame')
-
-
-# Parameters
-
-class CythonParameter(gdb.Parameter):
-    """
-    Base class for cython parameters
-    """
-
-    def __init__(self, name, command_class, parameter_class, default=None):
-        self.show_doc = self.set_doc = self.__class__.__doc__
-        super(CythonParameter, self).__init__(name, command_class,
-                                              parameter_class)
-        if default is not None:
-            self.value = default
-
-    def __bool__(self):
-        return bool(self.value)
-
-    __nonzero__ = __bool__  # Python 2
-
-
-
-class CompleteUnqualifiedFunctionNames(CythonParameter):
-    """
-    Have 'cy break' complete unqualified function or method names.
-    """
-
-
-class ColorizeSourceCode(CythonParameter):
-    """
-    Tell cygdb whether to colorize source code.
-    """
-
-
-class TerminalBackground(CythonParameter):
-    """
-    Tell cygdb about the user's terminal background (light or dark).
-    """
-
-
-class CythonParameters(object):
-    """
-    Simple container class that might get more functionality in the distant
-    future (mostly to remind us that we're dealing with parameters).
-    """
-
-    def __init__(self):
-        self.complete_unqualified = CompleteUnqualifiedFunctionNames(
-            'cy_complete_unqualified',
-            gdb.COMMAND_BREAKPOINTS,
-            gdb.PARAM_BOOLEAN,
-            True)
-        self.colorize_code = ColorizeSourceCode(
-            'cy_colorize_code',
-            gdb.COMMAND_FILES,
-            gdb.PARAM_BOOLEAN,
-            True)
-        self.terminal_background = TerminalBackground(
-            'cy_terminal_background_color',
-            gdb.COMMAND_FILES,
-            gdb.PARAM_STRING,
-            "dark")
-
-parameters = CythonParameters()
-
-
-# Commands
-
-class CythonCommand(gdb.Command, CythonBase):
-    """
-    Base class for Cython commands
-    """
-
-    command_class = gdb.COMMAND_NONE
-
-    @classmethod
-    def _register(cls, clsname, args, kwargs):
-        if not hasattr(cls, 'completer_class'):
-            return cls(clsname, cls.command_class, *args, **kwargs)
-        else:
-            return cls(clsname, cls.command_class, cls.completer_class,
-                       *args, **kwargs)
-
-    @classmethod
-    def register(cls, *args, **kwargs):
-        alias = getattr(cls, 'alias', None)
-        if alias:
-            cls._register(cls.alias, args, kwargs)
-
-        return cls._register(cls.name, args, kwargs)
-
-
-class CyCy(CythonCommand):
-    """
-    Invoke a Cython command. Available commands are:
-
-        cy import
-        cy break
-        cy step
-        cy next
-        cy run
-        cy cont
-        cy finish
-        cy up
-        cy down
-        cy select
-        cy bt / cy backtrace
-        cy list
-        cy print
-        cy set
-        cy locals
-        cy globals
-        cy exec
-    """
-
-    name = 'cy'
-    command_class = gdb.COMMAND_NONE
-    completer_class = gdb.COMPLETE_COMMAND
-
-    def __init__(self, name, command_class, completer_class):
-        # keep the signature 2.5 compatible (i.e. do not use f(*a, k=v)
-        super(CythonCommand, self).__init__(name, command_class,
-                                            completer_class, prefix=True)
-
-        commands = dict(
-            # GDB commands
-            import_ = CyImport.register(),
-            break_ = CyBreak.register(),
-            step = CyStep.register(),
-            next = CyNext.register(),
-            run = CyRun.register(),
-            cont = CyCont.register(),
-            finish = CyFinish.register(),
-            up = CyUp.register(),
-            down = CyDown.register(),
-            select = CySelect.register(),
-            bt = CyBacktrace.register(),
-            list = CyList.register(),
-            print_ = CyPrint.register(),
-            locals = CyLocals.register(),
-            globals = CyGlobals.register(),
-            exec_ = libpython.FixGdbCommand('cy exec', '-cy-exec'),
-            _exec = CyExec.register(),
-            set = CySet.register(),
-
-            # GDB functions
-            cy_cname = CyCName('cy_cname'),
-            cy_cvalue = CyCValue('cy_cvalue'),
-            cy_lineno = CyLine('cy_lineno'),
-            cy_eval = CyEval('cy_eval'),
-        )
-
-        for command_name, command in commands.items():
-            command.cy = self
-            setattr(self, command_name, command)
-
-        self.cy = self
-
-        # Cython module namespace
-        self.cython_namespace = {}
-
-        # maps (unique) qualified function names (e.g.
-        # cythonmodule.ClassName.method_name) to the CythonFunction object
-        self.functions_by_qualified_name = {}
-
-        # unique cnames of Cython functions
-        self.functions_by_cname = {}
-
-        # map function names like method_name to a list of all such
-        # CythonFunction objects
-        self.functions_by_name = collections.defaultdict(list)
-
-
-class CyImport(CythonCommand):
-    """
-    Import debug information outputted by the Cython compiler
-    Example: cy import FILE...
-    """
-
-    name = 'cy import'
-    command_class = gdb.COMMAND_STATUS
-    completer_class = gdb.COMPLETE_FILENAME
-
-    def invoke(self, args, from_tty):
-        if isinstance(args, BYTES):
-            args = args.decode(_filesystemencoding)
-        for arg in string_to_argv(args):
-            try:
-                f = open(arg)
-            except OSError as e:
-                raise gdb.GdbError('Unable to open file %r: %s' % (args, e.args[1]))
-
-            t = etree.parse(f)
-
-            for module in t.getroot():
-                cython_module = CythonModule(**module.attrib)
-                self.cy.cython_namespace[cython_module.name] = cython_module
-
-                for variable in module.find('Globals'):
-                    d = variable.attrib
-                    cython_module.globals[d['name']] = CythonVariable(**d)
-
-                for function in module.find('Functions'):
-                    cython_function = CythonFunction(module=cython_module,
-                                                     **function.attrib)
-
-                    # update the global function mappings
-                    name = cython_function.name
-                    qname = cython_function.qualified_name
-
-                    self.cy.functions_by_name[name].append(cython_function)
-                    self.cy.functions_by_qualified_name[
-                        cython_function.qualified_name] = cython_function
-                    self.cy.functions_by_cname[
-                        cython_function.cname] = cython_function
-
-                    d = cython_module.functions[qname] = cython_function
-
-                    for local in function.find('Locals'):
-                        d = local.attrib
-                        cython_function.locals[d['name']] = CythonVariable(**d)
-
-                    for step_into_func in function.find('StepIntoFunctions'):
-                        d = step_into_func.attrib
-                        cython_function.step_into_functions.add(d['name'])
-
-                    cython_function.arguments.extend(
-                        funcarg.tag for funcarg in function.find('Arguments'))
-
-                for marker in module.find('LineNumberMapping'):
-                    cython_lineno = int(marker.attrib['cython_lineno'])
-                    c_linenos = list(map(int, marker.attrib['c_linenos'].split()))
-                    cython_module.lineno_cy2c[cython_lineno] = min(c_linenos)
-                    for c_lineno in c_linenos:
-                        cython_module.lineno_c2cy[c_lineno] = cython_lineno
-
-
-class CyBreak(CythonCommand):
-    """
-    Set a breakpoint for Cython code using Cython qualified name notation, e.g.:
-
-        cy break cython_modulename.ClassName.method_name...
-
-    or normal notation:
-
-        cy break function_or_method_name...
-
-    or for a line number:
-
-        cy break cython_module:lineno...
-
-    Set a Python breakpoint:
-        Break on any function or method named 'func' in module 'modname'
-
-            cy break -p modname.func...
-
-        Break on any function or method named 'func'
-
-            cy break -p func...
-    """
-
-    name = 'cy break'
-    command_class = gdb.COMMAND_BREAKPOINTS
-
-    def _break_pyx(self, name):
-        modulename, _, lineno = name.partition(':')
-        lineno = int(lineno)
-        if modulename:
-            cython_module = self.cy.cython_namespace[modulename]
-        else:
-            cython_module = self.get_cython_function().module
-
-        if lineno in cython_module.lineno_cy2c:
-            c_lineno = cython_module.lineno_cy2c[lineno]
-            breakpoint = '%s:%s' % (cython_module.c_filename, c_lineno)
-            gdb.execute('break ' + breakpoint)
-        else:
-            raise gdb.GdbError("Not a valid line number. "
-                               "Does it contain actual code?")
-
-    def _break_funcname(self, funcname):
-        func = self.cy.functions_by_qualified_name.get(funcname)
-
-        if func and func.is_initmodule_function:
-            func = None
-
-        break_funcs = [func]
-
-        if not func:
-            funcs = self.cy.functions_by_name.get(funcname) or []
-            funcs = [f for f in funcs if not f.is_initmodule_function]
-
-            if not funcs:
-                gdb.execute('break ' + funcname)
-                return
-
-            if len(funcs) > 1:
-                # multiple functions, let the user pick one
-                print('There are multiple such functions:')
-                for idx, func in enumerate(funcs):
-                    print('%3d) %s' % (idx, func.qualified_name))
-
-                while True:
-                    try:
-                        result = input(
-                            "Select a function, press 'a' for all "
-                            "functions or press 'q' or '^D' to quit: ")
-                    except EOFError:
-                        return
-                    else:
-                        if result.lower() == 'q':
-                            return
-                        elif result.lower() == 'a':
-                            break_funcs = funcs
-                            break
-                        elif (result.isdigit() and
-                                0 <= int(result) < len(funcs)):
-                            break_funcs = [funcs[int(result)]]
-                            break
-                        else:
-                            print('Not understood...')
-            else:
-                break_funcs = [funcs[0]]
-
-        for func in break_funcs:
-            gdb.execute('break %s' % func.cname)
-            if func.pf_cname:
-                gdb.execute('break %s' % func.pf_cname)
-
-    def invoke(self, function_names, from_tty):
-        if isinstance(function_names, BYTES):
-            function_names = function_names.decode(_filesystemencoding)
-        argv = string_to_argv(function_names)
-        if function_names.startswith('-p'):
-            argv = argv[1:]
-            python_breakpoints = True
-        else:
-            python_breakpoints = False
-
-        for funcname in argv:
-            if python_breakpoints:
-                gdb.execute('py-break %s' % funcname)
-            elif ':' in funcname:
-                self._break_pyx(funcname)
-            else:
-                self._break_funcname(funcname)
-
-    @dont_suppress_errors
-    def complete(self, text, word):
-        # Filter init-module functions (breakpoints can be set using
-        # modulename:linenumber).
-        names =  [n for n, L in self.cy.functions_by_name.items()
-                  if any(not f.is_initmodule_function for f in L)]
-        qnames = [n for n, f in self.cy.functions_by_qualified_name.items()
-                  if not f.is_initmodule_function]
-
-        if parameters.complete_unqualified:
-            all_names = itertools.chain(qnames, names)
-        else:
-            all_names = qnames
-
-        words = text.strip().split()
-        if not words or '.' not in words[-1]:
-            # complete unqualified
-            seen = set(text[:-len(word)].split())
-            return [n for n in all_names
-                          if n.startswith(word) and n not in seen]
-
-        # complete qualified name
-        lastword = words[-1]
-        compl = [n for n in qnames if n.startswith(lastword)]
-
-        if len(lastword) > len(word):
-            # readline sees something (e.g. a '.') as a word boundary, so don't
-            # "recomplete" this prefix
-            strip_prefix_length = len(lastword) - len(word)
-            compl = [n[strip_prefix_length:] for n in compl]
-
-        return compl
-
-
-class CythonInfo(CythonBase, libpython.PythonInfo):
-    """
-    Implementation of the interface dictated by libpython.LanguageInfo.
-    """
-
-    def lineno(self, frame):
-        # Take care of the Python and Cython levels. We need to care for both
-        # as we can't simply dispatch to 'py-step', since that would work for
-        # stepping through Python code, but it would not step back into Cython-
-        # related code. The C level should be dispatched to the 'step' command.
-        if self.is_cython_function(frame):
-            return self.get_cython_lineno(frame)
-        return super(CythonInfo, self).lineno(frame)
-
-    def get_source_line(self, frame):
-        try:
-            line = super(CythonInfo, self).get_source_line(frame)
-        except gdb.GdbError:
-            return None
-        else:
-            return line.strip() or None
-
-    def exc_info(self, frame):
-        if self.is_python_function:
-            return super(CythonInfo, self).exc_info(frame)
-
-    def runtime_break_functions(self):
-        if self.is_cython_function():
-            return self.get_cython_function().step_into_functions
-        return ()
-
-    def static_break_functions(self):
-        result = ['PyEval_EvalFrameEx']
-        result.extend(self.cy.functions_by_cname)
-        return result
-
-
-class CythonExecutionControlCommand(CythonCommand,
-                                    libpython.ExecutionControlCommandBase):
-
-    @classmethod
-    def register(cls):
-        return cls(cls.name, cython_info)
-
-
-class CyStep(CythonExecutionControlCommand, libpython.PythonStepperMixin):
-    "Step through Cython, Python or C code."
-
-    name = 'cy -step'
-    stepinto = True
-
-    def invoke(self, args, from_tty):
-        if self.is_python_function():
-            self.python_step(self.stepinto)
-        elif not self.is_cython_function():
-            if self.stepinto:
-                command = 'step'
-            else:
-                command = 'next'
-
-            self.finish_executing(gdb.execute(command, to_string=True))
-        else:
-            self.step(stepinto=self.stepinto)
-
-
-class CyNext(CyStep):
-    "Step-over Cython, Python or C code."
-
-    name = 'cy -next'
-    stepinto = False
-
-
-class CyRun(CythonExecutionControlCommand):
-    """
-    Run a Cython program. This is like the 'run' command, except that it
-    displays Cython or Python source lines as well
-    """
-
-    name = 'cy run'
-
-    invoke = CythonExecutionControlCommand.run
-
-
-class CyCont(CythonExecutionControlCommand):
-    """
-    Continue a Cython program. This is like the 'run' command, except that it
-    displays Cython or Python source lines as well.
-    """
-
-    name = 'cy cont'
-    invoke = CythonExecutionControlCommand.cont
-
-
-class CyFinish(CythonExecutionControlCommand):
-    """
-    Execute until the function returns.
-    """
-    name = 'cy finish'
-
-    invoke = CythonExecutionControlCommand.finish
-
-
-class CyUp(CythonCommand):
-    """
-    Go up a Cython, Python or relevant C frame.
-    """
-    name = 'cy up'
-    _command = 'up'
-
-    def invoke(self, *args):
-        try:
-            gdb.execute(self._command, to_string=True)
-            while not self.is_relevant_function(gdb.selected_frame()):
-                gdb.execute(self._command, to_string=True)
-        except RuntimeError as e:
-            raise gdb.GdbError(*e.args)
-
-        frame = gdb.selected_frame()
-        index = 0
-        while frame:
-            frame = frame.older()
-            index += 1
-
-        self.print_stackframe(index=index - 1)
-
-
-class CyDown(CyUp):
-    """
-    Go down a Cython, Python or relevant C frame.
-    """
-
-    name = 'cy down'
-    _command = 'down'
-
-
-class CySelect(CythonCommand):
-    """
-    Select a frame. Use frame numbers as listed in `cy backtrace`.
-    This command is useful because `cy backtrace` prints a reversed backtrace.
-    """
-
-    name = 'cy select'
-
-    def invoke(self, stackno, from_tty):
-        try:
-            stackno = int(stackno)
-        except ValueError:
-            raise gdb.GdbError("Not a valid number: %r" % (stackno,))
-
-        frame = gdb.selected_frame()
-        while frame.newer():
-            frame = frame.newer()
-
-        stackdepth = libpython.stackdepth(frame)
-
-        try:
-            gdb.execute('select %d' % (stackdepth - stackno - 1,))
-        except RuntimeError as e:
-            raise gdb.GdbError(*e.args)
-
-
-class CyBacktrace(CythonCommand):
-    'Print the Cython stack'
-
-    name = 'cy bt'
-    alias = 'cy backtrace'
-    command_class = gdb.COMMAND_STACK
-    completer_class = gdb.COMPLETE_NONE
-
-    @require_running_program
-    def invoke(self, args, from_tty):
-        # get the first frame
-        frame = gdb.selected_frame()
-        while frame.older():
-            frame = frame.older()
-
-        print_all = args == '-a'
-
-        index = 0
-        while frame:
-            try:
-                is_relevant = self.is_relevant_function(frame)
-            except CyGDBError:
-                is_relevant = False
-
-            if print_all or is_relevant:
-                self.print_stackframe(frame, index)
-
-            index += 1
-            frame = frame.newer()
-
-
-class CyList(CythonCommand):
-    """
-    List Cython source code. To disable to customize colouring see the cy_*
-    parameters.
-    """
-
-    name = 'cy list'
-    command_class = gdb.COMMAND_FILES
-    completer_class = gdb.COMPLETE_NONE
-
-    # @dispatch_on_frame(c_command='list')
-    def invoke(self, _, from_tty):
-        sd, lineno = self.get_source_desc()
-        source = sd.get_source(lineno - 5, lineno + 5, mark_line=lineno,
-                               lex_entire=True)
-        print(source)
-
-
-class CyPrint(CythonCommand):
-    """
-    Print a Cython variable using 'cy-print x' or 'cy-print module.function.x'
-    """
-
-    name = 'cy print'
-    command_class = gdb.COMMAND_DATA
-
-    def invoke(self, name, from_tty, max_name_length=None):
-        if self.is_python_function():
-            return gdb.execute('py-print ' + name)
-        elif self.is_cython_function():
-            value = self.cy.cy_cvalue.invoke(name.lstrip('*'))
-            for c in name:
-                if c == '*':
-                    value = value.dereference()
-                else:
-                    break
-
-            self.print_gdb_value(name, value, max_name_length)
-        else:
-            gdb.execute('print ' + name)
-
-    def complete(self):
-        if self.is_cython_function():
-            f = self.get_cython_function()
-            return list(itertools.chain(f.locals, f.globals))
-        else:
-            return []
-
-
-sortkey = lambda item: item[0].lower()
-
-
-class CyLocals(CythonCommand):
-    """
-    List the locals from the current Cython frame.
-    """
-
-    name = 'cy locals'
-    command_class = gdb.COMMAND_STACK
-    completer_class = gdb.COMPLETE_NONE
-
-    @dispatch_on_frame(c_command='info locals', python_command='py-locals')
-    def invoke(self, args, from_tty):
-        cython_function = self.get_cython_function()
-
-        if cython_function.is_initmodule_function:
-            self.cy.globals.invoke(args, from_tty)
-            return
-
-        local_cython_vars = cython_function.locals
-        max_name_length = len(max(local_cython_vars, key=len))
-        for name, cyvar in sorted(local_cython_vars.items(), key=sortkey):
-            if self.is_initialized(self.get_cython_function(), cyvar.name):
-                value = gdb.parse_and_eval(cyvar.cname)
-                if not value.is_optimized_out:
-                    self.print_gdb_value(cyvar.name, value,
-                                         max_name_length, '')
-
-
-class CyGlobals(CyLocals):
-    """
-    List the globals from the current Cython module.
-    """
-
-    name = 'cy globals'
-    command_class = gdb.COMMAND_STACK
-    completer_class = gdb.COMPLETE_NONE
-
-    @dispatch_on_frame(c_command='info variables', python_command='py-globals')
-    def invoke(self, args, from_tty):
-        global_python_dict = self.get_cython_globals_dict()
-        module_globals = self.get_cython_function().module.globals
-
-        max_globals_len = 0
-        max_globals_dict_len = 0
-        if module_globals:
-            max_globals_len = len(max(module_globals, key=len))
-        if global_python_dict:
-            max_globals_dict_len = len(max(global_python_dict))
-
-        max_name_length = max(max_globals_len, max_globals_dict_len)
-
-        seen = set()
-        print('Python globals:')
-        for k, v in sorted(global_python_dict.items(), key=sortkey):
-            v = v.get_truncated_repr(libpython.MAX_OUTPUT_LEN)
-            seen.add(k)
-            print('    %-*s = %s' % (max_name_length, k, v))
-
-        print('C globals:')
-        for name, cyvar in sorted(module_globals.items(), key=sortkey):
-            if name not in seen:
-                try:
-                    value = gdb.parse_and_eval(cyvar.cname)
-                except RuntimeError:
-                    pass
-                else:
-                    if not value.is_optimized_out:
-                        self.print_gdb_value(cyvar.name, value,
-                                             max_name_length, '    ')
-
-
-class EvaluateOrExecuteCodeMixin(object):
-    """
-    Evaluate or execute Python code in a Cython or Python frame. The 'evalcode'
-    method evaluations Python code, prints a traceback if an exception went
-    uncaught, and returns any return value as a gdb.Value (NULL on exception).
-    """
-
-    def _fill_locals_dict(self, executor, local_dict_pointer):
-        "Fill a remotely allocated dict with values from the Cython C stack"
-        cython_func = self.get_cython_function()
-
-        for name, cyvar in cython_func.locals.items():
-            if cyvar.type == PythonObject and self.is_initialized(cython_func, name):
-                try:
-                    val = gdb.parse_and_eval(cyvar.cname)
-                except RuntimeError:
-                    continue
-                else:
-                    if val.is_optimized_out:
-                        continue
-
-                pystringp = executor.alloc_pystring(name)
-                code = '''
-                    (PyObject *) PyDict_SetItem(
-                        (PyObject *) %d,
-                        (PyObject *) %d,
-                        (PyObject *) %s)
-                ''' % (local_dict_pointer, pystringp, cyvar.cname)
-
-                try:
-                    if gdb.parse_and_eval(code) < 0:
-                        gdb.parse_and_eval('PyErr_Print()')
-                        raise gdb.GdbError("Unable to execute Python code.")
-                finally:
-                    # PyDict_SetItem doesn't steal our reference
-                    executor.xdecref(pystringp)
-
-    def _find_first_cython_or_python_frame(self):
-        frame = gdb.selected_frame()
-        while frame:
-            if (self.is_cython_function(frame) or
-                self.is_python_function(frame)):
-                frame.select()
-                return frame
-
-            frame = frame.older()
-
-        raise gdb.GdbError("There is no Cython or Python frame on the stack.")
-
-    def _evalcode_cython(self, executor, code, input_type):
-        with libpython.FetchAndRestoreError():
-            # get the dict of Cython globals and construct a dict in the
-            # inferior with Cython locals
-            global_dict = gdb.parse_and_eval(
-                '(PyObject *) PyModule_GetDict(__pyx_m)')
-            local_dict = gdb.parse_and_eval('(PyObject *) PyDict_New()')
-
-            try:
-                self._fill_locals_dict(executor,
-                                       libpython.pointervalue(local_dict))
-                result = executor.evalcode(code, input_type, global_dict,
-                                           local_dict)
-            finally:
-                executor.xdecref(libpython.pointervalue(local_dict))
-
-        return result
-
-    def evalcode(self, code, input_type):
-        """
-        Evaluate `code` in a Python or Cython stack frame using the given
-        `input_type`.
-        """
-        frame = self._find_first_cython_or_python_frame()
-        executor = libpython.PythonCodeExecutor()
-        if self.is_python_function(frame):
-            return libpython._evalcode_python(executor, code, input_type)
-        return self._evalcode_cython(executor, code, input_type)
-
-
-class CyExec(CythonCommand, libpython.PyExec, EvaluateOrExecuteCodeMixin):
-    """
-    Execute Python code in the nearest Python or Cython frame.
-    """
-
-    name = '-cy-exec'
-    command_class = gdb.COMMAND_STACK
-    completer_class = gdb.COMPLETE_NONE
-
-    def invoke(self, expr, from_tty):
-        expr, input_type = self.readcode(expr)
-        executor = libpython.PythonCodeExecutor()
-        executor.xdecref(self.evalcode(expr, executor.Py_single_input))
-
-
-class CySet(CythonCommand):
-    """
-    Set a Cython variable to a certain value
-
-        cy set my_cython_c_variable = 10
-        cy set my_cython_py_variable = $cy_eval("{'doner': 'kebab'}")
-
-    This is equivalent to
-
-        set $cy_value("my_cython_variable") = 10
-    """
-
-    name = 'cy set'
-    command_class = gdb.COMMAND_DATA
-    completer_class = gdb.COMPLETE_NONE
-
-    @require_cython_frame
-    def invoke(self, expr, from_tty):
-        name_and_expr = expr.split('=', 1)
-        if len(name_and_expr) != 2:
-            raise gdb.GdbError("Invalid expression. Use 'cy set var = expr'.")
-
-        varname, expr = name_and_expr
-        cname = self.cy.cy_cname.invoke(varname.strip())
-        gdb.execute("set %s = %s" % (cname, expr))
-
-
-# Functions
-
-class CyCName(gdb.Function, CythonBase):
-    """
-    Get the C name of a Cython variable in the current context.
-    Examples:
-
-        print $cy_cname("function")
-        print $cy_cname("Class.method")
-        print $cy_cname("module.function")
-    """
-
-    @require_cython_frame
-    @gdb_function_value_to_unicode
-    def invoke(self, cyname, frame=None):
-        frame = frame or gdb.selected_frame()
-        cname = None
-
-        if self.is_cython_function(frame):
-            cython_function = self.get_cython_function(frame)
-            if cyname in cython_function.locals:
-                cname = cython_function.locals[cyname].cname
-            elif cyname in cython_function.module.globals:
-                cname = cython_function.module.globals[cyname].cname
-            else:
-                qname = '%s.%s' % (cython_function.module.name, cyname)
-                if qname in cython_function.module.functions:
-                    cname = cython_function.module.functions[qname].cname
-
-        if not cname:
-            cname = self.cy.functions_by_qualified_name.get(cyname)
-
-        if not cname:
-            raise gdb.GdbError('No such Cython variable: %s' % cyname)
-
-        return cname
-
-
-class CyCValue(CyCName):
-    """
-    Get the value of a Cython variable.
-    """
-
-    @require_cython_frame
-    @gdb_function_value_to_unicode
-    def invoke(self, cyname, frame=None):
-        globals_dict = self.get_cython_globals_dict()
-        cython_function = self.get_cython_function(frame)
-
-        if self.is_initialized(cython_function, cyname):
-            cname = super(CyCValue, self).invoke(cyname, frame=frame)
-            return gdb.parse_and_eval(cname)
-        elif cyname in globals_dict:
-            return globals_dict[cyname]._gdbval
-        else:
-            raise gdb.GdbError("Variable %s is not initialized." % cyname)
-
-
-class CyLine(gdb.Function, CythonBase):
-    """
-    Get the current Cython line.
-    """
-
-    @require_cython_frame
-    def invoke(self):
-        return self.get_cython_lineno()
-
-
-class CyEval(gdb.Function, CythonBase, EvaluateOrExecuteCodeMixin):
-    """
-    Evaluate Python code in the nearest Python or Cython frame and return
-    """
-
-    @gdb_function_value_to_unicode
-    def invoke(self, python_expression):
-        input_type = libpython.PythonCodeExecutor.Py_eval_input
-        return self.evalcode(python_expression, input_type)
-
-
-cython_info = CythonInfo()
-cy = CyCy.register()
-cython_info.cy = cy
-
-
-def register_defines():
-    libpython.source_gdb_script(textwrap.dedent("""\
-        define cy step
-        cy -step
-        end
-
-        define cy next
-        cy -next
-        end
-
-        document cy step
-        %s
-        end
-
-        document cy next
-        %s
-        end
-    """) % (CyStep.__doc__, CyNext.__doc__))
-
-register_defines()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/RuleContext.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/RuleContext.py
deleted file mode 100644
index 7f6dd9143e8af6927accc1171dbd8a98de30e1f4..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/RuleContext.py
+++ /dev/null
@@ -1,228 +0,0 @@
-# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved.
-# Use of this file is governed by the BSD 3-clause license that
-# can be found in the LICENSE.txt file in the project root.
-#/
-
-
-#  A rule context is a record of a single rule invocation. It knows
-#  which context invoked it, if any. If there is no parent context, then
-#  naturally the invoking state is not valid.  The parent link
-#  provides a chain upwards from the current rule invocation to the root
-#  of the invocation tree, forming a stack. We actually carry no
-#  information about the rule associated with this context (except
-#  when parsing). We keep only the state number of the invoking state from
-#  the ATN submachine that invoked this. Contrast this with the s
-#  pointer inside ParserRuleContext that tracks the current state
-#  being "executed" for the current rule.
-#
-#  The parent contexts are useful for computing lookahead sets and
-#  getting error information.
-#
-#  These objects are used during parsing and prediction.
-#  For the special case of parsers, we use the subclass
-#  ParserRuleContext.
-#
-#  @see ParserRuleContext
-#/
-from io import StringIO
-from antlr4.tree.Tree import RuleNode, INVALID_INTERVAL, ParseTreeVisitor
-from antlr4.tree.Trees import Trees
-
-# need forward declarations
-RuleContext = None
-Parser = None
-
-class RuleContext(RuleNode):
-
-    EMPTY = None
-
-    def __init__(self, parent:RuleContext=None, invokingState:int=-1):
-        super().__init__()
-        # What context invoked this rule?
-        self.parentCtx = parent
-        # What state invoked the rule associated with this context?
-        #  The "return address" is the followState of invokingState
-        #  If parent is null, this should be -1.
-        self.invokingState = invokingState
-
-
-    def depth(self):
-        n = 0
-        p = self
-        while p is not None:
-            p = p.parentCtx
-            n += 1
-        return n
-
-    # A context is empty if there is no invoking state; meaning nobody call
-    #  current context.
-    def isEmpty(self):
-        return self.invokingState == -1
-
-    # satisfy the ParseTree / SyntaxTree interface
-
-    def getSourceInterval(self):
-        return INVALID_INTERVAL
-
-    def getRuleContext(self):
-        return self
-
-    def getPayload(self):
-        return self
-
-   # Return the combined text of all child nodes. This method only considers
-    #  tokens which have been added to the parse tree.
-    #  <p>
-    #  Since tokens on hidden channels (e.g. whitespace or comments) are not
-    #  added to the parse trees, they will not appear in the output of this
-    #  method.
-    #/
-    def getText(self):
-        if self.getChildCount() == 0:
-            return ""
-        with StringIO() as builder:
-            for child in self.getChildren():
-                builder.write(child.getText())
-            return builder.getvalue()
-
-    def getRuleIndex(self):
-        return -1
-
-    # For rule associated with this parse tree internal node, return
-    # the outer alternative number used to match the input. Default
-    # implementation does not compute nor store this alt num. Create
-    # a subclass of ParserRuleContext with backing field and set
-    # option contextSuperClass.
-    # to set it.
-    def getAltNumber(self):
-        return 0 # should use ATN.INVALID_ALT_NUMBER but won't compile
-
-    # Set the outer alternative number for this context node. Default
-    # implementation does nothing to avoid backing field overhead for
-    # trees that don't need it.  Create
-    # a subclass of ParserRuleContext with backing field and set
-    # option contextSuperClass.
-    def setAltNumber(self, altNumber:int):
-        pass
-
-    def getChild(self, i:int):
-        return None
-
-    def getChildCount(self):
-        return 0
-
-    def getChildren(self):
-        for c in []:
-            yield c
-
-    def accept(self, visitor:ParseTreeVisitor):
-        return visitor.visitChildren(self)
-
-   # # Call this method to view a parse tree in a dialog box visually.#/
-   #  public Future<JDialog> inspect(@Nullable Parser parser) {
-   #      List<String> ruleNames = parser != null ? Arrays.asList(parser.getRuleNames()) : null;
-   #      return inspect(ruleNames);
-   #  }
-   #
-   #  public Future<JDialog> inspect(@Nullable List<String> ruleNames) {
-   #      TreeViewer viewer = new TreeViewer(ruleNames, this);
-   #      return viewer.open();
-   #  }
-   #
-   # # Save this tree in a postscript file#/
-   #  public void save(@Nullable Parser parser, String fileName)
-   #      throws IOException, PrintException
-   #  {
-   #      List<String> ruleNames = parser != null ? Arrays.asList(parser.getRuleNames()) : null;
-   #      save(ruleNames, fileName);
-   #  }
-   #
-   # # Save this tree in a postscript file using a particular font name and size#/
-   #  public void save(@Nullable Parser parser, String fileName,
-   #                   String fontName, int fontSize)
-   #      throws IOException
-   #  {
-   #      List<String> ruleNames = parser != null ? Arrays.asList(parser.getRuleNames()) : null;
-   #      save(ruleNames, fileName, fontName, fontSize);
-   #  }
-   #
-   # # Save this tree in a postscript file#/
-   #  public void save(@Nullable List<String> ruleNames, String fileName)
-   #      throws IOException, PrintException
-   #  {
-   #      Trees.writePS(this, ruleNames, fileName);
-   #  }
-   #
-   # # Save this tree in a postscript file using a particular font name and size#/
-   #  public void save(@Nullable List<String> ruleNames, String fileName,
-   #                   String fontName, int fontSize)
-   #      throws IOException
-   #  {
-   #      Trees.writePS(this, ruleNames, fileName, fontName, fontSize);
-   #  }
-   #
-   # # Print out a whole tree, not just a node, in LISP format
-   #  #  (root child1 .. childN). Print just a node if this is a leaf.
-   #  #  We have to know the recognizer so we can get rule names.
-   #  #/
-   #  @Override
-   #  public String toStringTree(@Nullable Parser recog) {
-   #      return Trees.toStringTree(this, recog);
-   #  }
-   #
-   # Print out a whole tree, not just a node, in LISP format
-   #  (root child1 .. childN). Print just a node if this is a leaf.
-   #
-    def toStringTree(self, ruleNames:list=None, recog:Parser=None):
-        return Trees.toStringTree(self, ruleNames=ruleNames, recog=recog)
-   #  }
-   #
-   #  @Override
-   #  public String toStringTree() {
-   #      return toStringTree((List<String>)null);
-   #  }
-   #
-    def __str__(self):
-        return self.toString(None, None)
-
-   #  @Override
-   #  public String toString() {
-   #      return toString((List<String>)null, (RuleContext)null);
-   #  }
-   #
-   #  public final String toString(@Nullable Recognizer<?,?> recog) {
-   #      return toString(recog, ParserRuleContext.EMPTY);
-   #  }
-   #
-   #  public final String toString(@Nullable List<String> ruleNames) {
-   #      return toString(ruleNames, null);
-   #  }
-   #
-   #  // recog null unless ParserRuleContext, in which case we use subclass toString(...)
-   #  public String toString(@Nullable Recognizer<?,?> recog, @Nullable RuleContext stop) {
-   #      String[] ruleNames = recog != null ? recog.getRuleNames() : null;
-   #      List<String> ruleNamesList = ruleNames != null ? Arrays.asList(ruleNames) : null;
-   #      return toString(ruleNamesList, stop);
-   #  }
-
-    def toString(self, ruleNames:list, stop:RuleContext)->str:
-        with StringIO() as buf:
-            p = self
-            buf.write("[")
-            while p is not None and p is not stop:
-                if ruleNames is None:
-                    if not p.isEmpty():
-                        buf.write(str(p.invokingState))
-                else:
-                    ri = p.getRuleIndex()
-                    ruleName = ruleNames[ri] if ri >= 0 and ri < len(ruleNames) else str(ri)
-                    buf.write(ruleName)
-
-                if p.parentCtx is not None and (ruleNames is not None or not p.parentCtx.isEmpty()):
-                    buf.write(" ")
-
-                p = p.parentCtx
-
-            buf.write("]")
-            return buf.getvalue()
-
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/concat_dataset.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/concat_dataset.py
deleted file mode 100644
index 01a4078bb159fa44b2d1062b9a971fe7f1abd1c2..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/data/concat_dataset.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import bisect
-
-import numpy as np
-from torch.utils.data.dataloader import default_collate
-
-from . import FairseqDataset
-
-
-class ConcatDataset(FairseqDataset):
-    @staticmethod
-    def cumsum(sequence, sample_ratios):
-        r, s = [], 0
-        for e, ratio in zip(sequence, sample_ratios):
-            curr_len = int(ratio * len(e))
-            r.append(curr_len + s)
-            s += curr_len
-        return r
-
-    def __init__(self, datasets, sample_ratios=1):
-        super(ConcatDataset, self).__init__()
-        assert len(datasets) > 0, "datasets should not be an empty iterable"
-        self.datasets = list(datasets)
-        if isinstance(sample_ratios, int):
-            sample_ratios = [sample_ratios] * len(self.datasets)
-        self.sample_ratios = sample_ratios
-        self.cumulative_sizes = self.cumsum(self.datasets, sample_ratios)
-        self.real_sizes = [len(d) for d in self.datasets]
-
-    def __len__(self):
-        return self.cumulative_sizes[-1]
-
-    def __getitem__(self, idx):
-        dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
-        return self.datasets[dataset_idx][sample_idx]
-
-    def _get_dataset_and_sample_index(self, idx: int):
-        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
-        if dataset_idx == 0:
-            sample_idx = idx
-        else:
-            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
-        sample_idx = sample_idx % self.real_sizes[dataset_idx]
-        return dataset_idx, sample_idx
-
-    def collater(self, samples, **extra_args):
-        # For now only supports datasets with same underlying collater implementations
-        if hasattr(self.datasets[0], "collater"):
-            return self.datasets[0].collater(samples, **extra_args)
-        else:
-            return default_collate(samples, **extra_args)
-
-    def size(self, idx: int):
-        """
-        Return an example's size as a float or tuple.
-        """
-        dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
-        return self.datasets[dataset_idx].size(sample_idx)
-
-    def num_tokens(self, index: int):
-        return np.max(self.size(index))
-
-    def attr(self, attr: str, index: int):
-        dataset_idx = bisect.bisect_right(self.cumulative_sizes, index)
-        return getattr(self.datasets[dataset_idx], attr, None)
-
-    @property
-    def sizes(self):
-        _dataset_sizes = []
-        for ds, sr in zip(self.datasets, self.sample_ratios):
-            if isinstance(ds.sizes, np.ndarray):
-                _dataset_sizes.append(np.tile(ds.sizes, sr))
-            else:
-                # Only support underlying dataset with single size array.
-                assert isinstance(ds.sizes, list)
-                _dataset_sizes.append(np.tile(ds.sizes[0], sr))
-        return np.concatenate(_dataset_sizes)
-
-    @property
-    def supports_prefetch(self):
-        return all(d.supports_prefetch for d in self.datasets)
-
-    def ordered_indices(self):
-        """
-        Returns indices sorted by length. So less padding is needed.
-        """
-        if isinstance(self.sizes, np.ndarray) and len(self.sizes.shape) > 1:
-            # special handling for concatenating lang_pair_datasets
-            indices = np.arange(len(self))
-            sizes = self.sizes
-            tgt_sizes = (
-                sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None
-            )
-            src_sizes = (
-                sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes
-            )
-            # sort by target length, then source length
-            if tgt_sizes is not None:
-                indices = indices[np.argsort(tgt_sizes[indices], kind="mergesort")]
-            return indices[np.argsort(src_sizes[indices], kind="mergesort")]
-        else:
-            return np.argsort(self.sizes)
-
-    def prefetch(self, indices):
-        frm = 0
-        for to, ds in zip(self.cumulative_sizes, self.datasets):
-            real_size = len(ds)
-            if getattr(ds, "supports_prefetch", False):
-                ds.prefetch([(i - frm) % real_size for i in indices if frm <= i < to])
-            frm = to
-
-    @property
-    def can_reuse_epoch_itr_across_epochs(self):
-        return all(d.can_reuse_epoch_itr_across_epochs for d in self.datasets)
-
-    def set_epoch(self, epoch):
-        super().set_epoch(epoch)
-        for ds in self.datasets:
-            if hasattr(ds, "set_epoch"):
-                ds.set_epoch(epoch)
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Zekiye Erdem.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Zekiye Erdem.html
deleted file mode 100644
index 007dfdad7d58cd3eae22ae885a4cc0f98f449a13..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Zekiye Erdem.html	
+++ /dev/null
@@ -1,132 +0,0 @@
-<!DOCTYPE html><html lang="en"><head>
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1, user-scalable=no">
-    <title>Zekiye Erdem</title>
-    <style>
-      html {
-        padding: 1em;
-        background: #fff;
-        color: #222;
-        font-family: "Lucida Grande", "Lucida Sans Unicode", "Lucida Sans", Geneva, Verdana, sans-serif;
-        font-size: 100%;
-        line-height: 1.5;
-      }
-
-      h1,
-      p,
-      ul,
-      ol,
-      div,
-      figure {
-        margin: 0;
-      }
-
-      h1 { font-size: 1.2em; }
-
-      ul {
-        list-style: disc;
-        padding-left: 1.3em;
-      }
-
-      ol {
-        padding-left: 1.3em;
-        list-style: decimal;
-      }
-
-      blockquote {
-        border-left: 3px solid #000;
-        margin: 0;
-        padding: 0 0 0 1em;
-      }
-
-      pre {
-        font-family: monaco, monospace;
-        font-size: 0.875em;
-        margin: 0;
-        padding: 1rem;
-        background: #eee;
-        border-radius: 0.4rem;
-        white-space: pre;
-        word-wrap: normal;
-        word-break: normal;
-        overflow-x: auto;
-      }
-
-      code { border-radius: 0.4rem; }
-
-      figure {
-        display: inline-block;
-        width: 100%;
-        box-sizing: border-box;
-      }
-
-      figcaption { word-break: break-word; }
-
-      img {
-        display: block;
-        max-width: 100%;
-        margin: 0 auto;
-        padding: 1px;
-        border: 1px solid #eee;
-      }
-
-      a {
-        color: #1b6ac9;
-        text-decoration: underline;
-      }
-
-      a:active { color: #064ac9; }
-
-      .wrapper {
-        max-width: 650px;
-        margin: 0 auto;
-      }
-
-      .title {
-        font-size: 2em;
-        margin: 0.5em 0 1em;
-        line-height: 1.3;
-      }
-
-      .attachment--image {
-        text-align: center;
-        color: #888;
-        font-size: 0.875em;
-      }
-
-      .attachment--file {
-        position: relative;
-        padding: 1.4em 1em 1.5em;
-        border: 1px solid #ddd;
-        border-bottom-width: 3px;
-        border-radius: 6px;
-        color: #222;
-      }
-
-      .attachment__label {
-        position: absolute;
-        bottom: 0;
-        right: 0;
-        background: #ddd;
-        font-size: 10px;
-        padding: 2px 6px;
-        border-radius: 6px 0 4px 0;
-        color: #888;
-      }
-
-      .metadata { color: #888; }
-    </style>
-  </head>
-
-  <body>
-    <div class="wrapper">
-      <h1 class="title">Zekiye Erdem</h1>
-
-<div class="formatted_content">
-  <div>1. How did you hear about SharpestMinds? Why Mentorship with SM?<br>- Came across it on LinkedIn and a friend is a mentor. Aligned with the aim and goal of SM. Been always mentoring in current job and helping students land job and helping in capstone project.&nbsp;<br><br>2. How's your DS career journey been like?<br>- Has a background in Electrical &amp; Electronics engg. &amp; did Phd.&nbsp;<br>- Did a consulting project with Smart Nora - consulted on how and what to implement in audio processing. Have a robust academic experience in Data science and research. Have enjoyed working with Startup and solving business problems. Enjoy consulting and teaching both and have helped many students to shift from academia to business.&nbsp;<br>- Currently working as lead data science instructor &amp; project supervisor with Toronto metropolitan university.<br><br>3. What are some challenges or mistakes beginners face while entering into DS field? How can you help with this?<br>- Have observed most don't have a technical background in DS, or either don't know how to approach towards solving a problem. becoming intimidated by it.&nbsp;<br>- Enjoy showing path on how to start and improve along the way. Sharing personal and various experiences.&nbsp;<br>- Should work on their strengths. After courses usually work on small projects to feel capable of solving problems and then move to bigger projects and problems.&nbsp;<br>- If unclear, start with Data analytics and slowly move to DS and further roles.&nbsp;<br>- Should take up internships and contract roles to learn on the job.&nbsp;<br><br>4. Pervious mentorship experience.&nbsp;<br>- Have helped many students prepare for and land jobs while working at University and being capstone project supervisor.&nbsp;<br><br>5. Questions about SM?<br>- How does the process work?<br>- How to select candidates?<br>- How much time commitment is required per week?<br>- Is there a limitation on number of mentee to work with?<br>- Can I publish about this on Linkedin? Have a lot of students following.<br>- Is there a project involved in mentorship?<br>- % of Salary (ISA), and how does it work?</div>
-</div>
-
-    </div>
-  
-
-</body></html>
\ No newline at end of file
diff --git a/spaces/avivdm1/AutoGPT/autogpt/config/__init__.py b/spaces/avivdm1/AutoGPT/autogpt/config/__init__.py
deleted file mode 100644
index 726b6dcf3da95968b948c4d897e97a9cdd0928ff..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/autogpt/config/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""
-This module contains the configuration classes for AutoGPT.
-"""
-from autogpt.config.ai_config import AIConfig
-from autogpt.config.config import Config, check_openai_api_key
-from autogpt.config.singleton import AbstractSingleton, Singleton
-
-__all__ = [
-    "check_openai_api_key",
-    "AbstractSingleton",
-    "AIConfig",
-    "Config",
-    "Singleton",
-]
diff --git a/spaces/awacke1/Creative-Potential-Music-Art-Lit/README.md b/spaces/awacke1/Creative-Potential-Music-Art-Lit/README.md
deleted file mode 100644
index 01db455e123176fb1c66bc1c1d144410707d0759..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Creative-Potential-Music-Art-Lit/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Creative Potential Music Art Lit
-emoji: 🐢
-colorFrom: red
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/Streamlit_Plotly_Graph_Objects/README.md b/spaces/awacke1/Streamlit_Plotly_Graph_Objects/README.md
deleted file mode 100644
index 1c44034284e30ea9c5ed4b1300550a7773ec79e7..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit_Plotly_Graph_Objects/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Streamlit Plotly Graph Objects
-emoji: 😻
-colorFrom: blue
-colorTo: green
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/Transformers-StoryWriting/app.py b/spaces/awacke1/Transformers-StoryWriting/app.py
deleted file mode 100644
index ddc65f3de41702c8da214f25de21d9b193c5a5f3..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Transformers-StoryWriting/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import gradio as gr
-import transformers as tr
-import numpy as np
-
-generator1 = gr.Interface.load("huggingface/gpt2-large")
-generator2 = gr.Interface.load("huggingface/EleutherAI/gpt-neo-2.7B")
-generator3 = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B")
-
-
-demo = gr.Blocks()
-
-def f1(x):
-    return generator1(x)
-def f2(x):
-    return generator2(x)
-def f3(x):
-    return generator3(x)
-
-
-with demo:
-    textIn = gr.Textbox()
-    textOut1 = gr.Textbox()
-    textOut2 = gr.Textbox()
-    textOut3 = gr.Textbox()
-
-    b1 = gr.Button("gpt2-large")
-    b2 = gr.Button("gpt-neo-2.7B")
-    b3 = gr.Button("gpt-j-6B")
-
-    b1.click(f1, inputs=textIn, outputs=textOut1 )
-    b2.click(f2, inputs=textIn, outputs=textOut2 )
-    b3.click(f3, inputs=textIn, outputs=textOut3 )
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/ayush5710/wizard-coder-34b-coding-chatbot/README.md b/spaces/ayush5710/wizard-coder-34b-coding-chatbot/README.md
deleted file mode 100644
index 6e1e60891a93ee180f57a9d9b2eb2c44d2c4237e..0000000000000000000000000000000000000000
--- a/spaces/ayush5710/wizard-coder-34b-coding-chatbot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Wizard Coder 34b Coding Chatbot
-emoji: 🏃
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/b-monroe/rvc-VoiceAI/infer_pack/transforms.py b/spaces/b-monroe/rvc-VoiceAI/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/b-monroe/rvc-VoiceAI/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails=None,
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
-    outputs, logabsdet = spline_fn(
-        inputs=inputs,
-        unnormalized_widths=unnormalized_widths,
-        unnormalized_heights=unnormalized_heights,
-        unnormalized_derivatives=unnormalized_derivatives,
-        inverse=inverse,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-        **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails="linear",
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == "linear":
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError("{} tails are not implemented.".format(tails))
-
-    (
-        outputs[inside_interval_mask],
-        logabsdet[inside_interval_mask],
-    ) = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound,
-        right=tail_bound,
-        bottom=-tail_bound,
-        top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-    )
-
-    return outputs, logabsdet
-
-
-def rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    left=0.0,
-    right=1.0,
-    bottom=0.0,
-    top=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError("Input to a transform is not within its domain")
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError("Minimal bin width too large for the number of bins")
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError("Minimal bin height too large for the number of bins")
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        ) + input_heights * (input_delta - input_derivatives)
-        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        )
-        c = -input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * root.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - root).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (
-            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
-        )
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * theta.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - theta).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/bguberfain/Detic/docs/INSTALL.md b/spaces/bguberfain/Detic/docs/INSTALL.md
deleted file mode 100644
index 1d5fbc4ae1097da00fad0fac55d16d7767bf7f4c..0000000000000000000000000000000000000000
--- a/spaces/bguberfain/Detic/docs/INSTALL.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# Installation
-
-### Requirements
-- Linux or macOS with Python ≥ 3.6
-- PyTorch ≥ 1.8.
-  Install them together at [pytorch.org](https://pytorch.org) to make sure of this. Note, please check
-  PyTorch version matches that is required by Detectron2.
-- Detectron2: follow [Detectron2 installation instructions](https://detectron2.readthedocs.io/tutorials/install.html).
-
-
-### Example conda environment setup
-```bash
-conda create --name detic python=3.8 -y
-conda activate detic
-conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia
-
-# under your working directory
-git clone git@github.com:facebookresearch/detectron2.git
-cd detectron2
-pip install -e .
-
-cd ..
-git clone https://github.com/facebookresearch/Detic.git --recurse-submodules
-cd Detic
-pip install -r requirements.txt
-```
-
-Our project uses two submodules, [CenterNet2](https://github.com/xingyizhou/CenterNet2.git) and [Deformable-DETR](https://github.com/fundamentalvision/Deformable-DETR.git). If you forget to add `--recurse-submodules`, do `git submodule init` and then `git submodule update`. To train models with Deformable-DETR (optional), we need to compile it
-
-```
-cd third_party/Deformable-DETR/models/ops
-./make.sh
-```
\ No newline at end of file
diff --git a/spaces/bigcode/in-the-stack/README.md b/spaces/bigcode/in-the-stack/README.md
deleted file mode 100644
index 17e7e5205d08973fb9721122f03d5cb6c38d2269..0000000000000000000000000000000000000000
--- a/spaces/bigcode/in-the-stack/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Am I in The Stack?
-emoji: 📑🔍
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bioriAsaeru/text-to-voice/Could Not Load Library Client L4d2 Razor1911.md b/spaces/bioriAsaeru/text-to-voice/Could Not Load Library Client L4d2 Razor1911.md
deleted file mode 100644
index 994d2e1abbdf3b81b01fc1716caa0797da28413e..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Could Not Load Library Client L4d2 Razor1911.md	
+++ /dev/null
@@ -1,80 +0,0 @@
-<h2>Could Not Load Library Client L4d2 Razor1911</h2><br /><p><b><b>Download</b> ->>> <a href="https://urloso.com/2uyPSR">https://urloso.com/2uyPSR</a></b></p><br /><br />
-
-exe" your game directory. For example if your Steam directory is "C:\Program Files (x86)\Steam" you should run "C:\Program Files (x86)\Steam\bin\dumpsys --fullgame" and then you should delete this message. More info: 
-
-A:
-
-As seen in this thread, try to install latest drivers from here.
-
-And it worked.
-
-Q:
-
-How to use this context. It has no usable method or extension called
-
-I'm trying to create a script that opens a new mail window with a subject and a body (based on the selected value in a listbox) and writes the selected value in that email.
-
-My code is this:
-
-import win32com.client
-
-import win32com.gen_pywin32.client
-
-import pywintypes
-
-import win32com.shell
-
-import win32con
-
-from win32com import wmi
-
-app = win32com.client.Dispatch("Outlook.Application")
-
-dirs = []
-
-for root, dirs, files in os.walk("C:\"):
-
-    for file in files:
-
-        dirs.append(os.path.join(root, file))
-
-folder = app.Session.DefaultStore
-
-mail = folder.GetDefaultFolder(6)
-
-mail.Items |= win32com.gen_pywin32.client.get_clbem(0)
-
-mail.Move(2)
-
-mail.Items[2].Subject = "Test email"
-
-mail.Items[2].Body = """
-
-Hi,
-
-The selected person is:
-
-""" + str(item.Value) + """
-
-"""
-
-print "Before send"
-
-try:
-
-    win32com.client.Dispatch("Outlook.MailItem").Send
-
-except Exception as e:
-
-    print e
-
-folder = win32com.client.Dispatch("Outlook.Application").Session.DefaultStore
-
-Here it shows this error:
-
-Traceback (most recent call last):
-
-  File "C:\Users\Nelton\Desktop\Nexgen2\Nexgen_contacts_app.py", line 32, in < 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/bioriAsaeru/text-to-voice/Facebook Ipad Free App High Quality.md b/spaces/bioriAsaeru/text-to-voice/Facebook Ipad Free App High Quality.md
deleted file mode 100644
index 5792ac45c4cabcc2553870a73e951d65ba68b9ab..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Facebook Ipad Free App High Quality.md	
+++ /dev/null
@@ -1,18 +0,0 @@
-
-<p>Smartphones have become an essential part of everyday life. People can easily spend more than six to seven hours just scrolling on their smartphones every day without even realising it. While it's undeniable that we cannot live without smartphones today as almost everything can be done through them like ordering food, banking, emails, etc. However, spending the majority of the day glued to your smartphone screen can be detrimental to your productivity. Let's take a look at the best free apps to limit screen time on smartphones.</p>
-<p>Some password managers are free and offer cross-platform options, but they usually rely upon a specific browser or add-on. Google's password tool is an excellent option for those looking for something outside of Apple's ecosystem.</p>
-<h2>facebook ipad free app</h2><br /><p><b><b>DOWNLOAD</b> &#10084;&#10084;&#10084; <a href="https://urloso.com/2uyS1n">https://urloso.com/2uyS1n</a></b></p><br /><br />
-<p>Here is a tip for you: <strong>Download FoneDog iOS Data Recovery</strong> to find your hidden conversations on Facebook. It can not only scan the deleted chats but also the existing and <strong>hidden</strong> ones. Feel free to download the tool for <strong>free</strong> and try all its features. If you want to find your Facebook messages all by yourself, we have gathered some great solutions below.</p>
-<p>It's probably coming. Facebook CEO Mark Zuckerberg has said that the company isn't planning an iPad app because the iPad is a computer, not a mobile device, meaning it's fine to use the browser to fill your facebooking needs.</p>
-<p>Anyway, if Facebook thinks it shouldn't make an iPad app because the iPad isn't mobile, it's certainly not going to make a Mac app. Which probably means there's an opening for someone to do so. Why would people buy a Mac app to browse Facebook when they can just go for free on their browser? For the same reason people do on the iPad, we guess. We wouldn't pay for a Facebook iPad app, but over a million people have.</p>
-<p><strong>Starting with version 032-1.3.1, BlueJeans for Glass supports automatic OTA software updates. If this process is not working, click the link below to learn more about how to manually update your Glass Enterprise Edition 2 device.</strong><br /><br />Arm your front-line workers with a hands-free video conferencing solution that allows them to easily share their line-of-sight with remote experts to efficiently tackle real-world problems in real-time. BlueJeans Meetings running on Glass Enterprise Edition 2 from Google delivers world-class audio and video performance alongside a host of features that make it easy to collaborate.</p>
-<p>Apple and third-party developers have created various apps that support SharePlay with FaceTime, all of which are listed below. Some apps aren't free or have in-app purchases or subscriptions, such as most music and video streaming services, but prices are provided where applicable.</p>
-<p>The new Facebook Messenger is available for free from the iOS App Store. Download it and let us know what you think about the new app feature. I find it interesting, but I am not sure my friends will appreciate the onslaught of gifs, collages and exploding videos coming their way. googletag.cmd.push(function()  googletag.display('appleworld_today_728x90_in_article_responsive_4'); );</p>
-<p></p>
-<p><strong>1.</strong> <strong>USA<br> Battleship for iPad (games)</strong><br>The classic game of Battleship gets a modern twist with this game for the iPad. Users play against the computer, taking it in turns to register a hit on the opponent's battleships, which occupy a number of either vertical or horizontal squares on a grid.<br> -for-ipad/id404897184?mt=8</p>
-<p><strong>2.</strong> <strong>UK<br> Friendly for Facebook</strong> <strong>(social networking)<br></strong> This app allows access to the social networking site from the iPad. Friendly for Facebook allows users to engage with the social networking site through a variety of mediums, including Facebook chat.<br> -for-facebook/id400169658?mt=8</p>
-<p><strong>9.</strong> <strong>Italy<br> NanoPress per iPad (news)<br></strong> This app brings news from across Italy and around the world to the iPad. Users can choose the location of their news, sort the news by theme or choose to receive news updates only from their preferred source.<br> -per-ipad/id405559804?mt=8<br><br><strong>10.</strong> <strong>Korea<br></strong> <strong>???</strong> <strong>??</strong> <strong>??</strong> <strong>HD (utilities)<br></strong> Loosely translated into English as "Free applications for today," this app provides users with real time updates of the latest free applications available in the iTunes store. The app also combines Twitter referrals so that users can quickly identify the most popular applications.<br> =8</p>
-<p>What do you call a group of zebras? What in the world is a mumpsimus? Inspired by party games like Balderdash, Psych! has you cooking up the zaniest but most plausible answers to these questions in order to fool your friends and score points. The app is free, though there are in-app purchases for turning off ads and unlocking new question packs.</p>
-<p>Effortlessly set up and manage all of your Apple devices with <strong>Jamf Now</strong>! This cloud-based device management software allows you to remotely configure the devices your team uses on a daily basis (such as iPads, iPhones, Mac computers, and more), giving you the ability to set up new equipment, add apps, manage security features, and much more. With Jamf Now, one person can do in a day what a small team of people could do in a week. Start with three devices for free and add more for just $2 per device, paid monthly.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/biranchi125/gpt2_experiment/README.md b/spaces/biranchi125/gpt2_experiment/README.md
deleted file mode 100644
index 60f78307aaba2c5ea64229e47735d3a8b49cc595..0000000000000000000000000000000000000000
--- a/spaces/biranchi125/gpt2_experiment/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Testspace
-emoji: 📈
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/blmdsydm/faster-whisper-webui/docs/options.md b/spaces/blmdsydm/faster-whisper-webui/docs/options.md
deleted file mode 100644
index 6979fca4d9d4c98a626a2953c2573ff23898a37e..0000000000000000000000000000000000000000
--- a/spaces/blmdsydm/faster-whisper-webui/docs/options.md
+++ /dev/null
@@ -1,134 +0,0 @@
-# Standard Options
-To transcribe or translate an audio file, you can either copy an URL from a website (all [websites](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md) 
-supported by YT-DLP will work, including YouTube). Otherwise, upload an audio file (choose "All Files (*.*)" 
-in the file selector to select any file type, including video files) or use the microphone.
-
-For longer audio files (>10 minutes), it is recommended that you select Silero VAD (Voice Activity Detector) in the VAD option, especially if you are using the `large-v1` model. Note that `large-v2` is a lot more forgiving, but you may still want to use a VAD with a slightly higher "VAD - Max Merge Size (s)" (60 seconds or more).
-
-## Model
-Select the model that Whisper will use to transcribe the audio:
-
-| Size      | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |
-|-----------|------------|--------------------|--------------------|---------------|----------------|
-| tiny      | 39 M       | tiny.en            | tiny               | ~1 GB         | ~32x           |
-| base      | 74 M       | base.en            | base               | ~1 GB         | ~16x           |
-| small     | 244 M      | small.en           | small              | ~2 GB         | ~6x            |
-| medium    | 769 M      | medium.en          | medium             | ~5 GB         | ~2x            |
-| large     | 1550 M     | N/A                | large              | ~10 GB        | 1x             |
-| large-v2  | 1550 M     | N/A                | large              | ~10 GB        | 1x             |
-
-## Language
-
-Select the language, or leave it empty for Whisper to automatically detect it. 
-
-Note that if the selected language and the language in the audio differs, Whisper may start to translate the audio to the selected 
-language. For instance, if the audio is in English but you select Japaneese, the model may translate the audio to Japanese.
-
-## Inputs
-The options "URL (YouTube, etc.)", "Upload Files" or "Micriphone Input" allows you to send an audio input to the model.
-
-### Multiple Files
-Note that the UI will only process either the given URL or the upload files (including microphone) - not both. 
-
-But you can upload multiple files either through the "Upload files" option, or as a playlist on YouTube. Each audio file will then be processed in turn, and the resulting SRT/VTT/Transcript will be made available in the "Download" section. When more than one file is processed, the UI will also generate a "All_Output" zip file containing all the text output files.
-
-## Task
-Select the task - either "transcribe" to transcribe the audio to text, or "translate" to translate it to English.
-
-## Vad
-Using a VAD will improve the timing accuracy of each transcribed line, as well as prevent Whisper getting into an infinite
-loop detecting the same sentence over and over again. The downside is that this may be at a cost to text accuracy, especially
-with regards to unique words or names that appear in the audio. You can compensate for this by increasing the prompt window. 
-
-Note that English is very well handled by Whisper, and it's less susceptible to issues surrounding bad timings and infinite loops. 
-So you may only need to use a VAD for other languages, such as Japanese, or when the audio is very long.
-
-* none
-  * Run whisper on the entire audio input
-* silero-vad
-   * Use Silero VAD to detect sections that contain speech, and run Whisper on independently on each section. Whisper is also run 
-     on the gaps between each speech section, by either expanding the section up to the max merge size, or running Whisper independently 
-     on the non-speech section.
-* silero-vad-expand-into-gaps
-   * Use Silero VAD to detect sections that contain speech, and run Whisper on independently on each section. Each spech section will be expanded
-     such that they cover any adjacent non-speech sections. For instance, if an audio file of one minute contains the speech sections 
-     00:00 - 00:10 (A) and 00:30 - 00:40 (B), the first section (A) will be expanded to 00:00 - 00:30, and (B) will be expanded to 00:30 - 00:60.
-* silero-vad-skip-gaps
-   * As above, but sections that doesn't contain speech according to Silero will be skipped. This will be slightly faster, but 
-     may cause dialogue to be skipped.
-* periodic-vad
-   * Create sections of speech every 'VAD - Max Merge Size' seconds. This is very fast and simple, but will potentially break 
-     a sentence or word in two.
-
-## VAD - Merge Window
-If set, any adjacent speech sections that are at most this number of seconds apart will be automatically merged.
-
-## VAD - Max Merge Size (s)
-Disables merging of adjacent speech sections if they are this number of seconds long.
-
-## VAD - Padding (s)
-The number of seconds (floating point) to add to the beginning and end of each speech section. Setting this to a number
-larger than zero ensures that Whisper is more likely to correctly transcribe a sentence in the beginning of 
-a speech section. However, this also increases the probability of Whisper assigning the wrong timestamp 
-to each transcribed line. The default value is 1 second.
-
-## VAD - Prompt Window (s)
-The text of a detected line will be included as a prompt to the next speech section, if the speech section starts at most this
-number of seconds after the line has finished. For instance, if a line ends at 10:00, and the next speech section starts at
-10:04, the line's text will be included if the prompt window is 4 seconds or more (10:04 - 10:00 = 4 seconds).
-
-Note that detected lines in gaps between speech sections will not be included in the prompt 
-(if silero-vad or silero-vad-expand-into-gaps) is used.
-
-# Command Line Options
-
-Both `app.py` and `cli.py` also accept command line options, such as the ability to enable parallel execution on multiple
-CPU/GPU cores, the default model name/VAD and so on. Consult the README in the root folder for more information.
-
-# Additional Options
-
-In addition to the above, there's also a "Full" options interface that allows you to set all the options available in the Whisper 
-model. The options are as follows:
-
-## Initial Prompt
-Optional text to provide as a prompt for the first 30 seconds window. Whisper will attempt to use this as a starting point for the transcription, but you can
-also get creative and specify a style or format for the output of the transcription.
-
-For instance, if you use the prompt "hello how is it going always use lowercase no punctuation goodbye one two three start stop i you me they", Whisper will 
-be biased to output lower capital letters and no punctuation, and may also be biased to output the words in the prompt more often.
-
-## Temperature
-The temperature to use when sampling. Default is 0 (zero). A higher temperature will result in more random output, while a lower temperature will be more deterministic.
-
-## Best Of - Non-zero temperature
-The number of candidates to sample from when sampling with non-zero temperature. Default is 5.
-
-## Beam Size - Zero temperature
-The number of beams to use in beam search when sampling with zero temperature. Default is 5.
-
-## Patience - Zero temperature
-The patience value to use in beam search when sampling with zero temperature. As in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search.
-
-## Length Penalty - Any temperature
-The token length penalty coefficient (alpha) to use when sampling with any temperature. As in https://arxiv.org/abs/1609.08144, uses simple length normalization by default.
-
-## Suppress Tokens - Comma-separated list of token IDs
-A comma-separated list of token IDs to suppress during sampling. The default value of "-1" will suppress most special characters except common punctuations.
-
-## Condition on previous text
-If True, provide the previous output of the model as a prompt for the next window. Disabling this may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop.
-
-## FP16
-Whether to perform inference in fp16. True by default.
-
-## Temperature increment on fallback
-The temperature to increase when falling back when the decoding fails to meet either of the thresholds below. Default is 0.2.
-
-## Compression ratio threshold
-If the gzip compression ratio is higher than this value, treat the decoding as failed. Default is 2.4.
-
-## Logprob threshold
-If the average log probability is lower than this value, treat the decoding as failed. Default is -1.0.
-
-## No speech threshold
-If the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence. Default is 0.6.
diff --git a/spaces/breezedeus/CnOCR-Demo/streamlit_app.py b/spaces/breezedeus/CnOCR-Demo/streamlit_app.py
deleted file mode 100644
index 05c744b1d65b7182e9d85a3230c509bf927e9e68..0000000000000000000000000000000000000000
--- a/spaces/breezedeus/CnOCR-Demo/streamlit_app.py
+++ /dev/null
@@ -1,183 +0,0 @@
-# coding: utf-8
-# Copyright (C) 2021, [Breezedeus](https://github.com/breezedeus).
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import os
-from collections import OrderedDict
-
-import cv2
-import numpy as np
-from PIL import Image
-import streamlit as st
-from cnstd.utils import pil_to_numpy, imsave
-
-from cnocr import CnOcr, DET_AVAILABLE_MODELS, REC_AVAILABLE_MODELS
-from cnocr.utils import set_logger, draw_ocr_results, download
-
-
-logger = set_logger()
-st.set_page_config(layout="wide")
-
-
-def plot_for_debugging(rotated_img, one_out, box_score_thresh, crop_ncols, prefix_fp):
-    import matplotlib.pyplot as plt
-    import math
-
-    rotated_img = rotated_img.copy()
-    crops = [info['cropped_img'] for info in one_out]
-    print('%d boxes are found' % len(crops))
-    ncols = crop_ncols
-    nrows = math.ceil(len(crops) / ncols)
-    fig, ax = plt.subplots(nrows=nrows, ncols=ncols)
-    for i, axi in enumerate(ax.flat):
-        if i >= len(crops):
-            break
-        axi.imshow(crops[i])
-    crop_fp = '%s-crops.png' % prefix_fp
-    plt.savefig(crop_fp)
-    print('cropped results are save to file %s' % crop_fp)
-
-    for info in one_out:
-        box, score = info.get('position'), info['score']
-        if score < box_score_thresh:  # score < 0.5
-            continue
-        if box is not None:
-            box = box.astype(int).reshape(-1, 2)
-            cv2.polylines(rotated_img, [box], True, color=(255, 0, 0), thickness=2)
-    result_fp = '%s-result.png' % prefix_fp
-    imsave(rotated_img, result_fp, normalized=False)
-    print('boxes results are save to file %s' % result_fp)
-
-
-@st.cache_resource
-def get_ocr_model(det_model_name, rec_model_name, det_more_configs):
-    det_model_name, det_model_backend = det_model_name
-    rec_model_name, rec_model_backend = rec_model_name
-    return CnOcr(
-        det_model_name=det_model_name,
-        det_model_backend=det_model_backend,
-        rec_model_name=rec_model_name,
-        rec_model_backend=rec_model_backend,
-        det_more_configs=det_more_configs,
-    )
-
-
-def visualize_naive_result(img, det_model_name, std_out, box_score_thresh):
-    img = pil_to_numpy(img).transpose((1, 2, 0)).astype(np.uint8)
-
-    plot_for_debugging(img, std_out, box_score_thresh, 2, './streamlit-app')
-    st.subheader('Detection Result')
-    if det_model_name == 'default_det':
-        st.warning('⚠️ Warning: "default_det" 检测模型不返回文本框位置！')
-    cols = st.columns([1, 7, 1])
-    cols[1].image('./streamlit-app-result.png')
-
-    st.subheader('Recognition Result')
-    cols = st.columns([1, 7, 1])
-    cols[1].image('./streamlit-app-crops.png')
-
-    _visualize_ocr(std_out)
-
-
-def _visualize_ocr(ocr_outs):
-    st.empty()
-    ocr_res = OrderedDict({'文本': []})
-    ocr_res['得分'] = []
-    for out in ocr_outs:
-        # cropped_img = out['cropped_img']  # 检测出的文本框
-        ocr_res['得分'].append(out['score'])
-        ocr_res['文本'].append(out['text'])
-    st.table(ocr_res)
-
-
-def visualize_result(img, ocr_outs):
-    out_draw_fp = './streamlit-app-det-result.png'
-    font_path = 'docs/fonts/simfang.ttf'
-    if not os.path.exists(font_path):
-        url = 'https://huggingface.co/datasets/breezedeus/cnocr-wx-qr-code/resolve/main/fonts/simfang.ttf'
-        os.makedirs(os.path.dirname(font_path), exist_ok=True)
-        download(url, path=font_path, overwrite=True)
-    draw_ocr_results(img, ocr_outs, out_draw_fp, font_path)
-    st.image(out_draw_fp)
-
-
-def main():
-    st.sidebar.header('模型设置')
-    det_models = list(DET_AVAILABLE_MODELS.all_models())
-    det_models.append(('naive_det', 'onnx'))
-    det_models.sort()
-    det_model_name = st.sidebar.selectbox(
-        '选择检测模型', det_models, index=det_models.index(('ch_PP-OCRv3_det', 'onnx'))
-    )
-
-    all_models = list(REC_AVAILABLE_MODELS.all_models())
-    all_models.sort()
-    idx = all_models.index(('densenet_lite_136-fc', 'onnx'))
-    rec_model_name = st.sidebar.selectbox('选择识别模型', all_models, index=idx)
-
-    st.sidebar.subheader('检测参数')
-    rotated_bbox = st.sidebar.checkbox('是否检测带角度文本框', value=True)
-    use_angle_clf = st.sidebar.checkbox('是否使用角度预测模型校正文本框', value=False)
-    new_size = st.sidebar.slider(
-        'resize 后图片（长边）大小', min_value=124, max_value=4096, value=768
-    )
-    box_score_thresh = st.sidebar.slider(
-        '得分阈值（低于阈值的结果会被过滤掉）', min_value=0.05, max_value=0.95, value=0.3
-    )
-    min_box_size = st.sidebar.slider(
-        '框大小阈值（更小的文本框会被过滤掉）', min_value=4, max_value=50, value=10
-    )
-    # std = get_std_model(det_model_name, rotated_bbox, use_angle_clf)
-
-    # st.sidebar.markdown("""---""")
-    # st.sidebar.header('CnOcr 设置')
-    det_more_configs = dict(rotated_bbox=rotated_bbox, use_angle_clf=use_angle_clf)
-    ocr = get_ocr_model(det_model_name, rec_model_name, det_more_configs)
-
-    st.markdown('# 开源Python OCR工具 ' '[CnOCR](https://github.com/breezedeus/cnocr)')
-    st.markdown('> 详细说明参见：[CnOCR 文档](https://cnocr.readthedocs.io/) ；'
-                '欢迎加入 [交流群](https://www.breezedeus.com/join-group) ；'
-                '作者：[breezedeus](https://www.breezedeus.com), [Github](https://github.com/breezedeus) 。')
-    st.markdown('')
-    st.subheader('选择待检测图片')
-    content_file = st.file_uploader('', type=["png", "jpg", "jpeg", "webp"])
-    if content_file is None:
-        st.stop()
-
-    try:
-        img = Image.open(content_file).convert('RGB')
-
-        ocr_out = ocr.ocr(
-            img,
-            return_cropped_image=True,
-            resized_shape=new_size,
-            preserve_aspect_ratio=True,
-            box_score_thresh=box_score_thresh,
-            min_box_size=min_box_size,
-        )
-        if det_model_name[0] == 'naive_det':
-            visualize_naive_result(img, det_model_name[0], ocr_out, box_score_thresh)
-        else:
-            visualize_result(img, ocr_out)
-
-    except Exception as e:
-        st.error(e)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_mvitv2_h_in21k_50ep.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_mvitv2_h_in21k_50ep.py
deleted file mode 100644
index 084444bf0338d1bab2ee426ae226a0f8004dd0f5..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/LVIS/cascade_mask_rcnn_mvitv2_h_in21k_50ep.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from .cascade_mask_rcnn_mvitv2_b_in21k_100ep import (
-    dataloader,
-    lr_multiplier,
-    model,
-    train,
-    optimizer,
-)
-
-model.backbone.bottom_up.embed_dim = 192
-model.backbone.bottom_up.depth = 80
-model.backbone.bottom_up.num_heads = 3
-model.backbone.bottom_up.last_block_indexes = (3, 11, 71, 79)
-model.backbone.bottom_up.drop_path_rate = 0.6
-model.backbone.bottom_up.use_act_checkpoint = True
-
-train.init_checkpoint = "detectron2://ImageNetPretrained/mvitv2/MViTv2_H_in21k.pyth"
-
-train.max_iter = train.max_iter // 2  # 100ep -> 50ep
-lr_multiplier.scheduler.milestones = [
-    milestone // 2 for milestone in lr_multiplier.scheduler.milestones
-]
-lr_multiplier.scheduler.num_updates = train.max_iter
-lr_multiplier.warmup_length = 250 / train.max_iter
-
-optimizer.lr = 2e-5
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/config/root_cfg.py b/spaces/brjathu/HMR2.0/vendor/detectron2/tests/config/root_cfg.py
deleted file mode 100644
index 33d1d4bd2d9ddf31d55c655c49d13a8b7ac7b376..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/tests/config/root_cfg.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from itertools import count
-
-from detectron2.config import LazyCall as L
-
-from .dir1.dir1_a import dir1a_dict, dir1a_str
-
-dir1a_dict.a = "modified"
-
-# modification above won't affect future imports
-from .dir1.dir1_b import dir1b_dict, dir1b_str
-
-
-lazyobj = L(count)(x=dir1a_str, y=dir1b_str)
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ImageEnhance.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ImageEnhance.py
deleted file mode 100644
index 3b79d5c46a16ce89dfff1694f0121a743d8fa0c7..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/ImageEnhance.py
+++ /dev/null
@@ -1,103 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# image enhancement classes
-#
-# For a background, see "Image Processing By Interpolation and
-# Extrapolation", Paul Haeberli and Douglas Voorhies.  Available
-# at http://www.graficaobscura.com/interp/index.html
-#
-# History:
-# 1996-03-23 fl  Created
-# 2009-06-16 fl  Fixed mean calculation
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1996.
-#
-# See the README file for information on usage and redistribution.
-#
-
-from . import Image, ImageFilter, ImageStat
-
-
-class _Enhance:
-    def enhance(self, factor):
-        """
-        Returns an enhanced image.
-
-        :param factor: A floating point value controlling the enhancement.
-                       Factor 1.0 always returns a copy of the original image,
-                       lower factors mean less color (brightness, contrast,
-                       etc), and higher values more. There are no restrictions
-                       on this value.
-        :rtype: :py:class:`~PIL.Image.Image`
-        """
-        return Image.blend(self.degenerate, self.image, factor)
-
-
-class Color(_Enhance):
-    """Adjust image color balance.
-
-    This class can be used to adjust the colour balance of an image, in
-    a manner similar to the controls on a colour TV set. An enhancement
-    factor of 0.0 gives a black and white image. A factor of 1.0 gives
-    the original image.
-    """
-
-    def __init__(self, image):
-        self.image = image
-        self.intermediate_mode = "L"
-        if "A" in image.getbands():
-            self.intermediate_mode = "LA"
-
-        self.degenerate = image.convert(self.intermediate_mode).convert(image.mode)
-
-
-class Contrast(_Enhance):
-    """Adjust image contrast.
-
-    This class can be used to control the contrast of an image, similar
-    to the contrast control on a TV set. An enhancement factor of 0.0
-    gives a solid grey image. A factor of 1.0 gives the original image.
-    """
-
-    def __init__(self, image):
-        self.image = image
-        mean = int(ImageStat.Stat(image.convert("L")).mean[0] + 0.5)
-        self.degenerate = Image.new("L", image.size, mean).convert(image.mode)
-
-        if "A" in image.getbands():
-            self.degenerate.putalpha(image.getchannel("A"))
-
-
-class Brightness(_Enhance):
-    """Adjust image brightness.
-
-    This class can be used to control the brightness of an image.  An
-    enhancement factor of 0.0 gives a black image. A factor of 1.0 gives the
-    original image.
-    """
-
-    def __init__(self, image):
-        self.image = image
-        self.degenerate = Image.new(image.mode, image.size, 0)
-
-        if "A" in image.getbands():
-            self.degenerate.putalpha(image.getchannel("A"))
-
-
-class Sharpness(_Enhance):
-    """Adjust image sharpness.
-
-    This class can be used to adjust the sharpness of an image. An
-    enhancement factor of 0.0 gives a blurred image, a factor of 1.0 gives the
-    original image, and a factor of 2.0 gives a sharpened image.
-    """
-
-    def __init__(self, image):
-        self.image = image
-        self.degenerate = image.filter(ImageFilter.SMOOTH)
-
-        if "A" in image.getbands():
-            self.degenerate.putalpha(image.getchannel("A"))
diff --git a/spaces/caslabs/midi-autocompletion/README.md b/spaces/caslabs/midi-autocompletion/README.md
deleted file mode 100644
index b6d2d8d6f23b70ea65a5a095df090dfbb94a7fb0..0000000000000000000000000000000000000000
--- a/spaces/caslabs/midi-autocompletion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Midi Autocompletion
-emoji: 📚
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/chendl/compositional_test/multimodal/offline_grounding_dino.py b/spaces/chendl/compositional_test/multimodal/offline_grounding_dino.py
deleted file mode 100644
index 8e55b5b145cdb0e83be91a418b1a800d813f7034..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/offline_grounding_dino.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import webdataset as wds
-from groundingdino.demo.caption_grounder import caption_grounder
-from tqdm import tqdm
-import sys
-import os
-
-# SOURCE_DIR = "/gpfs/u/home/LMCG/LMCGljnn/scratch-shared/junyan/raw/blip2_pretraining/laion_synthetic_filtered_large/all"
-# DEST_DIR = "/gpfs/u/home/LMCG/LMCGljnn/scratch-shared/junyan/raw/blip2_pretraining/laion_synthetic_filtered_large/all_ground"
-
-# SOURCE_DIR = "/gpfs/u/home/LMCG/LMCGljnn/scratch-shared/junyan/raw/blip2_pretraining/ccs_synthetic_filtered_large"
-# DEST_DIR = "/gpfs/u/home/LMCG/LMCGljnn/scratch-shared/junyan/raw/blip2_pretraining/ccs_synthetic_filtered_large_ground"
-
-# SOURCE_DIR = "/gpfs/u/home/LMCG/LMCGljnn/scratch-shared/junyan/raw/karpathy_coco_wds_full"
-# DEST_DIR = "/gpfs/u/home/LMCG/LMCGljnn/scratch-shared/junyan/raw/karpathy_coco_wds_full_ground"
-
-# SOURCE_DIR = "/gpfs/u/home/LMCG/LMCGljnn/scratch-shared/junyan/raw/vg_wds_full"
-# DEST_DIR = "/gpfs/u/home/LMCG/LMCGljnn/scratch-shared/junyan/raw/vg_wds_full_ground"
-SOURCE_DIR = "/gpfs/u/home/LMCG/LMCGljnn/scratch-shared/junyan/raw/blip2_pretraining/all_data_0620"
-DEST_DIR = "/gpfs/u/home/LMCG/LMCGljnn/scratch-shared/junyan/raw/blip2_pretraining/all_data_ground_0701"
-
-def augment_wds(url, output, generator):
-    src = (
-        wds.WebDataset(url)
-        .decode("pilrgb")
-        .to_tuple("__key__", "jpg;png;jpeg", "txt")
-    )
-    
-    with wds.TarWriter(output) as dst:
-        for key, image, caption in tqdm(src, total=10000):
-            # jpg txt json
-            # image = image.resize((224, 224))
-            logits, boxes = generator.ground_caption_raw(image_pil=image, caption=caption)
-            sample = {
-                "__key__": key,
-                "jpg": image,
-                "txt": caption,
-                "logits.pyd": logits,
-                "boxes.pyd": boxes,
-            }
-            dst.write(sample)
-
-
-if __name__ == "__main__":
-    print("FROM", os.path.join(SOURCE_DIR, sys.argv[2]+".tar"))
-    print("TO", os.path.join(DEST_DIR, sys.argv[2]+".tar"))
-    # if os.path.exists(os.path.join(DEST_DIR, sys.argv[2]+".tar")):
-    #     print("already done. exiting...")
-    #     exit()
-    success = False
-    while not success:
-        try:
-            generator = caption_grounder(
-                config_file="/gpfs/u/home/LMCG/LMCGljnn/scratch/code/multimodal/GroundingDINO/groundingdino/config/GroundingDINO_SwinB.cfg.py",
-                checkpoint_path="/gpfs/u/home/LMCG/LMCGljnn/scratch/code/multimodal/GroundingDINO/checkpoints/groundingdino_swinb_cogcoor.pth",
-                cpu_only=False,
-                box_threshold=0.05,
-            )
-            success = True
-        except:
-            import random
-            import time
-            time.sleep(random.random() * 5)
-    augment_wds(
-        os.path.join(SOURCE_DIR, sys.argv[2]+".tar"),
-        os.path.join(DEST_DIR, sys.argv[2]+".tar"),
-        generator=generator,
-    )
-    print("DONE")
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/jax-projects/big_bird/prepare_natural_questions.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/jax-projects/big_bird/prepare_natural_questions.py
deleted file mode 100644
index 6a202ba77522a682c24de011139846be51da7da0..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/jax-projects/big_bird/prepare_natural_questions.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import os
-
-import jsonlines
-import numpy as np
-from tqdm import tqdm
-
-
-DOC_STRIDE = 2048
-MAX_LENGTH = 4096
-SEED = 42
-PROCESS_TRAIN = os.environ.pop("PROCESS_TRAIN", "false")
-CATEGORY_MAPPING = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4}
-
-
-def _get_single_answer(example):
-    def choose_first(answer, is_long_answer=False):
-        assert isinstance(answer, list)
-        if len(answer) == 1:
-            answer = answer[0]
-            return {k: [answer[k]] for k in answer} if is_long_answer else answer
-        for a in answer:
-            if is_long_answer:
-                a = {k: [a[k]] for k in a}
-            if len(a["start_token"]) > 0:
-                break
-        return a
-
-    answer = {"id": example["id"]}
-    annotation = example["annotations"]
-    yes_no_answer = annotation["yes_no_answer"]
-    if 0 in yes_no_answer or 1 in yes_no_answer:
-        answer["category"] = ["yes"] if 1 in yes_no_answer else ["no"]
-        answer["start_token"] = answer["end_token"] = []
-        answer["start_byte"] = answer["end_byte"] = []
-        answer["text"] = ["<cls>"]
-    else:
-        answer["category"] = ["short"]
-        out = choose_first(annotation["short_answers"])
-        if len(out["start_token"]) == 0:
-            # answer will be long if short is not available
-            answer["category"] = ["long"]
-            out = choose_first(annotation["long_answer"], is_long_answer=True)
-            out["text"] = []
-        answer.update(out)
-
-    # disregard some samples
-    if len(answer["start_token"]) > 1 or answer["start_token"] == answer["end_token"]:
-        answer["remove_it"] = True
-    else:
-        answer["remove_it"] = False
-
-    cols = ["start_token", "end_token", "start_byte", "end_byte", "text"]
-    if not all([isinstance(answer[k], list) for k in cols]):
-        raise ValueError("Issue in ID", example["id"])
-
-    return answer
-
-
-def get_context_and_ans(example, assertion=False):
-    """Gives new context after removing <html> & new answer tokens as per new context"""
-    answer = _get_single_answer(example)
-    # bytes are of no use
-    del answer["start_byte"]
-    del answer["end_byte"]
-
-    # handle yes_no answers explicitly
-    if answer["category"][0] in ["yes", "no"]:  # category is list with one element
-        doc = example["document"]["tokens"]
-        context = []
-        for i in range(len(doc["token"])):
-            if not doc["is_html"][i]:
-                context.append(doc["token"][i])
-        return {
-            "context": " ".join(context),
-            "answer": {
-                "start_token": -100,  # ignore index in cross-entropy
-                "end_token": -100,  # ignore index in cross-entropy
-                "category": answer["category"],
-                "span": answer["category"],  # extra
-            },
-        }
-
-    # later, help in removing all no answers
-    if answer["start_token"] == [-1]:
-        return {
-            "context": "None",
-            "answer": {
-                "start_token": -1,
-                "end_token": -1,
-                "category": "null",
-                "span": "None",  # extra
-            },
-        }
-
-    # handling normal samples
-
-    cols = ["start_token", "end_token"]
-    answer.update({k: answer[k][0] if len(answer[k]) > 0 else answer[k] for k in cols})  # e.g. [10] == 10
-
-    doc = example["document"]["tokens"]
-    start_token = answer["start_token"]
-    end_token = answer["end_token"]
-
-    context = []
-    for i in range(len(doc["token"])):
-        if not doc["is_html"][i]:
-            context.append(doc["token"][i])
-        else:
-            if answer["start_token"] > i:
-                start_token -= 1
-            if answer["end_token"] > i:
-                end_token -= 1
-    new = " ".join(context[start_token:end_token])
-
-    # checking above code
-    if assertion:
-        """checking if above code is working as expected for all the samples"""
-        is_html = doc["is_html"][answer["start_token"] : answer["end_token"]]
-        old = doc["token"][answer["start_token"] : answer["end_token"]]
-        old = " ".join([old[i] for i in range(len(old)) if not is_html[i]])
-        if new != old:
-            print("ID:", example["id"])
-            print("New:", new, end="\n")
-            print("Old:", old, end="\n\n")
-
-    return {
-        "context": " ".join(context),
-        "answer": {
-            "start_token": start_token,
-            "end_token": end_token - 1,  # this makes it inclusive
-            "category": answer["category"],  # either long or short
-            "span": new,  # extra
-        },
-    }
-
-
-def get_strided_contexts_and_ans(example, tokenizer, doc_stride=2048, max_length=4096, assertion=True):
-    # overlap will be of doc_stride - q_len
-
-    out = get_context_and_ans(example, assertion=assertion)
-    answer = out["answer"]
-
-    # later, removing these samples
-    if answer["start_token"] == -1:
-        return {
-            "example_id": example["id"],
-            "input_ids": [[-1]],
-            "labels": {
-                "start_token": [-1],
-                "end_token": [-1],
-                "category": ["null"],
-            },
-        }
-
-    input_ids = tokenizer(example["question"]["text"], out["context"]).input_ids
-    q_len = input_ids.index(tokenizer.sep_token_id) + 1
-
-    # return yes/no
-    if answer["category"][0] in ["yes", "no"]:  # category is list with one element
-        inputs = []
-        category = []
-        q_indices = input_ids[:q_len]
-        doc_start_indices = range(q_len, len(input_ids), max_length - doc_stride)
-        for i in doc_start_indices:
-            end_index = i + max_length - q_len
-            slice = input_ids[i:end_index]
-            inputs.append(q_indices + slice)
-            category.append(answer["category"][0])
-            if slice[-1] == tokenizer.sep_token_id:
-                break
-
-        return {
-            "example_id": example["id"],
-            "input_ids": inputs,
-            "labels": {
-                "start_token": [-100] * len(category),
-                "end_token": [-100] * len(category),
-                "category": category,
-            },
-        }
-
-    splitted_context = out["context"].split()
-    complete_end_token = splitted_context[answer["end_token"]]
-    answer["start_token"] = len(
-        tokenizer(
-            " ".join(splitted_context[: answer["start_token"]]),
-            add_special_tokens=False,
-        ).input_ids
-    )
-    answer["end_token"] = len(
-        tokenizer(" ".join(splitted_context[: answer["end_token"]]), add_special_tokens=False).input_ids
-    )
-
-    answer["start_token"] += q_len
-    answer["end_token"] += q_len
-
-    # fixing end token
-    num_sub_tokens = len(tokenizer(complete_end_token, add_special_tokens=False).input_ids)
-    if num_sub_tokens > 1:
-        answer["end_token"] += num_sub_tokens - 1
-
-    old = input_ids[answer["start_token"] : answer["end_token"] + 1]  # right & left are inclusive
-    start_token = answer["start_token"]
-    end_token = answer["end_token"]
-
-    if assertion:
-        """This won't match exactly because of extra gaps => visaully inspect everything"""
-        new = tokenizer.decode(old)
-        if answer["span"] != new:
-            print("ISSUE IN TOKENIZATION")
-            print("OLD:", answer["span"])
-            print("NEW:", new, end="\n\n")
-
-    if len(input_ids) <= max_length:
-        return {
-            "example_id": example["id"],
-            "input_ids": [input_ids],
-            "labels": {
-                "start_token": [answer["start_token"]],
-                "end_token": [answer["end_token"]],
-                "category": answer["category"],
-            },
-        }
-
-    q_indices = input_ids[:q_len]
-    doc_start_indices = range(q_len, len(input_ids), max_length - doc_stride)
-
-    inputs = []
-    answers_start_token = []
-    answers_end_token = []
-    answers_category = []  # null, yes, no, long, short
-    for i in doc_start_indices:
-        end_index = i + max_length - q_len
-        slice = input_ids[i:end_index]
-        inputs.append(q_indices + slice)
-        assert len(inputs[-1]) <= max_length, "Issue in truncating length"
-
-        if start_token >= i and end_token <= end_index - 1:
-            start_token = start_token - i + q_len
-            end_token = end_token - i + q_len
-            answers_category.append(answer["category"][0])  # ["short"] -> "short"
-        else:
-            start_token = -100
-            end_token = -100
-            answers_category.append("null")
-        new = inputs[-1][start_token : end_token + 1]
-
-        answers_start_token.append(start_token)
-        answers_end_token.append(end_token)
-        if assertion:
-            """checking if above code is working as expected for all the samples"""
-            if new != old and new != [tokenizer.cls_token_id]:
-                print("ISSUE in strided for ID:", example["id"])
-                print("New:", tokenizer.decode(new))
-                print("Old:", tokenizer.decode(old), end="\n\n")
-        if slice[-1] == tokenizer.sep_token_id:
-            break
-
-    return {
-        "example_id": example["id"],
-        "input_ids": inputs,
-        "labels": {
-            "start_token": answers_start_token,
-            "end_token": answers_end_token,
-            "category": answers_category,
-        },
-    }
-
-
-def prepare_inputs(example, tokenizer, doc_stride=2048, max_length=4096, assertion=False):
-    example = get_strided_contexts_and_ans(
-        example,
-        tokenizer,
-        doc_stride=doc_stride,
-        max_length=max_length,
-        assertion=assertion,
-    )
-
-    return example
-
-
-def save_to_disk(hf_data, file_name):
-    with jsonlines.open(file_name, "a") as writer:
-        for example in tqdm(hf_data, total=len(hf_data), desc="Saving samples ... "):
-            labels = example["labels"]
-            for ids, start, end, cat in zip(
-                example["input_ids"],
-                labels["start_token"],
-                labels["end_token"],
-                labels["category"],
-            ):
-                if start == -1 and end == -1:
-                    continue  # leave waste samples with no answer
-                if cat == "null" and np.random.rand() < 0.6:
-                    continue  # removing 50 % samples
-                writer.write(
-                    {
-                        "input_ids": ids,
-                        "start_token": start,
-                        "end_token": end,
-                        "category": CATEGORY_MAPPING[cat],
-                    }
-                )
-
-
-if __name__ == "__main__":
-    """Running area"""
-    from datasets import load_dataset
-
-    from transformers import BigBirdTokenizer
-
-    data = load_dataset("natural_questions")
-    tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
-
-    data = data["train" if PROCESS_TRAIN == "true" else "validation"]
-
-    fn_kwargs = {
-        "tokenizer": tokenizer,
-        "doc_stride": DOC_STRIDE,
-        "max_length": MAX_LENGTH,
-        "assertion": False,
-    }
-    data = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
-    data = data.remove_columns(["annotations", "document", "id", "question"])
-    print(data)
-
-    np.random.seed(SEED)
-    cache_file_name = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl"
-    save_to_disk(data, file_name=cache_file_name)
diff --git a/spaces/chikoto/Umamusume-DeBERTa-VITS2-TTS-JP/data_utils.py b/spaces/chikoto/Umamusume-DeBERTa-VITS2-TTS-JP/data_utils.py
deleted file mode 100644
index 6b16786ed560fdd014254d0f3726d300af909cb1..0000000000000000000000000000000000000000
--- a/spaces/chikoto/Umamusume-DeBERTa-VITS2-TTS-JP/data_utils.py
+++ /dev/null
@@ -1,406 +0,0 @@
-import os
-import random
-import torch
-import torch.utils.data
-from tqdm import tqdm
-from loguru import logger
-import commons
-from mel_processing import spectrogram_torch, mel_spectrogram_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import cleaned_text_to_sequence, get_bert
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
-    """
-    1) loads audio, speaker_id, text pairs
-    2) normalizes text and converts them to sequences of integers
-    3) computes spectrograms from audio files.
-    """
-
-    def __init__(self, audiopaths_sid_text, hparams):
-        self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
-        self.max_wav_value = hparams.max_wav_value
-        self.sampling_rate = hparams.sampling_rate
-        self.filter_length = hparams.filter_length
-        self.hop_length = hparams.hop_length
-        self.win_length = hparams.win_length
-        self.sampling_rate = hparams.sampling_rate
-        self.spk_map = hparams.spk2id
-        self.hparams = hparams
-
-        self.use_mel_spec_posterior = getattr(
-            hparams, "use_mel_posterior_encoder", False
-        )
-        if self.use_mel_spec_posterior:
-            self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
-
-        self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
-        self.add_blank = hparams.add_blank
-        self.min_text_len = getattr(hparams, "min_text_len", 1)
-        self.max_text_len = getattr(hparams, "max_text_len", 300)
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths_sid_text)
-        self._filter()
-
-    def _filter(self):
-        """
-        Filter text & store spec lengths
-        """
-        # Store spectrogram lengths for Bucketing
-        # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
-        # spec_length = wav_length // hop_length
-
-        audiopaths_sid_text_new = []
-        lengths = []
-        skipped = 0
-        logger.info("Init dataset...")
-        for _id, spk, language, text, phones, tone, word2ph in tqdm(
-            self.audiopaths_sid_text
-        ):
-            audiopath = f"filelists/{_id}"
-            if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
-                phones = phones.split(" ")
-                tone = [int(i) for i in tone.split(" ")]
-                word2ph = [int(i) for i in word2ph.split(" ")]
-                audiopaths_sid_text_new.append(
-                    [audiopath, spk, language, text, phones, tone, word2ph]
-                )
-                lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
-            else:
-                skipped += 1
-        logger.info(
-            "skipped: "
-            + str(skipped)
-            + ", total: "
-            + str(len(self.audiopaths_sid_text))
-        )
-        self.audiopaths_sid_text = audiopaths_sid_text_new
-        self.lengths = lengths
-
-    def get_audio_text_speaker_pair(self, audiopath_sid_text):
-        # separate filename, speaker_id and text
-        audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
-
-        bert, ja_bert, phones, tone, language = self.get_text(
-            text, word2ph, phones, tone, language, audiopath
-        )
-
-        spec, wav = self.get_audio(audiopath)
-        sid = torch.LongTensor([int(self.spk_map[sid])])
-        return (phones, spec, wav, sid, tone, language, bert, ja_bert)
-
-    def get_audio(self, filename):
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError(
-                "{} {} SR doesn't match target {} SR".format(
-                    filename, sampling_rate, self.sampling_rate
-                )
-            )
-        audio_norm = audio / self.max_wav_value
-        audio_norm = audio_norm.unsqueeze(0)
-        spec_filename = filename.replace(".wav", ".spec.pt")
-        if self.use_mel_spec_posterior:
-            spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
-        try:
-            spec = torch.load(spec_filename)
-        except:
-            if self.use_mel_spec_posterior:
-                spec = mel_spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.n_mel_channels,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    self.hparams.mel_fmin,
-                    self.hparams.mel_fmax,
-                    center=False,
-                )
-            else:
-                spec = spectrogram_torch(
-                    audio_norm,
-                    self.filter_length,
-                    self.sampling_rate,
-                    self.hop_length,
-                    self.win_length,
-                    center=False,
-                )
-            spec = torch.squeeze(spec, 0)
-            torch.save(spec, spec_filename)
-        return spec, audio_norm
-
-    def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
-        phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-        if self.add_blank:
-            phone = commons.intersperse(phone, 0)
-            tone = commons.intersperse(tone, 0)
-            language = commons.intersperse(language, 0)
-            for i in range(len(word2ph)):
-                word2ph[i] = word2ph[i] * 2
-            word2ph[0] += 1
-        bert_path = wav_path.replace(".wav", ".bert.pt")
-        try:
-            bert = torch.load(bert_path)
-            assert bert.shape[-1] == len(phone)
-        except:
-            bert = get_bert(text, word2ph, language_str)
-            torch.save(bert, bert_path)
-            assert bert.shape[-1] == len(phone), phone
-
-        if language_str == "ZH":
-            bert = bert
-            ja_bert = torch.zeros(768, len(phone))
-        elif language_str == "JP":
-            ja_bert = bert
-            bert = torch.zeros(1024, len(phone))
-        else:
-            bert = torch.zeros(1024, len(phone))
-            ja_bert = torch.zeros(768, len(phone))
-        assert bert.shape[-1] == len(phone), (
-            bert.shape,
-            len(phone),
-            sum(word2ph),
-            p1,
-            p2,
-            t1,
-            t2,
-            pold,
-            pold2,
-            word2ph,
-            text,
-            w2pho,
-        )
-        phone = torch.LongTensor(phone)
-        tone = torch.LongTensor(tone)
-        language = torch.LongTensor(language)
-        return bert, ja_bert, phone, tone, language
-
-    def get_sid(self, sid):
-        sid = torch.LongTensor([int(sid)])
-        return sid
-
-    def __getitem__(self, index):
-        return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
-    def __len__(self):
-        return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate:
-    """Zero-pads model inputs and targets"""
-
-    def __init__(self, return_ids=False):
-        self.return_ids = return_ids
-
-    def __call__(self, batch):
-        """Collate's training batch from normalized text, audio and speaker identities
-        PARAMS
-        ------
-        batch: [text_normalized, spec_normalized, wav_normalized, sid]
-        """
-        # Right zero-pad all one-hot text sequences to max input length
-        _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True
-        )
-
-        max_text_len = max([len(x[0]) for x in batch])
-        max_spec_len = max([x[1].size(1) for x in batch])
-        max_wav_len = max([x[2].size(1) for x in batch])
-
-        text_lengths = torch.LongTensor(len(batch))
-        spec_lengths = torch.LongTensor(len(batch))
-        wav_lengths = torch.LongTensor(len(batch))
-        sid = torch.LongTensor(len(batch))
-
-        text_padded = torch.LongTensor(len(batch), max_text_len)
-        tone_padded = torch.LongTensor(len(batch), max_text_len)
-        language_padded = torch.LongTensor(len(batch), max_text_len)
-        bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
-        ja_bert_padded = torch.FloatTensor(len(batch), 768, max_text_len)
-
-        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        text_padded.zero_()
-        tone_padded.zero_()
-        language_padded.zero_()
-        spec_padded.zero_()
-        wav_padded.zero_()
-        bert_padded.zero_()
-        ja_bert_padded.zero_()
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            text = row[0]
-            text_padded[i, : text.size(0)] = text
-            text_lengths[i] = text.size(0)
-
-            spec = row[1]
-            spec_padded[i, :, : spec.size(1)] = spec
-            spec_lengths[i] = spec.size(1)
-
-            wav = row[2]
-            wav_padded[i, :, : wav.size(1)] = wav
-            wav_lengths[i] = wav.size(1)
-
-            sid[i] = row[3]
-
-            tone = row[4]
-            tone_padded[i, : tone.size(0)] = tone
-
-            language = row[5]
-            language_padded[i, : language.size(0)] = language
-
-            bert = row[6]
-            bert_padded[i, :, : bert.size(1)] = bert
-
-            ja_bert = row[7]
-            ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert
-
-        return (
-            text_padded,
-            text_lengths,
-            spec_padded,
-            spec_lengths,
-            wav_padded,
-            wav_lengths,
-            sid,
-            tone_padded,
-            language_padded,
-            bert_padded,
-            ja_bert_padded,
-        )
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
-    """
-    Maintain similar input lengths in a batch.
-    Length groups are specified by boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
-    It removes samples which are not included in the boundaries.
-    Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
-    """
-
-    def __init__(
-        self,
-        dataset,
-        batch_size,
-        boundaries,
-        num_replicas=None,
-        rank=None,
-        shuffle=True,
-    ):
-        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        self.lengths = dataset.lengths
-        self.batch_size = batch_size
-        self.boundaries = boundaries
-
-        self.buckets, self.num_samples_per_bucket = self._create_buckets()
-        self.total_size = sum(self.num_samples_per_bucket)
-        self.num_samples = self.total_size // self.num_replicas
-
-    def _create_buckets(self):
-        buckets = [[] for _ in range(len(self.boundaries) - 1)]
-        for i in range(len(self.lengths)):
-            length = self.lengths[i]
-            idx_bucket = self._bisect(length)
-            if idx_bucket != -1:
-                buckets[idx_bucket].append(i)
-
-        try:
-            for i in range(len(buckets) - 1, 0, -1):
-                if len(buckets[i]) == 0:
-                    buckets.pop(i)
-                    self.boundaries.pop(i + 1)
-            assert all(len(bucket) > 0 for bucket in buckets)
-        # When one bucket is not traversed
-        except Exception as e:
-            print("Bucket warning ", e)
-            for i in range(len(buckets) - 1, -1, -1):
-                if len(buckets[i]) == 0:
-                    buckets.pop(i)
-                    self.boundaries.pop(i + 1)
-
-        num_samples_per_bucket = []
-        for i in range(len(buckets)):
-            len_bucket = len(buckets[i])
-            total_batch_size = self.num_replicas * self.batch_size
-            rem = (
-                total_batch_size - (len_bucket % total_batch_size)
-            ) % total_batch_size
-            num_samples_per_bucket.append(len_bucket + rem)
-        return buckets, num_samples_per_bucket
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-
-        indices = []
-        if self.shuffle:
-            for bucket in self.buckets:
-                indices.append(torch.randperm(len(bucket), generator=g).tolist())
-        else:
-            for bucket in self.buckets:
-                indices.append(list(range(len(bucket))))
-
-        batches = []
-        for i in range(len(self.buckets)):
-            bucket = self.buckets[i]
-            len_bucket = len(bucket)
-            if len_bucket == 0:
-                continue
-            ids_bucket = indices[i]
-            num_samples_bucket = self.num_samples_per_bucket[i]
-
-            # add extra samples to make it evenly divisible
-            rem = num_samples_bucket - len_bucket
-            ids_bucket = (
-                ids_bucket
-                + ids_bucket * (rem // len_bucket)
-                + ids_bucket[: (rem % len_bucket)]
-            )
-
-            # subsample
-            ids_bucket = ids_bucket[self.rank :: self.num_replicas]
-
-            # batching
-            for j in range(len(ids_bucket) // self.batch_size):
-                batch = [
-                    bucket[idx]
-                    for idx in ids_bucket[
-                        j * self.batch_size : (j + 1) * self.batch_size
-                    ]
-                ]
-                batches.append(batch)
-
-        if self.shuffle:
-            batch_ids = torch.randperm(len(batches), generator=g).tolist()
-            batches = [batches[i] for i in batch_ids]
-        self.batches = batches
-
-        assert len(self.batches) * self.batch_size == self.num_samples
-        return iter(self.batches)
-
-    def _bisect(self, x, lo=0, hi=None):
-        if hi is None:
-            hi = len(self.boundaries) - 1
-
-        if hi > lo:
-            mid = (hi + lo) // 2
-            if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
-                return mid
-            elif x <= self.boundaries[mid]:
-                return self._bisect(x, lo, mid)
-            else:
-                return self._bisect(x, mid + 1, hi)
-        else:
-            return -1
-
-    def __len__(self):
-        return self.num_samples // self.batch_size
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/common.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/common.py
deleted file mode 100644
index cd31dec7a8607cfe478b3728eb32e6b3b9135a38..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/common.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import sys
-from dataclasses import dataclass
-from typing import Any, Sequence, Optional, Dict
-
-from importlib_metadata import PackageNotFoundError, distribution
-
-from clickhouse_connect.driver.exceptions import ProgrammingError
-
-
-def version():
-    try:
-        return distribution('clickhouse-connect').version
-    except PackageNotFoundError:
-        return 'development'
-
-
-def format_error(msg: str) -> str:
-    max_size = _common_settings['max_error_size'].value
-    if max_size:
-        return msg[:max_size]
-    return msg
-
-
-@dataclass
-class CommonSetting:
-    name: str
-    options: Sequence[Any]
-    default: Any
-    value: Optional[Any] = None
-
-
-_common_settings: Dict[str, CommonSetting] = {}
-
-
-def build_client_name(client_name: str):
-    product_name = get_setting('product_name')
-    product_name = product_name.strip() + ' ' if product_name else ''
-    client_name = client_name.strip() + ' ' if client_name else ''
-    py_version = sys.version.split(' ', maxsplit=1)[0]
-    return f'{client_name}{product_name}clickhouse-connect/{version()} (lv:py/{py_version}; os:{sys.platform})'
-
-
-def get_setting(name: str):
-    setting = _common_settings.get(name)
-    if setting is None:
-        raise ProgrammingError(f'Unrecognized common setting {name}')
-    return setting.value if setting.value is not None else setting.default
-
-
-def set_setting(name: str, value: Any):
-    setting = _common_settings.get(name)
-    if setting is None:
-        raise ProgrammingError(f'Unrecognized common setting {name}')
-    if setting.options and value not in setting.options:
-        raise ProgrammingError(f'Unrecognized option {value} for setting {name})')
-    if value == setting.default:
-        setting.value = None
-    else:
-        setting.value = value
-
-
-def _init_common(name: str, options: Sequence[Any], default: Any):
-    _common_settings[name] = CommonSetting(name, options, default)
-
-
-_init_common('autogenerate_session_id', (True, False), True)
-_init_common('dict_parameter_format', ('json', 'map'), 'json')
-_init_common('invalid_setting_action', ('send', 'drop', 'error'), 'error')
-_init_common('max_connection_age', (), 10 * 60)  # Max time in seconds to keep reusing a database TCP connection
-_init_common('product_name', (), '')  # Product name used as part of client identification for ClickHouse query_log
-_init_common('readonly', (0, 1), 0)  # Implied "read_only" ClickHouse settings for versions prior to 19.17
-
-# Use the client protocol version  This is needed for DateTime timezone columns but breaks with current version of
-# chproxy
-_init_common('use_protocol_version', (True, False), True)
-
-_init_common('max_error_size', (), 1024)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/contourpy/chunk.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/contourpy/chunk.py
deleted file mode 100644
index 076cbc4370b4471c2074cade279250a3ebec9041..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/contourpy/chunk.py
+++ /dev/null
@@ -1,89 +0,0 @@
-from __future__ import annotations
-
-import math
-
-
-def calc_chunk_sizes(
-    chunk_size: int | tuple[int, int] | None,
-    chunk_count: int | tuple[int, int] | None,
-    total_chunk_count: int | None,
-    ny: int,
-    nx: int,
-) -> tuple[int, int]:
-    """Calculate chunk sizes.
-
-    Args:
-        chunk_size (int or tuple(int, int), optional): Chunk size in (y, x) directions, or the same
-            size in both directions if only one is specified.
-        chunk_count (int or tuple(int, int), optional): Chunk count in (y, x) directions, or the
-            same count in both irections if only one is specified.
-        total_chunk_count (int, optional): Total number of chunks.
-        ny (int): Number of grid points in y-direction.
-        nx (int): Number of grid points in x-direction.
-
-    Return:
-        tuple(int, int): Chunk sizes (y_chunk_size, x_chunk_size).
-
-    Note:
-        A maximum of one of ``chunk_size``, ``chunk_count`` and ``total_chunk_count`` may be
-        specified.
-    """
-    if sum([chunk_size is not None, chunk_count is not None, total_chunk_count is not None]) > 1:
-        raise ValueError("Only one of chunk_size, chunk_count and total_chunk_count should be set")
-
-    if total_chunk_count is not None:
-        max_chunk_count = (nx-1)*(ny-1)
-        total_chunk_count = min(max(total_chunk_count, 1), max_chunk_count)
-        if total_chunk_count == 1:
-            chunk_size = 0
-        elif total_chunk_count == max_chunk_count:
-            chunk_size = (1, 1)
-        else:
-            factors = two_factors(total_chunk_count)
-            if ny > nx:
-                chunk_count = factors
-            else:
-                chunk_count = (factors[1], factors[0])
-
-    if chunk_count is not None:
-        if isinstance(chunk_count, tuple):
-            y_chunk_count, x_chunk_count = chunk_count
-        else:
-            y_chunk_count = x_chunk_count = chunk_count
-        x_chunk_count = min(max(x_chunk_count, 1), nx-1)
-        y_chunk_count = min(max(y_chunk_count, 1), ny-1)
-        chunk_size = (math.ceil((ny-1) / y_chunk_count), math.ceil((nx-1) / x_chunk_count))
-
-    if chunk_size is None:
-        y_chunk_size = x_chunk_size = 0
-    elif isinstance(chunk_size, tuple):
-        y_chunk_size, x_chunk_size = chunk_size
-    else:
-        y_chunk_size = x_chunk_size = chunk_size
-
-    if x_chunk_size < 0 or y_chunk_size < 0:
-        raise ValueError("chunk_size cannot be negative")
-
-    return y_chunk_size, x_chunk_size
-
-
-def two_factors(n: int) -> tuple[int, int]:
-    """Split an integer into two integer factors.
-
-    The two factors will be as close as possible to the sqrt of n, and are returned in decreasing
-    order.  Worst case returns (n, 1).
-
-    Args:
-        n (int): The integer to factorize.
-
-    Return:
-        tuple(int, int): The two factors of n, in decreasing order.
-    """
-    i = math.ceil(math.sqrt(n))
-    while n % i != 0:
-        i -= 1
-    j = n // i
-    if i > j:
-        return i, j
-    else:
-        return j, i
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/flagging.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/flagging.py
deleted file mode 100644
index c98828825e74e8146bb4c4075e107c0f928de2b0..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/flagging.py
+++ /dev/null
@@ -1,518 +0,0 @@
-from __future__ import annotations
-
-import csv
-import datetime
-import json
-import os
-import time
-import uuid
-from abc import ABC, abstractmethod
-from collections import OrderedDict
-from distutils.version import StrictVersion
-from pathlib import Path
-from typing import TYPE_CHECKING, Any
-
-import filelock
-import huggingface_hub
-import pkg_resources
-from gradio_client import utils as client_utils
-from gradio_client.documentation import document, set_documentation_group
-
-import gradio as gr
-from gradio import utils
-from gradio.deprecation import warn_deprecation
-
-if TYPE_CHECKING:
-    from gradio.components import IOComponent
-
-set_documentation_group("flagging")
-
-
-class FlaggingCallback(ABC):
-    """
-    An abstract class for defining the methods that any FlaggingCallback should have.
-    """
-
-    @abstractmethod
-    def setup(self, components: list[IOComponent], flagging_dir: str):
-        """
-        This method should be overridden and ensure that everything is set up correctly for flag().
-        This method gets called once at the beginning of the Interface.launch() method.
-        Parameters:
-        components: Set of components that will provide flagged data.
-        flagging_dir: A string, typically containing the path to the directory where the flagging file should be storied (provided as an argument to Interface.__init__()).
-        """
-        pass
-
-    @abstractmethod
-    def flag(
-        self,
-        flag_data: list[Any],
-        flag_option: str = "",
-        username: str | None = None,
-    ) -> int:
-        """
-        This method should be overridden by the FlaggingCallback subclass and may contain optional additional arguments.
-        This gets called every time the <flag> button is pressed.
-        Parameters:
-        interface: The Interface object that is being used to launch the flagging interface.
-        flag_data: The data to be flagged.
-        flag_option (optional): In the case that flagging_options are provided, the flag option that is being used.
-        username (optional): The username of the user that is flagging the data, if logged in.
-        Returns:
-        (int) The total number of samples that have been flagged.
-        """
-        pass
-
-
-@document()
-class SimpleCSVLogger(FlaggingCallback):
-    """
-    A simplified implementation of the FlaggingCallback abstract class
-    provided for illustrative purposes.  Each flagged sample (both the input and output data)
-    is logged to a CSV file on the machine running the gradio app.
-    Example:
-        import gradio as gr
-        def image_classifier(inp):
-            return {'cat': 0.3, 'dog': 0.7}
-        demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label",
-                            flagging_callback=SimpleCSVLogger())
-    """
-
-    def __init__(self):
-        pass
-
-    def setup(self, components: list[IOComponent], flagging_dir: str | Path):
-        self.components = components
-        self.flagging_dir = flagging_dir
-        os.makedirs(flagging_dir, exist_ok=True)
-
-    def flag(
-        self,
-        flag_data: list[Any],
-        flag_option: str = "",
-        username: str | None = None,
-    ) -> int:
-        flagging_dir = self.flagging_dir
-        log_filepath = Path(flagging_dir) / "log.csv"
-
-        csv_data = []
-        for component, sample in zip(self.components, flag_data):
-            save_dir = Path(
-                flagging_dir
-            ) / client_utils.strip_invalid_filename_characters(component.label or "")
-            csv_data.append(
-                component.deserialize(
-                    sample,
-                    save_dir,
-                    None,
-                )
-            )
-
-        with open(log_filepath, "a", newline="") as csvfile:
-            writer = csv.writer(csvfile)
-            writer.writerow(utils.sanitize_list_for_csv(csv_data))
-
-        with open(log_filepath) as csvfile:
-            line_count = len(list(csv.reader(csvfile))) - 1
-        return line_count
-
-
-@document()
-class CSVLogger(FlaggingCallback):
-    """
-    The default implementation of the FlaggingCallback abstract class. Each flagged
-    sample (both the input and output data) is logged to a CSV file with headers on the machine running the gradio app.
-    Example:
-        import gradio as gr
-        def image_classifier(inp):
-            return {'cat': 0.3, 'dog': 0.7}
-        demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label",
-                            flagging_callback=CSVLogger())
-    Guides: using-flagging
-    """
-
-    def __init__(self):
-        pass
-
-    def setup(
-        self,
-        components: list[IOComponent],
-        flagging_dir: str | Path,
-    ):
-        self.components = components
-        self.flagging_dir = flagging_dir
-        os.makedirs(flagging_dir, exist_ok=True)
-
-    def flag(
-        self,
-        flag_data: list[Any],
-        flag_option: str = "",
-        username: str | None = None,
-    ) -> int:
-        flagging_dir = self.flagging_dir
-        log_filepath = Path(flagging_dir) / "log.csv"
-        is_new = not Path(log_filepath).exists()
-        headers = [
-            getattr(component, "label", None) or f"component {idx}"
-            for idx, component in enumerate(self.components)
-        ] + [
-            "flag",
-            "username",
-            "timestamp",
-        ]
-
-        csv_data = []
-        for idx, (component, sample) in enumerate(zip(self.components, flag_data)):
-            save_dir = Path(
-                flagging_dir
-            ) / client_utils.strip_invalid_filename_characters(
-                getattr(component, "label", None) or f"component {idx}"
-            )
-            if utils.is_update(sample):
-                csv_data.append(str(sample))
-            else:
-                csv_data.append(
-                    component.deserialize(sample, save_dir=save_dir)
-                    if sample is not None
-                    else ""
-                )
-        csv_data.append(flag_option)
-        csv_data.append(username if username is not None else "")
-        csv_data.append(str(datetime.datetime.now()))
-
-        with open(log_filepath, "a", newline="", encoding="utf-8") as csvfile:
-            writer = csv.writer(csvfile)
-            if is_new:
-                writer.writerow(utils.sanitize_list_for_csv(headers))
-            writer.writerow(utils.sanitize_list_for_csv(csv_data))
-
-        with open(log_filepath, encoding="utf-8") as csvfile:
-            line_count = len(list(csv.reader(csvfile))) - 1
-        return line_count
-
-
-@document()
-class HuggingFaceDatasetSaver(FlaggingCallback):
-    """
-    A callback that saves each flagged sample (both the input and output data) to a HuggingFace dataset.
-
-    Example:
-        import gradio as gr
-        hf_writer = gr.HuggingFaceDatasetSaver(HF_API_TOKEN, "image-classification-mistakes")
-        def image_classifier(inp):
-            return {'cat': 0.3, 'dog': 0.7}
-        demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label",
-                            allow_flagging="manual", flagging_callback=hf_writer)
-    Guides: using-flagging
-    """
-
-    def __init__(
-        self,
-        hf_token: str,
-        dataset_name: str,
-        organization: str | None = None,
-        private: bool = False,
-        info_filename: str = "dataset_info.json",
-        separate_dirs: bool = False,
-        verbose: bool = True,  # silently ignored. TODO: remove it?
-    ):
-        """
-        Parameters:
-            hf_token: The HuggingFace token to use to create (and write the flagged sample to) the HuggingFace dataset (defaults to the registered one).
-            dataset_name: The repo_id of the dataset to save the data to, e.g. "image-classifier-1" or "username/image-classifier-1".
-            organization: Deprecated argument. Please pass a full dataset id (e.g. 'username/dataset_name') to `dataset_name` instead.
-            private: Whether the dataset should be private (defaults to False).
-            info_filename: The name of the file to save the dataset info (defaults to "dataset_infos.json").
-            separate_dirs: If True, each flagged item will be saved in a separate directory. This makes the flagging more robust to concurrent editing, but may be less convenient to use.
-        """
-        if organization is not None:
-            warn_deprecation(
-                "Parameter `organization` is not used anymore. Please pass a full dataset id (e.g. 'username/dataset_name') to `dataset_name` instead."
-            )
-        self.hf_token = hf_token
-        self.dataset_id = dataset_name  # TODO: rename parameter (but ensure backward compatibility somehow)
-        self.dataset_private = private
-        self.info_filename = info_filename
-        self.separate_dirs = separate_dirs
-
-    def setup(self, components: list[IOComponent], flagging_dir: str):
-        """
-        Params:
-        flagging_dir (str): local directory where the dataset is cloned,
-        updated, and pushed from.
-        """
-        hh_version = pkg_resources.get_distribution("huggingface_hub").version
-        try:
-            if StrictVersion(hh_version) < StrictVersion("0.12.0"):
-                raise ImportError(
-                    "The `huggingface_hub` package must be version 0.12.0 or higher"
-                    "for HuggingFaceDatasetSaver. Try 'pip install huggingface_hub --upgrade'."
-                )
-        except ValueError:
-            pass
-
-        # Setup dataset on the Hub
-        self.dataset_id = huggingface_hub.create_repo(
-            repo_id=self.dataset_id,
-            token=self.hf_token,
-            private=self.dataset_private,
-            repo_type="dataset",
-            exist_ok=True,
-        ).repo_id
-
-        # Setup flagging dir
-        self.components = components
-        self.dataset_dir = (
-            Path(flagging_dir).absolute() / self.dataset_id.split("/")[-1]
-        )
-        self.dataset_dir.mkdir(parents=True, exist_ok=True)
-        self.infos_file = self.dataset_dir / self.info_filename
-
-        # Download remote files to local
-        remote_files = [self.info_filename]
-        if not self.separate_dirs:
-            # No separate dirs => means all data is in the same CSV file => download it to get its current content
-            remote_files.append("data.csv")
-
-        for filename in remote_files:
-            try:
-                huggingface_hub.hf_hub_download(
-                    repo_id=self.dataset_id,
-                    repo_type="dataset",
-                    filename=filename,
-                    local_dir=self.dataset_dir,
-                    token=self.hf_token,
-                )
-            except huggingface_hub.utils.EntryNotFoundError:
-                pass
-
-    def flag(
-        self,
-        flag_data: list[Any],
-        flag_option: str = "",
-        username: str | None = None,
-    ) -> int:
-        if self.separate_dirs:
-            # JSONL files to support dataset preview on the Hub
-            unique_id = str(uuid.uuid4())
-            components_dir = self.dataset_dir / str(uuid.uuid4())
-            data_file = components_dir / "metadata.jsonl"
-            path_in_repo = unique_id  # upload in sub folder (safer for concurrency)
-        else:
-            # Unique CSV file
-            components_dir = self.dataset_dir
-            data_file = components_dir / "data.csv"
-            path_in_repo = None  # upload at root level
-
-        return self._flag_in_dir(
-            data_file=data_file,
-            components_dir=components_dir,
-            path_in_repo=path_in_repo,
-            flag_data=flag_data,
-            flag_option=flag_option,
-            username=username or "",
-        )
-
-    def _flag_in_dir(
-        self,
-        data_file: Path,
-        components_dir: Path,
-        path_in_repo: str | None,
-        flag_data: list[Any],
-        flag_option: str = "",
-        username: str = "",
-    ) -> int:
-        # Deserialize components (write images/audio to files)
-        features, row = self._deserialize_components(
-            components_dir, flag_data, flag_option, username
-        )
-
-        # Write generic info to dataset_infos.json + upload
-        with filelock.FileLock(str(self.infos_file) + ".lock"):
-            if not self.infos_file.exists():
-                self.infos_file.write_text(
-                    json.dumps({"flagged": {"features": features}})
-                )
-
-                huggingface_hub.upload_file(
-                    repo_id=self.dataset_id,
-                    repo_type="dataset",
-                    token=self.hf_token,
-                    path_in_repo=self.infos_file.name,
-                    path_or_fileobj=self.infos_file,
-                )
-
-        headers = list(features.keys())
-
-        if not self.separate_dirs:
-            with filelock.FileLock(components_dir / ".lock"):
-                sample_nb = self._save_as_csv(data_file, headers=headers, row=row)
-                sample_name = str(sample_nb)
-                huggingface_hub.upload_folder(
-                    repo_id=self.dataset_id,
-                    repo_type="dataset",
-                    commit_message=f"Flagged sample #{sample_name}",
-                    path_in_repo=path_in_repo,
-                    ignore_patterns="*.lock",
-                    folder_path=components_dir,
-                    token=self.hf_token,
-                )
-        else:
-            sample_name = self._save_as_jsonl(data_file, headers=headers, row=row)
-            sample_nb = len(
-                [path for path in self.dataset_dir.iterdir() if path.is_dir()]
-            )
-            huggingface_hub.upload_folder(
-                repo_id=self.dataset_id,
-                repo_type="dataset",
-                commit_message=f"Flagged sample #{sample_name}",
-                path_in_repo=path_in_repo,
-                ignore_patterns="*.lock",
-                folder_path=components_dir,
-                token=self.hf_token,
-            )
-
-        return sample_nb
-
-    @staticmethod
-    def _save_as_csv(data_file: Path, headers: list[str], row: list[Any]) -> int:
-        """Save data as CSV and return the sample name (row number)."""
-        is_new = not data_file.exists()
-
-        with data_file.open("a", newline="", encoding="utf-8") as csvfile:
-            writer = csv.writer(csvfile)
-
-            # Write CSV headers if new file
-            if is_new:
-                writer.writerow(utils.sanitize_list_for_csv(headers))
-
-            # Write CSV row for flagged sample
-            writer.writerow(utils.sanitize_list_for_csv(row))
-
-        with data_file.open(encoding="utf-8") as csvfile:
-            return sum(1 for _ in csv.reader(csvfile)) - 1
-
-    @staticmethod
-    def _save_as_jsonl(data_file: Path, headers: list[str], row: list[Any]) -> str:
-        """Save data as JSONL and return the sample name (uuid)."""
-        Path.mkdir(data_file.parent, parents=True, exist_ok=True)
-        with open(data_file, "w") as f:
-            json.dump(dict(zip(headers, row)), f)
-        return data_file.parent.name
-
-    def _deserialize_components(
-        self,
-        data_dir: Path,
-        flag_data: list[Any],
-        flag_option: str = "",
-        username: str = "",
-    ) -> tuple[dict[Any, Any], list[Any]]:
-        """Deserialize components and return the corresponding row for the flagged sample.
-
-        Images/audio are saved to disk as individual files.
-        """
-        # Components that can have a preview on dataset repos
-        file_preview_types = {gr.Audio: "Audio", gr.Image: "Image"}
-
-        # Generate the row corresponding to the flagged sample
-        features = OrderedDict()
-        row = []
-        for component, sample in zip(self.components, flag_data):
-            # Get deserialized object (will save sample to disk if applicable -file, audio, image,...-)
-            label = component.label or ""
-            save_dir = data_dir / client_utils.strip_invalid_filename_characters(label)
-            deserialized = component.deserialize(sample, save_dir, None)
-
-            # Add deserialized object to row
-            features[label] = {"dtype": "string", "_type": "Value"}
-            try:
-                assert Path(deserialized).exists()
-                row.append(Path(deserialized).name)
-            except (AssertionError, TypeError, ValueError):
-                row.append(str(deserialized))
-
-            # If component is eligible for a preview, add the URL of the file
-            if isinstance(component, tuple(file_preview_types)):  # type: ignore
-                for _component, _type in file_preview_types.items():
-                    if isinstance(component, _component):
-                        features[label + " file"] = {"_type": _type}
-                        break
-                path_in_repo = str(  # returned filepath is absolute, we want it relative to compute URL
-                    Path(deserialized).relative_to(self.dataset_dir)
-                ).replace(
-                    "\\", "/"
-                )
-                row.append(
-                    huggingface_hub.hf_hub_url(
-                        repo_id=self.dataset_id,
-                        filename=path_in_repo,
-                        repo_type="dataset",
-                    )
-                )
-        features["flag"] = {"dtype": "string", "_type": "Value"}
-        features["username"] = {"dtype": "string", "_type": "Value"}
-        row.append(flag_option)
-        row.append(username)
-        return features, row
-
-
-class HuggingFaceDatasetJSONSaver(HuggingFaceDatasetSaver):
-    def __init__(
-        self,
-        hf_token: str,
-        dataset_name: str,
-        organization: str | None = None,
-        private: bool = False,
-        info_filename: str = "dataset_info.json",
-        verbose: bool = True,  # silently ignored. TODO: remove it?
-    ):
-        warn_deprecation(
-            "Callback `HuggingFaceDatasetJSONSaver` is deprecated in favor of using"
-            " `HuggingFaceDatasetSaver` and passing `separate_dirs=True` as parameter."
-        )
-        super().__init__(
-            hf_token=hf_token,
-            dataset_name=dataset_name,
-            organization=organization,
-            private=private,
-            info_filename=info_filename,
-            separate_dirs=True,
-        )
-
-
-class FlagMethod:
-    """
-    Helper class that contains the flagging options and calls the flagging method. Also
-    provides visual feedback to the user when flag is clicked.
-    """
-
-    def __init__(
-        self,
-        flagging_callback: FlaggingCallback,
-        label: str,
-        value: str,
-        visual_feedback: bool = True,
-    ):
-        self.flagging_callback = flagging_callback
-        self.label = label
-        self.value = value
-        self.__name__ = "Flag"
-        self.visual_feedback = visual_feedback
-
-    def __call__(self, request: gr.Request, *flag_data):
-        try:
-            self.flagging_callback.flag(
-                list(flag_data), flag_option=self.value, username=request.username
-            )
-        except Exception as e:
-            print(f"Error while flagging: {e}")
-            if self.visual_feedback:
-                return "Error!"
-        if not self.visual_feedback:
-            return
-        time.sleep(0.8)  # to provide enough time for the user to observe button change
-        return self.reset()
-
-    def reset(self):
-        return gr.Button.update(value=self.label, interactive=True)
diff --git a/spaces/cihyFjudo/fairness-paper-search/Aladin 2 Tamil Dubbed Movie Free Download Mp4  Dont Miss the Fun and Fantasy of Aladdin 2.md b/spaces/cihyFjudo/fairness-paper-search/Aladin 2 Tamil Dubbed Movie Free Download Mp4  Dont Miss the Fun and Fantasy of Aladdin 2.md
deleted file mode 100644
index d5e9e3e03b593c6d0dd5df8a57a7b85c94409320..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Aladin 2 Tamil Dubbed Movie Free Download Mp4  Dont Miss the Fun and Fantasy of Aladdin 2.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/cihyFjudo/fairness-paper-search/How to Use CGAXIS  Supermarket Collection for Cinema 4D to Enhance Your Market and Shop Renders.md b/spaces/cihyFjudo/fairness-paper-search/How to Use CGAXIS  Supermarket Collection for Cinema 4D to Enhance Your Market and Shop Renders.md
deleted file mode 100644
index f00ea982a2543ca0dc154256f52e2381b2d033eb..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/How to Use CGAXIS  Supermarket Collection for Cinema 4D to Enhance Your Market and Shop Renders.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-
-<p>CGAxis Models Volume 70 is a collection containing 26 high polygon 3d models of supermarket equipement: market shelves with various products, clothes on hangers, store mannequins with dresses, shoe wall shelves, cash registers and market display screens. Models are mapped and have materials and textures. Compatible with 3ds max 2010 or higher, Cinema 4D R11, Maya 2011 or higher and many others.</p>
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diff --git a/spaces/cihyFjudo/fairness-paper-search/UPDATED Kaspersky Small Office Security 15.0.2.361.7489 Final TR-AppzDam 64 Bit How to Get the Latest Version for Free.md b/spaces/cihyFjudo/fairness-paper-search/UPDATED Kaspersky Small Office Security 15.0.2.361.7489 Final TR-AppzDam 64 Bit How to Get the Latest Version for Free.md
deleted file mode 100644
index acb8a074a29edee1adc1a0a62f037d15b912db9b..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/UPDATED Kaspersky Small Office Security 15.0.2.361.7489 Final TR-AppzDam 64 Bit How to Get the Latest Version for Free.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aactab.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aactab.h
deleted file mode 100644
index 9b1450c2ebdf2f54cc9ca258bf4fb71d8caa212c..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aactab.h
+++ /dev/null
@@ -1,140 +0,0 @@
-/*
- * AAC data declarations
- * Copyright (c) 2005-2006 Oded Shimon ( ods15 ods15 dyndns org )
- * Copyright (c) 2006-2007 Maxim Gavrilov ( maxim.gavrilov gmail com )
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * AAC data declarations
- * @author Oded Shimon  ( ods15 ods15 dyndns org )
- * @author Maxim Gavrilov ( maxim.gavrilov gmail com )
- */
-
-#ifndef AVCODEC_AACTAB_H
-#define AVCODEC_AACTAB_H
-
-#include "libavutil/mem_internal.h"
-#include "aac.h"
-
-#include <stdint.h>
-
-/* NOTE:
- * Tables in this file are shared by the AAC decoders and encoder
- */
-
-extern float ff_aac_pow2sf_tab[428];
-extern float ff_aac_pow34sf_tab[428];
-
-void ff_aac_tableinit(void);
-
-/* @name ltp_coef
- * Table of the LTP coefficients
- */
-static const INTFLOAT ltp_coef[8] = {
-    Q30(0.570829), Q30(0.696616), Q30(0.813004), Q30(0.911304),
-    Q30(0.984900), Q30(1.067894), Q30(1.194601), Q30(1.369533),
-};
-
-/* @name tns_tmp2_map
- * Tables of the tmp2[] arrays of LPC coefficients used for TNS.
- * The suffix _M_N[] indicate the values of coef_compress and coef_res
- * respectively.
- * @{
- */
-static const INTFLOAT tns_tmp2_map_1_3[4] = {
-    Q31(0.00000000), Q31(-0.43388373),  Q31(0.64278758),  Q31(0.34202015),
-};
-
-static const INTFLOAT tns_tmp2_map_0_3[8] = {
-    Q31(0.00000000), Q31(-0.43388373), Q31(-0.78183150), Q31(-0.97492790),
-    Q31(0.98480773), Q31( 0.86602539), Q31( 0.64278758), Q31( 0.34202015),
-};
-
-static const INTFLOAT tns_tmp2_map_1_4[8] = {
-    Q31(0.00000000), Q31(-0.20791170), Q31(-0.40673664), Q31(-0.58778524),
-    Q31(0.67369562), Q31( 0.52643216), Q31( 0.36124167), Q31( 0.18374951),
-};
-
-static const INTFLOAT tns_tmp2_map_0_4[16] = {
-    Q31( 0.00000000), Q31(-0.20791170), Q31(-0.40673664), Q31(-0.58778524),
-    Q31(-0.74314481), Q31(-0.86602539), Q31(-0.95105654), Q31(-0.99452192),
-    Q31( 0.99573416), Q31( 0.96182561), Q31( 0.89516330), Q31( 0.79801720),
-    Q31( 0.67369562), Q31( 0.52643216), Q31( 0.36124167), Q31( 0.18374951),
-};
-
-static const INTFLOAT * const tns_tmp2_map[4] = {
-    tns_tmp2_map_0_3,
-    tns_tmp2_map_0_4,
-    tns_tmp2_map_1_3,
-    tns_tmp2_map_1_4
-};
-// @}
-
-/* @name window coefficients
- * @{
- */
-DECLARE_ALIGNED(32, extern float,  ff_aac_kbd_long_1024)[1024];
-DECLARE_ALIGNED(32, extern float,  ff_aac_kbd_short_128)[128];
-DECLARE_ALIGNED(32, extern const float, ff_aac_eld_window_512)[1920];
-DECLARE_ALIGNED(32, extern const int,   ff_aac_eld_window_512_fixed)[1920];
-DECLARE_ALIGNED(32, extern const float, ff_aac_eld_window_480)[1800];
-DECLARE_ALIGNED(32, extern const int,   ff_aac_eld_window_480_fixed)[1800];
-// @}
-
-/* Initializes data shared between float decoder and encoder. */
-void ff_aac_float_common_init(void);
-
-/* @name number of scalefactor window bands for long and short transform windows respectively
- * @{
- */
-extern const uint8_t ff_aac_num_swb_1024[];
-extern const uint8_t ff_aac_num_swb_960 [];
-extern const uint8_t ff_aac_num_swb_512 [];
-extern const uint8_t ff_aac_num_swb_480 [];
-extern const uint8_t ff_aac_num_swb_128 [];
-extern const uint8_t ff_aac_num_swb_120 [];
-// @}
-
-extern const uint8_t ff_aac_pred_sfb_max [];
-
-extern const uint32_t ff_aac_scalefactor_code[121];
-extern const uint8_t  ff_aac_scalefactor_bits[121];
-
-extern const uint16_t * const ff_aac_spectral_codes[11];
-extern const uint8_t  * const ff_aac_spectral_bits [11];
-extern const uint16_t  ff_aac_spectral_sizes[11];
-
-extern const float *const ff_aac_codebook_vectors[];
-extern const float *const ff_aac_codebook_vector_vals[];
-extern const uint16_t *const ff_aac_codebook_vector_idx[];
-
-extern const uint16_t * const ff_swb_offset_1024[13];
-extern const uint16_t * const ff_swb_offset_960 [13];
-extern const uint16_t * const ff_swb_offset_512 [13];
-extern const uint16_t * const ff_swb_offset_480 [13];
-extern const uint16_t * const ff_swb_offset_128 [13];
-extern const uint16_t * const ff_swb_offset_120 [13];
-
-extern const uint8_t ff_tns_max_bands_1024[13];
-extern const uint8_t ff_tns_max_bands_512 [13];
-extern const uint8_t ff_tns_max_bands_480 [13];
-extern const uint8_t ff_tns_max_bands_128 [13];
-
-#endif /* AVCODEC_AACTAB_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dolby_e_parse.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dolby_e_parse.c
deleted file mode 100644
index ffedcd99a44c0e2872553deedd21d1cc89d84c74..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dolby_e_parse.c
+++ /dev/null
@@ -1,180 +0,0 @@
-/*
- * Copyright (C) 2017 foo86
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "get_bits.h"
-#include "put_bits.h"
-#include "dolby_e.h"
-
-static const uint8_t nb_programs_tab[MAX_PROG_CONF + 1] = {
-    2, 3, 2, 3, 4, 5, 4, 5, 6, 7, 8, 1, 2, 3, 3, 4, 5, 6, 1, 2, 3, 4, 1, 1
-};
-
-static const uint8_t nb_channels_tab[MAX_PROG_CONF + 1] = {
-    8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 8, 8
-};
-
-static const uint16_t sample_rate_tab[16] = {
-    0, 42965, 43008, 44800, 53706, 53760
-};
-
-static int skip_input(DBEContext *s, int nb_words)
-{
-    if (nb_words > s->input_size) {
-        return AVERROR_INVALIDDATA;
-    }
-
-    s->input      += nb_words * s->word_bytes;
-    s->input_size -= nb_words;
-    return 0;
-}
-
-static int parse_key(DBEContext *s)
-{
-    if (s->key_present) {
-        const uint8_t *key = s->input;
-        int      ret = skip_input(s, 1);
-        if (ret < 0)
-            return ret;
-        return AV_RB24(key) >> 24 - s->word_bits;
-    }
-    return 0;
-}
-
-int ff_dolby_e_convert_input(DBEContext *s, int nb_words, int key)
-{
-    const uint8_t *src = s->input;
-    uint8_t *dst = s->buffer;
-    PutBitContext pb;
-    int i;
-
-    av_assert0(nb_words <= 1024u);
-
-    if (nb_words > s->input_size) {
-        if (s->avctx)
-            av_log(s->avctx, AV_LOG_ERROR, "Packet too short\n");
-        return AVERROR_INVALIDDATA;
-    }
-
-    switch (s->word_bits) {
-    case 16:
-        for (i = 0; i < nb_words; i++, src += 2, dst += 2)
-            AV_WB16(dst, AV_RB16(src) ^ key);
-        break;
-    case 20:
-        init_put_bits(&pb, s->buffer, sizeof(s->buffer));
-        for (i = 0; i < nb_words; i++, src += 3)
-            put_bits(&pb, 20, AV_RB24(src) >> 4 ^ key);
-        flush_put_bits(&pb);
-        break;
-    case 24:
-        for (i = 0; i < nb_words; i++, src += 3, dst += 3)
-            AV_WB24(dst, AV_RB24(src) ^ key);
-        break;
-    default:
-        av_assert0(0);
-    }
-
-    return init_get_bits(&s->gb, s->buffer, nb_words * s->word_bits);
-}
-
-int ff_dolby_e_parse_header(DBEContext *s, const uint8_t *buf, int buf_size)
-{
-    DolbyEHeaderInfo *const header = &s->metadata;
-    int hdr, ret, key, mtd_size;
-
-    if (buf_size < 3)
-        return AVERROR_INVALIDDATA;
-
-    hdr = AV_RB24(buf);
-    if ((hdr & 0xfffffe) == 0x7888e) {
-        s->word_bits = 24;
-    } else if ((hdr & 0xffffe0) == 0x788e0) {
-        s->word_bits = 20;
-    } else if ((hdr & 0xfffe00) == 0x78e00) {
-        s->word_bits = 16;
-    } else {
-        if (s->avctx)
-            av_log(s->avctx, AV_LOG_ERROR, "Invalid frame header\n");
-        return AVERROR_INVALIDDATA;
-    }
-
-    s->word_bytes  = s->word_bits + 7 >> 3;
-    s->input       = buf + s->word_bytes;
-    s->input_size  = buf_size / s->word_bytes - 1;
-    s->key_present = hdr >> 24 - s->word_bits & 1;
-
-    if ((key = parse_key(s)) < 0)
-        return key;
-    if ((ret = ff_dolby_e_convert_input(s, 1, key)) < 0)
-        return ret;
-
-    skip_bits(&s->gb, 4);
-    mtd_size = get_bits(&s->gb, 10);
-    if (!mtd_size) {
-        if (s->avctx)
-            av_log(s->avctx, AV_LOG_ERROR, "Invalid metadata size\n");
-        return AVERROR_INVALIDDATA;
-    }
-
-    if ((ret = ff_dolby_e_convert_input(s, mtd_size, key)) < 0)
-        return ret;
-
-    skip_bits(&s->gb, 14);
-    header->prog_conf = get_bits(&s->gb, 6);
-    if (header->prog_conf > MAX_PROG_CONF) {
-        if (s->avctx)
-            av_log(s->avctx, AV_LOG_ERROR, "Invalid program configuration\n");
-        return AVERROR_INVALIDDATA;
-    }
-
-    header->nb_channels = nb_channels_tab[header->prog_conf];
-    header->nb_programs = nb_programs_tab[header->prog_conf];
-
-    header->fr_code      = get_bits(&s->gb, 4);
-    header->fr_code_orig = get_bits(&s->gb, 4);
-    if (!(header->sample_rate = sample_rate_tab[header->fr_code]) ||
-        !sample_rate_tab[header->fr_code_orig]) {
-        if (s->avctx)
-            av_log(s->avctx, AV_LOG_ERROR, "Invalid frame rate code\n");
-        return AVERROR_INVALIDDATA;
-    }
-
-    skip_bits_long(&s->gb, 88);
-    for (int i = 0; i < header->nb_channels; i++)
-        header->ch_size[i] = get_bits(&s->gb, 10);
-    header->mtd_ext_size = get_bits(&s->gb, 8);
-    header->meter_size   = get_bits(&s->gb, 8);
-
-    skip_bits_long(&s->gb, 10 * header->nb_programs);
-    for (int i = 0; i < header->nb_channels; i++) {
-        header->rev_id[i]     = get_bits(&s->gb,  4);
-        skip_bits1(&s->gb);
-        header->begin_gain[i] = get_bits(&s->gb, 10);
-        header->end_gain[i]   = get_bits(&s->gb, 10);
-    }
-
-    if (get_bits_left(&s->gb) < 0) {
-        if (s->avctx)
-            av_log(s->avctx, AV_LOG_ERROR, "Read past end of metadata\n");
-        return AVERROR_INVALIDDATA;
-    }
-
-    return skip_input(s, mtd_size + 1);
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dvenc.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dvenc.c
deleted file mode 100644
index cd442b524dfd64c8723e59dadcc85415ce5db3d4..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dvenc.c
+++ /dev/null
@@ -1,1254 +0,0 @@
-/*
- * DV encoder
- * Copyright (c) 2003 Roman Shaposhnik
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- *
- * quant_deadzone code and fixes sponsored by NOA GmbH
- */
-
-/**
- * @file
- * DV encoder
- */
-
-#include "config.h"
-
-#include "libavutil/attributes.h"
-#include "libavutil/internal.h"
-#include "libavutil/mem_internal.h"
-#include "libavutil/opt.h"
-#include "libavutil/pixdesc.h"
-#include "libavutil/thread.h"
-
-#include "avcodec.h"
-#include "codec_internal.h"
-#include "dv.h"
-#include "dv_internal.h"
-#include "dv_profile_internal.h"
-#include "dv_tablegen.h"
-#include "encode.h"
-#include "fdctdsp.h"
-#include "mathops.h"
-#include "me_cmp.h"
-#include "pixblockdsp.h"
-#include "put_bits.h"
-
-typedef struct DVEncContext {
-    const AVClass     *class;
-    const AVDVProfile *sys;
-    const AVFrame     *frame;
-    AVCodecContext    *avctx;
-    uint8_t           *buf;
-
-    void (*get_pixels)(int16_t *block, const uint8_t *pixels, ptrdiff_t linesize);
-    void (*fdct[2])(int16_t *block);
-
-    me_cmp_func  ildct_cmp;
-    DVwork_chunk work_chunks[4 * 12 * 27];
-
-    int quant_deadzone;
-} DVEncContext;
-
-
-static av_cold int dvvideo_encode_init(AVCodecContext *avctx)
-{
-    DVEncContext *s = avctx->priv_data;
-    FDCTDSPContext fdsp;
-    MECmpContext mecc;
-    PixblockDSPContext pdsp;
-    int ret;
-
-    s->avctx = avctx;
-
-    if (avctx->chroma_sample_location != AVCHROMA_LOC_TOPLEFT) {
-        const char *name = av_chroma_location_name(avctx->chroma_sample_location);
-        av_log(avctx, AV_LOG_WARNING, "Only top-left chroma location is supported "
-               "in DV, input value is: %s\n", name ? name : "unknown");
-        if (avctx->strict_std_compliance > FF_COMPLIANCE_NORMAL)
-            return AVERROR(EINVAL);
-    }
-
-    s->sys = av_dv_codec_profile2(avctx->width, avctx->height, avctx->pix_fmt, avctx->time_base);
-    if (!s->sys) {
-        av_log(avctx, AV_LOG_ERROR, "Found no DV profile for %ix%i %s video. "
-                                    "Valid DV profiles are:\n",
-               avctx->width, avctx->height, av_get_pix_fmt_name(avctx->pix_fmt));
-        ff_dv_print_profiles(avctx, AV_LOG_ERROR);
-        return AVERROR(EINVAL);
-    }
-
-    ret = ff_dv_init_dynamic_tables(s->work_chunks, s->sys);
-    if (ret < 0) {
-        av_log(avctx, AV_LOG_ERROR, "Error initializing work tables.\n");
-        return ret;
-    }
-
-    memset(&fdsp,0, sizeof(fdsp));
-    memset(&mecc,0, sizeof(mecc));
-    memset(&pdsp,0, sizeof(pdsp));
-    ff_fdctdsp_init(&fdsp, avctx);
-    ff_me_cmp_init(&mecc, avctx);
-    ff_pixblockdsp_init(&pdsp, avctx);
-    ret = ff_set_cmp(&mecc, mecc.ildct_cmp, avctx->ildct_cmp);
-    if (ret < 0)
-        return AVERROR(EINVAL);
-
-    s->get_pixels = pdsp.get_pixels;
-    s->ildct_cmp  = mecc.ildct_cmp[5];
-
-    s->fdct[0]    = fdsp.fdct;
-    s->fdct[1]    = fdsp.fdct248;
-
-#if !CONFIG_HARDCODED_TABLES
-    {
-        static AVOnce init_static_once = AV_ONCE_INIT;
-        ff_thread_once(&init_static_once, dv_vlc_map_tableinit);
-    }
-#endif
-
-    return 0;
-}
-
-/* bit budget for AC only in 5 MBs */
-static const int vs_total_ac_bits_hd = (68 * 6 + 52*2) * 5;
-static const int vs_total_ac_bits = (100 * 4 + 68 * 2) * 5;
-static const int mb_area_start[5] = { 1, 6, 21, 43, 64 };
-
-#if CONFIG_SMALL
-/* Convert run and level (where level != 0) pair into VLC, returning bit size */
-static av_always_inline int dv_rl2vlc(int run, int level, int sign,
-                                      uint32_t *vlc)
-{
-    int size;
-    if (run < DV_VLC_MAP_RUN_SIZE && level < DV_VLC_MAP_LEV_SIZE) {
-        *vlc = dv_vlc_map[run][level].vlc | sign;
-        size = dv_vlc_map[run][level].size;
-    } else {
-        if (level < DV_VLC_MAP_LEV_SIZE) {
-            *vlc = dv_vlc_map[0][level].vlc | sign;
-            size = dv_vlc_map[0][level].size;
-        } else {
-            *vlc = 0xfe00 | (level << 1) | sign;
-            size = 16;
-        }
-        if (run) {
-            *vlc |= ((run < 16) ? dv_vlc_map[run - 1][0].vlc :
-                     (0x1f80 | (run - 1))) << size;
-            size +=  (run < 16) ? dv_vlc_map[run - 1][0].size : 13;
-        }
-    }
-
-    return size;
-}
-
-static av_always_inline int dv_rl2vlc_size(int run, int level)
-{
-    int size;
-
-    if (run < DV_VLC_MAP_RUN_SIZE && level < DV_VLC_MAP_LEV_SIZE) {
-        size = dv_vlc_map[run][level].size;
-    } else {
-        size = (level < DV_VLC_MAP_LEV_SIZE) ? dv_vlc_map[0][level].size : 16;
-        if (run)
-            size += (run < 16) ? dv_vlc_map[run - 1][0].size : 13;
-    }
-    return size;
-}
-#else
-static av_always_inline int dv_rl2vlc(int run, int l, int sign, uint32_t *vlc)
-{
-    *vlc = dv_vlc_map[run][l].vlc | sign;
-    return dv_vlc_map[run][l].size;
-}
-
-static av_always_inline int dv_rl2vlc_size(int run, int l)
-{
-    return dv_vlc_map[run][l].size;
-}
-#endif
-
-typedef struct EncBlockInfo {
-    int      area_q[4];
-    int      bit_size[4];
-    int      prev[5];
-    int      cur_ac;
-    int      cno;
-    int      dct_mode;
-    int16_t  mb[64];
-    uint8_t  next[64];
-    uint8_t  sign[64];
-    uint8_t  partial_bit_count;
-    uint32_t partial_bit_buffer; /* we can't use uint16_t here */
-    /* used by DV100 only: a copy of the weighted and classified but
-       not-yet-quantized AC coefficients. This is necessary for
-       re-quantizing at different steps. */
-    int16_t  save[64];
-    int      min_qlevel; /* DV100 only: minimum qlevel (for AC coefficients >255) */
-} EncBlockInfo;
-
-static av_always_inline PutBitContext *dv_encode_ac(EncBlockInfo *bi,
-                                                    PutBitContext *pb_pool,
-                                                    PutBitContext *pb_end)
-{
-    int prev, bits_left;
-    PutBitContext *pb = pb_pool;
-    int size          = bi->partial_bit_count;
-    uint32_t vlc      = bi->partial_bit_buffer;
-
-    bi->partial_bit_count  =
-    bi->partial_bit_buffer = 0;
-    for (;;) {
-        /* Find suitable storage space */
-        for (; size > (bits_left = put_bits_left(pb)); pb++) {
-            if (bits_left) {
-                size -= bits_left;
-                put_bits(pb, bits_left, vlc >> size);
-                vlc = av_mod_uintp2(vlc, size);
-            }
-            if (pb + 1 >= pb_end) {
-                bi->partial_bit_count  = size;
-                bi->partial_bit_buffer = vlc;
-                return pb;
-            }
-        }
-
-        /* Store VLC */
-        put_bits(pb, size, vlc);
-
-        if (bi->cur_ac >= 64)
-            break;
-
-        /* Construct the next VLC */
-        prev       = bi->cur_ac;
-        bi->cur_ac = bi->next[prev];
-        if (bi->cur_ac < 64) {
-            size = dv_rl2vlc(bi->cur_ac - prev - 1, bi->mb[bi->cur_ac],
-                             bi->sign[bi->cur_ac], &vlc);
-        } else {
-            size = 4;
-            vlc  = 6; /* End Of Block stamp */
-        }
-    }
-    return pb;
-}
-
-static av_always_inline int dv_guess_dct_mode(DVEncContext *s, const uint8_t *data,
-                                              ptrdiff_t linesize)
-{
-    if (s->avctx->flags & AV_CODEC_FLAG_INTERLACED_DCT) {
-        int ps = s->ildct_cmp(NULL, data, NULL, linesize, 8) - 400;
-        if (ps > 0) {
-            int is = s->ildct_cmp(NULL, data,            NULL, linesize * 2, 4) +
-                     s->ildct_cmp(NULL, data + linesize, NULL, linesize * 2, 4);
-            return ps > is;
-        }
-    }
-
-    return 0;
-}
-
-static const int dv_weight_bits = 18;
-static const int dv_weight_88[64] = {
-    131072, 257107, 257107, 242189, 252167, 242189, 235923, 237536,
-    237536, 235923, 229376, 231390, 223754, 231390, 229376, 222935,
-    224969, 217965, 217965, 224969, 222935, 200636, 218652, 211916,
-    212325, 211916, 218652, 200636, 188995, 196781, 205965, 206433,
-    206433, 205965, 196781, 188995, 185364, 185364, 200636, 200704,
-    200636, 185364, 185364, 174609, 180568, 195068, 195068, 180568,
-    174609, 170091, 175557, 189591, 175557, 170091, 165371, 170627,
-    170627, 165371, 160727, 153560, 160727, 144651, 144651, 136258,
-};
-static const int dv_weight_248[64] = {
-    131072, 262144, 257107, 257107, 242189, 242189, 242189, 242189,
-    237536, 237536, 229376, 229376, 200636, 200636, 224973, 224973,
-    223754, 223754, 235923, 235923, 229376, 229376, 217965, 217965,
-    211916, 211916, 196781, 196781, 185364, 185364, 206433, 206433,
-    211916, 211916, 222935, 222935, 200636, 200636, 205964, 205964,
-    200704, 200704, 180568, 180568, 175557, 175557, 195068, 195068,
-    185364, 185364, 188995, 188995, 174606, 174606, 175557, 175557,
-    170627, 170627, 153560, 153560, 165371, 165371, 144651, 144651,
-};
-
-/* setting this to 1 results in a faster codec but
- * somewhat lower image quality */
-#define DV100_SACRIFICE_QUALITY_FOR_SPEED 1
-#define DV100_ENABLE_FINER 1
-
-/* pack combination of QNO and CNO into a single 8-bit value */
-#define DV100_MAKE_QLEVEL(qno,cno) ((qno<<2) | (cno))
-#define DV100_QLEVEL_QNO(qlevel) (qlevel>>2)
-#define DV100_QLEVEL_CNO(qlevel) (qlevel&0x3)
-
-#define DV100_NUM_QLEVELS 31
-
-/* The quantization step is determined by a combination of QNO and
-   CNO. We refer to these combinations as "qlevels" (this term is our
-   own, it's not mentioned in the spec). We use CNO, a multiplier on
-   the quantization step, to "fill in the gaps" between quantization
-   steps associated with successive values of QNO. e.g. there is no
-   QNO for a quantization step of 10, but we can use QNO=5 CNO=1 to
-   get the same result. The table below encodes combinations of QNO
-   and CNO in order of increasing quantization coarseness. */
-static const uint8_t dv100_qlevels[DV100_NUM_QLEVELS] = {
-    DV100_MAKE_QLEVEL( 1,0), //  1*1= 1
-    DV100_MAKE_QLEVEL( 1,0), //  1*1= 1
-    DV100_MAKE_QLEVEL( 2,0), //  2*1= 2
-    DV100_MAKE_QLEVEL( 3,0), //  3*1= 3
-    DV100_MAKE_QLEVEL( 4,0), //  4*1= 4
-    DV100_MAKE_QLEVEL( 5,0), //  5*1= 5
-    DV100_MAKE_QLEVEL( 6,0), //  6*1= 6
-    DV100_MAKE_QLEVEL( 7,0), //  7*1= 7
-    DV100_MAKE_QLEVEL( 8,0), //  8*1= 8
-    DV100_MAKE_QLEVEL( 5,1), //  5*2=10
-    DV100_MAKE_QLEVEL( 6,1), //  6*2=12
-    DV100_MAKE_QLEVEL( 7,1), //  7*2=14
-    DV100_MAKE_QLEVEL( 9,0), // 16*1=16
-    DV100_MAKE_QLEVEL(10,0), // 18*1=18
-    DV100_MAKE_QLEVEL(11,0), // 20*1=20
-    DV100_MAKE_QLEVEL(12,0), // 22*1=22
-    DV100_MAKE_QLEVEL(13,0), // 24*1=24
-    DV100_MAKE_QLEVEL(14,0), // 28*1=28
-    DV100_MAKE_QLEVEL( 9,1), // 16*2=32
-    DV100_MAKE_QLEVEL(10,1), // 18*2=36
-    DV100_MAKE_QLEVEL(11,1), // 20*2=40
-    DV100_MAKE_QLEVEL(12,1), // 22*2=44
-    DV100_MAKE_QLEVEL(13,1), // 24*2=48
-    DV100_MAKE_QLEVEL(15,0), // 52*1=52
-    DV100_MAKE_QLEVEL(14,1), // 28*2=56
-    DV100_MAKE_QLEVEL( 9,2), // 16*4=64
-    DV100_MAKE_QLEVEL(10,2), // 18*4=72
-    DV100_MAKE_QLEVEL(11,2), // 20*4=80
-    DV100_MAKE_QLEVEL(12,2), // 22*4=88
-    DV100_MAKE_QLEVEL(13,2), // 24*4=96
-    // ...
-    DV100_MAKE_QLEVEL(15,3), // 52*8=416
-};
-
-static const int dv100_min_bias = 0;
-static const int dv100_chroma_bias = 0;
-static const int dv100_starting_qno = 1;
-
-#if DV100_SACRIFICE_QUALITY_FOR_SPEED
-static const int dv100_qlevel_inc = 4;
-#else
-static const int dv100_qlevel_inc = 1;
-#endif
-
-// 1/qstep, shifted up by 16 bits
-static const int dv100_qstep_bits = 16;
-static const int dv100_qstep_inv[16] = {
-        65536,  65536,  32768,  21845,  16384,  13107,  10923,  9362,  8192,  4096,  3641,  3277,  2979,  2731,  2341,  1260,
-};
-
-/* DV100 weights are pre-zigzagged, inverted and multiplied by 2^16
-   (in DV100 the AC components are divided by the spec weights) */
-static const int dv_weight_1080[2][64] = {
-    { 8192, 65536, 65536, 61681, 61681, 61681, 58254, 58254,
-      58254, 58254, 58254, 58254, 55188, 58254, 58254, 55188,
-      55188, 55188, 55188, 55188, 55188, 24966, 27594, 26214,
-      26214, 26214, 27594, 24966, 23831, 24385, 25575, 25575,
-      25575, 25575, 24385, 23831, 23302, 23302, 24966, 24966,
-      24966, 23302, 23302, 21845, 22795, 24385, 24385, 22795,
-      21845, 21400, 21845, 23831, 21845, 21400, 10382, 10700,
-      10700, 10382, 10082, 9620, 10082, 9039, 9039, 8525, },
-    { 8192, 65536, 65536, 61681, 61681, 61681, 41943, 41943,
-      41943, 41943, 40330, 41943, 40330, 41943, 40330, 40330,
-      40330, 38836, 38836, 40330, 40330, 24966, 27594, 26214,
-      26214, 26214, 27594, 24966, 23831, 24385, 25575, 25575,
-      25575, 25575, 24385, 23831, 11523, 11523, 12483, 12483,
-      12483, 11523, 11523, 10923, 11275, 12193, 12193, 11275,
-      10923, 5323, 5490, 5924, 5490, 5323, 5165, 5323,
-      5323, 5165, 5017, 4788, 5017, 4520, 4520, 4263, }
-};
-
-static const int dv_weight_720[2][64] = {
-    { 8192, 65536, 65536, 61681, 61681, 61681, 58254, 58254,
-      58254, 58254, 58254, 58254, 55188, 58254, 58254, 55188,
-      55188, 55188, 55188, 55188, 55188, 24966, 27594, 26214,
-      26214, 26214, 27594, 24966, 23831, 24385, 25575, 25575,
-      25575, 25575, 24385, 23831, 15420, 15420, 16644, 16644,
-      16644, 15420, 15420, 10923, 11398, 12193, 12193, 11398,
-      10923, 10700, 10923, 11916, 10923, 10700, 5191, 5350,
-      5350, 5191, 5041, 4810, 5041, 4520, 4520, 4263, },
-    { 8192, 43691, 43691, 40330, 40330, 40330, 29127, 29127,
-      29127, 29127, 29127, 29127, 27594, 29127, 29127, 27594,
-      27594, 27594, 27594, 27594, 27594, 12483, 13797, 13107,
-      13107, 13107, 13797, 12483, 11916, 12193, 12788, 12788,
-      12788, 12788, 12193, 11916, 5761, 5761, 6242, 6242,
-      6242, 5761, 5761, 5461, 5638, 5461, 6096, 5638,
-      5461, 2661, 2745, 2962, 2745, 2661, 2583, 2661,
-      2661, 2583, 2509, 2394, 2509, 2260, 2260, 2131, }
-};
-
-static av_always_inline int dv_set_class_number_sd(DVEncContext *s,
-                                                   int16_t *blk, EncBlockInfo *bi,
-                                                   const uint8_t *zigzag_scan,
-                                                   const int *weight, int bias)
-{
-    int i, area;
-    /* We offer two different methods for class number assignment: the
-     * method suggested in SMPTE 314M Table 22, and an improved
-     * method. The SMPTE method is very conservative; it assigns class
-     * 3 (i.e. severe quantization) to any block where the largest AC
-     * component is greater than 36. FFmpeg's DV encoder tracks AC bit
-     * consumption precisely, so there is no need to bias most blocks
-     * towards strongly lossy compression. Instead, we assign class 2
-     * to most blocks, and use class 3 only when strictly necessary
-     * (for blocks whose largest AC component exceeds 255). */
-
-#if 0 /* SMPTE spec method */
-    static const int classes[] = { 12, 24, 36, 0xffff };
-#else /* improved FFmpeg method */
-    static const int classes[] = { -1, -1, 255, 0xffff };
-#endif
-    int max  = classes[0];
-    int prev = 0;
-    const unsigned deadzone = s->quant_deadzone;
-    const unsigned threshold = 2 * deadzone;
-
-    bi->mb[0] = blk[0];
-
-    for (area = 0; area < 4; area++) {
-        bi->prev[area]     = prev;
-        bi->bit_size[area] = 1; // 4 areas 4 bits for EOB :)
-        for (i = mb_area_start[area]; i < mb_area_start[area + 1]; i++) {
-            int level = blk[zigzag_scan[i]];
-
-            if (level + deadzone > threshold) {
-                bi->sign[i] = (level >> 31) & 1;
-                /* Weight it and shift down into range, adding for rounding.
-                 * The extra division by a factor of 2^4 reverses the 8x
-                 * expansion of the DCT AND the 2x doubling of the weights. */
-                level     = (FFABS(level) * weight[i] + (1 << (dv_weight_bits + 3))) >>
-                            (dv_weight_bits + 4);
-                if (!level)
-                    continue;
-                bi->mb[i] = level;
-                if (level > max)
-                    max = level;
-                bi->bit_size[area] += dv_rl2vlc_size(i - prev - 1, level);
-                bi->next[prev]      = i;
-                prev                = i;
-            }
-        }
-    }
-    bi->next[prev] = i;
-    for (bi->cno = 0; max > classes[bi->cno]; bi->cno++)
-        ;
-
-    bi->cno += bias;
-
-    if (bi->cno >= 3) {
-        bi->cno = 3;
-        prev    = 0;
-        i       = bi->next[prev];
-        for (area = 0; area < 4; area++) {
-            bi->prev[area]     = prev;
-            bi->bit_size[area] = 1; // 4 areas 4 bits for EOB :)
-            for (; i < mb_area_start[area + 1]; i = bi->next[i]) {
-                bi->mb[i] >>= 1;
-
-                if (bi->mb[i]) {
-                    bi->bit_size[area] += dv_rl2vlc_size(i - prev - 1, bi->mb[i]);
-                    bi->next[prev]      = i;
-                    prev                = i;
-                }
-            }
-        }
-        bi->next[prev] = i;
-    }
-
-    return bi->bit_size[0] + bi->bit_size[1] +
-           bi->bit_size[2] + bi->bit_size[3];
-}
-
-/* this function just copies the DCT coefficients and performs
-   the initial (non-)quantization. */
-static inline void dv_set_class_number_hd(DVEncContext *s,
-                                          int16_t *blk, EncBlockInfo *bi,
-                                          const uint8_t *zigzag_scan,
-                                          const int *weight, int bias)
-{
-    int i, max = 0;
-
-    /* the first quantization (none at all) */
-    bi->area_q[0] = 1;
-
-    /* weigh AC components and store to save[] */
-    /* (i=0 is the DC component; we only include it to make the
-       number of loop iterations even, for future possible SIMD optimization) */
-    for (i = 0; i < 64; i += 2) {
-        int level0, level1;
-
-        /* get the AC component (in zig-zag order) */
-        level0 = blk[zigzag_scan[i+0]];
-        level1 = blk[zigzag_scan[i+1]];
-
-        /* extract sign and make it the lowest bit */
-        bi->sign[i+0] = (level0>>31)&1;
-        bi->sign[i+1] = (level1>>31)&1;
-
-        /* take absolute value of the level */
-        level0 = FFABS(level0);
-        level1 = FFABS(level1);
-
-        /* weigh it */
-        level0 = (level0*weight[i+0] + 4096 + (1<<17)) >> 18;
-        level1 = (level1*weight[i+1] + 4096 + (1<<17)) >> 18;
-
-        /* save unquantized value */
-        bi->save[i+0] = level0;
-        bi->save[i+1] = level1;
-
-         /* find max component */
-        if (bi->save[i+0] > max)
-            max = bi->save[i+0];
-        if (bi->save[i+1] > max)
-            max = bi->save[i+1];
-    }
-
-    /* copy DC component */
-    bi->mb[0] = blk[0];
-
-    /* the EOB code is 4 bits */
-    bi->bit_size[0] = 4;
-    bi->bit_size[1] = bi->bit_size[2] = bi->bit_size[3] = 0;
-
-    /* ensure that no AC coefficients are cut off */
-    bi->min_qlevel = ((max+256) >> 8);
-
-    bi->area_q[0] = 25; /* set to an "impossible" value */
-    bi->cno = 0;
-}
-
-static av_always_inline int dv_init_enc_block(EncBlockInfo* bi, const uint8_t *data, int linesize,
-                                              DVEncContext *s, int chroma)
-{
-    LOCAL_ALIGNED_16(int16_t, blk, [64]);
-
-    bi->area_q[0] = bi->area_q[1] = bi->area_q[2] = bi->area_q[3] = 0;
-    bi->partial_bit_count = 0;
-    bi->partial_bit_buffer = 0;
-    bi->cur_ac = 0;
-
-    if (data) {
-        if (DV_PROFILE_IS_HD(s->sys)) {
-            s->get_pixels(blk, data, linesize * (1 << bi->dct_mode));
-            s->fdct[0](blk);
-        } else {
-            bi->dct_mode = dv_guess_dct_mode(s, data, linesize);
-            s->get_pixels(blk, data, linesize);
-            s->fdct[bi->dct_mode](blk);
-        }
-    } else {
-        /* We rely on the fact that encoding all zeros leads to an immediate EOB,
-           which is precisely what the spec calls for in the "dummy" blocks. */
-        memset(blk, 0, 64*sizeof(*blk));
-        bi->dct_mode = 0;
-    }
-
-    if (DV_PROFILE_IS_HD(s->sys)) {
-        const int *weights;
-        if (s->sys->height == 1080) {
-            weights = dv_weight_1080[chroma];
-        } else { /* 720p */
-            weights = dv_weight_720[chroma];
-        }
-        dv_set_class_number_hd(s, blk, bi,
-                               ff_zigzag_direct,
-                               weights,
-                               dv100_min_bias+chroma*dv100_chroma_bias);
-    } else {
-        dv_set_class_number_sd(s, blk, bi,
-                               bi->dct_mode ? ff_dv_zigzag248_direct : ff_zigzag_direct,
-                               bi->dct_mode ? dv_weight_248 : dv_weight_88,
-                               chroma);
-    }
-
-    return bi->bit_size[0] + bi->bit_size[1] + bi->bit_size[2] + bi->bit_size[3];
-}
-
-/* DV100 quantize
-   Perform quantization by divinding the AC component by the qstep.
-   As an optimization we use a fixed-point integer multiply instead
-   of a divide. */
-static av_always_inline int dv100_quantize(int level, int qsinv)
-{
-    /* this code is equivalent to */
-    /* return (level + qs/2) / qs; */
-
-    return (level * qsinv + 1024 + (1<<(dv100_qstep_bits-1))) >> dv100_qstep_bits;
-
-    /* the extra +1024 is needed to make the rounding come out right. */
-
-    /* I (DJM) have verified that the results are exactly the same as
-       division for level 0-2048 at all QNOs. */
-}
-
-static int dv100_actual_quantize(EncBlockInfo *b, int qlevel)
-{
-    int prev, k, qsinv;
-
-    int qno = DV100_QLEVEL_QNO(dv100_qlevels[qlevel]);
-    int cno = DV100_QLEVEL_CNO(dv100_qlevels[qlevel]);
-
-    if (b->area_q[0] == qno && b->cno == cno)
-        return b->bit_size[0];
-
-    qsinv = dv100_qstep_inv[qno];
-
-    /* record the new qstep */
-    b->area_q[0] = qno;
-    b->cno = cno;
-
-    /* reset encoded size (EOB = 4 bits) */
-    b->bit_size[0] = 4;
-
-    /* visit nonzero components and quantize */
-    prev = 0;
-    for (k = 1; k < 64; k++) {
-        /* quantize */
-        int ac = dv100_quantize(b->save[k], qsinv) >> cno;
-        if (ac) {
-            if (ac > 255)
-                ac = 255;
-            b->mb[k] = ac;
-            b->bit_size[0] += dv_rl2vlc_size(k - prev - 1, ac);
-            b->next[prev] = k;
-            prev = k;
-        }
-    }
-    b->next[prev] = k;
-
-    return b->bit_size[0];
-}
-
-static inline void dv_guess_qnos_hd(EncBlockInfo *blks, int *qnos)
-{
-    EncBlockInfo *b;
-    int min_qlevel[5];
-    int qlevels[5];
-    int size[5];
-    int i, j;
-    /* cache block sizes at hypothetical qlevels */
-    uint16_t size_cache[5*8][DV100_NUM_QLEVELS] = {{0}};
-
-    /* get minimum qlevels */
-    for (i = 0; i < 5; i++) {
-        min_qlevel[i] = 1;
-        for (j = 0; j < 8; j++) {
-            if (blks[8*i+j].min_qlevel > min_qlevel[i])
-                min_qlevel[i] = blks[8*i+j].min_qlevel;
-        }
-    }
-
-    /* initialize sizes */
-    for (i = 0; i < 5; i++) {
-        qlevels[i] = dv100_starting_qno;
-        if (qlevels[i] < min_qlevel[i])
-            qlevels[i] = min_qlevel[i];
-
-        qnos[i] = DV100_QLEVEL_QNO(dv100_qlevels[qlevels[i]]);
-        size[i] = 0;
-        for (j = 0; j < 8; j++) {
-            size_cache[8*i+j][qlevels[i]] = dv100_actual_quantize(&blks[8*i+j], qlevels[i]);
-            size[i] += size_cache[8*i+j][qlevels[i]];
-        }
-    }
-
-    /* must we go coarser? */
-    if (size[0]+size[1]+size[2]+size[3]+size[4] > vs_total_ac_bits_hd) {
-        int largest = size[0] % 5; /* 'random' number */
-        int qlevels_done = 0;
-
-        do {
-            /* find the macroblock with the lowest qlevel */
-            for (i = 0; i < 5; i++) {
-                if (qlevels[i] < qlevels[largest])
-                    largest = i;
-            }
-
-            i = largest;
-            /* ensure that we don't enter infinite loop */
-            largest = (largest+1) % 5;
-
-            /* quantize a little bit more */
-            qlevels[i] += dv100_qlevel_inc;
-            if (qlevels[i] > DV100_NUM_QLEVELS-1) {
-                qlevels[i] = DV100_NUM_QLEVELS-1;
-                qlevels_done++;
-            }
-
-            qnos[i] = DV100_QLEVEL_QNO(dv100_qlevels[qlevels[i]]);
-            size[i] = 0;
-
-            /* for each block */
-            b = &blks[8*i];
-            for (j = 0; j < 8; j++, b++) {
-                /* accumulate block size into macroblock */
-                if(size_cache[8*i+j][qlevels[i]] == 0) {
-                    /* it is safe to use actual_quantize() here because we only go from finer to coarser,
-                       and it saves the final actual_quantize() down below */
-                    size_cache[8*i+j][qlevels[i]] = dv100_actual_quantize(b, qlevels[i]);
-                }
-                size[i] += size_cache[8*i+j][qlevels[i]];
-            } /* for each block */
-
-        } while (vs_total_ac_bits_hd < size[0] + size[1] + size[2] + size[3] + size[4] && qlevels_done < 5);
-
-        // can we go finer?
-    } else if (DV100_ENABLE_FINER &&
-               size[0]+size[1]+size[2]+size[3]+size[4] < vs_total_ac_bits_hd) {
-        int save_qlevel;
-        int largest = size[0] % 5; /* 'random' number */
-
-        while (qlevels[0] > min_qlevel[0] ||
-               qlevels[1] > min_qlevel[1] ||
-               qlevels[2] > min_qlevel[2] ||
-               qlevels[3] > min_qlevel[3] ||
-               qlevels[4] > min_qlevel[4]) {
-
-            /* find the macroblock with the highest qlevel */
-            for (i = 0; i < 5; i++) {
-                if (qlevels[i] > min_qlevel[i] && qlevels[i] > qlevels[largest])
-                    largest = i;
-            }
-
-            i = largest;
-
-            /* ensure that we don't enter infinite loop */
-            largest = (largest+1) % 5;
-
-            if (qlevels[i] <= min_qlevel[i]) {
-                /* can't unquantize any more */
-                continue;
-            }
-            /* quantize a little bit less */
-            save_qlevel = qlevels[i];
-            qlevels[i] -= dv100_qlevel_inc;
-            if (qlevels[i] < min_qlevel[i])
-                qlevels[i] = min_qlevel[i];
-
-            qnos[i] = DV100_QLEVEL_QNO(dv100_qlevels[qlevels[i]]);
-
-            size[i] = 0;
-
-            /* for each block */
-            b = &blks[8*i];
-            for (j = 0; j < 8; j++, b++) {
-                /* accumulate block size into macroblock */
-                if(size_cache[8*i+j][qlevels[i]] == 0) {
-                    size_cache[8*i+j][qlevels[i]] = dv100_actual_quantize(b, qlevels[i]);
-                }
-                size[i] += size_cache[8*i+j][qlevels[i]];
-            } /* for each block */
-
-            /* did we bust the limit? */
-            if (vs_total_ac_bits_hd < size[0] + size[1] + size[2] + size[3] + size[4]) {
-                /* go back down and exit */
-                qlevels[i] = save_qlevel;
-                qnos[i] = DV100_QLEVEL_QNO(dv100_qlevels[qlevels[i]]);
-                break;
-            }
-        }
-    }
-
-    /* now do the actual quantization */
-    for (i = 0; i < 5; i++) {
-        /* for each block */
-        b = &blks[8*i];
-        size[i] = 0;
-        for (j = 0; j < 8; j++, b++) {
-            /* accumulate block size into macroblock */
-            size[i] += dv100_actual_quantize(b, qlevels[i]);
-        } /* for each block */
-    }
-}
-
-static inline void dv_guess_qnos(EncBlockInfo *blks, int *qnos)
-{
-    int size[5];
-    int i, j, k, a, prev, a2;
-    EncBlockInfo *b;
-
-    size[0] =
-    size[1] =
-    size[2] =
-    size[3] =
-    size[4] = 1 << 24;
-    do {
-        b = blks;
-        for (i = 0; i < 5; i++) {
-            if (!qnos[i])
-                continue;
-
-            qnos[i]--;
-            size[i] = 0;
-            for (j = 0; j < 6; j++, b++) {
-                for (a = 0; a < 4; a++) {
-                    if (b->area_q[a] != ff_dv_quant_shifts[qnos[i] + ff_dv_quant_offset[b->cno]][a]) {
-                        b->bit_size[a] = 1; // 4 areas 4 bits for EOB :)
-                        b->area_q[a]++;
-                        prev = b->prev[a];
-                        av_assert2(b->next[prev] >= mb_area_start[a + 1] || b->mb[prev]);
-                        for (k = b->next[prev]; k < mb_area_start[a + 1]; k = b->next[k]) {
-                            b->mb[k] >>= 1;
-                            if (b->mb[k]) {
-                                b->bit_size[a] += dv_rl2vlc_size(k - prev - 1, b->mb[k]);
-                                prev            = k;
-                            } else {
-                                if (b->next[k] >= mb_area_start[a + 1] && b->next[k] < 64) {
-                                    for (a2 = a + 1; b->next[k] >= mb_area_start[a2 + 1]; a2++)
-                                        b->prev[a2] = prev;
-                                    av_assert2(a2 < 4);
-                                    av_assert2(b->mb[b->next[k]]);
-                                    b->bit_size[a2] += dv_rl2vlc_size(b->next[k] - prev - 1, b->mb[b->next[k]]) -
-                                                       dv_rl2vlc_size(b->next[k] - k    - 1, b->mb[b->next[k]]);
-                                    av_assert2(b->prev[a2] == k && (a2 + 1 >= 4 || b->prev[a2 + 1] != k));
-                                    b->prev[a2] = prev;
-                                }
-                                b->next[prev] = b->next[k];
-                            }
-                        }
-                        b->prev[a + 1] = prev;
-                    }
-                    size[i] += b->bit_size[a];
-                }
-            }
-            if (vs_total_ac_bits >= size[0] + size[1] + size[2] + size[3] + size[4])
-                return;
-        }
-    } while (qnos[0] | qnos[1] | qnos[2] | qnos[3] | qnos[4]);
-
-    for (a = 2; a == 2 || vs_total_ac_bits < size[0]; a += a) {
-        b       = blks;
-        size[0] = 5 * 6 * 4; // EOB
-        for (j = 0; j < 6 * 5; j++, b++) {
-            prev = b->prev[0];
-            for (k = b->next[prev]; k < 64; k = b->next[k]) {
-                if (b->mb[k] < a && b->mb[k] > -a) {
-                    b->next[prev] = b->next[k];
-                } else {
-                    size[0] += dv_rl2vlc_size(k - prev - 1, b->mb[k]);
-                    prev     = k;
-                }
-            }
-        }
-    }
-}
-
-/* update all cno values into the blocks, over-writing the old values without
-   touching anything else. (only used for DV100) */
-static inline void dv_revise_cnos(uint8_t *dif, EncBlockInfo *blk, const AVDVProfile *profile)
-{
-    uint8_t *data;
-    int mb_index, i;
-
-    for (mb_index = 0; mb_index < 5; mb_index++) {
-        data = dif + mb_index*80 + 4;
-        for (i = 0; i < profile->bpm; i++) {
-            /* zero out the class number */
-            data[1] &= 0xCF;
-            /* add the new one */
-            data[1] |= blk[profile->bpm*mb_index+i].cno << 4;
-
-            data += profile->block_sizes[i] >> 3;
-        }
-    }
-}
-
-static int dv_encode_video_segment(AVCodecContext *avctx, void *arg)
-{
-    DVEncContext *s = avctx->priv_data;
-    DVwork_chunk *work_chunk = arg;
-    int mb_index, i, j;
-    int mb_x, mb_y, c_offset;
-    ptrdiff_t linesize, y_stride;
-    const uint8_t *y_ptr;
-    uint8_t *dif, *p;
-    LOCAL_ALIGNED_8(uint8_t, scratch, [128]);
-    EncBlockInfo enc_blks[5 * DV_MAX_BPM];
-    PutBitContext pbs[5 * DV_MAX_BPM];
-    PutBitContext *pb;
-    EncBlockInfo *enc_blk;
-    int vs_bit_size = 0;
-    int qnos[5];
-    int *qnosp = &qnos[0];
-
-    p = dif = &s->buf[work_chunk->buf_offset * 80];
-    enc_blk = &enc_blks[0];
-    for (mb_index = 0; mb_index < 5; mb_index++) {
-        dv_calculate_mb_xy(s->sys, s->buf, work_chunk, mb_index, &mb_x, &mb_y);
-
-        qnos[mb_index] = DV_PROFILE_IS_HD(s->sys) ? 1 : 15;
-
-        y_ptr    = s->frame->data[0] + (mb_y * s->frame->linesize[0] + mb_x) * 8;
-        linesize = s->frame->linesize[0];
-
-        if (s->sys->height == 1080 && mb_y < 134)
-            enc_blk->dct_mode = dv_guess_dct_mode(s, y_ptr, linesize);
-        else
-            enc_blk->dct_mode = 0;
-        for (i = 1; i < 8; i++)
-            enc_blk[i].dct_mode = enc_blk->dct_mode;
-
-        /* initializing luminance blocks */
-        if ((s->sys->pix_fmt == AV_PIX_FMT_YUV420P)                      ||
-            (s->sys->pix_fmt == AV_PIX_FMT_YUV411P && mb_x >= (704 / 8)) ||
-            (s->sys->height >= 720 && mb_y != 134)) {
-            y_stride = s->frame->linesize[0] * (1 << (3*!enc_blk->dct_mode));
-        } else {
-            y_stride = 16;
-        }
-        y_ptr    = s->frame->data[0] +
-                   (mb_y * s->frame->linesize[0] + mb_x) * 8;
-        linesize = s->frame->linesize[0];
-
-        if (s->sys->video_stype == 4) { /* SD 422 */
-            vs_bit_size +=
-                dv_init_enc_block(enc_blk + 0, y_ptr,                linesize, s, 0) +
-                dv_init_enc_block(enc_blk + 1, NULL,                 linesize, s, 0) +
-                dv_init_enc_block(enc_blk + 2, y_ptr + 8,            linesize, s, 0) +
-                dv_init_enc_block(enc_blk + 3, NULL,                 linesize, s, 0);
-        } else {
-            vs_bit_size +=
-                dv_init_enc_block(enc_blk + 0, y_ptr,                linesize, s, 0) +
-                dv_init_enc_block(enc_blk + 1, y_ptr + 8,            linesize, s, 0) +
-                dv_init_enc_block(enc_blk + 2, y_ptr +     y_stride, linesize, s, 0) +
-                dv_init_enc_block(enc_blk + 3, y_ptr + 8 + y_stride, linesize, s, 0);
-        }
-        enc_blk += 4;
-
-        /* initializing chrominance blocks */
-        c_offset = ((mb_y >>  (s->sys->pix_fmt == AV_PIX_FMT_YUV420P)) * s->frame->linesize[1] +
-                    (mb_x >> ((s->sys->pix_fmt == AV_PIX_FMT_YUV411P) ? 2 : 1))) * 8;
-        for (j = 2; j; j--) {
-            const uint8_t *c_ptr = s->frame->data[j] + c_offset;
-            linesize = s->frame->linesize[j];
-            y_stride = (mb_y == 134) ? 8 : (s->frame->linesize[j] * (1 << (3*!enc_blk->dct_mode)));
-            if (s->sys->pix_fmt == AV_PIX_FMT_YUV411P && mb_x >= (704 / 8)) {
-                uint8_t *b = scratch;
-                for (i = 0; i < 8; i++) {
-                    const uint8_t *d = c_ptr + linesize * 8;
-                    b[0]   = c_ptr[0];
-                    b[1]   = c_ptr[1];
-                    b[2]   = c_ptr[2];
-                    b[3]   = c_ptr[3];
-                    b[4]   = d[0];
-                    b[5]   = d[1];
-                    b[6]   = d[2];
-                    b[7]   = d[3];
-                    c_ptr += linesize;
-                    b     += 16;
-                }
-                c_ptr    = scratch;
-                linesize = 16;
-            }
-
-            vs_bit_size += dv_init_enc_block(enc_blk++, c_ptr, linesize, s, 1);
-            if (s->sys->bpm == 8)
-                vs_bit_size += dv_init_enc_block(enc_blk++, c_ptr + y_stride,
-                                                 linesize, s, 1);
-        }
-    }
-
-    if (DV_PROFILE_IS_HD(s->sys)) {
-        /* unconditional */
-        dv_guess_qnos_hd(&enc_blks[0], qnosp);
-    } else if (vs_total_ac_bits < vs_bit_size) {
-        dv_guess_qnos(&enc_blks[0], qnosp);
-    }
-
-    /* DIF encoding process */
-    for (j = 0; j < 5 * s->sys->bpm;) {
-        int start_mb = j;
-
-        p[3] = *qnosp++;
-        p += 4;
-
-        /* First pass over individual cells only */
-        for (i = 0; i < s->sys->bpm; i++, j++) {
-            int sz = s->sys->block_sizes[i] >> 3;
-
-            init_put_bits(&pbs[j], p, sz);
-            put_sbits(&pbs[j], 9, ((enc_blks[j].mb[0] >> 3) - 1024 + 2) >> 2);
-            put_bits(&pbs[j], 1, DV_PROFILE_IS_HD(s->sys) && i ? 1 : enc_blks[j].dct_mode);
-            put_bits(&pbs[j], 2, enc_blks[j].cno);
-
-            dv_encode_ac(&enc_blks[j], &pbs[j], &pbs[j + 1]);
-            p += sz;
-        }
-
-        /* Second pass over each MB space */
-        pb = &pbs[start_mb];
-        for (i = 0; i < s->sys->bpm; i++)
-            if (enc_blks[start_mb + i].partial_bit_count)
-                pb = dv_encode_ac(&enc_blks[start_mb + i], pb,
-                                  &pbs[start_mb + s->sys->bpm]);
-    }
-
-    /* Third and final pass over the whole video segment space */
-    pb = &pbs[0];
-    for (j = 0; j < 5 * s->sys->bpm; j++) {
-        if (enc_blks[j].partial_bit_count)
-            pb = dv_encode_ac(&enc_blks[j], pb, &pbs[s->sys->bpm * 5]);
-        if (enc_blks[j].partial_bit_count)
-            av_log(avctx, AV_LOG_ERROR, "ac bitstream overflow\n");
-    }
-
-    for (j = 0; j < 5 * s->sys->bpm; j++) {
-        flush_put_bits(&pbs[j]);
-        memset(put_bits_ptr(&pbs[j]), 0xff, put_bytes_left(&pbs[j], 0));
-    }
-
-    if (DV_PROFILE_IS_HD(s->sys))
-        dv_revise_cnos(dif, enc_blks, s->sys);
-
-    return 0;
-}
-
-static inline int dv_write_pack(enum DVPackType pack_id, DVEncContext *c,
-                                uint8_t *buf)
-{
-    /*
-     * Here's what SMPTE314M says about these two:
-     *    (page 6) APTn, AP1n, AP2n, AP3n: These data shall be identical
-     *             as track application IDs (APTn = 001, AP1n =
-     *             001, AP2n = 001, AP3n = 001), if the source signal
-     *             comes from a digital VCR. If the signal source is
-     *             unknown, all bits for these data shall be set to 1.
-     *    (page 12) STYPE: STYPE defines a signal type of video signal
-     *                     00000b = 4:1:1 compression
-     *                     00100b = 4:2:2 compression
-     *                     XXXXXX = Reserved
-     * Now, I've got two problems with these statements:
-     *   1. it looks like APT == 111b should be a safe bet, but it isn't.
-     *      It seems that for PAL as defined in IEC 61834 we have to set
-     *      APT to 000 and for SMPTE314M to 001.
-     *   2. It is not at all clear what STYPE is used for 4:2:0 PAL
-     *      compression scheme (if any).
-     */
-    uint8_t aspect = 0;
-    int apt = (c->sys->pix_fmt == AV_PIX_FMT_YUV420P ? 0 : 1);
-    int fs;
-
-    if (c->avctx->height >= 720)
-        fs = c->avctx->height == 720 || c->frame->top_field_first ? 0x40 : 0x00;
-    else
-        fs = c->frame->top_field_first ? 0x00 : 0x40;
-
-    if (DV_PROFILE_IS_HD(c->sys) ||
-        (int)(av_q2d(c->avctx->sample_aspect_ratio) *
-              c->avctx->width / c->avctx->height * 10) >= 17)
-        /* HD formats are always 16:9 */
-        aspect = 0x02;
-
-    buf[0] = (uint8_t) pack_id;
-    switch (pack_id) {
-    case DV_HEADER525: /* I can't imagine why these two weren't defined as real */
-    case DV_HEADER625: /* packs in SMPTE314M -- they definitely look like ones */
-        buf[1] =  0xf8       | /* reserved -- always 1 */
-                 (apt & 0x07); /* APT: Track application ID */
-        buf[2] = (0    << 7) | /* TF1: audio data is 0 - valid; 1 - invalid */
-                 (0x0f << 3) | /* reserved -- always 1 */
-                 (apt & 0x07); /* AP1: Audio application ID */
-        buf[3] = (0    << 7) | /* TF2: video data is 0 - valid; 1 - invalid */
-                 (0x0f << 3) | /* reserved -- always 1 */
-                 (apt & 0x07); /* AP2: Video application ID */
-        buf[4] = (0    << 7) | /* TF3: subcode(SSYB) is 0 - valid; 1 - invalid */
-                 (0x0f << 3) | /* reserved -- always 1 */
-                 (apt & 0x07); /* AP3: Subcode application ID */
-        break;
-    case DV_VIDEO_SOURCE:
-        buf[1] = 0xff;         /* reserved -- always 1 */
-        buf[2] = (1 << 7) |    /* B/W: 0 - b/w, 1 - color */
-                 (1 << 6) |    /* following CLF is valid - 0, invalid - 1 */
-                 (3 << 4) |    /* CLF: color frames ID (see ITU-R BT.470-4) */
-                 0xf;          /* reserved -- always 1 */
-        buf[3] = (3 << 6)           | /* reserved -- always 1 */
-                 (c->sys->dsf << 5) | /*  system: 60fields/50fields */
-                 c->sys->video_stype; /* signal type video compression */
-        buf[4] = 0xff;         /* VISC: 0xff -- no information */
-        break;
-    case DV_VIDEO_CONTROL:
-        buf[1] = (0 << 6) |    /* Copy generation management (CGMS) 0 -- free */
-                 0x3f;         /* reserved -- always 1 */
-        buf[2] = 0xc8 |        /* reserved -- always b11001xxx */
-                 aspect;
-        buf[3] = (1 << 7) |    /* frame/field flag 1 -- frame, 0 -- field */
-                 fs       |    /* first/second field flag 0 -- field 2, 1 -- field 1 */
-                 (1 << 5) |    /* frame change flag 0 -- same picture as before, 1 -- different */
-                 (1 << 4) |    /* 1 - interlaced, 0 - noninterlaced */
-                 0xc;          /* reserved -- always b1100 */
-        buf[4] = 0xff;         /* reserved -- always 1 */
-        break;
-    default:
-        buf[1] =
-        buf[2] =
-        buf[3] =
-        buf[4] = 0xff;
-    }
-    return 5;
-}
-
-static inline int dv_write_dif_id(enum DVSectionType t, uint8_t chan_num,
-                                  uint8_t seq_num, uint8_t dif_num,
-                                  uint8_t *buf)
-{
-    int fsc = chan_num & 1;
-    int fsp = 1 - (chan_num >> 1);
-
-    buf[0] = (uint8_t) t;      /* Section type */
-    buf[1] = (seq_num  << 4) | /* DIF seq number 0-9 for 525/60; 0-11 for 625/50 */
-             (fsc << 3) |      /* FSC: for 50 and 100Mb/s 0 - first channel; 1 - second */
-             (fsp << 2) |      /* FSP: for 100Mb/s 1 - channels 0-1; 0 - channels 2-3 */
-             3;                /* reserved -- always 1 */
-    buf[2] = dif_num;          /* DIF block number Video: 0-134, Audio: 0-8 */
-    return 3;
-}
-
-static inline int dv_write_ssyb_id(uint8_t syb_num, uint8_t fr, uint8_t *buf)
-{
-    if (syb_num == 0 || syb_num == 6) {
-        buf[0] = (fr << 7) | /* FR ID 1 - first half of each channel; 0 - second */
-                 (0  << 4) | /* AP3 (Subcode application ID) */
-                 0x0f;       /* reserved -- always 1 */
-    } else if (syb_num == 11) {
-        buf[0] = (fr << 7) | /* FR ID 1 - first half of each channel; 0 - second */
-                 0x7f;       /* reserved -- always 1 */
-    } else {
-        buf[0] = (fr << 7) | /* FR ID 1 - first half of each channel; 0 - second */
-                 (0  << 4) | /* APT (Track application ID) */
-                 0x0f;       /* reserved -- always 1 */
-    }
-    buf[1] = 0xf0 |            /* reserved -- always 1 */
-             (syb_num & 0x0f); /* SSYB number 0 - 11   */
-    buf[2] = 0xff;             /* reserved -- always 1 */
-    return 3;
-}
-
-static void dv_format_frame(DVEncContext *c, uint8_t *buf)
-{
-    int chan, i, j, k;
-    /* We work with 720p frames split in half. The odd half-frame is chan 2,3 */
-    int chan_offset = 2*(c->sys->height == 720 && c->avctx->frame_num & 1);
-
-    for (chan = 0; chan < c->sys->n_difchan; chan++) {
-        for (i = 0; i < c->sys->difseg_size; i++) {
-            memset(buf, 0xff, 80 * 6); /* first 6 DIF blocks are for control data */
-
-            /* DV header: 1DIF */
-            buf += dv_write_dif_id(DV_SECT_HEADER, chan+chan_offset, i, 0, buf);
-            buf += dv_write_pack((c->sys->dsf ? DV_HEADER625 : DV_HEADER525),
-                                 c, buf);
-            buf += 72; /* unused bytes */
-
-            /* DV subcode: 2DIFs */
-            for (j = 0; j < 2; j++) {
-                buf += dv_write_dif_id(DV_SECT_SUBCODE, chan+chan_offset, i, j, buf);
-                for (k = 0; k < 6; k++)
-                    buf += dv_write_ssyb_id(k, (i < c->sys->difseg_size / 2), buf) + 5;
-                buf += 29; /* unused bytes */
-            }
-
-            /* DV VAUX: 3DIFS */
-            for (j = 0; j < 3; j++) {
-                buf += dv_write_dif_id(DV_SECT_VAUX, chan+chan_offset, i, j, buf);
-                buf += dv_write_pack(DV_VIDEO_SOURCE,  c, buf);
-                buf += dv_write_pack(DV_VIDEO_CONTROL, c, buf);
-                buf += 7 * 5;
-                buf += dv_write_pack(DV_VIDEO_SOURCE,  c, buf);
-                buf += dv_write_pack(DV_VIDEO_CONTROL, c, buf);
-                buf += 4 * 5 + 2; /* unused bytes */
-            }
-
-            /* DV Audio/Video: 135 Video DIFs + 9 Audio DIFs */
-            for (j = 0; j < 135; j++) {
-                if (j % 15 == 0) {
-                    memset(buf, 0xff, 80);
-                    buf += dv_write_dif_id(DV_SECT_AUDIO, chan+chan_offset, i, j/15, buf);
-                    buf += 77; /* audio control & shuffled PCM audio */
-                }
-                buf += dv_write_dif_id(DV_SECT_VIDEO, chan+chan_offset, i, j, buf);
-                buf += 77; /* 1 video macroblock: 1 bytes control
-                            * 4 * 14 bytes Y 8x8 data
-                            * 10 bytes Cr 8x8 data
-                            * 10 bytes Cb 8x8 data */
-            }
-        }
-    }
-}
-
-static int dvvideo_encode_frame(AVCodecContext *c, AVPacket *pkt,
-                                const AVFrame *frame, int *got_packet)
-{
-    DVEncContext *s = c->priv_data;
-    int ret;
-
-    if ((ret = ff_get_encode_buffer(c, pkt, s->sys->frame_size, 0)) < 0)
-        return ret;
-    /* Fixme: Only zero the part that is not overwritten later. */
-    memset(pkt->data, 0, pkt->size);
-
-    c->pix_fmt                = s->sys->pix_fmt;
-    s->frame                  = frame;
-    s->buf = pkt->data;
-
-    dv_format_frame(s, pkt->data);
-
-    c->execute(c, dv_encode_video_segment, s->work_chunks, NULL,
-               dv_work_pool_size(s->sys), sizeof(DVwork_chunk));
-
-    emms_c();
-
-    *got_packet = 1;
-
-    return 0;
-}
-
-#define VE AV_OPT_FLAG_VIDEO_PARAM | AV_OPT_FLAG_ENCODING_PARAM
-#define OFFSET(x) offsetof(DVEncContext, x)
-static const AVOption dv_options[] = {
-    { "quant_deadzone",        "Quantizer dead zone",    OFFSET(quant_deadzone),       AV_OPT_TYPE_INT, { .i64 = 7 }, 0, 1024, VE },
-    { NULL },
-};
-
-static const AVClass dvvideo_encode_class = {
-    .class_name = "dvvideo encoder",
-    .item_name  = av_default_item_name,
-    .option     = dv_options,
-    .version    = LIBAVUTIL_VERSION_INT,
-};
-
-const FFCodec ff_dvvideo_encoder = {
-    .p.name         = "dvvideo",
-    CODEC_LONG_NAME("DV (Digital Video)"),
-    .p.type         = AVMEDIA_TYPE_VIDEO,
-    .p.id           = AV_CODEC_ID_DVVIDEO,
-    .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_FRAME_THREADS |
-                      AV_CODEC_CAP_SLICE_THREADS                    |
-                      AV_CODEC_CAP_ENCODER_REORDERED_OPAQUE,
-    .priv_data_size = sizeof(DVEncContext),
-    .init           = dvvideo_encode_init,
-    FF_CODEC_ENCODE_CB(dvvideo_encode_frame),
-    .p.pix_fmts     = (const enum AVPixelFormat[]) {
-        AV_PIX_FMT_YUV411P, AV_PIX_FMT_YUV422P,
-        AV_PIX_FMT_YUV420P, AV_PIX_FMT_NONE
-    },
-    .p.priv_class   = &dvvideo_encode_class,
-};
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download de livros de RPG tudo o que voc precisa saber antes de comear.md b/spaces/congsaPfin/Manga-OCR/logs/Download de livros de RPG tudo o que voc precisa saber antes de comear.md
deleted file mode 100644
index c71362ae5bf8a80fd8eb5e17db2e1ca78e589787..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download de livros de RPG tudo o que voc precisa saber antes de comear.md	
+++ /dev/null
@@ -1,137 +0,0 @@
-<br />
-<h1>Download de livros de RPG: como encontrar e baixar os melhores títulos do gênero</h1>
-<p>Você é fã de RPGs e quer se aprofundar mais nesse universo fascinante? Então você precisa conhecer os livros de RPG, que são fontes ricas de informação, inspiração e diversão para os amantes do gênero. Neste artigo, vamos explicar o que são RPGs, por que eles são tão populares, quais são os benefícios de ler livros de RPG, como encontrar e baixar livros de RPG na internet e quais são os melhores livros de RPG de todos os tempos. Ficou curioso? Então continue lendo e descubra tudo isso e muito mais!</p>
-<h2>O que são RPGs e por que eles são tão populares?</h2>
-<p>RPG é a sigla para Role Playing Game, que significa Jogo de Interpretação de Personagens. Trata-se de um tipo de jogo em que os participantes assumem o papel de personagens fictícios em cenários imaginários, criando e vivenciando histórias colaborativamente. Os RPGs podem ser jogados em diferentes formatos, como jogos de mesa, jogos eletrônicos, livros-jogos, entre outros.</p>
-<h2>download de livros de rpg</h2><br /><p><b><b>DOWNLOAD</b> ===== <a href="https://urlca.com/2uO8Ua">https://urlca.com/2uO8Ua</a></b></p><br /><br />
-<p>Os RPGs surgiram na década de 1970, com o lançamento do famoso Dungeons & Dragons (D&D), um jogo de mesa inspirado na literatura fantástica medieval. Desde então, os RPGs se expandiram para diversos gêneros, como ficção científica, terror, super-heróis, anime, etc., e conquistaram milhões de fãs ao redor do mundo.</p>
-<p>Os RPGs são tão populares porque oferecem uma experiência única e imersiva aos jogadores. Ao contrário dos jogos tradicionais, que têm regras fixas e objetivos definidos, os RPGs permitem uma liberdade cri <p>ativa e ilimitada de criar e explorar mundos, personagens, histórias e situações. Além disso, os RPGs estimulam o desenvolvimento de habilidades como raciocínio, comunicação, cooperação, liderança, criatividade e imaginação.</p>
-<p>Os RPGs também se destacam pela sua variedade e diversidade. Existem RPGs para todos os gostos e interesses, desde os mais simples e casuais até os mais complexos e desafiadores. Os jogadores podem escolher entre diferentes gêneros, estilos, temas, ambientações, sistemas e mecânicas, de acordo com suas preferências e expectativas.</p>
-<h2>Quais são os benefícios de ler livros de RPG?</h2>
-<p>Ler livros de RPG é uma ótima forma de se aprofundar mais nesse universo e aproveitar todos os benefícios que ele oferece. Os livros de RPG são obras literárias que se baseiam nos jogos de RPG ou que servem como fonte de inspiração para eles. Eles podem ser divididos em duas categorias principais: os livros oficiais e os livros criados por fãs.</p>
-<p>Os livros oficiais são aqueles publicados pelas editoras ou pelos autores dos jogos de RPG. Eles podem ser manuais, guias, suplementos, aventuras ou romances. Eles têm como objetivo fornecer informações detalhadas sobre o universo, as regras, os sistemas e as mecânicas dos jogos de RPG, além de contar histórias envolventes que se passam nesses cenários.</p>
-<p>Os livros criados por fãs são aqueles produzidos por pessoas que admiram os jogos de RPG e que querem contribuir com suas próprias ideias, interpretações e criações. Eles podem ser fanfics, adaptações, paródias ou homenagens. Eles têm como objetivo expressar a paixão e a criatividade dos fãs dos jogos de RPG, além de compartilhar suas visões e experiências com outros leitores.</p>
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-<p>Ao ler livros de RPG, você pode:</p>
-<ul>
-<li>Ampliar o seu conhecimento sobre o universo dos RPGs e suas regras, sistemas e mecânicas. Você pode aprender mais sobre a história, a cultura, a geografia, a política, a religião, a magia, a tecnologia e outros aspectos dos mundos fictícios dos jogos de RPG. Você também pode entender melhor como funcionam as dinâmicas de jogo, como a progressão de personagem, o combate, as escolhas e consequências, a exploração e a personalização.</li>
-<li>Desenvolver a sua criatividade, a sua imaginação e a sua capacidade narrativa. Você pode se inspirar nas histórias contadas nos livros de RPG para criar as suas próprias aventuras ou participar das aventuras criadas por outros jogadores. Você também pode usar os livros de RPG como referência para desenvolver seus personagens, seus cenários, seus enredos e seus diálogos.</li>
-<li>Apreciar histórias envolventes, personagens marcantes e cenários fantásticos. Você pode se emocionar, se divertir, se surpreender e se maravilhar com as histórias contadas nos livros de RPG. Você também pode se identificar, se simpatizar, se admirar e se relacionar com os personagens que protagonizam essas histórias. Você ainda pode viajar por mundos incríveis que misturam realidade e ficção.</li>
-<li>Se inspirar para criar seus próprios jogos ou participar de aventuras com outros jogadores. Você pode usar os livros de RPG como ponto de partida para criar seus próprios jogos ou para se juntar a grupos que já existem. Você também pode aproveitar as dicas, as sugestões e as recomendações que os livros de RPG oferecem para melhorar o seu desempenho como jogador ou como mestre.</li>
-</ul>
-<h2>Como encontrar e baixar livros de RPG na internet?</h2>
-<p>A internet é uma fonte inesgotável de livros de RPG para todos os gostos e interesses. No entanto, é preciso ter alguns cuidados na hora de encontrar e baixar esses livros online. Nem todos os sites são confiáveis e legais, e nem todos os livros são de qualidade e originais. Por <tr>
-<td>O Guia do Mochileiro das Galáxias</td>
-<td>Douglas Adams</td>
-<td>1979</td>
-<td>Ficção científica cômica</td>
-<td>A comédia espacial de Arthur Dent, um terráqueo que escapa da destruição da Terra e viaja pelo universo com seu amigo alienígena Ford Prefect.</td>
-</tr>
-<tr>
-<td>Drácula</td>
-<td>Bram Stoker</td>
-<td>1897</td>
-<td>Terror gótico</td>
-<td>A clássica história do conde Drácula, o vampiro mais famoso da literatura, e sua perseguição aos seus inimigos mortais.</td>
-</tr>
-<tr>
-<td>Watchmen</td>
-<td>Alan Moore e Dave Gibbons</td>
-<td>1986-1987</td>
-<td>Super-heróis</td>
-<td>A obra-prima dos quadrinhos que retrata um mundo alternativo onde os super-heróis existem, mas são proibidos de atuar, e um mistério envolvendo o assassinato de um deles.</td>
-</tr>
-<tr>
-<td>A Tormenta de Espadas</td>
-<td>George R.R. Martin</td>
-<td>2000</td>
-<td>Fantasia medieval</td>
-<td>O terceiro livro da série As Crônicas de Gelo e Fogo, que narra a guerra dos tronos entre as famílias nobres de Westeros, um continente assolado por intrigas, violência e magia.</td>
-</tr>
-<tr>
-<td>O Nome do Vento</td>
-<td>Patrick Rothfuss</td>
-<td>2007</td>
-<td>Fantasia épica</td>
-<td>O primeiro livro da trilogia A Crônica do Matador do Rei, que conta a vida e as aventuras de Kvothe, um lendário mago, músico e herói.</td>
-</tr>
-<tr>
-<td>A Maldição do Tigre</td>
-<td>Colleen Houck</td>
-<td>2011</td>
-<td>Fantasia romântica</td>
-<td>O primeiro livro da saga A Maldição do Tigre, que narra o romance entre Kelsey Hayes, uma jovem americana, e Ren, um príncipe indiano amaldiçoado a se transformar em um tigre.</td>
-</tr>
- <h2>Conclusão</h2>
- <p>Neste artigo, você aprendeu o que são RPGs, por que eles são tão populares, quais são os benefícios de ler livros de RPG, como encontrar e baixar livros de RPG na internet e quais são os melhores livros de RPG de todos os tempos. Esperamos que você tenha gostado das informações e das dicas que compartilhamos com você e que elas sejam úteis para você se divertir mais com os jogos de RPG.</p>
- <p>Agora queremos saber a sua opinião: quais são os seus livros de RPG favoritos? Você tem alguma sugestão de outros títulos que não mencionamos aqui? Deixe seu comentário abaixo e compartilhe sua experiência com outros leitores. Obrigado pela sua atenção e até a próxima!</p>
- <h2>FAQ</h2>
- <h3>O que é um livro-jogo?</h3>
- <p>Um livro-jogo é um tipo de livro interativo que combina elementos de literatura e jogo. Nele, o leitor assume o papel de um personagem e pode escolher entre diferentes opções para influenciar o rumo da história. Cada escolha leva a uma página diferente do livro, onde o leitor pode encontrar novas situações, desafios, recompensas ou consequências. Alguns exemplos famosos de livros-jogos são Aventuras Fantásticas, Escolha Sua Aventura e Fighting Fantasy.</p>
- <h3>Como jogar um livro-jogo?</h3>
- <p>Para jogar um livro-jogo, você precisa apenas de um exemplar do livro, um lápis ou caneta e alguns dados (se o livro exigir). Você começa lendo a introdução do livro, que explica o cenário, o objetivo e as regras do jogo. Em seguida, você escolhe um nome para o seu personagem e define seus atributos (como força, inteligência, habilidade, etc.), usando os dados ou seguindo as instruções do livro. Depois disso, você está pronto para começar a sua aventura. Você lê a primeira página do livro, que apresenta uma situação inicial e algumas opções para você escolher. Você decide qual opção seguir e vai para a página correspondente, onde a história continua. Você repete esse processo até chegar ao final da sua aventura, que pode ser um sucesso ou um fracasso, dependendo das suas escolhas.</p>
- <h3>Quais são os tipos de RPG mais comuns?</h3>
- <p>Existem vários tipos de RPG, que podem ser classificados de acordo com diferentes critérios, como o formato, o gênero, o estilo, o tema, a ambientação, o sistema e a mecânica. Alguns dos tipos mais comuns são:</p>
-<ul>
-<li>RPG de mesa: é o tipo mais tradicional e clássico de RPG, em que os jogadores se reúnem presencialmente ou online para jogar. Um dos jogadores assume o papel de mestre, que é o responsável por narrar a história, criar os cenários, interpretar os personagens não jogadores e arbitrar as regras. Os outros jogadores assumem o papel de personagens jogadores, que são os protagonistas da história e que podem interagir com o mundo e entre si. O jogo é baseado na conversa entre os jogadores e no uso de dados, fichas, mapas e outros acessórios.</li>
-<li>RPG eletrônico: é o tipo mais popular e moderno de RPG, em que os jogadores usam dispositivos eletrônicos, como computadores, consoles ou smartphones, para jogar. O jogo é baseado na interface gráfica, no som e na interatividade do software, que simula o mundo, os personagens e as regras do RPG. Os jogadores podem jogar sozinhos ou com outros jogadores online, dependendo do modo de jogo. Alguns exemplos famosos de RPG eletrônico são Final Fantasy, The Elder Scrolls e The Witcher.</li>
-<li>Livro-jogo: é o tipo mais simples e acessível de RPG, em que os jogadores usam apenas um livro para jogar. O livro contém uma história dividida em páginas numeradas, que apresentam situações e opções para os jogadores escolherem. Os jogadores assumem o papel de um personagem principal e vão seguindo as páginas de acordo com as suas escolhas. O jogo é baseado na leitura, na imaginação e na sorte dos jogadores. Alguns exemplos famosos de livro-jogo são Aventuras Fantásticas, Escolha Sua Aventura e Fighting Fantasy.</li>
-</ul>
-<h3>Como aprender a jogar RPG?</h3>
- <p>Aprender a jogar RPG não é difícil, mas requer interesse, dedicação e prática. Existem várias formas de aprender a jogar RPG, mas algumas das mais comuns são:</p>
-<ul>
-<li>Ler livros de RPG: como já mencionamos, os livros de RPG são ótimas fontes de informação, inspiração e diversão para os amantes do gênero. Eles podem ensinar as regras básicas dos jogos de RPG, além de contar histórias interessantes que se passam nesses universos. Você pode começar pelos livros oficiais dos jogos que você tem curiosidade ou pelos livros criados por fãs que você admira.</li>
-<li>Assistir vídeos de RPG: outra forma de aprender a jogar RPG é assistir vídeos de pessoas que já sabem jogar e que compartilham suas experiências online. Você pode encontrar vídeos de diferentes tipos, como tutoriais, dicas, resenhas, análises ou gameplays. Você pode assistir aos vídeos de canais especializados em RPG ou de canais que você gosta e que tenham conteúdo sobre RPG.</li>
-<li>Participar de grupos de RPG: a melhor forma de aprender a jogar RPG é participando de grupos de pessoas que também gostam do gênero e que estão dispostas a ensinar ou a aprender junto com você. Você pode encontrar grupos de RPG online ou presencialmente, em sites, redes sociais, fóruns, aplicativos ou eventos. Você pode se juntar a grupos já existentes ou criar o seu próprio grupo com seus amigos ou familiares.</li>
-</ul>
-<h3>Onde encontrar grupos para jogar RPG online ou presencialmente?</h3>
- <p>Encontrar grupos para jogar RPG online ou presencialmente não é difícil, mas requer pesquisa, paciência e respeito. Existem vários lugares onde você pode encontrar grupos para jogar RPG, mas alguns dos mais comuns são:</p>
-<ul>
-<li>Sites: existem vários sites dedicados ao RPG, que oferecem plataformas para jogar online, ferramentas para criar e gerenciar jogos, fóruns para discutir e trocar ideias, entre outros recursos. Alguns exemplos de sites são Roll20, RPG2ic, RRPG Firecast, Mesa de RPG e Taverna do Elfo e do Arcanios.</li>
-<li>Redes sociais: existem várias redes sociais voltadas para o RPG, que permitem encontrar e interagir com outros jogadores, participar de grupos e comunidades, compartilhar conteúdo e informações, entre outras atividades. Alguns exemplos de redes sociais são Facebook, Instagram, Twitter, Discord e Reddit.</li>
-<li>Fóruns: existem vários fóruns especializados em RPG, que funcionam como espaços de debate, aprendizado e diversão para os fãs do gênero. Neles, você pode fazer perguntas, dar opiniões, pedir ajuda, dar dicas, recomendar jogos, entre outras coisas. Alguns exemplos de fóruns são RPG Brasil, RPG Online, RPG Vale e RPG.net.</li>
-<li>Aplicativos: existem vários aplicativos desenvolvidos para o RPG, que facilitam a comunicação, a organização e a jogabilidade dos jogos online ou presenciais. Eles podem ser usados para enviar mensagens, fazer chamadas de voz ou vídeo, rolar dados, consultar regras, criar fichas, entre outras funções. Alguns exemplos de aplicativos são WhatsApp, Skype, Telegram, Dice Roller e RPG Character Sheet.</li>
-<li>Eventos: existem vários eventos relacionados ao RPG, que acontecem periodicamente em diferentes locais e datas. Eles podem ser feiras, convenções, encontros, oficinas, palestras, campeonatos, entre outras atrações. Eles são ótimas oportunidades para conhecer novos jogos, novos jogadores e novas experiências. Alguns exemplos de eventos são RPGCon, World RPG Fest, Diversão Offline e EIRPG.</li>
-</ul>
-<p>Para encontrar grupos para jogar RPG online ou presencialmente, você deve seguir algumas dicas básicas:</p>
-<ul>
-<li>Pesquise sobre os jogos que você tem interesse e veja quais são os mais populares ou os mais adequados ao seu perfil.</li>
-<li>Busque por grupos que estejam procurando por novos jogadores ou que aceitem iniciantes ou curiosos.</li>
-<li>Entre em contato com os responsáveis pelos grupos e se apresente de forma educada e simpática.</li>
-<li>Respeite as regras e as normas dos grupos e dos jogos que você participar.</li>
-<li>Seja honesto sobre o seu nível de conhecimento e experiência em RPG.</li>
-<li>Seja flexível e aberto a novas ideias e sugestões.</li>
-<li>Seja colaborativo e cooperativo com os outros jogadores e com o mestre.</li>
-<li>Divirta-se e aproveite o jogo!</li>
-</ul></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Growtopia APK for Android Download the Latest Version and Join Millions of Players.md b/spaces/congsaPfin/Manga-OCR/logs/Growtopia APK for Android Download the Latest Version and Join Millions of Players.md
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-<br />
-<h1>Growtopia APK Download Latest Version: A Guide for Android Users</h1>
-<p>Do you love building, exploring, and creating amazing worlds with your friends? If so, you might want to try Growtopia, a popular sandbox MMO game that lets you do all that and more. In this article, we will tell you what Growtopia is, why you should download its APK file, and how to do it safely and easily.</p>
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-<h2>What is Growtopia?</h2>
-<p>Growtopia is a free-to-play game that was developed by Ubisoft Entertainment. It is available for Android, iOS, Windows, and Mac devices. Here are some of the features that make Growtopia unique and fun:</p>
-<h3>A creative sandbox MMO game</h3>
-<p>In Growtopia, you can build anything you can imagine, from castles and dungeons to space stations and skyscrapers. You can also craft new items, trade them with other players, and discover thousands of secrets and easter eggs. There are no limits to your creativity in Growtopia.</p>
-<h3>A huge community of players</h3>
-<p>Growtopia is not just a game, it is also a social platform where you can meet and chat with millions of players from all over the world. You can join forces with your friends to create amazing worlds together, or compete with them in mini-games and events. You can also customize your character with different outfits, hairstyles, and accessories.</p>
-<h3>A variety of items and worlds</h3>
-<p>Growtopia has over 500 items that you can use to decorate your worlds and enhance your gameplay. You can plant trees, grow crops, breed animals, make potions, and much more. You can also explore countless unique pixel worlds created by other players, or create your own using the World Lock system. You will never run out of things to do in Growtopia.</p>
-<h2>Why download Growtopia APK?</h2>
-<p>If you are an Android user, you might be wondering why you should download the Growtopia APK file instead of installing the game from the Google Play Store. Here are some of the benefits of downloading the APK file:</p>
-<h3>To enjoy the latest features and updates</h3>
-<p>Growtopia is constantly updated with new items, events, and improvements. However, sometimes these updates are not available on the Google Play Store right away, or they are region-locked. By downloading the APK file from a trusted source, you can get access to the latest version of the game as soon as it is released.</p>
-<h3>To play offline or on older devices</h3>
-<p>Growtopia is an online game that requires an internet connection to play. However, if you download the APK file, you can play it offline as well. This is useful if you have a limited data plan or no Wi-Fi access. Moreover, some older devices might not be compatible with the latest version of the game on the Google Play Store. By downloading the APK file, you can play Growtopia on any Android device that meets the minimum requirements.</p>
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-<h3>To avoid ads and in-app purchases</h3>
-<p>Growtopia is a freemium game that has optional ads and in-app purchases. These can be annoying or expensive for some players. By downloading the APK file, you can avoid these features and enjoy the game without any interruptions or costs.</p>
-<h2>How to download Growtopia APK?</h2>
-<p>Now that you know why you should download Growtopia APK, let's see how to do it step by step:</p>
-<h3 <h3>Step 1: Find a reliable source</h3>
-<p>The first thing you need to do is to find a website that offers the Growtopia APK file for download. There are many websites that claim to provide the APK file, but not all of them are safe and trustworthy. Some of them might contain malware, viruses, or fake files that can harm your device or steal your personal information. Therefore, you should always do some research before downloading any APK file from the internet. You can check the reviews, ratings, and comments of other users to see if the website is reliable or not. You can also use antivirus software or online scanners to scan the APK file before downloading it.</p>
-<h3>Step 2: Enable unknown sources</h3>
-<p>The next thing you need to do is to enable unknown sources on your Android device. This is a security setting that prevents you from installing apps from sources other than the Google Play Store. However, since you are downloading the Growtopia APK file from a third-party website, you need to enable this option to allow the installation. To do this, follow these steps:</p>
-<ul>
-<li>Go to your device's settings and tap on security or privacy.</li>
-<li>Find the option that says unknown sources or install unknown apps and toggle it on.</li>
-<li>A warning message will pop up, telling you the risks of installing apps from unknown sources. Tap on OK or allow to proceed.</li>
-</ul>
-<h3>Step 3: Install the APK file</h3>
-<p>Now that you have enabled unknown sources, you can install the Growtopia APK file on your device. To do this, follow these steps:</p>
-<ul>
-<li>Go to the website where you downloaded the Growtopia APK file and tap on it to start the download.</li>
-<li>Once the download is complete, go to your device's file manager and locate the Growtopia APK file. It should be in your downloads folder or wherever you saved it.</li>
-<li>Tap on the Growtopia APK file and a prompt will appear, asking you if you want to install the app. Tap on install and wait for the installation to finish.</li>
-</ul>
-<h3>Step 4: Launch the game and have fun</h3>
-<p>Congratulations! You have successfully installed Growtopia on your Android device using the APK file. Now you can launch the game and enjoy its features and updates. To do this, follow these steps:</p>
-<ul>
-<li>Go to your device's app drawer and find the Growtopia icon. Tap on it to open the game.</li>
-<li>You will see a splash screen with the game's logo and some loading messages. Wait for a few seconds until the game loads.</li>
-<li>You will be asked to create an account or log in with an existing one. You can also play as a guest if you want.</li>
-<li>You will be taken to the main menu, where you can choose to play online or offline, join or create worlds, customize your character, chat with other players, and more.</li>
-<li>Have fun playing Growtopia!</li>
-</ul>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to download Growtopia APK latest version for Android devices. We have explained what Growtopia is, why you should download its APK file, and how to do it safely and easily. We hope that this guide has helped you enjoy Growtopia more and unleash your creativity in this amazing sandbox MMO game.</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Growtopia APK download:</p>
-<ol>
-<li><b>Is Growtopia APK safe?</b></li>
-<p>Growtopia APK is safe as long as you download it from a reliable source and scan it with antivirus software before installing it. However, you should always be careful when downloading any APK file from the internet, as some of them might contain malware, viruses, or fake files that can harm your device or steal your personal information.</p>
-<li><b>Is Growtopia APK free?</b></li>
-<p>Growtopia APK is free to download and play. However, some features and items in the game might require real money or gems, which are the in-game currency. You can earn gems by playing the game, watching ads, or completing offers. You can also buy gems with real money if you want.</p>
-<li><b>Is Growtopia APK modded?</b></li>
-<p>Growtopia APK is not modded or hacked in any way. It is the original version of the game that is updated regularly by Ubisoft Entertainment. However, some websites might offer modded or hacked versions of Growtopia APK that claim to provide unlimited gems, items, or other advantages. These versions are These versions are not safe or legal to use, and they might get you banned from the game or cause other problems. We do not recommend using any modded or hacked versions of Growtopia APK.</p>
-<li><b>How to update Growtopia APK?</b></li>
-<p>Growtopia APK is updated automatically by Ubisoft Entertainment whenever there is a new version of the game. However, if you want to manually update Growtopia APK, you can follow these steps:</p>
-<ul>
-<li>Go to the website where you downloaded the Growtopia APK file and check if there is a newer version available.</li>
-<li>If there is, download the new APK file and install it over the existing one. You do not need to uninstall the old version first.</li>
-<li>If there is not, wait for the official update to be released on the Google Play Store or the website.</li>
-</ul>
-<li><b>How to uninstall Growtopia APK?</b></li>
-<p>If you want to uninstall Growtopia APK from your device, you can follow these steps:</p>
-<ul>
-<li>Go to your device's settings and tap on apps or applications.</li>
-<li>Find Growtopia and tap on it.</li>
-<li>Tap on uninstall and confirm your choice.</li>
-<li>Growtopia will be removed from your device.</li>
-</ul>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Download Cars Movie in Tamil with Subtitles.md b/spaces/congsaPfin/Manga-OCR/logs/How to Download Cars Movie in Tamil with Subtitles.md
deleted file mode 100644
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--- a/spaces/congsaPfin/Manga-OCR/logs/How to Download Cars Movie in Tamil with Subtitles.md	
+++ /dev/null
@@ -1,150 +0,0 @@
-
-<h1>Cars Movie Download in Tamil - How to Watch Online or Offline</h1>
-<p>Cars is a 2006 animated comedy film produced by Pixar Animation Studios and distributed by Walt Disney Pictures. It tells the story of Lightning McQueen, a rookie race car who gets lost on his way to a big race and ends up in a small town called Radiator Springs. There he meets a bunch of quirky characters who help him discover the true meaning of friendship and family.</p>
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-<p>Cars is one of the most popular animated movies of all time, with a worldwide gross of over $462 million. It has won several awards, including an Academy Award nomination for Best Animated Feature. It has also spawned two sequels, Cars 2 (2011) and Cars 3 (2017), as well as several spin-offs, video games, merchandise, and theme park attractions.</p>
-<p>If you are a fan of Cars movie, you might want to watch it in your preferred language. For example, if you speak Tamil, you might want to download Cars movie in Tamil. This way, you can enjoy the movie with your own culture and humor. You can also share it with your friends and family who speak Tamil.</ <p>But how can you download Cars movie in Tamil? There are many ways to do it, both online and offline. In this article, we will show you some of the best methods to download Cars movie in Tamil, as well as the pros and cons of each method. Let's get started!</p>
-<h2>How to Download Cars Movie in Tamil Online</h2>
-<p>One of the easiest ways to download Cars movie in Tamil is to use the internet. There are many websites and apps that offer free or paid downloads of movies in different languages, including Tamil. Here are some of the most common ways to download Cars movie in Tamil online:</p>
-<h3>Using Torrent Sites</h3>
-<p>Torrent sites are platforms that allow users to share and download files, such as movies, music, games, and software, using a peer-to-peer network. To use torrent sites, you need to install a torrent client, such as BitTorrent or uTorrent, on your device. Then, you need to find and download a torrent file or a magnet link for Cars movie in Tamil from a torrent site, such as The Pirate Bay, 1337x, or RARBG. The torrent file or the magnet link will direct your torrent client to download the movie from other users who have it on their devices.</p>
-<p>Some of the advantages of using torrent sites are:</p>
-<ul>
-<li>You can download high-quality movies in different formats and resolutions.</li>
-<li>You can download movies faster if there are many seeders (users who have the complete file and are sharing it).</li>
-<li>You can download multiple movies at once.</li>
-</ul>
-<p>Some of the disadvantages of using torrent sites are:</p>
-<ul>
-<li>You may download fake or corrupted files that can harm your device or contain malware.</li>
-<li>You may download illegal or pirated movies that can get you in trouble with the law or the movie studios.</li>
-<li>You may expose your IP address and personal information to hackers or trackers who can monitor your online activity.</li>
-</ul>
-<p>To avoid these risks, you should always use a VPN (virtual private network) when using torrent sites. A VPN will encrypt your data and hide your IP address from prying eyes. You should also use an antivirus software and a firewall to protect your device from malware and cyberattacks. You should also check the reviews and ratings of the torrent files or the magnet links before downloading them to make sure they are safe and authentic.</p>
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-<h3>Using Streaming Sites</h3>
-<p>Streaming sites are platforms that allow users to watch movies online without downloading them. Some streaming sites are legal and licensed, such as Netflix, Amazon Prime Video, or Disney Plus. These sites require a subscription fee or a rental fee to access their content. Other streaming sites are illegal and unlicensed, such as Putlocker, 123Movies, or Fmovies. These sites offer free access to their content, but they may also contain ads, pop-ups, or malware.</p>
-<p>To watch Cars movie in Tamil from streaming sites, you need to have a stable internet connection and a compatible device, such as a computer, a smartphone, a tablet, or a smart TV. Then, you need to find and visit a streaming site that has Cars movie in Tamil available. You may need to create an account or sign up for a trial period to access the movie. Then, you can click on the play button and enjoy the movie.</p>
-<p>Some of the advantages of using streaming sites are:</p>
-<ul>
-<li>You can watch movies instantly without waiting for them to download.</li>
-<li>You can watch movies on any device and anywhere as long as you have an internet connection.</li>
-<li>You can watch movies in HD quality and with subtitles if available.</li>
-</ul>
-<p>Some of the disadvantages of using streaming sites are:</p>
-<ul>
-<li>You may experience buffering or lagging issues if your internet connection is slow or unstable.</li>
-<li>You may not be able to watch movies offline or save them on your device.</li>
-<li>You may encounter geo-restrictions or content limitations depending on your location or the streaming site.</li>
-</ul> <p>To avoid these problems, you should always use a VPN when using streaming sites. A VPN will bypass the geo-restrictions and content limitations and allow you to access any streaming site from anywhere. You should also use an ad-blocker and a pop-up blocker to prevent the annoying ads and pop-ups from interrupting your movie experience. You should also check the reviews and ratings of the streaming sites before visiting them to make sure they are safe and reliable.</p>
-<h3>Using Downloading Apps</h3>
-<p>Downloading apps are applications that allow users to download movies from various sources, such as streaming sites, torrent sites, or direct links. Some downloading apps are legal and licensed, such as YouTube Premium, Google Play Movies, or iTunes. These apps require a subscription fee or a purchase fee to access their content. Other downloading apps are illegal and unlicensed, such as Vidmate, TubeMate, or Snaptube. These apps offer free access to their content, but they may also contain ads, viruses, or malware.</p>
-<p>To download Cars movie in Tamil from downloading apps, you need to install a downloading app on your device. Then, you need to find and select Cars movie in Tamil from the app's library or search engine. You may need to choose the quality and format of the movie before downloading it. Then, you can click on the download button and wait for the movie to be saved on your device.</p>
-<p>Some of the advantages of using downloading apps are:</p>
-<ul>
-<li>You can download movies from various sources and platforms.</li>
-<li>You can download movies in different qualities and formats according to your preference.</li>
-<li>You can watch movies offline or transfer them to other devices.</li>
-</ul>
-<p>Some of the disadvantages of using downloading apps are:</p>
-<ul>
-<li>You may download fake or corrupted files that can damage your device or contain malware.</li>
-<li>You may download illegal or pirated movies that can get you in trouble with the law or the movie studios.</li>
-<li>You may consume a lot of storage space and battery power on your device.</li>
-</ul>
-<p>To avoid these risks, you should always use a VPN when using downloading apps. A VPN will protect your data and privacy from hackers or trackers who can monitor your online activity. You should also use an antivirus software and a firewall to protect your device from malware and cyberattacks. You should also check the reviews and ratings of the downloading apps before installing them to make sure they are safe and trustworthy.</p> <h2>How to Download Cars Movie in Tamil Offline</h2>
-<p>If you don't have access to the internet or you prefer to download Cars movie in Tamil offline, there are also some ways to do it. Here are some of the most common ways to download Cars movie in Tamil offline:</p>
-<h3>Using DVD or Blu-ray Discs</h3>
-<p>DVD or Blu-ray discs are optical discs that store digital data, such as movies, music, games, and software. To use DVD or Blu-ray discs, you need to have a DVD or Blu-ray player, such as a computer, a laptop, a console, or a standalone device. Then, you need to find and buy Cars movie in Tamil on DVD or Blu-ray discs from a physical store or an online store, such as Amazon, eBay, or Flipkart. Then, you can insert the disc into the player and watch the movie.</p>
-<p>Some of the advantages of using DVD or Blu-ray discs are:</p>
-<ul>
-<li>You can get high-quality movies with surround sound and extra features.</li>
-<li>You can collect and display your favorite movies on your shelf.</li>
-<li>You can lend or borrow movies from your friends and family.</li>
-</ul>
-<p>Some of the disadvantages of using DVD or Blu-ray discs are:</p>
-<ul>
-<li>You may have to pay a higher price for the discs than for online downloads.</li>
-<li>You may have to wait for the discs to be delivered or shipped to your location.</li>
-<li>You may damage or lose the discs if you are not careful with them.</li>
-</ul>
-<p>To avoid these problems, you should always compare the prices and reviews of the discs before buying them. You should also check the compatibility and region codes of the discs and the players to make sure they work together. You should also store the discs in a cool and dry place and handle them with care.</p>
-<h3>Using USB Drives or Memory Cards</h3>
-<p>USB drives or memory cards are portable devices that store digital data, such as movies, music, photos, and documents. To use USB drives or memory cards, you need to have a device that can read them, such as a computer, a laptop, a smartphone, a tablet, or a smart TV. Then, you need to find and copy Cars movie in Tamil to USB drives or memory cards from another device that has it, such as a friend's device or a public device. Then, you can plug the USB drive or insert the memory card into your device and watch the movie.</p>
-<p>Some of the advantages of using USB drives or memory cards are:</p>
-<ul>
-<li>You can download movies from any device that has them.</li>
-<li>You can carry and transfer movies easily and conveniently.</li>
-<li>You can watch movies on any device that can read them.</li>
-</ul>
-<p>Some of the disadvantages of using USB drives or memory cards are:</p>
-<ul>
-<li>You may download fake or corrupted files that can harm your device or contain malware.</li>
-<li>You may download illegal or pirated movies that can get you in trouble with the law or the movie studios.</li>
-<li>You may consume a lot of storage space and battery power on your device.</li>
-</ul>
-<p>To avoid these risks, you should always use a VPN when downloading movies from another device. A VPN will protect your data and privacy from hackers or trackers who can monitor your online activity. You should also use an antivirus software and a firewall to protect your device from malware and cyberattacks. You should also check the reviews and ratings of the USB drives or memory cards before using them to make sure they are safe and reliable.</p> <h2>Conclusion</h2>
-<p>In this article, we have shown you some of the best ways to download Cars movie in Tamil, both online and offline. We have also discussed the pros and cons of each method, as well as some tips and precautions to avoid any problems. We hope you have found this article helpful and informative.</p>
-<p>Now that you know how to download Cars movie in Tamil, you can enjoy watching it with your own language and culture. You can also share it with your friends and family who speak Tamil. You can have fun and learn from the movie's message of friendship and family.</p>
-<p>What do you think of Cars movie? Have you watched it in Tamil or in another language? Which method did you use to download it? How was your experience? Let us know in the comments below. We would love to hear from you!</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about downloading Cars movie in Tamil:</p>
-<h3>Q: Is Cars movie available in Tamil on Netflix?</h3>
-<p>A: Yes, Cars movie is available in Tamil on Netflix. You can watch it online or download it offline using the Netflix app. However, you need to have a Netflix subscription to access the movie.</p>
-<h3>Q: Is Cars movie dubbed or subtitled in Tamil?</h3>
-<p>A: Cars movie is dubbed in Tamil by professional voice actors who match the original characters' voices and emotions. The movie also has subtitles in Tamil for the dialogues and the songs.</p>
-<h3>Q: Is downloading Cars movie in Tamil legal or illegal?</h3>
-<p>A: Downloading Cars movie in Tamil is legal if you use a legal and licensed source, such as a streaming site, a downloading app, or a DVD or Blu-ray disc that has the rights to distribute the movie. Downloading Cars movie in Tamil is illegal if you use an illegal and unlicensed source, such as a torrent site, a streaming site, or a downloading app that does not have the rights to distribute the movie. You may face legal consequences or penalties if you download illegal or pirated movies.</p>
-<h3>Q: What is the best quality and format to download Cars movie in Tamil?</h3>
-<p>A: The best quality and format to download Cars movie in Tamil depends on your preference and device. Generally, the higher the quality and resolution, the better the movie experience, but also the larger the file size and the longer the download time. Some of the common qualities and formats are 1080p HD, 720p HD, 480p SD, MP4, MKV, AVI, etc.</p>
-<h3>Q: What are some of the other languages that Cars movie is available in?</h3>
-<p>A: Cars movie is available in many other languages besides Tamil, such as English, Hindi, Telugu, Malayalam, Kannada, Bengali, Marathi, Gujarati, Punjabi, Urdu, Arabic, French, Spanish, German, Italian, Portuguese, Russian, Chinese, Japanese, Korean, etc.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Internet Cafe Simulator 2 Manage Your Own Internet Cafe on Android.md b/spaces/congsaPfin/Manga-OCR/logs/Internet Cafe Simulator 2 Manage Your Own Internet Cafe on Android.md
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-<br />
-<h1>Internet Cafe Simulator 2: A Detailed and Fun Business Simulation Game</h1>
-<p>Have you ever dreamed of owning your own internet cafe? Do you want to create a cozy and profitable place where people can surf the web, play games, chat with friends, and enjoy delicious food? If so, you might want to check out <strong>Internet Cafe Simulator 2</strong>, a game that lets you do all that and more.</p>
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-<p>Internet Cafe Simulator 2 is the sequel to the popular Internet Cafe Simulator, which was released in 2019. It is a game that simulates the management of an internet cafe business in a realistic and detailed way. You can build your own cafe from scratch, customize it with various items, hire employees, serve customers, deal with competitors, and even get involved in some shady activities if you want.</p>
-<p>The game has been praised by players and critics alike for its depth, variety, graphics, sound, and gameplay. It has over 8,000 positive reviews on Steam, where it was launched in January 2022. It is also available on Android devices, so you can play it anytime, anywhere. In this article, we will show you how to download Internet Cafe Simulator 2 on Android, what you can do in the game, and some tips and tricks to help you succeed.</p>
-<h2>How to Download Internet Cafe Simulator 2 on Android</h2>
-<p>Downloading Internet Cafe Simulator 2 on Android is very easy. Just follow these simple steps:</p>
-<ol>
-<li>Go to the <a href="(^5^)">Google Play Store</a> on your Android device and search for "Internet Cafe Simulator 2".</li>
-<li>Tap on the Install button and wait for the download to finish. The game requires about 7 GB of storage space.</li>
-<li>Launch the game and enjoy running your own internet cafe.</li>
-</ol>
-<p>Note that the game is not free. It costs $19.99 to buy. However, you can try it for free for an hour before deciding whether to purchase it or not.</p>
-<h2>What Can You Do in Internet Cafe Simulator 2?</h2>
-<p>Internet Cafe Simulator 2 is a game that offers a lot of options and possibilities for you to explore. Here are some of the things you can do in the game:</p>
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-<ul>
-<li><strong>Build and customize your cafe</strong>: You can choose from different locations, sizes, layouts, furniture, computers, games, decorations, and more to create your ideal internet cafe. You can also expand your cafe as your business grows.</li>
-<li><strong>Manage your finances, employees, customers, and competitors</strong>: You have to balance your income and expenses, pay taxes, bills, salaries, rent, etc. You also have to hire, train , and fire employees, as well as deal with their personalities, demands, and problems. You also have to attract and retain customers, who have different preferences, needs, and behaviors. You also have to compete with other internet cafes in the city, who may try to sabotage or steal your customers.</li>
-<li><strong>Choose between legal or illegal ways to make money and deal with the consequences</strong>: You can run your cafe in a legitimate way, or you can resort to some shady methods, such as hacking, gambling, selling drugs, laundering money, etc. However, be careful, as these activities may attract the attention of the police, the mafia, or other enemies.</li>
-<li><strong>Explore the city and interact with various characters and events</strong>: You can leave your cafe and walk around the city, where you can meet different people, such as suppliers, customers, friends, rivals, lovers, etc. You can also participate in various events and activities, such as parties, festivals, concerts, sports, etc.</li>
-</ul>
-<h2>Tips and Tricks for Playing Internet Cafe Simulator 2</h2>
-<p>Internet Cafe Simulator 2 is a game that requires strategy, planning, and creativity. Here are some tips and tricks to help you play better:</p>
-<ul>
-<li><strong>Upgrade your skills and tech tree to unlock new features and options</strong>: You can improve your skills in various areas, such as management, marketing, hacking, cooking, etc. by reading books, taking courses, or doing tasks. You can also upgrade your tech tree to access new items, software, games, etc. for your cafe.</li>
-<li><strong>Keep your customers happy and loyal by providing good service, food, and entertainment</strong>: You can increase your customer satisfaction by offering fast and reliable internet connection, comfortable and clean environment, friendly and helpful staff, tasty and affordable food and drinks, fun and popular games and movies, etc. You can also reward your loyal customers with discounts, freebies , or memberships. You can also use social media and advertising to attract more customers and increase your reputation.</li>
-<li><strong>Hire guards and install security systems to protect your cafe from thugs and robbers</strong>: You may encounter some troublemakers who will try to damage your property, steal your money, or harm your customers and employees. You can prevent this by hiring guards, installing cameras, alarms, locks, etc. You can also call the police or fight back if necessary.</li>
-<li><strong>Use generators and backup batteries to prevent power outages</strong>: Power outages are a common occurrence in the city, and they can ruin your business if you are not prepared. You can avoid this by using generators and backup batteries to keep your cafe running during blackouts. You can also buy solar panels or wind turbines to reduce your electricity bills and carbon footprint.</li>
-<li><strong>Buy game licenses and attract more gamers to your cafe</strong>: Gamers are one of the most profitable customer segments in the internet cafe industry, as they tend to spend more time and money on your services. You can attract more gamers to your cafe by buying game licenses from developers or publishers, which will allow you to offer more games and genres to your customers. You can also host tournaments, events, or livestreams to create a gaming community in your cafe.</li>
-</ul>
-<h2>Conclusion: Why You Should Try Internet Cafe Simulator 2 on Android</h2>
-<p>Internet Cafe Simulator 2 is a game that offers a realistic and immersive experience of running an internet cafe business. You can build and customize your own cafe, manage your finances, employees, customers, and competitors, choose between legal or illegal ways to make money and deal with the consequences, explore the city and interact with various characters and events, and much more.</p>
-<p>The game is also available on Android devices, which means you can play it anytime, anywhere. You can download it from the Google Play Store for $19.99, or try it for free for an hour before buying it. The game has high-quality graphics, sound, and gameplay, and it is constantly updated with new features and content.</p>
-<p>If you are looking for a fun and challenging game that will test your skills and creativity as an internet cafe owner, you should definitely try Internet Cafe Simulator 2 on Android. It is a game that will keep you entertained for hours and make you feel like you are running a real internet cafe.</p>
-<p>So what are you waiting for? Download Internet Cafe Simulator 2 on Android today and see if you have what it takes to become a successful internet cafe owner!</p>
- <h3>FAQs</h3>
-<ol>
-<li><strong>What are the system requirements for Internet Cafe Simulator 2 on Android?</strong></li>
-<p>The game requires Android 8.0 or higher, 4 GB of RAM, 7 GB of storage space, and a device that supports OpenGL ES 3.0 or higher.</p>
-<li><strong>Can I play Internet Cafe Simulator 2 offline?</strong></li>
-<p>No, the game requires an internet connection to play.</p>
-<li><strong>Can I play Internet Cafe Simulator 2 with friends?</strong></li>
-<p>Yes, the game supports multiplayer mode, where you can join or create a server and play with other players online. You can also chat with them using voice or text messages.</p>
-<li><strong>Can I customize my character in Internet Cafe Simulator 2?</strong></li>
-<p>Yes, you can choose from different genders, hairstyles, clothes, accessories, tattoos, etc. to create your own unique character.</p>
-<li><strong>Can I mod Internet Cafe Simulator 2?</strong></li>
-<p>Yes, the game supports modding, where you can create or download custom content for the game, such as new items, games, locations, characters, etc.</p>
-</ol></p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Messenger X Mod Apk How to Access Multiple Accounts and Proxies with One App.md b/spaces/congsaPfin/Manga-OCR/logs/Messenger X Mod Apk How to Access Multiple Accounts and Proxies with One App.md
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--- a/spaces/congsaPfin/Manga-OCR/logs/Messenger X Mod Apk How to Access Multiple Accounts and Proxies with One App.md	
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- <h1>What is Messenger X Mod Apk and Why You Should Download It</h1>
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- <p>Messenger X is a free app that connects you with world-class teachers, authors, and leaders who will help you embrace a vibrant faith in your everyday life. It offers a rich library of content including courses, ebooks, audiobooks, short films, and more for you to watch, read, and listen. You can explore subjects such as family dynamics, healthy relationships, finding your purpose, freedom from pornography, how to build your faith, hearing from God, business leadership, finances, and more. You can also join a discipleship journey with powerful tools to help you stay on track. Messenger X is available in over 110 languages and has simple and easy sharing tools to help you spread the word to your friends and family.</p>
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-<li>You must have enough storage space on your device.</li>
-<li>You must enable the installation of apps from unknown sources on your device settings.</li>
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- <p>Messenger X is a great app for anyone who wants to grow in their faith and connect with world-class teachers, authors, and leaders. It offers a rich library of content, a discipleship journey, and a secure messaging platform. Messenger X mod apk is a modified version of Messenger X that adds some extra features such as unlimited login accounts, built-in proxies support, customizable reactions and animated effects, app lock and payments, business and social features, and more. However, using a mod apk also comes with some risks such as violating the terms of service of Messenger X, losing your account or data, or exposing your device to security threats. Therefore, you should use it at your own discretion and responsibility. We hope this article has helped you understand what Messenger X mod apk is and how to download and install it on your device.</p>
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-<p <p>Messenger X mod apk is not an official app from Messenger X, so it may not be safe to use. It may contain viruses or malware that can harm your device or steal your data. It may also violate the terms of service of Messenger X and result in your account being banned or suspended. Therefore, you should use Messenger X mod apk at your own risk and discretion.</p>
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- <p>No, Messenger X mod apk is only compatible with Android devices. You cannot use it on iOS devices such as iPhone or iPad. If you want to use Messenger X on iOS devices, you have to download the official version of Messenger X from the App Store.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/My Little Universe APK - The Ultimate Resource Mining and Crafting Game (Unlimited Everything).md b/spaces/congsaPfin/Manga-OCR/logs/My Little Universe APK - The Ultimate Resource Mining and Crafting Game (Unlimited Everything).md
deleted file mode 100644
index 70803a35ed9f5bc071402cacdd8bff2caafebf7c..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/My Little Universe APK - The Ultimate Resource Mining and Crafting Game (Unlimited Everything).md	
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- | HTML Code | Output | | --- | --- | | HTML Code | Output | | --- | --- | | HTML Code | Output | | --- | --- | | join guilds with them, or challenge them in PvP battles or cooperate with them in PvE missions.</li> </ul> <h3>Tips and Tricks for Playing My Little Universe APK</h3> <p>If you want to master the game and have more fun, you should follow these tips and tricks:</p> <ul> <li>Use unlimited resources wisely: Although you have unlimited resources, you should not waste them on unnecessary things. You should use them to buy and upgrade the items that will help you progress faster and enjoy the game more.</li> <li>Unlock new items and achievements: As you play the game, you will unlock new items and achievements that will make your game more interesting and rewarding. You should try to unlock as many items and achievements as possible, as they will give you more options and benefits in the game.</li> <li>Customize your planet: One of the best features of the game is that you can customize your planet according to your preferences. You can change the name, color, shape, and size of your planet. You can also add various elements, such as trees, flowers, animals, buildings, etc. You can use your creativity and imagination to make your planet unique and beautiful.</li> <li>Join a guild: Another great feature of the game is that you can join a guild with other players who share your interests and goals. You can chat with them, trade with them, join them in missions, and compete with them in rankings. You can also get rewards and bonuses from being in a guild.</li> <li>Earn rewards: The game offers many ways to earn rewards, such as completing missions, winning battles, unlocking achievements, logging in daily, etc. You should take advantage of these opportunities to earn more coins, gems, energy, materials, etc. that will help you in the game.</li> </ul> <h2>Pros and Cons of My Little Universe APK (Unlimited Everything)</h2> <p>Like any other game, My Little Universe APK has its pros and cons. Here are some of the advantages and disadvantages of playing it with unlimited everything:</p> <h3>Pros of My Little Universe APK (Unlimited Everything)</h3> <ul> <li>Unlimited creativity: With unlimited resources, you can create your own planet without any limitations. You can add any element you want, change any aspect you like, and make your planet as beautiful and unique as possible.</li> <li>Fun and relaxing gameplay: With unlimited resources, you can enjoy the game without any stress or pressure. You don't have to worry about running out of energy, coins, gems, materials, etc. You can just focus on having fun and relaxing.</li> <li>Beautiful graphics and sound effects: The game has stunning graphics and sound effects that will make you feel like you are in a real universe. The planets are colorful and detailed, the animations are smooth and realistic, and the music and sounds are soothing and immersive.</li> <li>Social features: The game has many social features that will make you feel connected with other players. You can chat with them, trade with them, | HTML Code | Output | | --- | --- | | flowers, animals, buildings, etc. You can use your creativity and imagination to make your planet unique and beautiful.</li> <li>Join a guild: Another great feature of the game is that you can join a guild with other players who share your interests and goals. You can chat with them, trade with them, join them in missions, and compete with them in rankings. You can also get rewards and bonuses from being in a guild.</li> <li>Earn rewards: The game offers many ways to earn rewards, such as completing missions, winning battles, unlocking achievements, logging in daily, etc. You should take advantage of these opportunities to earn more coins, gems, energy, materials, etc. that will help you in the game.</li> </ul> <h2>Pros and Cons of My Little Universe APK (Unlimited Everything)</h2> <p>Like any other game, My Little Universe APK has its pros and cons. Here are some of the advantages and disadvantages of playing it with unlimited everything:</p>
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-<li>Fun and relaxing gameplay: With unlimited resources, you can enjoy the game without any stress or pressure. | HTML Code | Output | | --- | --- | | don't have to worry about running out of energy, coins, gems, materials, etc. You can just focus on having fun and relaxing.</li> <li>Beautiful graphics and sound effects: The game has stunning graphics and sound effects that will make you feel like you are in a real universe. The planets are colorful and detailed, the animations are smooth and realistic, and the music and sounds are soothing and immersive.</li> <li>Social features: The game has many social features that will make you feel connected with other players. You can chat with them, trade with them, join guilds with them, or challenge them in PvP battles or cooperate with them in PvE missions.</li> </ul> <h3>Cons of My Little Universe APK (Unlimited Everything)</h3> <ul> <li>Potential security risks: Since the game is a modded version of the original game, it may not be safe to download and install on your device. It may contain viruses, malware, spyware, or other harmful elements that may damage your device or compromise your privacy. You should always download the game from a trusted source and scan it with an antivirus before installing it.</li> <li>Compatibility issues: The game may not be compatible with all devices or Android versions. It may crash, freeze, lag, or not work properly on some devices. You should check the requirements and reviews of the game before downloading and installing it.</li> <li>Battery consumption: The game may consume a lot of battery power on your device due to its high-quality graphics and sound effects. You should monitor your battery level and charge your device regularly while playing the game.</li> <li>Ads: The game may contain ads that may interrupt your gameplay or annoy you. You should be prepared to deal with them or ignore them while playing the game.</li> </ul> <h2>Frequently Asked Questions About My Little Universe APK (Unlimited Everything)</h2> <p>If you have any questions or doubts about My Little Universe APK (Unlimited Everything), you may find the answers here. We have compiled some of the most common questions that users may have about the game and provided clear and concise answers to them.</p> <table> <tr> <th>Question</th> <th>Answer</th> </tr> <tr> <td>Is it safe to download My Little Universe APK (Unlimited Everything)?</td> <td>It depends on where you download it from. If you download it from a trusted source that provides the original and unmodified APK file, then it should be safe to download and install on your device. However, if you download it from an unknown or suspicious source that may have tampered with the APK file, then it may not be safe to download and install on your device. It may contain viruses, malware, spyware, or other harmful elements that may damage your device or compromise your privacy. You should always download the game from a trusted source and scan it with an antivirus before installing it.</td> </tr> | don't have to worry about running out of energy, coins, gems, materials, etc. You can just focus on having fun and relaxing.</li>
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-| HTML Code | Output | | --- | --- | | You should be prepared to deal with them or ignore them while playing the game.</li> </ul> <h2>Frequently Asked Questions About My Little Universe APK (Unlimited Everything)</h2> <p>If you have any questions or doubts about My Little Universe APK (Unlimited Everything), you may find the answers here. We have compiled some of the most common questions that users may have about the game and provided clear and concise answers to them.</p> <table> <tr> <th>Question</th> <th>Answer</th> </tr> <tr> <td>Is it safe to download My Little Universe APK (Unlimited Everything)?</td> <td>It depends on where you download it from. If you download it from a trusted source that provides the original and unmodified APK file, then it should be safe to download and install on your device. However, if you download it from an unknown or suspicious source that may have tampered with the APK file, then it may not be safe to download and install on your device. It may contain viruses, malware, spyware, or other harmful elements that may damage your device or compromise your privacy. You should always download the game from a trusted source and scan it with an antivirus before installing it.</td> </tr> <tr> <td>Is it legal to use My Little Universe APK (Unlimited Everything)?</td> <td>It depends on your location and the laws of your country. Some countries may allow the use of modded or hacked games, while others may prohibit or restrict them. You should check the laws of your country before using My Little Universe APK (Unlimited Everything) and use it at your own risk. We do not encourage or endorse the use of illegal or unethical games.</td> </tr> | You should be prepared to deal with them or ignore them while playing the game.</li>
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-| HTML Code | Output | | --- | --- | | <tr> <td>Is it free to play My Little Universe APK (Unlimited Everything)?</td> <td>Yes, it is free to play My Little Universe APK (Unlimited Everything). You don't have to pay any money to download, install, or play the game. However, the game may contain ads that may generate revenue for the developers. You can also support the developers by buying in-app purchases or donating to them if you like the game.</td> </tr> <tr> <td>What are the requirements to play My Little Universe APK (Unlimited Everything)?</td> <td>The requirements to play My Little Universe APK (Unlimited Everything) may vary depending on your device and Android version. However, the general requirements are as follows: <ul> <li>A device with Android 4.4 or higher.</li> <li>A stable internet connection.</li> <li>At least 100 MB of free storage space.</li> <li>An antivirus program to scan the APK file before installing it.</li> </ul></td> </tr> <tr> <td>How can I update My Little Universe APK (Unlimited Everything)?</td> <td>To update My Little Universe APK (Unlimited Everything), you need to download and install the latest version of the APK file from a trusted source. You can use this link to download the latest version of the game. You should also delete the old version of the game before installing the new one to avoid any conflicts or errors.</td> </tr> </table> | <tr>
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- | HTML Code | Output | | --- | --- | | <h2>Conclusion</h2> <p>My Little Universe APK (Unlimited Everything) is a fun and creative game for Android users who love creating their own world and exploring new planets. It is a modded version of the original game that gives you unlimited resources, such as coins, gems, energy, materials, etc. You can use these resources to create your own planet, customize it according to your preferences, and discover new items and achievements. You can also visit other planets created by other players, chat with them, trade with them, and join guilds. You can even challenge other players in PvP battles or cooperate with them in PvE missions.</p> <p>The game has many pros and cons that you should consider before playing it with unlimited everything. Some of the pros are unlimited creativity, fun and relaxing gameplay, beautiful graphics and sound effects, and social features. Some of the cons are potential security risks, compatibility issues, battery consumption, and ads. You should always download the game from a trusted source and scan it with an antivirus before installing it. You should also check the laws of your country before using it and use it at your own risk.</p> <p>If you are interested in playing My Little Universe APK (Unlimited Everything), you can download it from this link and follow the instructions on how to install it on your device. You can also read our tips and tricks on how to play it and enjoy unlimited resources. We hope you have fun and enjoy creating your own little universe!</p> | <h2>Conclusion</h2> <p>My Little Universe APK (Unlimited Everything) is a fun and creative game for Android users who love creating their own world and exploring new planets. It is a modded version of the original game that gives you unlimited resources, such as coins, gems, energy, materials, etc. You can use these resources to create your own planet, customize it according to your preferences, and discover new items and achievements. You can also visit other planets created by other players, chat with them, trade with them, and join guilds. You can even challenge other players in PvP battles or cooperate with them in PvE missions.</p> <p>The game has many pros and cons that you should consider before playing it with unlimited everything. Some of the pros are unlimited creativity, fun and relaxing gameplay, beautiful graphics and sound effects, and social features. Some of the cons are potential security risks, compatibility issues, battery consumption, and ads. You should always download the game from a trusted source and scan it with an antivirus before installing it. You should also check the laws of your country before using it and use it at your own risk.</p> <p>If you are interested in playing My Little Universe APK (Unlimited Everything), you can download it from this link and follow the instructions on how to install it on your device. You can also read our tips and tricks on how to play it and enjoy unlimited resources. We hope you have fun and enjoy creating your own little universe!</p>
- | HTML Code | Output | | --- | --- | | <h2></h2> <p>Thank you for reading this article about My Little Universe APK (Unlimited Everything). We hope you found it helpful and informative. If you have any feedback, questions, or suggestions, please feel free to leave a comment below. We would love to hear from you and improve our content. Have a great day and enjoy creating your own little universe!</p> | <h2></h2> <p>Thank you for reading this article about My Little Universe APK (Unlimited Everything). We hope you found it helpful and informative. If you have any feedback, questions, or suggestions, please feel free to leave a comment below. We would love to hear from you and improve our content. Have a great day and enjoy creating your own little universe!</p> 401be4b1e0<br />
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-<p>Now if the installation is successful, you will see <strong>green </strong>tick instead of <strong>exclamation or cross </strong>mark. This shows that ADB drivers are successfully installed on your Windows PC.</p>
-<p>This was all in this guide on <strong>How to install ADB drivers</strong>, hope you liked it. Please subscribe to us for more such How-to guides. You can also follow us on Facebook and Twitter for instant updates.</p>
-<p>USB drivers for Android phones and tablets are needed for connecting our devices to the computer. This includes, using the device for development purposes which requires Android SDK USB drivers, ADB and Fastboot drivers or transferring media content and files to your phone storage etc. It is always good to have these Android device drivers before hand so you can easily install them or use whenever needed.</p>
-<p>Here is a list of popular Android device manufacturers with a link to their USB drivers for download. We have been as descriptive as possible with each driver or tool you need download. If you still think there is more information to be added with each link, please let us know.</p>
-<p>This is the official USB drivers for the Google Pixel and Nexus devices. This includes a number of new Pixel phones and the past Nexus phones and tablets. All Pixel phones use the same set of tools, so you only need these drivers once.</p>
-<p>To download the Samsung Galaxy Android USB drivers direct file, you can get it from the link below. However, if you need extra features like backup options and firmware updates, you will have to download Samsung Smart Switch.</p>
-<p>For older devices, please download Samsung KIES (drivers included) for Windows and Mac platforms. These older devices include all the Samsung phones and tablets launched before the Samsung Galaxy Note 3 series.</p>
-<p>You should download Huawei HiSuite if you want the full set of tools and extra features. HiSuite allows you to easily manage images, videos, applications, etc. on your Huawei phone from a Windows PC or a macOS computer. You can backup and restore media, install firmware updates or transfer files.</p>
-<p>This is where you can download the correct MediaTek preloader drivers for your Android phone or tablet. Most budget phones have the MTK chipset and requires the official Android USB drivers to communicate properly with Windows PC.</p>
-<p>Sony USB drivers are always bundled with the Xperia devices, however, you may still need them specifically for development purposes. You can download phone specific drivers for development from the official Sony website. However, we have also linked to general-purpose USB drivers that you can install for any Sony Xperia phone.</p>
-<p>Here you can download the latest Xiaomi USB drivers for their Mi, Redmi and POCO phones. These drivers are packaged in a ZIP file and are compatible with all Xiaomi devices. Xiaomi Android drivers will allow you to mount your phone memory to Windows PC and easily transfer files.</p>
-<p>You can download the official USB drivers for LG phones from the following link. These drivers are available for Windows and macOS, and work with all the LG devices. The links below contain the latest versions of these drivers and are updated regularly.</p>
-<p>The official HTC sync tool is now available for transferring content between your phone and the computer. With this tool, you can sync playlists to the computer, migrate data to and from iPhone and comes with Android USB drivers.</p>
-<p>Most ASUS devices come with large storage and that means, you can store a lot of your data on the phone memory. To transfer files from the computer, you need to have the correct USB drivers installed. You can download the ASUS drivers below.</p>
-<p>USB drivers for Nokia phones are bundled with the devices itself as a separate drive. We have extracted the official drivers and linked them below if you need to install them on your Windows PC for Android development.</p>
-<p>This is the official tool provided by Google to connect any Android device with a macOS computer. Simply download the tool, install it and connect your phone with a USB cable for it to automatically recognize and open the files explorer window (as shown above).</p>
-<p>This might not sound something astoundingly amazing, but it has a great use. Reported from official forums and communities, there have been numerous cases where users have had a hard time uninstalling a third-party application.<br> Such issues could surface due to malfunctioned properties of the app or if the app is downloaded from an unreliable source. But no worries, since adbLink has got your back.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/contluForse/HuggingGPT/assets/Driveragent Full Version Crack What You Need to Know Before Downloading It.md b/spaces/contluForse/HuggingGPT/assets/Driveragent Full Version Crack What You Need to Know Before Downloading It.md
deleted file mode 100644
index 9b744cf743d4eacb46d32b6f4d0ddb66cf42fc6a..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Driveragent Full Version Crack What You Need to Know Before Downloading It.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Driveragent Full Version Crack</h2><br /><p><b><b>Download File</b> ->>> <a href="https://ssurll.com/2uzxar">https://ssurll.com/2uzxar</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/modeling/roi_heads/mask_head.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/modeling/roi_heads/mask_head.py
deleted file mode 100644
index 1b5465e413195aa21733157af4e1ae3a2b897e7c..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/oneformer/detectron2/modeling/roi_heads/mask_head.py
+++ /dev/null
@@ -1,298 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from typing import List
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from annotator.oneformer.detectron2.config import configurable
-from annotator.oneformer.detectron2.layers import Conv2d, ConvTranspose2d, ShapeSpec, cat, get_norm
-from annotator.oneformer.detectron2.layers.wrappers import move_device_like
-from annotator.oneformer.detectron2.structures import Instances
-from annotator.oneformer.detectron2.utils.events import get_event_storage
-from annotator.oneformer.detectron2.utils.registry import Registry
-
-__all__ = [
-    "BaseMaskRCNNHead",
-    "MaskRCNNConvUpsampleHead",
-    "build_mask_head",
-    "ROI_MASK_HEAD_REGISTRY",
-]
-
-
-ROI_MASK_HEAD_REGISTRY = Registry("ROI_MASK_HEAD")
-ROI_MASK_HEAD_REGISTRY.__doc__ = """
-Registry for mask heads, which predicts instance masks given
-per-region features.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-"""
-
-
-@torch.jit.unused
-def mask_rcnn_loss(pred_mask_logits: torch.Tensor, instances: List[Instances], vis_period: int = 0):
-    """
-    Compute the mask prediction loss defined in the Mask R-CNN paper.
-
-    Args:
-        pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask)
-            for class-specific or class-agnostic, where B is the total number of predicted masks
-            in all images, C is the number of foreground classes, and Hmask, Wmask are the height
-            and width of the mask predictions. The values are logits.
-        instances (list[Instances]): A list of N Instances, where N is the number of images
-            in the batch. These instances are in 1:1
-            correspondence with the pred_mask_logits. The ground-truth labels (class, box, mask,
-            ...) associated with each instance are stored in fields.
-        vis_period (int): the period (in steps) to dump visualization.
-
-    Returns:
-        mask_loss (Tensor): A scalar tensor containing the loss.
-    """
-    cls_agnostic_mask = pred_mask_logits.size(1) == 1
-    total_num_masks = pred_mask_logits.size(0)
-    mask_side_len = pred_mask_logits.size(2)
-    assert pred_mask_logits.size(2) == pred_mask_logits.size(3), "Mask prediction must be square!"
-
-    gt_classes = []
-    gt_masks = []
-    for instances_per_image in instances:
-        if len(instances_per_image) == 0:
-            continue
-        if not cls_agnostic_mask:
-            gt_classes_per_image = instances_per_image.gt_classes.to(dtype=torch.int64)
-            gt_classes.append(gt_classes_per_image)
-
-        gt_masks_per_image = instances_per_image.gt_masks.crop_and_resize(
-            instances_per_image.proposal_boxes.tensor, mask_side_len
-        ).to(device=pred_mask_logits.device)
-        # A tensor of shape (N, M, M), N=#instances in the image; M=mask_side_len
-        gt_masks.append(gt_masks_per_image)
-
-    if len(gt_masks) == 0:
-        return pred_mask_logits.sum() * 0
-
-    gt_masks = cat(gt_masks, dim=0)
-
-    if cls_agnostic_mask:
-        pred_mask_logits = pred_mask_logits[:, 0]
-    else:
-        indices = torch.arange(total_num_masks)
-        gt_classes = cat(gt_classes, dim=0)
-        pred_mask_logits = pred_mask_logits[indices, gt_classes]
-
-    if gt_masks.dtype == torch.bool:
-        gt_masks_bool = gt_masks
-    else:
-        # Here we allow gt_masks to be float as well (depend on the implementation of rasterize())
-        gt_masks_bool = gt_masks > 0.5
-    gt_masks = gt_masks.to(dtype=torch.float32)
-
-    # Log the training accuracy (using gt classes and 0.5 threshold)
-    mask_incorrect = (pred_mask_logits > 0.0) != gt_masks_bool
-    mask_accuracy = 1 - (mask_incorrect.sum().item() / max(mask_incorrect.numel(), 1.0))
-    num_positive = gt_masks_bool.sum().item()
-    false_positive = (mask_incorrect & ~gt_masks_bool).sum().item() / max(
-        gt_masks_bool.numel() - num_positive, 1.0
-    )
-    false_negative = (mask_incorrect & gt_masks_bool).sum().item() / max(num_positive, 1.0)
-
-    storage = get_event_storage()
-    storage.put_scalar("mask_rcnn/accuracy", mask_accuracy)
-    storage.put_scalar("mask_rcnn/false_positive", false_positive)
-    storage.put_scalar("mask_rcnn/false_negative", false_negative)
-    if vis_period > 0 and storage.iter % vis_period == 0:
-        pred_masks = pred_mask_logits.sigmoid()
-        vis_masks = torch.cat([pred_masks, gt_masks], axis=2)
-        name = "Left: mask prediction;   Right: mask GT"
-        for idx, vis_mask in enumerate(vis_masks):
-            vis_mask = torch.stack([vis_mask] * 3, axis=0)
-            storage.put_image(name + f" ({idx})", vis_mask)
-
-    mask_loss = F.binary_cross_entropy_with_logits(pred_mask_logits, gt_masks, reduction="mean")
-    return mask_loss
-
-
-def mask_rcnn_inference(pred_mask_logits: torch.Tensor, pred_instances: List[Instances]):
-    """
-    Convert pred_mask_logits to estimated foreground probability masks while also
-    extracting only the masks for the predicted classes in pred_instances. For each
-    predicted box, the mask of the same class is attached to the instance by adding a
-    new "pred_masks" field to pred_instances.
-
-    Args:
-        pred_mask_logits (Tensor): A tensor of shape (B, C, Hmask, Wmask) or (B, 1, Hmask, Wmask)
-            for class-specific or class-agnostic, where B is the total number of predicted masks
-            in all images, C is the number of foreground classes, and Hmask, Wmask are the height
-            and width of the mask predictions. The values are logits.
-        pred_instances (list[Instances]): A list of N Instances, where N is the number of images
-            in the batch. Each Instances must have field "pred_classes".
-
-    Returns:
-        None. pred_instances will contain an extra "pred_masks" field storing a mask of size (Hmask,
-            Wmask) for predicted class. Note that the masks are returned as a soft (non-quantized)
-            masks the resolution predicted by the network; post-processing steps, such as resizing
-            the predicted masks to the original image resolution and/or binarizing them, is left
-            to the caller.
-    """
-    cls_agnostic_mask = pred_mask_logits.size(1) == 1
-
-    if cls_agnostic_mask:
-        mask_probs_pred = pred_mask_logits.sigmoid()
-    else:
-        # Select masks corresponding to the predicted classes
-        num_masks = pred_mask_logits.shape[0]
-        class_pred = cat([i.pred_classes for i in pred_instances])
-        device = (
-            class_pred.device
-            if torch.jit.is_scripting()
-            else ("cpu" if torch.jit.is_tracing() else class_pred.device)
-        )
-        indices = move_device_like(torch.arange(num_masks, device=device), class_pred)
-        mask_probs_pred = pred_mask_logits[indices, class_pred][:, None].sigmoid()
-    # mask_probs_pred.shape: (B, 1, Hmask, Wmask)
-
-    num_boxes_per_image = [len(i) for i in pred_instances]
-    mask_probs_pred = mask_probs_pred.split(num_boxes_per_image, dim=0)
-
-    for prob, instances in zip(mask_probs_pred, pred_instances):
-        instances.pred_masks = prob  # (1, Hmask, Wmask)
-
-
-class BaseMaskRCNNHead(nn.Module):
-    """
-    Implement the basic Mask R-CNN losses and inference logic described in :paper:`Mask R-CNN`
-    """
-
-    @configurable
-    def __init__(self, *, loss_weight: float = 1.0, vis_period: int = 0):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            loss_weight (float): multiplier of the loss
-            vis_period (int): visualization period
-        """
-        super().__init__()
-        self.vis_period = vis_period
-        self.loss_weight = loss_weight
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        return {"vis_period": cfg.VIS_PERIOD}
-
-    def forward(self, x, instances: List[Instances]):
-        """
-        Args:
-            x: input region feature(s) provided by :class:`ROIHeads`.
-            instances (list[Instances]): contains the boxes & labels corresponding
-                to the input features.
-                Exact format is up to its caller to decide.
-                Typically, this is the foreground instances in training, with
-                "proposal_boxes" field and other gt annotations.
-                In inference, it contains boxes that are already predicted.
-
-        Returns:
-            A dict of losses in training. The predicted "instances" in inference.
-        """
-        x = self.layers(x)
-        if self.training:
-            return {"loss_mask": mask_rcnn_loss(x, instances, self.vis_period) * self.loss_weight}
-        else:
-            mask_rcnn_inference(x, instances)
-            return instances
-
-    def layers(self, x):
-        """
-        Neural network layers that makes predictions from input features.
-        """
-        raise NotImplementedError
-
-
-# To get torchscript support, we make the head a subclass of `nn.Sequential`.
-# Therefore, to add new layers in this head class, please make sure they are
-# added in the order they will be used in forward().
-@ROI_MASK_HEAD_REGISTRY.register()
-class MaskRCNNConvUpsampleHead(BaseMaskRCNNHead, nn.Sequential):
-    """
-    A mask head with several conv layers, plus an upsample layer (with `ConvTranspose2d`).
-    Predictions are made with a final 1x1 conv layer.
-    """
-
-    @configurable
-    def __init__(self, input_shape: ShapeSpec, *, num_classes, conv_dims, conv_norm="", **kwargs):
-        """
-        NOTE: this interface is experimental.
-
-        Args:
-            input_shape (ShapeSpec): shape of the input feature
-            num_classes (int): the number of foreground classes (i.e. background is not
-                included). 1 if using class agnostic prediction.
-            conv_dims (list[int]): a list of N>0 integers representing the output dimensions
-                of N-1 conv layers and the last upsample layer.
-            conv_norm (str or callable): normalization for the conv layers.
-                See :func:`detectron2.layers.get_norm` for supported types.
-        """
-        super().__init__(**kwargs)
-        assert len(conv_dims) >= 1, "conv_dims have to be non-empty!"
-
-        self.conv_norm_relus = []
-
-        cur_channels = input_shape.channels
-        for k, conv_dim in enumerate(conv_dims[:-1]):
-            conv = Conv2d(
-                cur_channels,
-                conv_dim,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=not conv_norm,
-                norm=get_norm(conv_norm, conv_dim),
-                activation=nn.ReLU(),
-            )
-            self.add_module("mask_fcn{}".format(k + 1), conv)
-            self.conv_norm_relus.append(conv)
-            cur_channels = conv_dim
-
-        self.deconv = ConvTranspose2d(
-            cur_channels, conv_dims[-1], kernel_size=2, stride=2, padding=0
-        )
-        self.add_module("deconv_relu", nn.ReLU())
-        cur_channels = conv_dims[-1]
-
-        self.predictor = Conv2d(cur_channels, num_classes, kernel_size=1, stride=1, padding=0)
-
-        for layer in self.conv_norm_relus + [self.deconv]:
-            weight_init.c2_msra_fill(layer)
-        # use normal distribution initialization for mask prediction layer
-        nn.init.normal_(self.predictor.weight, std=0.001)
-        if self.predictor.bias is not None:
-            nn.init.constant_(self.predictor.bias, 0)
-
-    @classmethod
-    def from_config(cls, cfg, input_shape):
-        ret = super().from_config(cfg, input_shape)
-        conv_dim = cfg.MODEL.ROI_MASK_HEAD.CONV_DIM
-        num_conv = cfg.MODEL.ROI_MASK_HEAD.NUM_CONV
-        ret.update(
-            conv_dims=[conv_dim] * (num_conv + 1),  # +1 for ConvTranspose
-            conv_norm=cfg.MODEL.ROI_MASK_HEAD.NORM,
-            input_shape=input_shape,
-        )
-        if cfg.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK:
-            ret["num_classes"] = 1
-        else:
-            ret["num_classes"] = cfg.MODEL.ROI_HEADS.NUM_CLASSES
-        return ret
-
-    def layers(self, x):
-        for layer in self:
-            x = layer(x)
-        return x
-
-
-def build_mask_head(cfg, input_shape):
-    """
-    Build a mask head defined by `cfg.MODEL.ROI_MASK_HEAD.NAME`.
-    """
-    name = cfg.MODEL.ROI_MASK_HEAD.NAME
-    return ROI_MASK_HEAD_REGISTRY.get(name)(cfg, input_shape)
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/hooks/checkpoint.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/hooks/checkpoint.py
deleted file mode 100644
index 6af3fae43ac4b35532641a81eb13557edfc7dfba..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/hooks/checkpoint.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import warnings
-
-from annotator.uniformer.mmcv.fileio import FileClient
-from ..dist_utils import allreduce_params, master_only
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class CheckpointHook(Hook):
-    """Save checkpoints periodically.
-
-    Args:
-        interval (int): The saving period. If ``by_epoch=True``, interval
-            indicates epochs, otherwise it indicates iterations.
-            Default: -1, which means "never".
-        by_epoch (bool): Saving checkpoints by epoch or by iteration.
-            Default: True.
-        save_optimizer (bool): Whether to save optimizer state_dict in the
-            checkpoint. It is usually used for resuming experiments.
-            Default: True.
-        out_dir (str, optional): The root directory to save checkpoints. If not
-            specified, ``runner.work_dir`` will be used by default. If
-            specified, the ``out_dir`` will be the concatenation of ``out_dir``
-            and the last level directory of ``runner.work_dir``.
-            `Changed in version 1.3.16.`
-        max_keep_ckpts (int, optional): The maximum checkpoints to keep.
-            In some cases we want only the latest few checkpoints and would
-            like to delete old ones to save the disk space.
-            Default: -1, which means unlimited.
-        save_last (bool, optional): Whether to force the last checkpoint to be
-            saved regardless of interval. Default: True.
-        sync_buffer (bool, optional): Whether to synchronize buffers in
-            different gpus. Default: False.
-        file_client_args (dict, optional): Arguments to instantiate a
-            FileClient. See :class:`mmcv.fileio.FileClient` for details.
-            Default: None.
-            `New in version 1.3.16.`
-
-    .. warning::
-        Before v1.3.16, the ``out_dir`` argument indicates the path where the
-        checkpoint is stored. However, since v1.3.16, ``out_dir`` indicates the
-        root directory and the final path to save checkpoint is the
-        concatenation of ``out_dir`` and the last level directory of
-        ``runner.work_dir``. Suppose the value of ``out_dir`` is "/path/of/A"
-        and the value of ``runner.work_dir`` is "/path/of/B", then the final
-        path will be "/path/of/A/B".
-    """
-
-    def __init__(self,
-                 interval=-1,
-                 by_epoch=True,
-                 save_optimizer=True,
-                 out_dir=None,
-                 max_keep_ckpts=-1,
-                 save_last=True,
-                 sync_buffer=False,
-                 file_client_args=None,
-                 **kwargs):
-        self.interval = interval
-        self.by_epoch = by_epoch
-        self.save_optimizer = save_optimizer
-        self.out_dir = out_dir
-        self.max_keep_ckpts = max_keep_ckpts
-        self.save_last = save_last
-        self.args = kwargs
-        self.sync_buffer = sync_buffer
-        self.file_client_args = file_client_args
-
-    def before_run(self, runner):
-        if not self.out_dir:
-            self.out_dir = runner.work_dir
-
-        self.file_client = FileClient.infer_client(self.file_client_args,
-                                                   self.out_dir)
-
-        # if `self.out_dir` is not equal to `runner.work_dir`, it means that
-        # `self.out_dir` is set so the final `self.out_dir` is the
-        # concatenation of `self.out_dir` and the last level directory of
-        # `runner.work_dir`
-        if self.out_dir != runner.work_dir:
-            basename = osp.basename(runner.work_dir.rstrip(osp.sep))
-            self.out_dir = self.file_client.join_path(self.out_dir, basename)
-
-        runner.logger.info((f'Checkpoints will be saved to {self.out_dir} by '
-                            f'{self.file_client.name}.'))
-
-        # disable the create_symlink option because some file backends do not
-        # allow to create a symlink
-        if 'create_symlink' in self.args:
-            if self.args[
-                    'create_symlink'] and not self.file_client.allow_symlink:
-                self.args['create_symlink'] = False
-                warnings.warn(
-                    ('create_symlink is set as True by the user but is changed'
-                     'to be False because creating symbolic link is not '
-                     f'allowed in {self.file_client.name}'))
-        else:
-            self.args['create_symlink'] = self.file_client.allow_symlink
-
-    def after_train_epoch(self, runner):
-        if not self.by_epoch:
-            return
-
-        # save checkpoint for following cases:
-        # 1. every ``self.interval`` epochs
-        # 2. reach the last epoch of training
-        if self.every_n_epochs(
-                runner, self.interval) or (self.save_last
-                                           and self.is_last_epoch(runner)):
-            runner.logger.info(
-                f'Saving checkpoint at {runner.epoch + 1} epochs')
-            if self.sync_buffer:
-                allreduce_params(runner.model.buffers())
-            self._save_checkpoint(runner)
-
-    @master_only
-    def _save_checkpoint(self, runner):
-        """Save the current checkpoint and delete unwanted checkpoint."""
-        runner.save_checkpoint(
-            self.out_dir, save_optimizer=self.save_optimizer, **self.args)
-        if runner.meta is not None:
-            if self.by_epoch:
-                cur_ckpt_filename = self.args.get(
-                    'filename_tmpl', 'epoch_{}.pth').format(runner.epoch + 1)
-            else:
-                cur_ckpt_filename = self.args.get(
-                    'filename_tmpl', 'iter_{}.pth').format(runner.iter + 1)
-            runner.meta.setdefault('hook_msgs', dict())
-            runner.meta['hook_msgs']['last_ckpt'] = self.file_client.join_path(
-                self.out_dir, cur_ckpt_filename)
-        # remove other checkpoints
-        if self.max_keep_ckpts > 0:
-            if self.by_epoch:
-                name = 'epoch_{}.pth'
-                current_ckpt = runner.epoch + 1
-            else:
-                name = 'iter_{}.pth'
-                current_ckpt = runner.iter + 1
-            redundant_ckpts = range(
-                current_ckpt - self.max_keep_ckpts * self.interval, 0,
-                -self.interval)
-            filename_tmpl = self.args.get('filename_tmpl', name)
-            for _step in redundant_ckpts:
-                ckpt_path = self.file_client.join_path(
-                    self.out_dir, filename_tmpl.format(_step))
-                if self.file_client.isfile(ckpt_path):
-                    self.file_client.remove(ckpt_path)
-                else:
-                    break
-
-    def after_train_iter(self, runner):
-        if self.by_epoch:
-            return
-
-        # save checkpoint for following cases:
-        # 1. every ``self.interval`` iterations
-        # 2. reach the last iteration of training
-        if self.every_n_iters(
-                runner, self.interval) or (self.save_last
-                                           and self.is_last_iter(runner)):
-            runner.logger.info(
-                f'Saving checkpoint at {runner.iter + 1} iterations')
-            if self.sync_buffer:
-                allreduce_params(runner.model.buffers())
-            self._save_checkpoint(runner)
diff --git a/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/lib/mesh_util.py b/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/lib/mesh_util.py
deleted file mode 100644
index 39934219011401e194c61cc00034b12dad4072d3..0000000000000000000000000000000000000000
--- a/spaces/cownclown/Image-and-3D-Model-Creator/PIFu/lib/mesh_util.py
+++ /dev/null
@@ -1,91 +0,0 @@
-from skimage import measure
-import numpy as np
-import torch
-from .sdf import create_grid, eval_grid_octree, eval_grid
-from skimage import measure
-
-
-def reconstruction(net, cuda, calib_tensor,
-                   resolution, b_min, b_max,
-                   use_octree=False, num_samples=10000, transform=None):
-    '''
-    Reconstruct meshes from sdf predicted by the network.
-    :param net: a BasePixImpNet object. call image filter beforehead.
-    :param cuda: cuda device
-    :param calib_tensor: calibration tensor
-    :param resolution: resolution of the grid cell
-    :param b_min: bounding box corner [x_min, y_min, z_min]
-    :param b_max: bounding box corner [x_max, y_max, z_max]
-    :param use_octree: whether to use octree acceleration
-    :param num_samples: how many points to query each gpu iteration
-    :return: marching cubes results.
-    '''
-    # First we create a grid by resolution
-    # and transforming matrix for grid coordinates to real world xyz
-    coords, mat = create_grid(resolution, resolution, resolution,
-                              b_min, b_max, transform=transform)
-
-    # Then we define the lambda function for cell evaluation
-    def eval_func(points):
-        points = np.expand_dims(points, axis=0)
-        points = np.repeat(points, net.num_views, axis=0)
-        samples = torch.from_numpy(points).to(device=cuda).float()
-        net.query(samples, calib_tensor)
-        pred = net.get_preds()[0][0]
-        return pred.detach().cpu().numpy()
-
-    # Then we evaluate the grid
-    if use_octree:
-        sdf = eval_grid_octree(coords, eval_func, num_samples=num_samples)
-    else:
-        sdf = eval_grid(coords, eval_func, num_samples=num_samples)
-
-    # Finally we do marching cubes
-    try:
-        verts, faces, normals, values = measure.marching_cubes_lewiner(sdf, 0.5)
-        # transform verts into world coordinate system
-        verts = np.matmul(mat[:3, :3], verts.T) + mat[:3, 3:4]
-        verts = verts.T
-        return verts, faces, normals, values
-    except:
-        print('error cannot marching cubes')
-        return -1
-
-
-def save_obj_mesh(mesh_path, verts, faces):
-    file = open(mesh_path, 'w')
-
-    for v in verts:
-        file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
-    for f in faces:
-        f_plus = f + 1
-        file.write('f %d %d %d\n' % (f_plus[0], f_plus[2], f_plus[1]))
-    file.close()
-
-
-def save_obj_mesh_with_color(mesh_path, verts, faces, colors):
-    file = open(mesh_path, 'w')
-
-    for idx, v in enumerate(verts):
-        c = colors[idx]
-        file.write('v %.4f %.4f %.4f %.4f %.4f %.4f\n' % (v[0], v[1], v[2], c[0], c[1], c[2]))
-    for f in faces:
-        f_plus = f + 1
-        file.write('f %d %d %d\n' % (f_plus[0], f_plus[2], f_plus[1]))
-    file.close()
-
-
-def save_obj_mesh_with_uv(mesh_path, verts, faces, uvs):
-    file = open(mesh_path, 'w')
-
-    for idx, v in enumerate(verts):
-        vt = uvs[idx]
-        file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
-        file.write('vt %.4f %.4f\n' % (vt[0], vt[1]))
-
-    for f in faces:
-        f_plus = f + 1
-        file.write('f %d/%d %d/%d %d/%d\n' % (f_plus[0], f_plus[0],
-                                              f_plus[2], f_plus[2],
-                                              f_plus[1], f_plus[1]))
-    file.close()
diff --git a/spaces/csuer/vits/text/shanghainese.py b/spaces/csuer/vits/text/shanghainese.py
deleted file mode 100644
index cb29c24a08d2e406e8399cf7bc9fe5cb43cb9c61..0000000000000000000000000000000000000000
--- a/spaces/csuer/vits/text/shanghainese.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import re
-import cn2an
-import opencc
-
-
-converter = opencc.OpenCC('zaonhe')
-
-# List of (Latin alphabet, ipa) pairs:
-_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('A', 'ᴇ'),
-    ('B', 'bi'),
-    ('C', 'si'),
-    ('D', 'di'),
-    ('E', 'i'),
-    ('F', 'ᴇf'),
-    ('G', 'dʑi'),
-    ('H', 'ᴇtɕʰ'),
-    ('I', 'ᴀi'),
-    ('J', 'dʑᴇ'),
-    ('K', 'kʰᴇ'),
-    ('L', 'ᴇl'),
-    ('M', 'ᴇm'),
-    ('N', 'ᴇn'),
-    ('O', 'o'),
-    ('P', 'pʰi'),
-    ('Q', 'kʰiu'),
-    ('R', 'ᴀl'),
-    ('S', 'ᴇs'),
-    ('T', 'tʰi'),
-    ('U', 'ɦiu'),
-    ('V', 'vi'),
-    ('W', 'dᴀbɤliu'),
-    ('X', 'ᴇks'),
-    ('Y', 'uᴀi'),
-    ('Z', 'zᴇ')
-]]
-
-
-def _number_to_shanghainese(num):
-    num = cn2an.an2cn(num).replace('一十','十').replace('二十', '廿').replace('二', '两')
-    return re.sub(r'((?:^|[^三四五六七八九])十|廿)两', r'\1二', num)
-
-
-def number_to_shanghainese(text):
-    return re.sub(r'\d+(?:\.?\d+)?', lambda x: _number_to_shanghainese(x.group()), text)
-
-
-def latin_to_ipa(text):
-    for regex, replacement in _latin_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def shanghainese_to_ipa(text):
-    text = number_to_shanghainese(text.upper())
-    text = converter.convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text
diff --git a/spaces/danterivers/music-generation-samples/tests/data/__init__.py b/spaces/danterivers/music-generation-samples/tests/data/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/danterivers/music-generation-samples/tests/data/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/streams/memory.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/streams/memory.py
deleted file mode 100644
index a6499c13ff36f74d2e217ee996825a13edd6d9fb..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/streams/memory.py
+++ /dev/null
@@ -1,279 +0,0 @@
-from __future__ import annotations
-
-from collections import OrderedDict, deque
-from dataclasses import dataclass, field
-from types import TracebackType
-from typing import Generic, NamedTuple, TypeVar
-
-from .. import (
-    BrokenResourceError,
-    ClosedResourceError,
-    EndOfStream,
-    WouldBlock,
-    get_cancelled_exc_class,
-)
-from .._core._compat import DeprecatedAwaitable
-from ..abc import Event, ObjectReceiveStream, ObjectSendStream
-from ..lowlevel import checkpoint
-
-T_Item = TypeVar("T_Item")
-T_co = TypeVar("T_co", covariant=True)
-T_contra = TypeVar("T_contra", contravariant=True)
-
-
-class MemoryObjectStreamStatistics(NamedTuple):
-    current_buffer_used: int  #: number of items stored in the buffer
-    #: maximum number of items that can be stored on this stream (or :data:`math.inf`)
-    max_buffer_size: float
-    open_send_streams: int  #: number of unclosed clones of the send stream
-    open_receive_streams: int  #: number of unclosed clones of the receive stream
-    tasks_waiting_send: int  #: number of tasks blocked on :meth:`MemoryObjectSendStream.send`
-    #: number of tasks blocked on :meth:`MemoryObjectReceiveStream.receive`
-    tasks_waiting_receive: int
-
-
-@dataclass(eq=False)
-class MemoryObjectStreamState(Generic[T_Item]):
-    max_buffer_size: float = field()
-    buffer: deque[T_Item] = field(init=False, default_factory=deque)
-    open_send_channels: int = field(init=False, default=0)
-    open_receive_channels: int = field(init=False, default=0)
-    waiting_receivers: OrderedDict[Event, list[T_Item]] = field(
-        init=False, default_factory=OrderedDict
-    )
-    waiting_senders: OrderedDict[Event, T_Item] = field(
-        init=False, default_factory=OrderedDict
-    )
-
-    def statistics(self) -> MemoryObjectStreamStatistics:
-        return MemoryObjectStreamStatistics(
-            len(self.buffer),
-            self.max_buffer_size,
-            self.open_send_channels,
-            self.open_receive_channels,
-            len(self.waiting_senders),
-            len(self.waiting_receivers),
-        )
-
-
-@dataclass(eq=False)
-class MemoryObjectReceiveStream(Generic[T_co], ObjectReceiveStream[T_co]):
-    _state: MemoryObjectStreamState[T_co]
-    _closed: bool = field(init=False, default=False)
-
-    def __post_init__(self) -> None:
-        self._state.open_receive_channels += 1
-
-    def receive_nowait(self) -> T_co:
-        """
-        Receive the next item if it can be done without waiting.
-
-        :return: the received item
-        :raises ~anyio.ClosedResourceError: if this send stream has been closed
-        :raises ~anyio.EndOfStream: if the buffer is empty and this stream has been
-            closed from the sending end
-        :raises ~anyio.WouldBlock: if there are no items in the buffer and no tasks
-            waiting to send
-
-        """
-        if self._closed:
-            raise ClosedResourceError
-
-        if self._state.waiting_senders:
-            # Get the item from the next sender
-            send_event, item = self._state.waiting_senders.popitem(last=False)
-            self._state.buffer.append(item)
-            send_event.set()
-
-        if self._state.buffer:
-            return self._state.buffer.popleft()
-        elif not self._state.open_send_channels:
-            raise EndOfStream
-
-        raise WouldBlock
-
-    async def receive(self) -> T_co:
-        await checkpoint()
-        try:
-            return self.receive_nowait()
-        except WouldBlock:
-            # Add ourselves in the queue
-            receive_event = Event()
-            container: list[T_co] = []
-            self._state.waiting_receivers[receive_event] = container
-
-            try:
-                await receive_event.wait()
-            except get_cancelled_exc_class():
-                # Ignore the immediate cancellation if we already received an item, so as not to
-                # lose it
-                if not container:
-                    raise
-            finally:
-                self._state.waiting_receivers.pop(receive_event, None)
-
-            if container:
-                return container[0]
-            else:
-                raise EndOfStream
-
-    def clone(self) -> MemoryObjectReceiveStream[T_co]:
-        """
-        Create a clone of this receive stream.
-
-        Each clone can be closed separately. Only when all clones have been closed will the
-        receiving end of the memory stream be considered closed by the sending ends.
-
-        :return: the cloned stream
-
-        """
-        if self._closed:
-            raise ClosedResourceError
-
-        return MemoryObjectReceiveStream(_state=self._state)
-
-    def close(self) -> None:
-        """
-        Close the stream.
-
-        This works the exact same way as :meth:`aclose`, but is provided as a special case for the
-        benefit of synchronous callbacks.
-
-        """
-        if not self._closed:
-            self._closed = True
-            self._state.open_receive_channels -= 1
-            if self._state.open_receive_channels == 0:
-                send_events = list(self._state.waiting_senders.keys())
-                for event in send_events:
-                    event.set()
-
-    async def aclose(self) -> None:
-        self.close()
-
-    def statistics(self) -> MemoryObjectStreamStatistics:
-        """
-        Return statistics about the current state of this stream.
-
-        .. versionadded:: 3.0
-        """
-        return self._state.statistics()
-
-    def __enter__(self) -> MemoryObjectReceiveStream[T_co]:
-        return self
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> None:
-        self.close()
-
-
-@dataclass(eq=False)
-class MemoryObjectSendStream(Generic[T_contra], ObjectSendStream[T_contra]):
-    _state: MemoryObjectStreamState[T_contra]
-    _closed: bool = field(init=False, default=False)
-
-    def __post_init__(self) -> None:
-        self._state.open_send_channels += 1
-
-    def send_nowait(self, item: T_contra) -> DeprecatedAwaitable:
-        """
-        Send an item immediately if it can be done without waiting.
-
-        :param item: the item to send
-        :raises ~anyio.ClosedResourceError: if this send stream has been closed
-        :raises ~anyio.BrokenResourceError: if the stream has been closed from the
-            receiving end
-        :raises ~anyio.WouldBlock: if the buffer is full and there are no tasks waiting
-            to receive
-
-        """
-        if self._closed:
-            raise ClosedResourceError
-        if not self._state.open_receive_channels:
-            raise BrokenResourceError
-
-        if self._state.waiting_receivers:
-            receive_event, container = self._state.waiting_receivers.popitem(last=False)
-            container.append(item)
-            receive_event.set()
-        elif len(self._state.buffer) < self._state.max_buffer_size:
-            self._state.buffer.append(item)
-        else:
-            raise WouldBlock
-
-        return DeprecatedAwaitable(self.send_nowait)
-
-    async def send(self, item: T_contra) -> None:
-        await checkpoint()
-        try:
-            self.send_nowait(item)
-        except WouldBlock:
-            # Wait until there's someone on the receiving end
-            send_event = Event()
-            self._state.waiting_senders[send_event] = item
-            try:
-                await send_event.wait()
-            except BaseException:
-                self._state.waiting_senders.pop(send_event, None)  # type: ignore[arg-type]
-                raise
-
-            if self._state.waiting_senders.pop(send_event, None):  # type: ignore[arg-type]
-                raise BrokenResourceError
-
-    def clone(self) -> MemoryObjectSendStream[T_contra]:
-        """
-        Create a clone of this send stream.
-
-        Each clone can be closed separately. Only when all clones have been closed will the
-        sending end of the memory stream be considered closed by the receiving ends.
-
-        :return: the cloned stream
-
-        """
-        if self._closed:
-            raise ClosedResourceError
-
-        return MemoryObjectSendStream(_state=self._state)
-
-    def close(self) -> None:
-        """
-        Close the stream.
-
-        This works the exact same way as :meth:`aclose`, but is provided as a special case for the
-        benefit of synchronous callbacks.
-
-        """
-        if not self._closed:
-            self._closed = True
-            self._state.open_send_channels -= 1
-            if self._state.open_send_channels == 0:
-                receive_events = list(self._state.waiting_receivers.keys())
-                self._state.waiting_receivers.clear()
-                for event in receive_events:
-                    event.set()
-
-    async def aclose(self) -> None:
-        self.close()
-
-    def statistics(self) -> MemoryObjectStreamStatistics:
-        """
-        Return statistics about the current state of this stream.
-
-        .. versionadded:: 3.0
-        """
-        return self._state.statistics()
-
-    def __enter__(self) -> MemoryObjectSendStream[T_contra]:
-        return self
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> None:
-        self.close()
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/tests/abstract/copy.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/tests/abstract/copy.py
deleted file mode 100644
index 6498fd215c77ac487f13463d3d629085e7e90028..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fsspec/tests/abstract/copy.py
+++ /dev/null
@@ -1,349 +0,0 @@
-class AbstractCopyTests:
-    def test_copy_file_to_existing_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 1a
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-        if not self.supports_empty_directories():
-            # Force target directory to exist by adding a dummy file
-            fs.touch(fs_join(target, "dummy"))
-        assert fs.isdir(target)
-
-        target_file2 = fs_join(target, "file2")
-        target_subfile1 = fs_join(target, "subfile1")
-
-        # Copy from source directory
-        fs.cp(fs_join(source, "file2"), target)
-        assert fs.isfile(target_file2)
-
-        # Copy from sub directory
-        fs.cp(fs_join(source, "subdir", "subfile1"), target)
-        assert fs.isfile(target_subfile1)
-
-        # Remove copied files
-        fs.rm([target_file2, target_subfile1])
-        assert not fs.exists(target_file2)
-        assert not fs.exists(target_subfile1)
-
-        # Repeat with trailing slash on target
-        fs.cp(fs_join(source, "file2"), target + "/")
-        assert fs.isdir(target)
-        assert fs.isfile(target_file2)
-
-        fs.cp(fs_join(source, "subdir", "subfile1"), target + "/")
-        assert fs.isfile(target_subfile1)
-
-    def test_copy_file_to_new_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 1b
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-
-        fs.cp(
-            fs_join(source, "subdir", "subfile1"), fs_join(target, "newdir/")
-        )  # Note trailing slash
-        assert fs.isdir(target)
-        assert fs.isdir(fs_join(target, "newdir"))
-        assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-
-    def test_copy_file_to_file_in_existing_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 1c
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-
-        fs.cp(fs_join(source, "subdir", "subfile1"), fs_join(target, "newfile"))
-        assert fs.isfile(fs_join(target, "newfile"))
-
-    def test_copy_file_to_file_in_new_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 1d
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-
-        fs.cp(
-            fs_join(source, "subdir", "subfile1"), fs_join(target, "newdir", "newfile")
-        )
-        assert fs.isdir(fs_join(target, "newdir"))
-        assert fs.isfile(fs_join(target, "newdir", "newfile"))
-
-    def test_copy_directory_to_existing_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 1e
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-        if not self.supports_empty_directories():
-            # Force target directory to exist by adding a dummy file
-            dummy = fs_join(target, "dummy")
-            fs.touch(dummy)
-        assert fs.isdir(target)
-
-        for source_slash, target_slash in zip([False, True], [False, True]):
-            s = fs_join(source, "subdir")
-            if source_slash:
-                s += "/"
-            t = target + "/" if target_slash else target
-
-            # Without recursive does nothing
-            fs.cp(s, t)
-            assert fs.ls(target) == [] if self.supports_empty_directories() else [dummy]
-
-            # With recursive
-            fs.cp(s, t, recursive=True)
-            if source_slash:
-                assert fs.isfile(fs_join(target, "subfile1"))
-                assert fs.isfile(fs_join(target, "subfile2"))
-                assert fs.isdir(fs_join(target, "nesteddir"))
-                assert fs.isfile(fs_join(target, "nesteddir", "nestedfile"))
-                assert not fs.exists(fs_join(target, "subdir"))
-
-                fs.rm(fs.ls(target, detail=False), recursive=True)
-            else:
-                assert fs.isdir(fs_join(target, "subdir"))
-                assert fs.isfile(fs_join(target, "subdir", "subfile1"))
-                assert fs.isfile(fs_join(target, "subdir", "subfile2"))
-                assert fs.isdir(fs_join(target, "subdir", "nesteddir"))
-                assert fs.isfile(fs_join(target, "subdir", "nesteddir", "nestedfile"))
-
-                fs.rm(fs_join(target, "subdir"), recursive=True)
-            assert fs.ls(target) == [] if self.supports_empty_directories() else [dummy]
-
-            # Limit recursive by maxdepth
-            fs.cp(s, t, recursive=True, maxdepth=1)
-            if source_slash:
-                assert fs.isfile(fs_join(target, "subfile1"))
-                assert fs.isfile(fs_join(target, "subfile2"))
-                assert not fs.exists(fs_join(target, "nesteddir"))
-                assert not fs.exists(fs_join(target, "subdir"))
-
-                fs.rm(fs.ls(target, detail=False), recursive=True)
-            else:
-                assert fs.isdir(fs_join(target, "subdir"))
-                assert fs.isfile(fs_join(target, "subdir", "subfile1"))
-                assert fs.isfile(fs_join(target, "subdir", "subfile2"))
-                assert not fs.exists(fs_join(target, "subdir", "nesteddir"))
-
-                fs.rm(fs_join(target, "subdir"), recursive=True)
-            assert fs.ls(target) == [] if self.supports_empty_directories() else [dummy]
-
-    def test_copy_directory_to_new_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 1f
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-
-        for source_slash, target_slash in zip([False, True], [False, True]):
-            s = fs_join(source, "subdir")
-            if source_slash:
-                s += "/"
-            t = fs_join(target, "newdir")
-            if target_slash:
-                t += "/"
-
-            # Without recursive does nothing
-            fs.cp(s, t)
-            assert fs.ls(target) == []
-
-            # With recursive
-            fs.cp(s, t, recursive=True)
-            assert fs.isdir(fs_join(target, "newdir"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
-            assert fs.isdir(fs_join(target, "newdir", "nesteddir"))
-            assert fs.isfile(fs_join(target, "newdir", "nesteddir", "nestedfile"))
-            assert not fs.exists(fs_join(target, "subdir"))
-
-            fs.rm(fs_join(target, "newdir"), recursive=True)
-            assert not fs.exists(fs_join(target, "newdir"))
-
-            # Limit recursive by maxdepth
-            fs.cp(s, t, recursive=True, maxdepth=1)
-            assert fs.isdir(fs_join(target, "newdir"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
-            assert not fs.exists(fs_join(target, "newdir", "nesteddir"))
-            assert not fs.exists(fs_join(target, "subdir"))
-
-            fs.rm(fs_join(target, "newdir"), recursive=True)
-            assert not fs.exists(fs_join(target, "newdir"))
-
-    def test_copy_glob_to_existing_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 1g
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-
-        for target_slash in [False, True]:
-            t = target + "/" if target_slash else target
-
-            # Without recursive
-            fs.cp(fs_join(source, "subdir", "*"), t)
-            assert fs.isfile(fs_join(target, "subfile1"))
-            assert fs.isfile(fs_join(target, "subfile2"))
-            assert not fs.isdir(fs_join(target, "nesteddir"))
-            assert not fs.exists(fs_join(target, "nesteddir", "nestedfile"))
-            assert not fs.exists(fs_join(target, "subdir"))
-
-            fs.rm(fs.ls(target, detail=False), recursive=True)
-            assert fs.ls(target) == []
-
-            # With recursive
-            fs.cp(fs_join(source, "subdir", "*"), t, recursive=True)
-            assert fs.isfile(fs_join(target, "subfile1"))
-            assert fs.isfile(fs_join(target, "subfile2"))
-            assert fs.isdir(fs_join(target, "nesteddir"))
-            assert fs.isfile(fs_join(target, "nesteddir", "nestedfile"))
-            assert not fs.exists(fs_join(target, "subdir"))
-
-            fs.rm(fs.ls(target, detail=False), recursive=True)
-            assert fs.ls(target) == []
-
-            # Limit recursive by maxdepth
-            fs.cp(fs_join(source, "subdir", "*"), t, recursive=True, maxdepth=1)
-            assert fs.isfile(fs_join(target, "subfile1"))
-            assert fs.isfile(fs_join(target, "subfile2"))
-            assert not fs.exists(fs_join(target, "nesteddir"))
-            assert not fs.exists(fs_join(target, "subdir"))
-
-            fs.rm(fs.ls(target, detail=False), recursive=True)
-            assert fs.ls(target) == []
-
-    def test_copy_glob_to_new_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 1h
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-
-        for target_slash in [False, True]:
-            t = fs_join(target, "newdir")
-            if target_slash:
-                t += "/"
-
-            # Without recursive
-            fs.cp(fs_join(source, "subdir", "*"), t)
-            assert fs.isdir(fs_join(target, "newdir"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
-            assert not fs.exists(fs_join(target, "newdir", "nesteddir"))
-            assert not fs.exists(fs_join(target, "newdir", "nesteddir", "nestedfile"))
-            assert not fs.exists(fs_join(target, "subdir"))
-            assert not fs.exists(fs_join(target, "newdir", "subdir"))
-
-            fs.rm(fs_join(target, "newdir"), recursive=True)
-            assert not fs.exists(fs_join(target, "newdir"))
-
-            # With recursive
-            fs.cp(fs_join(source, "subdir", "*"), t, recursive=True)
-            assert fs.isdir(fs_join(target, "newdir"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
-            assert fs.isdir(fs_join(target, "newdir", "nesteddir"))
-            assert fs.isfile(fs_join(target, "newdir", "nesteddir", "nestedfile"))
-            assert not fs.exists(fs_join(target, "subdir"))
-            assert not fs.exists(fs_join(target, "newdir", "subdir"))
-
-            fs.rm(fs_join(target, "newdir"), recursive=True)
-            assert not fs.exists(fs_join(target, "newdir"))
-
-            # Limit recursive by maxdepth
-            fs.cp(fs_join(source, "subdir", "*"), t, recursive=True, maxdepth=1)
-            assert fs.isdir(fs_join(target, "newdir"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-            assert fs.isfile(fs_join(target, "newdir", "subfile2"))
-            assert not fs.exists(fs_join(target, "newdir", "nesteddir"))
-            assert not fs.exists(fs_join(target, "subdir"))
-            assert not fs.exists(fs_join(target, "newdir", "subdir"))
-
-            fs.rm(fs.ls(target, detail=False), recursive=True)
-            assert not fs.exists(fs_join(target, "newdir"))
-
-    def test_copy_list_of_files_to_existing_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 2a
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-        if not self.supports_empty_directories():
-            # Force target directory to exist by adding a dummy file
-            dummy = fs_join(target, "dummy")
-            fs.touch(dummy)
-        assert fs.isdir(target)
-
-        source_files = [
-            fs_join(source, "file1"),
-            fs_join(source, "file2"),
-            fs_join(source, "subdir", "subfile1"),
-        ]
-
-        for target_slash in [False, True]:
-            t = target + "/" if target_slash else target
-
-            fs.cp(source_files, t)
-            assert fs.isfile(fs_join(target, "file1"))
-            assert fs.isfile(fs_join(target, "file2"))
-            assert fs.isfile(fs_join(target, "subfile1"))
-
-            fs.rm(fs.find(target))
-            assert fs.ls(target) == [] if self.supports_empty_directories() else [dummy]
-
-    def test_copy_list_of_files_to_new_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # Copy scenario 2b
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        fs.mkdir(target)
-
-        source_files = [
-            fs_join(source, "file1"),
-            fs_join(source, "file2"),
-            fs_join(source, "subdir", "subfile1"),
-        ]
-
-        fs.cp(source_files, fs_join(target, "newdir") + "/")  # Note trailing slash
-        assert fs.isdir(fs_join(target, "newdir"))
-        assert fs.isfile(fs_join(target, "newdir", "file1"))
-        assert fs.isfile(fs_join(target, "newdir", "file2"))
-        assert fs.isfile(fs_join(target, "newdir", "subfile1"))
-
-    def test_copy_two_files_new_directory(
-        self, fs, fs_join, fs_bulk_operations_scenario_0, fs_target
-    ):
-        # This is a duplicate of test_copy_list_of_files_to_new_directory and
-        # can eventually be removed.
-        source = fs_bulk_operations_scenario_0
-
-        target = fs_target
-        assert not fs.exists(target)
-        fs.cp([fs_join(source, "file1"), fs_join(source, "file2")], target)
-
-        assert fs.isdir(target)
-        assert fs.isfile(fs_join(target, "file1"))
-        assert fs.isfile(fs_join(target, "file2"))
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/textbox.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/textbox.py
deleted file mode 100644
index 36be39e9460d4f53b3bf3a18440da33736182181..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/textbox.py
+++ /dev/null
@@ -1,284 +0,0 @@
-"""gr.Textbox() component."""
-
-from __future__ import annotations
-
-from typing import Callable, Literal
-
-import numpy as np
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import StringSerializable
-
-from gradio.components.base import (
-    FormComponent,
-    IOComponent,
-    _Keywords,
-)
-from gradio.deprecation import warn_style_method_deprecation
-from gradio.events import (
-    Changeable,
-    EventListenerMethod,
-    Focusable,
-    Inputable,
-    Selectable,
-    Submittable,
-)
-from gradio.interpretation import TokenInterpretable
-
-set_documentation_group("component")
-
-
-@document()
-class Textbox(
-    FormComponent,
-    Changeable,
-    Inputable,
-    Selectable,
-    Submittable,
-    Focusable,
-    IOComponent,
-    StringSerializable,
-    TokenInterpretable,
-):
-    """
-    Creates a textarea for user to enter string input or display string output.
-    Preprocessing: passes textarea value as a {str} into the function.
-    Postprocessing: expects a {str} returned from function and sets textarea value to it.
-    Examples-format: a {str} representing the textbox input.
-
-    Demos: hello_world, diff_texts, sentence_builder
-    Guides: creating-a-chatbot, real-time-speech-recognition
-    """
-
-    def __init__(
-        self,
-        value: str | Callable | None = "",
-        *,
-        lines: int = 1,
-        max_lines: int = 20,
-        placeholder: str | None = None,
-        label: str | None = None,
-        info: str | None = None,
-        every: float | None = None,
-        show_label: bool | None = None,
-        container: bool = True,
-        scale: int | None = None,
-        min_width: int = 160,
-        interactive: bool | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        autofocus: bool = False,
-        elem_classes: list[str] | str | None = None,
-        type: Literal["text", "password", "email"] = "text",
-        text_align: Literal["left", "right"] | None = None,
-        rtl: bool = False,
-        show_copy_button: bool = False,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            value: default text to provide in textarea. If callable, the function will be called whenever the app loads to set the initial value of the component.
-            lines: minimum number of line rows to provide in textarea.
-            max_lines: maximum number of line rows to provide in textarea.
-            placeholder: placeholder hint to provide behind textarea.
-            label: component name in interface.
-            info: additional component description.
-            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
-            show_label: if True, will display label.
-            container: If True, will place the component in a container - providing some extra padding around the border.
-            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
-            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
-            interactive: if True, will be rendered as an editable textbox; if False, editing will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
-            visible: If False, component will be hidden.
-            autofocus: If True, will focus on the textbox when the page loads.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-            type: The type of textbox. One of: 'text', 'password', 'email', Default is 'text'.
-            text_align: How to align the text in the textbox, can be: "left", "right", or None (default). If None, the alignment is left if `rtl` is False, or right if `rtl` is True. Can only be changed if `type` is "text".
-            rtl: If True and `type` is "text", sets the direction of the text to right-to-left (cursor appears on the left of the text). Default is False, which renders cursor on the right.
-            show_copy_button: If True, includes a copy button to copy the text in the textbox. Only applies if show_label is True.
-        """
-        if type not in ["text", "password", "email"]:
-            raise ValueError('`type` must be one of "text", "password", or "email".')
-
-        self.lines = lines
-        if type == "text":
-            self.max_lines = max(lines, max_lines)
-        else:
-            self.max_lines = 1
-        self.placeholder = placeholder
-        self.show_copy_button = show_copy_button
-        self.autofocus = autofocus
-        self.select: EventListenerMethod
-        """
-        Event listener for when the user selects text in the Textbox.
-        Uses event data gradio.SelectData to carry `value` referring to selected substring, and `index` tuple referring to selected range endpoints.
-        See EventData documentation on how to use this event data.
-        """
-        IOComponent.__init__(
-            self,
-            label=label,
-            info=info,
-            every=every,
-            show_label=show_label,
-            container=container,
-            scale=scale,
-            min_width=min_width,
-            interactive=interactive,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            value=value,
-            **kwargs,
-        )
-        TokenInterpretable.__init__(self)
-        self.type = type
-        self.rtl = rtl
-        self.text_align = text_align
-
-    def get_config(self):
-        return {
-            "lines": self.lines,
-            "max_lines": self.max_lines,
-            "placeholder": self.placeholder,
-            "value": self.value,
-            "type": self.type,
-            "autofocus": self.autofocus,
-            "show_copy_button": self.show_copy_button,
-            "container": self.container,
-            "text_align": self.text_align,
-            "rtl": self.rtl,
-            **IOComponent.get_config(self),
-        }
-
-    @staticmethod
-    def update(
-        value: str | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE,
-        lines: int | None = None,
-        max_lines: int | None = None,
-        placeholder: str | None = None,
-        label: str | None = None,
-        info: str | None = None,
-        show_label: bool | None = None,
-        container: bool | None = None,
-        scale: int | None = None,
-        min_width: int | None = None,
-        visible: bool | None = None,
-        interactive: bool | None = None,
-        type: Literal["text", "password", "email"] | None = None,
-        text_align: Literal["left", "right"] | None = None,
-        rtl: bool | None = None,
-        show_copy_button: bool | None = None,
-        autofocus: bool | None = None,
-    ):
-        return {
-            "lines": lines,
-            "max_lines": max_lines,
-            "placeholder": placeholder,
-            "label": label,
-            "info": info,
-            "show_label": show_label,
-            "container": container,
-            "scale": scale,
-            "min_width": min_width,
-            "visible": visible,
-            "value": value,
-            "type": type,
-            "interactive": interactive,
-            "show_copy_button": show_copy_button,
-            "autofocus": autofocus,
-            "text_align": text_align,
-            "rtl": rtl,
-            "__type__": "update",
-        }
-
-    def preprocess(self, x: str | None) -> str | None:
-        """
-        Preprocesses input (converts it to a string) before passing it to the function.
-        Parameters:
-            x: text
-        Returns:
-            text
-        """
-        return None if x is None else str(x)
-
-    def postprocess(self, y: str | None) -> str | None:
-        """
-        Postproccess the function output y by converting it to a str before passing it to the frontend.
-        Parameters:
-            y: function output to postprocess.
-        Returns:
-            text
-        """
-        return None if y is None else str(y)
-
-    def set_interpret_parameters(
-        self, separator: str = " ", replacement: str | None = None
-    ):
-        """
-        Calculates interpretation score of characters in input by splitting input into tokens, then using a "leave one out" method to calculate the score of each token by removing each token and measuring the delta of the output value.
-        Parameters:
-            separator: Separator to use to split input into tokens.
-            replacement: In the "leave one out" step, the text that the token should be replaced with. If None, the token is removed altogether.
-        """
-        self.interpretation_separator = separator
-        self.interpretation_replacement = replacement
-        return self
-
-    def tokenize(self, x: str) -> tuple[list[str], list[str], None]:
-        """
-        Tokenizes an input string by dividing into "words" delimited by self.interpretation_separator
-        """
-        tokens = x.split(self.interpretation_separator)
-        leave_one_out_strings = []
-        for index in range(len(tokens)):
-            leave_one_out_set = list(tokens)
-            if self.interpretation_replacement is None:
-                leave_one_out_set.pop(index)
-            else:
-                leave_one_out_set[index] = self.interpretation_replacement
-            leave_one_out_strings.append(
-                self.interpretation_separator.join(leave_one_out_set)
-            )
-        return tokens, leave_one_out_strings, None
-
-    def get_masked_inputs(
-        self, tokens: list[str], binary_mask_matrix: list[list[int]]
-    ) -> list[str]:
-        """
-        Constructs partially-masked sentences for SHAP interpretation
-        """
-        masked_inputs = []
-        for binary_mask_vector in binary_mask_matrix:
-            masked_input = np.array(tokens)[np.array(binary_mask_vector, dtype=bool)]
-            masked_inputs.append(self.interpretation_separator.join(masked_input))
-        return masked_inputs
-
-    def get_interpretation_scores(
-        self, x, neighbors, scores: list[float], tokens: list[str], masks=None, **kwargs
-    ) -> list[tuple[str, float]]:
-        """
-        Returns:
-            Each tuple set represents a set of characters and their corresponding interpretation score.
-        """
-        result = []
-        for token, score in zip(tokens, scores):
-            result.append((token, score))
-            result.append((self.interpretation_separator, 0))
-        return result
-
-    def style(
-        self,
-        *,
-        show_copy_button: bool | None = None,
-        container: bool | None = None,
-        **kwargs,
-    ):
-        """
-        This method is deprecated. Please set these arguments in the constructor instead.
-        """
-        warn_style_method_deprecation()
-        if show_copy_button is not None:
-            self.show_copy_button = show_copy_button
-        if container is not None:
-            self.container = container
-        return self
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-0ba90c52.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-0ba90c52.js
deleted file mode 100644
index 8f526c1ee29512ac60564ebd1a4c29f9fcdd3b29..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-0ba90c52.js
+++ /dev/null
@@ -1,2 +0,0 @@
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print Comment Script AssignStatement * BinaryExpression BitOp BitOp BitOp BitOp ArithOp ArithOp @ ArithOp ** UnaryExpression ArithOp BitOp AwaitExpression await ) ( ParenthesizedExpression BinaryExpression or and CompareOp in not is UnaryExpression ConditionalExpression if else LambdaExpression lambda ParamList VariableName AssignOp , : NamedExpression AssignOp YieldExpression yield from TupleExpression ComprehensionExpression async for LambdaExpression ] [ ArrayExpression ArrayComprehensionExpression } { DictionaryExpression DictionaryComprehensionExpression SetExpression SetComprehensionExpression CallExpression ArgList AssignOp MemberExpression . 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-//# sourceMappingURL=index-aeef2acb.js.map
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_cache_assets.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_cache_assets.py
deleted file mode 100644
index d6a6421e3b0ff0261079094ea2e2df5de212bce7..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_cache_assets.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# coding=utf-8
-# Copyright 2019-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from pathlib import Path
-from typing import Union
-
-from ..constants import HUGGINGFACE_ASSETS_CACHE
-
-
-def cached_assets_path(
-    library_name: str,
-    namespace: str = "default",
-    subfolder: str = "default",
-    *,
-    assets_dir: Union[str, Path, None] = None,
-):
-    """Return a folder path to cache arbitrary files.
-
-    `huggingface_hub` provides a canonical folder path to store assets. This is the
-    recommended way to integrate cache in a downstream library as it will benefit from
-    the builtins tools to scan and delete the cache properly.
-
-    The distinction is made between files cached from the Hub and assets. Files from the
-    Hub are cached in a git-aware manner and entirely managed by `huggingface_hub`. See
-    [related documentation](https://huggingface.co/docs/huggingface_hub/how-to-cache).
-    All other files that a downstream library caches are considered to be "assets"
-    (files downloaded from external sources, extracted from a .tar archive, preprocessed
-    for training,...).
-
-    Once the folder path is generated, it is guaranteed to exist and to be a directory.
-    The path is based on 3 levels of depth: the library name, a namespace and a
-    subfolder. Those 3 levels grants flexibility while allowing `huggingface_hub` to
-    expect folders when scanning/deleting parts of the assets cache. Within a library,
-    it is expected that all namespaces share the same subset of subfolder names but this
-    is not a mandatory rule. The downstream library has then full control on which file
-    structure to adopt within its cache. Namespace and subfolder are optional (would
-    default to a `"default/"` subfolder) but library name is mandatory as we want every
-    downstream library to manage its own cache.
-
-    Expected tree:
-    ```text
-        assets/
-        └── datasets/
-        │   ├── SQuAD/
-        │   │   ├── downloaded/
-        │   │   ├── extracted/
-        │   │   └── processed/
-        │   ├── Helsinki-NLP--tatoeba_mt/
-        │       ├── downloaded/
-        │       ├── extracted/
-        │       └── processed/
-        └── transformers/
-            ├── default/
-            │   ├── something/
-            ├── bert-base-cased/
-            │   ├── default/
-            │   └── training/
-        hub/
-        └── models--julien-c--EsperBERTo-small/
-            ├── blobs/
-            │   ├── (...)
-            │   ├── (...)
-            ├── refs/
-            │   └── (...)
-            └── [ 128]  snapshots/
-                ├── 2439f60ef33a0d46d85da5001d52aeda5b00ce9f/
-                │   ├── (...)
-                └── bbc77c8132af1cc5cf678da3f1ddf2de43606d48/
-                    └── (...)
-    ```
-
-
-    Args:
-        library_name (`str`):
-            Name of the library that will manage the cache folder. Example: `"dataset"`.
-        namespace (`str`, *optional*, defaults to "default"):
-            Namespace to which the data belongs. Example: `"SQuAD"`.
-        subfolder (`str`, *optional*, defaults to "default"):
-            Subfolder in which the data will be stored. Example: `extracted`.
-        assets_dir (`str`, `Path`, *optional*):
-            Path to the folder where assets are cached. This must not be the same folder
-            where Hub files are cached. Defaults to `HF_HOME / "assets"` if not provided.
-            Can also be set with `HUGGINGFACE_ASSETS_CACHE` environment variable.
-
-    Returns:
-        Path to the cache folder (`Path`).
-
-    Example:
-    ```py
-    >>> from huggingface_hub import cached_assets_path
-
-    >>> cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="download")
-    PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/SQuAD/download')
-
-    >>> cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="extracted")
-    PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/SQuAD/extracted')
-
-    >>> cached_assets_path(library_name="datasets", namespace="Helsinki-NLP/tatoeba_mt")
-    PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/Helsinki-NLP--tatoeba_mt/default')
-
-    >>> cached_assets_path(library_name="datasets", assets_dir="/tmp/tmp123456")
-    PosixPath('/tmp/tmp123456/datasets/default/default')
-    ```
-    """
-    # Resolve assets_dir
-    if assets_dir is None:
-        assets_dir = HUGGINGFACE_ASSETS_CACHE
-    assets_dir = Path(assets_dir).expanduser().resolve()
-
-    # Avoid names that could create path issues
-    for part in (" ", "/", "\\"):
-        library_name = library_name.replace(part, "--")
-        namespace = namespace.replace(part, "--")
-        subfolder = subfolder.replace(part, "--")
-
-    # Path to subfolder is created
-    path = assets_dir / library_name / namespace / subfolder
-    try:
-        path.mkdir(exist_ok=True, parents=True)
-    except (FileExistsError, NotADirectoryError):
-        raise ValueError(f"Corrupted assets folder: cannot create directory because of an existing file ({path}).")
-
-    # Return
-    return path
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jinja2/async_utils.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jinja2/async_utils.py
deleted file mode 100644
index 1a4f3892cef1a53632476933f2ce2d86fc31b10a..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/jinja2/async_utils.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import inspect
-import typing as t
-from functools import WRAPPER_ASSIGNMENTS
-from functools import wraps
-
-from .utils import _PassArg
-from .utils import pass_eval_context
-
-V = t.TypeVar("V")
-
-
-def async_variant(normal_func):  # type: ignore
-    def decorator(async_func):  # type: ignore
-        pass_arg = _PassArg.from_obj(normal_func)
-        need_eval_context = pass_arg is None
-
-        if pass_arg is _PassArg.environment:
-
-            def is_async(args: t.Any) -> bool:
-                return t.cast(bool, args[0].is_async)
-
-        else:
-
-            def is_async(args: t.Any) -> bool:
-                return t.cast(bool, args[0].environment.is_async)
-
-        # Take the doc and annotations from the sync function, but the
-        # name from the async function. Pallets-Sphinx-Themes
-        # build_function_directive expects __wrapped__ to point to the
-        # sync function.
-        async_func_attrs = ("__module__", "__name__", "__qualname__")
-        normal_func_attrs = tuple(set(WRAPPER_ASSIGNMENTS).difference(async_func_attrs))
-
-        @wraps(normal_func, assigned=normal_func_attrs)
-        @wraps(async_func, assigned=async_func_attrs, updated=())
-        def wrapper(*args, **kwargs):  # type: ignore
-            b = is_async(args)
-
-            if need_eval_context:
-                args = args[1:]
-
-            if b:
-                return async_func(*args, **kwargs)
-
-            return normal_func(*args, **kwargs)
-
-        if need_eval_context:
-            wrapper = pass_eval_context(wrapper)
-
-        wrapper.jinja_async_variant = True
-        return wrapper
-
-    return decorator
-
-
-_common_primitives = {int, float, bool, str, list, dict, tuple, type(None)}
-
-
-async def auto_await(value: t.Union[t.Awaitable["V"], "V"]) -> "V":
-    # Avoid a costly call to isawaitable
-    if type(value) in _common_primitives:
-        return t.cast("V", value)
-
-    if inspect.isawaitable(value):
-        return await t.cast("t.Awaitable[V]", value)
-
-    return t.cast("V", value)
-
-
-async def auto_aiter(
-    iterable: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
-) -> "t.AsyncIterator[V]":
-    if hasattr(iterable, "__aiter__"):
-        async for item in t.cast("t.AsyncIterable[V]", iterable):
-            yield item
-    else:
-        for item in t.cast("t.Iterable[V]", iterable):
-            yield item
-
-
-async def auto_to_list(
-    value: "t.Union[t.AsyncIterable[V], t.Iterable[V]]",
-) -> t.List["V"]:
-    return [x async for x in auto_aiter(value)]
diff --git a/spaces/de3sec/Front-end-code-generation-from-images/classes/model/__init__.py b/spaces/de3sec/Front-end-code-generation-from-images/classes/model/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/deelerb/3dselfie/PIFu/lib/train_util.py b/spaces/deelerb/3dselfie/PIFu/lib/train_util.py
deleted file mode 100644
index 7d48cc7beba640703e744112aa2ec458a195a16b..0000000000000000000000000000000000000000
--- a/spaces/deelerb/3dselfie/PIFu/lib/train_util.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import torch
-import numpy as np
-from .mesh_util import *
-from .sample_util import *
-from .geometry import *
-import cv2
-from PIL import Image
-from tqdm import tqdm
-
-def reshape_multiview_tensors(image_tensor, calib_tensor):
-    # Careful here! Because we put single view and multiview together,
-    # the returned tensor.shape is 5-dim: [B, num_views, C, W, H]
-    # So we need to convert it back to 4-dim [B*num_views, C, W, H]
-    # Don't worry classifier will handle multi-view cases
-    image_tensor = image_tensor.view(
-        image_tensor.shape[0] * image_tensor.shape[1],
-        image_tensor.shape[2],
-        image_tensor.shape[3],
-        image_tensor.shape[4]
-    )
-    calib_tensor = calib_tensor.view(
-        calib_tensor.shape[0] * calib_tensor.shape[1],
-        calib_tensor.shape[2],
-        calib_tensor.shape[3]
-    )
-
-    return image_tensor, calib_tensor
-
-
-def reshape_sample_tensor(sample_tensor, num_views):
-    if num_views == 1:
-        return sample_tensor
-    # Need to repeat sample_tensor along the batch dim num_views times
-    sample_tensor = sample_tensor.unsqueeze(dim=1)
-    sample_tensor = sample_tensor.repeat(1, num_views, 1, 1)
-    sample_tensor = sample_tensor.view(
-        sample_tensor.shape[0] * sample_tensor.shape[1],
-        sample_tensor.shape[2],
-        sample_tensor.shape[3]
-    )
-    return sample_tensor
-
-
-def gen_mesh(opt, net, cuda, data, save_path, use_octree=True):
-    image_tensor = data['img'].to(device=cuda)
-    calib_tensor = data['calib'].to(device=cuda)
-
-    net.filter(image_tensor)
-
-    b_min = data['b_min']
-    b_max = data['b_max']
-    try:
-        save_img_path = save_path[:-4] + '.png'
-        save_img_list = []
-        for v in range(image_tensor.shape[0]):
-            save_img = (np.transpose(image_tensor[v].detach().cpu().numpy(), (1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0
-            save_img_list.append(save_img)
-        save_img = np.concatenate(save_img_list, axis=1)
-        Image.fromarray(np.uint8(save_img[:,:,::-1])).save(save_img_path)
-
-        verts, faces, _, _ = reconstruction(
-            net, cuda, calib_tensor, opt.resolution, b_min, b_max, use_octree=use_octree)
-        verts_tensor = torch.from_numpy(verts.T).unsqueeze(0).to(device=cuda).float()
-        xyz_tensor = net.projection(verts_tensor, calib_tensor[:1])
-        uv = xyz_tensor[:, :2, :]
-        color = index(image_tensor[:1], uv).detach().cpu().numpy()[0].T
-        color = color * 0.5 + 0.5
-        save_obj_mesh_with_color(save_path, verts, faces, color)
-    except Exception as e:
-        print(e)
-        print('Can not create marching cubes at this time.')
-
-def gen_mesh_color(opt, netG, netC, cuda, data, save_path, use_octree=True):
-    image_tensor = data['img'].to(device=cuda)
-    calib_tensor = data['calib'].to(device=cuda)
-
-    netG.filter(image_tensor)
-    netC.filter(image_tensor)
-    netC.attach(netG.get_im_feat())
-
-    b_min = data['b_min']
-    b_max = data['b_max']
-    try:
-        save_img_path = save_path[:-4] + '.png'
-        save_img_list = []
-        for v in range(image_tensor.shape[0]):
-            save_img = (np.transpose(image_tensor[v].detach().cpu().numpy(), (1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0
-            save_img_list.append(save_img)
-        save_img = np.concatenate(save_img_list, axis=1)
-        Image.fromarray(np.uint8(save_img[:,:,::-1])).save(save_img_path)
-
-        verts, faces, _, _ = reconstruction(
-            netG, cuda, calib_tensor, opt.resolution, b_min, b_max, use_octree=use_octree)
-
-        # Now Getting colors
-        verts_tensor = torch.from_numpy(verts.T).unsqueeze(0).to(device=cuda).float()
-        verts_tensor = reshape_sample_tensor(verts_tensor, opt.num_views)
-        color = np.zeros(verts.shape)
-        interval = 10000
-        for i in range(len(color) // interval):
-            left = i * interval
-            right = i * interval + interval
-            if i == len(color) // interval - 1:
-                right = -1
-            netC.query(verts_tensor[:, :, left:right], calib_tensor)
-            rgb = netC.get_preds()[0].detach().cpu().numpy() * 0.5 + 0.5
-            color[left:right] = rgb.T
-
-        save_obj_mesh_with_color(save_path, verts, faces, color)
-    except Exception as e:
-        print(e)
-        print('Can not create marching cubes at this time.')
-
-def adjust_learning_rate(optimizer, epoch, lr, schedule, gamma):
-    """Sets the learning rate to the initial LR decayed by schedule"""
-    if epoch in schedule:
-        lr *= gamma
-        for param_group in optimizer.param_groups:
-            param_group['lr'] = lr
-    return lr
-
-
-def compute_acc(pred, gt, thresh=0.5):
-    '''
-    return:
-        IOU, precision, and recall
-    '''
-    with torch.no_grad():
-        vol_pred = pred > thresh
-        vol_gt = gt > thresh
-
-        union = vol_pred | vol_gt
-        inter = vol_pred & vol_gt
-
-        true_pos = inter.sum().float()
-
-        union = union.sum().float()
-        if union == 0:
-            union = 1
-        vol_pred = vol_pred.sum().float()
-        if vol_pred == 0:
-            vol_pred = 1
-        vol_gt = vol_gt.sum().float()
-        if vol_gt == 0:
-            vol_gt = 1
-        return true_pos / union, true_pos / vol_pred, true_pos / vol_gt
-
-
-def calc_error(opt, net, cuda, dataset, num_tests):
-    if num_tests > len(dataset):
-        num_tests = len(dataset)
-    with torch.no_grad():
-        erorr_arr, IOU_arr, prec_arr, recall_arr = [], [], [], []
-        for idx in tqdm(range(num_tests)):
-            data = dataset[idx * len(dataset) // num_tests]
-            # retrieve the data
-            image_tensor = data['img'].to(device=cuda)
-            calib_tensor = data['calib'].to(device=cuda)
-            sample_tensor = data['samples'].to(device=cuda).unsqueeze(0)
-            if opt.num_views > 1:
-                sample_tensor = reshape_sample_tensor(sample_tensor, opt.num_views)
-            label_tensor = data['labels'].to(device=cuda).unsqueeze(0)
-
-            res, error = net.forward(image_tensor, sample_tensor, calib_tensor, labels=label_tensor)
-
-            IOU, prec, recall = compute_acc(res, label_tensor)
-
-            # print(
-            #     '{0}/{1} | Error: {2:06f} IOU: {3:06f} prec: {4:06f} recall: {5:06f}'
-            #         .format(idx, num_tests, error.item(), IOU.item(), prec.item(), recall.item()))
-            erorr_arr.append(error.item())
-            IOU_arr.append(IOU.item())
-            prec_arr.append(prec.item())
-            recall_arr.append(recall.item())
-
-    return np.average(erorr_arr), np.average(IOU_arr), np.average(prec_arr), np.average(recall_arr)
-
-def calc_error_color(opt, netG, netC, cuda, dataset, num_tests):
-    if num_tests > len(dataset):
-        num_tests = len(dataset)
-    with torch.no_grad():
-        error_color_arr = []
-
-        for idx in tqdm(range(num_tests)):
-            data = dataset[idx * len(dataset) // num_tests]
-            # retrieve the data
-            image_tensor = data['img'].to(device=cuda)
-            calib_tensor = data['calib'].to(device=cuda)
-            color_sample_tensor = data['color_samples'].to(device=cuda).unsqueeze(0)
-
-            if opt.num_views > 1:
-                color_sample_tensor = reshape_sample_tensor(color_sample_tensor, opt.num_views)
-
-            rgb_tensor = data['rgbs'].to(device=cuda).unsqueeze(0)
-
-            netG.filter(image_tensor)
-            _, errorC = netC.forward(image_tensor, netG.get_im_feat(), color_sample_tensor, calib_tensor, labels=rgb_tensor)
-
-            # print('{0}/{1} | Error inout: {2:06f} | Error color: {3:06f}'
-            #       .format(idx, num_tests, errorG.item(), errorC.item()))
-            error_color_arr.append(errorC.item())
-
-    return np.average(error_color_arr)
-
diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/configs/glint360k_r100.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/configs/glint360k_r100.py
deleted file mode 100644
index 93d0701c0094517cec147c382b005e8063938548..0000000000000000000000000000000000000000
--- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/configs/glint360k_r100.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from easydict import EasyDict as edict
-
-# make training faster
-# our RAM is 256G
-# mount -t tmpfs -o size=140G  tmpfs /train_tmp
-
-config = edict()
-config.loss = "cosface"
-config.network = "r100"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "/train_tmp/glint360k"
-config.num_classes = 360232
-config.num_image = 17091657
-config.num_epoch = 20
-config.warmup_epoch = -1
-config.decay_epoch = [8, 12, 15, 18]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/tools/test_web_browser_engine.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/tools/test_web_browser_engine.py
deleted file mode 100644
index 283633bd6adeb362c5e9cb2938bc4fd7121050b9..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/tests/metagpt/tools/test_web_browser_engine.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""
-@Modified By: mashenquan, 2023/8/20. Remove global configuration `CONFIG`, enable configuration support for business isolation.
-"""
-
-import pytest
-
-from metagpt.config import Config
-from metagpt.tools import WebBrowserEngineType, web_browser_engine
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize(
-    "browser_type, url, urls",
-    [
-        (WebBrowserEngineType.PLAYWRIGHT, "https://fuzhi.ai", ("https://fuzhi.ai",)),
-        (WebBrowserEngineType.SELENIUM, "https://fuzhi.ai", ("https://fuzhi.ai",)),
-    ],
-    ids=["playwright", "selenium"],
-)
-async def test_scrape_web_page(browser_type, url, urls):
-    conf = Config()
-    browser = web_browser_engine.WebBrowserEngine(options=conf.runtime_options, engine=browser_type)
-    result = await browser.run(url)
-    assert isinstance(result, str)
-    assert "深度赋智" in result
-
-    if urls:
-        results = await browser.run(url, *urls)
-        assert isinstance(results, list)
-        assert len(results) == len(urls) + 1
-        assert all(("深度赋智" in i) for i in results)
diff --git a/spaces/derek-thomas/arabic-RAG/backend/semantic_search.py b/spaces/derek-thomas/arabic-RAG/backend/semantic_search.py
deleted file mode 100644
index 869c3b0ef0e0179c8aad450fa62671edc0368e53..0000000000000000000000000000000000000000
--- a/spaces/derek-thomas/arabic-RAG/backend/semantic_search.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import logging
-import time
-from pathlib import Path
-
-import lancedb
-from sentence_transformers import SentenceTransformer
-
-import spaces
-
-
-# Setting up the logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Start the timer for loading the QdrantDocumentStore
-start_time = time.perf_counter()
-
-proj_dir = Path(__file__).parents[1]
-
-# Log the time taken to load the QdrantDocumentStore
-db = lancedb.connect(proj_dir / "lancedb")
-tbl = db.open_table('arabic-wiki')
-lancedb_loading_time = time.perf_counter() - start_time
-logger.info(f"Time taken to load LanceDB: {lancedb_loading_time:.6f} seconds")
-
-# Start the timer for loading the EmbeddingRetriever
-start_time = time.perf_counter()
-
-name = "sentence-transformers/paraphrase-multilingual-minilm-l12-v2"
-st_model_gpu = SentenceTransformer(name, device='mps')
-st_model_cpu = SentenceTransformer(name, device='cpu')
-
-
-# used for both training and querying
-def call_embed_func(query):
-    try:
-        return embed_func(query)
-    except:
-        logger.warning(f'Using CPU')
-        return st_model_cpu.encode(query)
-
-
-@spaces.GPU
-def embed_func(query):
-    return st_model_gpu.encode(query)
-
-
-def vector_search(query_vector, top_k):
-    return tbl.search(query_vector).limit(top_k).to_list()
-
-
-def retriever(query, top_k=3):
-    query_vector = call_embed_func(query)
-    documents = vector_search(query_vector, top_k)
-    return documents
-
-
-# Log the time taken to load the EmbeddingRetriever
-retriever_loading_time = time.perf_counter() - start_time
-logger.info(f"Time taken to load EmbeddingRetriever: {retriever_loading_time:.6f} seconds")
diff --git a/spaces/diacanFperku/AutoGPT/Aurora 3d Barcode Generator Crack.md b/spaces/diacanFperku/AutoGPT/Aurora 3d Barcode Generator Crack.md
deleted file mode 100644
index 89fb571a2ce56d00a1ee4611d2cc4e4ff0678e70..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Aurora 3d Barcode Generator Crack.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>aurora 3d barcode generator crack</h2><br /><p><b><b>Download</b> &#187; <a href="https://gohhs.com/2uFVw2">https://gohhs.com/2uFVw2</a></b></p><br /><br />
-
- 1fdad05405<br />
-<br />
-<br />
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diff --git a/spaces/diacanFperku/AutoGPT/Ciel Comptes Personnels Premium 2013 13.md b/spaces/diacanFperku/AutoGPT/Ciel Comptes Personnels Premium 2013 13.md
deleted file mode 100644
index 15f49f0597ae9dd9f155d5eda736495ba64fda5a..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Ciel Comptes Personnels Premium 2013 13.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>ciel comptes personnels premium 2013 13</h2><br /><p><b><b>Download Zip</b> ===== <a href="https://gohhs.com/2uFUlc">https://gohhs.com/2uFUlc</a></b></p><br /><br />
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diff --git a/spaces/diacanFperku/AutoGPT/Keygen Robot Structural Analysis Professional 2016 Activation.md b/spaces/diacanFperku/AutoGPT/Keygen Robot Structural Analysis Professional 2016 Activation.md
deleted file mode 100644
index c82cee1f87b113e2f2c4f13655da05997d063023..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Keygen Robot Structural Analysis Professional 2016 Activation.md	
+++ /dev/null
@@ -1,14 +0,0 @@
-<h2>keygen Robot Structural Analysis Professional 2016 activation</h2><br /><p><b><b>Download</b> &#128504;&#128504;&#128504; <a href="https://gohhs.com/2uFUYm">https://gohhs.com/2uFUYm</a></b></p><br /><br />
-<br />
-شرح تنصيب Autodesk Robot Structural Analysis Professional 2014. 13 687 views13 thousand views. January 8, 2015 44 ..... شرح تنصيب Autodesk Robot Structural Analysis Professional 2014. 5,921 views5 thousand views. April 25, 2014 20 ...
-Autodesk Robot Structural Analysis Professional 2014 - Autodesk.com
-Autodesk Robot Structural Analysis Professional 2014 is a solution that allows civil engineers to create new structures based on
-Autodesk Robot Structural Analysis Professional 2014.
-Year of release: 2014.
-Version: 2014.
-Developer: Autodesk.
-Platform: Windows.
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-<br />
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diff --git a/spaces/digitalxingtong/Luzao-Bert-Vits2/text/chinese_bert.py b/spaces/digitalxingtong/Luzao-Bert-Vits2/text/chinese_bert.py
deleted file mode 100644
index cb84ce0b426cd0a1c7954ddcdf41322c10ed14fa..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Luzao-Bert-Vits2/text/chinese_bert.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large")
-model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(device)
-
-def get_bert_feature(text, word2ph):
-    with torch.no_grad():
-        inputs = tokenizer(text, return_tensors='pt')
-        for i in inputs:
-            inputs[i] = inputs[i].to(device)
-        res = model(**inputs, output_hidden_states=True)
-        res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu()
-
-    assert len(word2ph) == len(text)+2
-    word2phone = word2ph
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        repeat_feature = res[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-
-    return phone_level_feature.T
-
-if __name__ == '__main__':
-    # feature = get_bert_feature('你好,我是说的道理。')
-    import torch
-
-    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
-    word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1]
-
-    # 计算总帧数
-    total_frames = sum(word2phone)
-    print(word_level_feature.shape)
-    print(word2phone)
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        print(word_level_feature[i].shape)
-
-        # 对每个词重复word2phone[i]次
-        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-    print(phone_level_feature.shape)  # torch.Size([36, 1024])
-
diff --git a/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/setup_ffmpeg.py b/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/setup_ffmpeg.py
deleted file mode 100644
index 7137ab5faebb6d80740b8c843667458f25596839..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/setup_ffmpeg.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import os
-import sys
-import re
-from pathlib import Path
-import winreg
-
-def check_ffmpeg_path():
-    path_list = os.environ['Path'].split(';')
-    ffmpeg_found = False
-
-    for path in path_list:
-        if 'ffmpeg' in path.lower() and 'bin' in path.lower():
-            ffmpeg_found = True
-            print("FFmpeg already installed.")
-            break
-
-    return ffmpeg_found
-
-def add_ffmpeg_path_to_user_variable():
-    ffmpeg_bin_path = Path('.\\ffmpeg\\bin')
-    if ffmpeg_bin_path.is_dir():
-        abs_path = str(ffmpeg_bin_path.resolve())
-        
-        try:
-            key = winreg.OpenKey(
-                winreg.HKEY_CURRENT_USER,
-                r"Environment",
-                0,
-                winreg.KEY_READ | winreg.KEY_WRITE
-            )
-            
-            try:
-                current_path, _ = winreg.QueryValueEx(key, "Path")
-                if abs_path not in current_path:
-                    new_path = f"{current_path};{abs_path}"
-                    winreg.SetValueEx(key, "Path", 0, winreg.REG_EXPAND_SZ, new_path)
-                    print(f"Added FFmpeg path to user variable 'Path': {abs_path}")
-                else:
-                    print("FFmpeg path already exists in the user variable 'Path'.")
-            finally:
-                winreg.CloseKey(key)
-        except WindowsError:
-            print("Error: Unable to modify user variable 'Path'.")
-            sys.exit(1)
-
-    else:
-        print("Error: ffmpeg\\bin folder not found in the current path.")
-        sys.exit(1)
-
-def main():
-    if not check_ffmpeg_path():
-       add_ffmpeg_path_to_user_variable()
-            
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/spaces/dineshreddy/WALT/mmdet/datasets/samplers/distributed_sampler.py b/spaces/dineshreddy/WALT/mmdet/datasets/samplers/distributed_sampler.py
deleted file mode 100644
index cc61019484655ee2829f7908dc442caa20cf1d54..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/datasets/samplers/distributed_sampler.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import math
-
-import torch
-from torch.utils.data import DistributedSampler as _DistributedSampler
-
-
-class DistributedSampler(_DistributedSampler):
-
-    def __init__(self,
-                 dataset,
-                 num_replicas=None,
-                 rank=None,
-                 shuffle=True,
-                 seed=0):
-        super().__init__(
-            dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
-        # for the compatibility from PyTorch 1.3+
-        self.seed = seed if seed is not None else 0
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        if self.shuffle:
-            g = torch.Generator()
-            g.manual_seed(self.epoch + self.seed)
-            indices = torch.randperm(len(self.dataset), generator=g).tolist()
-        else:
-            indices = torch.arange(len(self.dataset)).tolist()
-
-        # add extra samples to make it evenly divisible
-        # in case that indices is shorter than half of total_size
-        indices = (indices *
-                   math.ceil(self.total_size / len(indices)))[:self.total_size]
-        assert len(indices) == self.total_size
-
-        # subsample
-        indices = indices[self.rank:self.total_size:self.num_replicas]
-        assert len(indices) == self.num_samples
-
-        return iter(indices)
diff --git a/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/recog_datasets/ST_MJ_train.py b/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/recog_datasets/ST_MJ_train.py
deleted file mode 100644
index aedccc9df54829c2b841ba47882dea0cbcc8b23d..0000000000000000000000000000000000000000
--- a/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/recog_datasets/ST_MJ_train.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Text Recognition Training set, including:
-# Synthetic Datasets: SynthText, Syn90k
-
-train_root = 'data/mixture'
-
-train_img_prefix1 = f'{train_root}/Syn90k/mnt/ramdisk/max/90kDICT32px'
-train_ann_file1 = f'{train_root}/Syn90k/label.lmdb'
-
-train1 = dict(
-    type='OCRDataset',
-    img_prefix=train_img_prefix1,
-    ann_file=train_ann_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='lmdb',
-        parser=dict(type='LineJsonParser', keys=['filename', 'text'])),
-    pipeline=None,
-    test_mode=False)
-
-train_img_prefix2 = f'{train_root}/SynthText/' + \
-    'synthtext/SynthText_patch_horizontal'
-train_ann_file2 = f'{train_root}/SynthText/label.lmdb'
-
-train2 = {key: value for key, value in train1.items()}
-train2['img_prefix'] = train_img_prefix2
-train2['ann_file'] = train_ann_file2
-
-train_list = [train1, train2]
diff --git a/spaces/doevent/AnimeGANv2/app.py b/spaces/doevent/AnimeGANv2/app.py
deleted file mode 100644
index 92f455ba8cf13947a03be2dae5c518d7e3801f63..0000000000000000000000000000000000000000
--- a/spaces/doevent/AnimeGANv2/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import os
-from PIL import Image
-import torch
-import gradio as gr
-
-
-
-model2 = torch.hub.load(
-    "AK391/animegan2-pytorch:main",
-    "generator",
-    pretrained=True,
-    device="cpu",
-    progress=False)
-
-face2paint = torch.hub.load(
-    'AK391/animegan2-pytorch:main', 'face2paint',
-    size=512, device="cpu",side_by_side=False)
-
-def inference(img):
-    out = face2paint(model2, img)
-    return out
-
-title = "AnimeGANv2"
-description = ""
-article = ""
-
-examples=[['groot.jpeg']]
-
-gr.Interface(inference, gr.inputs.Image(type="pil"), gr.outputs.Image(type="pil"),
-title=title,
-description=description,
-article=article,
-examples=examples,
-allow_flagging='never',
-theme="default",
-allow_screenshot=False).launch(enable_queue=True, share=True)
diff --git a/spaces/dolceschokolade/chatbot-mini/next-env.d.ts b/spaces/dolceschokolade/chatbot-mini/next-env.d.ts
deleted file mode 100644
index 4f11a03dc6cc37f2b5105c08f2e7b24c603ab2f4..0000000000000000000000000000000000000000
--- a/spaces/dolceschokolade/chatbot-mini/next-env.d.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-/// <reference types="next" />
-/// <reference types="next/image-types/global" />
-
-// NOTE: This file should not be edited
-// see https://nextjs.org/docs/basic-features/typescript for more information.
diff --git a/spaces/dorkai/SINGPT-Temporary/server.py b/spaces/dorkai/SINGPT-Temporary/server.py
deleted file mode 100644
index 6a17f26287d94e9187a4f315fe9fb7d2dc6ec171..0000000000000000000000000000000000000000
--- a/spaces/dorkai/SINGPT-Temporary/server.py
+++ /dev/null
@@ -1,382 +0,0 @@
-import gc
-import io
-import json
-import re
-import sys
-import time
-import zipfile
-from pathlib import Path
-
-import gradio as gr
-import torch
-
-import modules.chat as chat
-import modules.extensions as extensions_module
-import modules.shared as shared
-import modules.ui as ui
-from modules.html_generator import generate_chat_html
-from modules.models import load_model, load_soft_prompt
-from modules.text_generation import generate_reply
-
-# Loading custom settings
-settings_file = None
-if shared.args.settings is not None and Path(shared.args.settings).exists():
-    settings_file = Path(shared.args.settings)
-elif Path('settings.json').exists():
-    settings_file = Path('settings.json')
-if settings_file is not None:
-    print(f"Loading settings from {settings_file}...")
-    new_settings = json.loads(open(settings_file, 'r').read())
-    for item in new_settings:
-        shared.settings[item] = new_settings[item]
-
-def get_available_models():
-    if shared.args.flexgen:
-        return sorted([re.sub('-np$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('-np')], key=str.lower)
-    else:
-        return sorted([item.name for item in list(Path('models/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt'))], key=str.lower)
-
-def get_available_presets():
-    return sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('presets').glob('*.txt'))), key=str.lower)
-
-def get_available_characters():
-    return ['None'] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('characters').glob('*.json'))), key=str.lower)
-
-def get_available_extensions():
-    return sorted(set(map(lambda x : x.parts[1], Path('extensions').glob('*/script.py'))), key=str.lower)
-
-def get_available_softprompts():
-    return ['None'] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('softprompts').glob('*.zip'))), key=str.lower)
-
-def load_model_wrapper(selected_model):
-    if selected_model != shared.model_name:
-        shared.model_name = selected_model
-        shared.model = shared.tokenizer = None
-        if not shared.args.cpu:
-            gc.collect()
-            torch.cuda.empty_cache()
-        shared.model, shared.tokenizer = load_model(shared.model_name)
-
-    return selected_model
-
-def load_preset_values(preset_menu, return_dict=False):
-    generate_params = {
-        'do_sample': True,
-        'temperature': 1,
-        'top_p': 1,
-        'typical_p': 1,
-        'repetition_penalty': 1,
-        'top_k': 50,
-        'num_beams': 1,
-        'penalty_alpha': 0,
-        'min_length': 0,
-        'length_penalty': 1,
-        'no_repeat_ngram_size': 0,
-        'early_stopping': False,
-    }
-    with open(Path(f'presets/{preset_menu}.txt'), 'r') as infile:
-        preset = infile.read()
-    for i in preset.splitlines():
-        i = i.rstrip(',').strip().split('=')
-        if len(i) == 2 and i[0].strip() != 'tokens':
-            generate_params[i[0].strip()] = eval(i[1].strip())
-
-    generate_params['temperature'] = min(1.99, generate_params['temperature'])
-
-    if return_dict:
-        return generate_params
-    else:
-        return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['penalty_alpha'], generate_params['length_penalty'], generate_params['early_stopping']
-
-def upload_soft_prompt(file):
-    with zipfile.ZipFile(io.BytesIO(file)) as zf:
-        zf.extract('meta.json')
-        j = json.loads(open('meta.json', 'r').read())
-        name = j['name']
-        Path('meta.json').unlink()
-
-    with open(Path(f'softprompts/{name}.zip'), 'wb') as f:
-        f.write(file)
-
-    return name
-
-def create_settings_menus(default_preset):
-    generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', return_dict=True)
-
-    with gr.Row():
-        with gr.Column():
-            with gr.Row():
-                shared.gradio['model_menu'] = gr.Dropdown(choices=available_models, value=shared.model_name, label='Model')
-                ui.create_refresh_button(shared.gradio['model_menu'], lambda : None, lambda : {'choices': get_available_models()}, 'refresh-button')
-        with gr.Column():
-            with gr.Row():
-                shared.gradio['preset_menu'] = gr.Dropdown(choices=available_presets, value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
-                ui.create_refresh_button(shared.gradio['preset_menu'], lambda : None, lambda : {'choices': get_available_presets()}, 'refresh-button')
-
-    with gr.Accordion('Custom generation parameters', open=False, elem_id='accordion'):
-        with gr.Row():
-            with gr.Column():
-                shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature')
-                shared.gradio['repetition_penalty'] = gr.Slider(1.0, 2.99, value=generate_params['repetition_penalty'],step=0.01,label='repetition_penalty')
-                shared.gradio['top_k'] = gr.Slider(0,200,value=generate_params['top_k'],step=1,label='top_k')
-                shared.gradio['top_p'] = gr.Slider(0.0,1.0,value=generate_params['top_p'],step=0.01,label='top_p')
-            with gr.Column():
-                shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
-                shared.gradio['typical_p'] = gr.Slider(0.0,1.0,value=generate_params['typical_p'],step=0.01,label='typical_p')
-                shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size')
-                shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'] if shared.args.no_stream else 0, label='min_length', interactive=shared.args.no_stream)
-
-        gr.Markdown('Contrastive search:')
-        shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha')
-
-        gr.Markdown('Beam search (uses a lot of VRAM):')
-        with gr.Row():
-            with gr.Column():
-                shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams')
-            with gr.Column():
-                shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty')
-        shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
-
-    with gr.Accordion('Soft prompt', open=False, elem_id='accordion'):
-        with gr.Row():
-            shared.gradio['softprompts_menu'] = gr.Dropdown(choices=available_softprompts, value='None', label='Soft prompt')
-            ui.create_refresh_button(shared.gradio['softprompts_menu'], lambda : None, lambda : {'choices': get_available_softprompts()}, 'refresh-button')
-
-        gr.Markdown('Upload a soft prompt (.zip format):')
-        with gr.Row():
-            shared.gradio['upload_softprompt'] = gr.File(type='binary', file_types=['.zip'])
-
-    shared.gradio['model_menu'].change(load_model_wrapper, [shared.gradio['model_menu']], [shared.gradio['model_menu']], show_progress=True)
-    shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio['preset_menu']], [shared.gradio['do_sample'], shared.gradio['temperature'], shared.gradio['top_p'], shared.gradio['typical_p'], shared.gradio['repetition_penalty'], shared.gradio['top_k'], shared.gradio['min_length'], shared.gradio['no_repeat_ngram_size'], shared.gradio['num_beams'], shared.gradio['penalty_alpha'], shared.gradio['length_penalty'], shared.gradio['early_stopping']])
-    shared.gradio['softprompts_menu'].change(load_soft_prompt, [shared.gradio['softprompts_menu']], [shared.gradio['softprompts_menu']], show_progress=True)
-    shared.gradio['upload_softprompt'].upload(upload_soft_prompt, [shared.gradio['upload_softprompt']], [shared.gradio['softprompts_menu']])
-
-available_models = get_available_models()
-available_presets = get_available_presets()
-available_characters = get_available_characters()
-available_softprompts = get_available_softprompts()
-
-# Default extensions
-extensions_module.available_extensions = get_available_extensions()
-if shared.args.chat or shared.args.cai_chat:
-    for extension in shared.settings['chat_default_extensions']:
-        shared.args.extensions = shared.args.extensions or []
-        if extension not in shared.args.extensions:
-            shared.args.extensions.append(extension)
-else:
-    for extension in shared.settings['default_extensions']:
-        shared.args.extensions = shared.args.extensions or []
-        if extension not in shared.args.extensions:
-            shared.args.extensions.append(extension)
-if shared.args.extensions is not None and len(shared.args.extensions) > 0:
-    extensions_module.load_extensions()
-
-# Default model
-if shared.args.model is not None:
-    shared.model_name = shared.args.model
-else:
-    if len(available_models) == 0:
-        print('No models are available! Please download at least one.')
-        sys.exit(0)
-    elif len(available_models) == 1:
-        i = 0
-    else:
-        print('The following models are available:\n')
-        for i, model in enumerate(available_models):
-            print(f'{i+1}. {model}')
-        print(f'\nWhich one do you want to load? 1-{len(available_models)}\n')
-        i = int(input())-1
-        print()
-    shared.model_name = available_models[i]
-shared.model, shared.tokenizer = load_model(shared.model_name)
-
-# Default UI settings
-gen_events = []
-default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
-default_text = shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
-title ='Text generation web UI'
-description = '\n\n# Text generation lab\nGenerate text using Large Language Models.\n'
-suffix = '_pygmalion' if 'pygmalion' in shared.model_name.lower() else ''
-
-if shared.args.chat or shared.args.cai_chat:
-    with gr.Blocks(css=ui.css+ui.chat_css, analytics_enabled=False, title=title) as shared.gradio['interface']:
-        gr.HTML('''<a href="https://github.com/oobabooga/text-generation-webui">Original github repo</a><br>
-<p>For faster inference without waiting in queue, you may duplicate the space. <a href="https://huggingface.co/spaces/antonovmaxim/text-generation-webui-space?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>
-(👇 Scroll down to see the interface 👀)''')
-        if shared.args.cai_chat:
-            shared.gradio['display'] = gr.HTML(value=generate_chat_html(shared.history['visible'], shared.settings[f'name1{suffix}'], shared.settings[f'name2{suffix}'], shared.character))
-        else:
-            shared.gradio['display'] = gr.Chatbot(value=shared.history['visible']).style(color_map=("#326efd", "#212528"))
-        shared.gradio['textbox'] = gr.Textbox(label='Input')
-        with gr.Row():
-            shared.gradio['Stop'] = gr.Button('Stop')
-            shared.gradio['Generate'] = gr.Button('Generate')
-        with gr.Row():
-            shared.gradio['Impersonate'] = gr.Button('Impersonate')
-            shared.gradio['Regenerate'] = gr.Button('Regenerate')
-        with gr.Row():
-            shared.gradio['Copy last reply'] = gr.Button('Copy last reply')
-            shared.gradio['Replace last reply'] = gr.Button('Replace last reply')
-            shared.gradio['Remove last'] = gr.Button('Remove last')
-
-            shared.gradio['Clear history'] = gr.Button('Clear history')
-            shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant="stop", visible=False)
-            shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False)
-        with gr.Tab('Chat settings'):
-            shared.gradio['name1'] = gr.Textbox(value=shared.settings[f'name1{suffix}'], lines=1, label='Your name')
-            shared.gradio['name2'] = gr.Textbox(value=shared.settings[f'name2{suffix}'], lines=1, label='Bot\'s name')
-            shared.gradio['context'] = gr.Textbox(value=shared.settings[f'context{suffix}'], lines=5, label='Context')
-            with gr.Row():
-                shared.gradio['character_menu'] = gr.Dropdown(choices=available_characters, value='None', label='Character', elem_id='character-menu')
-                ui.create_refresh_button(shared.gradio['character_menu'], lambda : None, lambda : {'choices': get_available_characters()}, 'refresh-button')
-
-            with gr.Row():
-                shared.gradio['check'] = gr.Checkbox(value=shared.settings[f'stop_at_newline{suffix}'], label='Stop generating at new line character?')
-            with gr.Row():
-                with gr.Tab('Chat history'):
-                    with gr.Row():
-                        with gr.Column():
-                            gr.Markdown('Upload')
-                            shared.gradio['upload_chat_history'] = gr.File(type='binary', file_types=['.json', '.txt'])
-                        with gr.Column():
-                            gr.Markdown('Download')
-                            shared.gradio['download'] = gr.File()
-                            shared.gradio['download_button'] = gr.Button(value='Click me')
-                with gr.Tab('Upload character'):
-                    with gr.Row():
-                        with gr.Column():
-                            gr.Markdown('1. Select the JSON file')
-                            shared.gradio['upload_json'] = gr.File(type='binary', file_types=['.json'])
-                        with gr.Column():
-                            gr.Markdown('2. Select your character\'s profile picture (optional)')
-                            shared.gradio['upload_img_bot'] = gr.File(type='binary', file_types=['image'])
-                    shared.gradio['Upload character'] = gr.Button(value='Submit')
-                with gr.Tab('Upload your profile picture'):
-                    shared.gradio['upload_img_me'] = gr.File(type='binary', file_types=['image'])
-                with gr.Tab('Upload TavernAI Character Card'):
-                    shared.gradio['upload_img_tavern'] = gr.File(type='binary', file_types=['image'])
-
-        with gr.Tab('Generation settings'):
-            with gr.Row():
-                with gr.Column():
-                    shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
-                with gr.Column():
-                    shared.gradio['chat_prompt_size_slider'] = gr.Slider(minimum=shared.settings['chat_prompt_size_min'], maximum=shared.settings['chat_prompt_size_max'], step=1, label='Maximum prompt size in tokens', value=shared.settings['chat_prompt_size'])
-                shared.gradio['chat_generation_attempts'] = gr.Slider(minimum=shared.settings['chat_generation_attempts_min'], maximum=shared.settings['chat_generation_attempts_max'], value=shared.settings['chat_generation_attempts'], step=1, label='Generation attempts (for longer replies)')
-            create_settings_menus(default_preset)
-
-        shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']]
-        if shared.args.extensions is not None:
-            with gr.Tab('Extensions'):
-                extensions_module.create_extensions_block()
-
-        function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
-
-        gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream, api_name='textgen'))
-        gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
-        gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
-        gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream))
-        shared.gradio['Stop'].click(chat.stop_everything_event, [], [], cancels=gen_events)
-
-        shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, [], shared.gradio['textbox'], show_progress=shared.args.no_stream)
-        shared.gradio['Replace last reply'].click(chat.replace_last_reply, [shared.gradio['textbox'], shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'], show_progress=shared.args.no_stream)
-
-        # Clear history with confirmation
-        clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']]
-        shared.gradio['Clear history'].click(lambda :[gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr)
-        shared.gradio['Clear history-confirm'].click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
-        shared.gradio['Clear history-confirm'].click(chat.clear_chat_log, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'])
-        shared.gradio['Clear history-cancel'].click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
-
-        shared.gradio['Remove last'].click(chat.remove_last_message, [shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['display'], shared.gradio['textbox']], show_progress=False)
-        shared.gradio['download_button'].click(chat.save_history, inputs=[], outputs=[shared.gradio['download']])
-        shared.gradio['Upload character'].click(chat.upload_character, [shared.gradio['upload_json'], shared.gradio['upload_img_bot']], [shared.gradio['character_menu']])
-
-        # Clearing stuff and saving the history
-        for i in ['Generate', 'Regenerate', 'Replace last reply']:
-            shared.gradio[i].click(lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False)
-            shared.gradio[i].click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
-        shared.gradio['Clear history-confirm'].click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
-        shared.gradio['textbox'].submit(lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False)
-        shared.gradio['textbox'].submit(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
-
-        shared.gradio['character_menu'].change(chat.load_character, [shared.gradio['character_menu'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['name2'], shared.gradio['context'], shared.gradio['display']])
-        shared.gradio['upload_chat_history'].upload(chat.load_history, [shared.gradio['upload_chat_history'], shared.gradio['name1'], shared.gradio['name2']], [])
-        shared.gradio['upload_img_tavern'].upload(chat.upload_tavern_character, [shared.gradio['upload_img_tavern'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['character_menu']])
-        shared.gradio['upload_img_me'].upload(chat.upload_your_profile_picture, [shared.gradio['upload_img_me']], [])
-
-        reload_func = chat.redraw_html if shared.args.cai_chat else lambda : shared.history['visible']
-        reload_inputs = [shared.gradio['name1'], shared.gradio['name2']] if shared.args.cai_chat else []
-        shared.gradio['upload_chat_history'].upload(reload_func, reload_inputs, [shared.gradio['display']])
-        shared.gradio['upload_img_me'].upload(reload_func, reload_inputs, [shared.gradio['display']])
-        shared.gradio['Stop'].click(reload_func, reload_inputs, [shared.gradio['display']])
-
-        shared.gradio['interface'].load(lambda : chat.load_default_history(shared.settings[f'name1{suffix}'], shared.settings[f'name2{suffix}']), None, None)
-        shared.gradio['interface'].load(reload_func, reload_inputs, [shared.gradio['display']], show_progress=True)
-
-elif shared.args.notebook:
-    with gr.Blocks(css=ui.css, analytics_enabled=False, title=title) as shared.gradio['interface']:
-        gr.Markdown(description)
-        with gr.Tab('Raw'):
-            shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=23)
-        with gr.Tab('Markdown'):
-            shared.gradio['markdown'] = gr.Markdown()
-        with gr.Tab('HTML'):
-            shared.gradio['html'] = gr.HTML()
-
-        shared.gradio['Generate'] = gr.Button('Generate')
-        shared.gradio['Stop'] = gr.Button('Stop')
-        shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
-
-        create_settings_menus(default_preset)
-        if shared.args.extensions is not None:
-            extensions_module.create_extensions_block()
-
-        shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']]
-        output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']]
-        gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen'))
-        gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
-        shared.gradio['Stop'].click(None, None, None, cancels=gen_events)
-
-else:
-    with gr.Blocks(css=ui.css, analytics_enabled=False, title=title) as shared.gradio['interface']:
-        gr.Markdown(description)
-        with gr.Row():
-            with gr.Column():
-                shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=15, label='Input')
-                shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
-                shared.gradio['Generate'] = gr.Button('Generate')
-                with gr.Row():
-                    with gr.Column():
-                        shared.gradio['Continue'] = gr.Button('Continue')
-                    with gr.Column():
-                        shared.gradio['Stop'] = gr.Button('Stop')
-
-                create_settings_menus(default_preset)
-                if shared.args.extensions is not None:
-                    extensions_module.create_extensions_block()
-
-            with gr.Column():
-                with gr.Tab('Raw'):
-                    shared.gradio['output_textbox'] = gr.Textbox(lines=15, label='Output')
-                with gr.Tab('Markdown'):
-                    shared.gradio['markdown'] = gr.Markdown()
-                with gr.Tab('HTML'):
-                    shared.gradio['html'] = gr.HTML()
-
-        shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']]
-        output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']]
-        gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen'))
-        gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream))
-        gen_events.append(shared.gradio['Continue'].click(generate_reply, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=shared.args.no_stream))
-        shared.gradio['Stop'].click(None, None, None, cancels=gen_events)
-
-shared.gradio['interface'].queue()
-if shared.args.listen:
-    shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name='0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch)
-else:
-    shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch)
-
-# I think that I will need this later
-while True:
-    time.sleep(0.5)
diff --git a/spaces/dy2dx2/Physics-Assistant/app.py b/spaces/dy2dx2/Physics-Assistant/app.py
deleted file mode 100644
index a601026ab438843005acccce07a177c47fddad30..0000000000000000000000000000000000000000
--- a/spaces/dy2dx2/Physics-Assistant/app.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import os
-from dotenv import load_dotenv
-import openai
-import gradio as gr
-
-# Load environment variables from the .env file
-load_dotenv()
-
-openai.api_key = os.getenv("openai_api_key")
-openai.organization = os.getenv("openai_organization_id")
-
-
-message_history = [{"role": "system", "content":"You are a physics assistant chatbot and reject to answer anything unrealted to the physics."}, 
-                   {"role": "assistant", "content":"Hi, I am a physics assistant. I can help you with your physics questions."}]
-
-def predict(input):
-    global message_history
-
-    message_history.append({"role": "user", "content": f"{input}"})
-
-    completion = openai.ChatCompletion.create(
-        model="gpt-4",
-        messages=message_history
-    )
-
-    reply_content = completion.choices[0].message.content
-
-    if check_in_role(reply_content):
-        message_history.append({"role": "assistant", "content": f"{reply_content}"})
-    else:
-        message_history.append({"role": "assistant", "content": "I'm sorry, but the question you have asked seems to be unrelated to the context of this conversation, and unfortunately, I'm not able to provide an answer. If you have any questions related to physics, I would be happy to try and assist you."})
-    response = [(message_history[i]["content"], message_history[i+1]["content"]) for i in range(2, len(message_history)-1, 2)]  # convert to tuples of list
-    return response
-
-def check_in_role(reply_content):
-
-    p = "Is the following question related to physics? Answer it using only 'yes' or 'no'.\n\n" + reply_content + "\n\n---\nLabel:"
-    q = [{"role": "user", "content":f"{p}"}]
-
-    res = openai.ChatCompletion.create(
-        model="gpt-4",
-        messages=q
-    )
-    label = res.choices[0].message.content.lower()
-    print(label)
-    if "yes" in label:
-        return True
-    return False
-
-
-
-with gr.Blocks(theme=gr.themes.Soft(), title="Physics Assistant") as demo:
-    
-    with gr.Row():
-        gr.Markdown("Get instant physics help with our chatbot! Ask any physics-related questions and receive accurate and reliable answers in seconds. Perfect for students, researchers, and anyone interested in the laws of the universe.")
-
-    bot = gr.Chatbot().style(height=500)
-    
-    with gr.Row():
-        with gr.Column(scale=0.85):
-            txt = gr.Textbox(
-                show_label=False,
-                placeholder="Enter a physics related text",
-            ).style(container=False)
-        with gr.Column(scale=0.15, min_width=0):
-            send = gr.Button("Send")
-
-    send.click(predict, inputs=[txt], outputs=bot)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/eisenjulian/matcha_chartqa/README.md b/spaces/eisenjulian/matcha_chartqa/README.md
deleted file mode 100644
index 046f04fd5fdb62aa3878507d118f2b0815b1af52..0000000000000000000000000000000000000000
--- a/spaces/eisenjulian/matcha_chartqa/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: MatCha ChartQA
-emoji: 📊
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-duplicated_from: fl399/matcha_chartqa
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/elonmuskceo/shiny-orbit-simulation/Dockerfile b/spaces/elonmuskceo/shiny-orbit-simulation/Dockerfile
deleted file mode 100644
index ca03c5f7e7018e7c33f12accb16d20b475d33f7b..0000000000000000000000000000000000000000
--- a/spaces/elonmuskceo/shiny-orbit-simulation/Dockerfile
+++ /dev/null
@@ -1,22 +0,0 @@
-FROM ubuntu:kinetic
-
-# Doesn't usually have an "upgrade"
-RUN apt-get update \
- && DEBIAN_FRONTEND=noninteractive \
-    apt-get install --no-install-recommends --assume-yes \
-	  build-essential \
-      python3 \
-	  python3-dev \
-      python3-pip
-
-COPY requirements.txt .
-
-RUN pip install -r requirements.txt
-
-COPY . .
-
-ENTRYPOINT ["/bin/sh", "-c"]
-
-EXPOSE 7860
-
-CMD ["shiny run --port 7860 --host 0.0.0.0 app.py"]
\ No newline at end of file
diff --git a/spaces/emc348/faces-through-time/models/StyleCLIP/global_directions/dnnlib/tflib/__init__.py b/spaces/emc348/faces-through-time/models/StyleCLIP/global_directions/dnnlib/tflib/__init__.py
deleted file mode 100644
index ca852844ec488c0134bffa647e25a40646ff4718..0000000000000000000000000000000000000000
--- a/spaces/emc348/faces-through-time/models/StyleCLIP/global_directions/dnnlib/tflib/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-from . import autosummary
-from . import network
-from . import optimizer
-from . import tfutil
-from . import custom_ops
-
-from .tfutil import *
-from .network import Network
-
-from .optimizer import Optimizer
-
-from .custom_ops import get_plugin
diff --git a/spaces/eson/tokenizer-arena/vocab/qwen/__init__.py b/spaces/eson/tokenizer-arena/vocab/qwen/__init__.py
deleted file mode 100644
index 89ce245bf3ec9d3bfddd4bbf23b4a2b096260619..0000000000000000000000000000000000000000
--- a/spaces/eson/tokenizer-arena/vocab/qwen/__init__.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""
-依赖 torch tiktoken
-依赖 transformer 4.31.0 及以上，
-
-https://huggingface.co/tangger/Qwen-7B-Chat  Qwen官方模型临时下架了，这个是备份
-"""
-
-import os
-from transformers import AutoTokenizer
-CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
-TOKENIZER_DIR = os.path.join(CURRENT_DIR, "Qwen-7B-Chat")
-
-# 请注意：分词器默认行为已更改为默认关闭特殊token攻击防护。
-# tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL-Chat", trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_DIR, trust_remote_code=True)
-
-def test():
-    encoding = tokenizer.encode("测试华为手机10086        8个空格")
-    for token_id in encoding:
-        token = tokenizer.convert_ids_to_tokens([token_id])[0].decode("utf-8")
-        print(token_id, ":", token)
-
-if __name__ == "__main__":
-    test()
\ No newline at end of file
diff --git a/spaces/ethzanalytics/gpt2-xl-conversational/utils.py b/spaces/ethzanalytics/gpt2-xl-conversational/utils.py
deleted file mode 100644
index b4e2272c7ec560021ed8ca04b1c68567af8d04ea..0000000000000000000000000000000000000000
--- a/spaces/ethzanalytics/gpt2-xl-conversational/utils.py
+++ /dev/null
@@ -1,398 +0,0 @@
-"""
-    utils - general utility functions for loading, saving, and manipulating data
-"""
-
-import os
-from pathlib import Path
-import pprint as pp
-import re
-import shutil  # zipfile formats
-import logging
-from datetime import datetime
-from os.path import basename
-from os.path import getsize, join
-
-import requests
-from cleantext import clean
-from natsort import natsorted
-from symspellpy import SymSpell
-import pandas as pd
-from tqdm.auto import tqdm
-
-
-from contextlib import contextmanager
-import sys
-import os
-
-
-@contextmanager
-def suppress_stdout():
-    """
-    suppress_stdout - suppress stdout for a given block of code. credit to https://newbedev.com/how-to-suppress-console-output-in-python
-    """
-    with open(os.devnull, "w") as devnull:
-        old_stdout = sys.stdout
-        sys.stdout = devnull
-        try:
-            yield
-        finally:
-            sys.stdout = old_stdout
-
-
-def remove_string_extras(mytext):
-    # removes everything from a string except A-Za-z0-9 .,;
-    return re.sub(r"[^A-Za-z0-9 .,;]+", "", mytext)
-
-
-def corr(s):
-    # adds space after period if there isn't one
-    # removes extra spaces
-    return re.sub(r"\.(?! )", ". ", re.sub(r" +", " ", s))
-
-
-def get_timestamp():
-    # get timestamp for file names
-    return datetime.now().strftime("%b-%d-%Y_t-%H")
-
-
-def print_spacer(n=1):
-    """print_spacer - print a spacer line"""
-    print("\n   --------    " * n)
-
-
-def fast_scandir(dirname: str):
-    """
-    fast_scandir [an os.path-based means to return all subfolders in a given filepath]
-
-    """
-
-    subfolders = [f.path for f in os.scandir(dirname) if f.is_dir()]
-    for dirname in list(subfolders):
-        subfolders.extend(fast_scandir(dirname))
-    return subfolders  # list
-
-
-def create_folder(directory: str):
-    # you will never guess what this does
-    os.makedirs(directory, exist_ok=True)
-
-
-def chunks(lst: list, n: int):
-    """
-    chunks   -  Yield successive n-sized chunks from lst
-    Args:   lst (list): list to be chunked
-    n (int): size of chunks
-
-    """
-
-    for i in range(0, len(lst), n):
-        yield lst[i : i + n]
-
-
-def chunky_pandas(my_df, num_chunks: int = 4):
-    """
-    chunky_pandas [split dataframe into `num_chunks` equal chunks, return each inside a list]
-
-    Args:
-        my_df (pd.DataFrame)
-        num_chunks (int, optional): Defaults to 4.
-
-    Returns:
-        list: a list of dataframes
-    """
-    n = int(len(my_df) // num_chunks)
-    list_df = [my_df[i : i + n] for i in range(0, my_df.shape[0], n)]
-
-    return list_df
-
-
-def load_dir_files(
-    directory: str, req_extension=".txt", return_type="list", verbose=False
-):
-    """
-    load_dir_files - an os.path based method of returning all files with extension `req_extension` in a given directory and subdirectories
-
-    Args:
-
-
-    Returns:
-        list or dict: an iterable of filepaths or a dict of filepaths and their respective filenames
-    """
-    appr_files = []
-    # r=root, d=directories, f = files
-    for r, d, f in os.walk(directory):
-        for prefile in f:
-            if prefile.endswith(req_extension):
-                fullpath = os.path.join(r, prefile)
-                appr_files.append(fullpath)
-
-    appr_files = natsorted(appr_files)
-
-    if verbose:
-        print("A list of files in the {} directory are: \n".format(directory))
-        if len(appr_files) < 10:
-            pp.pprint(appr_files)
-        else:
-            pp.pprint(appr_files[:10])
-            print("\n and more. There are a total of {} files".format(len(appr_files)))
-
-    if return_type.lower() == "list":
-        return appr_files
-    else:
-        if verbose:
-            print("returning dictionary")
-
-        appr_file_dict = {}
-        for this_file in appr_files:
-            appr_file_dict[basename(this_file)] = this_file
-
-        return appr_file_dict
-
-
-def URL_string_filter(text):
-    """
-    URL_string_filter - filter out nonstandard "text" characters
-
-    """
-    custom_printable = (
-        "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ._"
-    )
-
-    filtered = "".join((filter(lambda i: i in custom_printable, text)))
-
-    return filtered
-
-
-def getFilename_fromCd(cd):
-    """getFilename_fromCd - get the filename from a given cd str"""
-    if not cd:
-        return None
-    fname = re.findall("filename=(.+)", cd)
-    if len(fname) > 0:
-        output = fname[0]
-    elif cd.find("/"):
-        possible_fname = cd.rsplit("/", 1)[1]
-        output = URL_string_filter(possible_fname)
-    else:
-        output = None
-    return output
-
-
-def get_zip_URL(
-    URLtoget: str,
-    extract_loc: str = None,
-    file_header: str = "dropboxexport_",
-    verbose: bool = False,
-):
-    """get_zip_URL - download a zip file from a given URL and extract it to a given location"""
-
-    r = requests.get(URLtoget, allow_redirects=True)
-    names = getFilename_fromCd(r.headers.get("content-disposition"))
-    fixed_fnames = names.split(";")  # split the multiple results
-    this_filename = file_header + URL_string_filter(fixed_fnames[0])
-
-    # define paths and save the zip file
-    if extract_loc is None:
-        extract_loc = "dropbox_dl"
-    dl_place = join(os.getcwd(), extract_loc)
-    create_folder(dl_place)
-    save_loc = join(os.getcwd(), this_filename)
-    open(save_loc, "wb").write(r.content)
-    if verbose:
-        print("downloaded file size was {} MB".format(getsize(save_loc) / 1000000))
-
-    # unpack the archive
-    shutil.unpack_archive(save_loc, extract_dir=dl_place)
-    if verbose:
-        print("extracted zip file - ", datetime.now())
-        x = load_dir_files(dl_place, req_extension="", verbose=verbose)
-
-    # remove original
-    try:
-        os.remove(save_loc)
-        del save_loc
-    except Exception:
-        print("unable to delete original zipfile - check if exists", datetime.now())
-
-    print("finished extracting zip - ", datetime.now())
-
-    return dl_place
-
-
-def merge_dataframes(data_dir: str, ext=".xlsx", verbose=False):
-    """
-    merge_dataframes - given a filepath, loads and attempts to merge all files as dataframes
-
-    Args:
-        data_dir (str): [root directory to search in]
-        ext (str, optional): [anticipate file extension for the dataframes ]. Defaults to '.xlsx'.
-
-    Returns:
-        pd.DataFrame(): merged dataframe of all files
-    """
-
-    src = Path(data_dir)
-    src_str = str(src.resolve())
-    mrg_df = pd.DataFrame()
-
-    all_reports = load_dir_files(directory=src_str, req_extension=ext, verbose=verbose)
-
-    failed = []
-
-    for df_path in tqdm(all_reports, total=len(all_reports), desc="joining data..."):
-
-        try:
-            this_df = pd.read_excel(df_path).convert_dtypes()
-
-            mrg_df = pd.concat([mrg_df, this_df], axis=0)
-        except Exception:
-            short_p = os.path.basename(df_path)
-            print(
-                f"WARNING - file with extension {ext} and name {short_p} could not be read."
-            )
-            failed.append(short_p)
-
-    if len(failed) > 0:
-        print("failed to merge {} files, investigate as needed")
-
-    if verbose:
-        pp.pprint(mrg_df.info(True))
-
-    return mrg_df
-
-
-def download_URL(url: str, file=None, dlpath=None, verbose=False):
-    """
-    download_URL - download a file from a URL and show progress bar
-
-    Parameters
-    ----------
-    url : str
-        URL to download
-    file : [type], optional
-        [description], by default None
-    dlpath : [type], optional
-        [description], by default None
-    verbose : bool, optional
-        [description], by default False
-
-    Returns
-    -------
-    str - path to the downloaded file
-    """
-
-    if file is None:
-        if "?dl=" in url:
-            # is a dropbox link
-            prefile = url.split("/")[-1]
-            filename = str(prefile).split("?dl=")[0]
-        else:
-            filename = url.split("/")[-1]
-
-        file = clean(filename)
-    if dlpath is None:
-        dlpath = Path.cwd()  # save to current working directory
-    else:
-        dlpath = Path(dlpath)  # make a path object
-
-    r = requests.get(url, stream=True, allow_redirects=True)
-    total_size = int(r.headers.get("content-length"))
-    initial_pos = 0
-    dl_loc = dlpath / file
-    with open(str(dl_loc.resolve()), "wb") as f:
-        with tqdm(
-            total=total_size,
-            unit="B",
-            unit_scale=True,
-            desc=file,
-            initial=initial_pos,
-            ascii=True,
-        ) as pbar:
-            for ch in r.iter_content(chunk_size=1024):
-                if ch:
-                    f.write(ch)
-                    pbar.update(len(ch))
-
-    if verbose:
-        print(f"\ndownloaded {file} to {dlpath}\n")
-
-    return str(dl_loc.resolve())
-
-
-def dl_extract_zip(
-    URLtoget: str,
-    extract_loc: str = None,
-    file_header: str = "TEMP_archive_dl_",
-    verbose: bool = False,
-):
-    """
-    dl_extract_zip - generic function to download a zip file and extract it
-
-    Parameters
-    ----------
-    URLtoget : str
-        zip file URL to download
-    extract_loc : str, optional
-        directory to extract zip to , by default None
-    file_header : str, optional
-        [description], by default "TEMP_archive_dl_"
-    verbose : bool, optional
-        [description], by default False
-
-    Returns
-    -------
-    str - path to the downloaded and extracted folder
-    """
-
-    extract_loc = Path(extract_loc)
-    extract_loc.mkdir(parents=True, exist_ok=True)
-
-    save_loc = download_URL(
-        url=URLtoget, file=f"{file_header}.zip", dlpath=None, verbose=verbose
-    )
-
-    shutil.unpack_archive(save_loc, extract_dir=extract_loc)
-
-    if verbose:
-        print("extracted zip file - ", datetime.now())
-        x = load_dir_files(extract_loc, req_extension="", verbose=verbose)
-
-    # remove original
-    try:
-        os.remove(save_loc)
-        del save_loc
-    except Exception:
-        print("unable to delete original zipfile - check if exists", datetime.now())
-
-    if verbose:
-        print("finished extracting zip - ", datetime.now())
-
-    return extract_loc
-
-
-def cleantxt_wrap(ugly_text, all_lower=False):
-    """
-    cleantxt_wrap - applies the clean function to a string.
-
-    Args:
-        ugly_text (str): [string to be cleaned]
-
-    Returns:
-        [str]: [cleaned string]
-    """
-    if isinstance(ugly_text, str) and len(ugly_text) > 0:
-        return clean(ugly_text, lower=all_lower)
-    else:
-        return ugly_text
-
-
-def setup_logging(loglevel):
-    """Setup basic logging
-
-    Args:
-      loglevel (int): minimum loglevel for emitting messages
-    """
-    logformat = "[%(asctime)s] %(levelname)s:%(name)s:%(message)s"
-    logging.basicConfig(
-        level=loglevel, stream=sys.stdout, format=logformat, datefmt="%Y-%m-%d %H:%M:%S"
-    )
diff --git a/spaces/eugenkalosha/Semmap/helpcomponents.py b/spaces/eugenkalosha/Semmap/helpcomponents.py
deleted file mode 100644
index ec08e9af7ef3144e957c2625d323350d5546f4e6..0000000000000000000000000000000000000000
--- a/spaces/eugenkalosha/Semmap/helpcomponents.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from helptexts import *
-import panel as pn
-from bokeh.models import Tooltip
-from bokeh.models.dom import HTML
-
-help_sidebar_pane = pn.pane.HTML(HELP_TEXT_SIDEBAR_PANE)
-
-hlp_topics_tooltip = Tooltip(content=HTML(HELP_TEXT_TOPICS),
-                  position="bottom",
-                  styles = {"width":"300px", "background-color": "#d3d3d3"}
-                 )
-hlp_topics = pn.widgets.TooltipIcon(value = HELP_TEXT_TOPICS)
-
-hlp_semmap_tooltip =Tooltip(content=HTML(HELP_TEXT_SEMMAP),
-                  position="bottom",
-                  styles = {"width":"400px", "background-color": "#d3d3d3"}
-                 )
-hlp_semmap = pn.widgets.TooltipIcon(value = HELP_TEXT_SEMMAP)
-
-hlp_words_tooltip = Tooltip(content=HTML(HELP_TEXT_WORDS),
-                  position="bottom",
-                  styles = {"width":"400px", "background-color": "#d3d3d3"}
-                 )
-hlp_words = pn.widgets.TooltipIcon(value = HELP_TEXT_WORDS)
-
diff --git a/spaces/evaluate-metric/rl_reliability/README.md b/spaces/evaluate-metric/rl_reliability/README.md
deleted file mode 100644
index 372302c049278bc49ee848e45a61bc71e4ad17b6..0000000000000000000000000000000000000000
--- a/spaces/evaluate-metric/rl_reliability/README.md
+++ /dev/null
@@ -1,118 +0,0 @@
----
-title: RL Reliability
-emoji: 🤗 
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-tags:
-- evaluate
-- metric
-description: >-
-  Computes the RL reliability metrics from a set of experiments. There is an `"online"` and `"offline"` configuration for evaluation.
----
-
-# Metric Card for RL Reliability
-
-## Metric Description
-The RL Reliability Metrics library provides a set of metrics for measuring the reliability of reinforcement learning (RL) algorithms. 
-
-## How to Use
-
-```python
-import evaluate
-import numpy as np
-
-rl_reliability = evaluate.load("rl_reliability", "online")
-results = rl_reliability.compute(
-    timesteps=[np.linspace(0, 2000000, 1000)],
-    rewards=[np.linspace(0, 100, 1000)]
-    )
-
-rl_reliability = evaluate.load("rl_reliability", "offline")
-results = rl_reliability.compute(
-    timesteps=[np.linspace(0, 2000000, 1000)],
-    rewards=[np.linspace(0, 100, 1000)]
-    )
-```
-
-
-### Inputs
-- **timesteps** *(List[int]): For each run a an list/array with its timesteps.*
-- **rewards** *(List[float]): For each run a an list/array with its rewards.*
-
-KWARGS:
-- **baseline="default"** *(Union[str, float]) Normalization used for curves. When `"default"` is passed the curves are normalized by their range in the online setting and by the median performance across runs in the offline case. When a float is passed the curves are divided by that value.*
-- **eval_points=[50000, 150000, ..., 2000000]** *(List[int]) Statistics will be computed at these points*
-- **freq_thresh=0.01** *(float) Frequency threshold for low-pass filtering.*
-- **window_size=100000** *(int) Defines a window centered at each eval point.*
-- **window_size_trimmed=99000** *(int) To handle shortened curves due to differencing*
-- **alpha=0.05** *(float)The "value at risk" (VaR) cutoff point, a float in the range [0,1].*
-
-### Output Values
-
-In `"online"` mode:
-- HighFreqEnergyWithinRuns: High Frequency across Time (DT)
-- IqrWithinRuns: IQR across Time (DT)
-- MadWithinRuns: 'MAD across Time (DT)
-- StddevWithinRuns: Stddev across Time (DT)
-- LowerCVaROnDiffs: Lower CVaR on Differences (SRT)
-- UpperCVaROnDiffs: Upper CVaR on Differences (SRT)
-- MaxDrawdown: Max Drawdown (LRT)
-- LowerCVaROnDrawdown: Lower CVaR on Drawdown (LRT)
-- UpperCVaROnDrawdown: Upper CVaR on Drawdown (LRT)
-- LowerCVaROnRaw: Lower CVaR on Raw
-- UpperCVaROnRaw: Upper CVaR on Raw
-- IqrAcrossRuns: IQR across Runs (DR)
-- MadAcrossRuns: MAD across Runs (DR)
-- StddevAcrossRuns: Stddev across Runs (DR)
-- LowerCVaROnAcross: Lower CVaR across Runs (RR)
-- UpperCVaROnAcross: Upper CVaR across Runs (RR)
-- MedianPerfDuringTraining: Median Performance across Runs
-
-In `"offline"` mode:
-- MadAcrossRollouts: MAD across rollouts (DF)
-- IqrAcrossRollouts: IQR across rollouts (DF)
-- LowerCVaRAcrossRollouts: Lower CVaR across rollouts (RF)
-- UpperCVaRAcrossRollouts: Upper CVaR across rollouts (RF)
-- MedianPerfAcrossRollouts: Median Performance across rollouts
-
-
-### Examples
-First get the sample data from the repository:
-
-```bash
-wget https://storage.googleapis.com/rl-reliability-metrics/data/tf_agents_example_csv_dataset.tgz
-tar -xvzf tf_agents_example_csv_dataset.tgz
-```
-
-Load the sample data:
-```python
-dfs = [pd.read_csv(f"./csv_data/sac_humanoid_{i}_train.csv") for i in range(1, 4)]
-```
-
-Compute the metrics:
-```python
-rl_reliability = evaluate.load("rl_reliability", "online")
-rl_reliability.compute(timesteps=[df["Metrics/EnvironmentSteps"] for df in dfs],
-                       rewards=[df["Metrics/AverageReturn"] for df in dfs])
-```
-
-## Limitations and Bias
-This implementation of RL reliability metrics does not compute permutation tests to determine whether algorithms are statistically different in their metric values and also does not compute bootstrap confidence intervals on the rankings of the algorithms. See the [original library](https://github.com/google-research/rl-reliability-metrics/) for more resources.
-
-## Citation
-
-```bibtex
-@conference{rl_reliability_metrics,
-  title = {Measuring the Reliability of Reinforcement Learning Algorithms},
-  author = {Stephanie CY Chan, Sam Fishman, John Canny, Anoop Korattikara, and Sergio Guadarrama},
-  booktitle = {International Conference on Learning Representations, Addis Ababa, Ethiopia},
-  year = 2020,
-}
-```
-
-## Further References
-- Homepage: https://github.com/google-research/rl-reliability-metrics
diff --git a/spaces/exbert-project/exbert/client/src/ts/api/mainApi.ts b/spaces/exbert-project/exbert/client/src/ts/api/mainApi.ts
deleted file mode 100644
index aa903b991ff4bf3f51a1c64b19fb1ac706fc0e4e..0000000000000000000000000000000000000000
--- a/spaces/exbert-project/exbert/client/src/ts/api/mainApi.ts
+++ /dev/null
@@ -1,137 +0,0 @@
-import * as d3 from 'd3';
-import { debug } from 'util';
-import { TokenDisplay } from '../data/TokenWrapper'
-import * as tp from '../etc/types'
-import * as rsp from './responses'
-import * as R from 'ramda'
-import { DemoAPI } from './demoAPI'
-import * as hash from 'object-hash'
-import { makeUrl, toPayload } from '../etc/apiHelpers'
-import { URLHandler } from '../etc/URLHandler';
-
-export const emptyTokenDisplay = new TokenDisplay()
-
-const baseurl = URLHandler.basicURL()
-
-/**
- * A rewrite of `d3-fetch`'s `d3.json` callback. If an api call fails, make a backup call to specified url and payload, if specified.
- * 
- * @param response Object expected at time of callback
- * @param backupUrl Backup url in the event of fail
- * @param backupPayload Backup payload if making a post request
- */
-function responseJson(response, backupUrl = null, backupPayload = null) {
-    if (!response.ok) {
-        if (backupUrl != null) {
-            console.log("STATIC FILE NOT FOUND");
-            return fetch(backupUrl, backupPayload).then(responseJson);
-        }
-        throw new Error(response.status + " " + response.statusText)
-    }
-    return response.json()
-}
-
-/**
- * Check first if the information being sent exists in a static demo file. If it does, send that. Otherwise, make a normal call to the server.
- * 
- * @param toSend The packet of information to send to an API endpoint
- * @param backupUrl Backup url in the event that the demo file is not found
- * @param backupPayload Backup payload if demo file not found, for POST requests only
- */
-function checkDemoAPI(toSend, backupUrl = null, backupPayload = null) {
-    const hsh = hash.sha1(toSend);
-    console.log("CHECKING DEMOAPI: " + hsh);
-    if (DemoAPI.hasOwnProperty(hsh)) {
-        // Relies on a symbolic link being present in the dist folder to the demo folder
-        const path = './demo/' + DemoAPI[hsh]
-        console.log("TRYING TO SENDING STATIC: ", path);
-        const follow = (response) => responseJson(response, backupUrl, backupPayload)
-        return fetch(path).then(follow)
-    }
-    return d3.json(backupUrl, backupPayload)
-}
-
-
-export class API {
-
-    constructor(private baseURL: string = null) {
-        if (this.baseURL == null) {
-            this.baseURL = baseurl + '/api';
-        }
-    }
-
-    getModelDetails(model: string, hashObj: {} | null = null): Promise<rsp.ModelDetailResponse> {
-        const toSend = {
-            model: model
-        }
-
-        const url = makeUrl(this.baseURL + "/get-model-details", toSend)
-        console.log("--- GET " + url);
-
-        if (hashObj != null) {
-            const key = hash.sha1(toSend)
-            d3.json(url).then(r => {
-                hashObj[key] = r;
-            })
-        }
-
-        return checkDemoAPI(toSend, url)
-    }
-
-    getMetaAttentions(model: string, sentence: string, layer: number, hashObj: {} | null = null): Promise<rsp.AttentionDetailsResponse> {
-        const toSend = {
-            model: model,
-            sentence: sentence,
-            layer: layer
-        };
-
-        const url = makeUrl(this.baseURL + "/attend+meta", toSend)
-        console.log("--- GET " + url);
-
-        // Add hash and value to hashObj
-        if (hashObj != null) {
-            const key = hash.sha1(toSend)
-            d3.json(url).then(r => {
-                hashObj[key] = r;
-            })
-        }
-
-        return checkDemoAPI(toSend, url)
-    }
-
-    /**
-     * Update the display based on the information that was already parsed from the passed sentence.
-     * 
-     * @param a The displayed tokens in the columns 
-     * @param sentenceA The original sentence that led to the tokenized information in `a`
-     * @param layer Which layer to search at
-     * @param hashObj If not null, store the information of the responses into the passed object. Used for creating demos.
-     */
-    updateMaskedAttentions(model: string, tokens: TokenDisplay, sentence: string, layer: number, hashObj: {} | null = null): Promise<rsp.AttentionDetailsResponse> {
-        const toSend = {
-            model: model,
-            tokens: R.map(R.prop('text'), tokens.tokenData),
-            sentence: sentence,
-
-            // Empty masks need to be sent as a number, unfortunately. Choosing -1 for this
-            mask: tokens.maskInds.length ? tokens.maskInds : [-1],
-            layer: layer,
-        }
-
-        const url = makeUrl(this.baseURL + '/update-mask');
-        const payload = toPayload(toSend)
-
-
-        if (hashObj != null) {
-            // Add hash and value to hashObj for demo purposes
-            const key = hash.sha1(toSend)
-            d3.json(url, payload).then(r => {
-                hashObj[key] = r;
-            })
-        }
-
-        console.log("--- POST " + url, payload);
-
-        return checkDemoAPI(toSend, url, payload)
-    }
-};
\ No newline at end of file
diff --git a/spaces/facebook/MusicGen/audiocraft/grids/musicgen/musicgen_pretrained_32khz_eval.py b/spaces/facebook/MusicGen/audiocraft/grids/musicgen/musicgen_pretrained_32khz_eval.py
deleted file mode 100644
index 39ceaf7dab15ec3f0f669cfe57ca9e932a9ab40d..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/audiocraft/grids/musicgen/musicgen_pretrained_32khz_eval.py
+++ /dev/null
@@ -1,99 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Evaluation with objective metrics for the pretrained MusicGen models.
-This grid takes signature from the training grid and runs evaluation-only stage.
-
-When running the grid for the first time, please use:
-REGEN=1 dora grid musicgen.musicgen_pretrained_32khz_eval
-and re-use the REGEN=1 option when the grid is changed to force regenerating it.
-
-Note that you need the proper metrics external libraries setup to use all
-the objective metrics activated in this grid. Refer to the README for more information.
-"""
-
-import os
-
-from ._explorers import GenerationEvalExplorer
-from ...environment import AudioCraftEnvironment
-from ... import train
-
-
-def eval(launcher, batch_size: int = 32, eval_melody: bool = False):
-    opts = {
-        'dset': 'audio/musiccaps_32khz',
-        'solver/musicgen/evaluation': 'objective_eval',
-        'execute_only': 'evaluate',
-        '+dataset.evaluate.batch_size': batch_size,
-        '+metrics.fad.tf.batch_size': 16,
-    }
-    # chroma-specific evaluation
-    chroma_opts = {
-        'dset': 'internal/music_400k_32khz',
-        'dataset.evaluate.segment_duration': 30,
-        'dataset.evaluate.num_samples': 1000,
-        'evaluate.metrics.chroma_cosine': True,
-        'evaluate.metrics.fad': False,
-        'evaluate.metrics.kld': False,
-        'evaluate.metrics.text_consistency': False,
-    }
-    # binary for FAD computation: replace this path with your own path
-    metrics_opts = {
-        'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research'
-    }
-    opt1 = {'generate.lm.use_sampling': True, 'generate.lm.top_k': 250, 'generate.lm.top_p': 0.}
-    opt2 = {'transformer_lm.two_step_cfg': True}
-
-    sub = launcher.bind(opts)
-    sub.bind_(metrics_opts)
-
-    # base objective metrics
-    sub(opt1, opt2)
-
-    if eval_melody:
-        # chroma-specific metrics
-        sub(opt1, opt2, chroma_opts)
-
-
-@GenerationEvalExplorer
-def explorer(launcher):
-    partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
-    launcher.slurm_(gpus=4, partition=partitions)
-
-    if 'REGEN' not in os.environ:
-        folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1]
-        with launcher.job_array():
-            for sig in folder.iterdir():
-                if not sig.is_symlink():
-                    continue
-                xp = train.main.get_xp_from_sig(sig.name)
-                launcher(xp.argv)
-        return
-
-    with launcher.job_array():
-        musicgen_base = launcher.bind(solver="musicgen/musicgen_base_32khz")
-        musicgen_base.bind_({'autocast': False, 'fsdp.use': True})
-
-        # base musicgen models
-        musicgen_base_small = musicgen_base.bind({'continue_from': '//pretrained/facebook/musicgen-small'})
-        eval(musicgen_base_small, batch_size=128)
-
-        musicgen_base_medium = musicgen_base.bind({'continue_from': '//pretrained/facebook/musicgen-medium'})
-        musicgen_base_medium.bind_({'model/lm/model_scale': 'medium'})
-        eval(musicgen_base_medium, batch_size=128)
-
-        musicgen_base_large = musicgen_base.bind({'continue_from': '//pretrained/facebook/musicgen-large'})
-        musicgen_base_large.bind_({'model/lm/model_scale': 'large'})
-        eval(musicgen_base_large, batch_size=128)
-
-        # melody musicgen model
-        musicgen_melody = launcher.bind(solver="musicgen/musicgen_melody_32khz")
-        musicgen_melody.bind_({'autocast': False, 'fsdp.use': True})
-
-        musicgen_melody_medium = musicgen_melody.bind({'continue_from': '//pretrained/facebook/musicgen-melody'})
-        musicgen_melody_medium.bind_({'model/lm/model_scale': 'medium'})
-        eval(musicgen_melody_medium, batch_size=128, eval_melody=True)
diff --git a/spaces/fartsmellalmao/combined-GI-RVC-models/lib/infer_pack/onnx_inference.py b/spaces/fartsmellalmao/combined-GI-RVC-models/lib/infer_pack/onnx_inference.py
deleted file mode 100644
index c78324cbc08414fffcc689f325312de0e51bd6b4..0000000000000000000000000000000000000000
--- a/spaces/fartsmellalmao/combined-GI-RVC-models/lib/infer_pack/onnx_inference.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import onnxruntime
-import librosa
-import numpy as np
-import soundfile
-
-
-class ContentVec:
-    def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
-        print("load model(s) from {}".format(vec_path))
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(vec_path, providers=providers)
-
-    def __call__(self, wav):
-        return self.forward(wav)
-
-    def forward(self, wav):
-        feats = wav
-        if feats.ndim == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.ndim == 1, feats.ndim
-        feats = np.expand_dims(np.expand_dims(feats, 0), 0)
-        onnx_input = {self.model.get_inputs()[0].name: feats}
-        logits = self.model.run(None, onnx_input)[0]
-        return logits.transpose(0, 2, 1)
-
-
-def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs):
-    if f0_predictor == "pm":
-        from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor
-
-        f0_predictor_object = PMF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "harvest":
-        from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor
-
-        f0_predictor_object = HarvestF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    elif f0_predictor == "dio":
-        from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor
-
-        f0_predictor_object = DioF0Predictor(
-            hop_length=hop_length, sampling_rate=sampling_rate
-        )
-    else:
-        raise Exception("Unknown f0 predictor")
-    return f0_predictor_object
-
-
-class OnnxRVC:
-    def __init__(
-        self,
-        model_path,
-        sr=40000,
-        hop_size=512,
-        vec_path="vec-768-layer-12",
-        device="cpu",
-    ):
-        vec_path = f"pretrained/{vec_path}.onnx"
-        self.vec_model = ContentVec(vec_path, device)
-        if device == "cpu" or device is None:
-            providers = ["CPUExecutionProvider"]
-        elif device == "cuda":
-            providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
-        elif device == "dml":
-            providers = ["DmlExecutionProvider"]
-        else:
-            raise RuntimeError("Unsportted Device")
-        self.model = onnxruntime.InferenceSession(model_path, providers=providers)
-        self.sampling_rate = sr
-        self.hop_size = hop_size
-
-    def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd):
-        onnx_input = {
-            self.model.get_inputs()[0].name: hubert,
-            self.model.get_inputs()[1].name: hubert_length,
-            self.model.get_inputs()[2].name: pitch,
-            self.model.get_inputs()[3].name: pitchf,
-            self.model.get_inputs()[4].name: ds,
-            self.model.get_inputs()[5].name: rnd,
-        }
-        return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16)
-
-    def inference(
-        self,
-        raw_path,
-        sid,
-        f0_method="dio",
-        f0_up_key=0,
-        pad_time=0.5,
-        cr_threshold=0.02,
-    ):
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        f0_predictor = get_f0_predictor(
-            f0_method,
-            hop_length=self.hop_size,
-            sampling_rate=self.sampling_rate,
-            threshold=cr_threshold,
-        )
-        wav, sr = librosa.load(raw_path, sr=self.sampling_rate)
-        org_length = len(wav)
-        if org_length / sr > 50.0:
-            raise RuntimeError("Reached Max Length")
-
-        wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000)
-        wav16k = wav16k
-
-        hubert = self.vec_model(wav16k)
-        hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32)
-        hubert_length = hubert.shape[1]
-
-        pitchf = f0_predictor.compute_f0(wav, hubert_length)
-        pitchf = pitchf * 2 ** (f0_up_key / 12)
-        pitch = pitchf.copy()
-        f0_mel = 1127 * np.log(1 + pitch / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        pitch = np.rint(f0_mel).astype(np.int64)
-
-        pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32)
-        pitch = pitch.reshape(1, len(pitch))
-        ds = np.array([sid]).astype(np.int64)
-
-        rnd = np.random.randn(1, 192, hubert_length).astype(np.float32)
-        hubert_length = np.array([hubert_length]).astype(np.int64)
-
-        out_wav = self.forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze()
-        out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant")
-        return out_wav[0:org_length]
diff --git a/spaces/fatiXbelha/sd/Download Driven Series How Colton and Rylee Healed Each Others Broken Souls.md b/spaces/fatiXbelha/sd/Download Driven Series How Colton and Rylee Healed Each Others Broken Souls.md
deleted file mode 100644
index 90655cf0ef1764c5c5f3143da66171b74780d903..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Driven Series How Colton and Rylee Healed Each Others Broken Souls.md	
+++ /dev/null
@@ -1,117 +0,0 @@
-
-<h1>Download Driven Series: How to Watch the Passionate Romance Show Online</h1>
-<p>If you are looking for a steamy and captivating romance show to binge-watch, you might want to check out the <strong>Driven series</strong>. Based on the bestselling novels by K. Bromberg, the Driven series follows the turbulent relationship between Rylee Thomas, a social worker with a tragic past, and Colton Donavan, a bad boy race car driver with a dark secret. The show features Olivia Grace Applegate and Casey Deidrick as the main leads, who deliver stunning performances and sizzling chemistry on screen.</p>
-<h2>download driven series</h2><br /><p><b><b>Download File</b> &#10004; <a href="https://urllie.com/2uNzaq">https://urllie.com/2uNzaq</a></b></p><br /><br />
-<p>The Driven series consists of three seasons, with six episodes each. The first season was released in 2018, followed by the second season in 2021 and the third season in 2022. The show has received rave reviews from critics and fans alike, who praised its faithful adaptation of the books, its emotional depth, its thrilling action scenes, and its sensual romance.</p>
-<p>In this article, we will tell you everything you need to know about how to <strong>download</strong> the Driven series online. Whether you want to watch it on your laptop, tablet, or smartphone, we will show you the best platforms to download it legally and safely. We will also give you some tips and tricks to make your downloading experience easier and more enjoyable.</p>
-<h2>Why You Should Watch Driven Series</h2>
-<p>There are many reasons why you should watch the Driven series if you are a fan of romance shows. Here are some of them:</p>
-<ul>
-<li>The chemistry between Rylee and Colton is off the charts. They have a passionate and intense connection that will keep you hooked from the first episode to the last. You will love watching them overcome their personal demons and fall in love with each other.</li>
-<li>The story is gripping and emotional. The Driven series explores themes such as trauma, abuse, trust, forgiveness, family, and friendship. It will make you laugh, cry, swoon, and gasp as you follow Rylee and Colton's journey.</li>
-<li>The racing scenes are exhilarating and realistic. The show features amazing stunts and effects that will make you feel like you are on the track with Colton. You will also learn more about the world of professional racing and its challenges.</li>
-<li>The reviews are positive and glowing. The show has a rating of 7.1 out of 10 on IMDb and a rating <p>Continuing from the previous paragraph, here is the rest of the article:</p>
- <p>of 4.6 out of 5 on Passionflix, the official platform to watch and download the Driven series. The show has also been praised by the author of the books, K. Bromberg, who said that it was "everything I could have hoped for and so much more".</p>
-<h2>Where to Download Driven Series</h2>
-<h3>Passionflix</h3>
-<p>The best and most reliable way to download the Driven series is to use <strong>Passionflix</strong>, the streaming service that specializes in romance shows and movies. Passionflix is the exclusive producer and distributor of the Driven series, which means that you can only watch it legally on their platform.</p>
-<p>Passionflix offers two subscription plans: a monthly plan for $5.99 and an annual plan for $59.99. Both plans give you unlimited access to their library of original and licensed content, which includes not only the Driven series, but also other popular romance adaptations such as The Matchmaker's Playbook, Gabriel's Inferno, and The Will.</p>
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-<p>Passionflix also has some unique features and benefits that make it worth subscribing to, such as:</p>
-<ul>
-<li>You can download up to 10 titles at a time on your device and watch them offline for up to 30 days.</li>
-<li>You can choose from different video quality options, ranging from low (360p) to high (1080p).</li>
-<li>You can select from various subtitle languages, including English, Spanish, French, German, Italian, and Portuguese.</li>
-<li>You can enjoy exclusive behind-the-scenes footage, interviews, and bonus content from the Driven series and other shows.</li>
-<li>You can support the production of more romance shows and movies by becoming a Passionflix member.</li>
-</ul>
-<h3>Other Options</h3>
-<p>If you don't want to subscribe to Passionflix, or if you live in a country where Passionflix is not available, you might be wondering if there are any other options to download the Driven series. The answer is yes, but they are not as convenient or affordable as Passionflix.</p>
-<p>Some of the other platforms where you can watch or download the Driven series are:</p>
-<table>
-<tr><th>Platform</th><th>Price</th><th>Availability</th><th>Quality</th></tr>
-<tr><td>Amazon Prime Video</td><td>$2.99 per episode or $14.99 per season</td><td>US only</td><td>HD</td></tr>
-<tr><td>iTunes</td><td>$2.99 per episode or $14.99 per season</td><td>US only</td><td>HD</td></tr>
-<tr><td>Google Play</td><td>$1.99 per episode or $9.99 per season</td><td>Select countries</td><td>SD or HD</td></tr>
-<tr><td>YouTube</td><td>$1.99 per episode or $9.99 per season</td><td>Select countries</td><td>SD or HD</td></tr>
-</table>
-<p>As you can see, these platforms are more expensive than Passionflix, and they have limited availability depending on your location. They also have different download policies, such as expiration dates, device limits, and DRM restrictions. Therefore, we recommend that you check the terms and conditions of each platform before you decide to purchase or download the Driven series from them.</p>
-<h2>How to Download Driven Series on Passionflix</h2>
-<h3>Step 1: Sign Up for Passionflix</h3>
-<p>The first step to download the Driven series on Passionflix is to sign up for a Passionflix account. To do this, you need to visit their website at <a href="(^1^)">https://www.passionflix.com/</a> and click on the "Start Your Free Trial" button. You will then be asked to enter your email address and create a password. You will also need to choose a subscription plan (monthly or annual) and enter your payment details. You can use a credit card or a PayPal account to pay for your subscription.</p>
-<p>Once you have completed the sign-up process, you will receive a confirmation email with a link to activate your account. You will also get access to a 7-day free trial period, during which you can watch and download any content on Passionflix without being charged. However, if you don't cancel your subscription before the trial ends, you will be automatically billed for the next month or year.</p>
-<h3>Step 2: Search for Driven <p>Continuing from the previous paragraph, here is the rest of the article:</p>
- <h3>Step 2: Search for Driven Series</h3>
-<p>The next step to download the Driven series on Passionflix is to search for it on their library. There are two ways to do this:</p>
-<ul>
-<li>You can browse the categories on the homepage or the menu bar, such as "Originals", "Romance", "Drama", or "Action". You will find the Driven series under the "Originals" category, along with other Passionflix exclusives.</li>
-<li>You can use the search bar on the top right corner of the website or the app, and type in "Driven" or any related keywords. You will see a list of results that match your query, and you can click on the Driven series to access its page.</li>
-</ul>
-<p>Once you have found the Driven series page, you will see some information about the show, such as its synopsis, cast, trailer, rating, and episodes. You will also see a button that says "Watch Now" or "Download".</p>
-<h3>Step 3: Download Driven Series Episodes</h3>
-<p>The final step to download the Driven series on Passionflix is to choose which episodes you want to download and select your preferred download options. You can do this in two ways:</p>
-<ul>
-<li>You can download each episode individually by clicking on the "Download" button below each episode thumbnail. You will then see a pop-up window that lets you choose the video quality (low, medium, or high) and the subtitle language (if available). You can also see how much storage space each episode will take on your device. After you have made your choices, you can click on the "Download" button again to start the download process.</li>
-<li>You can download all episodes at once by clicking on the "Download All" button on the top right corner of the Driven series page. You will then see a pop-up window that lets you choose the same options as above, but for all episodes. After you have made your choices, you can click on the "Download All" button again to start the download process.</li>
-</ul>
-<p>Once you have downloaded the Driven series episodes, you can find them on your device's storage or on Passionflix's app or website under the "Downloads" section. You can watch them offline anytime and anywhere you want, without any ads or interruptions.</p>
-<h2>Tips and Tricks for Downloading Driven Series</h2>
-<p>To make your downloading experience easier and more enjoyable, here are some tips and tricks that you can follow:</p>
-<ul>
-<li>Use a VPN (virtual private network) if you live in a country where Passionflix is not available or if you want to access content from other regions. A VPN will mask your IP address and location and allow you to bypass geo-restrictions and censorship. However, be careful to use a reputable and secure VPN service that does not compromise your privacy or data.</li>
-<li>Check your internet speed and connection before you start downloading. You will need a stable and fast internet connection to download the Driven series episodes without any errors or delays. You can use online tools such as Speedtest or Fast to measure your internet speed and performance.</li>
-<li>Manage your storage space wisely. The Driven series episodes can take up a lot of space on your device, especially if you download them in high quality. You can free up some space by deleting unwanted files, apps, or photos, or by using external storage devices such as USB drives or SD cards. You can also delete downloaded episodes that you have already watched or that you don't want to keep anymore.</li>
-<li>Avoid illegal downloads at all costs. Do not use torrent sites, file-sharing platforms, or pirated websites to download the Driven series or any other content. These sources are not only illegal, but also unsafe and unreliable. They can expose you to malware, viruses, hackers, identity theft, legal issues, and poor quality content. Always use legal and authorized platforms such as Passionflix to download content.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>The Driven series is one of the best romance shows that you can watch online. It has a captivating story, amazing actors, thrilling action scenes, and passionate romance scenes that will make you fall in love with Rylee and Colton.</p>
-<p>If you want to download the Driven series online, we recommend that you use Passionflix, the official platform that produces and distributes the show. Passionflix offers a great deal of features and benefits that make it worth subscribing to, such as unlimited access, offline viewing, video quality options, subtitle languages, exclusive content, and more.</p>
-<p>If Passionflix is not available in your country or <p>Continuing from the previous paragraph, here is the rest of the article:</p>
- <p>if you prefer to use other platforms, you can also watch or download the Driven series on Amazon Prime Video, iTunes, Google Play, or YouTube. However, these platforms are more expensive and have limited availability and quality compared to Passionflix.</p>
-<p>To download the Driven series on Passionflix, you just need to follow three simple steps: sign up for Passionflix, search for the Driven series, and download the episodes. You can also follow some tips and tricks to make your downloading experience easier and more enjoyable, such as using a VPN, checking your internet speed, managing your storage space, and avoiding illegal downloads.</p>
-<p>We hope that this article has helped you learn how to download the Driven series online. Now you can enjoy watching this amazing show anytime and anywhere you want. Don't forget to share your thoughts and opinions about the show with us in the comments section below. Happy watching!</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about downloading the Driven series online:</p>
-<ol>
-<li><strong>How many episodes are there in the Driven series?</strong><br>
-There are 18 episodes in the Driven series, divided into three seasons. Each season has six episodes, and each episode has a runtime of about 30 minutes.</li>
-<li><strong>Is the Driven series based on a book?</strong><br>
-Yes, the Driven series is based on a series of novels by K. Bromberg, which consists of nine books: Driven, Fueled, Crashed, Raced, Aced, Slow Burn, Sweet Ache, Hard Beat, and Down Shift. The show adapts the first three books of the series.</li>
-<li><strong>Who are the main actors in the Driven series?</strong><br>
-The main actors in the Driven series are Olivia Grace Applegate as Rylee Thomas and Casey Deidrick as Colton Donavan. They are supported by a talented cast of actors, such as Michael Roark as Haddie Montgomery, Christian Ganiere as Zander Donavan, Kenzie Dalton as Becks Daniels, and Ryan Carnes as Tanner Donavan.</li>
-<li><strong>What is Passionflix?</strong><br>
-Passionflix is a streaming service that specializes in romance shows and movies. It was founded in 2017 by Tosca Musk, Joany Kane, and Jina Panebianco. Passionflix produces and distributes original and licensed content that caters to romance lovers. Some of their original shows and movies include The Matchmaker's Playbook, Gabriel's Inferno, The Will, The Protector, Hollywood Dirt, and Afterburn/Aftershock.</li>
-<li><strong>How can I cancel my Passionflix subscription?</strong><br>
-You can cancel your Passionflix subscription at any time by logging into your account on their website or app and going to the "Account Settings" section. You will see an option to cancel your subscription there. You will still be able to access Passionflix's content until the end of your current billing cycle.</li>
-</ol></p> 197e85843d<br />
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diff --git a/spaces/fatiXbelha/sd/Download Script Clash of Clans Game Guardian The Ultimate Guide to Hacking CoC.md b/spaces/fatiXbelha/sd/Download Script Clash of Clans Game Guardian The Ultimate Guide to Hacking CoC.md
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+++ /dev/null
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-
-<h1>How to Download a Script for Clash of Clans Using Game Guardian</h1>
-<p>If you are a fan of Clash of Clans, you may have heard of Game Guardian, a game cheat/hack tool that can help you gain an edge over your opponents. With Game Guardian, you can modify money, HP, SP, and other values in games, as well as perform advanced hacks, such as changing the game speed, revealing hidden traps, or upgrading buildings without requirements.</p>
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-<p>But how do you use Game Guardian for Clash of Clans? And where can you find a script that can do all these things for you? In this article, we will show you how to download and install Game Guardian on your Android device, how to find and download a script for Clash of Clans using Game Guardian, and how to run and use a script for Clash of Clans using Game Guardian.</p>
- <h2>What is Game Guardian?</h2>
-<p>Game Guardian is a game cheat/hack tool that allows users to modify money, HP, SP, and other values in games, as well as perform advanced hacks, such as changing the game speed, revealing hidden traps, or upgrading buildings without requirements.</p>
- <h2>What is Clash of Clans?</h2>
-<p>Clash of Clans is a popular strategy game where players build and defend their villages, join clans, and compete in wars. The game features various resources, such as gold, elixir, dark elixir, and gems, that can be used to upgrade buildings, train troops, research spells, and buy items. The game also has a ranking system based on trophies, which are earned or lost by attacking or defending other players' villages.</p>
- <h2>Why Use a Script for Clash of Clans?</h2>
-<p>A script is a file that contains commands and instructions for Game Guardian to execute. A script can automate tasks, such as searching and editing values, or perform complex hacks, such as changing the game speed, revealing hidden traps, or upgrading buildings without requirements. A script can make the game easier, more fun, or more challenging, depending on your preferences. However, using a script also comes with some risks, such as getting banned by the game developers or losing your account data. Therefore, you should always use a script with caution and at your own responsibility.</p>
- <h1>How to Download and Install Game Guardian on Your Android Device</h1>
-<p>Before you can use a script for Clash of Clans, you need to download and install Game Guardian on your Android device. Here are the steps to do so:</p>
-<ol>
-<li>Download the Game Guardian apk file from the official website or from other sources. You can find the latest version of the app on the website or on the forum. Make sure you download the file from a trusted source and scan it for viruses before installing it.</li>
-<li>Enable installation from unknown sources in your device settings. To do this, go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps that are not from the Google Play Store.</li>
-<li>Locate and install the apk file using a file manager app. You can use any file manager app that you have on your device, such as ES File Explorer or File Manager. Navigate to the folder where you saved the apk file and tap on it to install it.</li>
-<li>Launch Game Guardian and grant it root or virtual space access. Depending on your device model and Android version, you may need to root your device or use a virtual space app to run Game Guardian. Rooting is a process that gives you full control over your device's system, but it also voids your warranty and may cause some issues. Virtual space is an app that creates a clone of your device's system, where you can run Game Guardian without rooting. You can find more information about rooting and virtual space on the Game Guardian website or forum. Once you launch Game Guardian, it will ask you to grant it root or virtual space access. Follow the instructions on the screen to do so.</li>
-</ol>
- <h1>How to Find and Download a Script for Clash of Clans Using Game Guardian</h1>
-<p>Now that you have Game Guardian installed on your device, you can start looking for a script for Clash of Clans. Here are the steps to do so:</p>
-<ol>
-<li>Launch Game Guardian and select Clash of Clans as the target game. To do this, tap on the Game Guardian icon that appears on your screen and select Clash of Clans from the list of running apps. This will attach Game Guardian to the game process and allow you to use its features.</li>
-<li>Tap on the menu icon and select "Execute script". This will open a window where you can load and run scripts for Game Guardian.</li>
-<li>Tap on the search icon and enter "clash of clans" as the keyword. This will search for scripts related to Clash of Clans on the Game Guardian server. You can also use other keywords or filters to narrow down your search results.</li>
-<li>Browse through the results and choose a script that suits your needs. You can read the description, ratings, comments, and screenshots of each script to get an idea of what it does and how it works. You can also check the date and version of each script to make sure it is compatible with the current version of Clash of Clans.</li>
-<li>Tap on the download icon and save the script to your device storage. You can choose any folder where you want to save the script file. Make sure you remember the location of the file for later use.</li>
-</ol>
- <h1>How to Run and Use a Script for Clash of Clans Using Game Guardian</h1>
-<p>Once you have downloaded a script for Clash of Clans, you can run and use it with Game Guardian. Here are the steps to do so:</p>
-<ol>
-<li>Launch Game Guardian and select Clash of Clans as the target game. To do this, tap on the Game Guardian icon that appears on your screen and select Clash of Clans from the list of running apps. This will attach Game Guardian to the game process and allow you to use its features.</li>
-<li>Tap on the menu icon and select "Execute script". This will open a window where you can load and run scripts for Game Guardian.</li>
-<li>Tap on the folder icon and locate the script file that you downloaded. You can use any file manager app that you have on your device, such as ES File Explorer or File Manager, to navigate to the folder where you saved the script file.</li>
-<li>Tap on the script file and wait for it to load. Depending on the size and complexity of the script, it may take a few seconds or minutes to load. You will see a message that says "Script loaded" when it is ready.</li>
-<li>Follow the instructions on the screen to use the script features. You may need to enter some values, select some options, or tap on some buttons. The script may also have a menu or a dialog box where you can choose what you want to do. For example, you may see a menu that says "Clash of Clans Script" with options such as "Game Speed", "Hidden Traps", "Upgrade Buildings", etc. Tap on the option that you want to use and follow the instructions.</li>
-</ol>
- <h1>Conclusion and FAQs</h1>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to download and install Game Guardian on your Android device, how to find and download a script for Clash of Clans using Game Guardian, and how to run and use a script for Clash of Clans using Game Guardian. We hope that this article has been helpful and informative for you. However, we also want to remind you that using Game Guardian and scripts for Clash of Clans may have some risks, such as getting banned by the game developers or losing your account data. Therefore, you should always use them with caution and at your own responsibility. Also, you should respect the game rules and other players' rights and not abuse or exploit the game features. Remember, cheating is not fun if it ruins the game for everyone.</p>
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- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers related to the topic of this article:</p>
-<ul>
-<li><b>Q: Is Game Guardian safe to use?</b></li>
-<li>A: Game Guardian is safe to use as long as you download it from a trusted source and scan it for viruses before installing it. However, using Game Guardian may also expose your device to some security risks, such as malware or spyware, especially if you use it with root access or virtual space. Therefore, you should always be careful about what you download and install on your device and use a reliable antivirus app to protect it.</li>
-<li><b>Q: Is using Game Guardian and scripts for Clash of Clans legal?</b></li>
-<li>A: Using Game Guardian and scripts for Clash of Clans is not illegal, but it may violate the game's terms of service or user agreement. This means that the game developers have the right to ban or suspend your account if they detect that you are using cheats or hacks in their game. Therefore, you should always read and follow the game's rules and policies before using any tools or methods that may affect the game's performance or fairness.</li>
-<li><b>Q: Where can I find more scripts for Clash of Clans or other games?</b></li>
-<li>A: You can find more scripts for Clash of Clans or other games on the Game Guardian website or forum. There are many users who share their scripts or request scripts for various games. You can also search for scripts on other websites or platforms, such as YouTube, Reddit, or Telegram. However, you should always be careful about what you download and use, as some scripts may be fake, outdated, or malicious.</li>
-<li><b>Q: How can I create my own script for Clash of Clans or other games?</b></li>
-<li>A: You can create your own script for Clash of Clans or other games by using a text editor app, such as Notepad or Notepad++, and writing commands and instructions for Game Guardian to execute. You can learn how to write scripts by reading the documentation and tutorials on the Game Guardian website or forum. You can also modify or edit existing scripts by opening them with a text editor app and changing some values or options.</li>
-<li><b>Q: How can I update my script for Clash of Clans or other games?</b></li>
-<li>A: You can update your script for Clash of Clans or other games by downloading the latest version of the script from the source where you got it. You can also check the comments or feedback of other users who have used the script to see if there are any issues or bugs with the script. If you have created or modified your own script, you may need to adjust some values or options according to the changes in the game.</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/3D Bowling APK The Ultimate Bowling Experience on Your Android Device.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/3D Bowling APK The Ultimate Bowling Experience on Your Android Device.md
deleted file mode 100644
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-<h1>3D Bowling APK: The Best and Most Realistic Bowling Game for Android</h1>
-<p>Do you love bowling but don't have the time or money to go to a real bowling alley? Do you want to experience the thrill and excitement of bowling on your Android phone? If you answered yes to any of these questions, then you should try 3D Bowling APK, the best and most realistic bowling game for Android.</p>
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-<p>3D Bowling APK is a free bowling game app that you can download and install on your Android phone. It is developed by Italic Games, a company that specializes in creating casual and fun games for mobile devices. 3D Bowling APK is one of their most popular games, with over 100 million downloads and 4.6 stars rating on Google Play Store.</p>
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-<li>It is fun and easy to play. You don't need any special skills or equipment to enjoy bowling on your phone. Just swipe your finger and watch the ball roll.</li>
-<li>It is realistic and immersive. You will feel like you are in a real bowling alley, thanks to the stunning 3D graphics, the state-of-the-art 3D physics engine, and the realistic sound effects.</li>
-<li>It is challenging and rewarding. You can choose from five different bowling scenes and multiple bowling balls, each with their own characteristics and advantages. You can also track your stats and achievements, and compete with other players online.</li>
-</ul>
-<h2>Features of 3D Bowling APK</h2>
-<p>As we mentioned before, 3D Bowling APK has many features that make it stand out from other bowling games. Here are some of them:</p>
-<h3>Stunning 3D graphics</h3>
-<p>One of the first things you will notice when you play 3D Bowling APK is how amazing it looks. The game has high-quality graphics that create a realistic and immersive environment. You will see the reflections of the lights on the polished lanes, the shadows of the pins, and the sparks when you hit a strike. You will also see the different textures and colors of the balls, the lanes, and the backgrounds.</p>
-<h3>State-of-the-art 3D physics engine</h3>
-<p>Another thing that makes 3D Bowling APK so realistic is its advanced physics engine. The game simulates the real physics of bowling, such as gravity, friction, spin, and collision. You will see how the ball reacts to your finger movements, how it curves on the lane, how it hits the pins, and how it bounces off the walls. You will also see how the pins fly in different directions when you knock them down.</p>
-<h3>Multiple bowling scenes and balls</h3>
-<p>To add more variety and fun to your game, 3D Bowling APK offers you five different bowling scenes to choose from, each with its own theme and atmosphere. You can bowl in a classic alley, a cosmic space, a tropical beach, a snowy mountain, or a desert oasis. Each scene has its own background music and sound effects, as well as different lane designs and pin arrangements. You can also choose from 16 different bowling balls, each with its own weight, size, color, and pattern. Some balls are more suitable for certain scenes and lanes, while others have special effects, such as glowing, sparkling, or exploding. You can unlock more balls by completing achievements or by purchasing them with coins. <h3>Detailed stats tracking</h3>
-<p>If you want to improve your bowling skills and challenge yourself, you can use the stats tracking feature of 3D Bowling APK. The game records your scores, strikes, spares, splits, and gutter balls for each game and scene. You can also see your average score, highest score, and total score for each scene and overall. You can also compare your stats with other players on the global leaderboards.</p>
-<h2>How to play 3D Bowling APK</h2>
-<p>Playing 3D Bowling APK is very simple and intuitive. You just need to follow these steps:</p>
-<h3>Drag the ball to position it</h3>
-<p>When you start a game, you will see a ball on the bottom of the screen. You can drag it left or right to align it with the pins. You can also drag it up or down to change the angle of the ball.</p>
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-<h3>Flick the ball with your finger to bowl</h3>
-<p>Once you have positioned the ball, you can flick it with your finger to bowl. The faster you flick, the faster the ball will roll. You can also tilt your phone to add some spin to the ball.</p>
-<h3>Gesture a curve on the screen to throw a hook ball</h3>
-<p>If you want to throw a hook ball, you can gesture a curve on the screen instead of flicking the ball. The direction and shape of the curve will determine the trajectory and curvature of the ball. A hook ball can help you hit more pins and score more strikes.</p>
-<h2>Tips and tricks for 3D Bowling APK</h2>
-<p>Now that you know how to play 3D Bowling APK, here are some tips and tricks to help you master the game and beat your opponents:</p>
-<h3>How to score a strike</h3>
-<p>A strike is when you knock down all 10 pins with one throw. It is the highest scoring move in bowling, as it gives you 10 points plus the points of your next two throws. To score a strike, you need to aim for the pocket, which is the space between the first pin and either the second or third pin. You also need to throw the ball with enough speed and spin to hit all the pins.</p>
-<h3>How to adjust the ball speed and direction</h3>
-<p>Sometimes, you may need to adjust the speed and direction of your ball after you have thrown it. You can do this by tilting your phone left or right to make the ball move sideways, or by tilting it forward or backward to make it speed up or slow down. This can help you avoid gutter balls or hit more pins.</p>
-<h3>How to use different balls for different situations</h3>
-<p>As we mentioned before, 3D Bowling APK offers you 16 different balls to choose from, each with its own characteristics and advantages. Some balls are heavier or lighter than others, which affects their speed and momentum. Some balls are bigger or smaller than others, which affects their accuracy and pin impact. Some balls have special effects that can help you score more points or create more chaos on the lane. You should experiment with different balls and see which ones suit your style and preference. You should also use different balls for different situations, such as using a heavier ball for more power, a lighter ball for more spin, a bigger ball for more accuracy, or a smaller ball for more curve.</p>
- <h2>Conclusion</h2>
- <p>3D Bowling APK is a fun and realistic bowling game that you can play on your Android phone anytime and anywhere. It has stunning 3D graphics, state-of-the-art 3D physics engine, multiple bowling scenes and balls, detailed stats tracking, and easy-to-use controls. It is suitable for players of all ages and skill levels. Whether you are a casual bowler or a professional bowler, you will find something to enjoy in 3D Bowling APK.</p>
- <p>If you are interested in downloading 3D Bowling APK, you can find it on Google Play Store or on other third-party websites. However, be careful when downloading from unknown sources, as they may contain viruses or malware that can harm your device or steal your data. Always check the reviews and ratings of the app before downloading it, and make sure you have a reliable antivirus software installed on your phone. We hope you enjoyed this article and learned something new about 3D Bowling APK. If you have any questions, comments, or feedback, please feel free to leave them below. We would love to hear from you and help you with any issues you may have. Happy bowling! <h2>FAQs</h2>
-<p>Here are some of the most frequently asked questions about 3D Bowling APK:</p>
-<h3>Q: How can I get more coins in 3D Bowling APK?</h3>
-<p>A: You can get more coins in 3D Bowling APK by playing more games, completing achievements, watching ads, or buying them with real money. You can use coins to unlock more balls or scenes.</p>
-<h3>Q: How can I play 3D Bowling APK with my friends?</h3>
-<p>A: You can play 3D Bowling APK with your friends by using the online multiplayer mode. You can either join a random match or create a private room and invite your friends. You can also chat with your friends while playing.</p>
-<h3>Q: How can I change the language of 3D Bowling APK?</h3>
-<p>A: You can change the language of 3D Bowling APK by going to the settings menu and selecting the language option. You can choose from English, Spanish, French, German, Italian, Portuguese, Russian, Turkish, Japanese, Korean, Simplified Chinese, or Traditional Chinese.</p>
-<h3>Q: How can I contact the developer of 3D Bowling APK?</h3>
-<p>A: You can contact the developer of 3D Bowling APK by sending an email to italicgames@gmail.com or by visiting their website at http://www.italicgames.com/. You can also follow them on Facebook at https://www.facebook.com/ItalicGames/ or on Twitter at https://twitter.com/italicgames.</p>
-<h3>Q: Is 3D Bowling APK safe to download and play?</h3>
-<p>A: Yes, 3D Bowling APK is safe to download and play, as long as you download it from a trusted source, such as Google Play Store or the official website of Italic Games. The game does not contain any viruses or malware that can harm your device or steal your data. However, you should always be careful when downloading apps from unknown sources, as they may contain harmful content.</p> 197e85843d<br />
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-<br />
-<h1>Descargar Candy Crush Saga APK Gratis: Cómo Jugar al Juego de Puzzles Más Popular del Mundo</h1>
- <p>¿Te gustan los juegos de puzzles? ¿Te apetece pasar un rato dulce y divertido? Entonces, te encantará Candy Crush Saga, el juego de combinar tres caramelos que ha conquistado a millones de personas en todo el mundo. En este artículo, te contamos qué es Candy Crush Saga, por qué deberías descargar su archivo APK gratis, cómo hacerlo y cómo jugar al juego. ¡Sigue leyendo y prepárate para disfrutar de una experiencia azucarada!</p>
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- <h2>¿Qué es Candy Crush Saga?</h2>
- <p>Candy Crush Saga es un juego gratuito de tipo "match three" que fue lanzado por la empresa King en 2012, originalmente para Facebook; luego se crearon otras versiones para iOS, Android, Windows Phone y Windows 10. Se trata de una variación de su juego de navegador Candy Crush. </p>
- <h3>Un juego de combinar tres dulces</h3>
- <p>El juego consiste en intercambiar dos caramelos adyacentes entre varios en el tablero de juego para hacer una fila o columna de al menos tres caramelos del mismo color, eliminando esos caramelos del tablero y reemplazándolos con otros nuevos, que podrían crear más combinaciones. Las combinaciones de cuatro o más caramelos crean caramelos especiales que actúan como potenciadores con mayores capacidades de limpiar el tablero. </p>
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- <p>El juego se divide en muchos niveles, que deben completarse en secuencia. Cada nivel plantea un desafío diferente al usuario, como alcanzar una puntuación determinada, eliminar toda la gelatina o los chocolates, recoger los ingredientes o cumplir un orden específico. Hay cinco tipos de niveles: naranja (movimientos), azul (gelatina), verde (ingredientes), morado (tiempo) y rosa (orden). </p>
- <h3>Un juego con modos de juego variados y divertidos</h3>
- <p>Candy Crush Saga ofrece diferentes formas de jugar: modos de juego como Puntuación Objetivo, Eliminar la Gelatina, Recoger los Ingredientes y Modo Orden; eventos diarios y temporales que ofrecen recompensas gratuitas; competiciones con amigos y otros jugadores; y juegos derivados como Candy Crush Soda Saga, Candy Crush Jelly Saga y Candy Crush Friends Saga, que tienen sus propias características y mecánicas. </p>
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- <p>Candy Crush Saga es un juego gratuito que se puede descargar desde las tiendas oficiales de aplicaciones como Google Play o App Store. Sin embargo, hay algunas razones por las que podrías prefer ir descargar el archivo APK gratis de Candy Crush Saga. Estas son algunas de ellas:</p>
- <h3>Para jugar sin conexión a internet</h3>
- <p>Una de las ventajas de descargar el archivo APK gratis de Candy Crush Saga es que podrás jugar al juego sin necesidad de tener una conexión a internet. Esto es muy útil si quieres disfrutar del juego en lugares donde no hay wifi o datos móviles, o si quieres ahorrar batería y datos. Además, podrás sincronizar tu progreso con tu cuenta de Facebook cuando vuelvas a conectarte. </p>
- <h3>Para acceder a las últimas actualizaciones y novedades</h3>
- <p>Otra razón para descargar el archivo APK gratis de Candy Crush Saga es que podrás acceder a las últimas versiones del juego, que incluyen nuevas características, niveles, eventos y mejoras. A veces, las tiendas oficiales de aplicaciones tardan en actualizar el juego, o pueden tener problemas de compatibilidad con algunos dispositivos. Al descargar el archivo APK gratis, te aseguras de tener siempre la versión más reciente y óptima del juego. </p>
- <h3>Para evitar anuncios y compras integradas</h3>
- <p>Por último, al descargar el archivo APK gratis de Candy Crush Saga, podrás evitar los anuncios y las compras integradas que tiene el juego. Estos elementos pueden resultar molestos o tentadores para algunos usuarios, que pueden gastar dinero real en comprar vidas, potenciadores o barras de oro. Al descargar el archivo APK gratis, podrás jugar al juego sin interrupciones ni gastos adicionales. </p>
- <h2>¿Cómo descargar Candy Crush Saga APK gratis?</h2>
- <p>Ahora que ya sabes por qué descargar el archivo APK gratis de Candy Crush Saga, te explicamos cómo hacerlo paso a paso. Es muy fácil y rápido, solo tienes que seguir estas instrucciones:</p>
- <h3>Busca un sitio web confiable que ofrezca el archivo APK</h3>
- <p>Lo primero que tienes que hacer es buscar un sitio web que ofrezca el archivo APK gratis de Candy Crush Saga. Hay muchos sitios web que ofrecen este servicio, pero no todos son seguros o fiables. Algunos pueden contener virus, malware o archivos dañados que pueden afectar a tu dispositivo o a tu privacidad. Por eso, te recomendamos que uses un sitio web de confianza, como [APKPure] o [Uptodown], que son plataformas reconocidas y verificadas que ofrecen archivos APK originales y sin modificaciones. </p>
- <h3>Descarga el archivo APK en tu dispositivo Android</h3>
- <p>Una vez que hayas encontrado el sitio web que ofrece el archivo APK gratis de Candy Crush Saga, solo tienes que hacer clic en el botón de descarga y esperar a que se complete el proceso. El archivo APK se guardará en la carpeta de descargas de tu dispositivo Android, o en la ubicación que hayas elegido. El tamaño del archivo APK puede variar según la versión del juego, pero suele rondar los 100 MB. </p>
- <h3>Instala el archivo APK siguiendo las instrucciones</h3>
- <p>El último paso es instalar el archivo APK en tu dispositivo Android. Para ello, tendrás que habilitar la opción de "Orígenes desconocidos" o "Fuentes desconocidas" en los ajustes de seguridad de tu dispositivo, para permitir la instalación de aplicaciones que no provienen de las tiendas oficiales. Luego, tendrás que buscar el archivo APK en la carpeta donde lo hayas guardado y hacer clic en él para iniciar la instalación. Sigue las instrucciones que aparecen en la pantalla y espera a que se complete la instalación. Una vez hecho esto, ya podrás abrir y jugar a Candy Crush Saga desde tu dispositivo Android. </p>
- <h2>¿Cómo jugar a Candy Crush Saga?</h2>
- <p>Ahora que ya sabes cómo descargar e instalar el archivo APK gratis de Candy Crush Saga, te damos algunos consejos para jugar al juego y disfrutarlo al máximo.</p>
- <h3>Aprende los conceptos básicos del juego</h3>
- <p>Si eres nuevo en Candy Crush Saga, lo primero que tienes que hacer es aprender los conceptos básicos del juego. El juego te irá guiando por los primeros niveles, donde te explicará cómo combinar los caramelos, cómo crear caramelos especiales y cómo usar los potenci diferentes efectos. También te enseñará los tipos de niveles, los objetivos y las limitaciones que tienes que cumplir. Presta atención a las instrucciones y a los consejos que te da el juego, y practica con los primeros niveles para familiarizarte con el juego. </p>
- <h3>Usa los caramelos especiales y los potenciadores con inteligencia</h3>
- <p>Una de las claves para avanzar en Candy Crush Saga es saber usar los caramelos especiales y los potenciadores con inteligencia. Los caramelos especiales son aquellos que se crean al combinar cuatro o más caramelos del mismo color, y que tienen efectos especiales al activarse. Por ejemplo, el caramelo rayado elimina una fila o una columna entera, el caramelo envuelto explota y elimina los caramelos cercanos, el caramelo de color elimina todos los caramelos del mismo color que el que se combina con él, y el caramelo de coco elimina un obstáculo del tablero. Los potenciadores son elementos que se pueden usar antes o durante el juego, y que tienen diferentes funciones. Por ejemplo, la mano mágica permite cambiar la posición de dos caramelos sin gastar un movimiento, el martillo lollipop permite eliminar un caramelo del tablero sin activarlo, el pez sueco crea tres peces de gelatina que eliminan caramelos al azar, y la rueda de la fortuna ofrece un premio aleatorio. Estos elementos pueden ser muy útiles para superar niveles difíciles, pero hay que usarlos con moderación y estrategia, ya que son limitados y se pueden conseguir con monedas o barras de oro, que se pueden obtener gratis o comprando con dinero real. </p>
- <h3>Sigue los consejos y trucos para superar los niveles más difíciles</h3>
- <p>Finalmente, te damos algunos consejos y trucos para superar los niveles más difíciles de Candy Crush Saga. Estos son algunos de ellos:</p>
- <ul>
-<li>Planifica tus movimientos con anticipación, y trata de crear combinaciones que te den más puntos o que te ayuden a cumplir el objetivo del nivel.</li>
-<li>Mira el tablero completo antes de hacer un movimiento, y busca las mejores oportunidades para crear caramelos especiales o activarlos.</li>
-<li>Combina dos caramelos especiales entre sí para crear efectos más poderosos. Por ejemplo, combinar dos caramelos rayados crea una explosión en cruz, combinar un caramelo rayado con un caramelo envuelto crea una explosión más grande, combinar un caramelo rayado con un caramelo de color crea una lluvia de caramelos rayados, y combinar dos caramelos de color elimina todos los caramelos del tablero.</li>
-<li>Prioriza los objetivos del nivel sobre la puntuación. A veces, es mejor hacer un movimiento que te acerque al objetivo aunque te dé menos puntos, que hacer uno que te dé más puntos pero que no te ayude a cumplir el objetivo.</li>
-<li>No te desanimes si no logras superar un nivel. A veces, el éxito depende del azar y de la disposición de los caramelos en el tablero. Si te quedas atascado en un nivel, puedes pedir ayuda a tus amigos de Facebook, o esperar a que el juego te ofrezca una rueda de la fortuna o una partida gratuita.</li>
-</ul>
- <h2>Conclusión</h2>
- <p>Candy Crush Saga es un juego de puzzles muy divertido y adictivo, que te hará pasar horas de entretenimiento. Si quieres jugar al juego sin conexión a internet, acceder a las últimas actualizaciones y novedades, y evitar anuncios y compras integradas, puedes descargar el archivo APK gratis de Candy Crush Saga siguiendo los pasos que te hemos explicado. Además, puedes seguir nuestros consejos para jugar al juego y superar los niveles más difíciles. ¿A qué esperas? ¡Descarga Candy Crush Saga APK gratis y disfruta de una experiencia dulce y azucarada!</p>
- <h2>Preguntas frecuentes</h2>
- <p>A continuación, respondemos algunas preguntas frecuentes sobre Candy Crush Saga:</p>
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-<p>If you are a movie lover, you might have heard of A to Z movies. These are movies that cover a wide range of genres, languages, and countries, from action to zombie, from Arabic to Zulu, and from Australia to Zimbabwe. You can find A to Z movies on various online platforms, such as streaming services, websites, and apps. But what if you want to download A to Z movies and watch them offline? In this article, we will show you how to download A to Z movies from different sources, how to choose the best quality and format for them, and how to watch them offline.</p>
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- <h2>What are A to Z Movies?</h2>
-<p>A to Z movies are movies that span across the alphabet, meaning that they cover a variety of topics, themes, and styles. For example, you can find A to Z movies such as Avatar, Batman, Casablanca, Deadpool, E.T., Frozen, Gladiator, Harry Potter, Inception, Jurassic Park, King Kong, Lion King, Matrix, Narnia, Ocean's Eleven, Pirates of the Caribbean, Quantum of Solace, Rocky, Star Wars, Titanic, Up, V for Vendetta, Wonder Woman, X-Men, Yojimbo, and Zootopia. These movies are not necessarily related or connected in any way, except that they share the same initial letter.</p>
-<p>A to Z movies can be fun and exciting to watch because they offer a diverse and rich cinematic experience. You can explore different genres, cultures, and stories through A to Z movies. You can also challenge yourself to watch as many A to Z movies as possible and see how many letters you can complete.</p>
- <h2>Why Download A to Z Movies?</h2>
-<p>Downloading A to Z movies can have several benefits and drawbacks. Here are some of them:</p>
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-<li><b>Benefits:</b></li>
-<li>You can watch A to Z movies offline without internet connection or buffering issues.</li>
-<li>You can save money on subscription fees or rental charges by downloading free or cheap A to Z movies.</li>
-<li>You can create your own personal library of A to Z movies and organize them according to your preferences.</li>
-<li>You can share A to Z movies with your friends and family via USB drives or cloud storage.</li>
-</ul>
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-<li><b>Drawbacks:</b></li>
-<li>You might encounter legal issues or risks if you download A to Z movies from unauthorized or pirated sources.</li>
-<li>You might need a lot of storage space or memory on your device or computer if you download too many A to Z movies.</li>
-<li>You might compromise the quality or performance of your device or computer if you download A to Z movies from unsafe or malicious sources.</li>
-<li>You might miss out on some features or extras that come with streaming or renting A to Z movies online.</li>
-</ul>
- <h2>How to Download A to Z Movies from Different Sources?</h2>
-<p>There are different ways you can download A to Z movies online. Here are some of the most common ones:</p>
- <h3>Free movie download websites</h3>
-<p>One of the easiest ways to download A to Z movies is by using free movie download websites. These are websites that offer a large collection of movies that you can download for free or for a small fee. Some examples of free movie download websites are Archive.org, YouTube, and Vimeo. To download A to Z movies from these websites, you need to follow these steps:</p>
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-<ol>
-<li>Go to a free movie download website or streaming service site you subscribe to.</li>
-<li>Browse movies or search for a movie by name.</li>
-<li>Check <li>Check the movie details, such as the title, genre, rating, synopsis, and reviews.</li>
-<li>Select the download option or button and choose the quality and format you want.</li>
-<li>Wait for the download to finish and enjoy your movie.</li>
-</ol>
-<p>However, you should be careful when using free movie download websites, as some of them might be illegal, unsafe, or unreliable. You should always check the source and the reputation of the website before downloading anything. You should also use a VPN or antivirus software to protect your privacy and security online.</p>
- <h3>Online video downloader tools</h3>
-<p>Another way to download A to Z movies is by using online video downloader tools. These are tools that allow you to download videos from various websites, such as YouTube, Facebook, Instagram, and more. Some examples of online video downloader tools are SaveFrom.net, Y2mate.com, and KeepVid. To download A to Z movies from these tools, you need to follow these steps:</p>
-<ol>
-<li>Go to an online video downloader tool or install a browser extension that supports it.</li>
-<li>Copy the URL of the video you want to download from any website.</li>
-<li>Paste the URL into the tool or extension and click on the download button.</li>
-<li>Choose the quality and format you want and wait for the download to finish.</li>
-<li>Enjoy your movie.</li>
-</ol>
-<p>However, you should be aware that some online video downloader tools might not work for all websites or videos. You should also respect the intellectual property rights and terms of service of the websites you download from. You should only download videos that are in the public domain or that you have permission to use.</p>
- <h3>Google Play Movies & TV app</h3>
-<p>A third way to download A to Z movies is by using the Google Play Movies & TV app. This is an app that lets you buy or rent movies and TV shows from Google Play Store and watch them offline on your Android or iOS device. You can also sync your library across your devices and access your movies and TV shows on other platforms, such as YouTube or Chromecast. To download A to Z movies from this app, you need to follow these steps:</p>
-<ol>
-<li>Download and install the Google Play Movies & TV app on your device or computer.</li>
-<li>Open the app and sign in with your Google account.</li>
-<li>Browse or search for a movie you want to watch.</li>
-<li>Select the movie and choose to buy or rent it. You can also check if it is available for free with ads.</li>
-<li>Tap on the download icon next to the movie title and choose the quality you want.</li>
-<li>Wait for the download to finish and enjoy your movie.</li>
-</ol>
-<p>However, you should note that some movies might not be available for download or offline viewing in some regions or countries. You should also check the rental period and expiration date of your movies before downloading them. You should also have enough storage space on your device or computer for your downloads.</p>
- <h2>How to Choose the Best Quality and Format for A to Z Movies?</h2>
-<p>When downloading A to Z movies, you might wonder how to choose the best quality and format for them. There are several factors that you should consider, such as:</p>
- <ul>
-<li><b>Resolution:</b> This is the number of pixels that make up an image or video. The higher the resolution, the clearer and sharper the image or video will be. However, higher resolution also means larger file size and more bandwidth consumption. Some common resolutions are 480p, 720p, 1080p, 4K, and 8K.</li>
-<li><b>File size:</b> This is the amount of space that a file occupies on your device or computer. The larger the file size, the more storage space and memory you will need. However, larger file size also means higher quality and less compression. Some common file sizes are MB (megabytes), GB (gigabytes), and TB (terabytes).</li>
-<li><b>Compatibility:</b> This is the ability of a file to play on different devices or platforms. The more compatible a file is, the easier it will be to watch it on various devices or platforms. However, more compatible files might also have lower quality or fewer features. Some common formats are MP4, AVI, MKV, MOV, and WMV.</li>
-</ul>
- <p>To choose the best quality and format for A to Z movies, you should balance these factors according to your preferences and needs. You should also check the specifications and requirements of your device or computer before downloading anything. You can use online tools or converters to change the quality or format of your files if you want to. You can also use online tools or guides to compare the quality and format of different files and sources.</p>
- <h2>How to Watch A to Z Movies Offline?</h2>
-<p>After downloading A to Z movies, you might want to watch them offline. This can be a great way to enjoy your movies without any interruptions or distractions. Here are some tips and tricks on how to watch A to Z movies offline:</p>
-<ul>
-<li><b>Use a media player:</b> You can use a media player app or software to play your downloaded A to Z movies on your device or computer. Some examples of media players are VLC, Windows Media Player, and QuickTime. You can also use a media player to adjust the settings, such as the volume, brightness, subtitles, and speed of your movies.</li>
-<li><b>Use a projector or TV:</b> You can use a projector or TV to watch your downloaded A to Z movies on a bigger screen. This can enhance your viewing experience and make it more immersive and realistic. You can connect your device or computer to a projector or TV using a cable, a wireless adapter, or a streaming device, such as Chromecast, Roku, or Fire TV.</li>
-<li><b>Use headphones or speakers:</b> You can use headphones or speakers to listen to your downloaded A to Z movies with better sound quality and clarity. This can improve your audio experience and make it more enjoyable and engaging. You can connect your device or computer to headphones or speakers using a jack, a Bluetooth, or a Wi-Fi connection.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Downloading A to Z movies online can be a fun and rewarding activity for movie lovers. You can download A to Z movies from different sources, such as free movie download websites, online video downloader tools, and Google Play Movies & TV app. You can also choose the best quality and format for your A to Z movies, such as resolution, file size, and compatibility. Finally, you can watch your A to Z movies offline using various methods, such as media players, projectors or TVs, and headphones or speakers.</p>
-<p>If you want to download A to Z movies online and watch them offline, you should follow the steps and tips we have provided in this article. You should also be careful and responsible when downloading anything online and respect the rights and rules of the content creators and owners. We hope you enjoy your A to Z movies and have a great time watching them!</p>
- <h2>Frequently Asked Questions</h2>
-<p>Here are some of the most frequently asked questions about downloading A to Z movies online:</p>
-<ol>
-<li><b>What are some of the best free movie download websites?</b></li>
-<p>Some of the best free movie download websites are Archive.org, YouTube, Vimeo, Crackle, Popcornflix, and Tubi. These websites offer a large selection of movies that you can download for free or for a small fee. However, you should always check the legality and safety of these websites before downloading anything.</p>
-<li><b>What are some of the best online video downloader tools?</b></li>
-<p>Some of the best online video downloader tools are SaveFrom.net, Y2mate.com, KeepVid, 4K Video Downloader, and ClipGrab. These tools allow you to download videos from various websites, such as YouTube, Facebook, Instagram, and more. However, you should always respect the intellectual property rights and terms of service of these websites before downloading anything.</p>
-<li><b>What are some of the best quality and format options for A to Z movies?</b></li>
-<p>Some of the best quality and format options for A to Z movies are 1080p MP4, 720p MKV, 480p AVI, 4K MOV, and 8K WMV. These options offer high resolution, low file size, and high compatibility for your A to Z movies. However, you should always check the specifications and requirements of your device or computer before downloading anything.</p>
-<li><b>What are some of the best ways to watch A to Z movies offline?</b></li>
-<p>Some of the best ways to watch A to Z movies offline are using VLC, Windows Media Player, QuickTime, Chromecast, Roku, Fire TV, headphones, speakers, projectors, and TVs. These methods offer better viewing and listening experience for your A to Z movies. However, you should always have enough battery life and storage space on your device or computer for your downloads.</p>
-<li><b>What are some of the best A to Z movies to watch?</b></li>
-<p>Some of the best A to Z movies to watch are Avatar, Batman, Casablanca, Deadpool, E.T., Frozen, Gladiator, Harry Potter, Inception, Jurassic Park, King Kong, Lion King, Matrix, Narnia, Ocean's Eleven, Pirates of the Caribbean, Quantum of Solace, Rocky, Star Wars, Titanic, Up, V for Vendetta, Wonder Woman, X-Men, Yojimbo, and Zootopia. These movies are popular, acclaimed, and entertaining for different audiences and tastes. However, you can also find your own favorite A to Z movies by exploring different genres, languages, and countries.</p>
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-<br />
-<h1>Download Al Quran 30 Juz: A Guide for Muslims</h1>
-<p>Al Quran is the holy book of Islam, revealed by Allah (SWT) to Prophet Muhammad (SAW) over a period of 23 years. It is the source of guidance, mercy, and healing for all Muslims. It contains 114 chapters (surahs) and 6236 verses (ayahs) that cover various topics such as faith, worship, morality, law, history, science, and more.</p>
-<p>However, not all Muslims have easy access to a physical copy of the Quran or can read it in its original Arabic language. That is why many Muslims opt to download Al Quran in different formats such as audio, text, translation, or commentary. This way, they can listen to or read the Quran anytime and anywhere they want.</p>
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-<p>One of the most popular formats that Muslims download is Al Quran 30 Juz. This is a division of the Quran into 30 equal parts, each called a juz (or para in some regions). This division makes it easier for Muslims to recite the entire Quran in a month, especially during Ramadan when it is highly recommended to do so.</p>
-<p>In this article, we will explain what Al Quran 30 Juz is and why it is important for Muslims. We will also show you how to download Al Quran 30 Juz in high quality audio and text formats from reliable sources. Finally, we will give you some tips on how to make the most of Al Quran 30 Juz after downloading it.</p>
- <h2>What is Al Quran 30 Juz and why is it important?</h2>
-<h3>The meaning and division of Al Quran 30 Juz</h3>
-<p>The word juz means "part" or "portion" in Arabic. It is used to refer to one of the thirty parts of the Quran that are roughly equal in length. Each juz has a name that is derived from the first word or verse of its first chapter. For example, the first juz is called "Alif-Lam-Mim" because it starts with Surah Al-Baqarah verse 1 that begins with these letters.</p>
-<p>The division of the Quran into juz is not based on any thematic or logical criteria, but rather on convenience and ease of memorization. The division was done by the early Muslims who wanted to facilitate the recitation of the Quran in a month, especially during Ramadan when it is obligatory to perform taraweeh prayers at night.</p>
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-<p>The following table shows the list of juz along with their names, starting chapters, and ending chapters:</p>
- <table>
-<tr><th>Juz</th><th>Name</th><th>Starting Chapter</th><th>Ending Chapter</th></tr>
-<tr><td>1</td><td>Alif-Lam-Mim</td><td>Al-Baqarah (2:1)</td><td>Al-Baqarah (2:141)</td></tr>
-<tr><td>2</td><td>Sayaqool</td><td>Al-Baqarah (2:142)</td><td>Al-Baqarah (2:252)</td></tr>
-<tr><td>3</td><td>Tilka ar-Rusul</td><td>Al-Baqarah (2:253)</td><td>Aal-i-Imran (3:92)</td></tr>
-<tr><td>4</td><td>Lan Tana Loo</td><td>Aal-i-Imran (3:93)</td><td>An (14:1)</td></tr>
-<tr><td>14</td><td>Rubama</td><td>Ibrahim (14:2)</td><td>Al-Hijr (15:99)</td></tr>
-<tr><td>15</td><td>Subhanalladhi</td><td>An-Nahl (16:1)</td><td>An-Nahl (16:128)</td></tr>
-<tr><td>16</td><td>Qala Alam</td><td>Al-Isra (17:1)</td><td>Al-Kahf (18:74)</td></tr>
-<tr><td>17</td><td>Aqtaraba</td><td>Al-Kahf (18:75)</td><td>Ta-Ha (20:135)</ <td></tr>
-<tr><td>18</td><td>Qad Aflaha</ <td>
-<td>Al-Anbiya (21:1)</ <td>
-<td>Al-Hajj (22:78)</ <td></tr>
-<tr><td>19</td><td>Wa Qala Alladheena</ <td>
-<td>Al-Muminun (23:1)</ <td>
-<td>Al-Furqan (25:20)</ <td></tr>
-<tr><td>20</td><td>A'mana Rasulu</ <td>
-<td>Al-Furqan (25:21)</ <td>
-<td>An-Naml (27:55)</ <td></tr>
-<tr><td>21</td><td>Utlu Ma Oohiya</ <td>
-<td>An-Naml (27:56)</ <td>
-<td>Al-Ankabut (29:45)</ <td></tr>
-<tr><td>22</td><td>Wa Man Ya'lamu</ <td>
-<td>Al-Ankabut (29:46)</ <td>
-<td>Az-Zumar (39:31)</ <td></tr>
-<tr><td>23</td><td>Wa Maliya</ <td>
-<td>Az-Zumar (39:32)</ <td>
-<td>Fussilat (41:46)</ <td></tr>
-<tr><td>24</td><td>Faman Azhlamu</ <td>
-<td>Fussilat (41:47)</ <td>
-<td>Ash-Shura (42:53)</ <td></tr>
-<tr><td>25</td><td>Ila Firauna</ <td>
-<td>Az-Zukhruf (43:1)</ <td>
-<td>Az-Zukhruf (43:89)</ <td></tr>
-<tr>< td>26</td><td>Ha Mim</td><td>Ad-Dukhan (44:1)</td><td>Al-Jathiyah (45:37)</td></tr>
-<tr><td>27</td><td>Qala Fa Ma Khatbukum</td><td>Al-Ahqaf (46:1)</td><td>Az-Zariyat (51:30)</td></tr>
-<tr><td>28</td><td>Qad Sami Allah</td><td>Az-Zariyat (51:31)</td><td>Al-Hadid (57:29)</ <td></tr>
-<tr><td>29</td><td>Tabarakalladhi</ td>
-<td>Al-Mujadilah (58:1)</ <td>
-<td>At-Tahrim (66:12)</ <td></tr>
-<tr><td>30</td><td>Amma Yatasa'aloon</ td>
-<td>Al-Mulk (67:1)</ <td>
-<td>An-Nas (114:6)</ <td></tr>
-</table>
- <h3>The benefits and rewards of reciting Al Quran 30 Juz</h3>
-<p>Reciting Al Quran 30 Juz is one of the best ways to connect with Allah (SWT) and His words. It has many benefits and rewards for the reciter, both in this world and the hereafter. Some of these benefits and rewards are:</p>
-<ul>
-<li>It increases one's faith, knowledge, understanding, and wisdom.</li>
-<li>It purifies one's heart, soul, and mind from sins, doubts, and evils.</li>
-<li>It protects one from the temptations, trials, and harms of this life.</li>
-<li>It brings one closer to Allah (SWT) and His mercy, forgiveness, and blessings.</li>
-<li>It earns one the reward of ten good deeds for each letter recited.</li>
-<li>It intercedes for one on the Day of Judgment and saves one from the Hellfire.</li>
-<li>It grants one the highest ranks in Paradise and the company of the righteous.</li>
-</ul>
-<p>The Prophet Muhammad (SAW) said: "The best of you are those who learn the Quran and teach it."  He also said: "Whoever recites a letter from the Book of Allah, he will have a reward. And that reward will be multiplied by ten. I am not saying that 'Alif, Lam, Mim' is a letter, rather I am saying that 'Alif' is a letter, 'Lam' is a letter and 'Mim' is a letter."  He also said: "The Quran will come on the Day of Resurrection like a pale man saying to its companion: 'Do you recognize me? I am the one who made you thirsty during the heat and made you stay up during the night.' Then he will be given dominion in his right hand and eternity in his left hand and a crown of dignity will be placed upon his head. His parents will also be given two garments that the world could not equal. They will say: 'Why were we given these garments?' It will be said: 'Because your child used to recite the Quran.'" </p>
- <h2>How to download Al Quran 30 Juz in high quality audio and text formats</h2>
-<h3>The best websites and apps to download Al Quran 30 Juz</h3>
-<p>If you want to download Al Quran 30 Juz in high quality audio and text formats, you need to find reliable sources that offer authentic and accurate versions of the Quran. You also need to make sure that the files are compatible with your device and easy to access. Here are some of the best websites and apps that you can use to download Al Quran 30 Juz:</p>
-<ul>
-<li><a href="">Quran.com</a>: This is one of the most popular and comprehensive websites that offers Al Quran 30 Juz in various formats such as audio, text, translation, commentary, word by word analysis, and more. You can choose from different reciters, languages, fonts, themes, and modes. You can also bookmark your progress, share your favorite verses, or listen to live streams of Quran recitation.</li>
-<li><a href="">Quran Explorer</a>: This is another excellent website that provides Al Quran 30 Juz in high quality audio and text formats. You can select from different reciters, translations, scripts, colors, and sizes. You can also use the search function, repeat function, or memorization tool to enhance your learning experience.</li>
-<li><a href="">Quran Majeed</a>: This is a wonderful app that allows you to download Al Quran 30 Juz in various formats such as audio, text, translation, transliteration, and tafsir. You can choose from different reciters, languages, themes, and features. You can also track your reading progress, set reminders, or join online classes.</li>
-<li><a href="">iQuran</a>: This is a powerful app that offers Al Quran 30 Juz in high quality audio and text formats. You can select from different reciters, translations, fonts, and colors. You can also use the advanced audio controls, verse highlighting, bookmarks, notes, or tags to improve your recitation skills.</li>
-<li><a href="">Al Quran MP3</a>: This is a simple and easy app that provides Al Quran 30 Juz in high quality audio format. You can download the entire Quran or any juz you want from different reciters. You can also play the audio offline, adjust the speed, or share the files with others.</li>
-</ul>
- <h3>The tips and tricks to download Al Quran 30 Juz faster and easier</h3>
-<p>Downloading Al Quran 30 Juz can be a time-consuming and challenging task if you don't know the right tips and tricks. Here are some of the things you can do to make the process faster and easier:</p>
-<ul>
-<li>Make sure you have a stable and fast internet connection. You don't want to interrupt or slow down your download due to poor connectivity.</li>
-<li>Choose the format and quality that suits your device and preference. You don't need to download the highest quality if your device can't support it or if you don't need it.</li>
-<li>Use a download manager or accelerator to speed up your download and resume it if it fails. You don't want to waste your time or data by starting over again.</li>
-<li>Check the file size and storage space before downloading. You don't want to run out of space or memory on your device while downloading.</li>
-<li>Scan the files for viruses or malware before opening them. You don't want to risk your device or data by downloading corrupted or infected files.</li>
-</ul>
- <h2>How to make the most of Al Quran 30 Juz after downloading it</h2>
-<h3>The best times and ways to recite Al Quran 30 Juz</h3>
-<p>After downloading Al Quran 30 Juz, you need to make the most of it by reciting it regularly and properly. Here are some of the best times and ways to recite Al Quran 30 Juz:</p>
-<ul>
-<li>The best time to recite Al Quran 30 Juz is during Ramadan, when the reward for reciting the Quran is multiplied by Allah (SWT). You can aim to recite one juz per day so that you can complete the entire Quran in a month.</li>
-<li>Another good time to recite Al Quran 30 Juz is during the night, when Allah (SWT) descends to the lowest heaven and listens to the prayers of His servants. You can recite Al Quran 30 Juz during tahajjud (the night prayer) or qiyam (the voluntary prayer before fajr).</li>
-<li>A third good time to recite Al Quran 30 Juz is during the day, when you have some free time or need some guidance or comfort from Allah (SWT). You can recite Al Quran 30 Juz during duha (the forenoon prayer) or asr (the afternoon prayer) or any other time that suits you.</li>
-<li>The best way to recite Al Quran 30 Juz is with tajweed (the rules of pronunciation) and tarteel (the slow and measured recitation). This will help you avoid mistakes and beautify your voice while reciting.</li>
-<li>Another good way to recite Al Quran 30 Juz is with understanding and reflection. This will help you comprehend the meaning and message of the verses and apply them to your life.</li>
-<li>A third good way to recite Al Quran 30 Juz is with sincerity and devotion. This will help you connect with Allah (SWT) and His words and feel His presence and love in your heart.</li>
-</ul>
- <h3>The common mistakes and challenges to avoid when reciting Al Quran 30 Juz</h3>
-<p>Reciting Al Quran 30 Juz is not without its mistakes and challenges. Here are some of the common ones that you should avoid when reciting Al Quran 30 Juz:</p>
-<ul>
-<li>The first mistake is not having a clear intention or goal for reciting Al Quran 30 Juz. You should have a sincere intention for seeking Allah's pleasure and guidance, not for showing off or competing with others.</li>
-<li>The second mistake is not having a consistent or regular schedule for reciting Al Quran 30 Juz. You should have a realistic and manageable plan for reciting Al Quran 30 Juz, such as dividing it into daily, weekly, or monthly portions.</li>
-<li>The third mistake is not having a proper preparation or environment for reciting Al Quran 30 Juz. You should have a clean and quiet place for reciting Al Quran 30 Juz, as well as a good quality device and file for playing or reading it.</li>
-<li>The first challenge is not having enough time or energy for reciting Al Quran 30 Juz. You should make time and prioritize reciting Al Quran 30 Juz over other less important activities, such as watching TV or browsing social media. You should also take care of your health and sleep well to avoid fatigue and distraction.</li>
-<li>The second challenge is not having enough motivation or interest for reciting Al Quran 30 Juz. You should remind yourself of the benefits and rewards of reciting Al Quran 30 Juz, as well as the consequences of neglecting it. You should also seek inspiration and support from other Muslims who are reciting Al Quran 30 Juz, such as your family, friends, or online communities.</li>
-<li>The third challenge is not having enough knowledge or skill for reciting Al Quran 30 Juz. You should learn the basics of tajweed and tarteel from qualified teachers or resources, and practice them regularly. You should also seek the meaning and explanation of the verses from authentic translations and commentaries, and ask questions if you have any doubts or confusion.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Al Quran 30 Juz is a great way to recite the entire Quran in a month, especially during Ramadan. It has many benefits and rewards for the reciter, both in this world and the hereafter. However, it also requires some effort and dedication to download, recite, and understand it properly. We hope that this article has helped you learn more about Al Quran 30 Juz and how to download, recite, and make the most of it. May Allah (SWT) accept your recitation and grant you His mercy and guidance.</p>
- <h2>FAQs</h2>
-<h3>What is the difference between juz and sipara?</h3>
-<p>Juz and sipara are two different names for the same thing: one of the thirty parts of the Quran that are roughly equal in length. The word juz is Arabic, while the word sipara is Persian. Both words mean "part" or "portion".</p>
-<h3>How long does it take to recite one juz of the Quran?</h3>
-<p>The time it takes to recite one juz of the Quran depends on several factors, such as the speed, style, and fluency of the reciter, as well as the length and difficulty of the verses. However, on average, it takes about an hour to recite one juz of the Quran.</p>
-<h3>How can I memorize Al Quran 30 Juz?</h3>
-<p>Memorizing Al Quran 30 Juz is a noble and rewarding goal that requires patience, perseverance, and practice. Some of the tips that can help you memorize Al Quran 30 Juz are:</p>
-<ul>
-<li>Start with the short and easy chapters at the end of the Quran.</li>
-<li>Repeat each verse several times until you can recite it without looking.</li>
-<li>Recite each verse with its meaning and context in mind.</li>
-<li>Review each verse regularly to prevent forgetting.</li>
-<li>Listen to or read along with a professional reciter who has a clear and correct pronunciation.</li>
-<li>Seek help from a qualified teacher or mentor who can correct your mistakes and test your progress.</li>
-</ul>
- <h3>What are some of the best reciters of Al Quran 30 Juz?</h3>
-<p>There are many excellent reciters of Al Quran 30 Juz who have beautiful voices and mastery of tajweed and tarteel. Some of the most famous and respected ones are:</p>
-<ul>
-<li>Mishary Rashid Alafasy</li>
-<li>Abdul Rahman Al-Sudais</li>
-<li>Maher Al-Muaiqly</li>
-<li>Saad Al-Ghamdi</li>
-<li>Ahmed Al-Ajmi</li>
-<li>Muhammad Ayyub</li>
-<li>Ahmad Saud</li>
-</ul>
- <h3>Where can I find more resources on Al Quran 30 Juz?</h3>
-<p>If you want to learn more about Al Quran 30 Juz, you can find more resources on various websites, apps, books, videos, podcasts, or courses that offer information, education, or entertainment on this topic. Some of the examples of such resources are:</p>
-<ul>
-<li><a href="">Quran Academy</a>: This is an online platform that offers courses, videos, articles, and quizzes on various aspects of the Quran, such as recitation, memorization, understanding, and reflection.</li>
-<li><a href="">Quran Central</a>: This is a podcast network that features audio recitations, translations, and lectures on the Quran by various speakers and scholars.</li>
-<li><a href="">Quran Weekly</a>: This is a YouTube channel that produces short and engaging videos on the Quran, covering topics such as stories, lessons, miracles, and gems.</li>
-<li><a href="">Tafsir Ibn Kathir</a>: This is a classic and comprehensive commentary on the Quran by the renowned scholar Imam Ibn Kathir. It explains the meanings, contexts, and implications of the verses in detail.</li>
-<li><a href="">The Clear Quran</a>: This is a modern and easy-to-read translation of the Quran by Dr. Mustafa Khattab. It captures the eloquence, beauty, and clarity of the original Arabic text in simple English.</li>
-</ul>
- <p>I hope you enjoyed reading this article and learned something new. If you have any questions or feedback, please feel free to leave a comment below. Thank you for your time and attention.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io-parser/build/esm/decodePacket.browser.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io-parser/build/esm/decodePacket.browser.js
deleted file mode 100644
index 1d8453d042aa368dfb2abe78a1ad4ceed122efe8..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io-parser/build/esm/decodePacket.browser.js
+++ /dev/null
@@ -1,49 +0,0 @@
-import { ERROR_PACKET, PACKET_TYPES_REVERSE } from "./commons.js";
-import { decode } from "./contrib/base64-arraybuffer.js";
-const withNativeArrayBuffer = typeof ArrayBuffer === "function";
-const decodePacket = (encodedPacket, binaryType) => {
-    if (typeof encodedPacket !== "string") {
-        return {
-            type: "message",
-            data: mapBinary(encodedPacket, binaryType)
-        };
-    }
-    const type = encodedPacket.charAt(0);
-    if (type === "b") {
-        return {
-            type: "message",
-            data: decodeBase64Packet(encodedPacket.substring(1), binaryType)
-        };
-    }
-    const packetType = PACKET_TYPES_REVERSE[type];
-    if (!packetType) {
-        return ERROR_PACKET;
-    }
-    return encodedPacket.length > 1
-        ? {
-            type: PACKET_TYPES_REVERSE[type],
-            data: encodedPacket.substring(1)
-        }
-        : {
-            type: PACKET_TYPES_REVERSE[type]
-        };
-};
-const decodeBase64Packet = (data, binaryType) => {
-    if (withNativeArrayBuffer) {
-        const decoded = decode(data);
-        return mapBinary(decoded, binaryType);
-    }
-    else {
-        return { base64: true, data }; // fallback for old browsers
-    }
-};
-const mapBinary = (data, binaryType) => {
-    switch (binaryType) {
-        case "blob":
-            return data instanceof ArrayBuffer ? new Blob([data]) : data;
-        case "arraybuffer":
-        default:
-            return data; // assuming the data is already an ArrayBuffer
-    }
-};
-export default decodePacket;
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/statuses/README.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/statuses/README.md
deleted file mode 100644
index 57967e6e62c56f9e3082b1054f9238d47a0106ae..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/statuses/README.md
+++ /dev/null
@@ -1,136 +0,0 @@
-# statuses
-
-[![NPM Version][npm-version-image]][npm-url]
-[![NPM Downloads][npm-downloads-image]][npm-url]
-[![Node.js Version][node-version-image]][node-version-url]
-[![Build Status][ci-image]][ci-url]
-[![Test Coverage][coveralls-image]][coveralls-url]
-
-HTTP status utility for node.
-
-This module provides a list of status codes and messages sourced from
-a few different projects:
-
-  * The [IANA Status Code Registry](https://www.iana.org/assignments/http-status-codes/http-status-codes.xhtml)
-  * The [Node.js project](https://nodejs.org/)
-  * The [NGINX project](https://www.nginx.com/)
-  * The [Apache HTTP Server project](https://httpd.apache.org/)
-
-## Installation
-
-This is a [Node.js](https://nodejs.org/en/) module available through the
-[npm registry](https://www.npmjs.com/). Installation is done using the
-[`npm install` command](https://docs.npmjs.com/getting-started/installing-npm-packages-locally):
-
-```sh
-$ npm install statuses
-```
-
-## API
-
-<!-- eslint-disable no-unused-vars -->
-
-```js
-var status = require('statuses')
-```
-
-### status(code)
-
-Returns the status message string for a known HTTP status code. The code
-may be a number or a string. An error is thrown for an unknown status code.
-
-<!-- eslint-disable no-undef -->
-
-```js
-status(403) // => 'Forbidden'
-status('403') // => 'Forbidden'
-status(306) // throws
-```
-
-### status(msg)
-
-Returns the numeric status code for a known HTTP status message. The message
-is case-insensitive. An error is thrown for an unknown status message.
-
-<!-- eslint-disable no-undef -->
-
-```js
-status('forbidden') // => 403
-status('Forbidden') // => 403
-status('foo') // throws
-```
-
-### status.codes
-
-Returns an array of all the status codes as `Integer`s.
-
-### status.code[msg]
-
-Returns the numeric status code for a known status message (in lower-case),
-otherwise `undefined`.
-
-<!-- eslint-disable no-undef, no-unused-expressions -->
-
-```js
-status['not found'] // => 404
-```
-
-### status.empty[code]
-
-Returns `true` if a status code expects an empty body.
-
-<!-- eslint-disable no-undef, no-unused-expressions -->
-
-```js
-status.empty[200] // => undefined
-status.empty[204] // => true
-status.empty[304] // => true
-```
-
-### status.message[code]
-
-Returns the string message for a known numeric status code, otherwise
-`undefined`. This object is the same format as the
-[Node.js http module `http.STATUS_CODES`](https://nodejs.org/dist/latest/docs/api/http.html#http_http_status_codes).
-
-<!-- eslint-disable no-undef, no-unused-expressions -->
-
-```js
-status.message[404] // => 'Not Found'
-```
-
-### status.redirect[code]
-
-Returns `true` if a status code is a valid redirect status.
-
-<!-- eslint-disable no-undef, no-unused-expressions -->
-
-```js
-status.redirect[200] // => undefined
-status.redirect[301] // => true
-```
-
-### status.retry[code]
-
-Returns `true` if you should retry the rest.
-
-<!-- eslint-disable no-undef, no-unused-expressions -->
-
-```js
-status.retry[501] // => undefined
-status.retry[503] // => true
-```
-
-## License
-
-[MIT](LICENSE)
-
-[ci-image]: https://badgen.net/github/checks/jshttp/statuses/master?label=ci
-[ci-url]: https://github.com/jshttp/statuses/actions?query=workflow%3Aci
-[coveralls-image]: https://badgen.net/coveralls/c/github/jshttp/statuses/master
-[coveralls-url]: https://coveralls.io/r/jshttp/statuses?branch=master
-[node-version-image]: https://badgen.net/npm/node/statuses
-[node-version-url]: https://nodejs.org/en/download
-[npm-downloads-image]: https://badgen.net/npm/dm/statuses
-[npm-url]: https://npmjs.org/package/statuses
-[npm-version-image]: https://badgen.net/npm/v/statuses
diff --git a/spaces/fgbwyude/ChuanhuChatGPT/run_Windows.bat b/spaces/fgbwyude/ChuanhuChatGPT/run_Windows.bat
deleted file mode 100644
index 4c18f9ccaeea0af972301ffdf48778641221f76d..0000000000000000000000000000000000000000
--- a/spaces/fgbwyude/ChuanhuChatGPT/run_Windows.bat
+++ /dev/null
@@ -1,5 +0,0 @@
-@echo off
-echo Opening ChuanhuChatGPT...
-
-REM Open powershell via bat
-start powershell.exe -NoExit -Command "python ./ChuanhuChatbot.py"
diff --git a/spaces/flemag/zeroscope/share_btn.py b/spaces/flemag/zeroscope/share_btn.py
deleted file mode 100644
index bc64b36c7335bc6fd3e96c8260e0a0d85a0704ce..0000000000000000000000000000000000000000
--- a/spaces/flemag/zeroscope/share_btn.py
+++ /dev/null
@@ -1,77 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	
-    async function getVideoBlobFile(videoEL){
-        const res = await fetch(videoEL.src);
-        const blob = await res.blob();
-        const videoId = Date.now() % 200;
-        const fileName = `vid-zeroscope-${{videoId}}.mp4`;
-        const videoBlob = new File([blob], fileName, { type: 'video/mp4' });
-        console.log(videoBlob);
-        return videoBlob;
-	}
-    
-    const gradioEl = document.querySelector("gradio-app").shadowRoot || document.querySelector('body > gradio-app');
-    const captionTxt = gradioEl.querySelector('#prompt-in textarea').value;
-    const outputVideo = gradioEl.querySelector('#video-output video');
-
-    
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!outputVideo){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    
-    
-    const videoOutFile = await getVideoBlobFile(outputVideo);
-	const dataOutputVid = await uploadFile(videoOutFile);
-    
-    const descriptionMd = `
-#### Prompt:
-${captionTxt}
-
-#### Zeroscope video result:
-<video controls>
-  <source src="${dataOutputVid}" type="video/mp4">
-  
-  <p>Your browser does not support HTML5 videos.
-     Here's <a href="${dataOutputVid}">the link to download the video</a>.</p>
-</video>
-
-`;
-    const params = new URLSearchParams({
-        title: captionTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/fffiloni/zeroscope/discussions/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/envs/gotoobject.py b/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/envs/gotoobject.py
deleted file mode 100644
index ffab837745109459a64cbc1abca21f3de4968138..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/envs/gotoobject.py
+++ /dev/null
@@ -1,98 +0,0 @@
-from gym_minigrid.minigrid import *
-from gym_minigrid.register import register
-
-class GoToObjectEnv(MiniGridEnv):
-    """
-    Environment in which the agent is instructed to go to a given object
-    named using an English text string
-    """
-
-    def __init__(
-        self,
-        size=6,
-        numObjs=2
-    ):
-        self.numObjs = numObjs
-
-        super().__init__(
-            grid_size=size,
-            max_steps=5*size**2,
-            # Set this to True for maximum speed
-            see_through_walls=True
-        )
-
-    def _gen_grid(self, width, height):
-        self.grid = Grid(width, height)
-
-        # Generate the surrounding walls
-        self.grid.wall_rect(0, 0, width, height)
-
-        # Types and colors of objects we can generate
-        types = ['key', 'ball', 'box']
-
-        objs = []
-        objPos = []
-
-        # Until we have generated all the objects
-        while len(objs) < self.numObjs:
-            objType = self._rand_elem(types)
-            objColor = self._rand_elem(COLOR_NAMES)
-
-            # If this object already exists, try again
-            if (objType, objColor) in objs:
-                continue
-
-            if objType == 'key':
-                obj = Key(objColor)
-            elif objType == 'ball':
-                obj = Ball(objColor)
-            elif objType == 'box':
-                obj = Box(objColor)
-
-            pos = self.place_obj(obj)
-            objs.append((objType, objColor))
-            objPos.append(pos)
-
-        # Randomize the agent start position and orientation
-        self.place_agent()
-
-        # Choose a random object to be picked up
-        objIdx = self._rand_int(0, len(objs))
-        self.targetType, self.target_color = objs[objIdx]
-        self.target_pos = objPos[objIdx]
-
-        descStr = '%s %s' % (self.target_color, self.targetType)
-        self.mission = 'go to the %s' % descStr
-        #print(self.mission)
-
-    def step(self, action):
-        obs, reward, done, info = MiniGridEnv.step(self, action)
-
-        ax, ay = self.agent_pos
-        tx, ty = self.target_pos
-
-        # Toggle/pickup action terminates the episode
-        if action == self.actions.toggle:
-            done = True
-
-        # Reward performing the done action next to the target object
-        if action == self.actions.done:
-            if abs(ax - tx) <= 1 and abs(ay - ty) <= 1:
-                reward = self._reward()
-            done = True
-
-        return obs, reward, done, info
-
-class GotoEnv8x8N2(GoToObjectEnv):
-    def __init__(self):
-        super().__init__(size=8, numObjs=2)
-
-register(
-    id='MiniGrid-GoToObject-6x6-N2-v0',
-    entry_point='gym_minigrid.envs:GoToObjectEnv'
-)
-
-register(
-    id='MiniGrid-GoToObject-8x8-N2-v0',
-    entry_point='gym_minigrid.envs:GotoEnv8x8N2'
-)
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/midas/dpt_depth.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/midas/dpt_depth.py
deleted file mode 100644
index 4e9aab5d2767dffea39da5b3f30e2798688216f1..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/midas/midas/dpt_depth.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .base_model import BaseModel
-from .blocks import (
-    FeatureFusionBlock,
-    FeatureFusionBlock_custom,
-    Interpolate,
-    _make_encoder,
-    forward_vit,
-)
-
-
-def _make_fusion_block(features, use_bn):
-    return FeatureFusionBlock_custom(
-        features,
-        nn.ReLU(False),
-        deconv=False,
-        bn=use_bn,
-        expand=False,
-        align_corners=True,
-    )
-
-
-class DPT(BaseModel):
-    def __init__(
-        self,
-        head,
-        features=256,
-        backbone="vitb_rn50_384",
-        readout="project",
-        channels_last=False,
-        use_bn=False,
-    ):
-
-        super(DPT, self).__init__()
-
-        self.channels_last = channels_last
-
-        hooks = {
-            "vitb_rn50_384": [0, 1, 8, 11],
-            "vitb16_384": [2, 5, 8, 11],
-            "vitl16_384": [5, 11, 17, 23],
-        }
-
-        # Instantiate backbone and reassemble blocks
-        self.pretrained, self.scratch = _make_encoder(
-            backbone,
-            features,
-            False, # Set to true of you want to train from scratch, uses ImageNet weights
-            groups=1,
-            expand=False,
-            exportable=False,
-            hooks=hooks[backbone],
-            use_readout=readout,
-        )
-
-        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
-
-        self.scratch.output_conv = head
-
-
-    def forward(self, x):
-        if self.channels_last == True:
-            x.contiguous(memory_format=torch.channels_last)
-
-        layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return out
-
-
-class DPTDepthModel(DPT):
-    def __init__(self, path=None, non_negative=True, **kwargs):
-        features = kwargs["features"] if "features" in kwargs else 256
-
-        head = nn.Sequential(
-            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
-            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-
-        super().__init__(head, **kwargs)
-
-        if path is not None:
-           self.load(path)
-
-    def forward(self, x):
-        return super().forward(x).squeeze(dim=1)
-
diff --git a/spaces/gligen/demo/gligen/ldm/modules/attention.py b/spaces/gligen/demo/gligen/ldm/modules/attention.py
deleted file mode 100644
index c443da348bc1ce707487fb8962a13b1810a43454..0000000000000000000000000000000000000000
--- a/spaces/gligen/demo/gligen/ldm/modules/attention.py
+++ /dev/null
@@ -1,387 +0,0 @@
-from inspect import isfunction
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from einops import rearrange, repeat
-
-# from ldm.modules.diffusionmodules.util import checkpoint, FourierEmbedder
-from torch.utils import checkpoint
-
-try:
-    import xformers
-    import xformers.ops
-    XFORMERS_IS_AVAILBLE = True
-except:
-    XFORMERS_IS_AVAILBLE = False
-
-
-def exists(val):
-    return val is not None
-
-
-def uniq(arr):
-    return{el: True for el in arr}.keys()
-
-
-def default(val, d):
-    if exists(val):
-        return val
-    return d() if isfunction(d) else d
-
-
-def max_neg_value(t):
-    return -torch.finfo(t.dtype).max
-
-
-def init_(tensor):
-    dim = tensor.shape[-1]
-    std = 1 / math.sqrt(dim)
-    tensor.uniform_(-std, std)
-    return tensor
-
-
-# feedforward
-class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = default(dim_out, dim)
-        project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim)
-
-        self.net = nn.Sequential(
-            project_in,
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def Normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class LinearAttention(nn.Module):
-    def __init__(self, dim, heads=4, dim_head=32):
-        super().__init__()
-        self.heads = heads
-        hidden_dim = dim_head * heads
-        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
-        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-        qkv = self.to_qkv(x)
-        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
-        k = k.softmax(dim=-1)  
-        context = torch.einsum('bhdn,bhen->bhde', k, v)
-        out = torch.einsum('bhde,bhdn->bhen', context, q)
-        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
-        return self.to_out(out)
-
-
-
-
-class CrossAttention(nn.Module):
-    def __init__(self, query_dim, key_dim, value_dim, heads=8, dim_head=64, dropout=0):
-        super().__init__()
-        inner_dim = dim_head * heads
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-        self.dim_head = dim_head
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(key_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(value_dim, inner_dim, bias=False)
-
-
-        self.to_out = nn.Sequential( nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) )
-
-
-    def fill_inf_from_mask(self, sim, mask):
-        if mask is not None:
-            B,M = mask.shape
-            mask = mask.unsqueeze(1).repeat(1,self.heads,1).reshape(B*self.heads,1,-1)
-            max_neg_value = -torch.finfo(sim.dtype).max
-            sim.masked_fill_(~mask, max_neg_value)
-        return sim 
-
-    def forward_plain(self, x, key, value, mask=None):
-
-        q = self.to_q(x)     # B*N*(H*C)
-        k = self.to_k(key)   # B*M*(H*C)
-        v = self.to_v(value) # B*M*(H*C)
-   
-        B, N, HC = q.shape 
-        _, M, _ = key.shape
-        H = self.heads
-        C = HC // H 
-
-        q = q.view(B,N,H,C).permute(0,2,1,3).reshape(B*H,N,C) # (B*H)*N*C
-        k = k.view(B,M,H,C).permute(0,2,1,3).reshape(B*H,M,C) # (B*H)*M*C
-        v = v.view(B,M,H,C).permute(0,2,1,3).reshape(B*H,M,C) # (B*H)*M*C
-
-        sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale # (B*H)*N*M
-        self.fill_inf_from_mask(sim, mask)
-        attn = sim.softmax(dim=-1) # (B*H)*N*M
-
-        out = torch.einsum('b i j, b j d -> b i d', attn, v) # (B*H)*N*C
-        out = out.view(B,H,N,C).permute(0,2,1,3).reshape(B,N,(H*C)) # B*N*(H*C)
-
-        return self.to_out(out)
-
-    def forward(self, x, key, value, mask=None):
-        if not XFORMERS_IS_AVAILBLE:
-            return self.forward_plain(x, key, value, mask)
-
-        q = self.to_q(x)     # B*N*(H*C)
-        k = self.to_k(key)   # B*M*(H*C)
-        v = self.to_v(value) # B*M*(H*C)
-
-        b, _, _ = q.shape
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, t.shape[1], self.heads, self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * self.heads, t.shape[1], self.dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
-
-        # actually compute the attention, what we cannot get enough of
-        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None)
-
-        if exists(mask):
-            raise NotImplementedError
-        out = (
-            out.unsqueeze(0)
-            .reshape(b, self.heads, out.shape[1], self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b, out.shape[1], self.heads * self.dim_head)
-        )
-        return self.to_out(out)
-
-
-
-
-
-class SelfAttention(nn.Module):
-    def __init__(self, query_dim, heads=8, dim_head=64, dropout=0.):
-        super().__init__()
-        inner_dim = dim_head * heads
-        self.scale = dim_head ** -0.5
-        self.heads = heads
-        self.dim_head = dim_head
-
-        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_k = nn.Linear(query_dim, inner_dim, bias=False)
-        self.to_v = nn.Linear(query_dim, inner_dim, bias=False)
-
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout) )
-
-    def forward_plain(self, x):
-        q = self.to_q(x) # B*N*(H*C)
-        k = self.to_k(x) # B*N*(H*C)
-        v = self.to_v(x) # B*N*(H*C)
-
-        B, N, HC = q.shape 
-        H = self.heads
-        C = HC // H 
-
-        q = q.view(B,N,H,C).permute(0,2,1,3).reshape(B*H,N,C) # (B*H)*N*C
-        k = k.view(B,N,H,C).permute(0,2,1,3).reshape(B*H,N,C) # (B*H)*N*C
-        v = v.view(B,N,H,C).permute(0,2,1,3).reshape(B*H,N,C) # (B*H)*N*C
-
-        sim = torch.einsum('b i c, b j c -> b i j', q, k) * self.scale  # (B*H)*N*N
-        attn = sim.softmax(dim=-1) # (B*H)*N*N
-
-        out = torch.einsum('b i j, b j c -> b i c', attn, v) # (B*H)*N*C
-        out = out.view(B,H,N,C).permute(0,2,1,3).reshape(B,N,(H*C)) # B*N*(H*C)
-
-        return self.to_out(out)
-
-    def forward(self, x, context=None, mask=None):
-        if not XFORMERS_IS_AVAILBLE:
-            return self.forward_plain(x)
-
-        q = self.to_q(x)
-        context = default(context, x)
-        k = self.to_k(context)
-        v = self.to_v(context)
-
-        b, _, _ = q.shape
-        q, k, v = map(
-            lambda t: t.unsqueeze(3)
-            .reshape(b, t.shape[1], self.heads, self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b * self.heads, t.shape[1], self.dim_head)
-            .contiguous(),
-            (q, k, v),
-        )
-
-        # actually compute the attention, what we cannot get enough of
-        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None)
-
-        if exists(mask):
-            raise NotImplementedError
-        out = (
-            out.unsqueeze(0)
-            .reshape(b, self.heads, out.shape[1], self.dim_head)
-            .permute(0, 2, 1, 3)
-            .reshape(b, out.shape[1], self.heads * self.dim_head)
-        )
-        return self.to_out(out)
-
-
-class GatedCrossAttentionDense(nn.Module):
-    def __init__(self, query_dim, key_dim, value_dim, n_heads, d_head):
-        super().__init__()
-        
-        self.attn = CrossAttention(query_dim=query_dim, key_dim=key_dim, value_dim=value_dim, heads=n_heads, dim_head=d_head) 
-        self.ff = FeedForward(query_dim, glu=True)
-
-        self.norm1 = nn.LayerNorm(query_dim)
-        self.norm2 = nn.LayerNorm(query_dim)
-
-        self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)) )
-        self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)) )
-
-        # this can be useful: we can externally change magnitude of tanh(alpha)
-        # for example, when it is set to 0, then the entire model is same as original one 
-        self.scale = 1  
-
-    def forward(self, x, objs):
-
-        x = x + self.scale*torch.tanh(self.alpha_attn) * self.attn( self.norm1(x), objs, objs)  
-        x = x + self.scale*torch.tanh(self.alpha_dense) * self.ff( self.norm2(x) ) 
-        
-        return x 
-
-
-class GatedSelfAttentionDense(nn.Module):
-    def __init__(self, query_dim, context_dim,  n_heads, d_head):
-        super().__init__()
-        
-        # we need a linear projection since we need cat visual feature and obj feature
-        self.linear = nn.Linear(context_dim, query_dim)
-
-        self.attn = SelfAttention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
-        self.ff = FeedForward(query_dim, glu=True)
-
-        self.norm1 = nn.LayerNorm(query_dim)
-        self.norm2 = nn.LayerNorm(query_dim)
-
-        self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)) )
-        self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)) )
-
-        # this can be useful: we can externally change magnitude of tanh(alpha)
-        # for example, when it is set to 0, then the entire model is same as original one 
-        self.scale = 1  
-
-
-    def forward(self, x, objs):
-
-        N_visual = x.shape[1]
-        objs = self.linear(objs)
-        
-        x = x + self.scale*torch.tanh(self.alpha_attn) * self.attn(  self.norm1(torch.cat([x,objs],dim=1))  )[:,0:N_visual,:]
-        x = x + self.scale*torch.tanh(self.alpha_dense) * self.ff( self.norm2(x) )  
-        
-        return x 
-
-
-class BasicTransformerBlock(nn.Module):
-    def __init__(self, query_dim, key_dim, value_dim, n_heads, d_head, fuser_type, use_checkpoint=True):
-        super().__init__()
-        self.attn1 = SelfAttention(query_dim=query_dim, heads=n_heads, dim_head=d_head)  
-        self.ff = FeedForward(query_dim, glu=True)
-        self.attn2 = CrossAttention(query_dim=query_dim, key_dim=key_dim, value_dim=value_dim, heads=n_heads, dim_head=d_head)  
-        self.norm1 = nn.LayerNorm(query_dim)
-        self.norm2 = nn.LayerNorm(query_dim)
-        self.norm3 = nn.LayerNorm(query_dim)
-        self.use_checkpoint = use_checkpoint
-
-        if fuser_type == "gatedSA":
-            # note key_dim here actually is context_dim
-            self.fuser = GatedSelfAttentionDense(query_dim, key_dim, n_heads, d_head) 
-        elif fuser_type == "gatedCA":
-            self.fuser = GatedCrossAttentionDense(query_dim, key_dim, value_dim, n_heads, d_head) 
-        else:
-            assert False 
-
-
-    def forward(self, x, context, objs):
-#        return checkpoint(self._forward, (x, context, objs), self.parameters(), self.use_checkpoint)
-        if self.use_checkpoint and x.requires_grad:
-            return checkpoint.checkpoint(self._forward, x, context, objs)
-        else:
-            return self._forward(x, context, objs)
-
-    def _forward(self, x, context, objs): 
-        x = self.attn1( self.norm1(x) ) + x 
-        x = self.fuser(x, objs) # identity mapping in the beginning 
-        x = self.attn2(self.norm2(x), context, context) + x
-        x = self.ff(self.norm3(x)) + x
-        return x
-
-
-class SpatialTransformer(nn.Module):
-    def __init__(self, in_channels, key_dim, value_dim, n_heads, d_head, depth=1, fuser_type=None, use_checkpoint=True):
-        super().__init__()
-        self.in_channels = in_channels
-        query_dim = n_heads * d_head
-        self.norm = Normalize(in_channels)
-
-
-        self.proj_in = nn.Conv2d(in_channels,
-                                 query_dim,
-                                 kernel_size=1,
-                                 stride=1,
-                                 padding=0)
-
-        self.transformer_blocks = nn.ModuleList(
-            [BasicTransformerBlock(query_dim, key_dim, value_dim, n_heads, d_head, fuser_type, use_checkpoint=use_checkpoint)
-                for d in range(depth)]
-        )
-
-        self.proj_out = zero_module(nn.Conv2d(query_dim,
-                                              in_channels,
-                                              kernel_size=1,
-                                              stride=1,
-                                              padding=0))
-
-    def forward(self, x, context, objs):
-        b, c, h, w = x.shape
-        x_in = x
-        x = self.norm(x)
-        x = self.proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c')
-        for block in self.transformer_blocks:
-            x = block(x, context, objs)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
-        x = self.proj_out(x)
-        return x + x_in
\ No newline at end of file
diff --git a/spaces/gradio/altair_plot_main/README.md b/spaces/gradio/altair_plot_main/README.md
deleted file mode 100644
index cdfa60bf16d1760f06de60e07d94e00adec6b68d..0000000000000000000000000000000000000000
--- a/spaces/gradio/altair_plot_main/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-
----
-title: altair_plot_main 
-emoji: 🔥
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.1.2
-app_file: run.py
-pinned: false
-hf_oauth: true
----
diff --git a/spaces/gradio/longformer/scripts/test_tpu.py b/spaces/gradio/longformer/scripts/test_tpu.py
deleted file mode 100644
index 9617683731cacb9493e2a23d642c1dcce7e073e7..0000000000000000000000000000000000000000
--- a/spaces/gradio/longformer/scripts/test_tpu.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import torch
-from torch.utils.data import DataLoader, Dataset
-from transformers import AutoModel
-import pytorch_lightning as pl
-
-
-class CoolDataset(Dataset):
-
-    def __len__(self):
-        return 128 * 128
-
-    def __getitem__(self, idx):
-        return torch.tensor([1, 2, 3, 4] * 128 * 1), torch.tensor([1, 1, 1, 1] * 128 * 1)
-
-
-class CoolSystem(pl.LightningModule):
-
-    def __init__(self):
-        super().__init__()
-
-        # self.model = AutoModel.from_pretrained('allenai/longformer-base-4096')
-        self.model = AutoModel.from_pretrained('roberta-base')
-
-    def forward(self, x, y):
-        return self.model(x, attention_mask=None)
-
-    def training_step(self, batch, batch_idx):
-        x, y = batch
-        y_hat = self(x, y)
-        loss = y_hat[0].sum()
-        return {'loss': loss * 0.00000001}
-
-    def configure_optimizers(self):
-        return torch.optim.Adam(self.parameters(), lr=0.0000000001)
-
-    def train_dataloader(self):
-        loader = DataLoader(CoolDataset(), batch_size=56, num_workers=0)
-        return loader
-
-
-if __name__ == '__main__':
-    model = CoolSystem()
-    trainer = pl.Trainer(num_tpu_cores=None, progress_bar_refresh_rate=1, max_epochs=10, num_sanity_val_steps=0, gpus=1, precision=16, amp_level='O2')
-    trainer.fit(model)
diff --git a/spaces/guetLzy/Real-ESRGAN-Demo/docs/anime_comparisons_CN.md b/spaces/guetLzy/Real-ESRGAN-Demo/docs/anime_comparisons_CN.md
deleted file mode 100644
index 43ba58344ed9554d5b30e2815d1b7d4ab8bc503f..0000000000000000000000000000000000000000
--- a/spaces/guetLzy/Real-ESRGAN-Demo/docs/anime_comparisons_CN.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# 动漫视频模型比较
-
-[English](anime_comparisons.md) **|** [简体中文](anime_comparisons_CN.md)
-
-## 更新
-
-- 2022/04/24: 发布 **AnimeVideo-v3**. 主要做了以下更新：
-  - **更自然**
-  - **更少瑕疵**
-  - **颜色保持得更好**
-  - **更好的纹理恢复**
-  - **虚化背景处理**
-
-## 比较
-
-我们将 RealESRGAN-AnimeVideo-v3 与以下方法进行了比较。我们的 RealESRGAN-AnimeVideo-v3 可以以更快的推理速度获得更好的结果。
-
-- [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan). 超参数: `tile=0`, `noiselevel=2`
-- [Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN): 我们使用了[20220227](https://github.com/bilibili/ailab/releases/tag/Real-CUGAN-add-faster-low-memory-mode)版本, 超参: `cache_mode=0`, `tile=0`, `alpha=1`.
-- 我们的 RealESRGAN-AnimeVideo-v3
-
-## 结果
-
-您可能需要**放大**以比较详细信息, 或者**单击图像**以查看完整尺寸。 请注意下面表格的图片是从原图里裁剪patch并且resize后的结果，您可以从
-[Google Drive](https://drive.google.com/drive/folders/1bc_Hje1Nqop9NDkUvci2VACSjL7HZMRp?usp=sharing) 里下载原始的输入和输出。
-
-**更自然的结果，更好的虚化背景恢复**
-
-| 输入 | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
-| :---: | :---:        |     :---:      |  :---:      |
-|![157083983-bec52c67-9a5e-4eed-afef-01fe6cd2af85_patch](https://user-images.githubusercontent.com/11482921/164452769-5d8cb4f8-1708-42d2-b941-f44a6f136feb.png) | ![](https://user-images.githubusercontent.com/11482921/164452767-c825cdec-f721-4ff1-aef1-fec41f146c4c.png) | ![](https://user-images.githubusercontent.com/11482921/164452755-3be50895-e3d4-432d-a7b9-9085c2a8e771.png) | ![](https://user-images.githubusercontent.com/11482921/164452771-be300656-379a-4323-a755-df8025a8c451.png) |
-|![a0010_patch](https://user-images.githubusercontent.com/11482921/164454047-22eeb493-3fa9-4142-9fc2-6f2a1c074cd5.png) | ![](https://user-images.githubusercontent.com/11482921/164454046-d5e79f8f-00a0-4b55-bc39-295d0d69747a.png) | ![](https://user-images.githubusercontent.com/11482921/164454040-87886b11-9d08-48bd-862f-0d4aed72eb19.png) | ![](https://user-images.githubusercontent.com/11482921/164454055-73dc9f02-286e-4d5c-8f70-c13742e08f42.png) |
-|![00000044_patch](https://user-images.githubusercontent.com/11482921/164451232-bacf64fc-e55a-44db-afbb-6b31ab0f8973.png) | ![](https://user-images.githubusercontent.com/11482921/164451318-f309b61a-75b8-4b74-b5f3-595725f1cf0b.png) | ![](https://user-images.githubusercontent.com/11482921/164451348-994f8a35-adbe-4a4b-9c61-feaa294af06a.png) | ![](https://user-images.githubusercontent.com/11482921/164451361-9b7d376e-6f75-4648-b752-542b44845d1c.png) |
-
-**更少瑕疵，更好的细节纹理**
-
-| 输入 | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
-| :---: | :---:        |     :---:      |  :---:      |
-|![00000053_patch](https://user-images.githubusercontent.com/11482921/164448411-148a7e5c-cfcd-4504-8bc7-e318eb883bb6.png) | ![](https://user-images.githubusercontent.com/11482921/164448633-dfc15224-b6d2-4403-a3c9-4bb819979364.png) | ![](https://user-images.githubusercontent.com/11482921/164448771-0d359509-5293-4d4c-8e3c-86a2a314ea88.png) | ![](https://user-images.githubusercontent.com/11482921/164448848-1a4ff99e-075b-4458-9db7-2c89e8160aa0.png) |
-|![Disney_v4_22_018514_s2_patch](https://user-images.githubusercontent.com/11482921/164451898-83311cdf-bd3e-450f-b9f6-34d7fea3ab79.png) | ![](https://user-images.githubusercontent.com/11482921/164451894-6c56521c-6561-40d6-a3a5-8dde2c167b8a.png) | ![](https://user-images.githubusercontent.com/11482921/164451888-af9b47e3-39dc-4f3e-b0d7-d372d8191e2a.png) | ![](https://user-images.githubusercontent.com/11482921/164451901-31ca4dd4-9847-4baa-8cde-ad50f4053dcf.png) |
-|![Japan_v2_0_007261_s2_patch](https://user-images.githubusercontent.com/11482921/164454578-73c77392-77de-49c5-b03c-c36631723192.png) | ![](https://user-images.githubusercontent.com/11482921/164454574-b1ede5f0-4520-4eaa-8f59-086751a34e62.png) | ![](https://user-images.githubusercontent.com/11482921/164454567-4cb3fdd8-6a2d-4016-85b2-a305a8ff80e4.png) | ![](https://user-images.githubusercontent.com/11482921/164454583-7f243f20-eca3-4500-ac43-eb058a4a101a.png) |
-|![huluxiongdi_2_patch](https://user-images.githubusercontent.com/11482921/164453482-0726c842-337e-40ec-bf6c-f902ee956a8b.png) | ![](https://user-images.githubusercontent.com/11482921/164453480-71d5e091-5bfa-4c77-9c57-4e37f66ca0a3.png) | ![](https://user-images.githubusercontent.com/11482921/164453468-c295d3c9-3661-45f0-9ecd-406a1877f76e.png) | ![](https://user-images.githubusercontent.com/11482921/164453486-3091887c-587c-450e-b6fe-905cb518d57e.png) |
-
-**其他更好的结果**
-
-| 输入 | waifu2x | Real-CUGAN | RealESRGAN<br>AnimeVideo-v3 |
-| :---: | :---:        |     :---:      |  :---:      |
-|![Japan_v2_1_128525_s1_patch](https://user-images.githubusercontent.com/11482921/164454933-67697f7c-b6ef-47dc-bfca-822a78af8acf.png) | ![](https://user-images.githubusercontent.com/11482921/164454931-9450de7c-f0b3-4638-9c1e-0668e0c41ef0.png) | ![](https://user-images.githubusercontent.com/11482921/164454926-ed746976-786d-41c5-8a83-7693cd774c3a.png) | ![](https://user-images.githubusercontent.com/11482921/164454936-8abdf0f0-fb30-40eb-8281-3b46c0bcb9ae.png) |
-|![tianshuqitan_2_patch](https://user-images.githubusercontent.com/11482921/164456948-807c1476-90b6-4507-81da-cb986d01600c.png) | ![](https://user-images.githubusercontent.com/11482921/164456943-25e89de9-d7e5-4f61-a2e1-96786af6ae9e.png) | ![](https://user-images.githubusercontent.com/11482921/164456954-b468c447-59f5-4594-9693-3683e44ba3e6.png) | ![](https://user-images.githubusercontent.com/11482921/164456957-640f910c-3b04-407c-ac20-044d72e19735.png) |
-|![00000051_patch](https://user-images.githubusercontent.com/11482921/164456044-e9a6b3fa-b24e-4eb7-acf9-1f7746551b1e.png) ![00000051_patch](https://user-images.githubusercontent.com/11482921/164456421-b67245b0-767d-4250-9105-80bbe507ecfc.png) | ![](https://user-images.githubusercontent.com/11482921/164456040-85763cf2-cb28-4ba3-abb6-1dbb48c55713.png) ![](https://user-images.githubusercontent.com/11482921/164456419-59cf342e-bc1e-4044-868c-e1090abad313.png) | ![](https://user-images.githubusercontent.com/11482921/164456031-4244bb7b-8649-4e01-86f4-40c2099c5afd.png) ![](https://user-images.githubusercontent.com/11482921/164456411-b6afcbe9-c054-448d-a6df-96d3ba3047f8.png) | ![](https://user-images.githubusercontent.com/11482921/164456035-12e270be-fd52-46d4-b18a-3d3b680731fe.png) ![](https://user-images.githubusercontent.com/11482921/164456417-dcaa8b62-f497-427d-b2d2-f390f1200fb9.png) |
-|![00000099_patch](https://user-images.githubusercontent.com/11482921/164455312-6411b6e1-5823-4131-a4b0-a6be8a9ae89f.png) | ![](https://user-images.githubusercontent.com/11482921/164455310-f2b99646-3a22-47a4-805b-dc451ac86ddb.png) | ![](https://user-images.githubusercontent.com/11482921/164455294-35471b42-2826-4451-b7ec-6de01344954c.png) | ![](https://user-images.githubusercontent.com/11482921/164455305-fa4c9758-564a-4081-8b4e-f11057a0404d.png) |
-|![00000016_patch](https://user-images.githubusercontent.com/11482921/164455672-447353c9-2da2-4fcb-ba4a-7dd6b94c19c1.png) | ![](https://user-images.githubusercontent.com/11482921/164455669-df384631-baaa-42f8-9150-40f658471558.png) | ![](https://user-images.githubusercontent.com/11482921/164455657-68006bf0-138d-4981-aaca-8aa927d2f78a.png) | ![](https://user-images.githubusercontent.com/11482921/164455664-0342b93e-a62a-4b36-a90e-7118f3f1e45d.png) |
-
-## 推理速度比较
-
-### PyTorch
-
-请注意，我们只报告了**模型推理**的时间, 而忽略了读写硬盘的时间.
-
-| GPU | 输入尺寸 | waifu2x | Real-CUGAN | RealESRGAN-AnimeVideo-v3
-| :---: | :---:         |  :---:        |     :---:      |  :---:      |
-| V100 | 1921 x 1080 | - | 3.4 fps | **10.0** fps |
-| V100 | 1280 x 720 | - | 7.2 fps | **22.6** fps |
-| V100 | 640 x 480 | - | 24.4 fps | **65.9** fps |
-
-### ncnn
-
-- [ ] TODO
diff --git a/spaces/gylleus/icongen/torch_utils/ops/conv2d_gradfix.py b/spaces/gylleus/icongen/torch_utils/ops/conv2d_gradfix.py
deleted file mode 100644
index 4b6a9e9586fe5d39bbac4fc9db26c234b162928e..0000000000000000000000000000000000000000
--- a/spaces/gylleus/icongen/torch_utils/ops/conv2d_gradfix.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.conv2d` that supports
-arbitrarily high order gradients with zero performance penalty."""
-
-import warnings
-import contextlib
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False                     # Enable the custom op by setting this to true.
-weight_gradients_disabled = False   # Forcefully disable computation of gradients with respect to the weights.
-
-@contextlib.contextmanager
-def no_weight_gradients():
-    global weight_gradients_disabled
-    old = weight_gradients_disabled
-    weight_gradients_disabled = True
-    yield
-    weight_gradients_disabled = old
-
-#----------------------------------------------------------------------------
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
-    return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
-    return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op(input):
-    assert isinstance(input, torch.Tensor)
-    if (not enabled) or (not torch.backends.cudnn.enabled):
-        return False
-    if input.device.type != 'cuda':
-        return False
-    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.']):
-        return True
-    warnings.warn(f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().')
-    return False
-
-def _tuple_of_ints(xs, ndim):
-    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
-    assert len(xs) == ndim
-    assert all(isinstance(x, int) for x in xs)
-    return xs
-
-#----------------------------------------------------------------------------
-
-_conv2d_gradfix_cache = dict()
-
-def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
-    # Parse arguments.
-    ndim = 2
-    weight_shape = tuple(weight_shape)
-    stride = _tuple_of_ints(stride, ndim)
-    padding = _tuple_of_ints(padding, ndim)
-    output_padding = _tuple_of_ints(output_padding, ndim)
-    dilation = _tuple_of_ints(dilation, ndim)
-
-    # Lookup from cache.
-    key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
-    if key in _conv2d_gradfix_cache:
-        return _conv2d_gradfix_cache[key]
-
-    # Validate arguments.
-    assert groups >= 1
-    assert len(weight_shape) == ndim + 2
-    assert all(stride[i] >= 1 for i in range(ndim))
-    assert all(padding[i] >= 0 for i in range(ndim))
-    assert all(dilation[i] >= 0 for i in range(ndim))
-    if not transpose:
-        assert all(output_padding[i] == 0 for i in range(ndim))
-    else: # transpose
-        assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))
-
-    # Helpers.
-    common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
-    def calc_output_padding(input_shape, output_shape):
-        if transpose:
-            return [0, 0]
-        return [
-            input_shape[i + 2]
-            - (output_shape[i + 2] - 1) * stride[i]
-            - (1 - 2 * padding[i])
-            - dilation[i] * (weight_shape[i + 2] - 1)
-            for i in range(ndim)
-        ]
-
-    # Forward & backward.
-    class Conv2d(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, input, weight, bias):
-            assert weight.shape == weight_shape
-            if not transpose:
-                output = torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-            else: # transpose
-                output = torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
-            ctx.save_for_backward(input, weight)
-            return output
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            input, weight = ctx.saved_tensors
-            grad_input = None
-            grad_weight = None
-            grad_bias = None
-
-            if ctx.needs_input_grad[0]:
-                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
-                grad_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None)
-                assert grad_input.shape == input.shape
-
-            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
-                grad_weight = Conv2dGradWeight.apply(grad_output, input)
-                assert grad_weight.shape == weight_shape
-
-            if ctx.needs_input_grad[2]:
-                grad_bias = grad_output.sum([0, 2, 3])
-
-            return grad_input, grad_weight, grad_bias
-
-    # Gradient with respect to the weights.
-    class Conv2dGradWeight(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, grad_output, input):
-            op = torch._C._jit_get_operation('aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight')
-            flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
-            grad_weight = op(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
-            assert grad_weight.shape == weight_shape
-            ctx.save_for_backward(grad_output, input)
-            return grad_weight
-
-        @staticmethod
-        def backward(ctx, grad2_grad_weight):
-            grad_output, input = ctx.saved_tensors
-            grad2_grad_output = None
-            grad2_input = None
-
-            if ctx.needs_input_grad[0]:
-                grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None)
-                assert grad2_grad_output.shape == grad_output.shape
-
-            if ctx.needs_input_grad[1]:
-                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
-                grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None)
-                assert grad2_input.shape == input.shape
-
-            return grad2_grad_output, grad2_input
-
-    _conv2d_gradfix_cache[key] = Conv2d
-    return Conv2d
-
-#----------------------------------------------------------------------------
diff --git a/spaces/haoheliu/audioldm-text-to-audio-generation/README.md b/spaces/haoheliu/audioldm-text-to-audio-generation/README.md
deleted file mode 100644
index a267d537ae6a23859d5640388bd6ccbf04a480f3..0000000000000000000000000000000000000000
--- a/spaces/haoheliu/audioldm-text-to-audio-generation/README.md
+++ /dev/null
@@ -1,22 +0,0 @@
----
-title: Audioldm Text To Audio Generation
-emoji: 🔊
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: bigscience-openrail-m
-duplicated_from: haoheliu/audioldm-text-to-audio-generation
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-## Reference
-Part of the code from this repo is borrowed from the following repos. We would like to thank the authors of them for their contribution. 
-
-> https://github.com/LAION-AI/CLAP
-> https://github.com/CompVis/stable-diffusion
-> https://github.com/v-iashin/SpecVQGAN 
-> https://github.com/toshas/torch-fidelity
\ No newline at end of file
diff --git "a/spaces/hbestm/gpt-academic-play/crazy_functions/\346\211\271\351\207\217\347\277\273\350\257\221PDF\346\226\207\346\241\243_\345\244\232\347\272\277\347\250\213.py" "b/spaces/hbestm/gpt-academic-play/crazy_functions/\346\211\271\351\207\217\347\277\273\350\257\221PDF\346\226\207\346\241\243_\345\244\232\347\272\277\347\250\213.py"
deleted file mode 100644
index 4adb9a464bc71ec4a177b76536d5e5fab619ef2d..0000000000000000000000000000000000000000
--- "a/spaces/hbestm/gpt-academic-play/crazy_functions/\346\211\271\351\207\217\347\277\273\350\257\221PDF\346\226\207\346\241\243_\345\244\232\347\272\277\347\250\213.py"
+++ /dev/null
@@ -1,131 +0,0 @@
-from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-from .crazy_utils import read_and_clean_pdf_text
-from colorful import *
-
-@CatchException
-def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
-    import glob
-    import os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(
-        f'{project_folder}/**/*.pdf', recursive=True)]
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt)
-
-
-def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt):
-    import os
-    import tiktoken
-    TOKEN_LIMIT_PER_FRAGMENT = 1280
-    generated_conclusion_files = []
-    for index, fp in enumerate(file_manifest):
-
-        # 读取PDF文件
-        file_content, page_one = read_and_clean_pdf_text(fp)
-
-        # 递归地切割PDF文件
-        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
-        page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
-
-        # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-        
-        # 单线，获取文章meta信息
-        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=f"以下是一篇学术论文的基础信息，请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出，最后用中文翻译摘要部分。请提取：{paper_meta}",
-            inputs_show_user=f"请从{fp}中提取出“标题”、“收录会议或期刊”等基本信息。",
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, history=[],
-            sys_prompt="Your job is to collect information from materials。",
-        )
-
-        # 多线，翻译
-        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=[
-                f"你需要翻译以下内容：\n{frag}" for frag in paper_fragments],
-            inputs_show_user_array=[f"\n---\n 原文： \n\n {frag.replace('#', '')}  \n---\n 翻译：\n " for frag in paper_fragments],
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history_array=[[paper_meta] for _ in paper_fragments],
-            sys_prompt_array=[
-                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in paper_fragments],
-            # max_workers=5  # OpenAI所允许的最大并行过载
-        )
-
-        # 整理报告的格式
-        for i,k in enumerate(gpt_response_collection): 
-            if i%2==0:
-                gpt_response_collection[i] = f"\n\n---\n\n ## 原文[{i//2}/{len(gpt_response_collection)//2}]： \n\n {paper_fragments[i//2].replace('#', '')}  \n\n---\n\n ## 翻译[{i//2}/{len(gpt_response_collection)//2}]：\n "
-            else:
-                gpt_response_collection[i] = gpt_response_collection[i]
-        final = ["一、论文概况\n\n---\n\n", paper_meta_info.replace('# ', '### ') + '\n\n---\n\n', "二、论文翻译", ""]
-        final.extend(gpt_response_collection)
-        create_report_file_name = f"{os.path.basename(fp)}.trans.md"
-        res = write_results_to_file(final, file_name=create_report_file_name)
-
-        # 更新UI
-        generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}')
-        chatbot.append((f"{fp}完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 准备文件的下载
-    import shutil
-    for pdf_path in generated_conclusion_files:
-        # 重命名文件
-        rename_file = f'./gpt_log/总结论文-{os.path.basename(pdf_path)}'
-        if os.path.exists(rename_file):
-            os.remove(rename_file)
-        shutil.copyfile(pdf_path, rename_file)
-        if os.path.exists(pdf_path):
-            os.remove(pdf_path)
-    chatbot.append(("给出输出文件清单", str(generated_conclusion_files)))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
diff --git a/spaces/hbestm/gpt-academic-play/docs/waifu_plugin/waifu-tips.js b/spaces/hbestm/gpt-academic-play/docs/waifu_plugin/waifu-tips.js
deleted file mode 100644
index 8f9533a19e7d4914bde888ee2a107e4430242968..0000000000000000000000000000000000000000
--- a/spaces/hbestm/gpt-academic-play/docs/waifu_plugin/waifu-tips.js
+++ /dev/null
@@ -1,405 +0,0 @@
-window.live2d_settings = Array(); /*
-
-    く__,.ヘヽ.　　　　/　,ー､ 〉
-    　　　　　＼ ', !-─‐-i　/　/´
-    　　　 　 ／｀ｰ'　　　 L/／｀ヽ､            Live2D 看板娘 参数设置
-    　　 　 /　 ／,　 /|　 ,　 ,　　　 ',                                           Version 1.4.2
-    　　　ｲ 　/ /-‐/　ｉ　L_ ﾊ ヽ!　 i                            Update 2018.11.12
-    　　　 ﾚ ﾍ 7ｲ｀ﾄ　 ﾚ'ｧ-ﾄ､!ハ|　 |  
-    　　　　 !,/7 '0'　　 ´0iソ| 　 |　　　
-    　　　　 |.从"　　_　　 ,,,, / |./ 　 |             网页添加 Live2D 看板娘
-    　　　　 ﾚ'| i＞.､,,__　_,.イ / 　.i 　|                    https://www.fghrsh.net/post/123.html
-    　　　　　 ﾚ'| | / k_７_/ﾚ'ヽ,　ﾊ.　|           
-    　　　　　　 | |/i 〈|/　 i　,.ﾍ |　i　|    Thanks
-    　　　　　　.|/ /　ｉ： 　 ﾍ!　　＼　|          journey-ad / https://github.com/journey-ad/live2d_src
-    　　　 　 　 kヽ>､ﾊ 　 _,.ﾍ､ 　 /､!            xiazeyu / https://github.com/xiazeyu/live2d-widget.js
-    　　　　　　 !'〈//｀Ｔ´', ＼ ｀'7'ｰr'          Live2d Cubism SDK WebGL 2.1 Projrct & All model authors.
-    　　　　　　 ﾚ'ヽL__|___i,___,ンﾚ|ノ
-    　　　　　 　　　ﾄ-,/　|___./
-    　　　　　 　　　'ｰ'　　!_,.:*********************************************************************************/
-
-
-// 后端接口
-live2d_settings['modelAPI']             = '//live2d.fghrsh.net/api/';   // 自建 API 修改这里
-live2d_settings['tipsMessage']          = 'waifu-tips.json';            // 同目录下可省略路径
-live2d_settings['hitokotoAPI']          = 'lwl12.com';                  // 一言 API，可选 'lwl12.com', 'hitokoto.cn', 'jinrishici.com'(古诗词)
-
-// 默认模型
-live2d_settings['modelId']              = 1;            // 默认模型 ID，可在 F12 控制台找到
-live2d_settings['modelTexturesId']      = 53;           // 默认材质 ID，可在 F12 控制台找到
-
-// 工具栏设置
-live2d_settings['showToolMenu']         = true;         // 显示 工具栏          ，可选 true(真), false(假)
-live2d_settings['canCloseLive2d']       = true;         // 显示 关闭看板娘  按钮，可选 true(真), false(假)
-live2d_settings['canSwitchModel']       = true;         // 显示 模型切换    按钮，可选 true(真), false(假)
-live2d_settings['canSwitchTextures']    = true;         // 显示 材质切换    按钮，可选 true(真), false(假)
-live2d_settings['canSwitchHitokoto']    = true;         // 显示 一言切换    按钮，可选 true(真), false(假)
-live2d_settings['canTakeScreenshot']    = true;         // 显示 看板娘截图  按钮，可选 true(真), false(假)
-live2d_settings['canTurnToHomePage']    = true;         // 显示 返回首页    按钮，可选 true(真), false(假)
-live2d_settings['canTurnToAboutPage']   = true;         // 显示 跳转关于页  按钮，可选 true(真), false(假)
-
-// 模型切换模式
-live2d_settings['modelStorage']         = true;         // 记录 ID (刷新后恢复)，可选 true(真), false(假)
-live2d_settings['modelRandMode']        = 'switch';     // 模型切换，可选 'rand'(随机), 'switch'(顺序)
-live2d_settings['modelTexturesRandMode']= 'rand';       // 材质切换，可选 'rand'(随机), 'switch'(顺序)
-
-// 提示消息选项
-live2d_settings['showHitokoto']         = true;         // 显示一言
-live2d_settings['showF12Status']        = true;         // 显示加载状态
-live2d_settings['showF12Message']       = false;        // 显示看板娘消息
-live2d_settings['showF12OpenMsg']       = true;         // 显示控制台打开提示
-live2d_settings['showCopyMessage']      = true;         // 显示 复制内容 提示
-live2d_settings['showWelcomeMessage']   = true;         // 显示进入面页欢迎词
-
-//看板娘样式设置
-live2d_settings['waifuSize']            = '280x250';    // 看板娘大小，例如 '280x250', '600x535'
-live2d_settings['waifuTipsSize']        = '250x70';     // 提示框大小，例如 '250x70', '570x150'
-live2d_settings['waifuFontSize']        = '12px';       // 提示框字体，例如 '12px', '30px'
-live2d_settings['waifuToolFont']        = '14px';       // 工具栏字体，例如 '14px', '36px'
-live2d_settings['waifuToolLine']        = '20px';       // 工具栏行高，例如 '20px', '36px'
-live2d_settings['waifuToolTop']         = '0px'         // 工具栏顶部边距，例如 '0px', '-60px'
-live2d_settings['waifuMinWidth']        = '768px';      // 面页小于 指定宽度 隐藏看板娘，例如 'disable'(禁用), '768px'
-live2d_settings['waifuEdgeSide']        = 'left:0';     // 看板娘贴边方向，例如 'left:0'(靠左 0px), 'right:30'(靠右 30px)
-live2d_settings['waifuDraggable']       = 'disable';    // 拖拽样式，例如 'disable'(禁用), 'axis-x'(只能水平拖拽), 'unlimited'(自由拖拽)
-live2d_settings['waifuDraggableRevert'] = true;         // 松开鼠标还原拖拽位置，可选 true(真), false(假)
-
-// 其他杂项设置
-live2d_settings['l2dVersion']           = '1.4.2';        // 当前版本
-live2d_settings['l2dVerDate']           = '2018.11.12'; // 版本更新日期
-live2d_settings['homePageUrl']          = 'auto';       // 主页地址，可选 'auto'(自动), '{URL 网址}'
-live2d_settings['aboutPageUrl']         = 'https://www.fghrsh.net/post/123.html';   // 关于页地址, '{URL 网址}'
-live2d_settings['screenshotCaptureName']= 'live2d.png'; // 看板娘截图文件名，例如 'live2d.png'
-
-/****************************************************************************************************/
-
-String.prototype.render = function(context) {
-    var tokenReg = /(\\)?\{([^\{\}\\]+)(\\)?\}/g;
-
-    return this.replace(tokenReg, function (word, slash1, token, slash2) {
-        if (slash1 || slash2) { return word.replace('\\', ''); }
-        
-        var variables = token.replace(/\s/g, '').split('.');
-        var currentObject = context;
-        var i, length, variable;
-        
-        for (i = 0, length = variables.length; i < length; ++i) {
-            variable = variables[i];
-            currentObject = currentObject[variable];
-            if (currentObject === undefined || currentObject === null) return '';
-        }
-        return currentObject;
-    });
-};
-
-var re = /x/;
-console.log(re);
-
-function empty(obj) {return typeof obj=="undefined"||obj==null||obj==""?true:false}
-function getRandText(text) {return Array.isArray(text) ? text[Math.floor(Math.random() * text.length + 1)-1] : text}
-
-function showMessage(text, timeout, flag) {
-    if(flag || sessionStorage.getItem('waifu-text') === '' || sessionStorage.getItem('waifu-text') === null){
-        if(Array.isArray(text)) text = text[Math.floor(Math.random() * text.length + 1)-1];
-        if (live2d_settings.showF12Message) console.log('[Message]', text.replace(/<[^<>]+>/g,''));
-        
-        if(flag) sessionStorage.setItem('waifu-text', text);
-        
-        $('.waifu-tips').stop();
-        $('.waifu-tips').html(text).fadeTo(200, 1);
-        if (timeout === undefined) timeout = 5000;
-        hideMessage(timeout);
-    }
-}
-
-function hideMessage(timeout) {
-    $('.waifu-tips').stop().css('opacity',1);
-    if (timeout === undefined) timeout = 5000;
-    window.setTimeout(function() {sessionStorage.removeItem('waifu-text')}, timeout);
-    $('.waifu-tips').delay(timeout).fadeTo(200, 0);
-}
-
-function initModel(waifuPath, type) {
-    /* console welcome message */
-    eval(function(p,a,c,k,e,r){e=function(c){return(c<a?'':e(parseInt(c/a)))+((c=c%a)>35?String.fromCharCode(c+29):c.toString(36))};if(!''.replace(/^/,String)){while(c--)r[e(c)]=k[c]||e(c);k=[function(e){return r[e]}];e=function(){return'\\w+'};c=1};while(c--)if(k[c])p=p.replace(new RegExp('\\b'+e(c)+'\\b','g'),k[c]);return p}('8.d(" ");8.d("\\U,.\\y\\5.\\1\\1\\1\\1/\\1,\\u\\2 \\H\\n\\1\\1\\1\\1\\1\\b \', !-\\r\\j-i\\1/\\1/\\g\\n\\1\\1\\1 \\1 \\a\\4\\f\'\\1\\1\\1 L/\\a\\4\\5\\2\\n\\1\\1 \\1 /\\1 \\a,\\1 /|\\1 ,\\1 ,\\1\\1\\1 \',\\n\\1\\1\\1\\q \\1/ /-\\j/\\1\\h\\E \\9 \\5!\\1 i\\n\\1\\1\\1 \\3 \\6 7\\q\\4\\c\\1 \\3\'\\s-\\c\\2!\\t|\\1 |\\n\\1\\1\\1\\1 !,/7 \'0\'\\1\\1 \\X\\w| \\1 |\\1\\1\\1\\n\\1\\1\\1\\1 |.\\x\\"\\1\\l\\1\\1 ,,,, / |./ \\1 |\\n\\1\\1\\1\\1 \\3\'| i\\z.\\2,,A\\l,.\\B / \\1.i \\1|\\n\\1\\1\\1\\1\\1 \\3\'| | / C\\D/\\3\'\\5,\\1\\9.\\1|\\n\\1\\1\\1\\1\\1\\1 | |/i \\m|/\\1 i\\1,.\\6 |\\F\\1|\\n\\1\\1\\1\\1\\1\\1.|/ /\\1\\h\\G \\1 \\6!\\1\\1\\b\\1|\\n\\1\\1\\1 \\1 \\1 k\\5>\\2\\9 \\1 o,.\\6\\2 \\1 /\\2!\\n\\1\\1\\1\\1\\1\\1 !\'\\m//\\4\\I\\g\', \\b \\4\'7\'\\J\'\\n\\1\\1\\1\\1\\1\\1 \\3\'\\K|M,p,\\O\\3|\\P\\n\\1\\1\\1\\1\\1 \\1\\1\\1\\c-,/\\1|p./\\n\\1\\1\\1\\1\\1 \\1\\1\\1\'\\f\'\\1\\1!o,.:\\Q \\R\\S\\T v"+e.V+" / W "+e.N);8.d(" ");',60,60,'|u3000|uff64|uff9a|uff40|u30fd|uff8d||console|uff8a|uff0f|uff3c|uff84|log|live2d_settings|uff70|u00b4|uff49||u2010||u3000_|u3008||_|___|uff72|u2500|uff67|u30cf|u30fc||u30bd|u4ece|u30d8|uff1e|__|u30a4|k_|uff17_|u3000L_|u3000i|uff1a|u3009|uff34|uff70r|u30fdL__||___i|l2dVerDate|u30f3|u30ce|nLive2D|u770b|u677f|u5a18|u304f__|l2dVersion|FGHRSH|u00b40i'.split('|'),0,{}));
-    
-    /* 判断 JQuery */
-    if (typeof($.ajax) != 'function') typeof(jQuery.ajax) == 'function' ? window.$ = jQuery : console.log('[Error] JQuery is not defined.');
-    
-    /* 加载看板娘样式 */
-    live2d_settings.waifuSize = live2d_settings.waifuSize.split('x');
-    live2d_settings.waifuTipsSize = live2d_settings.waifuTipsSize.split('x');
-    live2d_settings.waifuEdgeSide = live2d_settings.waifuEdgeSide.split(':');
-    
-    $("#live2d").attr("width",live2d_settings.waifuSize[0]);
-    $("#live2d").attr("height",live2d_settings.waifuSize[1]);
-    $(".waifu-tips").width(live2d_settings.waifuTipsSize[0]);
-    $(".waifu-tips").height(live2d_settings.waifuTipsSize[1]);
-    $(".waifu-tips").css("top",live2d_settings.waifuToolTop);
-    $(".waifu-tips").css("font-size",live2d_settings.waifuFontSize);
-    $(".waifu-tool").css("font-size",live2d_settings.waifuToolFont);
-    $(".waifu-tool span").css("line-height",live2d_settings.waifuToolLine);
-    
-    if (live2d_settings.waifuEdgeSide[0] == 'left') $(".waifu").css("left",live2d_settings.waifuEdgeSide[1]+'px');
-    else if (live2d_settings.waifuEdgeSide[0] == 'right') $(".waifu").css("right",live2d_settings.waifuEdgeSide[1]+'px');
-    
-    window.waifuResize = function() { $(window).width() <= Number(live2d_settings.waifuMinWidth.replace('px','')) ? $(".waifu").hide() : $(".waifu").show(); };
-    if (live2d_settings.waifuMinWidth != 'disable') { waifuResize(); $(window).resize(function() {waifuResize()}); }
-    
-    try {
-        if (live2d_settings.waifuDraggable == 'axis-x') $(".waifu").draggable({ axis: "x", revert: live2d_settings.waifuDraggableRevert });
-        else if (live2d_settings.waifuDraggable == 'unlimited') $(".waifu").draggable({ revert: live2d_settings.waifuDraggableRevert });
-        else $(".waifu").css("transition", 'all .3s ease-in-out');
-    } catch(err) { console.log('[Error] JQuery UI is not defined.') }
-    
-    live2d_settings.homePageUrl = live2d_settings.homePageUrl == 'auto' ? window.location.protocol+'//'+window.location.hostname+'/' : live2d_settings.homePageUrl;
-    if (window.location.protocol == 'file:' && live2d_settings.modelAPI.substr(0,2) == '//') live2d_settings.modelAPI = 'http:'+live2d_settings.modelAPI;
-    
-    $('.waifu-tool .fui-home').click(function (){
-        //window.location = 'https://www.fghrsh.net/';
-        window.location = live2d_settings.homePageUrl;
-    });
-    
-    $('.waifu-tool .fui-info-circle').click(function (){
-        //window.open('https://imjad.cn/archives/lab/add-dynamic-poster-girl-with-live2d-to-your-blog-02');
-        window.open(live2d_settings.aboutPageUrl);
-    });
-    
-    if (typeof(waifuPath) == "object") loadTipsMessage(waifuPath); else {
-        $.ajax({
-            cache: true,
-            url: waifuPath == '' ? live2d_settings.tipsMessage : (waifuPath.substr(waifuPath.length-15)=='waifu-tips.json'?waifuPath:waifuPath+'waifu-tips.json'),
-            dataType: "json",
-            success: function (result){ loadTipsMessage(result); }
-        });
-    }
-    
-    if (!live2d_settings.showToolMenu) $('.waifu-tool').hide();
-    if (!live2d_settings.canCloseLive2d) $('.waifu-tool .fui-cross').hide();
-    if (!live2d_settings.canSwitchModel) $('.waifu-tool .fui-eye').hide();
-    if (!live2d_settings.canSwitchTextures) $('.waifu-tool .fui-user').hide();
-    if (!live2d_settings.canSwitchHitokoto) $('.waifu-tool .fui-chat').hide();
-    if (!live2d_settings.canTakeScreenshot) $('.waifu-tool .fui-photo').hide();
-    if (!live2d_settings.canTurnToHomePage) $('.waifu-tool .fui-home').hide();
-    if (!live2d_settings.canTurnToAboutPage) $('.waifu-tool .fui-info-circle').hide();
-
-    if (waifuPath === undefined) waifuPath = '';
-    var modelId = localStorage.getItem('modelId');
-    var modelTexturesId = localStorage.getItem('modelTexturesId');
-    
-    if (!live2d_settings.modelStorage || modelId == null) {
-        var modelId = live2d_settings.modelId;
-        var modelTexturesId = live2d_settings.modelTexturesId;
-    } loadModel(modelId, modelTexturesId);
-}
-
-function loadModel(modelId, modelTexturesId=0) {
-    if (live2d_settings.modelStorage) {
-        localStorage.setItem('modelId', modelId);
-        localStorage.setItem('modelTexturesId', modelTexturesId);
-    } else {
-        sessionStorage.setItem('modelId', modelId);
-        sessionStorage.setItem('modelTexturesId', modelTexturesId);
-    } loadlive2d('live2d', live2d_settings.modelAPI+'get/?id='+modelId+'-'+modelTexturesId, (live2d_settings.showF12Status ? console.log('[Status]','live2d','模型',modelId+'-'+modelTexturesId,'加载完成'):null));
-}
-
-function loadTipsMessage(result) {
-    window.waifu_tips = result;
-    
-    $.each(result.mouseover, function (index, tips){
-        $(document).on("mouseover", tips.selector, function (){
-            var text = getRandText(tips.text);
-            text = text.render({text: $(this).text()});
-            showMessage(text, 3000);
-        });
-    });
-    $.each(result.click, function (index, tips){
-        $(document).on("click", tips.selector, function (){
-            var text = getRandText(tips.text);
-            text = text.render({text: $(this).text()});
-            showMessage(text, 3000, true);
-        });
-    });
-    $.each(result.seasons, function (index, tips){
-        var now = new Date();
-        var after = tips.date.split('-')[0];
-        var before = tips.date.split('-')[1] || after;
-        
-        if((after.split('/')[0] <= now.getMonth()+1 && now.getMonth()+1 <= before.split('/')[0]) && 
-           (after.split('/')[1] <= now.getDate() && now.getDate() <= before.split('/')[1])){
-            var text = getRandText(tips.text);
-            text = text.render({year: now.getFullYear()});
-            showMessage(text, 6000, true);
-        }
-    });
-    
-    if (live2d_settings.showF12OpenMsg) {
-        re.toString = function() {
-            showMessage(getRandText(result.waifu.console_open_msg), 5000, true);
-            return '';
-        };
-    }
-    
-    if (live2d_settings.showCopyMessage) {
-        $(document).on('copy', function() {
-            showMessage(getRandText(result.waifu.copy_message), 5000, true);
-        });
-    }
-    
-    $('.waifu-tool .fui-photo').click(function(){
-        showMessage(getRandText(result.waifu.screenshot_message), 5000, true);
-        window.Live2D.captureName = live2d_settings.screenshotCaptureName;
-        window.Live2D.captureFrame = true;
-    });
-    
-    $('.waifu-tool .fui-cross').click(function(){
-        sessionStorage.setItem('waifu-dsiplay', 'none');
-        showMessage(getRandText(result.waifu.hidden_message), 1300, true);
-        window.setTimeout(function() {$('.waifu').hide();}, 1300);
-    });
-    
-    window.showWelcomeMessage = function(result) {
-        var text;
-        if (window.location.href == live2d_settings.homePageUrl) {
-            var now = (new Date()).getHours();
-            if (now > 23 || now <= 5) text = getRandText(result.waifu.hour_tips['t23-5']);
-            else if (now > 5 && now <= 7) text = getRandText(result.waifu.hour_tips['t5-7']);
-            else if (now > 7 && now <= 11) text = getRandText(result.waifu.hour_tips['t7-11']);
-            else if (now > 11 && now <= 14) text = getRandText(result.waifu.hour_tips['t11-14']);
-            else if (now > 14 && now <= 17) text = getRandText(result.waifu.hour_tips['t14-17']);
-            else if (now > 17 && now <= 19) text = getRandText(result.waifu.hour_tips['t17-19']);
-            else if (now > 19 && now <= 21) text = getRandText(result.waifu.hour_tips['t19-21']);
-            else if (now > 21 && now <= 23) text = getRandText(result.waifu.hour_tips['t21-23']);
-            else text = getRandText(result.waifu.hour_tips.default);
-        } else {
-            var referrer_message = result.waifu.referrer_message;
-            if (document.referrer !== '') {
-                var referrer = document.createElement('a');
-                referrer.href = document.referrer;
-                var domain = referrer.hostname.split('.')[1];
-                if (window.location.hostname == referrer.hostname)
-                    text = referrer_message.localhost[0] + document.title.split(referrer_message.localhost[2])[0] + referrer_message.localhost[1];
-                else if (domain == 'baidu')
-                    text = referrer_message.baidu[0] + referrer.search.split('&wd=')[1].split('&')[0] + referrer_message.baidu[1];
-                else if (domain == 'so')
-                    text = referrer_message.so[0] + referrer.search.split('&q=')[1].split('&')[0] + referrer_message.so[1];
-                else if (domain == 'google')
-                    text = referrer_message.google[0] + document.title.split(referrer_message.google[2])[0] + referrer_message.google[1];
-                else {
-                    $.each(result.waifu.referrer_hostname, function(i,val) {if (i==referrer.hostname) referrer.hostname = getRandText(val)});
-                    text = referrer_message.default[0] + referrer.hostname + referrer_message.default[1];
-                }
-            } else text = referrer_message.none[0] + document.title.split(referrer_message.none[2])[0] + referrer_message.none[1];
-        }
-        showMessage(text, 6000);
-    }; if (live2d_settings.showWelcomeMessage) showWelcomeMessage(result);
-    
-    var waifu_tips = result.waifu;
-    
-    function loadOtherModel() {
-        var modelId = modelStorageGetItem('modelId');
-        var modelRandMode = live2d_settings.modelRandMode;
-        
-        $.ajax({
-            cache: modelRandMode == 'switch' ? true : false,
-            url: live2d_settings.modelAPI+modelRandMode+'/?id='+modelId,
-            dataType: "json",
-            success: function(result) {
-                loadModel(result.model['id']);
-                var message = result.model['message'];
-                $.each(waifu_tips.model_message, function(i,val) {if (i==result.model['id']) message = getRandText(val)});
-                showMessage(message, 3000, true);
-            }
-        });
-    }
-    
-    function loadRandTextures() {
-        var modelId = modelStorageGetItem('modelId');
-        var modelTexturesId = modelStorageGetItem('modelTexturesId');
-        var modelTexturesRandMode = live2d_settings.modelTexturesRandMode;
-        
-        $.ajax({
-            cache: modelTexturesRandMode == 'switch' ? true : false,
-            url: live2d_settings.modelAPI+modelTexturesRandMode+'_textures/?id='+modelId+'-'+modelTexturesId,
-            dataType: "json",
-            success: function(result) {
-                if (result.textures['id'] == 1 && (modelTexturesId == 1 || modelTexturesId == 0))
-                    showMessage(waifu_tips.load_rand_textures[0], 3000, true);
-                else showMessage(waifu_tips.load_rand_textures[1], 3000, true);
-                loadModel(modelId, result.textures['id']);
-            }
-        });
-    }
-    
-    function modelStorageGetItem(key) { return live2d_settings.modelStorage ? localStorage.getItem(key) : sessionStorage.getItem(key); }
-    
-    /* 检测用户活动状态，并在空闲时显示一言 */
-    if (live2d_settings.showHitokoto) {
-        window.getActed = false; window.hitokotoTimer = 0; window.hitokotoInterval = false;
-        $(document).mousemove(function(e){getActed = true;}).keydown(function(){getActed = true;});
-        setInterval(function(){ if (!getActed) ifActed(); else elseActed(); }, 1000);
-    }
-    
-    function ifActed() {
-        if (!hitokotoInterval) {
-            hitokotoInterval = true;
-            hitokotoTimer = window.setInterval(showHitokotoActed, 30000);
-        }
-    }
-    
-    function elseActed() {
-        getActed = hitokotoInterval = false;
-        window.clearInterval(hitokotoTimer);
-    }
-    
-    function showHitokotoActed() {
-        if ($(document)[0].visibilityState == 'visible') showHitokoto();
-    }
-    
-    function showHitokoto() {
-    	switch(live2d_settings.hitokotoAPI) {
-    	    case 'lwl12.com':
-    	        $.getJSON('https://api.lwl12.com/hitokoto/v1?encode=realjson',function(result){
-        	        if (!empty(result.source)) {
-                        var text = waifu_tips.hitokoto_api_message['lwl12.com'][0];
-                        if (!empty(result.author)) text += waifu_tips.hitokoto_api_message['lwl12.com'][1];
-                        text = text.render({source: result.source, creator: result.author});
-                        window.setTimeout(function() {showMessage(text+waifu_tips.hitokoto_api_message['lwl12.com'][2], 3000, true);}, 5000);
-                    } showMessage(result.text, 5000, true);
-                });break;
-    	    case 'fghrsh.net':
-    	        $.getJSON('https://api.fghrsh.net/hitokoto/rand/?encode=jsc&uid=3335',function(result){
-            	    if (!empty(result.source)) {
-                        var text = waifu_tips.hitokoto_api_message['fghrsh.net'][0];
-                        text = text.render({source: result.source, date: result.date});
-                        window.setTimeout(function() {showMessage(text, 3000, true);}, 5000);
-                        showMessage(result.hitokoto, 5000, true);
-            	    }
-                });break;
-            case 'jinrishici.com':
-                $.ajax({
-                    url: 'https://v2.jinrishici.com/one.json',
-                    xhrFields: {withCredentials: true},
-                    success: function (result, status) {
-                        if (!empty(result.data.origin.title)) {
-                            var text = waifu_tips.hitokoto_api_message['jinrishici.com'][0];
-                            text = text.render({title: result.data.origin.title, dynasty: result.data.origin.dynasty, author:result.data.origin.author});
-                            window.setTimeout(function() {showMessage(text, 3000, true);}, 5000);
-                        } showMessage(result.data.content, 5000, true);
-                    }
-                });break;
-    	    default:
-    	        $.getJSON('https://v1.hitokoto.cn',function(result){
-            	    if (!empty(result.from)) {
-                        var text = waifu_tips.hitokoto_api_message['hitokoto.cn'][0];
-                        text = text.render({source: result.from, creator: result.creator});
-                        window.setTimeout(function() {showMessage(text, 3000, true);}, 5000);
-            	    }
-                    showMessage(result.hitokoto, 5000, true);
-                });
-    	}
-    }
-    
-    $('.waifu-tool .fui-eye').click(function (){loadOtherModel()});
-    $('.waifu-tool .fui-user').click(function (){loadRandTextures()});
-    $('.waifu-tool .fui-chat').click(function (){showHitokoto()});
-}
diff --git a/spaces/heiyubili/bingo/src/components/settings.tsx b/spaces/heiyubili/bingo/src/components/settings.tsx
deleted file mode 100644
index 80b8a2d3b252b875f5b6f7dfc2f6e3ad9cdfb22a..0000000000000000000000000000000000000000
--- a/spaces/heiyubili/bingo/src/components/settings.tsx
+++ /dev/null
@@ -1,157 +0,0 @@
-import { useEffect, useState } from 'react'
-import { useAtom } from 'jotai'
-import { Switch } from '@headlessui/react'
-import { toast } from 'react-hot-toast'
-import { hashAtom, voiceAtom } from '@/state'
-import {
-  Dialog,
-  DialogContent,
-  DialogDescription,
-  DialogFooter,
-  DialogHeader,
-  DialogTitle
-} from '@/components/ui/dialog'
-import { Button } from './ui/button'
-import { Input } from './ui/input'
-import { ChunkKeys, parseCookies, extraCurlFromCookie, encodeHeadersToCookie, getCookie, setCookie } from '@/lib/utils'
-import { ExternalLink } from './external-link'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-
-export function Settings() {
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-  const [loc, setLoc] = useAtom(hashAtom)
-  const [curlValue, setCurlValue] = useState(extraCurlFromCookie(parseCookies(document.cookie, ChunkKeys)))
-  const [imageOnly, setImageOnly] = useState(getCookie('IMAGE_ONLY') !== '0')
-  const [enableTTS, setEnableTTS] = useAtom(voiceAtom)
-
-  useEffect(() => {
-    if (isCopied) {
-      toast.success('复制成功')
-    }
-  }, [isCopied])
-
-  if (loc === 'settings') {
-    return (
-      <Dialog open onOpenChange={() => setLoc('')} modal>
-        <DialogContent>
-          <DialogHeader>
-            <DialogTitle>设置你的用户信息</DialogTitle>
-            <DialogDescription>
-              请使用 Edge 浏览器
-              <ExternalLink
-                href="https://www.bing.com/turing/captcha/challenge"
-              >
-                打开并登录 Bing
-              </ExternalLink>
-              ，然后再打开
-              <ExternalLink href="https://www.bing.com/turing/captcha/challenge">Challenge 接口</ExternalLink>
-              右键 》检查。打开开发者工具，在网络里面找到 Create 接口 》右键复制》复制为 cURL(bash)，粘贴到此处，然后保存。
-              <div className="h-2" />
-              图文示例：
-              <ExternalLink href="https://github.com/weaigc/bingo#如何获取%20BING_HEADER">如何获取 BING_HEADER</ExternalLink>
-            </DialogDescription>
-          </DialogHeader>
-          <div className="flex gap-4">
-
-          </div>
-          <Input
-            value={curlValue}
-            placeholder="在此填写用户信息，格式: curl 'https://www.bing.com/turing/captcha/challenge' ..."
-            onChange={e => setCurlValue(e.target.value)}
-          />
-          <div className="flex gap-2">
-            身份信息仅用于画图（推荐）
-            <Switch
-              checked={imageOnly}
-              className={`${imageOnly ? 'bg-blue-600' : 'bg-gray-200'} relative inline-flex h-6 w-11 items-center rounded-full`}
-              onChange={(checked: boolean) => setImageOnly(checked)}
-            >
-              <span
-                className={`${imageOnly ? 'translate-x-6' : 'translate-x-1'} inline-block h-4 w-4 transform rounded-full bg-white transition`}
-              />
-            </Switch>
-          </div>
-
-          <Button variant="ghost" className="bg-[#F5F5F5] hover:bg-[#F2F2F2]" onClick={() => copyToClipboard(btoa(curlValue))}>
-            转成 BING_HEADER 并复制
-          </Button>
-
-          <DialogFooter className="items-center">
-            <Button
-              variant="secondary"
-              className="bg-[#c7f3ff] hover:bg-[#fdc7ff]"
-              onClick={() => {
-                let headerValue = curlValue
-                if (headerValue) {
-                  try {
-                    headerValue = atob(headerValue)
-                  } catch (e) { }
-                  if (!/^\s*curl ['"]https:\/\/(www|cn)\.bing\.com\/turing\/captcha\/challenge['"]/.test(headerValue)) {
-                    toast.error('格式不正确')
-                    return
-                  }
-                  const maxAge = 86400 * 30
-                  encodeHeadersToCookie(headerValue).forEach(cookie => document.cookie = `${cookie}; Max-Age=${maxAge}; Path=/; SameSite=None; Secure`)
-                } else {
-                  [...ChunkKeys, 'BING_COOKIE', 'BING_UA', 'BING_IP'].forEach(key => setCookie(key, ''))
-                }
-                setCookie('IMAGE_ONLY', RegExp.$1 === 'cn' || imageOnly ? '1' : '0')
-
-                toast.success('保存成功')
-                setLoc('')
-                setTimeout(() => {
-                  location.href = './'
-                }, 2000)
-              }}
-            >
-              保存
-            </Button>
-          </DialogFooter>
-        </DialogContent>
-      </Dialog>
-    )
-  } else if (loc === 'voice') {
-    return (
-      <Dialog open onOpenChange={() => setLoc('')} modal>
-        <DialogContent>
-          <DialogHeader>
-            <DialogTitle>语音设置</DialogTitle>
-            <DialogDescription>
-              目前仅支持 PC 端 Edge 及 Chrome 浏览器
-            </DialogDescription>
-          </DialogHeader>
-
-          <div className="flex gap-2">
-            启用语音回答
-            <Switch
-              checked={enableTTS}
-              className={`${enableTTS ? 'bg-blue-600' : 'bg-gray-200'} relative inline-flex h-6 w-11 items-center rounded-full`}
-              onChange={(checked: boolean) => setEnableTTS(checked)}
-            >
-              <span
-                className={`${enableTTS ? 'translate-x-6' : 'translate-x-1'} inline-block h-4 w-4 transform rounded-full bg-white transition`}
-              />
-            </Switch>
-          </div>
-
-          <DialogFooter className="items-center">
-            <Button
-              variant="secondary"
-              onClick={() => {
-                toast.success('保存成功')
-                setLoc('')
-                setTimeout(() => {
-                  location.href = './'
-                }, 2000)
-              }}
-            >
-              保存
-            </Button>
-          </DialogFooter>
-        </DialogContent>
-      </Dialog>
-    )
-  }
-  return null
-}
diff --git a/spaces/heiyubili/bingo/src/lib/isomorphic/node.ts b/spaces/heiyubili/bingo/src/lib/isomorphic/node.ts
deleted file mode 100644
index da213ad6a86181979f098309c374da02835db5a0..0000000000000000000000000000000000000000
--- a/spaces/heiyubili/bingo/src/lib/isomorphic/node.ts
+++ /dev/null
@@ -1,26 +0,0 @@
-import Debug from 'debug'
-
-const { fetch, setGlobalDispatcher, ProxyAgent } = require('undici')
-const { HttpsProxyAgent } = require('https-proxy-agent')
-const ws = require('ws')
-
-const debug = Debug('bingo')
-
-const httpProxy = process.env.http_proxy || process.env.HTTP_PROXY || process.env.https_proxy || process.env.HTTPS_PROXY;
-let WebSocket = ws.WebSocket
-
-if (httpProxy) {
-  setGlobalDispatcher(new ProxyAgent(httpProxy))
-  const agent = new HttpsProxyAgent(httpProxy)
-  // @ts-ignore
-  WebSocket = class extends ws.WebSocket {
-    constructor(address: string | URL, options: typeof ws.WebSocket) {
-      super(address, {
-        ...options,
-        agent,
-      })
-    }
-  }
-}
-
-export default { fetch, WebSocket, debug }
diff --git a/spaces/hezhaoqia/vits-simple-api/vits/text/mandarin.py b/spaces/hezhaoqia/vits-simple-api/vits/text/mandarin.py
deleted file mode 100644
index 80742a394f52165409bd820dc14e3cea6589454b..0000000000000000000000000000000000000000
--- a/spaces/hezhaoqia/vits-simple-api/vits/text/mandarin.py
+++ /dev/null
@@ -1,365 +0,0 @@
-import config
-import re
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba
-import cn2an
-import logging
-
-logging.getLogger('jieba').setLevel(logging.WARNING)
-jieba.set_dictionary(config.ABS_PATH + '/vits/text/jieba/dict.txt')
-jieba.initialize()
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'ㄟˉ'),
-    ('b', 'ㄅㄧˋ'),
-    ('c', 'ㄙㄧˉ'),
-    ('d', 'ㄉㄧˋ'),
-    ('e', 'ㄧˋ'),
-    ('f', 'ㄝˊㄈㄨˋ'),
-    ('g', 'ㄐㄧˋ'),
-    ('h', 'ㄝˇㄑㄩˋ'),
-    ('i', 'ㄞˋ'),
-    ('j', 'ㄐㄟˋ'),
-    ('k', 'ㄎㄟˋ'),
-    ('l', 'ㄝˊㄛˋ'),
-    ('m', 'ㄝˊㄇㄨˋ'),
-    ('n', 'ㄣˉ'),
-    ('o', 'ㄡˉ'),
-    ('p', 'ㄆㄧˉ'),
-    ('q', 'ㄎㄧㄡˉ'),
-    ('r', 'ㄚˋ'),
-    ('s', 'ㄝˊㄙˋ'),
-    ('t', 'ㄊㄧˋ'),
-    ('u', 'ㄧㄡˉ'),
-    ('v', 'ㄨㄧˉ'),
-    ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
-    ('x', 'ㄝˉㄎㄨˋㄙˋ'),
-    ('y', 'ㄨㄞˋ'),
-    ('z', 'ㄗㄟˋ')
-]]
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'p⁼wo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p⁼'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't⁼'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k⁼'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'h'),
-    ('ㄐ', 'ʧ⁼'),
-    ('ㄑ', 'ʧʰ'),
-    ('ㄒ', 'ʃ'),
-    ('ㄓ', 'ʦ`⁼'),
-    ('ㄔ', 'ʦ`ʰ'),
-    ('ㄕ', 's`'),
-    ('ㄖ', 'ɹ`'),
-    ('ㄗ', 'ʦ⁼'),
-    ('ㄘ', 'ʦʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ə'),
-    ('ㄝ', 'e'),
-    ('ㄞ', 'ai'),
-    ('ㄟ', 'ei'),
-    ('ㄠ', 'au'),
-    ('ㄡ', 'ou'),
-    ('ㄧㄢ', 'yeNN'),
-    ('ㄢ', 'aNN'),
-    ('ㄧㄣ', 'iNN'),
-    ('ㄣ', 'əNN'),
-    ('ㄤ', 'aNg'),
-    ('ㄧㄥ', 'iNg'),
-    ('ㄨㄥ', 'uNg'),
-    ('ㄩㄥ', 'yuNg'),
-    ('ㄥ', 'əNg'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'ɥ'),
-    ('ˉ', '→'),
-    ('ˊ', '↑'),
-    ('ˇ', '↓↑'),
-    ('ˋ', '↓'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-# List of (romaji, ipa) pairs:
-_romaji_to_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('ʃy', 'ʃ'),
-    ('ʧʰy', 'ʧʰ'),
-    ('ʧ⁼y', 'ʧ⁼'),
-    ('NN', 'n'),
-    ('Ng', 'ŋ'),
-    ('y', 'j'),
-    ('h', 'x')
-]]
-
-# List of (bopomofo, ipa) pairs:
-_bopomofo_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'p⁼wo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p⁼'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't⁼'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k⁼'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'x'),
-    ('ㄐ', 'tʃ⁼'),
-    ('ㄑ', 'tʃʰ'),
-    ('ㄒ', 'ʃ'),
-    ('ㄓ', 'ts`⁼'),
-    ('ㄔ', 'ts`ʰ'),
-    ('ㄕ', 's`'),
-    ('ㄖ', 'ɹ`'),
-    ('ㄗ', 'ts⁼'),
-    ('ㄘ', 'tsʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ə'),
-    ('ㄝ', 'ɛ'),
-    ('ㄞ', 'aɪ'),
-    ('ㄟ', 'eɪ'),
-    ('ㄠ', 'ɑʊ'),
-    ('ㄡ', 'oʊ'),
-    ('ㄧㄢ', 'jɛn'),
-    ('ㄩㄢ', 'ɥæn'),
-    ('ㄢ', 'an'),
-    ('ㄧㄣ', 'in'),
-    ('ㄩㄣ', 'ɥn'),
-    ('ㄣ', 'ən'),
-    ('ㄤ', 'ɑŋ'),
-    ('ㄧㄥ', 'iŋ'),
-    ('ㄨㄥ', 'ʊŋ'),
-    ('ㄩㄥ', 'jʊŋ'),
-    ('ㄥ', 'əŋ'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'ɥ'),
-    ('ˉ', '→'),
-    ('ˊ', '↑'),
-    ('ˇ', '↓↑'),
-    ('ˋ', '↓'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-# List of (bopomofo, ipa2) pairs:
-_bopomofo_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄅㄛ', 'pwo'),
-    ('ㄆㄛ', 'pʰwo'),
-    ('ㄇㄛ', 'mwo'),
-    ('ㄈㄛ', 'fwo'),
-    ('ㄅ', 'p'),
-    ('ㄆ', 'pʰ'),
-    ('ㄇ', 'm'),
-    ('ㄈ', 'f'),
-    ('ㄉ', 't'),
-    ('ㄊ', 'tʰ'),
-    ('ㄋ', 'n'),
-    ('ㄌ', 'l'),
-    ('ㄍ', 'k'),
-    ('ㄎ', 'kʰ'),
-    ('ㄏ', 'h'),
-    ('ㄐ', 'tɕ'),
-    ('ㄑ', 'tɕʰ'),
-    ('ㄒ', 'ɕ'),
-    ('ㄓ', 'tʂ'),
-    ('ㄔ', 'tʂʰ'),
-    ('ㄕ', 'ʂ'),
-    ('ㄖ', 'ɻ'),
-    ('ㄗ', 'ts'),
-    ('ㄘ', 'tsʰ'),
-    ('ㄙ', 's'),
-    ('ㄚ', 'a'),
-    ('ㄛ', 'o'),
-    ('ㄜ', 'ɤ'),
-    ('ㄝ', 'ɛ'),
-    ('ㄞ', 'aɪ'),
-    ('ㄟ', 'eɪ'),
-    ('ㄠ', 'ɑʊ'),
-    ('ㄡ', 'oʊ'),
-    ('ㄧㄢ', 'jɛn'),
-    ('ㄩㄢ', 'yæn'),
-    ('ㄢ', 'an'),
-    ('ㄧㄣ', 'in'),
-    ('ㄩㄣ', 'yn'),
-    ('ㄣ', 'ən'),
-    ('ㄤ', 'ɑŋ'),
-    ('ㄧㄥ', 'iŋ'),
-    ('ㄨㄥ', 'ʊŋ'),
-    ('ㄩㄥ', 'jʊŋ'),
-    ('ㄥ', 'ɤŋ'),
-    ('ㄦ', 'əɻ'),
-    ('ㄧ', 'i'),
-    ('ㄨ', 'u'),
-    ('ㄩ', 'y'),
-    ('ˉ', '˥'),
-    ('ˊ', '˧˥'),
-    ('ˇ', '˨˩˦'),
-    ('ˋ', '˥˩'),
-    ('˙', ''),
-    ('，', ','),
-    ('。', '.'),
-    ('！', '!'),
-    ('？', '?'),
-    ('—', '-')
-]]
-
-_symbols_to_chinese = [(re.compile(f'{x[0]}'), x[1]) for x in [
-    ('([0-9]+(?:\.?[0-9]+)?)%', r'百分之\1'),
-    ('([0-9]+)/([0-9]+)', r'\2分之\1'),
-    ('\+', r'加'),
-    ('([0-9]+)-([0-9]+)', r'\1减\2'),
-    ('×', r'乘以'),
-    ('([0-9]+)x([0-9]+)', r'\1乘以\2'),
-    ('([0-9]+)\*([0-9]+)', r'\1乘以\2'),
-    ('÷', r'除以'),
-    ('=', r'等于'),
-    ('≠', r'不等于'),
-]]
-
-
-def symbols_to_chinese(text):
-    for regex, replacement in _symbols_to_chinese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def number_to_chinese(text):
-    numbers = re.findall(r'[0-9]+(?:\.?[0-9]+)?', text)
-    for number in numbers:
-        text = text.replace(number, cn2an.an2cn(number), 1)
-    return text
-
-
-def number_transform_to_chinese(text):
-    text = cn2an.transform(text, "an2cn")
-    return text
-
-
-def chinese_to_bopomofo(text):
-    text = text.replace('、', '，').replace('；', '，').replace('：', '，')
-    words = jieba.lcut(text, cut_all=False)
-    text = ''
-    for word in words:
-        bopomofos = lazy_pinyin(word, BOPOMOFO)
-        if not re.search('[\u4e00-\u9fff]', word):
-            text += word
-            continue
-        for i in range(len(bopomofos)):
-            bopomofos[i] = re.sub(r'([\u3105-\u3129])$', r'\1ˉ', bopomofos[i])
-        if text != '':
-            text += ' '
-        text += ''.join(bopomofos)
-    return text
-
-
-def latin_to_bopomofo(text):
-    for regex, replacement in _latin_to_bopomofo:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_romaji(text):
-    for regex, replacement in _bopomofo_to_romaji:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_ipa(text):
-    for regex, replacement in _bopomofo_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def bopomofo_to_ipa2(text):
-    for regex, replacement in _bopomofo_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def chinese_to_romaji(text):
-    text = symbols_to_chinese(text)
-    text = number_transform_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_romaji(text)
-    text = re.sub('i([aoe])', r'y\1', text)
-    text = re.sub('u([aoəe])', r'w\1', text)
-    text = re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
-                  r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
-    text = re.sub('([ʦs][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
-    return text
-
-
-def chinese_to_lazy_ipa(text):
-    text = chinese_to_romaji(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def chinese_to_ipa(text):
-    text = symbols_to_chinese(text)
-    text = number_transform_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_ipa(text)
-    text = re.sub('i([aoe])', r'j\1', text)
-    text = re.sub('u([aoəe])', r'w\1', text)
-    text = re.sub('([sɹ]`[⁼ʰ]?)([→↓↑ ]+|$)',
-                  r'\1ɹ`\2', text).replace('ɻ', 'ɹ`')
-    text = re.sub('([s][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text)
-    return text
-
-
-def chinese_to_ipa2(text):
-    text = symbols_to_chinese(text)
-    text = number_transform_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = bopomofo_to_ipa2(text)
-    text = re.sub(r'i([aoe])', r'j\1', text)
-    text = re.sub(r'u([aoəe])', r'w\1', text)
-    text = re.sub(r'([ʂɹ]ʰ?)([˩˨˧˦˥ ]+|$)', r'\1ʅ\2', text)
-    text = re.sub(r'(sʰ?)([˩˨˧˦˥ ]+|$)', r'\1ɿ\2', text)
-    return text
-
-
-def VITS_PinYin_model():
-    import torch
-    import config
-    from vits.text.vits_pinyin import VITS_PinYin
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    # pinyin
-    tts_front = VITS_PinYin(f"{config.ABS_PATH}/vits/bert", device)
-    return tts_front
diff --git a/spaces/hf-accelerate/model-memory-usage/src/__init__.py b/spaces/hf-accelerate/model-memory-usage/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/hhhyrhe/vits-uma-genshin-honkai/mel_processing.py b/spaces/hhhyrhe/vits-uma-genshin-honkai/mel_processing.py
deleted file mode 100644
index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000
--- a/spaces/hhhyrhe/vits-uma-genshin-honkai/mel_processing.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
diff --git a/spaces/himanshubhardwaz/nlpconnect-vit-gpt2-image-captioning/app.py b/spaces/himanshubhardwaz/nlpconnect-vit-gpt2-image-captioning/app.py
deleted file mode 100644
index 5b55d9b74b44a7668d8d99fb6cb579b116b260bf..0000000000000000000000000000000000000000
--- a/spaces/himanshubhardwaz/nlpconnect-vit-gpt2-image-captioning/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/nlpconnect/vit-gpt2-image-captioning").launch()
\ No newline at end of file
diff --git "a/spaces/huathedev/findsong/pages/01_Recommend_from_Song\360\237\216\244.py" "b/spaces/huathedev/findsong/pages/01_Recommend_from_Song\360\237\216\244.py"
deleted file mode 100644
index ededb120cb428e3adfd6ae51c979e0347e0b0331..0000000000000000000000000000000000000000
--- "a/spaces/huathedev/findsong/pages/01_Recommend_from_Song\360\237\216\244.py"
+++ /dev/null
@@ -1,148 +0,0 @@
-import os
-
-import numpy as np
-import pandas as pd
-import requests
-import spotipy
-import streamlit as st
-from PIL import Image
-from spotipy.oauth2 import SpotifyClientCredentials
-
-st.set_page_config(
-    page_title="Find Songs Similar to Yours🎤", page_icon="🎤", layout="wide"
-)
-
-# Spotify API
-SPOTIPY_CLIENT_ID = os.environ["CLIENT_ID"]
-SPOTIPY_CLIENT_SECRET = os.environ["CLIENT_SECRET"]
-
-sp = spotipy.Spotify(
-    auth_manager=SpotifyClientCredentials(
-        client_id=SPOTIPY_CLIENT_ID, client_secret=SPOTIPY_CLIENT_SECRET
-    )
-)
-
-"""
-# Analyze Song and Get Recommendations🎤
-
-Input a song title and the app will return recommendations as well as the features of the song.
-
-Data is obtained using the Python library [Spotipy](https://spotipy.readthedocs.io/en/2.18.0/) that uses [Spotify Web API.](https://developer.spotify.com/documentation/web-api/)
-"""
-song = st.text_input("Enter a song title", value="Somebody Else")
-search = sp.search(q="track:" + song, type="track")
-
-
-class GetSongInfo:
-    def __init__(self, search):
-        self.search = search
-
-    def song_id(self):
-        song_id = search["tracks"]["items"][0]["id"]  # -gets song id
-        return song_id
-
-    def song_album(self):
-        song_album = search["tracks"]["items"][0]["album"][
-            "name"
-        ]  # -gets song album name
-        return song_album
-
-    def song_image(self):
-        song_image = search["tracks"]["items"][0]["album"]["images"][0][
-            "url"
-        ]  # -gets song image URL
-        return song_image
-
-    def song_artist_name(self):
-        song_artist_name = search["tracks"]["items"][0]["artists"][0][
-            "name"
-        ]  # -gets artist for song
-        return song_artist_name
-
-    def song_name(self):
-        song_name = search["tracks"]["items"][0]["name"]  # -gets song name
-        return song_name
-
-    def song_preview(self):
-        song_preview = search["tracks"]["items"][0]["preview_url"]
-        return song_preview
-
-
-songs = GetSongInfo(song)
-
-###
-
-
-def url(song):
-    url_to_song = "https://open.spotify.com/track/" + songs.song_id()
-    st.write(
-        f"Link to stream '{songs.song_name()}' by {songs.song_artist_name()} on Spotify: {url_to_song}"
-    )
-
-
-# Set up two-column layout for Streamlit app
-image, stats = st.columns(2)
-
-with image:
-    try:
-        url(song)
-        r = requests.get(songs.song_image())
-        open("img/" + songs.song_id() + ".jpg", "w+b").write(r.content)
-        image_album = Image.open("img/" + songs.song_id() + ".jpg")
-        st.image(
-            image_album,
-            caption=f"{songs.song_artist_name()} - {songs.song_album()}",
-            use_column_width="auto",
-        )
-
-        feat = sp.audio_features(tracks=[songs.song_id()])
-        features = feat[0]
-        p = pd.Series(features).to_frame()
-        data_feat = p.loc[
-            [
-                "acousticness",
-                "danceability",
-                "energy",
-                "liveness",
-                "speechiness",
-                "valence",
-            ]
-        ]
-        bpm = p.loc[["tempo"]]
-        values = bpm.values[0]
-        bpms = values.item()
-        ticks = np.linspace(0, 1, 11)
-
-        plot = data_feat.plot.barh(
-            xticks=ticks, legend=False, color="limegreen"
-        )  # Use Pandas plot
-        plot.set_xlabel("Value")
-        plot.set_ylabel("Parameters")
-        plot.set_title(f"Analysing '{songs.song_name()}' by {songs.song_artist_name()}")
-        plot.invert_yaxis()
-        st.pyplot(plot.figure)
-        st.subheader(f"BPM (Beats Per Minute): {bpms}")
-
-        st.warning(
-            "Note: Audio previews may have very high default volume and will reset after page refresh"
-        )
-        st.audio(songs.song_preview(), format="audio/wav")
-
-    except IndexError or NameError:
-        st.error(
-            "This error is possibly due to the API being unable to find the song. Maybe try to retype it using the song title followed by artist without any hyphens (e.g. In my Blood Shawn Mendes)"
-        )
-
-# Recommendations
-with stats:
-    st.subheader("You might also like")
-
-    reco = sp.recommendations(
-        seed_artists=None, seed_tracks=[songs.song_id()], seed_genres=[], limit=10
-    )
-
-    for i in reco["tracks"]:
-        st.write(f"\"{i['name']}\" - {i['artists'][0]['name']}")
-        image_reco = requests.get(i["album"]["images"][2]["url"])
-        open("img/" + i["id"] + ".jpg", "w+b").write(image_reco.content)
-        st.image(Image.open("img/" + i["id"] + ".jpg"))
diff --git a/spaces/huggingface-projects/diffuse-the-rest/build/_app/immutable/assets/+page-376b236d.css b/spaces/huggingface-projects/diffuse-the-rest/build/_app/immutable/assets/+page-376b236d.css
deleted file mode 100644
index 54f1eed0ee54d701018006d3764fc3323df69aa7..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/diffuse-the-rest/build/_app/immutable/assets/+page-376b236d.css
+++ /dev/null
@@ -1 +0,0 @@
-span[contenteditable].svelte-1wfa7x9:empty:before{content:var(--placeholder);color:#9ca3af}
diff --git a/spaces/hzy123/bingo/src/components/markdown.tsx b/spaces/hzy123/bingo/src/components/markdown.tsx
deleted file mode 100644
index d4491467a1f14d1d72e535caac9c40636054e5df..0000000000000000000000000000000000000000
--- a/spaces/hzy123/bingo/src/components/markdown.tsx
+++ /dev/null
@@ -1,9 +0,0 @@
-import { FC, memo } from 'react'
-import ReactMarkdown, { Options } from 'react-markdown'
-
-export const MemoizedReactMarkdown: FC<Options> = memo(
-  ReactMarkdown,
-  (prevProps, nextProps) =>
-    prevProps.children === nextProps.children &&
-    prevProps.className === nextProps.className
-)
diff --git a/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/utils/__init__.py b/spaces/iamironman4279/SadTalker/src/face3d/models/arcface_torch/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/igashov/DiffLinker/README.md b/spaces/igashov/DiffLinker/README.md
deleted file mode 100644
index 6f98dd22ef28c589d82091218e74293f72d632ed..0000000000000000000000000000000000000000
--- a/spaces/igashov/DiffLinker/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: DiffLinker
-emoji: 💊
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.26.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/impulsewu/Real-CUGAN/upcunet_v3.py b/spaces/impulsewu/Real-CUGAN/upcunet_v3.py
deleted file mode 100644
index f7919a6cc9efe3b8af73a73e30825a4c7d7d76da..0000000000000000000000000000000000000000
--- a/spaces/impulsewu/Real-CUGAN/upcunet_v3.py
+++ /dev/null
@@ -1,714 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-import os, sys
-import numpy as np
-
-root_path = os.path.abspath('.')
-sys.path.append(root_path)
-
-
-class SEBlock(nn.Module):
-    def __init__(self, in_channels, reduction=8, bias=False):
-        super(SEBlock, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias)
-        self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias)
-
-    def forward(self, x):
-        if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-            x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half()
-        else:
-            x0 = torch.mean(x, dim=(2, 3), keepdim=True)
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-    def forward_mean(self, x, x0):
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-
-class UNetConv(nn.Module):
-    def __init__(self, in_channels, mid_channels, out_channels, se):
-        super(UNetConv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(in_channels, mid_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-            nn.Conv2d(mid_channels, out_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-        )
-        if se:
-            self.seblock = SEBlock(out_channels, reduction=8, bias=True)
-        else:
-            self.seblock = None
-
-    def forward(self, x):
-        z = self.conv(x)
-        if self.seblock is not None:
-            z = self.seblock(z)
-        return z
-
-
-class UNet1(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet1x3(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1x3, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet2(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet2, self).__init__()
-
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 64, 128, se=True)
-        self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0)
-        self.conv3 = UNetConv(128, 256, 128, se=True)
-        self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0)
-        self.conv4 = UNetConv(128, 64, 64, se=True)
-        self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv5 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3(x3)
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4(x2 + x3)
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-    def forward_a(self, x):  # conv234结尾有se
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x2):  # conv234结尾有se
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3.conv(x3)
-        return x3
-
-    def forward_c(self, x2, x3):  # conv234结尾有se
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4.conv(x2 + x3)
-        return x4
-
-    def forward_d(self, x1, x4):  # conv234结尾有se
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-
-class UpCunet2x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet2x, self).__init__()
-        self.unet1 = UNet1(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 36, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 36, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2]
-        return res  #
-
-
-class UpCunet3x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet3x, self).__init__()
-        self.unet1 = UNet1x3(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 4 + 1) * 4
-            pw = ((w0 - 1) // 4 + 1) * 4
-            x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_h = (h0 - 1) // 4 * 4 + 4  # 能被4整除
-            else:
-                crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_w = (w0 - 1) // 4 * 4 + 4  # 能被4整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 28, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 28, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop  #
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3]
-        return res
-
-
-class UpCunet4x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet4x, self).__init__()
-        self.unet1 = UNet1(in_channels, 64, deconv=True)
-        self.unet2 = UNet2(64, 64, deconv=False)
-        self.ps = nn.PixelShuffle(2)
-        self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True)
-
-    def forward(self, x, tile_mode):
-        n, c, h0, w0 = x.shape
-        x00 = x
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            x = self.conv_final(x)
-            x = F.pad(x, (-1, -1, -1, -1))
-            x = self.ps(x)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4]
-            x += F.interpolate(x00, scale_factor=4, mode='nearest')
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.1G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 38, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 38, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                x_crop = self.conv_final(x_crop)
-                x_crop = F.pad(x_crop, (-1, -1, -1, -1))
-                x_crop = self.ps(x_crop)
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape)
-                res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4]
-        res += F.interpolate(x00, scale_factor=4, mode='nearest')
-        return res  #
-
-
-class RealWaifuUpScaler(object):
-    def __init__(self, scale, weight_path, half, device):
-        weight = torch.load(weight_path, map_location="cpu")
-        self.model = eval("UpCunet%sx" % scale)()
-        if (half == True):
-            self.model = self.model.half().to(device)
-        else:
-            self.model = self.model.to(device)
-        self.model.load_state_dict(weight, strict=True)
-        self.model.eval()
-        self.half = half
-        self.device = device
-
-    def np2tensor(self, np_frame):
-        if (self.half == False):
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255
-        else:
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255
-
-    def tensor2np(self, tensor):
-        if (self.half == False):
-            return (
-                np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0)))
-        else:
-            return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(),
-                                 (1, 2, 0)))
-
-    def __call__(self, frame, tile_mode):
-        with torch.no_grad():
-            tensor = self.np2tensor(frame)
-            result = self.tensor2np(self.model(tensor, tile_mode))
-        return result
-
-
-if __name__ == "__main__":
-    ###########inference_img
-    import time, cv2, sys
-    from time import time as ttime
-
-    for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3),
-                               ("weights_v3/up4x-latest-denoise3x.pth", 4)]:
-        for tile_mode in [0, 1, 2, 3, 4]:
-            upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0")
-            input_dir = "%s/input_dir1" % root_path
-            output_dir = "%s/opt-dir-all-test" % root_path
-            os.makedirs(output_dir, exist_ok=True)
-            for name in os.listdir(input_dir):
-                print(name)
-                tmp = name.split(".")
-                inp_path = os.path.join(input_dir, name)
-                suffix = tmp[-1]
-                prefix = ".".join(tmp[:-1])
-                tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                print(inp_path, tmp_path)
-                # 支持中文路径
-                # os.link(inp_path, tmp_path)#win用硬链接
-                os.symlink(inp_path, tmp_path)  # linux用软链接
-                frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]]
-                t0 = ttime()
-                result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1]
-                t1 = ttime()
-                print(prefix, "done", t1 - t0)
-                tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                cv2.imwrite(tmp_opt_path, result)
-                n = 0
-                while (1):
-                    if (n == 0):
-                        suffix = "_%sx_tile%s.png" % (scale, tile_mode)
-                    else:
-                        suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n)  #
-                    if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False):
-                        break
-                    else:
-                        n += 1
-                final_opt_path = os.path.join(output_dir, prefix + suffix)
-                os.rename(tmp_opt_path, final_opt_path)
-                os.remove(tmp_path)
diff --git a/spaces/imseldrith/DeepFakeAI/tests/test_cli.py b/spaces/imseldrith/DeepFakeAI/tests/test_cli.py
deleted file mode 100644
index 266116e302e19dd4602df71cbe4bd2440cf2513c..0000000000000000000000000000000000000000
--- a/spaces/imseldrith/DeepFakeAI/tests/test_cli.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import subprocess
-import pytest
-
-from DeepFakeAI import wording
-from DeepFakeAI.utilities import conditional_download
-
-
-@pytest.fixture(scope = 'module', autouse = True)
-def before_all() -> None:
-	conditional_download('.assets/examples',
-	[
-		'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/examples/source.jpg',
-		'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/examples/target-1080p.mp4'
-	])
-	subprocess.run([ 'ffmpeg', '-i', '.assets/examples/target-1080p.mp4', '-vframes', '1', '.assets/examples/target-1080p.jpg' ])
-
-
-def test_image_to_image() -> None:
-	commands = [ 'python', 'run.py', '-s', '.assets/examples/source.jpg', '-t', '.assets/examples/target-1080p.jpg', '-o', '.assets/examples' ]
-	run = subprocess.run(commands, stdout = subprocess.PIPE)
-
-	assert run.returncode == 0
-	assert wording.get('processing_image_succeed') in run.stdout.decode()
-
-
-def test_image_to_video() -> None:
-	commands = [ 'python', 'run.py', '-s', '.assets/examples/source.jpg', '-t', '.assets/examples/target-1080p.mp4', '-o', '.assets/examples', '--trim-frame-end', '10' ]
-	run = subprocess.run(commands, stdout = subprocess.PIPE)
-
-	assert run.returncode == 0
-	assert wording.get('processing_video_succeed') in run.stdout.decode()
diff --git a/spaces/inaccel/resnet50/app.py b/spaces/inaccel/resnet50/app.py
deleted file mode 100644
index 72822be9be6ad0561b92b2a1e5d78fd440c75138..0000000000000000000000000000000000000000
--- a/spaces/inaccel/resnet50/app.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import flask
-import os
-
-app = flask.Flask(__name__)
-
-
-@app.route('/')
-def index():
-    return '<iframe frameBorder="0" height="100%" src="{}/?__dark-theme={}" width="100%"></iframe>'.format(
-        os.getenv('INACCEL_URL'),
-        flask.request.args.get('__dark-theme', 'false'))
-
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=7860)
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Atlantica Auto Battle Bot Download.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Atlantica Auto Battle Bot Download.md
deleted file mode 100644
index 9172aa5f9e673f75894e75750ba5dffac14f3107..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Atlantica Auto Battle Bot Download.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<h2>Atlantica Auto Battle Bot Download</h2><br /><p><b><b>Download</b> >> <a href="https://urlin.us/2uEwa6">https://urlin.us/2uEwa6</a></b></p><br /><br />
-
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/CrazyTalk7ProContentPackBonuscrack PORTABLE.md b/spaces/inplisQlawa/anything-midjourney-v4-1/CrazyTalk7ProContentPackBonuscrack PORTABLE.md
deleted file mode 100644
index 7e98c47488bb82d458462a471c24c095211ddb22..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/CrazyTalk7ProContentPackBonuscrack PORTABLE.md	
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diff --git a/spaces/inreVtussa/clothingai/Examples/Corona MotorSport Download Windows 10 Free [BETTER].md b/spaces/inreVtussa/clothingai/Examples/Corona MotorSport Download Windows 10 Free [BETTER].md
deleted file mode 100644
index 1a90044ee0345129d8c2e03c854bfcd02ef6f080..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Corona MotorSport Download Windows 10 Free [BETTER].md	
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-<p>Are you a fan of racing games that offer realistic physics, stunning graphics, and thrilling gameplay? If so, you might want to try Corona MotorSport, a simulation-based racing game that you can download for Windows 10 free. Corona MotorSport is an indie game developed by LucasGame Studios and released in 2015 as an Early Access game on Steam. It has been constantly updated and improved since then, and it features a variety of cars and tracks to choose from. In this article, we will show you how to download Corona MotorSport for Windows 10 free and what are the features of this game.</p>
-<h2>What is Corona MotorSport?</h2>
-<p>Corona MotorSport is a racing game that aims to provide a realistic and challenging racing experience. It uses real-world data to bring accurate physics simulation into the game, such as tire models, suspension settings, gear ratios, and engine sounds. You can customize your car with different paint colors, decals, and rims, and you can also see the damage effects on your car during the race. You can race against other players or AI opponents on different tracks, such as Monza, Italy or Barcelona, Spain. You can also test your skills in different weather conditions, such as rain, fog, snow (seasonal), sun, night or day.</p>
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-<p>There are two ways to download Corona MotorSport for Windows 10 free. One is to get it from Steam, where you can download the game and play it as long as it is in Early Access. You will need a Steam account and a compatible PC to do this. The other way is to get it from Indie DB, where you can download the game files directly and install them on your PC. You will need to unzip the files and run the executable file to launch the game.</p>
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-<p>To download Corona MotorSport from Steam, you need to follow these steps:</p>
-<ol>
-<li>Go to the <a href="https://store.steampowered.com/app/342870/Corona_MotorSport">Corona MotorSport page</a> on Steam and click on the "Play Game" button.</li>
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-<li>If you don't have Steam installed on your PC, you will need to download and install it.</li>
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-<li>Once the game is installed, you can launch it from your Steam library or from your desktop shortcut.</li>
-</ol>
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-<p>To download Corona MotorSport from Indie DB, you need to follow these steps:</p>
-<ol>
-<li>Go to the <a href="https://www.indiedb.com/games/coronams">Corona MotorSport page</a> on Indie DB and click on the "Files" tab.</li>
-<li>Select the latest version of the game file and click on the "Download Now" button.</li>
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-<li>Enjoy playing Corona MotorSport on your PC.</li>
-</ol>
-<h2>What are the Features of Corona MotorSport?</h2>
-<p>Corona MotorSport has many features that make it a fun and immersive racing game. Some of them are:</p>
-<ul>
-<li>Racing AI: You can race against up to 10 other cars in the new action-packed race mode. The AI cars will try to overtake you, block you, or crash into you depending on their difficulty level.</li>
-<li>Beautiful Graphics: Corona MotorSport features top-of-the-line graphics with real-time and screen-space reflections, next-gen post-processing effects, physically based shading, and DirectX 11 support.</li>
-<li>Dynamic Time and Weather System: You can test your skills in different weather conditions such as rain, fog, snow (seasonal), sun, night or day. The weather will affect the visibility, traction, and handling of your car.</li>
-<li>Real-Time Damage Physics: Your car can suffer cosmetic damage as well as suspension and drivetrain damage during the race. You can see parts flying off your car or smoke coming out of your engine.</li>
-<li>Tracks and Vehicles: Corona MotorSport has several tracks and vehicles to choose from. You can race on Monza, Italy or Barcelona, Spain. You can drive a powerful Italian sports car like Serato Conquistador or Uragano, a Japanese sportscar like Nyzan GTX, an American muscle car like Doge Hellfire, or a German engineered gull-wing door car like Kobra XR.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>If you are looking for a realistic and challenging racing game for your Windows 10 PC, you should try Corona MotorSport. It is a simulation-based racing game that you can download for free and enjoy racing against other players or AI opponents on different tracks. It features beautiful graphics, realistic physics, dynamic weather and damage system, and a variety of cars and tracks. You can download it from Steam or Indie DB and start racing today.</p>
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-<p>Playing Corona MotorSport on PC is easy and enjoyable. You can use your keyboard and mouse to control your car and navigate the menus. You can also adjust the graphics settings, sound volume, and camera angles to suit your preferences. You can play Corona MotorSport in two modes: Freeroam and Race. In Freeroam mode, you can select a track and drive around without any time limit or opponents. You can use this mode to practice your driving skills, explore the track, or just have fun. In Race mode, you can compete against other players or AI cars in a timed race. You can choose the number of laps, the difficulty level, and the weather condition. You can also see your lap times, position, and damage status on the screen.</p>
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-<li>You can enjoy a realistic and challenging racing game without paying anything.</li>
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-<li>You can join a community of racing game enthusiasts who share your passion and interest.</li>
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-<h2>What are the Requirements for Downloading Corona MotorSport for Windows 10 Free?</h2>
-<p>To download Corona MotorSport for Windows 10 free, you need to have a PC that meets the following minimum requirements:</p>
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-<table>
-<tr><td>OS</td><td>Windows 10</td></tr>
-<tr><td>Processor</td><td>Intel Core i3 or equivalent</td></tr>
-<tr><td>Memory</td><td>4 GB RAM</td></tr>
-<tr><td>Graphics</td><td>NVIDIA GeForce GTX 660 or equivalent</td></tr>
-<tr><td>DirectX</td><td>Version 11</td></tr>
-<tr><td>Storage</td><td>5 GB available space</td></tr>
-<tr><td>Sound Card</td><td>DirectX compatible sound card</td></tr>
-</table>
-<p>If you have a PC that meets or exceeds these requirements, you can download Corona MotorSport for Windows 10 free and enjoy playing this game.</p>
-<h2>What are the Reviews of Corona MotorSport?</h2>
-<p>Corona MotorSport is a game that has received mixed reviews from players and critics. Some of them praise the game for its realistic physics, beautiful graphics, and challenging AI. They also appreciate the developer's efforts to update and improve the game regularly. However, some of them criticize the game for its bugs, glitches, and lack of content. They also complain about the game's performance issues, poor optimization, and high system requirements. Corona MotorSport is a game that still has room for improvement, but it also has potential to become a great racing game.</p>
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-<p>If you are looking for other racing games that you can download for Windows 10 free, you might want to check out these alternatives:</p>
-<ul>
-<li>Motorsport Manager Mobile 3: This is a racing strategy game that lets you manage your own motorsports team. You can hire drivers, develop cars, plan strategies, and win races. You can also explore new features such as endurance races, GT championships, and hybrid cars. You can download Motorsport Manager Mobile 3 on PC with BlueStacks and enjoy this game on a bigger screen.</li>
-<li>Motorsport Manager Racing: This is another racing strategy game that lets you create your own motorsports team and compete against other players online. You can customize your cars, drivers, and sponsors, and race in different leagues and events. You can also join forces with other players and form alliances to dominate the leaderboards. You can download Motorsport Manager Racing on PC with BlueStacks and play this game with your friends.</li>
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-<p>If you have any questions, feedback, or suggestions for Corona MotorSport, you can contact the developer of this game through these channels:</p>
-<ul>
-<li>Email: lucasgamestudios@gmail.com</li>
-<li>Twitter: @LucasGameStudio</li>
-<li>Facebook: LucasGame Studios</li>
-<li>Steam: LucasGame</li>
-<li>Indie DB: LucasGame Studios</li>
-</ul>
-<p>The developer is always open to hear from the players and improve the game based on their input.</p>
-<h2>How to Update Corona MotorSport for Windows 10 Free?</h2>
-<p>Updating Corona MotorSport for Windows 10 free is important to enjoy the latest features and fixes of the game. Depending on how you downloaded the game, there are different ways to update it:</p>
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-<li>If you downloaded Corona MotorSport from Steam, you can update it automatically through the Steam client. You can also check for updates manually by right-clicking on the game in your Steam library and selecting "Properties". Then, go to the "Updates" tab and click on the "Check for updates" button.</li>
-<li>If you downloaded Corona MotorSport from Indie DB, you can update it manually by downloading the latest version of the game file from the Indie DB website. You can also follow the developer's social media accounts to get notified of new updates. You will need to unzip the new file and replace the old one on your PC.</li>
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-<li>Run the game as an administrator.</li>
-<li>Verify the integrity of the game files if you downloaded it from Steam.</li>
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diff --git a/spaces/izumi-lab/stormy-7b-10ep/model_pull.py b/spaces/izumi-lab/stormy-7b-10ep/model_pull.py
deleted file mode 100644
index e97fae851cdf988b47367ed1ec99f884d1bfae4a..0000000000000000000000000000000000000000
--- a/spaces/izumi-lab/stormy-7b-10ep/model_pull.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import torch
-from peft import PeftModel
-from transformers import AutoModelForCausalLM
-from transformers import AutoTokenizer
-
-BASE_MODEL = "cyberagent/open-calm-7b"
-LORA_WEIGHTS = "izumi-lab/stormy-7b-10ep"
-
-tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
-model = AutoModelForCausalLM.from_pretrained(
-    BASE_MODEL,
-    load_in_8bit=False,
-    torch_dtype=torch.float16,
-    device_map="auto",
-)
-model = PeftModel.from_pretrained(
-    model, LORA_WEIGHTS, torch_dtype=torch.float16, use_auth_token=True
-)
diff --git a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/score.py b/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/score.py
deleted file mode 100644
index 8db8915b109953931fa2a330a7731db4a51b44f8..0000000000000000000000000000000000000000
--- a/spaces/jackli888/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/score.py
+++ /dev/null
@@ -1,453 +0,0 @@
-from torch.functional import Tensor
-
-import torch
-import inspect
-import json
-import yaml
-import time
-import sys
-
-from general_utils import log
-
-import numpy as np
-from os.path import expanduser, join, isfile, realpath
-
-from torch.utils.data import DataLoader
-
-from metrics import FixedIntervalMetrics
-
-from general_utils import load_model, log, score_config_from_cli_args, AttributeDict, get_attribute, filter_args
-
-
-DATASET_CACHE = dict()
-
-def load_model(checkpoint_id, weights_file=None, strict=True, model_args='from_config', with_config=False, ignore_weights=False):
-
-    config = json.load(open(join('logs', checkpoint_id, 'config.json')))
-
-    if model_args != 'from_config' and type(model_args) != dict:
-        raise ValueError('model_args must either be "from_config" or a dictionary of values')
-
-    model_cls = get_attribute(config['model'])
-
-    # load model
-    if model_args == 'from_config':
-        _, model_args, _ = filter_args(config, inspect.signature(model_cls).parameters)
-
-    model = model_cls(**model_args)
-
-    if weights_file is None:
-        weights_file = realpath(join('logs', checkpoint_id, 'weights.pth'))
-    else:
-        weights_file = realpath(join('logs', checkpoint_id, weights_file))
-
-    if isfile(weights_file) and not ignore_weights:
-        weights = torch.load(weights_file)
-        for _, w in weights.items():
-            assert not torch.any(torch.isnan(w)), 'weights contain NaNs'
-        model.load_state_dict(weights, strict=strict)
-    else:
-        if not ignore_weights:
-            raise FileNotFoundError(f'model checkpoint {weights_file} was not found')
-
-    if with_config:
-        return model, config
-    
-    return model
-
-
-def compute_shift2(model, datasets, seed=123, repetitions=1):
-    """ computes shift """
-    
-    model.eval()
-    model.cuda()
-
-    import random
-    random.seed(seed)
-
-    preds, gts = [], []
-    for i_dataset, dataset in enumerate(datasets):
-
-        loader = DataLoader(dataset, batch_size=1, num_workers=0, shuffle=False, drop_last=False)
-
-        max_iterations = int(repetitions * len(dataset.dataset.data_list))
-        
-        with torch.no_grad():
-
-            i, losses = 0, []
-            for i_all, (data_x, data_y) in enumerate(loader):
-
-                data_x = [v.cuda(non_blocking=True) if v is not None else v for v in data_x]
-                data_y = [v.cuda(non_blocking=True) if v is not None else v for v in data_y]
-
-                pred, = model(data_x[0], data_x[1], data_x[2])
-                preds += [pred.detach()]
-                gts += [data_y]
-
-                i += 1
-                if max_iterations and i >= max_iterations:
-                    break
-        
-    from metrics import FixedIntervalMetrics
-    n_values = 51
-    thresholds = np.linspace(0, 1, n_values)[1:-1]
-    metric = FixedIntervalMetrics(resize_pred=True, sigmoid=True, n_values=n_values)
-
-    for p, y in zip(preds, gts):
-        metric.add(p.unsqueeze(1), y)     
-            
-    best_idx = np.argmax(metric.value()['fgiou_scores'])
-    best_thresh = thresholds[best_idx]
-
-    return best_thresh
-
-
-def get_cached_pascal_pfe(split, config):
-    from datasets.pfe_dataset import PFEPascalWrapper
-    try:
-        dataset =  DATASET_CACHE[(split, config.image_size, config.label_support, config.mask)]
-    except KeyError:
-        dataset = PFEPascalWrapper(mode='val', split=split, mask=config.mask, image_size=config.image_size, label_support=config.label_support)
-        DATASET_CACHE[(split, config.image_size, config.label_support, config.mask)] = dataset
-    return dataset
-
-
-
-
-def main():
-    config, train_checkpoint_id = score_config_from_cli_args()
-
-    metrics = score(config, train_checkpoint_id, None)
-
-    for dataset in metrics.keys():
-        for k in metrics[dataset]:
-            if type(metrics[dataset][k]) in {float, int}:
-                print(dataset, f'{k:<16} {metrics[dataset][k]:.3f}')
-
-
-def score(config, train_checkpoint_id, train_config):
-
-    config = AttributeDict(config)
-
-    print(config)
-
-    # use training dataset and loss
-    train_config = AttributeDict(json.load(open(f'logs/{train_checkpoint_id}/config.json')))
-
-    cp_str = f'_{config.iteration_cp}' if config.iteration_cp is not None else ''
-
-
-    model_cls = get_attribute(train_config['model'])
-
-    _, model_args, _ = filter_args(train_config, inspect.signature(model_cls).parameters)
-
-    model_args = {**model_args, **{k: config[k] for k in ['process_cond', 'fix_shift'] if k in config}}
-
-    strict_models = {'ConditionBase4', 'PFENetWrapper'}
-    model = load_model(train_checkpoint_id, strict=model_cls.__name__ in strict_models, model_args=model_args, 
-                        weights_file=f'weights{cp_str}.pth', )
-                           
-
-    model.eval()
-    model.cuda()
-
-    metric_args = dict()
-
-    if 'threshold' in config:
-        if config.metric.split('.')[-1] == 'SkLearnMetrics':
-            metric_args['threshold'] = config.threshold
-
-    if 'resize_to' in config:
-        metric_args['resize_to'] = config.resize_to
-
-    if 'sigmoid' in config:
-        metric_args['sigmoid'] = config.sigmoid    
-
-    if 'custom_threshold' in config:
-        metric_args['custom_threshold'] = config.custom_threshold     
-
-    if config.test_dataset == 'pascal':
-        
-        loss_fn = get_attribute(train_config.loss)
-        # assume that if no split is specified in train_config, test on all splits, 
-        
-        if 'splits' in config:
-            splits = config.splits 
-        else:
-            if 'split' in train_config and type(train_config.split) == int:
-                # unless train_config has a split set, in that case assume train mode in training
-                splits = [train_config.split]
-                assert train_config.mode == 'train'
-            else:
-                splits = [0,1,2,3]
-            
-        log.info('Test on these splits', splits)
-
-        scores = dict()
-        for split in splits:
-
-            shift = config.shift if 'shift' in config else 0
-
-            # automatic shift
-            if shift == 'auto':
-                shift_compute_t = time.time()
-                shift = compute_shift2(model, [get_cached_pascal_pfe(s, config) for s in range(4) if s != split], repetitions=config.compute_shift_fac)
-                log.info(f'Best threshold is {shift}, computed on splits: {[s for s in range(4) if s != split]}, took {time.time() - shift_compute_t:.1f}s')
-
-            dataset = get_cached_pascal_pfe(split, config)
-
-            eval_start_t = time.time()
-
-            loader = DataLoader(dataset, batch_size=1, num_workers=0, shuffle=False, drop_last=False)
-
-            assert config.batch_size is None or config.batch_size == 1, 'When PFE Dataset is used, batch size must be 1'
-
-            metric = FixedIntervalMetrics(resize_pred=True, sigmoid=True, custom_threshold=shift, **metric_args)
-
-            with torch.no_grad():
-
-                i, losses = 0, []
-                for i_all, (data_x, data_y) in enumerate(loader):
-
-                    data_x = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_x]
-                    data_y = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_y]
-
-                    if config.mask == 'separate':  # for old CondBase model
-                        pred, = model(data_x[0], data_x[1], data_x[2])
-                    else:
-                        # assert config.mask in {'text', 'highlight'}
-                        pred, _, _, _  = model(data_x[0], data_x[1], return_features=True)
-
-                    # loss = loss_fn(pred, data_y[0])
-                    metric.add(pred.unsqueeze(1) + shift, data_y)
-
-                    # losses += [float(loss)]
-
-                    i += 1
-                    if config.max_iterations and i >= config.max_iterations:
-                        break
-
-            #scores[split] = {m: s for m, s in zip(metric.names(), metric.value())}
-
-            log.info(f'Dataset length: {len(dataset)}, took {time.time() - eval_start_t:.1f}s to evaluate.')
-
-            print(metric.value()['mean_iou_scores'])
-
-            scores[split] = metric.scores()
-
-            log.info(f'Completed split {split}')
-        
-        key_prefix = config['name'] if 'name' in config else 'pas'
-
-        all_keys = set.intersection(*[set(v.keys()) for v in scores.values()])
-
-        valid_keys = [k for k in all_keys if all(v[k] is not None and isinstance(v[k], (int, float, np.float)) for v in scores.values())]
-
-        return {key_prefix: {k: np.mean([s[k] for s in scores.values()]) for k in valid_keys}}
-
-
-    if config.test_dataset == 'coco':
-        from datasets.coco_wrapper import COCOWrapper
-
-        coco_dataset = COCOWrapper('test', fold=train_config.fold, image_size=train_config.image_size, mask=config.mask,
-                                    with_class_label=True)
-
-        log.info('Dataset length', len(coco_dataset))
-        loader = DataLoader(coco_dataset, batch_size=config.batch_size, num_workers=2, shuffle=False, drop_last=False)
-        
-        metric = get_attribute(config.metric)(resize_pred=True, **metric_args)
-
-        shift = config.shift if 'shift' in config else 0
-
-        with torch.no_grad():
-
-            i, losses = 0, []
-            for i_all, (data_x, data_y) in enumerate(loader):
-                data_x = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_x]
-                data_y = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_y]
-
-                if config.mask == 'separate':  # for old CondBase model
-                    pred, = model(data_x[0], data_x[1], data_x[2])
-                else:
-                    # assert config.mask in {'text', 'highlight'}
-                    pred, _, _, _  = model(data_x[0], data_x[1], return_features=True)
-
-                metric.add([pred + shift], data_y)
-
-                i += 1
-                if config.max_iterations and i >= config.max_iterations:
-                    break                
-
-        key_prefix = config['name'] if 'name' in config else 'coco'      
-        return {key_prefix: metric.scores()}
-        #return {key_prefix: {k: v for k, v in zip(metric.names(), metric.value())}}
-
-
-    if config.test_dataset == 'phrasecut':
-        from datasets.phrasecut import PhraseCut
-
-        only_visual = config.only_visual is not None and config.only_visual
-        with_visual = config.with_visual is not None and config.with_visual
-
-        dataset = PhraseCut('test', 
-                            image_size=train_config.image_size,
-                            mask=config.mask, 
-                            with_visual=with_visual, only_visual=only_visual, aug_crop=False, 
-                            aug_color=False)
-
-        loader = DataLoader(dataset, batch_size=config.batch_size, num_workers=2, shuffle=False, drop_last=False)
-        metric = get_attribute(config.metric)(resize_pred=True, **metric_args)
-
-        shift = config.shift if 'shift' in config else 0
-
-
-        with torch.no_grad():
-
-            i, losses = 0, []
-            for i_all, (data_x, data_y) in enumerate(loader):
-                data_x = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_x]
-                data_y = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_y]
-
-                pred, _, _, _  = model(data_x[0], data_x[1], return_features=True)
-                metric.add([pred + shift], data_y)
-
-                i += 1
-                if config.max_iterations and i >= config.max_iterations:
-                    break                
-
-        key_prefix = config['name'] if 'name' in config else 'phrasecut'      
-        return {key_prefix: metric.scores()}
-        #return {key_prefix: {k: v for k, v in zip(metric.names(), metric.value())}}
-
-    if config.test_dataset == 'pascal_zs':
-        from third_party.JoEm.model.metric import Evaluator
-        from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC
-        from datasets.pascal_zeroshot import PascalZeroShot, PASCAL_VOC_CLASSES_ZS
-
-        from models.clipseg import CLIPSegMultiLabel
-
-        n_unseen = train_config.remove_classes[1]
-
-        pz = PascalZeroShot('val', n_unseen, image_size=352)
-        m = CLIPSegMultiLabel(model=train_config.name).cuda()
-        m.eval();
-
-        print(len(pz), n_unseen)
-        print('training removed', [c for class_set in PASCAL_VOC_CLASSES_ZS[:n_unseen // 2] for c in class_set])
-
-        print('unseen', [VOC[i] for i in get_unseen_idx(n_unseen)])
-        print('seen', [VOC[i] for i in get_seen_idx(n_unseen)])
-
-        loader = DataLoader(pz, batch_size=8)
-        evaluator = Evaluator(21, get_unseen_idx(n_unseen), get_seen_idx(n_unseen))
-
-        for i, (data_x, data_y) in enumerate(loader):
-            pred = m(data_x[0].cuda())
-            evaluator.add_batch(data_y[0].numpy(), pred.argmax(1).cpu().detach().numpy())
-            
-            if config.max_iter is not None and i > config.max_iter: 
-                break
-                
-        scores = evaluator.Mean_Intersection_over_Union()        
-        key_prefix = config['name'] if 'name' in config else 'pas_zs'      
-
-        return {key_prefix: {k: scores[k] for k in ['seen', 'unseen', 'harmonic', 'overall']}}
-
-    elif config.test_dataset in {'same_as_training', 'affordance'}:
-        loss_fn = get_attribute(train_config.loss)
-
-        metric_cls = get_attribute(config.metric)
-        metric = metric_cls(**metric_args)
-
-        if config.test_dataset == 'same_as_training':
-            dataset_cls = get_attribute(train_config.dataset)
-        elif config.test_dataset == 'affordance':
-            dataset_cls = get_attribute('datasets.lvis_oneshot3.LVIS_Affordance')
-            dataset_name = 'aff'
-        else:
-            dataset_cls = get_attribute('datasets.lvis_oneshot3.LVIS_OneShot')
-            dataset_name = 'lvis'
-
-        _, dataset_args, _ = filter_args(config, inspect.signature(dataset_cls).parameters)
-
-        dataset_args['image_size'] = train_config.image_size  # explicitly use training image size for evaluation
-
-        if model.__class__.__name__ == 'PFENetWrapper':
-            dataset_args['image_size'] = config.image_size
-
-        log.info('init dataset', str(dataset_cls))
-        dataset = dataset_cls(**dataset_args)
-
-        log.info(f'Score on {model.__class__.__name__} on {dataset_cls.__name__}')
-
-        data_loader = torch.utils.data.DataLoader(dataset, batch_size=config.batch_size, shuffle=config.shuffle)
-
-        # explicitly set prompts
-        if config.prompt == 'plain':
-            model.prompt_list = ['{}']
-        elif config.prompt == 'fixed':
-            model.prompt_list = ['a photo of a {}.']
-        elif config.prompt == 'shuffle':
-            model.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
-        elif config.prompt == 'shuffle_clip':
-            from models.clip_prompts import imagenet_templates
-            model.prompt_list = imagenet_templates
-
-        config.assume_no_unused_keys(exceptions=['max_iterations'])
-
-        t_start = time.time()
-
-        with torch.no_grad():  # TODO: switch to inference_mode (torch 1.9)
-            i, losses = 0, []
-            for data_x, data_y in data_loader:
-
-                data_x = [x.cuda() if isinstance(x, torch.Tensor) else x for x in data_x]
-                data_y = [x.cuda() if isinstance(x, torch.Tensor) else x for x in data_y]
-
-                if model.__class__.__name__ in {'ConditionBase4', 'PFENetWrapper'}:
-                    pred, = model(data_x[0], data_x[1], data_x[2])
-                    visual_q = None
-                else:
-                    pred, visual_q, _, _  = model(data_x[0], data_x[1], return_features=True)
-
-                loss = loss_fn(pred, data_y[0])
-
-                metric.add([pred], data_y)
-
-                losses += [float(loss)]
-
-                i += 1
-                if config.max_iterations and i >= config.max_iterations:
-                    break
-
-        # scores = {m: s for m, s in zip(metric.names(), metric.value())}
-        scores = metric.scores()
-
-        keys = set(scores.keys())
-        if dataset.negative_prob > 0 and 'mIoU' in keys:
-            keys.remove('mIoU')
-
-        name_mask = dataset.mask.replace('text_label', 'txt')[:3]
-        name_neg = '' if dataset.negative_prob == 0 else '_' + str(dataset.negative_prob)
-        
-        score_name = config.name if 'name' in config else f'{dataset_name}_{name_mask}{name_neg}'
-
-        scores = {score_name: {k: v for k,v in scores.items() if k in keys}}
-        scores[score_name].update({'test_loss': np.mean(losses)})
-
-        log.info(f'Evaluation took {time.time() - t_start:.1f}s')
-
-        return scores
-    else:
-        raise ValueError('invalid test dataset')
-
-
-
-
-
-
-
-
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/ai-clip-factory/src/lib/computeSha256.ts b/spaces/jbilcke-hf/ai-clip-factory/src/lib/computeSha256.ts
deleted file mode 100644
index cb6ef0604fca9653408012fd6cef2a58b6acaf47..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-clip-factory/src/lib/computeSha256.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-import { createHash } from 'node:crypto'
-
-/**
- * Returns a SHA256 hash using SHA-3 for the given `content`.
- *
- * @see https://en.wikipedia.org/wiki/SHA-3
- *
- * @param {String} content
- *
- * @returns {String}
- */
-export function computeSha256(strContent: string) {
-  return createHash('sha3-256').update(strContent).digest('hex')
-}
\ No newline at end of file
diff --git a/spaces/jbilcke-hf/ai-comic-factory/src/lib/loadImage.ts b/spaces/jbilcke-hf/ai-comic-factory/src/lib/loadImage.ts
deleted file mode 100644
index d2e7dcb6a548a9ce1937315486954e66e2c54746..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/ai-comic-factory/src/lib/loadImage.ts
+++ /dev/null
@@ -1,14 +0,0 @@
-export async function loadImage(image: string): Promise<HTMLImageElement> {
-  const img = new Image();
-  img.src = image;
-
-  const imgOnLoad = () => {
-    return new Promise<HTMLImageElement>((resolve, reject) => {
-      img.onload = () => { resolve(img) };
-      img.onerror = (err) => { reject(err) };
-    })
-  };
-
-  const loadImg = await imgOnLoad();
-  return loadImg
-}
\ No newline at end of file
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/click/globals.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/click/globals.py
deleted file mode 100644
index 480058f10dd6a8205d1bff0b94de7ae347a7629a..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/click/globals.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import typing as t
-from threading import local
-
-if t.TYPE_CHECKING:
-    import typing_extensions as te
-    from .core import Context
-
-_local = local()
-
-
-@t.overload
-def get_current_context(silent: "te.Literal[False]" = False) -> "Context":
-    ...
-
-
-@t.overload
-def get_current_context(silent: bool = ...) -> t.Optional["Context"]:
-    ...
-
-
-def get_current_context(silent: bool = False) -> t.Optional["Context"]:
-    """Returns the current click context.  This can be used as a way to
-    access the current context object from anywhere.  This is a more implicit
-    alternative to the :func:`pass_context` decorator.  This function is
-    primarily useful for helpers such as :func:`echo` which might be
-    interested in changing its behavior based on the current context.
-
-    To push the current context, :meth:`Context.scope` can be used.
-
-    .. versionadded:: 5.0
-
-    :param silent: if set to `True` the return value is `None` if no context
-                   is available.  The default behavior is to raise a
-                   :exc:`RuntimeError`.
-    """
-    try:
-        return t.cast("Context", _local.stack[-1])
-    except (AttributeError, IndexError) as e:
-        if not silent:
-            raise RuntimeError("There is no active click context.") from e
-
-    return None
-
-
-def push_context(ctx: "Context") -> None:
-    """Pushes a new context to the current stack."""
-    _local.__dict__.setdefault("stack", []).append(ctx)
-
-
-def pop_context() -> None:
-    """Removes the top level from the stack."""
-    _local.stack.pop()
-
-
-def resolve_color_default(color: t.Optional[bool] = None) -> t.Optional[bool]:
-    """Internal helper to get the default value of the color flag.  If a
-    value is passed it's returned unchanged, otherwise it's looked up from
-    the current context.
-    """
-    if color is not None:
-        return color
-
-    ctx = get_current_context(silent=True)
-
-    if ctx is not None:
-        return ctx.color
-
-    return None
diff --git a/spaces/johnowhitaker/color-guided-wikiart-diffusion/README.md b/spaces/johnowhitaker/color-guided-wikiart-diffusion/README.md
deleted file mode 100644
index 1b00b06294d4e42a151a942476188fc763bf5699..0000000000000000000000000000000000000000
--- a/spaces/johnowhitaker/color-guided-wikiart-diffusion/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
----
-title: Color Guided Wikiart Diffusion
-emoji: 🌈 
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: mit
-tags:
-  - pytorch
-  - diffusers
-  - unconditional-image-generation
-  - diffusion-models-class
----
-
-Gradio demo for color-guided diffusion as shown in [Unit 2 of the Diffusion Models Class](https://github.com/huggingface/diffusion-models-class/tree/main/unit2)
-
-Duplicate this space and add your own description here :)
diff --git a/spaces/juancopi81/whisper-youtube-2-hf_dataset/README.md b/spaces/juancopi81/whisper-youtube-2-hf_dataset/README.md
deleted file mode 100644
index 68dad13f3ce9383e6345b0bc4ca8518ff511f46b..0000000000000000000000000000000000000000
--- a/spaces/juancopi81/whisper-youtube-2-hf_dataset/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Whisper-youtube-2-hf Dataset
-emoji: 📚
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.10.0
-app_file: app.py
-pinned: false
-license: openrail
-duplicated_from: Whispering-GPT/whisper-youtube-2-hf_dataset
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/juancopi81/whisper-youtube-2-hf_dataset/storing/__init__.py b/spaces/juancopi81/whisper-youtube-2-hf_dataset/storing/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/jyseo/3DFuse/ldm/modules/midas/midas/dpt_depth.py b/spaces/jyseo/3DFuse/ldm/modules/midas/midas/dpt_depth.py
deleted file mode 100644
index 4e9aab5d2767dffea39da5b3f30e2798688216f1..0000000000000000000000000000000000000000
--- a/spaces/jyseo/3DFuse/ldm/modules/midas/midas/dpt_depth.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .base_model import BaseModel
-from .blocks import (
-    FeatureFusionBlock,
-    FeatureFusionBlock_custom,
-    Interpolate,
-    _make_encoder,
-    forward_vit,
-)
-
-
-def _make_fusion_block(features, use_bn):
-    return FeatureFusionBlock_custom(
-        features,
-        nn.ReLU(False),
-        deconv=False,
-        bn=use_bn,
-        expand=False,
-        align_corners=True,
-    )
-
-
-class DPT(BaseModel):
-    def __init__(
-        self,
-        head,
-        features=256,
-        backbone="vitb_rn50_384",
-        readout="project",
-        channels_last=False,
-        use_bn=False,
-    ):
-
-        super(DPT, self).__init__()
-
-        self.channels_last = channels_last
-
-        hooks = {
-            "vitb_rn50_384": [0, 1, 8, 11],
-            "vitb16_384": [2, 5, 8, 11],
-            "vitl16_384": [5, 11, 17, 23],
-        }
-
-        # Instantiate backbone and reassemble blocks
-        self.pretrained, self.scratch = _make_encoder(
-            backbone,
-            features,
-            False, # Set to true of you want to train from scratch, uses ImageNet weights
-            groups=1,
-            expand=False,
-            exportable=False,
-            hooks=hooks[backbone],
-            use_readout=readout,
-        )
-
-        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
-        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
-
-        self.scratch.output_conv = head
-
-
-    def forward(self, x):
-        if self.channels_last == True:
-            x.contiguous(memory_format=torch.channels_last)
-
-        layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
-
-        layer_1_rn = self.scratch.layer1_rn(layer_1)
-        layer_2_rn = self.scratch.layer2_rn(layer_2)
-        layer_3_rn = self.scratch.layer3_rn(layer_3)
-        layer_4_rn = self.scratch.layer4_rn(layer_4)
-
-        path_4 = self.scratch.refinenet4(layer_4_rn)
-        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
-        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
-        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
-        out = self.scratch.output_conv(path_1)
-
-        return out
-
-
-class DPTDepthModel(DPT):
-    def __init__(self, path=None, non_negative=True, **kwargs):
-        features = kwargs["features"] if "features" in kwargs else 256
-
-        head = nn.Sequential(
-            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
-            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
-            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
-            nn.ReLU(True),
-            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
-            nn.ReLU(True) if non_negative else nn.Identity(),
-            nn.Identity(),
-        )
-
-        super().__init__(head, **kwargs)
-
-        if path is not None:
-           self.load(path)
-
-    def forward(self, x):
-        return super().forward(x).squeeze(dim=1)
-
diff --git a/spaces/kainy/rvc_okiba_TTS/lib/infer_pack/models.py b/spaces/kainy/rvc_okiba_TTS/lib/infer_pack/models.py
deleted file mode 100644
index 3665d03bc0514a6ed07d3372ea24717dae1e0a65..0000000000000000000000000000000000000000
--- a/spaces/kainy/rvc_okiba_TTS/lib/infer_pack/models.py
+++ /dev/null
@@ -1,1142 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMs256NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-            nsff0 = nsff0[:, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(
-        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
-    ):  # 这里ds是id，[bs,1]
-        # print(1,pitch.shape)#[bs,t]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
-        # print(-2,pitchf.shape,z_slice.shape)
-        o = self.dec(z_slice, pitchf, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, pitch, nsff0, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-            nsff0 = nsff0[:, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs768NSFsid_nono(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder768(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
-        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, phone, phone_lengths, sid, rate=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
-        if rate:
-            head = int(z_p.shape[2] * rate)
-            z_p = z_p[:, :, -head:]
-            x_mask = x_mask[:, :, -head:]
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec(z * x_mask, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/kangvcar/RealChar/client/web/README.md b/spaces/kangvcar/RealChar/client/web/README.md
deleted file mode 100644
index 994c536353c1a49bf9913a79a06b6d9a7bc7d656..0000000000000000000000000000000000000000
--- a/spaces/kangvcar/RealChar/client/web/README.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# Getting Started with Create React App
-
-This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app).
-
-## Available Scripts
-
-***Remember to run `npm install` to install dependencies before running other commands.***
-
-In the project directory, you can run:
-
-### `npm start`
-
-Runs the app in the development mode.\
-Open [http://localhost:3000](http://localhost:3000) to view it in your browser.
-
-The page will reload when you make changes.\
-You may also see any lint errors in the console.
-
-Do not forget to run server with `uvicorn realtime_ai_character.main:app` in the root folder.
-
-### `npm test`
-
-Launches the test runner in the interactive watch mode.\
-See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information.
-
-### `npm run build`
-
-Builds the app for production to the `build` folder.\
-It correctly bundles React in production mode and optimizes the build for the best performance.
-
-The build is minified and the filenames include the hashes.\
-Your app is ready to be deployed!
-
-See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information.
-
-### `npm run eject`
-
-**Note: this is a one-way operation. Once you `eject`, you can't go back!**
-
-If you aren't satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project.
-
-Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you're on your own.
-
-You don't have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it.
-
-## Learn More
-
-You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started).
-
-To learn React, check out the [React documentation](https://reactjs.org/).
-
-### Code Splitting
-
-This section has moved here: [https://facebook.github.io/create-react-app/docs/code-splitting](https://facebook.github.io/create-react-app/docs/code-splitting)
-
-### Analyzing the Bundle Size
-
-This section has moved here: [https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size](https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size)
-
-### Making a Progressive Web App
-
-This section has moved here: [https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app](https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app)
-
-### Advanced Configuration
-
-This section has moved here: [https://facebook.github.io/create-react-app/docs/advanced-configuration](https://facebook.github.io/create-react-app/docs/advanced-configuration)
-
-### Deployment
-
-This section has moved here: [https://facebook.github.io/create-react-app/docs/deployment](https://facebook.github.io/create-react-app/docs/deployment)
-
-### `npm run build` fails to minify
-
-This section has moved here: [https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify](https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify)
diff --git a/spaces/keras-dreambooth/dreambooth-diffusion-akita-dog/app.py b/spaces/keras-dreambooth/dreambooth-diffusion-akita-dog/app.py
deleted file mode 100644
index 438342b70c21ed0f219da12ad73625168799902b..0000000000000000000000000000000000000000
--- a/spaces/keras-dreambooth/dreambooth-diffusion-akita-dog/app.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from huggingface_hub import from_pretrained_keras
-from keras_cv import models
-import gradio as gr
-import tensorflow as tf 
-
-# load keras model
-resolution = 512
-dreambooth_model = models.StableDiffusion(
-        img_width=resolution, img_height=resolution, jit_compile=True, 
-    )
-loaded_diffusion_model = from_pretrained_keras("keras-dreambooth/dreambooth_diffusion_akitainu")
-dreambooth_model._diffusion_model = loaded_diffusion_model
-
-
-# generate images
-def inference(prompt, negative_prompt, num_imgs_to_gen, num_steps, guidance_scale):
-    generated_images = dreambooth_model.text_to_image(
-        prompt,
-        negative_prompt=negative_prompt,
-        batch_size=num_imgs_to_gen,
-        num_steps=num_steps,
-        unconditional_guidance_scale=guidance_scale,
-    )
-    return generated_images 
-    
-    
-
-# pass function, input type for prompt, the output for multiple images
-gr.Interface(
-    inference, [
-        gr.Textbox(label="Positive Prompt", value="a photo of hks## toy"),
-        gr.Textbox(label="Negative Prompt", value="bad anatomy, soft blurry"),
-        gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1),
-        gr.Slider(label="Inference Steps",value=100),
-        gr.Number(label='Guidance scale', value=12),
-    ], [
-        gr.Gallery(show_label=False).style(grid=(1,2)),
-    ],
-    title="Keras Dreambooth - Aikta dog Demo 🐶",
-    description = "This model has been fine tuned to learn the concept of Akita dog-a famous and very cute dog of Japan. To use this demo, you should have {akt## dog} in the input",
-    examples = [["akt## dog as an anime character in overwatch", "((ugly)), blurry, ((bad anatomy)), duplicate", 4, 100, 12],
-               ["cute and adorable cartoon fluffy akt## dog with cap, fantasy, dreamlike, city scenario, surrealism, super cute, trending on artstation", "((ugly)), blurry, ((bad anatomy)), duplicate", 4, 100, 12]],
-    cache_examples=True
-    ).queue().launch(debug=True)
\ No newline at end of file
diff --git a/spaces/keremberke/awesome-yolov8-models/README.md b/spaces/keremberke/awesome-yolov8-models/README.md
deleted file mode 100644
index 7a9ebfc56a28bc19eb808c147f88eb94d0e885ae..0000000000000000000000000000000000000000
--- a/spaces/keremberke/awesome-yolov8-models/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Awesome YOLOv8 Models
-emoji: 💯
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.17.1
-app_file: app.py
-pinned: true
-license: mit
----
\ No newline at end of file
diff --git a/spaces/kevinwang676/Bark-Voice-Cloning/util/helper.py b/spaces/kevinwang676/Bark-Voice-Cloning/util/helper.py
deleted file mode 100644
index 185613661a2f450e55a5d2add1a1e75bc08f5c19..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Bark-Voice-Cloning/util/helper.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-from datetime import datetime
-from mutagen.wave import WAVE
-from mutagen.id3._frames import *
-
-def create_filename(path, seed, name, extension):
-    now = datetime.now()
-    date_str =now.strftime("%m-%d-%Y")
-    outputs_folder = os.path.join(os.getcwd(), path)
-    if not os.path.exists(outputs_folder):
-        os.makedirs(outputs_folder)
-
-    sub_folder = os.path.join(outputs_folder, date_str)
-    if not os.path.exists(sub_folder):
-        os.makedirs(sub_folder)
-
-    time_str = now.strftime("%H-%M-%S")
-    if seed == None:
-        file_name = f"{name}_{time_str}{extension}"
-    else:
-        file_name = f"{name}_{time_str}_s{seed}{extension}"
-    return os.path.join(sub_folder, file_name)
-
-
-def add_id3_tag(filename, text, speakername, seed):
-    audio = WAVE(filename)
-    if speakername == None:
-        speakername = "Unconditional"
-
-    # write id3 tag with text truncated to 60 chars, as a precaution...
-    audio["TIT2"] = TIT2(encoding=3, text=text[:60])
-    audio["TPE1"] = TPE1(encoding=3, text=f"Voice {speakername} using Seed={seed}")
-    audio["TPUB"] = TPUB(encoding=3, text="Bark by Suno AI")
-    audio["COMMENT"] = COMM(encoding=3, text="Generated with Bark GUI - Text-Prompted Generative Audio Model. Visit https://github.com/C0untFloyd/bark-gui")
-    audio.save()
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/data/__init__.py b/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/data/__init__.py
deleted file mode 100644
index 9a9761c518a1b07c5996165869742af0a52c82bc..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/data/__init__.py
+++ /dev/null
@@ -1,116 +0,0 @@
-"""This package includes all the modules related to data loading and preprocessing
-
- To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
- You need to implement four functions:
-    -- <__init__>:                      initialize the class, first call BaseDataset.__init__(self, opt).
-    -- <__len__>:                       return the size of dataset.
-    -- <__getitem__>:                   get a data point from data loader.
-    -- <modify_commandline_options>:    (optionally) add dataset-specific options and set default options.
-
-Now you can use the dataset class by specifying flag '--dataset_mode dummy'.
-See our template dataset class 'template_dataset.py' for more details.
-"""
-import numpy as np
-import importlib
-import torch.utils.data
-from face3d.data.base_dataset import BaseDataset
-
-
-def find_dataset_using_name(dataset_name):
-    """Import the module "data/[dataset_name]_dataset.py".
-
-    In the file, the class called DatasetNameDataset() will
-    be instantiated. It has to be a subclass of BaseDataset,
-    and it is case-insensitive.
-    """
-    dataset_filename = "data." + dataset_name + "_dataset"
-    datasetlib = importlib.import_module(dataset_filename)
-
-    dataset = None
-    target_dataset_name = dataset_name.replace('_', '') + 'dataset'
-    for name, cls in datasetlib.__dict__.items():
-        if name.lower() == target_dataset_name.lower() \
-           and issubclass(cls, BaseDataset):
-            dataset = cls
-
-    if dataset is None:
-        raise NotImplementedError("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name))
-
-    return dataset
-
-
-def get_option_setter(dataset_name):
-    """Return the static method <modify_commandline_options> of the dataset class."""
-    dataset_class = find_dataset_using_name(dataset_name)
-    return dataset_class.modify_commandline_options
-
-
-def create_dataset(opt, rank=0):
-    """Create a dataset given the option.
-
-    This function wraps the class CustomDatasetDataLoader.
-        This is the main interface between this package and 'train.py'/'test.py'
-
-    Example:
-        >>> from data import create_dataset
-        >>> dataset = create_dataset(opt)
-    """
-    data_loader = CustomDatasetDataLoader(opt, rank=rank)
-    dataset = data_loader.load_data()
-    return dataset
-
-class CustomDatasetDataLoader():
-    """Wrapper class of Dataset class that performs multi-threaded data loading"""
-
-    def __init__(self, opt, rank=0):
-        """Initialize this class
-
-        Step 1: create a dataset instance given the name [dataset_mode]
-        Step 2: create a multi-threaded data loader.
-        """
-        self.opt = opt
-        dataset_class = find_dataset_using_name(opt.dataset_mode)
-        self.dataset = dataset_class(opt)
-        self.sampler = None
-        print("rank %d %s dataset [%s] was created" % (rank, self.dataset.name, type(self.dataset).__name__))
-        if opt.use_ddp and opt.isTrain:
-            world_size = opt.world_size
-            self.sampler = torch.utils.data.distributed.DistributedSampler(
-                    self.dataset,
-                    num_replicas=world_size,
-                    rank=rank,
-                    shuffle=not opt.serial_batches
-                )
-            self.dataloader = torch.utils.data.DataLoader(
-                        self.dataset,
-                        sampler=self.sampler,
-                        num_workers=int(opt.num_threads / world_size), 
-                        batch_size=int(opt.batch_size / world_size), 
-                        drop_last=True)
-        else:
-            self.dataloader = torch.utils.data.DataLoader(
-                self.dataset,
-                batch_size=opt.batch_size,
-                shuffle=(not opt.serial_batches) and opt.isTrain,
-                num_workers=int(opt.num_threads),
-                drop_last=True
-            )
-
-    def set_epoch(self, epoch):
-        self.dataset.current_epoch = epoch
-        if self.sampler is not None:
-            self.sampler.set_epoch(epoch)
-
-    def load_data(self):
-        return self
-
-    def __len__(self):
-        """Return the number of data in the dataset"""
-        return min(len(self.dataset), self.opt.max_dataset_size)
-
-    def __iter__(self):
-        """Return a batch of data"""
-        for i, data in enumerate(self.dataloader):
-            if i * self.opt.batch_size >= self.opt.max_dataset_size:
-                break
-            yield data
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/options/train_options.py b/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/options/train_options.py
deleted file mode 100644
index 1337bfdd5f372b5c686a91b394a2aadbe5741f44..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/face3d/options/train_options.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""This script contains the training options for Deep3DFaceRecon_pytorch
-"""
-
-from .base_options import BaseOptions
-from util import util
-
-class TrainOptions(BaseOptions):
-    """This class includes training options.
-
-    It also includes shared options defined in BaseOptions.
-    """
-
-    def initialize(self, parser):
-        parser = BaseOptions.initialize(self, parser)
-        # dataset parameters
-        # for train
-        parser.add_argument('--data_root', type=str, default='./', help='dataset root')
-        parser.add_argument('--flist', type=str, default='datalist/train/masks.txt', help='list of mask names of training set')
-        parser.add_argument('--batch_size', type=int, default=32)
-        parser.add_argument('--dataset_mode', type=str, default='flist', help='chooses how datasets are loaded. [None | flist]')
-        parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
-        parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')
-        parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
-        parser.add_argument('--preprocess', type=str, default='shift_scale_rot_flip', help='scaling and cropping of images at load time [shift_scale_rot_flip | shift_scale | shift | shift_rot_flip ]')
-        parser.add_argument('--use_aug', type=util.str2bool, nargs='?', const=True, default=True, help='whether use data augmentation')
-
-        # for val
-        parser.add_argument('--flist_val', type=str, default='datalist/val/masks.txt', help='list of mask names of val set')
-        parser.add_argument('--batch_size_val', type=int, default=32)
-
-
-        # visualization parameters
-        parser.add_argument('--display_freq', type=int, default=1000, help='frequency of showing training results on screen')
-        parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')
-        
-        # network saving and loading parameters
-        parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results')
-        parser.add_argument('--save_epoch_freq', type=int, default=1, help='frequency of saving checkpoints at the end of epochs')
-        parser.add_argument('--evaluation_freq', type=int, default=5000, help='evaluation freq')
-        parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration')
-        parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
-        parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>, ...')
-        parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
-        parser.add_argument('--pretrained_name', type=str, default=None, help='resume training from another checkpoint')
-
-        # training parameters
-        parser.add_argument('--n_epochs', type=int, default=20, help='number of epochs with the initial learning rate')
-        parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate for adam')
-        parser.add_argument('--lr_policy', type=str, default='step', help='learning rate policy. [linear | step | plateau | cosine]')
-        parser.add_argument('--lr_decay_epochs', type=int, default=10, help='multiply by a gamma every lr_decay_epochs epoches')
-
-        self.isTrain = True
-        return parser
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/extract_kp_videos.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/extract_kp_videos.py
deleted file mode 100644
index 21616a3b4b5077ffdce99621395237b4edcff58c..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/extract_kp_videos.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import os
-import cv2
-import time
-import glob
-import argparse
-import face_alignment
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-from itertools import cycle
-
-from torch.multiprocessing import Pool, Process, set_start_method
-
-class KeypointExtractor():
-    def __init__(self, device):
-        self.detector = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, 
-                                                     device=device)   
-
-    def extract_keypoint(self, images, name=None, info=True):
-        if isinstance(images, list):
-            keypoints = []
-            if info:
-                i_range = tqdm(images,desc='landmark Det:')
-            else:
-                i_range = images
-
-            for image in i_range:
-                current_kp = self.extract_keypoint(image)
-                if np.mean(current_kp) == -1 and keypoints:
-                    keypoints.append(keypoints[-1])
-                else:
-                    keypoints.append(current_kp[None])
-
-            keypoints = np.concatenate(keypoints, 0)
-            np.savetxt(os.path.splitext(name)[0]+'.txt', keypoints.reshape(-1))
-            return keypoints
-        else:
-            while True:
-                try:
-                    keypoints = self.detector.get_landmarks_from_image(np.array(images))[0]
-                    break
-                except RuntimeError as e:
-                    if str(e).startswith('CUDA'):
-                        print("Warning: out of memory, sleep for 1s")
-                        time.sleep(1)
-                    else:
-                        print(e)
-                        break    
-                except TypeError:
-                    print('No face detected in this image')
-                    shape = [68, 2]
-                    keypoints = -1. * np.ones(shape)                    
-                    break
-            if name is not None:
-                np.savetxt(os.path.splitext(name)[0]+'.txt', keypoints.reshape(-1))
-            return keypoints
-
-def read_video(filename):
-    frames = []
-    cap = cv2.VideoCapture(filename)
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if ret:
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            frame = Image.fromarray(frame)
-            frames.append(frame)
-        else:
-            break
-    cap.release()
-    return frames
-
-def run(data):
-    filename, opt, device = data
-    os.environ['CUDA_VISIBLE_DEVICES'] = device
-    kp_extractor = KeypointExtractor()
-    images = read_video(filename)
-    name = filename.split('/')[-2:]
-    os.makedirs(os.path.join(opt.output_dir, name[-2]), exist_ok=True)
-    kp_extractor.extract_keypoint(
-        images, 
-        name=os.path.join(opt.output_dir, name[-2], name[-1])
-    )
-
-if __name__ == '__main__':
-    set_start_method('spawn')
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--input_dir', type=str, help='the folder of the input files')
-    parser.add_argument('--output_dir', type=str, help='the folder of the output files')
-    parser.add_argument('--device_ids', type=str, default='0,1')
-    parser.add_argument('--workers', type=int, default=4)
-
-    opt = parser.parse_args()
-    filenames = list()
-    VIDEO_EXTENSIONS_LOWERCASE = {'mp4'}
-    VIDEO_EXTENSIONS = VIDEO_EXTENSIONS_LOWERCASE.union({f.upper() for f in VIDEO_EXTENSIONS_LOWERCASE})
-    extensions = VIDEO_EXTENSIONS
-    
-    for ext in extensions:
-        os.listdir(f'{opt.input_dir}')
-        print(f'{opt.input_dir}/*.{ext}')
-        filenames = sorted(glob.glob(f'{opt.input_dir}/*.{ext}'))
-    print('Total number of videos:', len(filenames))
-    pool = Pool(opt.workers)
-    args_list = cycle([opt])
-    device_ids = opt.device_ids.split(",")
-    device_ids = cycle(device_ids)
-    for data in tqdm(pool.imap_unordered(run, zip(filenames, args_list, device_ids))):
-        None
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/utils/version_utils.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/utils/version_utils.py
deleted file mode 100644
index 963c45a2e8a86a88413ab6c18c22481fb9831985..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/utils/version_utils.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-import subprocess
-import warnings
-
-from packaging.version import parse
-
-
-def digit_version(version_str: str, length: int = 4):
-    """Convert a version string into a tuple of integers.
-
-    This method is usually used for comparing two versions. For pre-release
-    versions: alpha < beta < rc.
-
-    Args:
-        version_str (str): The version string.
-        length (int): The maximum number of version levels. Default: 4.
-
-    Returns:
-        tuple[int]: The version info in digits (integers).
-    """
-    assert 'parrots' not in version_str
-    version = parse(version_str)
-    assert version.release, f'failed to parse version {version_str}'
-    release = list(version.release)
-    release = release[:length]
-    if len(release) < length:
-        release = release + [0] * (length - len(release))
-    if version.is_prerelease:
-        mapping = {'a': -3, 'b': -2, 'rc': -1}
-        val = -4
-        # version.pre can be None
-        if version.pre:
-            if version.pre[0] not in mapping:
-                warnings.warn(f'unknown prerelease version {version.pre[0]}, '
-                              'version checking may go wrong')
-            else:
-                val = mapping[version.pre[0]]
-            release.extend([val, version.pre[-1]])
-        else:
-            release.extend([val, 0])
-
-    elif version.is_postrelease:
-        release.extend([1, version.post])
-    else:
-        release.extend([0, 0])
-    return tuple(release)
-
-
-def _minimal_ext_cmd(cmd):
-    # construct minimal environment
-    env = {}
-    for k in ['SYSTEMROOT', 'PATH', 'HOME']:
-        v = os.environ.get(k)
-        if v is not None:
-            env[k] = v
-    # LANGUAGE is used on win32
-    env['LANGUAGE'] = 'C'
-    env['LANG'] = 'C'
-    env['LC_ALL'] = 'C'
-    out = subprocess.Popen(
-        cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
-    return out
-
-
-def get_git_hash(fallback='unknown', digits=None):
-    """Get the git hash of the current repo.
-
-    Args:
-        fallback (str, optional): The fallback string when git hash is
-            unavailable. Defaults to 'unknown'.
-        digits (int, optional): kept digits of the hash. Defaults to None,
-            meaning all digits are kept.
-
-    Returns:
-        str: Git commit hash.
-    """
-
-    if digits is not None and not isinstance(digits, int):
-        raise TypeError('digits must be None or an integer')
-
-    try:
-        out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
-        sha = out.strip().decode('ascii')
-        if digits is not None:
-            sha = sha[:digits]
-    except OSError:
-        sha = fallback
-
-    return sha
diff --git a/spaces/krazyxki/V-1488abed/src/manage.ts b/spaces/krazyxki/V-1488abed/src/manage.ts
deleted file mode 100644
index 9db5a2e7ffb219c0b01fbcbcb92c5cabc5cbd9dd..0000000000000000000000000000000000000000
--- a/spaces/krazyxki/V-1488abed/src/manage.ts
+++ /dev/null
@@ -1,67 +0,0 @@
-import { Request, Response, Router } from "express";
-import { authPassword } from './proxy/auth';
-import { logger } from "./logger";
-import { keys } from "./keys";
-import { proxies } from "./proxies";
-import { proxyKeys } from "./proxy/proxy-keys";
-
-const handleAddKey = async (req: Request, res: Response) => {
-  const { key } = req.params;
-
-  await Promise.all(keys.parse(key).map(k => keys.add(k)));
-
-  res.status(200).json({ result: "ok" });
-};
-
-const handleAddProxy = async (req: Request, res: Response) => {
-  const { proxy } = req.params;
-
-  await Promise.all(proxies.parse(proxy).map(p => proxies.add(p)));
-
-  res.status(200).json({ result: "ok" });
-};
-
-const handleCloneKey = async (req: Request, res: Response) => {
-  const { key } = req.params;
-
-  const newKeys: string[] = [];
-  for (const k of keys.parse(key)) {
-    const newKey = await keys.clone(k);
-    if (newKey) {
-      newKeys.push(newKey.key);
-    }
-  }
-
-  res.status(200).json({ result: newKeys });
-};
-
-const handleGenerateProxyKey = async (req: Request, res: Response) => {
-  const key = proxyKeys.generate();
-
-  res.status(200).json({ result: key });
-};
-
-const handleRevokeProxyKeys = async (req: Request, res: Response) => {
-  const { keys } = req.body;
-
-  if (Array.isArray(keys)) {
-    keys.forEach((key) => proxyKeys.revoke(key));
-  }
-
-  res.status(200).json({ result: 'ok' });
-};
-
-const manageRouter = Router();
-
-manageRouter.use(authPassword);
-manageRouter.get("/gen", handleGenerateProxyKey);
-manageRouter.get("/revoke", handleRevokeProxyKeys);
-manageRouter.get("/addKey/:key", handleAddKey);
-manageRouter.get("/addProxy/:proxy", handleAddProxy);
-manageRouter.get("/cloneKey/:key", handleCloneKey);
-manageRouter.use((req, res) => {
-  logger.warn(`Unhandled manage request: ${req.method} ${req.path}`);
-  res.status(404).json({ error: "Not found" });
-});
-
-export const manage = manageRouter;
diff --git a/spaces/kukuhtw/AutoGPT/.github/PULL_REQUEST_TEMPLATE.md b/spaces/kukuhtw/AutoGPT/.github/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index a4f28a3d27d66d79cb95f2b8b847832172bb5f11..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/AutoGPT/.github/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,40 +0,0 @@
-<!-- ⚠️ At the moment any non-essential commands are not being merged.
-If you want to add non-essential commands to Auto-GPT, please create a plugin instead.
-We are expecting to ship plugin support within the week (PR #757).
-Resources:
-* https://github.com/Significant-Gravitas/Auto-GPT-Plugin-Template
--->
-
-<!-- 📢 Announcement
-We've recently noticed an increase in pull requests focusing on combining multiple changes. While the intentions behind these PRs are appreciated, it's essential to maintain a clean and manageable git history. To ensure the quality of our repository, we kindly ask you to adhere to the following guidelines when submitting PRs:
-
-Focus on a single, specific change.
-Do not include any unrelated or "extra" modifications.
-Provide clear documentation and explanations of the changes made.
-Ensure diffs are limited to the intended lines — no applying preferred formatting styles or line endings (unless that's what the PR is about).
-For guidance on committing only the specific lines you have changed, refer to this helpful video: https://youtu.be/8-hSNHHbiZg
-
-By following these guidelines, your PRs are more likely to be merged quickly after testing, as long as they align with the project's overall direction. -->
-
-### Background
-<!-- Provide a concise overview of the rationale behind this change. Include relevant context, prior discussions, or links to related issues. Ensure that the change aligns with the project's overall direction. -->
-
-### Changes
-<!-- Describe the specific, focused change made in this pull request. Detail the modifications clearly and avoid any unrelated or "extra" changes. -->
-
-### Documentation
-<!-- Explain how your changes are documented, such as in-code comments or external documentation. Ensure that the documentation is clear, concise, and easy to understand. -->
-
-### Test Plan
-<!-- Describe how you tested this functionality. Include steps to reproduce, relevant test cases, and any other pertinent information. -->
-
-### PR Quality Checklist
-- [ ] My pull request is atomic and focuses on a single change.
-- [ ] I have thoroughly tested my changes with multiple different prompts.
-- [ ] I have considered potential risks and mitigations for my changes.
-- [ ] I have documented my changes clearly and comprehensively.
-- [ ] I have not snuck in any "extra" small tweaks changes <!-- Submit these as separate Pull Requests, they are the easiest to merge! -->
-
-<!-- If you haven't added tests, please explain why. If you have, check the appropriate box. If you've ensured your PR is atomic and well-documented, check the corresponding boxes. -->
-
-<!-- By submitting this, I agree that my pull request should be closed if I do not fill this out or follow the guidelines. -->
diff --git a/spaces/kunkun11/home/README.md b/spaces/kunkun11/home/README.md
deleted file mode 100644
index b7d432c7fb3b6a93b6fd2379219167ca2ceb8d2e..0000000000000000000000000000000000000000
--- a/spaces/kunkun11/home/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Home
-emoji: 🏢
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 4.0.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpcore/_api.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpcore/_api.py
deleted file mode 100644
index 854235f5f6035031f0960d4a4b8834081d5df389..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpcore/_api.py
+++ /dev/null
@@ -1,92 +0,0 @@
-from contextlib import contextmanager
-from typing import Iterator, Optional, Union
-
-from ._models import URL, Extensions, HeaderTypes, Response
-from ._sync.connection_pool import ConnectionPool
-
-
-def request(
-    method: Union[bytes, str],
-    url: Union[URL, bytes, str],
-    *,
-    headers: HeaderTypes = None,
-    content: Union[bytes, Iterator[bytes], None] = None,
-    extensions: Optional[Extensions] = None,
-) -> Response:
-    """
-    Sends an HTTP request, returning the response.
-
-    ```
-    response = httpcore.request("GET", "https://www.example.com/")
-    ```
-
-    Arguments:
-        method: The HTTP method for the request. Typically one of `"GET"`,
-            `"OPTIONS"`, `"HEAD"`, `"POST"`, `"PUT"`, `"PATCH"`, or `"DELETE"`.
-        url: The URL of the HTTP request. Either as an instance of `httpcore.URL`,
-            or as str/bytes.
-        headers: The HTTP request headers. Either as a dictionary of str/bytes,
-            or as a list of two-tuples of str/bytes.
-        content: The content of the request body. Either as bytes,
-            or as a bytes iterator.
-        extensions: A dictionary of optional extra information included on the request.
-            Possible keys include `"timeout"`.
-
-    Returns:
-        An instance of `httpcore.Response`.
-    """
-    with ConnectionPool() as pool:
-        return pool.request(
-            method=method,
-            url=url,
-            headers=headers,
-            content=content,
-            extensions=extensions,
-        )
-
-
-@contextmanager
-def stream(
-    method: Union[bytes, str],
-    url: Union[URL, bytes, str],
-    *,
-    headers: HeaderTypes = None,
-    content: Union[bytes, Iterator[bytes], None] = None,
-    extensions: Optional[Extensions] = None,
-) -> Iterator[Response]:
-    """
-    Sends an HTTP request, returning the response within a content manager.
-
-    ```
-    with httpcore.stream("GET", "https://www.example.com/") as response:
-        ...
-    ```
-
-    When using the `stream()` function, the body of the response will not be
-    automatically read. If you want to access the response body you should
-    either use `content = response.read()`, or `for chunk in response.iter_content()`.
-
-    Arguments:
-        method: The HTTP method for the request. Typically one of `"GET"`,
-            `"OPTIONS"`, `"HEAD"`, `"POST"`, `"PUT"`, `"PATCH"`, or `"DELETE"`.
-        url: The URL of the HTTP request. Either as an instance of `httpcore.URL`,
-            or as str/bytes.
-        headers: The HTTP request headers. Either as a dictionary of str/bytes,
-            or as a list of two-tuples of str/bytes.
-        content: The content of the request body. Either as bytes,
-            or as a bytes iterator.
-        extensions: A dictionary of optional extra information included on the request.
-            Possible keys include `"timeout"`.
-
-    Returns:
-        An instance of `httpcore.Response`.
-    """
-    with ConnectionPool() as pool:
-        with pool.stream(
-            method=method,
-            url=url,
-            headers=headers,
-            content=content,
-            extensions=extensions,
-        ) as response:
-            yield response
diff --git a/spaces/laurabarreda/genre_prediction/predict_all.py b/spaces/laurabarreda/genre_prediction/predict_all.py
deleted file mode 100644
index e64c02272c44f490aa4914c8e3ba19f1bb9d7248..0000000000000000000000000000000000000000
--- a/spaces/laurabarreda/genre_prediction/predict_all.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import pandas as pd
-from sklearn import preprocessing
-import pickle
-from variables import *
-import streamlit as st
-from extract_all import *
-import numpy as np
-
-
-class Predict_all():
-    '''
-    This class can modify the entered data and apply the machine larning engineering
-    '''
-    genre_decode_dict = genre_decode_dict
-
-    def __init__(self, input):
-
-        self.input = input
-
-        if len(self.input) > 0:
-            object = Extract_all(self.input)
-            self.track_data = object.track_data
-            self.dict_to_df()
-            self.feature_engineering()
-            self.scaling()
-            self.get_genre_prediction()
-            self.show_predictions()
-
-
-    def dict_to_df(self):
-        '''
-        This function takes the given dictionary and converts it into a dataframe
-        '''
-        self.track_data = pd.DataFrame.from_dict(self.track_data, orient='index')
-
-
-    def feature_engineering(self):
-        '''
-        This function will modify the data from the dictionary in order to apply the ML model
-        '''
-        # Some of the and albums of the tracks have extra information added after a '-' or in between '()'
-        self.track_data['album'] = self.track_data['album'].str.split('(').str[0]
-        self.track_data['album'] = self.track_data['album'].str.split('-').str[0]
-
-        le = preprocessing.LabelEncoder()
-
-        self.track_data["artist_encoded"] = le.fit_transform(self.track_data["artist_name"])
-        self.track_data["album_encoded"] = le.fit_transform(self.track_data["album"])
-
-        self.track_data['artist_popularity'] = np.log1p(self.track_data.artist_popularity)
-        self.track_data['track_popularity'] = np.log1p(self.track_data.track_popularity)
-        self.track_data['key'] = np.log1p(self.track_data.key)
-        self.track_data['tempo'] = np.log1p(self.track_data.tempo)
-        self.track_data['duration_ms'] = np.log1p(self.track_data.duration_ms)
-        self.track_data['time_signature'] = np.log1p(self.track_data.time_signature)
-        self.track_data['artist_encoded'] = np.log1p(self.track_data.artist_encoded)
-        self.track_data['album_encoded'] = np.log1p(self.track_data.album_encoded)
-
-        # The column containing the track_id will be renamed to track_id
-        self.track_data.rename(columns={'Unnamed: 0' : 'track_id'}, inplace=True)
-
-
-    def scaling(self):
-        # Define the columns to use during the prediction and apply the model
-
-        scaler_path = 'scaler_all'
-        with open(scaler_path, 'rb') as archivo_entrada:
-            loaded_scaler = pickle.load(archivo_entrada)
-
-        self.X_track = self.track_data[['track_popularity', 'artist_popularity', 'danceability', 'energy', 'loudness', 'key', 
-                'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'duration_ms', 
-                'artist_encoded', 'album_encoded']]
-        
-        self.X_track = pd.DataFrame(loaded_scaler.transform(self.X_track), columns = self.X_track.columns)
-
-
-    def get_genre_prediction(self):
-        '''
-        This function will load the ML model and get the genre predictions from the given dataframe
-        '''
-        # Import the ML model as loaded_model and generate the predictions
-        model_path = 'my_model_all'
-        with open(model_path, 'rb') as archivo_entrada:
-            loaded_model = pickle.load(archivo_entrada)
-
-        # Apply the model
-        genre_predictions = loaded_model.predict(self.X_track)
-
-        # Add the predictions as an extra column in the dataframe, and decode them
-        self.track_data['genre'] = genre_predictions
-        self.track_data.genre = self.track_data.genre.map(genre_decode_dict_all)
-
-
-    def show_predictions(self):
-        '''
-        This function will get the individual predictions and show them to the user
-        '''
-        # Create the variables with the information to return to the user
-        track_name = self.track_data['track_name'].values[0]
-        artist_name = self.track_data['artist_name'].values[0]
-        track_genre = self.track_data['genre'].values[0]
-
-        st.write('Track name: ', track_name)
-        st.write('Artist name: ', artist_name)
-        st.write('Track genre: ', track_genre)
-
-
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Discjuggler 6 Serial Keygen Patch.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Discjuggler 6 Serial Keygen Patch.md
deleted file mode 100644
index e80d4d1052d0409522ab57c22709d3cf78fda3e7..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Discjuggler 6 Serial Keygen Patch.md	
+++ /dev/null
@@ -1,71 +0,0 @@
-<br />
-<h1>Discjuggler 6 Serial Keygen Patch: A Complete Guide</h1>
-<p>If you are looking for a reliable and versatile software to copy, burn and backup your CD/DVD/Blu-ray discs, you might want to consider Discjuggler 6. This software is one of the most popular and powerful applications in the market, with a range of features and functions that can meet your needs. However, Discjuggler 6 is not a free software, and you need a valid serial key to activate it. In this article, we will show you how to get Discjuggler 6 serial keygen patch, a tool that can generate working serial keys for Discjuggler 6. We will also explain what Discjuggler 6 can do for you, and how to use it effectively.</p>
-<h2>discjuggler 6 serial keygen patch</h2><br /><p><b><b>DOWNLOAD</b> &rArr; <a href="https://bytlly.com/2uGxYd">https://bytlly.com/2uGxYd</a></b></p><br /><br />
-<h2>What is Discjuggler 6?</h2>
-<p>Discjuggler 6 is a software that allows you to copy, burn and backup any disc with the DVD or CD structure, including preservation of menus and subtitles. You can also copy Blu-ray discs with BD-J structure to your hard drive if your computer is equipped with a Blu-ray player. The program also includes support for Pioneer BDRs (Blu-ray recorders) and can copy Blu Ray discs with AACS MKB version 2 protection.</p>
-<p>Discjuggler 6 can copy most movie DVDs to DVD-R/RW, DVD+R/RW/-R/-ROM, AVI, MPEG2, VCD, SVCD or Xvid. It can also backup audio CDs to MP3 or WMA files. With the Blu-ray disc burning feature, you can write Blu-ray discs in BD-R and BD-RE formats. Additionally, Discjuggler 6 can generate new keys if the Blu-ray disc uses AACS version 2 protection.</p>
-<p>Discjuggler 6 has an easy-to-use interface that makes it very user friendly. With this program, you can copy any DVD movies in DVDs to DVD-R/RW, DVDRW/ROM media including dual layer discs. Discjuggler 6 also supports compilation creation, so you can combine many movies on one disc. The movies will be played back using a regular DVD player or a stand-alone device without any special software or drivers required.</p>
-<p>Discjuggler 6 is the perfect tool for backing up your audio CDs to Audio CDs, MP3 or WMA files. It can also perform compression and streaming of your audio files if your player supports the VBR (variable bitrate) method. You can also burn audio CDs that contain all of the data converted to MP3, WMA or OGG format.</p>
-<p></p>
-<h2>How to get Discjuggler 6 serial keygen patch?</h2>
-<p>If you want to use Discjuggler 6 without any limitations or restrictions, you need a valid serial key to activate it. However, buying a serial key from the official website can be expensive and not affordable for everyone. That's why many people look for alternative ways to get Discjuggler 6 serial keygen patch, a tool that can generate working serial keys for Discjuggler 6.</p>
-<p>There are many websites that claim to offer Discjuggler 6 serial keygen patch for free download, but most of them are fake or contain viruses and malware that can harm your computer. Therefore, you need to be careful and choose a reliable source to get Discjuggler 6 serial keygen patch.</p>
-<p>One of the best websites that we recommend is <a href="https://oualie.dev/wp-content/uploads/2022/07/Discjuggler_6_Serial_Keygen_And_Crack.pdf">https://oualie.dev/wp-content/uploads/2022/07/Discjuggler_6_Serial_Keygen_And_Crack.pdf</a>. This website provides a safe and secure download link for Discjuggler 6 serial keygen patch, which is tested and verified by many users. You can download Discjuggler 6 serial keygen patch from this website without any risk or hassle.</p>
-<p>To download Discjuggler 6 serial keygen patch from this website, you need to follow these simple steps:</p>
-<ol>
-<li>Click on the download link provided on the website.</li>
-<li>Wait for a few seconds until the download process starts.</li>
-<li>Save the file on your computer and extract it using WinRAR or any other software.</li>
-<li>Run the file named "DiscJuggler_6_Serial_Keygen_Patch.exe" as administrator.</li>
-<li>Select your language and click on "Next".</li>
-<li>Accept the terms and conditions and click on "Next".</li>
-<li>Select the destination folder where you want to install Discjuggler 6 serial keygen patch and click on "Next".</li>
-<li>Wait for a few minutes until the installation process completes.</li>
-<li>Click on "Finish" and launch Discjuggler 6 serial keygen patch from your desktop.</li>
-<li>Select "Generate" and copy the generated serial key.</li>
-<li>Paste the serial key in the activation window of Discjuggler 6 and click on "OK".</li>
-<li>Congratulations! You have successfully activated Discjuggler 6 with Discjuggler 6 serial keygen patch.</li>
-</ol>
-<h2>How to use Discjuggler 6?</h2>
-<p>Once you have activated Discjuggler 6 with Discjuggler 6 serial keygen patch, you can start using it for your disc copying, burning and backup needs. Here are some tips on how to use Discjuggler 6 effectively:</p>
-<ul>
-<li>To copy a disc, insert the source disc in your drive and select "Copy CD/DVD" from the main menu of Discjuggler 6. Then select the destination drive where you want to write the copy and click on "Start".</li>
-<li>To burn a disc image file, select "Burn CD/DVD Image" from the main menu of Discjuggler 6. Then browse for the image file that you want to burn and select the drive where you want to write it. You can also adjust some settings such as write speed, verify mode and finalize disc before clicking on "Start".</li>
-<li>To backup a disc to your hard drive, select "Create CD/DVD Image" from the main menu of Discjuggler 6. Then insert the disc that you want to backup in your drive and select the folder where you want to save the image file. You can also choose some options such as compression level, split size and image format before clicking on "Start".</li>
-<li>To create a compilation disc, select "New CD/DVD" from the main menu of Discjuggler 6. Then drag and drop the files that you want to include in your compilation from your computer to the project window of Discjuggler 6. You can also arrange them in folders and subfolders as you like. Then select the drive where you want to write your compilation disc and click on "Start".</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Discjuggler 6 is a powerful software that can help you copy, burn and backup your CD/DVD/Blu-ray discs with ease and efficiency. However, it is not a free software and you need a valid serial key to activate it. If you don't want to spend money on buying a serial key from the official website, you can use Discjuggler 6 serial keygen patch, a tool that can generate working serial keys for Discjuggler 6. You can download Discjuggler 6 serial keygen patch from <a href="https://oualie.dev/wp-content/uploads/2022/07/Discjuggler_6_Serial_Keygen_And_Crack.pdf">https://oualie.dev/wp-content/uploads/2022/07/Discjuggler_6_Serial_Keygen_And_Crack.pdf</a>, which is a safe and reliable website that provides a secure download link for this tool. With this tool, you can activate Discjuggler 6 without any hassle or risk.</p>
-<h2>What are the benefits of Discjuggler 6 serial keygen patch?</h2>
-<p>By using Discjuggler 6 serial keygen patch, you can enjoy many benefits that can enhance your disc copying, burning and backup experience. Some of these benefits are:</p>
-<ul>
-<li>You can save money by not buying a serial key from the official website.</li>
-<li>You can use Discjuggler 6 without any limitations or restrictions.</li>
-<li>You can access all the features and functions of Discjuggler 6.</li>
-<li>You can copy, burn and backup any disc with the DVD or CD structure, including preservation of menus and subtitles.</li>
-<li>You can copy Blu-ray discs with BD-J structure to your hard drive if your computer is equipped with a Blu-ray player.</li>
-<li>You can copy Blu Ray discs with AACS MKB version 2 protection.</li>
-<li>You can copy most movie DVDs to DVD-R/RW, DVD+R/RW/-R/-ROM, AVI, MPEG2, VCD, SVCD or Xvid.</li>
-<li>You can backup audio CDs to MP3 or WMA files.</li>
-<li>You can write Blu-ray discs in BD-R and BD-RE formats.</li>
-<li>You can generate new keys if the Blu-ray disc uses AACS version 2 protection.</li>
-<li>You can create compilation discs by combining many movies on one disc.</li>
-<li>You can backup your audio CDs to Audio CDs, MP3 or WMA files.</li>
-<li>You can perform compression and streaming of your audio files if your player supports the VBR (variable bitrate) method.</li>
-<li>You can burn audio CDs that contain all of the data converted to MP3, WMA or OGG format.</li>
-</ul>
-<h2>How to uninstall Discjuggler 6 serial keygen patch?</h2>
-<p>If you want to uninstall Discjuggler 6 serial keygen patch from your computer, you need to follow these simple steps:</p>
-<ol>
-<li>Go to Start > Control Panel > Add or Remove Programs (Windows XP) or Programs and Features (Windows Vista/7/8/10).</li>
-<li>Find Discjuggler 6 serial keygen patch in the list of installed programs and click on Remove or Uninstall.</li>
-<li>Follow the instructions on the screen to complete the uninstallation process.</li>
-<li>Restart your computer if prompted.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Discjuggler 6 is a powerful software that can help you copy, burn and backup your CD/DVD/Blu-ray discs with ease and efficiency. However, it is not a free software and you need a valid serial key to activate it. If you don't want to spend money on buying a serial key from the official website, you can use Discjuggler 6 serial keygen patch, a tool that can generate working serial keys for Discjuggler 6. You can download Discjuggler 6 serial keygen patch from <a href="https://oualie.dev/wp-content/uploads/2022/07/Discjuggler_6_Serial_Keygen_And_Crack.pdf">https://oualie.dev/wp-content/uploads/2022/07/Discjuggler_6_Serial_Keygen_And_Crack.pdf</a>, which is a safe and reliable website that provides a secure download link for this tool. With this tool, you can activate Discjuggler 6 without any hassle or risk. You can also enjoy many benefits that can enhance your disc copying, burning and backup experience. However, if you want to uninstall Discjuggler 6 serial keygen patch from your computer, you can follow the simple steps mentioned above. We hope this article has been helpful for you and has answered your questions about Discjuggler 6 serial keygen patch.</p>
-<h2>Conclusion</h2>
-<p>Discjuggler 6 is a powerful software that can help you copy, burn and backup your CD/DVD/Blu-ray discs with ease and efficiency. However, it is not a free software and you need a valid serial key to activate it. If you don't want to spend money on buying a serial key from the official website, you can use Discjuggler 6 serial keygen patch, a tool that can generate working serial keys for Discjuggler 6. You can download Discjuggler 6 serial keygen patch from <a href="https://oualie.dev/wp-content/uploads/2022/07/Discjuggler_6_Serial_Keygen_And_Crack.pdf">https://oualie.dev/wp-content/uploads/2022/07/Discjuggler_6_Serial_Keygen_And_Crack.pdf</a>, which is a safe and reliable website that provides a secure download link for this tool. With this tool, you can activate Discjuggler 6 without any hassle or risk. You can also enjoy many benefits that can enhance your disc copying, burning and backup experience. However, if you want to uninstall Discjuggler 6 serial keygen patch from your computer, you can follow the simple steps mentioned above. We hope this article has been helpful for you and has answered your questions about Discjuggler 6 serial keygen patch.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/lllqqq/so-vits-svc-models-pcr/vencoder/__init__.py b/spaces/lllqqq/so-vits-svc-models-pcr/vencoder/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/llovantale/ChatGPT4/README.md b/spaces/llovantale/ChatGPT4/README.md
deleted file mode 100644
index 7938de14e5355209aaae713f289ca469181bbb17..0000000000000000000000000000000000000000
--- a/spaces/llovantale/ChatGPT4/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Chat-with-GPT4
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ysharma/ChatGPT4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/lsmyrtaj/cse6242-dataminers/app.py b/spaces/lsmyrtaj/cse6242-dataminers/app.py
deleted file mode 100644
index ea0936346181847bc82f7b368442ddda82647eb4..0000000000000000000000000000000000000000
--- a/spaces/lsmyrtaj/cse6242-dataminers/app.py
+++ /dev/null
@@ -1,283 +0,0 @@
-import streamlit as st
-from datetime import date, timedelta
-#from rest_api.fetch_data import (get_symbol_data)
-import pandas as pd
-from PIL import Image
-import time
-
-from plots import (
-    beta,
-    basic_portfolio,
-    # display_portfolio_return,
-    display_heat_map,
-    #circle_packing,
-    ER,
-    buble_interactive
-)
-
-### Koi
-from ef import(
-    ef_viz
-)
-def risk_str(num):
-    if num >=5 and num <15:
-        return 'Low Risk Aversion'
-    elif num >= 15 and num <25:
-        return 'Medium Risk Aversion'
-    elif num >= 25 and num <=35:
-        return 'High Risk Aversion'
-#### Koi
-        
-from sharp_ratio import(
-    cumulative_return,
-    
-    sharp_ratio_func
-)
-
-from arima import (
-    # get_model_accuracy,
-    arima_chart
-)
-
-
-        
-def load_heading():
-    """The function that displays the heading.
-        Provides instructions to the user
-    """
-    with st.container():
-        st.title('Dataminers')
-        header = st.subheader('This App performs historical portfolio analysis and future analysis ')
-        st.subheader('Please read the instructions carefully and enjoy!')
-        # st.text('This is some text.')
-
-
-def get_choices():
-    """Prompts the dialog to get the All Choices.
-    Returns:
-        An object of choices and an object of combined dataframes.
-    """
-    choices = {}
-    #tab1, tab2, tab3, tab4, tab5 = st.tabs(["Tickers", "Quantity", "Benchmark","Risk Free Return","Risk Aversion"])
-
-    tickers = st.sidebar.text_input('Enter stock tickers.', 'GOOG,AA,AVGO,AMD')
-
-    # Set the weights
-    weights_str = st.sidebar.text_input('Enter the investment quantities', '50,30,25,25')
-
-    benchmark = st.sidebar.selectbox(
-        'Select your ideal benchmark of return',
-        ('SP500', 'AOK', 'IXIC'))
-    if benchmark == 'IXIC':
-        st.sidebar.warning("You have selected a volatile benchmark.")
-    elif benchmark == 'SP500':
-        st.sidebar.success('You have selected a balanced benchmark')
-    elif benchmark == 'AOK':
-        st.sidebar.success('You have selected a conservative benchmark')
-
-    ### koi
-    rf = st.sidebar.number_input('Enter current rate of risk free return', min_value=0.001, max_value=1.00, value=0.041)
-
-
-    #A_coef_map = 
-    A_coef = st.sidebar.slider('Enter The Coefficient of Risk Aversion', min_value=5, max_value=35, value=30, step=5)
-
-    if A_coef > 20:
-        st.sidebar.success("You have selected a "+ risk_str(A_coef) +" investing style")
-        investing_style = 'Conservative'
-    elif A_coef >10 and A_coef <= 20:
-        st.sidebar.success("You have selected a "+risk_str(A_coef) +" investing style")
-        investing_style = 'Balanced'
-    elif A_coef <= 10:
-        st.sidebar.warning("You have selected a "+ risk_str(A_coef) +" investing style")
-        investing_style = 'Risky'
-
-    # Every form must have a submit button.
-    submitted = st.sidebar.button("Calculate")
-
-    symbols = []
-    reset = False
-
-    # Reusable Error Button DRY!
-    #def reset_app(error):
-    #    st.sidebar.write(f"{error}!")
-    #    st.sidebar.write(f"Check The Syntax")
-    #    reset = st.sidebar.button("RESET APP")
-
-    if submitted:
-        #with st.spinner('Running the calculations...'):
-        #    time.sleep(8)
-        #    st.success('Done!')
-        # convert  strings to lists
-        tickers_list = tickers.split(",")
-        weights_list = weights_str.split(",")
-        #crypto_symbols_list = crypto_symbols.split(",")
-        # Create the Symbols List
-        symbols.extend(tickers_list)
-        #symbols.extend(crypto_symbols_list)
-        # Convert Weights To Decimals
-        weights = []
-        for item in weights_list:
-            weights.append(float(item))
-
-        if reset:
-        #    # Clears all singleton caches:
-            #tickers = st.sidebar.selectbox('Enter 11 stock symbols.', ('GOOG','D','AAP','BLK'))
-        #    crypto_symbols = st.sidebar.text_input('Enter 2 crypto symbols only as below', 'BTC-USD,ETH-USD')
-            #weights_str = st.sidebar.text_input('Enter The Investment Weights', '0.3,0.3 ,0.3')
-
-            st.experimental_singleton.clear()
-
-
-        else:
-            # Submit an object with choices
-            choices = {
-
-                'symbols': symbols,
-                'weights': weights,
-                'benchmark': benchmark,
-                'investing_style': investing_style,
-                'risk-free-rate': rf,
-                'A-coef': A_coef
-
-            }
-            # Load combined_df
-            data = pd.read_csv('data_and_sp500.csv')
-            combined_df = data[tickers_list]
-            raw_data=pd.read_csv('us-shareprices-daily.csv', sep=';')
-            # return object of objects
-            return {
-                'choices': choices,
-                'combined_df': combined_df,
-                'data': data,
-                'raw_data':raw_data
-            }
-
-
-def run():
-    """The main function for running the script."""
-
-    load_heading()
-    choices = get_choices()
-    if choices:
-        st.success('''** Selected Tickers **''')     
-        buble_interactive(choices['data'],choices['choices'])
-        st.header('Tickers Beta')
-        """
-        The Capital Asset Pricing Model (CAPM) utilizes a formula to enable the application to calculate
-risk, return, and variability of return with respect to a benchmark. The application uses this
-benchmark, currently S&P 500 annual rate of return, to calculate the return of a stock using
-Figure 2 in Appendix A. Elements such as beta can be calculated using the formula in Appendix
-A Figure 1. The beta variable will serve as a variable to be used for calculating the variability of
-the stock with respect to the benchmark. This variability factor will prove useful for a variety of
-calculations such as understanding market risk and return. If the beta is equal to 1.0, the stock
-price is correlated with the market. When beta is smaller than 1.0, the stock is less volatile than
-the market. If beta is greater than 1.0, the stock is more volatile than the market.
-The CAPM model was run for 9 stocks, using 10-year daily historical data for initial test analysis.
-With this initial analysis, beta was calculated to determine the stock’s risk by measuring the
-price changes to the benchmark. By using CAPM model, annual expected return and portfolio
-return is calculated. The model results can be found in Appendix A.
-        """
-        
-        beta(choices['data'], choices['choices'])
-        ER(choices['data'], choices['choices'])
-        ##### EDIT HERE ##### koi
-        st.header('CAPM Model and the Efficient Frontier')
-        """
-    CAPM model measures systematic risks, however many of it's functions have unrealistic assumptions and rely heavily on a linear interpretation 
-    of the risks vs. returns relationship. It is better to use CAPM model in conjunction with the Efficient Frontier to better
-    graphically depict volatility (a measure of investment risk) for the defined rate of return. \n
-    Below we map the linear Utility function from the CAPM economic model along with the Efficient Frontier
-    Each circle depicted above is a variation of the portfolio with the same input asset, only different weights. 
-    Portfolios with higher volatilities have a yellower shade of hue, while portfolios with a higher return have a larger radius. \n
-    As you input different porfolio assets, take note of how diversification can improve a portfolio's risk versus reward profile.
-        """
-        ef_viz(choices['data'],choices['choices'])
-        """
-    There are in fact two components of the Efficient Frontier: the Efficient Frontier curve itself and the Minimum Variance Frontier. 
-    The lower curve, which is also the Minimum Variance Frontier will contain assets in the portfolio 
-    that has the lowest volatility. If our portfolio contains "safer" assets such as Governmental Bonds, the further to the right 
-    of the lower curve we will see a portfolio that contains only these "safe" assets, the portfolios on 
-    this curve, in theory, will have diminishing returns.\n
-    The upper curve, which is also the Efficient Frontier, contains portfolios that have marginally increasing returns as the risks 
-    increases. In theory, we want to pick a portfolio on this curve, as these portfolios contain more balanced weights of assets 
-    with acceptable trade-offs between risks and returns. \n
-    If an investor is more comfortable with investment risks, they can pick a portfolio on the right side of the Efficient Frontier. 
-    Whereas, a conservative investor might want to pick a portfolio from the left side of the Efficient Frontier. \n
-    Take notes of the assets' Betas and how that changes the shape of the curve as well. \n 
-    How does the shape of the curve change when 
-    the assets are of similar Beta vs when they are all different?\n 
-    Note the behavior of the curve when the portfolio contains only assets with Betas higher than 1 vs. when Betas are lower than 1.\n  
-
-        """
-        ##### ##### Koi
-        # Creates the title for streamlit
-        st.subheader('Portfolio Historical Normalized Cumulative Returns')
-        """
-Cumulative Returns:\n 
-The cumulative return of an asset is calculated by subtracting the original price paid from the current profit or loss. This answers the question, 
-what is the return on my initial investment?\n 
-The graph below shows the historical normalized cumulative returns for each of the chosen assets for the entire time period of the available data. 
-The default line chart shows tickers AA, AMD, AVGO, and GOOG and we can see that all have a positive cumulative return over the period of the available data. 
-Any of these assets purchased on the starting day and sold on the ending day for the period would have earned a return on their investment.\n
-This chart can also be used to analyze the correlation of the returns of the chosen assets over the displayed period. 
-Any segments of the line charts that show cumulative returns with similarly or oppositely angled segments can be considered to have some level of 
-correlation during those periods. 
-        """
-        basic_portfolio(choices['combined_df'])
-        """
-Negative Correlations (1): \n
-Occur for assets whose cumulative returns move in opposite directions. When one goes up the other goes down and vice versa. 
-These negatively correlated assets would offer some level of diversification protection to each other. 
-Perfectly negatively correlated stocks are sort of the goal, but unlikely to be common. 
-In most cases finding some level of negatively correlated stocks, should offer some level of diversification protection to your portfolio. 
-The amount of protection depends upon the calculated metric. Our tool includes some CAPM analysis, which attempts to relate the risk and return 
-and the correlation of assets to determine the expected portfolio returns versus the combined, hopefully reduced, risk.\n
-
-Positive Correlations (2):\n 
-Occur for assets whose cumulative returns move in concert. When one goes up the other also goes up and vice versa. 
-These positively correlated assets would not offer much or any diversification protection to each other.\n 
-        """
-        im = Image.open('1vs2.png')
-        col1, col2, col3 = st.columns([1,6,1])
-
-        with col1:
-            st.write("")
-    
-        with col2:
-            st.image(im, caption='Trends of Assets Correlations',use_column_width='auto')
-    
-        with col3:
-            st.write("")
-            
-            # Creates the title for streamlit
-        st.subheader('Heatmap Showing Correlation Of Assets')
-        """
-Heatmap: \n
-The Heat map shows the overall correlation of each asset to the other assets. Notice that the middle diagonal row is filled in with all 1’s. 
-That is because they are all perfectly correlated with themselves. A value of 1 equates to perfect correlation, -1 equates to perfect negative correlation, 
-and 0 equates to no correlation with values in between being relative to their distance from the extremes. A correlation value of .5 would mean 
-the asset moves half as much in the same direction as the correlated asset.  A values of -0.5 would mean it moves half as much in the opposite direction 
-as the correlated asset. \n
-The Heat map shows the correlation coefficient or value for each asset over the entire period to each other asset. 
-It also depicts the color of the intersection as darker for less correlation and lighter for more correlation, which could be either positive or negative. 
-The legend on the right indicates the absolute level of correlation for each color, again positive or negative associated to each color.\n 
-        """
-        
-        display_heat_map(choices['data'],choices['choices'])
-        #display_portfolio_return(choices['combined_df'], choices['choices'])
-        
-        cumulative_return(choices['combined_df'], choices['choices'])
-        sharp_ratio_func(choices['raw_data'], choices['choices'])
-
-        '''
-ARIMA:\n
-        '''
-
-        arima_chart(choices['choices']['symbols'])
-
-        
-
-if __name__ == "__main__":
-    run()
-
diff --git a/spaces/lunarflu/HF-QA-Demo-3/Dockerfile b/spaces/lunarflu/HF-QA-Demo-3/Dockerfile
deleted file mode 100644
index 0403c918cc1db8668a4b73e2000b104e50418f34..0000000000000000000000000000000000000000
--- a/spaces/lunarflu/HF-QA-Demo-3/Dockerfile
+++ /dev/null
@@ -1,20 +0,0 @@
-FROM debian:bullseye-slim
-
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN apt-get -y update && \
-    apt-get -y upgrade && \
-    apt-get -y install git python3.11 python3-pip
-
-COPY requirements.txt .
-RUN pip install --upgrade pip && \
-    pip install --no-cache-dir -r requirements.txt
-
-WORKDIR /hugging-face-qa-bot
-COPY . .
-
-RUN ls -la
-EXPOSE 8000
-
-ENTRYPOINT [ "python3", "-m", "api" ] # to run the api module
-# ENTRYPOINT [ "python3", "-m", "discord_bot" ] # to host the bot
diff --git a/spaces/magicr/BuboGPT/bubogpt/datasets/datasets/audio_caption/audio_caption_datasets.py b/spaces/magicr/BuboGPT/bubogpt/datasets/datasets/audio_caption/audio_caption_datasets.py
deleted file mode 100644
index b42a855bbd62c3d2effa9e8e84880aa82fa404f7..0000000000000000000000000000000000000000
--- a/spaces/magicr/BuboGPT/bubogpt/datasets/datasets/audio_caption/audio_caption_datasets.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import json
-import os 
-import torchaudio
-import random
-import tempfile
-
-from torch.utils.data import Dataset, default_collate
-import webdataset as wds
-from bubogpt.datasets.datasets.base_dataset import BaseDualDataset
-
-
-class GenericAudioDataset(BaseDualDataset):
-    def __init__(self, audio_processor, text_processor, location):
-        super().__init__(x_processor=audio_processor, text_processor=text_processor)
-
-        self.inner_dataset = wds.DataPipeline(
-            wds.ResampledShards(location),
-            wds.tarfile_to_samples(handler=wds.warn_and_continue),
-            wds.shuffle(1000, handler=wds.warn_and_continue),
-            wds.decode(wds.torch_audio, handler=wds.warn_and_continue),
-            wds.to_tuple("flac", "json", handler=wds.warn_and_continue),
-            wds.map_tuple(self.x_processor, handler=wds.warn_and_continue),
-            wds.map(self.to_dict, handler=wds.warn_and_continue),
-        )
-
-    def to_dict(self, sample):
-        return {
-            "audio": sample[0],
-            # [clips_per_video, channel, mel_bins, time_steps]
-            "text_input": self.text_processor(sample[1]["caption"]),
-        }
-
-
-class AudioCaptionDataset(BaseDualDataset):
-    def __init__(self, audio_processor, text_processor, audio_root, ann_paths):
-        """
-        vis_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        """
-        super().__init__(audio_processor, text_processor, audio_root, ann_paths)
-
-        self.audio_ids = {}
-        n = 0
-        for ann in self.annotation:
-            audio_id = ann["audio_id"]
-            if audio_id not in self.audio_ids.keys():
-                self.audio_ids[audio_id] = n
-                n += 1
-
-        with open("prompts/alignment_audio.txt") as f:
-            self.prompts = f.read().splitlines()
-        print(f"==> {self.__class__.__name__} using prompts: ", "\n  " + "\n  ".join(self.prompts))
-
-    def __getitem__(self, index):
-
-        # TODO this assumes image input, not general enough
-        ann = self.annotation[index]
-
-        audio_file = ann["audio_id"] + ".wav"
-        audio_path = os.path.join(self.x_root, audio_file)
-        audio = torchaudio.load(audio_path)
-        audio = self.x_processor(audio)
-        caption = self.text_processor(ann["caption"])
-
-        return {
-            "audio": audio,
-            "text_input": caption,
-            # "audio_id": self.audio_ids[ann["audio_id"]],
-            "prompt": random.choice(self.prompts),
-        }
diff --git a/spaces/manhkhanhUIT/BOPBTL/Global/models/NonLocal_feature_mapping_model.py b/spaces/manhkhanhUIT/BOPBTL/Global/models/NonLocal_feature_mapping_model.py
deleted file mode 100644
index 1b9bb1031d8c1fe399fb4fa61e875027a6cfc4a5..0000000000000000000000000000000000000000
--- a/spaces/manhkhanhUIT/BOPBTL/Global/models/NonLocal_feature_mapping_model.py
+++ /dev/null
@@ -1,202 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import os
-import functools
-from torch.autograd import Variable
-from util.image_pool import ImagePool
-from .base_model import BaseModel
-from . import networks
-import math
-
-
-class Mapping_Model_with_mask(nn.Module):
-    def __init__(self, nc, mc=64, n_blocks=3, norm="instance", padding_type="reflect", opt=None):
-        super(Mapping_Model_with_mask, self).__init__()
-
-        norm_layer = networks.get_norm_layer(norm_type=norm)
-        activation = nn.ReLU(True)
-        model = []
-
-        tmp_nc = 64
-        n_up = 4
-
-        for i in range(n_up):
-            ic = min(tmp_nc * (2 ** i), mc)
-            oc = min(tmp_nc * (2 ** (i + 1)), mc)
-            model += [nn.Conv2d(ic, oc, 3, 1, 1), norm_layer(oc), activation]
-
-        self.before_NL = nn.Sequential(*model)
-
-        if opt.NL_res:
-            self.NL = networks.NonLocalBlock2D_with_mask_Res(
-                mc,
-                mc,
-                opt.NL_fusion_method,
-                opt.correlation_renormalize,
-                opt.softmax_temperature,
-                opt.use_self,
-                opt.cosin_similarity,
-            )
-            print("You are using NL + Res")
-
-        model = []
-        for i in range(n_blocks):
-            model += [
-                networks.ResnetBlock(
-                    mc,
-                    padding_type=padding_type,
-                    activation=activation,
-                    norm_layer=norm_layer,
-                    opt=opt,
-                    dilation=opt.mapping_net_dilation,
-                )
-            ]
-
-        for i in range(n_up - 1):
-            ic = min(64 * (2 ** (4 - i)), mc)
-            oc = min(64 * (2 ** (3 - i)), mc)
-            model += [nn.Conv2d(ic, oc, 3, 1, 1), norm_layer(oc), activation]
-        model += [nn.Conv2d(tmp_nc * 2, tmp_nc, 3, 1, 1)]
-        if opt.feat_dim > 0 and opt.feat_dim < 64:
-            model += [norm_layer(tmp_nc), activation, nn.Conv2d(tmp_nc, opt.feat_dim, 1, 1)]
-        # model += [nn.Conv2d(64, 1, 1, 1, 0)]
-        self.after_NL = nn.Sequential(*model)
-        
-    
-    def forward(self, input, mask):
-        x1 = self.before_NL(input)
-        del input
-        x2 = self.NL(x1, mask)
-        del x1, mask
-        x3 = self.after_NL(x2)
-        del x2
-
-        return x3
-
-class Mapping_Model_with_mask_2(nn.Module): ## Multi-Scale Patch Attention
-    def __init__(self, nc, mc=64, n_blocks=3, norm="instance", padding_type="reflect", opt=None):
-        super(Mapping_Model_with_mask_2, self).__init__()
-
-        norm_layer = networks.get_norm_layer(norm_type=norm)
-        activation = nn.ReLU(True)
-        model = []
-
-        tmp_nc = 64
-        n_up = 4
-
-        for i in range(n_up):
-            ic = min(tmp_nc * (2 ** i), mc)
-            oc = min(tmp_nc * (2 ** (i + 1)), mc)
-            model += [nn.Conv2d(ic, oc, 3, 1, 1), norm_layer(oc), activation]
-
-        for i in range(2):
-            model += [
-                networks.ResnetBlock(
-                    mc,
-                    padding_type=padding_type,
-                    activation=activation,
-                    norm_layer=norm_layer,
-                    opt=opt,
-                    dilation=opt.mapping_net_dilation,
-                )
-            ]
-
-        print("Mapping: You are using multi-scale patch attention, conv combine + mask input")
-
-        self.before_NL = nn.Sequential(*model)
-
-        if opt.mapping_exp==1:
-            self.NL_scale_1=networks.Patch_Attention_4(mc,mc,8)
-
-        model = []
-        for i in range(2):
-            model += [
-                networks.ResnetBlock(
-                    mc,
-                    padding_type=padding_type,
-                    activation=activation,
-                    norm_layer=norm_layer,
-                    opt=opt,
-                    dilation=opt.mapping_net_dilation,
-                )
-            ]
-
-        self.res_block_1 = nn.Sequential(*model)
-
-        if opt.mapping_exp==1:
-            self.NL_scale_2=networks.Patch_Attention_4(mc,mc,4)
-
-        model = []
-        for i in range(2):
-            model += [
-                networks.ResnetBlock(
-                    mc,
-                    padding_type=padding_type,
-                    activation=activation,
-                    norm_layer=norm_layer,
-                    opt=opt,
-                    dilation=opt.mapping_net_dilation,
-                )
-            ]
-        
-        self.res_block_2 = nn.Sequential(*model)
-        
-        if opt.mapping_exp==1:
-            self.NL_scale_3=networks.Patch_Attention_4(mc,mc,2)
-        # self.NL_scale_3=networks.Patch_Attention_2(mc,mc,2)
-
-        model = []
-        for i in range(2):
-            model += [
-                networks.ResnetBlock(
-                    mc,
-                    padding_type=padding_type,
-                    activation=activation,
-                    norm_layer=norm_layer,
-                    opt=opt,
-                    dilation=opt.mapping_net_dilation,
-                )
-            ]
-
-        for i in range(n_up - 1):
-            ic = min(64 * (2 ** (4 - i)), mc)
-            oc = min(64 * (2 ** (3 - i)), mc)
-            model += [nn.Conv2d(ic, oc, 3, 1, 1), norm_layer(oc), activation]
-        model += [nn.Conv2d(tmp_nc * 2, tmp_nc, 3, 1, 1)]
-        if opt.feat_dim > 0 and opt.feat_dim < 64:
-            model += [norm_layer(tmp_nc), activation, nn.Conv2d(tmp_nc, opt.feat_dim, 1, 1)]
-        # model += [nn.Conv2d(64, 1, 1, 1, 0)]
-        self.after_NL = nn.Sequential(*model)
-        
-    
-    def forward(self, input, mask):
-        x1 = self.before_NL(input)
-        x2 = self.NL_scale_1(x1,mask)
-        x3 = self.res_block_1(x2)
-        x4 = self.NL_scale_2(x3,mask)
-        x5 = self.res_block_2(x4)
-        x6 = self.NL_scale_3(x5,mask)
-        x7 = self.after_NL(x6)
-        return x7
-
-    def inference_forward(self, input, mask):
-        x1 = self.before_NL(input)
-        del input
-        x2 = self.NL_scale_1.inference_forward(x1,mask)
-        del x1
-        x3 = self.res_block_1(x2)
-        del x2
-        x4 = self.NL_scale_2.inference_forward(x3,mask)
-        del x3
-        x5 = self.res_block_2(x4)
-        del x4
-        x6 = self.NL_scale_3.inference_forward(x5,mask)
-        del x5
-        x7 = self.after_NL(x6)
-        del x6
-        return x7   
\ No newline at end of file
diff --git a/spaces/mateuseap/magic-vocals/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/mateuseap/magic-vocals/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index ee3171bcb7c4a5066560723108b56e055f18be45..0000000000000000000000000000000000000000
--- a/spaces/mateuseap/magic-vocals/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class DioF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.dio(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        for index, pitch in enumerate(f0):
-            f0[index] = round(pitch, 1)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/merve/anonymization/source/measuring-fairness/slider.js b/spaces/merve/anonymization/source/measuring-fairness/slider.js
deleted file mode 100644
index efcbc18387d0d0cb957e34f75bb20a83131dda8e..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/source/measuring-fairness/slider.js
+++ /dev/null
@@ -1,139 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-
-
-
-
-
-
-
-window.makeSlider = function(){
-
-  var width = 300
-  var height = 30
-
-  var x = d3.scaleLinear()
-    .domain([.99, .6])
-    .range([0, width])
-    .clamp(true)
-
-  var rv = {}
-  rv.threshold = .5
-  rv.setSlider = makeSetSlider(students, 'threshold')
-  rv.setSliderF = makeSetSlider(students.filter(d => !d.isMale), 'threshold_f')
-  rv.setSliderM = makeSetSlider(students.filter(d => d.isMale), 'threshold_m')
-
-  var allActiveSel = d3.selectAll('.threshold-rect')
-  var allHandleSel = d3.selectAll('.threshold-handle')
-
-  var gatedSel = d3.select('.gated')
-
-  function makeSetSlider(data, key){
-    var text = key.split('_')[1]
-
-
-    var drag = d3.drag()
-      .on('drag', function(d){
-        updateThreshold(x.invert(d3.mouse(this)[0]))
-        // console.log(d3.event.x)
-
-        if (text && slider.threshold_f && (slider.threshold_f > 0.9042 || slider.threshold_f - slider.threshold_m > .05)){
-          gatedSel.classed('opened', 1)
-          svg.classed('no-blink', 1)
-        }
-
-        if (key == 'threshold') svg.classed('no-blink', 1)
-      })
-
-    var svg = d3.select('.slider.' + key).html('')
-      .append('svg').at({width, height})
-      .call(drag)
-      .st({cursor: 'pointer'})
-
-    if (key == 'threshold_m') svg.classed('no-blink', 1)
-
-
-
-    svg.append('rect').at({width, height, fill: lcolors.well})
-
-    var rectSel = svg.append('rect.threshold-rect')
-      .at({width, height, fill: lcolors.sick})
-
-    var handleSel = svg.append('g.threshold-handle')
-    handleSel.append('text.cursor')
-      .text('▲')
-      .at({textAnchor: 'middle', fontSize: 10, y: height, dy: '.8em'})
-    handleSel.append('circle')
-      .at({cy: height, r: 30, fill: 'rgba(0,0,0,0)'})
-
-    var labelText = 'Model Aggressiveness _→'
-    var _replacement = !text ? '' : 'On ' + (text == 'f' ? 'Women ' : 'Men ')
-
-    var labelText = '_Model Aggressiveness →'
-    var _replacement = !text ? '' : (text == 'f' ? 'Adult ' : 'Adult ')
-
-    var labelText = '_Model Decision Point'
-    var _replacement = !text ? '' : (text == 'f' ? 'Adult ' : 'Adult ')
-
-    var labelText = 'Model Decision Point_'
-    var _replacement = !text ? '' : (text == 'f' ? ' for Adults ' : ' for Children ')
-
-    var labelText = '_ Model Aggressiveness →'
-    var _replacement = !text ? '' : (text == 'f' ? ' Adult ' : 'Child ')
-
-
-    svg.append('text.axis').text(labelText.replace('_', _replacement))
-      .at({y: height/2, dy: '.33em', dx: 10})
-      .st({pointerEvents: 'none'})
-
-
-
-    function updateThreshold(threshold, skipDom){
-      rv[key] = threshold
-      data.forEach(d => d.threshold = threshold)
-
-      mini.updateAll()
-
-      rectSel.at({width: x(threshold)})
-      handleSel.translate(x(threshold), 0)
-
-      if (skipDom) return
-
-      if (key == 'threshold'){
-        allActiveSel.at({width: x(threshold)})
-        allHandleSel.translate(x(threshold), 0)
-      }
-
-      sel.rectSel.at({fill: d => d.grade > d.threshold ? lcolors.sick : lcolors.well})
-      sel.textSel
-        .st({
-          strokeWidth: d => d.grade > d.threshold == d.isSick ? 0 : .6,
-        })
-
-    }
-
-    return updateThreshold
-  }
-
-  return rv
-}
-
-
-
-
-
-
-if (window.init) window.init()
diff --git a/spaces/merve/data-leak/public/measuring-fairness/style.css b/spaces/merve/data-leak/public/measuring-fairness/style.css
deleted file mode 100644
index 27a4ab72371dd17fe64ae938268ef37f7fb16247..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/public/measuring-fairness/style.css
+++ /dev/null
@@ -1,274 +0,0 @@
-/* Copyright 2020 Google LLC. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-
-@media (max-width: 925px)  {
-  #graph > div{
-    position: relative;
-    top: 25px;
-  }
-}
-
-
-
-body{
-  --colors-well: rgb(179, 201, 204);
-  --colors-sick: rgb(241, 85, 85);
-  --lcolors-well: rgb(217, 228, 230);
-  --lcolors-sick: rgb(246, 145, 145);
-  --dcolors-well: rgb(63, 70, 71);
-  --dcolors-sick: rgb(84, 30, 30);
-}
-
-
-.tooltip {
-  top: -1000px;
-  position: fixed;
-  padding: 10px;
-  background: rgba(255, 255, 255, .90);
-  border: 1px solid lightgray;
-  pointer-events: none;
-}
-.tooltip-hidden{
-  opacity: 0;
-  transition: all .3s;
-  transition-delay: .1s;
-}
-
-@media (max-width: 590px){
-  div.tooltip{
-    bottom: -1px;
-    width: calc(100%);
-    left: -1px !important;
-    right: -1px !important;
-    top: auto !important;
-    width: auto !important;
-  }
-}
-
-svg{
-  overflow: visible;
-}
-
-.domain{
-  display: none;
-}
-
-text{
-  /*pointer-events: none;*/
-  /*text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;*/
-}
-
-
-
-#graph > div{
-  margin-top: 20px;
-}
-
-
-#end{
-  height: 600px;
-}
-
-
-.mono{
-  font-family: monospace;
-}
-
-
-
-
-.mini .axis{
-  font-size: 10px;
-  line-height: 12px !important;
-  position: relative;
-  top: 40px;
-}
-
-.axis{
-  font-size: 12px;
-}
-.axis{
-  color: #999;
-}
-.axis text{
-  fill: #999;
-}
-.axis line{
-  stroke: #ccc;
-}
-
-div.axis b{
-  margin-bottom: -10px;
-  display: block;
-}
-
-.init-hidden{
-  opacity: 0;
-}
-
-
-.highlight{
-  color: #fff;
-  padding-left: 3px;
-  padding-right: 3px;
-  padding-top: 1px;
-  padding-bottom: 1px;
-  border-radius: 3px;
-}
-
-.highlight.grey{ background: var(--colors-well); }
-.highlight.box{ 
-  border: 1px solid #000;
-  border-radius: 0px;
-  color: #000;
-  padding-bottom: 2px;
-}
-
-.weepeople {
-  font-family: "WeePeople";
-}
-
-
-wee{
-  font-family: "WeePeople";
-  font-size: 30px;
-  height: 22px;
-  display: inline;
-  position: relative;
-  top: 5px;
-  color: var(--colors-well);
-  padding: 1px;
-  margin: -1px;
-  line-height: 3px;
-}
-wee.sick{
-  color: var(--colors-sick);
-}
-
-wee.bg-sick{
-  background: var(--lcolors-sick);
-}
-wee.bg-well{
-  background: var(--lcolors-well);
-}
-
-bg{
-  background: var(--lcolors-well);
-  padding-left: 2px;
-  padding-right: 2px;
-}
-
-bg.sick{
-  background: var(--lcolors-sick);
-}
-
-wee.sick.bg-well{
-  -webkit-text-stroke: .6px var(--dcolors-sick);
-}
-wee.well.bg-sick{
-  -webkit-text-stroke: .6px var(--dcolors-well);
-}
-
-
-
-.equation{
-  margin: 7px;
-  position: relative;
-}
-
-
-.gated #hidden{
-  visibility: hidden;
-}
-
-.gated.opened #hidden{
-  visibility: unset;
-}
-.gated.opened #default{
-  display: none;
-}
-
-.gated #default{
-  height: 0px;
-}
-
-
-
-
-
-
-
-text.weepeople{
-  stroke: #000;
-  stroke-width: 0;
-  /*stroke-width: .2;*/
-}
-
-
-
-
-.post-summary, .headline{
-  display: none;
-}
-
-
-i{
-  pointer-events: none;
-}
-
-.slider{
-  position: relative;
-  z-index: 100;
-}
-
-
-
-
-
-.cursor{
-  animation-duration: 1s;
-  animation-name: bgblink;
-  display: inline-block;
-  animation-iteration-count: infinite;
-  animation-direction: alternate;
-  cursor: pointer;
-  transition: opacity .5s;
-  stroke: #000;
-} 
-
-@keyframes bgblink {
-  from {
-    /*fill: black;*/
-    stroke-width: 0px;
-  }
-
-  to {
-    /*fill: green;*/
-    stroke-width: 16px;
-  }
-}
-
-.no-blink .cursor{
-  /*background: rgba(255,255,0,0) !important;*/
-  animation: 0;
-}
-
-
-
-#adjust-text{
-  padding-top: 15px;
-  display: block;
-}
diff --git a/spaces/merve/fill-in-the-blank/public/measuring-fairness/graph-scroll.css b/spaces/merve/fill-in-the-blank/public/measuring-fairness/graph-scroll.css
deleted file mode 100644
index e3757d99ca305478165c6f7e4781ec0ce95b6291..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/public/measuring-fairness/graph-scroll.css
+++ /dev/null
@@ -1,119 +0,0 @@
-#container{
-  position: relative;
-  width: auto;
-}
-
-#sections{
-  width: 340px;
-}
-
-#sections > div{
-  background: white;
-  opacity: .2;
-  margin-bottom: 400px;
-  line-height: 1.4em;
-  transition: opacity .2s;
-}
-#sections > div:first-child{
-  opacity: 1;
-}
-#sections > div:last-child{
-  /*padding-bottom: 80vh;*/
-  padding-bottom: 80px;
-  margin-bottom: 0px;
-}
-#sections > div:first-child > h1{
-  padding-top: 40px;
-}
-
-#sections > div.graph-scroll-active{
-  opacity: 1;
-}
-
-#graph{
-  margin-left: 40px;
-  width: 500px;
-  position: -webkit-sticky;
-  position: sticky;
-  top: 0px;
-  float: right;
-  height: 580px;
-  font-family: 'Google Sans', sans-serif;
-
-}
-
-.slider{
-  font-family: 'Google Sans', sans-serif;
-}
-
-#sections h1{
-  text-align: left !important;
-}
-
-@media (max-width: 1000px) and (min-width: 926px){
-  #sections{
-    margin-left: 20px;
-  }
-}
-
-@media (max-width: 925px)  {
-  #container{
-    margin-left: 0px;
-  }
-
-  #graph{
-    width: 100%;
-    margin-left: 10px;
-    float: none;
-    max-width: 500px;
-    margin: 0px auto;
-  }
-
-  #graph > div{
-    position: relative;
-    top: 0px;
-  }
-  #sections{
-    width: auto;
-    position: relative;
-    margin: 0px auto;
-  }
-
-  #sections > div{
-    background: rgba(255,255,255,.8);
-    padding: 10px;
-    border-top: 1px solid;
-    border-bottom: 1px solid;
-    margin-bottom: 80vh;
-    width: calc(100vw - 20px);
-    margin-left: -5px;
-  }
-
-  #sections > div > *{
-    max-width: 750px;
-  }
-  .mini, .slider, i, .gated{
-    margin: 0px auto;
-  }
-
-  #sections > div:first-child{
-    opacity: 1;
-    margin-top: -140px;
-  }
-
-  #sections > div:last-child{
-    padding-bottom: 0px;
-    margin-bottom: 0px;
-  }
-
-
-  #sections h1{
-    margin: 10px;
-    padding-top: 0px !important;
-  }
-
-  #sections h3{
-    margin-top: .5em;
-  }
-
-}
diff --git a/spaces/merve/hidden-bias/source/third_party/umap.js b/spaces/merve/hidden-bias/source/third_party/umap.js
deleted file mode 100644
index 13bb989b285114e7a79d0a213422997c19a3c2f0..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/source/third_party/umap.js
+++ /dev/null
@@ -1,6864 +0,0 @@
-// https://github.com/pair-code/umap-js Copyright 2019 Google
-(function webpackUniversalModuleDefinition(root, factory) {
-    if(typeof exports === 'object' && typeof module === 'object')
-        module.exports = factory();
-    else if(typeof define === 'function' && define.amd)
-        define([], factory);
-    else {
-        var a = factory();
-        for(var i in a) (typeof exports === 'object' ? exports : root)[i] = a[i];
-    }
-})(window, function() {
-return /******/ (function(modules) { // webpackBootstrap
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-/******/        // Create a new module (and put it into the cache)
-/******/        var module = installedModules[moduleId] = {
-/******/            i: moduleId,
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-/******/        // Flag the module as loaded
-/******/        module.l = true;
-/******/
-/******/        // Return the exports of the module
-/******/        return module.exports;
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-/******/    __webpack_require__.m = modules;
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-/******/        if(typeof Symbol !== 'undefined' && Symbol.toStringTag) {
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-/******/        Object.defineProperty(exports, '__esModule', { value: true });
-/******/    };
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-/******/    // create a fake namespace object
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-/******/            function getModuleExports() { return module; };
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-/************************************************************************/
-/******/ ([
-/* 0 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-
-const toString = Object.prototype.toString;
-
-function isAnyArray(object) {
-  return toString.call(object).endsWith('Array]');
-}
-
-module.exports = isAnyArray;
-
-
-/***/ }),
-/* 1 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-var __values = (this && this.__values) || function (o) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0;
-    if (m) return m.call(o);
-    return {
-        next: function () {
-            if (o && i >= o.length) o = void 0;
-            return { value: o && o[i++], done: !o };
-        }
-    };
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-function tauRandInt(n, random) {
-    return Math.floor(random() * n);
-}
-exports.tauRandInt = tauRandInt;
-function tauRand(random) {
-    return random();
-}
-exports.tauRand = tauRand;
-function norm(vec) {
-    var e_1, _a;
-    var result = 0;
-    try {
-        for (var vec_1 = __values(vec), vec_1_1 = vec_1.next(); !vec_1_1.done; vec_1_1 = vec_1.next()) {
-            var item = vec_1_1.value;
-            result += Math.pow(item, 2);
-        }
-    }
-    catch (e_1_1) { e_1 = { error: e_1_1 }; }
-    finally {
-        try {
-            if (vec_1_1 && !vec_1_1.done && (_a = vec_1.return)) _a.call(vec_1);
-        }
-        finally { if (e_1) throw e_1.error; }
-    }
-    return Math.sqrt(result);
-}
-exports.norm = norm;
-function empty(n) {
-    var output = [];
-    for (var i = 0; i < n; i++) {
-        output.push(undefined);
-    }
-    return output;
-}
-exports.empty = empty;
-function range(n) {
-    return empty(n).map(function (_, i) { return i; });
-}
-exports.range = range;
-function filled(n, v) {
-    return empty(n).map(function () { return v; });
-}
-exports.filled = filled;
-function zeros(n) {
-    return filled(n, 0);
-}
-exports.zeros = zeros;
-function ones(n) {
-    return filled(n, 1);
-}
-exports.ones = ones;
-function linear(a, b, len) {
-    return empty(len).map(function (_, i) {
-        return a + i * ((b - a) / (len - 1));
-    });
-}
-exports.linear = linear;
-function sum(input) {
-    return input.reduce(function (sum, val) { return sum + val; });
-}
-exports.sum = sum;
-function mean(input) {
-    return sum(input) / input.length;
-}
-exports.mean = mean;
-function max(input) {
-    var max = 0;
-    for (var i = 0; i < input.length; i++) {
-        max = input[i] > max ? input[i] : max;
-    }
-    return max;
-}
-exports.max = max;
-function max2d(input) {
-    var max = 0;
-    for (var i = 0; i < input.length; i++) {
-        for (var j = 0; j < input[i].length; j++) {
-            max = input[i][j] > max ? input[i][j] : max;
-        }
-    }
-    return max;
-}
-exports.max2d = max2d;
-function rejectionSample(nSamples, poolSize, random) {
-    var result = zeros(nSamples);
-    for (var i = 0; i < nSamples; i++) {
-        var rejectSample = true;
-        while (rejectSample) {
-            var j = tauRandInt(poolSize, random);
-            var broken = false;
-            for (var k = 0; k < i; k++) {
-                if (j === result[k]) {
-                    broken = true;
-                    break;
-                }
-            }
-            if (!broken) {
-                rejectSample = false;
-            }
-            result[i] = j;
-        }
-    }
-    return result;
-}
-exports.rejectionSample = rejectionSample;
-function reshape2d(x, a, b) {
-    var rows = [];
-    var count = 0;
-    var index = 0;
-    if (x.length !== a * b) {
-        throw new Error('Array dimensions must match input length.');
-    }
-    for (var i = 0; i < a; i++) {
-        var col = [];
-        for (var j = 0; j < b; j++) {
-            col.push(x[index]);
-            index += 1;
-        }
-        rows.push(col);
-        count += 1;
-    }
-    return rows;
-}
-exports.reshape2d = reshape2d;
-
-
-/***/ }),
-/* 2 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-var __importStar = (this && this.__importStar) || function (mod) {
-    if (mod && mod.__esModule) return mod;
-    var result = {};
-    if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
-    result["default"] = mod;
-    return result;
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-var utils = __importStar(__webpack_require__(1));
-function makeHeap(nPoints, size) {
-    var makeArrays = function (fillValue) {
-        return utils.empty(nPoints).map(function () {
-            return utils.filled(size, fillValue);
-        });
-    };
-    var heap = [];
-    heap.push(makeArrays(-1));
-    heap.push(makeArrays(Infinity));
-    heap.push(makeArrays(0));
-    return heap;
-}
-exports.makeHeap = makeHeap;
-function rejectionSample(nSamples, poolSize, random) {
-    var result = utils.zeros(nSamples);
-    for (var i = 0; i < nSamples; i++) {
-        var rejectSample = true;
-        var j = 0;
-        while (rejectSample) {
-            j = utils.tauRandInt(poolSize, random);
-            var broken = false;
-            for (var k = 0; k < i; k++) {
-                if (j === result[k]) {
-                    broken = true;
-                    break;
-                }
-            }
-            if (!broken)
-                rejectSample = false;
-        }
-        result[i] = j;
-    }
-    return result;
-}
-exports.rejectionSample = rejectionSample;
-function heapPush(heap, row, weight, index, flag) {
-    row = Math.floor(row);
-    var indices = heap[0][row];
-    var weights = heap[1][row];
-    var isNew = heap[2][row];
-    if (weight >= weights[0]) {
-        return 0;
-    }
-    for (var i = 0; i < indices.length; i++) {
-        if (index === indices[i]) {
-            return 0;
-        }
-    }
-    return uncheckedHeapPush(heap, row, weight, index, flag);
-}
-exports.heapPush = heapPush;
-function uncheckedHeapPush(heap, row, weight, index, flag) {
-    var indices = heap[0][row];
-    var weights = heap[1][row];
-    var isNew = heap[2][row];
-    if (weight >= weights[0]) {
-        return 0;
-    }
-    weights[0] = weight;
-    indices[0] = index;
-    isNew[0] = flag;
-    var i = 0;
-    var iSwap = 0;
-    while (true) {
-        var ic1 = 2 * i + 1;
-        var ic2 = ic1 + 1;
-        var heapShape2 = heap[0][0].length;
-        if (ic1 >= heapShape2) {
-            break;
-        }
-        else if (ic2 >= heapShape2) {
-            if (weights[ic1] > weight) {
-                iSwap = ic1;
-            }
-            else {
-                break;
-            }
-        }
-        else if (weights[ic1] >= weights[ic2]) {
-            if (weight < weights[ic1]) {
-                iSwap = ic1;
-            }
-            else {
-                break;
-            }
-        }
-        else {
-            if (weight < weights[ic2]) {
-                iSwap = ic2;
-            }
-            else {
-                break;
-            }
-        }
-        weights[i] = weights[iSwap];
-        indices[i] = indices[iSwap];
-        isNew[i] = isNew[iSwap];
-        i = iSwap;
-    }
-    weights[i] = weight;
-    indices[i] = index;
-    isNew[i] = flag;
-    return 1;
-}
-exports.uncheckedHeapPush = uncheckedHeapPush;
-function buildCandidates(currentGraph, nVertices, nNeighbors, maxCandidates, random) {
-    var candidateNeighbors = makeHeap(nVertices, maxCandidates);
-    for (var i = 0; i < nVertices; i++) {
-        for (var j = 0; j < nNeighbors; j++) {
-            if (currentGraph[0][i][j] < 0) {
-                continue;
-            }
-            var idx = currentGraph[0][i][j];
-            var isn = currentGraph[2][i][j];
-            var d = utils.tauRand(random);
-            heapPush(candidateNeighbors, i, d, idx, isn);
-            heapPush(candidateNeighbors, idx, d, i, isn);
-            currentGraph[2][i][j] = 0;
-        }
-    }
-    return candidateNeighbors;
-}
-exports.buildCandidates = buildCandidates;
-function deheapSort(heap) {
-    var indices = heap[0];
-    var weights = heap[1];
-    for (var i = 0; i < indices.length; i++) {
-        var indHeap = indices[i];
-        var distHeap = weights[i];
-        for (var j = 0; j < indHeap.length - 1; j++) {
-            var indHeapIndex = indHeap.length - j - 1;
-            var distHeapIndex = distHeap.length - j - 1;
-            var temp1 = indHeap[0];
-            indHeap[0] = indHeap[indHeapIndex];
-            indHeap[indHeapIndex] = temp1;
-            var temp2 = distHeap[0];
-            distHeap[0] = distHeap[distHeapIndex];
-            distHeap[distHeapIndex] = temp2;
-            siftDown(distHeap, indHeap, distHeapIndex, 0);
-        }
-    }
-    return { indices: indices, weights: weights };
-}
-exports.deheapSort = deheapSort;
-function siftDown(heap1, heap2, ceiling, elt) {
-    while (elt * 2 + 1 < ceiling) {
-        var leftChild = elt * 2 + 1;
-        var rightChild = leftChild + 1;
-        var swap = elt;
-        if (heap1[swap] < heap1[leftChild]) {
-            swap = leftChild;
-        }
-        if (rightChild < ceiling && heap1[swap] < heap1[rightChild]) {
-            swap = rightChild;
-        }
-        if (swap === elt) {
-            break;
-        }
-        else {
-            var temp1 = heap1[elt];
-            heap1[elt] = heap1[swap];
-            heap1[swap] = temp1;
-            var temp2 = heap2[elt];
-            heap2[elt] = heap2[swap];
-            heap2[swap] = temp2;
-            elt = swap;
-        }
-    }
-}
-function smallestFlagged(heap, row) {
-    var ind = heap[0][row];
-    var dist = heap[1][row];
-    var flag = heap[2][row];
-    var minDist = Infinity;
-    var resultIndex = -1;
-    for (var i = 0; i > ind.length; i++) {
-        if (flag[i] === 1 && dist[i] < minDist) {
-            minDist = dist[i];
-            resultIndex = i;
-        }
-    }
-    if (resultIndex >= 0) {
-        flag[resultIndex] = 0;
-        return Math.floor(ind[resultIndex]);
-    }
-    else {
-        return -1;
-    }
-}
-exports.smallestFlagged = smallestFlagged;
-
-
-/***/ }),
-/* 3 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-var __read = (this && this.__read) || function (o, n) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator];
-    if (!m) return o;
-    var i = m.call(o), r, ar = [], e;
-    try {
-        while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);
-    }
-    catch (error) { e = { error: error }; }
-    finally {
-        try {
-            if (r && !r.done && (m = i["return"])) m.call(i);
-        }
-        finally { if (e) throw e.error; }
-    }
-    return ar;
-};
-var __spread = (this && this.__spread) || function () {
-    for (var ar = [], i = 0; i < arguments.length; i++) ar = ar.concat(__read(arguments[i]));
-    return ar;
-};
-var __values = (this && this.__values) || function (o) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0;
-    if (m) return m.call(o);
-    return {
-        next: function () {
-            if (o && i >= o.length) o = void 0;
-            return { value: o && o[i++], done: !o };
-        }
-    };
-};
-var __importStar = (this && this.__importStar) || function (mod) {
-    if (mod && mod.__esModule) return mod;
-    var result = {};
-    if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
-    result["default"] = mod;
-    return result;
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-var _a;
-var utils = __importStar(__webpack_require__(1));
-var SparseMatrix = (function () {
-    function SparseMatrix(rows, cols, values, dims) {
-        this.entries = new Map();
-        this.nRows = 0;
-        this.nCols = 0;
-        this.rows = __spread(rows);
-        this.cols = __spread(cols);
-        this.values = __spread(values);
-        for (var i = 0; i < values.length; i++) {
-            var key = this.makeKey(this.rows[i], this.cols[i]);
-            this.entries.set(key, i);
-        }
-        this.nRows = dims[0];
-        this.nCols = dims[1];
-    }
-    SparseMatrix.prototype.makeKey = function (row, col) {
-        return row + ":" + col;
-    };
-    SparseMatrix.prototype.checkDims = function (row, col) {
-        var withinBounds = row < this.nRows && col < this.nCols;
-        if (!withinBounds) {
-            throw new Error('array index out of bounds');
-        }
-    };
-    SparseMatrix.prototype.set = function (row, col, value) {
-        this.checkDims(row, col);
-        var key = this.makeKey(row, col);
-        if (!this.entries.has(key)) {
-            this.rows.push(row);
-            this.cols.push(col);
-            this.values.push(value);
-            this.entries.set(key, this.values.length - 1);
-        }
-        else {
-            var index = this.entries.get(key);
-            this.values[index] = value;
-        }
-    };
-    SparseMatrix.prototype.get = function (row, col, defaultValue) {
-        if (defaultValue === void 0) { defaultValue = 0; }
-        this.checkDims(row, col);
-        var key = this.makeKey(row, col);
-        if (this.entries.has(key)) {
-            var index = this.entries.get(key);
-            return this.values[index];
-        }
-        else {
-            return defaultValue;
-        }
-    };
-    SparseMatrix.prototype.getDims = function () {
-        return [this.nRows, this.nCols];
-    };
-    SparseMatrix.prototype.getRows = function () {
-        return __spread(this.rows);
-    };
-    SparseMatrix.prototype.getCols = function () {
-        return __spread(this.cols);
-    };
-    SparseMatrix.prototype.getValues = function () {
-        return __spread(this.values);
-    };
-    SparseMatrix.prototype.forEach = function (fn) {
-        for (var i = 0; i < this.values.length; i++) {
-            fn(this.values[i], this.rows[i], this.cols[i]);
-        }
-    };
-    SparseMatrix.prototype.map = function (fn) {
-        var vals = [];
-        for (var i = 0; i < this.values.length; i++) {
-            vals.push(fn(this.values[i], this.rows[i], this.cols[i]));
-        }
-        var dims = [this.nRows, this.nCols];
-        return new SparseMatrix(this.rows, this.cols, vals, dims);
-    };
-    SparseMatrix.prototype.toArray = function () {
-        var _this = this;
-        var rows = utils.empty(this.nRows);
-        var output = rows.map(function () {
-            return utils.zeros(_this.nCols);
-        });
-        for (var i = 0; i < this.values.length; i++) {
-            output[this.rows[i]][this.cols[i]] = this.values[i];
-        }
-        return output;
-    };
-    return SparseMatrix;
-}());
-exports.SparseMatrix = SparseMatrix;
-function transpose(matrix) {
-    var cols = [];
-    var rows = [];
-    var vals = [];
-    matrix.forEach(function (value, row, col) {
-        cols.push(row);
-        rows.push(col);
-        vals.push(value);
-    });
-    var dims = [matrix.nCols, matrix.nRows];
-    return new SparseMatrix(rows, cols, vals, dims);
-}
-exports.transpose = transpose;
-function identity(size) {
-    var _a = __read(size, 1), rows = _a[0];
-    var matrix = new SparseMatrix([], [], [], size);
-    for (var i = 0; i < rows; i++) {
-        matrix.set(i, i, 1);
-    }
-    return matrix;
-}
-exports.identity = identity;
-function pairwiseMultiply(a, b) {
-    return elementWise(a, b, function (x, y) { return x * y; });
-}
-exports.pairwiseMultiply = pairwiseMultiply;
-function add(a, b) {
-    return elementWise(a, b, function (x, y) { return x + y; });
-}
-exports.add = add;
-function subtract(a, b) {
-    return elementWise(a, b, function (x, y) { return x - y; });
-}
-exports.subtract = subtract;
-function maximum(a, b) {
-    return elementWise(a, b, function (x, y) { return (x > y ? x : y); });
-}
-exports.maximum = maximum;
-function multiplyScalar(a, scalar) {
-    return a.map(function (value) {
-        return value * scalar;
-    });
-}
-exports.multiplyScalar = multiplyScalar;
-function eliminateZeros(m) {
-    var zeroIndices = new Set();
-    var values = m.getValues();
-    var rows = m.getRows();
-    var cols = m.getCols();
-    for (var i = 0; i < values.length; i++) {
-        if (values[i] === 0) {
-            zeroIndices.add(i);
-        }
-    }
-    var removeByZeroIndex = function (_, index) { return !zeroIndices.has(index); };
-    var nextValues = values.filter(removeByZeroIndex);
-    var nextRows = rows.filter(removeByZeroIndex);
-    var nextCols = cols.filter(removeByZeroIndex);
-    return new SparseMatrix(nextRows, nextCols, nextValues, m.getDims());
-}
-exports.eliminateZeros = eliminateZeros;
-function normalize(m, normType) {
-    if (normType === void 0) { normType = "l2"; }
-    var e_1, _a;
-    var normFn = normFns[normType];
-    var colsByRow = new Map();
-    m.forEach(function (_, row, col) {
-        var cols = colsByRow.get(row) || [];
-        cols.push(col);
-        colsByRow.set(row, cols);
-    });
-    var nextMatrix = new SparseMatrix([], [], [], m.getDims());
-    var _loop_1 = function (row) {
-        var cols = colsByRow.get(row).sort();
-        var vals = cols.map(function (col) { return m.get(row, col); });
-        var norm = normFn(vals);
-        for (var i = 0; i < norm.length; i++) {
-            nextMatrix.set(row, cols[i], norm[i]);
-        }
-    };
-    try {
-        for (var _b = __values(colsByRow.keys()), _c = _b.next(); !_c.done; _c = _b.next()) {
-            var row = _c.value;
-            _loop_1(row);
-        }
-    }
-    catch (e_1_1) { e_1 = { error: e_1_1 }; }
-    finally {
-        try {
-            if (_c && !_c.done && (_a = _b.return)) _a.call(_b);
-        }
-        finally { if (e_1) throw e_1.error; }
-    }
-    return nextMatrix;
-}
-exports.normalize = normalize;
-var normFns = (_a = {},
-    _a["max"] = function (xs) {
-        var max = -Infinity;
-        for (var i = 0; i < xs.length; i++) {
-            max = xs[i] > max ? xs[i] : max;
-        }
-        return xs.map(function (x) { return x / max; });
-    },
-    _a["l1"] = function (xs) {
-        var sum = 0;
-        for (var i = 0; i < xs.length; i++) {
-            sum += xs[i];
-        }
-        return xs.map(function (x) { return x / sum; });
-    },
-    _a["l2"] = function (xs) {
-        var sum = 0;
-        for (var i = 0; i < xs.length; i++) {
-            sum += Math.pow(xs[i], 2);
-        }
-        return xs.map(function (x) { return Math.sqrt(Math.pow(x, 2) / sum); });
-    },
-    _a);
-function elementWise(a, b, op) {
-    var visited = new Set();
-    var rows = [];
-    var cols = [];
-    var vals = [];
-    var operate = function (row, col) {
-        rows.push(row);
-        cols.push(col);
-        var nextValue = op(a.get(row, col), b.get(row, col));
-        vals.push(nextValue);
-    };
-    var valuesA = a.getValues();
-    var rowsA = a.getRows();
-    var colsA = a.getCols();
-    for (var i = 0; i < valuesA.length; i++) {
-        var row = rowsA[i];
-        var col = colsA[i];
-        var key = row + ":" + col;
-        visited.add(key);
-        operate(row, col);
-    }
-    var valuesB = b.getValues();
-    var rowsB = b.getRows();
-    var colsB = b.getCols();
-    for (var i = 0; i < valuesB.length; i++) {
-        var row = rowsB[i];
-        var col = colsB[i];
-        var key = row + ":" + col;
-        if (visited.has(key))
-            continue;
-        operate(row, col);
-    }
-    var dims = [a.nRows, a.nCols];
-    return new SparseMatrix(rows, cols, vals, dims);
-}
-function getCSR(x) {
-    var entries = [];
-    x.forEach(function (value, row, col) {
-        entries.push({ value: value, row: row, col: col });
-    });
-    entries.sort(function (a, b) {
-        if (a.row === b.row) {
-            return a.col - b.col;
-        }
-        else {
-            return a.row - b.col;
-        }
-    });
-    var indices = [];
-    var values = [];
-    var indptr = [];
-    var currentRow = -1;
-    for (var i = 0; i < entries.length; i++) {
-        var _a = entries[i], row = _a.row, col = _a.col, value = _a.value;
-        if (row !== currentRow) {
-            currentRow = row;
-            indptr.push(i);
-        }
-        indices.push(col);
-        values.push(value);
-    }
-    return { indices: indices, values: values, indptr: indptr };
-}
-exports.getCSR = getCSR;
-
-
-/***/ }),
-/* 4 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-var __read = (this && this.__read) || function (o, n) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator];
-    if (!m) return o;
-    var i = m.call(o), r, ar = [], e;
-    try {
-        while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);
-    }
-    catch (error) { e = { error: error }; }
-    finally {
-        try {
-            if (r && !r.done && (m = i["return"])) m.call(i);
-        }
-        finally { if (e) throw e.error; }
-    }
-    return ar;
-};
-var __spread = (this && this.__spread) || function () {
-    for (var ar = [], i = 0; i < arguments.length; i++) ar = ar.concat(__read(arguments[i]));
-    return ar;
-};
-var __values = (this && this.__values) || function (o) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0;
-    if (m) return m.call(o);
-    return {
-        next: function () {
-            if (o && i >= o.length) o = void 0;
-            return { value: o && o[i++], done: !o };
-        }
-    };
-};
-var __importStar = (this && this.__importStar) || function (mod) {
-    if (mod && mod.__esModule) return mod;
-    var result = {};
-    if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
-    result["default"] = mod;
-    return result;
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-var utils = __importStar(__webpack_require__(1));
-var FlatTree = (function () {
-    function FlatTree(hyperplanes, offsets, children, indices) {
-        this.hyperplanes = hyperplanes;
-        this.offsets = offsets;
-        this.children = children;
-        this.indices = indices;
-    }
-    return FlatTree;
-}());
-exports.FlatTree = FlatTree;
-function makeForest(data, nNeighbors, nTrees, random) {
-    var leafSize = Math.max(10, nNeighbors);
-    var trees = utils
-        .range(nTrees)
-        .map(function (_, i) { return makeTree(data, leafSize, i, random); });
-    var forest = trees.map(function (tree) { return flattenTree(tree, leafSize); });
-    return forest;
-}
-exports.makeForest = makeForest;
-function makeTree(data, leafSize, n, random) {
-    if (leafSize === void 0) { leafSize = 30; }
-    var indices = utils.range(data.length);
-    var tree = makeEuclideanTree(data, indices, leafSize, n, random);
-    return tree;
-}
-function makeEuclideanTree(data, indices, leafSize, q, random) {
-    if (leafSize === void 0) { leafSize = 30; }
-    if (indices.length > leafSize) {
-        var splitResults = euclideanRandomProjectionSplit(data, indices, random);
-        var indicesLeft = splitResults.indicesLeft, indicesRight = splitResults.indicesRight, hyperplane = splitResults.hyperplane, offset = splitResults.offset;
-        var leftChild = makeEuclideanTree(data, indicesLeft, leafSize, q + 1, random);
-        var rightChild = makeEuclideanTree(data, indicesRight, leafSize, q + 1, random);
-        var node = { leftChild: leftChild, rightChild: rightChild, isLeaf: false, hyperplane: hyperplane, offset: offset };
-        return node;
-    }
-    else {
-        var node = { indices: indices, isLeaf: true };
-        return node;
-    }
-}
-function euclideanRandomProjectionSplit(data, indices, random) {
-    var dim = data[0].length;
-    var leftIndex = utils.tauRandInt(indices.length, random);
-    var rightIndex = utils.tauRandInt(indices.length, random);
-    rightIndex += leftIndex === rightIndex ? 1 : 0;
-    rightIndex = rightIndex % indices.length;
-    var left = indices[leftIndex];
-    var right = indices[rightIndex];
-    var hyperplaneOffset = 0;
-    var hyperplaneVector = utils.zeros(dim);
-    for (var i = 0; i < hyperplaneVector.length; i++) {
-        hyperplaneVector[i] = data[left][i] - data[right][i];
-        hyperplaneOffset -=
-            (hyperplaneVector[i] * (data[left][i] + data[right][i])) / 2.0;
-    }
-    var nLeft = 0;
-    var nRight = 0;
-    var side = utils.zeros(indices.length);
-    for (var i = 0; i < indices.length; i++) {
-        var margin = hyperplaneOffset;
-        for (var d = 0; d < dim; d++) {
-            margin += hyperplaneVector[d] * data[indices[i]][d];
-        }
-        if (margin === 0) {
-            side[i] = utils.tauRandInt(2, random);
-            if (side[i] === 0) {
-                nLeft += 1;
-            }
-            else {
-                nRight += 1;
-            }
-        }
-        else if (margin > 0) {
-            side[i] = 0;
-            nLeft += 1;
-        }
-        else {
-            side[i] = 1;
-            nRight += 1;
-        }
-    }
-    var indicesLeft = utils.zeros(nLeft);
-    var indicesRight = utils.zeros(nRight);
-    nLeft = 0;
-    nRight = 0;
-    for (var i in utils.range(side.length)) {
-        if (side[i] === 0) {
-            indicesLeft[nLeft] = indices[i];
-            nLeft += 1;
-        }
-        else {
-            indicesRight[nRight] = indices[i];
-            nRight += 1;
-        }
-    }
-    return {
-        indicesLeft: indicesLeft,
-        indicesRight: indicesRight,
-        hyperplane: hyperplaneVector,
-        offset: hyperplaneOffset,
-    };
-}
-function flattenTree(tree, leafSize) {
-    var nNodes = numNodes(tree);
-    var nLeaves = numLeaves(tree);
-    var hyperplanes = utils
-        .range(nNodes)
-        .map(function () { return utils.zeros(tree.hyperplane.length); });
-    var offsets = utils.zeros(nNodes);
-    var children = utils.range(nNodes).map(function () { return [-1, -1]; });
-    var indices = utils
-        .range(nLeaves)
-        .map(function () { return utils.range(leafSize).map(function () { return -1; }); });
-    recursiveFlatten(tree, hyperplanes, offsets, children, indices, 0, 0);
-    return new FlatTree(hyperplanes, offsets, children, indices);
-}
-function recursiveFlatten(tree, hyperplanes, offsets, children, indices, nodeNum, leafNum) {
-    var _a;
-    if (tree.isLeaf) {
-        children[nodeNum][0] = -leafNum;
-        (_a = indices[leafNum]).splice.apply(_a, __spread([0, tree.indices.length], tree.indices));
-        leafNum += 1;
-        return { nodeNum: nodeNum, leafNum: leafNum };
-    }
-    else {
-        hyperplanes[nodeNum] = tree.hyperplane;
-        offsets[nodeNum] = tree.offset;
-        children[nodeNum][0] = nodeNum + 1;
-        var oldNodeNum = nodeNum;
-        var res = recursiveFlatten(tree.leftChild, hyperplanes, offsets, children, indices, nodeNum + 1, leafNum);
-        nodeNum = res.nodeNum;
-        leafNum = res.leafNum;
-        children[oldNodeNum][1] = nodeNum + 1;
-        res = recursiveFlatten(tree.rightChild, hyperplanes, offsets, children, indices, nodeNum + 1, leafNum);
-        return { nodeNum: res.nodeNum, leafNum: res.leafNum };
-    }
-}
-function numNodes(tree) {
-    if (tree.isLeaf) {
-        return 1;
-    }
-    else {
-        return 1 + numNodes(tree.leftChild) + numNodes(tree.rightChild);
-    }
-}
-function numLeaves(tree) {
-    if (tree.isLeaf) {
-        return 1;
-    }
-    else {
-        return numLeaves(tree.leftChild) + numLeaves(tree.rightChild);
-    }
-}
-function makeLeafArray(rpForest) {
-    var e_1, _a;
-    if (rpForest.length > 0) {
-        var output = [];
-        try {
-            for (var rpForest_1 = __values(rpForest), rpForest_1_1 = rpForest_1.next(); !rpForest_1_1.done; rpForest_1_1 = rpForest_1.next()) {
-                var tree = rpForest_1_1.value;
-                output.push.apply(output, __spread(tree.indices));
-            }
-        }
-        catch (e_1_1) { e_1 = { error: e_1_1 }; }
-        finally {
-            try {
-                if (rpForest_1_1 && !rpForest_1_1.done && (_a = rpForest_1.return)) _a.call(rpForest_1);
-            }
-            finally { if (e_1) throw e_1.error; }
-        }
-        return output;
-    }
-    else {
-        return [[-1]];
-    }
-}
-exports.makeLeafArray = makeLeafArray;
-function selectSide(hyperplane, offset, point, random) {
-    var margin = offset;
-    for (var d = 0; d < point.length; d++) {
-        margin += hyperplane[d] * point[d];
-    }
-    if (margin === 0) {
-        var side = utils.tauRandInt(2, random);
-        return side;
-    }
-    else if (margin > 0) {
-        return 0;
-    }
-    else {
-        return 1;
-    }
-}
-function searchFlatTree(point, tree, random) {
-    var node = 0;
-    while (tree.children[node][0] > 0) {
-        var side = selectSide(tree.hyperplanes[node], tree.offsets[node], point, random);
-        if (side === 0) {
-            node = tree.children[node][0];
-        }
-        else {
-            node = tree.children[node][1];
-        }
-    }
-    var index = -1 * tree.children[node][0];
-    return tree.indices[index];
-}
-exports.searchFlatTree = searchFlatTree;
-
-
-/***/ }),
-/* 5 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-Object.defineProperty(exports, "__esModule", { value: true });
-var umap_1 = __webpack_require__(6);
-exports.UMAP = umap_1.UMAP;
-
-
-/***/ }),
-/* 6 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
-    return new (P || (P = Promise))(function (resolve, reject) {
-        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
-        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
-        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
-        step((generator = generator.apply(thisArg, _arguments || [])).next());
-    });
-};
-var __generator = (this && this.__generator) || function (thisArg, body) {
-    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
-    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
-    function verb(n) { return function (v) { return step([n, v]); }; }
-    function step(op) {
-        if (f) throw new TypeError("Generator is already executing.");
-        while (_) try {
-            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
-            if (y = 0, t) op = [op[0] & 2, t.value];
-            switch (op[0]) {
-                case 0: case 1: t = op; break;
-                case 4: _.label++; return { value: op[1], done: false };
-                case 5: _.label++; y = op[1]; op = [0]; continue;
-                case 7: op = _.ops.pop(); _.trys.pop(); continue;
-                default:
-                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
-                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
-                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
-                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
-                    if (t[2]) _.ops.pop();
-                    _.trys.pop(); continue;
-            }
-            op = body.call(thisArg, _);
-        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
-        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
-    }
-};
-var __read = (this && this.__read) || function (o, n) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator];
-    if (!m) return o;
-    var i = m.call(o), r, ar = [], e;
-    try {
-        while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);
-    }
-    catch (error) { e = { error: error }; }
-    finally {
-        try {
-            if (r && !r.done && (m = i["return"])) m.call(i);
-        }
-        finally { if (e) throw e.error; }
-    }
-    return ar;
-};
-var __spread = (this && this.__spread) || function () {
-    for (var ar = [], i = 0; i < arguments.length; i++) ar = ar.concat(__read(arguments[i]));
-    return ar;
-};
-var __importStar = (this && this.__importStar) || function (mod) {
-    if (mod && mod.__esModule) return mod;
-    var result = {};
-    if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
-    result["default"] = mod;
-    return result;
-};
-var __importDefault = (this && this.__importDefault) || function (mod) {
-    return (mod && mod.__esModule) ? mod : { "default": mod };
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-var heap = __importStar(__webpack_require__(2));
-var matrix = __importStar(__webpack_require__(3));
-var nnDescent = __importStar(__webpack_require__(7));
-var tree = __importStar(__webpack_require__(4));
-var utils = __importStar(__webpack_require__(1));
-var ml_levenberg_marquardt_1 = __importDefault(__webpack_require__(8));
-var SMOOTH_K_TOLERANCE = 1e-5;
-var MIN_K_DIST_SCALE = 1e-3;
-var UMAP = (function () {
-    function UMAP(params) {
-        if (params === void 0) { params = {}; }
-        var _this = this;
-        this.learningRate = 1.0;
-        this.localConnectivity = 1.0;
-        this.minDist = 0.1;
-        this.nComponents = 2;
-        this.nEpochs = 0;
-        this.nNeighbors = 15;
-        this.negativeSampleRate = 5;
-        this.random = Math.random;
-        this.repulsionStrength = 1.0;
-        this.setOpMixRatio = 1.0;
-        this.spread = 1.0;
-        this.transformQueueSize = 4.0;
-        this.targetMetric = "categorical";
-        this.targetWeight = 0.5;
-        this.targetNNeighbors = this.nNeighbors;
-        this.distanceFn = euclidean;
-        this.isInitialized = false;
-        this.rpForest = [];
-        this.embedding = [];
-        this.optimizationState = new OptimizationState();
-        var setParam = function (key) {
-            if (params[key] !== undefined)
-                _this[key] = params[key];
-        };
-        setParam('distanceFn');
-        setParam('learningRate');
-        setParam('localConnectivity');
-        setParam('minDist');
-        setParam('nComponents');
-        setParam('nEpochs');
-        setParam('nNeighbors');
-        setParam('negativeSampleRate');
-        setParam('random');
-        setParam('repulsionStrength');
-        setParam('setOpMixRatio');
-        setParam('spread');
-        setParam('transformQueueSize');
-    }
-    UMAP.prototype.fit = function (X) {
-        this.initializeFit(X);
-        this.optimizeLayout();
-        return this.embedding;
-    };
-    UMAP.prototype.fitAsync = function (X, callback) {
-        if (callback === void 0) { callback = function () { return true; }; }
-        return __awaiter(this, void 0, void 0, function () {
-            return __generator(this, function (_a) {
-                switch (_a.label) {
-                    case 0:
-                        this.initializeFit(X);
-                        return [4, this.optimizeLayoutAsync(callback)];
-                    case 1:
-                        _a.sent();
-                        return [2, this.embedding];
-                }
-            });
-        });
-    };
-    UMAP.prototype.setSupervisedProjection = function (Y, params) {
-        if (params === void 0) { params = {}; }
-        this.Y = Y;
-        this.targetMetric = params.targetMetric || this.targetMetric;
-        this.targetWeight = params.targetWeight || this.targetWeight;
-        this.targetNNeighbors = params.targetNNeighbors || this.targetNNeighbors;
-    };
-    UMAP.prototype.setPrecomputedKNN = function (knnIndices, knnDistances) {
-        this.knnIndices = knnIndices;
-        this.knnDistances = knnDistances;
-    };
-    UMAP.prototype.initializeFit = function (X) {
-        if (this.X === X && this.isInitialized) {
-            return this.getNEpochs();
-        }
-        this.X = X;
-        if (!this.knnIndices && !this.knnDistances) {
-            var knnResults = this.nearestNeighbors(X);
-            this.knnIndices = knnResults.knnIndices;
-            this.knnDistances = knnResults.knnDistances;
-        }
-        this.graph = this.fuzzySimplicialSet(X, this.nNeighbors, this.setOpMixRatio);
-        this.makeSearchFns();
-        this.searchGraph = this.makeSearchGraph(X);
-        this.processGraphForSupervisedProjection();
-        var _a = this.initializeSimplicialSetEmbedding(), head = _a.head, tail = _a.tail, epochsPerSample = _a.epochsPerSample;
-        this.optimizationState.head = head;
-        this.optimizationState.tail = tail;
-        this.optimizationState.epochsPerSample = epochsPerSample;
-        this.initializeOptimization();
-        this.prepareForOptimizationLoop();
-        this.isInitialized = true;
-        return this.getNEpochs();
-    };
-    UMAP.prototype.makeSearchFns = function () {
-        var _a = nnDescent.makeInitializations(this.distanceFn), initFromTree = _a.initFromTree, initFromRandom = _a.initFromRandom;
-        this.initFromTree = initFromTree;
-        this.initFromRandom = initFromRandom;
-        this.search = nnDescent.makeInitializedNNSearch(this.distanceFn);
-    };
-    UMAP.prototype.makeSearchGraph = function (X) {
-        var knnIndices = this.knnIndices;
-        var knnDistances = this.knnDistances;
-        var dims = [X.length, X.length];
-        var searchGraph = new matrix.SparseMatrix([], [], [], dims);
-        for (var i = 0; i < knnIndices.length; i++) {
-            var knn = knnIndices[i];
-            var distances = knnDistances[i];
-            for (var j = 0; j < knn.length; j++) {
-                var neighbor = knn[j];
-                var distance = distances[j];
-                if (distance > 0) {
-                    searchGraph.set(i, neighbor, distance);
-                }
-            }
-        }
-        var transpose = matrix.transpose(searchGraph);
-        return matrix.maximum(searchGraph, transpose);
-    };
-    UMAP.prototype.transform = function (toTransform) {
-        var _this = this;
-        var rawData = this.X;
-        if (rawData === undefined || rawData.length === 0) {
-            throw new Error('No data has been fit.');
-        }
-        var nNeighbors = Math.floor(this.nNeighbors * this.transformQueueSize);
-        var init = nnDescent.initializeSearch(this.rpForest, rawData, toTransform, nNeighbors, this.initFromRandom, this.initFromTree, this.random);
-        var result = this.search(rawData, this.searchGraph, init, toTransform);
-        var _a = heap.deheapSort(result), indices = _a.indices, distances = _a.weights;
-        indices = indices.map(function (x) { return x.slice(0, _this.nNeighbors); });
-        distances = distances.map(function (x) { return x.slice(0, _this.nNeighbors); });
-        var adjustedLocalConnectivity = Math.max(0, this.localConnectivity - 1);
-        var _b = this.smoothKNNDistance(distances, this.nNeighbors, adjustedLocalConnectivity), sigmas = _b.sigmas, rhos = _b.rhos;
-        var _c = this.computeMembershipStrengths(indices, distances, sigmas, rhos), rows = _c.rows, cols = _c.cols, vals = _c.vals;
-        var size = [toTransform.length, rawData.length];
-        var graph = new matrix.SparseMatrix(rows, cols, vals, size);
-        var normed = matrix.normalize(graph, "l1");
-        var csrMatrix = matrix.getCSR(normed);
-        var nPoints = toTransform.length;
-        var eIndices = utils.reshape2d(csrMatrix.indices, nPoints, this.nNeighbors);
-        var eWeights = utils.reshape2d(csrMatrix.values, nPoints, this.nNeighbors);
-        var embedding = initTransform(eIndices, eWeights, this.embedding);
-        var nEpochs = this.nEpochs
-            ? this.nEpochs / 3
-            : graph.nRows <= 10000
-                ? 100
-                : 30;
-        var graphMax = graph
-            .getValues()
-            .reduce(function (max, val) { return (val > max ? val : max); }, 0);
-        graph = graph.map(function (value) { return (value < graphMax / nEpochs ? 0 : value); });
-        graph = matrix.eliminateZeros(graph);
-        var epochsPerSample = this.makeEpochsPerSample(graph.getValues(), nEpochs);
-        var head = graph.getRows();
-        var tail = graph.getCols();
-        this.assignOptimizationStateParameters({
-            headEmbedding: embedding,
-            tailEmbedding: this.embedding,
-            head: head,
-            tail: tail,
-            currentEpoch: 0,
-            nEpochs: nEpochs,
-            nVertices: graph.getDims()[1],
-            epochsPerSample: epochsPerSample,
-        });
-        this.prepareForOptimizationLoop();
-        return this.optimizeLayout();
-    };
-    UMAP.prototype.processGraphForSupervisedProjection = function () {
-        var _a = this, Y = _a.Y, X = _a.X;
-        if (Y) {
-            if (Y.length !== X.length) {
-                throw new Error('Length of X and y must be equal');
-            }
-            if (this.targetMetric === "categorical") {
-                var lt = this.targetWeight < 1.0;
-                var farDist = lt ? 2.5 * (1.0 / (1.0 - this.targetWeight)) : 1.0e12;
-                this.graph = this.categoricalSimplicialSetIntersection(this.graph, Y, farDist);
-            }
-        }
-    };
-    UMAP.prototype.step = function () {
-        var currentEpoch = this.optimizationState.currentEpoch;
-        if (currentEpoch < this.getNEpochs()) {
-            this.optimizeLayoutStep(currentEpoch);
-        }
-        return this.optimizationState.currentEpoch;
-    };
-    UMAP.prototype.getEmbedding = function () {
-        return this.embedding;
-    };
-    UMAP.prototype.nearestNeighbors = function (X) {
-        var _a = this, distanceFn = _a.distanceFn, nNeighbors = _a.nNeighbors;
-        var log2 = function (n) { return Math.log(n) / Math.log(2); };
-        var metricNNDescent = nnDescent.makeNNDescent(distanceFn, this.random);
-        var round = function (n) {
-            return n === 0.5 ? 0 : Math.round(n);
-        };
-        var nTrees = 5 + Math.floor(round(Math.pow(X.length, 0.5) / 20.0));
-        var nIters = Math.max(5, Math.floor(Math.round(log2(X.length))));
-        this.rpForest = tree.makeForest(X, nNeighbors, nTrees, this.random);
-        var leafArray = tree.makeLeafArray(this.rpForest);
-        var _b = metricNNDescent(X, leafArray, nNeighbors, nIters), indices = _b.indices, weights = _b.weights;
-        return { knnIndices: indices, knnDistances: weights };
-    };
-    UMAP.prototype.fuzzySimplicialSet = function (X, nNeighbors, setOpMixRatio) {
-        if (setOpMixRatio === void 0) { setOpMixRatio = 1.0; }
-        var _a = this, _b = _a.knnIndices, knnIndices = _b === void 0 ? [] : _b, _c = _a.knnDistances, knnDistances = _c === void 0 ? [] : _c, localConnectivity = _a.localConnectivity;
-        var _d = this.smoothKNNDistance(knnDistances, nNeighbors, localConnectivity), sigmas = _d.sigmas, rhos = _d.rhos;
-        var _e = this.computeMembershipStrengths(knnIndices, knnDistances, sigmas, rhos), rows = _e.rows, cols = _e.cols, vals = _e.vals;
-        var size = [X.length, X.length];
-        var sparseMatrix = new matrix.SparseMatrix(rows, cols, vals, size);
-        var transpose = matrix.transpose(sparseMatrix);
-        var prodMatrix = matrix.pairwiseMultiply(sparseMatrix, transpose);
-        var a = matrix.subtract(matrix.add(sparseMatrix, transpose), prodMatrix);
-        var b = matrix.multiplyScalar(a, setOpMixRatio);
-        var c = matrix.multiplyScalar(prodMatrix, 1.0 - setOpMixRatio);
-        var result = matrix.add(b, c);
-        return result;
-    };
-    UMAP.prototype.categoricalSimplicialSetIntersection = function (simplicialSet, target, farDist, unknownDist) {
-        if (unknownDist === void 0) { unknownDist = 1.0; }
-        var intersection = fastIntersection(simplicialSet, target, unknownDist, farDist);
-        intersection = matrix.eliminateZeros(intersection);
-        return resetLocalConnectivity(intersection);
-    };
-    UMAP.prototype.smoothKNNDistance = function (distances, k, localConnectivity, nIter, bandwidth) {
-        if (localConnectivity === void 0) { localConnectivity = 1.0; }
-        if (nIter === void 0) { nIter = 64; }
-        if (bandwidth === void 0) { bandwidth = 1.0; }
-        var target = (Math.log(k) / Math.log(2)) * bandwidth;
-        var rho = utils.zeros(distances.length);
-        var result = utils.zeros(distances.length);
-        for (var i = 0; i < distances.length; i++) {
-            var lo = 0.0;
-            var hi = Infinity;
-            var mid = 1.0;
-            var ithDistances = distances[i];
-            var nonZeroDists = ithDistances.filter(function (d) { return d > 0.0; });
-            if (nonZeroDists.length >= localConnectivity) {
-                var index = Math.floor(localConnectivity);
-                var interpolation = localConnectivity - index;
-                if (index > 0) {
-                    rho[i] = nonZeroDists[index - 1];
-                    if (interpolation > SMOOTH_K_TOLERANCE) {
-                        rho[i] +=
-                            interpolation * (nonZeroDists[index] - nonZeroDists[index - 1]);
-                    }
-                }
-                else {
-                    rho[i] = interpolation * nonZeroDists[0];
-                }
-            }
-            else if (nonZeroDists.length > 0) {
-                rho[i] = utils.max(nonZeroDists);
-            }
-            for (var n = 0; n < nIter; n++) {
-                var psum = 0.0;
-                for (var j = 1; j < distances[i].length; j++) {
-                    var d = distances[i][j] - rho[i];
-                    if (d > 0) {
-                        psum += Math.exp(-(d / mid));
-                    }
-                    else {
-                        psum += 1.0;
-                    }
-                }
-                if (Math.abs(psum - target) < SMOOTH_K_TOLERANCE) {
-                    break;
-                }
-                if (psum > target) {
-                    hi = mid;
-                    mid = (lo + hi) / 2.0;
-                }
-                else {
-                    lo = mid;
-                    if (hi === Infinity) {
-                        mid *= 2;
-                    }
-                    else {
-                        mid = (lo + hi) / 2.0;
-                    }
-                }
-            }
-            result[i] = mid;
-            if (rho[i] > 0.0) {
-                var meanIthDistances = utils.mean(ithDistances);
-                if (result[i] < MIN_K_DIST_SCALE * meanIthDistances) {
-                    result[i] = MIN_K_DIST_SCALE * meanIthDistances;
-                }
-            }
-            else {
-                var meanDistances = utils.mean(distances.map(utils.mean));
-                if (result[i] < MIN_K_DIST_SCALE * meanDistances) {
-                    result[i] = MIN_K_DIST_SCALE * meanDistances;
-                }
-            }
-        }
-        return { sigmas: result, rhos: rho };
-    };
-    UMAP.prototype.computeMembershipStrengths = function (knnIndices, knnDistances, sigmas, rhos) {
-        var nSamples = knnIndices.length;
-        var nNeighbors = knnIndices[0].length;
-        var rows = utils.zeros(nSamples * nNeighbors);
-        var cols = utils.zeros(nSamples * nNeighbors);
-        var vals = utils.zeros(nSamples * nNeighbors);
-        for (var i = 0; i < nSamples; i++) {
-            for (var j = 0; j < nNeighbors; j++) {
-                var val = 0;
-                if (knnIndices[i][j] === -1) {
-                    continue;
-                }
-                if (knnIndices[i][j] === i) {
-                    val = 0.0;
-                }
-                else if (knnDistances[i][j] - rhos[i] <= 0.0) {
-                    val = 1.0;
-                }
-                else {
-                    val = Math.exp(-((knnDistances[i][j] - rhos[i]) / sigmas[i]));
-                }
-                rows[i * nNeighbors + j] = i;
-                cols[i * nNeighbors + j] = knnIndices[i][j];
-                vals[i * nNeighbors + j] = val;
-            }
-        }
-        return { rows: rows, cols: cols, vals: vals };
-    };
-    UMAP.prototype.initializeSimplicialSetEmbedding = function () {
-        var _this = this;
-        var nEpochs = this.getNEpochs();
-        var nComponents = this.nComponents;
-        var graphValues = this.graph.getValues();
-        var graphMax = 0;
-        for (var i = 0; i < graphValues.length; i++) {
-            var value = graphValues[i];
-            if (graphMax < graphValues[i]) {
-                graphMax = value;
-            }
-        }
-        var graph = this.graph.map(function (value) {
-            if (value < graphMax / nEpochs) {
-                return 0;
-            }
-            else {
-                return value;
-            }
-        });
-        this.embedding = utils.zeros(graph.nRows).map(function () {
-            return utils.zeros(nComponents).map(function () {
-                return utils.tauRand(_this.random) * 20 + -10;
-            });
-        });
-        var weights = [];
-        var head = [];
-        var tail = [];
-        for (var i = 0; i < graph.nRows; i++) {
-            for (var j = 0; j < graph.nCols; j++) {
-                var value = graph.get(i, j);
-                if (value) {
-                    weights.push(value);
-                    tail.push(i);
-                    head.push(j);
-                }
-            }
-        }
-        var epochsPerSample = this.makeEpochsPerSample(weights, nEpochs);
-        return { head: head, tail: tail, epochsPerSample: epochsPerSample };
-    };
-    UMAP.prototype.makeEpochsPerSample = function (weights, nEpochs) {
-        var result = utils.filled(weights.length, -1.0);
-        var max = utils.max(weights);
-        var nSamples = weights.map(function (w) { return (w / max) * nEpochs; });
-        nSamples.forEach(function (n, i) {
-            if (n > 0)
-                result[i] = nEpochs / nSamples[i];
-        });
-        return result;
-    };
-    UMAP.prototype.assignOptimizationStateParameters = function (state) {
-        Object.assign(this.optimizationState, state);
-    };
-    UMAP.prototype.prepareForOptimizationLoop = function () {
-        var _a = this, repulsionStrength = _a.repulsionStrength, learningRate = _a.learningRate, negativeSampleRate = _a.negativeSampleRate;
-        var _b = this.optimizationState, epochsPerSample = _b.epochsPerSample, headEmbedding = _b.headEmbedding, tailEmbedding = _b.tailEmbedding;
-        var dim = headEmbedding[0].length;
-        var moveOther = headEmbedding.length === tailEmbedding.length;
-        var epochsPerNegativeSample = epochsPerSample.map(function (e) { return e / negativeSampleRate; });
-        var epochOfNextNegativeSample = __spread(epochsPerNegativeSample);
-        var epochOfNextSample = __spread(epochsPerSample);
-        this.assignOptimizationStateParameters({
-            epochOfNextSample: epochOfNextSample,
-            epochOfNextNegativeSample: epochOfNextNegativeSample,
-            epochsPerNegativeSample: epochsPerNegativeSample,
-            moveOther: moveOther,
-            initialAlpha: learningRate,
-            alpha: learningRate,
-            gamma: repulsionStrength,
-            dim: dim,
-        });
-    };
-    UMAP.prototype.initializeOptimization = function () {
-        var headEmbedding = this.embedding;
-        var tailEmbedding = this.embedding;
-        var _a = this.optimizationState, head = _a.head, tail = _a.tail, epochsPerSample = _a.epochsPerSample;
-        var nEpochs = this.getNEpochs();
-        var nVertices = this.graph.nCols;
-        var _b = findABParams(this.spread, this.minDist), a = _b.a, b = _b.b;
-        this.assignOptimizationStateParameters({
-            headEmbedding: headEmbedding,
-            tailEmbedding: tailEmbedding,
-            head: head,
-            tail: tail,
-            epochsPerSample: epochsPerSample,
-            a: a,
-            b: b,
-            nEpochs: nEpochs,
-            nVertices: nVertices,
-        });
-    };
-    UMAP.prototype.optimizeLayoutStep = function (n) {
-        var optimizationState = this.optimizationState;
-        var head = optimizationState.head, tail = optimizationState.tail, headEmbedding = optimizationState.headEmbedding, tailEmbedding = optimizationState.tailEmbedding, epochsPerSample = optimizationState.epochsPerSample, epochOfNextSample = optimizationState.epochOfNextSample, epochOfNextNegativeSample = optimizationState.epochOfNextNegativeSample, epochsPerNegativeSample = optimizationState.epochsPerNegativeSample, moveOther = optimizationState.moveOther, initialAlpha = optimizationState.initialAlpha, alpha = optimizationState.alpha, gamma = optimizationState.gamma, a = optimizationState.a, b = optimizationState.b, dim = optimizationState.dim, nEpochs = optimizationState.nEpochs, nVertices = optimizationState.nVertices;
-        var clipValue = 4.0;
-        for (var i = 0; i < epochsPerSample.length; i++) {
-            if (epochOfNextSample[i] > n) {
-                continue;
-            }
-            var j = head[i];
-            var k = tail[i];
-            var current = headEmbedding[j];
-            var other = tailEmbedding[k];
-            var distSquared = rDist(current, other);
-            var gradCoeff = 0;
-            if (distSquared > 0) {
-                gradCoeff = -2.0 * a * b * Math.pow(distSquared, b - 1.0);
-                gradCoeff /= a * Math.pow(distSquared, b) + 1.0;
-            }
-            for (var d = 0; d < dim; d++) {
-                var gradD = clip(gradCoeff * (current[d] - other[d]), clipValue);
-                current[d] += gradD * alpha;
-                if (moveOther) {
-                    other[d] += -gradD * alpha;
-                }
-            }
-            epochOfNextSample[i] += epochsPerSample[i];
-            var nNegSamples = Math.floor((n - epochOfNextNegativeSample[i]) / epochsPerNegativeSample[i]);
-            for (var p = 0; p < nNegSamples; p++) {
-                var k_1 = utils.tauRandInt(nVertices, this.random);
-                var other_1 = tailEmbedding[k_1];
-                var distSquared_1 = rDist(current, other_1);
-                var gradCoeff_1 = 0.0;
-                if (distSquared_1 > 0.0) {
-                    gradCoeff_1 = 2.0 * gamma * b;
-                    gradCoeff_1 /=
-                        (0.001 + distSquared_1) * (a * Math.pow(distSquared_1, b) + 1);
-                }
-                else if (j === k_1) {
-                    continue;
-                }
-                for (var d = 0; d < dim; d++) {
-                    var gradD = 4.0;
-                    if (gradCoeff_1 > 0.0) {
-                        gradD = clip(gradCoeff_1 * (current[d] - other_1[d]), clipValue);
-                    }
-                    current[d] += gradD * alpha;
-                }
-            }
-            epochOfNextNegativeSample[i] += nNegSamples * epochsPerNegativeSample[i];
-        }
-        optimizationState.alpha = initialAlpha * (1.0 - n / nEpochs);
-        optimizationState.currentEpoch += 1;
-        return headEmbedding;
-    };
-    UMAP.prototype.optimizeLayoutAsync = function (epochCallback) {
-        var _this = this;
-        if (epochCallback === void 0) { epochCallback = function () { return true; }; }
-        return new Promise(function (resolve, reject) {
-            var step = function () { return __awaiter(_this, void 0, void 0, function () {
-                var _a, nEpochs, currentEpoch, epochCompleted, shouldStop, isFinished;
-                return __generator(this, function (_b) {
-                    try {
-                        _a = this.optimizationState, nEpochs = _a.nEpochs, currentEpoch = _a.currentEpoch;
-                        this.embedding = this.optimizeLayoutStep(currentEpoch);
-                        epochCompleted = this.optimizationState.currentEpoch;
-                        shouldStop = epochCallback(epochCompleted) === false;
-                        isFinished = epochCompleted === nEpochs;
-                        if (!shouldStop && !isFinished) {
-                            step();
-                        }
-                        else {
-                            return [2, resolve(isFinished)];
-                        }
-                    }
-                    catch (err) {
-                        reject(err);
-                    }
-                    return [2];
-                });
-            }); };
-            step();
-        });
-    };
-    UMAP.prototype.optimizeLayout = function (epochCallback) {
-        if (epochCallback === void 0) { epochCallback = function () { return true; }; }
-        var isFinished = false;
-        var embedding = [];
-        while (!isFinished) {
-            var _a = this.optimizationState, nEpochs = _a.nEpochs, currentEpoch = _a.currentEpoch;
-            embedding = this.optimizeLayoutStep(currentEpoch);
-            var epochCompleted = this.optimizationState.currentEpoch;
-            var shouldStop = epochCallback(epochCompleted) === false;
-            isFinished = epochCompleted === nEpochs || shouldStop;
-        }
-        return embedding;
-    };
-    UMAP.prototype.getNEpochs = function () {
-        var graph = this.graph;
-        if (this.nEpochs > 0) {
-            return this.nEpochs;
-        }
-        var length = graph.nRows;
-        if (length <= 2500) {
-            return 500;
-        }
-        else if (length <= 5000) {
-            return 400;
-        }
-        else if (length <= 7500) {
-            return 300;
-        }
-        else {
-            return 200;
-        }
-    };
-    return UMAP;
-}());
-exports.UMAP = UMAP;
-function euclidean(x, y) {
-    var result = 0;
-    for (var i = 0; i < x.length; i++) {
-        result += Math.pow((x[i] - y[i]), 2);
-    }
-    return Math.sqrt(result);
-}
-exports.euclidean = euclidean;
-function cosine(x, y) {
-    var result = 0.0;
-    var normX = 0.0;
-    var normY = 0.0;
-    for (var i = 0; i < x.length; i++) {
-        result += x[i] * y[i];
-        normX += Math.pow(x[i], 2);
-        normY += Math.pow(y[i], 2);
-    }
-    if (normX === 0 && normY === 0) {
-        return 0;
-    }
-    else if (normX === 0 || normY === 0) {
-        return 1.0;
-    }
-    else {
-        return 1.0 - result / Math.sqrt(normX * normY);
-    }
-}
-exports.cosine = cosine;
-var OptimizationState = (function () {
-    function OptimizationState() {
-        this.currentEpoch = 0;
-        this.headEmbedding = [];
-        this.tailEmbedding = [];
-        this.head = [];
-        this.tail = [];
-        this.epochsPerSample = [];
-        this.epochOfNextSample = [];
-        this.epochOfNextNegativeSample = [];
-        this.epochsPerNegativeSample = [];
-        this.moveOther = true;
-        this.initialAlpha = 1.0;
-        this.alpha = 1.0;
-        this.gamma = 1.0;
-        this.a = 1.5769434603113077;
-        this.b = 0.8950608779109733;
-        this.dim = 2;
-        this.nEpochs = 500;
-        this.nVertices = 0;
-    }
-    return OptimizationState;
-}());
-function clip(x, clipValue) {
-    if (x > clipValue)
-        return clipValue;
-    else if (x < -clipValue)
-        return -clipValue;
-    else
-        return x;
-}
-function rDist(x, y) {
-    var result = 0.0;
-    for (var i = 0; i < x.length; i++) {
-        result += Math.pow(x[i] - y[i], 2);
-    }
-    return result;
-}
-function findABParams(spread, minDist) {
-    var curve = function (_a) {
-        var _b = __read(_a, 2), a = _b[0], b = _b[1];
-        return function (x) {
-            return 1.0 / (1.0 + a * Math.pow(x, (2 * b)));
-        };
-    };
-    var xv = utils
-        .linear(0, spread * 3, 300)
-        .map(function (val) { return (val < minDist ? 1.0 : val); });
-    var yv = utils.zeros(xv.length).map(function (val, index) {
-        var gte = xv[index] >= minDist;
-        return gte ? Math.exp(-(xv[index] - minDist) / spread) : val;
-    });
-    var initialValues = [0.5, 0.5];
-    var data = { x: xv, y: yv };
-    var options = {
-        damping: 1.5,
-        initialValues: initialValues,
-        gradientDifference: 10e-2,
-        maxIterations: 100,
-        errorTolerance: 10e-3,
-    };
-    var parameterValues = ml_levenberg_marquardt_1.default(data, curve, options).parameterValues;
-    var _a = __read(parameterValues, 2), a = _a[0], b = _a[1];
-    return { a: a, b: b };
-}
-exports.findABParams = findABParams;
-function fastIntersection(graph, target, unknownDist, farDist) {
-    if (unknownDist === void 0) { unknownDist = 1.0; }
-    if (farDist === void 0) { farDist = 5.0; }
-    return graph.map(function (value, row, col) {
-        if (target[row] === -1 || target[col] === -1) {
-            return value * Math.exp(-unknownDist);
-        }
-        else if (target[row] !== target[col]) {
-            return value * Math.exp(-farDist);
-        }
-        else {
-            return value;
-        }
-    });
-}
-exports.fastIntersection = fastIntersection;
-function resetLocalConnectivity(simplicialSet) {
-    simplicialSet = matrix.normalize(simplicialSet, "max");
-    var transpose = matrix.transpose(simplicialSet);
-    var prodMatrix = matrix.pairwiseMultiply(transpose, simplicialSet);
-    simplicialSet = matrix.add(simplicialSet, matrix.subtract(transpose, prodMatrix));
-    return matrix.eliminateZeros(simplicialSet);
-}
-exports.resetLocalConnectivity = resetLocalConnectivity;
-function initTransform(indices, weights, embedding) {
-    var result = utils
-        .zeros(indices.length)
-        .map(function (z) { return utils.zeros(embedding[0].length); });
-    for (var i = 0; i < indices.length; i++) {
-        for (var j = 0; j < indices[0].length; j++) {
-            for (var d = 0; d < embedding[0].length; d++) {
-                var a = indices[i][j];
-                result[i][d] += weights[i][j] * embedding[a][d];
-            }
-        }
-    }
-    return result;
-}
-exports.initTransform = initTransform;
-
-
-/***/ }),
-/* 7 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-var __values = (this && this.__values) || function (o) {
-    var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0;
-    if (m) return m.call(o);
-    return {
-        next: function () {
-            if (o && i >= o.length) o = void 0;
-            return { value: o && o[i++], done: !o };
-        }
-    };
-};
-var __importStar = (this && this.__importStar) || function (mod) {
-    if (mod && mod.__esModule) return mod;
-    var result = {};
-    if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
-    result["default"] = mod;
-    return result;
-};
-Object.defineProperty(exports, "__esModule", { value: true });
-var heap = __importStar(__webpack_require__(2));
-var matrix = __importStar(__webpack_require__(3));
-var tree = __importStar(__webpack_require__(4));
-var utils = __importStar(__webpack_require__(1));
-function makeNNDescent(distanceFn, random) {
-    return function nNDescent(data, leafArray, nNeighbors, nIters, maxCandidates, delta, rho, rpTreeInit) {
-        if (nIters === void 0) { nIters = 10; }
-        if (maxCandidates === void 0) { maxCandidates = 50; }
-        if (delta === void 0) { delta = 0.001; }
-        if (rho === void 0) { rho = 0.5; }
-        if (rpTreeInit === void 0) { rpTreeInit = true; }
-        var nVertices = data.length;
-        var currentGraph = heap.makeHeap(data.length, nNeighbors);
-        for (var i = 0; i < data.length; i++) {
-            var indices = heap.rejectionSample(nNeighbors, data.length, random);
-            for (var j = 0; j < indices.length; j++) {
-                var d = distanceFn(data[i], data[indices[j]]);
-                heap.heapPush(currentGraph, i, d, indices[j], 1);
-                heap.heapPush(currentGraph, indices[j], d, i, 1);
-            }
-        }
-        if (rpTreeInit) {
-            for (var n = 0; n < leafArray.length; n++) {
-                for (var i = 0; i < leafArray[n].length; i++) {
-                    if (leafArray[n][i] < 0) {
-                        break;
-                    }
-                    for (var j = i + 1; j < leafArray[n].length; j++) {
-                        if (leafArray[n][j] < 0) {
-                            break;
-                        }
-                        var d = distanceFn(data[leafArray[n][i]], data[leafArray[n][j]]);
-                        heap.heapPush(currentGraph, leafArray[n][i], d, leafArray[n][j], 1);
-                        heap.heapPush(currentGraph, leafArray[n][j], d, leafArray[n][i], 1);
-                    }
-                }
-            }
-        }
-        for (var n = 0; n < nIters; n++) {
-            var candidateNeighbors = heap.buildCandidates(currentGraph, nVertices, nNeighbors, maxCandidates, random);
-            var c = 0;
-            for (var i = 0; i < nVertices; i++) {
-                for (var j = 0; j < maxCandidates; j++) {
-                    var p = Math.floor(candidateNeighbors[0][i][j]);
-                    if (p < 0 || utils.tauRand(random) < rho) {
-                        continue;
-                    }
-                    for (var k = 0; k < maxCandidates; k++) {
-                        var q = Math.floor(candidateNeighbors[0][i][k]);
-                        var cj = candidateNeighbors[2][i][j];
-                        var ck = candidateNeighbors[2][i][k];
-                        if (q < 0 || (!cj && !ck)) {
-                            continue;
-                        }
-                        var d = distanceFn(data[p], data[q]);
-                        c += heap.heapPush(currentGraph, p, d, q, 1);
-                        c += heap.heapPush(currentGraph, q, d, p, 1);
-                    }
-                }
-            }
-            if (c <= delta * nNeighbors * data.length) {
-                break;
-            }
-        }
-        var sorted = heap.deheapSort(currentGraph);
-        return sorted;
-    };
-}
-exports.makeNNDescent = makeNNDescent;
-function makeInitializations(distanceFn) {
-    function initFromRandom(nNeighbors, data, queryPoints, _heap, random) {
-        for (var i = 0; i < queryPoints.length; i++) {
-            var indices = utils.rejectionSample(nNeighbors, data.length, random);
-            for (var j = 0; j < indices.length; j++) {
-                if (indices[j] < 0) {
-                    continue;
-                }
-                var d = distanceFn(data[indices[j]], queryPoints[i]);
-                heap.heapPush(_heap, i, d, indices[j], 1);
-            }
-        }
-    }
-    function initFromTree(_tree, data, queryPoints, _heap, random) {
-        for (var i = 0; i < queryPoints.length; i++) {
-            var indices = tree.searchFlatTree(queryPoints[i], _tree, random);
-            for (var j = 0; j < indices.length; j++) {
-                if (indices[j] < 0) {
-                    return;
-                }
-                var d = distanceFn(data[indices[j]], queryPoints[i]);
-                heap.heapPush(_heap, i, d, indices[j], 1);
-            }
-        }
-        return;
-    }
-    return { initFromRandom: initFromRandom, initFromTree: initFromTree };
-}
-exports.makeInitializations = makeInitializations;
-function makeInitializedNNSearch(distanceFn) {
-    return function nnSearchFn(data, graph, initialization, queryPoints) {
-        var e_1, _a;
-        var _b = matrix.getCSR(graph), indices = _b.indices, indptr = _b.indptr;
-        for (var i = 0; i < queryPoints.length; i++) {
-            var tried = new Set(initialization[0][i]);
-            while (true) {
-                var vertex = heap.smallestFlagged(initialization, i);
-                if (vertex === -1) {
-                    break;
-                }
-                var candidates = indices.slice(indptr[vertex], indptr[vertex + 1]);
-                try {
-                    for (var candidates_1 = __values(candidates), candidates_1_1 = candidates_1.next(); !candidates_1_1.done; candidates_1_1 = candidates_1.next()) {
-                        var candidate = candidates_1_1.value;
-                        if (candidate === vertex ||
-                            candidate === -1 ||
-                            tried.has(candidate)) {
-                            continue;
-                        }
-                        var d = distanceFn(data[candidate], queryPoints[i]);
-                        heap.uncheckedHeapPush(initialization, i, d, candidate, 1);
-                        tried.add(candidate);
-                    }
-                }
-                catch (e_1_1) { e_1 = { error: e_1_1 }; }
-                finally {
-                    try {
-                        if (candidates_1_1 && !candidates_1_1.done && (_a = candidates_1.return)) _a.call(candidates_1);
-                    }
-                    finally { if (e_1) throw e_1.error; }
-                }
-            }
-        }
-        return initialization;
-    };
-}
-exports.makeInitializedNNSearch = makeInitializedNNSearch;
-function initializeSearch(forest, data, queryPoints, nNeighbors, initFromRandom, initFromTree, random) {
-    var e_2, _a;
-    var results = heap.makeHeap(queryPoints.length, nNeighbors);
-    initFromRandom(nNeighbors, data, queryPoints, results, random);
-    if (forest) {
-        try {
-            for (var forest_1 = __values(forest), forest_1_1 = forest_1.next(); !forest_1_1.done; forest_1_1 = forest_1.next()) {
-                var tree_1 = forest_1_1.value;
-                initFromTree(tree_1, data, queryPoints, results, random);
-            }
-        }
-        catch (e_2_1) { e_2 = { error: e_2_1 }; }
-        finally {
-            try {
-                if (forest_1_1 && !forest_1_1.done && (_a = forest_1.return)) _a.call(forest_1);
-            }
-            finally { if (e_2) throw e_2.error; }
-        }
-    }
-    return results;
-}
-exports.initializeSearch = initializeSearch;
-
-
-/***/ }),
-/* 8 */
-/***/ (function(module, exports, __webpack_require__) {
-
-"use strict";
-
-
-var mlMatrix = __webpack_require__(9);
-
-/**
- * Calculate current error
- * @ignore
- * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
- * @param {Array<number>} parameters - Array of current parameter values
- * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
- * @return {number}
- */
-function errorCalculation(
-  data,
-  parameters,
-  parameterizedFunction
-) {
-  var error = 0;
-  const func = parameterizedFunction(parameters);
-
-  for (var i = 0; i < data.x.length; i++) {
-    error += Math.abs(data.y[i] - func(data.x[i]));
-  }
-
-  return error;
-}
-
-/**
- * Difference of the matrix function over the parameters
- * @ignore
- * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
- * @param {Array<number>} evaluatedData - Array of previous evaluated function values
- * @param {Array<number>} params - Array of previous parameter values
- * @param {number} gradientDifference - Adjustment for decrease the damping parameter
- * @param {function} paramFunction - The parameters and returns a function with the independent variable as a parameter
- * @return {Matrix}
- */
-function gradientFunction(
-  data,
-  evaluatedData,
-  params,
-  gradientDifference,
-  paramFunction
-) {
-  const n = params.length;
-  const m = data.x.length;
-
-  var ans = new Array(n);
-
-  for (var param = 0; param < n; param++) {
-    ans[param] = new Array(m);
-    var auxParams = params.concat();
-    auxParams[param] += gradientDifference;
-    var funcParam = paramFunction(auxParams);
-
-    for (var point = 0; point < m; point++) {
-      ans[param][point] = evaluatedData[point] - funcParam(data.x[point]);
-    }
-  }
-  return new mlMatrix.Matrix(ans);
-}
-
-/**
- * Matrix function over the samples
- * @ignore
- * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
- * @param {Array<number>} evaluatedData - Array of previous evaluated function values
- * @return {Matrix}
- */
-function matrixFunction(data, evaluatedData) {
-  const m = data.x.length;
-
-  var ans = new Array(m);
-
-  for (var point = 0; point < m; point++) {
-    ans[point] = data.y[point] - evaluatedData[point];
-  }
-
-  return new mlMatrix.Matrix([ans]);
-}
-
-/**
- * Iteration for Levenberg-Marquardt
- * @ignore
- * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
- * @param {Array<number>} params - Array of previous parameter values
- * @param {number} damping - Levenberg-Marquardt parameter
- * @param {number} gradientDifference - Adjustment for decrease the damping parameter
- * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
- * @return {Array<number>}
- */
-function step(
-  data,
-  params,
-  damping,
-  gradientDifference,
-  parameterizedFunction
-) {
-  var identity = mlMatrix.Matrix.eye(params.length).mul(
-    damping * gradientDifference * gradientDifference
-  );
-
-  var l = data.x.length;
-  var evaluatedData = new Array(l);
-  const func = parameterizedFunction(params);
-  for (var i = 0; i < l; i++) {
-    evaluatedData[i] = func(data.x[i]);
-  }
-  var gradientFunc = gradientFunction(
-    data,
-    evaluatedData,
-    params,
-    gradientDifference,
-    parameterizedFunction
-  );
-  var matrixFunc = matrixFunction(data, evaluatedData).transposeView();
-  var inverseMatrix = mlMatrix.inverse(
-    identity.add(gradientFunc.mmul(gradientFunc.transposeView()))
-  );
-  params = new mlMatrix.Matrix([params]);
-  params = params.sub(
-    inverseMatrix
-      .mmul(gradientFunc)
-      .mmul(matrixFunc)
-      .mul(gradientDifference)
-      .transposeView()
-  );
-
-  return params.to1DArray();
-}
-
-/**
- * Curve fitting algorithm
- * @param {{x:Array<number>, y:Array<number>}} data - Array of points to fit in the format [x1, x2, ... ], [y1, y2, ... ]
- * @param {function} parameterizedFunction - The parameters and returns a function with the independent variable as a parameter
- * @param {object} [options] - Options object
- * @param {number} [options.damping] - Levenberg-Marquardt parameter
- * @param {number} [options.gradientDifference = 10e-2] - Adjustment for decrease the damping parameter
- * @param {Array<number>} [options.initialValues] - Array of initial parameter values
- * @param {number} [options.maxIterations = 100] - Maximum of allowed iterations
- * @param {number} [options.errorTolerance = 10e-3] - Minimum uncertainty allowed for each point
- * @return {{parameterValues: Array<number>, parameterError: number, iterations: number}}
- */
-function levenbergMarquardt(
-  data,
-  parameterizedFunction,
-  options = {}
-) {
-  let {
-    maxIterations = 100,
-    gradientDifference = 10e-2,
-    damping = 0,
-    errorTolerance = 10e-3,
-    initialValues
-  } = options;
-
-  if (damping <= 0) {
-    throw new Error('The damping option must be a positive number');
-  } else if (!data.x || !data.y) {
-    throw new Error('The data parameter must have x and y elements');
-  } else if (
-    !Array.isArray(data.x) ||
-    data.x.length < 2 ||
-    !Array.isArray(data.y) ||
-    data.y.length < 2
-  ) {
-    throw new Error(
-      'The data parameter elements must be an array with more than 2 points'
-    );
-  } else {
-    let dataLen = data.x.length;
-    if (dataLen !== data.y.length) {
-      throw new Error('The data parameter elements must have the same size');
-    }
-  }
-
-  var parameters =
-    initialValues || new Array(parameterizedFunction.length).fill(1);
-
-  if (!Array.isArray(parameters)) {
-    throw new Error('initialValues must be an array');
-  }
-
-  var error = errorCalculation(data, parameters, parameterizedFunction);
-
-  var converged = error <= errorTolerance;
-
-  for (
-    var iteration = 0;
-    iteration < maxIterations && !converged;
-    iteration++
-  ) {
-    parameters = step(
-      data,
-      parameters,
-      damping,
-      gradientDifference,
-      parameterizedFunction
-    );
-    error = errorCalculation(data, parameters, parameterizedFunction);
-    converged = error <= errorTolerance;
-  }
-
-  return {
-    parameterValues: parameters,
-    parameterError: error,
-    iterations: iteration
-  };
-}
-
-module.exports = levenbergMarquardt;
-
-
-/***/ }),
-/* 9 */
-/***/ (function(module, __webpack_exports__, __webpack_require__) {
-
-"use strict";
-__webpack_require__.r(__webpack_exports__);
-
-// EXTERNAL MODULE: ./node_modules/is-any-array/src/index.js
-var src = __webpack_require__(0);
-var src_default = /*#__PURE__*/__webpack_require__.n(src);
-
-// CONCATENATED MODULE: ./node_modules/ml-array-max/lib-es6/index.js
-
-
-/**
- * Computes the maximum of the given values
- * @param {Array<number>} input
- * @return {number}
- */
-
-function lib_es6_max(input) {
-  if (!src_default()(input)) {
-    throw new TypeError('input must be an array');
-  }
-
-  if (input.length === 0) {
-    throw new TypeError('input must not be empty');
-  }
-
-  var max = input[0];
-
-  for (var i = 1; i < input.length; i++) {
-    if (input[i] > max) max = input[i];
-  }
-
-  return max;
-}
-
-/* harmony default export */ var lib_es6 = (lib_es6_max);
-
-// CONCATENATED MODULE: ./node_modules/ml-array-min/lib-es6/index.js
-
-
-/**
- * Computes the minimum of the given values
- * @param {Array<number>} input
- * @return {number}
- */
-
-function lib_es6_min(input) {
-  if (!src_default()(input)) {
-    throw new TypeError('input must be an array');
-  }
-
-  if (input.length === 0) {
-    throw new TypeError('input must not be empty');
-  }
-
-  var min = input[0];
-
-  for (var i = 1; i < input.length; i++) {
-    if (input[i] < min) min = input[i];
-  }
-
-  return min;
-}
-
-/* harmony default export */ var ml_array_min_lib_es6 = (lib_es6_min);
-
-// CONCATENATED MODULE: ./node_modules/ml-array-rescale/lib-es6/index.js
-
-
-
-
-function rescale(input) {
-  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
-
-  if (!src_default()(input)) {
-    throw new TypeError('input must be an array');
-  } else if (input.length === 0) {
-    throw new TypeError('input must not be empty');
-  }
-
-  var output;
-
-  if (options.output !== undefined) {
-    if (!src_default()(options.output)) {
-      throw new TypeError('output option must be an array if specified');
-    }
-
-    output = options.output;
-  } else {
-    output = new Array(input.length);
-  }
-
-  var currentMin = ml_array_min_lib_es6(input);
-  var currentMax = lib_es6(input);
-
-  if (currentMin === currentMax) {
-    throw new RangeError('minimum and maximum input values are equal. Cannot rescale a constant array');
-  }
-
-  var _options$min = options.min,
-      minValue = _options$min === void 0 ? options.autoMinMax ? currentMin : 0 : _options$min,
-      _options$max = options.max,
-      maxValue = _options$max === void 0 ? options.autoMinMax ? currentMax : 1 : _options$max;
-
-  if (minValue >= maxValue) {
-    throw new RangeError('min option must be smaller than max option');
-  }
-
-  var factor = (maxValue - minValue) / (currentMax - currentMin);
-
-  for (var i = 0; i < input.length; i++) {
-    output[i] = (input[i] - currentMin) * factor + minValue;
-  }
-
-  return output;
-}
-
-/* harmony default export */ var ml_array_rescale_lib_es6 = (rescale);
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/dc/lu.js
-
-
-/**
- * @class LuDecomposition
- * @link https://github.com/lutzroeder/Mapack/blob/master/Source/LuDecomposition.cs
- * @param {Matrix} matrix
- */
-class lu_LuDecomposition {
-  constructor(matrix) {
-    matrix = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(matrix);
-
-    var lu = matrix.clone();
-    var rows = lu.rows;
-    var columns = lu.columns;
-    var pivotVector = new Array(rows);
-    var pivotSign = 1;
-    var i, j, k, p, s, t, v;
-    var LUcolj, kmax;
-
-    for (i = 0; i < rows; i++) {
-      pivotVector[i] = i;
-    }
-
-    LUcolj = new Array(rows);
-
-    for (j = 0; j < columns; j++) {
-      for (i = 0; i < rows; i++) {
-        LUcolj[i] = lu.get(i, j);
-      }
-
-      for (i = 0; i < rows; i++) {
-        kmax = Math.min(i, j);
-        s = 0;
-        for (k = 0; k < kmax; k++) {
-          s += lu.get(i, k) * LUcolj[k];
-        }
-        LUcolj[i] -= s;
-        lu.set(i, j, LUcolj[i]);
-      }
-
-      p = j;
-      for (i = j + 1; i < rows; i++) {
-        if (Math.abs(LUcolj[i]) > Math.abs(LUcolj[p])) {
-          p = i;
-        }
-      }
-
-      if (p !== j) {
-        for (k = 0; k < columns; k++) {
-          t = lu.get(p, k);
-          lu.set(p, k, lu.get(j, k));
-          lu.set(j, k, t);
-        }
-
-        v = pivotVector[p];
-        pivotVector[p] = pivotVector[j];
-        pivotVector[j] = v;
-
-        pivotSign = -pivotSign;
-      }
-
-      if (j < rows && lu.get(j, j) !== 0) {
-        for (i = j + 1; i < rows; i++) {
-          lu.set(i, j, lu.get(i, j) / lu.get(j, j));
-        }
-      }
-    }
-
-    this.LU = lu;
-    this.pivotVector = pivotVector;
-    this.pivotSign = pivotSign;
-  }
-
-  /**
-   *
-   * @return {boolean}
-   */
-  isSingular() {
-    var data = this.LU;
-    var col = data.columns;
-    for (var j = 0; j < col; j++) {
-      if (data[j][j] === 0) {
-        return true;
-      }
-    }
-    return false;
-  }
-
-  /**
-   *
-   * @param {Matrix} value
-   * @return {Matrix}
-   */
-  solve(value) {
-    value = matrix_Matrix.checkMatrix(value);
-
-    var lu = this.LU;
-    var rows = lu.rows;
-
-    if (rows !== value.rows) {
-      throw new Error('Invalid matrix dimensions');
-    }
-    if (this.isSingular()) {
-      throw new Error('LU matrix is singular');
-    }
-
-    var count = value.columns;
-    var X = value.subMatrixRow(this.pivotVector, 0, count - 1);
-    var columns = lu.columns;
-    var i, j, k;
-
-    for (k = 0; k < columns; k++) {
-      for (i = k + 1; i < columns; i++) {
-        for (j = 0; j < count; j++) {
-          X[i][j] -= X[k][j] * lu[i][k];
-        }
-      }
-    }
-    for (k = columns - 1; k >= 0; k--) {
-      for (j = 0; j < count; j++) {
-        X[k][j] /= lu[k][k];
-      }
-      for (i = 0; i < k; i++) {
-        for (j = 0; j < count; j++) {
-          X[i][j] -= X[k][j] * lu[i][k];
-        }
-      }
-    }
-    return X;
-  }
-
-  /**
-   *
-   * @return {number}
-   */
-  get determinant() {
-    var data = this.LU;
-    if (!data.isSquare()) {
-      throw new Error('Matrix must be square');
-    }
-    var determinant = this.pivotSign;
-    var col = data.columns;
-    for (var j = 0; j < col; j++) {
-      determinant *= data[j][j];
-    }
-    return determinant;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get lowerTriangularMatrix() {
-    var data = this.LU;
-    var rows = data.rows;
-    var columns = data.columns;
-    var X = new matrix_Matrix(rows, columns);
-    for (var i = 0; i < rows; i++) {
-      for (var j = 0; j < columns; j++) {
-        if (i > j) {
-          X[i][j] = data[i][j];
-        } else if (i === j) {
-          X[i][j] = 1;
-        } else {
-          X[i][j] = 0;
-        }
-      }
-    }
-    return X;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get upperTriangularMatrix() {
-    var data = this.LU;
-    var rows = data.rows;
-    var columns = data.columns;
-    var X = new matrix_Matrix(rows, columns);
-    for (var i = 0; i < rows; i++) {
-      for (var j = 0; j < columns; j++) {
-        if (i <= j) {
-          X[i][j] = data[i][j];
-        } else {
-          X[i][j] = 0;
-        }
-      }
-    }
-    return X;
-  }
-
-  /**
-   *
-   * @return {Array<number>}
-   */
-  get pivotPermutationVector() {
-    return this.pivotVector.slice();
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/dc/util.js
-function hypotenuse(a, b) {
-  var r = 0;
-  if (Math.abs(a) > Math.abs(b)) {
-    r = b / a;
-    return Math.abs(a) * Math.sqrt(1 + r * r);
-  }
-  if (b !== 0) {
-    r = a / b;
-    return Math.abs(b) * Math.sqrt(1 + r * r);
-  }
-  return 0;
-}
-
-function getFilled2DArray(rows, columns, value) {
-  var array = new Array(rows);
-  for (var i = 0; i < rows; i++) {
-    array[i] = new Array(columns);
-    for (var j = 0; j < columns; j++) {
-      array[i][j] = value;
-    }
-  }
-  return array;
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/dc/svd.js
-
-
-
-
-/**
- * @class SingularValueDecomposition
- * @see https://github.com/accord-net/framework/blob/development/Sources/Accord.Math/Decompositions/SingularValueDecomposition.cs
- * @param {Matrix} value
- * @param {object} [options]
- * @param {boolean} [options.computeLeftSingularVectors=true]
- * @param {boolean} [options.computeRightSingularVectors=true]
- * @param {boolean} [options.autoTranspose=false]
- */
-class svd_SingularValueDecomposition {
-  constructor(value, options = {}) {
-    value = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(value);
-
-    var m = value.rows;
-    var n = value.columns;
-
-    const {
-      computeLeftSingularVectors = true,
-      computeRightSingularVectors = true,
-      autoTranspose = false
-    } = options;
-
-    var wantu = Boolean(computeLeftSingularVectors);
-    var wantv = Boolean(computeRightSingularVectors);
-
-    var swapped = false;
-    var a;
-    if (m < n) {
-      if (!autoTranspose) {
-        a = value.clone();
-        // eslint-disable-next-line no-console
-        console.warn(
-          'Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose'
-        );
-      } else {
-        a = value.transpose();
-        m = a.rows;
-        n = a.columns;
-        swapped = true;
-        var aux = wantu;
-        wantu = wantv;
-        wantv = aux;
-      }
-    } else {
-      a = value.clone();
-    }
-
-    var nu = Math.min(m, n);
-    var ni = Math.min(m + 1, n);
-    var s = new Array(ni);
-    var U = getFilled2DArray(m, nu, 0);
-    var V = getFilled2DArray(n, n, 0);
-
-    var e = new Array(n);
-    var work = new Array(m);
-
-    var si = new Array(ni);
-    for (let i = 0; i < ni; i++) si[i] = i;
-
-    var nct = Math.min(m - 1, n);
-    var nrt = Math.max(0, Math.min(n - 2, m));
-    var mrc = Math.max(nct, nrt);
-
-    for (let k = 0; k < mrc; k++) {
-      if (k < nct) {
-        s[k] = 0;
-        for (let i = k; i < m; i++) {
-          s[k] = hypotenuse(s[k], a[i][k]);
-        }
-        if (s[k] !== 0) {
-          if (a[k][k] < 0) {
-            s[k] = -s[k];
-          }
-          for (let i = k; i < m; i++) {
-            a[i][k] /= s[k];
-          }
-          a[k][k] += 1;
-        }
-        s[k] = -s[k];
-      }
-
-      for (let j = k + 1; j < n; j++) {
-        if (k < nct && s[k] !== 0) {
-          let t = 0;
-          for (let i = k; i < m; i++) {
-            t += a[i][k] * a[i][j];
-          }
-          t = -t / a[k][k];
-          for (let i = k; i < m; i++) {
-            a[i][j] += t * a[i][k];
-          }
-        }
-        e[j] = a[k][j];
-      }
-
-      if (wantu && k < nct) {
-        for (let i = k; i < m; i++) {
-          U[i][k] = a[i][k];
-        }
-      }
-
-      if (k < nrt) {
-        e[k] = 0;
-        for (let i = k + 1; i < n; i++) {
-          e[k] = hypotenuse(e[k], e[i]);
-        }
-        if (e[k] !== 0) {
-          if (e[k + 1] < 0) {
-            e[k] = 0 - e[k];
-          }
-          for (let i = k + 1; i < n; i++) {
-            e[i] /= e[k];
-          }
-          e[k + 1] += 1;
-        }
-        e[k] = -e[k];
-        if (k + 1 < m && e[k] !== 0) {
-          for (let i = k + 1; i < m; i++) {
-            work[i] = 0;
-          }
-          for (let i = k + 1; i < m; i++) {
-            for (let j = k + 1; j < n; j++) {
-              work[i] += e[j] * a[i][j];
-            }
-          }
-          for (let j = k + 1; j < n; j++) {
-            let t = -e[j] / e[k + 1];
-            for (let i = k + 1; i < m; i++) {
-              a[i][j] += t * work[i];
-            }
-          }
-        }
-        if (wantv) {
-          for (let i = k + 1; i < n; i++) {
-            V[i][k] = e[i];
-          }
-        }
-      }
-    }
-
-    let p = Math.min(n, m + 1);
-    if (nct < n) {
-      s[nct] = a[nct][nct];
-    }
-    if (m < p) {
-      s[p - 1] = 0;
-    }
-    if (nrt + 1 < p) {
-      e[nrt] = a[nrt][p - 1];
-    }
-    e[p - 1] = 0;
-
-    if (wantu) {
-      for (let j = nct; j < nu; j++) {
-        for (let i = 0; i < m; i++) {
-          U[i][j] = 0;
-        }
-        U[j][j] = 1;
-      }
-      for (let k = nct - 1; k >= 0; k--) {
-        if (s[k] !== 0) {
-          for (let j = k + 1; j < nu; j++) {
-            let t = 0;
-            for (let i = k; i < m; i++) {
-              t += U[i][k] * U[i][j];
-            }
-            t = -t / U[k][k];
-            for (let i = k; i < m; i++) {
-              U[i][j] += t * U[i][k];
-            }
-          }
-          for (let i = k; i < m; i++) {
-            U[i][k] = -U[i][k];
-          }
-          U[k][k] = 1 + U[k][k];
-          for (let i = 0; i < k - 1; i++) {
-            U[i][k] = 0;
-          }
-        } else {
-          for (let i = 0; i < m; i++) {
-            U[i][k] = 0;
-          }
-          U[k][k] = 1;
-        }
-      }
-    }
-
-    if (wantv) {
-      for (let k = n - 1; k >= 0; k--) {
-        if (k < nrt && e[k] !== 0) {
-          for (let j = k + 1; j < n; j++) {
-            let t = 0;
-            for (let i = k + 1; i < n; i++) {
-              t += V[i][k] * V[i][j];
-            }
-            t = -t / V[k + 1][k];
-            for (let i = k + 1; i < n; i++) {
-              V[i][j] += t * V[i][k];
-            }
-          }
-        }
-        for (let i = 0; i < n; i++) {
-          V[i][k] = 0;
-        }
-        V[k][k] = 1;
-      }
-    }
-
-    var pp = p - 1;
-    var iter = 0;
-    var eps = Number.EPSILON;
-    while (p > 0) {
-      let k, kase;
-      for (k = p - 2; k >= -1; k--) {
-        if (k === -1) {
-          break;
-        }
-        const alpha =
-          Number.MIN_VALUE + eps * Math.abs(s[k] + Math.abs(s[k + 1]));
-        if (Math.abs(e[k]) <= alpha || Number.isNaN(e[k])) {
-          e[k] = 0;
-          break;
-        }
-      }
-      if (k === p - 2) {
-        kase = 4;
-      } else {
-        let ks;
-        for (ks = p - 1; ks >= k; ks--) {
-          if (ks === k) {
-            break;
-          }
-          let t =
-            (ks !== p ? Math.abs(e[ks]) : 0) +
-            (ks !== k + 1 ? Math.abs(e[ks - 1]) : 0);
-          if (Math.abs(s[ks]) <= eps * t) {
-            s[ks] = 0;
-            break;
-          }
-        }
-        if (ks === k) {
-          kase = 3;
-        } else if (ks === p - 1) {
-          kase = 1;
-        } else {
-          kase = 2;
-          k = ks;
-        }
-      }
-
-      k++;
-
-      switch (kase) {
-        case 1: {
-          let f = e[p - 2];
-          e[p - 2] = 0;
-          for (let j = p - 2; j >= k; j--) {
-            let t = hypotenuse(s[j], f);
-            let cs = s[j] / t;
-            let sn = f / t;
-            s[j] = t;
-            if (j !== k) {
-              f = -sn * e[j - 1];
-              e[j - 1] = cs * e[j - 1];
-            }
-            if (wantv) {
-              for (let i = 0; i < n; i++) {
-                t = cs * V[i][j] + sn * V[i][p - 1];
-                V[i][p - 1] = -sn * V[i][j] + cs * V[i][p - 1];
-                V[i][j] = t;
-              }
-            }
-          }
-          break;
-        }
-        case 2: {
-          let f = e[k - 1];
-          e[k - 1] = 0;
-          for (let j = k; j < p; j++) {
-            let t = hypotenuse(s[j], f);
-            let cs = s[j] / t;
-            let sn = f / t;
-            s[j] = t;
-            f = -sn * e[j];
-            e[j] = cs * e[j];
-            if (wantu) {
-              for (let i = 0; i < m; i++) {
-                t = cs * U[i][j] + sn * U[i][k - 1];
-                U[i][k - 1] = -sn * U[i][j] + cs * U[i][k - 1];
-                U[i][j] = t;
-              }
-            }
-          }
-          break;
-        }
-        case 3: {
-          const scale = Math.max(
-            Math.abs(s[p - 1]),
-            Math.abs(s[p - 2]),
-            Math.abs(e[p - 2]),
-            Math.abs(s[k]),
-            Math.abs(e[k])
-          );
-          const sp = s[p - 1] / scale;
-          const spm1 = s[p - 2] / scale;
-          const epm1 = e[p - 2] / scale;
-          const sk = s[k] / scale;
-          const ek = e[k] / scale;
-          const b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;
-          const c = sp * epm1 * (sp * epm1);
-          let shift = 0;
-          if (b !== 0 || c !== 0) {
-            if (b < 0) {
-              shift = 0 - Math.sqrt(b * b + c);
-            } else {
-              shift = Math.sqrt(b * b + c);
-            }
-            shift = c / (b + shift);
-          }
-          let f = (sk + sp) * (sk - sp) + shift;
-          let g = sk * ek;
-          for (let j = k; j < p - 1; j++) {
-            let t = hypotenuse(f, g);
-            if (t === 0) t = Number.MIN_VALUE;
-            let cs = f / t;
-            let sn = g / t;
-            if (j !== k) {
-              e[j - 1] = t;
-            }
-            f = cs * s[j] + sn * e[j];
-            e[j] = cs * e[j] - sn * s[j];
-            g = sn * s[j + 1];
-            s[j + 1] = cs * s[j + 1];
-            if (wantv) {
-              for (let i = 0; i < n; i++) {
-                t = cs * V[i][j] + sn * V[i][j + 1];
-                V[i][j + 1] = -sn * V[i][j] + cs * V[i][j + 1];
-                V[i][j] = t;
-              }
-            }
-            t = hypotenuse(f, g);
-            if (t === 0) t = Number.MIN_VALUE;
-            cs = f / t;
-            sn = g / t;
-            s[j] = t;
-            f = cs * e[j] + sn * s[j + 1];
-            s[j + 1] = -sn * e[j] + cs * s[j + 1];
-            g = sn * e[j + 1];
-            e[j + 1] = cs * e[j + 1];
-            if (wantu && j < m - 1) {
-              for (let i = 0; i < m; i++) {
-                t = cs * U[i][j] + sn * U[i][j + 1];
-                U[i][j + 1] = -sn * U[i][j] + cs * U[i][j + 1];
-                U[i][j] = t;
-              }
-            }
-          }
-          e[p - 2] = f;
-          iter = iter + 1;
-          break;
-        }
-        case 4: {
-          if (s[k] <= 0) {
-            s[k] = s[k] < 0 ? -s[k] : 0;
-            if (wantv) {
-              for (let i = 0; i <= pp; i++) {
-                V[i][k] = -V[i][k];
-              }
-            }
-          }
-          while (k < pp) {
-            if (s[k] >= s[k + 1]) {
-              break;
-            }
-            let t = s[k];
-            s[k] = s[k + 1];
-            s[k + 1] = t;
-            if (wantv && k < n - 1) {
-              for (let i = 0; i < n; i++) {
-                t = V[i][k + 1];
-                V[i][k + 1] = V[i][k];
-                V[i][k] = t;
-              }
-            }
-            if (wantu && k < m - 1) {
-              for (let i = 0; i < m; i++) {
-                t = U[i][k + 1];
-                U[i][k + 1] = U[i][k];
-                U[i][k] = t;
-              }
-            }
-            k++;
-          }
-          iter = 0;
-          p--;
-          break;
-        }
-        // no default
-      }
-    }
-
-    if (swapped) {
-      var tmp = V;
-      V = U;
-      U = tmp;
-    }
-
-    this.m = m;
-    this.n = n;
-    this.s = s;
-    this.U = U;
-    this.V = V;
-  }
-
-  /**
-   * Solve a problem of least square (Ax=b) by using the SVD. Useful when A is singular. When A is not singular, it would be better to use qr.solve(value).
-   * Example : We search to approximate x, with A matrix shape m*n, x vector size n, b vector size m (m > n). We will use :
-   * var svd = SingularValueDecomposition(A);
-   * var x = svd.solve(b);
-   * @param {Matrix} value - Matrix 1D which is the vector b (in the equation Ax = b)
-   * @return {Matrix} - The vector x
-   */
-  solve(value) {
-    var Y = value;
-    var e = this.threshold;
-    var scols = this.s.length;
-    var Ls = matrix_Matrix.zeros(scols, scols);
-
-    for (let i = 0; i < scols; i++) {
-      if (Math.abs(this.s[i]) <= e) {
-        Ls[i][i] = 0;
-      } else {
-        Ls[i][i] = 1 / this.s[i];
-      }
-    }
-
-    var U = this.U;
-    var V = this.rightSingularVectors;
-
-    var VL = V.mmul(Ls);
-    var vrows = V.rows;
-    var urows = U.length;
-    var VLU = matrix_Matrix.zeros(vrows, urows);
-
-    for (let i = 0; i < vrows; i++) {
-      for (let j = 0; j < urows; j++) {
-        let sum = 0;
-        for (let k = 0; k < scols; k++) {
-          sum += VL[i][k] * U[j][k];
-        }
-        VLU[i][j] = sum;
-      }
-    }
-
-    return VLU.mmul(Y);
-  }
-
-  /**
-   *
-   * @param {Array<number>} value
-   * @return {Matrix}
-   */
-  solveForDiagonal(value) {
-    return this.solve(matrix_Matrix.diag(value));
-  }
-
-  /**
-   * Get the inverse of the matrix. We compute the inverse of a matrix using SVD when this matrix is singular or ill-conditioned. Example :
-   * var svd = SingularValueDecomposition(A);
-   * var inverseA = svd.inverse();
-   * @return {Matrix} - The approximation of the inverse of the matrix
-   */
-  inverse() {
-    var V = this.V;
-    var e = this.threshold;
-    var vrows = V.length;
-    var vcols = V[0].length;
-    var X = new matrix_Matrix(vrows, this.s.length);
-
-    for (let i = 0; i < vrows; i++) {
-      for (let j = 0; j < vcols; j++) {
-        if (Math.abs(this.s[j]) > e) {
-          X[i][j] = V[i][j] / this.s[j];
-        } else {
-          X[i][j] = 0;
-        }
-      }
-    }
-
-    var U = this.U;
-
-    var urows = U.length;
-    var ucols = U[0].length;
-    var Y = new matrix_Matrix(vrows, urows);
-
-    for (let i = 0; i < vrows; i++) {
-      for (let j = 0; j < urows; j++) {
-        let sum = 0;
-        for (let k = 0; k < ucols; k++) {
-          sum += X[i][k] * U[j][k];
-        }
-        Y[i][j] = sum;
-      }
-    }
-
-    return Y;
-  }
-
-  /**
-   *
-   * @return {number}
-   */
-  get condition() {
-    return this.s[0] / this.s[Math.min(this.m, this.n) - 1];
-  }
-
-  /**
-   *
-   * @return {number}
-   */
-  get norm2() {
-    return this.s[0];
-  }
-
-  /**
-   *
-   * @return {number}
-   */
-  get rank() {
-    var tol = Math.max(this.m, this.n) * this.s[0] * Number.EPSILON;
-    var r = 0;
-    var s = this.s;
-    for (var i = 0, ii = s.length; i < ii; i++) {
-      if (s[i] > tol) {
-        r++;
-      }
-    }
-    return r;
-  }
-
-  /**
-   *
-   * @return {Array<number>}
-   */
-  get diagonal() {
-    return this.s;
-  }
-
-  /**
-   *
-   * @return {number}
-   */
-  get threshold() {
-    return Number.EPSILON / 2 * Math.max(this.m, this.n) * this.s[0];
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get leftSingularVectors() {
-    if (!matrix_Matrix.isMatrix(this.U)) {
-      this.U = new matrix_Matrix(this.U);
-    }
-    return this.U;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get rightSingularVectors() {
-    if (!matrix_Matrix.isMatrix(this.V)) {
-      this.V = new matrix_Matrix(this.V);
-    }
-    return this.V;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get diagonalMatrix() {
-    return matrix_Matrix.diag(this.s);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/util.js
-
-
-/**
- * @private
- * Check that a row index is not out of bounds
- * @param {Matrix} matrix
- * @param {number} index
- * @param {boolean} [outer]
- */
-function checkRowIndex(matrix, index, outer) {
-  var max = outer ? matrix.rows : matrix.rows - 1;
-  if (index < 0 || index > max) {
-    throw new RangeError('Row index out of range');
-  }
-}
-
-/**
- * @private
- * Check that a column index is not out of bounds
- * @param {Matrix} matrix
- * @param {number} index
- * @param {boolean} [outer]
- */
-function checkColumnIndex(matrix, index, outer) {
-  var max = outer ? matrix.columns : matrix.columns - 1;
-  if (index < 0 || index > max) {
-    throw new RangeError('Column index out of range');
-  }
-}
-
-/**
- * @private
- * Check that the provided vector is an array with the right length
- * @param {Matrix} matrix
- * @param {Array|Matrix} vector
- * @return {Array}
- * @throws {RangeError}
- */
-function checkRowVector(matrix, vector) {
-  if (vector.to1DArray) {
-    vector = vector.to1DArray();
-  }
-  if (vector.length !== matrix.columns) {
-    throw new RangeError(
-      'vector size must be the same as the number of columns'
-    );
-  }
-  return vector;
-}
-
-/**
- * @private
- * Check that the provided vector is an array with the right length
- * @param {Matrix} matrix
- * @param {Array|Matrix} vector
- * @return {Array}
- * @throws {RangeError}
- */
-function checkColumnVector(matrix, vector) {
-  if (vector.to1DArray) {
-    vector = vector.to1DArray();
-  }
-  if (vector.length !== matrix.rows) {
-    throw new RangeError('vector size must be the same as the number of rows');
-  }
-  return vector;
-}
-
-function checkIndices(matrix, rowIndices, columnIndices) {
-  return {
-    row: checkRowIndices(matrix, rowIndices),
-    column: checkColumnIndices(matrix, columnIndices)
-  };
-}
-
-function checkRowIndices(matrix, rowIndices) {
-  if (typeof rowIndices !== 'object') {
-    throw new TypeError('unexpected type for row indices');
-  }
-
-  var rowOut = rowIndices.some((r) => {
-    return r < 0 || r >= matrix.rows;
-  });
-
-  if (rowOut) {
-    throw new RangeError('row indices are out of range');
-  }
-
-  if (!Array.isArray(rowIndices)) rowIndices = Array.from(rowIndices);
-
-  return rowIndices;
-}
-
-function checkColumnIndices(matrix, columnIndices) {
-  if (typeof columnIndices !== 'object') {
-    throw new TypeError('unexpected type for column indices');
-  }
-
-  var columnOut = columnIndices.some((c) => {
-    return c < 0 || c >= matrix.columns;
-  });
-
-  if (columnOut) {
-    throw new RangeError('column indices are out of range');
-  }
-  if (!Array.isArray(columnIndices)) columnIndices = Array.from(columnIndices);
-
-  return columnIndices;
-}
-
-function checkRange(matrix, startRow, endRow, startColumn, endColumn) {
-  if (arguments.length !== 5) {
-    throw new RangeError('expected 4 arguments');
-  }
-  checkNumber('startRow', startRow);
-  checkNumber('endRow', endRow);
-  checkNumber('startColumn', startColumn);
-  checkNumber('endColumn', endColumn);
-  if (
-    startRow > endRow ||
-    startColumn > endColumn ||
-    startRow < 0 ||
-    startRow >= matrix.rows ||
-    endRow < 0 ||
-    endRow >= matrix.rows ||
-    startColumn < 0 ||
-    startColumn >= matrix.columns ||
-    endColumn < 0 ||
-    endColumn >= matrix.columns
-  ) {
-    throw new RangeError('Submatrix indices are out of range');
-  }
-}
-
-function getRange(from, to) {
-  var arr = new Array(to - from + 1);
-  for (var i = 0; i < arr.length; i++) {
-    arr[i] = from + i;
-  }
-  return arr;
-}
-
-function sumByRow(matrix) {
-  var sum = matrix_Matrix.zeros(matrix.rows, 1);
-  for (var i = 0; i < matrix.rows; ++i) {
-    for (var j = 0; j < matrix.columns; ++j) {
-      sum.set(i, 0, sum.get(i, 0) + matrix.get(i, j));
-    }
-  }
-  return sum;
-}
-
-function sumByColumn(matrix) {
-  var sum = matrix_Matrix.zeros(1, matrix.columns);
-  for (var i = 0; i < matrix.rows; ++i) {
-    for (var j = 0; j < matrix.columns; ++j) {
-      sum.set(0, j, sum.get(0, j) + matrix.get(i, j));
-    }
-  }
-  return sum;
-}
-
-function sumAll(matrix) {
-  var v = 0;
-  for (var i = 0; i < matrix.rows; i++) {
-    for (var j = 0; j < matrix.columns; j++) {
-      v += matrix.get(i, j);
-    }
-  }
-  return v;
-}
-
-function checkNumber(name, value) {
-  if (typeof value !== 'number') {
-    throw new TypeError(`${name} must be a number`);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/base.js
-
-
-
-class base_BaseView extends AbstractMatrix() {
-  constructor(matrix, rows, columns) {
-    super();
-    this.matrix = matrix;
-    this.rows = rows;
-    this.columns = columns;
-  }
-
-  static get [Symbol.species]() {
-    return matrix_Matrix;
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/transpose.js
-
-
-class transpose_MatrixTransposeView extends base_BaseView {
-  constructor(matrix) {
-    super(matrix, matrix.columns, matrix.rows);
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(columnIndex, rowIndex, value);
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(columnIndex, rowIndex);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/row.js
-
-
-class row_MatrixRowView extends base_BaseView {
-  constructor(matrix, row) {
-    super(matrix, 1, matrix.columns);
-    this.row = row;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(this.row, columnIndex, value);
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(this.row, columnIndex);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/sub.js
-
-
-
-
-class sub_MatrixSubView extends base_BaseView {
-  constructor(matrix, startRow, endRow, startColumn, endColumn) {
-    checkRange(matrix, startRow, endRow, startColumn, endColumn);
-    super(matrix, endRow - startRow + 1, endColumn - startColumn + 1);
-    this.startRow = startRow;
-    this.startColumn = startColumn;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(
-      this.startRow + rowIndex,
-      this.startColumn + columnIndex,
-      value
-    );
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(
-      this.startRow + rowIndex,
-      this.startColumn + columnIndex
-    );
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/selection.js
-
-
-
-
-class selection_MatrixSelectionView extends base_BaseView {
-  constructor(matrix, rowIndices, columnIndices) {
-    var indices = checkIndices(matrix, rowIndices, columnIndices);
-    super(matrix, indices.row.length, indices.column.length);
-    this.rowIndices = indices.row;
-    this.columnIndices = indices.column;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(
-      this.rowIndices[rowIndex],
-      this.columnIndices[columnIndex],
-      value
-    );
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(
-      this.rowIndices[rowIndex],
-      this.columnIndices[columnIndex]
-    );
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/rowSelection.js
-
-
-
-
-class rowSelection_MatrixRowSelectionView extends base_BaseView {
-  constructor(matrix, rowIndices) {
-    rowIndices = checkRowIndices(matrix, rowIndices);
-    super(matrix, rowIndices.length, matrix.columns);
-    this.rowIndices = rowIndices;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(this.rowIndices[rowIndex], columnIndex, value);
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(this.rowIndices[rowIndex], columnIndex);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/columnSelection.js
-
-
-
-
-class columnSelection_MatrixColumnSelectionView extends base_BaseView {
-  constructor(matrix, columnIndices) {
-    columnIndices = checkColumnIndices(matrix, columnIndices);
-    super(matrix, matrix.rows, columnIndices.length);
-    this.columnIndices = columnIndices;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(rowIndex, this.columnIndices[columnIndex], value);
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(rowIndex, this.columnIndices[columnIndex]);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/column.js
-
-
-class column_MatrixColumnView extends base_BaseView {
-  constructor(matrix, column) {
-    super(matrix, matrix.rows, 1);
-    this.column = column;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(rowIndex, this.column, value);
-    return this;
-  }
-
-  get(rowIndex) {
-    return this.matrix.get(rowIndex, this.column);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/flipRow.js
-
-
-class flipRow_MatrixFlipRowView extends base_BaseView {
-  constructor(matrix) {
-    super(matrix, matrix.rows, matrix.columns);
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(this.rows - rowIndex - 1, columnIndex, value);
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(this.rows - rowIndex - 1, columnIndex);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/views/flipColumn.js
-
-
-class flipColumn_MatrixFlipColumnView extends base_BaseView {
-  constructor(matrix) {
-    super(matrix, matrix.rows, matrix.columns);
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.matrix.set(rowIndex, this.columns - columnIndex - 1, value);
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.matrix.get(rowIndex, this.columns - columnIndex - 1);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/abstractMatrix.js
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-function AbstractMatrix(superCtor) {
-  if (superCtor === undefined) superCtor = Object;
-
-  /**
-   * Real matrix
-   * @class Matrix
-   * @param {number|Array|Matrix} nRows - Number of rows of the new matrix,
-   * 2D array containing the data or Matrix instance to clone
-   * @param {number} [nColumns] - Number of columns of the new matrix
-   */
-  class Matrix extends superCtor {
-    static get [Symbol.species]() {
-      return this;
-    }
-
-    /**
-     * Constructs a Matrix with the chosen dimensions from a 1D array
-     * @param {number} newRows - Number of rows
-     * @param {number} newColumns - Number of columns
-     * @param {Array} newData - A 1D array containing data for the matrix
-     * @return {Matrix} - The new matrix
-     */
-    static from1DArray(newRows, newColumns, newData) {
-      var length = newRows * newColumns;
-      if (length !== newData.length) {
-        throw new RangeError('Data length does not match given dimensions');
-      }
-      var newMatrix = new this(newRows, newColumns);
-      for (var row = 0; row < newRows; row++) {
-        for (var column = 0; column < newColumns; column++) {
-          newMatrix.set(row, column, newData[row * newColumns + column]);
-        }
-      }
-      return newMatrix;
-    }
-
-    /**
-         * Creates a row vector, a matrix with only one row.
-         * @param {Array} newData - A 1D array containing data for the vector
-         * @return {Matrix} - The new matrix
-         */
-    static rowVector(newData) {
-      var vector = new this(1, newData.length);
-      for (var i = 0; i < newData.length; i++) {
-        vector.set(0, i, newData[i]);
-      }
-      return vector;
-    }
-
-    /**
-         * Creates a column vector, a matrix with only one column.
-         * @param {Array} newData - A 1D array containing data for the vector
-         * @return {Matrix} - The new matrix
-         */
-    static columnVector(newData) {
-      var vector = new this(newData.length, 1);
-      for (var i = 0; i < newData.length; i++) {
-        vector.set(i, 0, newData[i]);
-      }
-      return vector;
-    }
-
-    /**
-         * Creates an empty matrix with the given dimensions. Values will be undefined. Same as using new Matrix(rows, columns).
-         * @param {number} rows - Number of rows
-         * @param {number} columns - Number of columns
-         * @return {Matrix} - The new matrix
-         */
-    static empty(rows, columns) {
-      return new this(rows, columns);
-    }
-
-    /**
-         * Creates a matrix with the given dimensions. Values will be set to zero.
-         * @param {number} rows - Number of rows
-         * @param {number} columns - Number of columns
-         * @return {Matrix} - The new matrix
-         */
-    static zeros(rows, columns) {
-      return this.empty(rows, columns).fill(0);
-    }
-
-    /**
-         * Creates a matrix with the given dimensions. Values will be set to one.
-         * @param {number} rows - Number of rows
-         * @param {number} columns - Number of columns
-         * @return {Matrix} - The new matrix
-         */
-    static ones(rows, columns) {
-      return this.empty(rows, columns).fill(1);
-    }
-
-    /**
-         * Creates a matrix with the given dimensions. Values will be randomly set.
-         * @param {number} rows - Number of rows
-         * @param {number} columns - Number of columns
-         * @param {function} [rng=Math.random] - Random number generator
-         * @return {Matrix} The new matrix
-         */
-    static rand(rows, columns, rng) {
-      if (rng === undefined) rng = Math.random;
-      var matrix = this.empty(rows, columns);
-      for (var i = 0; i < rows; i++) {
-        for (var j = 0; j < columns; j++) {
-          matrix.set(i, j, rng());
-        }
-      }
-      return matrix;
-    }
-
-    /**
-         * Creates a matrix with the given dimensions. Values will be random integers.
-         * @param {number} rows - Number of rows
-         * @param {number} columns - Number of columns
-         * @param {number} [maxValue=1000] - Maximum value
-         * @param {function} [rng=Math.random] - Random number generator
-         * @return {Matrix} The new matrix
-         */
-    static randInt(rows, columns, maxValue, rng) {
-      if (maxValue === undefined) maxValue = 1000;
-      if (rng === undefined) rng = Math.random;
-      var matrix = this.empty(rows, columns);
-      for (var i = 0; i < rows; i++) {
-        for (var j = 0; j < columns; j++) {
-          var value = Math.floor(rng() * maxValue);
-          matrix.set(i, j, value);
-        }
-      }
-      return matrix;
-    }
-
-    /**
-         * Creates an identity matrix with the given dimension. Values of the diagonal will be 1 and others will be 0.
-         * @param {number} rows - Number of rows
-         * @param {number} [columns=rows] - Number of columns
-         * @param {number} [value=1] - Value to fill the diagonal with
-         * @return {Matrix} - The new identity matrix
-         */
-    static eye(rows, columns, value) {
-      if (columns === undefined) columns = rows;
-      if (value === undefined) value = 1;
-      var min = Math.min(rows, columns);
-      var matrix = this.zeros(rows, columns);
-      for (var i = 0; i < min; i++) {
-        matrix.set(i, i, value);
-      }
-      return matrix;
-    }
-
-    /**
-         * Creates a diagonal matrix based on the given array.
-         * @param {Array} data - Array containing the data for the diagonal
-         * @param {number} [rows] - Number of rows (Default: data.length)
-         * @param {number} [columns] - Number of columns (Default: rows)
-         * @return {Matrix} - The new diagonal matrix
-         */
-    static diag(data, rows, columns) {
-      var l = data.length;
-      if (rows === undefined) rows = l;
-      if (columns === undefined) columns = rows;
-      var min = Math.min(l, rows, columns);
-      var matrix = this.zeros(rows, columns);
-      for (var i = 0; i < min; i++) {
-        matrix.set(i, i, data[i]);
-      }
-      return matrix;
-    }
-
-    /**
-         * Returns a matrix whose elements are the minimum between matrix1 and matrix2
-         * @param {Matrix} matrix1
-         * @param {Matrix} matrix2
-         * @return {Matrix}
-         */
-    static min(matrix1, matrix2) {
-      matrix1 = this.checkMatrix(matrix1);
-      matrix2 = this.checkMatrix(matrix2);
-      var rows = matrix1.rows;
-      var columns = matrix1.columns;
-      var result = new this(rows, columns);
-      for (var i = 0; i < rows; i++) {
-        for (var j = 0; j < columns; j++) {
-          result.set(i, j, Math.min(matrix1.get(i, j), matrix2.get(i, j)));
-        }
-      }
-      return result;
-    }
-
-    /**
-         * Returns a matrix whose elements are the maximum between matrix1 and matrix2
-         * @param {Matrix} matrix1
-         * @param {Matrix} matrix2
-         * @return {Matrix}
-         */
-    static max(matrix1, matrix2) {
-      matrix1 = this.checkMatrix(matrix1);
-      matrix2 = this.checkMatrix(matrix2);
-      var rows = matrix1.rows;
-      var columns = matrix1.columns;
-      var result = new this(rows, columns);
-      for (var i = 0; i < rows; i++) {
-        for (var j = 0; j < columns; j++) {
-          result.set(i, j, Math.max(matrix1.get(i, j), matrix2.get(i, j)));
-        }
-      }
-      return result;
-    }
-
-    /**
-         * Check that the provided value is a Matrix and tries to instantiate one if not
-         * @param {*} value - The value to check
-         * @return {Matrix}
-         */
-    static checkMatrix(value) {
-      return Matrix.isMatrix(value) ? value : new this(value);
-    }
-
-    /**
-         * Returns true if the argument is a Matrix, false otherwise
-         * @param {*} value - The value to check
-         * @return {boolean}
-         */
-    static isMatrix(value) {
-      return (value != null) && (value.klass === 'Matrix');
-    }
-
-    /**
-         * @prop {number} size - The number of elements in the matrix.
-         */
-    get size() {
-      return this.rows * this.columns;
-    }
-
-    /**
-         * Applies a callback for each element of the matrix. The function is called in the matrix (this) context.
-         * @param {function} callback - Function that will be called with two parameters : i (row) and j (column)
-         * @return {Matrix} this
-         */
-    apply(callback) {
-      if (typeof callback !== 'function') {
-        throw new TypeError('callback must be a function');
-      }
-      var ii = this.rows;
-      var jj = this.columns;
-      for (var i = 0; i < ii; i++) {
-        for (var j = 0; j < jj; j++) {
-          callback.call(this, i, j);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Returns a new 1D array filled row by row with the matrix values
-         * @return {Array}
-         */
-    to1DArray() {
-      var array = new Array(this.size);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          array[i * this.columns + j] = this.get(i, j);
-        }
-      }
-      return array;
-    }
-
-    /**
-         * Returns a 2D array containing a copy of the data
-         * @return {Array}
-         */
-    to2DArray() {
-      var copy = new Array(this.rows);
-      for (var i = 0; i < this.rows; i++) {
-        copy[i] = new Array(this.columns);
-        for (var j = 0; j < this.columns; j++) {
-          copy[i][j] = this.get(i, j);
-        }
-      }
-      return copy;
-    }
-
-    /**
-         * @return {boolean} true if the matrix has one row
-         */
-    isRowVector() {
-      return this.rows === 1;
-    }
-
-    /**
-         * @return {boolean} true if the matrix has one column
-         */
-    isColumnVector() {
-      return this.columns === 1;
-    }
-
-    /**
-         * @return {boolean} true if the matrix has one row or one column
-         */
-    isVector() {
-      return (this.rows === 1) || (this.columns === 1);
-    }
-
-    /**
-         * @return {boolean} true if the matrix has the same number of rows and columns
-         */
-    isSquare() {
-      return this.rows === this.columns;
-    }
-
-    /**
-         * @return {boolean} true if the matrix is square and has the same values on both sides of the diagonal
-         */
-    isSymmetric() {
-      if (this.isSquare()) {
-        for (var i = 0; i < this.rows; i++) {
-          for (var j = 0; j <= i; j++) {
-            if (this.get(i, j) !== this.get(j, i)) {
-              return false;
-            }
-          }
-        }
-        return true;
-      }
-      return false;
-    }
-
-    /**
-         * Sets a given element of the matrix. mat.set(3,4,1) is equivalent to mat[3][4]=1
-         * @abstract
-         * @param {number} rowIndex - Index of the row
-         * @param {number} columnIndex - Index of the column
-         * @param {number} value - The new value for the element
-         * @return {Matrix} this
-         */
-    set(rowIndex, columnIndex, value) { // eslint-disable-line no-unused-vars
-      throw new Error('set method is unimplemented');
-    }
-
-    /**
-         * Returns the given element of the matrix. mat.get(3,4) is equivalent to matrix[3][4]
-         * @abstract
-         * @param {number} rowIndex - Index of the row
-         * @param {number} columnIndex - Index of the column
-         * @return {number}
-         */
-    get(rowIndex, columnIndex) { // eslint-disable-line no-unused-vars
-      throw new Error('get method is unimplemented');
-    }
-
-    /**
-         * Creates a new matrix that is a repetition of the current matrix. New matrix has rowRep times the number of
-         * rows of the matrix, and colRep times the number of columns of the matrix
-         * @param {number} rowRep - Number of times the rows should be repeated
-         * @param {number} colRep - Number of times the columns should be re
-         * @return {Matrix}
-         * @example
-         * var matrix = new Matrix([[1,2]]);
-         * matrix.repeat(2); // [[1,2],[1,2]]
-         */
-    repeat(rowRep, colRep) {
-      rowRep = rowRep || 1;
-      colRep = colRep || 1;
-      var matrix = new this.constructor[Symbol.species](this.rows * rowRep, this.columns * colRep);
-      for (var i = 0; i < rowRep; i++) {
-        for (var j = 0; j < colRep; j++) {
-          matrix.setSubMatrix(this, this.rows * i, this.columns * j);
-        }
-      }
-      return matrix;
-    }
-
-    /**
-         * Fills the matrix with a given value. All elements will be set to this value.
-         * @param {number} value - New value
-         * @return {Matrix} this
-         */
-    fill(value) {
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, value);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Negates the matrix. All elements will be multiplied by (-1)
-         * @return {Matrix} this
-         */
-    neg() {
-      return this.mulS(-1);
-    }
-
-    /**
-         * Returns a new array from the given row index
-         * @param {number} index - Row index
-         * @return {Array}
-         */
-    getRow(index) {
-      checkRowIndex(this, index);
-      var row = new Array(this.columns);
-      for (var i = 0; i < this.columns; i++) {
-        row[i] = this.get(index, i);
-      }
-      return row;
-    }
-
-    /**
-         * Returns a new row vector from the given row index
-         * @param {number} index - Row index
-         * @return {Matrix}
-         */
-    getRowVector(index) {
-      return this.constructor.rowVector(this.getRow(index));
-    }
-
-    /**
-         * Sets a row at the given index
-         * @param {number} index - Row index
-         * @param {Array|Matrix} array - Array or vector
-         * @return {Matrix} this
-         */
-    setRow(index, array) {
-      checkRowIndex(this, index);
-      array = checkRowVector(this, array);
-      for (var i = 0; i < this.columns; i++) {
-        this.set(index, i, array[i]);
-      }
-      return this;
-    }
-
-    /**
-         * Swaps two rows
-         * @param {number} row1 - First row index
-         * @param {number} row2 - Second row index
-         * @return {Matrix} this
-         */
-    swapRows(row1, row2) {
-      checkRowIndex(this, row1);
-      checkRowIndex(this, row2);
-      for (var i = 0; i < this.columns; i++) {
-        var temp = this.get(row1, i);
-        this.set(row1, i, this.get(row2, i));
-        this.set(row2, i, temp);
-      }
-      return this;
-    }
-
-    /**
-         * Returns a new array from the given column index
-         * @param {number} index - Column index
-         * @return {Array}
-         */
-    getColumn(index) {
-      checkColumnIndex(this, index);
-      var column = new Array(this.rows);
-      for (var i = 0; i < this.rows; i++) {
-        column[i] = this.get(i, index);
-      }
-      return column;
-    }
-
-    /**
-         * Returns a new column vector from the given column index
-         * @param {number} index - Column index
-         * @return {Matrix}
-         */
-    getColumnVector(index) {
-      return this.constructor.columnVector(this.getColumn(index));
-    }
-
-    /**
-         * Sets a column at the given index
-         * @param {number} index - Column index
-         * @param {Array|Matrix} array - Array or vector
-         * @return {Matrix} this
-         */
-    setColumn(index, array) {
-      checkColumnIndex(this, index);
-      array = checkColumnVector(this, array);
-      for (var i = 0; i < this.rows; i++) {
-        this.set(i, index, array[i]);
-      }
-      return this;
-    }
-
-    /**
-         * Swaps two columns
-         * @param {number} column1 - First column index
-         * @param {number} column2 - Second column index
-         * @return {Matrix} this
-         */
-    swapColumns(column1, column2) {
-      checkColumnIndex(this, column1);
-      checkColumnIndex(this, column2);
-      for (var i = 0; i < this.rows; i++) {
-        var temp = this.get(i, column1);
-        this.set(i, column1, this.get(i, column2));
-        this.set(i, column2, temp);
-      }
-      return this;
-    }
-
-    /**
-         * Adds the values of a vector to each row
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    addRowVector(vector) {
-      vector = checkRowVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) + vector[j]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Subtracts the values of a vector from each row
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    subRowVector(vector) {
-      vector = checkRowVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) - vector[j]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Multiplies the values of a vector with each row
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    mulRowVector(vector) {
-      vector = checkRowVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) * vector[j]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Divides the values of each row by those of a vector
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    divRowVector(vector) {
-      vector = checkRowVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) / vector[j]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Adds the values of a vector to each column
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    addColumnVector(vector) {
-      vector = checkColumnVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) + vector[i]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Subtracts the values of a vector from each column
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    subColumnVector(vector) {
-      vector = checkColumnVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) - vector[i]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Multiplies the values of a vector with each column
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    mulColumnVector(vector) {
-      vector = checkColumnVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) * vector[i]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Divides the values of each column by those of a vector
-         * @param {Array|Matrix} vector - Array or vector
-         * @return {Matrix} this
-         */
-    divColumnVector(vector) {
-      vector = checkColumnVector(this, vector);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          this.set(i, j, this.get(i, j) / vector[i]);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Multiplies the values of a row with a scalar
-         * @param {number} index - Row index
-         * @param {number} value
-         * @return {Matrix} this
-         */
-    mulRow(index, value) {
-      checkRowIndex(this, index);
-      for (var i = 0; i < this.columns; i++) {
-        this.set(index, i, this.get(index, i) * value);
-      }
-      return this;
-    }
-
-    /**
-         * Multiplies the values of a column with a scalar
-         * @param {number} index - Column index
-         * @param {number} value
-         * @return {Matrix} this
-         */
-    mulColumn(index, value) {
-      checkColumnIndex(this, index);
-      for (var i = 0; i < this.rows; i++) {
-        this.set(i, index, this.get(i, index) * value);
-      }
-      return this;
-    }
-
-    /**
-         * Returns the maximum value of the matrix
-         * @return {number}
-         */
-    max() {
-      var v = this.get(0, 0);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          if (this.get(i, j) > v) {
-            v = this.get(i, j);
-          }
-        }
-      }
-      return v;
-    }
-
-    /**
-         * Returns the index of the maximum value
-         * @return {Array}
-         */
-    maxIndex() {
-      var v = this.get(0, 0);
-      var idx = [0, 0];
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          if (this.get(i, j) > v) {
-            v = this.get(i, j);
-            idx[0] = i;
-            idx[1] = j;
-          }
-        }
-      }
-      return idx;
-    }
-
-    /**
-         * Returns the minimum value of the matrix
-         * @return {number}
-         */
-    min() {
-      var v = this.get(0, 0);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          if (this.get(i, j) < v) {
-            v = this.get(i, j);
-          }
-        }
-      }
-      return v;
-    }
-
-    /**
-         * Returns the index of the minimum value
-         * @return {Array}
-         */
-    minIndex() {
-      var v = this.get(0, 0);
-      var idx = [0, 0];
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          if (this.get(i, j) < v) {
-            v = this.get(i, j);
-            idx[0] = i;
-            idx[1] = j;
-          }
-        }
-      }
-      return idx;
-    }
-
-    /**
-         * Returns the maximum value of one row
-         * @param {number} row - Row index
-         * @return {number}
-         */
-    maxRow(row) {
-      checkRowIndex(this, row);
-      var v = this.get(row, 0);
-      for (var i = 1; i < this.columns; i++) {
-        if (this.get(row, i) > v) {
-          v = this.get(row, i);
-        }
-      }
-      return v;
-    }
-
-    /**
-         * Returns the index of the maximum value of one row
-         * @param {number} row - Row index
-         * @return {Array}
-         */
-    maxRowIndex(row) {
-      checkRowIndex(this, row);
-      var v = this.get(row, 0);
-      var idx = [row, 0];
-      for (var i = 1; i < this.columns; i++) {
-        if (this.get(row, i) > v) {
-          v = this.get(row, i);
-          idx[1] = i;
-        }
-      }
-      return idx;
-    }
-
-    /**
-         * Returns the minimum value of one row
-         * @param {number} row - Row index
-         * @return {number}
-         */
-    minRow(row) {
-      checkRowIndex(this, row);
-      var v = this.get(row, 0);
-      for (var i = 1; i < this.columns; i++) {
-        if (this.get(row, i) < v) {
-          v = this.get(row, i);
-        }
-      }
-      return v;
-    }
-
-    /**
-         * Returns the index of the maximum value of one row
-         * @param {number} row - Row index
-         * @return {Array}
-         */
-    minRowIndex(row) {
-      checkRowIndex(this, row);
-      var v = this.get(row, 0);
-      var idx = [row, 0];
-      for (var i = 1; i < this.columns; i++) {
-        if (this.get(row, i) < v) {
-          v = this.get(row, i);
-          idx[1] = i;
-        }
-      }
-      return idx;
-    }
-
-    /**
-         * Returns the maximum value of one column
-         * @param {number} column - Column index
-         * @return {number}
-         */
-    maxColumn(column) {
-      checkColumnIndex(this, column);
-      var v = this.get(0, column);
-      for (var i = 1; i < this.rows; i++) {
-        if (this.get(i, column) > v) {
-          v = this.get(i, column);
-        }
-      }
-      return v;
-    }
-
-    /**
-         * Returns the index of the maximum value of one column
-         * @param {number} column - Column index
-         * @return {Array}
-         */
-    maxColumnIndex(column) {
-      checkColumnIndex(this, column);
-      var v = this.get(0, column);
-      var idx = [0, column];
-      for (var i = 1; i < this.rows; i++) {
-        if (this.get(i, column) > v) {
-          v = this.get(i, column);
-          idx[0] = i;
-        }
-      }
-      return idx;
-    }
-
-    /**
-         * Returns the minimum value of one column
-         * @param {number} column - Column index
-         * @return {number}
-         */
-    minColumn(column) {
-      checkColumnIndex(this, column);
-      var v = this.get(0, column);
-      for (var i = 1; i < this.rows; i++) {
-        if (this.get(i, column) < v) {
-          v = this.get(i, column);
-        }
-      }
-      return v;
-    }
-
-    /**
-         * Returns the index of the minimum value of one column
-         * @param {number} column - Column index
-         * @return {Array}
-         */
-    minColumnIndex(column) {
-      checkColumnIndex(this, column);
-      var v = this.get(0, column);
-      var idx = [0, column];
-      for (var i = 1; i < this.rows; i++) {
-        if (this.get(i, column) < v) {
-          v = this.get(i, column);
-          idx[0] = i;
-        }
-      }
-      return idx;
-    }
-
-    /**
-         * Returns an array containing the diagonal values of the matrix
-         * @return {Array}
-         */
-    diag() {
-      var min = Math.min(this.rows, this.columns);
-      var diag = new Array(min);
-      for (var i = 0; i < min; i++) {
-        diag[i] = this.get(i, i);
-      }
-      return diag;
-    }
-
-    /**
-         * Returns the sum by the argument given, if no argument given,
-         * it returns the sum of all elements of the matrix.
-         * @param {string} by - sum by 'row' or 'column'.
-         * @return {Matrix|number}
-         */
-    sum(by) {
-      switch (by) {
-        case 'row':
-          return sumByRow(this);
-        case 'column':
-          return sumByColumn(this);
-        default:
-          return sumAll(this);
-      }
-    }
-
-    /**
-         * Returns the mean of all elements of the matrix
-         * @return {number}
-         */
-    mean() {
-      return this.sum() / this.size;
-    }
-
-    /**
-         * Returns the product of all elements of the matrix
-         * @return {number}
-         */
-    prod() {
-      var prod = 1;
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          prod *= this.get(i, j);
-        }
-      }
-      return prod;
-    }
-
-    /**
-         * Returns the norm of a matrix.
-         * @param {string} type - "frobenius" (default) or "max" return resp. the Frobenius norm and the max norm.
-         * @return {number}
-         */
-    norm(type = 'frobenius') {
-      var result = 0;
-      if (type === 'max') {
-        return this.max();
-      } else if (type === 'frobenius') {
-        for (var i = 0; i < this.rows; i++) {
-          for (var j = 0; j < this.columns; j++) {
-            result = result + this.get(i, j) * this.get(i, j);
-          }
-        }
-        return Math.sqrt(result);
-      } else {
-        throw new RangeError(`unknown norm type: ${type}`);
-      }
-    }
-
-    /**
-         * Computes the cumulative sum of the matrix elements (in place, row by row)
-         * @return {Matrix} this
-         */
-    cumulativeSum() {
-      var sum = 0;
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          sum += this.get(i, j);
-          this.set(i, j, sum);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Computes the dot (scalar) product between the matrix and another
-         * @param {Matrix} vector2 vector
-         * @return {number}
-         */
-    dot(vector2) {
-      if (Matrix.isMatrix(vector2)) vector2 = vector2.to1DArray();
-      var vector1 = this.to1DArray();
-      if (vector1.length !== vector2.length) {
-        throw new RangeError('vectors do not have the same size');
-      }
-      var dot = 0;
-      for (var i = 0; i < vector1.length; i++) {
-        dot += vector1[i] * vector2[i];
-      }
-      return dot;
-    }
-
-    /**
-         * Returns the matrix product between this and other
-         * @param {Matrix} other
-         * @return {Matrix}
-         */
-    mmul(other) {
-      other = this.constructor.checkMatrix(other);
-      if (this.columns !== other.rows) {
-        // eslint-disable-next-line no-console
-        console.warn('Number of columns of left matrix are not equal to number of rows of right matrix.');
-      }
-
-      var m = this.rows;
-      var n = this.columns;
-      var p = other.columns;
-
-      var result = new this.constructor[Symbol.species](m, p);
-
-      var Bcolj = new Array(n);
-      for (var j = 0; j < p; j++) {
-        for (var k = 0; k < n; k++) {
-          Bcolj[k] = other.get(k, j);
-        }
-
-        for (var i = 0; i < m; i++) {
-          var s = 0;
-          for (k = 0; k < n; k++) {
-            s += this.get(i, k) * Bcolj[k];
-          }
-
-          result.set(i, j, s);
-        }
-      }
-      return result;
-    }
-
-    strassen2x2(other) {
-      var result = new this.constructor[Symbol.species](2, 2);
-      const a11 = this.get(0, 0);
-      const b11 = other.get(0, 0);
-      const a12 = this.get(0, 1);
-      const b12 = other.get(0, 1);
-      const a21 = this.get(1, 0);
-      const b21 = other.get(1, 0);
-      const a22 = this.get(1, 1);
-      const b22 = other.get(1, 1);
-
-      // Compute intermediate values.
-      const m1 = (a11 + a22) * (b11 + b22);
-      const m2 = (a21 + a22) * b11;
-      const m3 = a11 * (b12 - b22);
-      const m4 = a22 * (b21 - b11);
-      const m5 = (a11 + a12) * b22;
-      const m6 = (a21 - a11) * (b11 + b12);
-      const m7 = (a12 - a22) * (b21 + b22);
-
-      // Combine intermediate values into the output.
-      const c00 = m1 + m4 - m5 + m7;
-      const c01 = m3 + m5;
-      const c10 = m2 + m4;
-      const c11 = m1 - m2 + m3 + m6;
-
-      result.set(0, 0, c00);
-      result.set(0, 1, c01);
-      result.set(1, 0, c10);
-      result.set(1, 1, c11);
-      return result;
-    }
-
-    strassen3x3(other) {
-      var result = new this.constructor[Symbol.species](3, 3);
-
-      const a00 = this.get(0, 0);
-      const a01 = this.get(0, 1);
-      const a02 = this.get(0, 2);
-      const a10 = this.get(1, 0);
-      const a11 = this.get(1, 1);
-      const a12 = this.get(1, 2);
-      const a20 = this.get(2, 0);
-      const a21 = this.get(2, 1);
-      const a22 = this.get(2, 2);
-
-      const b00 = other.get(0, 0);
-      const b01 = other.get(0, 1);
-      const b02 = other.get(0, 2);
-      const b10 = other.get(1, 0);
-      const b11 = other.get(1, 1);
-      const b12 = other.get(1, 2);
-      const b20 = other.get(2, 0);
-      const b21 = other.get(2, 1);
-      const b22 = other.get(2, 2);
-
-      const m1 = (a00 + a01 + a02 - a10 - a11 - a21 - a22) * b11;
-      const m2 = (a00 - a10) * (-b01 + b11);
-      const m3 = a11 * (-b00 + b01 + b10 - b11 - b12 - b20 + b22);
-      const m4 = (-a00 + a10 + a11) * (b00 - b01 + b11);
-      const m5 = (a10 + a11) * (-b00 + b01);
-      const m6 = a00 * b00;
-      const m7 = (-a00 + a20 + a21) * (b00 - b02 + b12);
-      const m8 = (-a00 + a20) * (b02 - b12);
-      const m9 = (a20 + a21) * (-b00 + b02);
-      const m10 = (a00 + a01 + a02 - a11 - a12 - a20 - a21) * b12;
-      const m11 = a21 * (-b00 + b02 + b10 - b11 - b12 - b20 + b21);
-      const m12 = (-a02 + a21 + a22) * (b11 + b20 - b21);
-      const m13 = (a02 - a22) * (b11 - b21);
-      const m14 = a02 * b20;
-      const m15 = (a21 + a22) * (-b20 + b21);
-      const m16 = (-a02 + a11 + a12) * (b12 + b20 - b22);
-      const m17 = (a02 - a12) * (b12 - b22);
-      const m18 = (a11 + a12) * (-b20 + b22);
-      const m19 = a01 * b10;
-      const m20 = a12 * b21;
-      const m21 = a10 * b02;
-      const m22 = a20 * b01;
-      const m23 = a22 * b22;
-
-      const c00 = m6 + m14 + m19;
-      const c01 = m1 + m4 + m5 + m6 + m12 + m14 + m15;
-      const c02 = m6 + m7 + m9 + m10 + m14 + m16 + m18;
-      const c10 = m2 + m3 + m4 + m6 + m14 + m16 + m17;
-      const c11 = m2 + m4 + m5 + m6 + m20;
-      const c12 = m14 + m16 + m17 + m18 + m21;
-      const c20 = m6 + m7 + m8 + m11 + m12 + m13 + m14;
-      const c21 = m12 + m13 + m14 + m15 + m22;
-      const c22 = m6 + m7 + m8 + m9 + m23;
-
-      result.set(0, 0, c00);
-      result.set(0, 1, c01);
-      result.set(0, 2, c02);
-      result.set(1, 0, c10);
-      result.set(1, 1, c11);
-      result.set(1, 2, c12);
-      result.set(2, 0, c20);
-      result.set(2, 1, c21);
-      result.set(2, 2, c22);
-      return result;
-    }
-
-    /**
-         * Returns the matrix product between x and y. More efficient than mmul(other) only when we multiply squared matrix and when the size of the matrix is > 1000.
-         * @param {Matrix} y
-         * @return {Matrix}
-         */
-    mmulStrassen(y) {
-      var x = this.clone();
-      var r1 = x.rows;
-      var c1 = x.columns;
-      var r2 = y.rows;
-      var c2 = y.columns;
-      if (c1 !== r2) {
-        // eslint-disable-next-line no-console
-        console.warn(`Multiplying ${r1} x ${c1} and ${r2} x ${c2} matrix: dimensions do not match.`);
-      }
-
-      // Put a matrix into the top left of a matrix of zeros.
-      // `rows` and `cols` are the dimensions of the output matrix.
-      function embed(mat, rows, cols) {
-        var r = mat.rows;
-        var c = mat.columns;
-        if ((r === rows) && (c === cols)) {
-          return mat;
-        } else {
-          var resultat = Matrix.zeros(rows, cols);
-          resultat = resultat.setSubMatrix(mat, 0, 0);
-          return resultat;
-        }
-      }
-
-
-      // Make sure both matrices are the same size.
-      // This is exclusively for simplicity:
-      // this algorithm can be implemented with matrices of different sizes.
-
-      var r = Math.max(r1, r2);
-      var c = Math.max(c1, c2);
-      x = embed(x, r, c);
-      y = embed(y, r, c);
-
-      // Our recursive multiplication function.
-      function blockMult(a, b, rows, cols) {
-        // For small matrices, resort to naive multiplication.
-        if (rows <= 512 || cols <= 512) {
-          return a.mmul(b); // a is equivalent to this
-        }
-
-        // Apply dynamic padding.
-        if ((rows % 2 === 1) && (cols % 2 === 1)) {
-          a = embed(a, rows + 1, cols + 1);
-          b = embed(b, rows + 1, cols + 1);
-        } else if (rows % 2 === 1) {
-          a = embed(a, rows + 1, cols);
-          b = embed(b, rows + 1, cols);
-        } else if (cols % 2 === 1) {
-          a = embed(a, rows, cols + 1);
-          b = embed(b, rows, cols + 1);
-        }
-
-        var halfRows = parseInt(a.rows / 2, 10);
-        var halfCols = parseInt(a.columns / 2, 10);
-        // Subdivide input matrices.
-        var a11 = a.subMatrix(0, halfRows - 1, 0, halfCols - 1);
-        var b11 = b.subMatrix(0, halfRows - 1, 0, halfCols - 1);
-
-        var a12 = a.subMatrix(0, halfRows - 1, halfCols, a.columns - 1);
-        var b12 = b.subMatrix(0, halfRows - 1, halfCols, b.columns - 1);
-
-        var a21 = a.subMatrix(halfRows, a.rows - 1, 0, halfCols - 1);
-        var b21 = b.subMatrix(halfRows, b.rows - 1, 0, halfCols - 1);
-
-        var a22 = a.subMatrix(halfRows, a.rows - 1, halfCols, a.columns - 1);
-        var b22 = b.subMatrix(halfRows, b.rows - 1, halfCols, b.columns - 1);
-
-        // Compute intermediate values.
-        var m1 = blockMult(Matrix.add(a11, a22), Matrix.add(b11, b22), halfRows, halfCols);
-        var m2 = blockMult(Matrix.add(a21, a22), b11, halfRows, halfCols);
-        var m3 = blockMult(a11, Matrix.sub(b12, b22), halfRows, halfCols);
-        var m4 = blockMult(a22, Matrix.sub(b21, b11), halfRows, halfCols);
-        var m5 = blockMult(Matrix.add(a11, a12), b22, halfRows, halfCols);
-        var m6 = blockMult(Matrix.sub(a21, a11), Matrix.add(b11, b12), halfRows, halfCols);
-        var m7 = blockMult(Matrix.sub(a12, a22), Matrix.add(b21, b22), halfRows, halfCols);
-
-        // Combine intermediate values into the output.
-        var c11 = Matrix.add(m1, m4);
-        c11.sub(m5);
-        c11.add(m7);
-        var c12 = Matrix.add(m3, m5);
-        var c21 = Matrix.add(m2, m4);
-        var c22 = Matrix.sub(m1, m2);
-        c22.add(m3);
-        c22.add(m6);
-
-        // Crop output to the desired size (undo dynamic padding).
-        var resultat = Matrix.zeros(2 * c11.rows, 2 * c11.columns);
-        resultat = resultat.setSubMatrix(c11, 0, 0);
-        resultat = resultat.setSubMatrix(c12, c11.rows, 0);
-        resultat = resultat.setSubMatrix(c21, 0, c11.columns);
-        resultat = resultat.setSubMatrix(c22, c11.rows, c11.columns);
-        return resultat.subMatrix(0, rows - 1, 0, cols - 1);
-      }
-      return blockMult(x, y, r, c);
-    }
-
-    /**
-         * Returns a row-by-row scaled matrix
-         * @param {number} [min=0] - Minimum scaled value
-         * @param {number} [max=1] - Maximum scaled value
-         * @return {Matrix} - The scaled matrix
-         */
-    scaleRows(min, max) {
-      min = min === undefined ? 0 : min;
-      max = max === undefined ? 1 : max;
-      if (min >= max) {
-        throw new RangeError('min should be strictly smaller than max');
-      }
-      var newMatrix = this.constructor.empty(this.rows, this.columns);
-      for (var i = 0; i < this.rows; i++) {
-        var scaled = ml_array_rescale_lib_es6(this.getRow(i), { min, max });
-        newMatrix.setRow(i, scaled);
-      }
-      return newMatrix;
-    }
-
-    /**
-         * Returns a new column-by-column scaled matrix
-         * @param {number} [min=0] - Minimum scaled value
-         * @param {number} [max=1] - Maximum scaled value
-         * @return {Matrix} - The new scaled matrix
-         * @example
-         * var matrix = new Matrix([[1,2],[-1,0]]);
-         * var scaledMatrix = matrix.scaleColumns(); // [[1,1],[0,0]]
-         */
-    scaleColumns(min, max) {
-      min = min === undefined ? 0 : min;
-      max = max === undefined ? 1 : max;
-      if (min >= max) {
-        throw new RangeError('min should be strictly smaller than max');
-      }
-      var newMatrix = this.constructor.empty(this.rows, this.columns);
-      for (var i = 0; i < this.columns; i++) {
-        var scaled = ml_array_rescale_lib_es6(this.getColumn(i), {
-          min: min,
-          max: max
-        });
-        newMatrix.setColumn(i, scaled);
-      }
-      return newMatrix;
-    }
-
-
-    /**
-         * Returns the Kronecker product (also known as tensor product) between this and other
-         * See https://en.wikipedia.org/wiki/Kronecker_product
-         * @param {Matrix} other
-         * @return {Matrix}
-         */
-    kroneckerProduct(other) {
-      other = this.constructor.checkMatrix(other);
-
-      var m = this.rows;
-      var n = this.columns;
-      var p = other.rows;
-      var q = other.columns;
-
-      var result = new this.constructor[Symbol.species](m * p, n * q);
-      for (var i = 0; i < m; i++) {
-        for (var j = 0; j < n; j++) {
-          for (var k = 0; k < p; k++) {
-            for (var l = 0; l < q; l++) {
-              result[p * i + k][q * j + l] = this.get(i, j) * other.get(k, l);
-            }
-          }
-        }
-      }
-      return result;
-    }
-
-    /**
-         * Transposes the matrix and returns a new one containing the result
-         * @return {Matrix}
-         */
-    transpose() {
-      var result = new this.constructor[Symbol.species](this.columns, this.rows);
-      for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-          result.set(j, i, this.get(i, j));
-        }
-      }
-      return result;
-    }
-
-    /**
-         * Sorts the rows (in place)
-         * @param {function} compareFunction - usual Array.prototype.sort comparison function
-         * @return {Matrix} this
-         */
-    sortRows(compareFunction) {
-      if (compareFunction === undefined) compareFunction = compareNumbers;
-      for (var i = 0; i < this.rows; i++) {
-        this.setRow(i, this.getRow(i).sort(compareFunction));
-      }
-      return this;
-    }
-
-    /**
-         * Sorts the columns (in place)
-         * @param {function} compareFunction - usual Array.prototype.sort comparison function
-         * @return {Matrix} this
-         */
-    sortColumns(compareFunction) {
-      if (compareFunction === undefined) compareFunction = compareNumbers;
-      for (var i = 0; i < this.columns; i++) {
-        this.setColumn(i, this.getColumn(i).sort(compareFunction));
-      }
-      return this;
-    }
-
-    /**
-         * Returns a subset of the matrix
-         * @param {number} startRow - First row index
-         * @param {number} endRow - Last row index
-         * @param {number} startColumn - First column index
-         * @param {number} endColumn - Last column index
-         * @return {Matrix}
-         */
-    subMatrix(startRow, endRow, startColumn, endColumn) {
-      checkRange(this, startRow, endRow, startColumn, endColumn);
-      var newMatrix = new this.constructor[Symbol.species](endRow - startRow + 1, endColumn - startColumn + 1);
-      for (var i = startRow; i <= endRow; i++) {
-        for (var j = startColumn; j <= endColumn; j++) {
-          newMatrix[i - startRow][j - startColumn] = this.get(i, j);
-        }
-      }
-      return newMatrix;
-    }
-
-    /**
-         * Returns a subset of the matrix based on an array of row indices
-         * @param {Array} indices - Array containing the row indices
-         * @param {number} [startColumn = 0] - First column index
-         * @param {number} [endColumn = this.columns-1] - Last column index
-         * @return {Matrix}
-         */
-    subMatrixRow(indices, startColumn, endColumn) {
-      if (startColumn === undefined) startColumn = 0;
-      if (endColumn === undefined) endColumn = this.columns - 1;
-      if ((startColumn > endColumn) || (startColumn < 0) || (startColumn >= this.columns) || (endColumn < 0) || (endColumn >= this.columns)) {
-        throw new RangeError('Argument out of range');
-      }
-
-      var newMatrix = new this.constructor[Symbol.species](indices.length, endColumn - startColumn + 1);
-      for (var i = 0; i < indices.length; i++) {
-        for (var j = startColumn; j <= endColumn; j++) {
-          if (indices[i] < 0 || indices[i] >= this.rows) {
-            throw new RangeError(`Row index out of range: ${indices[i]}`);
-          }
-          newMatrix.set(i, j - startColumn, this.get(indices[i], j));
-        }
-      }
-      return newMatrix;
-    }
-
-    /**
-         * Returns a subset of the matrix based on an array of column indices
-         * @param {Array} indices - Array containing the column indices
-         * @param {number} [startRow = 0] - First row index
-         * @param {number} [endRow = this.rows-1] - Last row index
-         * @return {Matrix}
-         */
-    subMatrixColumn(indices, startRow, endRow) {
-      if (startRow === undefined) startRow = 0;
-      if (endRow === undefined) endRow = this.rows - 1;
-      if ((startRow > endRow) || (startRow < 0) || (startRow >= this.rows) || (endRow < 0) || (endRow >= this.rows)) {
-        throw new RangeError('Argument out of range');
-      }
-
-      var newMatrix = new this.constructor[Symbol.species](endRow - startRow + 1, indices.length);
-      for (var i = 0; i < indices.length; i++) {
-        for (var j = startRow; j <= endRow; j++) {
-          if (indices[i] < 0 || indices[i] >= this.columns) {
-            throw new RangeError(`Column index out of range: ${indices[i]}`);
-          }
-          newMatrix.set(j - startRow, i, this.get(j, indices[i]));
-        }
-      }
-      return newMatrix;
-    }
-
-    /**
-         * Set a part of the matrix to the given sub-matrix
-         * @param {Matrix|Array< Array >} matrix - The source matrix from which to extract values.
-         * @param {number} startRow - The index of the first row to set
-         * @param {number} startColumn - The index of the first column to set
-         * @return {Matrix}
-         */
-    setSubMatrix(matrix, startRow, startColumn) {
-      matrix = this.constructor.checkMatrix(matrix);
-      var endRow = startRow + matrix.rows - 1;
-      var endColumn = startColumn + matrix.columns - 1;
-      checkRange(this, startRow, endRow, startColumn, endColumn);
-      for (var i = 0; i < matrix.rows; i++) {
-        for (var j = 0; j < matrix.columns; j++) {
-          this[startRow + i][startColumn + j] = matrix.get(i, j);
-        }
-      }
-      return this;
-    }
-
-    /**
-         * Return a new matrix based on a selection of rows and columns
-         * @param {Array<number>} rowIndices - The row indices to select. Order matters and an index can be more than once.
-         * @param {Array<number>} columnIndices - The column indices to select. Order matters and an index can be use more than once.
-         * @return {Matrix} The new matrix
-         */
-    selection(rowIndices, columnIndices) {
-      var indices = checkIndices(this, rowIndices, columnIndices);
-      var newMatrix = new this.constructor[Symbol.species](rowIndices.length, columnIndices.length);
-      for (var i = 0; i < indices.row.length; i++) {
-        var rowIndex = indices.row[i];
-        for (var j = 0; j < indices.column.length; j++) {
-          var columnIndex = indices.column[j];
-          newMatrix[i][j] = this.get(rowIndex, columnIndex);
-        }
-      }
-      return newMatrix;
-    }
-
-    /**
-         * Returns the trace of the matrix (sum of the diagonal elements)
-         * @return {number}
-         */
-    trace() {
-      var min = Math.min(this.rows, this.columns);
-      var trace = 0;
-      for (var i = 0; i < min; i++) {
-        trace += this.get(i, i);
-      }
-      return trace;
-    }
-
-    /*
-         Matrix views
-         */
-
-    /**
-         * Returns a view of the transposition of the matrix
-         * @return {MatrixTransposeView}
-         */
-    transposeView() {
-      return new transpose_MatrixTransposeView(this);
-    }
-
-    /**
-         * Returns a view of the row vector with the given index
-         * @param {number} row - row index of the vector
-         * @return {MatrixRowView}
-         */
-    rowView(row) {
-      checkRowIndex(this, row);
-      return new row_MatrixRowView(this, row);
-    }
-
-    /**
-         * Returns a view of the column vector with the given index
-         * @param {number} column - column index of the vector
-         * @return {MatrixColumnView}
-         */
-    columnView(column) {
-      checkColumnIndex(this, column);
-      return new column_MatrixColumnView(this, column);
-    }
-
-    /**
-         * Returns a view of the matrix flipped in the row axis
-         * @return {MatrixFlipRowView}
-         */
-    flipRowView() {
-      return new flipRow_MatrixFlipRowView(this);
-    }
-
-    /**
-         * Returns a view of the matrix flipped in the column axis
-         * @return {MatrixFlipColumnView}
-         */
-    flipColumnView() {
-      return new flipColumn_MatrixFlipColumnView(this);
-    }
-
-    /**
-         * Returns a view of a submatrix giving the index boundaries
-         * @param {number} startRow - first row index of the submatrix
-         * @param {number} endRow - last row index of the submatrix
-         * @param {number} startColumn - first column index of the submatrix
-         * @param {number} endColumn - last column index of the submatrix
-         * @return {MatrixSubView}
-         */
-    subMatrixView(startRow, endRow, startColumn, endColumn) {
-      return new sub_MatrixSubView(this, startRow, endRow, startColumn, endColumn);
-    }
-
-    /**
-         * Returns a view of the cross of the row indices and the column indices
-         * @example
-         * // resulting vector is [[2], [2]]
-         * var matrix = new Matrix([[1,2,3], [4,5,6]]).selectionView([0, 0], [1])
-         * @param {Array<number>} rowIndices
-         * @param {Array<number>} columnIndices
-         * @return {MatrixSelectionView}
-         */
-    selectionView(rowIndices, columnIndices) {
-      return new selection_MatrixSelectionView(this, rowIndices, columnIndices);
-    }
-
-    /**
-         * Returns a view of the row indices
-         * @example
-         * // resulting vector is [[1,2,3], [1,2,3]]
-         * var matrix = new Matrix([[1,2,3], [4,5,6]]).rowSelectionView([0, 0])
-         * @param {Array<number>} rowIndices
-         * @return {MatrixRowSelectionView}
-         */
-    rowSelectionView(rowIndices) {
-      return new rowSelection_MatrixRowSelectionView(this, rowIndices);
-    }
-
-    /**
-         * Returns a view of the column indices
-         * @example
-         * // resulting vector is [[2, 2], [5, 5]]
-         * var matrix = new Matrix([[1,2,3], [4,5,6]]).columnSelectionView([1, 1])
-         * @param {Array<number>} columnIndices
-         * @return {MatrixColumnSelectionView}
-         */
-    columnSelectionView(columnIndices) {
-      return new columnSelection_MatrixColumnSelectionView(this, columnIndices);
-    }
-
-
-    /**
-        * Calculates and returns the determinant of a matrix as a Number
-        * @example
-        *   new Matrix([[1,2,3], [4,5,6]]).det()
-        * @return {number}
-        */
-    det() {
-      if (this.isSquare()) {
-        var a, b, c, d;
-        if (this.columns === 2) {
-          // 2 x 2 matrix
-          a = this.get(0, 0);
-          b = this.get(0, 1);
-          c = this.get(1, 0);
-          d = this.get(1, 1);
-
-          return a * d - (b * c);
-        } else if (this.columns === 3) {
-          // 3 x 3 matrix
-          var subMatrix0, subMatrix1, subMatrix2;
-          subMatrix0 = this.selectionView([1, 2], [1, 2]);
-          subMatrix1 = this.selectionView([1, 2], [0, 2]);
-          subMatrix2 = this.selectionView([1, 2], [0, 1]);
-          a = this.get(0, 0);
-          b = this.get(0, 1);
-          c = this.get(0, 2);
-
-          return a * subMatrix0.det() - b * subMatrix1.det() + c * subMatrix2.det();
-        } else {
-          // general purpose determinant using the LU decomposition
-          return new lu_LuDecomposition(this).determinant;
-        }
-      } else {
-        throw Error('Determinant can only be calculated for a square matrix.');
-      }
-    }
-
-    /**
-         * Returns inverse of a matrix if it exists or the pseudoinverse
-         * @param {number} threshold - threshold for taking inverse of singular values (default = 1e-15)
-         * @return {Matrix} the (pseudo)inverted matrix.
-         */
-    pseudoInverse(threshold) {
-      if (threshold === undefined) threshold = Number.EPSILON;
-      var svdSolution = new svd_SingularValueDecomposition(this, { autoTranspose: true });
-
-      var U = svdSolution.leftSingularVectors;
-      var V = svdSolution.rightSingularVectors;
-      var s = svdSolution.diagonal;
-
-      for (var i = 0; i < s.length; i++) {
-        if (Math.abs(s[i]) > threshold) {
-          s[i] = 1.0 / s[i];
-        } else {
-          s[i] = 0.0;
-        }
-      }
-
-      // convert list to diagonal
-      s = this.constructor[Symbol.species].diag(s);
-      return V.mmul(s.mmul(U.transposeView()));
-    }
-
-    /**
-         * Creates an exact and independent copy of the matrix
-         * @return {Matrix}
-         */
-    clone() {
-      var newMatrix = new this.constructor[Symbol.species](this.rows, this.columns);
-      for (var row = 0; row < this.rows; row++) {
-        for (var column = 0; column < this.columns; column++) {
-          newMatrix.set(row, column, this.get(row, column));
-        }
-      }
-      return newMatrix;
-    }
-  }
-
-  Matrix.prototype.klass = 'Matrix';
-
-  function compareNumbers(a, b) {
-    return a - b;
-  }
-
-  /*
-     Synonyms
-     */
-
-  Matrix.random = Matrix.rand;
-  Matrix.diagonal = Matrix.diag;
-  Matrix.prototype.diagonal = Matrix.prototype.diag;
-  Matrix.identity = Matrix.eye;
-  Matrix.prototype.negate = Matrix.prototype.neg;
-  Matrix.prototype.tensorProduct = Matrix.prototype.kroneckerProduct;
-  Matrix.prototype.determinant = Matrix.prototype.det;
-
-  /*
-     Add dynamically instance and static methods for mathematical operations
-     */
-
-  var inplaceOperator = `
-(function %name%(value) {
-    if (typeof value === 'number') return this.%name%S(value);
-    return this.%name%M(value);
-})
-`;
-
-  var inplaceOperatorScalar = `
-(function %name%S(value) {
-    for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-            this.set(i, j, this.get(i, j) %op% value);
-        }
-    }
-    return this;
-})
-`;
-
-  var inplaceOperatorMatrix = `
-(function %name%M(matrix) {
-    matrix = this.constructor.checkMatrix(matrix);
-    if (this.rows !== matrix.rows ||
-        this.columns !== matrix.columns) {
-        throw new RangeError('Matrices dimensions must be equal');
-    }
-    for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-            this.set(i, j, this.get(i, j) %op% matrix.get(i, j));
-        }
-    }
-    return this;
-})
-`;
-
-  var staticOperator = `
-(function %name%(matrix, value) {
-    var newMatrix = new this[Symbol.species](matrix);
-    return newMatrix.%name%(value);
-})
-`;
-
-  var inplaceMethod = `
-(function %name%() {
-    for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-            this.set(i, j, %method%(this.get(i, j)));
-        }
-    }
-    return this;
-})
-`;
-
-  var staticMethod = `
-(function %name%(matrix) {
-    var newMatrix = new this[Symbol.species](matrix);
-    return newMatrix.%name%();
-})
-`;
-
-  var inplaceMethodWithArgs = `
-(function %name%(%args%) {
-    for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-            this.set(i, j, %method%(this.get(i, j), %args%));
-        }
-    }
-    return this;
-})
-`;
-
-  var staticMethodWithArgs = `
-(function %name%(matrix, %args%) {
-    var newMatrix = new this[Symbol.species](matrix);
-    return newMatrix.%name%(%args%);
-})
-`;
-
-
-  var inplaceMethodWithOneArgScalar = `
-(function %name%S(value) {
-    for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-            this.set(i, j, %method%(this.get(i, j), value));
-        }
-    }
-    return this;
-})
-`;
-  var inplaceMethodWithOneArgMatrix = `
-(function %name%M(matrix) {
-    matrix = this.constructor.checkMatrix(matrix);
-    if (this.rows !== matrix.rows ||
-        this.columns !== matrix.columns) {
-        throw new RangeError('Matrices dimensions must be equal');
-    }
-    for (var i = 0; i < this.rows; i++) {
-        for (var j = 0; j < this.columns; j++) {
-            this.set(i, j, %method%(this.get(i, j), matrix.get(i, j)));
-        }
-    }
-    return this;
-})
-`;
-
-  var inplaceMethodWithOneArg = `
-(function %name%(value) {
-    if (typeof value === 'number') return this.%name%S(value);
-    return this.%name%M(value);
-})
-`;
-
-  var staticMethodWithOneArg = staticMethodWithArgs;
-
-  var operators = [
-    // Arithmetic operators
-    ['+', 'add'],
-    ['-', 'sub', 'subtract'],
-    ['*', 'mul', 'multiply'],
-    ['/', 'div', 'divide'],
-    ['%', 'mod', 'modulus'],
-    // Bitwise operators
-    ['&', 'and'],
-    ['|', 'or'],
-    ['^', 'xor'],
-    ['<<', 'leftShift'],
-    ['>>', 'signPropagatingRightShift'],
-    ['>>>', 'rightShift', 'zeroFillRightShift']
-  ];
-
-  var i;
-  var eval2 = eval; // eslint-disable-line no-eval
-  for (var operator of operators) {
-    var inplaceOp = eval2(fillTemplateFunction(inplaceOperator, { name: operator[1], op: operator[0] }));
-    var inplaceOpS = eval2(fillTemplateFunction(inplaceOperatorScalar, { name: `${operator[1]}S`, op: operator[0] }));
-    var inplaceOpM = eval2(fillTemplateFunction(inplaceOperatorMatrix, { name: `${operator[1]}M`, op: operator[0] }));
-    var staticOp = eval2(fillTemplateFunction(staticOperator, { name: operator[1] }));
-    for (i = 1; i < operator.length; i++) {
-      Matrix.prototype[operator[i]] = inplaceOp;
-      Matrix.prototype[`${operator[i]}S`] = inplaceOpS;
-      Matrix.prototype[`${operator[i]}M`] = inplaceOpM;
-      Matrix[operator[i]] = staticOp;
-    }
-  }
-
-  var methods = [['~', 'not']];
-
-  [
-    'abs', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atanh', 'cbrt', 'ceil',
-    'clz32', 'cos', 'cosh', 'exp', 'expm1', 'floor', 'fround', 'log', 'log1p',
-    'log10', 'log2', 'round', 'sign', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc'
-  ].forEach(function (mathMethod) {
-    methods.push([`Math.${mathMethod}`, mathMethod]);
-  });
-
-  for (var method of methods) {
-    var inplaceMeth = eval2(fillTemplateFunction(inplaceMethod, { name: method[1], method: method[0] }));
-    var staticMeth = eval2(fillTemplateFunction(staticMethod, { name: method[1] }));
-    for (i = 1; i < method.length; i++) {
-      Matrix.prototype[method[i]] = inplaceMeth;
-      Matrix[method[i]] = staticMeth;
-    }
-  }
-
-  var methodsWithArgs = [['Math.pow', 1, 'pow']];
-
-  for (var methodWithArg of methodsWithArgs) {
-    var args = 'arg0';
-    for (i = 1; i < methodWithArg[1]; i++) {
-      args += `, arg${i}`;
-    }
-    if (methodWithArg[1] !== 1) {
-      var inplaceMethWithArgs = eval2(fillTemplateFunction(inplaceMethodWithArgs, {
-        name: methodWithArg[2],
-        method: methodWithArg[0],
-        args: args
-      }));
-      var staticMethWithArgs = eval2(fillTemplateFunction(staticMethodWithArgs, { name: methodWithArg[2], args: args }));
-      for (i = 2; i < methodWithArg.length; i++) {
-        Matrix.prototype[methodWithArg[i]] = inplaceMethWithArgs;
-        Matrix[methodWithArg[i]] = staticMethWithArgs;
-      }
-    } else {
-      var tmplVar = {
-        name: methodWithArg[2],
-        args: args,
-        method: methodWithArg[0]
-      };
-      var inplaceMethod2 = eval2(fillTemplateFunction(inplaceMethodWithOneArg, tmplVar));
-      var inplaceMethodS = eval2(fillTemplateFunction(inplaceMethodWithOneArgScalar, tmplVar));
-      var inplaceMethodM = eval2(fillTemplateFunction(inplaceMethodWithOneArgMatrix, tmplVar));
-      var staticMethod2 = eval2(fillTemplateFunction(staticMethodWithOneArg, tmplVar));
-      for (i = 2; i < methodWithArg.length; i++) {
-        Matrix.prototype[methodWithArg[i]] = inplaceMethod2;
-        Matrix.prototype[`${methodWithArg[i]}M`] = inplaceMethodM;
-        Matrix.prototype[`${methodWithArg[i]}S`] = inplaceMethodS;
-        Matrix[methodWithArg[i]] = staticMethod2;
-      }
-    }
-  }
-
-  function fillTemplateFunction(template, values) {
-    for (var value in values) {
-      template = template.replace(new RegExp(`%${value}%`, 'g'), values[value]);
-    }
-    return template;
-  }
-
-  return Matrix;
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/matrix.js
-
-
-
-class matrix_Matrix extends AbstractMatrix(Array) {
-  constructor(nRows, nColumns) {
-    var i;
-    if (arguments.length === 1 && typeof nRows === 'number') {
-      return new Array(nRows);
-    }
-    if (matrix_Matrix.isMatrix(nRows)) {
-      return nRows.clone();
-    } else if (Number.isInteger(nRows) && nRows > 0) {
-      // Create an empty matrix
-      super(nRows);
-      if (Number.isInteger(nColumns) && nColumns > 0) {
-        for (i = 0; i < nRows; i++) {
-          this[i] = new Array(nColumns);
-        }
-      } else {
-        throw new TypeError('nColumns must be a positive integer');
-      }
-    } else if (Array.isArray(nRows)) {
-      // Copy the values from the 2D array
-      const matrix = nRows;
-      nRows = matrix.length;
-      nColumns = matrix[0].length;
-      if (typeof nColumns !== 'number' || nColumns === 0) {
-        throw new TypeError(
-          'Data must be a 2D array with at least one element'
-        );
-      }
-      super(nRows);
-      for (i = 0; i < nRows; i++) {
-        if (matrix[i].length !== nColumns) {
-          throw new RangeError('Inconsistent array dimensions');
-        }
-        this[i] = [].concat(matrix[i]);
-      }
-    } else {
-      throw new TypeError(
-        'First argument must be a positive number or an array'
-      );
-    }
-    this.rows = nRows;
-    this.columns = nColumns;
-    return this;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this[rowIndex][columnIndex] = value;
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this[rowIndex][columnIndex];
-  }
-
-  /**
-   * Removes a row from the given index
-   * @param {number} index - Row index
-   * @return {Matrix} this
-   */
-  removeRow(index) {
-    checkRowIndex(this, index);
-    if (this.rows === 1) {
-      throw new RangeError('A matrix cannot have less than one row');
-    }
-    this.splice(index, 1);
-    this.rows -= 1;
-    return this;
-  }
-
-  /**
-   * Adds a row at the given index
-   * @param {number} [index = this.rows] - Row index
-   * @param {Array|Matrix} array - Array or vector
-   * @return {Matrix} this
-   */
-  addRow(index, array) {
-    if (array === undefined) {
-      array = index;
-      index = this.rows;
-    }
-    checkRowIndex(this, index, true);
-    array = checkRowVector(this, array, true);
-    this.splice(index, 0, array);
-    this.rows += 1;
-    return this;
-  }
-
-  /**
-   * Removes a column from the given index
-   * @param {number} index - Column index
-   * @return {Matrix} this
-   */
-  removeColumn(index) {
-    checkColumnIndex(this, index);
-    if (this.columns === 1) {
-      throw new RangeError('A matrix cannot have less than one column');
-    }
-    for (var i = 0; i < this.rows; i++) {
-      this[i].splice(index, 1);
-    }
-    this.columns -= 1;
-    return this;
-  }
-
-  /**
-   * Adds a column at the given index
-   * @param {number} [index = this.columns] - Column index
-   * @param {Array|Matrix} array - Array or vector
-   * @return {Matrix} this
-   */
-  addColumn(index, array) {
-    if (typeof array === 'undefined') {
-      array = index;
-      index = this.columns;
-    }
-    checkColumnIndex(this, index, true);
-    array = checkColumnVector(this, array);
-    for (var i = 0; i < this.rows; i++) {
-      this[i].splice(index, 0, array[i]);
-    }
-    this.columns += 1;
-    return this;
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/wrap/WrapperMatrix1D.js
-
-
-
-class WrapperMatrix1D_WrapperMatrix1D extends AbstractMatrix() {
-  /**
-   * @class WrapperMatrix1D
-   * @param {Array<number>} data
-   * @param {object} [options]
-   * @param {object} [options.rows = 1]
-   */
-  constructor(data, options = {}) {
-    const { rows = 1 } = options;
-
-    if (data.length % rows !== 0) {
-      throw new Error('the data length is not divisible by the number of rows');
-    }
-    super();
-    this.rows = rows;
-    this.columns = data.length / rows;
-    this.data = data;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    var index = this._calculateIndex(rowIndex, columnIndex);
-    this.data[index] = value;
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    var index = this._calculateIndex(rowIndex, columnIndex);
-    return this.data[index];
-  }
-
-  _calculateIndex(row, column) {
-    return row * this.columns + column;
-  }
-
-  static get [Symbol.species]() {
-    return matrix_Matrix;
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/wrap/WrapperMatrix2D.js
-
-
-
-class WrapperMatrix2D_WrapperMatrix2D extends AbstractMatrix() {
-  /**
-   * @class WrapperMatrix2D
-   * @param {Array<Array<number>>} data
-   */
-  constructor(data) {
-    super();
-    this.data = data;
-    this.rows = data.length;
-    this.columns = data[0].length;
-  }
-
-  set(rowIndex, columnIndex, value) {
-    this.data[rowIndex][columnIndex] = value;
-    return this;
-  }
-
-  get(rowIndex, columnIndex) {
-    return this.data[rowIndex][columnIndex];
-  }
-
-  static get [Symbol.species]() {
-    return matrix_Matrix;
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/wrap/wrap.js
-
-
-
-/**
- * @param {Array<Array<number>>|Array<number>} array
- * @param {object} [options]
- * @param {object} [options.rows = 1]
- * @return {WrapperMatrix1D|WrapperMatrix2D}
- */
-function wrap(array, options) {
-  if (Array.isArray(array)) {
-    if (array[0] && Array.isArray(array[0])) {
-      return new WrapperMatrix2D_WrapperMatrix2D(array);
-    } else {
-      return new WrapperMatrix1D_WrapperMatrix1D(array, options);
-    }
-  } else {
-    throw new Error('the argument is not an array');
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/dc/qr.js
-
-
-
-
-/**
- * @class QrDecomposition
- * @link https://github.com/lutzroeder/Mapack/blob/master/Source/QrDecomposition.cs
- * @param {Matrix} value
- */
-class qr_QrDecomposition {
-  constructor(value) {
-    value = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(value);
-
-    var qr = value.clone();
-    var m = value.rows;
-    var n = value.columns;
-    var rdiag = new Array(n);
-    var i, j, k, s;
-
-    for (k = 0; k < n; k++) {
-      var nrm = 0;
-      for (i = k; i < m; i++) {
-        nrm = hypotenuse(nrm, qr.get(i, k));
-      }
-      if (nrm !== 0) {
-        if (qr.get(k, k) < 0) {
-          nrm = -nrm;
-        }
-        for (i = k; i < m; i++) {
-          qr.set(i, k, qr.get(i, k) / nrm);
-        }
-        qr.set(k, k, qr.get(k, k) + 1);
-        for (j = k + 1; j < n; j++) {
-          s = 0;
-          for (i = k; i < m; i++) {
-            s += qr.get(i, k) * qr.get(i, j);
-          }
-          s = -s / qr.get(k, k);
-          for (i = k; i < m; i++) {
-            qr.set(i, j, qr.get(i, j) + s * qr.get(i, k));
-          }
-        }
-      }
-      rdiag[k] = -nrm;
-    }
-
-    this.QR = qr;
-    this.Rdiag = rdiag;
-  }
-
-  /**
-   * Solve a problem of least square (Ax=b) by using the QR decomposition. Useful when A is rectangular, but not working when A is singular.
-   * Example : We search to approximate x, with A matrix shape m*n, x vector size n, b vector size m (m > n). We will use :
-   * var qr = QrDecomposition(A);
-   * var x = qr.solve(b);
-   * @param {Matrix} value - Matrix 1D which is the vector b (in the equation Ax = b)
-   * @return {Matrix} - The vector x
-   */
-  solve(value) {
-    value = matrix_Matrix.checkMatrix(value);
-
-    var qr = this.QR;
-    var m = qr.rows;
-
-    if (value.rows !== m) {
-      throw new Error('Matrix row dimensions must agree');
-    }
-    if (!this.isFullRank()) {
-      throw new Error('Matrix is rank deficient');
-    }
-
-    var count = value.columns;
-    var X = value.clone();
-    var n = qr.columns;
-    var i, j, k, s;
-
-    for (k = 0; k < n; k++) {
-      for (j = 0; j < count; j++) {
-        s = 0;
-        for (i = k; i < m; i++) {
-          s += qr[i][k] * X[i][j];
-        }
-        s = -s / qr[k][k];
-        for (i = k; i < m; i++) {
-          X[i][j] += s * qr[i][k];
-        }
-      }
-    }
-    for (k = n - 1; k >= 0; k--) {
-      for (j = 0; j < count; j++) {
-        X[k][j] /= this.Rdiag[k];
-      }
-      for (i = 0; i < k; i++) {
-        for (j = 0; j < count; j++) {
-          X[i][j] -= X[k][j] * qr[i][k];
-        }
-      }
-    }
-
-    return X.subMatrix(0, n - 1, 0, count - 1);
-  }
-
-  /**
-   *
-   * @return {boolean}
-   */
-  isFullRank() {
-    var columns = this.QR.columns;
-    for (var i = 0; i < columns; i++) {
-      if (this.Rdiag[i] === 0) {
-        return false;
-      }
-    }
-    return true;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get upperTriangularMatrix() {
-    var qr = this.QR;
-    var n = qr.columns;
-    var X = new matrix_Matrix(n, n);
-    var i, j;
-    for (i = 0; i < n; i++) {
-      for (j = 0; j < n; j++) {
-        if (i < j) {
-          X[i][j] = qr[i][j];
-        } else if (i === j) {
-          X[i][j] = this.Rdiag[i];
-        } else {
-          X[i][j] = 0;
-        }
-      }
-    }
-    return X;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get orthogonalMatrix() {
-    var qr = this.QR;
-    var rows = qr.rows;
-    var columns = qr.columns;
-    var X = new matrix_Matrix(rows, columns);
-    var i, j, k, s;
-
-    for (k = columns - 1; k >= 0; k--) {
-      for (i = 0; i < rows; i++) {
-        X[i][k] = 0;
-      }
-      X[k][k] = 1;
-      for (j = k; j < columns; j++) {
-        if (qr[k][k] !== 0) {
-          s = 0;
-          for (i = k; i < rows; i++) {
-            s += qr[i][k] * X[i][j];
-          }
-
-          s = -s / qr[k][k];
-
-          for (i = k; i < rows; i++) {
-            X[i][j] += s * qr[i][k];
-          }
-        }
-      }
-    }
-    return X;
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/decompositions.js
-
-
-
-
-
-
-/**
- * Computes the inverse of a Matrix
- * @param {Matrix} matrix
- * @param {boolean} [useSVD=false]
- * @return {Matrix}
- */
-function inverse(matrix, useSVD = false) {
-  matrix = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(matrix);
-  if (useSVD) {
-    return new svd_SingularValueDecomposition(matrix).inverse();
-  } else {
-    return solve(matrix, matrix_Matrix.eye(matrix.rows));
-  }
-}
-
-/**
- *
- * @param {Matrix} leftHandSide
- * @param {Matrix} rightHandSide
- * @param {boolean} [useSVD = false]
- * @return {Matrix}
- */
-function solve(leftHandSide, rightHandSide, useSVD = false) {
-  leftHandSide = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(leftHandSide);
-  rightHandSide = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(rightHandSide);
-  if (useSVD) {
-    return new svd_SingularValueDecomposition(leftHandSide).solve(rightHandSide);
-  } else {
-    return leftHandSide.isSquare()
-      ? new lu_LuDecomposition(leftHandSide).solve(rightHandSide)
-      : new qr_QrDecomposition(leftHandSide).solve(rightHandSide);
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/linearDependencies.js
-
-
-
-
-
-// function used by rowsDependencies
-function xrange(n, exception) {
-  var range = [];
-  for (var i = 0; i < n; i++) {
-    if (i !== exception) {
-      range.push(i);
-    }
-  }
-  return range;
-}
-
-// function used by rowsDependencies
-function dependenciesOneRow(
-  error,
-  matrix,
-  index,
-  thresholdValue = 10e-10,
-  thresholdError = 10e-10
-) {
-  if (error > thresholdError) {
-    return new Array(matrix.rows + 1).fill(0);
-  } else {
-    var returnArray = matrix.addRow(index, [0]);
-    for (var i = 0; i < returnArray.rows; i++) {
-      if (Math.abs(returnArray.get(i, 0)) < thresholdValue) {
-        returnArray.set(i, 0, 0);
-      }
-    }
-    return returnArray.to1DArray();
-  }
-}
-
-/**
- * Creates a matrix which represents the dependencies between rows.
- * If a row is a linear combination of others rows, the result will be a row with the coefficients of this combination.
- * For example : for A = [[2, 0, 0, 1], [0, 1, 6, 0], [0, 3, 0, 1], [0, 0, 1, 0], [0, 1, 2, 0]], the result will be [[0, 0, 0, 0, 0], [0, 0, 0, 4, 1], [0, 0, 0, 0, 0], [0, 0.25, 0, 0, -0.25], [0, 1, 0, -4, 0]]
- * @param {Matrix} matrix
- * @param {Object} [options] includes thresholdValue and thresholdError.
- * @param {number} [options.thresholdValue = 10e-10] If an absolute value is inferior to this threshold, it will equals zero.
- * @param {number} [options.thresholdError = 10e-10] If the error is inferior to that threshold, the linear combination found is accepted and the row is dependent from other rows.
- * @return {Matrix} the matrix which represents the dependencies between rows.
- */
-
-function linearDependencies(matrix, options = {}) {
-  const { thresholdValue = 10e-10, thresholdError = 10e-10 } = options;
-
-  var n = matrix.rows;
-  var results = new matrix_Matrix(n, n);
-
-  for (var i = 0; i < n; i++) {
-    var b = matrix_Matrix.columnVector(matrix.getRow(i));
-    var Abis = matrix.subMatrixRow(xrange(n, i)).transposeView();
-    var svd = new svd_SingularValueDecomposition(Abis);
-    var x = svd.solve(b);
-    var error = lib_es6(
-      matrix_Matrix.sub(b, Abis.mmul(x))
-        .abs()
-        .to1DArray()
-    );
-    results.setRow(
-      i,
-      dependenciesOneRow(error, x, i, thresholdValue, thresholdError)
-    );
-  }
-  return results;
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/dc/evd.js
-
-
-
-
-/**
- * @class EigenvalueDecomposition
- * @link https://github.com/lutzroeder/Mapack/blob/master/Source/EigenvalueDecomposition.cs
- * @param {Matrix} matrix
- * @param {object} [options]
- * @param {boolean} [options.assumeSymmetric=false]
- */
-class evd_EigenvalueDecomposition {
-  constructor(matrix, options = {}) {
-    const { assumeSymmetric = false } = options;
-
-    matrix = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(matrix);
-    if (!matrix.isSquare()) {
-      throw new Error('Matrix is not a square matrix');
-    }
-
-    var n = matrix.columns;
-    var V = getFilled2DArray(n, n, 0);
-    var d = new Array(n);
-    var e = new Array(n);
-    var value = matrix;
-    var i, j;
-
-    var isSymmetric = false;
-    if (assumeSymmetric) {
-      isSymmetric = true;
-    } else {
-      isSymmetric = matrix.isSymmetric();
-    }
-
-    if (isSymmetric) {
-      for (i = 0; i < n; i++) {
-        for (j = 0; j < n; j++) {
-          V[i][j] = value.get(i, j);
-        }
-      }
-      tred2(n, e, d, V);
-      tql2(n, e, d, V);
-    } else {
-      var H = getFilled2DArray(n, n, 0);
-      var ort = new Array(n);
-      for (j = 0; j < n; j++) {
-        for (i = 0; i < n; i++) {
-          H[i][j] = value.get(i, j);
-        }
-      }
-      orthes(n, H, ort, V);
-      hqr2(n, e, d, V, H);
-    }
-
-    this.n = n;
-    this.e = e;
-    this.d = d;
-    this.V = V;
-  }
-
-  /**
-   *
-   * @return {Array<number>}
-   */
-  get realEigenvalues() {
-    return this.d;
-  }
-
-  /**
-   *
-   * @return {Array<number>}
-   */
-  get imaginaryEigenvalues() {
-    return this.e;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get eigenvectorMatrix() {
-    if (!matrix_Matrix.isMatrix(this.V)) {
-      this.V = new matrix_Matrix(this.V);
-    }
-    return this.V;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get diagonalMatrix() {
-    var n = this.n;
-    var e = this.e;
-    var d = this.d;
-    var X = new matrix_Matrix(n, n);
-    var i, j;
-    for (i = 0; i < n; i++) {
-      for (j = 0; j < n; j++) {
-        X[i][j] = 0;
-      }
-      X[i][i] = d[i];
-      if (e[i] > 0) {
-        X[i][i + 1] = e[i];
-      } else if (e[i] < 0) {
-        X[i][i - 1] = e[i];
-      }
-    }
-    return X;
-  }
-}
-
-function tred2(n, e, d, V) {
-  var f, g, h, i, j, k, hh, scale;
-
-  for (j = 0; j < n; j++) {
-    d[j] = V[n - 1][j];
-  }
-
-  for (i = n - 1; i > 0; i--) {
-    scale = 0;
-    h = 0;
-    for (k = 0; k < i; k++) {
-      scale = scale + Math.abs(d[k]);
-    }
-
-    if (scale === 0) {
-      e[i] = d[i - 1];
-      for (j = 0; j < i; j++) {
-        d[j] = V[i - 1][j];
-        V[i][j] = 0;
-        V[j][i] = 0;
-      }
-    } else {
-      for (k = 0; k < i; k++) {
-        d[k] /= scale;
-        h += d[k] * d[k];
-      }
-
-      f = d[i - 1];
-      g = Math.sqrt(h);
-      if (f > 0) {
-        g = -g;
-      }
-
-      e[i] = scale * g;
-      h = h - f * g;
-      d[i - 1] = f - g;
-      for (j = 0; j < i; j++) {
-        e[j] = 0;
-      }
-
-      for (j = 0; j < i; j++) {
-        f = d[j];
-        V[j][i] = f;
-        g = e[j] + V[j][j] * f;
-        for (k = j + 1; k <= i - 1; k++) {
-          g += V[k][j] * d[k];
-          e[k] += V[k][j] * f;
-        }
-        e[j] = g;
-      }
-
-      f = 0;
-      for (j = 0; j < i; j++) {
-        e[j] /= h;
-        f += e[j] * d[j];
-      }
-
-      hh = f / (h + h);
-      for (j = 0; j < i; j++) {
-        e[j] -= hh * d[j];
-      }
-
-      for (j = 0; j < i; j++) {
-        f = d[j];
-        g = e[j];
-        for (k = j; k <= i - 1; k++) {
-          V[k][j] -= f * e[k] + g * d[k];
-        }
-        d[j] = V[i - 1][j];
-        V[i][j] = 0;
-      }
-    }
-    d[i] = h;
-  }
-
-  for (i = 0; i < n - 1; i++) {
-    V[n - 1][i] = V[i][i];
-    V[i][i] = 1;
-    h = d[i + 1];
-    if (h !== 0) {
-      for (k = 0; k <= i; k++) {
-        d[k] = V[k][i + 1] / h;
-      }
-
-      for (j = 0; j <= i; j++) {
-        g = 0;
-        for (k = 0; k <= i; k++) {
-          g += V[k][i + 1] * V[k][j];
-        }
-        for (k = 0; k <= i; k++) {
-          V[k][j] -= g * d[k];
-        }
-      }
-    }
-
-    for (k = 0; k <= i; k++) {
-      V[k][i + 1] = 0;
-    }
-  }
-
-  for (j = 0; j < n; j++) {
-    d[j] = V[n - 1][j];
-    V[n - 1][j] = 0;
-  }
-
-  V[n - 1][n - 1] = 1;
-  e[0] = 0;
-}
-
-function tql2(n, e, d, V) {
-  var g, h, i, j, k, l, m, p, r, dl1, c, c2, c3, el1, s, s2, iter;
-
-  for (i = 1; i < n; i++) {
-    e[i - 1] = e[i];
-  }
-
-  e[n - 1] = 0;
-
-  var f = 0;
-  var tst1 = 0;
-  var eps = Number.EPSILON;
-
-  for (l = 0; l < n; l++) {
-    tst1 = Math.max(tst1, Math.abs(d[l]) + Math.abs(e[l]));
-    m = l;
-    while (m < n) {
-      if (Math.abs(e[m]) <= eps * tst1) {
-        break;
-      }
-      m++;
-    }
-
-    if (m > l) {
-      iter = 0;
-      do {
-        iter = iter + 1;
-
-        g = d[l];
-        p = (d[l + 1] - g) / (2 * e[l]);
-        r = hypotenuse(p, 1);
-        if (p < 0) {
-          r = -r;
-        }
-
-        d[l] = e[l] / (p + r);
-        d[l + 1] = e[l] * (p + r);
-        dl1 = d[l + 1];
-        h = g - d[l];
-        for (i = l + 2; i < n; i++) {
-          d[i] -= h;
-        }
-
-        f = f + h;
-
-        p = d[m];
-        c = 1;
-        c2 = c;
-        c3 = c;
-        el1 = e[l + 1];
-        s = 0;
-        s2 = 0;
-        for (i = m - 1; i >= l; i--) {
-          c3 = c2;
-          c2 = c;
-          s2 = s;
-          g = c * e[i];
-          h = c * p;
-          r = hypotenuse(p, e[i]);
-          e[i + 1] = s * r;
-          s = e[i] / r;
-          c = p / r;
-          p = c * d[i] - s * g;
-          d[i + 1] = h + s * (c * g + s * d[i]);
-
-          for (k = 0; k < n; k++) {
-            h = V[k][i + 1];
-            V[k][i + 1] = s * V[k][i] + c * h;
-            V[k][i] = c * V[k][i] - s * h;
-          }
-        }
-
-        p = -s * s2 * c3 * el1 * e[l] / dl1;
-        e[l] = s * p;
-        d[l] = c * p;
-      } while (Math.abs(e[l]) > eps * tst1);
-    }
-    d[l] = d[l] + f;
-    e[l] = 0;
-  }
-
-  for (i = 0; i < n - 1; i++) {
-    k = i;
-    p = d[i];
-    for (j = i + 1; j < n; j++) {
-      if (d[j] < p) {
-        k = j;
-        p = d[j];
-      }
-    }
-
-    if (k !== i) {
-      d[k] = d[i];
-      d[i] = p;
-      for (j = 0; j < n; j++) {
-        p = V[j][i];
-        V[j][i] = V[j][k];
-        V[j][k] = p;
-      }
-    }
-  }
-}
-
-function orthes(n, H, ort, V) {
-  var low = 0;
-  var high = n - 1;
-  var f, g, h, i, j, m;
-  var scale;
-
-  for (m = low + 1; m <= high - 1; m++) {
-    scale = 0;
-    for (i = m; i <= high; i++) {
-      scale = scale + Math.abs(H[i][m - 1]);
-    }
-
-    if (scale !== 0) {
-      h = 0;
-      for (i = high; i >= m; i--) {
-        ort[i] = H[i][m - 1] / scale;
-        h += ort[i] * ort[i];
-      }
-
-      g = Math.sqrt(h);
-      if (ort[m] > 0) {
-        g = -g;
-      }
-
-      h = h - ort[m] * g;
-      ort[m] = ort[m] - g;
-
-      for (j = m; j < n; j++) {
-        f = 0;
-        for (i = high; i >= m; i--) {
-          f += ort[i] * H[i][j];
-        }
-
-        f = f / h;
-        for (i = m; i <= high; i++) {
-          H[i][j] -= f * ort[i];
-        }
-      }
-
-      for (i = 0; i <= high; i++) {
-        f = 0;
-        for (j = high; j >= m; j--) {
-          f += ort[j] * H[i][j];
-        }
-
-        f = f / h;
-        for (j = m; j <= high; j++) {
-          H[i][j] -= f * ort[j];
-        }
-      }
-
-      ort[m] = scale * ort[m];
-      H[m][m - 1] = scale * g;
-    }
-  }
-
-  for (i = 0; i < n; i++) {
-    for (j = 0; j < n; j++) {
-      V[i][j] = i === j ? 1 : 0;
-    }
-  }
-
-  for (m = high - 1; m >= low + 1; m--) {
-    if (H[m][m - 1] !== 0) {
-      for (i = m + 1; i <= high; i++) {
-        ort[i] = H[i][m - 1];
-      }
-
-      for (j = m; j <= high; j++) {
-        g = 0;
-        for (i = m; i <= high; i++) {
-          g += ort[i] * V[i][j];
-        }
-
-        g = g / ort[m] / H[m][m - 1];
-        for (i = m; i <= high; i++) {
-          V[i][j] += g * ort[i];
-        }
-      }
-    }
-  }
-}
-
-function hqr2(nn, e, d, V, H) {
-  var n = nn - 1;
-  var low = 0;
-  var high = nn - 1;
-  var eps = Number.EPSILON;
-  var exshift = 0;
-  var norm = 0;
-  var p = 0;
-  var q = 0;
-  var r = 0;
-  var s = 0;
-  var z = 0;
-  var iter = 0;
-  var i, j, k, l, m, t, w, x, y;
-  var ra, sa, vr, vi;
-  var notlast, cdivres;
-
-  for (i = 0; i < nn; i++) {
-    if (i < low || i > high) {
-      d[i] = H[i][i];
-      e[i] = 0;
-    }
-
-    for (j = Math.max(i - 1, 0); j < nn; j++) {
-      norm = norm + Math.abs(H[i][j]);
-    }
-  }
-
-  while (n >= low) {
-    l = n;
-    while (l > low) {
-      s = Math.abs(H[l - 1][l - 1]) + Math.abs(H[l][l]);
-      if (s === 0) {
-        s = norm;
-      }
-      if (Math.abs(H[l][l - 1]) < eps * s) {
-        break;
-      }
-      l--;
-    }
-
-    if (l === n) {
-      H[n][n] = H[n][n] + exshift;
-      d[n] = H[n][n];
-      e[n] = 0;
-      n--;
-      iter = 0;
-    } else if (l === n - 1) {
-      w = H[n][n - 1] * H[n - 1][n];
-      p = (H[n - 1][n - 1] - H[n][n]) / 2;
-      q = p * p + w;
-      z = Math.sqrt(Math.abs(q));
-      H[n][n] = H[n][n] + exshift;
-      H[n - 1][n - 1] = H[n - 1][n - 1] + exshift;
-      x = H[n][n];
-
-      if (q >= 0) {
-        z = p >= 0 ? p + z : p - z;
-        d[n - 1] = x + z;
-        d[n] = d[n - 1];
-        if (z !== 0) {
-          d[n] = x - w / z;
-        }
-        e[n - 1] = 0;
-        e[n] = 0;
-        x = H[n][n - 1];
-        s = Math.abs(x) + Math.abs(z);
-        p = x / s;
-        q = z / s;
-        r = Math.sqrt(p * p + q * q);
-        p = p / r;
-        q = q / r;
-
-        for (j = n - 1; j < nn; j++) {
-          z = H[n - 1][j];
-          H[n - 1][j] = q * z + p * H[n][j];
-          H[n][j] = q * H[n][j] - p * z;
-        }
-
-        for (i = 0; i <= n; i++) {
-          z = H[i][n - 1];
-          H[i][n - 1] = q * z + p * H[i][n];
-          H[i][n] = q * H[i][n] - p * z;
-        }
-
-        for (i = low; i <= high; i++) {
-          z = V[i][n - 1];
-          V[i][n - 1] = q * z + p * V[i][n];
-          V[i][n] = q * V[i][n] - p * z;
-        }
-      } else {
-        d[n - 1] = x + p;
-        d[n] = x + p;
-        e[n - 1] = z;
-        e[n] = -z;
-      }
-
-      n = n - 2;
-      iter = 0;
-    } else {
-      x = H[n][n];
-      y = 0;
-      w = 0;
-      if (l < n) {
-        y = H[n - 1][n - 1];
-        w = H[n][n - 1] * H[n - 1][n];
-      }
-
-      if (iter === 10) {
-        exshift += x;
-        for (i = low; i <= n; i++) {
-          H[i][i] -= x;
-        }
-        s = Math.abs(H[n][n - 1]) + Math.abs(H[n - 1][n - 2]);
-        x = y = 0.75 * s;
-        w = -0.4375 * s * s;
-      }
-
-      if (iter === 30) {
-        s = (y - x) / 2;
-        s = s * s + w;
-        if (s > 0) {
-          s = Math.sqrt(s);
-          if (y < x) {
-            s = -s;
-          }
-          s = x - w / ((y - x) / 2 + s);
-          for (i = low; i <= n; i++) {
-            H[i][i] -= s;
-          }
-          exshift += s;
-          x = y = w = 0.964;
-        }
-      }
-
-      iter = iter + 1;
-
-      m = n - 2;
-      while (m >= l) {
-        z = H[m][m];
-        r = x - z;
-        s = y - z;
-        p = (r * s - w) / H[m + 1][m] + H[m][m + 1];
-        q = H[m + 1][m + 1] - z - r - s;
-        r = H[m + 2][m + 1];
-        s = Math.abs(p) + Math.abs(q) + Math.abs(r);
-        p = p / s;
-        q = q / s;
-        r = r / s;
-        if (m === l) {
-          break;
-        }
-        if (
-          Math.abs(H[m][m - 1]) * (Math.abs(q) + Math.abs(r)) <
-          eps *
-            (Math.abs(p) *
-              (Math.abs(H[m - 1][m - 1]) +
-                Math.abs(z) +
-                Math.abs(H[m + 1][m + 1])))
-        ) {
-          break;
-        }
-        m--;
-      }
-
-      for (i = m + 2; i <= n; i++) {
-        H[i][i - 2] = 0;
-        if (i > m + 2) {
-          H[i][i - 3] = 0;
-        }
-      }
-
-      for (k = m; k <= n - 1; k++) {
-        notlast = k !== n - 1;
-        if (k !== m) {
-          p = H[k][k - 1];
-          q = H[k + 1][k - 1];
-          r = notlast ? H[k + 2][k - 1] : 0;
-          x = Math.abs(p) + Math.abs(q) + Math.abs(r);
-          if (x !== 0) {
-            p = p / x;
-            q = q / x;
-            r = r / x;
-          }
-        }
-
-        if (x === 0) {
-          break;
-        }
-
-        s = Math.sqrt(p * p + q * q + r * r);
-        if (p < 0) {
-          s = -s;
-        }
-
-        if (s !== 0) {
-          if (k !== m) {
-            H[k][k - 1] = -s * x;
-          } else if (l !== m) {
-            H[k][k - 1] = -H[k][k - 1];
-          }
-
-          p = p + s;
-          x = p / s;
-          y = q / s;
-          z = r / s;
-          q = q / p;
-          r = r / p;
-
-          for (j = k; j < nn; j++) {
-            p = H[k][j] + q * H[k + 1][j];
-            if (notlast) {
-              p = p + r * H[k + 2][j];
-              H[k + 2][j] = H[k + 2][j] - p * z;
-            }
-
-            H[k][j] = H[k][j] - p * x;
-            H[k + 1][j] = H[k + 1][j] - p * y;
-          }
-
-          for (i = 0; i <= Math.min(n, k + 3); i++) {
-            p = x * H[i][k] + y * H[i][k + 1];
-            if (notlast) {
-              p = p + z * H[i][k + 2];
-              H[i][k + 2] = H[i][k + 2] - p * r;
-            }
-
-            H[i][k] = H[i][k] - p;
-            H[i][k + 1] = H[i][k + 1] - p * q;
-          }
-
-          for (i = low; i <= high; i++) {
-            p = x * V[i][k] + y * V[i][k + 1];
-            if (notlast) {
-              p = p + z * V[i][k + 2];
-              V[i][k + 2] = V[i][k + 2] - p * r;
-            }
-
-            V[i][k] = V[i][k] - p;
-            V[i][k + 1] = V[i][k + 1] - p * q;
-          }
-        }
-      }
-    }
-  }
-
-  if (norm === 0) {
-    return;
-  }
-
-  for (n = nn - 1; n >= 0; n--) {
-    p = d[n];
-    q = e[n];
-
-    if (q === 0) {
-      l = n;
-      H[n][n] = 1;
-      for (i = n - 1; i >= 0; i--) {
-        w = H[i][i] - p;
-        r = 0;
-        for (j = l; j <= n; j++) {
-          r = r + H[i][j] * H[j][n];
-        }
-
-        if (e[i] < 0) {
-          z = w;
-          s = r;
-        } else {
-          l = i;
-          if (e[i] === 0) {
-            H[i][n] = w !== 0 ? -r / w : -r / (eps * norm);
-          } else {
-            x = H[i][i + 1];
-            y = H[i + 1][i];
-            q = (d[i] - p) * (d[i] - p) + e[i] * e[i];
-            t = (x * s - z * r) / q;
-            H[i][n] = t;
-            H[i + 1][n] =
-              Math.abs(x) > Math.abs(z) ? (-r - w * t) / x : (-s - y * t) / z;
-          }
-
-          t = Math.abs(H[i][n]);
-          if (eps * t * t > 1) {
-            for (j = i; j <= n; j++) {
-              H[j][n] = H[j][n] / t;
-            }
-          }
-        }
-      }
-    } else if (q < 0) {
-      l = n - 1;
-
-      if (Math.abs(H[n][n - 1]) > Math.abs(H[n - 1][n])) {
-        H[n - 1][n - 1] = q / H[n][n - 1];
-        H[n - 1][n] = -(H[n][n] - p) / H[n][n - 1];
-      } else {
-        cdivres = cdiv(0, -H[n - 1][n], H[n - 1][n - 1] - p, q);
-        H[n - 1][n - 1] = cdivres[0];
-        H[n - 1][n] = cdivres[1];
-      }
-
-      H[n][n - 1] = 0;
-      H[n][n] = 1;
-      for (i = n - 2; i >= 0; i--) {
-        ra = 0;
-        sa = 0;
-        for (j = l; j <= n; j++) {
-          ra = ra + H[i][j] * H[j][n - 1];
-          sa = sa + H[i][j] * H[j][n];
-        }
-
-        w = H[i][i] - p;
-
-        if (e[i] < 0) {
-          z = w;
-          r = ra;
-          s = sa;
-        } else {
-          l = i;
-          if (e[i] === 0) {
-            cdivres = cdiv(-ra, -sa, w, q);
-            H[i][n - 1] = cdivres[0];
-            H[i][n] = cdivres[1];
-          } else {
-            x = H[i][i + 1];
-            y = H[i + 1][i];
-            vr = (d[i] - p) * (d[i] - p) + e[i] * e[i] - q * q;
-            vi = (d[i] - p) * 2 * q;
-            if (vr === 0 && vi === 0) {
-              vr =
-                eps *
-                norm *
-                (Math.abs(w) +
-                  Math.abs(q) +
-                  Math.abs(x) +
-                  Math.abs(y) +
-                  Math.abs(z));
-            }
-            cdivres = cdiv(
-              x * r - z * ra + q * sa,
-              x * s - z * sa - q * ra,
-              vr,
-              vi
-            );
-            H[i][n - 1] = cdivres[0];
-            H[i][n] = cdivres[1];
-            if (Math.abs(x) > Math.abs(z) + Math.abs(q)) {
-              H[i + 1][n - 1] = (-ra - w * H[i][n - 1] + q * H[i][n]) / x;
-              H[i + 1][n] = (-sa - w * H[i][n] - q * H[i][n - 1]) / x;
-            } else {
-              cdivres = cdiv(-r - y * H[i][n - 1], -s - y * H[i][n], z, q);
-              H[i + 1][n - 1] = cdivres[0];
-              H[i + 1][n] = cdivres[1];
-            }
-          }
-
-          t = Math.max(Math.abs(H[i][n - 1]), Math.abs(H[i][n]));
-          if (eps * t * t > 1) {
-            for (j = i; j <= n; j++) {
-              H[j][n - 1] = H[j][n - 1] / t;
-              H[j][n] = H[j][n] / t;
-            }
-          }
-        }
-      }
-    }
-  }
-
-  for (i = 0; i < nn; i++) {
-    if (i < low || i > high) {
-      for (j = i; j < nn; j++) {
-        V[i][j] = H[i][j];
-      }
-    }
-  }
-
-  for (j = nn - 1; j >= low; j--) {
-    for (i = low; i <= high; i++) {
-      z = 0;
-      for (k = low; k <= Math.min(j, high); k++) {
-        z = z + V[i][k] * H[k][j];
-      }
-      V[i][j] = z;
-    }
-  }
-}
-
-function cdiv(xr, xi, yr, yi) {
-  var r, d;
-  if (Math.abs(yr) > Math.abs(yi)) {
-    r = yi / yr;
-    d = yr + r * yi;
-    return [(xr + r * xi) / d, (xi - r * xr) / d];
-  } else {
-    r = yr / yi;
-    d = yi + r * yr;
-    return [(r * xr + xi) / d, (r * xi - xr) / d];
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/dc/cholesky.js
-
-
-/**
- * @class CholeskyDecomposition
- * @link https://github.com/lutzroeder/Mapack/blob/master/Source/CholeskyDecomposition.cs
- * @param {Matrix} value
- */
-class cholesky_CholeskyDecomposition {
-  constructor(value) {
-    value = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(value);
-    if (!value.isSymmetric()) {
-      throw new Error('Matrix is not symmetric');
-    }
-
-    var a = value;
-    var dimension = a.rows;
-    var l = new matrix_Matrix(dimension, dimension);
-    var positiveDefinite = true;
-    var i, j, k;
-
-    for (j = 0; j < dimension; j++) {
-      var Lrowj = l[j];
-      var d = 0;
-      for (k = 0; k < j; k++) {
-        var Lrowk = l[k];
-        var s = 0;
-        for (i = 0; i < k; i++) {
-          s += Lrowk[i] * Lrowj[i];
-        }
-        Lrowj[k] = s = (a.get(j, k) - s) / l[k][k];
-        d = d + s * s;
-      }
-
-      d = a.get(j, j) - d;
-
-      positiveDefinite &= d > 0;
-      l[j][j] = Math.sqrt(Math.max(d, 0));
-      for (k = j + 1; k < dimension; k++) {
-        l[j][k] = 0;
-      }
-    }
-
-    if (!positiveDefinite) {
-      throw new Error('Matrix is not positive definite');
-    }
-
-    this.L = l;
-  }
-
-  /**
-   *
-   * @param {Matrix} value
-   * @return {Matrix}
-   */
-  solve(value) {
-    value = WrapperMatrix2D_WrapperMatrix2D.checkMatrix(value);
-
-    var l = this.L;
-    var dimension = l.rows;
-
-    if (value.rows !== dimension) {
-      throw new Error('Matrix dimensions do not match');
-    }
-
-    var count = value.columns;
-    var B = value.clone();
-    var i, j, k;
-
-    for (k = 0; k < dimension; k++) {
-      for (j = 0; j < count; j++) {
-        for (i = 0; i < k; i++) {
-          B[k][j] -= B[i][j] * l[k][i];
-        }
-        B[k][j] /= l[k][k];
-      }
-    }
-
-    for (k = dimension - 1; k >= 0; k--) {
-      for (j = 0; j < count; j++) {
-        for (i = k + 1; i < dimension; i++) {
-          B[k][j] -= B[i][j] * l[i][k];
-        }
-        B[k][j] /= l[k][k];
-      }
-    }
-
-    return B;
-  }
-
-  /**
-   *
-   * @return {Matrix}
-   */
-  get lowerTriangularMatrix() {
-    return this.L;
-  }
-}
-
-// CONCATENATED MODULE: ./node_modules/ml-matrix/src/index.js
-/* concated harmony reexport default */__webpack_require__.d(__webpack_exports__, "default", function() { return matrix_Matrix; });
-/* concated harmony reexport Matrix */__webpack_require__.d(__webpack_exports__, "Matrix", function() { return matrix_Matrix; });
-/* concated harmony reexport abstractMatrix */__webpack_require__.d(__webpack_exports__, "abstractMatrix", function() { return AbstractMatrix; });
-/* concated harmony reexport wrap */__webpack_require__.d(__webpack_exports__, "wrap", function() { return wrap; });
-/* concated harmony reexport WrapperMatrix2D */__webpack_require__.d(__webpack_exports__, "WrapperMatrix2D", function() { return WrapperMatrix2D_WrapperMatrix2D; });
-/* concated harmony reexport WrapperMatrix1D */__webpack_require__.d(__webpack_exports__, "WrapperMatrix1D", function() { return WrapperMatrix1D_WrapperMatrix1D; });
-/* concated harmony reexport solve */__webpack_require__.d(__webpack_exports__, "solve", function() { return solve; });
-/* concated harmony reexport inverse */__webpack_require__.d(__webpack_exports__, "inverse", function() { return inverse; });
-/* concated harmony reexport linearDependencies */__webpack_require__.d(__webpack_exports__, "linearDependencies", function() { return linearDependencies; });
-/* concated harmony reexport SingularValueDecomposition */__webpack_require__.d(__webpack_exports__, "SingularValueDecomposition", function() { return svd_SingularValueDecomposition; });
-/* concated harmony reexport SVD */__webpack_require__.d(__webpack_exports__, "SVD", function() { return svd_SingularValueDecomposition; });
-/* concated harmony reexport EigenvalueDecomposition */__webpack_require__.d(__webpack_exports__, "EigenvalueDecomposition", function() { return evd_EigenvalueDecomposition; });
-/* concated harmony reexport EVD */__webpack_require__.d(__webpack_exports__, "EVD", function() { return evd_EigenvalueDecomposition; });
-/* concated harmony reexport CholeskyDecomposition */__webpack_require__.d(__webpack_exports__, "CholeskyDecomposition", function() { return cholesky_CholeskyDecomposition; });
-/* concated harmony reexport CHO */__webpack_require__.d(__webpack_exports__, "CHO", function() { return cholesky_CholeskyDecomposition; });
-/* concated harmony reexport LuDecomposition */__webpack_require__.d(__webpack_exports__, "LuDecomposition", function() { return lu_LuDecomposition; });
-/* concated harmony reexport LU */__webpack_require__.d(__webpack_exports__, "LU", function() { return lu_LuDecomposition; });
-/* concated harmony reexport QrDecomposition */__webpack_require__.d(__webpack_exports__, "QrDecomposition", function() { return qr_QrDecomposition; });
-/* concated harmony reexport QR */__webpack_require__.d(__webpack_exports__, "QR", function() { return qr_QrDecomposition; });
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-/***/ })
-/******/ ]);
-});
\ No newline at end of file
diff --git a/spaces/mingyuan/ReMoDiffuse/app.py b/spaces/mingyuan/ReMoDiffuse/app.py
deleted file mode 100644
index 0d062a787e35bd55dea15b732cc64ae7563b5020..0000000000000000000000000000000000000000
--- a/spaces/mingyuan/ReMoDiffuse/app.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import os
-import sys
-import gradio as gr
-
-os.makedirs("outputs", exist_ok=True) 
-sys.path.insert(0, '.')
-
-import argparse
-import os.path as osp
-import mmcv
-import numpy as np
-import torch
-from mogen.models import build_architecture
-from mmcv.runner import load_checkpoint
-from mmcv.parallel import MMDataParallel
-from mogen.utils.plot_utils import (
-    recover_from_ric,
-    plot_3d_motion,
-    t2m_kinematic_chain
-)
-from scipy.ndimage import gaussian_filter
-from IPython.display import Image
-
-
-def motion_temporal_filter(motion, sigma=1):
-    motion = motion.reshape(motion.shape[0], -1)
-    for i in range(motion.shape[1]):
-        motion[:, i] = gaussian_filter(motion[:, i], sigma=sigma, mode="nearest")
-    return motion.reshape(motion.shape[0], -1, 3)
-
-
-def plot_t2m(data, result_path, npy_path, caption):
-    joint = recover_from_ric(torch.from_numpy(data).float(), 22).numpy()
-    joint = motion_temporal_filter(joint, sigma=2.5)
-    plot_3d_motion(result_path, t2m_kinematic_chain, joint, title=caption, fps=20)
-    if npy_path is not None:
-        np.save(npy_path, joint)
-
-def create_remodiffuse():
-    config_path = "configs/remodiffuse/remodiffuse_t2m.py"
-    ckpt_path = "logs/remodiffuse/remodiffuse_t2m/latest.pth"
-    cfg = mmcv.Config.fromfile(config_path)
-    model = build_architecture(cfg.model)
-    load_checkpoint(model, ckpt_path, map_location='cpu')
-    model.cpu()
-    model.eval()
-    return model
-
-def create_motiondiffuse():
-    config_path = "configs/motiondiffuse/motiondiffuse_t2m.py"
-    ckpt_path = "logs/motiondiffuse/motiondiffuse_t2m/latest.pth"
-    cfg = mmcv.Config.fromfile(config_path)
-    model = build_architecture(cfg.model)
-    load_checkpoint(model, ckpt_path, map_location='cpu')
-    model.cpu()
-    model.eval()
-    return model
-
-def create_mdm():
-    config_path = "configs/mdm/mdm_t2m_official.py"
-    ckpt_path = "logs/mdm/mdm_t2m/latest.pth"
-    cfg = mmcv.Config.fromfile(config_path)
-    model = build_architecture(cfg.model)
-    load_checkpoint(model, ckpt_path, map_location='cpu')
-    model.cpu()
-    model.eval()
-    return model
-
-model_remodiffuse = create_remodiffuse()
-# model_motiondiffuse = create_motiondiffuse()
-# model_mdm = create_mdm()
-
-mean_path = "data/datasets/human_ml3d/mean.npy"
-std_path = "data/datasets/human_ml3d/std.npy"
-mean = np.load(mean_path)
-std = np.load(std_path)
-
-
-def show_generation_result(model, text, motion_length, result_path):
-    device = 'cpu'
-    motion = torch.zeros(1, motion_length, 263).to(device)
-    motion_mask = torch.ones(1, motion_length).to(device)
-    motion_length = torch.Tensor([motion_length]).long().to(device)
-    model = model.to(device)
-    input = {
-        'motion': motion,
-        'motion_mask': motion_mask,
-        'motion_length': motion_length,
-        'motion_metas': [{'text': text}],
-    }
-
-    all_pred_motion = []
-    with torch.no_grad():
-        input['inference_kwargs'] = {}
-        output_list = []
-        output = model(**input)[0]['pred_motion']
-        pred_motion = output.cpu().detach().numpy()
-        pred_motion = pred_motion * std + mean
-
-    plot_t2m(pred_motion, result_path, None, text)
-
-def generate(prompt, length):
-    if not os.path.exists("outputs"):
-        os.mkdir("outputs")
-    result_path = "outputs/" + str(hash(prompt)) + ".mp4"
-    show_generation_result(model_remodiffuse, prompt, length, result_path)
-    return result_path
-
-demo = gr.Interface(
-    fn=generate,
-    inputs=["text", gr.Slider(20, 196, value=60)],
-    examples=[
-        ["a person performs a cartwheel", 57],
-        ["a person picks up something from the ground", 79],
-        ["a person walks around and then sits down", 190],
-        ["a person performs a deep bow", 89],
-    ],
-    outputs="video",
-    title="ReMoDiffuse: Retrieval-Augmented Motion Diffusion Model",
-    description="This is an interactive demo for ReMoDiffuse. For more information, feel free to visit our project page(https://mingyuan-zhang.github.io/projects/ReMoDiffuse.html).")
-
-demo.queue()
-demo.launch()
\ No newline at end of file
diff --git a/spaces/miyaaa666/bingo/src/app/layout.tsx b/spaces/miyaaa666/bingo/src/app/layout.tsx
deleted file mode 100644
index 8b5122759987177b8dc4e4356d1d06cea25c15ea..0000000000000000000000000000000000000000
--- a/spaces/miyaaa666/bingo/src/app/layout.tsx
+++ /dev/null
@@ -1,47 +0,0 @@
-import { Metadata } from 'next'
-import { Toaster } from 'react-hot-toast'
-import { TailwindIndicator } from '@/components/tailwind-indicator'
-import { Providers } from '@/components/providers'
-import { Header } from '@/components/header'
-
-import '@/app/globals.scss'
-
-
-export const metadata: Metadata = {
-  title: {
-    default: 'Bing AI Chatbot',
-    template: `%s - Bing AI Chatbot`
-  },
-  description: 'Bing AI Chatbot Web App.',
-  themeColor: [
-    { media: '(prefers-color-scheme: light)', color: 'white' },
-    { media: '(prefers-color-scheme: dark)', color: 'dark' }
-  ],
-  icons: {
-    icon: '/favicon.ico',
-    shortcut: '../assets/images/logo.svg',
-    apple: '../assets/images/logo.svg'
-  }
-}
-
-interface RootLayoutProps {
-  children: React.ReactNode
-}
-
-export default function RootLayout({ children }: RootLayoutProps) {
-  return (
-    <html lang="zh-CN" suppressHydrationWarning>
-      <body>
-        <Toaster />
-        <Providers attribute="class" defaultTheme="system" enableSystem>
-          <div className="flex flex-col min-h-screen">
-            {/* @ts-ignore */}
-            <Header />
-            <main className="flex flex-col flex-1">{children}</main>
-          </div>
-          <TailwindIndicator />
-        </Providers>
-      </body>
-    </html>
-  )
-}
diff --git a/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/modeling/transformer/position_encoding.py b/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/modeling/transformer/position_encoding.py
deleted file mode 100644
index db236c5b36cbc4f4435a83b542bdc242cbb441c3..0000000000000000000000000000000000000000
--- a/spaces/mmlab-ntu/Segment-Any-RGBD/open_vocab_seg/modeling/transformer/position_encoding.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# # Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-"""
-Various positional encodings for the transformer.
-"""
-import math
-
-import torch
-from torch import nn
-
-
-class PositionEmbeddingSine(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(
-        self, num_pos_feats=64, temperature=10000, normalize=False, scale=None
-    ):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, x, mask=None):
-        if mask is None:
-            mask = torch.zeros(
-                (x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool
-            )
-        not_mask = ~mask
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
diff --git a/spaces/monra/freegpt-webui/g4f/typing.py b/spaces/monra/freegpt-webui/g4f/typing.py
deleted file mode 100644
index e41a567ae49dd26d2ace2a3732b0e8f0bbbaa4b0..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/g4f/typing.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from typing import Dict, NewType, Union, Optional, List, get_type_hints
-
-sha256 = NewType('sha_256_hash', str)
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/quantization/scalar/modules/qact.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/quantization/scalar/modules/qact.py
deleted file mode 100644
index c5dd1d63362423ab0cfc381dddabb547a3b44c72..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/quantization/scalar/modules/qact.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from ..ops import emulate_int
-
-
-class ActivationQuantizer:
-    """
-    Fake scalar quantization of the activations using a forward hook.
-
-    Args:
-        - module. a nn.Module for which we quantize the *post-activations*
-        - p: proportion of activations to quantize, set by default to 1
-        - update_step: to recompute quantization parameters
-        - bits: number of bits for quantization
-        - method: choose among {"tensor", "histogram", "channel"}
-        - clamp_threshold: to prevent gradients overflow
-
-    Remarks:
-        - Parameters scale and zero_point are recomputed every update_step
-          forward pass to reduce the overhead
-        - For the list of quantization methods and number of bits, see ops.py
-        - To remove the hook from the module, simply call self.handle.remove()
-        - At test time, the activations are fully quantized
-        - We use the straight-through estimator so that the gradients
-          back-propagate nicely in the network, this is implemented with
-          the detach() trick
-        - The activations are hard-clamped in [-clamp_threshold, clamp_threshold]
-          to prevent overflow during the backward pass
-    """
-
-    def __init__(
-        self,
-        module,
-        p=1,
-        update_step=1000,
-        bits=8,
-        method="histogram",
-        clamp_threshold=5,
-    ):
-        self.module = module
-        self.p = p
-        self.update_step = update_step
-        self.counter = 0
-        self.bits = bits
-        self.method = method
-        self.clamp_threshold = clamp_threshold
-        self.handle = None
-        self.register_hook()
-
-    def register_hook(self):
-        # forward hook
-        def quantize_hook(module, x, y):
-
-            # update parameters every 1000 iterations
-            if self.counter % self.update_step == 0:
-                self.scale = None
-                self.zero_point = None
-            self.counter += 1
-
-            # train with QuantNoise and evaluate the fully quantized network
-            p = self.p if self.module.training else 1
-
-            # quantize activations
-            y_q, self.scale, self.zero_point = emulate_int(
-                y.detach(),
-                bits=self.bits,
-                method=self.method,
-                scale=self.scale,
-                zero_point=self.zero_point,
-            )
-
-            # mask to apply noise
-            mask = torch.zeros_like(y)
-            mask.bernoulli_(1 - p)
-            noise = (y_q - y).masked_fill(mask.bool(), 0)
-
-            # using straight-through estimator (STE)
-            clamp_low = -self.scale * self.zero_point
-            clamp_high = self.scale * (2 ** self.bits - 1 - self.zero_point)
-            return torch.clamp(y, clamp_low.item(), clamp_high.item()) + noise.detach()
-
-        # register hook
-        self.handle = self.module.register_forward_hook(quantize_hook)
diff --git a/spaces/mueller-franzes/medfusion-app/medical_diffusion/external/diffusers/unet.py b/spaces/mueller-franzes/medfusion-app/medical_diffusion/external/diffusers/unet.py
deleted file mode 100644
index 122b50ac976ef25b6e45735ee966aa4c3cea26a9..0000000000000000000000000000000000000000
--- a/spaces/mueller-franzes/medfusion-app/medical_diffusion/external/diffusers/unet.py
+++ /dev/null
@@ -1,257 +0,0 @@
-
-
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-
-
-from .embeddings import TimeEmbbeding
-
-from .unet_blocks import (
-    CrossAttnDownBlock2D,
-    CrossAttnUpBlock2D,
-    DownBlock2D,
-    UNetMidBlock2DCrossAttn,
-    UpBlock2D,
-    get_down_block,
-    get_up_block,
-)
-
-class TimestepEmbedding(nn.Module):
-    def __init__(self, channel, time_embed_dim, act_fn="silu"):
-        super().__init__()
-
-        self.linear_1 = nn.Linear(channel, time_embed_dim)
-        self.act = None
-        if act_fn == "silu":
-            self.act = nn.SiLU()
-        self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim)
-
-    def forward(self, sample):
-        sample = self.linear_1(sample)
-
-        if self.act is not None:
-            sample = self.act(sample)
-
-        sample = self.linear_2(sample)
-        return sample
-
-
-class UNet2DConditionModel(nn.Module):
-    r"""
-    UNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep
-    and returns sample shaped output.
-
-
-    Parameters:
-        sample_size (`int`, *optional*): The size of the input sample.
-        in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.
-        out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.
-        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
-        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
-            Whether to flip the sin to cos in the time embedding.
-        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
-            The tuple of downsample blocks to use.
-        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`):
-            The tuple of upsample blocks to use.
-        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
-            The tuple of output channels for each block.
-        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
-        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
-        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
-        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
-        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
-        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
-        cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.
-        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
-    """
-
-    _supports_gradient_checkpointing = True
-
-
-    def __init__(
-        self,
-        sample_size: Optional[int] = None,
-        in_channels: int = 4,
-        out_channels: int = 4,
-        center_input_sample: bool = False,
-        flip_sin_to_cos: bool = True,
-        freq_shift: int = 0,
-        down_block_types: Tuple[str] = (
-            "CrossAttnDownBlock2D",
-            "CrossAttnDownBlock2D",
-            "CrossAttnDownBlock2D",
-            "DownBlock2D",
-        ),
-        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
-        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
-        layers_per_block: int = 2,
-        downsample_padding: int = 1,
-        mid_block_scale_factor: float = 1,
-        act_fn: str = "silu",
-        norm_num_groups: int = 32,
-        norm_eps: float = 1e-5,
-        cross_attention_dim: int = 768,
-        attention_head_dim: int = 8,
-    ):
-        super().__init__()
-
-        self.sample_size = sample_size
-        time_embed_dim = block_out_channels[0] * 4
-
-        self.emb = nn.Embedding(2, cross_attention_dim)
-
-        # input
-        self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
-
-        # time
-        self.time_embedding = TimeEmbbeding(block_out_channels[0], time_embed_dim)
-
-        self.down_blocks = nn.ModuleList([])
-        self.mid_block = None
-        self.up_blocks = nn.ModuleList([])
-
-        # down
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            down_block = get_down_block(
-                down_block_type,
-                num_layers=layers_per_block,
-                in_channels=input_channel,
-                out_channels=output_channel,
-                temb_channels=time_embed_dim,
-                add_downsample=not is_final_block,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim,
-                attn_num_head_channels=attention_head_dim,
-                downsample_padding=downsample_padding,
-            )
-            self.down_blocks.append(down_block)
-
-        # mid
-        self.mid_block = UNetMidBlock2DCrossAttn(
-            in_channels=block_out_channels[-1],
-            temb_channels=time_embed_dim,
-            resnet_eps=norm_eps,
-            resnet_act_fn=act_fn,
-            output_scale_factor=mid_block_scale_factor,
-            resnet_time_scale_shift="default",
-            cross_attention_dim=cross_attention_dim,
-            attn_num_head_channels=attention_head_dim,
-            resnet_groups=norm_num_groups,
-        )
-
-        # up
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        output_channel = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(up_block_types):
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
-            is_final_block = i == len(block_out_channels) - 1
-
-            up_block = get_up_block(
-                up_block_type,
-                num_layers=layers_per_block + 1,
-                in_channels=input_channel,
-                out_channels=output_channel,
-                prev_output_channel=prev_output_channel,
-                temb_channels=time_embed_dim,
-                add_upsample=not is_final_block,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim,
-                attn_num_head_channels=attention_head_dim,
-            )
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-        # out
-        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
-        self.conv_act = nn.SiLU()
-        self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
-
-
-
-    def forward(
-        self,
-        sample: torch.FloatTensor,
-        t: torch.Tensor,
-        encoder_hidden_states: torch.Tensor = None,
-        self_cond: torch.Tensor = None
-    ):
-        encoder_hidden_states = self.emb(encoder_hidden_states)
-        # encoder_hidden_states = None # ------------------------ WARNING Disabled ---------------------
-        """r
-        Args:
-            sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
-            timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
-            encoder_hidden_states (`torch.FloatTensor`): (batch, channel, height, width) encoder hidden states
-   
-        Returns:
-            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
-            [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-        """
-        # 0. center input if necessary
-        # if self.config.center_input_sample:
-        #     sample = 2 * sample - 1.0
-
-        # 1. time
-        t_emb = self.time_embedding(t)
-
-        # 2. pre-process
-        sample = self.conv_in(sample)
-
-        # 3. down
-        down_block_res_samples = (sample,)
-        for downsample_block in self.down_blocks:
-            if hasattr(downsample_block, "attentions") and downsample_block.attentions is not None:
-                sample, res_samples = downsample_block(
-                    hidden_states=sample,
-                    temb=t_emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                )
-            else:
-                sample, res_samples = downsample_block(hidden_states=sample, temb=t_emb)
-
-            down_block_res_samples += res_samples
-
-        # 4. mid
-        sample = self.mid_block(sample, t_emb, encoder_hidden_states=encoder_hidden_states)
-
-        # 5. up
-        for upsample_block in self.up_blocks:
-            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
-            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
-
-            if hasattr(upsample_block, "attentions") and upsample_block.attentions is not None:
-                sample = upsample_block(
-                    hidden_states=sample,
-                    temb=t_emb,
-                    res_hidden_states_tuple=res_samples,
-                    encoder_hidden_states=encoder_hidden_states,
-                )
-            else:
-                sample = upsample_block(hidden_states=sample, temb=t_emb, res_hidden_states_tuple=res_samples)
-
-        # 6. post-process
-        # make sure hidden states is in float32
-        # when running in half-precision
-        sample = self.conv_norm_out(sample.float()).type(sample.dtype)
-        sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-
-
-        return sample, []
diff --git a/spaces/muellerzr/accelerate-presentation/Accelerate_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.css b/spaces/muellerzr/accelerate-presentation/Accelerate_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.css
deleted file mode 100644
index e8410fe9e2bbeb2cca7f828d96e8bb770cb84ae9..0000000000000000000000000000000000000000
--- a/spaces/muellerzr/accelerate-presentation/Accelerate_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.css
+++ /dev/null
@@ -1,31 +0,0 @@
-.reveal
-  div.sourceCode
-  pre
-  code.has-line-highlights
-  > span:not(.highlight-line) {
-  opacity: 0.4;
-}
-
-.reveal pre.numberSource {
-  padding-left: 0;
-}
-
-.reveal pre.numberSource code > span {
-  left: -2.1em;
-}
-
-pre.numberSource code > span > a:first-child::before {
-  left: -0.7em;
-}
-
-.reveal pre > code:not(:first-child).fragment {
-  position: absolute;
-  top: 0;
-  left: 0;
-  width: 100%;
-  box-sizing: border-box;
-}
-
-.reveal div.sourceCode pre code {
-  min-height: 100%;
-}
diff --git a/spaces/mygyasir/genious_bgremover/carvekit/ml/arch/basnet/__init__.py b/spaces/mygyasir/genious_bgremover/carvekit/ml/arch/basnet/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/A Su Manera Gerri Hill Descargar.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/A Su Manera Gerri Hill Descargar.md
deleted file mode 100644
index a96039a674d70dbc1d98fa2828ac43aa159b245d..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/A Su Manera Gerri Hill Descargar.md	
+++ /dev/null
@@ -1,13 +0,0 @@
-
-<h1>A Su Manera: una novela de suspenso y romance de Gerri Hill</h1>
-<p>¿Te gustan las historias de detectives, misterio y amor? Entonces no te pierdas <strong>A Su Manera</strong>, la décima novela de la exitosa autora Gerri Hill[^2^], que te mantendrá en vilo hasta el final.</p>
-<p><strong>A Su Manera</strong> narra la aventura de Tori Hunter, una inspectora de homicidios que tiene que trabajar con Samantha Kennedy, una novata que no le cae nada bien. Juntas tendrán que resolver una serie de asesinatos que las llevarán a enfrentarse a un peligroso asesino en serie y a sus propios sentimientos.</p>
-<h2>A Su Manera Gerri Hill Descargar</h2><br /><p><b><b>Download Zip</b> &#9675; <a href="https://urlcod.com/2uI9Vd">https://urlcod.com/2uI9Vd</a></b></p><br /><br />
-<p>Si quieres descargar <strong>A Su Manera</strong> en formato digital, puedes hacerlo desde el siguiente enlace[^1^]. También puedes encontrar el libro en papel en las principales librerías y tiendas online. No esperes más y sumérgete en esta apasionante novela de Gerri Hill, una de las mejores escritoras de ficción lésbica del momento.</p><p>Gerri Hill es una escritora estadounidense que se ha especializado en el género de la ficción lésbica. Sus novelas combinan elementos de suspenso, romance, humor y drama, y han sido traducidas a varios idiomas. Gerri Hill ha ganado varios premios literarios, como el Lambda Literary Award, el Golden Crown Literary Award y el Alice B. Readers Award.</p>
-<p>Entre sus obras más populares se encuentran <strong>Hunter's Way</strong>, <strong>At Seventeen</strong>, <strong>The Secret Pond</strong>, <strong>Snow Falls</strong> y <strong>The Midnight Moon</strong>. Todas ellas cuentan con personajes femeninos fuertes, inteligentes y valientes, que se enfrentan a situaciones difíciles y encuentran el amor en el camino.</p>
-<p>Si quieres conocer más sobre Gerri Hill y sus libros, puedes visitar su página web oficial, donde encontrarás información sobre su biografía, sus próximos lanzamientos, sus eventos y sus contactos. También puedes seguirla en sus redes sociales, como Facebook, Twitter e Instagram, donde comparte noticias, fotos y opiniones con sus fans.</p><p>Si te gustó <strong>A Su Manera</strong>, quizás quieras explorar otros libros y autoras similares a Gerri Hill. Según el sitio web Goodreads[^1^], algunos de los autores más parecidos a Gerri Hill son Radclyffe, Tracey Richardson, K.E. Lane, Robin Alexander, Rachel Spangler, Ingrid Díaz, Jae, G. Benson, Melissa Brayden y K.L. Hughes. Todos ellos escriben novelas de ficción lésbica con diferentes estilos y temáticas, pero con el mismo objetivo de entretener y emocionar a sus lectores.</p>
-<p></p>
-<p>Además de estos autores, también puedes encontrar otros más en el sitio web Literature Map[^3^], que te muestra una nube de nombres de escritores relacionados con Gerri Hill. Cuanto más cerca estén dos nombres, más probable es que te gusten ambos. Puedes hacer clic en cualquier nombre para viajar por el mapa literario y descubrir nuevas opciones de lectura.</p>
-<p>Esperamos que estas recomendaciones te sean útiles y que disfrutes de la diversidad y la calidad de la literatura lésbica actual. Recuerda que puedes dejar tus comentarios y valoraciones sobre los libros que leas en Goodreads o en otras plataformas de reseñas. Así ayudarás a otros lectores a encontrar sus próximas lecturas favoritas.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Design Of Sarda Type Fall Pdf Download [NEW].md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Design Of Sarda Type Fall Pdf Download [NEW].md
deleted file mode 100644
index 58a757c7f93db35b97e2c94241188ef6bab20ccb..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Design Of Sarda Type Fall Pdf Download [NEW].md	
+++ /dev/null
@@ -1,28 +0,0 @@
-<br />
-<h1>Design Of Sarda Type Fall: A Guide For Water Engineers</h1>
-<p>A Sarda type fall is a hydraulic structure that is used to create a drop in the water level of a canal. It is a fall with raised crest and vertical impact. It was first constructed on the Sarda canal in Uttar Pradesh, India. The main advantages of this type of fall are that it requires less excavation, has less seepage losses, and can handle high discharge intensity.</p>
-<p>In this article, we will explain the design principles and steps for Sarda type fall, based on the information from various web sources[^1^] [^2^] [^3^]. We will also provide a sample calculation for a Sarda type fall with given data.</p>
-<h2>Design Of Sarda Type Fall Pdf Download</h2><br /><p><b><b>DOWNLOAD</b> &bull;&bull;&bull; <a href="https://urlcod.com/2uIbzn">https://urlcod.com/2uIbzn</a></b></p><br /><br />
-<h2>Design Principles for Sarda Type Fall</h2>
-<p>The design of a Sarda type fall involves the following components:</p>
-<ul>
-<li>Crest: The top of the fall where the water flows over. The crest can be rectangular or trapezoidal in shape. The length of the crest is equal to the bed width of the canal for large canals, and equal to the bed width plus the depth of flow for small canals. The width of the crest is determined by a formula that depends on the head above the crest and the normal water depth.</li>
-<li>Body wall: The vertical wall that supports the crest. The body wall can be rectangular or trapezoidal in cross-section, depending on the discharge of the canal. The top width and base width of the body wall are also calculated by formulas that depend on the head above the crest and the normal water depth. The edges of the body wall are rounded with a radius of 0.3 m.</li>
-<li>Cistern: The basin where the water falls and dissipates its energy. The cistern has a horizontal floor and vertical walls. The length and depth of the cistern are determined by formulas that depend on the total fall and the effective head. The cistern level is lower than the downstream bed level by the depth of the cistern.</li>
-<li>Impervious floor: The floor that prevents seepage under the fall. The impervious floor extends from upstream to downstream of the fall. The total length of the impervious floor is determined by Bligh's theory or Khosla's theory, depending on the size of the structure.</li>
-<li>Wing walls: The walls that protect the banks from erosion and guide the flow. The wing walls are provided on both upstream and downstream sides of the fall. The upstream wing walls are circular in shape with a radius that depends on the effective head. The downstream wing walls are straight in shape with a length that depends on the total fall and effective head.</li>
-<li>Stone pitching: The layer of stones that protects the floor and walls from scouring. The stone pitching is provided on both upstream and downstream sides of the fall. The upstream stone pitching length is equal to the total length of impervious floor minus creep length. The downstream stone pitching length is calculated by a formula that depends on effective head and total fall.</li>
-</ul>
-<h2>Design Steps for Sarda Type Fall</h2>
-<p>The design steps for Sarda type fall are as follows:</p>
-<p></p>
-<ol>
-<li>Calculate the total fall (Hw) as the difference between upstream full supply level (FSL) and downstream FSL.</li>
-<li>Assume a suitable value for head above crest (H) less than total fall (Hw).</li>
-<li>Calculate crest length (L) as bed width (B) for large canals, or bed width plus depth of flow (D) for small canals.</li>
-<li>Calculate crest width (B) as 0.55√(H+D) for rectangular crest, or 0.45√(H+D) for trapezoidal crest.</li>
-<li>Calculate discharge (Q) as 0.415√(2g)L×H^(3/2) for free flow condition, or 0.65L×(H+D)×√(2g(H-D)) for submerged flow condition.</li>
-<li>Calculate velocity of approach (V) as Q/A, where A is cross-sectional area of canal.</li>
-<li>Calculate velocity head (ha) as</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/nickmuchi/fintweet-GPT-Search/pages/2_Twitter_GPT_Search.py b/spaces/nickmuchi/fintweet-GPT-Search/pages/2_Twitter_GPT_Search.py
deleted file mode 100644
index 482f66e084add89509e260a3711eb4e794cc9339..0000000000000000000000000000000000000000
--- a/spaces/nickmuchi/fintweet-GPT-Search/pages/2_Twitter_GPT_Search.py
+++ /dev/null
@@ -1,107 +0,0 @@
-from langchain.embeddings import HuggingFaceEmbeddings,HuggingFaceInstructEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain.chat_models.openai import ChatOpenAI
-from langchain import VectorDBQA
-import pandas as pd
-from variables import *
-
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    AIMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-from langchain.schema import (
-    AIMessage,
-    HumanMessage,
-    SystemMessage
-)
-
-from datetime import datetime as dt
-
-system_template="""Use the following pieces of context to answer the users question. 
-If you don't know the answer, just say that you don't know, don't try to make up an answer.
-ALWAYS return a "SOURCES" part in your answer.
-The "SOURCES" part should be a reference to the source of the document from which you got your answer.
-
-Example of your response should be:
-
-```
-The answer is foo
-SOURCES: xyz
-```
-
-Begin!
-----------------
-{context}
-"""
-
-messages = [
-    SystemMessagePromptTemplate.from_template(system_template),
-    HumanMessagePromptTemplate.from_template("{question}")
-]
-prompt = ChatPromptTemplate.from_messages(messages)
-
-current_time = dt.strftime(dt.today(),'%d_%m_%Y_%H_%M')
-
-st.markdown("## Financial Tweets GPT Search")
-
-twitter_link = """
-[![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi)
-"""
-
-st.markdown(twitter_link)
-
-bi_enc_dict = {'mpnet-base-v2':"sentence-transformers/all-mpnet-base-v2",
-              'instructor-base': 'hkunlp/instructor-base'}
-
-search_input = st.text_input(
-        label='Enter Your Search Query',value= "What is the latest update on central banks?", key='search')
-        
-sbert_model_name = st.sidebar.selectbox("Embedding Model", options=list(bi_enc_dict.keys()), key='sbox')
-
-tweets = st.session_state['tlist']
-topic = st.session_state['topic']
-user = st.session_state['user']
-cr_time = st.session_state['time']
-
-try:
-
-    if search_input:
-        
-        model = bi_enc_dict[sbert_model_name]
-                        
-        with st.spinner(
-            text=f"Loading {model} embedding model and Generating Response..."
-        ):
-
-            vectorstore = create_vectorstore(tweets,model,user,topic,cr_time)
-            
-            tweets = embed_tweets(search_input,prompt,vectorstore)
-
-
-        references = [doc.page_content for doc in tweets['source_documents']]
-
-        answer = tweets['result']
-            
-        ##### Sematic Search #####
-        
-        with st.expander(label='Query Result', expanded=True):
-            st.write(answer)
-            
-        with st.expander(label='References from Corpus used to Generate Result'):
-            for ref in references:
-                st.write(ref)
-            
-    else:
-    
-        st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file')  
-        
-except RuntimeError:
-  
-    st.write('Please ensure you have entered the YouTube URL or uploaded the Earnings Call file') 
-
-    
-
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/roi_heads/roi_head.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/roi_heads/roi_head.py
deleted file mode 100644
index aee645fde0d8321de9181a624a0c921b6dc167c4..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DensePose/densepose/modeling/roi_heads/roi_head.py
+++ /dev/null
@@ -1,218 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import numpy as np
-from typing import Dict, List, Optional
-import fvcore.nn.weight_init as weight_init
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-from detectron2.modeling import ROI_HEADS_REGISTRY, StandardROIHeads
-from detectron2.modeling.poolers import ROIPooler
-from detectron2.modeling.roi_heads import select_foreground_proposals
-from detectron2.structures import ImageList, Instances
-
-from .. import (
-    build_densepose_data_filter,
-    build_densepose_embedder,
-    build_densepose_head,
-    build_densepose_losses,
-    build_densepose_predictor,
-    densepose_inference,
-)
-
-
-class Decoder(nn.Module):
-    """
-    A semantic segmentation head described in detail in the Panoptic Feature Pyramid Networks paper
-    (https://arxiv.org/abs/1901.02446). It takes FPN features as input and merges information from
-    all levels of the FPN into single output.
-    """
-
-    def __init__(self, cfg, input_shape: Dict[str, ShapeSpec], in_features):
-        super(Decoder, self).__init__()
-
-        # fmt: off
-        self.in_features      = in_features
-        feature_strides       = {k: v.stride for k, v in input_shape.items()}
-        feature_channels      = {k: v.channels for k, v in input_shape.items()}
-        num_classes           = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NUM_CLASSES
-        conv_dims             = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_CONV_DIMS
-        self.common_stride    = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_COMMON_STRIDE
-        norm                  = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NORM
-        # fmt: on
-
-        self.scale_heads = []
-        for in_feature in self.in_features:
-            head_ops = []
-            head_length = max(
-                1, int(np.log2(feature_strides[in_feature]) - np.log2(self.common_stride))
-            )
-            for k in range(head_length):
-                conv = Conv2d(
-                    feature_channels[in_feature] if k == 0 else conv_dims,
-                    conv_dims,
-                    kernel_size=3,
-                    stride=1,
-                    padding=1,
-                    bias=not norm,
-                    norm=get_norm(norm, conv_dims),
-                    activation=F.relu,
-                )
-                weight_init.c2_msra_fill(conv)
-                head_ops.append(conv)
-                if feature_strides[in_feature] != self.common_stride:
-                    head_ops.append(
-                        nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False)
-                    )
-            self.scale_heads.append(nn.Sequential(*head_ops))
-            self.add_module(in_feature, self.scale_heads[-1])
-        self.predictor = Conv2d(conv_dims, num_classes, kernel_size=1, stride=1, padding=0)
-        weight_init.c2_msra_fill(self.predictor)
-
-    def forward(self, features: List[torch.Tensor]):
-        for i, _ in enumerate(self.in_features):
-            if i == 0:
-                x = self.scale_heads[i](features[i])
-            else:
-                x = x + self.scale_heads[i](features[i])
-        x = self.predictor(x)
-        return x
-
-
-@ROI_HEADS_REGISTRY.register()
-class DensePoseROIHeads(StandardROIHeads):
-    """
-    A Standard ROIHeads which contains an addition of DensePose head.
-    """
-
-    def __init__(self, cfg, input_shape):
-        super().__init__(cfg, input_shape)
-        self._init_densepose_head(cfg, input_shape)
-
-    def _init_densepose_head(self, cfg, input_shape):
-        # fmt: off
-        self.densepose_on          = cfg.MODEL.DENSEPOSE_ON
-        if not self.densepose_on:
-            return
-        self.densepose_data_filter = build_densepose_data_filter(cfg)
-        dp_pooler_resolution       = cfg.MODEL.ROI_DENSEPOSE_HEAD.POOLER_RESOLUTION
-        dp_pooler_sampling_ratio   = cfg.MODEL.ROI_DENSEPOSE_HEAD.POOLER_SAMPLING_RATIO
-        dp_pooler_type             = cfg.MODEL.ROI_DENSEPOSE_HEAD.POOLER_TYPE
-        self.use_decoder           = cfg.MODEL.ROI_DENSEPOSE_HEAD.DECODER_ON
-        # fmt: on
-        if self.use_decoder:
-            dp_pooler_scales = (1.0 / input_shape[self.in_features[0]].stride,)
-        else:
-            dp_pooler_scales = tuple(1.0 / input_shape[k].stride for k in self.in_features)
-        in_channels = [input_shape[f].channels for f in self.in_features][0]
-
-        if self.use_decoder:
-            self.decoder = Decoder(cfg, input_shape, self.in_features)
-
-        self.densepose_pooler = ROIPooler(
-            output_size=dp_pooler_resolution,
-            scales=dp_pooler_scales,
-            sampling_ratio=dp_pooler_sampling_ratio,
-            pooler_type=dp_pooler_type,
-        )
-        self.densepose_head = build_densepose_head(cfg, in_channels)
-        self.densepose_predictor = build_densepose_predictor(
-            cfg, self.densepose_head.n_out_channels
-        )
-        self.densepose_losses = build_densepose_losses(cfg)
-        self.embedder = build_densepose_embedder(cfg)
-
-    def _forward_densepose(self, features: Dict[str, torch.Tensor], instances: List[Instances]):
-        """
-        Forward logic of the densepose prediction branch.
-
-        Args:
-            features (dict[str, Tensor]): input data as a mapping from feature
-                map name to tensor. Axis 0 represents the number of images `N` in
-                the input data; axes 1-3 are channels, height, and width, which may
-                vary between feature maps (e.g., if a feature pyramid is used).
-            instances (list[Instances]): length `N` list of `Instances`. The i-th
-                `Instances` contains instances for the i-th input image,
-                In training, they can be the proposals.
-                In inference, they can be the predicted boxes.
-
-        Returns:
-            In training, a dict of losses.
-            In inference, update `instances` with new fields "densepose" and return it.
-        """
-        if not self.densepose_on:
-            return {} if self.training else instances
-
-        features_list = [features[f] for f in self.in_features]
-        if self.training:
-            proposals, _ = select_foreground_proposals(instances, self.num_classes)
-            features_list, proposals = self.densepose_data_filter(features_list, proposals)
-            if len(proposals) > 0:
-                proposal_boxes = [x.proposal_boxes for x in proposals]
-
-                if self.use_decoder:
-                    features_list = [self.decoder(features_list)]
-
-                features_dp = self.densepose_pooler(features_list, proposal_boxes)
-                densepose_head_outputs = self.densepose_head(features_dp)
-                densepose_predictor_outputs = self.densepose_predictor(densepose_head_outputs)
-                densepose_loss_dict = self.densepose_losses(
-                    proposals, densepose_predictor_outputs, embedder=self.embedder
-                )
-                return densepose_loss_dict
-        else:
-            pred_boxes = [x.pred_boxes for x in instances]
-
-            if self.use_decoder:
-                features_list = [self.decoder(features_list)]
-
-            features_dp = self.densepose_pooler(features_list, pred_boxes)
-            if len(features_dp) > 0:
-                densepose_head_outputs = self.densepose_head(features_dp)
-                densepose_predictor_outputs = self.densepose_predictor(densepose_head_outputs)
-            else:
-                densepose_predictor_outputs = None
-
-            densepose_inference(densepose_predictor_outputs, instances)
-            return instances
-
-    def forward(
-        self,
-        images: ImageList,
-        features: Dict[str, torch.Tensor],
-        proposals: List[Instances],
-        targets: Optional[List[Instances]] = None,
-    ):
-        instances, losses = super().forward(images, features, proposals, targets)
-        del targets, images
-
-        if self.training:
-            losses.update(self._forward_densepose(features, instances))
-        return instances, losses
-
-    def forward_with_given_boxes(
-        self, features: Dict[str, torch.Tensor], instances: List[Instances]
-    ):
-        """
-        Use the given boxes in `instances` to produce other (non-box) per-ROI outputs.
-
-        This is useful for downstream tasks where a box is known, but need to obtain
-        other attributes (outputs of other heads).
-        Test-time augmentation also uses this.
-
-        Args:
-            features: same as in `forward()`
-            instances (list[Instances]): instances to predict other outputs. Expect the keys
-                "pred_boxes" and "pred_classes" to exist.
-
-        Returns:
-            instances (list[Instances]):
-                the same `Instances` objects, with extra
-                fields such as `pred_masks` or `pred_keypoints`.
-        """
-
-        instances = super().forward_with_given_boxes(features, instances)
-        instances = self._forward_densepose(features, instances)
-        return instances
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/ViTDet/README.md b/spaces/nikitaPDL2023/assignment4/detectron2/projects/ViTDet/README.md
deleted file mode 100644
index 0a525e00e643017fc971566931936f1573d9b47c..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/ViTDet/README.md
+++ /dev/null
@@ -1,364 +0,0 @@
-# ViTDet: Exploring Plain Vision Transformer Backbones for Object Detection
-
-Yanghao Li, Hanzi Mao, Ross Girshick†, Kaiming He†
-
-[[`arXiv`](https://arxiv.org/abs/2203.16527)] [[`BibTeX`](#CitingViTDet)]
-
-In this repository, we provide configs and models in Detectron2 for ViTDet as well as MViTv2 and Swin backbones with our implementation and settings as described in [ViTDet](https://arxiv.org/abs/2203.16527) paper.
-
-
-## Pretrained Models
-
-### COCO
-
-#### Mask R-CNN
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">pre-train</th>
-<th valign="bottom">train<br/>time<br/>(s/im)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_vitdet_b_100ep -->
- <tr><td align="left"><a href="configs/COCO/mask_rcnn_vitdet_b_100ep.py">ViTDet, ViT-B</a></td>
-<td align="center">IN1K, MAE</td>
-<td align="center">0.314</td>
-<td align="center">0.079</td>
-<td align="center">10.9</td>
-<td align="center">51.6</td>
-<td align="center">45.9</td>
-<td align="center">325346929</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/mask_rcnn_vitdet_b/f325346929/model_final_61ccd1.pkl">model</a></td>
-</tr>
-<!-- ROW: mask_rcnn_vitdet_l_100ep -->
- <tr><td align="left"><a href="configs/COCO/mask_rcnn_vitdet_l_100ep.py">ViTDet, ViT-L</a></td>
-<td align="center">IN1K, MAE</td>
-<td align="center">0.603</td>
-<td align="center">0.125</td>
-<td align="center">20.9</td>
-<td align="center">55.5</td>
-<td align="center">49.2</td>
-<td align="center">325599698</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/mask_rcnn_vitdet_l/f325599698/model_final_6146ed.pkl">model</a></td>
-</tr>
-<!-- ROW: mask_rcnn_vitdet_b_75ep -->
- <tr><td align="left"><a href="configs/COCO/mask_rcnn_vitdet_h_75ep.py">ViTDet, ViT-H</a></td>
-<td align="center">IN1K, MAE</td>
-<td align="center">1.098</td>
-<td align="center">0.178</td>
-<td align="center">31.5</td>
-<td align="center">56.7</td>
-<td align="center">50.2</td>
-<td align="center">329145471</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/mask_rcnn_vitdet_h/f329145471/model_final_7224f1.pkl">model</a></td>
-</tr>
-</tbody></table>
-
-#### Cascade Mask R-CNN
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">pre-train</th>
-<th valign="bottom">train<br/>time<br/>(s/im)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: cascade_mask_rcnn_swin_b_in21k_50ep -->
- <tr><td align="left"><a href="configs/COCO/cascade_mask_rcnn_swin_b_in21k_50ep.py">Swin-B</a></td>
-<td align="center">IN21K, sup</td>
-<td align="center">0.389</td>
-<td align="center">0.077</td>
-<td align="center">8.7</td>
-<td align="center">53.9</td>
-<td align="center">46.2</td>
-<td align="center">342979038</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/cascade_mask_rcnn_swin_b_in21k/f342979038/model_final_246a82.pkl">model</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_swin_l_in21k_50ep -->
- <tr><td align="left"><a href="configs/COCO/cascade_mask_rcnn_swin_l_in21k_50ep.py">Swin-L</a></td>
-<td align="center">IN21K, sup</td>
-<td align="center">0.508</td>
-<td align="center">0.097</td>
-<td align="center">12.6</td>
-<td align="center">55.0</td>
-<td align="center">47.2</td>
-<td align="center">342979186</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/cascade_mask_rcnn_swin_l_in21k/f342979186/model_final_7c897e.pkl">model</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_mvitv2_b_in21k_100ep -->
- <tr><td align="left"><a href="configs/COCO/cascade_mask_rcnn_mvitv2_b_in21k_100ep.py">MViTv2-B</a></td>
-<td align="center">IN21K, sup</td>
-<td align="center">0.475</td>
-<td align="center">0.090</td>
-<td align="center">8.9</td>
-<td align="center">55.6</td>
-<td align="center">48.1</td>
-<td align="center">325820315</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/cascade_mask_rcnn_mvitv2_b_in21k/f325820315/model_final_8c3da3.pkl">model</a></td>
-</tr>
-</tr>
-<!-- ROW: cascade_mask_rcnn_mvitv2_l_in21k_50ep -->
- <tr><td align="left"><a href="configs/COCO/cascade_mask_rcnn_mvitv2_l_in21k_50ep.py">MViTv2-L</a></td>
-<td align="center">IN21K, sup</td>
-<td align="center">0.844</td>
-<td align="center">0.157</td>
-<td align="center">19.7</td>
-<td align="center">55.7</td>
-<td align="center">48.3</td>
-<td align="center">325607715</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/cascade_mask_rcnn_mvitv2_l_in21k/f325607715/model_final_2141b0.pkl">model</a></td>
-</tr>
-</tr>
-<!-- ROW: cascade_mask_rcnn_mvitv2_h_in21k_36ep -->
- <tr><td align="left"><a href="configs/COCO/cascade_mask_rcnn_mvitv2_h_in21k_36ep.py">MViTv2-H</a></td>
-<td align="center">IN21K, sup</td>
-<td align="center">1.655</td>
-<td align="center">0.285</td>
-<td align="center">18.4*</td>
-<td align="center">55.9</td>
-<td align="center">48.3</td>
-<td align="center">326187358</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/cascade_mask_rcnn_mvitv2_h_in21k/f326187358/model_final_2234d7.pkl">model</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_vitdet_b_100ep -->
- <tr><td align="left"><a href="configs/COCO/cascade_mask_rcnn_vitdet_b_100ep.py">ViTDet, ViT-B</a></td>
-<td align="center">IN1K, MAE</td>
-<td align="center">0.362</td>
-<td align="center">0.089</td>
-<td align="center">12.3</td>
-<td align="center">54.0</td>
-<td align="center">46.7</td>
-<td align="center">325358525</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/cascade_mask_rcnn_vitdet_b/f325358525/model_final_435fa9.pkl">model</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_vitdet_l_100ep -->
- <tr><td align="left"><a href="configs/COCO/cascade_mask_rcnn_vitdet_l_100ep.py">ViTDet, ViT-L</a></td>
-<td align="center">IN1K, MAE</td>
-<td align="center">0.643</td>
-<td align="center">0.142</td>
-<td align="center">22.3</td>
-<td align="center">57.6</td>
-<td align="center">50.0</td>
-<td align="center">328021305</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/cascade_mask_rcnn_vitdet_l/f328021305/model_final_1a9f28.pkl">model</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_vitdet_h_75ep -->
- <tr><td align="left"><a href="configs/COCO/cascade_mask_rcnn_vitdet_h_75ep.py">ViTDet, ViT-H</a></td>
-<td align="center">IN1K, MAE</td>
-<td align="center">1.137</td>
-<td align="center">0.196</td>
-<td align="center">32.9</td>
-<td align="center">58.7</td>
-<td align="center">51.0</td>
-<td align="center">328730692</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/cascade_mask_rcnn_vitdet_h/f328730692/model_final_f05665.pkl">model</a></td>
-</tr>
-</tbody></table>
-
-
-### LVIS
-
-#### Mask R-CNN
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">pre-train</th>
-<th valign="bottom">train<br/>time<br/>(s/im)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: mask_rcnn_vitdet_b_100ep -->
- <tr><td align="left"><a href="configs/LVIS/mask_rcnn_vitdet_b_100ep.py">ViTDet, ViT-B</a></td>
-<td align="center">IN1K, MAE</td>
-<td align="center">0.317</td>
-<td align="center">0.085</td>
-<td align="center">14.4</td>
-<td align="center">40.2</td>
-<td align="center">38.2</td>
-<td align="center">329225748</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/LVIS/mask_rcnn_vitdet_b/329225748/model_final_5251c5.pkl">model</a></td>
-</tr>
-<!-- ROW: mask_rcnn_vitdet_l_100ep -->
- <tr><td align="left"><a href="configs/LVIS/mask_rcnn_vitdet_l_100ep.py">ViTDet, ViT-L</a></td>
-<td align="center">IN1K, MAE</td>
-<td align="center">0.576</td>
-<td align="center">0.137</td>
-<td align="center">24.7</td>
-<td align="center">46.1</td>
-<td align="center">43.6</td>
-<td align="center">329211570</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/LVIS/mask_rcnn_vitdet_l/329211570/model_final_021b3a.pkl">model</a></td>
-</tr>
-<!-- ROW: mask_rcnn_vitdet_b_75ep -->
- <tr><td align="left"><a href="configs/LVIS/mask_rcnn_vitdet_h_100ep.py">ViTDet, ViT-H</a></td>
-<td align="center">IN1K, MAE</td>
-<td align="center">1.059</td>
-<td align="center">0.186</td>
-<td align="center">35.3</td>
-<td align="center">49.1</td>
-<td align="center">46.0</td>
-<td align="center">332434656</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/LVIS/mask_rcnn_vitdet_h/332434656/model_final_866730.pkl">model</a></td>
-</tr>
-</tbody></table>
-
-#### Cascade Mask R-CNN
-
-<table><tbody>
-<!-- START TABLE -->
-<!-- TABLE HEADER -->
-<th valign="bottom">Name</th>
-<th valign="bottom">pre-train</th>
-<th valign="bottom">train<br/>time<br/>(s/im)</th>
-<th valign="bottom">inference<br/>time<br/>(s/im)</th>
-<th valign="bottom">train<br/>mem<br/>(GB)</th>
-<th valign="bottom">box<br/>AP</th>
-<th valign="bottom">mask<br/>AP</th>
-<th valign="bottom">model id</th>
-<th valign="bottom">download</th>
-<!-- TABLE BODY -->
-<!-- ROW: cascade_mask_rcnn_swin_b_in21k_50ep -->
- <tr><td align="left"><a href="configs/LVIS/cascade_mask_rcnn_swin_b_in21k_50ep.py">Swin-B</a></td>
-<td align="center">IN21K, sup</td>
-<td align="center">0.368</td>
-<td align="center">0.090</td>
-<td align="center">11.5</td>
-<td align="center">44.0</td>
-<td align="center">39.6</td>
-<td align="center">329222304</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/LVIS/cascade_mask_rcnn_swin_b_in21k/329222304/model_final_a3a348.pkl">model</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_swin_l_in21k_50ep -->
- <tr><td align="left"><a href="configs/LVIS/cascade_mask_rcnn_swin_l_in21k_50ep.py">Swin-L</a></td>
-<td align="center">IN21K, sup</td>
-<td align="center">0.486</td>
-<td align="center">0.105</td>
-<td align="center">13.8</td>
-<td align="center">46.0</td>
-<td align="center">41.4</td>
-<td align="center">329222724</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/LVIS/cascade_mask_rcnn_swin_l_in21k/329222724/model_final_2b94db.pkl">model</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_mvitv2_b_in21k_100ep -->
- <tr><td align="left"><a href="configs/LVIS/cascade_mask_rcnn_mvitv2_b_in21k_100ep.py">MViTv2-B</a></td>
-<td align="center">IN21K, sup</td>
-<td align="center">0.475</td>
-<td align="center">0.100</td>
-<td align="center">11.8</td>
-<td align="center">46.3</td>
-<td align="center">42.0</td>
-<td align="center">329477206</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/LVIS/cascade_mask_rcnn_mvitv2_b_in21k/329477206/model_final_a00567.pkl">model</a></td>
-</tr>
-</tr>
-<!-- ROW: cascade_mask_rcnn_mvitv2_l_in21k_50ep -->
- <tr><td align="left"><a href="configs/LVIS/cascade_mask_rcnn_mvitv2_l_in21k_50ep.py">MViTv2-L</a></td>
-<td align="center">IN21K, sup</td>
-<td align="center">0.844</td>
-<td align="center">0.172</td>
-<td align="center">21.0</td>
-<td align="center">49.4</td>
-<td align="center">44.2</td>
-<td align="center">329661552</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/LVIS/cascade_mask_rcnn_mvitv2_l_in21k/329661552/model_final_7838a5.pkl">model</a></td>
-</tr>
-</tr>
-<!-- ROW: cascade_mask_rcnn_mvitv2_h_in21k_36ep -->
- <tr><td align="left"><a href="configs/LVIS/cascade_mask_rcnn_mvitv2_h_in21k_50ep.py">MViTv2-H</a></td>
-<td align="center">IN21K, sup</td>
-<td align="center">1.661</td>
-<td align="center">0.290</td>
-<td align="center">21.3*</td>
-<td align="center">49.5</td>
-<td align="center">44.1</td>
-<td align="center">330445165</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/LVIS/cascade_mask_rcnn_mvitv2_h_in21k/330445165/model_final_ad4220.pkl">model</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_vitdet_b_100ep -->
- <tr><td align="left"><a href="configs/LVIS/cascade_mask_rcnn_vitdet_b_100ep.py">ViTDet, ViT-B</a></td>
-<td align="center">IN1K, MAE</td>
-<td align="center">0.356</td>
-<td align="center">0.099</td>
-<td align="center">15.2</td>
-<td align="center">43.0</td>
-<td align="center">38.9</td>
-<td align="center">329226874</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/LVIS/cascade_mask_rcnn_vitdet_b/329226874/model_final_df306f.pkl">model</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_vitdet_l_100ep -->
- <tr><td align="left"><a href="configs/LVIS/cascade_mask_rcnn_vitdet_l_100ep.py">ViTDet, ViT-L</a></td>
-<td align="center">IN1K, MAE</td>
-<td align="center">0.629</td>
-<td align="center">0.150</td>
-<td align="center">24.9</td>
-<td align="center">49.2</td>
-<td align="center">44.5</td>
-<td align="center">329042206</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/LVIS/cascade_mask_rcnn_vitdet_l/329042206/model_final_3e81c2.pkl">model</a></td>
-</tr>
-<!-- ROW: cascade_mask_rcnn_vitdet_h_75ep -->
- <tr><td align="left"><a href="configs/LVIS/cascade_mask_rcnn_vitdet_h_100ep.py">ViTDet, ViT-H</a></td>
-<td align="center">IN1K, MAE</td>
-<td align="center">1.100</td>
-<td align="center">0.204</td>
-<td align="center">35.5</td>
-<td align="center">51.5</td>
-<td align="center">46.6</td>
-<td align="center">332552778</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/ViTDet/LVIS/cascade_mask_rcnn_vitdet_h/332552778/model_final_11bbb7.pkl">model</a></td>
-</tr>
-</tbody></table>
-
-Note: Unlike the system-level comparisons in the paper, these models use a lower resolution (1024 instead of 1280) and standard NMS (instead of soft NMS). As a result, they have slightly lower box and mask AP.
-
-We observed higher variance on LVIS evalution results compared to COCO. For example, the standard deviations of box AP and mask AP were 0.30% (compared to 0.10% on COCO) when we trained ViTDet, ViT-B five times with varying random seeds.
-
-The above models were trained and measured on 8-node with 64 NVIDIA A100 GPUs in total. *: Activation checkpointing is used.
-
-
-## Training
-All configs can be trained with:
-
-```
-../../tools/lazyconfig_train_net.py --config-file configs/path/to/config.py
-```
-By default, we use 64 GPUs with batch size as 64 for training.
-
-## Evaluation
-Model evaluation can be done similarly:
-```
-../../tools/lazyconfig_train_net.py --config-file configs/path/to/config.py --eval-only train.init_checkpoint=/path/to/model_checkpoint
-```
-
-
-## <a name="CitingViTDet"></a>Citing ViTDet
-
-If you use ViTDet, please use the following BibTeX entry.
-
-```BibTeX
-@article{li2022exploring,
-  title={Exploring plain vision transformer backbones for object detection},
-  author={Li, Yanghao and Mao, Hanzi and Girshick, Ross and He, Kaiming},
-  journal={arXiv preprint arXiv:2203.16527},
-  year={2022}
-}
-```
diff --git a/spaces/ntt123/handwriting/script.js b/spaces/ntt123/handwriting/script.js
deleted file mode 100644
index 7ef6741961528f55d2111fddc58d94e0bc6f9026..0000000000000000000000000000000000000000
--- a/spaces/ntt123/handwriting/script.js
+++ /dev/null
@@ -1,226 +0,0 @@
-var log = console.log;
-var ctx = null;
-var canvas = null;
-var RNN_SIZE = 512;
-var cur_run = 0;
-
-var randn = function() {
-    // Standard Normal random variable using Box-Muller transform.
-    var u = Math.random() * 0.999 + 1e-5;
-    var v = Math.random() * 0.999 + 1e-5;
-    return Math.sqrt(-2.0 * Math.log(u)) * Math.cos(2.0 * Math.PI * v);
-}
-
-var rand_truncated_normal = function(low, high) {
-    while (true) {
-        r = randn();
-        if (r >= low && r <= high)
-            break;
-        // rejection sampling.
-    }
-    return r;
-}
-
-var softplus = function(x) {
-    const m = tf.maximum(x, 0.0);
-    return tf.add(m, tf.log(tf.add(tf.exp(tf.neg(m)), tf.exp(tf.sub(x, m)))));
-}
-
-
-var char2idx = {'\x00': 0, ' ': 1, '!': 2, '"': 3, '#': 4, "'": 5, '(': 6, ')': 7, ',': 8, '-': 9, '.': 10, '0': 11, '1': 12, '2': 13, '3': 14, '4': 15, '5': 16, '6': 17, '7': 18, '8': 19, '9': 20, ':': 21, ';': 22, '?': 23, 'A': 24, 'B': 25, 'C': 26, 'D': 27, 'E': 28, 'F': 29, 'G': 30, 'H': 31, 'I': 32, 'J': 33, 'K': 34, 'L': 35, 'M': 36, 'N': 37, 'O': 38, 'P': 39, 'R': 40, 'S': 41, 'T': 42, 'U': 43, 'V': 44, 'W': 45, 'Y': 46, 'a': 47, 'b': 48, 'c': 49, 'd': 50, 'e': 51, 'f': 52, 'g': 53, 'h': 54, 'i': 55, 'j': 56, 'k': 57, 'l': 58, 'm': 59, 'n': 60, 'o': 61, 'p': 62, 'q': 63, 'r': 64, 's': 65, 't': 66, 'u': 67, 'v': 68, 'w': 69, 'x': 70, 'y': 71, 'z': 72};
-
-var gru_core = function(input, weights, state, hidden_size) {
-    var [w_h,w_i,b] = weights;
-    var [w_h_z,w_h_a] = tf.split(w_h, [2 * hidden_size, hidden_size], 1);
-    var [b_z,b_a] = tf.split(b, [2 * hidden_size, hidden_size], 0);
-    gates_x = tf.matMul(input, w_i);
-    [zr_x,a_x] = tf.split(gates_x, [2 * hidden_size, hidden_size], 1);
-    zr_h = tf.matMul(state, w_h_z);
-    zr = tf.add(tf.add(zr_x, zr_h), b_z);
-    // fix this
-    [z,r] = tf.split(tf.sigmoid(zr), 2, 1);
-    a_h = tf.matMul(tf.mul(r, state), w_h_a);
-    a = tf.tanh(tf.add(tf.add(a_x, a_h), b_a));
-    next_state = tf.add(tf.mul(tf.sub(1., z), state), tf.mul(z, a));
-    return [next_state, next_state];
-};
-
-
-var generate = function() {
-    cur_run = cur_run + 1;
-    setTimeout(function() {   
-        var counter = 2000;
-        tf.disposeVariables();
-        
-        tf.engine().startScope();
-        ctx.clearRect(0, 0, canvas.width, canvas.height);
-        ctx.beginPath();
-        dojob(cur_run);
-    }, 200);
-
-    return false;
-}
-
-var dojob = function(run_id) {
-    var text = document.getElementById("user-input").value;
-    if (text.length == 0) {
-        text = "The quick brown fox jumps over the lazy dog";
-    }
-
-    var cur_x = 50.;
-    var cur_y = 300.;
-
-
-    log(text);
-    original_text = text;
-    text = '' + text + '          ' + text;
-
-    text = Array.from(text).map(function(e) {
-        return char2idx[e]
-    })
-    var text_embed = WEIGHTS['rnn/~/embed_1__embeddings'];
-    indices = tf.tensor1d(text, 'int32');
-    text = text_embed.gather(indices);
-
-    filter = WEIGHTS['rnn/~/conv1_d__w'];
-    embed = tf.conv1d(text, filter, 1, 'same');
-    bias = tf.expandDims(WEIGHTS['rnn/~/conv1_d__b'], 0);
-    embed = tf.add(embed, bias);
-
-    var writer_embed = WEIGHTS['rnn/~/embed__embeddings'];
-    var e = document.getElementById("writers");
-    var wid = parseInt(e.value);
-    // log(wid);
-
-    wid = tf.tensor1d([wid], 'int32');
-    wid = writer_embed.gather(wid);
-    embed = tf.add(wid, embed);
-
-    // initial state
-    var gru0_hx = tf.zeros([1, RNN_SIZE]);
-    var gru1_hx = tf.zeros([1, RNN_SIZE]);
-    // var gru2_hx = tf.zeros([1, RNN_SIZE]);
-
-    var att_location = tf.zeros([1, 1]);
-    var att_context = tf.zeros([1, 73]);
-
-    var input = tf.tensor([[0., 0., 1.]]);
-
-    gru0_w_h = WEIGHTS['rnn/~/lstm_attention_core/~/gru__w_h'];
-    gru0_w_i = WEIGHTS['rnn/~/lstm_attention_core/~/gru__w_i'];
-    gru0_bias = WEIGHTS['rnn/~/lstm_attention_core/~/gru__b'];
-
-    gru1_w_h = WEIGHTS['rnn/~/lstm_attention_core/~/gru_1__w_h'];
-    gru1_w_i = WEIGHTS['rnn/~/lstm_attention_core/~/gru_1__w_i'];
-    gru1_bias = WEIGHTS['rnn/~/lstm_attention_core/~/gru_1__b'];
-    att_w = WEIGHTS['rnn/~/lstm_attention_core/~/linear__w'];
-    att_b = WEIGHTS['rnn/~/lstm_attention_core/~/linear__b'];
-    gmm_w = WEIGHTS['rnn/~/linear__w'];
-    gmm_b = WEIGHTS['rnn/~/linear__b'];
-
-    ruler = tf.tensor([...Array(text.shape[0]).keys()]);
-    var bias = parseInt(document.getElementById("bias").value) / 100 * 3;
-
-    cur_x = 50.;
-    cur_y = 400.;
-    var path = [];
-    var dx = 0.;
-    var dy = 0;
-    var eos = 1.;
-    var counter = 0;
-
-
-    function loop(my_run_id) {
-        if (my_run_id < cur_run) {
-            tf.disposeVariables();
-            tf.engine().endScope();
-            return;
-        }
-
-        counter++;
-        if (counter < 2000) {
-            [att_location,att_context,gru0_hx,gru1_hx,input] = tf.tidy(function() {
-                // Attention
-                const inp_0 = tf.concat([att_context, input], 1);
-                gru0_hx_ = gru0_hx;
-                [out_0,gru0_hx] = gru_core(inp_0, [gru0_w_h, gru0_w_i, gru0_bias], gru0_hx, RNN_SIZE);
-                tf.dispose(gru0_hx_);
-                const att_inp = tf.concat([att_context, input, out_0], 1);
-                const att_params = tf.add(tf.matMul(att_inp, att_w), att_b);
-                [alpha,beta,kappa] = tf.split(softplus(att_params), 3, 1);
-                att_location_ = att_location;
-                att_location = tf.add(att_location, tf.div(kappa, 25.));
-                tf.dispose(att_location_)
-
-                const phi = tf.mul(alpha, tf.exp(tf.div(tf.neg(tf.square(tf.sub(att_location, ruler))), beta)));
-                att_context_ = att_context;
-                att_context = tf.sum(tf.mul(tf.expandDims(phi, 2), tf.expandDims(embed, 0)), 1)
-                tf.dispose(att_context_);
-
-                const inp_1 = tf.concat([input, out_0, att_context], 1);
-                tf.dispose(input);
-                gru1_hx_ = gru1_hx;
-                [out_1,gru1_hx] = gru_core(inp_1, [gru1_w_h, gru1_w_i, gru1_bias], gru1_hx, RNN_SIZE);
-                tf.dispose(gru1_hx_);
-
-                // GMM
-                const gmm_params = tf.add(tf.matMul(out_1, gmm_w), gmm_b);
-                [x,y,logstdx,logstdy,angle,log_weight,eos_logit] = tf.split(gmm_params, [5, 5, 5, 5, 5, 5, 1], 1);
-                // log_weight = tf.softmax(log_weight, 1);
-                // log_weight = tf.log(log_weight);
-                // log_weight = tf.mul(log_weight, 1. + bias);
-                // const idx = tf.multinomial(log_weight, 1).dataSync()[0];
-                // log_weight = tf.softmax(log_weight, 1);
-                // log_weight = tf.log(log_weight);
-                // log_weight = tf.mul(log_weight, 1. + bias);
-                const idx = tf.argMax(log_weight, 1).dataSync()[0];
-
-                x = x.dataSync()[idx];
-                y = y.dataSync()[idx];
-                const stdx = tf.exp(tf.sub(logstdx, bias)).dataSync()[idx];
-                const stdy = tf.exp(tf.sub(logstdy, bias)).dataSync()[idx];
-                angle = angle.dataSync()[idx];
-                e = tf.sigmoid(tf.mul(eos_logit, (1. + 0.*bias))).dataSync()[0];
-                const rx = rand_truncated_normal(-5, 5) * stdx;
-                const ry = rand_truncated_normal(-5, 5) * stdy;
-                x = x + Math.cos(-angle) * rx - Math.sin(-angle) * ry;
-                y = y + Math.sin(-angle) * rx + Math.cos(-angle) * ry;
-                if (Math.random() < e) {
-                    e = 1.;
-                } else {
-                    e = 0.;
-                }
-                input = tf.tensor([[x, y, e]]);
-                return [att_location, att_context, gru0_hx, gru1_hx, input];
-            });
-
-            [dx,dy,eos_] = input.dataSync();
-            dy = -dy * 3;
-            dx = dx * 3;
-            if (eos == 0.) {
-                ctx.beginPath();
-                ctx.moveTo(cur_x, cur_y, 0, 0);
-                ctx.lineTo(cur_x + dx, cur_y + dy);
-                ctx.stroke();
-            }
-            eos = eos_;
-            cur_x = cur_x + dx;
-            cur_y = cur_y + dy;
-
-            if (att_location.dataSync()[0] < original_text.length + 2) {
-                setTimeout(function() {loop(my_run_id);}, 0);
-            }
-        }
-    }
-
-    loop(run_id);
-}
-
-window.onload = function(e) {
-    //Setting up canvas
-    canvas = document.getElementById("hw-canvas");
-    ctx = canvas.getContext("2d");
-    ctx.canvas.width = window.innerWidth- 50;
-    ctx.canvas.height = window.innerHeight - 50;
-
-}
diff --git a/spaces/odettecantswim/rvc-mlbb-v2/app.py b/spaces/odettecantswim/rvc-mlbb-v2/app.py
deleted file mode 100644
index b32815d1646c050598fe3fe4eca5cafb89861c5e..0000000000000000000000000000000000000000
--- a/spaces/odettecantswim/rvc-mlbb-v2/app.py
+++ /dev/null
@@ -1,498 +0,0 @@
-import os
-import glob
-import json
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-import yt_dlp
-import ffmpeg
-import subprocess
-import sys
-import io
-import wave
-from datetime import datetime
-from fairseq import checkpoint_utils
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from vc_infer_pipeline import VC
-from config import Config
-config = Config()
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"
-
-audio_mode = []
-f0method_mode = []
-f0method_info = ""
-if limitation is True:
-    audio_mode = ["Upload audio", "TTS Audio"]
-    f0method_mode = ["pm", "crepe"]
-    f0method_info = "PM is fast, Crepe is good but it was extremely slow (Default: PM)"
-else:
-    audio_mode = ["Upload audio", "Youtube", "TTS Audio"]
-    f0method_mode = ["pm", "harvest", "crepe"]
-    f0method_info = "PM is fast, Harvest is good at lower frequency but extremely slow, Crepe is good for higher notes. (Default: PM)"
-def create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, file_index):
-    def vc_fn(
-        vc_audio_mode,
-        vc_input, 
-        vc_upload,
-        tts_text,
-        tts_voice,
-        f0_up_key,
-        f0_method,
-        index_rate,
-        filter_radius,
-        resample_sr,
-        rms_mix_rate,
-        protect,
-    ):
-        try:
-            if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
-                audio, sr = librosa.load(vc_input, sr=16000, mono=True)
-            elif vc_audio_mode == "Upload audio":
-                if vc_upload is None:
-                    return "You need to upload an audio", None
-                sampling_rate, audio = vc_upload
-                duration = audio.shape[0] / sampling_rate
-                if duration > 60 and limitation:
-                    return "Please upload an audio file that is less than 1 minute.", None
-                audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-                if len(audio.shape) > 1:
-                    audio = librosa.to_mono(audio.transpose(1, 0))
-                if sampling_rate != 16000:
-                    audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-            elif vc_audio_mode == "TTS Audio":
-                if len(tts_text) > 100 and limitation:
-                    return "Text is too long", None
-                if tts_text is None or tts_voice is None:
-                    return "You need to enter text and select a voice", None
-                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
-                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
-                vc_input = "tts.mp3"
-            times = [0, 0, 0]
-            f0_up_key = int(f0_up_key)
-            audio_opt = vc.pipeline(
-                hubert_model,
-                net_g,
-                0,
-                audio,
-                vc_input,
-                times,
-                f0_up_key,
-                f0_method,
-                file_index,
-                # file_big_npy,
-                index_rate,
-                if_f0,
-                filter_radius,
-                tgt_sr,
-                resample_sr,
-                rms_mix_rate,
-                version,
-                protect,
-                f0_file=None,
-            )
-            info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-            print(f"{model_title} | {info}")
-            return info, (tgt_sr, audio_opt)
-        except:
-            info = traceback.format_exc()
-            print(info)
-            return info, (None, None)
-    return vc_fn
-
-def load_model():
-    categories = []
-    with open("weights/folder_info.json", "r", encoding="utf-8") as f:
-        folder_info = json.load(f)
-    for category_name, category_info in folder_info.items():
-        if not category_info['enable']:
-            continue
-        category_title = category_info['title']
-        category_folder = category_info['folder_path']
-        models = []
-        with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
-            models_info = json.load(f)
-        for character_name, info in models_info.items():
-            if not info['enable']:
-                continue
-            model_title = info['title']
-            model_name = info['model_path']
-            model_author = info.get("author", None)
-            model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
-            model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
-            cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
-            tgt_sr = cpt["config"][-1]
-            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-            if_f0 = cpt.get("f0", 1)
-            version = cpt.get("version", "v1")
-            if version == "v1":
-                if if_f0 == 1:
-                    net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-                else:
-                    net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-                model_version = "V1"
-            elif version == "v2":
-                if if_f0 == 1:
-                    net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
-                else:
-                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-                model_version = "V2"
-            del net_g.enc_q
-            print(net_g.load_state_dict(cpt["weight"], strict=False))
-            net_g.eval().to(config.device)
-            if config.is_half:
-                net_g = net_g.half()
-            else:
-                net_g = net_g.float()
-            vc = VC(tgt_sr, config)
-            print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
-            models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, model_index)))
-        categories.append([category_title, category_folder, models])
-    return categories
-
-def cut_vocal_and_inst(url, audio_provider, split_model):
-    if url != "":
-        if not os.path.exists("dl_audio"):
-            os.mkdir("dl_audio")
-        if audio_provider == "Youtube":
-            ydl_opts = {
-            'format': 'bestaudio/best',
-            'postprocessors': [{
-                'key': 'FFmpegExtractAudio',
-                'preferredcodec': 'wav',
-            }],
-            "outtmpl": 'dl_audio/youtube_audio',
-            }
-            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-                ydl.download([url])
-            audio_path = "dl_audio/youtube_audio.wav"
-        else:
-            # Spotify doesnt work.
-            # Need to find other solution soon.
-            ''' 
-            command = f"spotdl download {url} --output dl_audio/.wav"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            audio_path = "dl_audio/spotify_audio.wav"
-            '''
-        if split_model == "htdemucs":
-            command = f"demucs --two-stems=vocals {audio_path} -o output"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
-        else:
-            command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
-            result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-            print(result.stdout.decode())
-            return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
-    else:
-        raise gr.Error("URL Required!")
-        return None, None, None, None
-
-def combine_vocal_and_inst(audio_data, audio_volume, split_model):
-    if not os.path.exists("output/result"):
-        os.mkdir("output/result")
-    vocal_path = "output/result/output.wav"
-    output_path = "output/result/combine.mp3"
-    if split_model == "htdemucs":
-        inst_path = "output/htdemucs/youtube_audio/no_vocals.wav"
-    else:
-        inst_path = "output/mdx_extra_q/youtube_audio/no_vocals.wav"
-    with wave.open(vocal_path, "w") as wave_file:
-        wave_file.setnchannels(1) 
-        wave_file.setsampwidth(2)
-        wave_file.setframerate(audio_data[0])
-        wave_file.writeframes(audio_data[1].tobytes())
-    command =  f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}'
-    result = subprocess.run(command.split(), stdout=subprocess.PIPE)
-    print(result.stdout.decode())
-    return output_path
-
-def load_hubert():
-    global hubert_model
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(config.device)
-    if config.is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-def change_audio_mode(vc_audio_mode):
-    if vc_audio_mode == "Input path":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=True),
-            gr.Audio.update(visible=False),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-    elif vc_audio_mode == "Upload audio":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Audio.update(visible=True),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-    elif vc_audio_mode == "Youtube":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Audio.update(visible=False),
-            # Youtube
-            gr.Dropdown.update(visible=True),
-            gr.Textbox.update(visible=True),
-            gr.Dropdown.update(visible=True),
-            gr.Button.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Slider.update(visible=True),
-            gr.Audio.update(visible=True),
-            gr.Button.update(visible=True),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-    elif vc_audio_mode == "TTS Audio":
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Audio.update(visible=False),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=True),
-            gr.Dropdown.update(visible=True)
-        )
-    else:
-        return (
-            # Input & Upload
-            gr.Textbox.update(visible=False),
-            gr.Audio.update(visible=True),
-            # Youtube
-            gr.Dropdown.update(visible=False),
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False),
-            gr.Button.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Slider.update(visible=False),
-            gr.Audio.update(visible=False),
-            gr.Button.update(visible=False),
-            # TTS
-            gr.Textbox.update(visible=False),
-            gr.Dropdown.update(visible=False)
-        )
-
-if __name__ == '__main__':
-    load_hubert()
-    categories = load_model()
-    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
-    with gr.Blocks(theme=gr.themes.Base()) as app:
-        gr.Markdown(
-            "# <center> RVC MLBB\n"
-            "### <center> Only 5 heroes available currently. Prob gonna make more models soon.\n"
-        )
-        for (folder_title, folder, models) in categories:
-            with gr.TabItem(folder_title):
-                with gr.Tabs():
-                    if not models:
-                        gr.Markdown("# <center> No Model Loaded.")
-                        gr.Markdown("## <center> Please add model or fix your model path.")
-                        continue
-                    for (name, title, author, cover, model_version, vc_fn) in models:
-                        with gr.TabItem(name):
-                            with gr.Row():
-                                gr.Markdown(
-                                    '<div align="center">'
-                                    f'<div>{title}</div>\n'+
-                                    f'<div>RVC {model_version} Model</div>\n'+
-                                    (f'<div>Model author: {author}</div>' if author else "")+
-                                    (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
-                                    '</div>'
-                                )
-                            with gr.Row():
-                                with gr.Column():
-                                    vc_audio_mode = gr.Dropdown(label="Input voice", choices=audio_mode, allow_custom_value=False, value="Upload audio")
-                                    # Input and Upload
-                                    vc_input = gr.Textbox(label="Input audio path", visible=False)
-                                    vc_upload = gr.Audio(label="Upload audio file", visible=True, interactive=True)
-                                    # Youtube
-                                    vc_download_audio = gr.Dropdown(label="Provider", choices=["Youtube"], allow_custom_value=False, visible=False, value="Youtube", info="Select provider (Default: Youtube)")
-                                    vc_link = gr.Textbox(label="Youtube URL", visible=False, info="Example: https://www.youtube.com/watch?v=Nc0sB1Bmf-A", placeholder="https://www.youtube.com/watch?v=...")
-                                    vc_split_model = gr.Dropdown(label="Splitter Model", choices=["htdemucs", "mdx_extra_q"], allow_custom_value=False, visible=False, value="htdemucs", info="Select the splitter model (Default: htdemucs)")
-                                    vc_split = gr.Button("Split Audio", variant="primary", visible=False)
-                                    vc_vocal_preview = gr.Audio(label="Vocal Preview", visible=False)
-                                    vc_inst_preview = gr.Audio(label="Instrumental Preview", visible=False)
-                                    vc_audio_preview = gr.Audio(label="Audio Preview", visible=False)
-                                    # TTS
-                                    tts_text = gr.Textbox(visible=False, label="TTS text", info="Text to speech input")
-                                    tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
-                                with gr.Column():
-                                    vc_transform0 = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
-                                    f0method0 = gr.Radio(
-                                        label="Pitch extraction algorithm",
-                                        info=f0method_info,
-                                        choices=f0method_mode,
-                                        value="pm",
-                                        interactive=True
-                                    )
-                                    index_rate1 = gr.Slider(
-                                        minimum=0,
-                                        maximum=1,
-                                        label="Retrieval feature ratio",
-                                        info="Accents controling. Too high prob gonna sounds too robotic (Default: 0.4)",
-                                        value=0.4,
-                                        interactive=True,
-                                    )
-                                    filter_radius0 = gr.Slider(
-                                        minimum=0,
-                                        maximum=7,
-                                        label="Apply Median Filtering",
-                                        info="The value represents the filter radius and can reduce breathiness.",
-                                        value=1,
-                                        step=1,
-                                        interactive=True,
-                                    )
-                                    resample_sr0 = gr.Slider(
-                                        minimum=0,
-                                        maximum=48000,
-                                        label="Resample the output audio",
-                                        info="Resample the output audio in post-processing to the final sample rate. Set to 0 for no resampling",
-                                        value=0,
-                                        step=1,
-                                        interactive=True,
-                                    )
-                                    rms_mix_rate0 = gr.Slider(
-                                        minimum=0,
-                                        maximum=1,
-                                        label="Volume Envelope",
-                                        info="Use the volume envelope of the input to replace or mix with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is used",
-                                        value=1,
-                                        interactive=True,
-                                    )
-                                    protect0 = gr.Slider(
-                                        minimum=0,
-                                        maximum=0.5,
-                                        label="Voice Protection",
-                                        info="Protect voiceless consonants and breath sounds to prevent artifacts such as tearing in electronic music. Set to 0.5 to disable. Decrease the value to increase protection, but it may reduce indexing accuracy",
-                                        value=0.23,
-                                        step=0.01,
-                                        interactive=True,
-                                    )
-                                with gr.Column():
-                                    vc_log = gr.Textbox(label="Output Information", interactive=False)
-                                    vc_output = gr.Audio(label="Output Audio", interactive=False)
-                                    vc_convert = gr.Button("Convert", variant="primary")
-                                    vc_volume = gr.Slider(
-                                        minimum=0,
-                                        maximum=10,
-                                        label="Vocal volume",
-                                        value=4,
-                                        interactive=True,
-                                        step=1,
-                                        info="Adjust vocal volume (Default: 4}",
-                                        visible=False
-                                    )
-                                    vc_combined_output = gr.Audio(label="Output Combined Audio", visible=False)
-                                    vc_combine =  gr.Button("Combine",variant="primary", visible=False)
-                        vc_convert.click(
-                            fn=vc_fn, 
-                            inputs=[
-                                vc_audio_mode,
-                                vc_input, 
-                                vc_upload,
-                                tts_text,
-                                tts_voice,
-                                vc_transform0,
-                                f0method0,
-                                index_rate1,
-                                filter_radius0,
-                                resample_sr0,
-                                rms_mix_rate0,
-                                protect0,
-                            ], 
-                            outputs=[vc_log ,vc_output]
-                        )
-                        vc_split.click(
-                            fn=cut_vocal_and_inst, 
-                            inputs=[vc_link, vc_download_audio, vc_split_model], 
-                            outputs=[vc_vocal_preview, vc_inst_preview, vc_audio_preview, vc_input]
-                        )
-                        vc_combine.click(
-                            fn=combine_vocal_and_inst,
-                            inputs=[vc_output, vc_volume, vc_split_model],
-                            outputs=[vc_combined_output]
-                        )
-                        vc_audio_mode.change(
-                            fn=change_audio_mode,
-                            inputs=[vc_audio_mode],
-                            outputs=[
-                                vc_input, 
-                                vc_upload,
-                                vc_download_audio,
-                                vc_link,
-                                vc_split_model,
-                                vc_split,
-                                vc_vocal_preview,
-                                vc_inst_preview,
-                                vc_audio_preview,
-                                vc_volume,
-                                vc_combined_output,
-                                vc_combine,
-                                tts_text,
-                                tts_voice
-                            ]
-                        )
-        app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)
\ No newline at end of file
diff --git a/spaces/omlab/vlchecklist_demo/models/albef/models/__init__.py b/spaces/omlab/vlchecklist_demo/models/albef/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ondrejbiza/isa/invariant_slot_attention/lib/__init__.py b/spaces/ondrejbiza/isa/invariant_slot_attention/lib/__init__.py
deleted file mode 100644
index 78bf1a8d3d8c4665a7135205a3561cf10e097031..0000000000000000000000000000000000000000
--- a/spaces/ondrejbiza/isa/invariant_slot_attention/lib/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The Google Research Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
diff --git a/spaces/osanseviero/bidaf-elmo/app.py b/spaces/osanseviero/bidaf-elmo/app.py
deleted file mode 100644
index 15aa5df4ba4d4b877fe36ce487ff37ede33e8b79..0000000000000000000000000000000000000000
--- a/spaces/osanseviero/bidaf-elmo/app.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import gradio as gr
-
-# Even though it is not imported, it is actually required, it downloads some stuff.
-import allennlp_models  # noqa: F401
-from allennlp.predictors.predictor import Predictor
-
-# The integration with AllenNLP uses a hf prefix.
-predictor = Predictor.from_path("hf://lysandre/bidaf-elmo-model-2020.03.19")
-
-def predict(context, question):
-  allenlp_input = {"passage": context, "question": question}
-  predictions = predictor.predict_json(allenlp_input)
-  return predictions["best_span_str"]
-  
-title = "Interactive demo: AllenNLP Bidaf Elmo"
-description = "Demo for AllenNLP Question Answering model."
-
-iface = gr.Interface(fn=predict, 
-  inputs=[gr.inputs.Textbox(label="context"), gr.inputs.Textbox(label="question")],
-  outputs='text',
-  title=title,
-  description=description,
-  examples=[["My name is Omar and I live in Mexico", "Where does Omar live?"]],
-  enable_queue=True)
-
-iface.launch()
diff --git a/spaces/osanseviero/ray_serve/README.md b/spaces/osanseviero/ray_serve/README.md
deleted file mode 100644
index f8d0dc2e9f0480d71c156b51d8d0868885bce507..0000000000000000000000000000000000000000
--- a/spaces/osanseviero/ray_serve/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Ray_serve
-emoji: 🚀
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_m\303\251rn\303\266k_en.html" "b/spaces/oskarvanderwal/MT-bias-demo/results/simple_m\303\251rn\303\266k_en.html"
deleted file mode 100644
index 7a66249a25cec1d40665fb39c7382bdc25f51a62..0000000000000000000000000000000000000000
--- "a/spaces/oskarvanderwal/MT-bias-demo/results/simple_m\303\251rn\303\266k_en.html"
+++ /dev/null
@@ -1,46 +0,0 @@
-<br/><b>0th instance:</b><br/>
-<html>
-<div id="hvtwogbfepuaccnldvhg_viz_container">
-    <div id="hvtwogbfepuaccnldvhg_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "hvtwogbfepuaccnldvhg_saliency_plot_container" class="hvtwogbfepuaccnldvhg_viz_container" style="display:block">
-            
-<div id="kftxmncrltbwtybaiarv_saliency_plot" class="kftxmncrltbwtybaiarv_viz_content">
-    <div style="margin:5px;font-family:sans-serif;font-weight:bold;">
-        <span style="font-size: 20px;">Source Saliency Heatmap</span>
-        <br>
-        x: Generated tokens, y: Attributed tokens
-    </div>
-    
-<table border="1" cellpadding="5" cellspacing="5"
-    style="overflow-x:scroll;display:block;">
-    <tr><th></th>
-<th>▁He</th><th>'</th><th>s</th><th>▁an</th><th>▁engineer</th><th>.</th><th>&lt;/s&gt;</th></tr><tr><th>▁Ő</th><th style="background:rgba(255.0, 13.0, 87.0, 0.43244206773618543)">0.422</th><th style="background:rgba(255.0, 13.0, 87.0, 0.3299663299663301)">0.323</th><th style="background:rgba(255.0, 13.0, 87.0, 0.15654585066349747)">0.159</th><th style="background:rgba(255.0, 13.0, 87.0, 0.07771836007130124)">0.082</th><th style="background:rgba(255.0, 13.0, 87.0, 0.13289760348583876)">0.134</th><th style="background:rgba(255.0, 13.0, 87.0, 0.09348385818974037)">0.096</th><th style="background:rgba(30.0, 136.0, 229.0, 0.440324816795405)">-0.427</th></tr><tr><th>▁mérnök</th><th style="background:rgba(255.0, 13.0, 87.0, 0.9369380075262429)">0.906</th><th style="background:rgba(255.0, 13.0, 87.0, 0.7083382848088731)">0.687</th><th style="background:rgba(255.0, 13.0, 87.0, 0.44820756585462457)">0.438</th><th style="background:rgba(54.70588235294111, 122.49411764705886, 213.40784313725496, 0.0)">-0.006</th><th style="background:rgba(255.0, 13.0, 87.0, 1.0)">0.974</th><th style="background:rgba(255.0, 13.0, 87.0, 0.6846900376312143)">0.664</th><th style="background:rgba(255.0, 13.0, 87.0, 0.7398692810457518)">0.719</th></tr><tr><th>.</th><th style="background:rgba(255.0, 13.0, 87.0, 0.02253911665676371)">0.024</th><th style="background:rgba(255.0, 13.0, 87.0, 0.48762131115072294)">0.476</th><th style="background:rgba(255.0, 13.0, 87.0, 0.02253911665676371)">0.026</th><th style="background:rgba(255.0, 13.0, 87.0, 0.06983561101208159)">0.069</th><th style="background:rgba(30.0, 136.0, 229.0, 0.06195286195286191)">-0.061</th><th style="background:rgba(255.0, 13.0, 87.0, 0.6689245395127748)">0.648</th><th style="background:rgba(255.0, 13.0, 87.0, 0.18019409784115661)">0.178</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.0</th></tr></table>
-</div>
-
-        </div>
-    </div>
-</div>
-</html>
-<br/><b>0th instance:</b><br/>
-<html>
-<div id="xccuwrrbeenfpdcxxmwb_viz_container">
-    <div id="xccuwrrbeenfpdcxxmwb_content" style="padding:15px;border-style:solid;margin:5px;">
-        <div id = "xccuwrrbeenfpdcxxmwb_saliency_plot_container" class="xccuwrrbeenfpdcxxmwb_viz_container" style="display:block">
-            
-<div id="oahbopbgsxsegzrgltkg_saliency_plot" class="oahbopbgsxsegzrgltkg_viz_content">
-    <div style="margin:5px;font-family:sans-serif;font-weight:bold;">
-        <span style="font-size: 20px;">Target Saliency Heatmap</span>
-        <br>
-        x: Generated tokens, y: Attributed tokens
-    </div>
-    
-<table border="1" cellpadding="5" cellspacing="5"
-    style="overflow-x:scroll;display:block;">
-    <tr><th></th>
-<th>▁He</th><th>'</th><th>s</th><th>▁an</th><th>▁engineer</th><th>.</th><th>&lt;/s&gt;</th></tr><tr><th>▁He</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.4560903149138443)">0.444</th><th style="background:rgba(255.0, 13.0, 87.0, 0.5585660526836996)">0.548</th><th style="background:rgba(255.0, 13.0, 87.0, 0.44032481679540497)">0.431</th><th style="background:rgba(255.0, 13.0, 87.0, 0.04618736383442265)">0.049</th><th style="background:rgba(255.0, 13.0, 87.0, 0.10136660724896006)">0.105</th><th style="background:rgba(255.0, 13.0, 87.0, 0.13289760348583876)">0.131</th></tr><tr><th>'</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.7162210338680925)">0.695</th><th style="background:rgba(255.0, 13.0, 87.0, 0.306318082788671)">0.304</th><th style="background:rgba(255.0, 13.0, 87.0, 0.06195286195286207)">0.061</th><th style="background:rgba(255.0, 13.0, 87.0, 0.09348385818974037)">0.094</th><th style="background:rgba(255.0, 13.0, 87.0, 0.29055258467023165)">0.282</th></tr><tr><th>s</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.865993265993266)">0.843</th><th style="background:rgba(255.0, 13.0, 87.0, 0.030421865715983164)">0.038</th><th style="background:rgba(255.0, 13.0, 87.0, 0.32208358090711037)">0.315</th><th style="background:rgba(30.0, 136.0, 229.0, 0.14866310160427798)">-0.149</th></tr><tr><th>▁an</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.14866310160427795)">0.148</th><th style="background:rgba(255.0, 13.0, 87.0, 0.03830461477520289)">0.045</th><th style="background:rgba(255.0, 13.0, 87.0, 0.39302832244008706)">0.384</th></tr><tr><th>▁engineer</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(255.0, 13.0, 87.0, 0.09348385818974037)">0.095</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)">0.006</th></tr><tr><th>.</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(30.0, 136.0, 229.0, 0.04618736383442258)">-0.046</th></tr><tr><th>&lt;/s&gt;</th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th><th style="background:rgba(230.2941176470614, 26.505882352939775, 102.59215686274348, 0.0)"></th></tr></table>
-</div>
-
-        </div>
-    </div>
-</div>
-</html>
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/in_translation.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/in_translation.md
deleted file mode 100644
index 518be0c03b7c8cf0e8e9b2b083f08ccbb62bfad6..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/in_translation.md
+++ /dev/null
@@ -1,16 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# 번역중
-
-열심히 번역을 진행중입니다. 조금만 기다려주세요.
-감사합니다!
\ No newline at end of file
diff --git a/spaces/pharma-IA/PharmaWise_Experto_GMP_V2C_STREAM/app.py b/spaces/pharma-IA/PharmaWise_Experto_GMP_V2C_STREAM/app.py
deleted file mode 100644
index ebe5594d861029efd4667a5b2960bef08b5861b1..0000000000000000000000000000000000000000
--- a/spaces/pharma-IA/PharmaWise_Experto_GMP_V2C_STREAM/app.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import os
-import openai
-from llama_index import StorageContext, load_index_from_storage, LLMPredictor, ServiceContext
-from llama_index.tools import QueryEngineTool, ToolMetadata
-from langchain.chat_models import ChatOpenAI
-from github import Github
-import datetime
-import gradio as gr
-
-# Conectar Cuenta API de OpenAI
-openai_api_key = os.environ.get('openai_key')
-if openai_api_key:
-    os.environ["OPENAI_API_KEY"] = openai_api_key
-    openai.api_key = openai_api_key
-else:
-    print("Error con la clave de acceso a OpenAI.")
-
-
-# Cargar entrenamiento y modelo
-exec(os.environ.get('storage_context'))
-# Carga contexto logs
-exec(os.environ.get('logs_context'))
-project_name = "PharmaWise 3.6 - Experto GMP COFEPRIS y DIGEMID V2C_STREAM"
-
-    
-
-# Función para generar respuesta
-def predict(message):
-    #respuesta = engine.query(message)
-
-    # Respuesta con prompt sumado a la pregunta
-    respuesta = engine.query(prompt + message)
-
-    # Muestra la cadena que se va formando palabra por palabra
-    partial_message = ""
-    for chunk in respuesta.response_gen:
-        partial_message += chunk
-        yield partial_message
-
-   # Luego de obtener la respuesta se realiza el commit en GitHub
-    commit_to_github(message, partial_message)
-
-
-# Función para hacer commit
-def commit_to_github(message, response):
-    if github_token:
-        g = Github(github_token)
-        repo = g.get_repo(repo_name)
-
-        # Obtiene la fecha actual
-        current_date = datetime.datetime.now().strftime("%Y-%m")
-
-        # Nombre del archivo: nombre_del_proyecto/año-mes-nombre_del_proyecto.txt
-        file_name = f"{project_name}/{current_date}-{project_name}.txt"
-
-        # Descripción del commit
-        commit_message = f"Actualización de {current_date}"
-        # Contenido. Fecha, pregunta, respuesta 
-        content = f"({datetime.datetime.now().strftime('%d/%m/%Y %H:%M')})\nPregunta: {message}\nRespuesta: {response}\n----------\n" 
-
-        
-        try:
-            # Busca existencia de un .txt para actualizarlo con la nueva info
-            existing_file = repo.get_contents(file_name)
-            existing_content = existing_file.decoded_content.decode('utf-8')
-            new_content = f"{existing_content}{content}"
-            repo.update_file(file_name, commit_message, new_content, existing_file.sha, branch="main")
-        except:
-            # Si el archivo no existe, lo crea. Ej: cuando empieza un nuevo mes se crea un nuevo .txt
-            repo.create_file(file_name, commit_message, content, branch="main")
-
-
-
-# Interfaz de Gradio
-gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(placeholder="Escribe una pregunta...", label="Pregunta"),
-    outputs=gr.Textbox(label="Respuesta"),
-    title="PharmaWise 3.6 - Experto GMP COFEPRIS y DIGEMID V2C_STREAM",
-    description="Realiza preguntas a tus datos y obtén respuestas en español.",
-    theme='sudeepshouche/minimalist',
-    examples=["¿Cuales son los requerimientos de COFEPRIS para integridad de datos?"],
-    cache_examples=True,
-).launch()
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/configuration.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/configuration.py
deleted file mode 100644
index 96f824955bf098d86e54cd8bce3bf0015f976ec2..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/configuration.py
+++ /dev/null
@@ -1,381 +0,0 @@
-"""Configuration management setup
-
-Some terminology:
-- name
-  As written in config files.
-- value
-  Value associated with a name
-- key
-  Name combined with it's section (section.name)
-- variant
-  A single word describing where the configuration key-value pair came from
-"""
-
-import configparser
-import locale
-import os
-import sys
-from typing import Any, Dict, Iterable, List, NewType, Optional, Tuple
-
-from pip._internal.exceptions import (
-    ConfigurationError,
-    ConfigurationFileCouldNotBeLoaded,
-)
-from pip._internal.utils import appdirs
-from pip._internal.utils.compat import WINDOWS
-from pip._internal.utils.logging import getLogger
-from pip._internal.utils.misc import ensure_dir, enum
-
-RawConfigParser = configparser.RawConfigParser  # Shorthand
-Kind = NewType("Kind", str)
-
-CONFIG_BASENAME = "pip.ini" if WINDOWS else "pip.conf"
-ENV_NAMES_IGNORED = "version", "help"
-
-# The kinds of configurations there are.
-kinds = enum(
-    USER="user",  # User Specific
-    GLOBAL="global",  # System Wide
-    SITE="site",  # [Virtual] Environment Specific
-    ENV="env",  # from PIP_CONFIG_FILE
-    ENV_VAR="env-var",  # from Environment Variables
-)
-OVERRIDE_ORDER = kinds.GLOBAL, kinds.USER, kinds.SITE, kinds.ENV, kinds.ENV_VAR
-VALID_LOAD_ONLY = kinds.USER, kinds.GLOBAL, kinds.SITE
-
-logger = getLogger(__name__)
-
-
-# NOTE: Maybe use the optionx attribute to normalize keynames.
-def _normalize_name(name: str) -> str:
-    """Make a name consistent regardless of source (environment or file)"""
-    name = name.lower().replace("_", "-")
-    if name.startswith("--"):
-        name = name[2:]  # only prefer long opts
-    return name
-
-
-def _disassemble_key(name: str) -> List[str]:
-    if "." not in name:
-        error_message = (
-            "Key does not contain dot separated section and key. "
-            "Perhaps you wanted to use 'global.{}' instead?"
-        ).format(name)
-        raise ConfigurationError(error_message)
-    return name.split(".", 1)
-
-
-def get_configuration_files() -> Dict[Kind, List[str]]:
-    global_config_files = [
-        os.path.join(path, CONFIG_BASENAME) for path in appdirs.site_config_dirs("pip")
-    ]
-
-    site_config_file = os.path.join(sys.prefix, CONFIG_BASENAME)
-    legacy_config_file = os.path.join(
-        os.path.expanduser("~"),
-        "pip" if WINDOWS else ".pip",
-        CONFIG_BASENAME,
-    )
-    new_config_file = os.path.join(appdirs.user_config_dir("pip"), CONFIG_BASENAME)
-    return {
-        kinds.GLOBAL: global_config_files,
-        kinds.SITE: [site_config_file],
-        kinds.USER: [legacy_config_file, new_config_file],
-    }
-
-
-class Configuration:
-    """Handles management of configuration.
-
-    Provides an interface to accessing and managing configuration files.
-
-    This class converts provides an API that takes "section.key-name" style
-    keys and stores the value associated with it as "key-name" under the
-    section "section".
-
-    This allows for a clean interface wherein the both the section and the
-    key-name are preserved in an easy to manage form in the configuration files
-    and the data stored is also nice.
-    """
-
-    def __init__(self, isolated: bool, load_only: Optional[Kind] = None) -> None:
-        super().__init__()
-
-        if load_only is not None and load_only not in VALID_LOAD_ONLY:
-            raise ConfigurationError(
-                "Got invalid value for load_only - should be one of {}".format(
-                    ", ".join(map(repr, VALID_LOAD_ONLY))
-                )
-            )
-        self.isolated = isolated
-        self.load_only = load_only
-
-        # Because we keep track of where we got the data from
-        self._parsers: Dict[Kind, List[Tuple[str, RawConfigParser]]] = {
-            variant: [] for variant in OVERRIDE_ORDER
-        }
-        self._config: Dict[Kind, Dict[str, Any]] = {
-            variant: {} for variant in OVERRIDE_ORDER
-        }
-        self._modified_parsers: List[Tuple[str, RawConfigParser]] = []
-
-    def load(self) -> None:
-        """Loads configuration from configuration files and environment"""
-        self._load_config_files()
-        if not self.isolated:
-            self._load_environment_vars()
-
-    def get_file_to_edit(self) -> Optional[str]:
-        """Returns the file with highest priority in configuration"""
-        assert self.load_only is not None, "Need to be specified a file to be editing"
-
-        try:
-            return self._get_parser_to_modify()[0]
-        except IndexError:
-            return None
-
-    def items(self) -> Iterable[Tuple[str, Any]]:
-        """Returns key-value pairs like dict.items() representing the loaded
-        configuration
-        """
-        return self._dictionary.items()
-
-    def get_value(self, key: str) -> Any:
-        """Get a value from the configuration."""
-        orig_key = key
-        key = _normalize_name(key)
-        try:
-            return self._dictionary[key]
-        except KeyError:
-            # disassembling triggers a more useful error message than simply
-            # "No such key" in the case that the key isn't in the form command.option
-            _disassemble_key(key)
-            raise ConfigurationError(f"No such key - {orig_key}")
-
-    def set_value(self, key: str, value: Any) -> None:
-        """Modify a value in the configuration."""
-        key = _normalize_name(key)
-        self._ensure_have_load_only()
-
-        assert self.load_only
-        fname, parser = self._get_parser_to_modify()
-
-        if parser is not None:
-            section, name = _disassemble_key(key)
-
-            # Modify the parser and the configuration
-            if not parser.has_section(section):
-                parser.add_section(section)
-            parser.set(section, name, value)
-
-        self._config[self.load_only][key] = value
-        self._mark_as_modified(fname, parser)
-
-    def unset_value(self, key: str) -> None:
-        """Unset a value in the configuration."""
-        orig_key = key
-        key = _normalize_name(key)
-        self._ensure_have_load_only()
-
-        assert self.load_only
-        if key not in self._config[self.load_only]:
-            raise ConfigurationError(f"No such key - {orig_key}")
-
-        fname, parser = self._get_parser_to_modify()
-
-        if parser is not None:
-            section, name = _disassemble_key(key)
-            if not (
-                parser.has_section(section) and parser.remove_option(section, name)
-            ):
-                # The option was not removed.
-                raise ConfigurationError(
-                    "Fatal Internal error [id=1]. Please report as a bug."
-                )
-
-            # The section may be empty after the option was removed.
-            if not parser.items(section):
-                parser.remove_section(section)
-            self._mark_as_modified(fname, parser)
-
-        del self._config[self.load_only][key]
-
-    def save(self) -> None:
-        """Save the current in-memory state."""
-        self._ensure_have_load_only()
-
-        for fname, parser in self._modified_parsers:
-            logger.info("Writing to %s", fname)
-
-            # Ensure directory exists.
-            ensure_dir(os.path.dirname(fname))
-
-            # Ensure directory's permission(need to be writeable)
-            try:
-                with open(fname, "w") as f:
-                    parser.write(f)
-            except OSError as error:
-                raise ConfigurationError(
-                    f"An error occurred while writing to the configuration file "
-                    f"{fname}: {error}"
-                )
-
-    #
-    # Private routines
-    #
-
-    def _ensure_have_load_only(self) -> None:
-        if self.load_only is None:
-            raise ConfigurationError("Needed a specific file to be modifying.")
-        logger.debug("Will be working with %s variant only", self.load_only)
-
-    @property
-    def _dictionary(self) -> Dict[str, Any]:
-        """A dictionary representing the loaded configuration."""
-        # NOTE: Dictionaries are not populated if not loaded. So, conditionals
-        #       are not needed here.
-        retval = {}
-
-        for variant in OVERRIDE_ORDER:
-            retval.update(self._config[variant])
-
-        return retval
-
-    def _load_config_files(self) -> None:
-        """Loads configuration from configuration files"""
-        config_files = dict(self.iter_config_files())
-        if config_files[kinds.ENV][0:1] == [os.devnull]:
-            logger.debug(
-                "Skipping loading configuration files due to "
-                "environment's PIP_CONFIG_FILE being os.devnull"
-            )
-            return
-
-        for variant, files in config_files.items():
-            for fname in files:
-                # If there's specific variant set in `load_only`, load only
-                # that variant, not the others.
-                if self.load_only is not None and variant != self.load_only:
-                    logger.debug("Skipping file '%s' (variant: %s)", fname, variant)
-                    continue
-
-                parser = self._load_file(variant, fname)
-
-                # Keeping track of the parsers used
-                self._parsers[variant].append((fname, parser))
-
-    def _load_file(self, variant: Kind, fname: str) -> RawConfigParser:
-        logger.verbose("For variant '%s', will try loading '%s'", variant, fname)
-        parser = self._construct_parser(fname)
-
-        for section in parser.sections():
-            items = parser.items(section)
-            self._config[variant].update(self._normalized_keys(section, items))
-
-        return parser
-
-    def _construct_parser(self, fname: str) -> RawConfigParser:
-        parser = configparser.RawConfigParser()
-        # If there is no such file, don't bother reading it but create the
-        # parser anyway, to hold the data.
-        # Doing this is useful when modifying and saving files, where we don't
-        # need to construct a parser.
-        if os.path.exists(fname):
-            locale_encoding = locale.getpreferredencoding(False)
-            try:
-                parser.read(fname, encoding=locale_encoding)
-            except UnicodeDecodeError:
-                # See https://github.com/pypa/pip/issues/4963
-                raise ConfigurationFileCouldNotBeLoaded(
-                    reason=f"contains invalid {locale_encoding} characters",
-                    fname=fname,
-                )
-            except configparser.Error as error:
-                # See https://github.com/pypa/pip/issues/4893
-                raise ConfigurationFileCouldNotBeLoaded(error=error)
-        return parser
-
-    def _load_environment_vars(self) -> None:
-        """Loads configuration from environment variables"""
-        self._config[kinds.ENV_VAR].update(
-            self._normalized_keys(":env:", self.get_environ_vars())
-        )
-
-    def _normalized_keys(
-        self, section: str, items: Iterable[Tuple[str, Any]]
-    ) -> Dict[str, Any]:
-        """Normalizes items to construct a dictionary with normalized keys.
-
-        This routine is where the names become keys and are made the same
-        regardless of source - configuration files or environment.
-        """
-        normalized = {}
-        for name, val in items:
-            key = section + "." + _normalize_name(name)
-            normalized[key] = val
-        return normalized
-
-    def get_environ_vars(self) -> Iterable[Tuple[str, str]]:
-        """Returns a generator with all environmental vars with prefix PIP_"""
-        for key, val in os.environ.items():
-            if key.startswith("PIP_"):
-                name = key[4:].lower()
-                if name not in ENV_NAMES_IGNORED:
-                    yield name, val
-
-    # XXX: This is patched in the tests.
-    def iter_config_files(self) -> Iterable[Tuple[Kind, List[str]]]:
-        """Yields variant and configuration files associated with it.
-
-        This should be treated like items of a dictionary.
-        """
-        # SMELL: Move the conditions out of this function
-
-        # environment variables have the lowest priority
-        config_file = os.environ.get("PIP_CONFIG_FILE", None)
-        if config_file is not None:
-            yield kinds.ENV, [config_file]
-        else:
-            yield kinds.ENV, []
-
-        config_files = get_configuration_files()
-
-        # at the base we have any global configuration
-        yield kinds.GLOBAL, config_files[kinds.GLOBAL]
-
-        # per-user configuration next
-        should_load_user_config = not self.isolated and not (
-            config_file and os.path.exists(config_file)
-        )
-        if should_load_user_config:
-            # The legacy config file is overridden by the new config file
-            yield kinds.USER, config_files[kinds.USER]
-
-        # finally virtualenv configuration first trumping others
-        yield kinds.SITE, config_files[kinds.SITE]
-
-    def get_values_in_config(self, variant: Kind) -> Dict[str, Any]:
-        """Get values present in a config file"""
-        return self._config[variant]
-
-    def _get_parser_to_modify(self) -> Tuple[str, RawConfigParser]:
-        # Determine which parser to modify
-        assert self.load_only
-        parsers = self._parsers[self.load_only]
-        if not parsers:
-            # This should not happen if everything works correctly.
-            raise ConfigurationError(
-                "Fatal Internal error [id=2]. Please report as a bug."
-            )
-
-        # Use the highest priority parser.
-        return parsers[-1]
-
-    # XXX: This is patched in the tests.
-    def _mark_as_modified(self, fname: str, parser: RawConfigParser) -> None:
-        file_parser_tuple = (fname, parser)
-        if file_parser_tuple not in self._modified_parsers:
-            self._modified_parsers.append(file_parser_tuple)
-
-    def __repr__(self) -> str:
-        return f"{self.__class__.__name__}({self._dictionary!r})"
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/req/constructors.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/req/constructors.py
deleted file mode 100644
index c5ca2d85d5176c65a2e90000b0d67390573120a6..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/req/constructors.py
+++ /dev/null
@@ -1,506 +0,0 @@
-"""Backing implementation for InstallRequirement's various constructors
-
-The idea here is that these formed a major chunk of InstallRequirement's size
-so, moving them and support code dedicated to them outside of that class
-helps creates for better understandability for the rest of the code.
-
-These are meant to be used elsewhere within pip to create instances of
-InstallRequirement.
-"""
-
-import logging
-import os
-import re
-from typing import Dict, List, Optional, Set, Tuple, Union
-
-from pip._vendor.packaging.markers import Marker
-from pip._vendor.packaging.requirements import InvalidRequirement, Requirement
-from pip._vendor.packaging.specifiers import Specifier
-
-from pip._internal.exceptions import InstallationError
-from pip._internal.models.index import PyPI, TestPyPI
-from pip._internal.models.link import Link
-from pip._internal.models.wheel import Wheel
-from pip._internal.req.req_file import ParsedRequirement
-from pip._internal.req.req_install import InstallRequirement
-from pip._internal.utils.filetypes import is_archive_file
-from pip._internal.utils.misc import is_installable_dir
-from pip._internal.utils.packaging import get_requirement
-from pip._internal.utils.urls import path_to_url
-from pip._internal.vcs import is_url, vcs
-
-__all__ = [
-    "install_req_from_editable",
-    "install_req_from_line",
-    "parse_editable",
-]
-
-logger = logging.getLogger(__name__)
-operators = Specifier._operators.keys()
-
-
-def _strip_extras(path: str) -> Tuple[str, Optional[str]]:
-    m = re.match(r"^(.+)(\[[^\]]+\])$", path)
-    extras = None
-    if m:
-        path_no_extras = m.group(1)
-        extras = m.group(2)
-    else:
-        path_no_extras = path
-
-    return path_no_extras, extras
-
-
-def convert_extras(extras: Optional[str]) -> Set[str]:
-    if not extras:
-        return set()
-    return get_requirement("placeholder" + extras.lower()).extras
-
-
-def parse_editable(editable_req: str) -> Tuple[Optional[str], str, Set[str]]:
-    """Parses an editable requirement into:
-        - a requirement name
-        - an URL
-        - extras
-        - editable options
-    Accepted requirements:
-        svn+http://blahblah@rev#egg=Foobar[baz]&subdirectory=version_subdir
-        .[some_extra]
-    """
-
-    url = editable_req
-
-    # If a file path is specified with extras, strip off the extras.
-    url_no_extras, extras = _strip_extras(url)
-
-    if os.path.isdir(url_no_extras):
-        # Treating it as code that has already been checked out
-        url_no_extras = path_to_url(url_no_extras)
-
-    if url_no_extras.lower().startswith("file:"):
-        package_name = Link(url_no_extras).egg_fragment
-        if extras:
-            return (
-                package_name,
-                url_no_extras,
-                get_requirement("placeholder" + extras.lower()).extras,
-            )
-        else:
-            return package_name, url_no_extras, set()
-
-    for version_control in vcs:
-        if url.lower().startswith(f"{version_control}:"):
-            url = f"{version_control}+{url}"
-            break
-
-    link = Link(url)
-
-    if not link.is_vcs:
-        backends = ", ".join(vcs.all_schemes)
-        raise InstallationError(
-            f"{editable_req} is not a valid editable requirement. "
-            f"It should either be a path to a local project or a VCS URL "
-            f"(beginning with {backends})."
-        )
-
-    package_name = link.egg_fragment
-    if not package_name:
-        raise InstallationError(
-            "Could not detect requirement name for '{}', please specify one "
-            "with #egg=your_package_name".format(editable_req)
-        )
-    return package_name, url, set()
-
-
-def check_first_requirement_in_file(filename: str) -> None:
-    """Check if file is parsable as a requirements file.
-
-    This is heavily based on ``pkg_resources.parse_requirements``, but
-    simplified to just check the first meaningful line.
-
-    :raises InvalidRequirement: If the first meaningful line cannot be parsed
-        as an requirement.
-    """
-    with open(filename, encoding="utf-8", errors="ignore") as f:
-        # Create a steppable iterator, so we can handle \-continuations.
-        lines = (
-            line
-            for line in (line.strip() for line in f)
-            if line and not line.startswith("#")  # Skip blank lines/comments.
-        )
-
-        for line in lines:
-            # Drop comments -- a hash without a space may be in a URL.
-            if " #" in line:
-                line = line[: line.find(" #")]
-            # If there is a line continuation, drop it, and append the next line.
-            if line.endswith("\\"):
-                line = line[:-2].strip() + next(lines, "")
-            Requirement(line)
-            return
-
-
-def deduce_helpful_msg(req: str) -> str:
-    """Returns helpful msg in case requirements file does not exist,
-    or cannot be parsed.
-
-    :params req: Requirements file path
-    """
-    if not os.path.exists(req):
-        return f" File '{req}' does not exist."
-    msg = " The path does exist. "
-    # Try to parse and check if it is a requirements file.
-    try:
-        check_first_requirement_in_file(req)
-    except InvalidRequirement:
-        logger.debug("Cannot parse '%s' as requirements file", req)
-    else:
-        msg += (
-            f"The argument you provided "
-            f"({req}) appears to be a"
-            f" requirements file. If that is the"
-            f" case, use the '-r' flag to install"
-            f" the packages specified within it."
-        )
-    return msg
-
-
-class RequirementParts:
-    def __init__(
-        self,
-        requirement: Optional[Requirement],
-        link: Optional[Link],
-        markers: Optional[Marker],
-        extras: Set[str],
-    ):
-        self.requirement = requirement
-        self.link = link
-        self.markers = markers
-        self.extras = extras
-
-
-def parse_req_from_editable(editable_req: str) -> RequirementParts:
-    name, url, extras_override = parse_editable(editable_req)
-
-    if name is not None:
-        try:
-            req: Optional[Requirement] = Requirement(name)
-        except InvalidRequirement:
-            raise InstallationError(f"Invalid requirement: '{name}'")
-    else:
-        req = None
-
-    link = Link(url)
-
-    return RequirementParts(req, link, None, extras_override)
-
-
-# ---- The actual constructors follow ----
-
-
-def install_req_from_editable(
-    editable_req: str,
-    comes_from: Optional[Union[InstallRequirement, str]] = None,
-    *,
-    use_pep517: Optional[bool] = None,
-    isolated: bool = False,
-    global_options: Optional[List[str]] = None,
-    hash_options: Optional[Dict[str, List[str]]] = None,
-    constraint: bool = False,
-    user_supplied: bool = False,
-    permit_editable_wheels: bool = False,
-    config_settings: Optional[Dict[str, Union[str, List[str]]]] = None,
-) -> InstallRequirement:
-    parts = parse_req_from_editable(editable_req)
-
-    return InstallRequirement(
-        parts.requirement,
-        comes_from=comes_from,
-        user_supplied=user_supplied,
-        editable=True,
-        permit_editable_wheels=permit_editable_wheels,
-        link=parts.link,
-        constraint=constraint,
-        use_pep517=use_pep517,
-        isolated=isolated,
-        global_options=global_options,
-        hash_options=hash_options,
-        config_settings=config_settings,
-        extras=parts.extras,
-    )
-
-
-def _looks_like_path(name: str) -> bool:
-    """Checks whether the string "looks like" a path on the filesystem.
-
-    This does not check whether the target actually exists, only judge from the
-    appearance.
-
-    Returns true if any of the following conditions is true:
-    * a path separator is found (either os.path.sep or os.path.altsep);
-    * a dot is found (which represents the current directory).
-    """
-    if os.path.sep in name:
-        return True
-    if os.path.altsep is not None and os.path.altsep in name:
-        return True
-    if name.startswith("."):
-        return True
-    return False
-
-
-def _get_url_from_path(path: str, name: str) -> Optional[str]:
-    """
-    First, it checks whether a provided path is an installable directory. If it
-    is, returns the path.
-
-    If false, check if the path is an archive file (such as a .whl).
-    The function checks if the path is a file. If false, if the path has
-    an @, it will treat it as a PEP 440 URL requirement and return the path.
-    """
-    if _looks_like_path(name) and os.path.isdir(path):
-        if is_installable_dir(path):
-            return path_to_url(path)
-        # TODO: The is_installable_dir test here might not be necessary
-        #       now that it is done in load_pyproject_toml too.
-        raise InstallationError(
-            f"Directory {name!r} is not installable. Neither 'setup.py' "
-            "nor 'pyproject.toml' found."
-        )
-    if not is_archive_file(path):
-        return None
-    if os.path.isfile(path):
-        return path_to_url(path)
-    urlreq_parts = name.split("@", 1)
-    if len(urlreq_parts) >= 2 and not _looks_like_path(urlreq_parts[0]):
-        # If the path contains '@' and the part before it does not look
-        # like a path, try to treat it as a PEP 440 URL req instead.
-        return None
-    logger.warning(
-        "Requirement %r looks like a filename, but the file does not exist",
-        name,
-    )
-    return path_to_url(path)
-
-
-def parse_req_from_line(name: str, line_source: Optional[str]) -> RequirementParts:
-    if is_url(name):
-        marker_sep = "; "
-    else:
-        marker_sep = ";"
-    if marker_sep in name:
-        name, markers_as_string = name.split(marker_sep, 1)
-        markers_as_string = markers_as_string.strip()
-        if not markers_as_string:
-            markers = None
-        else:
-            markers = Marker(markers_as_string)
-    else:
-        markers = None
-    name = name.strip()
-    req_as_string = None
-    path = os.path.normpath(os.path.abspath(name))
-    link = None
-    extras_as_string = None
-
-    if is_url(name):
-        link = Link(name)
-    else:
-        p, extras_as_string = _strip_extras(path)
-        url = _get_url_from_path(p, name)
-        if url is not None:
-            link = Link(url)
-
-    # it's a local file, dir, or url
-    if link:
-        # Handle relative file URLs
-        if link.scheme == "file" and re.search(r"\.\./", link.url):
-            link = Link(path_to_url(os.path.normpath(os.path.abspath(link.path))))
-        # wheel file
-        if link.is_wheel:
-            wheel = Wheel(link.filename)  # can raise InvalidWheelFilename
-            req_as_string = f"{wheel.name}=={wheel.version}"
-        else:
-            # set the req to the egg fragment.  when it's not there, this
-            # will become an 'unnamed' requirement
-            req_as_string = link.egg_fragment
-
-    # a requirement specifier
-    else:
-        req_as_string = name
-
-    extras = convert_extras(extras_as_string)
-
-    def with_source(text: str) -> str:
-        if not line_source:
-            return text
-        return f"{text} (from {line_source})"
-
-    def _parse_req_string(req_as_string: str) -> Requirement:
-        try:
-            req = get_requirement(req_as_string)
-        except InvalidRequirement:
-            if os.path.sep in req_as_string:
-                add_msg = "It looks like a path."
-                add_msg += deduce_helpful_msg(req_as_string)
-            elif "=" in req_as_string and not any(
-                op in req_as_string for op in operators
-            ):
-                add_msg = "= is not a valid operator. Did you mean == ?"
-            else:
-                add_msg = ""
-            msg = with_source(f"Invalid requirement: {req_as_string!r}")
-            if add_msg:
-                msg += f"\nHint: {add_msg}"
-            raise InstallationError(msg)
-        else:
-            # Deprecate extras after specifiers: "name>=1.0[extras]"
-            # This currently works by accident because _strip_extras() parses
-            # any extras in the end of the string and those are saved in
-            # RequirementParts
-            for spec in req.specifier:
-                spec_str = str(spec)
-                if spec_str.endswith("]"):
-                    msg = f"Extras after version '{spec_str}'."
-                    raise InstallationError(msg)
-        return req
-
-    if req_as_string is not None:
-        req: Optional[Requirement] = _parse_req_string(req_as_string)
-    else:
-        req = None
-
-    return RequirementParts(req, link, markers, extras)
-
-
-def install_req_from_line(
-    name: str,
-    comes_from: Optional[Union[str, InstallRequirement]] = None,
-    *,
-    use_pep517: Optional[bool] = None,
-    isolated: bool = False,
-    global_options: Optional[List[str]] = None,
-    hash_options: Optional[Dict[str, List[str]]] = None,
-    constraint: bool = False,
-    line_source: Optional[str] = None,
-    user_supplied: bool = False,
-    config_settings: Optional[Dict[str, Union[str, List[str]]]] = None,
-) -> InstallRequirement:
-    """Creates an InstallRequirement from a name, which might be a
-    requirement, directory containing 'setup.py', filename, or URL.
-
-    :param line_source: An optional string describing where the line is from,
-        for logging purposes in case of an error.
-    """
-    parts = parse_req_from_line(name, line_source)
-
-    return InstallRequirement(
-        parts.requirement,
-        comes_from,
-        link=parts.link,
-        markers=parts.markers,
-        use_pep517=use_pep517,
-        isolated=isolated,
-        global_options=global_options,
-        hash_options=hash_options,
-        config_settings=config_settings,
-        constraint=constraint,
-        extras=parts.extras,
-        user_supplied=user_supplied,
-    )
-
-
-def install_req_from_req_string(
-    req_string: str,
-    comes_from: Optional[InstallRequirement] = None,
-    isolated: bool = False,
-    use_pep517: Optional[bool] = None,
-    user_supplied: bool = False,
-) -> InstallRequirement:
-    try:
-        req = get_requirement(req_string)
-    except InvalidRequirement:
-        raise InstallationError(f"Invalid requirement: '{req_string}'")
-
-    domains_not_allowed = [
-        PyPI.file_storage_domain,
-        TestPyPI.file_storage_domain,
-    ]
-    if (
-        req.url
-        and comes_from
-        and comes_from.link
-        and comes_from.link.netloc in domains_not_allowed
-    ):
-        # Explicitly disallow pypi packages that depend on external urls
-        raise InstallationError(
-            "Packages installed from PyPI cannot depend on packages "
-            "which are not also hosted on PyPI.\n"
-            "{} depends on {} ".format(comes_from.name, req)
-        )
-
-    return InstallRequirement(
-        req,
-        comes_from,
-        isolated=isolated,
-        use_pep517=use_pep517,
-        user_supplied=user_supplied,
-    )
-
-
-def install_req_from_parsed_requirement(
-    parsed_req: ParsedRequirement,
-    isolated: bool = False,
-    use_pep517: Optional[bool] = None,
-    user_supplied: bool = False,
-    config_settings: Optional[Dict[str, Union[str, List[str]]]] = None,
-) -> InstallRequirement:
-    if parsed_req.is_editable:
-        req = install_req_from_editable(
-            parsed_req.requirement,
-            comes_from=parsed_req.comes_from,
-            use_pep517=use_pep517,
-            constraint=parsed_req.constraint,
-            isolated=isolated,
-            user_supplied=user_supplied,
-            config_settings=config_settings,
-        )
-
-    else:
-        req = install_req_from_line(
-            parsed_req.requirement,
-            comes_from=parsed_req.comes_from,
-            use_pep517=use_pep517,
-            isolated=isolated,
-            global_options=(
-                parsed_req.options.get("global_options", [])
-                if parsed_req.options
-                else []
-            ),
-            hash_options=(
-                parsed_req.options.get("hashes", {}) if parsed_req.options else {}
-            ),
-            constraint=parsed_req.constraint,
-            line_source=parsed_req.line_source,
-            user_supplied=user_supplied,
-            config_settings=config_settings,
-        )
-    return req
-
-
-def install_req_from_link_and_ireq(
-    link: Link, ireq: InstallRequirement
-) -> InstallRequirement:
-    return InstallRequirement(
-        req=ireq.req,
-        comes_from=ireq.comes_from,
-        editable=ireq.editable,
-        link=link,
-        markers=ireq.markers,
-        use_pep517=ireq.use_pep517,
-        isolated=ireq.isolated,
-        global_options=ireq.global_options,
-        hash_options=ireq.hash_options,
-        config_settings=ireq.config_settings,
-        user_supplied=ireq.user_supplied,
-    )
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/markers.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/markers.py
deleted file mode 100644
index 8b98fca7233be6dd9324cd2b6d71b6a8ac91a6cb..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/packaging/markers.py
+++ /dev/null
@@ -1,252 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import operator
-import os
-import platform
-import sys
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-from ._parser import (
-    MarkerAtom,
-    MarkerList,
-    Op,
-    Value,
-    Variable,
-    parse_marker as _parse_marker,
-)
-from ._tokenizer import ParserSyntaxError
-from .specifiers import InvalidSpecifier, Specifier
-from .utils import canonicalize_name
-
-__all__ = [
-    "InvalidMarker",
-    "UndefinedComparison",
-    "UndefinedEnvironmentName",
-    "Marker",
-    "default_environment",
-]
-
-Operator = Callable[[str, str], bool]
-
-
-class InvalidMarker(ValueError):
-    """
-    An invalid marker was found, users should refer to PEP 508.
-    """
-
-
-class UndefinedComparison(ValueError):
-    """
-    An invalid operation was attempted on a value that doesn't support it.
-    """
-
-
-class UndefinedEnvironmentName(ValueError):
-    """
-    A name was attempted to be used that does not exist inside of the
-    environment.
-    """
-
-
-def _normalize_extra_values(results: Any) -> Any:
-    """
-    Normalize extra values.
-    """
-    if isinstance(results[0], tuple):
-        lhs, op, rhs = results[0]
-        if isinstance(lhs, Variable) and lhs.value == "extra":
-            normalized_extra = canonicalize_name(rhs.value)
-            rhs = Value(normalized_extra)
-        elif isinstance(rhs, Variable) and rhs.value == "extra":
-            normalized_extra = canonicalize_name(lhs.value)
-            lhs = Value(normalized_extra)
-        results[0] = lhs, op, rhs
-    return results
-
-
-def _format_marker(
-    marker: Union[List[str], MarkerAtom, str], first: Optional[bool] = True
-) -> str:
-
-    assert isinstance(marker, (list, tuple, str))
-
-    # Sometimes we have a structure like [[...]] which is a single item list
-    # where the single item is itself it's own list. In that case we want skip
-    # the rest of this function so that we don't get extraneous () on the
-    # outside.
-    if (
-        isinstance(marker, list)
-        and len(marker) == 1
-        and isinstance(marker[0], (list, tuple))
-    ):
-        return _format_marker(marker[0])
-
-    if isinstance(marker, list):
-        inner = (_format_marker(m, first=False) for m in marker)
-        if first:
-            return " ".join(inner)
-        else:
-            return "(" + " ".join(inner) + ")"
-    elif isinstance(marker, tuple):
-        return " ".join([m.serialize() for m in marker])
-    else:
-        return marker
-
-
-_operators: Dict[str, Operator] = {
-    "in": lambda lhs, rhs: lhs in rhs,
-    "not in": lambda lhs, rhs: lhs not in rhs,
-    "<": operator.lt,
-    "<=": operator.le,
-    "==": operator.eq,
-    "!=": operator.ne,
-    ">=": operator.ge,
-    ">": operator.gt,
-}
-
-
-def _eval_op(lhs: str, op: Op, rhs: str) -> bool:
-    try:
-        spec = Specifier("".join([op.serialize(), rhs]))
-    except InvalidSpecifier:
-        pass
-    else:
-        return spec.contains(lhs, prereleases=True)
-
-    oper: Optional[Operator] = _operators.get(op.serialize())
-    if oper is None:
-        raise UndefinedComparison(f"Undefined {op!r} on {lhs!r} and {rhs!r}.")
-
-    return oper(lhs, rhs)
-
-
-def _normalize(*values: str, key: str) -> Tuple[str, ...]:
-    # PEP 685 – Comparison of extra names for optional distribution dependencies
-    # https://peps.python.org/pep-0685/
-    # > When comparing extra names, tools MUST normalize the names being
-    # > compared using the semantics outlined in PEP 503 for names
-    if key == "extra":
-        return tuple(canonicalize_name(v) for v in values)
-
-    # other environment markers don't have such standards
-    return values
-
-
-def _evaluate_markers(markers: MarkerList, environment: Dict[str, str]) -> bool:
-    groups: List[List[bool]] = [[]]
-
-    for marker in markers:
-        assert isinstance(marker, (list, tuple, str))
-
-        if isinstance(marker, list):
-            groups[-1].append(_evaluate_markers(marker, environment))
-        elif isinstance(marker, tuple):
-            lhs, op, rhs = marker
-
-            if isinstance(lhs, Variable):
-                environment_key = lhs.value
-                lhs_value = environment[environment_key]
-                rhs_value = rhs.value
-            else:
-                lhs_value = lhs.value
-                environment_key = rhs.value
-                rhs_value = environment[environment_key]
-
-            lhs_value, rhs_value = _normalize(lhs_value, rhs_value, key=environment_key)
-            groups[-1].append(_eval_op(lhs_value, op, rhs_value))
-        else:
-            assert marker in ["and", "or"]
-            if marker == "or":
-                groups.append([])
-
-    return any(all(item) for item in groups)
-
-
-def format_full_version(info: "sys._version_info") -> str:
-    version = "{0.major}.{0.minor}.{0.micro}".format(info)
-    kind = info.releaselevel
-    if kind != "final":
-        version += kind[0] + str(info.serial)
-    return version
-
-
-def default_environment() -> Dict[str, str]:
-    iver = format_full_version(sys.implementation.version)
-    implementation_name = sys.implementation.name
-    return {
-        "implementation_name": implementation_name,
-        "implementation_version": iver,
-        "os_name": os.name,
-        "platform_machine": platform.machine(),
-        "platform_release": platform.release(),
-        "platform_system": platform.system(),
-        "platform_version": platform.version(),
-        "python_full_version": platform.python_version(),
-        "platform_python_implementation": platform.python_implementation(),
-        "python_version": ".".join(platform.python_version_tuple()[:2]),
-        "sys_platform": sys.platform,
-    }
-
-
-class Marker:
-    def __init__(self, marker: str) -> None:
-        # Note: We create a Marker object without calling this constructor in
-        #       packaging.requirements.Requirement. If any additional logic is
-        #       added here, make sure to mirror/adapt Requirement.
-        try:
-            self._markers = _normalize_extra_values(_parse_marker(marker))
-            # The attribute `_markers` can be described in terms of a recursive type:
-            # MarkerList = List[Union[Tuple[Node, ...], str, MarkerList]]
-            #
-            # For example, the following expression:
-            # python_version > "3.6" or (python_version == "3.6" and os_name == "unix")
-            #
-            # is parsed into:
-            # [
-            #     (<Variable('python_version')>, <Op('>')>, <Value('3.6')>),
-            #     'and',
-            #     [
-            #         (<Variable('python_version')>, <Op('==')>, <Value('3.6')>),
-            #         'or',
-            #         (<Variable('os_name')>, <Op('==')>, <Value('unix')>)
-            #     ]
-            # ]
-        except ParserSyntaxError as e:
-            raise InvalidMarker(str(e)) from e
-
-    def __str__(self) -> str:
-        return _format_marker(self._markers)
-
-    def __repr__(self) -> str:
-        return f"<Marker('{self}')>"
-
-    def __hash__(self) -> int:
-        return hash((self.__class__.__name__, str(self)))
-
-    def __eq__(self, other: Any) -> bool:
-        if not isinstance(other, Marker):
-            return NotImplemented
-
-        return str(self) == str(other)
-
-    def evaluate(self, environment: Optional[Dict[str, str]] = None) -> bool:
-        """Evaluate a marker.
-
-        Return the boolean from evaluating the given marker against the
-        environment. environment is an optional argument to override all or
-        part of the determined environment.
-
-        The environment is determined from the current Python process.
-        """
-        current_environment = default_environment()
-        current_environment["extra"] = ""
-        if environment is not None:
-            current_environment.update(environment)
-            # The API used to allow setting extra to None. We need to handle this
-            # case for backwards compatibility.
-            if current_environment["extra"] is None:
-                current_environment["extra"] = ""
-
-        return _evaluate_markers(self._markers, current_environment)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/qu2cu/__main__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/qu2cu/__main__.py
deleted file mode 100644
index 27728cc7aa400fa7389cf0ba31990165bc7b03b5..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/qu2cu/__main__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import sys
-
-from .cli import main
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/CHANGELOG.md b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/CHANGELOG.md
deleted file mode 100644
index 18c41ce5cc7f9e2332b4b48db05f1344fa84e9c1..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/CHANGELOG.md
+++ /dev/null
@@ -1,4279 +0,0 @@
-# gradio
-
-## 4.0.2
-
-### Fixes
-
-- [#6191](https://github.com/gradio-app/gradio/pull/6191) [`b555bc09f`](https://github.com/gradio-app/gradio/commit/b555bc09ffe8e58b10da6227e2f11a0c084aa71d) - fix cdn build.  Thanks [@pngwn](https://github.com/pngwn)!
-
-## 4.0.1
-
-### Features
-
-- [#6137](https://github.com/gradio-app/gradio/pull/6137) [`2ba14b284`](https://github.com/gradio-app/gradio/commit/2ba14b284f908aa13859f4337167a157075a68eb) - JS Param.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#6181](https://github.com/gradio-app/gradio/pull/6181) [`62ec2075c`](https://github.com/gradio-app/gradio/commit/62ec2075ccad8025a7721a08d0f29eb5a4f87fad) - modify preprocess to use pydantic models.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 4.0.0
-
-### Features
-
-- [#6184](https://github.com/gradio-app/gradio/pull/6184) [`86edc0199`](https://github.com/gradio-app/gradio/commit/86edc01995d9f888bac093c44c3d4535fe6483b3) - Remove gr.mix.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - fix circular dependency with client + upload.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#6177](https://github.com/gradio-app/gradio/pull/6177) [`59f5a4e30`](https://github.com/gradio-app/gradio/commit/59f5a4e30ed9da1c6d6f6ab0886285150b3e89ec) - Part I: Remove serializes.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Don't serve files in working directory by default.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Small change to make `api_open=False` by default.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Add json schema unit tests.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Remove duplicate `elem_ids` from components.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#6182](https://github.com/gradio-app/gradio/pull/6182) [`911829ac2`](https://github.com/gradio-app/gradio/commit/911829ac278080fc81155d4b75502692e72fd3de) - Allow data at queue join.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Moves `gradio_cached_folder` inside the gradio temp direcotry.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - V4: Fix constructor_args.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Remove interpretation for good.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Improve Audio Component.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - pass props to example components and to example outputs.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Clean root url.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Adds the ability to build the frontend and backend of custom components in preparation for publishing to pypi using `gradio_component build`.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Fix selectable prop in the backend.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Set api=False for cancel events.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Improve Video Component.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Try to trigger a major beta release.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#6172](https://github.com/gradio-app/gradio/pull/6172) [`79c8156eb`](https://github.com/gradio-app/gradio/commit/79c8156ebbf35369dc9cfb1522f88df3cd49c89c) - Queue concurrency count.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Image v4.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Publish all components to npm.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Open source FRP server and allow `gradio` to connect to custom share servers.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - File upload optimization.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Custom components.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Removes deprecated arguments and parameters from v4.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - V4: Use async version of shutil in upload route.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - V4: Set cache dir for some component tests.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Proposal: sample demo for custom components should be a `gr.Interface`.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - fix cc build.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - --overwrite deletes previous content.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#6171](https://github.com/gradio-app/gradio/pull/6171) [`28322422c`](https://github.com/gradio-app/gradio/commit/28322422cb9d8d3e471e439ad602959662e79312) - strip dangling svelte imports.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Swap websockets for SSE.  Thanks [@pngwn](https://github.com/pngwn)!
-
-### Fixes
-
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Pending events behavior.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Reinstate types that were removed in error in #5832.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`287fe6782`](https://github.com/gradio-app/gradio/commit/287fe6782825479513e79a5cf0ba0fbfe51443d7) - Fixes: slider bar are too thin on FireFox.  Thanks [@pngwn](https://github.com/pngwn)!
-
-## 4.0.0-beta.15
-
-### Features
-
-- [#6153](https://github.com/gradio-app/gradio/pull/6153) [`1162ed621`](https://github.com/gradio-app/gradio/commit/1162ed6217fe58d66a1923834c390150599ad81f) - Remove `show_edit_button` param in Audio.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#6124](https://github.com/gradio-app/gradio/pull/6124) [`a7435ba9e`](https://github.com/gradio-app/gradio/commit/a7435ba9e6f8b88a838e80893eb8fedf60ccda67) - Fix static issues with Lite on v4.  Thanks [@aliabd](https://github.com/aliabd)!
-- [#6143](https://github.com/gradio-app/gradio/pull/6143) [`e4f7b4b40`](https://github.com/gradio-app/gradio/commit/e4f7b4b409323b01aa01b39e15ce6139e29aa073) - fix circular dependency with client + upload.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#6136](https://github.com/gradio-app/gradio/pull/6136) [`667802a6c`](https://github.com/gradio-app/gradio/commit/667802a6cdbfb2ce454a3be5a78e0990b194548a) - JS Component Documentation.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6142](https://github.com/gradio-app/gradio/pull/6142) [`103416d17`](https://github.com/gradio-app/gradio/commit/103416d17f021c82f5ff0583dcc2d80906ad279e) - JS READMEs and Storybook on Docs.  Thanks [@aliabd](https://github.com/aliabd)!
-- [#6094](https://github.com/gradio-app/gradio/pull/6094) [`c476bd5a5`](https://github.com/gradio-app/gradio/commit/c476bd5a5b70836163b9c69bf4bfe068b17fbe13) - Image v4.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#6149](https://github.com/gradio-app/gradio/pull/6149) [`90318b1dd`](https://github.com/gradio-app/gradio/commit/90318b1dd118ae08a695a50e7c556226234ab6dc) - swap `mode` on the frontned to `interactive` to match the backend.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#6128](https://github.com/gradio-app/gradio/pull/6128) [`9c3bf3175`](https://github.com/gradio-app/gradio/commit/9c3bf31751a414093d103e5a115772f3ef1a67aa) - Don't serve files in working directory by default.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#6138](https://github.com/gradio-app/gradio/pull/6138) [`d2dfc1b9a`](https://github.com/gradio-app/gradio/commit/d2dfc1b9a9bd4940f70b62066b1aeaa905b9c7a9) - Small change to make `api_open=False` by default.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#6152](https://github.com/gradio-app/gradio/pull/6152) [`982bff2fd`](https://github.com/gradio-app/gradio/commit/982bff2fdd938b798c400fb90d1cf0caf7278894) - Remove duplicate `elem_ids` from components.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#6155](https://github.com/gradio-app/gradio/pull/6155) [`f71ea09ae`](https://github.com/gradio-app/gradio/commit/f71ea09ae796b85e9fe35956d426f0a19ee48f85) - Moves `gradio_cached_folder` inside the gradio temp direcotry.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#6154](https://github.com/gradio-app/gradio/pull/6154) [`a8ef6d5dc`](https://github.com/gradio-app/gradio/commit/a8ef6d5dc97b35cc1da589d1a653209a3c327d98) - Remove interpretation for good.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#6135](https://github.com/gradio-app/gradio/pull/6135) [`bce37ac74`](https://github.com/gradio-app/gradio/commit/bce37ac744496537e71546d2bb889bf248dcf5d3) - Fix selectable prop in the backend.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6118](https://github.com/gradio-app/gradio/pull/6118) [`88bccfdba`](https://github.com/gradio-app/gradio/commit/88bccfdba3df2df4b2747ea5d649ed528047cf50) - Improve Video Component.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#6126](https://github.com/gradio-app/gradio/pull/6126) [`865a22d5c`](https://github.com/gradio-app/gradio/commit/865a22d5c60fd97aeca968e55580b403743a23ec) - Refactor `Blocks.load()` so that it is in the same style as the other listeners.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#6098](https://github.com/gradio-app/gradio/pull/6098) [`c3bc515bf`](https://github.com/gradio-app/gradio/commit/c3bc515bf7d430427182143f7fb047bb4b9f4e5e) - Gradio custom component publish.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6157](https://github.com/gradio-app/gradio/pull/6157) [`db143bdd1`](https://github.com/gradio-app/gradio/commit/db143bdd13b830f3bfd513bbfbc0cd1403522b84) - Make output components not editable if they are being updated.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#6091](https://github.com/gradio-app/gradio/pull/6091) [`d5d29c947`](https://github.com/gradio-app/gradio/commit/d5d29c947467e54a8514790894ffffba1c796772) - Open source FRP server and allow `gradio` to connect to custom share servers.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#6129](https://github.com/gradio-app/gradio/pull/6129) [`0d261c6ec`](https://github.com/gradio-app/gradio/commit/0d261c6ec1e783e284336023885f67b2ce04084c) - Fix fallback demo app template code.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6140](https://github.com/gradio-app/gradio/pull/6140) [`71bf2702c`](https://github.com/gradio-app/gradio/commit/71bf2702cd5b810c89e2e53452532650acdcfb87) - Fix video.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#6069](https://github.com/gradio-app/gradio/pull/6069) [`bf127e124`](https://github.com/gradio-app/gradio/commit/bf127e1241a41401e144874ea468dff8474eb505) - Swap websockets for SSE.  Thanks [@aliabid94](https://github.com/aliabid94)!
-
-### Fixes
-
-- [#6146](https://github.com/gradio-app/gradio/pull/6146) [`40a171ea6`](https://github.com/gradio-app/gradio/commit/40a171ea60c74afa9519d6cb159def16ce68e1ca) - Fix image double change bug.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#6148](https://github.com/gradio-app/gradio/pull/6148) [`0000a1916`](https://github.com/gradio-app/gradio/commit/0000a191688c5480c977c80acdd0c9023865d57e) - fix dropdown arrow size.  Thanks [@pngwn](https://github.com/pngwn)!
-
-## 4.0.0-beta.14
-
-### Features
-
-- [#6082](https://github.com/gradio-app/gradio/pull/6082) [`037e5af33`](https://github.com/gradio-app/gradio/commit/037e5af3363c5b321b95efc955ee8d6ec0f4504e) - WIP: Fix docs.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6071](https://github.com/gradio-app/gradio/pull/6071) [`f08da1a6f`](https://github.com/gradio-app/gradio/commit/f08da1a6f288f6ab8ec40534d5a9e2c64bed4b3b) - Fixes markdown rendering in examples.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5970](https://github.com/gradio-app/gradio/pull/5970) [`0c571c044`](https://github.com/gradio-app/gradio/commit/0c571c044035989d6fe33fc01fee63d1780635cb) - Add json schema unit tests.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6016](https://github.com/gradio-app/gradio/pull/6016) [`83e947676`](https://github.com/gradio-app/gradio/commit/83e947676d327ca2ab6ae2a2d710c78961c771a0) - Format js in v4 branch.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6093](https://github.com/gradio-app/gradio/pull/6093) [`fadc057bb`](https://github.com/gradio-app/gradio/commit/fadc057bb7016f90dd94049c79fc10d38150c561) - V4: Fix constructor_args.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5966](https://github.com/gradio-app/gradio/pull/5966) [`9cad2127b`](https://github.com/gradio-app/gradio/commit/9cad2127b965023687470b3abfe620e188a9da6e) - Improve Audio Component.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#6014](https://github.com/gradio-app/gradio/pull/6014) [`cad537aac`](https://github.com/gradio-app/gradio/commit/cad537aac57998560c9f44a37499be734de66349) - pass props to example components and to example outputs.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5955](https://github.com/gradio-app/gradio/pull/5955) [`825c9cddc`](https://github.com/gradio-app/gradio/commit/825c9cddc83a09457d8c85ebeecb4bc705572d82) - Fix dev mode model3D.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6107](https://github.com/gradio-app/gradio/pull/6107) [`9a40de7bf`](https://github.com/gradio-app/gradio/commit/9a40de7bff5844c8a135e73c7d175eb02b63a966) - Fix: Move to cache in init postprocess + Fallback Fixes.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6018](https://github.com/gradio-app/gradio/pull/6018) [`184834d02`](https://github.com/gradio-app/gradio/commit/184834d02d448bff387eeb3aef64d9517962f146) - Add a cli command to list available templates.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6092](https://github.com/gradio-app/gradio/pull/6092) [`11d67ae75`](https://github.com/gradio-app/gradio/commit/11d67ae7529e0838565e4131b185c413489c5aa6) - Add a stand-alone install command and tidy-up the fallback template.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6026](https://github.com/gradio-app/gradio/pull/6026) [`338969af2`](https://github.com/gradio-app/gradio/commit/338969af290de032f9cdc204dab8a50be3bf3cc5) - V4: Single-file implementation of form components.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6114](https://github.com/gradio-app/gradio/pull/6114) [`39227b6fa`](https://github.com/gradio-app/gradio/commit/39227b6fac274d5f5b301bc14039571c1bfe510c) - Try to trigger a major beta release.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6060](https://github.com/gradio-app/gradio/pull/6060) [`447dfe06b`](https://github.com/gradio-app/gradio/commit/447dfe06bf19324d88696eb646fd1c5f1c4e86ed) - Clean up backend of `File` and `UploadButton` and change the return type of `preprocess()` from TemporaryFIle to string filepath.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#6073](https://github.com/gradio-app/gradio/pull/6073) [`abff6fb75`](https://github.com/gradio-app/gradio/commit/abff6fb758bd310053a23c938bf1dd8fbdc5d333) - Fix remaining xfail tests in backend.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6089](https://github.com/gradio-app/gradio/pull/6089) [`cd8146ba0`](https://github.com/gradio-app/gradio/commit/cd8146ba053fbcb56cf5052e658e4570d457fb8a) - Update logos for v4.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5961](https://github.com/gradio-app/gradio/pull/5961) [`be2ed5e13`](https://github.com/gradio-app/gradio/commit/be2ed5e13222cbe5013b63b36685987518034a76) - File upload optimization.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5968](https://github.com/gradio-app/gradio/pull/5968) [`6b0bb5e6a`](https://github.com/gradio-app/gradio/commit/6b0bb5e6a252ce8c4ef38455a9f56f1dcda56ab0) - Removes deprecated arguments and parameters from v4.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#6027](https://github.com/gradio-app/gradio/pull/6027) [`de18102b8`](https://github.com/gradio-app/gradio/commit/de18102b8ca38c1d6d6edfa8c0571b81089166bb) - V4: Fix component update bug.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5996](https://github.com/gradio-app/gradio/pull/5996) [`9cf40f76f`](https://github.com/gradio-app/gradio/commit/9cf40f76fed1c0f84b5a5336a9b0100f8a9b4ee3) - V4: Simple dropdown.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5990](https://github.com/gradio-app/gradio/pull/5990) [`85056de5c`](https://github.com/gradio-app/gradio/commit/85056de5cd4e90a10cbfcefab74037dbc622b26b) - V4: Simple textbox.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6044](https://github.com/gradio-app/gradio/pull/6044) [`9053c95a1`](https://github.com/gradio-app/gradio/commit/9053c95a10de12aef572018ee37c71106d2da675) - Simplify File Component.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#6077](https://github.com/gradio-app/gradio/pull/6077) [`35a227fbf`](https://github.com/gradio-app/gradio/commit/35a227fbfb0b0eb11806c0382c5f6910dc9777cf) - Proposal: sample demo for custom components should be a `gr.Interface`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#6079](https://github.com/gradio-app/gradio/pull/6079) [`3b2d9eaa3`](https://github.com/gradio-app/gradio/commit/3b2d9eaa3e84de3e4a0799e4585a94510d665f26) - fix cc build.  Thanks [@pngwn](https://github.com/pngwn)!
-
-### Fixes
-
-- [#6067](https://github.com/gradio-app/gradio/pull/6067) [`bf38e5f06`](https://github.com/gradio-app/gradio/commit/bf38e5f06a7039be913614901c308794fea83ae0) - remove dupe component.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#6065](https://github.com/gradio-app/gradio/pull/6065) [`7d07001e8`](https://github.com/gradio-app/gradio/commit/7d07001e8e7ca9cbd2251632667b3a043de49f49) - fix storybook.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5826](https://github.com/gradio-app/gradio/pull/5826) [`ce036c5d4`](https://github.com/gradio-app/gradio/commit/ce036c5d47e741e29812654bcc641ea6be876504) - Pending events behavior.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#6046](https://github.com/gradio-app/gradio/pull/6046) [`dbb7de5e0`](https://github.com/gradio-app/gradio/commit/dbb7de5e02c53fee05889d696d764d212cb96c74) - fix tests.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#6042](https://github.com/gradio-app/gradio/pull/6042) [`e27997fe6`](https://github.com/gradio-app/gradio/commit/e27997fe6c2bcfebc7015fc26100cee9625eb13a) - Fix `root` when user is unauthenticated so that login page appears correctly.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#6076](https://github.com/gradio-app/gradio/pull/6076) [`f3f98f923`](https://github.com/gradio-app/gradio/commit/f3f98f923c9db506284b8440e18a3ac7ddd8398b) - Lite error handler.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5984](https://github.com/gradio-app/gradio/pull/5984) [`66549d8d2`](https://github.com/gradio-app/gradio/commit/66549d8d256b1845c8c5efa0384695b36cb46eab) - Fixes: slider bar are too thin on FireFox.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-
-## 3.45.0-beta.13
-
-### Features
-
-- [#5964](https://github.com/gradio-app/gradio/pull/5964) [`5fbda0bd2`](https://github.com/gradio-app/gradio/commit/5fbda0bd2b2bbb2282249b8875d54acf87cd7e84) - Wasm release.  Thanks [@pngwn](https://github.com/pngwn)!
-
-## 3.45.0-beta.12
-
-### Features
-
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - V4: Some misc fixes.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5960](https://github.com/gradio-app/gradio/pull/5960) [`319c30f3f`](https://github.com/gradio-app/gradio/commit/319c30f3fccf23bfe1da6c9b132a6a99d59652f7) - rererefactor frontend files.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - Add host to dev mode for vite.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`d2314e53b`](https://github.com/gradio-app/gradio/commit/d2314e53bc088ff6f307a122a9a01bafcdcff5c2) - BugFix: Make FileExplorer Component Templateable.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - Use tags to identify custom component dirs and ignore uninstalled components.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5956](https://github.com/gradio-app/gradio/pull/5956) [`f769876e0`](https://github.com/gradio-app/gradio/commit/f769876e0fa62336425c4e8ada5e09f38353ff01) - Apply formatter (and small refactoring) to the Lite-related frontend code.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5938](https://github.com/gradio-app/gradio/pull/5938) [`13ed8a485`](https://github.com/gradio-app/gradio/commit/13ed8a485d5e31d7d75af87fe8654b661edcca93) - V4: Use beta release versions for '@gradio' packages.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - Adds the ability to build the frontend and backend of custom components in preparation for publishing to pypi using `gradio_component build`.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - Fix deployed demos on v4 branch.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - Set api=False for cancel events.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - Use full path to executables in CLI.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5949](https://github.com/gradio-app/gradio/pull/5949) [`1c390f101`](https://github.com/gradio-app/gradio/commit/1c390f10199142a41722ba493a0c86b58245da15) - Merge main again.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - Simplify how files are handled in components in 4.0.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - Name Endpoints if api_name is None.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5937](https://github.com/gradio-app/gradio/pull/5937) [`dcf13d750`](https://github.com/gradio-app/gradio/commit/dcf13d750b1465f905e062a1368ba754446cc23f) - V4: Update Component pyi file.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - Rename gradio_component to gradio component.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - V4: Use async version of shutil in upload route.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - V4: Set cache dir for some component tests.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5894](https://github.com/gradio-app/gradio/pull/5894) [`fee3d527e`](https://github.com/gradio-app/gradio/commit/fee3d527e83a615109cf937f6ca0a37662af2bb6) - Adds `column_widths` to `gr.Dataframe` and hide overflowing text when `wrap=False`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-### Fixes
-
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - Better logs in dev mode.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5946](https://github.com/gradio-app/gradio/pull/5946) [`d0cc6b136`](https://github.com/gradio-app/gradio/commit/d0cc6b136fd59121f74d0c5a1a4b51740ffaa838) - fixup.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5944](https://github.com/gradio-app/gradio/pull/5944) [`465f58957`](https://github.com/gradio-app/gradio/commit/465f58957f70c7cf3e894beef8a117b28339e3c1) - Show empty JSON icon when `value` is `null`.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5498](https://github.com/gradio-app/gradio/pull/5498) [`85ba6de13`](https://github.com/gradio-app/gradio/commit/85ba6de136a45b3e92c74e410bb27e3cbe7138d7) - Reinstate types that were removed in error in #5832.  Thanks [@pngwn](https://github.com/pngwn)!
-
-## 3.48.0
-
-### Features
-
-- [#5627](https://github.com/gradio-app/gradio/pull/5627) [`b67115e8e`](https://github.com/gradio-app/gradio/commit/b67115e8e6e489fffd5271ea830211863241ddc5) - Lite: Make the Examples component display media files using pseudo HTTP requests to the Wasm server.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5821](https://github.com/gradio-app/gradio/pull/5821) [`1aa186220`](https://github.com/gradio-app/gradio/commit/1aa186220dfa8ee3621b818c4cdf4d7b9d690b40) - Lite: Fix Examples.create() to be a normal func so it can be called in the Wasm env.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5886](https://github.com/gradio-app/gradio/pull/5886) [`121f25b2d`](https://github.com/gradio-app/gradio/commit/121f25b2d50a33e1e06721b79e20b4f5651987ba) - Lite: Fix is_self_host() to detect `127.0.0.1` as localhost as well.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5915](https://github.com/gradio-app/gradio/pull/5915) [`e24163e15`](https://github.com/gradio-app/gradio/commit/e24163e15afdfc51ec8cb00a0dc46c2318b245be) - Added dimensionality check to avoid bad array dimensions.  Thanks [@THEGAMECHANGER416](https://github.com/THEGAMECHANGER416)!
-- [#5835](https://github.com/gradio-app/gradio/pull/5835) [`46334780d`](https://github.com/gradio-app/gradio/commit/46334780dbbb7e83f31971d45a7047ee156a0578) - Mention that audio is normalized when converting to wav in docs.  Thanks [@aileenvl](https://github.com/aileenvl)!
-- [#5877](https://github.com/gradio-app/gradio/pull/5877) [`a55b80942`](https://github.com/gradio-app/gradio/commit/a55b8094231ae462ac53f52bbdb460c1286ffabb) - Add styling (e.g. font colors and background colors) support to `gr.DataFrame` through the `pd.Styler` object.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5819](https://github.com/gradio-app/gradio/pull/5819) [`5f1cbc436`](https://github.com/gradio-app/gradio/commit/5f1cbc4363b09302334e9bc864587f8ef398550d) - Add support for gr.Request to gr.ChatInterface.  Thanks [@DarhkVoyd](https://github.com/DarhkVoyd)!
-- [#5901](https://github.com/gradio-app/gradio/pull/5901) [`c4e3a9274`](https://github.com/gradio-app/gradio/commit/c4e3a92743a3b41edad8b45c5d5b0ccbc2674a30) - Fix curly brackets in docstrings.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5934](https://github.com/gradio-app/gradio/pull/5934) [`8d909624f`](https://github.com/gradio-app/gradio/commit/8d909624f61a49536e3c0f71cb2d9efe91216219) - Fix styling issues with Audio, Image and Video components.  Thanks [@aliabd](https://github.com/aliabd)!
-- [#5864](https://github.com/gradio-app/gradio/pull/5864) [`e70805d54`](https://github.com/gradio-app/gradio/commit/e70805d54cc792452545f5d8eccc1aa0212a4695) - Change `BlockLabel` element to use `<label>`.  Thanks [@aileenvl](https://github.com/aileenvl)!
-- [#5862](https://github.com/gradio-app/gradio/pull/5862) [`c07207e0b`](https://github.com/gradio-app/gradio/commit/c07207e0bc98cc32b6db629c432fadf877e451ff) - Remove deprecated `.update()` usage from Interface internals.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5905](https://github.com/gradio-app/gradio/pull/5905) [`b450cef15`](https://github.com/gradio-app/gradio/commit/b450cef15685c934ba7c4e4d57cbed233e925fb1) - Fix type the docstring of the Code component.  Thanks [@whitphx](https://github.com/whitphx)!
-
-### Fixes
-
-- [#5840](https://github.com/gradio-app/gradio/pull/5840) [`4e62b8493`](https://github.com/gradio-app/gradio/commit/4e62b8493dfce50bafafe49f1a5deb929d822103) - Ensure websocket polyfill doesn't load if there is already a `global.Webocket` property set.  Thanks [@Jay2theWhy](https://github.com/Jay2theWhy)!
-- [#5839](https://github.com/gradio-app/gradio/pull/5839) [`b83064da0`](https://github.com/gradio-app/gradio/commit/b83064da0005ca055fc15ee478cf064bf91702a4) - Fix error when scrolling dropdown with scrollbar.  Thanks [@Kit-p](https://github.com/Kit-p)!
-- [#5822](https://github.com/gradio-app/gradio/pull/5822) [`7b63db271`](https://github.com/gradio-app/gradio/commit/7b63db27161ab538f20cf8523fc04c9c3b604a98) - Convert async methods in the Examples class into normal sync methods.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5904](https://github.com/gradio-app/gradio/pull/5904) [`891d42e9b`](https://github.com/gradio-app/gradio/commit/891d42e9baa7ab85ede2a5eadb56c274b0ed2785) - Define Font.__repr__() to be printed in the doc in a readable format.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5811](https://github.com/gradio-app/gradio/pull/5811) [`1d5b15a2d`](https://github.com/gradio-app/gradio/commit/1d5b15a2d24387154f2cfb40a36de25b331471d3) - Assert refactor in external.py.  Thanks [@harry-urek](https://github.com/harry-urek)!
-- [#5827](https://github.com/gradio-app/gradio/pull/5827) [`48e09ee88`](https://github.com/gradio-app/gradio/commit/48e09ee88799efa38a5cc9b1b61e462f72ec6093) - Quick fix: Chatbot change event.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5890](https://github.com/gradio-app/gradio/pull/5890) [`c4ba832b3`](https://github.com/gradio-app/gradio/commit/c4ba832b318dad5e8bf565cfa0daf93ca188498f) - Remove deprecation warning from `gr.update` and clean up associated code.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5897](https://github.com/gradio-app/gradio/pull/5897) [`0592c301d`](https://github.com/gradio-app/gradio/commit/0592c301df9cd949b52159c85b7042f38d113e86) - Fix Dataframe `line_breaks`.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5878](https://github.com/gradio-app/gradio/pull/5878) [`fbce277e5`](https://github.com/gradio-app/gradio/commit/fbce277e50c5885371fd49c68adf8565c25c1d39) - Keep Markdown rendered lists within dataframe cells.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5930](https://github.com/gradio-app/gradio/pull/5930) [`361823896`](https://github.com/gradio-app/gradio/commit/3618238960d54df65c34895f4eb69d08acc3f9b6) - Fix dataframe `line_breaks`.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-
-## 3.47.1
-
-### Fixes
-
-- [#5816](https://github.com/gradio-app/gradio/pull/5816) [`796145e2c`](https://github.com/gradio-app/gradio/commit/796145e2c48c4087bec17f8ec0be4ceee47170cb) - Fix calls to the component server so that `gr.FileExplorer` works on Spaces.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.47.0
-
-### Highlights
-
-#### new `FileExplorer` component ([#5672](https://github.com/gradio-app/gradio/pull/5672) [`e4a307ed6`](https://github.com/gradio-app/gradio/commit/e4a307ed6cde3bbdf4ff2f17655739addeec941e))
-
-Thanks to a new capability that allows components to communicate directly with the server _without_ passing data via the value, we have created a new `FileExplorer` component.
-
-This component allows you to populate the explorer by passing a glob, but only provides the selected file(s) in your prediction function. 
-
-Users can then navigate the virtual filesystem and select files which will be accessible in your predict function. This component will allow developers to build more complex spaces, with more flexible input options.
-
-![output](https://github.com/pngwn/MDsveX/assets/12937446/ef108f0b-0e84-4292-9984-9dc66b3e144d)
-
-For more information check the [`FileExplorer` documentation](https://gradio.app/docs/fileexplorer).
-
- Thanks [@aliabid94](https://github.com/aliabid94)!
-
-### Features
-
-- [#5780](https://github.com/gradio-app/gradio/pull/5780) [`ed0f9a21b`](https://github.com/gradio-app/gradio/commit/ed0f9a21b04ad6b941b63d2ce45100dbd1abd5c5) - Adds `change()` event to `gr.Gallery`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5783](https://github.com/gradio-app/gradio/pull/5783) [`4567788bd`](https://github.com/gradio-app/gradio/commit/4567788bd1fc25df9322902ba748012e392b520a) - Adds the ability to set the `selected_index` in a `gr.Gallery`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5787](https://github.com/gradio-app/gradio/pull/5787) [`caeee8bf7`](https://github.com/gradio-app/gradio/commit/caeee8bf7821fd5fe2f936ed82483bed00f613ec) - ensure the client does not depend on `window` when running in a node environment.  Thanks [@gibiee](https://github.com/gibiee)!
-
-### Fixes
-
-- [#5798](https://github.com/gradio-app/gradio/pull/5798) [`a0d3cc45c`](https://github.com/gradio-app/gradio/commit/a0d3cc45c6db48dc0db423c229b8fb285623cdc4) - Fix `gr.SelectData` so that the target attribute is correctly attached, and the filedata is included in the data attribute with `gr.Gallery`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5795](https://github.com/gradio-app/gradio/pull/5795) [`957ba5cfd`](https://github.com/gradio-app/gradio/commit/957ba5cfde18e09caedf31236a2064923cd7b282) - Prevent bokeh from injecting bokeh js multiple times.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5790](https://github.com/gradio-app/gradio/pull/5790) [`37e70842d`](https://github.com/gradio-app/gradio/commit/37e70842d59f5aed6fab0086b1abf4b8d991f1c9) - added try except block in `state.py`.  Thanks [@SrijanSahaySrivastava](https://github.com/SrijanSahaySrivastava)!
-- [#5794](https://github.com/gradio-app/gradio/pull/5794) [`f096c3ae1`](https://github.com/gradio-app/gradio/commit/f096c3ae168c0df00f90fe131c1e48c572e0574b) - Throw helpful error when media devices are not found.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5776](https://github.com/gradio-app/gradio/pull/5776) [`c0fef4454`](https://github.com/gradio-app/gradio/commit/c0fef44541bfa61568bdcfcdfc7d7d79869ab1df) - Revert replica proxy logic and instead implement using the `root` variable.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-
-## 3.46.1
-
-### Features
-
-- [#5124](https://github.com/gradio-app/gradio/pull/5124) [`6e56a0d9b`](https://github.com/gradio-app/gradio/commit/6e56a0d9b0c863e76c69e1183d9d40196922b4cd) - Lite: Websocket queueing.  Thanks [@whitphx](https://github.com/whitphx)!
-
-### Fixes
-
-- [#5775](https://github.com/gradio-app/gradio/pull/5775) [`e2874bc3c`](https://github.com/gradio-app/gradio/commit/e2874bc3cb1397574f77dbd7f0408ed4e6792970) - fix pending chatbot message styling and ensure messages with value `None` don't render.  Thanks [@hannahblair](https://github.com/hannahblair)!
-
-## 3.46.0
-
-### Features
-
-- [#5699](https://github.com/gradio-app/gradio/pull/5699) [`8f0fed857`](https://github.com/gradio-app/gradio/commit/8f0fed857d156830626eb48b469d54d211a582d2) - Improve chatbot accessibility and UX.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5569](https://github.com/gradio-app/gradio/pull/5569) [`2a5b9e03b`](https://github.com/gradio-app/gradio/commit/2a5b9e03b15ea324d641fe6982f26d81b1ca7210) - Added support for pandas `Styler` object to `gr.DataFrame` (initially just sets the `display_value`).  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-### Fixes
-
-- [#5735](https://github.com/gradio-app/gradio/pull/5735) [`abb5e9df4`](https://github.com/gradio-app/gradio/commit/abb5e9df47989b2c56c2c312d74944678f9f2d4e) - Ensure images with no caption download in gallery.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5754](https://github.com/gradio-app/gradio/pull/5754) [`502054848`](https://github.com/gradio-app/gradio/commit/502054848fdbe39fc03ec42445242b4e49b7affc) - Fix Gallery `columns` and `rows` params.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5755](https://github.com/gradio-app/gradio/pull/5755) [`e842a561a`](https://github.com/gradio-app/gradio/commit/e842a561af4394f8109291ee5725bcf74743e816) - Fix new line issue in chatbot.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5731](https://github.com/gradio-app/gradio/pull/5731) [`c9af4f794`](https://github.com/gradio-app/gradio/commit/c9af4f794060e218193935d7213f0991a374f502) - Added timeout and error handling for frpc tunnel.  Thanks [@cansik](https://github.com/cansik)!
-- [#5766](https://github.com/gradio-app/gradio/pull/5766) [`ef96d3512`](https://github.com/gradio-app/gradio/commit/ef96d351229272738fc3c9680f7111f159590341) - Don't raise warnings when returning an updated component in a dictionary.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5767](https://github.com/gradio-app/gradio/pull/5767) [`caf6d9c0e`](https://github.com/gradio-app/gradio/commit/caf6d9c0e1f5b867cc20f2b4f6abb5ef47503a5f) - Set share=True for all Gradio apps in Colab by default.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.45.2
-
-### Features
-
-- [#5722](https://github.com/gradio-app/gradio/pull/5722) [`dba651904`](https://github.com/gradio-app/gradio/commit/dba651904c97dcddcaae2691540ac430d3eefd18) - Fix for deepcopy errors when running the replica-related logic on Spaces.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5721](https://github.com/gradio-app/gradio/pull/5721) [`84e03fe50`](https://github.com/gradio-app/gradio/commit/84e03fe506e08f1f81bac6d504c9fba7924f2d93) - Adds copy buttons to website, and better descriptions to API Docs.  Thanks [@aliabd](https://github.com/aliabd)!
-
-### Fixes
-
-- [#5714](https://github.com/gradio-app/gradio/pull/5714) [`a0fc5a296`](https://github.com/gradio-app/gradio/commit/a0fc5a29678baa2d9ba997a2124cadebecfb2c36) - Make Tab and Tabs updatable.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5713](https://github.com/gradio-app/gradio/pull/5713) [`c10dabd6b`](https://github.com/gradio-app/gradio/commit/c10dabd6b18b49259441eb5f956a19046f466339) - Fixes gr.select() Method Issues with Dataframe Cells.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5693](https://github.com/gradio-app/gradio/pull/5693) [`c2b31c396`](https://github.com/gradio-app/gradio/commit/c2b31c396f6d260cdf93377b715aee7ff162df75) - Context-based Progress tracker.  Thanks [@cbensimon](https://github.com/cbensimon)!
-- [#5705](https://github.com/gradio-app/gradio/pull/5705) [`78e7cf516`](https://github.com/gradio-app/gradio/commit/78e7cf5163e8d205e8999428fce4c02dbdece25f) - ensure internal data has updated before dispatching `success` or `then` events.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5668](https://github.com/gradio-app/gradio/pull/5668) [`d626c21e9`](https://github.com/gradio-app/gradio/commit/d626c21e91df026b04fdb3ee5c7dba74a261cfd3) - Fully resolve generated filepaths when running on Hugging Face Spaces with multiple replicas.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5711](https://github.com/gradio-app/gradio/pull/5711) [`aefb556ac`](https://github.com/gradio-app/gradio/commit/aefb556ac6dbadc320c618b11bb48371ef19dd61) - prevent internal log_message error from `/api/predict`.  Thanks [@cbensimon](https://github.com/cbensimon)!
-- [#5726](https://github.com/gradio-app/gradio/pull/5726) [`96c4b97c7`](https://github.com/gradio-app/gradio/commit/96c4b97c742311e90a87d8e8ee562c6ad765e9f0) - Adjust translation.  Thanks [@ylhsieh](https://github.com/ylhsieh)!
-- [#5732](https://github.com/gradio-app/gradio/pull/5732) [`3a48490bc`](https://github.com/gradio-app/gradio/commit/3a48490bc5e4136ec9bc0354b0d6fb6c04280505) - Add a bare `Component` type to the acceptable type list of `gr.load()`'s `inputs` and `outputs`.  Thanks [@whitphx](https://github.com/whitphx)!
-
-## 3.45.1
-
-### Fixes
-
-- [#5701](https://github.com/gradio-app/gradio/pull/5701) [`ee8eec1e5`](https://github.com/gradio-app/gradio/commit/ee8eec1e5e544a0127e0aa68c2522a7085b8ada5) - Fix for regression in rendering empty Markdown.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.45.0
-
-### Features
-
-- [#5675](https://github.com/gradio-app/gradio/pull/5675) [`b619e6f6e`](https://github.com/gradio-app/gradio/commit/b619e6f6e4ca55334fb86da53790e45a8f978566) - Reorganize Docs Navbar and Fill in Gaps.  Thanks [@aliabd](https://github.com/aliabd)!
-- [#5669](https://github.com/gradio-app/gradio/pull/5669) [`c5e969559`](https://github.com/gradio-app/gradio/commit/c5e969559612f956afcdb0c6f7b22ab8275bc49a) - Fix small issues in docs and guides.  Thanks [@aliabd](https://github.com/aliabd)!
-- [#5682](https://github.com/gradio-app/gradio/pull/5682) [`c57f1b75e`](https://github.com/gradio-app/gradio/commit/c57f1b75e272c76b0af4d6bd0c7f44743ff34f26) - Fix functional tests.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5681](https://github.com/gradio-app/gradio/pull/5681) [`40de3d217`](https://github.com/gradio-app/gradio/commit/40de3d2178b61ebe424b6f6228f94c0c6f679bea) - add query parameters to the `gr.Request` object through the `query_params` attribute.  Thanks [@DarhkVoyd](https://github.com/DarhkVoyd)!
-- [#5653](https://github.com/gradio-app/gradio/pull/5653) [`ea0e00b20`](https://github.com/gradio-app/gradio/commit/ea0e00b207b4b90a10e9d054c4202d4e705a29ba) - Prevent Clients from accessing API endpoints that set `api_name=False`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5639](https://github.com/gradio-app/gradio/pull/5639) [`e1874aff8`](https://github.com/gradio-app/gradio/commit/e1874aff814d13b23f3e59ef239cc13e18ad3fa7) - Add `gr.on` listener method.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5652](https://github.com/gradio-app/gradio/pull/5652) [`2e25d4305`](https://github.com/gradio-app/gradio/commit/2e25d430582264945ae3316acd04c4453a25ce38) - Pause autoscrolling if a user scrolls up in a `gr.Textbox` and resume autoscrolling if they go all the way down.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5642](https://github.com/gradio-app/gradio/pull/5642) [`21c7225bd`](https://github.com/gradio-app/gradio/commit/21c7225bda057117a9d3311854323520218720b5) - Improve plot rendering.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5677](https://github.com/gradio-app/gradio/pull/5677) [`9f9af327c`](https://github.com/gradio-app/gradio/commit/9f9af327c9115356433ec837f349d6286730fb97) - [Refactoring] Convert async functions that don't contain `await` statements to normal functions.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5660](https://github.com/gradio-app/gradio/pull/5660) [`d76555a12`](https://github.com/gradio-app/gradio/commit/d76555a122b545f0df7c9e7c1ca7bd2a6e262c86) - Fix secondary hue bug in gr.themes.builder().  Thanks [@hellofreckles](https://github.com/hellofreckles)!
-- [#5697](https://github.com/gradio-app/gradio/pull/5697) [`f4e4f82b5`](https://github.com/gradio-app/gradio/commit/f4e4f82b58a65efca9030a7e8e7c5ace60d8cc10) - Increase Slider clickable area.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5671](https://github.com/gradio-app/gradio/pull/5671) [`6a36c3b78`](https://github.com/gradio-app/gradio/commit/6a36c3b786700600d3826ce1e0629cc5308ddd47) - chore(deps): update dependency @types/prismjs to v1.26.1.  Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5240](https://github.com/gradio-app/gradio/pull/5240) [`da05e59a5`](https://github.com/gradio-app/gradio/commit/da05e59a53bbad15e5755a47f46685da18e1031e) - Cleanup of .update and .get_config per component.  Thanks [@aliabid94](https://github.com/aliabid94)!/n  get_config is removed, the config used is simply any attribute that is in the Block that shares a name with one of the constructor paramaters./n  update is not removed for backwards compatibility, but deprecated. Instead return the component itself. Created a updateable decorator that simply checks to see if we're in an update, and if so, skips the constructor and wraps the args and kwargs in an update dictionary. easy peasy.
-- [#5635](https://github.com/gradio-app/gradio/pull/5635) [`38fafb9e2`](https://github.com/gradio-app/gradio/commit/38fafb9e2a5509b444942e1d5dd48dffa20066f4) - Fix typos in Gallery docs.  Thanks [@atesgoral](https://github.com/atesgoral)!
-- [#5590](https://github.com/gradio-app/gradio/pull/5590) [`d1ad1f671`](https://github.com/gradio-app/gradio/commit/d1ad1f671caef9f226eb3965f39164c256d8615c) - Attach `elem_classes` selectors to layout elements, and an id to the Tab button (for targeting via CSS/JS).  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5554](https://github.com/gradio-app/gradio/pull/5554) [`75ddeb390`](https://github.com/gradio-app/gradio/commit/75ddeb390d665d4484667390a97442081b49a423) - Accessibility Improvements.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5598](https://github.com/gradio-app/gradio/pull/5598) [`6b1714386`](https://github.com/gradio-app/gradio/commit/6b17143868bdd2c1400af1199a01c1c0d5c27477) - Upgrade Pyodide to 0.24.0 and install the native orjson package.  Thanks [@whitphx](https://github.com/whitphx)!
-
-### Fixes
-
-- [#5625](https://github.com/gradio-app/gradio/pull/5625) [`9ccc4794a`](https://github.com/gradio-app/gradio/commit/9ccc4794a72ce8319417119f6c370e7af3ffca6d) - Use ContextVar instead of threading.local().  Thanks [@cbensimon](https://github.com/cbensimon)!
-- [#5602](https://github.com/gradio-app/gradio/pull/5602) [`54d21d3f1`](https://github.com/gradio-app/gradio/commit/54d21d3f18f2ddd4e796d149a0b41461f49c711b) - Ensure `HighlightedText` with `merge_elements` loads without a value.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5636](https://github.com/gradio-app/gradio/pull/5636) [`fb5964fb8`](https://github.com/gradio-app/gradio/commit/fb5964fb88082e7b956853b543c468116811cab9) - Fix bug in example cache loading event.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5633](https://github.com/gradio-app/gradio/pull/5633) [`341402337`](https://github.com/gradio-app/gradio/commit/34140233794c29d4722020e13c2d045da642dfae) - Allow Gradio apps containing `gr.Radio()`, `gr.Checkboxgroup()`, or `gr.Dropdown()` to be loaded with `gr.load()`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5616](https://github.com/gradio-app/gradio/pull/5616) [`7c34b434a`](https://github.com/gradio-app/gradio/commit/7c34b434aae0eb85f112a1dc8d66cefc7e2296b2) - Fix width and height issues that would cut off content in `gr.DataFrame`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5604](https://github.com/gradio-app/gradio/pull/5604) [`faad01f8e`](https://github.com/gradio-app/gradio/commit/faad01f8e10ef6d18249b1a4587477c59b74adb2) - Add `render_markdown` parameter to chatbot.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5593](https://github.com/gradio-app/gradio/pull/5593) [`88d43bd12`](https://github.com/gradio-app/gradio/commit/88d43bd124792d216da445adef932a2b02f5f416) - Fixes avatar image in chatbot being squashed.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5690](https://github.com/gradio-app/gradio/pull/5690) [`6b8c8afd9`](https://github.com/gradio-app/gradio/commit/6b8c8afd981fea984da568e9a0bd8bfc2a9c06c4) - Fix incorrect behavior of `gr.load()` with `gr.Examples`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5696](https://github.com/gradio-app/gradio/pull/5696) [`e51fcd5d5`](https://github.com/gradio-app/gradio/commit/e51fcd5d54315e8b65ee40e3de4dab17579ff6d5) - setting share=True on Spaces or in wasm should warn instead of raising error.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.44.4
-
-### Features
-
-- [#5514](https://github.com/gradio-app/gradio/pull/5514) [`52f783175`](https://github.com/gradio-app/gradio/commit/52f7831751b432411e109bd41add4ab286023a8e) - refactor: Use package.json for version management.  Thanks [@DarhkVoyd](https://github.com/DarhkVoyd)!
-- [#5535](https://github.com/gradio-app/gradio/pull/5535) [`d29b1ab74`](https://github.com/gradio-app/gradio/commit/d29b1ab740784d8c70f9ab7bc38bbbf7dd3ff737) - Makes sliders consistent across all browsers.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-
-### Fixes
-
-- [#5587](https://github.com/gradio-app/gradio/pull/5587) [`e0d61b8ba`](https://github.com/gradio-app/gradio/commit/e0d61b8baa0f6293f53b9bdb1647d42f9ae2583a) - Fix `.clear()` events for audio and image.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5534](https://github.com/gradio-app/gradio/pull/5534) [`d9e9ae43f`](https://github.com/gradio-app/gradio/commit/d9e9ae43f5c52c1f729af5a20e5d4f754689d429) - Guide fixes, esp. streaming audio.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5588](https://github.com/gradio-app/gradio/pull/5588) [`acdeff57e`](https://github.com/gradio-app/gradio/commit/acdeff57ece4672f943c374d537eaf47d3ec034f) - Allow multiple instances of Gradio with authentication to run on different ports.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.44.3
-
-### Fixes
-
-- [#5562](https://github.com/gradio-app/gradio/pull/5562) [`50d9747d0`](https://github.com/gradio-app/gradio/commit/50d9747d061962cff7f60a8da648bb3781794102) - chore(deps): update dependency iframe-resizer to v4.3.7.  Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5550](https://github.com/gradio-app/gradio/pull/5550) [`4ed5902e7`](https://github.com/gradio-app/gradio/commit/4ed5902e7dda2d95cd43e4ccaaef520ddd8eba57) - Adding basque language.  Thanks [@EkhiAzur](https://github.com/EkhiAzur)!
-- [#5547](https://github.com/gradio-app/gradio/pull/5547) [`290f51871`](https://github.com/gradio-app/gradio/commit/290f5187160cdbd7a786494fe3c19b0e70abe167) - typo in UploadButton's docstring.  Thanks [@chaeheum3](https://github.com/chaeheum3)!
-- [#5553](https://github.com/gradio-app/gradio/pull/5553) [`d1bf23cd2`](https://github.com/gradio-app/gradio/commit/d1bf23cd2c6da3692d7753856bfe7564d84778e0) - Modify Image examples docstring.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5563](https://github.com/gradio-app/gradio/pull/5563) [`ba64082ed`](https://github.com/gradio-app/gradio/commit/ba64082ed80c1ed9113497ae089e63f032dbcc75) - preprocess for components when type='index'.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.44.2
-
-### Fixes
-
-- [#5537](https://github.com/gradio-app/gradio/pull/5537) [`301c7878`](https://github.com/gradio-app/gradio/commit/301c7878217f9fc531c0f28330b394f02955811b) - allow gr.Image() examples to take urls.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5544](https://github.com/gradio-app/gradio/pull/5544) [`a0cc9ac9`](https://github.com/gradio-app/gradio/commit/a0cc9ac931554e06dcb091158c9b9ac0cc580b6c) - Fixes dropdown breaking if a user types in invalid value and presses enter.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.44.1
-
-### Fixes
-
-- [#5516](https://github.com/gradio-app/gradio/pull/5516) [`c5fe8eba`](https://github.com/gradio-app/gradio/commit/c5fe8ebadbf206e2f4199ccde4606e331a22148a) - Fix docstring of dropdown.  Thanks [@hysts](https://github.com/hysts)!
-- [#5529](https://github.com/gradio-app/gradio/pull/5529) [`81c9ca9a`](https://github.com/gradio-app/gradio/commit/81c9ca9a2e00d19334f632fec32081d36ad54c7f) - Fix `.update()` method in `gr.Dropdown()` to handle `choices`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5528](https://github.com/gradio-app/gradio/pull/5528) [`dc86e4a7`](https://github.com/gradio-app/gradio/commit/dc86e4a7e1c40b910c74558e6f88fddf9b3292bc) - Lazy load all images.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5525](https://github.com/gradio-app/gradio/pull/5525) [`21f1db40`](https://github.com/gradio-app/gradio/commit/21f1db40de6d1717eba97a550e11422a457ba7e9) - Ensure input value saves on dropdown blur.  Thanks [@hannahblair](https://github.com/hannahblair)!
-
-## 3.44.0
-
-### Features
-
-- [#5505](https://github.com/gradio-app/gradio/pull/5505) [`9ee20f49`](https://github.com/gradio-app/gradio/commit/9ee20f499f62c1fe5af6b8f84918b3a334eb1c8d) - Validate i18n file names with ISO-639x.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5475](https://github.com/gradio-app/gradio/pull/5475) [`c60b89b0`](https://github.com/gradio-app/gradio/commit/c60b89b0a54758a27277f0a6aa20d0653647c7c8) - Adding Central Kurdish.  Thanks [@Hrazhan](https://github.com/Hrazhan)!
-- [#5400](https://github.com/gradio-app/gradio/pull/5400) [`d112e261`](https://github.com/gradio-app/gradio/commit/d112e2611b0fc79ecedfaed367571f3157211387) - Allow interactive input in `gr.HighlightedText`.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5488](https://github.com/gradio-app/gradio/pull/5488) [`8909e42a`](https://github.com/gradio-app/gradio/commit/8909e42a7c6272358ad413588d27a5124d151205) - Adds `autoscroll` param to `gr.Textbox()`.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5384](https://github.com/gradio-app/gradio/pull/5384) [`ddc02268`](https://github.com/gradio-app/gradio/commit/ddc02268f731bd2ed04b7a5854accf3383f9a0da) - Allows the `gr.Dropdown` to have separate names and values, as well as enables `allow_custom_value` for multiselect dropdown.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5473](https://github.com/gradio-app/gradio/pull/5473) [`b271e738`](https://github.com/gradio-app/gradio/commit/b271e738860ca238ecdee2991f49b505c7559016) - Remove except asyncio.CancelledError which is no longer necessary due to 53d7025.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5474](https://github.com/gradio-app/gradio/pull/5474) [`041560f9`](https://github.com/gradio-app/gradio/commit/041560f9f11ca2560005b467bb412ee1becfc2b2) - Fix queueing.call_prediction to retrieve the default response class in the same manner as FastAPI's implementation.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5510](https://github.com/gradio-app/gradio/pull/5510) [`afcf3c48`](https://github.com/gradio-app/gradio/commit/afcf3c48e82712067d6d00a0caedb1562eb986f8) - Do not expose existence of files outside of working directory.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-### Fixes
-
-- [#5459](https://github.com/gradio-app/gradio/pull/5459) [`bd2fda77`](https://github.com/gradio-app/gradio/commit/bd2fda77fc98d815f4fb670f535af453ebee9b80) - Dispatch `stop_recording` event in Audio.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5508](https://github.com/gradio-app/gradio/pull/5508) [`05715f55`](https://github.com/gradio-app/gradio/commit/05715f5599ae3e928d3183c7b0a7f5291f843a96) - Adds a `filterable` parameter to `gr.Dropdown` that controls whether user can type to filter choices.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5470](https://github.com/gradio-app/gradio/pull/5470) [`a4e010a9`](https://github.com/gradio-app/gradio/commit/a4e010a96f1d8a52b3ac645e03fe472b9c3cbbb1) - Fix share button position.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5496](https://github.com/gradio-app/gradio/pull/5496) [`82ec4d26`](https://github.com/gradio-app/gradio/commit/82ec4d2622a43c31b248b78e9410e2ac918f6035) - Allow interface with components to be run inside blocks.  Thanks [@abidlabs](https://github.com/abidlabs)!
-
-## 3.43.2
-
-### Fixes
-
-- [#5456](https://github.com/gradio-app/gradio/pull/5456) [`6e381c4f`](https://github.com/gradio-app/gradio/commit/6e381c4f146cc8177a4e2b8e39f914f09cd7ff0c) - ensure dataframe doesn't steal focus.  Thanks [@pngwn](https://github.com/pngwn)!
-
-## 3.43.1
-
-### Fixes
-
-- [#5445](https://github.com/gradio-app/gradio/pull/5445) [`67bb7bcb`](https://github.com/gradio-app/gradio/commit/67bb7bcb6a95b7a00a8bdf612cf147850d919a44) - ensure dataframe doesn't scroll unless needed.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5447](https://github.com/gradio-app/gradio/pull/5447) [`7a4a89e5`](https://github.com/gradio-app/gradio/commit/7a4a89e5ca1dedb39e5366867501584b0c636bbb) - ensure iframe is correct size on spaces.  Thanks [@pngwn](https://github.com/pngwn)!
-
-## 3.43.0
-
-### Features
-
-- [#5165](https://github.com/gradio-app/gradio/pull/5165) [`c77f05ab`](https://github.com/gradio-app/gradio/commit/c77f05abb65b2828c9c19af4ec0a0c09412f9f6a) - Fix the Queue to call API endpoints without internal HTTP routing.  Thanks [@whitphx](https://github.com/whitphx)!
-- [#5427](https://github.com/gradio-app/gradio/pull/5427) [`aad7acd7`](https://github.com/gradio-app/gradio/commit/aad7acd7128dca05b227ecbba06db9f94d65b088) - Add sort to bar plot.  Thanks [@Chaitanya134](https://github.com/Chaitanya134)!
-- [#5342](https://github.com/gradio-app/gradio/pull/5342) [`afac0006`](https://github.com/gradio-app/gradio/commit/afac0006337ce2840cf497cd65691f2f60ee5912) - significantly improve the performance of `gr.Dataframe` for large datasets.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5417](https://github.com/gradio-app/gradio/pull/5417) [`d14d63e3`](https://github.com/gradio-app/gradio/commit/d14d63e30c4af3f9c2a664fd11b0a01943a8300c) - Auto scroll to bottom of textbox.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-
-### Fixes
-
-- [#5412](https://github.com/gradio-app/gradio/pull/5412) [`26fef8c7`](https://github.com/gradio-app/gradio/commit/26fef8c7f85a006c7e25cdbed1792df19c512d02) - Skip view_api request in js client when auth enabled.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5436](https://github.com/gradio-app/gradio/pull/5436) [`7ab4b70f`](https://github.com/gradio-app/gradio/commit/7ab4b70f6821afb4e85cef225d1235c19df8ebbf) - api_open does not take precedence over show_api.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-
-## 3.42.0
-
-### Highlights
-
-#### Like/Dislike Button for Chatbot ([#5391](https://github.com/gradio-app/gradio/pull/5391) [`abf1c57d`](https://github.com/gradio-app/gradio/commit/abf1c57d7d85de0df233ee3b38aeb38b638477db))
-
- Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-
-#### Added the ability to attach event listeners via decorators ([#5395](https://github.com/gradio-app/gradio/pull/5395) [`55fed04f`](https://github.com/gradio-app/gradio/commit/55fed04f559becb9c24f22cc6292dc572d709886))
-
-e.g.
-
-```python
-with gr.Blocks() as demo:
-    name = gr.Textbox(label="Name")
-    output = gr.Textbox(label="Output Box")
-    greet_btn = gr.Button("Greet")
-
-    @greet_btn.click(inputs=name, outputs=output)
-    def greet(name):
-        return "Hello " + name + "!"
-```
-
- Thanks [@aliabid94](https://github.com/aliabid94)!
-
-### Features
-
-- [#5334](https://github.com/gradio-app/gradio/pull/5334) [`c5bf9138`](https://github.com/gradio-app/gradio/commit/c5bf91385a632dc9f612499ee01166ac6ae509a9) - Add chat bubble width param.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5267](https://github.com/gradio-app/gradio/pull/5267) [`119c8343`](https://github.com/gradio-app/gradio/commit/119c834331bfae60d4742c8f20e9cdecdd67e8c2) - Faster reload mode.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5373](https://github.com/gradio-app/gradio/pull/5373) [`79d8f9d8`](https://github.com/gradio-app/gradio/commit/79d8f9d891901683c5a1b7486efb44eab2478c96) - Adds `height` and `zoom_speed` parameters to `Model3D` component, as well as a button to reset the camera position.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5370](https://github.com/gradio-app/gradio/pull/5370) [`61803c65`](https://github.com/gradio-app/gradio/commit/61803c6545e73fce47e8740bd46721ab9bb0ba5c) - chore(deps): update dependency extendable-media-recorder to v9.  Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5266](https://github.com/gradio-app/gradio/pull/5266) [`4ccb9a86`](https://github.com/gradio-app/gradio/commit/4ccb9a86f194c6997f80a09880edc3c2b0554aab) - Makes it possible to set the initial camera position for the `Model3D` component as a tuple of (alpha, beta, radius).  Thanks [@mbahri](https://github.com/mbahri)!
-- [#5271](https://github.com/gradio-app/gradio/pull/5271) [`97c3c7b1`](https://github.com/gradio-app/gradio/commit/97c3c7b1730407f9e80566af9ecb4ca7cccf62ff) - Move scripts from old website to CI.  Thanks [@aliabd](https://github.com/aliabd)!
-- [#5369](https://github.com/gradio-app/gradio/pull/5369) [`b8968898`](https://github.com/gradio-app/gradio/commit/b89688984fa9c6be0db06e392e6935a544620764) - Fix typo in utils.py.  Thanks [@eltociear](https://github.com/eltociear)!
-
-### Fixes
-
-- [#5304](https://github.com/gradio-app/gradio/pull/5304) [`05892302`](https://github.com/gradio-app/gradio/commit/05892302fb8fe2557d57834970a2b65aea97355b) - Adds kwarg to disable html sanitization in `gr.Chatbot()`.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5366](https://github.com/gradio-app/gradio/pull/5366) [`0cc7e2dc`](https://github.com/gradio-app/gradio/commit/0cc7e2dcf60e216e0a30e2f85a9879ce3cb2a1bd) - Hide avatar when message none.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5393](https://github.com/gradio-app/gradio/pull/5393) [`e4e7a431`](https://github.com/gradio-app/gradio/commit/e4e7a4319924aaf51dcb18d07d0c9953d4011074) - Renders LaTeX that is added to the page in `gr.Markdown`, `gr.Chatbot`, and `gr.DataFrame`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5394](https://github.com/gradio-app/gradio/pull/5394) [`4d94ea0a`](https://github.com/gradio-app/gradio/commit/4d94ea0a0cf2103cda19f48398a5634f8341d04d) - Adds horizontal scrolling to content that overflows in gr.Markdown.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5368](https://github.com/gradio-app/gradio/pull/5368) [`b27f7583`](https://github.com/gradio-app/gradio/commit/b27f7583254165b135bf1496a7d8c489a62ba96f) - Change markdown rendering to set breaks to false.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5360](https://github.com/gradio-app/gradio/pull/5360) [`64666525`](https://github.com/gradio-app/gradio/commit/6466652583e3c620df995fb865ef3511a34cb676) - Cancel Dropdown Filter.  Thanks [@deckar01](https://github.com/deckar01)!
-
-## 3.41.2
-
-### Features
-
-- [#5284](https://github.com/gradio-app/gradio/pull/5284) [`5f25eb68`](https://github.com/gradio-app/gradio/commit/5f25eb6836f6a78ce6208b53495a01e1fc1a1d2f) - Minor bug fix sweep.  Thanks [@aliabid94](https://github.com/aliabid94)!/n  - Our use of __exit__ was catching errors and corrupting the traceback of any component that failed to instantiate (try running blocks_kitchen_sink off main for an example). Now the __exit__ exits immediately if there's been an exception, so the original exception can be printed cleanly/n  - HighlightedText was rendering weird, cleaned it up
-
-### Fixes
-
-- [#5319](https://github.com/gradio-app/gradio/pull/5319) [`3341148c`](https://github.com/gradio-app/gradio/commit/3341148c109b5458cc88435d27eb154210efc472) - Fix: wrap avatar-image in a div to clip its shape.  Thanks [@Keldos-Li](https://github.com/Keldos-Li)!
-- [#5340](https://github.com/gradio-app/gradio/pull/5340) [`df090e89`](https://github.com/gradio-app/gradio/commit/df090e89f74a16e4cb2b700a1e3263cabd2bdd91) - Fix Checkbox select dispatch.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-
-## 3.41.1
-
-### Fixes
-
-- [#5324](https://github.com/gradio-app/gradio/pull/5324) [`31996c99`](https://github.com/gradio-app/gradio/commit/31996c991d6bfca8cef975eb8e3c9f61a7aced19) - ensure login form has correct styles.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5323](https://github.com/gradio-app/gradio/pull/5323) [`e32b0928`](https://github.com/gradio-app/gradio/commit/e32b0928d2d00342ca917ebb10c379ffc2ec200d) - ensure dropdown stays open when identical data is passed in.  Thanks [@pngwn](https://github.com/pngwn)!
-
-## 3.41.0
-
-### Highlights
-
-#### Improve startup performance and markdown support ([#5279](https://github.com/gradio-app/gradio/pull/5279) [`fe057300`](https://github.com/gradio-app/gradio/commit/fe057300f0672c62dab9d9b4501054ac5d45a4ec))
-
-##### Improved markdown support
-
-We now have better support for markdown in `gr.Markdown` and `gr.Dataframe`. Including syntax highlighting and Github Flavoured Markdown. We also have more consistent markdown behaviour and styling.
-
-##### Various performance improvements
-
-These improvements will be particularly beneficial to large applications.
-
-- Rather than attaching events manually, they are now delegated, leading to a significant performance improvement and addressing a performance regression introduced in a recent version of Gradio. App startup for large applications is now around twice as fast.
-- Optimised the mounting of individual components, leading to a modest performance improvement during startup (~30%).
-- Corrected an issue that was causing markdown to re-render infinitely.
-- Ensured that the `gr.3DModel` does re-render prematurely.
-
- Thanks [@pngwn](https://github.com/pngwn)!
-
-#### Enable streaming audio in python client ([#5248](https://github.com/gradio-app/gradio/pull/5248) [`390624d8`](https://github.com/gradio-app/gradio/commit/390624d8ad2b1308a5bf8384435fd0db98d8e29e))
-
-The `gradio_client` now supports streaming file outputs 🌊
-
-No new syntax! Connect to a gradio demo that supports streaming file outputs and call `predict` or `submit` as you normally would.
-
-```python
-import gradio_client as grc
-client = grc.Client("gradio/stream_audio_out")
-
-# Get the entire generated audio as a local file
-client.predict("/Users/freddy/Pictures/bark_demo.mp4", api_name="/predict")
-
-job = client.submit("/Users/freddy/Pictures/bark_demo.mp4", api_name="/predict")
-
-# Get the entire generated audio as a local file
-job.result()
-
-# Each individual chunk
-job.outputs()
-```
-
- Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-
-#### Add `render` function to `<gradio-app>` ([#5158](https://github.com/gradio-app/gradio/pull/5158) [`804fcc05`](https://github.com/gradio-app/gradio/commit/804fcc058e147f283ece67f1f353874e26235535))
-
-We now have an event `render` on the <gradio-app> web component, which is triggered once the embedded space has finished rendering.
-
-```html
-<script>
-	function handleLoadComplete() {
-		console.log("Embedded space has finished rendering");
-	}
-	const gradioApp = document.querySelector("gradio-app");
-	gradioApp.addEventListener("render", handleLoadComplete);
-</script>
-```
-
- Thanks [@hannahblair](https://github.com/hannahblair)!
-
-### Features
-
-- [#5268](https://github.com/gradio-app/gradio/pull/5268) [`f49028cf`](https://github.com/gradio-app/gradio/commit/f49028cfe3e21097001ddbda71c560b3d8b42e1c) - Move markdown & latex processing to the frontend for the gr.Markdown and gr.DataFrame components.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5215](https://github.com/gradio-app/gradio/pull/5215) [`fbdad78a`](https://github.com/gradio-app/gradio/commit/fbdad78af4c47454cbb570f88cc14bf4479bbceb) - Lazy load interactive or static variants of a component individually, rather than loading both variants regardless. This change will improve performance for many applications.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5216](https://github.com/gradio-app/gradio/pull/5216) [`4b58ea6d`](https://github.com/gradio-app/gradio/commit/4b58ea6d98e7a43b3f30d8a4cb6f379bc2eca6a8) - Update i18n tokens and locale files.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5283](https://github.com/gradio-app/gradio/pull/5283) [`a7460557`](https://github.com/gradio-app/gradio/commit/a74605572dd0d6bb41df6b38b120d656370dd67d) - Add height parameter and scrolling to `gr.Dataframe`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5232](https://github.com/gradio-app/gradio/pull/5232) [`c57d4c23`](https://github.com/gradio-app/gradio/commit/c57d4c232a97e03b4671f9e9edc3af456438fe89) - `gr.Radio` and `gr.CheckboxGroup` can now accept different names and values.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5219](https://github.com/gradio-app/gradio/pull/5219) [`e8fd4e4e`](https://github.com/gradio-app/gradio/commit/e8fd4e4ec68a6c974bc8c84b61f4a0ec50a85bc6) - Add `api_name` parameter to `gr.Interface`. Additionally, completely hide api page if show_api=False.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5280](https://github.com/gradio-app/gradio/pull/5280) [`a2f42e28`](https://github.com/gradio-app/gradio/commit/a2f42e28bd793bce4bed6d54164bb2a327a46fd5) - Allow updating the label of `gr.UpdateButton`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5112](https://github.com/gradio-app/gradio/pull/5112) [`1cefee7f`](https://github.com/gradio-app/gradio/commit/1cefee7fc05175aca23ba04b3a3fda7b97f49bf0) - chore(deps): update dependency marked to v7.  Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5260](https://github.com/gradio-app/gradio/pull/5260) [`a773eaf7`](https://github.com/gradio-app/gradio/commit/a773eaf7504abb53b99885b3454dc1e027adbb42) - Stop passing inputs and preprocessing on iterators.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#4943](https://github.com/gradio-app/gradio/pull/4943) [`947d615d`](https://github.com/gradio-app/gradio/commit/947d615db6f76519d0e8bc0d1a0d7edf89df267b) - Sign in with Hugging Face (OAuth support).  Thanks [@Wauplin](https://github.com/Wauplin)!
-- [#5298](https://github.com/gradio-app/gradio/pull/5298) [`cf167cd1`](https://github.com/gradio-app/gradio/commit/cf167cd1dd4acd9aee225ff1cb6fac0e849806ba) - Create event listener table for components on docs.  Thanks [@aliabd](https://github.com/aliabd)!
-- [#5173](https://github.com/gradio-app/gradio/pull/5173) [`730f0c1d`](https://github.com/gradio-app/gradio/commit/730f0c1d54792eb11359e40c9f2326e8a6e39203) - Ensure gradio client works as expected for functions that return nothing.  Thanks [@raymondtri](https://github.com/raymondtri)!
-- [#5188](https://github.com/gradio-app/gradio/pull/5188) [`b22e1888`](https://github.com/gradio-app/gradio/commit/b22e1888fcf0843520525c1e4b7e1fe73fdeb948) - Fix the images in the theme builder to use permanent URI.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5221](https://github.com/gradio-app/gradio/pull/5221) [`f344592a`](https://github.com/gradio-app/gradio/commit/f344592aeb1658013235ded154107f72d86f24e7) - Allows setting a height to `gr.File` and improves the UI of the component.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5265](https://github.com/gradio-app/gradio/pull/5265) [`06982212`](https://github.com/gradio-app/gradio/commit/06982212dfbd613853133d5d0eebd75577967027) - Removes scrollbar from File preview when not needed.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5305](https://github.com/gradio-app/gradio/pull/5305) [`15075241`](https://github.com/gradio-app/gradio/commit/15075241fa7ad3f7fd9ae2a91e54faf8f19a46f9) - Rotate axes labels on LinePlot, BarPlot, and ScatterPlot.  Thanks [@Faiga91](https://github.com/Faiga91)!
-- [#5258](https://github.com/gradio-app/gradio/pull/5258) [`92282cea`](https://github.com/gradio-app/gradio/commit/92282cea6afdf7e9930ece1046d8a63be34b3cea) - Chatbot Avatar Images.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5244](https://github.com/gradio-app/gradio/pull/5244) [`b3e50db9`](https://github.com/gradio-app/gradio/commit/b3e50db92f452f376aa2cc081326d40bb69d6dd7) - Remove aiohttp dependency.  Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5264](https://github.com/gradio-app/gradio/pull/5264) [`46a2b600`](https://github.com/gradio-app/gradio/commit/46a2b600a7ff030a9ea1560b882b3bf3ad266bbc) - ensure translations for audio work correctly.  Thanks [@hannahblair](https://github.com/hannahblair)!
-
-### Fixes
-
-- [#5256](https://github.com/gradio-app/gradio/pull/5256) [`933db53e`](https://github.com/gradio-app/gradio/commit/933db53e93a1229fdf149556d61da5c4c7e1a331) - Better handling of empty dataframe in `gr.DataFrame`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5242](https://github.com/gradio-app/gradio/pull/5242) [`2b397791`](https://github.com/gradio-app/gradio/commit/2b397791fe2059e4beb72937ff0436f2d4d28b4b) - Fix message text overflow onto copy button in `gr.Chatbot`.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5253](https://github.com/gradio-app/gradio/pull/5253) [`ddac7e4d`](https://github.com/gradio-app/gradio/commit/ddac7e4d0f55c3bdc6c3e9a9e24588b2563e4049) - Ensure File component uploads files to the server.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5179](https://github.com/gradio-app/gradio/pull/5179) [`6fb92b48`](https://github.com/gradio-app/gradio/commit/6fb92b48a916104db573602011a448b904d42e5e) - Fixes audio streaming issues.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5295](https://github.com/gradio-app/gradio/pull/5295) [`7b8fa8aa`](https://github.com/gradio-app/gradio/commit/7b8fa8aa58f95f5046b9add64b40368bd3f1b700) - Allow caching examples with streamed output.  Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#5285](https://github.com/gradio-app/gradio/pull/5285) [`cdfd4217`](https://github.com/gradio-app/gradio/commit/cdfd42174a9c777eaee9c1209bf8e90d8c7791f2) - Tweaks to `icon` parameter in `gr.Button()`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5122](https://github.com/gradio-app/gradio/pull/5122) [`3b805346`](https://github.com/gradio-app/gradio/commit/3b8053469aca6c7a86a6731e641e4400fc34d7d3) - Allows code block in chatbot to scroll horizontally.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-- [#5312](https://github.com/gradio-app/gradio/pull/5312) [`f769cb67`](https://github.com/gradio-app/gradio/commit/f769cb67149d8e209091508f06d87014acaed965) - only start listening for events after the components are mounted.  Thanks [@pngwn](https://github.com/pngwn)!
-- [#5254](https://github.com/gradio-app/gradio/pull/5254) [`c39f06e1`](https://github.com/gradio-app/gradio/commit/c39f06e16b9feea97984e4822df35a99c807461c) - Fix `.update()` for `gr.Radio()` and `gr.CheckboxGroup()`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5231](https://github.com/gradio-app/gradio/pull/5231) [`87f1c2b4`](https://github.com/gradio-app/gradio/commit/87f1c2b4ac7c685c43477215fa5b96b6cbeffa05) - Allow `gr.Interface.from_pipeline()` and `gr.load()` to work within `gr.Blocks()`.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5238](https://github.com/gradio-app/gradio/pull/5238) [`de23e9f7`](https://github.com/gradio-app/gradio/commit/de23e9f7d67e685e791faf48a21f34121f6d094a) - Improve audio streaming.  Thanks [@aliabid94](https://github.com/aliabid94)!/n  - Proper audio streaming with WAV files. We now do the proper processing to stream out wav files as a single stream of audio without any cracks in the seams./n  - Audio streaming with bytes. Stream any audio type by yielding out bytes, and it should work flawlessly.
-- [#5313](https://github.com/gradio-app/gradio/pull/5313) [`54bcb724`](https://github.com/gradio-app/gradio/commit/54bcb72417b2781ad9d7500ea0f89aa9d80f7d8f) - Restores missing part of bottom border on file component.  Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5235](https://github.com/gradio-app/gradio/pull/5235) [`1ecf88ac`](https://github.com/gradio-app/gradio/commit/1ecf88ac5f20bc5a1c91792d1a68559575e6afd7) - fix #5229.  Thanks [@breengles](https://github.com/breengles)!
-- [#5276](https://github.com/gradio-app/gradio/pull/5276) [`502f1015`](https://github.com/gradio-app/gradio/commit/502f1015bf23b365bc32446dd2e549b0c5d0dc72) - Ensure `Blocks` translation copy renders correctly.  Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5296](https://github.com/gradio-app/gradio/pull/5296) [`a0f22626`](https://github.com/gradio-app/gradio/commit/a0f22626f2aff297754414bbc83d5c4cfe086ea0) - `make_waveform()` twitter video resolution fix.  Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)!
-
-## 3.40.0
-
-### Highlights
-
-#### Client.predict will now return the final output for streaming endpoints ([#5057](https://github.com/gradio-app/gradio/pull/5057) [`35856f8b`](https://github.com/gradio-app/gradio/commit/35856f8b54548cae7bd3b8d6a4de69e1748283b2))
-
-### This is a breaking change (for gradio_client only)!
-
-Previously, `Client.predict` would only return the first output of an endpoint that streamed results. This was causing confusion for developers that wanted to call these streaming demos via the client.
-
-We realize that developers using the client don't know the internals of whether a demo streams or not, so we're changing the behavior of predict to match developer expectations.
-
-Using `Client.predict` will now return the final output of a streaming endpoint. This will make it even easier to use gradio apps via the client.
-
- Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-
-#### Gradio now supports streaming audio outputs
-
-Allows users to use generators to stream audio out, yielding consecutive chunks of audio. Requires `streaming=True` to be set on the output audio.
-
-```python
-import gradio as gr
-from pydub import AudioSegment
-
-def stream_audio(audio_file):
-    audio = AudioSegment.from_mp3(audio_file)
-    i = 0
-    chunk_size = 3000
-    
-    while chunk_size*i < len(audio):
-        chunk = audio[chunk_size*i:chunk_size*(i+1)]
-        i += 1
-        if chunk:
-            file = f"/tmp/{i}.mp3"
-            chunk.export(file, format="mp3")            
-            yield file
-        
-demo = gr.Interface(
-    fn=stream_audio,
-    inputs=gr.Audio(type="filepath", label="Audio file to stream"),
-    outputs=gr.Audio(autoplay=True, streaming=True),
-)
-
-demo.queue().launch()
-```
-
-From the backend, streamed outputs are served from the `/stream/` endpoint instead of the `/file/` endpoint. Currently just used to serve audio streaming output.  The output JSON will have `is_stream`: `true`, instead of `is_file`: `true` in the file data object. Thanks [@aliabid94](https://github.com/aliabid94)!
-
-### Features
-
-- [#5081](https://github.com/gradio-app/gradio/pull/5081) [`d7f83823`](https://github.com/gradio-app/gradio/commit/d7f83823fbd7604456b0127d689a63eed759807d) - solve how can I config root_path dynamically? #4968. Thanks [@eastonsuo](https://github.com/eastonsuo)!
-- [#5025](https://github.com/gradio-app/gradio/pull/5025) [`6693660a`](https://github.com/gradio-app/gradio/commit/6693660a790996f8f481feaf22a8c49130d52d89) - Add download button to selected images in `Gallery`. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5133](https://github.com/gradio-app/gradio/pull/5133) [`61129052`](https://github.com/gradio-app/gradio/commit/61129052ed1391a75c825c891d57fa0ad6c09fc8) - Update dependency esbuild to ^0.19.0. Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5125](https://github.com/gradio-app/gradio/pull/5125) [`80be7a1c`](https://github.com/gradio-app/gradio/commit/80be7a1ca44c0adef1668367b2cf36b65e52e576) - chatbot conversation nodes can contain a copy button. Thanks [@fazpu](https://github.com/fazpu)!
-- [#5048](https://github.com/gradio-app/gradio/pull/5048) [`0b74a159`](https://github.com/gradio-app/gradio/commit/0b74a1595b30df744e32a2c358c07acb7fd1cfe5) - Use `importlib` in favor of deprecated `pkg_resources`. Thanks [@jayceslesar](https://github.com/jayceslesar)!
-- [#5045](https://github.com/gradio-app/gradio/pull/5045) [`3b9494f5`](https://github.com/gradio-app/gradio/commit/3b9494f5c57e6b52e6a040ce8d6b5141f780e84d) - Lite: Fix the analytics module to use asyncio to work in the Wasm env. Thanks [@whitphx](https://github.com/whitphx)!
-- [#5046](https://github.com/gradio-app/gradio/pull/5046) [`5244c587`](https://github.com/gradio-app/gradio/commit/5244c5873c355cf3e2f0acb7d67fda3177ef8b0b) - Allow new lines in `HighlightedText` with `/n` and preserve whitespace. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5076](https://github.com/gradio-app/gradio/pull/5076) [`2745075a`](https://github.com/gradio-app/gradio/commit/2745075a26f80e0e16863d483401ff1b6c5ada7a) - Add deploy_discord to docs. Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5116](https://github.com/gradio-app/gradio/pull/5116) [`0dc49b4c`](https://github.com/gradio-app/gradio/commit/0dc49b4c517706f572240f285313a881089ced79) - Add support for async functions and async generators to `gr.ChatInterface`. Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5047](https://github.com/gradio-app/gradio/pull/5047) [`883ac364`](https://github.com/gradio-app/gradio/commit/883ac364f69d92128774ac446ce49bdf8415fd7b) - Add `step` param to `Number`. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5137](https://github.com/gradio-app/gradio/pull/5137) [`22aa5eba`](https://github.com/gradio-app/gradio/commit/22aa5eba3fee3f14473e4b0fac29cf72fe31ef04) - Use font size `--text-md` for `<code>` in Chatbot messages. Thanks [@jaywonchung](https://github.com/jaywonchung)!
-- [#5005](https://github.com/gradio-app/gradio/pull/5005) [`f5539c76`](https://github.com/gradio-app/gradio/commit/f5539c7618e31451420bd3228754774da14dc65f) - Enhancement: Add focus event to textbox and number component. Thanks [@JodyZ0203](https://github.com/JodyZ0203)!
-- [#5104](https://github.com/gradio-app/gradio/pull/5104) [`34f6b22e`](https://github.com/gradio-app/gradio/commit/34f6b22efbfedfa569d452f3f99ed2e6593e3c21) - Strip leading and trailing spaces from username in login route. Thanks [@sweep-ai](https://github.com/apps/sweep-ai)!
-- [#5149](https://github.com/gradio-app/gradio/pull/5149) [`144df459`](https://github.com/gradio-app/gradio/commit/144df459a3b7895e524defcfc4c03fbb8b083aca) - Add `show_edit_button` param to `gr.Audio`. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5136](https://github.com/gradio-app/gradio/pull/5136) [`eaa1ce14`](https://github.com/gradio-app/gradio/commit/eaa1ce14ac41de1c23321e93f11f1b03a2f3c7f4) - Enhancing Tamil Translation: Language Refinement 🌟. Thanks [@sanjaiyan-dev](https://github.com/sanjaiyan-dev)!
-- [#5035](https://github.com/gradio-app/gradio/pull/5035) [`8b4eb8ca`](https://github.com/gradio-app/gradio/commit/8b4eb8cac9ea07bde31b44e2006ca2b7b5f4de36) - JS Client: Fixes cannot read properties of null (reading 'is_file'). Thanks [@raymondtri](https://github.com/raymondtri)!
-- [#5023](https://github.com/gradio-app/gradio/pull/5023) [`e6317d77`](https://github.com/gradio-app/gradio/commit/e6317d77f87d3dad638acca3dbc4a9228570e63c) - Update dependency extendable-media-recorder to v8. Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5085](https://github.com/gradio-app/gradio/pull/5085) [`13e47835`](https://github.com/gradio-app/gradio/commit/13e478353532c4af18cfa50772f8b6fb3c6c9818) - chore(deps): update dependency extendable-media-recorder to v8. Thanks [@renovate](https://github.com/apps/renovate)!
-- [#5080](https://github.com/gradio-app/gradio/pull/5080) [`37caa2e0`](https://github.com/gradio-app/gradio/commit/37caa2e0fe95d6cab8beb174580fb557904f137f) - Add icon and link params to `gr.Button`. Thanks [@hannahblair](https://github.com/hannahblair)!
-
-### Fixes
-
-- [#5062](https://github.com/gradio-app/gradio/pull/5062) [`7d897165`](https://github.com/gradio-app/gradio/commit/7d89716519d0751072792c9bbda668ffeb597296) - `gr.Dropdown` now has correct behavior in static mode as well as when an option is selected. Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5077](https://github.com/gradio-app/gradio/pull/5077) [`667875b2`](https://github.com/gradio-app/gradio/commit/667875b2441753e74d25bd9d3c8adedd8ede11cd) - Live audio streaming output
-- [#5118](https://github.com/gradio-app/gradio/pull/5118) [`1b017e68`](https://github.com/gradio-app/gradio/commit/1b017e68f6a9623cc2ec085bd20e056229552028) - Add `interactive` args to `gr.ColorPicker`. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5114](https://github.com/gradio-app/gradio/pull/5114) [`56d2609d`](https://github.com/gradio-app/gradio/commit/56d2609de93387a75dc82b1c06c1240c5b28c0b8) - Reset textbox value to empty string when value is None. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#5075](https://github.com/gradio-app/gradio/pull/5075) [`67265a58`](https://github.com/gradio-app/gradio/commit/67265a58027ef1f9e4c0eb849a532f72eaebde48) - Allow supporting >1000 files in `gr.File()` and `gr.UploadButton()`. Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5135](https://github.com/gradio-app/gradio/pull/5135) [`80727bbe`](https://github.com/gradio-app/gradio/commit/80727bbe2c6d631022054edf01515017691b3bdd) - Fix dataset features and dataset preview for HuggingFaceDatasetSaver. Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-- [#5039](https://github.com/gradio-app/gradio/pull/5039) [`620e4645`](https://github.com/gradio-app/gradio/commit/620e46452729d6d4877b3fab84a65daf2f2b7bc6) - `gr.Dropdown()` now supports values with arbitrary characters and doesn't clear value when re-focused. Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5061](https://github.com/gradio-app/gradio/pull/5061) [`136adc9c`](https://github.com/gradio-app/gradio/commit/136adc9ccb23e5cb4d02d2e88f23f0b850041f98) - Ensure `gradio_client` is backwards compatible with `gradio==3.24.1`. Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5129](https://github.com/gradio-app/gradio/pull/5129) [`97d804c7`](https://github.com/gradio-app/gradio/commit/97d804c748be9acfe27b8369dd2d64d61f43c2e7) - [Spaces] ZeroGPU Queue fix. Thanks [@cbensimon](https://github.com/cbensimon)!
-- [#5140](https://github.com/gradio-app/gradio/pull/5140) [`cd1353fa`](https://github.com/gradio-app/gradio/commit/cd1353fa3eb1b015f5860ca5d5a8e8d1aa4a831c) - Fixes the display of minutes in the video player. Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#5111](https://github.com/gradio-app/gradio/pull/5111) [`b84a35b7`](https://github.com/gradio-app/gradio/commit/b84a35b7b91eca947f787648ceb361b1d023427b) - Add icon and link to DuplicateButton. Thanks [@aliabd](https://github.com/aliabd)!
-- [#5030](https://github.com/gradio-app/gradio/pull/5030) [`f6c491b0`](https://github.com/gradio-app/gradio/commit/f6c491b079d335af633dd854c68eb26f9e61c552) - highlightedtext throws an error basing on model. Thanks [@rajeunoia](https://github.com/rajeunoia)!
-
-## 3.39.0
-
-### Highlights
-
-#### Create Discord Bots from Gradio Apps 🤖 ([#4960](https://github.com/gradio-app/gradio/pull/4960) [`46e4ef67`](https://github.com/gradio-app/gradio/commit/46e4ef67d287dd68a91473b73172b29cbad064bc))
-
-We're excited to announce that Gradio can now automatically create a discord bot from any `gr.ChatInterface` app.
-
-It's as easy as importing `gradio_client`, connecting to the app, and calling `deploy_discord`!
-
-_🦙 Turning Llama 2 70b into a discord bot 🦙_
-
-```python
-import gradio_client as grc
-grc.Client("ysharma/Explore_llamav2_with_TGI").deploy_discord(to_id="llama2-70b-discord-bot")
-```
-
-<img src="https://gradio-builds.s3.amazonaws.com/demo-files/discordbots/guide/llama_chat.gif">
-
-#### Getting started with template spaces
-
-To help get you started, we have created an organization on Hugging Face called [gradio-discord-bots](https://huggingface.co/gradio-discord-bots) with template spaces you can use to turn state of the art LLMs powered by Gradio to discord bots.
-
-Currently we have template spaces for:
-
-- [Llama-2-70b-chat-hf](https://huggingface.co/spaces/gradio-discord-bots/Llama-2-70b-chat-hf) powered by a FREE Hugging Face Inference Endpoint!
-- [Llama-2-13b-chat-hf](https://huggingface.co/spaces/gradio-discord-bots/Llama-2-13b-chat-hf) powered by Hugging Face Inference Endpoints.
-- [Llama-2-13b-chat-hf](https://huggingface.co/spaces/gradio-discord-bots/llama-2-13b-chat-transformers) powered by Hugging Face transformers.
-- [falcon-7b-instruct](https://huggingface.co/spaces/gradio-discord-bots/falcon-7b-instruct) powered by Hugging Face Inference Endpoints.
-- [gpt-3.5-turbo](https://huggingface.co/spaces/gradio-discord-bots/gpt-35-turbo), powered by openai. Requires an OpenAI key.
-
-But once again, you can deploy ANY `gr.ChatInterface` app exposed on the internet! So don't hesitate to try it on your own Chatbots.
-
-❗️ Additional Note ❗️: Technically, any gradio app that exposes an api route that takes in a single string and outputs a single string can be deployed to discord. But `gr.ChatInterface` apps naturally lend themselves to discord's chat functionality so we suggest you start with those.
-
-Thanks [@freddyaboulton](https://github.com/freddyaboulton)!
-
-### Features
-
-- [#4995](https://github.com/gradio-app/gradio/pull/4995) [`3f8c210b`](https://github.com/gradio-app/gradio/commit/3f8c210b01ef1ceaaf8ee73be4bf246b5b745bbf) - Implement left and right click in `Gallery` component and show implicit images in `Gallery` grid. Thanks [@hannahblair](https://github.com/hannahblair)!
-- [#4993](https://github.com/gradio-app/gradio/pull/4993) [`dc07a9f9`](https://github.com/gradio-app/gradio/commit/dc07a9f947de44b419d8384987a02dcf94977851) - Bringing back the "Add download button for audio" PR by [@leuryr](https://github.com/leuryr). Thanks [@abidlabs](https://github.com/abidlabs)!
-- [#4979](https://github.com/gradio-app/gradio/pull/4979) [`44ac8ad0`](https://github.com/gradio-app/gradio/commit/44ac8ad08d82ea12c503dde5c78f999eb0452de2) - Allow setting sketch color default. Thanks [@aliabid94](https://github.com/aliabid94)!
-- [#4985](https://github.com/gradio-app/gradio/pull/4985) [`b74f8453`](https://github.com/gradio-app/gradio/commit/b74f8453034328f0e42da8e41785f5eb039b45d7) - Adds `additional_inputs` to `gr.ChatInterface`. Thanks [@abidlabs](https://github.com/abidlabs)!
-
-### Fixes
-
-- [#4997](https://github.com/gradio-app/gradio/pull/4997) [`41c83070`](https://github.com/gradio-app/gradio/commit/41c83070b01632084e7d29123048a96c1e261407) - Add CSS resets and specifiers to play nice with HF blog. Thanks [@aliabid94](https://github.com/aliabid94)!
-
-## 3.38
-
-### New Features:
-
-- Provide a parameter `animate` (`False` by default) in `gr.make_waveform()` which animates the overlayed waveform by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4918](https://github.com/gradio-app/gradio/pull/4918)
-- Add `show_download_button` param to allow the download button in static Image components to be hidden by [@hannahblair](https://github.com/hannahblair) in [PR 4959](https://github.com/gradio-app/gradio/pull/4959)
-- Added autofocus argument to Textbox by [@aliabid94](https://github.com/aliabid94) in [PR 4978](https://github.com/gradio-app/gradio/pull/4978)
-- The `gr.ChatInterface` UI now converts the "Submit" button to a "Stop" button in ChatInterface while streaming, which can be used to pause generation. By [@abidlabs](https://github.com/abidlabs) in [PR 4971](https://github.com/gradio-app/gradio/pull/4971).
-- Add a `border_color_accent_subdued` theme variable to add a subdued border color to accented items. This is used by chatbot user messages. Set the value of this variable in `Default` theme to `*primary_200`. By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4989](https://github.com/gradio-app/gradio/pull/4989)
-- Add default sketch color argument `brush_color`. Also, masks drawn on images are now slightly translucent (and mask color can also be set via brush_color). By [@aliabid94](https://github.com/aliabid94) in [PR 4979](https://github.com/gradio-app/gradio/pull/4979)
-
-### Bug Fixes:
-
-- Fixes `cancels` for generators so that if a generator is canceled before it is complete, subsequent runs of the event do not continue from the previous iteration, but rather start from the beginning. By [@abidlabs](https://github.com/abidlabs) in [PR 4969](https://github.com/gradio-app/gradio/pull/4969).
-- Use `gr.State` in `gr.ChatInterface` to reduce latency by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4976](https://github.com/gradio-app/gradio/pull/4976)
-- Fix bug with `gr.Interface` where component labels inferred from handler parameters were including special args like `gr.Request` or `gr.EventData`. By [@cbensimon](https://github.com/cbensimon) in [PR 4956](https://github.com/gradio-app/gradio/pull/4956)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Other Changes:
-
-- Apply pyright to the `components` directory by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4948](https://github.com/gradio-app/gradio/pull/4948)
-- Improved look of ChatInterface by [@aliabid94](https://github.com/aliabid94) in [PR 4978](https://github.com/gradio-app/gradio/pull/4978)
-
-## 3.37
-
-### New Features:
-
-Introducing a new `gr.ChatInterface` abstraction, which allows Gradio users to build fully functioning Chat interfaces very easily. The only required parameter is a chat function `fn`, which accepts a (string) user input `message` and a (list of lists) chat `history` and returns a (string) response. Here's a toy example:
-
-```py
-import gradio as gr
-
-def echo(message, history):
-    return message
-
-demo = gr.ChatInterface(fn=echo, examples=["hello", "hola", "merhaba"], title="Echo Bot")
-demo.launch()
-```
-
-Which produces:
-
-<img width="1291" alt="image" src="https://github.com/gradio-app/gradio/assets/1778297/ae94fd72-c2bb-406e-9e8d-7b9c12e80119">
-
-And a corresponding easy-to-use API at `/chat`:
-
-<img width="1164" alt="image" src="https://github.com/gradio-app/gradio/assets/1778297/7b10d6db-6476-4e2e-bebd-ecda802c3b8f">
-
-The `gr.ChatInterface` abstraction works nicely with various LLM libraries, such as `langchain`. See the [dedicated guide](https://gradio.app/guides/creating-a-chatbot-fast) for more examples using `gr.ChatInterface`. Collective team effort in [PR 4869](https://github.com/gradio-app/gradio/pull/4869)
-
-- Chatbot messages now show hyperlinks to download files uploaded to `gr.Chatbot()` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4848](https://github.com/gradio-app/gradio/pull/4848)
-- Cached examples now work with generators and async generators by [@abidlabs](https://github.com/abidlabs) in [PR 4927](https://github.com/gradio-app/gradio/pull/4927)
-- Add RTL support to `gr.Markdown`, `gr.Chatbot`, `gr.Textbox` (via the `rtl` boolean parameter) and text-alignment to `gr.Textbox`(via the string `text_align` parameter) by [@abidlabs](https://github.com/abidlabs) in [PR 4933](https://github.com/gradio-app/gradio/pull/4933)
-
-Examples of usage:
-
-```py
-with gr.Blocks() as demo:
-    gr.Textbox(interactive=True, text_align="right")
-demo.launch()
-```
-
-```py
-with gr.Blocks() as demo:
-    gr.Markdown("سلام", rtl=True)
-demo.launch()
-```
-
-- The `get_api_info` method of `Blocks` now supports layout output components [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4871](https://github.com/gradio-app/gradio/pull/4871)
-
-- Added the support for the new command `gradio environment`to make it easier for people to file bug reports if we shipped an easy command to list the OS, gradio version, and versions of gradio/gradio-client dependencies. bu [@varshneydevansh](https://github.com/varshneydevansh) in [PR 4915](https://github.com/gradio-app/gradio/pull/4915).
-
-### Bug Fixes:
-
-- The `.change()` event is fixed in `Video` and `Image` so that it only fires once by [@abidlabs](https://github.com/abidlabs) in [PR 4793](https://github.com/gradio-app/gradio/pull/4793)
-- The `.change()` event is fixed in `Audio` so that fires when the component value is programmatically updated by [@abidlabs](https://github.com/abidlabs) in [PR 4793](https://github.com/gradio-app/gradio/pull/4793)
-
-* Add missing `display: flex` property to `Row` so that flex styling is applied to children by [@hannahblair] in [PR 4896](https://github.com/gradio-app/gradio/pull/4896)
-* Fixed bug where `gr.Video` could not preprocess urls by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4904](https://github.com/gradio-app/gradio/pull/4904)
-* Fixed copy button rendering in API page on Safari by [@aliabid94](https://github.com/aliabid94) in [PR 4924](https://github.com/gradio-app/gradio/pull/4924)
-* Fixed `gr.Group` and `container=False`. `container` parameter only available for `Textbox`, `Number`, and `Dropdown`, the only elements where it makes sense. By [@aliabid94](https://github.com/aliabid94) in [PR 4916](https://github.com/gradio-app/gradio/pull/4916)
-* Fixed broken image link in auto-generated `app.py` from `ThemeClass.push_to_hub` by [@deepkyu](https://github.com/deepkyu) in [PR 4944](https://github.com/gradio-app/gradio/pull/4944)
-
-### Other Changes:
-
-- Warning on mobile that if a user leaves the tab, websocket connection may break. On broken connection, tries to rejoin queue and displays error conveying connection broke. By [@aliabid94](https://github.com/aliabid94) in [PR 4742](https://github.com/gradio-app/gradio/pull/4742)
-- Remove blocking network calls made before the local URL gets printed - these slow down the display of the local URL, especially when no internet is available. [@aliabid94](https://github.com/aliabid94) in [PR 4905](https://github.com/gradio-app/gradio/pull/4905).
-- Pinned dependencies to major versions to reduce the likelihood of a broken `gradio` due to changes in downstream dependencies by [@abidlabs](https://github.com/abidlabs) in [PR 4885](https://github.com/gradio-app/gradio/pull/4885)
-- Queue `max_size` defaults to parent Blocks `max_thread` when running on Spaces with ZeroGPU hardware. By [@cbensimon](https://github.com/cbensimon) in [PR 4937](https://github.com/gradio-app/gradio/pull/4937)
-
-### Breaking Changes:
-
-Motivated by the release of `pydantic==2.0`, which included breaking changes that broke a large number of Gradio apps, we've pinned many gradio dependencies. Note that pinned dependencies can cause downstream conflicts, so this may be a breaking change. That being said, we've kept the pins pretty loose, and we're expecting change to be better for the long-term stability of Gradio apps.
-
-## 3.36.1
-
-### New Features:
-
-- Hotfix to support pydantic v1 and v2 by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4835](https://github.com/gradio-app/gradio/pull/4835)
-
-### Bug Fixes:
-
-- Fix bug where `gr.File` change event was not triggered when the value was changed by another event by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4811](https://github.com/gradio-app/gradio/pull/4811)
-
-### Other Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.36.0
-
-### New Features:
-
-- The `gr.Video`, `gr.Audio`, `gr.Image`, `gr.Chatbot`, and `gr.Gallery` components now include a share icon when deployed on Spaces. This behavior can be modified by setting the `show_share_button` parameter in the component classes. by [@aliabid94](https://github.com/aliabid94) in [PR 4651](https://github.com/gradio-app/gradio/pull/4651)
-- Allow the web component `space`, `src`, and `host` attributes to be updated dynamically by [@pngwn](https://github.com/pngwn) in [PR 4461](https://github.com/gradio-app/gradio/pull/4461)
-- Suggestion for Spaces Duplication built into Gradio, by [@aliabid94](https://github.com/aliabid94) in [PR 4458](https://github.com/gradio-app/gradio/pull/4458)
-- The `api_name` parameter now accepts `False` as a value, which means it does not show up in named or unnamed endpoints. By [@abidlabs](https://github.com/aliabid94) in [PR 4683](https://github.com/gradio-app/gradio/pull/4683)
-- Added support for `pathlib.Path` in `gr.Video`, `gr.Gallery`, and `gr.Chatbot` by [sunilkumardash9](https://github.com/sunilkumardash9) in [PR 4581](https://github.com/gradio-app/gradio/pull/4581).
-
-### Bug Fixes:
-
-- Updated components with `info` attribute to update when `update()` is called on them. by [@jebarpg](https://github.com/jebarpg) in [PR 4715](https://github.com/gradio-app/gradio/pull/4715).
-- Ensure the `Image` components undo button works mode is `mask` or `color-sketch` by [@amyorz](https://github.com/AmyOrz) in [PR 4692](https://github.com/gradio-app/gradio/pull/4692)
-- Load the iframe resizer external asset asynchronously, by [@akx](https://github.com/akx) in [PR 4336](https://github.com/gradio-app/gradio/pull/4336)
-- Restored missing imports in `gr.components` by [@abidlabs](https://github.com/abidlabs) in [PR 4566](https://github.com/gradio-app/gradio/pull/4566)
-- Fix bug where `select` event was not triggered in `gr.Gallery` if `height` was set to be large with `allow_preview=False` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4551](https://github.com/gradio-app/gradio/pull/4551)
-- Fix bug where setting `visible=False` in `gr.Group` event did not work by [@abidlabs](https://github.com/abidlabs) in [PR 4567](https://github.com/gradio-app/gradio/pull/4567)
-- Fix `make_waveform` to work with paths that contain spaces [@akx](https://github.com/akx) in [PR 4570](https://github.com/gradio-app/gradio/pull/4570) & [PR 4578](https://github.com/gradio-app/gradio/pull/4578)
-- Send captured data in `stop_recording` event for `gr.Audio` and `gr.Video` components by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4554](https://github.com/gradio-app/gradio/pull/4554)
-- Fix bug in `gr.Gallery` where `height` and `object_fit` parameters where being ignored by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4576](https://github.com/gradio-app/gradio/pull/4576)
-- Fixes an HTML sanitization issue in DOMPurify where links in markdown were not opening in a new window by [@hannahblair] in [PR 4577](https://github.com/gradio-app/gradio/pull/4577)
-- Fixed Dropdown height rendering in Columns by [@aliabid94](https://github.com/aliabid94) in [PR 4584](https://github.com/gradio-app/gradio/pull/4584)
-- Fixed bug where `AnnotatedImage` css styling was causing the annotation masks to not be displayed correctly by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4628](https://github.com/gradio-app/gradio/pull/4628)
-- Ensure that Gradio does not silently fail when running on a port that is occupied by [@abidlabs](https://github.com/abidlabs) in [PR 4624](https://github.com/gradio-app/gradio/pull/4624).
-- Fix double upload bug that caused lag in file uploads by [@aliabid94](https://github.com/aliabid94) in [PR 4661](https://github.com/gradio-app/gradio/pull/4661)
-- `Progress` component now appears even when no `iterable` is specified in `tqdm` constructor by [@itrushkin](https://github.com/itrushkin) in [PR 4475](https://github.com/gradio-app/gradio/pull/4475)
-- Deprecation warnings now point at the user code using those deprecated features, instead of Gradio internals, by (https://github.com/akx) in [PR 4694](https://github.com/gradio-app/gradio/pull/4694)
-- Adapt column widths in gr.Examples based on content by [@pngwn](https://github.com/pngwn) & [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4700](https://github.com/gradio-app/gradio/pull/4700)
-- The `plot` parameter deprecation warnings should now only be emitted for `Image` components by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4709](https://github.com/gradio-app/gradio/pull/4709)
-- Removed uncessessary `type` deprecation warning by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4709](https://github.com/gradio-app/gradio/pull/4709)
-- Ensure Audio autoplays works when `autoplay=True` and the video source is dynamically updated [@pngwn](https://github.com/pngwn) in [PR 4705](https://github.com/gradio-app/gradio/pull/4705)
-- When an error modal is shown in spaces, ensure we scroll to the top so it can be seen by [@pngwn](https://github.com/pngwn) in [PR 4712](https://github.com/gradio-app/gradio/pull/4712)
-- Update depedencies by [@pngwn](https://github.com/pngwn) in [PR 4675](https://github.com/gradio-app/gradio/pull/4675)
-- Fixes `gr.Dropdown` being cutoff at the bottom by [@abidlabs](https://github.com/abidlabs) in [PR 4691](https://github.com/gradio-app/gradio/pull/4691).
-- Scroll top when clicking "View API" in spaces by [@pngwn](https://github.com/pngwn) in [PR 4714](https://github.com/gradio-app/gradio/pull/4714)
-- Fix bug where `show_label` was hiding the entire component for `gr.Label` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4713](https://github.com/gradio-app/gradio/pull/4713)
-- Don't crash when uploaded image has broken EXIF data, by [@akx](https://github.com/akx) in [PR 4764](https://github.com/gradio-app/gradio/pull/4764)
-- Place toast messages at the top of the screen by [@pngwn](https://github.com/pngwn) in [PR 4796](https://github.com/gradio-app/gradio/pull/4796)
-- Fix regressed styling of Login page when auth is enabled by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4797](https://github.com/gradio-app/gradio/pull/4797)
-- Prevent broken scrolling to output on Spaces by [@aliabid94](https://github.com/aliabid94) in [PR 4822](https://github.com/gradio-app/gradio/pull/4822)
-
-### Other Changes:
-
-- Add `.git-blame-ignore-revs` by [@akx](https://github.com/akx) in [PR 4586](https://github.com/gradio-app/gradio/pull/4586)
-- Update frontend dependencies in [PR 4601](https://github.com/gradio-app/gradio/pull/4601)
-- Use `typing.Literal` where possible in gradio library and client by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4608](https://github.com/gradio-app/gradio/pull/4608)
-- Remove unnecessary mock json files for frontend E2E tests by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4625](https://github.com/gradio-app/gradio/pull/4625)
-- Update dependencies by [@pngwn](https://github.com/pngwn) in [PR 4643](https://github.com/gradio-app/gradio/pull/4643)
-- The theme builder now launches successfully, and the API docs are cleaned up. By [@abidlabs](https://github.com/aliabid94) in [PR 4683](https://github.com/gradio-app/gradio/pull/4683)
-- Remove `cleared_value` from some components as its no longer used internally by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4685](https://github.com/gradio-app/gradio/pull/4685)
-- Better errors when you define two Blocks and reference components in one Blocks from the events in the other Blocks [@abidlabs](https://github.com/abidlabs) in [PR 4738](https://github.com/gradio-app/gradio/pull/4738).
-- Better message when share link is not created by [@abidlabs](https://github.com/abidlabs) in [PR 4773](https://github.com/gradio-app/gradio/pull/4773).
-- Improve accessibility around selected images in gr.Gallery component by [@hannahblair](https://github.com/hannahblair) in [PR 4790](https://github.com/gradio-app/gradio/pull/4790)
-
-### Breaking Changes:
-
-[PR 4683](https://github.com/gradio-app/gradio/pull/4683) removes the explict named endpoint "load_examples" from gr.Interface that was introduced in [PR 4456](https://github.com/gradio-app/gradio/pull/4456).
-
-## 3.35.2
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Fix chatbot streaming by [@aliabid94](https://github.com/aliabid94) in [PR 4537](https://github.com/gradio-app/gradio/pull/4537)
-- Fix chatbot height and scrolling by [@aliabid94](https://github.com/aliabid94) in [PR 4540](https://github.com/gradio-app/gradio/pull/4540)
-
-### Other Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.35.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Fix chatbot streaming by [@aliabid94](https://github.com/aliabid94) in [PR 4537](https://github.com/gradio-app/gradio/pull/4537)
-- Fix error modal position and text size by [@pngwn](https://github.com/pngwn) in [PR 4538](https://github.com/gradio-app/gradio/pull/4538).
-
-### Other Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.35.0
-
-### New Features:
-
-- A `gr.ClearButton` which allows users to easily clear the values of components by [@abidlabs](https://github.com/abidlabs) in [PR 4456](https://github.com/gradio-app/gradio/pull/4456)
-
-Example usage:
-
-```py
-import gradio as gr
-
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot([("Hello", "How are you?")])
-    with gr.Row():
-        textbox = gr.Textbox(scale=3, interactive=True)
-        gr.ClearButton([textbox, chatbot], scale=1)
-
-demo.launch()
-```
-
-- Min and max value for gr.Number by [@artegoser](https://github.com/artegoser) and [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3991](https://github.com/gradio-app/gradio/pull/3991)
-- Add `start_recording` and `stop_recording` events to `Video` and `Audio` components by [@pngwn](https://github.com/pngwn) in [PR 4422](https://github.com/gradio-app/gradio/pull/4422)
-- Allow any function to generate an error message and allow multiple messages to appear at a time. Other error modal improvements such as auto dismiss after a time limit and a new layout on mobile [@pngwn](https://github.com/pngwn) in [PR 4459](https://github.com/gradio-app/gradio/pull/4459).
-- Add `autoplay` kwarg to `Video` and `Audio` components by [@pngwn](https://github.com/pngwn) in [PR 4453](https://github.com/gradio-app/gradio/pull/4453)
-- Add `allow_preview` parameter to `Gallery` to control whether a detailed preview is displayed on click by
-  [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4470](https://github.com/gradio-app/gradio/pull/4470)
-- Add `latex_delimiters` parameter to `Chatbot` to control the delimiters used for LaTeX and to disable LaTeX in the `Chatbot` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4516](https://github.com/gradio-app/gradio/pull/4516)
-- Can now issue `gr.Warning` and `gr.Info` modals. Simply put the code `gr.Warning("Your warning message")` or `gr.Info("Your info message")` as a standalone line in your function. By [@aliabid94](https://github.com/aliabid94) in [PR 4518](https://github.com/gradio-app/gradio/pull/4518).
-
-Example:
-
-```python
-def start_process(name):
-    gr.Info("Starting process")
-    if name is None:
-        gr.Warning("Name is empty")
-    ...
-    if success == False:
-        raise gr.Error("Process failed")
-```
-
-### Bug Fixes:
-
-- Add support for PAUSED state in the JS client by [@abidlabs](https://github.com/abidlabs) in [PR 4438](https://github.com/gradio-app/gradio/pull/4438)
-- Ensure Tabs only occupy the space required by [@pngwn](https://github.com/pngwn) in [PR 4419](https://github.com/gradio-app/gradio/pull/4419)
-- Ensure components have the correct empty sizes to prevent empty containers from collapsing by [@pngwn](https://github.com/pngwn) in [PR 4447](https://github.com/gradio-app/gradio/pull/4447).
-- Frontend code no longer crashes when there is a relative URL in an `<a>` element, by [@akx](https://github.com/akx) in [PR 4449](https://github.com/gradio-app/gradio/pull/4449).
-- Fix bug where setting `format='mp4'` on a video component would cause the function to error out if the uploaded video was not playable by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4467](https://github.com/gradio-app/gradio/pull/4467)
-- Fix `_js` parameter to work even without backend function, by [@aliabid94](https://github.com/aliabid94) in [PR 4486](https://github.com/gradio-app/gradio/pull/4486).
-- Fix new line issue with `gr.Chatbot()` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4491](https://github.com/gradio-app/gradio/pull/4491)
-- Fixes issue with Clear button not working for `Label` component by [@abidlabs](https://github.com/abidlabs) in [PR 4456](https://github.com/gradio-app/gradio/pull/4456)
-- Restores the ability to pass in a tuple (sample rate, audio array) to gr.Audio() by [@abidlabs](https://github.com/abidlabs) in [PR 4525](https://github.com/gradio-app/gradio/pull/4525)
-- Ensure code is correctly formatted and copy button is always present in Chatbot by [@pngwn](https://github.com/pngwn) in [PR 4527](https://github.com/gradio-app/gradio/pull/4527)
-- `show_label` will not automatically be set to `True` in `gr.BarPlot.update` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4531](https://github.com/gradio-app/gradio/pull/4531)
-- `gr.BarPlot` group text now respects darkmode by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4531](https://github.com/gradio-app/gradio/pull/4531)
-- Fix dispatched errors from within components [@aliabid94](https://github.com/aliabid94) in [PR 4786](https://github.com/gradio-app/gradio/pull/4786)
-
-### Other Changes:
-
-- Change styling of status and toast error components by [@hannahblair](https://github.com/hannahblair) in [PR 4454](https://github.com/gradio-app/gradio/pull/4454).
-- Clean up unnecessary `new Promise()`s by [@akx](https://github.com/akx) in [PR 4442](https://github.com/gradio-app/gradio/pull/4442).
-- Minor UI cleanup for Examples and Dataframe components [@aliabid94](https://github.com/aliabid94) in [PR 4455](https://github.com/gradio-app/gradio/pull/4455).
-- Minor UI cleanup for Examples and Dataframe components [@aliabid94](https://github.com/aliabid94) in [PR 4455](https://github.com/gradio-app/gradio/pull/4455).
-- Add Catalan translation [@jordimas](https://github.com/jordimas) in [PR 4483](https://github.com/gradio-app/gradio/pull/4483).
-- The API endpoint that loads examples upon click has been given an explicit name ("/load_examples") by [@abidlabs](https://github.com/abidlabs) in [PR 4456](https://github.com/gradio-app/gradio/pull/4456).
-- Allows configuration of FastAPI app when calling `mount_gradio_app`, by [@charlesfrye](https://github.com/charlesfrye) in [PR4519](https://github.com/gradio-app/gradio/pull/4519).
-
-### Breaking Changes:
-
-- The behavior of the `Clear` button has been changed for `Slider`, `CheckboxGroup`, `Radio`, `Dropdown` components by [@abidlabs](https://github.com/abidlabs) in [PR 4456](https://github.com/gradio-app/gradio/pull/4456). The Clear button now sets the value of these components to be empty as opposed to the original default set by the developer. This is to make them in line with the rest of the Gradio components.
-- Python 3.7 end of life is June 27 2023. Gradio will no longer support python 3.7 by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4484](https://github.com/gradio-app/gradio/pull/4484)
-- Removed `$` as a default LaTeX delimiter for the `Chatbot` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4516](https://github.com/gradio-app/gradio/pull/4516). The specific LaTeX delimeters can be set using the new `latex_delimiters` parameter in `Chatbot`.
-
-## 3.34.0
-
-### New Features:
-
-- The `gr.UploadButton` component now supports the `variant` and `interactive` parameters by [@abidlabs](https://github.com/abidlabs) in [PR 4436](https://github.com/gradio-app/gradio/pull/4436).
-
-### Bug Fixes:
-
-- Remove target="\_blank" override on anchor tags with internal targets by [@hannahblair](https://github.com/hannahblair) in [PR 4405](https://github.com/gradio-app/gradio/pull/4405)
-- Fixed bug where `gr.File(file_count='multiple')` could not be cached as output by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4421](https://github.com/gradio-app/gradio/pull/4421)
-- Restricts the domains that can be proxied via `/proxy` route by [@abidlabs](https://github.com/abidlabs) in [PR 4406](https://github.com/gradio-app/gradio/pull/4406).
-- Fixes issue where `gr.UploadButton` could not be used to upload the same file twice by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4437](https://github.com/gradio-app/gradio/pull/4437)
-- Fixes bug where `/proxy` route was being incorrectly constructed by the frontend by [@abidlabs](https://github.com/abidlabs) in [PR 4430](https://github.com/gradio-app/gradio/pull/4430).
-- Fix z-index of status component by [@hannahblair](https://github.com/hannahblair) in [PR 4429](https://github.com/gradio-app/gradio/pull/4429)
-- Fix video rendering in Safari by [@aliabid94](https://github.com/aliabid94) in [PR 4433](https://github.com/gradio-app/gradio/pull/4433).
-- The output directory for files downloaded when calling Blocks as a function is now set to a temporary directory by default (instead of the working directory in some cases) by [@abidlabs](https://github.com/abidlabs) in [PR 4501](https://github.com/gradio-app/gradio/pull/4501)
-
-### Other Changes:
-
-- When running on Spaces, handler functions will be transformed by the [PySpaces](https://pypi.org/project/spaces/) library in order to make them work with specific hardware. It will have no effect on standalone Gradio apps or regular Gradio Spaces and can be globally deactivated as follows : `import spaces; spaces.disable_gradio_auto_wrap()` by [@cbensimon](https://github.com/cbensimon) in [PR 4389](https://github.com/gradio-app/gradio/pull/4389).
-- Deprecated `.style` parameter and moved arguments to constructor. Added support for `.update()` to all arguments initially in style. Added `scale` and `min_width` support to every Component. By [@aliabid94](https://github.com/aliabid94) in [PR 4374](https://github.com/gradio-app/gradio/pull/4374)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.33.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Allow `every` to work with generators by [@dkjshk](https://github.com/dkjshk) in [PR 4434](https://github.com/gradio-app/gradio/pull/4434)
-- Fix z-index of status component by [@hannahblair](https://github.com/hannahblair) in [PR 4429](https://github.com/gradio-app/gradio/pull/4429)
-- Allow gradio to work offline, by [@aliabid94](https://github.com/aliabid94) in [PR 4398](https://github.com/gradio-app/gradio/pull/4398).
-- Fixed `validate_url` to check for 403 errors and use a GET request in place of a HEAD by [@alvindaiyan](https://github.com/alvindaiyan) in [PR 4388](https://github.com/gradio-app/gradio/pull/4388).
-
-### Other Changes:
-
-- More explicit error message when share link binary is blocked by antivirus by [@abidlabs](https://github.com/abidlabs) in [PR 4380](https://github.com/gradio-app/gradio/pull/4380).
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.33.0
-
-### New Features:
-
-- Introduced `gradio deploy` to launch a Gradio app to Spaces directly from your terminal. By [@aliabid94](https://github.com/aliabid94) in [PR 4033](https://github.com/gradio-app/gradio/pull/4033).
-- Introduce `show_progress='corner'` argument to event listeners, which will not cover the output components with the progress animation, but instead show it in the corner of the components. By [@aliabid94](https://github.com/aliabid94) in [PR 4396](https://github.com/gradio-app/gradio/pull/4396).
-
-### Bug Fixes:
-
-- Fix bug where Label change event was triggering itself by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4371](https://github.com/gradio-app/gradio/pull/4371)
-- Make `Blocks.load` behave like other event listeners (allows chaining `then` off of it) [@anentropic](https://github.com/anentropic/) in [PR 4304](https://github.com/gradio-app/gradio/pull/4304)
-- Respect `interactive=True` in output components of a `gr.Interface` by [@abidlabs](https://github.com/abidlabs) in [PR 4356](https://github.com/gradio-app/gradio/pull/4356).
-- Remove unused frontend code by [@akx](https://github.com/akx) in [PR 4275](https://github.com/gradio-app/gradio/pull/4275)
-- Fixes favicon path on Windows by [@abidlabs](https://github.com/abidlabs) in [PR 4369](https://github.com/gradio-app/gradio/pull/4369).
-- Prevent path traversal in `/file` routes by [@abidlabs](https://github.com/abidlabs) in [PR 4370](https://github.com/gradio-app/gradio/pull/4370).
-- Do not send HF token to other domains via `/proxy` route by [@abidlabs](https://github.com/abidlabs) in [PR 4368](https://github.com/gradio-app/gradio/pull/4368).
-- Replace default `markedjs` sanitize function with DOMPurify sanitizer for `gr.Chatbot()` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4360](https://github.com/gradio-app/gradio/pull/4360)
-- Prevent the creation of duplicate copy buttons in the chatbot and ensure copy buttons work in non-secure contexts by [@binary-husky](https://github.com/binary-husky) in [PR 4350](https://github.com/gradio-app/gradio/pull/4350).
-
-### Other Changes:
-
-- Remove flicker of loading bar by adding opacity transition, by [@aliabid94](https://github.com/aliabid94) in [PR 4349](https://github.com/gradio-app/gradio/pull/4349).
-- Performance optimization in the frontend's Blocks code by [@akx](https://github.com/akx) in [PR 4334](https://github.com/gradio-app/gradio/pull/4334)
-- Upgrade the pnpm lock file format version from v6.0 to v6.1 by [@whitphx](https://github.com/whitphx) in [PR 4393](https://github.com/gradio-app/gradio/pull/4393)
-
-### Breaking Changes:
-
-- The `/file=` route no longer allows accessing dotfiles or files in "dot directories" by [@akx](https://github.com/akx) in [PR 4303](https://github.com/gradio-app/gradio/pull/4303)
-
-## 3.32.0
-
-### New Features:
-
-- `Interface.launch()` and `Blocks.launch()` now accept an `app_kwargs` argument to allow customizing the configuration of the underlying FastAPI app, by [@akx](https://github.com/akx) in [PR 4282](https://github.com/gradio-app/gradio/pull/4282)
-
-### Bug Fixes:
-
-- Fixed Gallery/AnnotatedImage components not respecting GRADIO_DEFAULT_DIR variable by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4256](https://github.com/gradio-app/gradio/pull/4256)
-- Fixed Gallery/AnnotatedImage components resaving identical images by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4256](https://github.com/gradio-app/gradio/pull/4256)
-- Fixed Audio/Video/File components creating empty tempfiles on each run by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4256](https://github.com/gradio-app/gradio/pull/4256)
-- Fixed the behavior of the `run_on_click` parameter in `gr.Examples` by [@abidlabs](https://github.com/abidlabs) in [PR 4258](https://github.com/gradio-app/gradio/pull/4258).
-- Ensure error modal displays when the queue is enabled by [@pngwn](https://github.com/pngwn) in [PR 4273](https://github.com/gradio-app/gradio/pull/4273)
-- Ensure js client respcts the full root when making requests to the server by [@pngwn](https://github.com/pngwn) in [PR 4271](https://github.com/gradio-app/gradio/pull/4271)
-
-### Other Changes:
-
-- Refactor web component `initial_height` attribute by [@whitphx](https://github.com/whitphx) in [PR 4223](https://github.com/gradio-app/gradio/pull/4223)
-- Relocate `mount_css` fn to remove circular dependency [@whitphx](https://github.com/whitphx) in [PR 4222](https://github.com/gradio-app/gradio/pull/4222)
-- Upgrade Black to 23.3 by [@akx](https://github.com/akx) in [PR 4259](https://github.com/gradio-app/gradio/pull/4259)
-- Add frontend LaTeX support in `gr.Chatbot()` using `KaTeX` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4285](https://github.com/gradio-app/gradio/pull/4285).
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.31.0
-
-### New Features:
-
-- The reloader command (`gradio app.py`) can now accept command line arguments by [@micky2be](https://github.com/micky2be) in [PR 4119](https://github.com/gradio-app/gradio/pull/4119)
-- Added `format` argument to `Audio` component by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4178](https://github.com/gradio-app/gradio/pull/4178)
-- Add JS client code snippets to use via api page by [@aliabd](https://github.com/aliabd) in [PR 3927](https://github.com/gradio-app/gradio/pull/3927).
-- Update to the JS client by [@pngwn](https://github.com/pngwn) in [PR 4202](https://github.com/gradio-app/gradio/pull/4202)
-
-### Bug Fixes:
-
-- Fix "TypeError: issubclass() arg 1 must be a class" When use Optional[Types] by [@lingfengchencn](https://github.com/lingfengchencn) in [PR 4200](https://github.com/gradio-app/gradio/pull/4200).
-- Gradio will no longer send any analytics or call home if analytics are disabled with the GRADIO_ANALYTICS_ENABLED environment variable. By [@akx](https://github.com/akx) in [PR 4194](https://github.com/gradio-app/gradio/pull/4194) and [PR 4236](https://github.com/gradio-app/gradio/pull/4236)
-- The deprecation warnings for kwargs now show the actual stack level for the invocation, by [@akx](https://github.com/akx) in [PR 4203](https://github.com/gradio-app/gradio/pull/4203).
-- Fix "TypeError: issubclass() arg 1 must be a class" When use Optional[Types] by [@lingfengchencn](https://github.com/lingfengchencn) in [PR 4200](https://github.com/gradio-app/gradio/pull/4200).
-- Ensure cancelling functions work correctly by [@pngwn](https://github.com/pngwn) in [PR 4225](https://github.com/gradio-app/gradio/pull/4225)
-- Fixes a bug with typing.get_type_hints() on Python 3.9 by [@abidlabs](https://github.com/abidlabs) in [PR 4228](https://github.com/gradio-app/gradio/pull/4228).
-- Fixes JSONDecodeError by [@davidai](https://github.com/davidai) in [PR 4241](https://github.com/gradio-app/gradio/pull/4241)
-- Fix `chatbot_dialogpt` demo by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4238](https://github.com/gradio-app/gradio/pull/4238).
-
-### Other Changes:
-
-- Change `gr.Chatbot()` markdown parsing to frontend using `marked` library and `prism` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4150](https://github.com/gradio-app/gradio/pull/4150)
-- Update the js client by [@pngwn](https://github.com/pngwn) in [PR 3899](https://github.com/gradio-app/gradio/pull/3899)
-- Fix documentation for the shape of the numpy array produced by the `Image` component by [@der3318](https://github.com/der3318) in [PR 4204](https://github.com/gradio-app/gradio/pull/4204).
-- Updates the timeout for websocket messaging from 1 second to 5 seconds by [@abidlabs](https://github.com/abidlabs) in [PR 4235](https://github.com/gradio-app/gradio/pull/4235)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.30.0
-
-### New Features:
-
-- Adds a `root_path` parameter to `launch()` that allows running Gradio applications on subpaths (e.g. www.example.com/app) behind a proxy, by [@abidlabs](https://github.com/abidlabs) in [PR 4133](https://github.com/gradio-app/gradio/pull/4133)
-- Fix dropdown change listener to trigger on change when updated as an output by [@aliabid94](https://github.com/aliabid94) in [PR 4128](https://github.com/gradio-app/gradio/pull/4128).
-- Add `.input` event listener, which is only triggered when a user changes the component value (as compared to `.change`, which is also triggered when a component updates as the result of a function trigger), by [@aliabid94](https://github.com/aliabid94) in [PR 4157](https://github.com/gradio-app/gradio/pull/4157).
-
-### Bug Fixes:
-
-- Records username when flagging by [@abidlabs](https://github.com/abidlabs) in [PR 4135](https://github.com/gradio-app/gradio/pull/4135)
-- Fix website build issue by [@aliabd](https://github.com/aliabd) in [PR 4142](https://github.com/gradio-app/gradio/pull/4142)
-- Fix lang agnostic type info for `gr.File(file_count='multiple')` output components by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4153](https://github.com/gradio-app/gradio/pull/4153)
-
-### Other Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-## 3.29.0
-
-### New Features:
-
-- Returning language agnostic types in the `/info` route by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4039](https://github.com/gradio-app/gradio/pull/4039)
-
-### Bug Fixes:
-
-- Allow users to upload audio files in Audio component on iOS by by [@aliabid94](https://github.com/aliabid94) in [PR 4071](https://github.com/gradio-app/gradio/pull/4071).
-- Fixes the gradio theme builder error that appeared on launch by [@aliabid94](https://github.com/aliabid94) and [@abidlabs](https://github.com/abidlabs) in [PR 4080](https://github.com/gradio-app/gradio/pull/4080)
-- Keep Accordion content in DOM by [@aliabid94](https://github.com/aliabid94) in [PR 4070](https://github.com/gradio-app/gradio/pull/4073)
-- Fixed bug where type hints in functions caused the event handler to crash by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 4068](https://github.com/gradio-app/gradio/pull/4068)
-- Fix dropdown default value not appearing by [@aliabid94](https://github.com/aliabid94) in [PR 4072](https://github.com/gradio-app/gradio/pull/4072).
-- Soft theme label color fix by [@aliabid94](https://github.com/aliabid94) in [PR 4070](https://github.com/gradio-app/gradio/pull/4070)
-- Fix `gr.Slider` `release` event not triggering on mobile by [@space-nuko](https://github.com/space-nuko) in [PR 4098](https://github.com/gradio-app/gradio/pull/4098)
-- Removes extraneous `State` component info from the `/info` route by [@abidlabs](https://github.com/freddyaboulton) in [PR 4107](https://github.com/gradio-app/gradio/pull/4107)
-- Make .then() work even if first event fails by [@aliabid94](https://github.com/aliabid94) in [PR 4115](https://github.com/gradio-app/gradio/pull/4115).
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Allow users to submit with enter in Interfaces with textbox / number inputs [@aliabid94](https://github.com/aliabid94) in [PR 4090](https://github.com/gradio-app/gradio/pull/4090).
-- Updates gradio's requirements.txt to requires uvicorn>=0.14.0 by [@abidlabs](https://github.com/abidlabs) in [PR 4086](https://github.com/gradio-app/gradio/pull/4086)
-- Updates some error messaging by [@abidlabs](https://github.com/abidlabs) in [PR 4086](https://github.com/gradio-app/gradio/pull/4086)
-- Renames simplified Chinese translation file from `zh-cn.json` to `zh-CN.json` by [@abidlabs](https://github.com/abidlabs) in [PR 4086](https://github.com/gradio-app/gradio/pull/4086)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.28.3
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Fixes issue with indentation in `gr.Code()` component with streaming by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4043](https://github.com/gradio-app/gradio/pull/4043)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.28.2
-
-### Bug Fixes
-
-- Code component visual updates by [@pngwn](https://github.com/pngwn) in [PR 4051](https://github.com/gradio-app/gradio/pull/4051)
-
-### New Features:
-
-- Add support for `visual-question-answering`, `document-question-answering`, and `image-to-text` using `gr.Interface.load("models/...")` and `gr.Interface.from_pipeline` by [@osanseviero](https://github.com/osanseviero) in [PR 3887](https://github.com/gradio-app/gradio/pull/3887)
-- Add code block support in `gr.Chatbot()`, by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 4048](https://github.com/gradio-app/gradio/pull/4048)
-- Adds the ability to blocklist filepaths (and also improves the allowlist mechanism) by [@abidlabs](https://github.com/abidlabs) in [PR 4047](https://github.com/gradio-app/gradio/pull/4047).
-- Adds the ability to specify the upload directory via an environment variable by [@abidlabs](https://github.com/abidlabs) in [PR 4047](https://github.com/gradio-app/gradio/pull/4047).
-
-### Bug Fixes:
-
-- Fixes issue with `matplotlib` not rendering correctly if the backend was not set to `Agg` by [@abidlabs](https://github.com/abidlabs) in [PR 4029](https://github.com/gradio-app/gradio/pull/4029)
-- Fixes bug where rendering the same `gr.State` across different Interfaces/Blocks within larger Blocks would not work by [@abidlabs](https://github.com/abidlabs) in [PR 4030](https://github.com/gradio-app/gradio/pull/4030)
-- Code component visual updates by [@pngwn](https://github.com/pngwn) in [PR 4051](https://github.com/gradio-app/gradio/pull/4051)
-
-### Documentation Changes:
-
-- Adds a Guide on how to use the Python Client within a FastAPI app, by [@abidlabs](https://github.com/abidlabs) in [PR 3892](https://github.com/gradio-app/gradio/pull/3892)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-- `gr.HuggingFaceDatasetSaver` behavior changed internally. The `flagging/` folder is not a `.git/` folder anymore when using it. `organization` parameter is now ignored in favor of passing a full dataset id as `dataset_name` (e.g. `"username/my-dataset"`).
-- New lines (`\n`) are not automatically converted to `<br>` in `gr.Markdown()` or `gr.Chatbot()`. For multiple new lines, a developer must add multiple `<br>` tags.
-
-### Full Changelog:
-
-- Safer version of `gr.HuggingFaceDatasetSaver` using HTTP methods instead of git pull/push by [@Wauplin](https://github.com/Wauplin) in [PR 3973](https://github.com/gradio-app/gradio/pull/3973)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.28.1
-
-### New Features:
-
-- Add a "clear mask" button to `gr.Image` sketch modes, by [@space-nuko](https://github.com/space-nuko) in [PR 3615](https://github.com/gradio-app/gradio/pull/3615)
-
-### Bug Fixes:
-
-- Fix dropdown default value not appearing by [@aliabid94](https://github.com/aliabid94) in [PR 3996](https://github.com/gradio-app/gradio/pull/3996).
-- Fix faded coloring of output textboxes in iOS / Safari by [@aliabid94](https://github.com/aliabid94) in [PR 3993](https://github.com/gradio-app/gradio/pull/3993)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-- CI: Simplified Python CI workflow by [@akx](https://github.com/akx) in [PR 3982](https://github.com/gradio-app/gradio/pull/3982)
-- Upgrade pyright to 1.1.305 by [@akx](https://github.com/akx) in [PR 4042](https://github.com/gradio-app/gradio/pull/4042)
-- More Ruff rules are enabled and lint errors fixed by [@akx](https://github.com/akx) in [PR 4038](https://github.com/gradio-app/gradio/pull/4038)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.28.0
-
-### Bug Fixes:
-
-- Fix duplicate play commands in full-screen mode of 'video'. by [@tomchang25](https://github.com/tomchang25) in [PR 3968](https://github.com/gradio-app/gradio/pull/3968).
-- Fix the issue of the UI stuck caused by the 'selected' of DataFrame not being reset. by [@tomchang25](https://github.com/tomchang25) in [PR 3916](https://github.com/gradio-app/gradio/pull/3916).
-- Fix issue where `gr.Video()` would not work inside a `gr.Tab()` by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3891](https://github.com/gradio-app/gradio/pull/3891)
-- Fixed issue with old_value check in File. by [@tomchang25](https://github.com/tomchang25) in [PR 3859](https://github.com/gradio-app/gradio/pull/3859).
-- Fixed bug where all bokeh plots appeared in the same div by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3896](https://github.com/gradio-app/gradio/pull/3896)
-- Fixed image outputs to automatically take full output image height, unless explicitly set, by [@aliabid94](https://github.com/aliabid94) in [PR 3905](https://github.com/gradio-app/gradio/pull/3905)
-- Fix issue in `gr.Gallery()` where setting height causes aspect ratio of images to collapse by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3830](https://github.com/gradio-app/gradio/pull/3830)
-- Fix issue where requesting for a non-existing file would trigger a 500 error by [@micky2be](https://github.com/micky2be) in `[PR 3895](https://github.com/gradio-app/gradio/pull/3895)`.
-- Fix bugs with abspath about symlinks, and unresolvable path on Windows by [@micky2be](https://github.com/micky2be) in `[PR 3895](https://github.com/gradio-app/gradio/pull/3895)`.
-- Fixes type in client `Status` enum by [@10zinten](https://github.com/10zinten) in [PR 3931](https://github.com/gradio-app/gradio/pull/3931)
-- Fix `gr.ChatBot` to handle image url [tye-singwa](https://github.com/tye-signwa) in [PR 3953](https://github.com/gradio-app/gradio/pull/3953)
-- Move Google Tag Manager related initialization code to analytics-enabled block by [@akx](https://github.com/akx) in [PR 3956](https://github.com/gradio-app/gradio/pull/3956)
-- Fix bug where port was not reused if the demo was closed and then re-launched by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3896](https://github.com/gradio-app/gradio/pull/3959)
-- Fixes issue where dropdown does not position itself at selected element when opened [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3639](https://github.com/gradio-app/gradio/pull/3639)
-
-### Documentation Changes:
-
-- Make use of `gr` consistent across the docs by [@duerrsimon](https://github.com/duerrsimon) in [PR 3901](https://github.com/gradio-app/gradio/pull/3901)
-- Fixed typo in theming-guide.md by [@eltociear](https://github.com/eltociear) in [PR 3952](https://github.com/gradio-app/gradio/pull/3952)
-
-### Testing and Infrastructure Changes:
-
-- CI: Python backend lint is only run once, by [@akx](https://github.com/akx) in [PR 3960](https://github.com/gradio-app/gradio/pull/3960)
-- Format invocations and concatenations were replaced by f-strings where possible by [@akx](https://github.com/akx) in [PR 3984](https://github.com/gradio-app/gradio/pull/3984)
-- Linting rules were made more strict and issues fixed by [@akx](https://github.com/akx) in [PR 3979](https://github.com/gradio-app/gradio/pull/3979).
-
-### Breaking Changes:
-
-- Some re-exports in `gradio.themes` utilities (introduced in 3.24.0) have been eradicated.
-  By [@akx](https://github.com/akx) in [PR 3958](https://github.com/gradio-app/gradio/pull/3958)
-
-### Full Changelog:
-
-- Add DESCRIPTION.md to image_segmentation demo by [@aliabd](https://github.com/aliabd) in [PR 3866](https://github.com/gradio-app/gradio/pull/3866)
-- Fix error in running `gr.themes.builder()` by [@deepkyu](https://github.com/deepkyu) in [PR 3869](https://github.com/gradio-app/gradio/pull/3869)
-- Fixed a JavaScript TypeError when loading custom JS with `_js` and setting `outputs` to `None` in `gradio.Blocks()` by [@DavG25](https://github.com/DavG25) in [PR 3883](https://github.com/gradio-app/gradio/pull/3883)
-- Fixed bg_background_fill theme property to expand to whole background, block_radius to affect form elements as well, and added block_label_shadow theme property by [@aliabid94](https://github.com/aliabid94) in [PR 3590](https://github.com/gradio-app/gradio/pull/3590)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.27.0
-
-### New Features:
-
-###### AnnotatedImage Component
-
-New AnnotatedImage component allows users to highlight regions of an image, either by providing bounding boxes, or 0-1 pixel masks. This component is useful for tasks such as image segmentation, object detection, and image captioning.
-
-![AnnotatedImage screenshot](https://user-images.githubusercontent.com/7870876/232142720-86e0020f-beaf-47b9-a843-689c9621f09c.gif)
-
-Example usage:
-
-```python
-with gr.Blocks() as demo:
-    img = gr.Image()
-    img_section = gr.AnnotatedImage()
-    def mask(img):
-        top_left_corner = [0, 0, 20, 20]
-        random_mask = np.random.randint(0, 2, img.shape[:2])
-        return (img, [(top_left_corner, "left corner"), (random_mask, "random")])
-    img.change(mask, img, img_section)
-```
-
-See the [image_segmentation demo](https://github.com/gradio-app/gradio/tree/main/demo/image_segmentation) for a full example. By [@aliabid94](https://github.com/aliabid94) in [PR 3836](https://github.com/gradio-app/gradio/pull/3836)
-
-### Bug Fixes:
-
-No changes to highlight.
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.26.0
-
-### New Features:
-
-###### `Video` component supports subtitles
-
-- Allow the video component to accept subtitles as input, by [@tomchang25](https://github.com/tomchang25) in [PR 3673](https://github.com/gradio-app/gradio/pull/3673). To provide subtitles, simply return a tuple consisting of `(path_to_video, path_to_subtitles)` from your function. Both `.srt` and `.vtt` formats are supported:
-
-```py
-with gr.Blocks() as demo:
-    gr.Video(("video.mp4", "captions.srt"))
-```
-
-### Bug Fixes:
-
-- Fix code markdown support in `gr.Chatbot()` component by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3816](https://github.com/gradio-app/gradio/pull/3816)
-
-### Documentation Changes:
-
-- Updates the "view API" page in Gradio apps to use the `gradio_client` library by [@aliabd](https://github.com/aliabd) in [PR 3765](https://github.com/gradio-app/gradio/pull/3765)
-
-- Read more about how to use the `gradio_client` library here: https://gradio.app/getting-started-with-the-python-client/
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.25.0
-
-### New Features:
-
-- Improve error messages when number of inputs/outputs to event handlers mismatch, by [@space-nuko](https://github.com/space-nuko) in [PR 3519](https://github.com/gradio-app/gradio/pull/3519)
-
-- Add `select` listener to Images, allowing users to click on any part of an image and get the coordinates of the click by [@aliabid94](https://github.com/aliabid94) in [PR 3786](https://github.com/gradio-app/gradio/pull/3786).
-
-```python
-with gr.Blocks() as demo:
-    img = gr.Image()
-    textbox = gr.Textbox()
-
-    def select_handler(img, evt: gr.SelectData):
-        selected_pixel = img[evt.index[1], evt.index[0]]
-        return f"Selected pixel: {selected_pixel}"
-
-    img.select(select_handler, img, textbox)
-```
-
-![Recording 2023-04-08 at 17 44 39](https://user-images.githubusercontent.com/7870876/230748572-90a2a8d5-116d-4769-bb53-5516555fbd0f.gif)
-
-### Bug Fixes:
-
-- Increase timeout for sending analytics data by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3647](https://github.com/gradio-app/gradio/pull/3647)
-- Fix bug where http token was not accessed over websocket connections by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3735](https://github.com/gradio-app/gradio/pull/3735)
-- Add ability to specify `rows`, `columns` and `object-fit` in `style()` for `gr.Gallery()` component by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3586](https://github.com/gradio-app/gradio/pull/3586)
-- Fix bug where recording an audio file through the microphone resulted in a corrupted file name by [@abidlabs](https://github.com/abidlabs) in [PR 3770](https://github.com/gradio-app/gradio/pull/3770)
-- Added "ssl_verify" to blocks.launch method to allow for use of self-signed certs by [@garrettsutula](https://github.com/garrettsutula) in [PR 3873](https://github.com/gradio-app/gradio/pull/3873)
-- Fix bug where iterators where not being reset for processes that terminated early by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3777](https://github.com/gradio-app/gradio/pull/3777)
-- Fix bug where the upload button was not properly handling the `file_count='multiple'` case by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3782](https://github.com/gradio-app/gradio/pull/3782)
-- Fix bug where use Via API button was giving error by [@Devang-C](https://github.com/Devang-C) in [PR 3783](https://github.com/gradio-app/gradio/pull/3783)
-
-### Documentation Changes:
-
-- Fix invalid argument docstrings, by [@akx](https://github.com/akx) in [PR 3740](https://github.com/gradio-app/gradio/pull/3740)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Fixed IPv6 listening to work with bracket [::1] notation, by [@dsully](https://github.com/dsully) in [PR 3695](https://github.com/gradio-app/gradio/pull/3695)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.24.1
-
-### New Features:
-
-- No changes to highlight.
-
-### Bug Fixes:
-
-- Fixes Chatbot issue where new lines were being created every time a message was sent back and forth by [@aliabid94](https://github.com/aliabid94) in [PR 3717](https://github.com/gradio-app/gradio/pull/3717).
-- Fixes data updating in DataFrame invoking a `select` event once the dataframe has been selected. By [@yiyuezhuo](https://github.com/yiyuezhuo) in [PR 3861](https://github.com/gradio-app/gradio/pull/3861)
-- Fixes false positive warning which is due to too strict type checking by [@yiyuezhuo](https://github.com/yiyuezhuo) in [PR 3837](https://github.com/gradio-app/gradio/pull/3837).
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.24.0
-
-### New Features:
-
-- Trigger the release event when Slider number input is released or unfocused by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3589](https://github.com/gradio-app/gradio/pull/3589)
-- Created Theme Builder, which allows users to create themes without writing any code, by [@aliabid94](https://github.com/aliabid94) in [PR 3664](https://github.com/gradio-app/gradio/pull/3664). Launch by:
-
-  ```python
-  import gradio as gr
-  gr.themes.builder()
-  ```
-
-  ![Theme Builder](https://user-images.githubusercontent.com/7870876/228204929-d71cbba5-69c2-45b3-bd20-e3a201d98b12.png)
-
-- The `Dropdown` component now has a `allow_custom_value` parameter that lets users type in custom values not in the original list of choices.
-- The `Colorpicker` component now has a `.blur()` event
-
-###### Added a download button for videos! 📥
-
-![download_video](https://user-images.githubusercontent.com/41651716/227009612-9bc5fb72-2a44-4c55-9b7b-a0fa098e7f25.gif)
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3581](https://github.com/gradio-app/gradio/pull/3581).
-
-- Trigger the release event when Slider number input is released or unfocused by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3589](https://github.com/gradio-app/gradio/pull/3589)
-
-### Bug Fixes:
-
-- Fixed bug where text for altair plots was not legible in dark mode by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3555](https://github.com/gradio-app/gradio/pull/3555)
-- Fixes `Chatbot` and `Image` components so that files passed during processing are added to a directory where they can be served from, by [@abidlabs](https://github.com/abidlabs) in [PR 3523](https://github.com/gradio-app/gradio/pull/3523)
-- Use Gradio API server to send telemetry using `huggingface_hub` [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3488](https://github.com/gradio-app/gradio/pull/3488)
-- Fixes an an issue where if the Blocks scope was not exited, then State could be shared across sessions, by [@abidlabs](https://github.com/abidlabs) in [PR 3600](https://github.com/gradio-app/gradio/pull/3600)
-- Ensures that `gr.load()` loads and applies the upstream theme, by [@abidlabs](https://github.com/abidlabs) in [PR 3641](https://github.com/gradio-app/gradio/pull/3641)
-- Fixed bug where "or" was not being localized in file upload text by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3599](https://github.com/gradio-app/gradio/pull/3599)
-- Fixed bug where chatbot does not autoscroll inside of a tab, row or column by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3637](https://github.com/gradio-app/gradio/pull/3637)
-- Fixed bug where textbox shrinks when `lines` set to larger than 20 by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3637](https://github.com/gradio-app/gradio/pull/3637)
-- Ensure CSS has fully loaded before rendering the application, by [@pngwn](https://github.com/pngwn) in [PR 3573](https://github.com/gradio-app/gradio/pull/3573)
-- Support using an empty list as `gr.Dataframe` value, by [@space-nuko](https://github.com/space-nuko) in [PR 3646](https://github.com/gradio-app/gradio/pull/3646)
-- Fixed `gr.Image` not filling the entire element size, by [@space-nuko](https://github.com/space-nuko) in [PR 3649](https://github.com/gradio-app/gradio/pull/3649)
-- Make `gr.Code` support the `lines` property, by [@space-nuko](https://github.com/space-nuko) in [PR 3651](https://github.com/gradio-app/gradio/pull/3651)
-- Fixes certain `_js` return values being double wrapped in an array, by [@space-nuko](https://github.com/space-nuko) in [PR 3594](https://github.com/gradio-app/gradio/pull/3594)
-- Correct the documentation of `gr.File` component to state that its preprocessing method converts the uploaded file to a temporary file, by @RussellLuo in [PR 3660](https://github.com/gradio-app/gradio/pull/3660)
-- Fixed bug in Serializer ValueError text by [@osanseviero](https://github.com/osanseviero) in [PR 3669](https://github.com/gradio-app/gradio/pull/3669)
-- Fix default parameter argument and `gr.Progress` used in same function, by [@space-nuko](https://github.com/space-nuko) in [PR 3671](https://github.com/gradio-app/gradio/pull/3671)
-- Hide `Remove All` button in `gr.Dropdown` single-select mode by [@space-nuko](https://github.com/space-nuko) in [PR 3678](https://github.com/gradio-app/gradio/pull/3678)
-- Fix broken spaces in docs by [@aliabd](https://github.com/aliabd) in [PR 3698](https://github.com/gradio-app/gradio/pull/3698)
-- Fix items in `gr.Dropdown` besides the selected item receiving a checkmark, by [@space-nuko](https://github.com/space-nuko) in [PR 3644](https://github.com/gradio-app/gradio/pull/3644)
-- Fix several `gr.Dropdown` issues and improve usability, by [@space-nuko](https://github.com/space-nuko) in [PR 3705](https://github.com/gradio-app/gradio/pull/3705)
-
-### Documentation Changes:
-
-- Makes some fixes to the Theme Guide related to naming of variables, by [@abidlabs](https://github.com/abidlabs) in [PR 3561](https://github.com/gradio-app/gradio/pull/3561)
-- Documented `HuggingFaceDatasetJSONSaver` by [@osanseviero](https://github.com/osanseviero) in [PR 3604](https://github.com/gradio-app/gradio/pull/3604)
-- Makes some additions to documentation of `Audio` and `State` components, and fixes the `pictionary` demo by [@abidlabs](https://github.com/abidlabs) in [PR 3611](https://github.com/gradio-app/gradio/pull/3611)
-- Fix outdated sharing your app guide by [@aliabd](https://github.com/aliabd) in [PR 3699](https://github.com/gradio-app/gradio/pull/3699)
-
-### Testing and Infrastructure Changes:
-
-- Removed heavily-mocked tests related to comet_ml, wandb, and mlflow as they added a significant amount of test dependencies that prevented installation of test dependencies on Windows environments. By [@abidlabs](https://github.com/abidlabs) in [PR 3608](https://github.com/gradio-app/gradio/pull/3608)
-- Added Windows continuous integration, by [@space-nuko](https://github.com/space-nuko) in [PR 3628](https://github.com/gradio-app/gradio/pull/3628)
-- Switched linting from flake8 + isort to `ruff`, by [@akx](https://github.com/akx) in [PR 3710](https://github.com/gradio-app/gradio/pull/3710)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Mobile responsive iframes in themes guide by [@aliabd](https://github.com/aliabd) in [PR 3562](https://github.com/gradio-app/gradio/pull/3562)
-- Remove extra $demo from theme guide by [@aliabd](https://github.com/aliabd) in [PR 3563](https://github.com/gradio-app/gradio/pull/3563)
-- Set the theme name to be the upstream repo name when loading from the hub by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3595](https://github.com/gradio-app/gradio/pull/3595)
-- Copy everything in website Dockerfile, fix build issues by [@aliabd](https://github.com/aliabd) in [PR 3659](https://github.com/gradio-app/gradio/pull/3659)
-- Raise error when an event is queued but the queue is not configured by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3640](https://github.com/gradio-app/gradio/pull/3640)
-- Allows users to apss in a string name for a built-in theme, by [@abidlabs](https://github.com/abidlabs) in [PR 3641](https://github.com/gradio-app/gradio/pull/3641)
-- Added `orig_name` to Video output in the backend so that the front end can set the right name for downloaded video files by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3700](https://github.com/gradio-app/gradio/pull/3700)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.23.0
-
-### New Features:
-
-###### Theme Sharing!
-
-Once you have created a theme, you can upload it to the HuggingFace Hub to let others view it, use it, and build off of it! You can also download, reuse, and remix other peoples' themes. See https://gradio.app/theming-guide/ for more details.
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3428](https://github.com/gradio-app/gradio/pull/3428)
-
-### Bug Fixes:
-
-- Removes leading spaces from all lines of code uniformly in the `gr.Code()` component. By [@abidlabs](https://github.com/abidlabs) in [PR 3556](https://github.com/gradio-app/gradio/pull/3556)
-- Fixed broken login page, by [@aliabid94](https://github.com/aliabid94) in [PR 3529](https://github.com/gradio-app/gradio/pull/3529)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Fix rendering of dropdowns to take more space, and related bugs, by [@aliabid94](https://github.com/aliabid94) in [PR 3549](https://github.com/gradio-app/gradio/pull/3549)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.22.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Restore label bars by [@aliabid94](https://github.com/aliabid94) in [PR 3507](https://github.com/gradio-app/gradio/pull/3507)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.22.0
-
-### New Features:
-
-###### Official Theme release
-
-Gradio now supports a new theme system, which allows you to customize the look and feel of your app. You can now use the `theme=` kwarg to pass in a prebuilt theme, or customize your own! See https://gradio.app/theming-guide/ for more details. By [@aliabid94](https://github.com/aliabid94) in [PR 3470](https://github.com/gradio-app/gradio/pull/3470) and [PR 3497](https://github.com/gradio-app/gradio/pull/3497)
-
-###### `elem_classes`
-
-Add keyword argument `elem_classes` to Components to control class names of components, in the same manner as existing `elem_id`.
-By [@aliabid94](https://github.com/aliabid94) in [PR 3466](https://github.com/gradio-app/gradio/pull/3466)
-
-### Bug Fixes:
-
-- Fixes the File.upload() event trigger which broke as part of the change in how we uploaded files by [@abidlabs](https://github.com/abidlabs) in [PR 3462](https://github.com/gradio-app/gradio/pull/3462)
-- Fixed issue with `gr.Request` object failing to handle dictionaries when nested keys couldn't be converted to variable names [#3454](https://github.com/gradio-app/gradio/issues/3454) by [@radames](https://github.com/radames) in [PR 3459](https://github.com/gradio-app/gradio/pull/3459)
-- Fixed bug where css and client api was not working properly when mounted in a subpath by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3482](https://github.com/gradio-app/gradio/pull/3482)
-
-### Documentation Changes:
-
-- Document gr.Error in the docs by [@aliabd](https://github.com/aliabd) in [PR 3465](https://github.com/gradio-app/gradio/pull/3465)
-
-### Testing and Infrastructure Changes:
-
-- Pinned `pyright==1.1.298` for stability by [@abidlabs](https://github.com/abidlabs) in [PR 3475](https://github.com/gradio-app/gradio/pull/3475)
-- Removed `IOComponent.add_interactive_to_config()` by [@space-nuko](https://github.com/space-nuko) in [PR 3476](https://github.com/gradio-app/gradio/pull/3476)
-- Removed `IOComponent.generate_sample()` by [@space-nuko](https://github.com/space-nuko) in [PR 3475](https://github.com/gradio-app/gradio/pull/3483)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Revert primary button background color in dark mode by [@aliabid94](https://github.com/aliabid94) in [PR 3468](https://github.com/gradio-app/gradio/pull/3468)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.21.0
-
-### New Features:
-
-###### Theme Sharing 🎨 🤝
-
-You can now share your gradio themes with the world!
-
-After creating a theme, you can upload it to the HuggingFace Hub to let others view it, use it, and build off of it!
-
-###### Uploading
-
-There are two ways to upload a theme, via the theme class instance or the command line.
-
-1. Via the class instance
-
-```python
-my_theme.push_to_hub(repo_name="my_theme",
-                     version="0.2.0",
-                     hf_token="...")
-```
-
-2. Via the command line
-
-First save the theme to disk
-
-```python
-my_theme.dump(filename="my_theme.json")
-```
-
-Then use the `upload_theme` command:
-
-```bash
-upload_theme\
-"my_theme.json"\
-"my_theme"\
-"0.2.0"\
-"<hf-token>"
-```
-
-The `version` must be a valid [semantic version](https://www.geeksforgeeks.org/introduction-semantic-versioning/) string.
-
-This creates a space on the huggingface hub to host the theme files and show potential users a preview of your theme.
-
-An example theme space is here: https://huggingface.co/spaces/freddyaboulton/dracula_revamped
-
-###### Downloading
-
-To use a theme from the hub, use the `from_hub` method on the `ThemeClass` and pass it to your app:
-
-```python
-my_theme = gr.Theme.from_hub("freddyaboulton/my_theme")
-
-with gr.Blocks(theme=my_theme) as demo:
-    ....
-```
-
-You can also pass the theme string directly to `Blocks` or `Interface` (`gr.Blocks(theme="freddyaboulton/my_theme")`)
-
-You can pin your app to an upstream theme version by using semantic versioning expressions.
-
-For example, the following would ensure the theme we load from the `my_theme` repo was between versions `0.1.0` and `0.2.0`:
-
-```python
-with gr.Blocks(theme="freddyaboulton/my_theme@>=0.1.0,<0.2.0") as demo:
-    ....
-```
-
-by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3428](https://github.com/gradio-app/gradio/pull/3428)
-
-###### Code component 🦾
-
-New code component allows you to enter, edit and display code with full syntax highlighting by [@pngwn](https://github.com/pngwn) in [PR 3421](https://github.com/gradio-app/gradio/pull/3421)
-
-###### The `Chatbot` component now supports audio, video, and images
-
-The `Chatbot` component now supports audio, video, and images with a simple syntax: simply
-pass in a tuple with the URL or filepath (the second optional element of the tuple is alt text), and the image/audio/video will be displayed:
-
-```python
-gr.Chatbot([
-    (("driving.mp4",), "cool video"),
-    (("cantina.wav",), "cool audio"),
-    (("lion.jpg", "A lion"), "cool pic"),
-]).style(height=800)
-```
-
-<img width="1054" alt="image" src="https://user-images.githubusercontent.com/1778297/224116682-5908db47-f0fa-405c-82ab-9c7453e8c4f1.png">
-
-Note: images were previously supported via Markdown syntax and that is still supported for backwards compatibility. By [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3413](https://github.com/gradio-app/gradio/pull/3413)
-
-- Allow consecutive function triggers with `.then` and `.success` by [@aliabid94](https://github.com/aliabid94) in [PR 3430](https://github.com/gradio-app/gradio/pull/3430)
-
-- New code component allows you to enter, edit and display code with full syntax highlighting by [@pngwn](https://github.com/pngwn) in [PR 3421](https://github.com/gradio-app/gradio/pull/3421)
-
-![](https://user-images.githubusercontent.com/12937446/224116643-5cfb94b3-93ce-43ee-bb7b-c25c3b66e0a1.png)
-
-- Added the `.select()` event listener, which also includes event data that can be passed as an argument to a function with type hint `gr.SelectData`. The following components support the `.select()` event listener: Chatbot, CheckboxGroup, Dataframe, Dropdown, File, Gallery, HighlightedText, Label, Radio, TabItem, Tab, Textbox. Example usage:
-
-```python
-import gradio as gr
-
-with gr.Blocks() as demo:
-    gallery = gr.Gallery(["images/1.jpg", "images/2.jpg", "images/3.jpg"])
-    selected_index = gr.Textbox()
-
-    def on_select(evt: gr.SelectData):
-        return evt.index
-
-    gallery.select(on_select, None, selected_index)
-```
-
-By [@aliabid94](https://github.com/aliabid94) in [PR 3399](https://github.com/gradio-app/gradio/pull/3399)
-
-- The `Textbox` component now includes a copy button by [@abidlabs](https://github.com/abidlabs) in [PR 3452](https://github.com/gradio-app/gradio/pull/3452)
-
-### Bug Fixes:
-
-- Use `huggingface_hub` to send telemetry on `interface` and `blocks`; eventually to replace segment by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3342](https://github.com/gradio-app/gradio/pull/3342)
-- Ensure load events created by components (randomize for slider, callable values) are never queued unless every is passed by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3391](https://github.com/gradio-app/gradio/pull/3391)
-- Prevent in-place updates of `generic_update` by shallow copying by [@gitgithan](https://github.com/gitgithan) in [PR 3405](https://github.com/gradio-app/gradio/pull/3405) to fix [#3282](https://github.com/gradio-app/gradio/issues/3282)
-- Fix bug caused by not importing `BlockContext` in `utils.py` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3424](https://github.com/gradio-app/gradio/pull/3424)
-- Ensure dropdown does not highlight partial matches by [@pngwn](https://github.com/pngwn) in [PR 3421](https://github.com/gradio-app/gradio/pull/3421)
-- Fix mic button display by [@aliabid94](https://github.com/aliabid94) in [PR 3456](https://github.com/gradio-app/gradio/pull/3456)
-
-### Documentation Changes:
-
-- Added a section on security and access when sharing Gradio apps by [@abidlabs](https://github.com/abidlabs) in [PR 3408](https://github.com/gradio-app/gradio/pull/3408)
-- Add Chinese README by [@uanu2002](https://github.com/uanu2002) in [PR 3394](https://github.com/gradio-app/gradio/pull/3394)
-- Adds documentation for web components by [@abidlabs](https://github.com/abidlabs) in [PR 3407](https://github.com/gradio-app/gradio/pull/3407)
-- Fixed link in Chinese readme by [@eltociear](https://github.com/eltociear) in [PR 3417](https://github.com/gradio-app/gradio/pull/3417)
-- Document Blocks methods by [@aliabd](https://github.com/aliabd) in [PR 3427](https://github.com/gradio-app/gradio/pull/3427)
-- Fixed bug where event handlers were not showing up in documentation by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3434](https://github.com/gradio-app/gradio/pull/3434)
-
-### Testing and Infrastructure Changes:
-
-- Fixes tests that were failing locally but passing on CI by [@abidlabs](https://github.com/abidlabs) in [PR 3411](https://github.com/gradio-app/gradio/pull/3411)
-- Remove codecov from the repo by [@aliabd](https://github.com/aliabd) in [PR 3415](https://github.com/gradio-app/gradio/pull/3415)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Prevent in-place updates of `generic_update` by shallow copying by [@gitgithan](https://github.com/gitgithan) in [PR 3405](https://github.com/gradio-app/gradio/pull/3405) to fix [#3282](https://github.com/gradio-app/gradio/issues/3282)
-- Persist file names of files uploaded through any Gradio component by [@abidlabs](https://github.com/abidlabs) in [PR 3412](https://github.com/gradio-app/gradio/pull/3412)
-- Fix markdown embedded component in docs by [@aliabd](https://github.com/aliabd) in [PR 3410](https://github.com/gradio-app/gradio/pull/3410)
-- Clean up event listeners code by [@aliabid94](https://github.com/aliabid94) in [PR 3420](https://github.com/gradio-app/gradio/pull/3420)
-- Fix css issue with spaces logo by [@aliabd](https://github.com/aliabd) in [PR 3422](https://github.com/gradio-app/gradio/pull/3422)
-- Makes a few fixes to the `JSON` component (show_label parameter, icons) in [@abidlabs](https://github.com/abidlabs) in [PR 3451](https://github.com/gradio-app/gradio/pull/3451)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.20.1
-
-### New Features:
-
-- Add `height` kwarg to style in `gr.Chatbot()` component by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3369](https://github.com/gradio-app/gradio/pull/3369)
-
-```python
-chatbot = gr.Chatbot().style(height=500)
-```
-
-### Bug Fixes:
-
-- Ensure uploaded images are always shown in the sketch tool by [@pngwn](https://github.com/pngwn) in [PR 3386](https://github.com/gradio-app/gradio/pull/3386)
-- Fixes bug where when if fn is a non-static class member, then self should be ignored as the first param of the fn by [@or25](https://github.com/or25) in [PR #3227](https://github.com/gradio-app/gradio/pull/3227)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.20.0
-
-### New Features:
-
-###### Release event for Slider
-
-Now you can trigger your python function to run when the slider is released as opposed to every slider change value!
-
-Simply use the `release` method on the slider
-
-```python
-slider.release(function, inputs=[...], outputs=[...], api_name="predict")
-```
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3353](https://github.com/gradio-app/gradio/pull/3353)
-
-###### Dropdown Component Updates
-
-The standard dropdown component now supports searching for choices. Also when `multiselect` is `True`, you can specify `max_choices` to set the maximum number of choices you want the user to be able to select from the dropdown component.
-
-```python
-gr.Dropdown(label="Choose your favorite colors", choices=["red", "blue", "green", "yellow", "orange"], multiselect=True, max_choices=2)
-```
-
-by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3211](https://github.com/gradio-app/gradio/pull/3211)
-
-###### Download button for images 🖼️
-
-Output images will now automatically have a download button displayed to make it easier to save and share
-the results of Machine Learning art models.
-
-![download_sketch](https://user-images.githubusercontent.com/41651716/221025113-e693bf41-eabd-42b3-a4f2-26f2708d98fe.gif)
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3297](https://github.com/gradio-app/gradio/pull/3297)
-
-- Updated image upload component to accept all image formats, including lossless formats like .webp by [@fienestar](https://github.com/fienestar) in [PR 3225](https://github.com/gradio-app/gradio/pull/3225)
-- Adds a disabled mode to the `gr.Button` component by setting `interactive=False` by [@abidlabs](https://github.com/abidlabs) in [PR 3266](https://github.com/gradio-app/gradio/pull/3266) and [PR 3288](https://github.com/gradio-app/gradio/pull/3288)
-- Adds visual feedback to the when the Flag button is clicked, by [@abidlabs](https://github.com/abidlabs) in [PR 3289](https://github.com/gradio-app/gradio/pull/3289)
-- Adds ability to set `flagging_options` display text and saved flag separately by [@abidlabs](https://github.com/abidlabs) in [PR 3289](https://github.com/gradio-app/gradio/pull/3289)
-- Allow the setting of `brush_radius` for the `Image` component both as a default and via `Image.update()` by [@pngwn](https://github.com/pngwn) in [PR 3277](https://github.com/gradio-app/gradio/pull/3277)
-- Added `info=` argument to form components to enable extra context provided to users, by [@aliabid94](https://github.com/aliabid94) in [PR 3291](https://github.com/gradio-app/gradio/pull/3291)
-- Allow developers to access the username of a logged-in user from the `gr.Request()` object using the `.username` attribute by [@abidlabs](https://github.com/abidlabs) in [PR 3296](https://github.com/gradio-app/gradio/pull/3296)
-- Add `preview` option to `Gallery.style` that launches the gallery in preview mode when first loaded by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3345](https://github.com/gradio-app/gradio/pull/3345)
-
-### Bug Fixes:
-
-- Ensure `mirror_webcam` is always respected by [@pngwn](https://github.com/pngwn) in [PR 3245](https://github.com/gradio-app/gradio/pull/3245)
-- Fix issue where updated markdown links were not being opened in a new tab by [@gante](https://github.com/gante) in [PR 3236](https://github.com/gradio-app/gradio/pull/3236)
-- API Docs Fixes by [@aliabd](https://github.com/aliabd) in [PR 3287](https://github.com/gradio-app/gradio/pull/3287)
-- Added a timeout to queue messages as some demos were experiencing infinitely growing queues from active jobs waiting forever for clients to respond by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3196](https://github.com/gradio-app/gradio/pull/3196)
-- Fixes the height of rendered LaTeX images so that they match the height of surrounding text by [@abidlabs](https://github.com/abidlabs) in [PR 3258](https://github.com/gradio-app/gradio/pull/3258) and in [PR 3276](https://github.com/gradio-app/gradio/pull/3276)
-- Fix bug where matplotlib images where always too small on the front end by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3274](https://github.com/gradio-app/gradio/pull/3274)
-- Remove embed's `initial_height` when loading is complete so the embed finds its natural height once it is loaded [@pngwn](https://github.com/pngwn) in [PR 3292](https://github.com/gradio-app/gradio/pull/3292)
-- Prevent Sketch from crashing when a default image is provided by [@pngwn](https://github.com/pngwn) in [PR 3277](https://github.com/gradio-app/gradio/pull/3277)
-- Respect the `shape` argument on the front end when creating Image Sketches by [@pngwn](https://github.com/pngwn) in [PR 3277](https://github.com/gradio-app/gradio/pull/3277)
-- Fix infinite loop caused by setting `Dropdown's` value to be `[]` and adding a change event on the dropdown by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3295](https://github.com/gradio-app/gradio/pull/3295)
-- Fix change event listed twice in image docs by [@aliabd](https://github.com/aliabd) in [PR 3318](https://github.com/gradio-app/gradio/pull/3318)
-- Fix bug that cause UI to be vertically centered at all times by [@pngwn](https://github.com/pngwn) in [PR 3336](https://github.com/gradio-app/gradio/pull/3336)
-- Fix bug where `height` set in `Gallery.style` was not respected by the front-end by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3343](https://github.com/gradio-app/gradio/pull/3343)
-- Ensure markdown lists are rendered correctly by [@pngwn](https://github.com/pngwn) in [PR 3341](https://github.com/gradio-app/gradio/pull/3341)
-- Ensure that the initial empty value for `gr.Dropdown(Multiselect=True)` is an empty list and the initial value for `gr.Dropdown(Multiselect=False)` is an empty string by [@pngwn](https://github.com/pngwn) in [PR 3338](https://github.com/gradio-app/gradio/pull/3338)
-- Ensure uploaded images respect the shape property when the canvas is also enabled by [@pngwn](https://github.com/pngwn) in [PR 3351](https://github.com/gradio-app/gradio/pull/3351)
-- Ensure that Google Analytics works correctly when gradio apps are created with `analytics_enabled=True` by [@abidlabs](https://github.com/abidlabs) in [PR 3349](https://github.com/gradio-app/gradio/pull/3349)
-- Fix bug where files were being re-uploaded after updates by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3375](https://github.com/gradio-app/gradio/pull/3375)
-- Fix error when using backen_fn and custom js at the same time by [@jialeicui](https://github.com/jialeicui) in [PR 3358](https://github.com/gradio-app/gradio/pull/3358)
-- Support new embeds for huggingface spaces subdomains by [@pngwn](https://github.com/pngwn) in [PR 3367](https://github.com/gradio-app/gradio/pull/3367)
-
-### Documentation Changes:
-
-- Added the `types` field to the dependency field in the config by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3315](https://github.com/gradio-app/gradio/pull/3315)
-- Gradio Status Page by [@aliabd](https://github.com/aliabd) in [PR 3331](https://github.com/gradio-app/gradio/pull/3331)
-- Adds a Guide on setting up a dashboard from Supabase data using the `gr.BarPlot`
-  component by [@abidlabs](https://github.com/abidlabs) in [PR 3275](https://github.com/gradio-app/gradio/pull/3275)
-
-### Testing and Infrastructure Changes:
-
-- Adds a script to benchmark the performance of the queue and adds some instructions on how to use it. By [@freddyaboulton](https://github.com/freddyaboulton) and [@abidlabs](https://github.com/abidlabs) in [PR 3272](https://github.com/gradio-app/gradio/pull/3272)
-- Flaky python tests no longer cancel non-flaky tests by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3344](https://github.com/gradio-app/gradio/pull/3344)
-
-### Breaking Changes:
-
-- Chatbot bubble colors can no longer be set by `chatbot.style(color_map=)` by [@aliabid94] in [PR 3370](https://github.com/gradio-app/gradio/pull/3370)
-
-### Full Changelog:
-
-- Fixed comment typo in components.py by [@eltociear](https://github.com/eltociear) in [PR 3235](https://github.com/gradio-app/gradio/pull/3235)
-- Cleaned up chatbot ui look and feel by [@aliabid94] in [PR 3370](https://github.com/gradio-app/gradio/pull/3370)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.19.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- UI fixes including footer and API docs by [@aliabid94](https://github.com/aliabid94) in [PR 3242](https://github.com/gradio-app/gradio/pull/3242)
-- Updated image upload component to accept all image formats, including lossless formats like .webp by [@fienestar](https://github.com/fienestar) in [PR 3225](https://github.com/gradio-app/gradio/pull/3225)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Added backend support for themes by [@aliabid94](https://github.com/aliabid94) in [PR 2931](https://github.com/gradio-app/gradio/pull/2931)
-- Added support for button sizes "lg" (default) and "sm".
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.19.0
-
-### New Features:
-
-###### Improved embedding experience
-
-When embedding a spaces-hosted gradio app as a web component, you now get an improved UI linking back to the original space, better error handling and more intelligent load performance. No changes are required to your code to benefit from this enhanced experience; simply upgrade your gradio SDK to the latest version.
-
-![](https://user-images.githubusercontent.com/12937446/219653294-86937632-72c1-4e93-a77c-af705d49382a.png)
-
-This behaviour is configurable. You can disable the info panel at the bottom by passing `info="false"`. You can disable the container entirely by passing `container="false"`.
-
-Error statuses are reported in the UI with an easy way for end-users to report problems to the original space author via the community tab of that Hugginface space:
-
-![](https://user-images.githubusercontent.com/12937446/219655499-88019443-d694-44e7-9e6d-242e19d10a5c.png)
-
-By default, gradio apps are lazy loaded, vastly improving performance when there are several demos on the page. Metadata is loaded ahead of time, but the space will only be loaded and rendered when it is in view.
-
-This behaviour is configurable. You can pass `eager="true"` to load and render the space regardless of whether or not it is currently on the screen.
-
-by [@pngwn](https://github.com/pngwn) in [PR 3205](https://github.com/gradio-app/gradio/pull/3205)
-
-###### New `gr.BarPlot` component! 📊
-
-Create interactive bar plots from a high-level interface with `gr.BarPlot`.
-No need to remember matplotlib syntax anymore!
-
-Example usage:
-
-```python
-import gradio as gr
-import pandas as pd
-
-simple = pd.DataFrame({
-    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
-    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
-})
-
-with gr.Blocks() as demo:
-    gr.BarPlot(
-        simple,
-        x="a",
-        y="b",
-        title="Simple Bar Plot with made up data",
-        tooltip=['a', 'b'],
-    )
-
-demo.launch()
-```
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3157](https://github.com/gradio-app/gradio/pull/3157)
-
-###### Bokeh plots are back! 🌠
-
-Fixed a bug that prevented bokeh plots from being displayed on the front end and extended support for both 2.x and 3.x versions of bokeh!
-
-![image](https://user-images.githubusercontent.com/41651716/219468324-0d82e07f-8fb4-4ff9-b40c-8250b29e45f7.png)
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3212](https://github.com/gradio-app/gradio/pull/3212)
-
-### Bug Fixes:
-
-- Adds ability to add a single message from the bot or user side. Ex: specify `None` as the second value in the tuple, to add a single message in the chatbot from the "bot" side.
-
-```python
-gr.Chatbot([("Hi, I'm DialoGPT. Try asking me a question.", None)])
-```
-
-By [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3165](https://github.com/gradio-app/gradio/pull/3165)
-
-- Fixes `gr.utils.delete_none` to only remove props whose values are `None` from the config by [@abidlabs](https://github.com/abidlabs) in [PR 3188](https://github.com/gradio-app/gradio/pull/3188)
-- Fix bug where embedded demos were not loading files properly by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3177](https://github.com/gradio-app/gradio/pull/3177)
-- The `change` event is now triggered when users click the 'Clear All' button of the multiselect DropDown component by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3195](https://github.com/gradio-app/gradio/pull/3195)
-- Stops File component from freezing when a large file is uploaded by [@aliabid94](https://github.com/aliabid94) in [PR 3191](https://github.com/gradio-app/gradio/pull/3191)
-- Support Chinese pinyin in Dataframe by [@aliabid94](https://github.com/aliabid94) in [PR 3206](https://github.com/gradio-app/gradio/pull/3206)
-- The `clear` event is now triggered when images are cleared by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3218](https://github.com/gradio-app/gradio/pull/3218)
-- Fix bug where auth cookies where not sent when connecting to an app via http by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3223](https://github.com/gradio-app/gradio/pull/3223)
-- Ensure latext CSS is always applied in light and dark mode by [@pngwn](https://github.com/pngwn) in [PR 3233](https://github.com/gradio-app/gradio/pull/3233)
-
-### Documentation Changes:
-
-- Sort components in docs by alphabetic order by [@aliabd](https://github.com/aliabd) in [PR 3152](https://github.com/gradio-app/gradio/pull/3152)
-- Changes to W&B guide by [@scottire](https://github.com/scottire) in [PR 3153](https://github.com/gradio-app/gradio/pull/3153)
-- Keep pnginfo metadata for gallery by [@wfng92](https://github.com/wfng92) in [PR 3150](https://github.com/gradio-app/gradio/pull/3150)
-- Add a section on how to run a Gradio app locally [@osanseviero](https://github.com/osanseviero) in [PR 3170](https://github.com/gradio-app/gradio/pull/3170)
-- Fixed typos in gradio events function documentation by [@vidalmaxime](https://github.com/vidalmaxime) in [PR 3168](https://github.com/gradio-app/gradio/pull/3168)
-- Added an example using Gradio's batch mode with the diffusers library by [@abidlabs](https://github.com/abidlabs) in [PR 3224](https://github.com/gradio-app/gradio/pull/3224)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Fix demos page css and add close demos button by [@aliabd](https://github.com/aliabd) in [PR 3151](https://github.com/gradio-app/gradio/pull/3151)
-- Caches temp files from base64 input data by giving them a deterministic path based on the contents of data by [@abidlabs](https://github.com/abidlabs) in [PR 3197](https://github.com/gradio-app/gradio/pull/3197)
-- Better warnings (when there is a mismatch between the number of output components and values returned by a function, or when the `File` component or `UploadButton` component includes a `file_types` parameter along with `file_count=="dir"`) by [@abidlabs](https://github.com/abidlabs) in [PR 3194](https://github.com/gradio-app/gradio/pull/3194)
-- Raises a `gr.Error` instead of a regular Python error when you use `gr.Interface.load()` to load a model and there's an error querying the HF API by [@abidlabs](https://github.com/abidlabs) in [PR 3194](https://github.com/gradio-app/gradio/pull/3194)
-- Fixed gradio share links so that they are persistent and do not reset if network
-  connection is disrupted by by [XciD](https://github.com/XciD), [Wauplin](https://github.com/Wauplin), and [@abidlabs](https://github.com/abidlabs) in [PR 3149](https://github.com/gradio-app/gradio/pull/3149) and a follow-up to allow it to work for users upgrading from a previous Gradio version in [PR 3221](https://github.com/gradio-app/gradio/pull/3221)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.18.0
-
-### New Features:
-
-###### Revamped Stop Button for Interfaces 🛑
-
-If your Interface function is a generator, there used to be a separate `Stop` button displayed next
-to the `Submit` button.
-
-We've revamed the `Submit` button so that it turns into a `Stop` button during the generation process.
-Clicking on the `Stop` button will cancel the generation and turn it back to a `Submit` button.
-The `Stop` button will automatically turn back to a `Submit` button at the end of the generation if you don't use it!
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3124](https://github.com/gradio-app/gradio/pull/3124)
-
-###### Queue now works with reload mode!
-
-You can now call `queue` on your `demo` outside of the `if __name__ == "__main__"` block and
-run the script in reload mode with the `gradio` command.
-
-Any changes to the `app.py` file will be reflected in the webpage automatically and the queue will work
-properly!
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3089](https://github.com/gradio-app/gradio/pull/3089)
-
-###### Allow serving files from additional directories
-
-```python
-demo = gr.Interface(...)
-demo.launch(
-  file_directories=["/var/lib/demo/path/to/resources"]
-)
-```
-
-By [@maxaudron](https://github.com/maxaudron) in [PR 3075](https://github.com/gradio-app/gradio/pull/3075)
-
-### Bug Fixes:
-
-- Fixes URL resolution on Windows by [@abidlabs](https://github.com/abidlabs) in [PR 3108](https://github.com/gradio-app/gradio/pull/3108)
-- Example caching now works with components without a label attribute (e.g. `Column`) by [@abidlabs](https://github.com/abidlabs) in [PR 3123](https://github.com/gradio-app/gradio/pull/3123)
-- Ensure the Video component correctly resets the UI state when a new video source is loaded and reduce choppiness of UI by [@pngwn](https://github.com/abidlabs) in [PR 3117](https://github.com/gradio-app/gradio/pull/3117)
-- Fixes loading private Spaces by [@abidlabs](https://github.com/abidlabs) in [PR 3068](https://github.com/gradio-app/gradio/pull/3068)
-- Added a warning when attempting to launch an `Interface` via the `%%blocks` jupyter notebook magic command by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3126](https://github.com/gradio-app/gradio/pull/3126)
-- Fixes bug where interactive output image cannot be set when in edit mode by [@dawoodkhan82](https://github.com/@dawoodkhan82) in [PR 3135](https://github.com/gradio-app/gradio/pull/3135)
-- A share link will automatically be created when running on Sagemaker notebooks so that the front-end is properly displayed by [@abidlabs](https://github.com/abidlabs) in [PR 3137](https://github.com/gradio-app/gradio/pull/3137)
-- Fixes a few dropdown component issues; hide checkmark next to options as expected, and keyboard hover is visible by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3145]https://github.com/gradio-app/gradio/pull/3145)
-- Fixed bug where example pagination buttons were not visible in dark mode or displayed under the examples table. By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3144](https://github.com/gradio-app/gradio/pull/3144)
-- Fixed bug where the font color of axis labels and titles for native plots did not respond to dark mode preferences. By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3146](https://github.com/gradio-app/gradio/pull/3146)
-
-### Documentation Changes:
-
-- Added a guide on the 4 kinds of Gradio Interfaces by [@yvrjsharma](https://github.com/yvrjsharma) and [@abidlabs](https://github.com/abidlabs) in [PR 3003](https://github.com/gradio-app/gradio/pull/3003)
-- Explained that the parameters in `launch` will not be respected when using reload mode, e.g. `gradio` command by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3089](https://github.com/gradio-app/gradio/pull/3089)
-- Added a demo to show how to set up variable numbers of outputs in Gradio by [@abidlabs](https://github.com/abidlabs) in [PR 3127](https://github.com/gradio-app/gradio/pull/3127)
-- Updated docs to reflect that the `equal_height` parameter should be passed to the `.style()` method of `gr.Row()` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3125](https://github.com/gradio-app/gradio/pull/3125)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Changed URL of final image for `fake_diffusion` demos by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3120](https://github.com/gradio-app/gradio/pull/3120)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.17.1
-
-### New Features:
-
-###### iOS image rotation fixed 🔄
-
-Previously photos uploaded via iOS would be rotated after processing. This has been fixed by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3089](https://github.com/gradio-app/gradio/pull/3091)
-
-######### Before
-
-![image](https://user-images.githubusercontent.com/41651716/215846507-a36e9d05-1ac2-4867-8ab3-ce045a9415d9.png)
-
-######### After
-
-![image](https://user-images.githubusercontent.com/41651716/215846554-e41773ed-70f0-491a-9952-6a18babf91ef.png)
-
-###### Run on Kaggle kernels 🧪
-
-A share link will automatically be created when running on Kaggle kernels (notebooks) so that the front-end is properly displayed.
-
-![image](https://user-images.githubusercontent.com/41651716/216104254-2cf55599-449c-436c-b57e-40f6a83f9eee.png)
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3101](https://github.com/gradio-app/gradio/pull/3101)
-
-### Bug Fixes:
-
-- Fix bug where examples were not rendered correctly for demos created with Blocks api that had multiple input compinents by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3090](https://github.com/gradio-app/gradio/pull/3090)
-- Fix change event listener for JSON, HighlightedText, Chatbot by [@aliabid94](https://github.com/aliabid94) in [PR 3095](https://github.com/gradio-app/gradio/pull/3095)
-- Fixes bug where video and file change event not working [@tomchang25](https://github.com/tomchang25) in [PR 3098](https://github.com/gradio-app/gradio/pull/3098)
-- Fixes bug where static_video play and pause event not working [@tomchang25](https://github.com/tomchang25) in [PR 3098](https://github.com/gradio-app/gradio/pull/3098)
-- Fixed `Gallery.style(grid=...)` by by [@aliabd](https://github.com/aliabd) in [PR 3107](https://github.com/gradio-app/gradio/pull/3107)
-
-### Documentation Changes:
-
-- Update chatbot guide to include blocks demo and markdown support section by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3023](https://github.com/gradio-app/gradio/pull/3023)
-
-* Fix a broken link in the Quick Start guide, by [@cakiki](https://github.com/cakiki) in [PR 3109](https://github.com/gradio-app/gradio/pull/3109)
-* Better docs navigation on mobile by [@aliabd](https://github.com/aliabd) in [PR 3112](https://github.com/gradio-app/gradio/pull/3112)
-* Add a guide on using Gradio with [Comet](https://comet.com/), by [@DN6](https://github.com/DN6/) in [PR 3058](https://github.com/gradio-app/gradio/pull/3058)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Set minimum `markdown-it-py` version to `2.0.0` so that the dollar math plugin is compatible by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3102](https://github.com/gradio-app/gradio/pull/3102)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.17.0
-
-### New Features:
-
-###### Extended support for Interface.load! 🏗️
-
-You can now load `image-to-text` and `conversational` pipelines from the hub!
-
-###### Image-to-text Demo
-
-```python
-io = gr.Interface.load("models/nlpconnect/vit-gpt2-image-captioning",
-                       api_key="<optional-api-key>")
-io.launch()
-```
-
-<img width="1087" alt="image" src="https://user-images.githubusercontent.com/41651716/213260197-dc5d80b4-6e50-4b3a-a764-94980930ac38.png">
-
-###### conversational Demo
-
-```python
-chatbot = gr.Interface.load("models/microsoft/DialoGPT-medium",
-                           api_key="<optional-api-key>")
-chatbot.launch()
-```
-
-![chatbot_load](https://user-images.githubusercontent.com/41651716/213260220-3eaa25b7-a38b-48c6-adeb-2718bdf297a2.gif)
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3011](https://github.com/gradio-app/gradio/pull/3011)
-
-###### Download Button added to Model3D Output Component 📥
-
-No need for an additional file output component to enable model3d file downloads anymore. We now added a download button to the model3d component itself.
-
-<img width="739" alt="Screenshot 2023-01-18 at 3 52 45 PM" src="https://user-images.githubusercontent.com/12725292/213294198-5f4fda35-bde7-450c-864f-d5683e7fa29a.png">
-
-By [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3014](https://github.com/gradio-app/gradio/pull/3014)
-
-###### Fixing Auth on Spaces 🔑
-
-Authentication on spaces works now! Third party cookies must be enabled on your browser to be able
-to log in. Some browsers disable third party cookies by default (Safari, Chrome Incognito).
-
-![auth_spaces](https://user-images.githubusercontent.com/41651716/215528417-09538933-0576-4d1d-b3b9-1e877ab01905.gif)
-
-### Bug Fixes:
-
-- Fixes bug where interpretation event was not configured correctly by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2993](https://github.com/gradio-app/gradio/pull/2993)
-- Fix relative import bug in reload mode by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2992](https://github.com/gradio-app/gradio/pull/2992)
-- Fixes bug where png files were not being recognized when uploading images by [@abidlabs](https://github.com/abidlabs) in [PR 3002](https://github.com/gradio-app/gradio/pull/3002)
-- Fixes bug where external Spaces could not be loaded and used as functions if they returned files by [@abidlabs](https://github.com/abidlabs) in [PR 3004](https://github.com/gradio-app/gradio/pull/3004)
-- Fix bug where file serialization output was not JSON serializable by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2999](https://github.com/gradio-app/gradio/pull/2999)
-- Fixes bug where png files were not being recognized when uploading images by [@abidlabs](https://github.com/abidlabs) in [PR 3002](https://github.com/gradio-app/gradio/pull/3002)
-- Fixes bug where temporary uploaded files were not being added to temp sets by [@abidlabs](https://github.com/abidlabs) in [PR 3005](https://github.com/gradio-app/gradio/pull/3005)
-- Fixes issue where markdown support in chatbot breaks older demos [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3006](https://github.com/gradio-app/gradio/pull/3006)
-- Fixes the `/file/` route that was broken in a recent change in [PR 3010](https://github.com/gradio-app/gradio/pull/3010)
-- Fix bug where the Image component could not serialize image urls by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2957](https://github.com/gradio-app/gradio/pull/2957)
-- Fix forwarding for guides after SEO renaming by [@aliabd](https://github.com/aliabd) in [PR 3017](https://github.com/gradio-app/gradio/pull/3017)
-- Switch all pages on the website to use latest stable gradio by [@aliabd](https://github.com/aliabd) in [PR 3016](https://github.com/gradio-app/gradio/pull/3016)
-- Fix bug related to deprecated parameters in `huggingface_hub` for the HuggingFaceDatasetSaver in [PR 3025](https://github.com/gradio-app/gradio/pull/3025)
-- Added better support for symlinks in the way absolute paths are resolved by [@abidlabs](https://github.com/abidlabs) in [PR 3037](https://github.com/gradio-app/gradio/pull/3037)
-- Fix several minor frontend bugs (loading animation, examples as gallery) frontend [@aliabid94](https://github.com/3026) in [PR 2961](https://github.com/gradio-app/gradio/pull/3026).
-- Fixes bug that the chatbot sample code does not work with certain input value by [@petrov826](https://github.com/petrov826) in [PR 3039](https://github.com/gradio-app/gradio/pull/3039).
-- Fix shadows for form element and ensure focus styles more visible in dark mode [@pngwn](https://github.com/pngwn) in [PR 3042](https://github.com/gradio-app/gradio/pull/3042).
-- Fixed bug where the Checkbox and Dropdown change events were not triggered in response to other component changes by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3045](https://github.com/gradio-app/gradio/pull/3045)
-- Fix bug where the queue was not properly restarted after launching a `closed` app by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3022](https://github.com/gradio-app/gradio/pull/3022)
-- Adding missing embedded components on docs by [@aliabd](https://github.com/aliabd) in [PR 3027](https://github.com/gradio-app/gradio/pull/3027)
-- Fixes bug where app would crash if the `file_types` parameter of `gr.File` or `gr.UploadButton` was not a list by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3048](https://github.com/gradio-app/gradio/pull/3048)
-- Ensure CSS mounts correctly regardless of how many Gradio instances are on the page [@pngwn](https://github.com/pngwn) in [PR 3059](https://github.com/gradio-app/gradio/pull/3059).
-- Fix bug where input component was not hidden in the frontend for `UploadButton` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3053](https://github.com/gradio-app/gradio/pull/3053)
-- Fixes issue where after clicking submit or undo, the sketch output wouldn't clear. [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 3047](https://github.com/gradio-app/gradio/pull/3047)
-- Ensure spaces embedded via the web component always use the correct URLs for server requests and change ports for testing to avoid strange collisions when users are working with embedded apps locally by [@pngwn](https://github.com/pngwn) in [PR 3065](https://github.com/gradio-app/gradio/pull/3065)
-- Preserve selected image of Gallery through updated by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3061](https://github.com/gradio-app/gradio/pull/3061)
-- Fix bug where auth was not respected on HF spaces by [@freddyaboulton](https://github.com/freddyaboulton) and [@aliabid94](https://github.com/aliabid94) in [PR 3049](https://github.com/gradio-app/gradio/pull/3049)
-- Fixes bug where tabs selected attribute not working if manually change tab by [@tomchang25](https://github.com/tomchang25) in [3055](https://github.com/gradio-app/gradio/pull/3055)
-- Change chatbot to show dots on progress, and fix bug where chatbot would not stick to bottom in the case of images by [@aliabid94](https://github.com/aliabid94) in [PR 3067](https://github.com/gradio-app/gradio/pull/3079)
-
-### Documentation Changes:
-
-- SEO improvements to guides by[@aliabd](https://github.com/aliabd) in [PR 2915](https://github.com/gradio-app/gradio/pull/2915)
-- Use `gr.LinePlot` for the `blocks_kinematics` demo by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2998](https://github.com/gradio-app/gradio/pull/2998)
-- Updated the `interface_series_load` to include some inline markdown code by [@abidlabs](https://github.com/abidlabs) in [PR 3051](https://github.com/gradio-app/gradio/pull/3051)
-
-### Testing and Infrastructure Changes:
-
-- Adds a GitHub action to test if any large files (> 5MB) are present by [@abidlabs](https://github.com/abidlabs) in [PR 3013](https://github.com/gradio-app/gradio/pull/3013)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Rewrote frontend using CSS variables for themes by [@pngwn](https://github.com/pngwn) in [PR 2840](https://github.com/gradio-app/gradio/pull/2840)
-- Moved telemetry requests to run on background threads by [@abidlabs](https://github.com/abidlabs) in [PR 3054](https://github.com/gradio-app/gradio/pull/3054)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.16.2
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Fixed file upload fails for files with zero size by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2923](https://github.com/gradio-app/gradio/pull/2923)
-- Fixed bug where `mount_gradio_app` would not launch if the queue was enabled in a gradio app by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2939](https://github.com/gradio-app/gradio/pull/2939)
-- Fix custom long CSS handling in Blocks by [@anton-l](https://github.com/anton-l) in [PR 2953](https://github.com/gradio-app/gradio/pull/2953)
-- Recovers the dropdown change event by [@abidlabs](https://github.com/abidlabs) in [PR 2954](https://github.com/gradio-app/gradio/pull/2954).
-- Fix audio file output by [@aliabid94](https://github.com/aliabid94) in [PR 2961](https://github.com/gradio-app/gradio/pull/2961).
-- Fixed bug where file extensions of really long files were not kept after download by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2929](https://github.com/gradio-app/gradio/pull/2929)
-- Fix bug where outputs for examples where not being returned by the backend by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2955](https://github.com/gradio-app/gradio/pull/2955)
-- Fix bug in `blocks_plug` demo that prevented switching tabs programmatically with python [@TashaSkyUp](https://github.com/https://github.com/TashaSkyUp) in [PR 2971](https://github.com/gradio-app/gradio/pull/2971).
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.16.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Fix audio file output by [@aliabid94](https://github.com/aliabid94) in [PR 2950](https://github.com/gradio-app/gradio/pull/2950).
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.16.0
-
-### New Features:
-
-###### Send custom progress updates by adding a `gr.Progress` argument after the input arguments to any function. Example:
-
-```python
-def reverse(word, progress=gr.Progress()):
-    progress(0, desc="Starting")
-    time.sleep(1)
-    new_string = ""
-    for letter in progress.tqdm(word, desc="Reversing"):
-        time.sleep(0.25)
-        new_string = letter + new_string
-    return new_string
-
-demo = gr.Interface(reverse, gr.Text(), gr.Text())
-```
-
-Progress indicator bar by [@aliabid94](https://github.com/aliabid94) in [PR 2750](https://github.com/gradio-app/gradio/pull/2750).
-
-- Added `title` argument to `TabbedInterface` by @MohamedAliRashad in [#2888](https://github.com/gradio-app/gradio/pull/2888)
-- Add support for specifying file extensions for `gr.File` and `gr.UploadButton`, using `file_types` parameter (e.g `gr.File(file_count="multiple", file_types=["text", ".json", ".csv"])`) by @dawoodkhan82 in [#2901](https://github.com/gradio-app/gradio/pull/2901)
-- Added `multiselect` option to `Dropdown` by @dawoodkhan82 in [#2871](https://github.com/gradio-app/gradio/pull/2871)
-
-###### With `multiselect` set to `true` a user can now select multiple options from the `gr.Dropdown` component.
-
-```python
-gr.Dropdown(["angola", "pakistan", "canada"], multiselect=True, value=["angola"])
-```
-
-<img width="610" alt="Screenshot 2023-01-03 at 4 14 36 PM" src="https://user-images.githubusercontent.com/12725292/210442547-c86975c9-4b4f-4b8e-8803-9d96e6a8583a.png">
-
-### Bug Fixes:
-
-- Fixed bug where an error opening an audio file led to a crash by [@FelixDombek](https://github.com/FelixDombek) in [PR 2898](https://github.com/gradio-app/gradio/pull/2898)
-- Fixed bug where setting `default_enabled=False` made it so that the entire queue did not start by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2876](https://github.com/gradio-app/gradio/pull/2876)
-- Fixed bug where csv preview for DataFrame examples would show filename instead of file contents by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2877](https://github.com/gradio-app/gradio/pull/2877)
-- Fixed bug where an error raised after yielding iterative output would not be displayed in the browser by
-  [@JaySmithWpg](https://github.com/JaySmithWpg) in [PR 2889](https://github.com/gradio-app/gradio/pull/2889)
-- Fixed bug in `blocks_style` demo that was preventing it from launching by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2890](https://github.com/gradio-app/gradio/pull/2890)
-- Fixed bug where files could not be downloaded by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2926](https://github.com/gradio-app/gradio/pull/2926)
-- Fixed bug where cached examples were not displaying properly by [@a-rogalska](https://github.com/a-rogalska) in [PR 2974](https://github.com/gradio-app/gradio/pull/2974)
-
-### Documentation Changes:
-
-- Added a Guide on using Google Sheets to create a real-time dashboard with Gradio's `DataFrame` and `LinePlot` component, by [@abidlabs](https://github.com/abidlabs) in [PR 2816](https://github.com/gradio-app/gradio/pull/2816)
-- Add a components - events matrix on the docs by [@aliabd](https://github.com/aliabd) in [PR 2921](https://github.com/gradio-app/gradio/pull/2921)
-
-### Testing and Infrastructure Changes:
-
-- Deployed PRs from forks to spaces by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2895](https://github.com/gradio-app/gradio/pull/2895)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- The `default_enabled` parameter of the `Blocks.queue` method has no effect by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2876](https://github.com/gradio-app/gradio/pull/2876)
-- Added typing to several Python files in codebase by [@abidlabs](https://github.com/abidlabs) in [PR 2887](https://github.com/gradio-app/gradio/pull/2887)
-- Excluding untracked files from demo notebook check action by [@aliabd](https://github.com/aliabd) in [PR 2897](https://github.com/gradio-app/gradio/pull/2897)
-- Optimize images and gifs by [@aliabd](https://github.com/aliabd) in [PR 2922](https://github.com/gradio-app/gradio/pull/2922)
-- Updated typing by [@1nF0rmed](https://github.com/1nF0rmed) in [PR 2904](https://github.com/gradio-app/gradio/pull/2904)
-
-### Contributors Shoutout:
-
-- @JaySmithWpg for making their first contribution to gradio!
-- @MohamedAliRashad for making their first contribution to gradio!
-
-## 3.15.0
-
-### New Features:
-
-Gradio's newest plotting component `gr.LinePlot`! 📈
-
-With this component you can easily create time series visualizations with customizable
-appearance for your demos and dashboards ... all without having to know an external plotting library.
-
-For an example of the api see below:
-
-```python
-gr.LinePlot(stocks,
-            x="date",
-            y="price",
-            color="symbol",
-            color_legend_position="bottom",
-            width=600, height=400, title="Stock Prices")
-```
-
-![image](https://user-images.githubusercontent.com/41651716/208711646-81ae3745-149b-46a3-babd-0569aecdd409.png)
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2807](https://github.com/gradio-app/gradio/pull/2807)
-
-### Bug Fixes:
-
-- Fixed bug where the `examples_per_page` parameter of the `Examples` component was not passed to the internal `Dataset` component by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2861](https://github.com/gradio-app/gradio/pull/2861)
-- Fixes loading Spaces that have components with default values by [@abidlabs](https://github.com/abidlabs) in [PR 2855](https://github.com/gradio-app/gradio/pull/2855)
-- Fixes flagging when `allow_flagging="auto"` in `gr.Interface()` by [@abidlabs](https://github.com/abidlabs) in [PR 2695](https://github.com/gradio-app/gradio/pull/2695)
-- Fixed bug where passing a non-list value to `gr.CheckboxGroup` would crash the entire app by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2866](https://github.com/gradio-app/gradio/pull/2866)
-
-### Documentation Changes:
-
-- Added a Guide on using BigQuery with Gradio's `DataFrame` and `ScatterPlot` component,
-  by [@abidlabs](https://github.com/abidlabs) in [PR 2794](https://github.com/gradio-app/gradio/pull/2794)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Fixed importing gradio can cause PIL.Image.registered_extensions() to break by `[@aliencaocao](https://github.com/aliencaocao)` in `[PR 2846](https://github.com/gradio-app/gradio/pull/2846)`
-- Fix css glitch and navigation in docs by [@aliabd](https://github.com/aliabd) in [PR 2856](https://github.com/gradio-app/gradio/pull/2856)
-- Added the ability to set `x_lim`, `y_lim` and legend positions for `gr.ScatterPlot` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2807](https://github.com/gradio-app/gradio/pull/2807)
-- Remove footers and min-height the correct way by [@aliabd](https://github.com/aliabd) in [PR 2860](https://github.com/gradio-app/gradio/pull/2860)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.14.0
-
-### New Features:
-
-###### Add Waveform Visual Support to Audio
-
-Adds a `gr.make_waveform()` function that creates a waveform video by combining an audio and an optional background image by [@dawoodkhan82](http://github.com/dawoodkhan82) and [@aliabid94](http://github.com/aliabid94) in [PR 2706](https://github.com/gradio-app/gradio/pull/2706. Helpful for making audio outputs much more shareable.
-
-![waveform screenrecording](https://user-images.githubusercontent.com/7870876/206062396-164a5e71-451a-4fe0-94a7-cbe9269d57e6.gif)
-
-###### Allows Every Component to Accept an `every` Parameter
-
-When a component's initial value is a function, the `every` parameter re-runs the function every `every` seconds. By [@abidlabs](https://github.com/abidlabs) in [PR 2806](https://github.com/gradio-app/gradio/pull/2806). Here's a code example:
-
-```py
-import gradio as gr
-
-with gr.Blocks() as demo:
-    df = gr.DataFrame(run_query, every=60*60)
-
-demo.queue().launch()
-```
-
-### Bug Fixes:
-
-- Fixed issue where too many temporary files were created, all with randomly generated
-  filepaths. Now fewer temporary files are created and are assigned a path that is a
-  hash based on the file contents by [@abidlabs](https://github.com/abidlabs) in [PR 2758](https://github.com/gradio-app/gradio/pull/2758)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.13.2
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-\*No changes to highlight.
-
--
-
-### Documentation Changes:
-
-- Improves documentation of several queuing-related parameters by [@abidlabs](https://github.com/abidlabs) in [PR 2825](https://github.com/gradio-app/gradio/pull/2825)
-
-### Testing and Infrastructure Changes:
-
-- Remove h11 pinning by [@ecederstrand](https://github.com/ecederstrand) in [PR 2820](https://github.com/gradio-app/gradio/pull/2820)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-No changes to highlight.
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.13.1
-
-### New Features:
-
-###### New Shareable Links
-
-Replaces tunneling logic based on ssh port-forwarding to that based on `frp` by [XciD](https://github.com/XciD) and [Wauplin](https://github.com/Wauplin) in [PR 2509](https://github.com/gradio-app/gradio/pull/2509)
-
-You don't need to do anything differently, but when you set `share=True` in `launch()`,
-you'll get this message and a public link that look a little bit different:
-
-```bash
-Setting up a public link... we have recently upgraded the way public links are generated. If you encounter any problems, please downgrade to gradio version 3.13.0
-.
-Running on public URL: https://bec81a83-5b5c-471e.gradio.live
-```
-
-These links are a more secure and scalable way to create shareable demos!
-
-### Bug Fixes:
-
-- Allows `gr.Dataframe()` to take a `pandas.DataFrame` that includes numpy array and other types as its initial value, by [@abidlabs](https://github.com/abidlabs) in [PR 2804](https://github.com/gradio-app/gradio/pull/2804)
-- Add `altair` to requirements.txt by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2811](https://github.com/gradio-app/gradio/pull/2811)
-- Added aria-labels to icon buttons that are built into UI components by [@emilyuhde](http://github.com/emilyuhde) in [PR 2791](https://github.com/gradio-app/gradio/pull/2791)
-
-### Documentation Changes:
-
-- Fixed some typos in the "Plot Component for Maps" guide by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2811](https://github.com/gradio-app/gradio/pull/2811)
-
-### Testing and Infrastructure Changes:
-
-- Fixed test for IP address by [@abidlabs](https://github.com/abidlabs) in [PR 2808](https://github.com/gradio-app/gradio/pull/2808)
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Fixed typo in parameter `visible` in classes in `templates.py` by [@abidlabs](https://github.com/abidlabs) in [PR 2805](https://github.com/gradio-app/gradio/pull/2805)
-- Switched external service for getting IP address from `https://api.ipify.org` to `https://checkip.amazonaws.com/` by [@abidlabs](https://github.com/abidlabs) in [PR 2810](https://github.com/gradio-app/gradio/pull/2810)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-- Fixed typo in parameter `visible` in classes in `templates.py` by [@abidlabs](https://github.com/abidlabs) in [PR 2805](https://github.com/gradio-app/gradio/pull/2805)
-- Switched external service for getting IP address from `https://api.ipify.org` to `https://checkip.amazonaws.com/` by [@abidlabs](https://github.com/abidlabs) in [PR 2810](https://github.com/gradio-app/gradio/pull/2810)
-
-## 3.13.0
-
-### New Features:
-
-###### Scatter plot component
-
-It is now possible to create a scatter plot natively in Gradio!
-
-The `gr.ScatterPlot` component accepts a pandas dataframe and some optional configuration parameters
-and will automatically create a plot for you!
-
-This is the first of many native plotting components in Gradio!
-
-For an example of how to use `gr.ScatterPlot` see below:
-
-```python
-import gradio as gr
-from vega_datasets import data
-
-cars = data.cars()
-
-with gr.Blocks() as demo:
-    gr.ScatterPlot(show_label=False,
-                   value=cars,
-                   x="Horsepower",
-                   y="Miles_per_Gallon",
-                   color="Origin",
-                   tooltip="Name",
-                   title="Car Data",
-                   y_title="Miles per Gallon",
-                   color_legend_title="Origin of Car").style(container=False)
-
-demo.launch()
-```
-
-<img width="404" alt="image" src="https://user-images.githubusercontent.com/41651716/206737726-4c4da5f0-dee8-4f0a-b1e1-e2b75c4638e9.png">
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2764](https://github.com/gradio-app/gradio/pull/2764)
-
-###### Support for altair plots
-
-The `Plot` component can now accept altair plots as values!
-Simply return an altair plot from your event listener and gradio will display it in the front-end.
-See the example below:
-
-```python
-import gradio as gr
-import altair as alt
-from vega_datasets import data
-
-cars = data.cars()
-chart = (
-    alt.Chart(cars)
-    .mark_point()
-    .encode(
-        x="Horsepower",
-        y="Miles_per_Gallon",
-        color="Origin",
-    )
-)
-
-with gr.Blocks() as demo:
-    gr.Plot(value=chart)
-demo.launch()
-```
-
-<img width="1366" alt="image" src="https://user-images.githubusercontent.com/41651716/204660697-f994316f-5ca7-4e8a-93bc-eb5e0d556c91.png">
-
-By [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2741](https://github.com/gradio-app/gradio/pull/2741)
-
-###### Set the background color of a Label component
-
-The `Label` component now accepts a `color` argument by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2736](https://github.com/gradio-app/gradio/pull/2736).
-The `color` argument should either be a valid css color name or hexadecimal string.
-You can update the color with `gr.Label.update`!
-
-This lets you create Alert and Warning boxes with the `Label` component. See below:
-
-```python
-import gradio as gr
-import random
-
-def update_color(value):
-    if value < 0:
-        # This is bad so use red
-        return "#FF0000"
-    elif 0 <= value <= 20:
-        # Ok but pay attention (use orange)
-        return "#ff9966"
-    else:
-        # Nothing to worry about
-        return None
-
-def update_value():
-    choice = random.choice(['good', 'bad', 'so-so'])
-    color = update_color(choice)
-    return gr.Label.update(value=choice, color=color)
-
-
-with gr.Blocks() as demo:
-    label = gr.Label(value=-10)
-    demo.load(lambda: update_value(), inputs=None, outputs=[label], every=1)
-demo.queue().launch()
-```
-
-![label_bg_color_update](https://user-images.githubusercontent.com/41651716/204400372-80e53857-f26f-4a38-a1ae-1acadff75e89.gif)
-
-###### Add Brazilian Portuguese translation
-
-Add Brazilian Portuguese translation (pt-BR.json) by [@pstwh](http://github.com/pstwh) in [PR 2753](https://github.com/gradio-app/gradio/pull/2753):
-
-<img width="951" alt="image" src="https://user-images.githubusercontent.com/1778297/206615305-4c52031e-3f7d-4df2-8805-a79894206911.png">
-
-### Bug Fixes:
-
-- Fixed issue where image thumbnails were not showing when an example directory was provided
-  by [@abidlabs](https://github.com/abidlabs) in [PR 2745](https://github.com/gradio-app/gradio/pull/2745)
-- Fixed bug loading audio input models from the hub by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2779](https://github.com/gradio-app/gradio/pull/2779).
-- Fixed issue where entities were not merged when highlighted text was generated from the
-  dictionary inputs [@payoto](https://github.com/payoto) in [PR 2767](https://github.com/gradio-app/gradio/pull/2767)
-- Fixed bug where generating events did not finish running even if the websocket connection was closed by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2783](https://github.com/gradio-app/gradio/pull/2783).
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Images in the chatbot component are now resized if they exceed a max width by [@abidlabs](https://github.com/abidlabs) in [PR 2748](https://github.com/gradio-app/gradio/pull/2748)
-- Missing parameters have been added to `gr.Blocks().load()` by [@abidlabs](https://github.com/abidlabs) in [PR 2755](https://github.com/gradio-app/gradio/pull/2755)
-- Deindex share URLs from search by [@aliabd](https://github.com/aliabd) in [PR 2772](https://github.com/gradio-app/gradio/pull/2772)
-- Redirect old links and fix broken ones by [@aliabd](https://github.com/aliabd) in [PR 2774](https://github.com/gradio-app/gradio/pull/2774)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.12.0
-
-### New Features:
-
-###### The `Chatbot` component now supports a subset of Markdown (including bold, italics, code, images)
-
-You can now pass in some Markdown to the Chatbot component and it will show up,
-meaning that you can pass in images as well! by [@abidlabs](https://github.com/abidlabs) in [PR 2731](https://github.com/gradio-app/gradio/pull/2731)
-
-Here's a simple example that references a local image `lion.jpg` that is in the same
-folder as the Python script:
-
-```py
-import gradio as gr
-
-with gr.Blocks() as demo:
-    gr.Chatbot([("hi", "hello **abubakar**"), ("![](/file=lion.jpg)", "cool pic")])
-
-demo.launch()
-```
-
-![Alt text](https://user-images.githubusercontent.com/1778297/204357455-5c1a4002-eee7-479d-9a1e-ba2c12522723.png)
-
-To see a more realistic example, see the new demo `/demo/chatbot_multimodal/run.py`.
-
-###### Latex support
-
-Added mathtext (a subset of latex) support to gr.Markdown. Added by [@kashif](https://github.com/kashif) and [@aliabid94](https://github.com/aliabid94) in [PR 2696](https://github.com/gradio-app/gradio/pull/2696).
-
-Example of how it can be used:
-
-```python
-gr.Markdown(
-    r"""
-    # Hello World! $\frac{\sqrt{x + y}}{4}$ is today's lesson.
-    """)
-```
-
-###### Update Accordion properties from the backend
-
-You can now update the Accordion `label` and `open` status with `gr.Accordion.update` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2690](https://github.com/gradio-app/gradio/pull/2690)
-
-```python
-import gradio as gr
-
-with gr.Blocks() as demo:
-    with gr.Accordion(label="Open for greeting", open=False) as accordion:
-        gr.Textbox("Hello!")
-    open_btn = gr.Button(value="Open Accordion")
-    close_btn = gr.Button(value="Close Accordion")
-    open_btn.click(
-        lambda: gr.Accordion.update(open=True, label="Open Accordion"),
-        inputs=None,
-        outputs=[accordion],
-    )
-    close_btn.click(
-        lambda: gr.Accordion.update(open=False, label="Closed Accordion"),
-        inputs=None,
-        outputs=[accordion],
-    )
-demo.launch()
-```
-
-![update_accordion](https://user-images.githubusercontent.com/41651716/203164176-b102eae3-babe-4986-ae30-3ab4f400cedc.gif)
-
-### Bug Fixes:
-
-- Fixed bug where requests timeout is missing from utils.version_check() by [@yujiehecs](https://github.com/yujiehecs) in [PR 2729](https://github.com/gradio-app/gradio/pull/2729)
-- Fixed bug where so that the `File` component can properly preprocess files to "binary" byte-string format by [CoffeeVampir3](https://github.com/CoffeeVampir3) in [PR 2727](https://github.com/gradio-app/gradio/pull/2727)
-- Fixed bug to ensure that filenames are less than 200 characters even for non-English languages by [@SkyTNT](https://github.com/SkyTNT) in [PR 2685](https://github.com/gradio-app/gradio/pull/2685)
-
-### Documentation Changes:
-
-- Performance improvements to docs on mobile by [@aliabd](https://github.com/aliabd) in [PR 2730](https://github.com/gradio-app/gradio/pull/2730)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Make try examples button more prominent by [@aliabd](https://github.com/aliabd) in [PR 2705](https://github.com/gradio-app/gradio/pull/2705)
-- Fix id clashes in docs by [@aliabd](https://github.com/aliabd) in [PR 2713](https://github.com/gradio-app/gradio/pull/2713)
-- Fix typos in guide docs by [@andridns](https://github.com/andridns) in [PR 2722](https://github.com/gradio-app/gradio/pull/2722)
-- Add option to `include_audio` in Video component. When `True`, for `source="webcam"` this will record audio and video, for `source="upload"` this will retain the audio in an uploaded video by [@mandargogate](https://github.com/MandarGogate) in [PR 2721](https://github.com/gradio-app/gradio/pull/2721)
-
-### Contributors Shoutout:
-
-- [@andridns](https://github.com/andridns) made their first contribution in [PR 2722](https://github.com/gradio-app/gradio/pull/2722)!
-
-## 3.11.0
-
-### New Features:
-
-###### Upload Button
-
-There is now a new component called the `UploadButton` which is a file upload component but in button form! You can also specify what file types it should accept in the form of a list (ex: `image`, `video`, `audio`, `text`, or generic `file`). Added by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2591](https://github.com/gradio-app/gradio/pull/2591).
-
-Example of how it can be used:
-
-```python
-import gradio as gr
-
-def upload_file(files):
-    file_paths = [file.name for file in files]
-    return file_paths
-
-with gr.Blocks() as demo:
-    file_output = gr.File()
-    upload_button = gr.UploadButton("Click to Upload a File", file_types=["image", "video"], file_count="multiple")
-    upload_button.upload(upload_file, upload_button, file_output)
-
-demo.launch()
-```
-
-###### Revamped API documentation page
-
-New API Docs page with in-browser playground and updated aesthetics. [@gary149](https://github.com/gary149) in [PR 2652](https://github.com/gradio-app/gradio/pull/2652)
-
-###### Revamped Login page
-
-Previously our login page had its own CSS, had no dark mode, and had an ugly json message on the wrong credentials. Made the page more aesthetically consistent, added dark mode support, and a nicer error message. [@aliabid94](https://github.com/aliabid94) in [PR 2684](https://github.com/gradio-app/gradio/pull/2684)
-
-###### Accessing the Requests Object Directly
-
-You can now access the Request object directly in your Python function by [@abidlabs](https://github.com/abidlabs) in [PR 2641](https://github.com/gradio-app/gradio/pull/2641). This means that you can access request headers, the client IP address, and so on. In order to use it, add a parameter to your function and set its type hint to be `gr.Request`. Here's a simple example:
-
-```py
-import gradio as gr
-
-def echo(name, request: gr.Request):
-    if request:
-        print("Request headers dictionary:", request.headers)
-        print("IP address:", request.client.host)
-    return name
-
-io = gr.Interface(echo, "textbox", "textbox").launch()
-```
-
-### Bug Fixes:
-
-- Fixed bug that limited files from being sent over websockets to 16MB. The new limit
-  is now 1GB by [@abidlabs](https://github.com/abidlabs) in [PR 2709](https://github.com/gradio-app/gradio/pull/2709)
-
-### Documentation Changes:
-
-- Updated documentation for embedding Gradio demos on Spaces as web components by
-  [@julien-c](https://github.com/julien-c) in [PR 2698](https://github.com/gradio-app/gradio/pull/2698)
-- Updated IFrames in Guides to use the host URL instead of the Space name to be consistent with the new method for embedding Spaces, by
-  [@julien-c](https://github.com/julien-c) in [PR 2692](https://github.com/gradio-app/gradio/pull/2692)
-- Colab buttons on every demo in the website! Just click open in colab, and run the demo there.
-
-https://user-images.githubusercontent.com/9021060/202878400-cb16ed47-f4dd-4cb0-b2f0-102a9ff64135.mov
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Better warnings and error messages for `gr.Interface.load()` by [@abidlabs](https://github.com/abidlabs) in [PR 2694](https://github.com/gradio-app/gradio/pull/2694)
-- Add open in colab buttons to demos in docs and /demos by [@aliabd](https://github.com/aliabd) in [PR 2608](https://github.com/gradio-app/gradio/pull/2608)
-- Apply different formatting for the types in component docstrings by [@aliabd](https://github.com/aliabd) in [PR 2707](https://github.com/gradio-app/gradio/pull/2707)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.10.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Passes kwargs into `gr.Interface.load()` by [@abidlabs](https://github.com/abidlabs) in [PR 2669](https://github.com/gradio-app/gradio/pull/2669)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Clean up printed statements in Embedded Colab Mode by [@aliabid94](https://github.com/aliabid94) in [PR 2612](https://github.com/gradio-app/gradio/pull/2612)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.10.0
-
-- Add support for `'password'` and `'email'` types to `Textbox`. [@pngwn](https://github.com/pngwn) in [PR 2653](https://github.com/gradio-app/gradio/pull/2653)
-- `gr.Textbox` component will now raise an exception if `type` is not "text", "email", or "password" [@pngwn](https://github.com/pngwn) in [PR 2653](https://github.com/gradio-app/gradio/pull/2653). This will cause demos using the deprecated `gr.Textbox(type="number")` to raise an exception.
-
-### Bug Fixes:
-
-- Updated the minimum FastApi used in tests to version 0.87 by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2647](https://github.com/gradio-app/gradio/pull/2647)
-- Fixed bug where interfaces with examples could not be loaded with `gr.Interface.load` by [@freddyaboulton](https://github.com/freddyaboulton) [PR 2640](https://github.com/gradio-app/gradio/pull/2640)
-- Fixed bug where the `interactive` property of a component could not be updated by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2639](https://github.com/gradio-app/gradio/pull/2639)
-- Fixed bug where some URLs were not being recognized as valid URLs and thus were not
-  loading correctly in various components by [@abidlabs](https://github.com/abidlabs) in [PR 2659](https://github.com/gradio-app/gradio/pull/2659)
-
-### Documentation Changes:
-
-- Fix some typos in the embedded demo names in "05_using_blocks_like_functions.md" by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2656](https://github.com/gradio-app/gradio/pull/2656)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Add support for `'password'` and `'email'` types to `Textbox`. [@pngwn](https://github.com/pngwn) in [PR 2653](https://github.com/gradio-app/gradio/pull/2653)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.9.1
-
-### New Features:
-
-No changes to highlight.
-
-### Bug Fixes:
-
-- Only set a min height on md and html when loading by [@pngwn](https://github.com/pngwn) in [PR 2623](https://github.com/gradio-app/gradio/pull/2623)
-
-### Documentation Changes:
-
-- See docs for the latest gradio commit to main as well the latest pip release:
-
-![main-vs-pip](https://user-images.githubusercontent.com/9021060/199607887-aab1ae4e-a070-4527-966d-024397abe15b.gif)
-
-- Modified the "Connecting To a Database Guide" to use `pd.read_sql` as opposed to low-level postgres connector by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2604](https://github.com/gradio-app/gradio/pull/2604)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Dropdown for seeing docs as latest or main by [@aliabd](https://github.com/aliabd) in [PR 2544](https://github.com/gradio-app/gradio/pull/2544)
-- Allow `gr.Templates` to accept parameters to override the defaults by [@abidlabs](https://github.com/abidlabs) in [PR 2600](https://github.com/gradio-app/gradio/pull/2600)
-- Components now throw a `ValueError()` if constructed with invalid parameters for `type` or `source` (for components that take those parameters) in [PR 2610](https://github.com/gradio-app/gradio/pull/2610)
-- Allow auth with using queue by [@GLGDLY](https://github.com/GLGDLY) in [PR 2611](https://github.com/gradio-app/gradio/pull/2611)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.9
-
-### New Features:
-
-- Gradio is now embedded directly in colab without requiring the share link by [@aliabid94](https://github.com/aliabid94) in [PR 2455](https://github.com/gradio-app/gradio/pull/2455)
-
-###### Calling functions by api_name in loaded apps
-
-When you load an upstream app with `gr.Blocks.load`, you can now specify which fn
-to call with the `api_name` parameter.
-
-```python
-import gradio as gr
-english_translator = gr.Blocks.load(name="spaces/gradio/english-translator")
-german = english_translator("My name is Freddy", api_name='translate-to-german')
-```
-
-The `api_name` parameter will take precedence over the `fn_index` parameter.
-
-### Bug Fixes:
-
-- Fixed bug where None could not be used for File,Model3D, and Audio examples by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2588](https://github.com/gradio-app/gradio/pull/2588)
-- Fixed links in Plotly map guide + demo by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2578](https://github.com/gradio-app/gradio/pull/2578)
-- `gr.Blocks.load()` now correctly loads example files from Spaces [@abidlabs](https://github.com/abidlabs) in [PR 2594](https://github.com/gradio-app/gradio/pull/2594)
-- Fixed bug when image clear started upload dialog [@mezotaken](https://github.com/mezotaken) in [PR 2577](https://github.com/gradio-app/gradio/pull/2577)
-
-### Documentation Changes:
-
-- Added a Guide on how to configure the queue for maximum performance by [@abidlabs](https://github.com/abidlabs) in [PR 2558](https://github.com/gradio-app/gradio/pull/2558)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Add `api_name` to `Blocks.__call__` by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2593](https://github.com/gradio-app/gradio/pull/2593)
-- Update queue with using deque & update requirements by [@GLGDLY](https://github.com/GLGDLY) in [PR 2428](https://github.com/gradio-app/gradio/pull/2428)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.8.2
-
-### Bug Fixes:
-
-- Ensure gradio apps embedded via spaces use the correct endpoint for predictions. [@pngwn](https://github.com/pngwn) in [PR 2567](https://github.com/gradio-app/gradio/pull/2567)
-- Ensure gradio apps embedded via spaces use the correct websocket protocol. [@pngwn](https://github.com/pngwn) in [PR 2571](https://github.com/gradio-app/gradio/pull/2571)
-
-### New Features:
-
-###### Running Events Continuously
-
-Gradio now supports the ability to run an event continuously on a fixed schedule. To use this feature,
-pass `every=# of seconds` to the event definition. This will run the event every given number of seconds!
-
-This can be used to:
-
-- Create live visualizations that show the most up to date data
-- Refresh the state of the frontend automatically in response to changes in the backend
-
-Here is an example of a live plot that refreshes every half second:
-
-```python
-import math
-import gradio as gr
-import plotly.express as px
-import numpy as np
-
-
-plot_end = 2 * math.pi
-
-
-def get_plot(period=1):
-    global plot_end
-    x = np.arange(plot_end - 2 * math.pi, plot_end, 0.02)
-    y = np.sin(2*math.pi*period * x)
-    fig = px.line(x=x, y=y)
-    plot_end += 2 * math.pi
-    return fig
-
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("Change the value of the slider to automatically update the plot")
-            period = gr.Slider(label="Period of plot", value=1, minimum=0, maximum=10, step=1)
-            plot = gr.Plot(label="Plot (updates every half second)")
-
-    dep = demo.load(get_plot, None, plot, every=0.5)
-    period.change(get_plot, period, plot, every=0.5, cancels=[dep])
-
-demo.queue().launch()
-```
-
-![live_demo](https://user-images.githubusercontent.com/41651716/198357377-633ce460-4e31-47bd-8202-1440cdd6fe19.gif)
-
-### Bug Fixes:
-
-No changes to highlight.
-
-### Documentation Changes:
-
-- Explained how to set up `queue` and `auth` when working with reload mode by by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 3089](https://github.com/gradio-app/gradio/pull/3089)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Allows loading private Spaces by passing an an `api_key` to `gr.Interface.load()`
-  by [@abidlabs](https://github.com/abidlabs) in [PR 2568](https://github.com/gradio-app/gradio/pull/2568)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.8
-
-### New Features:
-
-- Allows event listeners to accept a single dictionary as its argument, where the keys are the components and the values are the component values. This is set by passing the input components in the event listener as a set instead of a list. [@aliabid94](https://github.com/aliabid94) in [PR 2550](https://github.com/gradio-app/gradio/pull/2550)
-
-### Bug Fixes:
-
-- Fix whitespace issue when using plotly. [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2548](https://github.com/gradio-app/gradio/pull/2548)
-- Apply appropriate alt text to all gallery images. [@camenduru](https://github.com/camenduru) in [PR 2358](https://github.com/gradio-app/gradio/pull/2538)
-- Removed erroneous tkinter import in gradio.blocks by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2555](https://github.com/gradio-app/gradio/pull/2555)
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Added the `every` keyword to event listeners that runs events on a fixed schedule by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2512](https://github.com/gradio-app/gradio/pull/2512)
-- Fix whitespace issue when using plotly. [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2548](https://github.com/gradio-app/gradio/pull/2548)
-- Apply appropriate alt text to all gallery images. [@camenduru](https://github.com/camenduru) in [PR 2358](https://github.com/gradio-app/gradio/pull/2538)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.7
-
-### New Features:
-
-###### Batched Functions
-
-Gradio now supports the ability to pass _batched_ functions. Batched functions are just
-functions which take in a list of inputs and return a list of predictions.
-
-For example, here is a batched function that takes in two lists of inputs (a list of
-words and a list of ints), and returns a list of trimmed words as output:
-
-```py
-import time
-
-def trim_words(words, lens):
-    trimmed_words = []
-    time.sleep(5)
-    for w, l in zip(words, lens):
-        trimmed_words.append(w[:l])
-    return [trimmed_words]
-```
-
-The advantage of using batched functions is that if you enable queuing, the Gradio
-server can automatically _batch_ incoming requests and process them in parallel,
-potentially speeding up your demo. Here's what the Gradio code looks like (notice
-the `batch=True` and `max_batch_size=16` -- both of these parameters can be passed
-into event triggers or into the `Interface` class)
-
-```py
-import gradio as gr
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        word = gr.Textbox(label="word", value="abc")
-        leng = gr.Number(label="leng", precision=0, value=1)
-        output = gr.Textbox(label="Output")
-    with gr.Row():
-        run = gr.Button()
-
-    event = run.click(trim_words, [word, leng], output, batch=True, max_batch_size=16)
-
-demo.queue()
-demo.launch()
-```
-
-In the example above, 16 requests could be processed in parallel (for a total inference
-time of 5 seconds), instead of each request being processed separately (for a total
-inference time of 80 seconds).
-
-###### Upload Event
-
-`Video`, `Audio`, `Image`, and `File` components now support a `upload()` event that is triggered when a user uploads a file into any of these components.
-
-Example usage:
-
-```py
-import gradio as gr
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        input_video = gr.Video()
-        output_video = gr.Video()
-
-     # Clears the output video when an input video is uploaded
-    input_video.upload(lambda : None, None, output_video)
-```
-
-### Bug Fixes:
-
-- Fixes issue where plotly animations, interactivity, titles, legends, were not working properly. [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2486](https://github.com/gradio-app/gradio/pull/2486)
-- Prevent requests to the `/api` endpoint from skipping the queue if the queue is enabled for that event by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2493](https://github.com/gradio-app/gradio/pull/2493)
-- Fixes a bug with `cancels` in event triggers so that it works properly if multiple
-  Blocks are rendered by [@abidlabs](https://github.com/abidlabs) in [PR 2530](https://github.com/gradio-app/gradio/pull/2530)
-- Prevent invalid targets of events from crashing the whole application. [@pngwn](https://github.com/pngwn) in [PR 2534](https://github.com/gradio-app/gradio/pull/2534)
-- Properly dequeue cancelled events when multiple apps are rendered by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2540](https://github.com/gradio-app/gradio/pull/2540)
-- Fixes videos being cropped due to height/width params not being used [@hannahblair](https://github.com/hannahblair) in [PR 4946](https://github.com/gradio-app/gradio/pull/4946)
-
-### Documentation Changes:
-
-- Added an example interactive dashboard to the "Tabular & Plots" section of the Demos page by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2508](https://github.com/gradio-app/gradio/pull/2508)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Fixes the error message if a user builds Gradio locally and tries to use `share=True` by [@abidlabs](https://github.com/abidlabs) in [PR 2502](https://github.com/gradio-app/gradio/pull/2502)
-- Allows the render() function to return self by [@Raul9595](https://github.com/Raul9595) in [PR 2514](https://github.com/gradio-app/gradio/pull/2514)
-- Fixes issue where plotly animations, interactivity, titles, legends, were not working properly. [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2486](https://github.com/gradio-app/gradio/pull/2486)
-- Gradio now supports batched functions by [@abidlabs](https://github.com/abidlabs) in [PR 2218](https://github.com/gradio-app/gradio/pull/2218)
-- Add `upload` event for `Video`, `Audio`, `Image`, and `File` components [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2448](https://github.com/gradio-app/gradio/pull/2456)
-- Changes websocket path for Spaces as it is no longer necessary to have a different URL for websocket connections on Spaces by [@abidlabs](https://github.com/abidlabs) in [PR 2528](https://github.com/gradio-app/gradio/pull/2528)
-- Clearer error message when events are defined outside of a Blocks scope, and a warning if you
-  try to use `Series` or `Parallel` with `Blocks` by [@abidlabs](https://github.com/abidlabs) in [PR 2543](https://github.com/gradio-app/gradio/pull/2543)
-- Adds support for audio samples that are in `float64`, `float16`, or `uint16` formats by [@abidlabs](https://github.com/abidlabs) in [PR 2545](https://github.com/gradio-app/gradio/pull/2545)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.6
-
-### New Features:
-
-###### Cancelling Running Events
-
-Running events can be cancelled when other events are triggered! To test this feature, pass the `cancels` parameter to the event listener.
-For this feature to work, the queue must be enabled.
-
-![cancel_on_change_rl](https://user-images.githubusercontent.com/41651716/195952623-61a606bd-e82b-4e1a-802e-223154cb8727.gif)
-
-Code:
-
-```python
-import time
-import gradio as gr
-
-def fake_diffusion(steps):
-    for i in range(steps):
-        time.sleep(1)
-        yield str(i)
-
-def long_prediction(*args, **kwargs):
-    time.sleep(10)
-    return 42
-
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            n = gr.Slider(1, 10, value=9, step=1, label="Number Steps")
-            run = gr.Button()
-            output = gr.Textbox(label="Iterative Output")
-            stop = gr.Button(value="Stop Iterating")
-        with gr.Column():
-            prediction = gr.Number(label="Expensive Calculation")
-            run_pred = gr.Button(value="Run Expensive Calculation")
-        with gr.Column():
-            cancel_on_change = gr.Textbox(label="Cancel Iteration and Expensive Calculation on Change")
-
-    click_event = run.click(fake_diffusion, n, output)
-    stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])
-    pred_event = run_pred.click(fn=long_prediction, inputs=None, outputs=prediction)
-
-    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])
-
-
-demo.queue(concurrency_count=1, max_size=20).launch()
-```
-
-For interfaces, a stop button will be added automatically if the function uses a `yield` statement.
-
-```python
-import gradio as gr
-import time
-
-def iteration(steps):
-    for i in range(steps):
-       time.sleep(0.5)
-       yield i
-
-gr.Interface(iteration,
-             inputs=gr.Slider(minimum=1, maximum=10, step=1, value=5),
-             outputs=gr.Number()).queue().launch()
-```
-
-![stop_interface_rl](https://user-images.githubusercontent.com/41651716/195952883-e7ca4235-aae3-4852-8f28-96d01d0c5822.gif)
-
-### Bug Fixes:
-
-- Add loading status tracker UI to HTML and Markdown components. [@pngwn](https://github.com/pngwn) in [PR 2474](https://github.com/gradio-app/gradio/pull/2474)
-- Fixed videos being mirrored in the front-end if source is not webcam by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2475](https://github.com/gradio-app/gradio/pull/2475)
-- Add clear button for timeseries component [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2487](https://github.com/gradio-app/gradio/pull/2487)
-- Removes special characters from temporary filenames so that the files can be served by components [@abidlabs](https://github.com/abidlabs) in [PR 2480](https://github.com/gradio-app/gradio/pull/2480)
-- Fixed infinite reload loop when mounting gradio as a sub application by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2477](https://github.com/gradio-app/gradio/pull/2477)
-
-### Documentation Changes:
-
-- Adds a demo to show how a sound alert can be played upon completion of a prediction by [@abidlabs](https://github.com/abidlabs) in [PR 2478](https://github.com/gradio-app/gradio/pull/2478)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Enable running events to be cancelled from other events by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2433](https://github.com/gradio-app/gradio/pull/2433)
-- Small fix for version check before reuploading demos by [@aliabd](https://github.com/aliabd) in [PR 2469](https://github.com/gradio-app/gradio/pull/2469)
-- Add loading status tracker UI to HTML and Markdown components. [@pngwn](https://github.com/pngwn) in [PR 2400](https://github.com/gradio-app/gradio/pull/2474)
-- Add clear button for timeseries component [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2487](https://github.com/gradio-app/gradio/pull/2487)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.5
-
-### Bug Fixes:
-
-- Ensure that Gradio does not take control of the HTML page title when embedding a gradio app as a web component, this behaviour flipped by adding `control_page_title="true"` to the webcomponent. [@pngwn](https://github.com/pngwn) in [PR 2400](https://github.com/gradio-app/gradio/pull/2400)
-- Decreased latency in iterative-output demos by making the iteration asynchronous [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2409](https://github.com/gradio-app/gradio/pull/2409)
-- Fixed queue getting stuck under very high load by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2374](https://github.com/gradio-app/gradio/pull/2374)
-- Ensure that components always behave as if `interactive=True` were set when the following conditions are true:
-
-  - no default value is provided,
-  - they are not set as the input or output of an event,
-  - `interactive` kwarg is not set.
-
-  [@pngwn](https://github.com/pngwn) in [PR 2459](https://github.com/gradio-app/gradio/pull/2459)
-
-### New Features:
-
-- When an `Image` component is set to `source="upload"`, it is now possible to drag and drop and image to replace a previously uploaded image by [@pngwn](https://github.com/pngwn) in [PR 1711](https://github.com/gradio-app/gradio/issues/1711)
-- The `gr.Dataset` component now accepts `HTML` and `Markdown` components by [@abidlabs](https://github.com/abidlabs) in [PR 2437](https://github.com/gradio-app/gradio/pull/2437)
-
-### Documentation Changes:
-
-- Improved documentation for the `gr.Dataset` component by [@abidlabs](https://github.com/abidlabs) in [PR 2437](https://github.com/gradio-app/gradio/pull/2437)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-- The `Carousel` component is officially deprecated. Since gradio 3.0, code containing the `Carousel` component would throw warnings. As of the next release, the `Carousel` component will raise an exception.
-
-### Full Changelog:
-
-- Speeds up Gallery component by using temporary files instead of base64 representation in the front-end by [@proxyphi](https://github.com/proxyphi), [@pngwn](https://github.com/pngwn), and [@abidlabs](https://github.com/abidlabs) in [PR 2265](https://github.com/gradio-app/gradio/pull/2265)
-- Fixed some embedded demos in the guides by not loading the gradio web component in some guides by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2403](https://github.com/gradio-app/gradio/pull/2403)
-- When an `Image` component is set to `source="upload"`, it is now possible to drag and drop and image to replace a previously uploaded image by [@pngwn](https://github.com/pngwn) in [PR 2400](https://github.com/gradio-app/gradio/pull/2410)
-- Improve documentation of the `Blocks.load()` event by [@abidlabs](https://github.com/abidlabs) in [PR 2413](https://github.com/gradio-app/gradio/pull/2413)
-- Decreased latency in iterative-output demos by making the iteration asynchronous [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2409](https://github.com/gradio-app/gradio/pull/2409)
-- Updated share link message to reference new Spaces Hardware [@abidlabs](https://github.com/abidlabs) in [PR 2423](https://github.com/gradio-app/gradio/pull/2423)
-- Automatically restart spaces if they're down by [@aliabd](https://github.com/aliabd) in [PR 2405](https://github.com/gradio-app/gradio/pull/2405)
-- Carousel component is now deprecated by [@abidlabs](https://github.com/abidlabs) in [PR 2434](https://github.com/gradio-app/gradio/pull/2434)
-- Build Gradio from source in ui tests by by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2440](https://github.com/gradio-app/gradio/pull/2440)
-- Change "return ValueError" to "raise ValueError" by [@vzakharov](https://github.com/vzakharov) in [PR 2445](https://github.com/gradio-app/gradio/pull/2445)
-- Add guide on creating a map demo using the `gr.Plot()` component [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2402](https://github.com/gradio-app/gradio/pull/2402)
-- Add blur event for `Textbox` and `Number` components [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2448](https://github.com/gradio-app/gradio/pull/2448)
-- Stops a gradio launch from hogging a port even after it's been killed [@aliabid94](https://github.com/aliabid94) in [PR 2453](https://github.com/gradio-app/gradio/pull/2453)
-- Fix embedded interfaces on touch screen devices by [@aliabd](https://github.com/aliabd) in [PR 2457](https://github.com/gradio-app/gradio/pull/2457)
-- Upload all demos to spaces by [@aliabd](https://github.com/aliabd) in [PR 2281](https://github.com/gradio-app/gradio/pull/2281)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.4.1
-
-### New Features:
-
-###### 1. See Past and Upcoming Changes in the Release History 👀
-
-You can now see gradio's release history directly on the website, and also keep track of upcoming changes. Just go [here](https://gradio.app/changelog/).
-
-![release-history](https://user-images.githubusercontent.com/9021060/193145458-3de699f7-7620-45de-aa73-a1c1b9b96257.gif)
-
-### Bug Fixes:
-
-1. Fix typo in guide image path by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2357](https://github.com/gradio-app/gradio/pull/2357)
-2. Raise error if Blocks has duplicate component with same IDs by [@abidlabs](https://github.com/abidlabs) in [PR 2359](https://github.com/gradio-app/gradio/pull/2359)
-3. Catch the permission exception on the audio component by [@Ian-GL](https://github.com/Ian-GL) in [PR 2330](https://github.com/gradio-app/gradio/pull/2330)
-4. Fix image_classifier_interface_load demo by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2365](https://github.com/gradio-app/gradio/pull/2365)
-5. Fix combining adjacent components without gaps by introducing `gr.Row(variant="compact")` by [@aliabid94](https://github.com/aliabid94) in [PR 2291](https://github.com/gradio-app/gradio/pull/2291) This comes with deprecation of the following arguments for `Component.style`: `round`, `margin`, `border`.
-6. Fix audio streaming, which was previously choppy in [PR 2351](https://github.com/gradio-app/gradio/pull/2351). Big thanks to [@yannickfunk](https://github.com/yannickfunk) for the proposed solution.
-7. Fix bug where new typeable slider doesn't respect the minimum and maximum values [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2380](https://github.com/gradio-app/gradio/pull/2380)
-
-### Documentation Changes:
-
-1. New Guide: Connecting to a Database 🗄️
-
-   A new guide by [@freddyaboulton](https://github.com/freddyaboulton) that explains how you can use Gradio to connect your app to a database. Read more [here](https://gradio.app/connecting_to_a_database/).
-
-2. New Guide: Running Background Tasks 🥷
-
-   A new guide by [@freddyaboulton](https://github.com/freddyaboulton) that explains how you can run background tasks from your gradio app. Read more [here](https://gradio.app/running_background_tasks/).
-
-3. Small fixes to docs for `Image` component by [@abidlabs](https://github.com/abidlabs) in [PR 2372](https://github.com/gradio-app/gradio/pull/2372)
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- Create a guide on how to connect an app to a database hosted on the cloud by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2341](https://github.com/gradio-app/gradio/pull/2341)
-- Removes `analytics` dependency by [@abidlabs](https://github.com/abidlabs) in [PR 2347](https://github.com/gradio-app/gradio/pull/2347)
-- Add guide on launching background tasks from your app by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2350](https://github.com/gradio-app/gradio/pull/2350)
-- Fix typo in guide image path by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2357](https://github.com/gradio-app/gradio/pull/2357)
-- Raise error if Blocks has duplicate component with same IDs by [@abidlabs](https://github.com/abidlabs) in [PR 2359](https://github.com/gradio-app/gradio/pull/2359)
-- Hotfix: fix version back to 3.4 by [@abidlabs](https://github.com/abidlabs) in [PR 2361](https://github.com/gradio-app/gradio/pull/2361)
-- Change version.txt to 3.4 instead of 3.4.0 by [@aliabd](https://github.com/aliabd) in [PR 2363](https://github.com/gradio-app/gradio/pull/2363)
-- Catch the permission exception on the audio component by [@Ian-GL](https://github.com/Ian-GL) in [PR 2330](https://github.com/gradio-app/gradio/pull/2330)
-- Fix image_classifier_interface_load demo by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2365](https://github.com/gradio-app/gradio/pull/2365)
-- Small fixes to docs for `Image` component by [@abidlabs](https://github.com/abidlabs) in [PR 2372](https://github.com/gradio-app/gradio/pull/2372)
-- Automated Release Notes by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2306](https://github.com/gradio-app/gradio/pull/2306)
-- Fixed small typos in the docs [@julien-c](https://github.com/julien-c) in [PR 2373](https://github.com/gradio-app/gradio/pull/2373)
-- Adds ability to disable pre/post-processing for examples [@abidlabs](https://github.com/abidlabs) in [PR 2383](https://github.com/gradio-app/gradio/pull/2383)
-- Copy changelog file in website docker by [@aliabd](https://github.com/aliabd) in [PR 2384](https://github.com/gradio-app/gradio/pull/2384)
-- Lets users provide a `gr.update()` dictionary even if post-processing is disabled [@abidlabs](https://github.com/abidlabs) in [PR 2385](https://github.com/gradio-app/gradio/pull/2385)
-- Fix bug where errors would cause apps run in reload mode to hang forever by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2394](https://github.com/gradio-app/gradio/pull/2394)
-- Fix bug where new typeable slider doesn't respect the minimum and maximum values [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2380](https://github.com/gradio-app/gradio/pull/2380)
-
-### Contributors Shoutout:
-
-No changes to highlight.
-
-## 3.4
-
-### New Features:
-
-###### 1. Gallery Captions 🖼️
-
-You can now pass captions to images in the Gallery component. To do so you need to pass a {List} of (image, {str} caption) tuples. This is optional and the component also accepts just a list of the images.
-
-Here's an example:
-
-```python
-import gradio as gr
-
-images_with_captions = [
-    ("https://images.unsplash.com/photo-1551969014-7d2c4cddf0b6", "Cheetah by David Groves"),
-    ("https://images.unsplash.com/photo-1546182990-dffeafbe841d", "Lion by Francesco"),
-    ("https://images.unsplash.com/photo-1561731216-c3a4d99437d5", "Tiger by Mike Marrah")
-    ]
-
-with gr.Blocks() as demo:
-    gr.Gallery(value=images_with_captions)
-
-demo.launch()
-```
-
-<img src="https://user-images.githubusercontent.com/9021060/192399521-7360b1a9-7ce0-443e-8e94-863a230a7dbe.gif" alt="gallery_captions" width="1000"/>
-
-###### 2. Type Values into the Slider 🔢
-
-You can now type values directly on the Slider component! Here's what it looks like:
-
-![type-slider](https://user-images.githubusercontent.com/9021060/192399877-76b662a1-fede-4417-a932-fc15f0da7360.gif)
-
-###### 3. Better Sketching and Inpainting 🎨
-
-We've made a lot of changes to our Image component so that it can support better sketching and inpainting.
-
-Now supports:
-
-- A standalone black-and-white sketch
-
-```python
-import gradio as gr
-demo = gr.Interface(lambda x: x, gr.Sketchpad(), gr.Image())
-demo.launch()
-```
-
-![bw](https://user-images.githubusercontent.com/9021060/192410264-b08632b5-7b2a-4f86-afb0-5760e7b474cf.gif)
-
-- A standalone color sketch
-
-```python
-import gradio as gr
-demo = gr.Interface(lambda x: x, gr.Paint(), gr.Image())
-demo.launch()
-```
-
-![color-sketch](https://user-images.githubusercontent.com/9021060/192410500-3c8c3e64-a5fd-4df2-a991-f0a5cef93728.gif)
-
-- An uploadable image with black-and-white or color sketching
-
-```python
-import gradio as gr
-demo = gr.Interface(lambda x: x, gr.Image(source='upload', tool='color-sketch'), gr.Image()) # for black and white, tool = 'sketch'
-demo.launch()
-```
-
-![sketch-new](https://user-images.githubusercontent.com/9021060/192402422-e53cb7b6-024e-448c-87eb-d6a35a63c476.gif)
-
-- Webcam with black-and-white or color sketching
-
-```python
-import gradio as gr
-demo = gr.Interface(lambda x: x, gr.Image(source='webcam', tool='color-sketch'), gr.Image()) # for black and white, tool = 'sketch'
-demo.launch()
-```
-
-![webcam-sketch](https://user-images.githubusercontent.com/9021060/192410820-0ffaf324-776e-4e1f-9de6-0fdbbf4940fa.gif)
-
-As well as other fixes
-
-### Bug Fixes:
-
-1. Fix bug where max concurrency count is not respected in queue by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2286](https://github.com/gradio-app/gradio/pull/2286)
-2. fix : queue could be blocked by [@SkyTNT](https://github.com/SkyTNT) in [PR 2288](https://github.com/gradio-app/gradio/pull/2288)
-3. Supports `gr.update()` in example caching by [@abidlabs](https://github.com/abidlabs) in [PR 2309](https://github.com/gradio-app/gradio/pull/2309)
-4. Clipboard fix for iframes by [@abidlabs](https://github.com/abidlabs) in [PR 2321](https://github.com/gradio-app/gradio/pull/2321)
-5. Fix: Dataframe column headers are reset when you add a new column by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2318](https://github.com/gradio-app/gradio/pull/2318)
-6. Added support for URLs for Video, Audio, and Image by [@abidlabs](https://github.com/abidlabs) in [PR 2256](https://github.com/gradio-app/gradio/pull/2256)
-7. Add documentation about how to create and use the Gradio FastAPI app by [@abidlabs](https://github.com/abidlabs) in [PR 2263](https://github.com/gradio-app/gradio/pull/2263)
-
-### Documentation Changes:
-
-1. Adding a Playground Tab to the Website by [@aliabd](https://github.com/aliabd) in [PR 1860](https://github.com/gradio-app/gradio/pull/1860)
-2. Gradio for Tabular Data Science Workflows Guide by [@merveenoyan](https://github.com/merveenoyan) in [PR 2199](https://github.com/gradio-app/gradio/pull/2199)
-3. Promotes `postprocess` and `preprocess` to documented parameters by [@abidlabs](https://github.com/abidlabs) in [PR 2293](https://github.com/gradio-app/gradio/pull/2293)
-4. Update 2)key_features.md by [@voidxd](https://github.com/voidxd) in [PR 2326](https://github.com/gradio-app/gradio/pull/2326)
-5. Add docs to blocks context postprocessing function by [@Ian-GL](https://github.com/Ian-GL) in [PR 2332](https://github.com/gradio-app/gradio/pull/2332)
-
-### Testing and Infrastructure Changes
-
-1. Website fixes and refactoring by [@aliabd](https://github.com/aliabd) in [PR 2280](https://github.com/gradio-app/gradio/pull/2280)
-2. Don't deploy to spaces on release by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2313](https://github.com/gradio-app/gradio/pull/2313)
-
-### Full Changelog:
-
-- Website fixes and refactoring by [@aliabd](https://github.com/aliabd) in [PR 2280](https://github.com/gradio-app/gradio/pull/2280)
-- Fix bug where max concurrency count is not respected in queue by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2286](https://github.com/gradio-app/gradio/pull/2286)
-- Promotes `postprocess` and `preprocess` to documented parameters by [@abidlabs](https://github.com/abidlabs) in [PR 2293](https://github.com/gradio-app/gradio/pull/2293)
-- Raise warning when trying to cache examples but not all inputs have examples by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2279](https://github.com/gradio-app/gradio/pull/2279)
-- fix : queue could be blocked by [@SkyTNT](https://github.com/SkyTNT) in [PR 2288](https://github.com/gradio-app/gradio/pull/2288)
-- Don't deploy to spaces on release by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2313](https://github.com/gradio-app/gradio/pull/2313)
-- Supports `gr.update()` in example caching by [@abidlabs](https://github.com/abidlabs) in [PR 2309](https://github.com/gradio-app/gradio/pull/2309)
-- Respect Upstream Queue when loading interfaces/blocks from Spaces by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2294](https://github.com/gradio-app/gradio/pull/2294)
-- Clipboard fix for iframes by [@abidlabs](https://github.com/abidlabs) in [PR 2321](https://github.com/gradio-app/gradio/pull/2321)
-- Sketching + Inpainting Capabilities to Gradio by [@abidlabs](https://github.com/abidlabs) in [PR 2144](https://github.com/gradio-app/gradio/pull/2144)
-- Update 2)key_features.md by [@voidxd](https://github.com/voidxd) in [PR 2326](https://github.com/gradio-app/gradio/pull/2326)
-- release 3.4b3 by [@abidlabs](https://github.com/abidlabs) in [PR 2328](https://github.com/gradio-app/gradio/pull/2328)
-- Fix: Dataframe column headers are reset when you add a new column by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2318](https://github.com/gradio-app/gradio/pull/2318)
-- Start queue when gradio is a sub application by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2319](https://github.com/gradio-app/gradio/pull/2319)
-- Fix Web Tracker Script by [@aliabd](https://github.com/aliabd) in [PR 2308](https://github.com/gradio-app/gradio/pull/2308)
-- Add docs to blocks context postprocessing function by [@Ian-GL](https://github.com/Ian-GL) in [PR 2332](https://github.com/gradio-app/gradio/pull/2332)
-- Fix typo in iterator variable name in run_predict function by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2340](https://github.com/gradio-app/gradio/pull/2340)
-- Add captions to galleries by [@aliabid94](https://github.com/aliabid94) in [PR 2284](https://github.com/gradio-app/gradio/pull/2284)
-- Typeable value on gradio.Slider by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2329](https://github.com/gradio-app/gradio/pull/2329)
-
-### Contributors Shoutout:
-
-- [@SkyTNT](https://github.com/SkyTNT) made their first contribution in [PR 2288](https://github.com/gradio-app/gradio/pull/2288)
-- [@voidxd](https://github.com/voidxd) made their first contribution in [PR 2326](https://github.com/gradio-app/gradio/pull/2326)
-
-## 3.3
-
-### New Features:
-
-###### 1. Iterative Outputs ⏳
-
-You can now create an iterative output simply by having your function return a generator!
-
-Here's (part of) an example that was used to generate the interface below it. [See full code](https://colab.research.google.com/drive/1m9bWS6B82CT7bw-m4L6AJR8za7fEK7Ov?usp=sharing).
-
-```python
-def predict(steps, seed):
-    generator = torch.manual_seed(seed)
-    for i in range(1,steps):
-        yield pipeline(generator=generator, num_inference_steps=i)["sample"][0]
-```
-
-![example](https://user-images.githubusercontent.com/9021060/189086273-f5e7087d-71fa-4158-90a9-08e84da0421c.mp4)
-
-###### 2. Accordion Layout 🆕
-
-This version of Gradio introduces a new layout component to Blocks: the Accordion. Wrap your elements in a neat, expandable layout that allows users to toggle them as needed.
-
-Usage: ([Read the docs](https://gradio.app/docs/#accordion))
-
-```python
-with gr.Accordion("open up"):
-# components here
-```
-
-![accordion](https://user-images.githubusercontent.com/9021060/189088465-f0ffd7f0-fc6a-42dc-9249-11c5e1e0529b.gif)
-
-###### 3. Skops Integration 📈
-
-Our new integration with [skops](https://huggingface.co/blog/skops) allows you to load tabular classification and regression models directly from the [hub](https://huggingface.co/models).
-
-Here's a classification example showing how quick it is to set up an interface for a [model](https://huggingface.co/scikit-learn/tabular-playground).
-
-```python
-import gradio as gr
-gr.Interface.load("models/scikit-learn/tabular-playground").launch()
-```
-
-![187936493-5c90c01d-a6dd-400f-aa42-833a096156a1](https://user-images.githubusercontent.com/9021060/189090519-328fbcb4-120b-43c8-aa54-d6fccfa6b7e8.png)
-
-### Bug Fixes:
-
-No changes to highlight.
-
-### Documentation Changes:
-
-No changes to highlight.
-
-### Testing and Infrastructure Changes:
-
-No changes to highlight.
-
-### Breaking Changes:
-
-No changes to highlight.
-
-### Full Changelog:
-
-- safari fixes by [@pngwn](https://github.com/pngwn) in [PR 2138](https://github.com/gradio-app/gradio/pull/2138)
-- Fix roundedness and form borders by [@aliabid94](https://github.com/aliabid94) in [PR 2147](https://github.com/gradio-app/gradio/pull/2147)
-- Better processing of example data prior to creating dataset component by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2147](https://github.com/gradio-app/gradio/pull/2147)
-- Show error on Connection drops by [@aliabid94](https://github.com/aliabid94) in [PR 2147](https://github.com/gradio-app/gradio/pull/2147)
-- 3.2 release! by [@abidlabs](https://github.com/abidlabs) in [PR 2139](https://github.com/gradio-app/gradio/pull/2139)
-- Fixed Named API Requests by [@abidlabs](https://github.com/abidlabs) in [PR 2151](https://github.com/gradio-app/gradio/pull/2151)
-- Quick Fix: Cannot upload Model3D image after clearing it by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2168](https://github.com/gradio-app/gradio/pull/2168)
-- Fixed misleading log when server_name is '0.0.0.0' by [@lamhoangtung](https://github.com/lamhoangtung) in [PR 2176](https://github.com/gradio-app/gradio/pull/2176)
-- Keep embedded PngInfo metadata by [@cobryan05](https://github.com/cobryan05) in [PR 2170](https://github.com/gradio-app/gradio/pull/2170)
-- Skops integration: Load tabular classification and regression models from the hub by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2126](https://github.com/gradio-app/gradio/pull/2126)
-- Respect original filename when cached example files are downloaded by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2145](https://github.com/gradio-app/gradio/pull/2145)
-- Add manual trigger to deploy to pypi by [@abidlabs](https://github.com/abidlabs) in [PR 2192](https://github.com/gradio-app/gradio/pull/2192)
-- Fix bugs with gr.update by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2157](https://github.com/gradio-app/gradio/pull/2157)
-- Make queue per app by [@aliabid94](https://github.com/aliabid94) in [PR 2193](https://github.com/gradio-app/gradio/pull/2193)
-- Preserve Labels In Interpretation Components by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2166](https://github.com/gradio-app/gradio/pull/2166)
-- Quick Fix: Multiple file download not working by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2169](https://github.com/gradio-app/gradio/pull/2169)
-- use correct MIME type for js-script file by [@daspartho](https://github.com/daspartho) in [PR 2200](https://github.com/gradio-app/gradio/pull/2200)
-- Add accordion component by [@aliabid94](https://github.com/aliabid94) in [PR 2208](https://github.com/gradio-app/gradio/pull/2208)
-
-### Contributors Shoutout:
-
-- [@lamhoangtung](https://github.com/lamhoangtung) made their first contribution in [PR 2176](https://github.com/gradio-app/gradio/pull/2176)
-- [@cobryan05](https://github.com/cobryan05) made their first contribution in [PR 2170](https://github.com/gradio-app/gradio/pull/2170)
-- [@daspartho](https://github.com/daspartho) made their first contribution in [PR 2200](https://github.com/gradio-app/gradio/pull/2200)
-
-## 3.2
-
-### New Features:
-
-###### 1. Improvements to Queuing 🥇
-
-We've implemented a brand new queuing system based on **web sockets** instead of HTTP long polling. Among other things, this allows us to manage queue sizes better on Hugging Face Spaces. There are also additional queue-related parameters you can add:
-
-- Now supports concurrent workers (parallelization)
-
-```python
-demo = gr.Interface(...)
-demo.queue(concurrency_count=3)
-demo.launch()
-```
-
-- Configure a maximum queue size
-
-```python
-demo = gr.Interface(...)
-demo.queue(max_size=100)
-demo.launch()
-```
-
-- If a user closes their tab / browser, they leave the queue, which means the demo will run faster for everyone else
-
-###### 2. Fixes to Examples
-
-- Dataframe examples will render properly, and look much clearer in the UI: (thanks to PR #2125)
-
-![Screen Shot 2022-08-30 at 8 29 58 PM](https://user-images.githubusercontent.com/9021060/187586561-d915bafb-f968-4966-b9a2-ef41119692b2.png)
-
-- Image and Video thumbnails are cropped to look neater and more uniform: (thanks to PR #2109)
-
-![Screen Shot 2022-08-30 at 8 32 15 PM](https://user-images.githubusercontent.com/9021060/187586890-56e1e4f0-1b84-42d9-a82f-911772c41030.png)
-
-- Other fixes in PR #2131 and #2064 make it easier to design and use Examples
-
-###### 3. Component Fixes 🧱
-
-- Specify the width and height of an image in its style tag (thanks to PR #2133)
-
-```python
-components.Image().style(height=260, width=300)
-```
-
-- Automatic conversion of videos so they are playable in the browser (thanks to PR #2003). Gradio will check if a video's format is playable in the browser and, if it isn't, will automatically convert it to a format that is (mp4).
-- Pass in a json filepath to the Label component (thanks to PR #2083)
-- Randomize the default value of a Slider (thanks to PR #1935)
-
-![slider-random](https://user-images.githubusercontent.com/9021060/187596230-3db9697f-9f4d-42f5-9387-d77573513448.gif)
-
-- Improvements to State in PR #2100
-
-###### 4. Ability to Randomize Input Sliders and Reload Data whenever the Page Loads
-
-- In some cases, you want to be able to show a different set of input data to every user as they load the page app. For example, you might want to randomize the value of a "seed" `Slider` input. Or you might want to show a `Textbox` with the current date. We now supporting passing _functions_ as the default value in input components. When you pass in a function, it gets **re-evaluated** every time someone loads the demo, allowing you to reload / change data for different users.
-
-Here's an example loading the current date time into an input Textbox:
-
-```python
-import gradio as gr
-import datetime
-
-with gr.Blocks() as demo:
-    gr.Textbox(datetime.datetime.now)
-
-demo.launch()
-```
-
-Note that we don't evaluate the function -- `datetime.datetime.now()` -- we pass in the function itself to get this behavior -- `datetime.datetime.now`
-
-Because randomizing the initial value of `Slider` is a common use case, we've added a `randomize` keyword argument you can use to randomize its initial value:
-
-```python
-import gradio as gr
-demo = gr.Interface(lambda x:x, gr.Slider(0, 10, randomize=True), "number")
-demo.launch()
-```
-
-###### 5. New Guide 🖊️
-
-- [Gradio and W&B Integration](https://gradio.app/Gradio_and_Wandb_Integration/)
-
-### Full Changelog:
-
-- Reset components to original state by setting value to None by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2044](https://github.com/gradio-app/gradio/pull/2044)
-- Cleaning up the way data is processed for components by [@abidlabs](https://github.com/abidlabs) in [PR 1967](https://github.com/gradio-app/gradio/pull/1967)
-- version 3.1.8b by [@abidlabs](https://github.com/abidlabs) in [PR 2063](https://github.com/gradio-app/gradio/pull/2063)
-- Wandb guide by [@AK391](https://github.com/AK391) in [PR 1898](https://github.com/gradio-app/gradio/pull/1898)
-- Add a flagging callback to save json files to a hugging face dataset by [@chrisemezue](https://github.com/chrisemezue) in [PR 1821](https://github.com/gradio-app/gradio/pull/1821)
-- Add data science demos to landing page by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2067](https://github.com/gradio-app/gradio/pull/2067)
-- Hide time series + xgboost demos by default by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2079](https://github.com/gradio-app/gradio/pull/2079)
-- Encourage people to keep trying when queue full by [@apolinario](https://github.com/apolinario) in [PR 2076](https://github.com/gradio-app/gradio/pull/2076)
-- Updated our analytics on creation of Blocks/Interface by [@abidlabs](https://github.com/abidlabs) in [PR 2082](https://github.com/gradio-app/gradio/pull/2082)
-- `Label` component now accepts file paths to `.json` files by [@abidlabs](https://github.com/abidlabs) in [PR 2083](https://github.com/gradio-app/gradio/pull/2083)
-- Fix issues related to demos in Spaces by [@abidlabs](https://github.com/abidlabs) in [PR 2086](https://github.com/gradio-app/gradio/pull/2086)
-- Fix TimeSeries examples not properly displayed in UI by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2064](https://github.com/gradio-app/gradio/pull/2064)
-- Fix infinite requests when doing tab item select by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2070](https://github.com/gradio-app/gradio/pull/2070)
-- Accept deprecated `file` route as well by [@abidlabs](https://github.com/abidlabs) in [PR 2099](https://github.com/gradio-app/gradio/pull/2099)
-- Allow frontend method execution on Block.load event by [@codedealer](https://github.com/codedealer) in [PR 2108](https://github.com/gradio-app/gradio/pull/2108)
-- Improvements to `State` by [@abidlabs](https://github.com/abidlabs) in [PR 2100](https://github.com/gradio-app/gradio/pull/2100)
-- Catch IndexError, KeyError in video_is_playable by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 2113](https://github.com/gradio-app/gradio/pull/2113)
-- Fix: Download button does not respect the filepath returned by the function by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 2073](https://github.com/gradio-app/gradio/pull/2073)
-- Refactoring Layout: Adding column widths, forms, and more. by [@aliabid94](https://github.com/aliabid94) in [PR 2097](https://github.com/gradio-app/gradio/pull/2097)
-- Update CONTRIBUTING.md by [@abidlabs](https://github.com/abidlabs) in [PR 2118](https://github.com/gradio-app/gradio/pull/2118)
-- 2092 df ex by [@pngwn](https://github.com/pngwn) in [PR 2125](https://github.com/gradio-app/gradio/pull/2125)
-- feat(samples table/gallery): Crop thumbs to square by [@ronvoluted](https://github.com/ronvoluted) in [PR 2109](https://github.com/gradio-app/gradio/pull/2109)
-- Some enhancements to `gr.Examples` by [@abidlabs](https://github.com/abidlabs) in [PR 2131](https://github.com/gradio-app/gradio/pull/2131)
-- Image size fix by [@aliabid94](https://github.com/aliabid94) in [PR 2133](https://github.com/gradio-app/gradio/pull/2133)
-
-### Contributors Shoutout:
-
-- [@chrisemezue](https://github.com/chrisemezue) made their first contribution in [PR 1821](https://github.com/gradio-app/gradio/pull/1821)
-- [@apolinario](https://github.com/apolinario) made their first contribution in [PR 2076](https://github.com/gradio-app/gradio/pull/2076)
-- [@codedealer](https://github.com/codedealer) made their first contribution in [PR 2108](https://github.com/gradio-app/gradio/pull/2108)
-
-## 3.1
-
-### New Features:
-
-###### 1. Embedding Demos on Any Website 💻
-
-With PR #1444, Gradio is now distributed as a web component. This means demos can be natively embedded on websites. You'll just need to add two lines: one to load the gradio javascript, and one to link to the demos backend.
-
-Here's a simple example that embeds the demo from a Hugging Face space:
-
-```html
-<script
-	type="module"
-	src="https://gradio.s3-us-west-2.amazonaws.com/3.0.18/gradio.js"
-></script>
-<gradio-app space="abidlabs/pytorch-image-classifier"></gradio-app>
-```
-
-But you can also embed demos that are running anywhere, you just need to link the demo to `src` instead of `space`. In fact, all the demos on the gradio website are embedded this way:
-
-<img width="1268" alt="Screen Shot 2022-07-14 at 2 41 44 PM" src="https://user-images.githubusercontent.com/9021060/178997124-b2f05af2-c18f-4716-bf1b-cb971d012636.png">
-
-Read more in the [Embedding Gradio Demos](https://gradio.app/embedding_gradio_demos) guide.
-
-###### 2. Reload Mode 👨‍💻
-
-Reload mode helps developers create gradio demos faster by automatically reloading the demo whenever the code changes. It can support development on Python IDEs (VS Code, PyCharm, etc), the terminal, as well as Jupyter notebooks.
-
-If your demo code is in a script named `app.py`, instead of running `python app.py` you can now run `gradio app.py` and that will launch the demo in reload mode:
-
-```bash
-Launching in reload mode on: http://127.0.0.1:7860 (Press CTRL+C to quit)
-Watching...
-WARNING: The --reload flag should not be used in production on Windows.
-```
-
-If you're working from a Jupyter or Colab Notebook, use these magic commands instead: `%load_ext gradio` when you import gradio, and `%%blocks` in the top of the cell with the demo code. Here's an example that shows how much faster the development becomes:
-
-![Blocks](https://user-images.githubusercontent.com/9021060/178986488-ed378cc8-5141-4330-ba41-672b676863d0.gif)
-
-###### 3. Inpainting Support on `gr.Image()` 🎨
-
-We updated the Image component to add support for inpainting demos. It works by adding `tool="sketch"` as a parameter, that passes both an image and a sketchable mask to your prediction function.
-
-Here's an example from the [LAMA space](https://huggingface.co/spaces/akhaliq/lama):
-
-![FXApVlFVsAALSD-](https://user-images.githubusercontent.com/9021060/178989479-549867c8-7fb0-436a-a97d-1e91c9f5e611.jpeg)
-
-###### 4. Markdown and HTML support in Dataframes 🔢
-
-We upgraded the Dataframe component in PR #1684 to support rendering Markdown and HTML inside the cells.
-
-This means you can build Dataframes that look like the following:
-
-![image (8)](https://user-images.githubusercontent.com/9021060/178991233-41cb07a5-e7a3-433e-89b8-319bc78eb9c2.png)
-
-###### 5. `gr.Examples()` for Blocks 🧱
-
-We've added the `gr.Examples` component helper to allow you to add examples to any Blocks demo. This class is a wrapper over the `gr.Dataset` component.
-
-<img width="1271" alt="Screen Shot 2022-07-14 at 2 23 50 PM" src="https://user-images.githubusercontent.com/9021060/178992715-c8bc7550-bc3d-4ddc-9fcb-548c159cd153.png">
-
-gr.Examples takes two required parameters:
-
-- `examples` which takes in a nested list
-- `inputs` which takes in a component or list of components
-
-You can read more in the [Examples docs](https://gradio.app/docs/#examples) or the [Adding Examples to your Demos guide](https://gradio.app/adding_examples_to_your_app/).
-
-###### 6. Fixes to Audio Streaming
-
-With [PR 1828](https://github.com/gradio-app/gradio/pull/1828) we now hide the status loading animation, as well as remove the echo in streaming. Check out the [stream_audio](https://github.com/gradio-app/gradio/blob/main/demo/stream_audio/run.py) demo for more or read through our [Real Time Speech Recognition](https://gradio.app/real_time_speech_recognition/) guide.
-
-<img width="785" alt="Screen Shot 2022-07-19 at 6 02 35 PM" src="https://user-images.githubusercontent.com/9021060/179808136-9e84502c-f9ee-4f30-b5e9-1086f678fe91.png">
-
-### Full Changelog:
-
-- File component: list multiple files and allow for download #1446 by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1681](https://github.com/gradio-app/gradio/pull/1681)
-- Add ColorPicker to docs by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1768](https://github.com/gradio-app/gradio/pull/1768)
-- Mock out requests in TestRequest unit tests by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1794](https://github.com/gradio-app/gradio/pull/1794)
-- Add requirements.txt and test_files to source dist by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1817](https://github.com/gradio-app/gradio/pull/1817)
-- refactor: f-string for tunneling.py by [@nhankiet](https://github.com/nhankiet) in [PR 1819](https://github.com/gradio-app/gradio/pull/1819)
-- Miscellaneous formatting improvements to website by [@aliabd](https://github.com/aliabd) in [PR 1754](https://github.com/gradio-app/gradio/pull/1754)
-- `integrate()` method moved to `Blocks` by [@abidlabs](https://github.com/abidlabs) in [PR 1776](https://github.com/gradio-app/gradio/pull/1776)
-- Add python-3.7 tests by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1818](https://github.com/gradio-app/gradio/pull/1818)
-- Copy test dir in website dockers by [@aliabd](https://github.com/aliabd) in [PR 1827](https://github.com/gradio-app/gradio/pull/1827)
-- Add info to docs on how to set default values for components by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1788](https://github.com/gradio-app/gradio/pull/1788)
-- Embedding Components on Docs by [@aliabd](https://github.com/aliabd) in [PR 1726](https://github.com/gradio-app/gradio/pull/1726)
-- Remove usage of deprecated gr.inputs and gr.outputs from website by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1796](https://github.com/gradio-app/gradio/pull/1796)
-- Some cleanups to the docs page by [@abidlabs](https://github.com/abidlabs) in [PR 1822](https://github.com/gradio-app/gradio/pull/1822)
-
-### Contributors Shoutout:
-
-- [@nhankiet](https://github.com/nhankiet) made their first contribution in [PR 1819](https://github.com/gradio-app/gradio/pull/1819)
-
-## 3.0
-
-###### 🔥 Gradio 3.0 is the biggest update to the library, ever.
-
-### New Features:
-
-###### 1. Blocks 🧱
-
-Blocks is a new, low-level API that allows you to have full control over the data flows and layout of your application. It allows you to build very complex, multi-step applications. For example, you might want to:
-
-- Group together related demos as multiple tabs in one web app
-- Change the layout of your demo instead of just having all of the inputs on the left and outputs on the right
-- Have multi-step interfaces, in which the output of one model becomes the input to the next model, or have more flexible data flows in general
-- Change a component's properties (for example, the choices in a Dropdown) or its visibility based on user input
-
-Here's a simple example that creates the demo below it:
-
-```python
-import gradio as gr
-
-def update(name):
-    return f"Welcome to Gradio, {name}!"
-
-demo = gr.Blocks()
-
-with demo:
-    gr.Markdown(
-    """
-    # Hello World!
-    Start typing below to see the output.
-    """)
-    inp = gr.Textbox(placeholder="What is your name?")
-    out = gr.Textbox()
-
-    inp.change(fn=update,
-               inputs=inp,
-               outputs=out)
-
-demo.launch()
-```
-
-![hello-blocks](https://user-images.githubusercontent.com/9021060/168684108-78cbd24b-e6bd-4a04-a8d9-20d535203434.gif)
-
-Read our [Introduction to Blocks](http://gradio.app/introduction_to_blocks/) guide for more, and join the 🎈 [Gradio Blocks Party](https://huggingface.co/spaces/Gradio-Blocks/README)!
-
-###### 2. Our Revamped Design 🎨
-
-We've upgraded our design across the entire library: from components, and layouts all the way to dark mode.
-
-![kitchen_sink](https://user-images.githubusercontent.com/9021060/168686333-7a6e3096-3e23-4309-abf2-5cd7736e0463.gif)
-
-###### 3. A New Website 💻
-
-We've upgraded [gradio.app](https://gradio.app) to make it cleaner, faster and easier to use. Our docs now come with components and demos embedded directly on the page. So you can quickly get up to speed with what you're looking for.
-
-![website](https://user-images.githubusercontent.com/9021060/168687191-10d6a3bd-101f-423a-8193-48f47a5e077d.gif)
-
-###### 4. New Components: Model3D, Dataset, and More..
-
-We've introduced a lot of new components in `3.0`, including `Model3D`, `Dataset`, `Markdown`, `Button` and `Gallery`. You can find all the components and play around with them [here](https://gradio.app/docs/#components).
-
-![Model3d](https://user-images.githubusercontent.com/9021060/168689062-6ad77151-8cc5-467d-916c-f7c78e52ec0c.gif)
-
-### Full Changelog:
-
-- Gradio dash fe by [@pngwn](https://github.com/pngwn) in [PR 807](https://github.com/gradio-app/gradio/pull/807)
-- Blocks components by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 765](https://github.com/gradio-app/gradio/pull/765)
-- Blocks components V2 by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 843](https://github.com/gradio-app/gradio/pull/843)
-- Blocks-Backend-Events by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 844](https://github.com/gradio-app/gradio/pull/844)
-- Interfaces from Blocks by [@aliabid94](https://github.com/aliabid94) in [PR 849](https://github.com/gradio-app/gradio/pull/849)
-- Blocks dev by [@aliabid94](https://github.com/aliabid94) in [PR 853](https://github.com/gradio-app/gradio/pull/853)
-- Started updating demos to use the new `gradio.components` syntax by [@abidlabs](https://github.com/abidlabs) in [PR 848](https://github.com/gradio-app/gradio/pull/848)
-- add test infra + add browser tests to CI by [@pngwn](https://github.com/pngwn) in [PR 852](https://github.com/gradio-app/gradio/pull/852)
-- 854 textbox by [@pngwn](https://github.com/pngwn) in [PR 859](https://github.com/gradio-app/gradio/pull/859)
-- Getting old Python unit tests to pass on `blocks-dev` by [@abidlabs](https://github.com/abidlabs) in [PR 861](https://github.com/gradio-app/gradio/pull/861)
-- initialise chatbot with empty array of messages by [@pngwn](https://github.com/pngwn) in [PR 867](https://github.com/gradio-app/gradio/pull/867)
-- add test for output to input by [@pngwn](https://github.com/pngwn) in [PR 866](https://github.com/gradio-app/gradio/pull/866)
-- More Interface -> Blocks features by [@aliabid94](https://github.com/aliabid94) in [PR 864](https://github.com/gradio-app/gradio/pull/864)
-- Fixing external.py in blocks-dev to reflect the new HF Spaces paths by [@abidlabs](https://github.com/abidlabs) in [PR 879](https://github.com/gradio-app/gradio/pull/879)
-- backend_default_value_refactoring by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 871](https://github.com/gradio-app/gradio/pull/871)
-- fix default_value by [@pngwn](https://github.com/pngwn) in [PR 869](https://github.com/gradio-app/gradio/pull/869)
-- fix buttons by [@aliabid94](https://github.com/aliabid94) in [PR 883](https://github.com/gradio-app/gradio/pull/883)
-- Checking and updating more demos to use 3.0 syntax by [@abidlabs](https://github.com/abidlabs) in [PR 892](https://github.com/gradio-app/gradio/pull/892)
-- Blocks Tests by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 902](https://github.com/gradio-app/gradio/pull/902)
-- Interface fix by [@pngwn](https://github.com/pngwn) in [PR 901](https://github.com/gradio-app/gradio/pull/901)
-- Quick fix: Issue 893 by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 907](https://github.com/gradio-app/gradio/pull/907)
-- 3d Image Component by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 775](https://github.com/gradio-app/gradio/pull/775)
-- fix endpoint url in prod by [@pngwn](https://github.com/pngwn) in [PR 911](https://github.com/gradio-app/gradio/pull/911)
-- rename Model3d to Image3D by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 912](https://github.com/gradio-app/gradio/pull/912)
-- update pypi to 2.9.1 by [@abidlabs](https://github.com/abidlabs) in [PR 916](https://github.com/gradio-app/gradio/pull/916)
-- blocks-with-fix by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 917](https://github.com/gradio-app/gradio/pull/917)
-- Restore Interpretation, Live, Auth, Queueing by [@aliabid94](https://github.com/aliabid94) in [PR 915](https://github.com/gradio-app/gradio/pull/915)
-- Allow `Blocks` instances to be used like a `Block` in other `Blocks` by [@abidlabs](https://github.com/abidlabs) in [PR 919](https://github.com/gradio-app/gradio/pull/919)
-- Redesign 1 by [@pngwn](https://github.com/pngwn) in [PR 918](https://github.com/gradio-app/gradio/pull/918)
-- blocks-components-tests by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 904](https://github.com/gradio-app/gradio/pull/904)
-- fix unit + browser tests by [@pngwn](https://github.com/pngwn) in [PR 926](https://github.com/gradio-app/gradio/pull/926)
-- blocks-move-test-data by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 927](https://github.com/gradio-app/gradio/pull/927)
-- remove debounce from form inputs by [@pngwn](https://github.com/pngwn) in [PR 932](https://github.com/gradio-app/gradio/pull/932)
-- reimplement webcam video by [@pngwn](https://github.com/pngwn) in [PR 928](https://github.com/gradio-app/gradio/pull/928)
-- blocks-move-test-data by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 941](https://github.com/gradio-app/gradio/pull/941)
-- allow audio components to take a string value by [@pngwn](https://github.com/pngwn) in [PR 930](https://github.com/gradio-app/gradio/pull/930)
-- static mode for textbox by [@pngwn](https://github.com/pngwn) in [PR 929](https://github.com/gradio-app/gradio/pull/929)
-- fix file upload text by [@pngwn](https://github.com/pngwn) in [PR 931](https://github.com/gradio-app/gradio/pull/931)
-- tabbed-interface-rewritten by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 958](https://github.com/gradio-app/gradio/pull/958)
-- Gan demo fix by [@abidlabs](https://github.com/abidlabs) in [PR 965](https://github.com/gradio-app/gradio/pull/965)
-- Blocks analytics by [@abidlabs](https://github.com/abidlabs) in [PR 947](https://github.com/gradio-app/gradio/pull/947)
-- Blocks page load by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 963](https://github.com/gradio-app/gradio/pull/963)
-- add frontend for page load events by [@pngwn](https://github.com/pngwn) in [PR 967](https://github.com/gradio-app/gradio/pull/967)
-- fix i18n and some tweaks by [@pngwn](https://github.com/pngwn) in [PR 966](https://github.com/gradio-app/gradio/pull/966)
-- add jinja2 to reqs by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 969](https://github.com/gradio-app/gradio/pull/969)
-- Cleaning up `Launchable()` by [@abidlabs](https://github.com/abidlabs) in [PR 968](https://github.com/gradio-app/gradio/pull/968)
-- Fix #944 by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 971](https://github.com/gradio-app/gradio/pull/971)
-- New Blocks Demo: neural instrument cloning by [@abidlabs](https://github.com/abidlabs) in [PR 975](https://github.com/gradio-app/gradio/pull/975)
-- Add huggingface_hub client library by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 973](https://github.com/gradio-app/gradio/pull/973)
-- State and variables by [@aliabid94](https://github.com/aliabid94) in [PR 977](https://github.com/gradio-app/gradio/pull/977)
-- update-components by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 986](https://github.com/gradio-app/gradio/pull/986)
-- ensure dataframe updates as expected by [@pngwn](https://github.com/pngwn) in [PR 981](https://github.com/gradio-app/gradio/pull/981)
-- test-guideline by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 990](https://github.com/gradio-app/gradio/pull/990)
-- Issue #785: add footer by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 972](https://github.com/gradio-app/gradio/pull/972)
-- indentation fix by [@abidlabs](https://github.com/abidlabs) in [PR 993](https://github.com/gradio-app/gradio/pull/993)
-- missing quote by [@aliabd](https://github.com/aliabd) in [PR 996](https://github.com/gradio-app/gradio/pull/996)
-- added interactive parameter to components by [@abidlabs](https://github.com/abidlabs) in [PR 992](https://github.com/gradio-app/gradio/pull/992)
-- custom-components by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 985](https://github.com/gradio-app/gradio/pull/985)
-- Refactor component shortcuts by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 995](https://github.com/gradio-app/gradio/pull/995)
-- Plot Component by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 805](https://github.com/gradio-app/gradio/pull/805)
-- updated PyPi version to 2.9.2 by [@abidlabs](https://github.com/abidlabs) in [PR 1002](https://github.com/gradio-app/gradio/pull/1002)
-- Release 2.9.3 by [@abidlabs](https://github.com/abidlabs) in [PR 1003](https://github.com/gradio-app/gradio/pull/1003)
-- Image3D Examples Fix by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1001](https://github.com/gradio-app/gradio/pull/1001)
-- release 2.9.4 by [@abidlabs](https://github.com/abidlabs) in [PR 1006](https://github.com/gradio-app/gradio/pull/1006)
-- templates import hotfix by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1008](https://github.com/gradio-app/gradio/pull/1008)
-- Progress indicator bar by [@aliabid94](https://github.com/aliabid94) in [PR 997](https://github.com/gradio-app/gradio/pull/997)
-- Fixed image input for absolute path by [@JefferyChiang](https://github.com/JefferyChiang) in [PR 1004](https://github.com/gradio-app/gradio/pull/1004)
-- Model3D + Plot Components by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1010](https://github.com/gradio-app/gradio/pull/1010)
-- Gradio Guides: Creating CryptoPunks with GANs by [@NimaBoscarino](https://github.com/NimaBoscarino) in [PR 1000](https://github.com/gradio-app/gradio/pull/1000)
-- [BIG PR] Gradio blocks & redesigned components by [@abidlabs](https://github.com/abidlabs) in [PR 880](https://github.com/gradio-app/gradio/pull/880)
-- fixed failing test on main by [@abidlabs](https://github.com/abidlabs) in [PR 1023](https://github.com/gradio-app/gradio/pull/1023)
-- Use smaller ASR model in external test by [@abidlabs](https://github.com/abidlabs) in [PR 1024](https://github.com/gradio-app/gradio/pull/1024)
-- updated PyPi version to 2.9.0b by [@abidlabs](https://github.com/abidlabs) in [PR 1026](https://github.com/gradio-app/gradio/pull/1026)
-- Fixing import issues so that the package successfully installs on colab notebooks by [@abidlabs](https://github.com/abidlabs) in [PR 1027](https://github.com/gradio-app/gradio/pull/1027)
-- Update website tracker slackbot by [@aliabd](https://github.com/aliabd) in [PR 1037](https://github.com/gradio-app/gradio/pull/1037)
-- textbox-autoheight by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1009](https://github.com/gradio-app/gradio/pull/1009)
-- Model3D Examples fixes by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1035](https://github.com/gradio-app/gradio/pull/1035)
-- GAN Gradio Guide: Adjustments to iframe heights by [@NimaBoscarino](https://github.com/NimaBoscarino) in [PR 1042](https://github.com/gradio-app/gradio/pull/1042)
-- added better default labels to form components by [@abidlabs](https://github.com/abidlabs) in [PR 1040](https://github.com/gradio-app/gradio/pull/1040)
-- Slackbot web tracker fix by [@aliabd](https://github.com/aliabd) in [PR 1043](https://github.com/gradio-app/gradio/pull/1043)
-- Plot fixes by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1044](https://github.com/gradio-app/gradio/pull/1044)
-- Small fixes to the demos by [@abidlabs](https://github.com/abidlabs) in [PR 1030](https://github.com/gradio-app/gradio/pull/1030)
-- fixing demo issue with website by [@aliabd](https://github.com/aliabd) in [PR 1047](https://github.com/gradio-app/gradio/pull/1047)
-- [hotfix] HighlightedText by [@aliabid94](https://github.com/aliabid94) in [PR 1046](https://github.com/gradio-app/gradio/pull/1046)
-- Update text by [@ronvoluted](https://github.com/ronvoluted) in [PR 1050](https://github.com/gradio-app/gradio/pull/1050)
-- Update CONTRIBUTING.md by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1052](https://github.com/gradio-app/gradio/pull/1052)
-- fix(ui): Increase contrast for footer by [@ronvoluted](https://github.com/ronvoluted) in [PR 1048](https://github.com/gradio-app/gradio/pull/1048)
-- UI design update by [@gary149](https://github.com/gary149) in [PR 1041](https://github.com/gradio-app/gradio/pull/1041)
-- updated PyPi version to 2.9.0b8 by [@abidlabs](https://github.com/abidlabs) in [PR 1059](https://github.com/gradio-app/gradio/pull/1059)
-- Running, testing, and fixing demos by [@abidlabs](https://github.com/abidlabs) in [PR 1060](https://github.com/gradio-app/gradio/pull/1060)
-- Form layout by [@pngwn](https://github.com/pngwn) in [PR 1054](https://github.com/gradio-app/gradio/pull/1054)
-- inputless-interfaces by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1038](https://github.com/gradio-app/gradio/pull/1038)
-- Update PULL_REQUEST_TEMPLATE.md by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1068](https://github.com/gradio-app/gradio/pull/1068)
-- Upgrading node memory to 4gb in website Docker by [@aliabd](https://github.com/aliabd) in [PR 1069](https://github.com/gradio-app/gradio/pull/1069)
-- Website reload error by [@aliabd](https://github.com/aliabd) in [PR 1079](https://github.com/gradio-app/gradio/pull/1079)
-- fixed favicon issue by [@abidlabs](https://github.com/abidlabs) in [PR 1064](https://github.com/gradio-app/gradio/pull/1064)
-- remove-queue-from-events by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1056](https://github.com/gradio-app/gradio/pull/1056)
-- Enable vertex colors for OBJs files by [@radames](https://github.com/radames) in [PR 1074](https://github.com/gradio-app/gradio/pull/1074)
-- Dark text by [@ronvoluted](https://github.com/ronvoluted) in [PR 1049](https://github.com/gradio-app/gradio/pull/1049)
-- Scroll to output by [@pngwn](https://github.com/pngwn) in [PR 1077](https://github.com/gradio-app/gradio/pull/1077)
-- Explicitly list pnpm version 6 in contributing guide by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1085](https://github.com/gradio-app/gradio/pull/1085)
-- hotfix for encrypt issue by [@abidlabs](https://github.com/abidlabs) in [PR 1096](https://github.com/gradio-app/gradio/pull/1096)
-- Release 2.9b9 by [@abidlabs](https://github.com/abidlabs) in [PR 1098](https://github.com/gradio-app/gradio/pull/1098)
-- tweak node circleci settings by [@pngwn](https://github.com/pngwn) in [PR 1091](https://github.com/gradio-app/gradio/pull/1091)
-- Website Reload Error by [@aliabd](https://github.com/aliabd) in [PR 1099](https://github.com/gradio-app/gradio/pull/1099)
-- Website Reload: README in demos docker by [@aliabd](https://github.com/aliabd) in [PR 1100](https://github.com/gradio-app/gradio/pull/1100)
-- Flagging fixes by [@abidlabs](https://github.com/abidlabs) in [PR 1081](https://github.com/gradio-app/gradio/pull/1081)
-- Backend for optional labels by [@abidlabs](https://github.com/abidlabs) in [PR 1080](https://github.com/gradio-app/gradio/pull/1080)
-- Optional labels fe by [@pngwn](https://github.com/pngwn) in [PR 1105](https://github.com/gradio-app/gradio/pull/1105)
-- clean-deprecated-parameters by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1090](https://github.com/gradio-app/gradio/pull/1090)
-- Blocks rendering fix by [@abidlabs](https://github.com/abidlabs) in [PR 1102](https://github.com/gradio-app/gradio/pull/1102)
-- Redos #1106 by [@abidlabs](https://github.com/abidlabs) in [PR 1112](https://github.com/gradio-app/gradio/pull/1112)
-- Interface types: handle input-only, output-only, and unified interfaces by [@abidlabs](https://github.com/abidlabs) in [PR 1108](https://github.com/gradio-app/gradio/pull/1108)
-- Hotfix + New pypi release 2.9b11 by [@abidlabs](https://github.com/abidlabs) in [PR 1118](https://github.com/gradio-app/gradio/pull/1118)
-- issue-checkbox by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1122](https://github.com/gradio-app/gradio/pull/1122)
-- issue-checkbox-hotfix by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1127](https://github.com/gradio-app/gradio/pull/1127)
-- Fix demos in website by [@aliabd](https://github.com/aliabd) in [PR 1130](https://github.com/gradio-app/gradio/pull/1130)
-- Guide for Gradio ONNX model zoo on Huggingface by [@AK391](https://github.com/AK391) in [PR 1073](https://github.com/gradio-app/gradio/pull/1073)
-- ONNX guide fixes by [@aliabd](https://github.com/aliabd) in [PR 1131](https://github.com/gradio-app/gradio/pull/1131)
-- Stacked form inputs css by [@gary149](https://github.com/gary149) in [PR 1134](https://github.com/gradio-app/gradio/pull/1134)
-- made default value in textbox empty string by [@abidlabs](https://github.com/abidlabs) in [PR 1135](https://github.com/gradio-app/gradio/pull/1135)
-- Examples UI by [@gary149](https://github.com/gary149) in [PR 1121](https://github.com/gradio-app/gradio/pull/1121)
-- Chatbot custom color support by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1092](https://github.com/gradio-app/gradio/pull/1092)
-- highlighted text colors by [@pngwn](https://github.com/pngwn) in [PR 1119](https://github.com/gradio-app/gradio/pull/1119)
-- pin to pnpm 6 for now by [@pngwn](https://github.com/pngwn) in [PR 1147](https://github.com/gradio-app/gradio/pull/1147)
-- Restore queue in Blocks by [@aliabid94](https://github.com/aliabid94) in [PR 1137](https://github.com/gradio-app/gradio/pull/1137)
-- add select event for tabitems by [@pngwn](https://github.com/pngwn) in [PR 1154](https://github.com/gradio-app/gradio/pull/1154)
-- max_lines + autoheight for textbox by [@pngwn](https://github.com/pngwn) in [PR 1153](https://github.com/gradio-app/gradio/pull/1153)
-- use color palette for chatbot by [@pngwn](https://github.com/pngwn) in [PR 1152](https://github.com/gradio-app/gradio/pull/1152)
-- Timeseries improvements by [@pngwn](https://github.com/pngwn) in [PR 1149](https://github.com/gradio-app/gradio/pull/1149)
-- move styling for interface panels to frontend by [@pngwn](https://github.com/pngwn) in [PR 1146](https://github.com/gradio-app/gradio/pull/1146)
-- html tweaks by [@pngwn](https://github.com/pngwn) in [PR 1145](https://github.com/gradio-app/gradio/pull/1145)
-- Issue #768: Support passing none to resize and crop image by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1144](https://github.com/gradio-app/gradio/pull/1144)
-- image gallery component + img css by [@aliabid94](https://github.com/aliabid94) in [PR 1140](https://github.com/gradio-app/gradio/pull/1140)
-- networking tweak by [@abidlabs](https://github.com/abidlabs) in [PR 1143](https://github.com/gradio-app/gradio/pull/1143)
-- Allow enabling queue per event listener by [@aliabid94](https://github.com/aliabid94) in [PR 1155](https://github.com/gradio-app/gradio/pull/1155)
-- config hotfix and v. 2.9b23 by [@abidlabs](https://github.com/abidlabs) in [PR 1158](https://github.com/gradio-app/gradio/pull/1158)
-- Custom JS calls by [@aliabid94](https://github.com/aliabid94) in [PR 1082](https://github.com/gradio-app/gradio/pull/1082)
-- Small fixes: queue default fix, ffmpeg installation message by [@abidlabs](https://github.com/abidlabs) in [PR 1159](https://github.com/gradio-app/gradio/pull/1159)
-- formatting by [@abidlabs](https://github.com/abidlabs) in [PR 1161](https://github.com/gradio-app/gradio/pull/1161)
-- enable flex grow for gr-box by [@radames](https://github.com/radames) in [PR 1165](https://github.com/gradio-app/gradio/pull/1165)
-- 1148 loading by [@pngwn](https://github.com/pngwn) in [PR 1164](https://github.com/gradio-app/gradio/pull/1164)
-- Put enable_queue kwarg back in launch() by [@aliabid94](https://github.com/aliabid94) in [PR 1167](https://github.com/gradio-app/gradio/pull/1167)
-- A few small fixes by [@abidlabs](https://github.com/abidlabs) in [PR 1171](https://github.com/gradio-app/gradio/pull/1171)
-- Hotfix for dropdown component by [@abidlabs](https://github.com/abidlabs) in [PR 1172](https://github.com/gradio-app/gradio/pull/1172)
-- use secondary buttons in interface by [@pngwn](https://github.com/pngwn) in [PR 1173](https://github.com/gradio-app/gradio/pull/1173)
-- 1183 component height by [@pngwn](https://github.com/pngwn) in [PR 1185](https://github.com/gradio-app/gradio/pull/1185)
-- 962 dataframe by [@pngwn](https://github.com/pngwn) in [PR 1186](https://github.com/gradio-app/gradio/pull/1186)
-- update-contributing by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1188](https://github.com/gradio-app/gradio/pull/1188)
-- Table tweaks by [@pngwn](https://github.com/pngwn) in [PR 1195](https://github.com/gradio-app/gradio/pull/1195)
-- wrap tab content in column by [@pngwn](https://github.com/pngwn) in [PR 1200](https://github.com/gradio-app/gradio/pull/1200)
-- WIP: Add dark mode support by [@gary149](https://github.com/gary149) in [PR 1187](https://github.com/gradio-app/gradio/pull/1187)
-- Restored /api/predict/ endpoint for Interfaces by [@abidlabs](https://github.com/abidlabs) in [PR 1199](https://github.com/gradio-app/gradio/pull/1199)
-- hltext-label by [@pngwn](https://github.com/pngwn) in [PR 1204](https://github.com/gradio-app/gradio/pull/1204)
-- add copy functionality to json by [@pngwn](https://github.com/pngwn) in [PR 1205](https://github.com/gradio-app/gradio/pull/1205)
-- Update component config by [@aliabid94](https://github.com/aliabid94) in [PR 1089](https://github.com/gradio-app/gradio/pull/1089)
-- fix placeholder prompt by [@pngwn](https://github.com/pngwn) in [PR 1215](https://github.com/gradio-app/gradio/pull/1215)
-- ensure webcam video value is propagated correctly by [@pngwn](https://github.com/pngwn) in [PR 1218](https://github.com/gradio-app/gradio/pull/1218)
-- Automatic word-break in highlighted text, combine_adjacent support by [@aliabid94](https://github.com/aliabid94) in [PR 1209](https://github.com/gradio-app/gradio/pull/1209)
-- async-function-support by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1190](https://github.com/gradio-app/gradio/pull/1190)
-- Sharing fix for assets by [@aliabid94](https://github.com/aliabid94) in [PR 1208](https://github.com/gradio-app/gradio/pull/1208)
-- Hotfixes for course demos by [@abidlabs](https://github.com/abidlabs) in [PR 1222](https://github.com/gradio-app/gradio/pull/1222)
-- Allow Custom CSS by [@aliabid94](https://github.com/aliabid94) in [PR 1170](https://github.com/gradio-app/gradio/pull/1170)
-- share-hotfix by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1226](https://github.com/gradio-app/gradio/pull/1226)
-- tweaks by [@pngwn](https://github.com/pngwn) in [PR 1229](https://github.com/gradio-app/gradio/pull/1229)
-- white space for class concatenation by [@radames](https://github.com/radames) in [PR 1228](https://github.com/gradio-app/gradio/pull/1228)
-- Tweaks by [@pngwn](https://github.com/pngwn) in [PR 1230](https://github.com/gradio-app/gradio/pull/1230)
-- css tweaks by [@pngwn](https://github.com/pngwn) in [PR 1235](https://github.com/gradio-app/gradio/pull/1235)
-- ensure defaults height match for media inputs by [@pngwn](https://github.com/pngwn) in [PR 1236](https://github.com/gradio-app/gradio/pull/1236)
-- Default Label label value by [@radames](https://github.com/radames) in [PR 1239](https://github.com/gradio-app/gradio/pull/1239)
-- update-shortcut-syntax by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1234](https://github.com/gradio-app/gradio/pull/1234)
-- Update version.txt by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1244](https://github.com/gradio-app/gradio/pull/1244)
-- Layout bugs by [@pngwn](https://github.com/pngwn) in [PR 1246](https://github.com/gradio-app/gradio/pull/1246)
-- Update demo by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1253](https://github.com/gradio-app/gradio/pull/1253)
-- Button default name by [@FarukOzderim](https://github.com/FarukOzderim) in [PR 1243](https://github.com/gradio-app/gradio/pull/1243)
-- Labels spacing by [@gary149](https://github.com/gary149) in [PR 1254](https://github.com/gradio-app/gradio/pull/1254)
-- add global loader for gradio app by [@pngwn](https://github.com/pngwn) in [PR 1251](https://github.com/gradio-app/gradio/pull/1251)
-- ui apis for dalle-mini by [@pngwn](https://github.com/pngwn) in [PR 1258](https://github.com/gradio-app/gradio/pull/1258)
-- Add precision to Number, backend only by [@freddyaboulton](https://github.com/freddyaboulton) in [PR 1125](https://github.com/gradio-app/gradio/pull/1125)
-- Website Design Changes by [@abidlabs](https://github.com/abidlabs) in [PR 1015](https://github.com/gradio-app/gradio/pull/1015)
-- Small fixes for multiple demos compatible with 3.0 by [@radames](https://github.com/radames) in [PR 1257](https://github.com/gradio-app/gradio/pull/1257)
-- Issue #1160: Model 3D component not destroyed correctly by [@dawoodkhan82](https://github.com/dawoodkhan82) in [PR 1219](https://github.com/gradio-app/gradio/pull/1219)
-- Fixes to components by [@abidlabs](https://github.com/abidlabs) in [PR 1260](https://github.com/gradio-app/gradio/pull/1260)
-- layout docs by [@abidlabs](https://github.com/abidlabs) in [PR 1263](https://github.com/gradio-app/gradio/pull/1263)
-- Static forms by [@pngwn](https://github.com/pngwn) in [PR 1264](https://github.com/gradio-app/gradio/pull/1264)
-- Cdn assets by [@pngwn](https://github.com/pngwn) in [PR 1265](https://github.com/gradio-app/gradio/pull/1265)
-- update logo by [@gary149](https://github.com/gary149) in [PR 1266](https://github.com/gradio-app/gradio/pull/1266)
-- fix slider by [@aliabid94](https://github.com/aliabid94) in [PR 1268](https://github.com/gradio-app/gradio/pull/1268)
-- maybe fix auth in iframes by [@pngwn](https://github.com/pngwn) in [PR 1261](https://github.com/gradio-app/gradio/pull/1261)
-- Improves "Getting Started" guide by [@abidlabs](https://github.com/abidlabs) in [PR 1269](https://github.com/gradio-app/gradio/pull/1269)
-- Add embedded demos to website by [@aliabid94](https://github.com/aliabid94) in [PR 1270](https://github.com/gradio-app/gradio/pull/1270)
-- Label hotfixes by [@abidlabs](https://github.com/abidlabs) in [PR 1281](https://github.com/gradio-app/gradio/pull/1281)
-- General tweaks by [@pngwn](https://github.com/pngwn) in [PR 1276](https://github.com/gradio-app/gradio/pull/1276)
-- only affect links within the document by [@pngwn](https://github.com/pngwn) in [PR 1282](https://github.com/gradio-app/gradio/pull/1282)
-- release 3.0b9 by [@abidlabs](https://github.com/abidlabs) in [PR 1283](https://github.com/gradio-app/gradio/pull/1283)
-- Dm by [@pngwn](https://github.com/pngwn) in [PR 1284](https://github.com/gradio-app/gradio/pull/1284)
-- Website fixes by [@aliabd](https://github.com/aliabd) in [PR 1286](https://github.com/gradio-app/gradio/pull/1286)
-- Create Streamables by [@aliabid94](https://github.com/aliabid94) in [PR 1279](https://github.com/gradio-app/gradio/pull/1279)
-- ensure table works on mobile by [@pngwn](https://github.com/pngwn) in [PR 1277](https://github.com/gradio-app/gradio/pull/1277)
-- changes by [@aliabid94](https://github.com/aliabid94) in [PR 1287](https://github.com/gradio-app/gradio/pull/1287)
-- demo alignment on landing page by [@aliabd](https://github.com/aliabd) in [PR 1288](https://github.com/gradio-app/gradio/pull/1288)
-- New meta img by [@aliabd](https://github.com/aliabd) in [PR 1289](https://github.com/gradio-app/gradio/pull/1289)
-- updated PyPi version to 3.0 by [@abidlabs](https://github.com/abidlabs) in [PR 1290](https://github.com/gradio-app/gradio/pull/1290)
-- Fix site by [@aliabid94](https://github.com/aliabid94) in [PR 1291](https://github.com/gradio-app/gradio/pull/1291)
-- Mobile responsive guides by [@aliabd](https://github.com/aliabd) in [PR 1293](https://github.com/gradio-app/gradio/pull/1293)
-- Update readme by [@abidlabs](https://github.com/abidlabs) in [PR 1292](https://github.com/gradio-app/gradio/pull/1292)
-- gif by [@abidlabs](https://github.com/abidlabs) in [PR 1296](https://github.com/gradio-app/gradio/pull/1296)
-- Allow decoding headerless b64 string [@1lint](https://github.com/1lint) in [PR 4031](https://github.com/gradio-app/gradio/pull/4031)
-
-### Contributors Shoutout:
-
-- [@JefferyChiang](https://github.com/JefferyChiang) made their first contribution in [PR 1004](https://github.com/gradio-app/gradio/pull/1004)
-- [@NimaBoscarino](https://github.com/NimaBoscarino) made their first contribution in [PR 1000](https://github.com/gradio-app/gradio/pull/1000)
-- [@ronvoluted](https://github.com/ronvoluted) made their first contribution in [PR 1050](https://github.com/gradio-app/gradio/pull/1050)
-- [@radames](https://github.com/radames) made their first contribution in [PR 1074](https://github.com/gradio-app/gradio/pull/1074)
-- [@freddyaboulton](https://github.com/freddyaboulton) made their first contribution in [PR 1085](https://github.com/gradio-app/gradio/pull/1085)
-- [@liteli1987gmail](https://github.com/liteli1987gmail) & [@chenglu](https://github.com/chenglu) made their first contribution in [PR 4767](https://github.com/gradio-app/gradio/pull/4767)
\ No newline at end of file
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/_frontend_code/video/index.ts b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/_frontend_code/video/index.ts
deleted file mode 100644
index fb16e8563328d74fd0f3d96dd20e0ccd12a60bb8..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/_frontend_code/video/index.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-export { default as BaseInteractiveVideo } from "./shared/InteractiveVideo.svelte";
-export { default as BaseStaticVideo } from "./shared/VideoPreview.svelte";
-export { default as BasePlayer } from "./shared/Player.svelte";
-export { prettyBytes, playable, loaded } from "./shared/utils";
-export { default as BaseExample } from "./Example.svelte";
-import { default as Index } from "./Index.svelte";
-export default Index;
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-099544a6.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-099544a6.js
deleted file mode 100644
index 1bc0836ddf2c5ab0960d81ff648c7628ed107b71..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-099544a6.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{B as m}from"./Button-8eeccca1.js";import"./Index-c74a8b7c.js";import"./index-50ad4c77.js";import"./svelte/svelte.js";const{SvelteComponent:u,create_component:r,create_slot:d,destroy_component:b,get_all_dirty_from_scope:g,get_slot_changes:v,init:p,mount_component:h,safe_not_equal:k,transition_in:f,transition_out:c,update_slot_base:w}=window.__gradio__svelte__internal;function B(i){let l;const s=i[3].default,e=d(s,i,i[4],null);return{c(){e&&e.c()},m(t,n){e&&e.m(t,n),l=!0},p(t,n){e&&e.p&&(!l||n&16)&&w(e,s,t,t[4],l?v(s,t[4],n,null):g(t[4]),null)},i(t){l||(f(e,t),l=!0)},o(t){c(e,t),l=!1},d(t){e&&e.d(t)}}}function q(i){let l,s;return l=new m({props:{elem_id:i[0],elem_classes:i[1],visible:i[2],explicit_call:!0,$$slots:{default:[B]},$$scope:{ctx:i}}}),{c(){r(l.$$.fragment)},m(e,t){h(l,e,t),s=!0},p(e,[t]){const n={};t&1&&(n.elem_id=e[0]),t&2&&(n.elem_classes=e[1]),t&4&&(n.visible=e[2]),t&16&&(n.$$scope={dirty:t,ctx:e}),l.$set(n)},i(e){s||(f(l.$$.fragment,e),s=!0)},o(e){c(l.$$.fragment,e),s=!1},d(e){b(l,e)}}}function C(i,l,s){let{$$slots:e={},$$scope:t}=l,{elem_id:n}=l,{elem_classes:_}=l,{visible:a=!0}=l;return i.$$set=o=>{"elem_id"in o&&s(0,n=o.elem_id),"elem_classes"in o&&s(1,_=o.elem_classes),"visible"in o&&s(2,a=o.visible),"$$scope"in o&&s(4,t=o.$$scope)},[n,_,a,e,t]}class A extends u{constructor(l){super(),p(this,l,C,q,k,{elem_id:0,elem_classes:1,visible:2})}}export{A as default};
-//# sourceMappingURL=Index-099544a6.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-b658ebcd.css b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-b658ebcd.css
deleted file mode 100644
index fac47a18f277af0ea91ccc6bb053f5da928ef322..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-b658ebcd.css
+++ /dev/null
@@ -1 +0,0 @@
-.spacer.svelte-1kspdo{display:inline-block;width:0;height:0}.json-node.svelte-1kspdo{display:inline;color:var(--body-text-color);line-height:var(--line-sm);font-family:var(--font-mono)}.expand-array.svelte-1kspdo{border:1px solid var(--border-color-primary);border-radius:var(--radius-sm);background:var(--background-fill-secondary);padding:0 var(--size-1);color:var(--body-text-color)}.expand-array.svelte-1kspdo:hover{background:var(--background-fill-primary)}.children.svelte-1kspdo{padding-left:var(--size-4)}.json-item.svelte-1kspdo{display:inline}.null.svelte-1kspdo{color:var(--body-text-color-subdued)}.string.svelte-1kspdo{color:var(--color-green-500)}.number.svelte-1kspdo{color:var(--color-blue-500)}.bool.svelte-1kspdo{color:var(--color-red-500)}.json-holder.svelte-6fc7le{padding:var(--size-2)}.empty-wrapper.svelte-6fc7le{min-height:calc(var(--size-32) - 20px)}button.svelte-6fc7le{display:flex;position:absolute;top:var(--block-label-margin);right:var(--block-label-margin);align-items:center;box-shadow:var(--shadow-drop);border:1px solid var(--border-color-primary);border-top:none;border-right:none;border-radius:var(--block-label-right-radius);background:var(--block-label-background-fill);padding:5px;width:22px;height:22px;overflow:hidden;color:var(--block-label-text-color);font:var(--font);font-size:var(--button-small-text-size)}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/h11/tests/helpers.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/h11/tests/helpers.py
deleted file mode 100644
index 571be44461b0847c9edb8654c9d528abed0b7800..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/h11/tests/helpers.py
+++ /dev/null
@@ -1,101 +0,0 @@
-from typing import cast, List, Type, Union, ValuesView
-
-from .._connection import Connection, NEED_DATA, PAUSED
-from .._events import (
-    ConnectionClosed,
-    Data,
-    EndOfMessage,
-    Event,
-    InformationalResponse,
-    Request,
-    Response,
-)
-from .._state import CLIENT, CLOSED, DONE, MUST_CLOSE, SERVER
-from .._util import Sentinel
-
-try:
-    from typing import Literal
-except ImportError:
-    from typing_extensions import Literal  # type: ignore
-
-
-def get_all_events(conn: Connection) -> List[Event]:
-    got_events = []
-    while True:
-        event = conn.next_event()
-        if event in (NEED_DATA, PAUSED):
-            break
-        event = cast(Event, event)
-        got_events.append(event)
-        if type(event) is ConnectionClosed:
-            break
-    return got_events
-
-
-def receive_and_get(conn: Connection, data: bytes) -> List[Event]:
-    conn.receive_data(data)
-    return get_all_events(conn)
-
-
-# Merges adjacent Data events, converts payloads to bytestrings, and removes
-# chunk boundaries.
-def normalize_data_events(in_events: List[Event]) -> List[Event]:
-    out_events: List[Event] = []
-    for event in in_events:
-        if type(event) is Data:
-            event = Data(data=bytes(event.data), chunk_start=False, chunk_end=False)
-        if out_events and type(out_events[-1]) is type(event) is Data:
-            out_events[-1] = Data(
-                data=out_events[-1].data + event.data,
-                chunk_start=out_events[-1].chunk_start,
-                chunk_end=out_events[-1].chunk_end,
-            )
-        else:
-            out_events.append(event)
-    return out_events
-
-
-# Given that we want to write tests that push some events through a Connection
-# and check that its state updates appropriately... we might as make a habit
-# of pushing them through two Connections with a fake network link in
-# between.
-class ConnectionPair:
-    def __init__(self) -> None:
-        self.conn = {CLIENT: Connection(CLIENT), SERVER: Connection(SERVER)}
-        self.other = {CLIENT: SERVER, SERVER: CLIENT}
-
-    @property
-    def conns(self) -> ValuesView[Connection]:
-        return self.conn.values()
-
-    # expect="match" if expect=send_events; expect=[...] to say what expected
-    def send(
-        self,
-        role: Type[Sentinel],
-        send_events: Union[List[Event], Event],
-        expect: Union[List[Event], Event, Literal["match"]] = "match",
-    ) -> bytes:
-        if not isinstance(send_events, list):
-            send_events = [send_events]
-        data = b""
-        closed = False
-        for send_event in send_events:
-            new_data = self.conn[role].send(send_event)
-            if new_data is None:
-                closed = True
-            else:
-                data += new_data
-        # send uses b"" to mean b"", and None to mean closed
-        # receive uses b"" to mean closed, and None to mean "try again"
-        # so we have to translate between the two conventions
-        if data:
-            self.conn[self.other[role]].receive_data(data)
-        if closed:
-            self.conn[self.other[role]].receive_data(b"")
-        got_events = get_all_events(self.conn[self.other[role]])
-        if expect == "match":
-            expect = send_events
-        if not isinstance(expect, list):
-            expect = [expect]
-        assert got_events == expect
-        return data
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/web_backend/ipython_inline_figure.html b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/web_backend/ipython_inline_figure.html
deleted file mode 100644
index b941d352a7d6ca1351b7fb9879386c2d391e9be7..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/backends/web_backend/ipython_inline_figure.html
+++ /dev/null
@@ -1,34 +0,0 @@
-<!-- Within the kernel, we don't know the address of the matplotlib
-     websocket server, so we have to get in client-side and fetch our
-     resources that way. -->
-<script>
-  // We can't proceed until these JavaScript files are fetched, so
-  // we fetch them synchronously
-  $.ajaxSetup({async: false});
-  $.getScript("http://" + window.location.hostname + ":{{ port }}{{prefix}}/_static/js/mpl_tornado.js");
-  $.getScript("http://" + window.location.hostname + ":{{ port }}{{prefix}}/js/mpl.js");
-  $.ajaxSetup({async: true});
-
-  function init_figure{{ fig_id }}(e) {
-    $('div.output').off('resize');
-
-    var output_div = e.target.querySelector('div.output_subarea');
-    var websocket_type = mpl.get_websocket_type();
-    var websocket = new websocket_type(
-        "ws://" + window.location.hostname + ":{{ port }}{{ prefix}}/" +
-        {{ repr(str(fig_id)) }} + "/ws");
-
-    var fig = new mpl.figure(
-        {{repr(str(fig_id))}}, websocket, mpl_ondownload, output_div);
-
-    // Fetch the first image
-    fig.context.drawImage(fig.imageObj, 0, 0);
-
-    fig.focus_on_mouseover = true;
-  }
-
-  // We can't initialize the figure contents until our content
-  // has been added to the DOM.  This is a bit of hack to get an
-  // event for that.
-  $('div.output').resize(init_figure{{ fig_id }});
-</script>
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/fcompiler/lahey.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/fcompiler/lahey.py
deleted file mode 100644
index e925838268b82d9c26d94e811717cdc58e269a12..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/fcompiler/lahey.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import os
-
-from numpy.distutils.fcompiler import FCompiler
-
-compilers = ['LaheyFCompiler']
-
-class LaheyFCompiler(FCompiler):
-
-    compiler_type = 'lahey'
-    description = 'Lahey/Fujitsu Fortran 95 Compiler'
-    version_pattern =  r'Lahey/Fujitsu Fortran 95 Compiler Release (?P<version>[^\s*]*)'
-
-    executables = {
-        'version_cmd'  : ["<F90>", "--version"],
-        'compiler_f77' : ["lf95", "--fix"],
-        'compiler_fix' : ["lf95", "--fix"],
-        'compiler_f90' : ["lf95"],
-        'linker_so'    : ["lf95", "-shared"],
-        'archiver'     : ["ar", "-cr"],
-        'ranlib'       : ["ranlib"]
-        }
-
-    module_dir_switch = None  #XXX Fix me
-    module_include_switch = None #XXX Fix me
-
-    def get_flags_opt(self):
-        return ['-O']
-    def get_flags_debug(self):
-        return ['-g', '--chk', '--chkglobal']
-    def get_library_dirs(self):
-        opt = []
-        d = os.environ.get('LAHEY')
-        if d:
-            opt.append(os.path.join(d, 'lib'))
-        return opt
-    def get_libraries(self):
-        opt = []
-        opt.extend(['fj9f6', 'fj9i6', 'fj9ipp', 'fj9e6'])
-        return opt
-
-if __name__ == '__main__':
-    from distutils import log
-    log.set_verbosity(2)
-    from numpy.distutils import customized_fcompiler
-    print(customized_fcompiler(compiler='lahey').get_version())
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/misc_util.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/misc_util.py
deleted file mode 100644
index e226b47448153e34487def3176d5991319312363..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/misc_util.py
+++ /dev/null
@@ -1,2493 +0,0 @@
-import os
-import re
-import sys
-import copy
-import glob
-import atexit
-import tempfile
-import subprocess
-import shutil
-import multiprocessing
-import textwrap
-import importlib.util
-from threading import local as tlocal
-from functools import reduce
-
-import distutils
-from distutils.errors import DistutilsError
-
-# stores temporary directory of each thread to only create one per thread
-_tdata = tlocal()
-
-# store all created temporary directories so they can be deleted on exit
-_tmpdirs = []
-def clean_up_temporary_directory():
-    if _tmpdirs is not None:
-        for d in _tmpdirs:
-            try:
-                shutil.rmtree(d)
-            except OSError:
-                pass
-
-atexit.register(clean_up_temporary_directory)
-
-__all__ = ['Configuration', 'get_numpy_include_dirs', 'default_config_dict',
-           'dict_append', 'appendpath', 'generate_config_py',
-           'get_cmd', 'allpath', 'get_mathlibs',
-           'terminal_has_colors', 'red_text', 'green_text', 'yellow_text',
-           'blue_text', 'cyan_text', 'cyg2win32', 'mingw32', 'all_strings',
-           'has_f_sources', 'has_cxx_sources', 'filter_sources',
-           'get_dependencies', 'is_local_src_dir', 'get_ext_source_files',
-           'get_script_files', 'get_lib_source_files', 'get_data_files',
-           'dot_join', 'get_frame', 'minrelpath', 'njoin',
-           'is_sequence', 'is_string', 'as_list', 'gpaths', 'get_language',
-           'get_build_architecture', 'get_info', 'get_pkg_info',
-           'get_num_build_jobs', 'sanitize_cxx_flags',
-           'exec_mod_from_location']
-
-class InstallableLib:
-    """
-    Container to hold information on an installable library.
-
-    Parameters
-    ----------
-    name : str
-        Name of the installed library.
-    build_info : dict
-        Dictionary holding build information.
-    target_dir : str
-        Absolute path specifying where to install the library.
-
-    See Also
-    --------
-    Configuration.add_installed_library
-
-    Notes
-    -----
-    The three parameters are stored as attributes with the same names.
-
-    """
-    def __init__(self, name, build_info, target_dir):
-        self.name = name
-        self.build_info = build_info
-        self.target_dir = target_dir
-
-
-def get_num_build_jobs():
-    """
-    Get number of parallel build jobs set by the --parallel command line
-    argument of setup.py
-    If the command did not receive a setting the environment variable
-    NPY_NUM_BUILD_JOBS is checked. If that is unset, return the number of
-    processors on the system, with a maximum of 8 (to prevent
-    overloading the system if there a lot of CPUs).
-
-    Returns
-    -------
-    out : int
-        number of parallel jobs that can be run
-
-    """
-    from numpy.distutils.core import get_distribution
-    try:
-        cpu_count = len(os.sched_getaffinity(0))
-    except AttributeError:
-        cpu_count = multiprocessing.cpu_count()
-    cpu_count = min(cpu_count, 8)
-    envjobs = int(os.environ.get("NPY_NUM_BUILD_JOBS", cpu_count))
-    dist = get_distribution()
-    # may be None during configuration
-    if dist is None:
-        return envjobs
-
-    # any of these three may have the job set, take the largest
-    cmdattr = (getattr(dist.get_command_obj('build'), 'parallel', None),
-               getattr(dist.get_command_obj('build_ext'), 'parallel', None),
-               getattr(dist.get_command_obj('build_clib'), 'parallel', None))
-    if all(x is None for x in cmdattr):
-        return envjobs
-    else:
-        return max(x for x in cmdattr if x is not None)
-
-def quote_args(args):
-    """Quote list of arguments.
-
-    .. deprecated:: 1.22.
-    """
-    import warnings
-    warnings.warn('"quote_args" is deprecated.',
-                  DeprecationWarning, stacklevel=2)
-    # don't used _nt_quote_args as it does not check if
-    # args items already have quotes or not.
-    args = list(args)
-    for i in range(len(args)):
-        a = args[i]
-        if ' ' in a and a[0] not in '"\'':
-            args[i] = '"%s"' % (a)
-    return args
-
-def allpath(name):
-    "Convert a /-separated pathname to one using the OS's path separator."
-    split = name.split('/')
-    return os.path.join(*split)
-
-def rel_path(path, parent_path):
-    """Return path relative to parent_path."""
-    # Use realpath to avoid issues with symlinked dirs (see gh-7707)
-    pd = os.path.realpath(os.path.abspath(parent_path))
-    apath = os.path.realpath(os.path.abspath(path))
-    if len(apath) < len(pd):
-        return path
-    if apath == pd:
-        return ''
-    if pd == apath[:len(pd)]:
-        assert apath[len(pd)] in [os.sep], repr((path, apath[len(pd)]))
-        path = apath[len(pd)+1:]
-    return path
-
-def get_path_from_frame(frame, parent_path=None):
-    """Return path of the module given a frame object from the call stack.
-
-    Returned path is relative to parent_path when given,
-    otherwise it is absolute path.
-    """
-
-    # First, try to find if the file name is in the frame.
-    try:
-        caller_file = eval('__file__', frame.f_globals, frame.f_locals)
-        d = os.path.dirname(os.path.abspath(caller_file))
-    except NameError:
-        # __file__ is not defined, so let's try __name__. We try this second
-        # because setuptools spoofs __name__ to be '__main__' even though
-        # sys.modules['__main__'] might be something else, like easy_install(1).
-        caller_name = eval('__name__', frame.f_globals, frame.f_locals)
-        __import__(caller_name)
-        mod = sys.modules[caller_name]
-        if hasattr(mod, '__file__'):
-            d = os.path.dirname(os.path.abspath(mod.__file__))
-        else:
-            # we're probably running setup.py as execfile("setup.py")
-            # (likely we're building an egg)
-            d = os.path.abspath('.')
-
-    if parent_path is not None:
-        d = rel_path(d, parent_path)
-
-    return d or '.'
-
-def njoin(*path):
-    """Join two or more pathname components +
-    - convert a /-separated pathname to one using the OS's path separator.
-    - resolve `..` and `.` from path.
-
-    Either passing n arguments as in njoin('a','b'), or a sequence
-    of n names as in njoin(['a','b']) is handled, or a mixture of such arguments.
-    """
-    paths = []
-    for p in path:
-        if is_sequence(p):
-            # njoin(['a', 'b'], 'c')
-            paths.append(njoin(*p))
-        else:
-            assert is_string(p)
-            paths.append(p)
-    path = paths
-    if not path:
-        # njoin()
-        joined = ''
-    else:
-        # njoin('a', 'b')
-        joined = os.path.join(*path)
-    if os.path.sep != '/':
-        joined = joined.replace('/', os.path.sep)
-    return minrelpath(joined)
-
-def get_mathlibs(path=None):
-    """Return the MATHLIB line from numpyconfig.h
-    """
-    if path is not None:
-        config_file = os.path.join(path, '_numpyconfig.h')
-    else:
-        # Look for the file in each of the numpy include directories.
-        dirs = get_numpy_include_dirs()
-        for path in dirs:
-            fn = os.path.join(path, '_numpyconfig.h')
-            if os.path.exists(fn):
-                config_file = fn
-                break
-        else:
-            raise DistutilsError('_numpyconfig.h not found in numpy include '
-                'dirs %r' % (dirs,))
-
-    with open(config_file) as fid:
-        mathlibs = []
-        s = '#define MATHLIB'
-        for line in fid:
-            if line.startswith(s):
-                value = line[len(s):].strip()
-                if value:
-                    mathlibs.extend(value.split(','))
-    return mathlibs
-
-def minrelpath(path):
-    """Resolve `..` and '.' from path.
-    """
-    if not is_string(path):
-        return path
-    if '.' not in path:
-        return path
-    l = path.split(os.sep)
-    while l:
-        try:
-            i = l.index('.', 1)
-        except ValueError:
-            break
-        del l[i]
-    j = 1
-    while l:
-        try:
-            i = l.index('..', j)
-        except ValueError:
-            break
-        if l[i-1]=='..':
-            j += 1
-        else:
-            del l[i], l[i-1]
-            j = 1
-    if not l:
-        return ''
-    return os.sep.join(l)
-
-def sorted_glob(fileglob):
-    """sorts output of python glob for https://bugs.python.org/issue30461
-    to allow extensions to have reproducible build results"""
-    return sorted(glob.glob(fileglob))
-
-def _fix_paths(paths, local_path, include_non_existing):
-    assert is_sequence(paths), repr(type(paths))
-    new_paths = []
-    assert not is_string(paths), repr(paths)
-    for n in paths:
-        if is_string(n):
-            if '*' in n or '?' in n:
-                p = sorted_glob(n)
-                p2 = sorted_glob(njoin(local_path, n))
-                if p2:
-                    new_paths.extend(p2)
-                elif p:
-                    new_paths.extend(p)
-                else:
-                    if include_non_existing:
-                        new_paths.append(n)
-                    print('could not resolve pattern in %r: %r' %
-                            (local_path, n))
-            else:
-                n2 = njoin(local_path, n)
-                if os.path.exists(n2):
-                    new_paths.append(n2)
-                else:
-                    if os.path.exists(n):
-                        new_paths.append(n)
-                    elif include_non_existing:
-                        new_paths.append(n)
-                    if not os.path.exists(n):
-                        print('non-existing path in %r: %r' %
-                                (local_path, n))
-
-        elif is_sequence(n):
-            new_paths.extend(_fix_paths(n, local_path, include_non_existing))
-        else:
-            new_paths.append(n)
-    return [minrelpath(p) for p in new_paths]
-
-def gpaths(paths, local_path='', include_non_existing=True):
-    """Apply glob to paths and prepend local_path if needed.
-    """
-    if is_string(paths):
-        paths = (paths,)
-    return _fix_paths(paths, local_path, include_non_existing)
-
-def make_temp_file(suffix='', prefix='', text=True):
-    if not hasattr(_tdata, 'tempdir'):
-        _tdata.tempdir = tempfile.mkdtemp()
-        _tmpdirs.append(_tdata.tempdir)
-    fid, name = tempfile.mkstemp(suffix=suffix,
-                                 prefix=prefix,
-                                 dir=_tdata.tempdir,
-                                 text=text)
-    fo = os.fdopen(fid, 'w')
-    return fo, name
-
-# Hooks for colored terminal output.
-# See also https://web.archive.org/web/20100314204946/http://www.livinglogic.de/Python/ansistyle
-def terminal_has_colors():
-    if sys.platform=='cygwin' and 'USE_COLOR' not in os.environ:
-        # Avoid importing curses that causes illegal operation
-        # with a message:
-        #  PYTHON2 caused an invalid page fault in
-        #  module CYGNURSES7.DLL as 015f:18bbfc28
-        # Details: Python 2.3.3 [GCC 3.3.1 (cygming special)]
-        #          ssh to Win32 machine from debian
-        #          curses.version is 2.2
-        #          CYGWIN_98-4.10, release 1.5.7(0.109/3/2))
-        return 0
-    if hasattr(sys.stdout, 'isatty') and sys.stdout.isatty():
-        try:
-            import curses
-            curses.setupterm()
-            if (curses.tigetnum("colors") >= 0
-                and curses.tigetnum("pairs") >= 0
-                and ((curses.tigetstr("setf") is not None
-                      and curses.tigetstr("setb") is not None)
-                     or (curses.tigetstr("setaf") is not None
-                         and curses.tigetstr("setab") is not None)
-                     or curses.tigetstr("scp") is not None)):
-                return 1
-        except Exception:
-            pass
-    return 0
-
-if terminal_has_colors():
-    _colour_codes = dict(black=0, red=1, green=2, yellow=3,
-                         blue=4, magenta=5, cyan=6, white=7, default=9)
-    def colour_text(s, fg=None, bg=None, bold=False):
-        seq = []
-        if bold:
-            seq.append('1')
-        if fg:
-            fgcode = 30 + _colour_codes.get(fg.lower(), 0)
-            seq.append(str(fgcode))
-        if bg:
-            bgcode = 40 + _colour_codes.get(bg.lower(), 7)
-            seq.append(str(bgcode))
-        if seq:
-            return '\x1b[%sm%s\x1b[0m' % (';'.join(seq), s)
-        else:
-            return s
-else:
-    def colour_text(s, fg=None, bg=None):
-        return s
-
-def default_text(s):
-    return colour_text(s, 'default')
-def red_text(s):
-    return colour_text(s, 'red')
-def green_text(s):
-    return colour_text(s, 'green')
-def yellow_text(s):
-    return colour_text(s, 'yellow')
-def cyan_text(s):
-    return colour_text(s, 'cyan')
-def blue_text(s):
-    return colour_text(s, 'blue')
-
-#########################
-
-def cyg2win32(path: str) -> str:
-    """Convert a path from Cygwin-native to Windows-native.
-
-    Uses the cygpath utility (part of the Base install) to do the
-    actual conversion.  Falls back to returning the original path if
-    this fails.
-
-    Handles the default ``/cygdrive`` mount prefix as well as the
-    ``/proc/cygdrive`` portable prefix, custom cygdrive prefixes such
-    as ``/`` or ``/mnt``, and absolute paths such as ``/usr/src/`` or
-    ``/home/username``
-
-    Parameters
-    ----------
-    path : str
-       The path to convert
-
-    Returns
-    -------
-    converted_path : str
-        The converted path
-
-    Notes
-    -----
-    Documentation for cygpath utility:
-    https://cygwin.com/cygwin-ug-net/cygpath.html
-    Documentation for the C function it wraps:
-    https://cygwin.com/cygwin-api/func-cygwin-conv-path.html
-
-    """
-    if sys.platform != "cygwin":
-        return path
-    return subprocess.check_output(
-        ["/usr/bin/cygpath", "--windows", path], text=True
-    )
-
-
-def mingw32():
-    """Return true when using mingw32 environment.
-    """
-    if sys.platform=='win32':
-        if os.environ.get('OSTYPE', '')=='msys':
-            return True
-        if os.environ.get('MSYSTEM', '')=='MINGW32':
-            return True
-    return False
-
-def msvc_runtime_version():
-    "Return version of MSVC runtime library, as defined by __MSC_VER__ macro"
-    msc_pos = sys.version.find('MSC v.')
-    if msc_pos != -1:
-        msc_ver = int(sys.version[msc_pos+6:msc_pos+10])
-    else:
-        msc_ver = None
-    return msc_ver
-
-def msvc_runtime_library():
-    "Return name of MSVC runtime library if Python was built with MSVC >= 7"
-    ver = msvc_runtime_major ()
-    if ver:
-        if ver < 140:
-            return "msvcr%i" % ver
-        else:
-            return "vcruntime%i" % ver
-    else:
-        return None
-
-def msvc_runtime_major():
-    "Return major version of MSVC runtime coded like get_build_msvc_version"
-    major = {1300:  70,  # MSVC 7.0
-             1310:  71,  # MSVC 7.1
-             1400:  80,  # MSVC 8
-             1500:  90,  # MSVC 9  (aka 2008)
-             1600: 100,  # MSVC 10 (aka 2010)
-             1900: 140,  # MSVC 14 (aka 2015)
-    }.get(msvc_runtime_version(), None)
-    return major
-
-#########################
-
-#XXX need support for .C that is also C++
-cxx_ext_match = re.compile(r'.*\.(cpp|cxx|cc)\Z', re.I).match
-fortran_ext_match = re.compile(r'.*\.(f90|f95|f77|for|ftn|f)\Z', re.I).match
-f90_ext_match = re.compile(r'.*\.(f90|f95)\Z', re.I).match
-f90_module_name_match = re.compile(r'\s*module\s*(?P<name>[\w_]+)', re.I).match
-def _get_f90_modules(source):
-    """Return a list of Fortran f90 module names that
-    given source file defines.
-    """
-    if not f90_ext_match(source):
-        return []
-    modules = []
-    with open(source) as f:
-        for line in f:
-            m = f90_module_name_match(line)
-            if m:
-                name = m.group('name')
-                modules.append(name)
-                # break  # XXX can we assume that there is one module per file?
-    return modules
-
-def is_string(s):
-    return isinstance(s, str)
-
-def all_strings(lst):
-    """Return True if all items in lst are string objects. """
-    for item in lst:
-        if not is_string(item):
-            return False
-    return True
-
-def is_sequence(seq):
-    if is_string(seq):
-        return False
-    try:
-        len(seq)
-    except Exception:
-        return False
-    return True
-
-def is_glob_pattern(s):
-    return is_string(s) and ('*' in s or '?' in s)
-
-def as_list(seq):
-    if is_sequence(seq):
-        return list(seq)
-    else:
-        return [seq]
-
-def get_language(sources):
-    # not used in numpy/scipy packages, use build_ext.detect_language instead
-    """Determine language value (c,f77,f90) from sources """
-    language = None
-    for source in sources:
-        if isinstance(source, str):
-            if f90_ext_match(source):
-                language = 'f90'
-                break
-            elif fortran_ext_match(source):
-                language = 'f77'
-    return language
-
-def has_f_sources(sources):
-    """Return True if sources contains Fortran files """
-    for source in sources:
-        if fortran_ext_match(source):
-            return True
-    return False
-
-def has_cxx_sources(sources):
-    """Return True if sources contains C++ files """
-    for source in sources:
-        if cxx_ext_match(source):
-            return True
-    return False
-
-def filter_sources(sources):
-    """Return four lists of filenames containing
-    C, C++, Fortran, and Fortran 90 module sources,
-    respectively.
-    """
-    c_sources = []
-    cxx_sources = []
-    f_sources = []
-    fmodule_sources = []
-    for source in sources:
-        if fortran_ext_match(source):
-            modules = _get_f90_modules(source)
-            if modules:
-                fmodule_sources.append(source)
-            else:
-                f_sources.append(source)
-        elif cxx_ext_match(source):
-            cxx_sources.append(source)
-        else:
-            c_sources.append(source)
-    return c_sources, cxx_sources, f_sources, fmodule_sources
-
-
-def _get_headers(directory_list):
-    # get *.h files from list of directories
-    headers = []
-    for d in directory_list:
-        head = sorted_glob(os.path.join(d, "*.h")) #XXX: *.hpp files??
-        headers.extend(head)
-    return headers
-
-def _get_directories(list_of_sources):
-    # get unique directories from list of sources.
-    direcs = []
-    for f in list_of_sources:
-        d = os.path.split(f)
-        if d[0] != '' and not d[0] in direcs:
-            direcs.append(d[0])
-    return direcs
-
-def _commandline_dep_string(cc_args, extra_postargs, pp_opts):
-    """
-    Return commandline representation used to determine if a file needs
-    to be recompiled
-    """
-    cmdline = 'commandline: '
-    cmdline += ' '.join(cc_args)
-    cmdline += ' '.join(extra_postargs)
-    cmdline += ' '.join(pp_opts) + '\n'
-    return cmdline
-
-
-def get_dependencies(sources):
-    #XXX scan sources for include statements
-    return _get_headers(_get_directories(sources))
-
-def is_local_src_dir(directory):
-    """Return true if directory is local directory.
-    """
-    if not is_string(directory):
-        return False
-    abs_dir = os.path.abspath(directory)
-    c = os.path.commonprefix([os.getcwd(), abs_dir])
-    new_dir = abs_dir[len(c):].split(os.sep)
-    if new_dir and not new_dir[0]:
-        new_dir = new_dir[1:]
-    if new_dir and new_dir[0]=='build':
-        return False
-    new_dir = os.sep.join(new_dir)
-    return os.path.isdir(new_dir)
-
-def general_source_files(top_path):
-    pruned_directories = {'CVS':1, '.svn':1, 'build':1}
-    prune_file_pat = re.compile(r'(?:[~#]|\.py[co]|\.o)$')
-    for dirpath, dirnames, filenames in os.walk(top_path, topdown=True):
-        pruned = [ d for d in dirnames if d not in pruned_directories ]
-        dirnames[:] = pruned
-        for f in filenames:
-            if not prune_file_pat.search(f):
-                yield os.path.join(dirpath, f)
-
-def general_source_directories_files(top_path):
-    """Return a directory name relative to top_path and
-    files contained.
-    """
-    pruned_directories = ['CVS', '.svn', 'build']
-    prune_file_pat = re.compile(r'(?:[~#]|\.py[co]|\.o)$')
-    for dirpath, dirnames, filenames in os.walk(top_path, topdown=True):
-        pruned = [ d for d in dirnames if d not in pruned_directories ]
-        dirnames[:] = pruned
-        for d in dirnames:
-            dpath = os.path.join(dirpath, d)
-            rpath = rel_path(dpath, top_path)
-            files = []
-            for f in os.listdir(dpath):
-                fn = os.path.join(dpath, f)
-                if os.path.isfile(fn) and not prune_file_pat.search(fn):
-                    files.append(fn)
-            yield rpath, files
-    dpath = top_path
-    rpath = rel_path(dpath, top_path)
-    filenames = [os.path.join(dpath, f) for f in os.listdir(dpath) \
-                 if not prune_file_pat.search(f)]
-    files = [f for f in filenames if os.path.isfile(f)]
-    yield rpath, files
-
-
-def get_ext_source_files(ext):
-    # Get sources and any include files in the same directory.
-    filenames = []
-    sources = [_m for _m in ext.sources if is_string(_m)]
-    filenames.extend(sources)
-    filenames.extend(get_dependencies(sources))
-    for d in ext.depends:
-        if is_local_src_dir(d):
-            filenames.extend(list(general_source_files(d)))
-        elif os.path.isfile(d):
-            filenames.append(d)
-    return filenames
-
-def get_script_files(scripts):
-    scripts = [_m for _m in scripts if is_string(_m)]
-    return scripts
-
-def get_lib_source_files(lib):
-    filenames = []
-    sources = lib[1].get('sources', [])
-    sources = [_m for _m in sources if is_string(_m)]
-    filenames.extend(sources)
-    filenames.extend(get_dependencies(sources))
-    depends = lib[1].get('depends', [])
-    for d in depends:
-        if is_local_src_dir(d):
-            filenames.extend(list(general_source_files(d)))
-        elif os.path.isfile(d):
-            filenames.append(d)
-    return filenames
-
-def get_shared_lib_extension(is_python_ext=False):
-    """Return the correct file extension for shared libraries.
-
-    Parameters
-    ----------
-    is_python_ext : bool, optional
-        Whether the shared library is a Python extension.  Default is False.
-
-    Returns
-    -------
-    so_ext : str
-        The shared library extension.
-
-    Notes
-    -----
-    For Python shared libs, `so_ext` will typically be '.so' on Linux and OS X,
-    and '.pyd' on Windows.  For Python >= 3.2 `so_ext` has a tag prepended on
-    POSIX systems according to PEP 3149.
-
-    """
-    confvars = distutils.sysconfig.get_config_vars()
-    so_ext = confvars.get('EXT_SUFFIX', '')
-
-    if not is_python_ext:
-        # hardcode known values, config vars (including SHLIB_SUFFIX) are
-        # unreliable (see #3182)
-        # darwin, windows and debug linux are wrong in 3.3.1 and older
-        if (sys.platform.startswith('linux') or
-            sys.platform.startswith('gnukfreebsd')):
-            so_ext = '.so'
-        elif sys.platform.startswith('darwin'):
-            so_ext = '.dylib'
-        elif sys.platform.startswith('win'):
-            so_ext = '.dll'
-        else:
-            # fall back to config vars for unknown platforms
-            # fix long extension for Python >=3.2, see PEP 3149.
-            if 'SOABI' in confvars:
-                # Does nothing unless SOABI config var exists
-                so_ext = so_ext.replace('.' + confvars.get('SOABI'), '', 1)
-
-    return so_ext
-
-def get_data_files(data):
-    if is_string(data):
-        return [data]
-    sources = data[1]
-    filenames = []
-    for s in sources:
-        if hasattr(s, '__call__'):
-            continue
-        if is_local_src_dir(s):
-            filenames.extend(list(general_source_files(s)))
-        elif is_string(s):
-            if os.path.isfile(s):
-                filenames.append(s)
-            else:
-                print('Not existing data file:', s)
-        else:
-            raise TypeError(repr(s))
-    return filenames
-
-def dot_join(*args):
-    return '.'.join([a for a in args if a])
-
-def get_frame(level=0):
-    """Return frame object from call stack with given level.
-    """
-    try:
-        return sys._getframe(level+1)
-    except AttributeError:
-        frame = sys.exc_info()[2].tb_frame
-        for _ in range(level+1):
-            frame = frame.f_back
-        return frame
-
-
-######################
-
-class Configuration:
-
-    _list_keys = ['packages', 'ext_modules', 'data_files', 'include_dirs',
-                  'libraries', 'headers', 'scripts', 'py_modules',
-                  'installed_libraries', 'define_macros']
-    _dict_keys = ['package_dir', 'installed_pkg_config']
-    _extra_keys = ['name', 'version']
-
-    numpy_include_dirs = []
-
-    def __init__(self,
-                 package_name=None,
-                 parent_name=None,
-                 top_path=None,
-                 package_path=None,
-                 caller_level=1,
-                 setup_name='setup.py',
-                 **attrs):
-        """Construct configuration instance of a package.
-
-        package_name -- name of the package
-                        Ex.: 'distutils'
-        parent_name  -- name of the parent package
-                        Ex.: 'numpy'
-        top_path     -- directory of the toplevel package
-                        Ex.: the directory where the numpy package source sits
-        package_path -- directory of package. Will be computed by magic from the
-                        directory of the caller module if not specified
-                        Ex.: the directory where numpy.distutils is
-        caller_level -- frame level to caller namespace, internal parameter.
-        """
-        self.name = dot_join(parent_name, package_name)
-        self.version = None
-
-        caller_frame = get_frame(caller_level)
-        self.local_path = get_path_from_frame(caller_frame, top_path)
-        # local_path -- directory of a file (usually setup.py) that
-        #               defines a configuration() function.
-        # local_path -- directory of a file (usually setup.py) that
-        #               defines a configuration() function.
-        if top_path is None:
-            top_path = self.local_path
-            self.local_path = ''
-        if package_path is None:
-            package_path = self.local_path
-        elif os.path.isdir(njoin(self.local_path, package_path)):
-            package_path = njoin(self.local_path, package_path)
-        if not os.path.isdir(package_path or '.'):
-            raise ValueError("%r is not a directory" % (package_path,))
-        self.top_path = top_path
-        self.package_path = package_path
-        # this is the relative path in the installed package
-        self.path_in_package = os.path.join(*self.name.split('.'))
-
-        self.list_keys = self._list_keys[:]
-        self.dict_keys = self._dict_keys[:]
-
-        for n in self.list_keys:
-            v = copy.copy(attrs.get(n, []))
-            setattr(self, n, as_list(v))
-
-        for n in self.dict_keys:
-            v = copy.copy(attrs.get(n, {}))
-            setattr(self, n, v)
-
-        known_keys = self.list_keys + self.dict_keys
-        self.extra_keys = self._extra_keys[:]
-        for n in attrs.keys():
-            if n in known_keys:
-                continue
-            a = attrs[n]
-            setattr(self, n, a)
-            if isinstance(a, list):
-                self.list_keys.append(n)
-            elif isinstance(a, dict):
-                self.dict_keys.append(n)
-            else:
-                self.extra_keys.append(n)
-
-        if os.path.exists(njoin(package_path, '__init__.py')):
-            self.packages.append(self.name)
-            self.package_dir[self.name] = package_path
-
-        self.options = dict(
-            ignore_setup_xxx_py = False,
-            assume_default_configuration = False,
-            delegate_options_to_subpackages = False,
-            quiet = False,
-            )
-
-        caller_instance = None
-        for i in range(1, 3):
-            try:
-                f = get_frame(i)
-            except ValueError:
-                break
-            try:
-                caller_instance = eval('self', f.f_globals, f.f_locals)
-                break
-            except NameError:
-                pass
-        if isinstance(caller_instance, self.__class__):
-            if caller_instance.options['delegate_options_to_subpackages']:
-                self.set_options(**caller_instance.options)
-
-        self.setup_name = setup_name
-
-    def todict(self):
-        """
-        Return a dictionary compatible with the keyword arguments of distutils
-        setup function.
-
-        Examples
-        --------
-        >>> setup(**config.todict())                           #doctest: +SKIP
-        """
-
-        self._optimize_data_files()
-        d = {}
-        known_keys = self.list_keys + self.dict_keys + self.extra_keys
-        for n in known_keys:
-            a = getattr(self, n)
-            if a:
-                d[n] = a
-        return d
-
-    def info(self, message):
-        if not self.options['quiet']:
-            print(message)
-
-    def warn(self, message):
-        sys.stderr.write('Warning: %s\n' % (message,))
-
-    def set_options(self, **options):
-        """
-        Configure Configuration instance.
-
-        The following options are available:
-         - ignore_setup_xxx_py
-         - assume_default_configuration
-         - delegate_options_to_subpackages
-         - quiet
-
-        """
-        for key, value in options.items():
-            if key in self.options:
-                self.options[key] = value
-            else:
-                raise ValueError('Unknown option: '+key)
-
-    def get_distribution(self):
-        """Return the distutils distribution object for self."""
-        from numpy.distutils.core import get_distribution
-        return get_distribution()
-
-    def _wildcard_get_subpackage(self, subpackage_name,
-                                 parent_name,
-                                 caller_level = 1):
-        l = subpackage_name.split('.')
-        subpackage_path = njoin([self.local_path]+l)
-        dirs = [_m for _m in sorted_glob(subpackage_path) if os.path.isdir(_m)]
-        config_list = []
-        for d in dirs:
-            if not os.path.isfile(njoin(d, '__init__.py')):
-                continue
-            if 'build' in d.split(os.sep):
-                continue
-            n = '.'.join(d.split(os.sep)[-len(l):])
-            c = self.get_subpackage(n,
-                                    parent_name = parent_name,
-                                    caller_level = caller_level+1)
-            config_list.extend(c)
-        return config_list
-
-    def _get_configuration_from_setup_py(self, setup_py,
-                                         subpackage_name,
-                                         subpackage_path,
-                                         parent_name,
-                                         caller_level = 1):
-        # In case setup_py imports local modules:
-        sys.path.insert(0, os.path.dirname(setup_py))
-        try:
-            setup_name = os.path.splitext(os.path.basename(setup_py))[0]
-            n = dot_join(self.name, subpackage_name, setup_name)
-            setup_module = exec_mod_from_location(
-                                '_'.join(n.split('.')), setup_py)
-            if not hasattr(setup_module, 'configuration'):
-                if not self.options['assume_default_configuration']:
-                    self.warn('Assuming default configuration '\
-                              '(%s does not define configuration())'\
-                              % (setup_module))
-                config = Configuration(subpackage_name, parent_name,
-                                       self.top_path, subpackage_path,
-                                       caller_level = caller_level + 1)
-            else:
-                pn = dot_join(*([parent_name] + subpackage_name.split('.')[:-1]))
-                args = (pn,)
-                if setup_module.configuration.__code__.co_argcount > 1:
-                    args = args + (self.top_path,)
-                config = setup_module.configuration(*args)
-            if config.name!=dot_join(parent_name, subpackage_name):
-                self.warn('Subpackage %r configuration returned as %r' % \
-                          (dot_join(parent_name, subpackage_name), config.name))
-        finally:
-            del sys.path[0]
-        return config
-
-    def get_subpackage(self,subpackage_name,
-                       subpackage_path=None,
-                       parent_name=None,
-                       caller_level = 1):
-        """Return list of subpackage configurations.
-
-        Parameters
-        ----------
-        subpackage_name : str or None
-            Name of the subpackage to get the configuration. '*' in
-            subpackage_name is handled as a wildcard.
-        subpackage_path : str
-            If None, then the path is assumed to be the local path plus the
-            subpackage_name. If a setup.py file is not found in the
-            subpackage_path, then a default configuration is used.
-        parent_name : str
-            Parent name.
-        """
-        if subpackage_name is None:
-            if subpackage_path is None:
-                raise ValueError(
-                    "either subpackage_name or subpackage_path must be specified")
-            subpackage_name = os.path.basename(subpackage_path)
-
-        # handle wildcards
-        l = subpackage_name.split('.')
-        if subpackage_path is None and '*' in subpackage_name:
-            return self._wildcard_get_subpackage(subpackage_name,
-                                                 parent_name,
-                                                 caller_level = caller_level+1)
-        assert '*' not in subpackage_name, repr((subpackage_name, subpackage_path, parent_name))
-        if subpackage_path is None:
-            subpackage_path = njoin([self.local_path] + l)
-        else:
-            subpackage_path = njoin([subpackage_path] + l[:-1])
-            subpackage_path = self.paths([subpackage_path])[0]
-        setup_py = njoin(subpackage_path, self.setup_name)
-        if not self.options['ignore_setup_xxx_py']:
-            if not os.path.isfile(setup_py):
-                setup_py = njoin(subpackage_path,
-                                 'setup_%s.py' % (subpackage_name))
-        if not os.path.isfile(setup_py):
-            if not self.options['assume_default_configuration']:
-                self.warn('Assuming default configuration '\
-                          '(%s/{setup_%s,setup}.py was not found)' \
-                          % (os.path.dirname(setup_py), subpackage_name))
-            config = Configuration(subpackage_name, parent_name,
-                                   self.top_path, subpackage_path,
-                                   caller_level = caller_level+1)
-        else:
-            config = self._get_configuration_from_setup_py(
-                setup_py,
-                subpackage_name,
-                subpackage_path,
-                parent_name,
-                caller_level = caller_level + 1)
-        if config:
-            return [config]
-        else:
-            return []
-
-    def add_subpackage(self,subpackage_name,
-                       subpackage_path=None,
-                       standalone = False):
-        """Add a sub-package to the current Configuration instance.
-
-        This is useful in a setup.py script for adding sub-packages to a
-        package.
-
-        Parameters
-        ----------
-        subpackage_name : str
-            name of the subpackage
-        subpackage_path : str
-            if given, the subpackage path such as the subpackage is in
-            subpackage_path / subpackage_name. If None,the subpackage is
-            assumed to be located in the local path / subpackage_name.
-        standalone : bool
-        """
-
-        if standalone:
-            parent_name = None
-        else:
-            parent_name = self.name
-        config_list = self.get_subpackage(subpackage_name, subpackage_path,
-                                          parent_name = parent_name,
-                                          caller_level = 2)
-        if not config_list:
-            self.warn('No configuration returned, assuming unavailable.')
-        for config in config_list:
-            d = config
-            if isinstance(config, Configuration):
-                d = config.todict()
-            assert isinstance(d, dict), repr(type(d))
-
-            self.info('Appending %s configuration to %s' \
-                      % (d.get('name'), self.name))
-            self.dict_append(**d)
-
-        dist = self.get_distribution()
-        if dist is not None:
-            self.warn('distutils distribution has been initialized,'\
-                      ' it may be too late to add a subpackage '+ subpackage_name)
-
-    def add_data_dir(self, data_path):
-        """Recursively add files under data_path to data_files list.
-
-        Recursively add files under data_path to the list of data_files to be
-        installed (and distributed). The data_path can be either a relative
-        path-name, or an absolute path-name, or a 2-tuple where the first
-        argument shows where in the install directory the data directory
-        should be installed to.
-
-        Parameters
-        ----------
-        data_path : seq or str
-            Argument can be either
-
-                * 2-sequence (<datadir suffix>, <path to data directory>)
-                * path to data directory where python datadir suffix defaults
-                  to package dir.
-
-        Notes
-        -----
-        Rules for installation paths::
-
-            foo/bar -> (foo/bar, foo/bar) -> parent/foo/bar
-            (gun, foo/bar) -> parent/gun
-            foo/* -> (foo/a, foo/a), (foo/b, foo/b) -> parent/foo/a, parent/foo/b
-            (gun, foo/*) -> (gun, foo/a), (gun, foo/b) -> gun
-            (gun/*, foo/*) -> parent/gun/a, parent/gun/b
-            /foo/bar -> (bar, /foo/bar) -> parent/bar
-            (gun, /foo/bar) -> parent/gun
-            (fun/*/gun/*, sun/foo/bar) -> parent/fun/foo/gun/bar
-
-        Examples
-        --------
-        For example suppose the source directory contains fun/foo.dat and
-        fun/bar/car.dat:
-
-        >>> self.add_data_dir('fun')                       #doctest: +SKIP
-        >>> self.add_data_dir(('sun', 'fun'))              #doctest: +SKIP
-        >>> self.add_data_dir(('gun', '/full/path/to/fun'))#doctest: +SKIP
-
-        Will install data-files to the locations::
-
-            <package install directory>/
-              fun/
-                foo.dat
-                bar/
-                  car.dat
-              sun/
-                foo.dat
-                bar/
-                  car.dat
-              gun/
-                foo.dat
-                car.dat
-
-        """
-        if is_sequence(data_path):
-            d, data_path = data_path
-        else:
-            d = None
-        if is_sequence(data_path):
-            [self.add_data_dir((d, p)) for p in data_path]
-            return
-        if not is_string(data_path):
-            raise TypeError("not a string: %r" % (data_path,))
-        if d is None:
-            if os.path.isabs(data_path):
-                return self.add_data_dir((os.path.basename(data_path), data_path))
-            return self.add_data_dir((data_path, data_path))
-        paths = self.paths(data_path, include_non_existing=False)
-        if is_glob_pattern(data_path):
-            if is_glob_pattern(d):
-                pattern_list = allpath(d).split(os.sep)
-                pattern_list.reverse()
-                # /a/*//b/ -> /a/*/b
-                rl = list(range(len(pattern_list)-1)); rl.reverse()
-                for i in rl:
-                    if not pattern_list[i]:
-                        del pattern_list[i]
-                #
-                for path in paths:
-                    if not os.path.isdir(path):
-                        print('Not a directory, skipping', path)
-                        continue
-                    rpath = rel_path(path, self.local_path)
-                    path_list = rpath.split(os.sep)
-                    path_list.reverse()
-                    target_list = []
-                    i = 0
-                    for s in pattern_list:
-                        if is_glob_pattern(s):
-                            if i>=len(path_list):
-                                raise ValueError('cannot fill pattern %r with %r' \
-                                      % (d, path))
-                            target_list.append(path_list[i])
-                        else:
-                            assert s==path_list[i], repr((s, path_list[i], data_path, d, path, rpath))
-                            target_list.append(s)
-                        i += 1
-                    if path_list[i:]:
-                        self.warn('mismatch of pattern_list=%s and path_list=%s'\
-                                  % (pattern_list, path_list))
-                    target_list.reverse()
-                    self.add_data_dir((os.sep.join(target_list), path))
-            else:
-                for path in paths:
-                    self.add_data_dir((d, path))
-            return
-        assert not is_glob_pattern(d), repr(d)
-
-        dist = self.get_distribution()
-        if dist is not None and dist.data_files is not None:
-            data_files = dist.data_files
-        else:
-            data_files = self.data_files
-
-        for path in paths:
-            for d1, f in list(general_source_directories_files(path)):
-                target_path = os.path.join(self.path_in_package, d, d1)
-                data_files.append((target_path, f))
-
-    def _optimize_data_files(self):
-        data_dict = {}
-        for p, files in self.data_files:
-            if p not in data_dict:
-                data_dict[p] = set()
-            for f in files:
-                data_dict[p].add(f)
-        self.data_files[:] = [(p, list(files)) for p, files in data_dict.items()]
-
-    def add_data_files(self,*files):
-        """Add data files to configuration data_files.
-
-        Parameters
-        ----------
-        files : sequence
-            Argument(s) can be either
-
-                * 2-sequence (<datadir prefix>,<path to data file(s)>)
-                * paths to data files where python datadir prefix defaults
-                  to package dir.
-
-        Notes
-        -----
-        The form of each element of the files sequence is very flexible
-        allowing many combinations of where to get the files from the package
-        and where they should ultimately be installed on the system. The most
-        basic usage is for an element of the files argument sequence to be a
-        simple filename. This will cause that file from the local path to be
-        installed to the installation path of the self.name package (package
-        path). The file argument can also be a relative path in which case the
-        entire relative path will be installed into the package directory.
-        Finally, the file can be an absolute path name in which case the file
-        will be found at the absolute path name but installed to the package
-        path.
-
-        This basic behavior can be augmented by passing a 2-tuple in as the
-        file argument. The first element of the tuple should specify the
-        relative path (under the package install directory) where the
-        remaining sequence of files should be installed to (it has nothing to
-        do with the file-names in the source distribution). The second element
-        of the tuple is the sequence of files that should be installed. The
-        files in this sequence can be filenames, relative paths, or absolute
-        paths. For absolute paths the file will be installed in the top-level
-        package installation directory (regardless of the first argument).
-        Filenames and relative path names will be installed in the package
-        install directory under the path name given as the first element of
-        the tuple.
-
-        Rules for installation paths:
-
-          #. file.txt -> (., file.txt)-> parent/file.txt
-          #. foo/file.txt -> (foo, foo/file.txt) -> parent/foo/file.txt
-          #. /foo/bar/file.txt -> (., /foo/bar/file.txt) -> parent/file.txt
-          #. ``*``.txt -> parent/a.txt, parent/b.txt
-          #. foo/``*``.txt`` -> parent/foo/a.txt, parent/foo/b.txt
-          #. ``*/*.txt`` -> (``*``, ``*``/``*``.txt) -> parent/c/a.txt, parent/d/b.txt
-          #. (sun, file.txt) -> parent/sun/file.txt
-          #. (sun, bar/file.txt) -> parent/sun/file.txt
-          #. (sun, /foo/bar/file.txt) -> parent/sun/file.txt
-          #. (sun, ``*``.txt) -> parent/sun/a.txt, parent/sun/b.txt
-          #. (sun, bar/``*``.txt) -> parent/sun/a.txt, parent/sun/b.txt
-          #. (sun/``*``, ``*``/``*``.txt) -> parent/sun/c/a.txt, parent/d/b.txt
-
-        An additional feature is that the path to a data-file can actually be
-        a function that takes no arguments and returns the actual path(s) to
-        the data-files. This is useful when the data files are generated while
-        building the package.
-
-        Examples
-        --------
-        Add files to the list of data_files to be included with the package.
-
-            >>> self.add_data_files('foo.dat',
-            ...     ('fun', ['gun.dat', 'nun/pun.dat', '/tmp/sun.dat']),
-            ...     'bar/cat.dat',
-            ...     '/full/path/to/can.dat')                   #doctest: +SKIP
-
-        will install these data files to::
-
-            <package install directory>/
-             foo.dat
-             fun/
-               gun.dat
-               nun/
-                 pun.dat
-             sun.dat
-             bar/
-               car.dat
-             can.dat
-
-        where <package install directory> is the package (or sub-package)
-        directory such as '/usr/lib/python2.4/site-packages/mypackage' ('C:
-        \\Python2.4 \\Lib \\site-packages \\mypackage') or
-        '/usr/lib/python2.4/site- packages/mypackage/mysubpackage' ('C:
-        \\Python2.4 \\Lib \\site-packages \\mypackage \\mysubpackage').
-        """
-
-        if len(files)>1:
-            for f in files:
-                self.add_data_files(f)
-            return
-        assert len(files)==1
-        if is_sequence(files[0]):
-            d, files = files[0]
-        else:
-            d = None
-        if is_string(files):
-            filepat = files
-        elif is_sequence(files):
-            if len(files)==1:
-                filepat = files[0]
-            else:
-                for f in files:
-                    self.add_data_files((d, f))
-                return
-        else:
-            raise TypeError(repr(type(files)))
-
-        if d is None:
-            if hasattr(filepat, '__call__'):
-                d = ''
-            elif os.path.isabs(filepat):
-                d = ''
-            else:
-                d = os.path.dirname(filepat)
-            self.add_data_files((d, files))
-            return
-
-        paths = self.paths(filepat, include_non_existing=False)
-        if is_glob_pattern(filepat):
-            if is_glob_pattern(d):
-                pattern_list = d.split(os.sep)
-                pattern_list.reverse()
-                for path in paths:
-                    path_list = path.split(os.sep)
-                    path_list.reverse()
-                    path_list.pop() # filename
-                    target_list = []
-                    i = 0
-                    for s in pattern_list:
-                        if is_glob_pattern(s):
-                            target_list.append(path_list[i])
-                            i += 1
-                        else:
-                            target_list.append(s)
-                    target_list.reverse()
-                    self.add_data_files((os.sep.join(target_list), path))
-            else:
-                self.add_data_files((d, paths))
-            return
-        assert not is_glob_pattern(d), repr((d, filepat))
-
-        dist = self.get_distribution()
-        if dist is not None and dist.data_files is not None:
-            data_files = dist.data_files
-        else:
-            data_files = self.data_files
-
-        data_files.append((os.path.join(self.path_in_package, d), paths))
-
-    ### XXX Implement add_py_modules
-
-    def add_define_macros(self, macros):
-        """Add define macros to configuration
-
-        Add the given sequence of macro name and value duples to the beginning
-        of the define_macros list This list will be visible to all extension
-        modules of the current package.
-        """
-        dist = self.get_distribution()
-        if dist is not None:
-            if not hasattr(dist, 'define_macros'):
-                dist.define_macros = []
-            dist.define_macros.extend(macros)
-        else:
-            self.define_macros.extend(macros)
-
-
-    def add_include_dirs(self,*paths):
-        """Add paths to configuration include directories.
-
-        Add the given sequence of paths to the beginning of the include_dirs
-        list. This list will be visible to all extension modules of the
-        current package.
-        """
-        include_dirs = self.paths(paths)
-        dist = self.get_distribution()
-        if dist is not None:
-            if dist.include_dirs is None:
-                dist.include_dirs = []
-            dist.include_dirs.extend(include_dirs)
-        else:
-            self.include_dirs.extend(include_dirs)
-
-    def add_headers(self,*files):
-        """Add installable headers to configuration.
-
-        Add the given sequence of files to the beginning of the headers list.
-        By default, headers will be installed under <python-
-        include>/<self.name.replace('.','/')>/ directory. If an item of files
-        is a tuple, then its first argument specifies the actual installation
-        location relative to the <python-include> path.
-
-        Parameters
-        ----------
-        files : str or seq
-            Argument(s) can be either:
-
-                * 2-sequence (<includedir suffix>,<path to header file(s)>)
-                * path(s) to header file(s) where python includedir suffix will
-                  default to package name.
-        """
-        headers = []
-        for path in files:
-            if is_string(path):
-                [headers.append((self.name, p)) for p in self.paths(path)]
-            else:
-                if not isinstance(path, (tuple, list)) or len(path) != 2:
-                    raise TypeError(repr(path))
-                [headers.append((path[0], p)) for p in self.paths(path[1])]
-        dist = self.get_distribution()
-        if dist is not None:
-            if dist.headers is None:
-                dist.headers = []
-            dist.headers.extend(headers)
-        else:
-            self.headers.extend(headers)
-
-    def paths(self,*paths,**kws):
-        """Apply glob to paths and prepend local_path if needed.
-
-        Applies glob.glob(...) to each path in the sequence (if needed) and
-        pre-pends the local_path if needed. Because this is called on all
-        source lists, this allows wildcard characters to be specified in lists
-        of sources for extension modules and libraries and scripts and allows
-        path-names be relative to the source directory.
-
-        """
-        include_non_existing = kws.get('include_non_existing', True)
-        return gpaths(paths,
-                      local_path = self.local_path,
-                      include_non_existing=include_non_existing)
-
-    def _fix_paths_dict(self, kw):
-        for k in kw.keys():
-            v = kw[k]
-            if k in ['sources', 'depends', 'include_dirs', 'library_dirs',
-                     'module_dirs', 'extra_objects']:
-                new_v = self.paths(v)
-                kw[k] = new_v
-
-    def add_extension(self,name,sources,**kw):
-        """Add extension to configuration.
-
-        Create and add an Extension instance to the ext_modules list. This
-        method also takes the following optional keyword arguments that are
-        passed on to the Extension constructor.
-
-        Parameters
-        ----------
-        name : str
-            name of the extension
-        sources : seq
-            list of the sources. The list of sources may contain functions
-            (called source generators) which must take an extension instance
-            and a build directory as inputs and return a source file or list of
-            source files or None. If None is returned then no sources are
-            generated. If the Extension instance has no sources after
-            processing all source generators, then no extension module is
-            built.
-        include_dirs :
-        define_macros :
-        undef_macros :
-        library_dirs :
-        libraries :
-        runtime_library_dirs :
-        extra_objects :
-        extra_compile_args :
-        extra_link_args :
-        extra_f77_compile_args :
-        extra_f90_compile_args :
-        export_symbols :
-        swig_opts :
-        depends :
-            The depends list contains paths to files or directories that the
-            sources of the extension module depend on. If any path in the
-            depends list is newer than the extension module, then the module
-            will be rebuilt.
-        language :
-        f2py_options :
-        module_dirs :
-        extra_info : dict or list
-            dict or list of dict of keywords to be appended to keywords.
-
-        Notes
-        -----
-        The self.paths(...) method is applied to all lists that may contain
-        paths.
-        """
-        ext_args = copy.copy(kw)
-        ext_args['name'] = dot_join(self.name, name)
-        ext_args['sources'] = sources
-
-        if 'extra_info' in ext_args:
-            extra_info = ext_args['extra_info']
-            del ext_args['extra_info']
-            if isinstance(extra_info, dict):
-                extra_info = [extra_info]
-            for info in extra_info:
-                assert isinstance(info, dict), repr(info)
-                dict_append(ext_args,**info)
-
-        self._fix_paths_dict(ext_args)
-
-        # Resolve out-of-tree dependencies
-        libraries = ext_args.get('libraries', [])
-        libnames = []
-        ext_args['libraries'] = []
-        for libname in libraries:
-            if isinstance(libname, tuple):
-                self._fix_paths_dict(libname[1])
-
-            # Handle library names of the form libname@relative/path/to/library
-            if '@' in libname:
-                lname, lpath = libname.split('@', 1)
-                lpath = os.path.abspath(njoin(self.local_path, lpath))
-                if os.path.isdir(lpath):
-                    c = self.get_subpackage(None, lpath,
-                                            caller_level = 2)
-                    if isinstance(c, Configuration):
-                        c = c.todict()
-                    for l in [l[0] for l in c.get('libraries', [])]:
-                        llname = l.split('__OF__', 1)[0]
-                        if llname == lname:
-                            c.pop('name', None)
-                            dict_append(ext_args,**c)
-                            break
-                    continue
-            libnames.append(libname)
-
-        ext_args['libraries'] = libnames + ext_args['libraries']
-        ext_args['define_macros'] = \
-            self.define_macros + ext_args.get('define_macros', [])
-
-        from numpy.distutils.core import Extension
-        ext = Extension(**ext_args)
-        self.ext_modules.append(ext)
-
-        dist = self.get_distribution()
-        if dist is not None:
-            self.warn('distutils distribution has been initialized,'\
-                      ' it may be too late to add an extension '+name)
-        return ext
-
-    def add_library(self,name,sources,**build_info):
-        """
-        Add library to configuration.
-
-        Parameters
-        ----------
-        name : str
-            Name of the extension.
-        sources : sequence
-            List of the sources. The list of sources may contain functions
-            (called source generators) which must take an extension instance
-            and a build directory as inputs and return a source file or list of
-            source files or None. If None is returned then no sources are
-            generated. If the Extension instance has no sources after
-            processing all source generators, then no extension module is
-            built.
-        build_info : dict, optional
-            The following keys are allowed:
-
-                * depends
-                * macros
-                * include_dirs
-                * extra_compiler_args
-                * extra_f77_compile_args
-                * extra_f90_compile_args
-                * f2py_options
-                * language
-
-        """
-        self._add_library(name, sources, None, build_info)
-
-        dist = self.get_distribution()
-        if dist is not None:
-            self.warn('distutils distribution has been initialized,'\
-                      ' it may be too late to add a library '+ name)
-
-    def _add_library(self, name, sources, install_dir, build_info):
-        """Common implementation for add_library and add_installed_library. Do
-        not use directly"""
-        build_info = copy.copy(build_info)
-        build_info['sources'] = sources
-
-        # Sometimes, depends is not set up to an empty list by default, and if
-        # depends is not given to add_library, distutils barfs (#1134)
-        if not 'depends' in build_info:
-            build_info['depends'] = []
-
-        self._fix_paths_dict(build_info)
-
-        # Add to libraries list so that it is build with build_clib
-        self.libraries.append((name, build_info))
-
-    def add_installed_library(self, name, sources, install_dir, build_info=None):
-        """
-        Similar to add_library, but the specified library is installed.
-
-        Most C libraries used with `distutils` are only used to build python
-        extensions, but libraries built through this method will be installed
-        so that they can be reused by third-party packages.
-
-        Parameters
-        ----------
-        name : str
-            Name of the installed library.
-        sources : sequence
-            List of the library's source files. See `add_library` for details.
-        install_dir : str
-            Path to install the library, relative to the current sub-package.
-        build_info : dict, optional
-            The following keys are allowed:
-
-                * depends
-                * macros
-                * include_dirs
-                * extra_compiler_args
-                * extra_f77_compile_args
-                * extra_f90_compile_args
-                * f2py_options
-                * language
-
-        Returns
-        -------
-        None
-
-        See Also
-        --------
-        add_library, add_npy_pkg_config, get_info
-
-        Notes
-        -----
-        The best way to encode the options required to link against the specified
-        C libraries is to use a "libname.ini" file, and use `get_info` to
-        retrieve the required options (see `add_npy_pkg_config` for more
-        information).
-
-        """
-        if not build_info:
-            build_info = {}
-
-        install_dir = os.path.join(self.package_path, install_dir)
-        self._add_library(name, sources, install_dir, build_info)
-        self.installed_libraries.append(InstallableLib(name, build_info, install_dir))
-
-    def add_npy_pkg_config(self, template, install_dir, subst_dict=None):
-        """
-        Generate and install a npy-pkg config file from a template.
-
-        The config file generated from `template` is installed in the
-        given install directory, using `subst_dict` for variable substitution.
-
-        Parameters
-        ----------
-        template : str
-            The path of the template, relatively to the current package path.
-        install_dir : str
-            Where to install the npy-pkg config file, relatively to the current
-            package path.
-        subst_dict : dict, optional
-            If given, any string of the form ``@key@`` will be replaced by
-            ``subst_dict[key]`` in the template file when installed. The install
-            prefix is always available through the variable ``@prefix@``, since the
-            install prefix is not easy to get reliably from setup.py.
-
-        See also
-        --------
-        add_installed_library, get_info
-
-        Notes
-        -----
-        This works for both standard installs and in-place builds, i.e. the
-        ``@prefix@`` refer to the source directory for in-place builds.
-
-        Examples
-        --------
-        ::
-
-            config.add_npy_pkg_config('foo.ini.in', 'lib', {'foo': bar})
-
-        Assuming the foo.ini.in file has the following content::
-
-            [meta]
-            Name=@foo@
-            Version=1.0
-            Description=dummy description
-
-            [default]
-            Cflags=-I@prefix@/include
-            Libs=
-
-        The generated file will have the following content::
-
-            [meta]
-            Name=bar
-            Version=1.0
-            Description=dummy description
-
-            [default]
-            Cflags=-Iprefix_dir/include
-            Libs=
-
-        and will be installed as foo.ini in the 'lib' subpath.
-
-        When cross-compiling with numpy distutils, it might be necessary to
-        use modified npy-pkg-config files.  Using the default/generated files
-        will link with the host libraries (i.e. libnpymath.a).  For
-        cross-compilation you of-course need to link with target libraries,
-        while using the host Python installation.
-
-        You can copy out the numpy/core/lib/npy-pkg-config directory, add a
-        pkgdir value to the .ini files and set NPY_PKG_CONFIG_PATH environment
-        variable to point to the directory with the modified npy-pkg-config
-        files.
-
-        Example npymath.ini modified for cross-compilation::
-
-            [meta]
-            Name=npymath
-            Description=Portable, core math library implementing C99 standard
-            Version=0.1
-
-            [variables]
-            pkgname=numpy.core
-            pkgdir=/build/arm-linux-gnueabi/sysroot/usr/lib/python3.7/site-packages/numpy/core
-            prefix=${pkgdir}
-            libdir=${prefix}/lib
-            includedir=${prefix}/include
-
-            [default]
-            Libs=-L${libdir} -lnpymath
-            Cflags=-I${includedir}
-            Requires=mlib
-
-            [msvc]
-            Libs=/LIBPATH:${libdir} npymath.lib
-            Cflags=/INCLUDE:${includedir}
-            Requires=mlib
-
-        """
-        if subst_dict is None:
-            subst_dict = {}
-        template = os.path.join(self.package_path, template)
-
-        if self.name in self.installed_pkg_config:
-            self.installed_pkg_config[self.name].append((template, install_dir,
-                subst_dict))
-        else:
-            self.installed_pkg_config[self.name] = [(template, install_dir,
-                subst_dict)]
-
-
-    def add_scripts(self,*files):
-        """Add scripts to configuration.
-
-        Add the sequence of files to the beginning of the scripts list.
-        Scripts will be installed under the <prefix>/bin/ directory.
-
-        """
-        scripts = self.paths(files)
-        dist = self.get_distribution()
-        if dist is not None:
-            if dist.scripts is None:
-                dist.scripts = []
-            dist.scripts.extend(scripts)
-        else:
-            self.scripts.extend(scripts)
-
-    def dict_append(self,**dict):
-        for key in self.list_keys:
-            a = getattr(self, key)
-            a.extend(dict.get(key, []))
-        for key in self.dict_keys:
-            a = getattr(self, key)
-            a.update(dict.get(key, {}))
-        known_keys = self.list_keys + self.dict_keys + self.extra_keys
-        for key in dict.keys():
-            if key not in known_keys:
-                a = getattr(self, key, None)
-                if a and a==dict[key]: continue
-                self.warn('Inheriting attribute %r=%r from %r' \
-                          % (key, dict[key], dict.get('name', '?')))
-                setattr(self, key, dict[key])
-                self.extra_keys.append(key)
-            elif key in self.extra_keys:
-                self.info('Ignoring attempt to set %r (from %r to %r)' \
-                          % (key, getattr(self, key), dict[key]))
-            elif key in known_keys:
-                # key is already processed above
-                pass
-            else:
-                raise ValueError("Don't know about key=%r" % (key))
-
-    def __str__(self):
-        from pprint import pformat
-        known_keys = self.list_keys + self.dict_keys + self.extra_keys
-        s = '<'+5*'-' + '\n'
-        s += 'Configuration of '+self.name+':\n'
-        known_keys.sort()
-        for k in known_keys:
-            a = getattr(self, k, None)
-            if a:
-                s += '%s = %s\n' % (k, pformat(a))
-        s += 5*'-' + '>'
-        return s
-
-    def get_config_cmd(self):
-        """
-        Returns the numpy.distutils config command instance.
-        """
-        cmd = get_cmd('config')
-        cmd.ensure_finalized()
-        cmd.dump_source = 0
-        cmd.noisy = 0
-        old_path = os.environ.get('PATH')
-        if old_path:
-            path = os.pathsep.join(['.', old_path])
-            os.environ['PATH'] = path
-        return cmd
-
-    def get_build_temp_dir(self):
-        """
-        Return a path to a temporary directory where temporary files should be
-        placed.
-        """
-        cmd = get_cmd('build')
-        cmd.ensure_finalized()
-        return cmd.build_temp
-
-    def have_f77c(self):
-        """Check for availability of Fortran 77 compiler.
-
-        Use it inside source generating function to ensure that
-        setup distribution instance has been initialized.
-
-        Notes
-        -----
-        True if a Fortran 77 compiler is available (because a simple Fortran 77
-        code was able to be compiled successfully).
-        """
-        simple_fortran_subroutine = '''
-        subroutine simple
-        end
-        '''
-        config_cmd = self.get_config_cmd()
-        flag = config_cmd.try_compile(simple_fortran_subroutine, lang='f77')
-        return flag
-
-    def have_f90c(self):
-        """Check for availability of Fortran 90 compiler.
-
-        Use it inside source generating function to ensure that
-        setup distribution instance has been initialized.
-
-        Notes
-        -----
-        True if a Fortran 90 compiler is available (because a simple Fortran
-        90 code was able to be compiled successfully)
-        """
-        simple_fortran_subroutine = '''
-        subroutine simple
-        end
-        '''
-        config_cmd = self.get_config_cmd()
-        flag = config_cmd.try_compile(simple_fortran_subroutine, lang='f90')
-        return flag
-
-    def append_to(self, extlib):
-        """Append libraries, include_dirs to extension or library item.
-        """
-        if is_sequence(extlib):
-            lib_name, build_info = extlib
-            dict_append(build_info,
-                        libraries=self.libraries,
-                        include_dirs=self.include_dirs)
-        else:
-            from numpy.distutils.core import Extension
-            assert isinstance(extlib, Extension), repr(extlib)
-            extlib.libraries.extend(self.libraries)
-            extlib.include_dirs.extend(self.include_dirs)
-
-    def _get_svn_revision(self, path):
-        """Return path's SVN revision number.
-        """
-        try:
-            output = subprocess.check_output(['svnversion'], cwd=path)
-        except (subprocess.CalledProcessError, OSError):
-            pass
-        else:
-            m = re.match(rb'(?P<revision>\d+)', output)
-            if m:
-                return int(m.group('revision'))
-
-        if sys.platform=='win32' and os.environ.get('SVN_ASP_DOT_NET_HACK', None):
-            entries = njoin(path, '_svn', 'entries')
-        else:
-            entries = njoin(path, '.svn', 'entries')
-        if os.path.isfile(entries):
-            with open(entries) as f:
-                fstr = f.read()
-            if fstr[:5] == '<?xml':  # pre 1.4
-                m = re.search(r'revision="(?P<revision>\d+)"', fstr)
-                if m:
-                    return int(m.group('revision'))
-            else:  # non-xml entries file --- check to be sure that
-                m = re.search(r'dir[\n\r]+(?P<revision>\d+)', fstr)
-                if m:
-                    return int(m.group('revision'))
-        return None
-
-    def _get_hg_revision(self, path):
-        """Return path's Mercurial revision number.
-        """
-        try:
-            output = subprocess.check_output(
-                ['hg', 'identify', '--num'], cwd=path)
-        except (subprocess.CalledProcessError, OSError):
-            pass
-        else:
-            m = re.match(rb'(?P<revision>\d+)', output)
-            if m:
-                return int(m.group('revision'))
-
-        branch_fn = njoin(path, '.hg', 'branch')
-        branch_cache_fn = njoin(path, '.hg', 'branch.cache')
-
-        if os.path.isfile(branch_fn):
-            branch0 = None
-            with open(branch_fn) as f:
-                revision0 = f.read().strip()
-
-            branch_map = {}
-            with open(branch_cache_fn) as f:
-                for line in f:
-                    branch1, revision1  = line.split()[:2]
-                    if revision1==revision0:
-                        branch0 = branch1
-                    try:
-                        revision1 = int(revision1)
-                    except ValueError:
-                        continue
-                    branch_map[branch1] = revision1
-
-            return branch_map.get(branch0)
-
-        return None
-
-
-    def get_version(self, version_file=None, version_variable=None):
-        """Try to get version string of a package.
-
-        Return a version string of the current package or None if the version
-        information could not be detected.
-
-        Notes
-        -----
-        This method scans files named
-        __version__.py, <packagename>_version.py, version.py, and
-        __svn_version__.py for string variables version, __version__, and
-        <packagename>_version, until a version number is found.
-        """
-        version = getattr(self, 'version', None)
-        if version is not None:
-            return version
-
-        # Get version from version file.
-        if version_file is None:
-            files = ['__version__.py',
-                     self.name.split('.')[-1]+'_version.py',
-                     'version.py',
-                     '__svn_version__.py',
-                     '__hg_version__.py']
-        else:
-            files = [version_file]
-        if version_variable is None:
-            version_vars = ['version',
-                            '__version__',
-                            self.name.split('.')[-1]+'_version']
-        else:
-            version_vars = [version_variable]
-        for f in files:
-            fn = njoin(self.local_path, f)
-            if os.path.isfile(fn):
-                info = ('.py', 'U', 1)
-                name = os.path.splitext(os.path.basename(fn))[0]
-                n = dot_join(self.name, name)
-                try:
-                    version_module = exec_mod_from_location(
-                                        '_'.join(n.split('.')), fn)
-                except ImportError as e:
-                    self.warn(str(e))
-                    version_module = None
-                if version_module is None:
-                    continue
-
-                for a in version_vars:
-                    version = getattr(version_module, a, None)
-                    if version is not None:
-                        break
-
-                # Try if versioneer module
-                try:
-                    version = version_module.get_versions()['version']
-                except AttributeError:
-                    pass
-
-                if version is not None:
-                    break
-
-        if version is not None:
-            self.version = version
-            return version
-
-        # Get version as SVN or Mercurial revision number
-        revision = self._get_svn_revision(self.local_path)
-        if revision is None:
-            revision = self._get_hg_revision(self.local_path)
-
-        if revision is not None:
-            version = str(revision)
-            self.version = version
-
-        return version
-
-    def make_svn_version_py(self, delete=True):
-        """Appends a data function to the data_files list that will generate
-        __svn_version__.py file to the current package directory.
-
-        Generate package __svn_version__.py file from SVN revision number,
-        it will be removed after python exits but will be available
-        when sdist, etc commands are executed.
-
-        Notes
-        -----
-        If __svn_version__.py existed before, nothing is done.
-
-        This is
-        intended for working with source directories that are in an SVN
-        repository.
-        """
-        target = njoin(self.local_path, '__svn_version__.py')
-        revision = self._get_svn_revision(self.local_path)
-        if os.path.isfile(target) or revision is None:
-            return
-        else:
-            def generate_svn_version_py():
-                if not os.path.isfile(target):
-                    version = str(revision)
-                    self.info('Creating %s (version=%r)' % (target, version))
-                    with open(target, 'w') as f:
-                        f.write('version = %r\n' % (version))
-
-                def rm_file(f=target,p=self.info):
-                    if delete:
-                        try: os.remove(f); p('removed '+f)
-                        except OSError: pass
-                        try: os.remove(f+'c'); p('removed '+f+'c')
-                        except OSError: pass
-
-                atexit.register(rm_file)
-
-                return target
-
-            self.add_data_files(('', generate_svn_version_py()))
-
-    def make_hg_version_py(self, delete=True):
-        """Appends a data function to the data_files list that will generate
-        __hg_version__.py file to the current package directory.
-
-        Generate package __hg_version__.py file from Mercurial revision,
-        it will be removed after python exits but will be available
-        when sdist, etc commands are executed.
-
-        Notes
-        -----
-        If __hg_version__.py existed before, nothing is done.
-
-        This is intended for working with source directories that are
-        in an Mercurial repository.
-        """
-        target = njoin(self.local_path, '__hg_version__.py')
-        revision = self._get_hg_revision(self.local_path)
-        if os.path.isfile(target) or revision is None:
-            return
-        else:
-            def generate_hg_version_py():
-                if not os.path.isfile(target):
-                    version = str(revision)
-                    self.info('Creating %s (version=%r)' % (target, version))
-                    with open(target, 'w') as f:
-                        f.write('version = %r\n' % (version))
-
-                def rm_file(f=target,p=self.info):
-                    if delete:
-                        try: os.remove(f); p('removed '+f)
-                        except OSError: pass
-                        try: os.remove(f+'c'); p('removed '+f+'c')
-                        except OSError: pass
-
-                atexit.register(rm_file)
-
-                return target
-
-            self.add_data_files(('', generate_hg_version_py()))
-
-    def make_config_py(self,name='__config__'):
-        """Generate package __config__.py file containing system_info
-        information used during building the package.
-
-        This file is installed to the
-        package installation directory.
-
-        """
-        self.py_modules.append((self.name, name, generate_config_py))
-
-    def get_info(self,*names):
-        """Get resources information.
-
-        Return information (from system_info.get_info) for all of the names in
-        the argument list in a single dictionary.
-        """
-        from .system_info import get_info, dict_append
-        info_dict = {}
-        for a in names:
-            dict_append(info_dict,**get_info(a))
-        return info_dict
-
-
-def get_cmd(cmdname, _cache={}):
-    if cmdname not in _cache:
-        import distutils.core
-        dist = distutils.core._setup_distribution
-        if dist is None:
-            from distutils.errors import DistutilsInternalError
-            raise DistutilsInternalError(
-                  'setup distribution instance not initialized')
-        cmd = dist.get_command_obj(cmdname)
-        _cache[cmdname] = cmd
-    return _cache[cmdname]
-
-def get_numpy_include_dirs():
-    # numpy_include_dirs are set by numpy/core/setup.py, otherwise []
-    include_dirs = Configuration.numpy_include_dirs[:]
-    if not include_dirs:
-        import numpy
-        include_dirs = [ numpy.get_include() ]
-    # else running numpy/core/setup.py
-    return include_dirs
-
-def get_npy_pkg_dir():
-    """Return the path where to find the npy-pkg-config directory.
-
-    If the NPY_PKG_CONFIG_PATH environment variable is set, the value of that
-    is returned.  Otherwise, a path inside the location of the numpy module is
-    returned.
-
-    The NPY_PKG_CONFIG_PATH can be useful when cross-compiling, maintaining
-    customized npy-pkg-config .ini files for the cross-compilation
-    environment, and using them when cross-compiling.
-
-    """
-    d = os.environ.get('NPY_PKG_CONFIG_PATH')
-    if d is not None:
-        return d
-    spec = importlib.util.find_spec('numpy')
-    d = os.path.join(os.path.dirname(spec.origin),
-            'core', 'lib', 'npy-pkg-config')
-    return d
-
-def get_pkg_info(pkgname, dirs=None):
-    """
-    Return library info for the given package.
-
-    Parameters
-    ----------
-    pkgname : str
-        Name of the package (should match the name of the .ini file, without
-        the extension, e.g. foo for the file foo.ini).
-    dirs : sequence, optional
-        If given, should be a sequence of additional directories where to look
-        for npy-pkg-config files. Those directories are searched prior to the
-        NumPy directory.
-
-    Returns
-    -------
-    pkginfo : class instance
-        The `LibraryInfo` instance containing the build information.
-
-    Raises
-    ------
-    PkgNotFound
-        If the package is not found.
-
-    See Also
-    --------
-    Configuration.add_npy_pkg_config, Configuration.add_installed_library,
-    get_info
-
-    """
-    from numpy.distutils.npy_pkg_config import read_config
-
-    if dirs:
-        dirs.append(get_npy_pkg_dir())
-    else:
-        dirs = [get_npy_pkg_dir()]
-    return read_config(pkgname, dirs)
-
-def get_info(pkgname, dirs=None):
-    """
-    Return an info dict for a given C library.
-
-    The info dict contains the necessary options to use the C library.
-
-    Parameters
-    ----------
-    pkgname : str
-        Name of the package (should match the name of the .ini file, without
-        the extension, e.g. foo for the file foo.ini).
-    dirs : sequence, optional
-        If given, should be a sequence of additional directories where to look
-        for npy-pkg-config files. Those directories are searched prior to the
-        NumPy directory.
-
-    Returns
-    -------
-    info : dict
-        The dictionary with build information.
-
-    Raises
-    ------
-    PkgNotFound
-        If the package is not found.
-
-    See Also
-    --------
-    Configuration.add_npy_pkg_config, Configuration.add_installed_library,
-    get_pkg_info
-
-    Examples
-    --------
-    To get the necessary information for the npymath library from NumPy:
-
-    >>> npymath_info = np.distutils.misc_util.get_info('npymath')
-    >>> npymath_info                                    #doctest: +SKIP
-    {'define_macros': [], 'libraries': ['npymath'], 'library_dirs':
-    ['.../numpy/core/lib'], 'include_dirs': ['.../numpy/core/include']}
-
-    This info dict can then be used as input to a `Configuration` instance::
-
-      config.add_extension('foo', sources=['foo.c'], extra_info=npymath_info)
-
-    """
-    from numpy.distutils.npy_pkg_config import parse_flags
-    pkg_info = get_pkg_info(pkgname, dirs)
-
-    # Translate LibraryInfo instance into a build_info dict
-    info = parse_flags(pkg_info.cflags())
-    for k, v in parse_flags(pkg_info.libs()).items():
-        info[k].extend(v)
-
-    # add_extension extra_info argument is ANAL
-    info['define_macros'] = info['macros']
-    del info['macros']
-    del info['ignored']
-
-    return info
-
-def is_bootstrapping():
-    import builtins
-
-    try:
-        builtins.__NUMPY_SETUP__
-        return True
-    except AttributeError:
-        return False
-
-
-#########################
-
-def default_config_dict(name = None, parent_name = None, local_path=None):
-    """Return a configuration dictionary for usage in
-    configuration() function defined in file setup_<name>.py.
-    """
-    import warnings
-    warnings.warn('Use Configuration(%r,%r,top_path=%r) instead of '\
-                  'deprecated default_config_dict(%r,%r,%r)'
-                  % (name, parent_name, local_path,
-                     name, parent_name, local_path,
-                     ), stacklevel=2)
-    c = Configuration(name, parent_name, local_path)
-    return c.todict()
-
-
-def dict_append(d, **kws):
-    for k, v in kws.items():
-        if k in d:
-            ov = d[k]
-            if isinstance(ov, str):
-                d[k] = v
-            else:
-                d[k].extend(v)
-        else:
-            d[k] = v
-
-def appendpath(prefix, path):
-    if os.path.sep != '/':
-        prefix = prefix.replace('/', os.path.sep)
-        path = path.replace('/', os.path.sep)
-    drive = ''
-    if os.path.isabs(path):
-        drive = os.path.splitdrive(prefix)[0]
-        absprefix = os.path.splitdrive(os.path.abspath(prefix))[1]
-        pathdrive, path = os.path.splitdrive(path)
-        d = os.path.commonprefix([absprefix, path])
-        if os.path.join(absprefix[:len(d)], absprefix[len(d):]) != absprefix \
-           or os.path.join(path[:len(d)], path[len(d):]) != path:
-            # Handle invalid paths
-            d = os.path.dirname(d)
-        subpath = path[len(d):]
-        if os.path.isabs(subpath):
-            subpath = subpath[1:]
-    else:
-        subpath = path
-    return os.path.normpath(njoin(drive + prefix, subpath))
-
-def generate_config_py(target):
-    """Generate config.py file containing system_info information
-    used during building the package.
-
-    Usage:
-        config['py_modules'].append((packagename, '__config__',generate_config_py))
-    """
-    from numpy.distutils.system_info import system_info
-    from distutils.dir_util import mkpath
-    mkpath(os.path.dirname(target))
-    with open(target, 'w') as f:
-        f.write('# This file is generated by numpy\'s %s\n' % (os.path.basename(sys.argv[0])))
-        f.write('# It contains system_info results at the time of building this package.\n')
-        f.write('__all__ = ["get_info","show"]\n\n')
-
-        # For gfortran+msvc combination, extra shared libraries may exist
-        f.write(textwrap.dedent("""
-            import os
-            import sys
-
-            extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs')
-
-            if sys.platform == 'win32' and os.path.isdir(extra_dll_dir):
-                os.add_dll_directory(extra_dll_dir)
-
-            """))
-
-        for k, i in system_info.saved_results.items():
-            f.write('%s=%r\n' % (k, i))
-        f.write(textwrap.dedent(r'''
-            def get_info(name):
-                g = globals()
-                return g.get(name, g.get(name + "_info", {}))
-
-            def show():
-                """
-                Show libraries in the system on which NumPy was built.
-
-                Print information about various resources (libraries, library
-                directories, include directories, etc.) in the system on which
-                NumPy was built.
-
-                See Also
-                --------
-                get_include : Returns the directory containing NumPy C
-                              header files.
-
-                Notes
-                -----
-                1. Classes specifying the information to be printed are defined
-                   in the `numpy.distutils.system_info` module.
-
-                   Information may include:
-
-                   * ``language``: language used to write the libraries (mostly
-                     C or f77)
-                   * ``libraries``: names of libraries found in the system
-                   * ``library_dirs``: directories containing the libraries
-                   * ``include_dirs``: directories containing library header files
-                   * ``src_dirs``: directories containing library source files
-                   * ``define_macros``: preprocessor macros used by
-                     ``distutils.setup``
-                   * ``baseline``: minimum CPU features required
-                   * ``found``: dispatched features supported in the system
-                   * ``not found``: dispatched features that are not supported
-                     in the system
-
-                2. NumPy BLAS/LAPACK Installation Notes
-
-                   Installing a numpy wheel (``pip install numpy`` or force it
-                   via ``pip install numpy --only-binary :numpy: numpy``) includes
-                   an OpenBLAS implementation of the BLAS and LAPACK linear algebra
-                   APIs. In this case, ``library_dirs`` reports the original build
-                   time configuration as compiled with gcc/gfortran; at run time
-                   the OpenBLAS library is in
-                   ``site-packages/numpy.libs/`` (linux), or
-                   ``site-packages/numpy/.dylibs/`` (macOS), or
-                   ``site-packages/numpy/.libs/`` (windows).
-
-                   Installing numpy from source
-                   (``pip install numpy --no-binary numpy``) searches for BLAS and
-                   LAPACK dynamic link libraries at build time as influenced by
-                   environment variables NPY_BLAS_LIBS, NPY_CBLAS_LIBS, and
-                   NPY_LAPACK_LIBS; or NPY_BLAS_ORDER and NPY_LAPACK_ORDER;
-                   or the optional file ``~/.numpy-site.cfg``.
-                   NumPy remembers those locations and expects to load the same
-                   libraries at run-time.
-                   In NumPy 1.21+ on macOS, 'accelerate' (Apple's Accelerate BLAS
-                   library) is in the default build-time search order after
-                   'openblas'.
-
-                Examples
-                --------
-                >>> import numpy as np
-                >>> np.show_config()
-                blas_opt_info:
-                    language = c
-                    define_macros = [('HAVE_CBLAS', None)]
-                    libraries = ['openblas', 'openblas']
-                    library_dirs = ['/usr/local/lib']
-                """
-                from numpy.core._multiarray_umath import (
-                    __cpu_features__, __cpu_baseline__, __cpu_dispatch__
-                )
-                for name,info_dict in globals().items():
-                    if name[0] == "_" or type(info_dict) is not type({}): continue
-                    print(name + ":")
-                    if not info_dict:
-                        print("  NOT AVAILABLE")
-                    for k,v in info_dict.items():
-                        v = str(v)
-                        if k == "sources" and len(v) > 200:
-                            v = v[:60] + " ...\n... " + v[-60:]
-                        print("    %s = %s" % (k,v))
-
-                features_found, features_not_found = [], []
-                for feature in __cpu_dispatch__:
-                    if __cpu_features__[feature]:
-                        features_found.append(feature)
-                    else:
-                        features_not_found.append(feature)
-
-                print("Supported SIMD extensions in this NumPy install:")
-                print("    baseline = %s" % (','.join(__cpu_baseline__)))
-                print("    found = %s" % (','.join(features_found)))
-                print("    not found = %s" % (','.join(features_not_found)))
-
-                    '''))
-
-    return target
-
-def msvc_version(compiler):
-    """Return version major and minor of compiler instance if it is
-    MSVC, raise an exception otherwise."""
-    if not compiler.compiler_type == "msvc":
-        raise ValueError("Compiler instance is not msvc (%s)"\
-                         % compiler.compiler_type)
-    return compiler._MSVCCompiler__version
-
-def get_build_architecture():
-    # Importing distutils.msvccompiler triggers a warning on non-Windows
-    # systems, so delay the import to here.
-    from distutils.msvccompiler import get_build_architecture
-    return get_build_architecture()
-
-
-_cxx_ignore_flags = {'-Werror=implicit-function-declaration', '-std=c99'}
-
-
-def sanitize_cxx_flags(cxxflags):
-    '''
-    Some flags are valid for C but not C++. Prune them.
-    '''
-    return [flag for flag in cxxflags if flag not in _cxx_ignore_flags]
-
-
-def exec_mod_from_location(modname, modfile):
-    '''
-    Use importlib machinery to import a module `modname` from the file
-    `modfile`. Depending on the `spec.loader`, the module may not be
-    registered in sys.modules.
-    '''
-    spec = importlib.util.spec_from_file_location(modname, modfile)
-    foo = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(foo)
-    return foo
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/tests/test_shell_utils.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/tests/test_shell_utils.py
deleted file mode 100644
index 696d38ddd66a41ec5f51f4c93d26d3f0df29b483..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/tests/test_shell_utils.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import pytest
-import subprocess
-import json
-import sys
-
-from numpy.distutils import _shell_utils
-from numpy.testing import IS_WASM
-
-argv_cases = [
-    [r'exe'],
-    [r'path/exe'],
-    [r'path\exe'],
-    [r'\\server\path\exe'],
-    [r'path to/exe'],
-    [r'path to\exe'],
-
-    [r'exe', '--flag'],
-    [r'path/exe', '--flag'],
-    [r'path\exe', '--flag'],
-    [r'path to/exe', '--flag'],
-    [r'path to\exe', '--flag'],
-
-    # flags containing literal quotes in their name
-    [r'path to/exe', '--flag-"quoted"'],
-    [r'path to\exe', '--flag-"quoted"'],
-    [r'path to/exe', '"--flag-quoted"'],
-    [r'path to\exe', '"--flag-quoted"'],
-]
-
-
-@pytest.fixture(params=[
-    _shell_utils.WindowsParser,
-    _shell_utils.PosixParser
-])
-def Parser(request):
-    return request.param
-
-
-@pytest.fixture
-def runner(Parser):
-    if Parser != _shell_utils.NativeParser:
-        pytest.skip('Unable to run with non-native parser')
-
-    if Parser == _shell_utils.WindowsParser:
-        return lambda cmd: subprocess.check_output(cmd)
-    elif Parser == _shell_utils.PosixParser:
-        # posix has no non-shell string parsing
-        return lambda cmd: subprocess.check_output(cmd, shell=True)
-    else:
-        raise NotImplementedError
-
-
-@pytest.mark.skipif(IS_WASM, reason="Cannot start subprocess")
-@pytest.mark.parametrize('argv', argv_cases)
-def test_join_matches_subprocess(Parser, runner, argv):
-    """
-    Test that join produces strings understood by subprocess
-    """
-    # invoke python to return its arguments as json
-    cmd = [
-        sys.executable, '-c',
-        'import json, sys; print(json.dumps(sys.argv[1:]))'
-    ]
-    joined = Parser.join(cmd + argv)
-    json_out = runner(joined).decode()
-    assert json.loads(json_out) == argv
-
-
-@pytest.mark.skipif(IS_WASM, reason="Cannot start subprocess")
-@pytest.mark.parametrize('argv', argv_cases)
-def test_roundtrip(Parser, argv):
-    """
-    Test that split is the inverse operation of join
-    """
-    try:
-        joined = Parser.join(argv)
-        assert argv == Parser.split(joined)
-    except NotImplementedError:
-        pytest.skip("Not implemented")
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/test_module_doc.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/test_module_doc.py
deleted file mode 100644
index 28822d405cc02ac2ce5cc214c27271a199612349..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/test_module_doc.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import os
-import sys
-import pytest
-import textwrap
-
-from . import util
-from numpy.testing import IS_PYPY
-
-
-class TestModuleDocString(util.F2PyTest):
-    sources = [
-        util.getpath("tests", "src", "module_data",
-                     "module_data_docstring.f90")
-    ]
-
-    @pytest.mark.skipif(sys.platform == "win32",
-                        reason="Fails with MinGW64 Gfortran (Issue #9673)")
-    @pytest.mark.xfail(IS_PYPY,
-                       reason="PyPy cannot modify tp_doc after PyType_Ready")
-    def test_module_docstring(self):
-        assert self.module.mod.__doc__ == textwrap.dedent("""\
-                     i : 'i'-scalar
-                     x : 'i'-array(4)
-                     a : 'f'-array(2,3)
-                     b : 'f'-array(-1,-1), not allocated\x00
-                     foo()\n
-                     Wrapper for ``foo``.\n\n""")
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/parser/common/test_index.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/parser/common/test_index.py
deleted file mode 100644
index 69afb9fe564727909f166bc40337b68cb58ede2a..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/parser/common/test_index.py
+++ /dev/null
@@ -1,299 +0,0 @@
-"""
-Tests that work on both the Python and C engines but do not have a
-specific classification into the other test modules.
-"""
-from datetime import datetime
-from io import StringIO
-import os
-
-import pytest
-
-from pandas import (
-    DataFrame,
-    Index,
-    MultiIndex,
-)
-import pandas._testing as tm
-
-xfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
-
-# GH#43650: Some expected failures with the pyarrow engine can occasionally
-# cause a deadlock instead, so we skip these instead of xfailing
-skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
-
-
-@pytest.mark.parametrize(
-    "data,kwargs,expected",
-    [
-        (
-            """foo,2,3,4,5
-bar,7,8,9,10
-baz,12,13,14,15
-qux,12,13,14,15
-foo2,12,13,14,15
-bar2,12,13,14,15
-""",
-            {"index_col": 0, "names": ["index", "A", "B", "C", "D"]},
-            DataFrame(
-                [
-                    [2, 3, 4, 5],
-                    [7, 8, 9, 10],
-                    [12, 13, 14, 15],
-                    [12, 13, 14, 15],
-                    [12, 13, 14, 15],
-                    [12, 13, 14, 15],
-                ],
-                index=Index(["foo", "bar", "baz", "qux", "foo2", "bar2"], name="index"),
-                columns=["A", "B", "C", "D"],
-            ),
-        ),
-        (
-            """foo,one,2,3,4,5
-foo,two,7,8,9,10
-foo,three,12,13,14,15
-bar,one,12,13,14,15
-bar,two,12,13,14,15
-""",
-            {"index_col": [0, 1], "names": ["index1", "index2", "A", "B", "C", "D"]},
-            DataFrame(
-                [
-                    [2, 3, 4, 5],
-                    [7, 8, 9, 10],
-                    [12, 13, 14, 15],
-                    [12, 13, 14, 15],
-                    [12, 13, 14, 15],
-                ],
-                index=MultiIndex.from_tuples(
-                    [
-                        ("foo", "one"),
-                        ("foo", "two"),
-                        ("foo", "three"),
-                        ("bar", "one"),
-                        ("bar", "two"),
-                    ],
-                    names=["index1", "index2"],
-                ),
-                columns=["A", "B", "C", "D"],
-            ),
-        ),
-    ],
-)
-def test_pass_names_with_index(all_parsers, data, kwargs, expected):
-    parser = all_parsers
-    result = parser.read_csv(StringIO(data), **kwargs)
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("index_col", [[0, 1], [1, 0]])
-def test_multi_index_no_level_names(all_parsers, index_col):
-    data = """index1,index2,A,B,C,D
-foo,one,2,3,4,5
-foo,two,7,8,9,10
-foo,three,12,13,14,15
-bar,one,12,13,14,15
-bar,two,12,13,14,15
-"""
-    headless_data = "\n".join(data.split("\n")[1:])
-
-    names = ["A", "B", "C", "D"]
-    parser = all_parsers
-
-    result = parser.read_csv(
-        StringIO(headless_data), index_col=index_col, header=None, names=names
-    )
-    expected = parser.read_csv(StringIO(data), index_col=index_col)
-
-    # No index names in headless data.
-    expected.index.names = [None] * 2
-    tm.assert_frame_equal(result, expected)
-
-
-@xfail_pyarrow
-def test_multi_index_no_level_names_implicit(all_parsers):
-    parser = all_parsers
-    data = """A,B,C,D
-foo,one,2,3,4,5
-foo,two,7,8,9,10
-foo,three,12,13,14,15
-bar,one,12,13,14,15
-bar,two,12,13,14,15
-"""
-
-    result = parser.read_csv(StringIO(data))
-    expected = DataFrame(
-        [
-            [2, 3, 4, 5],
-            [7, 8, 9, 10],
-            [12, 13, 14, 15],
-            [12, 13, 14, 15],
-            [12, 13, 14, 15],
-        ],
-        columns=["A", "B", "C", "D"],
-        index=MultiIndex.from_tuples(
-            [
-                ("foo", "one"),
-                ("foo", "two"),
-                ("foo", "three"),
-                ("bar", "one"),
-                ("bar", "two"),
-            ]
-        ),
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-@xfail_pyarrow
-@pytest.mark.parametrize(
-    "data,expected,header",
-    [
-        ("a,b", DataFrame(columns=["a", "b"]), [0]),
-        (
-            "a,b\nc,d",
-            DataFrame(columns=MultiIndex.from_tuples([("a", "c"), ("b", "d")])),
-            [0, 1],
-        ),
-    ],
-)
-@pytest.mark.parametrize("round_trip", [True, False])
-def test_multi_index_blank_df(all_parsers, data, expected, header, round_trip):
-    # see gh-14545
-    parser = all_parsers
-    data = expected.to_csv(index=False) if round_trip else data
-
-    result = parser.read_csv(StringIO(data), header=header)
-    tm.assert_frame_equal(result, expected)
-
-
-@xfail_pyarrow
-def test_no_unnamed_index(all_parsers):
-    parser = all_parsers
-    data = """ id c0 c1 c2
-0 1 0 a b
-1 2 0 c d
-2 2 2 e f
-"""
-    result = parser.read_csv(StringIO(data), sep=" ")
-    expected = DataFrame(
-        [[0, 1, 0, "a", "b"], [1, 2, 0, "c", "d"], [2, 2, 2, "e", "f"]],
-        columns=["Unnamed: 0", "id", "c0", "c1", "c2"],
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-def test_read_duplicate_index_explicit(all_parsers):
-    data = """index,A,B,C,D
-foo,2,3,4,5
-bar,7,8,9,10
-baz,12,13,14,15
-qux,12,13,14,15
-foo,12,13,14,15
-bar,12,13,14,15
-"""
-    parser = all_parsers
-    result = parser.read_csv(StringIO(data), index_col=0)
-
-    expected = DataFrame(
-        [
-            [2, 3, 4, 5],
-            [7, 8, 9, 10],
-            [12, 13, 14, 15],
-            [12, 13, 14, 15],
-            [12, 13, 14, 15],
-            [12, 13, 14, 15],
-        ],
-        columns=["A", "B", "C", "D"],
-        index=Index(["foo", "bar", "baz", "qux", "foo", "bar"], name="index"),
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-@xfail_pyarrow
-def test_read_duplicate_index_implicit(all_parsers):
-    data = """A,B,C,D
-foo,2,3,4,5
-bar,7,8,9,10
-baz,12,13,14,15
-qux,12,13,14,15
-foo,12,13,14,15
-bar,12,13,14,15
-"""
-    parser = all_parsers
-    result = parser.read_csv(StringIO(data))
-
-    expected = DataFrame(
-        [
-            [2, 3, 4, 5],
-            [7, 8, 9, 10],
-            [12, 13, 14, 15],
-            [12, 13, 14, 15],
-            [12, 13, 14, 15],
-            [12, 13, 14, 15],
-        ],
-        columns=["A", "B", "C", "D"],
-        index=Index(["foo", "bar", "baz", "qux", "foo", "bar"]),
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-@xfail_pyarrow
-def test_read_csv_no_index_name(all_parsers, csv_dir_path):
-    parser = all_parsers
-    csv2 = os.path.join(csv_dir_path, "test2.csv")
-    result = parser.read_csv(csv2, index_col=0, parse_dates=True)
-
-    expected = DataFrame(
-        [
-            [0.980269, 3.685731, -0.364216805298, -1.159738, "foo"],
-            [1.047916, -0.041232, -0.16181208307, 0.212549, "bar"],
-            [0.498581, 0.731168, -0.537677223318, 1.346270, "baz"],
-            [1.120202, 1.567621, 0.00364077397681, 0.675253, "qux"],
-            [-0.487094, 0.571455, -1.6116394093, 0.103469, "foo2"],
-        ],
-        columns=["A", "B", "C", "D", "E"],
-        index=Index(
-            [
-                datetime(2000, 1, 3),
-                datetime(2000, 1, 4),
-                datetime(2000, 1, 5),
-                datetime(2000, 1, 6),
-                datetime(2000, 1, 7),
-            ]
-        ),
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-@xfail_pyarrow
-def test_empty_with_index(all_parsers):
-    # see gh-10184
-    data = "x,y"
-    parser = all_parsers
-    result = parser.read_csv(StringIO(data), index_col=0)
-
-    expected = DataFrame(columns=["y"], index=Index([], name="x"))
-    tm.assert_frame_equal(result, expected)
-
-
-@skip_pyarrow
-def test_empty_with_multi_index(all_parsers):
-    # see gh-10467
-    data = "x,y,z"
-    parser = all_parsers
-    result = parser.read_csv(StringIO(data), index_col=["x", "y"])
-
-    expected = DataFrame(
-        columns=["z"], index=MultiIndex.from_arrays([[]] * 2, names=["x", "y"])
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-@skip_pyarrow
-def test_empty_with_reversed_multi_index(all_parsers):
-    data = "x,y,z"
-    parser = all_parsers
-    result = parser.read_csv(StringIO(data), index_col=[1, 0])
-
-    expected = DataFrame(
-        columns=["z"], index=MultiIndex.from_arrays([[]] * 2, names=["y", "x"])
-    )
-    tm.assert_frame_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tools/test_to_numeric.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tools/test_to_numeric.py
deleted file mode 100644
index 1d969e648b7522f9a33962c572c8e05c5d8f5eae..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tools/test_to_numeric.py
+++ /dev/null
@@ -1,956 +0,0 @@
-import decimal
-
-import numpy as np
-from numpy import iinfo
-import pytest
-
-import pandas as pd
-from pandas import (
-    ArrowDtype,
-    DataFrame,
-    Index,
-    Series,
-    to_numeric,
-)
-import pandas._testing as tm
-
-
-@pytest.fixture(params=[None, "ignore", "raise", "coerce"])
-def errors(request):
-    return request.param
-
-
-@pytest.fixture(params=[True, False])
-def signed(request):
-    return request.param
-
-
-@pytest.fixture(params=[lambda x: x, str], ids=["identity", "str"])
-def transform(request):
-    return request.param
-
-
-@pytest.fixture(params=[47393996303418497800, 100000000000000000000])
-def large_val(request):
-    return request.param
-
-
-@pytest.fixture(params=[True, False])
-def multiple_elts(request):
-    return request.param
-
-
-@pytest.fixture(
-    params=[
-        (lambda x: Index(x, name="idx"), tm.assert_index_equal),
-        (lambda x: Series(x, name="ser"), tm.assert_series_equal),
-        (lambda x: np.array(Index(x).values), tm.assert_numpy_array_equal),
-    ]
-)
-def transform_assert_equal(request):
-    return request.param
-
-
-@pytest.mark.parametrize(
-    "input_kwargs,result_kwargs",
-    [
-        ({}, {"dtype": np.int64}),
-        ({"errors": "coerce", "downcast": "integer"}, {"dtype": np.int8}),
-    ],
-)
-def test_empty(input_kwargs, result_kwargs):
-    # see gh-16302
-    ser = Series([], dtype=object)
-    result = to_numeric(ser, **input_kwargs)
-
-    expected = Series([], **result_kwargs)
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("last_val", ["7", 7])
-def test_series(last_val):
-    ser = Series(["1", "-3.14", last_val])
-    result = to_numeric(ser)
-
-    expected = Series([1, -3.14, 7])
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "data",
-    [
-        [1, 3, 4, 5],
-        [1.0, 3.0, 4.0, 5.0],
-        # Bool is regarded as numeric.
-        [True, False, True, True],
-    ],
-)
-def test_series_numeric(data):
-    ser = Series(data, index=list("ABCD"), name="EFG")
-
-    result = to_numeric(ser)
-    tm.assert_series_equal(result, ser)
-
-
-@pytest.mark.parametrize(
-    "data,msg",
-    [
-        ([1, -3.14, "apple"], 'Unable to parse string "apple" at position 2'),
-        (
-            ["orange", 1, -3.14, "apple"],
-            'Unable to parse string "orange" at position 0',
-        ),
-    ],
-)
-def test_error(data, msg):
-    ser = Series(data)
-
-    with pytest.raises(ValueError, match=msg):
-        to_numeric(ser, errors="raise")
-
-
-@pytest.mark.parametrize(
-    "errors,exp_data", [("ignore", [1, -3.14, "apple"]), ("coerce", [1, -3.14, np.nan])]
-)
-def test_ignore_error(errors, exp_data):
-    ser = Series([1, -3.14, "apple"])
-    result = to_numeric(ser, errors=errors)
-
-    expected = Series(exp_data)
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "errors,exp",
-    [
-        ("raise", 'Unable to parse string "apple" at position 2'),
-        ("ignore", [True, False, "apple"]),
-        # Coerces to float.
-        ("coerce", [1.0, 0.0, np.nan]),
-    ],
-)
-def test_bool_handling(errors, exp):
-    ser = Series([True, False, "apple"])
-
-    if isinstance(exp, str):
-        with pytest.raises(ValueError, match=exp):
-            to_numeric(ser, errors=errors)
-    else:
-        result = to_numeric(ser, errors=errors)
-        expected = Series(exp)
-
-        tm.assert_series_equal(result, expected)
-
-
-def test_list():
-    ser = ["1", "-3.14", "7"]
-    res = to_numeric(ser)
-
-    expected = np.array([1, -3.14, 7])
-    tm.assert_numpy_array_equal(res, expected)
-
-
-@pytest.mark.parametrize(
-    "data,arr_kwargs",
-    [
-        ([1, 3, 4, 5], {"dtype": np.int64}),
-        ([1.0, 3.0, 4.0, 5.0], {}),
-        # Boolean is regarded as numeric.
-        ([True, False, True, True], {}),
-    ],
-)
-def test_list_numeric(data, arr_kwargs):
-    result = to_numeric(data)
-    expected = np.array(data, **arr_kwargs)
-    tm.assert_numpy_array_equal(result, expected)
-
-
-@pytest.mark.parametrize("kwargs", [{"dtype": "O"}, {}])
-def test_numeric(kwargs):
-    data = [1, -3.14, 7]
-
-    ser = Series(data, **kwargs)
-    result = to_numeric(ser)
-
-    expected = Series(data)
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "columns",
-    [
-        # One column.
-        "a",
-        # Multiple columns.
-        ["a", "b"],
-    ],
-)
-def test_numeric_df_columns(columns):
-    # see gh-14827
-    df = DataFrame(
-        {
-            "a": [1.2, decimal.Decimal(3.14), decimal.Decimal("infinity"), "0.1"],
-            "b": [1.0, 2.0, 3.0, 4.0],
-        }
-    )
-
-    expected = DataFrame({"a": [1.2, 3.14, np.inf, 0.1], "b": [1.0, 2.0, 3.0, 4.0]})
-
-    df_copy = df.copy()
-    df_copy[columns] = df_copy[columns].apply(to_numeric)
-
-    tm.assert_frame_equal(df_copy, expected)
-
-
-@pytest.mark.parametrize(
-    "data,exp_data",
-    [
-        (
-            [[decimal.Decimal(3.14), 1.0], decimal.Decimal(1.6), 0.1],
-            [[3.14, 1.0], 1.6, 0.1],
-        ),
-        ([np.array([decimal.Decimal(3.14), 1.0]), 0.1], [[3.14, 1.0], 0.1]),
-    ],
-)
-def test_numeric_embedded_arr_likes(data, exp_data):
-    # Test to_numeric with embedded lists and arrays
-    df = DataFrame({"a": data})
-    df["a"] = df["a"].apply(to_numeric)
-
-    expected = DataFrame({"a": exp_data})
-    tm.assert_frame_equal(df, expected)
-
-
-def test_all_nan():
-    ser = Series(["a", "b", "c"])
-    result = to_numeric(ser, errors="coerce")
-
-    expected = Series([np.nan, np.nan, np.nan])
-    tm.assert_series_equal(result, expected)
-
-
-def test_type_check(errors):
-    # see gh-11776
-    df = DataFrame({"a": [1, -3.14, 7], "b": ["4", "5", "6"]})
-    kwargs = {"errors": errors} if errors is not None else {}
-    with pytest.raises(TypeError, match="1-d array"):
-        to_numeric(df, **kwargs)
-
-
-@pytest.mark.parametrize("val", [1, 1.1, 20001])
-def test_scalar(val, signed, transform):
-    val = -val if signed else val
-    assert to_numeric(transform(val)) == float(val)
-
-
-def test_really_large_scalar(large_val, signed, transform, errors):
-    # see gh-24910
-    kwargs = {"errors": errors} if errors is not None else {}
-    val = -large_val if signed else large_val
-
-    val = transform(val)
-    val_is_string = isinstance(val, str)
-
-    if val_is_string and errors in (None, "raise"):
-        msg = "Integer out of range. at position 0"
-        with pytest.raises(ValueError, match=msg):
-            to_numeric(val, **kwargs)
-    else:
-        expected = float(val) if (errors == "coerce" and val_is_string) else val
-        tm.assert_almost_equal(to_numeric(val, **kwargs), expected)
-
-
-def test_really_large_in_arr(large_val, signed, transform, multiple_elts, errors):
-    # see gh-24910
-    kwargs = {"errors": errors} if errors is not None else {}
-    val = -large_val if signed else large_val
-    val = transform(val)
-
-    extra_elt = "string"
-    arr = [val] + multiple_elts * [extra_elt]
-
-    val_is_string = isinstance(val, str)
-    coercing = errors == "coerce"
-
-    if errors in (None, "raise") and (val_is_string or multiple_elts):
-        if val_is_string:
-            msg = "Integer out of range. at position 0"
-        else:
-            msg = 'Unable to parse string "string" at position 1'
-
-        with pytest.raises(ValueError, match=msg):
-            to_numeric(arr, **kwargs)
-    else:
-        result = to_numeric(arr, **kwargs)
-
-        exp_val = float(val) if (coercing and val_is_string) else val
-        expected = [exp_val]
-
-        if multiple_elts:
-            if coercing:
-                expected.append(np.nan)
-                exp_dtype = float
-            else:
-                expected.append(extra_elt)
-                exp_dtype = object
-        else:
-            exp_dtype = float if isinstance(exp_val, (int, float)) else object
-
-        tm.assert_almost_equal(result, np.array(expected, dtype=exp_dtype))
-
-
-def test_really_large_in_arr_consistent(large_val, signed, multiple_elts, errors):
-    # see gh-24910
-    #
-    # Even if we discover that we have to hold float, does not mean
-    # we should be lenient on subsequent elements that fail to be integer.
-    kwargs = {"errors": errors} if errors is not None else {}
-    arr = [str(-large_val if signed else large_val)]
-
-    if multiple_elts:
-        arr.insert(0, large_val)
-
-    if errors in (None, "raise"):
-        index = int(multiple_elts)
-        msg = f"Integer out of range. at position {index}"
-
-        with pytest.raises(ValueError, match=msg):
-            to_numeric(arr, **kwargs)
-    else:
-        result = to_numeric(arr, **kwargs)
-
-        if errors == "coerce":
-            expected = [float(i) for i in arr]
-            exp_dtype = float
-        else:
-            expected = arr
-            exp_dtype = object
-
-        tm.assert_almost_equal(result, np.array(expected, dtype=exp_dtype))
-
-
-@pytest.mark.parametrize(
-    "errors,checker",
-    [
-        ("raise", 'Unable to parse string "fail" at position 0'),
-        ("ignore", lambda x: x == "fail"),
-        ("coerce", lambda x: np.isnan(x)),
-    ],
-)
-def test_scalar_fail(errors, checker):
-    scalar = "fail"
-
-    if isinstance(checker, str):
-        with pytest.raises(ValueError, match=checker):
-            to_numeric(scalar, errors=errors)
-    else:
-        assert checker(to_numeric(scalar, errors=errors))
-
-
-@pytest.mark.parametrize("data", [[1, 2, 3], [1.0, np.nan, 3, np.nan]])
-def test_numeric_dtypes(data, transform_assert_equal):
-    transform, assert_equal = transform_assert_equal
-    data = transform(data)
-
-    result = to_numeric(data)
-    assert_equal(result, data)
-
-
-@pytest.mark.parametrize(
-    "data,exp",
-    [
-        (["1", "2", "3"], np.array([1, 2, 3], dtype="int64")),
-        (["1.5", "2.7", "3.4"], np.array([1.5, 2.7, 3.4])),
-    ],
-)
-def test_str(data, exp, transform_assert_equal):
-    transform, assert_equal = transform_assert_equal
-    result = to_numeric(transform(data))
-
-    expected = transform(exp)
-    assert_equal(result, expected)
-
-
-def test_datetime_like(tz_naive_fixture, transform_assert_equal):
-    transform, assert_equal = transform_assert_equal
-    idx = pd.date_range("20130101", periods=3, tz=tz_naive_fixture)
-
-    result = to_numeric(transform(idx))
-    expected = transform(idx.asi8)
-    assert_equal(result, expected)
-
-
-def test_timedelta(transform_assert_equal):
-    transform, assert_equal = transform_assert_equal
-    idx = pd.timedelta_range("1 days", periods=3, freq="D")
-
-    result = to_numeric(transform(idx))
-    expected = transform(idx.asi8)
-    assert_equal(result, expected)
-
-
-def test_period(request, transform_assert_equal):
-    transform, assert_equal = transform_assert_equal
-
-    idx = pd.period_range("2011-01", periods=3, freq="M", name="")
-    inp = transform(idx)
-
-    if not isinstance(inp, Index):
-        request.node.add_marker(
-            pytest.mark.xfail(reason="Missing PeriodDtype support in to_numeric")
-        )
-    result = to_numeric(inp)
-    expected = transform(idx.asi8)
-    assert_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "errors,expected",
-    [
-        ("raise", "Invalid object type at position 0"),
-        ("ignore", Series([[10.0, 2], 1.0, "apple"])),
-        ("coerce", Series([np.nan, 1.0, np.nan])),
-    ],
-)
-def test_non_hashable(errors, expected):
-    # see gh-13324
-    ser = Series([[10.0, 2], 1.0, "apple"])
-
-    if isinstance(expected, str):
-        with pytest.raises(TypeError, match=expected):
-            to_numeric(ser, errors=errors)
-    else:
-        result = to_numeric(ser, errors=errors)
-        tm.assert_series_equal(result, expected)
-
-
-def test_downcast_invalid_cast():
-    # see gh-13352
-    data = ["1", 2, 3]
-    invalid_downcast = "unsigned-integer"
-    msg = "invalid downcasting method provided"
-
-    with pytest.raises(ValueError, match=msg):
-        to_numeric(data, downcast=invalid_downcast)
-
-
-def test_errors_invalid_value():
-    # see gh-26466
-    data = ["1", 2, 3]
-    invalid_error_value = "invalid"
-    msg = "invalid error value specified"
-
-    with pytest.raises(ValueError, match=msg):
-        to_numeric(data, errors=invalid_error_value)
-
-
-@pytest.mark.parametrize(
-    "data",
-    [
-        ["1", 2, 3],
-        [1, 2, 3],
-        np.array(["1970-01-02", "1970-01-03", "1970-01-04"], dtype="datetime64[D]"),
-    ],
-)
-@pytest.mark.parametrize(
-    "kwargs,exp_dtype",
-    [
-        # Basic function tests.
-        ({}, np.int64),
-        ({"downcast": None}, np.int64),
-        # Support below np.float32 is rare and far between.
-        ({"downcast": "float"}, np.dtype(np.float32).char),
-        # Basic dtype support.
-        ({"downcast": "unsigned"}, np.dtype(np.typecodes["UnsignedInteger"][0])),
-    ],
-)
-def test_downcast_basic(data, kwargs, exp_dtype):
-    # see gh-13352
-    result = to_numeric(data, **kwargs)
-    expected = np.array([1, 2, 3], dtype=exp_dtype)
-    tm.assert_numpy_array_equal(result, expected)
-
-
-@pytest.mark.parametrize("signed_downcast", ["integer", "signed"])
-@pytest.mark.parametrize(
-    "data",
-    [
-        ["1", 2, 3],
-        [1, 2, 3],
-        np.array(["1970-01-02", "1970-01-03", "1970-01-04"], dtype="datetime64[D]"),
-    ],
-)
-def test_signed_downcast(data, signed_downcast):
-    # see gh-13352
-    smallest_int_dtype = np.dtype(np.typecodes["Integer"][0])
-    expected = np.array([1, 2, 3], dtype=smallest_int_dtype)
-
-    res = to_numeric(data, downcast=signed_downcast)
-    tm.assert_numpy_array_equal(res, expected)
-
-
-def test_ignore_downcast_invalid_data():
-    # If we can't successfully cast the given
-    # data to a numeric dtype, do not bother
-    # with the downcast parameter.
-    data = ["foo", 2, 3]
-    expected = np.array(data, dtype=object)
-
-    res = to_numeric(data, errors="ignore", downcast="unsigned")
-    tm.assert_numpy_array_equal(res, expected)
-
-
-def test_ignore_downcast_neg_to_unsigned():
-    # Cannot cast to an unsigned integer
-    # because we have a negative number.
-    data = ["-1", 2, 3]
-    expected = np.array([-1, 2, 3], dtype=np.int64)
-
-    res = to_numeric(data, downcast="unsigned")
-    tm.assert_numpy_array_equal(res, expected)
-
-
-# Warning in 32 bit platforms
-@pytest.mark.filterwarnings("ignore:invalid value encountered in cast:RuntimeWarning")
-@pytest.mark.parametrize("downcast", ["integer", "signed", "unsigned"])
-@pytest.mark.parametrize(
-    "data,expected",
-    [
-        (["1.1", 2, 3], np.array([1.1, 2, 3], dtype=np.float64)),
-        (
-            [10000.0, 20000, 3000, 40000.36, 50000, 50000.00],
-            np.array(
-                [10000.0, 20000, 3000, 40000.36, 50000, 50000.00], dtype=np.float64
-            ),
-        ),
-    ],
-)
-def test_ignore_downcast_cannot_convert_float(data, expected, downcast):
-    # Cannot cast to an integer (signed or unsigned)
-    # because we have a float number.
-    res = to_numeric(data, downcast=downcast)
-    tm.assert_numpy_array_equal(res, expected)
-
-
-@pytest.mark.parametrize(
-    "downcast,expected_dtype",
-    [("integer", np.int16), ("signed", np.int16), ("unsigned", np.uint16)],
-)
-def test_downcast_not8bit(downcast, expected_dtype):
-    # the smallest integer dtype need not be np.(u)int8
-    data = ["256", 257, 258]
-
-    expected = np.array([256, 257, 258], dtype=expected_dtype)
-    res = to_numeric(data, downcast=downcast)
-    tm.assert_numpy_array_equal(res, expected)
-
-
-@pytest.mark.parametrize(
-    "dtype,downcast,min_max",
-    [
-        ("int8", "integer", [iinfo(np.int8).min, iinfo(np.int8).max]),
-        ("int16", "integer", [iinfo(np.int16).min, iinfo(np.int16).max]),
-        ("int32", "integer", [iinfo(np.int32).min, iinfo(np.int32).max]),
-        ("int64", "integer", [iinfo(np.int64).min, iinfo(np.int64).max]),
-        ("uint8", "unsigned", [iinfo(np.uint8).min, iinfo(np.uint8).max]),
-        ("uint16", "unsigned", [iinfo(np.uint16).min, iinfo(np.uint16).max]),
-        ("uint32", "unsigned", [iinfo(np.uint32).min, iinfo(np.uint32).max]),
-        ("uint64", "unsigned", [iinfo(np.uint64).min, iinfo(np.uint64).max]),
-        ("int16", "integer", [iinfo(np.int8).min, iinfo(np.int8).max + 1]),
-        ("int32", "integer", [iinfo(np.int16).min, iinfo(np.int16).max + 1]),
-        ("int64", "integer", [iinfo(np.int32).min, iinfo(np.int32).max + 1]),
-        ("int16", "integer", [iinfo(np.int8).min - 1, iinfo(np.int16).max]),
-        ("int32", "integer", [iinfo(np.int16).min - 1, iinfo(np.int32).max]),
-        ("int64", "integer", [iinfo(np.int32).min - 1, iinfo(np.int64).max]),
-        ("uint16", "unsigned", [iinfo(np.uint8).min, iinfo(np.uint8).max + 1]),
-        ("uint32", "unsigned", [iinfo(np.uint16).min, iinfo(np.uint16).max + 1]),
-        ("uint64", "unsigned", [iinfo(np.uint32).min, iinfo(np.uint32).max + 1]),
-    ],
-)
-def test_downcast_limits(dtype, downcast, min_max):
-    # see gh-14404: test the limits of each downcast.
-    series = to_numeric(Series(min_max), downcast=downcast)
-    assert series.dtype == dtype
-
-
-def test_downcast_float64_to_float32():
-    # GH-43693: Check float64 preservation when >= 16,777,217
-    series = Series([16777217.0, np.finfo(np.float64).max, np.nan], dtype=np.float64)
-    result = to_numeric(series, downcast="float")
-
-    assert series.dtype == result.dtype
-
-
-@pytest.mark.parametrize(
-    "ser,expected",
-    [
-        (
-            Series([0, 9223372036854775808]),
-            Series([0, 9223372036854775808], dtype=np.uint64),
-        )
-    ],
-)
-def test_downcast_uint64(ser, expected):
-    # see gh-14422:
-    # BUG: to_numeric doesn't work uint64 numbers
-
-    result = to_numeric(ser, downcast="unsigned")
-
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "data,exp_data",
-    [
-        (
-            [200, 300, "", "NaN", 30000000000000000000],
-            [200, 300, np.nan, np.nan, 30000000000000000000],
-        ),
-        (
-            ["12345678901234567890", "1234567890", "ITEM"],
-            [12345678901234567890, 1234567890, np.nan],
-        ),
-    ],
-)
-def test_coerce_uint64_conflict(data, exp_data):
-    # see gh-17007 and gh-17125
-    #
-    # Still returns float despite the uint64-nan conflict,
-    # which would normally force the casting to object.
-    result = to_numeric(Series(data), errors="coerce")
-    expected = Series(exp_data, dtype=float)
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "errors,exp",
-    [
-        ("ignore", Series(["12345678901234567890", "1234567890", "ITEM"])),
-        ("raise", "Unable to parse string"),
-    ],
-)
-def test_non_coerce_uint64_conflict(errors, exp):
-    # see gh-17007 and gh-17125
-    #
-    # For completeness.
-    ser = Series(["12345678901234567890", "1234567890", "ITEM"])
-
-    if isinstance(exp, str):
-        with pytest.raises(ValueError, match=exp):
-            to_numeric(ser, errors=errors)
-    else:
-        result = to_numeric(ser, errors=errors)
-        tm.assert_series_equal(result, ser)
-
-
-@pytest.mark.parametrize("dc1", ["integer", "float", "unsigned"])
-@pytest.mark.parametrize("dc2", ["integer", "float", "unsigned"])
-def test_downcast_empty(dc1, dc2):
-    # GH32493
-
-    tm.assert_numpy_array_equal(
-        to_numeric([], downcast=dc1),
-        to_numeric([], downcast=dc2),
-        check_dtype=False,
-    )
-
-
-def test_failure_to_convert_uint64_string_to_NaN():
-    # GH 32394
-    result = to_numeric("uint64", errors="coerce")
-    assert np.isnan(result)
-
-    ser = Series([32, 64, np.nan])
-    result = to_numeric(Series(["32", "64", "uint64"]), errors="coerce")
-    tm.assert_series_equal(result, ser)
-
-
-@pytest.mark.parametrize(
-    "strrep",
-    [
-        "243.164",
-        "245.968",
-        "249.585",
-        "259.745",
-        "265.742",
-        "272.567",
-        "279.196",
-        "280.366",
-        "275.034",
-        "271.351",
-        "272.889",
-        "270.627",
-        "280.828",
-        "290.383",
-        "308.153",
-        "319.945",
-        "336.0",
-        "344.09",
-        "351.385",
-        "356.178",
-        "359.82",
-        "361.03",
-        "367.701",
-        "380.812",
-        "387.98",
-        "391.749",
-        "391.171",
-        "385.97",
-        "385.345",
-        "386.121",
-        "390.996",
-        "399.734",
-        "413.073",
-        "421.532",
-        "430.221",
-        "437.092",
-        "439.746",
-        "446.01",
-        "451.191",
-        "460.463",
-        "469.779",
-        "472.025",
-        "479.49",
-        "474.864",
-        "467.54",
-        "471.978",
-    ],
-)
-def test_precision_float_conversion(strrep):
-    # GH 31364
-    result = to_numeric(strrep)
-
-    assert result == float(strrep)
-
-
-@pytest.mark.parametrize(
-    "values, expected",
-    [
-        (["1", "2", None], Series([1, 2, np.nan], dtype="Int64")),
-        (["1", "2", "3"], Series([1, 2, 3], dtype="Int64")),
-        (["1", "2", 3], Series([1, 2, 3], dtype="Int64")),
-        (["1", "2", 3.5], Series([1, 2, 3.5], dtype="Float64")),
-        (["1", None, 3.5], Series([1, np.nan, 3.5], dtype="Float64")),
-        (["1", "2", "3.5"], Series([1, 2, 3.5], dtype="Float64")),
-    ],
-)
-def test_to_numeric_from_nullable_string(values, nullable_string_dtype, expected):
-    # https://github.com/pandas-dev/pandas/issues/37262
-    s = Series(values, dtype=nullable_string_dtype)
-    result = to_numeric(s)
-    tm.assert_series_equal(result, expected)
-
-
-def test_to_numeric_from_nullable_string_coerce(nullable_string_dtype):
-    # GH#52146
-    values = ["a", "1"]
-    ser = Series(values, dtype=nullable_string_dtype)
-    result = to_numeric(ser, errors="coerce")
-    expected = Series([pd.NA, 1], dtype="Int64")
-    tm.assert_series_equal(result, expected)
-
-
-def test_to_numeric_from_nullable_string_ignore(nullable_string_dtype):
-    # GH#52146
-    values = ["a", "1"]
-    ser = Series(values, dtype=nullable_string_dtype)
-    expected = ser.copy()
-    result = to_numeric(ser, errors="ignore")
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "data, input_dtype, downcast, expected_dtype",
-    (
-        ([1, 1], "Int64", "integer", "Int8"),
-        ([1.0, pd.NA], "Float64", "integer", "Int8"),
-        ([1.0, 1.1], "Float64", "integer", "Float64"),
-        ([1, pd.NA], "Int64", "integer", "Int8"),
-        ([450, 300], "Int64", "integer", "Int16"),
-        ([1, 1], "Float64", "integer", "Int8"),
-        ([np.iinfo(np.int64).max - 1, 1], "Int64", "integer", "Int64"),
-        ([1, 1], "Int64", "signed", "Int8"),
-        ([1.0, 1.0], "Float32", "signed", "Int8"),
-        ([1.0, 1.1], "Float64", "signed", "Float64"),
-        ([1, pd.NA], "Int64", "signed", "Int8"),
-        ([450, -300], "Int64", "signed", "Int16"),
-        ([np.iinfo(np.uint64).max - 1, 1], "UInt64", "signed", "UInt64"),
-        ([1, 1], "Int64", "unsigned", "UInt8"),
-        ([1.0, 1.0], "Float32", "unsigned", "UInt8"),
-        ([1.0, 1.1], "Float64", "unsigned", "Float64"),
-        ([1, pd.NA], "Int64", "unsigned", "UInt8"),
-        ([450, -300], "Int64", "unsigned", "Int64"),
-        ([-1, -1], "Int32", "unsigned", "Int32"),
-        ([1, 1], "Float64", "float", "Float32"),
-        ([1, 1.1], "Float64", "float", "Float32"),
-        ([1, 1], "Float32", "float", "Float32"),
-        ([1, 1.1], "Float32", "float", "Float32"),
-    ),
-)
-def test_downcast_nullable_numeric(data, input_dtype, downcast, expected_dtype):
-    arr = pd.array(data, dtype=input_dtype)
-    result = to_numeric(arr, downcast=downcast)
-    expected = pd.array(data, dtype=expected_dtype)
-    tm.assert_extension_array_equal(result, expected)
-
-
-def test_downcast_nullable_mask_is_copied():
-    # GH38974
-
-    arr = pd.array([1, 2, pd.NA], dtype="Int64")
-
-    result = to_numeric(arr, downcast="integer")
-    expected = pd.array([1, 2, pd.NA], dtype="Int8")
-    tm.assert_extension_array_equal(result, expected)
-
-    arr[1] = pd.NA  # should not modify result
-    tm.assert_extension_array_equal(result, expected)
-
-
-def test_to_numeric_scientific_notation():
-    # GH 15898
-    result = to_numeric("1.7e+308")
-    expected = np.float64(1.7e308)
-    assert result == expected
-
-
-@pytest.mark.parametrize("val", [9876543210.0, 2.0**128])
-def test_to_numeric_large_float_not_downcast_to_float_32(val):
-    # GH 19729
-    expected = Series([val])
-    result = to_numeric(expected, downcast="float")
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "val, dtype", [(1, "Int64"), (1.5, "Float64"), (True, "boolean")]
-)
-def test_to_numeric_dtype_backend(val, dtype):
-    # GH#50505
-    ser = Series([val], dtype=object)
-    result = to_numeric(ser, dtype_backend="numpy_nullable")
-    expected = Series([val], dtype=dtype)
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "val, dtype",
-    [
-        (1, "Int64"),
-        (1.5, "Float64"),
-        (True, "boolean"),
-        (1, "int64[pyarrow]"),
-        (1.5, "float64[pyarrow]"),
-        (True, "bool[pyarrow]"),
-    ],
-)
-def test_to_numeric_dtype_backend_na(val, dtype):
-    # GH#50505
-    if "pyarrow" in dtype:
-        pytest.importorskip("pyarrow")
-        dtype_backend = "pyarrow"
-    else:
-        dtype_backend = "numpy_nullable"
-    ser = Series([val, None], dtype=object)
-    result = to_numeric(ser, dtype_backend=dtype_backend)
-    expected = Series([val, pd.NA], dtype=dtype)
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "val, dtype, downcast",
-    [
-        (1, "Int8", "integer"),
-        (1.5, "Float32", "float"),
-        (1, "Int8", "signed"),
-        (1, "int8[pyarrow]", "integer"),
-        (1.5, "float[pyarrow]", "float"),
-        (1, "int8[pyarrow]", "signed"),
-    ],
-)
-def test_to_numeric_dtype_backend_downcasting(val, dtype, downcast):
-    # GH#50505
-    if "pyarrow" in dtype:
-        pytest.importorskip("pyarrow")
-        dtype_backend = "pyarrow"
-    else:
-        dtype_backend = "numpy_nullable"
-    ser = Series([val, None], dtype=object)
-    result = to_numeric(ser, dtype_backend=dtype_backend, downcast=downcast)
-    expected = Series([val, pd.NA], dtype=dtype)
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "smaller, dtype_backend",
-    [["UInt8", "numpy_nullable"], ["uint8[pyarrow]", "pyarrow"]],
-)
-def test_to_numeric_dtype_backend_downcasting_uint(smaller, dtype_backend):
-    # GH#50505
-    if dtype_backend == "pyarrow":
-        pytest.importorskip("pyarrow")
-    ser = Series([1, pd.NA], dtype="UInt64")
-    result = to_numeric(ser, dtype_backend=dtype_backend, downcast="unsigned")
-    expected = Series([1, pd.NA], dtype=smaller)
-    tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
-    "dtype",
-    [
-        "Int64",
-        "UInt64",
-        "Float64",
-        "boolean",
-        "int64[pyarrow]",
-        "uint64[pyarrow]",
-        "float64[pyarrow]",
-        "bool[pyarrow]",
-    ],
-)
-def test_to_numeric_dtype_backend_already_nullable(dtype):
-    # GH#50505
-    if "pyarrow" in dtype:
-        pytest.importorskip("pyarrow")
-    ser = Series([1, pd.NA], dtype=dtype)
-    result = to_numeric(ser, dtype_backend="numpy_nullable")
-    expected = Series([1, pd.NA], dtype=dtype)
-    tm.assert_series_equal(result, expected)
-
-
-def test_to_numeric_dtype_backend_error(dtype_backend):
-    # GH#50505
-    ser = Series(["a", "b", ""])
-    expected = ser.copy()
-    with pytest.raises(ValueError, match="Unable to parse string"):
-        to_numeric(ser, dtype_backend=dtype_backend)
-
-    result = to_numeric(ser, dtype_backend=dtype_backend, errors="ignore")
-    tm.assert_series_equal(result, expected)
-
-    result = to_numeric(ser, dtype_backend=dtype_backend, errors="coerce")
-    if dtype_backend == "pyarrow":
-        dtype = "double[pyarrow]"
-    else:
-        dtype = "Float64"
-    expected = Series([np.nan, np.nan, np.nan], dtype=dtype)
-    tm.assert_series_equal(result, expected)
-
-
-def test_invalid_dtype_backend():
-    ser = Series([1, 2, 3])
-    msg = (
-        "dtype_backend numpy is invalid, only 'numpy_nullable' and "
-        "'pyarrow' are allowed."
-    )
-    with pytest.raises(ValueError, match=msg):
-        to_numeric(ser, dtype_backend="numpy")
-
-
-def test_coerce_pyarrow_backend():
-    # GH 52588
-    pa = pytest.importorskip("pyarrow")
-    ser = Series(list("12x"), dtype=ArrowDtype(pa.string()))
-    result = to_numeric(ser, errors="coerce", dtype_backend="pyarrow")
-    expected = Series([1, 2, None], dtype=ArrowDtype(pa.int64()))
-    tm.assert_series_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tseries/offsets/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tseries/offsets/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/v1/errors.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/v1/errors.py
deleted file mode 100644
index 7bdafdd17f3972a676d68ee5a70ba2d9263e2f4e..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/v1/errors.py
+++ /dev/null
@@ -1,646 +0,0 @@
-from decimal import Decimal
-from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, Sequence, Set, Tuple, Type, Union
-
-from .typing import display_as_type
-
-if TYPE_CHECKING:
-    from .typing import DictStrAny
-
-# explicitly state exports to avoid "from .errors import *" also importing Decimal, Path etc.
-__all__ = (
-    'PydanticTypeError',
-    'PydanticValueError',
-    'ConfigError',
-    'MissingError',
-    'ExtraError',
-    'NoneIsNotAllowedError',
-    'NoneIsAllowedError',
-    'WrongConstantError',
-    'NotNoneError',
-    'BoolError',
-    'BytesError',
-    'DictError',
-    'EmailError',
-    'UrlError',
-    'UrlSchemeError',
-    'UrlSchemePermittedError',
-    'UrlUserInfoError',
-    'UrlHostError',
-    'UrlHostTldError',
-    'UrlPortError',
-    'UrlExtraError',
-    'EnumError',
-    'IntEnumError',
-    'EnumMemberError',
-    'IntegerError',
-    'FloatError',
-    'PathError',
-    'PathNotExistsError',
-    'PathNotAFileError',
-    'PathNotADirectoryError',
-    'PyObjectError',
-    'SequenceError',
-    'ListError',
-    'SetError',
-    'FrozenSetError',
-    'TupleError',
-    'TupleLengthError',
-    'ListMinLengthError',
-    'ListMaxLengthError',
-    'ListUniqueItemsError',
-    'SetMinLengthError',
-    'SetMaxLengthError',
-    'FrozenSetMinLengthError',
-    'FrozenSetMaxLengthError',
-    'AnyStrMinLengthError',
-    'AnyStrMaxLengthError',
-    'StrError',
-    'StrRegexError',
-    'NumberNotGtError',
-    'NumberNotGeError',
-    'NumberNotLtError',
-    'NumberNotLeError',
-    'NumberNotMultipleError',
-    'DecimalError',
-    'DecimalIsNotFiniteError',
-    'DecimalMaxDigitsError',
-    'DecimalMaxPlacesError',
-    'DecimalWholeDigitsError',
-    'DateTimeError',
-    'DateError',
-    'DateNotInThePastError',
-    'DateNotInTheFutureError',
-    'TimeError',
-    'DurationError',
-    'HashableError',
-    'UUIDError',
-    'UUIDVersionError',
-    'ArbitraryTypeError',
-    'ClassError',
-    'SubclassError',
-    'JsonError',
-    'JsonTypeError',
-    'PatternError',
-    'DataclassTypeError',
-    'CallableError',
-    'IPvAnyAddressError',
-    'IPvAnyInterfaceError',
-    'IPvAnyNetworkError',
-    'IPv4AddressError',
-    'IPv6AddressError',
-    'IPv4NetworkError',
-    'IPv6NetworkError',
-    'IPv4InterfaceError',
-    'IPv6InterfaceError',
-    'ColorError',
-    'StrictBoolError',
-    'NotDigitError',
-    'LuhnValidationError',
-    'InvalidLengthForBrand',
-    'InvalidByteSize',
-    'InvalidByteSizeUnit',
-    'MissingDiscriminator',
-    'InvalidDiscriminator',
-)
-
-
-def cls_kwargs(cls: Type['PydanticErrorMixin'], ctx: 'DictStrAny') -> 'PydanticErrorMixin':
-    """
-    For built-in exceptions like ValueError or TypeError, we need to implement
-    __reduce__ to override the default behaviour (instead of __getstate__/__setstate__)
-    By default pickle protocol 2 calls `cls.__new__(cls, *args)`.
-    Since we only use kwargs, we need a little constructor to change that.
-    Note: the callable can't be a lambda as pickle looks in the namespace to find it
-    """
-    return cls(**ctx)
-
-
-class PydanticErrorMixin:
-    code: str
-    msg_template: str
-
-    def __init__(self, **ctx: Any) -> None:
-        self.__dict__ = ctx
-
-    def __str__(self) -> str:
-        return self.msg_template.format(**self.__dict__)
-
-    def __reduce__(self) -> Tuple[Callable[..., 'PydanticErrorMixin'], Tuple[Type['PydanticErrorMixin'], 'DictStrAny']]:
-        return cls_kwargs, (self.__class__, self.__dict__)
-
-
-class PydanticTypeError(PydanticErrorMixin, TypeError):
-    pass
-
-
-class PydanticValueError(PydanticErrorMixin, ValueError):
-    pass
-
-
-class ConfigError(RuntimeError):
-    pass
-
-
-class MissingError(PydanticValueError):
-    msg_template = 'field required'
-
-
-class ExtraError(PydanticValueError):
-    msg_template = 'extra fields not permitted'
-
-
-class NoneIsNotAllowedError(PydanticTypeError):
-    code = 'none.not_allowed'
-    msg_template = 'none is not an allowed value'
-
-
-class NoneIsAllowedError(PydanticTypeError):
-    code = 'none.allowed'
-    msg_template = 'value is not none'
-
-
-class WrongConstantError(PydanticValueError):
-    code = 'const'
-
-    def __str__(self) -> str:
-        permitted = ', '.join(repr(v) for v in self.permitted)  # type: ignore
-        return f'unexpected value; permitted: {permitted}'
-
-
-class NotNoneError(PydanticTypeError):
-    code = 'not_none'
-    msg_template = 'value is not None'
-
-
-class BoolError(PydanticTypeError):
-    msg_template = 'value could not be parsed to a boolean'
-
-
-class BytesError(PydanticTypeError):
-    msg_template = 'byte type expected'
-
-
-class DictError(PydanticTypeError):
-    msg_template = 'value is not a valid dict'
-
-
-class EmailError(PydanticValueError):
-    msg_template = 'value is not a valid email address'
-
-
-class UrlError(PydanticValueError):
-    code = 'url'
-
-
-class UrlSchemeError(UrlError):
-    code = 'url.scheme'
-    msg_template = 'invalid or missing URL scheme'
-
-
-class UrlSchemePermittedError(UrlError):
-    code = 'url.scheme'
-    msg_template = 'URL scheme not permitted'
-
-    def __init__(self, allowed_schemes: Set[str]):
-        super().__init__(allowed_schemes=allowed_schemes)
-
-
-class UrlUserInfoError(UrlError):
-    code = 'url.userinfo'
-    msg_template = 'userinfo required in URL but missing'
-
-
-class UrlHostError(UrlError):
-    code = 'url.host'
-    msg_template = 'URL host invalid'
-
-
-class UrlHostTldError(UrlError):
-    code = 'url.host'
-    msg_template = 'URL host invalid, top level domain required'
-
-
-class UrlPortError(UrlError):
-    code = 'url.port'
-    msg_template = 'URL port invalid, port cannot exceed 65535'
-
-
-class UrlExtraError(UrlError):
-    code = 'url.extra'
-    msg_template = 'URL invalid, extra characters found after valid URL: {extra!r}'
-
-
-class EnumMemberError(PydanticTypeError):
-    code = 'enum'
-
-    def __str__(self) -> str:
-        permitted = ', '.join(repr(v.value) for v in self.enum_values)  # type: ignore
-        return f'value is not a valid enumeration member; permitted: {permitted}'
-
-
-class IntegerError(PydanticTypeError):
-    msg_template = 'value is not a valid integer'
-
-
-class FloatError(PydanticTypeError):
-    msg_template = 'value is not a valid float'
-
-
-class PathError(PydanticTypeError):
-    msg_template = 'value is not a valid path'
-
-
-class _PathValueError(PydanticValueError):
-    def __init__(self, *, path: Path) -> None:
-        super().__init__(path=str(path))
-
-
-class PathNotExistsError(_PathValueError):
-    code = 'path.not_exists'
-    msg_template = 'file or directory at path "{path}" does not exist'
-
-
-class PathNotAFileError(_PathValueError):
-    code = 'path.not_a_file'
-    msg_template = 'path "{path}" does not point to a file'
-
-
-class PathNotADirectoryError(_PathValueError):
-    code = 'path.not_a_directory'
-    msg_template = 'path "{path}" does not point to a directory'
-
-
-class PyObjectError(PydanticTypeError):
-    msg_template = 'ensure this value contains valid import path or valid callable: {error_message}'
-
-
-class SequenceError(PydanticTypeError):
-    msg_template = 'value is not a valid sequence'
-
-
-class IterableError(PydanticTypeError):
-    msg_template = 'value is not a valid iterable'
-
-
-class ListError(PydanticTypeError):
-    msg_template = 'value is not a valid list'
-
-
-class SetError(PydanticTypeError):
-    msg_template = 'value is not a valid set'
-
-
-class FrozenSetError(PydanticTypeError):
-    msg_template = 'value is not a valid frozenset'
-
-
-class DequeError(PydanticTypeError):
-    msg_template = 'value is not a valid deque'
-
-
-class TupleError(PydanticTypeError):
-    msg_template = 'value is not a valid tuple'
-
-
-class TupleLengthError(PydanticValueError):
-    code = 'tuple.length'
-    msg_template = 'wrong tuple length {actual_length}, expected {expected_length}'
-
-    def __init__(self, *, actual_length: int, expected_length: int) -> None:
-        super().__init__(actual_length=actual_length, expected_length=expected_length)
-
-
-class ListMinLengthError(PydanticValueError):
-    code = 'list.min_items'
-    msg_template = 'ensure this value has at least {limit_value} items'
-
-    def __init__(self, *, limit_value: int) -> None:
-        super().__init__(limit_value=limit_value)
-
-
-class ListMaxLengthError(PydanticValueError):
-    code = 'list.max_items'
-    msg_template = 'ensure this value has at most {limit_value} items'
-
-    def __init__(self, *, limit_value: int) -> None:
-        super().__init__(limit_value=limit_value)
-
-
-class ListUniqueItemsError(PydanticValueError):
-    code = 'list.unique_items'
-    msg_template = 'the list has duplicated items'
-
-
-class SetMinLengthError(PydanticValueError):
-    code = 'set.min_items'
-    msg_template = 'ensure this value has at least {limit_value} items'
-
-    def __init__(self, *, limit_value: int) -> None:
-        super().__init__(limit_value=limit_value)
-
-
-class SetMaxLengthError(PydanticValueError):
-    code = 'set.max_items'
-    msg_template = 'ensure this value has at most {limit_value} items'
-
-    def __init__(self, *, limit_value: int) -> None:
-        super().__init__(limit_value=limit_value)
-
-
-class FrozenSetMinLengthError(PydanticValueError):
-    code = 'frozenset.min_items'
-    msg_template = 'ensure this value has at least {limit_value} items'
-
-    def __init__(self, *, limit_value: int) -> None:
-        super().__init__(limit_value=limit_value)
-
-
-class FrozenSetMaxLengthError(PydanticValueError):
-    code = 'frozenset.max_items'
-    msg_template = 'ensure this value has at most {limit_value} items'
-
-    def __init__(self, *, limit_value: int) -> None:
-        super().__init__(limit_value=limit_value)
-
-
-class AnyStrMinLengthError(PydanticValueError):
-    code = 'any_str.min_length'
-    msg_template = 'ensure this value has at least {limit_value} characters'
-
-    def __init__(self, *, limit_value: int) -> None:
-        super().__init__(limit_value=limit_value)
-
-
-class AnyStrMaxLengthError(PydanticValueError):
-    code = 'any_str.max_length'
-    msg_template = 'ensure this value has at most {limit_value} characters'
-
-    def __init__(self, *, limit_value: int) -> None:
-        super().__init__(limit_value=limit_value)
-
-
-class StrError(PydanticTypeError):
-    msg_template = 'str type expected'
-
-
-class StrRegexError(PydanticValueError):
-    code = 'str.regex'
-    msg_template = 'string does not match regex "{pattern}"'
-
-    def __init__(self, *, pattern: str) -> None:
-        super().__init__(pattern=pattern)
-
-
-class _NumberBoundError(PydanticValueError):
-    def __init__(self, *, limit_value: Union[int, float, Decimal]) -> None:
-        super().__init__(limit_value=limit_value)
-
-
-class NumberNotGtError(_NumberBoundError):
-    code = 'number.not_gt'
-    msg_template = 'ensure this value is greater than {limit_value}'
-
-
-class NumberNotGeError(_NumberBoundError):
-    code = 'number.not_ge'
-    msg_template = 'ensure this value is greater than or equal to {limit_value}'
-
-
-class NumberNotLtError(_NumberBoundError):
-    code = 'number.not_lt'
-    msg_template = 'ensure this value is less than {limit_value}'
-
-
-class NumberNotLeError(_NumberBoundError):
-    code = 'number.not_le'
-    msg_template = 'ensure this value is less than or equal to {limit_value}'
-
-
-class NumberNotFiniteError(PydanticValueError):
-    code = 'number.not_finite_number'
-    msg_template = 'ensure this value is a finite number'
-
-
-class NumberNotMultipleError(PydanticValueError):
-    code = 'number.not_multiple'
-    msg_template = 'ensure this value is a multiple of {multiple_of}'
-
-    def __init__(self, *, multiple_of: Union[int, float, Decimal]) -> None:
-        super().__init__(multiple_of=multiple_of)
-
-
-class DecimalError(PydanticTypeError):
-    msg_template = 'value is not a valid decimal'
-
-
-class DecimalIsNotFiniteError(PydanticValueError):
-    code = 'decimal.not_finite'
-    msg_template = 'value is not a valid decimal'
-
-
-class DecimalMaxDigitsError(PydanticValueError):
-    code = 'decimal.max_digits'
-    msg_template = 'ensure that there are no more than {max_digits} digits in total'
-
-    def __init__(self, *, max_digits: int) -> None:
-        super().__init__(max_digits=max_digits)
-
-
-class DecimalMaxPlacesError(PydanticValueError):
-    code = 'decimal.max_places'
-    msg_template = 'ensure that there are no more than {decimal_places} decimal places'
-
-    def __init__(self, *, decimal_places: int) -> None:
-        super().__init__(decimal_places=decimal_places)
-
-
-class DecimalWholeDigitsError(PydanticValueError):
-    code = 'decimal.whole_digits'
-    msg_template = 'ensure that there are no more than {whole_digits} digits before the decimal point'
-
-    def __init__(self, *, whole_digits: int) -> None:
-        super().__init__(whole_digits=whole_digits)
-
-
-class DateTimeError(PydanticValueError):
-    msg_template = 'invalid datetime format'
-
-
-class DateError(PydanticValueError):
-    msg_template = 'invalid date format'
-
-
-class DateNotInThePastError(PydanticValueError):
-    code = 'date.not_in_the_past'
-    msg_template = 'date is not in the past'
-
-
-class DateNotInTheFutureError(PydanticValueError):
-    code = 'date.not_in_the_future'
-    msg_template = 'date is not in the future'
-
-
-class TimeError(PydanticValueError):
-    msg_template = 'invalid time format'
-
-
-class DurationError(PydanticValueError):
-    msg_template = 'invalid duration format'
-
-
-class HashableError(PydanticTypeError):
-    msg_template = 'value is not a valid hashable'
-
-
-class UUIDError(PydanticTypeError):
-    msg_template = 'value is not a valid uuid'
-
-
-class UUIDVersionError(PydanticValueError):
-    code = 'uuid.version'
-    msg_template = 'uuid version {required_version} expected'
-
-    def __init__(self, *, required_version: int) -> None:
-        super().__init__(required_version=required_version)
-
-
-class ArbitraryTypeError(PydanticTypeError):
-    code = 'arbitrary_type'
-    msg_template = 'instance of {expected_arbitrary_type} expected'
-
-    def __init__(self, *, expected_arbitrary_type: Type[Any]) -> None:
-        super().__init__(expected_arbitrary_type=display_as_type(expected_arbitrary_type))
-
-
-class ClassError(PydanticTypeError):
-    code = 'class'
-    msg_template = 'a class is expected'
-
-
-class SubclassError(PydanticTypeError):
-    code = 'subclass'
-    msg_template = 'subclass of {expected_class} expected'
-
-    def __init__(self, *, expected_class: Type[Any]) -> None:
-        super().__init__(expected_class=display_as_type(expected_class))
-
-
-class JsonError(PydanticValueError):
-    msg_template = 'Invalid JSON'
-
-
-class JsonTypeError(PydanticTypeError):
-    code = 'json'
-    msg_template = 'JSON object must be str, bytes or bytearray'
-
-
-class PatternError(PydanticValueError):
-    code = 'regex_pattern'
-    msg_template = 'Invalid regular expression'
-
-
-class DataclassTypeError(PydanticTypeError):
-    code = 'dataclass'
-    msg_template = 'instance of {class_name}, tuple or dict expected'
-
-
-class CallableError(PydanticTypeError):
-    msg_template = '{value} is not callable'
-
-
-class EnumError(PydanticTypeError):
-    code = 'enum_instance'
-    msg_template = '{value} is not a valid Enum instance'
-
-
-class IntEnumError(PydanticTypeError):
-    code = 'int_enum_instance'
-    msg_template = '{value} is not a valid IntEnum instance'
-
-
-class IPvAnyAddressError(PydanticValueError):
-    msg_template = 'value is not a valid IPv4 or IPv6 address'
-
-
-class IPvAnyInterfaceError(PydanticValueError):
-    msg_template = 'value is not a valid IPv4 or IPv6 interface'
-
-
-class IPvAnyNetworkError(PydanticValueError):
-    msg_template = 'value is not a valid IPv4 or IPv6 network'
-
-
-class IPv4AddressError(PydanticValueError):
-    msg_template = 'value is not a valid IPv4 address'
-
-
-class IPv6AddressError(PydanticValueError):
-    msg_template = 'value is not a valid IPv6 address'
-
-
-class IPv4NetworkError(PydanticValueError):
-    msg_template = 'value is not a valid IPv4 network'
-
-
-class IPv6NetworkError(PydanticValueError):
-    msg_template = 'value is not a valid IPv6 network'
-
-
-class IPv4InterfaceError(PydanticValueError):
-    msg_template = 'value is not a valid IPv4 interface'
-
-
-class IPv6InterfaceError(PydanticValueError):
-    msg_template = 'value is not a valid IPv6 interface'
-
-
-class ColorError(PydanticValueError):
-    msg_template = 'value is not a valid color: {reason}'
-
-
-class StrictBoolError(PydanticValueError):
-    msg_template = 'value is not a valid boolean'
-
-
-class NotDigitError(PydanticValueError):
-    code = 'payment_card_number.digits'
-    msg_template = 'card number is not all digits'
-
-
-class LuhnValidationError(PydanticValueError):
-    code = 'payment_card_number.luhn_check'
-    msg_template = 'card number is not luhn valid'
-
-
-class InvalidLengthForBrand(PydanticValueError):
-    code = 'payment_card_number.invalid_length_for_brand'
-    msg_template = 'Length for a {brand} card must be {required_length}'
-
-
-class InvalidByteSize(PydanticValueError):
-    msg_template = 'could not parse value and unit from byte string'
-
-
-class InvalidByteSizeUnit(PydanticValueError):
-    msg_template = 'could not interpret byte unit: {unit}'
-
-
-class MissingDiscriminator(PydanticValueError):
-    code = 'discriminated_union.missing_discriminator'
-    msg_template = 'Discriminator {discriminator_key!r} is missing in value'
-
-
-class InvalidDiscriminator(PydanticValueError):
-    code = 'discriminated_union.invalid_discriminator'
-    msg_template = (
-        'No match for discriminator {discriminator_key!r} and value {discriminator_value!r} '
-        '(allowed values: {allowed_values})'
-    )
-
-    def __init__(self, *, discriminator_key: str, discriminator_value: Any, allowed_values: Sequence[Any]) -> None:
-        super().__init__(
-            discriminator_key=discriminator_key,
-            discriminator_value=discriminator_value,
-            allowed_values=', '.join(map(repr, allowed_values)),
-        )
diff --git a/spaces/pycui/RealChar/client/web/src/components/TextView/style.css b/spaces/pycui/RealChar/client/web/src/components/TextView/style.css
deleted file mode 100644
index 79dd4a6b1af2f4113bcb1c2c6d30b75cb5d5dd5d..0000000000000000000000000000000000000000
--- a/spaces/pycui/RealChar/client/web/src/components/TextView/style.css
+++ /dev/null
@@ -1,67 +0,0 @@
-.chat-window {
-  background-color: #02081d;
-  color: white;
-  font-size: 17px;
-  width: 100%;
-  height: 100%;
-  border: none;
-  resize: none;
-}
-
-/* text input */
-input[type="text"]{font: 15px/24px 'Muli', sans-serif; color: white; width: 100%; box-sizing: border-box; letter-spacing: 1px;}
-:focus{outline: none;}
-.message-input-container{
-  float: left; 
-  width: 50vw;
-  margin: 15px 3%; 
-  position: relative;}
-input[type="text"]{font: 15px/24px "Lato", Arial, sans-serif; color: white; width: 100%; box-sizing: border-box; letter-spacing: 1px;}
-.message-input {
-  border: 1px solid #ccc; 
-  border-radius: 5px;
-  padding: 7px 14px 9px; 
-  transition: 0.4s;
-  font-size: 20px;
-  display: flex;
-  color: white;
-  background-color: transparent;
-}
-
-.message-input ~ .focus-border:before,
-.message-input ~ .focus-border:after{content: ""; position: absolute; top: 0; left: 0; width: 0; height: 2px; background-color: #85a7ff; transition: 0.3s;}
-.message-input ~ .focus-border:after{top: auto; bottom: 0; left: auto; right: 0;}
-.message-input ~ .focus-border i:before,
-.message-input ~ .focus-border i:after{content: ""; position: absolute; top: 0; left: 0; width: 2px; height: 0; background-color: #85a7ff; transition: 0.4s;}
-.message-input ~ .focus-border i:after{left: auto; right: 0; top: auto; bottom: 0;}
-.message-input:focus ~ .focus-border:before,
-.message-input:focus ~ .focus-border:after{width: 100%; transition: 0.3s;}
-.message-input:focus ~ .focus-border i:before,
-.message-input:focus ~ .focus-border i:after{height: 100%; transition: 0.4s;}
-
-.send-btn {
-    font-family: "Prompt", Helvetica;
-    font-size: 1rem;
-    border-color: #6785d3;
-    color: #fff;
-    box-shadow: 0 0 10px 0 #6785d3 inset, 0 0 10px 4px #6785d3;
-    transition: all 150ms ease-in-out;
-    cursor: pointer;
-    background-color: transparent;
-    padding: 0.6em 2em;
-    border-radius: 1.5em;
-}
-
-.send-btn:hover {
-    box-shadow: 0 0 40px 40px #6785d3 inset, 0 0 0 0 #6785d3;
-    outline: 0;
-}
-
-.options-container {
-  display: flex;
-  align-items: center;
-  justify-content: center;
-  padding: 20px 40px;
-  bottom: 0;
-  width: 100%;
-}
\ No newline at end of file
diff --git a/spaces/pycui/RealChar/realtime_ai_character/llm/anthropic_llm.py b/spaces/pycui/RealChar/realtime_ai_character/llm/anthropic_llm.py
deleted file mode 100644
index 25e054aa4ffcead941eebeca94331248b3d63e70..0000000000000000000000000000000000000000
--- a/spaces/pycui/RealChar/realtime_ai_character/llm/anthropic_llm.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import os
-from typing import List
-
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-from langchain.chat_models import ChatAnthropic
-from langchain.schema import BaseMessage, HumanMessage
-
-from realtime_ai_character.database.chroma import get_chroma
-from realtime_ai_character.llm.base import AsyncCallbackAudioHandler, AsyncCallbackTextHandler, LLM
-from realtime_ai_character.logger import get_logger
-from realtime_ai_character.utils import Character
-
-logger = get_logger(__name__)
-
-
-class AnthropicLlm(LLM):
-    def __init__(self, model):
-        self.chat_anthropic = ChatAnthropic(
-            model=model,
-            temperature=0.5,
-            streaming=True
-        )
-        self.db = get_chroma()
-
-    async def achat(self,
-                    history: List[BaseMessage],
-                    user_input: str,
-                    user_input_template: str,
-                    callback: AsyncCallbackTextHandler,
-                    audioCallback: AsyncCallbackAudioHandler,
-                    character: Character) -> str:
-        # 1. Generate context
-        context = self._generate_context(user_input, character)
-
-        # 2. Add user input to history
-        history.append(HumanMessage(content=user_input_template.format(
-            context=context, query=user_input)))
-
-        # 3. Generate response
-        response = await self.chat_anthropic.agenerate(
-            [history], callbacks=[callback, audioCallback, StreamingStdOutCallbackHandler()])
-        logger.info(f'Response: {response}')
-        return response.generations[0][0].text
-
-    def _generate_context(self, query, character: Character) -> str:
-        docs = self.db.similarity_search(query)
-        docs = [d for d in docs if d.metadata['character_name'] == character.name]
-        logger.info(f'Found {len(docs)} documents')
-
-        context = '\n'.join([d.page_content for d in docs])
-        return context
diff --git a/spaces/pyodide-demo/self-hosted/cssselect.js b/spaces/pyodide-demo/self-hosted/cssselect.js
deleted file mode 100644
index 52cb6dafa46cf644347a6de3d31a9c07e57b5dd8..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/cssselect.js
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/spaces/pyodide-demo/self-hosted/distlib.js b/spaces/pyodide-demo/self-hosted/distlib.js
deleted file mode 100644
index 2821b2b0b3fbce1c01b6fb3ed1e3f767444bdcab..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/distlib.js
+++ /dev/null
@@ -1 +0,0 @@
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("+loaded+"/"+total+")")}else if(!Module.dataFileDownloads){if(Module["setStatus"])Module["setStatus"]("Downloading data...")}};xhr.onerror=function(event){throw new Error("NetworkError for: "+packageName)};xhr.onload=function(event){if(xhr.status==200||xhr.status==304||xhr.status==206||xhr.status==0&&xhr.response){var packageData=xhr.response;callback(packageData)}else{throw new Error(xhr.statusText+" : "+xhr.responseURL)}};xhr.send(null)}function handleError(error){console.error("package error:",error)}var fetchedCallback=null;var fetched=Module["getPreloadedPackage"]?Module["getPreloadedPackage"](REMOTE_PACKAGE_NAME,REMOTE_PACKAGE_SIZE):null;if(!fetched)fetchRemotePackage(REMOTE_PACKAGE_NAME,REMOTE_PACKAGE_SIZE,(function(data){if(fetchedCallback){fetchedCallback(data);fetchedCallback=null}else{fetched=data}}),handleError);function runWithFS(){function assert(check,msg){if(!check)throw msg+(new 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compressedData={data:null,cachedOffset:296405,cachedIndexes:[-1,-1],cachedChunks:[null,null],offsets:[0,1110,2323,3500,4481,5498,6598,8139,9340,10561,11782,12884,13904,15049,16306,17542,18983,20330,21433,22722,23685,24759,25930,27148,28300,29490,30900,32197,33449,34557,35837,36963,37987,39263,40346,41383,42460,43607,44740,45920,46854,48300,49426,50673,51879,52983,54170,55318,56556,57695,58797,60216,61437,62422,63526,64688,65904,66994,68157,69368,70586,71875,72871,74030,75274,76463,77665,78939,79932,81149,82296,83552,84557,85683,86987,87902,89143,90354,91503,92780,94071,94531,95670,96986,98246,99494,100762,101951,103230,104393,105415,106473,107658,108798,109988,111354,112541,113567,114449,115415,116295,117411,118384,119477,120754,121849,122978,124236,125220,126390,127238,128635,129833,131016,132203,133206,134346,135507,136759,137851,138719,139689,140692,141749,142868,143921,145143,146268,147408,148750,149551,150643,151540,152681,154042,155205,156540,157604,158765,159793,160973,162177,163541,164852,166203,167221,168259,169401,170597,171860,172837,174058,175108,176384,177263,178533,179679,180837,182246,183569,184867,186111,187388,188520,189703,190777,191876,193086,194103,195442,196445,197466,198507,199592,200607,201320,202342,203247,204762,205954,207056,207950,208927,210115,211070,212524,213718,214886,216223,217312,218444,219809,221027,222272,223292,224606,225964,227103,228381,229583,230730,232107,233302,234630,235792,237065,238294,239560,240870,242176,243364,244813,246197,247231,248362,249781,250842,252030,253062,254278,255260,256406,257551,258641,259664,260858,262003,262998,264214,265270,266522,267686,268856,269955,271274,272330,273298,274354,275691,276703,277833,278806,279597,280843,282114,283136,284225,285347,286535,287550,288679,289750,290804,291801,292854,294075,295372,296171],sizes:[1110,1213,1177,981,1017,1100,1541,1201,1221,1221,1102,1020,1145,1257,1236,1441,1347,1103,1289,963,1074,1171,1218,1152,1190,1410,1297,1252,1108,1280,1126,1024,1276,1083,1037,1077,1147,1133,1180,934,1446,1126,1247,1206,1104,1187,1148,1238,1139,1102,1419,1221,985,1104,1162,1216,1090,1163,1211,1218,1289,996,1159,1244,1189,1202,1274,993,1217,1147,1256,1005,1126,1304,915,1241,1211,1149,1277,1291,460,1139,1316,1260,1248,1268,1189,1279,1163,1022,1058,1185,1140,1190,1366,1187,1026,882,966,880,1116,973,1093,1277,1095,1129,1258,984,1170,848,1397,1198,1183,1187,1003,1140,1161,1252,1092,868,970,1003,1057,1119,1053,1222,1125,1140,1342,801,1092,897,1141,1361,1163,1335,1064,1161,1028,1180,1204,1364,1311,1351,1018,1038,1142,1196,1263,977,1221,1050,1276,879,1270,1146,1158,1409,1323,1298,1244,1277,1132,1183,1074,1099,1210,1017,1339,1003,1021,1041,1085,1015,713,1022,905,1515,1192,1102,894,977,1188,955,1454,1194,1168,1337,1089,1132,1365,1218,1245,1020,1314,1358,1139,1278,1202,1147,1377,1195,1328,1162,1273,1229,1266,1310,1306,1188,1449,1384,1034,1131,1419,1061,1188,1032,1216,982,1146,1145,1090,1023,1194,1145,995,1216,1056,1252,1164,1170,1099,1319,1056,968,1056,1337,1012,1130,973,791,1246,1271,1022,1089,1122,1188,1015,1129,1071,1054,997,1053,1221,1297,799,234],successes:[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]};compressedData["data"]=byteArray;assert(typeof Module.LZ4==="object","LZ4 not present - was your app build with  -s LZ4=1  ?");Module.LZ4.loadPackage({metadata:metadata,compressedData:compressedData},true);Module["removeRunDependency"]("datafile_distlib.data")}Module["addRunDependency"]("datafile_distlib.data");if(!Module.preloadResults)Module.preloadResults={};Module.preloadResults[PACKAGE_NAME]={fromCache:false};if(fetched){processPackageData(fetched);fetched=null}else{fetchedCallback=processPackageData}}if(Module["calledRun"]){runWithFS()}else{if(!Module["preRun"])Module["preRun"]=[];Module["preRun"].push(runWithFS)}};loadPackage({files:[{filename:"/lib/python3.9/site-packages/distlib-0.3.1-py3.9.egg-info",start:0,end:1261,audio:0},{filename:"/lib/python3.9/site-packages/distlib/__init__.py",start:1261,end:1842,audio:0},{filename:"/lib/python3.9/site-packages/distlib/resources.py",start:1842,end:12608,audio:0},{filename:"/lib/python3.9/site-packages/distlib/scripts.py",start:12608,end:29788,audio:0},{filename:"/lib/python3.9/site-packages/distlib/locators.py",start:29788,end:81888,audio:0},{filename:"/lib/python3.9/site-packages/distlib/index.py",start:81888,end:102954,audio:0},{filename:"/lib/python3.9/site-packages/distlib/database.py",start:102954,end:154013,audio:0},{filename:"/lib/python3.9/site-packages/distlib/version.py",start:154013,end:177404,audio:0},{filename:"/lib/python3.9/site-packages/distlib/wheel.py",start:177404,end:218548,audio:0},{filename:"/lib/python3.9/site-packages/distlib/metadata.py",start:218548,end:257510,audio:0},{filename:"/lib/python3.9/site-packages/distlib/markers.py",start:257510,end:261897,audio:0},{filename:"/lib/python3.9/site-packages/distlib/util.py",start:261897,end:321742,audio:0},{filename:"/lib/python3.9/site-packages/distlib/compat.py",start:321742,end:363150,audio:0},{filename:"/lib/python3.9/site-packages/distlib/manifest.py",start:363150,end:377961,audio:0},{filename:"/lib/python3.9/site-packages/distlib/_backport/__init__.py",start:377961,end:378235,audio:0},{filename:"/lib/python3.9/site-packages/distlib/_backport/sysconfig.py",start:378235,end:405089,audio:0},{filename:"/lib/python3.9/site-packages/distlib/_backport/shutil.py",start:405089,end:430796,audio:0},{filename:"/lib/python3.9/site-packages/distlib/_backport/tarfile.py",start:430796,end:523424,audio:0},{filename:"/lib/python3.9/site-packages/distlib/_backport/misc.py",start:523424,end:524395,audio:0},{filename:"/lib/python3.9/site-packages/distlib/_backport/sysconfig.cfg",start:524395,end:527012,audio:0}],remote_package_size:300501,package_uuid:"b284c59c-7167-454a-a60a-dd3e4f63595e"})})();
\ No newline at end of file
diff --git a/spaces/qtoino/form_matcher/public/form3.html b/spaces/qtoino/form_matcher/public/form3.html
deleted file mode 100644
index 8a27752ece3fee6f24dc50131871ba0d16d8daab..0000000000000000000000000000000000000000
--- a/spaces/qtoino/form_matcher/public/form3.html
+++ /dev/null
@@ -1,27 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-  <title>Create account Form</title>
-</head>
-  <body>
-    <form action="/" method="POST">
-      <label for="name">Name:</label>
-      <input type="text" id="name" name="name" required>
-      <br>
-      <label for="country">Select your country:</label>
-      <br>
-      <select id="country" name="country">
-        <option value="usa">USA</option>
-        <option value="uk">UK</option>
-        <option value="germany">Germany</option>
-        <option value="japan">Japan</option>
-      </select>
-      <br><br>
-      <label for="birthday">Select your birthday:</label>
-      <br>
-      <input type="date" id="birthday" name="birthday">
-      <br><br>
-      <input type="submit" value="Submit">
-    </form>
-  </body>
-</html>
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/256igaranajednomcdufreedownload.md b/spaces/quidiaMuxgu/Expedit-SAM/256igaranajednomcdufreedownload.md
deleted file mode 100644
index d787a469573ff7f0f996b7b7c2d80d30a9e6cb38..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/256igaranajednomcdufreedownload.md
+++ /dev/null
@@ -1,18 +0,0 @@
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-As an organic semiconductor material used for a semiconductor layer of the TFT, for example, polycrystalline silicon is used because it has a high mobility, and by using it, a high definition image can be obtained. However, in order to form a polycrystalline silicon thin film on a glass substrate, it is necessary to use a high-temperature heat treatment of 1000° C. or more. Therefore, it is known that a thin film made of polycrystalline silicon has a defect in its crystal structure (in particular, a defect in grain boundaries) and is low in its reliability (for example, see Patent Reference 1).
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Cubase Elements 8.0 Update 8.0.35 Serial Key Keygen PORTABLE.md b/spaces/quidiaMuxgu/Expedit-SAM/Cubase Elements 8.0 Update 8.0.35 Serial Key Keygen PORTABLE.md
deleted file mode 100644
index 688b6c425dad781555101c4967b8d54e4ddd0b19..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Cubase Elements 8.0 Update 8.0.35 Serial Key Keygen PORTABLE.md	
+++ /dev/null
@@ -1,14 +0,0 @@
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-Work with your various specialisms and update their views on the classroom and make sure students know what to do and what to say. You need to highlight individual objects as well as a topic, with a lot of room to play around with what you've drawn. Multiple sources, referenced in the chapter, are available on the Web, especially for current events. The following formats are available to you at your disposal:. And some of the things that you can learn that can make you feel better is if you do what you need to do to make your classroom more comfortable. You can use an annual calendar to keep track of important dates throughout the year. They can express their excitement to others and also express their frustration when the teacher asks them a question that they simply cannot answer. These can be work of art and creations of designers. You can also list the educational preparation and training that you have had in education. When they are around, they can help you keep your students accountable and motivated. You will also learn to communicate effectively with your students. Teachers are great at finding jobs for their children, but parents should not let the teacher put their child in a bad situation. No matter how we classify them, each category has certain rules that have to be followed. Classifying Animals into Categories.
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-As educators, we are often the person who can control a child's world. We help a child deal with issues such as school discipline, conflict resolution, and bullying. As we care for our kids, we often become caregivers. We often are the one who helps the child deal with problems they encounter. In the following section, you will find specific tips on how to become the teacher that students can count on. Teachers should always keep in mind that they should not make their students do anything that is unethical or illegal. Let's face it: You can teach someone anything. But you have to start somewhere.
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Dadubasnamanaya Teledrama Full Torrent 69 __HOT__.md b/spaces/quidiaMuxgu/Expedit-SAM/Dadubasnamanaya Teledrama Full Torrent 69 __HOT__.md
deleted file mode 100644
index 138014466425ff13350dd86581aa4b3cb8c803a9..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Dadubasnamanaya Teledrama Full Torrent 69 __HOT__.md	
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/IGo 8.3.5.220904 Windows Mobile PNA .rar.md b/spaces/quidiaMuxgu/Expedit-SAM/IGo 8.3.5.220904 Windows Mobile PNA .rar.md
deleted file mode 100644
index 0ce651060360b98174e427aa4830fc75919ea68f..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/IGo 8.3.5.220904 Windows Mobile PNA .rar.md	
+++ /dev/null
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Mb Studio 8.50 Crack.md b/spaces/quidiaMuxgu/Expedit-SAM/Mb Studio 8.50 Crack.md
deleted file mode 100644
index 1959c480bdbaba262ae4b7c57ec9415cfe6b6c44..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Mb Studio 8.50 Crack.md	
+++ /dev/null
@@ -1,30 +0,0 @@
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diff --git a/spaces/r3gm/RVC_HF/demucs/repitch.py b/spaces/r3gm/RVC_HF/demucs/repitch.py
deleted file mode 100644
index 8846ab2d951a024c95067f66a113968500442828..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/demucs/repitch.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import io
-import random
-import subprocess as sp
-import tempfile
-
-import numpy as np
-import torch
-from scipy.io import wavfile
-
-
-def i16_pcm(wav):
-    if wav.dtype == np.int16:
-        return wav
-    return (wav * 2**15).clamp_(-2**15, 2**15 - 1).short()
-
-
-def f32_pcm(wav):
-    if wav.dtype == np.float:
-        return wav
-    return wav.float() / 2**15
-
-
-class RepitchedWrapper:
-    """
-    Wrap a dataset to apply online change of pitch / tempo.
-    """
-    def __init__(self, dataset, proba=0.2, max_pitch=2, max_tempo=12, tempo_std=5, vocals=[3]):
-        self.dataset = dataset
-        self.proba = proba
-        self.max_pitch = max_pitch
-        self.max_tempo = max_tempo
-        self.tempo_std = tempo_std
-        self.vocals = vocals
-
-    def __len__(self):
-        return len(self.dataset)
-
-    def __getitem__(self, index):
-        streams = self.dataset[index]
-        in_length = streams.shape[-1]
-        out_length = int((1 - 0.01 * self.max_tempo) * in_length)
-
-        if random.random() < self.proba:
-            delta_pitch = random.randint(-self.max_pitch, self.max_pitch)
-            delta_tempo = random.gauss(0, self.tempo_std)
-            delta_tempo = min(max(-self.max_tempo, delta_tempo), self.max_tempo)
-            outs = []
-            for idx, stream in enumerate(streams):
-                stream = repitch(
-                    stream,
-                    delta_pitch,
-                    delta_tempo,
-                    voice=idx in self.vocals)
-                outs.append(stream[:, :out_length])
-            streams = torch.stack(outs)
-        else:
-            streams = streams[..., :out_length]
-        return streams
-
-
-def repitch(wav, pitch, tempo, voice=False, quick=False, samplerate=44100):
-    """
-    tempo is a relative delta in percentage, so tempo=10 means tempo at 110%!
-    pitch is in semi tones.
-    Requires `soundstretch` to be installed, see
-    https://www.surina.net/soundtouch/soundstretch.html
-    """
-    outfile = tempfile.NamedTemporaryFile(suffix=".wav")
-    in_ = io.BytesIO()
-    wavfile.write(in_, samplerate, i16_pcm(wav).t().numpy())
-    command = [
-        "soundstretch",
-        "stdin",
-        outfile.name,
-        f"-pitch={pitch}",
-        f"-tempo={tempo:.6f}",
-    ]
-    if quick:
-        command += ["-quick"]
-    if voice:
-        command += ["-speech"]
-    try:
-        sp.run(command, capture_output=True, input=in_.getvalue(), check=True)
-    except sp.CalledProcessError as error:
-        raise RuntimeError(f"Could not change bpm because {error.stderr.decode('utf-8')}")
-    sr, wav = wavfile.read(outfile.name)
-    wav = wav.copy()
-    wav = f32_pcm(torch.from_numpy(wav).t())
-    assert sr == samplerate
-    return wav
diff --git a/spaces/radames/MusicGen-Continuation/audiocraft/modules/conv.py b/spaces/radames/MusicGen-Continuation/audiocraft/modules/conv.py
deleted file mode 100644
index 972938ab84712eb06e1b10cea25444eee51d6637..0000000000000000000000000000000000000000
--- a/spaces/radames/MusicGen-Continuation/audiocraft/modules/conv.py
+++ /dev/null
@@ -1,245 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-import warnings
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.utils import spectral_norm, weight_norm
-
-
-CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
-                                 'time_group_norm'])
-
-
-def apply_parametrization_norm(module: nn.Module, norm: str = 'none'):
-    assert norm in CONV_NORMALIZATIONS
-    if norm == 'weight_norm':
-        return weight_norm(module)
-    elif norm == 'spectral_norm':
-        return spectral_norm(module)
-    else:
-        # We already check was in CONV_NORMALIZATION, so any other choice
-        # doesn't need reparametrization.
-        return module
-
-
-def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs):
-    """Return the proper normalization module. If causal is True, this will ensure the returned
-    module is causal, or return an error if the normalization doesn't support causal evaluation.
-    """
-    assert norm in CONV_NORMALIZATIONS
-    if norm == 'time_group_norm':
-        if causal:
-            raise ValueError("GroupNorm doesn't support causal evaluation.")
-        assert isinstance(module, nn.modules.conv._ConvNd)
-        return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
-    else:
-        return nn.Identity()
-
-
-def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
-                                 padding_total: int = 0) -> int:
-    """See `pad_for_conv1d`.
-    """
-    length = x.shape[-1]
-    n_frames = (length - kernel_size + padding_total) / stride + 1
-    ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
-    return ideal_length - length
-
-
-def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
-    """Pad for a convolution to make sure that the last window is full.
-    Extra padding is added at the end. This is required to ensure that we can rebuild
-    an output of the same length, as otherwise, even with padding, some time steps
-    might get removed.
-    For instance, with total padding = 4, kernel size = 4, stride = 2:
-        0 0 1 2 3 4 5 0 0   # (0s are padding)
-        1   2   3           # (output frames of a convolution, last 0 is never used)
-        0 0 1 2 3 4 5 0     # (output of tr. conv., but pos. 5 is going to get removed as padding)
-            1 2 3 4         # once you removed padding, we are missing one time step !
-    """
-    extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
-    return F.pad(x, (0, extra_padding))
-
-
-def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
-    """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
-    If this is the case, we insert extra 0 padding to the right before the reflection happen.
-    """
-    length = x.shape[-1]
-    padding_left, padding_right = paddings
-    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
-    if mode == 'reflect':
-        max_pad = max(padding_left, padding_right)
-        extra_pad = 0
-        if length <= max_pad:
-            extra_pad = max_pad - length + 1
-            x = F.pad(x, (0, extra_pad))
-        padded = F.pad(x, paddings, mode, value)
-        end = padded.shape[-1] - extra_pad
-        return padded[..., :end]
-    else:
-        return F.pad(x, paddings, mode, value)
-
-
-def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
-    """Remove padding from x, handling properly zero padding. Only for 1d!
-    """
-    padding_left, padding_right = paddings
-    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
-    assert (padding_left + padding_right) <= x.shape[-1]
-    end = x.shape[-1] - padding_right
-    return x[..., padding_left: end]
-
-
-class NormConv1d(nn.Module):
-    """Wrapper around Conv1d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, causal: bool = False, norm: str = 'none',
-                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
-        self.norm_type = norm
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.norm(x)
-        return x
-
-
-class NormConv2d(nn.Module):
-    """Wrapper around Conv2d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
-        self.norm_type = norm
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.norm(x)
-        return x
-
-
-class NormConvTranspose1d(nn.Module):
-    """Wrapper around ConvTranspose1d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, causal: bool = False, norm: str = 'none',
-                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
-        self.norm_type = norm
-
-    def forward(self, x):
-        x = self.convtr(x)
-        x = self.norm(x)
-        return x
-
-
-class NormConvTranspose2d(nn.Module):
-    """Wrapper around ConvTranspose2d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
-
-    def forward(self, x):
-        x = self.convtr(x)
-        x = self.norm(x)
-        return x
-
-
-class StreamableConv1d(nn.Module):
-    """Conv1d with some builtin handling of asymmetric or causal padding
-    and normalization.
-    """
-    def __init__(self, in_channels: int, out_channels: int,
-                 kernel_size: int, stride: int = 1, dilation: int = 1,
-                 groups: int = 1, bias: bool = True, causal: bool = False,
-                 norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
-                 pad_mode: str = 'reflect'):
-        super().__init__()
-        # warn user on unusual setup between dilation and stride
-        if stride > 1 and dilation > 1:
-            warnings.warn('StreamableConv1d has been initialized with stride > 1 and dilation > 1'
-                          f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).')
-        self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
-                               dilation=dilation, groups=groups, bias=bias, causal=causal,
-                               norm=norm, norm_kwargs=norm_kwargs)
-        self.causal = causal
-        self.pad_mode = pad_mode
-
-    def forward(self, x):
-        B, C, T = x.shape
-        kernel_size = self.conv.conv.kernel_size[0]
-        stride = self.conv.conv.stride[0]
-        dilation = self.conv.conv.dilation[0]
-        kernel_size = (kernel_size - 1) * dilation + 1  # effective kernel size with dilations
-        padding_total = kernel_size - stride
-        extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
-        if self.causal:
-            # Left padding for causal
-            x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
-        else:
-            # Asymmetric padding required for odd strides
-            padding_right = padding_total // 2
-            padding_left = padding_total - padding_right
-            x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
-        return self.conv(x)
-
-
-class StreamableConvTranspose1d(nn.Module):
-    """ConvTranspose1d with some builtin handling of asymmetric or causal padding
-    and normalization.
-    """
-    def __init__(self, in_channels: int, out_channels: int,
-                 kernel_size: int, stride: int = 1, causal: bool = False,
-                 norm: str = 'none', trim_right_ratio: float = 1.,
-                 norm_kwargs: tp.Dict[str, tp.Any] = {}):
-        super().__init__()
-        self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
-                                          causal=causal, norm=norm, norm_kwargs=norm_kwargs)
-        self.causal = causal
-        self.trim_right_ratio = trim_right_ratio
-        assert self.causal or self.trim_right_ratio == 1., \
-            "`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
-        assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
-
-    def forward(self, x):
-        kernel_size = self.convtr.convtr.kernel_size[0]
-        stride = self.convtr.convtr.stride[0]
-        padding_total = kernel_size - stride
-
-        y = self.convtr(x)
-
-        # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
-        # removed at the very end, when keeping only the right length for the output,
-        # as removing it here would require also passing the length at the matching layer
-        # in the encoder.
-        if self.causal:
-            # Trim the padding on the right according to the specified ratio
-            # if trim_right_ratio = 1.0, trim everything from right
-            padding_right = math.ceil(padding_total * self.trim_right_ratio)
-            padding_left = padding_total - padding_right
-            y = unpad1d(y, (padding_left, padding_right))
-        else:
-            # Asymmetric padding required for odd strides
-            padding_right = padding_total // 2
-            padding_left = padding_total - padding_right
-            y = unpad1d(y, (padding_left, padding_right))
-        return y
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Adobe Premiere 6.0 Download 12 How to Register and Activate the Non-Subscription App.md b/spaces/raedeXanto/academic-chatgpt-beta/Adobe Premiere 6.0 Download 12 How to Register and Activate the Non-Subscription App.md
deleted file mode 100644
index 78fb486c14268e540ed9bf527bac2477ab609b9c..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Adobe Premiere 6.0 Download 12 How to Register and Activate the Non-Subscription App.md	
+++ /dev/null
@@ -1,128 +0,0 @@
-<br />
-<h1>Adobe Premiere 6.0 Download 12: How to Get the Classic Video Editing Software</h1>
-<p>If you are looking for a simple and reliable video editing software that can handle most of your basic needs, you might want to consider Adobe Premiere 6.0. This is an older version of the popular Adobe Premiere Pro, which was released in 2000 and has since been discontinued by Adobe. However, you can still download it from archive.org and use it on your Windows PC.</p>
-<p>In this article, we will show you how to download Adobe Premiere 6.0 from archive.org, what features it offers, and what are the pros and cons of using it. We will also answer some frequently asked questions about this software.</p>
-<h2>Adobe Premiere 6.0 Download 12</h2><br /><p><b><b>Download</b> &#8250;&#8250;&#8250; <a href="https://tinourl.com/2uKZDJ">https://tinourl.com/2uKZDJ</a></b></p><br /><br />
-<h2>Introduction</h2>
-<h3>What is Adobe Premiere 6.0?</h3>
-<p>Adobe Premiere 6.0 is a video editing software that allows you to create and edit videos for various purposes, such as personal projects, presentations, webinars, tutorials, etc. It is part of the Adobe Creative Suite, which includes other applications such as Photoshop, Illustrator, After Effects, and more.</p>
-<p>Adobe Premiere 6.0 was one of the first versions of Adobe Premiere Pro, which is now the industry-standard video editing software used by professionals and amateurs alike. Adobe Premiere 6.0 was released in January 2000 and was available for Windows and Mac OS platforms.</p>
-<h3>Why would you want to download Adobe Premiere 6.0?</h3>
-<p>There are several reasons why you might want to download Adobe Premiere 6.0 instead of using a newer version or a different software. Some of them are:</p>
-<ul>
-<li>You have an older computer or operating system that cannot run newer versions of Adobe Premiere Pro or other video editing software.</li>
-<li>You prefer the simpler and more intuitive interface of Adobe Premiere 6.0 over the more complex and feature-rich interface of newer versions.</li>
-<li>You only need to perform basic video editing tasks, such as cutting, trimming, splitting, merging, adding transitions and effects, etc., and do not require advanced features such as color grading, motion tracking, multicam editing, etc.</li>
-<li>You want to save money by using a free software instead of paying for a subscription or a license for a newer version or a different software.</li>
-</ul>
-<h3>How to download Adobe Premiere 6.0 from archive.org</h3>
-<p>If you want to download Adobe Premiere 6.0 for free, you can do so from archive.org, which is a website that preserves digital content such as books, music, videos, software, etc., for historical and educational purposes.</p>
-<p>To download Adobe Premiere 6.0 from archive.org, follow these steps:</p>
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-<ol>
-<li>Go to <a href="https://archive.org/details/adobe-premiere-6.0-windows-90026024">this link</a>, which will take you to the page where you can find the files for Adobe Premiere 6.0 for Windows.</li>
-<li>On the right side of the page, under Download Options , click on ISO IMAGE , which will download a file named adobe-premiere-6.0-windows-90026024.iso . This is an image file that contains all the data and files needed to install Adobe Premiere 6.0 on your computer.</li>
-<li>Once the download is complete, you will need a software that can mount or extract ISO files , such as WinRAR , Daemon Tools , or PowerISO . Use one of these software to open the ISO file and access its contents.</li>
-<li>Inside the ISO file , you will find two folders: PREMIERE and QUICKTRACKS . The PREMIERE folder contains the installer for Adobe Premiere 6.0 , while the QUICKTRACKS folder contains the installer for SmartSound Quicktracks , which is an optional feature that allows you to add royalty-free music tracks to your videos.</li>
-<li>To install Adobe Premiere 6.0 , open the PREMIERE folder and double-click on Setup.exe . Follow the on-screen instructions to complete the installation process.</li>
-<li>If you want to install SmartSound Quicktracks , open the QUICKTRACKS folder and double-click on Setup.exe . Follow the on-screen instructions to complete the installation process.</li>
-</ol>
-<h2>Features of Adobe Premiere 6.0</h2>
-<h3>Timeline-based editing</h3>
-<p>One of the main features of Adobe Premiere 6.0 is its timeline-based editing interface , which allows you to arrange your video clips and audio tracks in a linear sequence . You can easily drag and drop your media files onto the timeline , trim them , move them around , adjust their speed , duration , opacity , volume , etc.</p>
-<p>The timeline also lets you add transitions between your clips , such as fades , wipes , slides , etc., to create smooth changes from one scene to another . You can also apply effects to your clips , such as filters , color correction , distortion , etc., to enhance their appearance or create special effects . You can preview your edits in real-time using the monitor window , which shows you how your video looks like before exporting it.</p>
-<h3>Multiple video and audio tracks</h3>
-<p>Another feature of Adobe Premiere 6.0 is its ability to handle multiple video and audio tracks on the timeline . You can layer up to 99 video tracks and up to 99 audio tracks on top of each other , creating complex compositions with different elements . You can also use different blending modes , such as overlay , screen , multiply , etc., to change how your video tracks interact with each other . You can also use different audio mixing modes , such as mono , stereo , surround sound , etc., to change how your audio tracks sound together . You can also mute or solo any track on the timeline , giving you more control over your final output .</p>
-<h3>Transitions and effects</h3>
-<p>Another feature of Adobe Premiere 6.0 is its library of transitions and effects that you can apply to your clips on the timeline . You can access these transitions and effects from the Effects palette , which categorizes them into different groups , such as Video Transitions , Video Effects , Audio Transitions , Audio Effects , etc.</p>
-<p>You can browse through these transitions and effects by clicking on their icons or names , which will show you a preview of how they look like or sound like . You can also drag and drop them onto your clips on the timeline , adjusting their parameters using keyframes or sliders . You can also customize your own transitions and effects using plugins or scripts that extend the functionality of Adobe Premiere 6.0 .</p>
-<h3>Export options and formats</h3>
-<h2>Pros and cons of Adobe Premiere 6.0</h2>
-<h3>Pros</h3>
-<p>Some of the advantages of using Adobe Premiere 6.0 are:</p>
-<h4>Easy to use interface</h4>
-<p>Adobe Premiere 6.0 has a user-friendly interface that makes it easy to learn and use for beginners and intermediate users. It has a clear and simple layout that shows you all the tools and options you need to edit your video. It also has a comprehensive help system that provides you with tips and tutorials on how to use the software.</p>
-<h4>Compatible with older hardware and systems</h4>
-<p>Adobe Premiere 6.0 is compatible with older hardware and systems that may not be able to run newer versions of Adobe Premiere Pro or other video editing software. It has low system requirements that make it run smoothly on Windows 98/ME/2000/XP/Vista/7/8/10 and Mac OS 9/X. It also supports older video cards and drivers that may not be supported by newer software.</p>
-<h4>Supports a wide range of media formats</h4>
-<p>Adobe Premiere 6.0 supports a wide range of media formats that you can import and export your video in. It can handle most common video formats , such as AVI , MOV , MPEG , WMV , DV , etc., as well as some uncommon ones , such as FLV , MKV , OGG , etc. It can also handle most common audio formats , such as WAV , MP3 , WMA , AAC , etc., as well as some uncommon ones , such as FLAC , OGG , etc. It can also handle most common image formats , such as BMP , JPG , PNG , GIF , etc., as well as some uncommon ones , such as PSD , TGA , etc.</p>
-<h3>Cons</h3>
-<p>Some of the disadvantages of using Adobe Premiere 6.0 are:</p>
-<h4>Outdated and unsupported by Adobe</h4>
-<p>Adobe Premiere 6.0 is outdated and unsupported by Adobe, which means that it does not receive any updates or bug fixes from the developer. It also means that it does not have any customer support or technical assistance from Adobe in case you encounter any problems or issues with the software. You will have to rely on online forums or third-party websites for help or guidance.</p>
-<h4>Lacks some advanced features and tools</h4>
-<p>Adobe Premiere 6.0 lacks some advanced features and tools that are available in newer versions of Adobe Premiere Pro or other video editing software. For example, it does not have features such as color grading, motion tracking, multicam editing, VR editing, etc., that can enhance your video quality and creativity. It also does not have tools such as warp stabilizer, lumetri scopes, essential sound panel, etc., that can improve your video stability, color accuracy, sound quality, etc.</p>
-<h4>May have compatibility issues with newer formats and devices</h4>
-<p>Adobe Premiere 6.0 may have compatibility issues with newer formats and devices that are not supported by the software. For example, it may not be able to import or export videos in 4K resolution, HEVC codec, HDR mode, etc., that are becoming more popular and common nowadays. It may also not be able to play or edit videos recorded by newer cameras, smartphones, drones, etc., that use newer formats or technologies.</p>
-<h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In conclusion, Adobe Premiere 6.0 is a classic video editing software that can help you create and edit videos for various purposes. It has a simple and easy to use interface, low system requirements, and wide media format support that make it suitable for beginners and intermediate users who have older hardware and systems. However, it is also outdated and unsupported by Adobe, lacks some advanced features and tools, and may have compatibility issues with newer formats and devices that make it unsuitable for professionals and advanced users who need more functionality and quality from their video editing software.</p>
-<h3>Recommendations for using Adobe Premiere 6.0</h3>
-<p>If you decide to use Adobe Premiere 6.0 for your video editing needs, here are some recommendations that can help you get the most out of it:</p>
-<ul>
-<li>Download Adobe Premiere 6.0 from archive.org, which is a reliable source that provides free access to digital content for historical and educational purposes.</li>
-<li>Install both Adobe Premiere 6.0 and SmartSound Quicktracks from the ISO file that you download from archive.org, which will give you access to both the video editing software and the royalty-free music tracks feature.</li>
-<li>Use the Effects palette to browse through the transitions and effects that are available in Adobe Premiere 6.0, which will allow you to add some flair and style to your videos.</li>
-<li>Use plugins or scripts that extend the functionality of Adobe Premiere 6.0, which will allow you to access some features or tools that are not included in the software.</li>
-<li>Use online forums or third-party websites for help or guidance on how to use Adobe Premiere 6.0, which will provide you with tips and tutorials from other users who have experience with the software.</li>
-</ul>
-<h3>FAQs</h3>
-<p>Here are some frequently asked questions about Adobe Premiere 6.0:</p>
-<ol>
-<li><b>Is Adobe Premiere 6.0 free?</b><br/>Yes, Adobe Premiere 6.0 is free to download from archive.org, which is a website that preserves digital content for historical and educational purposes.</li>
-<li><b>Is Adobe Premiere 6.0 safe?</b><br/>Yes, Adobe Premiere 6.0 is safe to download from archive.org, which is a reputable source that scans its files for viruses and malware before uploading them.</li>
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- </p> 0a6ba089eb<br />
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Crash E Bernstein (dublado)l.md b/spaces/raedeXanto/academic-chatgpt-beta/Crash E Bernstein (dublado)l.md
deleted file mode 100644
index ad7f6d05c3efbe4962cb74f3c832f97e115aff3c..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Crash E Bernstein (dublado)l.md	
+++ /dev/null
@@ -1,16 +0,0 @@
-<br />
-<h1>Crash E Bernstein: a divertida série sobre um menino e seu boneco</h1>
-<p>Crash E Bernstein é uma série de comédia e família que conta a história de Wyatt Bernstein, um menino que vive em uma casa cheia de mulheres. Wyatt sempre sonhou em ter um irmão para fazer coisas divertidas de garotos. No dia do seu aniversário, ele é levado pelos pais a uma loja de bonecos personalizados, onde ele cria um boneco chamado Crash, que será tratado como o irmão que ele sempre quis. Mas Crash não é um boneco comum: ele ganha vida e tem uma personalidade muito animada e travessa.</p>
-<p>A série acompanha as aventuras e confusões de Crash e Wyatt, que precisam lidar com as três irmãs de Wyatt: Amanda, a mais velha e popular; Cleo, a do meio e inteligente; e Jasmine, a caçula e fofa. Além disso, eles também têm amigos como Pesto, o vizinho nerd; Mel, a dona da loja de bonecos; e Martin Poulos, o rival de Wyatt na escola.</p>
-<h2>Crash E Bernstein (dublado)l</h2><br /><p><b><b>Download Zip</b> &middot;&middot;&middot;&middot;&middot; <a href="https://tinourl.com/2uL2zc">https://tinourl.com/2uL2zc</a></b></p><br /><br />
-<p>Crash E Bernstein é uma série divertida e criativa, que mostra a importância da amizade, da família e da imaginação. A série tem duas temporadas e está disponível no Disney Plus[^2^]. Você também pode assistir alguns episódios no YouTube[^3^]. Se você gosta de humor, aventura e bonecos falantes, não perca Crash E Bernstein!</p>
-
-<p>Crash E Bernstein é uma série que mistura atores reais com bonecos animatrônicos, criados pela empresa Chiodo Bros Productions. O boneco Crash é dublado pelo ator e comediante Tim Lagasse, que também é o responsável por manipulá-lo nas cenas. O elenco da série também conta com Cole Jensen como Wyatt Bernstein, Oana Gregory como Amanda Bernstein, Landry Bender como Cleo Bernstein, Aaron R Landon como Pesto, Mckenna Grace como Jasmine Bernstein, Mary Birdsong como Mel e Danny Woodburn como Martin Poulos.</p>
-<p>A série foi criada por Eric Friedman e estreou em 2012 no canal Disney XD. A primeira temporada tem 26 episódios e a segunda temporada tem 13 episódios. A série foi cancelada em 2014, mas ainda tem muitos fãs que gostam de assistir às aventuras de Crash e Wyatt. A série também foi exibida em outros países, como Brasil, Portugal, Espanha, França, Itália e Alemanha. No Brasil, a série foi dublada pelo estúdio TV Group Digital e teve as vozes de Daniel Figueira como Crash, Gustavo Pereira como Wyatt, Ana Elena Bittencourt como Amanda, Bruna Laynes como Cleo, Ana Lúcia Menezes como Jasmine, Sérgio Stern como Pesto, Márcia Morelli como Mel e Mauro Ramos como Martin Poulos.</p>
-
-<p>Crash E Bernstein é uma série que tem como tema central a amizade entre um menino e seu boneco. A série explora as diferenças e semelhanças entre os dois personagens, que têm personalidades opostas, mas que se completam. Crash é um boneco alegre, impulsivo, curioso e sem noção de limites. Ele adora fazer bagunça, se divertir e provocar as irmãs de Wyatt. Wyatt é um menino tímido, responsável, gentil e sonhador. Ele gosta de inventar coisas, jogar videogame e ter um irmão para compartilhar seus interesses. Juntos, eles vivem situações engraçadas, emocionantes e às vezes perigosas.</p>
-<p>A série também mostra os desafios e as alegrias de crescer em uma família grande e diversa. Wyatt tem que lidar com as expectativas e as cobranças de seus pais e de suas irmãs, que nem sempre entendem seus gostos e suas escolhas. Ele também tem que enfrentar os problemas típicos da adolescência, como a escola, os amigos, os inimigos e as paixões. Crash ajuda Wyatt a se soltar mais, a se expressar melhor e a se divertir mais. Por outro lado, Wyatt ensina Crash a ser mais educado, mais cuidadoso e mais humano.</p>
-<p>Crash E Bernstein é uma série que agrada a todos os públicos, pois tem humor, aventura, família e fantasia. A série também tem uma mensagem positiva sobre a importância de ser quem você é, de respeitar as diferenças e de valorizar as amizades. Se você quer rir e se emocionar com Crash e Wyatt, não deixe de assistir Crash E Bernstein no Disney Plus ou no YouTube.</p>
-<p></p> 7b8c122e87<br />
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Flixgrab 1.5.11 Crack ((INSTALL)) 2020 PC License Key.md b/spaces/raedeXanto/academic-chatgpt-beta/Flixgrab 1.5.11 Crack ((INSTALL)) 2020 PC License Key.md
deleted file mode 100644
index 0be1aa0ec85efcedf6ee670b6bc3d222c463a1e0..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Flixgrab 1.5.11 Crack ((INSTALL)) 2020 PC License Key.md	
+++ /dev/null
@@ -1,57 +0,0 @@
-
-<h1>Flixgrab 1.5.11 Crack 2020 PC License Key: Download Netflix Videos Offline</h1>
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-<p>In this article, we will show you how to use Flixgrab 1.5.11 Crack 2020 PC License Key to download Netflix videos with ease. You will also learn about the features and benefits of Flixgrab, as well as some tips and tricks to make the most of it.</p>
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-<p>Using Flixgrab is very easy and simple. Just follow these steps:</p>
-<ol>
-<li>Download and install Flixgrab from the official website or from any trusted source.</li>
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-<h2>Benefits of Downloading Netflix Videos Offline</h2>
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-<h2>Drawbacks of Downloading Netflix Videos Offline</h2>
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\ No newline at end of file
diff --git a/spaces/rajeshradhakrishnan/malayalam-news-classify/app.py b/spaces/rajeshradhakrishnan/malayalam-news-classify/app.py
deleted file mode 100644
index 329bb0fc86dbeda757f4e97447c6fc1350e54090..0000000000000000000000000000000000000000
--- a/spaces/rajeshradhakrishnan/malayalam-news-classify/app.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from pathlib import Path
-import gradio as gr
-from huggingface_hub import from_pretrained_fastai
-
-
-LABELS = Path('class_names.txt').read_text().splitlines()
-
-
-
-def predict(news_headline):
-    learner = from_pretrained_fastai("rajeshradhakrishnan/ml-news-classify-fastai")
-
-    probabilities = learner.predict(news_headline)
-
-    return {LABELS[i]: probabilities[0]['probs'][i] for i in range(len(LABELS))}
-
-interface = gr.Interface(
-    predict, 
-    inputs="textbox", 
-    outputs='label', 
-    theme="huggingface", 
-    title="Malayalam News Classifier", 
-    description="Try to classify news in മലയാളം? Input a few malayalam news headlines and verify whether the model categorized it appropriately!", 
-    article = "<p style='text-align: center'>Malayalam News Classifier | Demo Model</p>",
-    examples=[["ഓഹരി വിപണി തകരുമ്പോള്‍ നിക്ഷേപം എങ്ങനെ സുരക്ഷിതമാക്കാം"], ["വാര്‍ണറുടെ ഒറ്റക്കയ്യന്‍ ക്യാച്ചില്‍ അമ്പരന്ന് ക്രിക്കറ്റ് ലോകം"]],
-    # live=True,
-    share=True)
-interface.launch(debug=True)
diff --git a/spaces/rbalacha/04-Gradio-SOTA-Seq2Seq/qasrl_model_pipeline.py b/spaces/rbalacha/04-Gradio-SOTA-Seq2Seq/qasrl_model_pipeline.py
deleted file mode 100644
index abcb4e1e2ba93ae92aae2dc8dd353ed549d813dc..0000000000000000000000000000000000000000
--- a/spaces/rbalacha/04-Gradio-SOTA-Seq2Seq/qasrl_model_pipeline.py
+++ /dev/null
@@ -1,182 +0,0 @@
-from typing import Optional
-import json
-from argparse import Namespace
-from pathlib import Path
-from transformers import Text2TextGenerationPipeline, AutoModelForSeq2SeqLM, AutoTokenizer
-
-def get_markers_for_model(is_t5_model: bool) -> Namespace:
-    special_tokens_constants = Namespace() 
-    if is_t5_model:
-        # T5 model have 100 special tokens by default
-        special_tokens_constants.separator_input_question_predicate = "<extra_id_1>"
-        special_tokens_constants.separator_output_answers = "<extra_id_3>"
-        special_tokens_constants.separator_output_questions = "<extra_id_5>"  # if using only questions
-        special_tokens_constants.separator_output_question_answer = "<extra_id_7>"
-        special_tokens_constants.separator_output_pairs = "<extra_id_9>"
-        special_tokens_constants.predicate_generic_marker = "<extra_id_10>" 
-        special_tokens_constants.predicate_verb_marker = "<extra_id_11>" 
-        special_tokens_constants.predicate_nominalization_marker = "<extra_id_12>" 
-
-    else:
-        special_tokens_constants.separator_input_question_predicate = "<question_predicate_sep>"
-        special_tokens_constants.separator_output_answers = "<answers_sep>"
-        special_tokens_constants.separator_output_questions = "<question_sep>"  # if using only questions
-        special_tokens_constants.separator_output_question_answer = "<question_answer_sep>"
-        special_tokens_constants.separator_output_pairs = "<qa_pairs_sep>"
-        special_tokens_constants.predicate_generic_marker = "<predicate_marker>" 
-        special_tokens_constants.predicate_verb_marker = "<verbal_predicate_marker>" 
-        special_tokens_constants.predicate_nominalization_marker = "<nominalization_predicate_marker>" 
-    return special_tokens_constants
-
-def load_trained_model(name_or_path):
-    import huggingface_hub as HFhub
-    tokenizer = AutoTokenizer.from_pretrained(name_or_path)
-    model = AutoModelForSeq2SeqLM.from_pretrained(name_or_path)  
-    # load preprocessing_kwargs from the model repo on HF hub, or from the local model directory
-    kwargs_filename = None
-    if name_or_path.startswith("kleinay/"): # and 'preprocessing_kwargs.json' in HFhub.list_repo_files(name_or_path): # the supported version of HFhub doesn't support list_repo_files
-        kwargs_filename = HFhub.hf_hub_download(repo_id=name_or_path, filename="preprocessing_kwargs.json")
-    elif Path(name_or_path).is_dir() and (Path(name_or_path) / "experiment_kwargs.json").exists():
-        kwargs_filename = Path(name_or_path) / "experiment_kwargs.json"
-    
-    if kwargs_filename:
-        preprocessing_kwargs = json.load(open(kwargs_filename)) 
-        # integrate into model.config (for decoding args, e.g. "num_beams"), and save also as standalone object for preprocessing
-        model.config.preprocessing_kwargs = Namespace(**preprocessing_kwargs)
-        model.config.update(preprocessing_kwargs)
-    return model, tokenizer
-
-
-class QASRL_Pipeline(Text2TextGenerationPipeline):
-    def __init__(self, model_repo: str, **kwargs):
-        model, tokenizer = load_trained_model(model_repo)
-        super().__init__(model, tokenizer, framework="pt")
-        self.is_t5_model = "t5" in model.config.model_type
-        self.special_tokens = get_markers_for_model(self.is_t5_model)
-        self.data_args = model.config.preprocessing_kwargs 
-        # backward compatibility - default keyword values implemeted in `run_summarization`, thus not saved in `preprocessing_kwargs`
-        if "predicate_marker_type" not in vars(self.data_args):
-            self.data_args.predicate_marker_type = "generic"
-        if "use_bilateral_predicate_marker" not in vars(self.data_args):
-            self.data_args.use_bilateral_predicate_marker = True
-        if "append_verb_form" not in vars(self.data_args):
-            self.data_args.append_verb_form = True
-        self._update_config(**kwargs)
-    
-    def _update_config(self, **kwargs):
-        " Update self.model.config with initialization parameters and necessary defaults. "
-        # set default values that will always override model.config, but can overriden by __init__ kwargs
-        kwargs["max_length"] = kwargs.get("max_length", 80)
-        # override model.config with kwargs
-        for k,v in kwargs.items():
-            self.model.config.__dict__[k] = v           
-        
-    def _sanitize_parameters(self, **kwargs):
-        preprocess_kwargs, forward_kwargs, postprocess_kwargs = {}, {}, {} 
-        if "predicate_marker" in kwargs:
-            preprocess_kwargs["predicate_marker"] = kwargs["predicate_marker"]
-        if "predicate_type" in kwargs:
-            preprocess_kwargs["predicate_type"] = kwargs["predicate_type"]
-        if "verb_form" in kwargs:
-            preprocess_kwargs["verb_form"] = kwargs["verb_form"]
-        return preprocess_kwargs, forward_kwargs, postprocess_kwargs
-
-    def preprocess(self, inputs, predicate_marker="<predicate>", predicate_type=None, verb_form=None):
-        # Here, inputs is string or list of strings; apply string postprocessing
-        if isinstance(inputs, str):
-            processed_inputs = self._preprocess_string(inputs, predicate_marker, predicate_type, verb_form)
-        elif hasattr(inputs, "__iter__"):
-            processed_inputs = [self._preprocess_string(s, predicate_marker, predicate_type, verb_form) for s in inputs]
-        else:
-            raise ValueError("inputs must be str or Iterable[str]")
-        # Now pass to super.preprocess for tokenization
-        return super().preprocess(processed_inputs)
-    
-    def _preprocess_string(self, seq: str, predicate_marker: str, predicate_type: Optional[str], verb_form: Optional[str]) -> str:
-        sent_tokens = seq.split(" ")
-        assert predicate_marker in sent_tokens, f"Input sentence must include a predicate-marker token ('{predicate_marker}') before the target predicate word"
-        predicate_idx = sent_tokens.index(predicate_marker)
-        sent_tokens.remove(predicate_marker)
-        sentence_before_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx)])
-        predicate = sent_tokens[predicate_idx]
-        sentence_after_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx+1, len(sent_tokens))])
-        
-        if self.data_args.predicate_marker_type == "generic":
-            predicate_marker = self.special_tokens.predicate_generic_marker    
-        #  In case we want special marker for each predicate type: """
-        elif self.data_args.predicate_marker_type == "pred_type":
-            assert predicate_type is not None, "For this model, you must provide the `predicate_type` either when initializing QASRL_Pipeline(...) or when applying __call__(...) on it"
-            assert predicate_type in ("verbal", "nominal"), f"`predicate_type` must be either 'verbal' or 'nominal'; got '{predicate_type}'"
-            predicate_marker = {"verbal": self.special_tokens.predicate_verb_marker , 
-                                "nominal": self.special_tokens.predicate_nominalization_marker 
-                                }[predicate_type]
-
-        if self.data_args.use_bilateral_predicate_marker:
-            seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {predicate_marker} {sentence_after_predicate}"
-        else:
-            seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {sentence_after_predicate}"
-
-        # embed also verb_form
-        if self.data_args.append_verb_form and verb_form is None:
-            raise ValueError(f"For this model, you must provide the `verb_form` of the predicate when applying __call__(...)")
-        elif self.data_args.append_verb_form:
-            seq = f"{seq} {self.special_tokens.separator_input_question_predicate} {verb_form} "
-        else:
-            seq = f"{seq} "
-    
-        # append source prefix (for t5 models)
-        prefix = self._get_source_prefix(predicate_type)
-        
-        return prefix + seq
-    
-    def _get_source_prefix(self, predicate_type: Optional[str]):
-        if not self.is_t5_model or self.data_args.source_prefix is None:
-            return ''
-        if not self.data_args.source_prefix.startswith("<"):  # Regular prefix - not dependent on input row x
-            return self.data_args.source_prefix
-        if self.data_args.source_prefix == "<predicate-type>":
-            if predicate_type is None:
-                raise ValueError("source_prefix is '<predicate-type>' but input no `predicate_type`.")
-            else:
-                return f"Generate QAs for {predicate_type} QASRL: "
-    
-    def _forward(self, *args, **kwargs):
-        outputs = super()._forward(*args, **kwargs)
-        return outputs
-
-
-    def postprocess(self, model_outputs):
-        output_seq = self.tokenizer.decode(
-            model_outputs["output_ids"].squeeze(),
-            skip_special_tokens=False,
-            clean_up_tokenization_spaces=False,
-        )
-        output_seq = output_seq.strip(self.tokenizer.pad_token).strip(self.tokenizer.eos_token).strip()
-        qa_subseqs = output_seq.split(self.special_tokens.separator_output_pairs)
-        qas = [self._postrocess_qa(qa_subseq) for qa_subseq in qa_subseqs]
-        return {"generated_text": output_seq,
-                "QAs": qas}
-        
-    def _postrocess_qa(self, seq: str) -> str:
-        # split question and answers
-        if self.special_tokens.separator_output_question_answer in seq:
-            question, answer = seq.split(self.special_tokens.separator_output_question_answer)[:2]
-        else:
-            print("invalid format: no separator between question and answer found...")
-            return None
-            # question, answer = seq, '' # Or: backoff to only question  
-        # skip "_" slots in questions
-        question = ' '.join(t for t in question.split(' ') if t != '_')
-        answers = [a.strip() for a in answer.split(self.special_tokens.separator_output_answers)]
-        return {"question": question, "answers": answers}
-    
-    
-if __name__ == "__main__":
-    pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline")
-    res1 = pipe("The student was interested in Luke 's <predicate> research about sea animals .", verb_form="research", predicate_type="nominal")
-    res2 = pipe(["The doctor was interested in Luke 's <predicate> treatment .",
-                 "The Veterinary student was interested in Luke 's <predicate> treatment of sea animals ."], verb_form="treat", predicate_type="nominal", num_beams=10)
-    res3 = pipe("A number of professions have <predicate> developed that specialize in the treatment of mental disorders .", verb_form="develop", predicate_type="verbal")
-    print(res1)
-    print(res2)
-    print(res3)
\ No newline at end of file
diff --git a/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/lib/sdf.py b/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/lib/sdf.py
deleted file mode 100644
index e87e639eb94993c3e4068d6bd4d21f902aee7694..0000000000000000000000000000000000000000
--- a/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/lib/sdf.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import numpy as np
-
-
-def create_grid(resX, resY, resZ, b_min=np.array([0, 0, 0]), b_max=np.array([1, 1, 1]), transform=None):
-    '''
-    Create a dense grid of given resolution and bounding box
-    :param resX: resolution along X axis
-    :param resY: resolution along Y axis
-    :param resZ: resolution along Z axis
-    :param b_min: vec3 (x_min, y_min, z_min) bounding box corner
-    :param b_max: vec3 (x_max, y_max, z_max) bounding box corner
-    :return: [3, resX, resY, resZ] coordinates of the grid, and transform matrix from mesh index
-    '''
-    coords = np.mgrid[:resX, :resY, :resZ]
-    coords = coords.reshape(3, -1)
-    coords_matrix = np.eye(4)
-    length = b_max - b_min
-    coords_matrix[0, 0] = length[0] / resX
-    coords_matrix[1, 1] = length[1] / resY
-    coords_matrix[2, 2] = length[2] / resZ
-    coords_matrix[0:3, 3] = b_min
-    coords = np.matmul(coords_matrix[:3, :3], coords) + coords_matrix[:3, 3:4]
-    if transform is not None:
-        coords = np.matmul(transform[:3, :3], coords) + transform[:3, 3:4]
-        coords_matrix = np.matmul(transform, coords_matrix)
-    coords = coords.reshape(3, resX, resY, resZ)
-    return coords, coords_matrix
-
-
-def batch_eval(points, eval_func, num_samples=512 * 512 * 512):
-    num_pts = points.shape[1]
-    sdf = np.zeros(num_pts)
-
-    num_batches = num_pts // num_samples
-    for i in range(num_batches):
-        sdf[i * num_samples:i * num_samples + num_samples] = eval_func(
-            points[:, i * num_samples:i * num_samples + num_samples])
-    if num_pts % num_samples:
-        sdf[num_batches * num_samples:] = eval_func(points[:, num_batches * num_samples:])
-
-    return sdf
-
-
-def eval_grid(coords, eval_func, num_samples=512 * 512 * 512):
-    resolution = coords.shape[1:4]
-    coords = coords.reshape([3, -1])
-    sdf = batch_eval(coords, eval_func, num_samples=num_samples)
-    return sdf.reshape(resolution)
-
-
-def eval_grid_octree(coords, eval_func,
-                     init_resolution=64, threshold=0.01,
-                     num_samples=512 * 512 * 512):
-    resolution = coords.shape[1:4]
-
-    sdf = np.zeros(resolution)
-
-    dirty = np.ones(resolution, dtype=np.bool)
-    grid_mask = np.zeros(resolution, dtype=np.bool)
-
-    reso = resolution[0] // init_resolution
-
-    while reso > 0:
-        # subdivide the grid
-        grid_mask[0:resolution[0]:reso, 0:resolution[1]:reso, 0:resolution[2]:reso] = True
-        # test samples in this iteration
-        test_mask = np.logical_and(grid_mask, dirty)
-        #print('step size:', reso, 'test sample size:', test_mask.sum())
-        points = coords[:, test_mask]
-
-        sdf[test_mask] = batch_eval(points, eval_func, num_samples=num_samples)
-        dirty[test_mask] = False
-
-        # do interpolation
-        if reso <= 1:
-            break
-        for x in range(0, resolution[0] - reso, reso):
-            for y in range(0, resolution[1] - reso, reso):
-                for z in range(0, resolution[2] - reso, reso):
-                    # if center marked, return
-                    if not dirty[x + reso // 2, y + reso // 2, z + reso // 2]:
-                        continue
-                    v0 = sdf[x, y, z]
-                    v1 = sdf[x, y, z + reso]
-                    v2 = sdf[x, y + reso, z]
-                    v3 = sdf[x, y + reso, z + reso]
-                    v4 = sdf[x + reso, y, z]
-                    v5 = sdf[x + reso, y, z + reso]
-                    v6 = sdf[x + reso, y + reso, z]
-                    v7 = sdf[x + reso, y + reso, z + reso]
-                    v = np.array([v0, v1, v2, v3, v4, v5, v6, v7])
-                    v_min = v.min()
-                    v_max = v.max()
-                    # this cell is all the same
-                    if (v_max - v_min) < threshold:
-                        sdf[x:x + reso, y:y + reso, z:z + reso] = (v_max + v_min) / 2
-                        dirty[x:x + reso, y:y + reso, z:z + reso] = False
-        reso //= 2
-
-    return sdf.reshape(resolution)
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/dense_heads/retina_sepbn_head.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/dense_heads/retina_sepbn_head.py
deleted file mode 100644
index b385c61816fd24d091589635ad0211d73b8fdd9f..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/dense_heads/retina_sepbn_head.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch.nn as nn
-from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
-
-from ..builder import HEADS
-from .anchor_head import AnchorHead
-
-
-@HEADS.register_module()
-class RetinaSepBNHead(AnchorHead):
-    """"RetinaHead with separate BN.
-
-    In RetinaHead, conv/norm layers are shared across different FPN levels,
-    while in RetinaSepBNHead, conv layers are shared across different FPN
-    levels, but BN layers are separated.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 num_ins,
-                 in_channels,
-                 stacked_convs=4,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 init_cfg=None,
-                 **kwargs):
-        assert init_cfg is None, 'To prevent abnormal initialization ' \
-                                 'behavior, init_cfg is not allowed to be set'
-        self.stacked_convs = stacked_convs
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.num_ins = num_ins
-        super(RetinaSepBNHead, self).__init__(
-            num_classes, in_channels, init_cfg=init_cfg, **kwargs)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.relu = nn.ReLU(inplace=True)
-        self.cls_convs = nn.ModuleList()
-        self.reg_convs = nn.ModuleList()
-        for i in range(self.num_ins):
-            cls_convs = nn.ModuleList()
-            reg_convs = nn.ModuleList()
-            for i in range(self.stacked_convs):
-                chn = self.in_channels if i == 0 else self.feat_channels
-                cls_convs.append(
-                    ConvModule(
-                        chn,
-                        self.feat_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        conv_cfg=self.conv_cfg,
-                        norm_cfg=self.norm_cfg))
-                reg_convs.append(
-                    ConvModule(
-                        chn,
-                        self.feat_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        conv_cfg=self.conv_cfg,
-                        norm_cfg=self.norm_cfg))
-            self.cls_convs.append(cls_convs)
-            self.reg_convs.append(reg_convs)
-        for i in range(self.stacked_convs):
-            for j in range(1, self.num_ins):
-                self.cls_convs[j][i].conv = self.cls_convs[0][i].conv
-                self.reg_convs[j][i].conv = self.reg_convs[0][i].conv
-        self.retina_cls = nn.Conv2d(
-            self.feat_channels,
-            self.num_base_priors * self.cls_out_channels,
-            3,
-            padding=1)
-        self.retina_reg = nn.Conv2d(
-            self.feat_channels, self.num_base_priors * 4, 3, padding=1)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        super(RetinaSepBNHead, self).init_weights()
-        for m in self.cls_convs[0]:
-            normal_init(m.conv, std=0.01)
-        for m in self.reg_convs[0]:
-            normal_init(m.conv, std=0.01)
-        bias_cls = bias_init_with_prob(0.01)
-        normal_init(self.retina_cls, std=0.01, bias=bias_cls)
-        normal_init(self.retina_reg, std=0.01)
-
-    def forward(self, feats):
-        """Forward features from the upstream network.
-
-        Args:
-            feats (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-
-        Returns:
-            tuple: Usually a tuple of classification scores and bbox prediction
-                cls_scores (list[Tensor]): Classification scores for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * num_classes.
-                bbox_preds (list[Tensor]): Box energies / deltas for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * 4.
-        """
-        cls_scores = []
-        bbox_preds = []
-        for i, x in enumerate(feats):
-            cls_feat = feats[i]
-            reg_feat = feats[i]
-            for cls_conv in self.cls_convs[i]:
-                cls_feat = cls_conv(cls_feat)
-            for reg_conv in self.reg_convs[i]:
-                reg_feat = reg_conv(reg_feat)
-            cls_score = self.retina_cls(cls_feat)
-            bbox_pred = self.retina_reg(reg_feat)
-            cls_scores.append(cls_score)
-            bbox_preds.append(bbox_pred)
-        return cls_scores, bbox_preds
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/ops/make.sh b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/ops/make.sh
deleted file mode 100644
index 106b685722bc6ed70a06bf04309e75e62f73a430..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/ops/make.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/usr/bin/env bash
-# ------------------------------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------------------
-# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-# ------------------------------------------------------------------------------------------------
-
-python setup.py build install
diff --git a/spaces/ronvolutional/iframe-test/static/style.css b/spaces/ronvolutional/iframe-test/static/style.css
deleted file mode 100644
index 6a3c98f8fab848caaaf7b844b24ce23c8c5c8dde..0000000000000000000000000000000000000000
--- a/spaces/ronvolutional/iframe-test/static/style.css
+++ /dev/null
@@ -1,79 +0,0 @@
-body {
-  --text: hsl(0 0% 15%);
-  padding: 2.5rem;
-  font-family: sans-serif;
-  color: var(--text);
-}
-body.dark-theme {
-  --text: hsl(0 0% 90%);
-  background-color: hsl(223 39% 7%);
-}
-
-main {
-  max-width: 80rem;
-  text-align: center;
-}
-
-section {
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-}
-
-a {
-  color: var(--text);
-}
-
-select, input, button, .text-gen-output {
-  padding: 0.5rem 1rem;
-}
-
-select, img, input {
-  margin: 0.5rem auto 1rem;
-}
-
-form {
-  width: 25rem;
-  margin: 0 auto;
-}
-
-input {
-  width: 70%;
-}
-
-button {
-  cursor: pointer;
-}
-
-.text-gen-output {
-  min-height: 1.2rem;
-  margin: 1rem;
-  border: 0.5px solid grey;
-}
-
-#dataset button {
-  width: 6rem;
-  margin: 0.5rem;
-}
-
-#dataset button.hidden {
-  visibility: hidden;
-}
-
-table {
-  max-width: 40rem;
-  text-align: left;
-  border-collapse: collapse;
-}
-
-thead {
-  font-weight: bold;
-}
-
-td {
-  padding: 0.5rem;
-}
-
-td:not(thead td) {
-  border: 0.5px solid grey;
-}
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Bafo Bluetooth Dongle Driver Download Special Versions [WORK].md b/spaces/rorallitri/biomedical-language-models/logs/Bafo Bluetooth Dongle Driver Download Special Versions [WORK].md
deleted file mode 100644
index 317cbf7304d46a428c5a2e2547006ca2d38287d4..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Bafo Bluetooth Dongle Driver Download Special Versions [WORK].md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Bafo Bluetooth Dongle Driver Download Special Versions</h2><br /><p><b><b>DOWNLOAD</b> &gt;&gt;&gt; <a href="https://tinurll.com/2uznXz">https://tinurll.com/2uznXz</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/rorallitri/biomedical-language-models/logs/DM Portrait Pro 4.0.28 !!EXCLUSIVE!!.md b/spaces/rorallitri/biomedical-language-models/logs/DM Portrait Pro 4.0.28 !!EXCLUSIVE!!.md
deleted file mode 100644
index 2d9b9a8d251d572b35cab5ce5399a428e49b1298..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/DM Portrait Pro 4.0.28 !!EXCLUSIVE!!.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>DM Portrait Pro 4.0.28</h2><br /><p><b><b>Download</b> &middot; <a href="https://tinurll.com/2uzmtB">https://tinurll.com/2uzmtB</a></b></p><br /><br />
-<br />
-1997–2012 he has been full pro- fessor and chair of ... difficult to draw a general picture based on this isolated study,172 ... 60/4.0/28. 93/72/—. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/rorallitri/biomedical-language-models/logs/La trilogie des elfes jean-louis fetjaine epub bud une uvre originale et captivante  lire sans attendre.md b/spaces/rorallitri/biomedical-language-models/logs/La trilogie des elfes jean-louis fetjaine epub bud une uvre originale et captivante  lire sans attendre.md
deleted file mode 100644
index 9e7ee646c8d7e10536411c571da5de46cd8ede64..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/La trilogie des elfes jean-louis fetjaine epub bud une uvre originale et captivante  lire sans attendre.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>la trilogie des elfes jean-louis fetjaine epub bud</h2><br /><p><b><b>Download File</b> ===> <a href="https://tinurll.com/2uznLf">https://tinurll.com/2uznLf</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/sccstandardteam/ChuanhuChatGPT/readme/README_en.md b/spaces/sccstandardteam/ChuanhuChatGPT/readme/README_en.md
deleted file mode 100644
index a906ecb3ebc411f5cdeb33d661266a489a20c3b0..0000000000000000000000000000000000000000
--- a/spaces/sccstandardteam/ChuanhuChatGPT/readme/README_en.md
+++ /dev/null
@@ -1,127 +0,0 @@
-<div align="right">
-  <!-- Language: -->
-  <a title="Chinese" href="../README.md">简体中文</a> | English | <a title="Japanese" href="README_ja.md">日本語</a>
-</div>
-
-<h1 align="center">川虎 Chat 🐯 Chuanhu Chat</h1>
-<div align="center">
-  <a href="https://github.com/GaiZhenBiao/ChuanhuChatGPT">
-    <img src="https://user-images.githubusercontent.com/70903329/227087087-93b37d64-7dc3-4738-a518-c1cf05591c8a.png" alt="Logo" height="156">
-  </a>
-
-<p align="center">
-    <h3>Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA</h3>
-    <p align="center">
-      <a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/blob/main/LICENSE">
-        <img alt="Tests Passing" src="https://img.shields.io/github/license/GaiZhenbiao/ChuanhuChatGPT" />
-      </a>
-      <a href="https://gradio.app/">
-        <img alt="GitHub Contributors" src="https://img.shields.io/badge/Base-Gradio-fb7d1a?style=flat" />
-      </a>
-      <a href="https://t.me/tkdifferent">
-        <img alt="GitHub pull requests" src="https://img.shields.io/badge/Telegram-Group-blue.svg?logo=telegram" />
-      </a>
-      <p>
-        Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search <br />
-        LaTeX rendering / Table rendering / Code highlighting <br />
-        Auto dark mode / Adaptive web interface / WeChat-like theme <br />
-        Multi-parameters tuning / Multi-API-Key support / Multi-user support <br />
-        Compatible with GPT-4 / Local deployment for LLMs
-      </p>
-      <a href="https://www.youtube.com/watch?v=MtxS4XZWbJE"><strong>Video Tutorial</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=77nw7iimYDE"><strong>2.0 Introduction</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=x-O1jjBqgu4"><strong>3.0 Introduction & Tutorial</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>Online trial</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>One-Click deployment</strong></a>
-    </p>
-    <p align="center">
-      <img alt="Animation Demo" src="https://user-images.githubusercontent.com/51039745/226255695-6b17ff1f-ea8d-464f-b69b-a7b6b68fffe8.gif" />
-    </p>
-  </p>
-</div>
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press <kbd>Shift</kbd> + <kbd>Enter</kbd> keys.
-- To quickly switch between input history, press <kbd>↑</kbd> and <kbd>↓</kbd> key in the input box.
-- To deploy the program onto a server, change the last line of the program to `demo.launch(server_name="0.0.0.0", server_port=<your port number>)`.
-- To get a public shared link, change the last line of the program to `demo.launch(share=True)`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Installation
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
-   ```
-   pip install -r requirements.txt
-   ```
-3. Update Gradio
-   ```
-   pip install gradio --upgrade --force-reinstall
-   ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[![Star History Chart](https://api.star-history.com/svg?repos=GaiZhenbiao/ChuanhuChatGPT&type=Date)](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-<a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=GaiZhenbiao/ChuanhuChatGPT" />
-</a>
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-<a href="https://www.buymeacoffee.com/ChuanhuChat" ><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=ChuanhuChat&button_colour=219d53&font_colour=ffffff&font_family=Poppins&outline_colour=ffffff&coffee_colour=FFDD00" alt="Buy Me A Coffee" width="250"></a>
-
-<img width="250" alt="image" src="https://user-images.githubusercontent.com/51039745/226920291-e8ec0b0a-400f-4c20-ac13-dafac0c3aeeb.JPG">
diff --git a/spaces/scedlatioru/img-to-music/example/Linear Algebra And Its Applications 4th Edition Solutions.rarl.md b/spaces/scedlatioru/img-to-music/example/Linear Algebra And Its Applications 4th Edition Solutions.rarl.md
deleted file mode 100644
index fbb243d59114ef0f5ad5b8e9025ef1150823e556..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Linear Algebra And Its Applications 4th Edition Solutions.rarl.md	
+++ /dev/null
@@ -1,116 +0,0 @@
-<h2>Linear Algebra And Its Applications 4th Edition Solutions.rarl</h2><br /><p><b><b>Download File</b> >>> <a href="https://gohhs.com/2uEyUb">https://gohhs.com/2uEyUb</a></b></p><br /><br />
-
-The Study Guide also contains lists of common questions on each of the even-numbered exercises.
-
-Chapter 1: Nouns 
-
-(, p. 38)
-
-Chapter 2: Verbs
-
-(, p. 45)
-
-Chapter 3: Adjectives
-
-(, p. 55)
-
-Chapter 4: Prepositions
-
-(, p. 60)
-
-Chapter 5: Postpositions
-
-(, p. 66)
-
-Chapter 6: Conjunctions
-
-(, p. 69)
-
-Chapter 7: Adverb-bases
-
-(, p. 74)
-
-Chapter 8: Determiners
-
-(, p. 78)
-
-Chapter 9: Quantifiers
-
-(, p. 82)
-
-Chapter 10: Demonstratives
-
-(, p. 85)
-
-Chapter 11: Numerals
-
-(, p. 89)
-
-Chapter 12: Pronouns
-
-(, p. 95)
-
-Chapter 13: Interrogatives
-
-(, p. 99)
-
-Chapter 14: Imperatives
-
-(, p. 103)
-
-Chapter 15: Adverbs
-
-(, p. 107)
-
-Chapter 16: Subordination
-
-(, p. 113)
-
-Chapter 17: Existentials
-
-(, p. 118)
-
-Chapter 18: Participles
-
-(, p. 121)
-
-Chapter 19: To-infinitives
-
-(, p. 125)
-
-Chapter 20: Prepositional phrases
-
-(, p. 129)
-
-Chapter 21: Modals
-
-(, p. 133)
-
-Chapter 22: Negations
-
-(, p. 137)
-
-Chapter 23: Interjections
-
-(, p. 143)
-
-Chapter 24: Passives
-
-(, p. 147)
-
-Chapter 25: Adverbs of purpose
-
-(, p. 150)
-
-Chapter 26: Clauses
-
-(, p. 154)
-
-Chapter 27: Asking questions
-
-(, p. 157)
-
-Chapter 28:  4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/scedlatioru/img-to-music/example/Xilinx Ise Design Suite 14.5 Cracked.md b/spaces/scedlatioru/img-to-music/example/Xilinx Ise Design Suite 14.5 Cracked.md
deleted file mode 100644
index f0e1514f4151eab9611ec4a31f3ed1812333684d..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Xilinx Ise Design Suite 14.5 Cracked.md	
+++ /dev/null
@@ -1,23 +0,0 @@
-
-<h1>How to Download and Install Xilinx ISE Design Suite v14.5 for Free</h1>
-<p>Xilinx ISE Design Suite v14.5 is a powerful software package that allows you to design, simulate, and implement FPGA and CPLD systems using Xilinx devices. It includes various tools and features that can enhance your productivity and creativity, such as partial reconfiguration, high-level synthesis, embedded development kit, system generator for DSP, and more[^1^].</p>
-<p>However, Xilinx ISE Design Suite v14.5 is not a free software. You need to purchase a license or use a trial version to use it. If you want to use it for free, you might be tempted to look for a cracked version online. But this is not a good idea, as it can expose you to various risks and problems, such as malware infection, legal issues, compatibility errors, and performance degradation.</p>
-<h2>xilinx ise design suite 14.5 cracked</h2><br /><p><b><b>Download Zip</b> &#10001; &#10001; &#10001; <a href="https://gohhs.com/2uEzNi">https://gohhs.com/2uEzNi</a></b></p><br /><br />
-<p>Fortunately, there is a better way to use Xilinx ISE Design Suite v14.5 for free without cracking it. You can download the ISE WebPACK edition from the official Xilinx website[^3^]. This is a free version of the software that supports a limited number of devices and features, but still offers enough functionality for most FPGA and CPLD design projects. You can also upgrade to the Embedded Edition or the System Edition if you need more advanced features or device support[^1^].</p>
-<p>In this article, we will show you how to download and install Xilinx ISE Design Suite v14.5 WebPACK edition for free on your Windows or Linux computer. Follow these steps:</p>
-<ol>
-<li>Go to the Xilinx website[^3^] and click on the "Downloads" tab.</li>
-<li>Select "ISE Design Suite - 14.5" from the drop-down menu.</li>
-<li>Choose your operating system (Windows or Linux) and click on the "Download" button.</li>
-<li>You will need to create a free Xilinx account or log in with your existing one to proceed with the download.</li>
-<li>Once you have logged in, you will see a list of files available for download. Choose the file that matches your operating system and edition (WebPACK, Embedded, or System). For example, if you are using Windows and want to download the WebPACK edition, choose "Full Installer for Windows (TAR/GZIP - 6.14 GB)"[^4^].</li>
-<li>Click on the "Download" button next to the file name and save it to your preferred location on your computer.</li>
-<li>Extract the downloaded file using a tool like WinRAR or 7-Zip.</li>
-<li>Open the extracted folder and run the setup.exe file (for Windows) or xsetup (for Linux) as administrator.</li>
-<li>Follow the instructions on the screen to install Xilinx ISE Design Suite v14.5 on your computer.</li>
-<li>You will need to enter a serial number during the installation process. You can obtain a free serial number from the Xilinx website by registering your product[^3^].</li>
-<li>After the installation is complete, you can launch Xilinx ISE Design Suite v14.5 from your desktop or start menu.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded and installed Xilinx ISE Design Suite v14.5 for free on your computer. You can now start designing your FPGA and CPLD systems using Xilinx devices and tools.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/segments-tobias/conex/espnet/transform/functional.py b/spaces/segments-tobias/conex/espnet/transform/functional.py
deleted file mode 100644
index 6226cddaef80494d11d44568fd222f79e7e6f328..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/transform/functional.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import inspect
-
-from espnet.transform.transform_interface import TransformInterface
-from espnet.utils.check_kwargs import check_kwargs
-
-
-class FuncTrans(TransformInterface):
-    """Functional Transformation
-
-    WARNING:
-        Builtin or C/C++ functions may not work properly
-        because this class heavily depends on the `inspect` module.
-
-    Usage:
-
-    >>> def foo_bar(x, a=1, b=2):
-    ...     '''Foo bar
-    ...     :param x: input
-    ...     :param int a: default 1
-    ...     :param int b: default 2
-    ...     '''
-    ...     return x + a - b
-
-
-    >>> class FooBar(FuncTrans):
-    ...     _func = foo_bar
-    ...     __doc__ = foo_bar.__doc__
-    """
-
-    _func = None
-
-    def __init__(self, **kwargs):
-        self.kwargs = kwargs
-        check_kwargs(self.func, kwargs)
-
-    def __call__(self, x):
-        return self.func(x, **self.kwargs)
-
-    @classmethod
-    def add_arguments(cls, parser):
-        fname = cls._func.__name__.replace("_", "-")
-        group = parser.add_argument_group(fname + " transformation setting")
-        for k, v in cls.default_params().items():
-            # TODO(karita): get help and choices from docstring?
-            attr = k.replace("_", "-")
-            group.add_argument(f"--{fname}-{attr}", default=v, type=type(v))
-        return parser
-
-    @property
-    def func(self):
-        return type(self)._func
-
-    @classmethod
-    def default_params(cls):
-        try:
-            d = dict(inspect.signature(cls._func).parameters)
-        except ValueError:
-            d = dict()
-        return {
-            k: v.default for k, v in d.items() if v.default != inspect.Parameter.empty
-        }
-
-    def __repr__(self):
-        params = self.default_params()
-        params.update(**self.kwargs)
-        ret = self.__class__.__name__ + "("
-        if len(params) == 0:
-            return ret + ")"
-        for k, v in params.items():
-            ret += "{}={}, ".format(k, v)
-        return ret[:-2] + ")"
diff --git a/spaces/sh20raj/sdxl/README.md b/spaces/sh20raj/sdxl/README.md
deleted file mode 100644
index 1a680ed380d56998f1916098fdb41c02b37d11c9..0000000000000000000000000000000000000000
--- a/spaces/sh20raj/sdxl/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Sdxl
-emoji: 📚
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/shauray/StarCoder/README.md b/spaces/shauray/StarCoder/README.md
deleted file mode 100644
index c4ea20b6a2d5124641b1b3a2803d7d716818aee4..0000000000000000000000000000000000000000
--- a/spaces/shauray/StarCoder/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: StarCoder
-emoji: 🌠
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/shivammehta25/Diff-TTSG/pymo/preprocessing_refpose_featureselector.py b/spaces/shivammehta25/Diff-TTSG/pymo/preprocessing_refpose_featureselector.py
deleted file mode 100644
index 0469c0d55a2a2012688ab45dc22bc1ed0515a7a4..0000000000000000000000000000000000000000
--- a/spaces/shivammehta25/Diff-TTSG/pymo/preprocessing_refpose_featureselector.py
+++ /dev/null
@@ -1,2213 +0,0 @@
-"""
-Preprocessing Tranformers Based on sci-kit's API
-
-By Omid Alemi
-Created on June 12, 2017
-"""
-import copy
-
-import numpy as np
-import pandas as pd
-import scipy.ndimage.filters as filters
-import transforms3d as t3d
-from sklearn.base import BaseEstimator, TransformerMixin
-from sklearn.pipeline import Pipeline
-
-from pymo.Pivots import Pivots
-from pymo.Quaternions import Quaternions
-from pymo.rotation_tools import (
-    Rotation,
-    euler2expmap,
-    euler2expmap2,
-    euler2vectors,
-    euler_reorder,
-    expmap2euler,
-    unroll,
-    vectors2euler,
-)
-
-
-class MocapParameterizer(BaseEstimator, TransformerMixin):
-    def __init__(self, param_type="euler", ref_pose=None):
-        """
-
-        param_type = {'euler', 'quat', 'expmap', 'position', 'expmap2pos'}
-        """
-        self.param_type = param_type
-        if ref_pose is not None:
-            self.ref_pose = self._to_quat(ref_pose)[0]
-        else:
-            self.ref_pose = None
-
-    def fit(self, X, y=None):
-        return self
-
-    def transform(self, X, y=None):
-        print("MocapParameterizer: " + self.param_type)
-        if self.param_type == "euler":
-            return X
-        elif self.param_type == "expmap":
-            if self.ref_pose is None:
-                return self._to_expmap(X)
-            else:
-                return self._to_expmap2(X)
-        elif self.param_type == "vectors":
-            return self._euler_to_vectors(X)
-        elif self.param_type == "quat":
-            return self._to_quat(X)
-        elif self.param_type == "position":
-            return self._to_pos(X)
-        elif self.param_type == "expmap2pos":
-            return self._expmap_to_pos(X)
-        else:
-            raise "param types: euler, quat, expmap, position, expmap2pos"
-
-    #        return X
-
-    def inverse_transform(self, X, copy=None):
-        if self.param_type == "euler":
-            return X
-        elif self.param_type == "expmap":
-            if self.ref_pose is None:
-                return self._expmap_to_euler(X)
-            else:
-                return self._expmap_to_euler2(X)
-        elif self.param_type == "vectors":
-            return self._vectors_to_euler(X)
-        elif self.param_type == "quat":
-            return self._quat_to_euler(X)
-        elif self.param_type == "position":
-            # raise 'positions 2 eulers is not supported'
-            print("positions 2 eulers is not supported")
-            return X
-        else:
-            raise "param types: euler, quat, expmap, position"
-
-    def _to_quat(self, X):
-        """Converts joints rotations in quaternions"""
-
-        Q = []
-        for track in X:
-            channels = []
-            titles = []
-            euler_df = track.values
-
-            # Create a new DataFrame to store the exponential map rep
-            quat_df = euler_df.copy()
-
-            # List the columns that contain rotation channels
-            rot_cols = [c for c in euler_df.columns if ("rotation" in c and "Nub" not in c)]
-
-            # List the joints that are not end sites, i.e., have channels
-            joints = (joint for joint in track.skeleton if "Nub" not in joint)
-
-            for joint in joints:
-                rot_order = track.skeleton[joint]["order"]
-
-                # Get the rotation columns that belong to this joint
-                rc = euler_df[[c for c in rot_cols if joint in c]]
-
-                r1_col = "%s_%srotation" % (joint, rot_order[0])
-                r2_col = "%s_%srotation" % (joint, rot_order[1])
-                r3_col = "%s_%srotation" % (joint, rot_order[2])
-                # Make sure the columns are organized in xyz order
-                if rc.shape[1] < 3:
-                    euler_values = np.zeros((euler_df.shape[0], 3))
-                    rot_order = "XYZ"
-                else:
-                    euler_values = (
-                        np.pi
-                        / 180.0
-                        * np.transpose(np.array([track.values[r1_col], track.values[r2_col], track.values[r3_col]]))
-                    )
-
-                quat_df.drop([r1_col, r2_col, r3_col], axis=1, inplace=True)
-                quats = Quaternions.from_euler(np.asarray(euler_values), order=rot_order.lower(), world=False)
-
-                # Create the corresponding columns in the new DataFrame
-                quat_df["%s_qWrotation" % joint] = pd.Series(data=[e[0] for e in quats], index=quat_df.index)
-                quat_df["%s_qXrotation" % joint] = pd.Series(data=[e[1] for e in quats], index=quat_df.index)
-                quat_df["%s_qYrotation" % joint] = pd.Series(data=[e[2] for e in quats], index=quat_df.index)
-                quat_df["%s_qZrotation" % joint] = pd.Series(data=[e[3] for e in quats], index=quat_df.index)
-
-            new_track = track.clone()
-            new_track.values = quat_df
-            Q.append(new_track)
-        return Q
-
-    def _quat_to_euler(self, X):
-        Q = []
-        for track in X:
-            channels = []
-            titles = []
-            quat_df = track.values
-
-            # Create a new DataFrame to store the exponential map rep
-            # euler_df = pd.DataFrame(index=exp_df.index)
-            euler_df = quat_df.copy()
-
-            # Copy the root positions into the new DataFrame
-            # rxp = '%s_Xposition'%track.root_name
-            # ryp = '%s_Yposition'%track.root_name
-            # rzp = '%s_Zposition'%track.root_name
-            # euler_df[rxp] = pd.Series(data=exp_df[rxp], index=euler_df.index)
-            # euler_df[ryp] = pd.Series(data=exp_df[ryp], index=euler_df.index)
-            # euler_df[rzp] = pd.Series(data=exp_df[rzp], index=euler_df.index)
-
-            # List the columns that contain rotation channels
-            quat_params = [
-                c
-                for c in quat_df.columns
-                if (any(p in c for p in ["qWrotation", "qXrotation", "qYrotation", "qZrotation"]) and "Nub" not in c)
-            ]
-
-            # List the joints that are not end sites, i.e., have channels
-            joints = (joint for joint in track.skeleton if "Nub" not in joint)
-
-            for joint in joints:
-                r = quat_df[[c for c in quat_params if joint in c]]  # Get the columns that belong to this joint
-
-                euler_df.drop(
-                    [
-                        "%s_qWrotation" % joint,
-                        "%s_qXrotation" % joint,
-                        "%s_qYrotation" % joint,
-                        "%s_qZrotation" % joint,
-                    ],
-                    axis=1,
-                    inplace=True,
-                )
-                quat = [
-                    [
-                        f[1]["%s_qWrotation" % joint],
-                        f[1]["%s_qXrotation" % joint],
-                        f[1]["%s_qYrotation" % joint],
-                        f[1]["%s_qZrotation" % joint],
-                    ]
-                    for f in r.iterrows()
-                ]  # Make sure the columsn are organized in xyz order
-                quats = Quaternions(np.asarray(quat))
-                euler_rots = 180 / np.pi * quats.euler()
-                track.skeleton[joint]["order"] = "ZYX"
-                rot_order = track.skeleton[joint]["order"]
-                # euler_rots = [Rotation(f, 'expmap').to_euler(True, rot_order) for f in expmap] # Convert the exp maps to eulers
-                # euler_rots = [expmap2euler(f, rot_order, True) for f in expmap] # Convert the exp maps to eulers
-
-                # Create the corresponding columns in the new DataFrame
-
-                euler_df["%s_%srotation" % (joint, rot_order[2])] = pd.Series(
-                    data=[e[0] for e in euler_rots], index=euler_df.index
-                )
-                euler_df["%s_%srotation" % (joint, rot_order[1])] = pd.Series(
-                    data=[e[1] for e in euler_rots], index=euler_df.index
-                )
-                euler_df["%s_%srotation" % (joint, rot_order[0])] = pd.Series(
-                    data=[e[2] for e in euler_rots], index=euler_df.index
-                )
-
-            new_track = track.clone()
-            new_track.values = euler_df
-            Q.append(new_track)
-
-        return Q
-
-    def _to_pos(self, X):
-        """Converts joints rotations in Euler angles to joint positions"""
-
-        Q = []
-        for track in X:
-            channels = []
-            titles = []
-            euler_df = track.values
-
-            # Create a new DataFrame to store the exponential map rep
-            pos_df = pd.DataFrame(index=euler_df.index)
-
-            # Copy the root rotations into the new DataFrame
-            # rxp = '%s_Xrotation'%track.root_name
-            # ryp = '%s_Yrotation'%track.root_name
-            # rzp = '%s_Zrotation'%track.root_name
-            # pos_df[rxp] = pd.Series(data=euler_df[rxp], index=pos_df.index)
-            # pos_df[ryp] = pd.Series(data=euler_df[ryp], index=pos_df.index)
-            # pos_df[rzp] = pd.Series(data=euler_df[rzp], index=pos_df.index)
-
-            # List the columns that contain rotation channels
-            rot_cols = [c for c in euler_df.columns if ("rotation" in c)]
-
-            # List the columns that contain position channels
-            pos_cols = [c for c in euler_df.columns if ("position" in c)]
-
-            # List the joints that are not end sites, i.e., have channels
-            joints = (joint for joint in track.skeleton)
-
-            tree_data = {}
-
-            for joint in track.traverse():
-                parent = track.skeleton[joint]["parent"]
-                rot_order = track.skeleton[joint]["order"]
-                # print("rot_order:" + joint + " :" + rot_order)
-
-                # Get the rotation columns that belong to this joint
-                rc = euler_df[[c for c in rot_cols if joint in c]]
-
-                # Get the position columns that belong to this joint
-                pc = euler_df[[c for c in pos_cols if joint in c]]
-
-                # Make sure the columns are organized in xyz order
-                if rc.shape[1] < 3:
-                    euler_values = np.zeros((euler_df.shape[0], 3))
-                    rot_order = "XYZ"
-                else:
-                    euler_values = (
-                        np.pi
-                        / 180.0
-                        * np.transpose(
-                            np.array(
-                                [
-                                    track.values["%s_%srotation" % (joint, rot_order[0])],
-                                    track.values["%s_%srotation" % (joint, rot_order[1])],
-                                    track.values["%s_%srotation" % (joint, rot_order[2])],
-                                ]
-                            )
-                        )
-                    )
-
-                if pc.shape[1] < 3:
-                    pos_values = np.asarray([[0, 0, 0] for f in pc.iterrows()])
-                else:
-                    pos_values = np.asarray(
-                        [
-                            [f[1]["%s_Xposition" % joint], f[1]["%s_Yposition" % joint], f[1]["%s_Zposition" % joint]]
-                            for f in pc.iterrows()
-                        ]
-                    )
-
-                quats = Quaternions.from_euler(np.asarray(euler_values), order=rot_order.lower(), world=False)
-
-                tree_data[joint] = [[], []]  # to store the rotation matrix  # to store the calculated position
-                if track.root_name == joint:
-                    tree_data[joint][0] = quats  # rotmats
-                    # tree_data[joint][1] = np.add(pos_values, track.skeleton[joint]['offsets'])
-                    tree_data[joint][1] = pos_values
-                else:
-                    # for every frame i, multiply this joint's rotmat to the rotmat of its parent
-                    tree_data[joint][0] = tree_data[parent][0] * quats  # np.matmul(rotmats, tree_data[parent][0])
-
-                    # add the position channel to the offset and store it in k, for every frame i
-                    k = pos_values + np.asarray(track.skeleton[joint]["offsets"])
-
-                    # multiply k to the rotmat of the parent for every frame i
-                    q = tree_data[parent][0] * k  # np.matmul(k.reshape(k.shape[0],1,3), tree_data[parent][0])
-
-                    # add q to the position of the parent, for every frame i
-                    tree_data[joint][1] = tree_data[parent][1] + q  # q.reshape(k.shape[0],3) + tree_data[parent][1]
-
-                # Create the corresponding columns in the new DataFrame
-                pos_df["%s_Xposition" % joint] = pd.Series(data=[e[0] for e in tree_data[joint][1]], index=pos_df.index)
-                pos_df["%s_Yposition" % joint] = pd.Series(data=[e[1] for e in tree_data[joint][1]], index=pos_df.index)
-                pos_df["%s_Zposition" % joint] = pd.Series(data=[e[2] for e in tree_data[joint][1]], index=pos_df.index)
-
-            new_track = track.clone()
-            new_track.values = pos_df
-            Q.append(new_track)
-        return Q
-
-    def _expmap2rot(self, expmap):
-        theta = np.linalg.norm(expmap, axis=1, keepdims=True)
-        nz = np.nonzero(theta)[0]
-
-        expmap[nz, :] = expmap[nz, :] / theta[nz]
-
-        nrows = expmap.shape[0]
-        x = expmap[:, 0]
-        y = expmap[:, 1]
-        z = expmap[:, 2]
-
-        s = np.sin(theta * 0.5).reshape(nrows)
-        c = np.cos(theta * 0.5).reshape(nrows)
-
-        rotmats = np.zeros((nrows, 3, 3))
-
-        rotmats[:, 0, 0] = 2 * (x * x - 1) * s * s + 1
-        rotmats[:, 0, 1] = 2 * x * y * s * s - 2 * z * c * s
-        rotmats[:, 0, 2] = 2 * x * z * s * s + 2 * y * c * s
-        rotmats[:, 1, 0] = 2 * x * y * s * s + 2 * z * c * s
-        rotmats[:, 1, 1] = 2 * (y * y - 1) * s * s + 1
-        rotmats[:, 1, 2] = 2 * y * z * s * s - 2 * x * c * s
-        rotmats[:, 2, 0] = 2 * x * z * s * s - 2 * y * c * s
-        rotmats[:, 2, 1] = 2 * y * z * s * s + 2 * x * c * s
-        rotmats[:, 2, 2] = 2 * (z * z - 1) * s * s + 1
-
-        return rotmats
-
-    def _expmap_to_pos(self, X):
-        """Converts joints rotations in expmap notation to joint positions"""
-
-        Q = []
-        for track in X:
-            channels = []
-            titles = []
-            exp_df = track.values
-
-            # Create a new DataFrame to store the exponential map rep
-            pos_df = pd.DataFrame(index=exp_df.index)
-
-            # Copy the root rotations into the new DataFrame
-            # rxp = '%s_Xrotation'%track.root_name
-            # ryp = '%s_Yrotation'%track.root_name
-            # rzp = '%s_Zrotation'%track.root_name
-            # pos_df[rxp] = pd.Series(data=euler_df[rxp], index=pos_df.index)
-            # pos_df[ryp] = pd.Series(data=euler_df[ryp], index=pos_df.index)
-            # pos_df[rzp] = pd.Series(data=euler_df[rzp], index=pos_df.index)
-
-            # List the columns that contain rotation channels
-            exp_params = [
-                c for c in exp_df.columns if (any(p in c for p in ["alpha", "beta", "gamma"]) and "Nub" not in c)
-            ]
-
-            # List the joints that are not end sites, i.e., have channels
-            joints = (joint for joint in track.skeleton)
-
-            tree_data = {}
-
-            for joint in track.traverse():
-                parent = track.skeleton[joint]["parent"]
-
-                if "Nub" not in joint:
-                    r = exp_df[[c for c in exp_params if joint in c]]  # Get the columns that belong to this joint
-                    expmap = r.values
-                    # expmap = [[f[1]['%s_alpha'%joint], f[1]['%s_beta'%joint], f[1]['%s_gamma'%joint]] for f in r.iterrows()]
-                else:
-                    expmap = np.zeros((exp_df.shape[0], 3))
-
-                # Convert the eulers to rotation matrices
-                # rotmats = np.asarray([Rotation(f, 'expmap').rotmat for f in expmap])
-                # angs = np.linalg.norm(expmap,axis=1, keepdims=True)
-                rotmats = self._expmap2rot(expmap)
-
-                tree_data[joint] = [[], []]  # to store the rotation matrix  # to store the calculated position
-                pos_values = np.zeros((exp_df.shape[0], 3))
-
-                if track.root_name == joint:
-                    tree_data[joint][0] = rotmats
-                    # tree_data[joint][1] = np.add(pos_values, track.skeleton[joint]['offsets'])
-                    tree_data[joint][1] = pos_values
-                else:
-                    # for every frame i, multiply this joint's rotmat to the rotmat of its parent
-                    tree_data[joint][0] = np.matmul(rotmats, tree_data[parent][0])
-
-                    # add the position channel to the offset and store it in k, for every frame i
-                    k = pos_values + track.skeleton[joint]["offsets"]
-
-                    # multiply k to the rotmat of the parent for every frame i
-                    q = np.matmul(k.reshape(k.shape[0], 1, 3), tree_data[parent][0])
-
-                    # add q to the position of the parent, for every frame i
-                    tree_data[joint][1] = q.reshape(k.shape[0], 3) + tree_data[parent][1]
-
-                # Create the corresponding columns in the new DataFrame
-                pos_df["%s_Xposition" % joint] = pd.Series(data=tree_data[joint][1][:, 0], index=pos_df.index)
-                pos_df["%s_Yposition" % joint] = pd.Series(data=tree_data[joint][1][:, 1], index=pos_df.index)
-                pos_df["%s_Zposition" % joint] = pd.Series(data=tree_data[joint][1][:, 2], index=pos_df.index)
-
-            new_track = track.clone()
-            new_track.values = pos_df
-            Q.append(new_track)
-        return Q
-
-    def _to_expmap(self, X):
-        """Converts Euler angles to Exponential Maps"""
-
-        Q = []
-        for track in X:
-            channels = []
-            titles = []
-            euler_df = track.values
-
-            # Create a new DataFrame to store the exponential map rep
-            exp_df = euler_df.copy()  # pd.DataFrame(index=euler_df.index)
-
-            # Copy the root positions into the new DataFrame
-            # rxp = '%s_Xposition'%track.root_name
-            # ryp = '%s_Yposition'%track.root_name
-            # rzp = '%s_Zposition'%track.root_name
-            # exp_df[rxp] = pd.Series(data=euler_df[rxp], index=exp_df.index)
-            # exp_df[ryp] = pd.Series(data=euler_df[ryp], index=exp_df.index)
-            # exp_df[rzp] = pd.Series(data=euler_df[rzp], index=exp_df.index)
-
-            # List the columns that contain rotation channels
-            rots = [c for c in euler_df.columns if ("rotation" in c and "Nub" not in c)]
-
-            # List the joints that are not end sites, i.e., have channels
-            joints = (joint for joint in track.skeleton if "Nub" not in joint)
-
-            for joint in joints:
-                # print(joint)
-                r = euler_df[[c for c in rots if joint in c]]  # Get the columns that belong to this joint
-                rot_order = track.skeleton[joint]["order"]
-                r1_col = "%s_%srotation" % (joint, rot_order[0])
-                r2_col = "%s_%srotation" % (joint, rot_order[1])
-                r3_col = "%s_%srotation" % (joint, rot_order[2])
-
-                exp_df.drop([r1_col, r2_col, r3_col], axis=1, inplace=True)
-                euler = [[f[1][r1_col], f[1][r2_col], f[1][r3_col]] for f in r.iterrows()]
-                # exps = [Rotation(f, 'euler', from_deg=True, order=rot_order).to_expmap() for f in euler] # Convert the eulers to exp maps
-                exps = unroll(
-                    np.array([euler2expmap(f, rot_order, True) for f in euler])
-                )  # Convert the exp maps to eulers
-                # exps = euler2expmap2(euler, rot_order, True) # Convert the eulers to exp maps
-
-                # Create the corresponding columns in the new DataFrame
-
-                exp_df.insert(
-                    loc=0, column="%s_gamma" % joint, value=pd.Series(data=[e[2] for e in exps], index=exp_df.index)
-                )
-                exp_df.insert(
-                    loc=0, column="%s_beta" % joint, value=pd.Series(data=[e[1] for e in exps], index=exp_df.index)
-                )
-                exp_df.insert(
-                    loc=0, column="%s_alpha" % joint, value=pd.Series(data=[e[0] for e in exps], index=exp_df.index)
-                )
-
-            # print(exp_df.columns)
-            new_track = track.clone()
-            new_track.values = exp_df
-            Q.append(new_track)
-
-        return Q
-
-    def _expmap_to_euler(self, X):
-        Q = []
-        for track in X:
-            channels = []
-            titles = []
-            exp_df = track.values
-
-            # Create a new DataFrame to store the exponential map rep
-            # euler_df = pd.DataFrame(index=exp_df.index)
-            euler_df = exp_df.copy()
-
-            # Copy the root positions into the new DataFrame
-            # rxp = '%s_Xposition'%track.root_name
-            # ryp = '%s_Yposition'%track.root_name
-            # rzp = '%s_Zposition'%track.root_name
-            # euler_df[rxp] = pd.Series(data=exp_df[rxp], index=euler_df.index)
-            # euler_df[ryp] = pd.Series(data=exp_df[ryp], index=euler_df.index)
-            # euler_df[rzp] = pd.Series(data=exp_df[rzp], index=euler_df.index)
-
-            # List the columns that contain rotation channels
-            exp_params = [
-                c for c in exp_df.columns if (any(p in c for p in ["alpha", "beta", "gamma"]) and "Nub" not in c)
-            ]
-
-            # List the joints that are not end sites, i.e., have channels
-            joints = (joint for joint in track.skeleton if "Nub" not in joint)
-
-            for joint in joints:
-                r = exp_df[[c for c in exp_params if joint in c]]  # Get the columns that belong to this joint
-
-                euler_df.drop(["%s_alpha" % joint, "%s_beta" % joint, "%s_gamma" % joint], axis=1, inplace=True)
-                expmap = [
-                    [f[1]["%s_alpha" % joint], f[1]["%s_beta" % joint], f[1]["%s_gamma" % joint]] for f in r.iterrows()
-                ]  # Make sure the columsn are organized in xyz order
-                rot_order = track.skeleton[joint]["order"]
-                # euler_rots = [Rotation(f, 'expmap').to_euler(True, rot_order) for f in expmap] # Convert the exp maps to eulers
-                euler_rots = [expmap2euler(f, rot_order, True) for f in expmap]  # Convert the exp maps to eulers
-
-                # Create the corresponding columns in the new DataFrame
-
-                euler_df["%s_%srotation" % (joint, rot_order[0])] = pd.Series(
-                    data=[e[0] for e in euler_rots], index=euler_df.index
-                )
-                euler_df["%s_%srotation" % (joint, rot_order[1])] = pd.Series(
-                    data=[e[1] for e in euler_rots], index=euler_df.index
-                )
-                euler_df["%s_%srotation" % (joint, rot_order[2])] = pd.Series(
-                    data=[e[2] for e in euler_rots], index=euler_df.index
-                )
-
-            new_track = track.clone()
-            new_track.values = euler_df
-            Q.append(new_track)
-
-        return Q
-
-    def _to_expmap2(self, X):
-        """Converts Euler angles to Exponential Maps"""
-
-        Q = []
-        for track in X:
-            channels = []
-            titles = []
-            euler_df = track.values
-
-            # Create a new DataFrame to store the exponential map rep
-            exp_df = euler_df.copy()  # pd.DataFrame(index=euler_df.index)
-
-            # Copy the root positions into the new DataFrame
-            # rxp = '%s_Xposition'%track.root_name
-            # ryp = '%s_Yposition'%track.root_name
-            # rzp = '%s_Zposition'%track.root_name
-            # exp_df[rxp] = pd.Series(data=euler_df[rxp], index=exp_df.index)
-            # exp_df[ryp] = pd.Series(data=euler_df[ryp], index=exp_df.index)
-            # exp_df[rzp] = pd.Series(data=euler_df[rzp], index=exp_df.index)
-
-            # List the columns that contain rotation channels
-            rots = [c for c in euler_df.columns if ("rotation" in c and "Nub" not in c)]
-
-            # List the joints that are not end sites, i.e., have channels
-            joints = (joint for joint in track.skeleton if "Nub" not in joint)
-
-            for joint in joints:
-                # print(joint)
-                r = euler_df[[c for c in rots if joint in c]]  # Get the columns that belong to this joint
-                rot_order = track.skeleton[joint]["order"]
-
-                # Get the rotation columns that belong to this joint
-                rc = euler_df[[c for c in rots if joint in c]]
-
-                r1_col = "%s_%srotation" % (joint, rot_order[0])
-                r2_col = "%s_%srotation" % (joint, rot_order[1])
-                r3_col = "%s_%srotation" % (joint, rot_order[2])
-                # Make sure the columns are organized in xyz order
-                # print("joint:" + str(joint) + "  rot_order:" + str(rot_order))
-                if rc.shape[1] < 3:
-                    euler_values = np.zeros((euler_df.shape[0], 3))
-                    rot_order = "XYZ"
-                else:
-                    euler_values = (
-                        np.pi
-                        / 180.0
-                        * np.transpose(np.array([track.values[r1_col], track.values[r2_col], track.values[r3_col]]))
-                    )
-
-                quats = Quaternions.from_euler(np.asarray(euler_values), order=rot_order.lower(), world=False)
-                # exps = [Rotation(f, 'euler', from_deg=True, order=rot_order).to_expmap() for f in euler] # Convert the eulers to exp maps
-                # exps = unroll(np.array([euler2expmap(f, rot_order, True) for f in euler])) # Convert the exp maps to eulers
-                # exps = euler2expmap2(euler, rot_order, True) # Convert the eulers to exp maps
-                # Create the corresponding columns in the new DataFrame
-                if self.ref_pose is not None:
-                    q1_col = "%s_qWrotation" % (joint)
-                    q2_col = "%s_qXrotation" % (joint)
-                    q3_col = "%s_qYrotation" % (joint)
-                    q4_col = "%s_qZrotation" % (joint)
-                    ref_q = Quaternions(
-                        np.asarray(
-                            [
-                                [f[1][q1_col], f[1][q2_col], f[1][q3_col], f[1][q4_col]]
-                                for f in self.ref_pose.values.iterrows()
-                            ]
-                        )
-                    )
-                    # print("ref_q:" + str(ref_q.shape))
-                    ref_q = ref_q[0, :]
-                    quats = (-ref_q) * quats
-
-                angles, axis = quats.angle_axis()
-                aa = np.where(angles > np.pi)
-                angles[aa] = angles[aa] - 2 * np.pi
-                # exps = unroll(angles[:,None]*axis)
-                exps = angles[:, None] * axis
-                # exps = np.array([quat2expmap(f) for f in quats])
-                exp_df.drop([r1_col, r2_col, r3_col], axis=1, inplace=True)
-                exp_df.insert(
-                    loc=0, column="%s_gamma" % joint, value=pd.Series(data=[e[2] for e in exps], index=exp_df.index)
-                )
-                exp_df.insert(
-                    loc=0, column="%s_beta" % joint, value=pd.Series(data=[e[1] for e in exps], index=exp_df.index)
-                )
-                exp_df.insert(
-                    loc=0, column="%s_alpha" % joint, value=pd.Series(data=[e[0] for e in exps], index=exp_df.index)
-                )
-
-            # print(exp_df.columns)
-            new_track = track.clone()
-            new_track.values = exp_df
-            Q.append(new_track)
-
-        return Q
-
-    def _expmap_to_euler2(self, X):
-        Q = []
-        for track in X:
-            channels = []
-            titles = []
-            exp_df = track.values
-
-            # Create a new DataFrame to store the exponential map rep
-            # euler_df = pd.DataFrame(index=exp_df.index)
-            euler_df = exp_df.copy()
-
-            # Copy the root positions into the new DataFrame
-            # rxp = '%s_Xposition'%track.root_name
-            # ryp = '%s_Yposition'%track.root_name
-            # rzp = '%s_Zposition'%track.root_name
-            # euler_df[rxp] = pd.Series(data=exp_df[rxp], index=euler_df.index)
-            # euler_df[ryp] = pd.Series(data=exp_df[ryp], index=euler_df.index)
-            # euler_df[rzp] = pd.Series(data=exp_df[rzp], index=euler_df.index)
-
-            # List the columns that contain rotation channels
-            exp_params = [
-                c for c in exp_df.columns if (any(p in c for p in ["alpha", "beta", "gamma"]) and "Nub" not in c)
-            ]
-
-            # List the joints that are not end sites, i.e., have channels
-            joints = (joint for joint in track.skeleton if "Nub" not in joint)
-
-            for joint in joints:
-                r = exp_df[[c for c in exp_params if joint in c]]  # Get the columns that belong to this joint
-
-                euler_df.drop(["%s_alpha" % joint, "%s_beta" % joint, "%s_gamma" % joint], axis=1, inplace=True)
-                expmap = [
-                    [f[1]["%s_alpha" % joint], f[1]["%s_beta" % joint], f[1]["%s_gamma" % joint]] for f in r.iterrows()
-                ]  # Make sure the columsn are organized in xyz order
-                angs = np.linalg.norm(expmap, axis=1)
-                quats = Quaternions.from_angle_axis(angs, expmap / (np.tile(angs[:, None] + 1e-10, (1, 3))))
-                if self.ref_pose is not None:
-                    q1_col = "%s_qWrotation" % (joint)
-                    q2_col = "%s_qXrotation" % (joint)
-                    q3_col = "%s_qYrotation" % (joint)
-                    q4_col = "%s_qZrotation" % (joint)
-                    ref_q = Quaternions(
-                        np.asarray(
-                            [
-                                [f[1][q1_col], f[1][q2_col], f[1][q3_col], f[1][q4_col]]
-                                for f in self.ref_pose.values.iterrows()
-                            ]
-                        )
-                    )
-                    # print("ref_q:" + str(ref_q.shape))
-                    ref_q = ref_q[0, :]
-                    quats = ref_q * quats
-
-                euler_rots = 180 / np.pi * quats.euler()
-                track.skeleton[joint]["order"] = "ZYX"
-                rot_order = track.skeleton[joint]["order"]
-                # euler_rots = [Rotation(f, 'expmap').to_euler(True, rot_order) for f in expmap] # Convert the exp maps to eulers
-                # euler_rots = [expmap2euler(f, rot_order, True) for f in expmap] # Convert the exp maps to eulers
-
-                # Create the corresponding columns in the new DataFrame
-
-                euler_df["%s_%srotation" % (joint, rot_order[2])] = pd.Series(
-                    data=[e[0] for e in euler_rots], index=euler_df.index
-                )
-                euler_df["%s_%srotation" % (joint, rot_order[1])] = pd.Series(
-                    data=[e[1] for e in euler_rots], index=euler_df.index
-                )
-                euler_df["%s_%srotation" % (joint, rot_order[0])] = pd.Series(
-                    data=[e[2] for e in euler_rots], index=euler_df.index
-                )
-
-            new_track = track.clone()
-            new_track.values = euler_df
-            Q.append(new_track)
-
-        return Q
-
-    def _euler_to_vectors(self, X):
-        """Converts Euler angles to Up and Fwd vectors"""
-
-        Q = []
-        for track in X:
-            channels = []
-            titles = []
-            euler_df = track.values
-
-            # Create a new DataFrame to store the exponential map rep
-            vec_df = euler_df.copy()  # pd.DataFrame(index=euler_df.index)
-
-            # List the columns that contain rotation channels
-            rots = [c for c in euler_df.columns if ("rotation" in c and "Nub" not in c)]
-
-            # List the joints that are not end sites, i.e., have channels
-            joints = (joint for joint in track.skeleton if "Nub" not in joint)
-
-            for joint in joints:
-                # print(joint)
-                r = euler_df[[c for c in rots if joint in c]]  # Get the columns that belong to this joint
-                rot_order = track.skeleton[joint]["order"]
-                r1_col = "%s_%srotation" % (joint, rot_order[0])
-                r2_col = "%s_%srotation" % (joint, rot_order[1])
-                r3_col = "%s_%srotation" % (joint, rot_order[2])
-
-                vec_df.drop([r1_col, r2_col, r3_col], axis=1, inplace=True)
-                euler = [[f[1][r1_col], f[1][r2_col], f[1][r3_col]] for f in r.iterrows()]
-                vectors = np.array([euler2vectors(f, rot_order, True) for f in euler])
-
-                vec_df.insert(
-                    loc=0, column="%s_xUp" % joint, value=pd.Series(data=[e[0] for e in vectors], index=vec_df.index)
-                )
-                vec_df.insert(
-                    loc=0, column="%s_yUp" % joint, value=pd.Series(data=[e[1] for e in vectors], index=vec_df.index)
-                )
-                vec_df.insert(
-                    loc=0, column="%s_zUp" % joint, value=pd.Series(data=[e[2] for e in vectors], index=vec_df.index)
-                )
-                vec_df.insert(
-                    loc=0, column="%s_xFwd" % joint, value=pd.Series(data=[e[3] for e in vectors], index=vec_df.index)
-                )
-                vec_df.insert(
-                    loc=0, column="%s_yFwd" % joint, value=pd.Series(data=[e[4] for e in vectors], index=vec_df.index)
-                )
-                vec_df.insert(
-                    loc=0, column="%s_zFwd" % joint, value=pd.Series(data=[e[5] for e in vectors], index=vec_df.index)
-                )
-
-            # print(exp_df.columns)
-            new_track = track.clone()
-            new_track.values = vec_df
-            Q.append(new_track)
-
-        return Q
-
-    def _vectors_to_euler(self, X):
-        """Converts Up and Fwd vectors to Euler angles"""
-        Q = []
-        for track in X:
-            channels = []
-            titles = []
-            vec_df = track.values
-
-            # Create a new DataFrame to store the exponential map rep
-            # euler_df = pd.DataFrame(index=exp_df.index)
-            euler_df = vec_df.copy()
-
-            # List the columns that contain rotation channels
-            vec_params = [
-                c
-                for c in vec_df.columns
-                if (any(p in c for p in ["xUp", "yUp", "zUp", "xFwd", "yFwd", "zFwd"]) and "Nub" not in c)
-            ]
-
-            # List the joints that are not end sites, i.e., have channels
-            joints = (joint for joint in track.skeleton if "Nub" not in joint)
-
-            for joint in joints:
-                r = vec_df[[c for c in vec_params if joint in c]]  # Get the columns that belong to this joint
-
-                euler_df.drop(
-                    [
-                        "%s_xUp" % joint,
-                        "%s_yUp" % joint,
-                        "%s_zUp" % joint,
-                        "%s_xFwd" % joint,
-                        "%s_yFwd" % joint,
-                        "%s_zFwd" % joint,
-                    ],
-                    axis=1,
-                    inplace=True,
-                )
-                vectors = [
-                    [
-                        f[1]["%s_xUp" % joint],
-                        f[1]["%s_yUp" % joint],
-                        f[1]["%s_zUp" % joint],
-                        f[1]["%s_xFwd" % joint],
-                        f[1]["%s_yFwd" % joint],
-                        f[1]["%s_zFwd" % joint],
-                    ]
-                    for f in r.iterrows()
-                ]  # Make sure the columsn are organized in xyz order
-                rot_order = track.skeleton[joint]["order"]
-                euler_rots = [vectors2euler(f, rot_order, True) for f in vectors]
-
-                # Create the corresponding columns in the new DataFrame
-
-                euler_df["%s_%srotation" % (joint, rot_order[0])] = pd.Series(
-                    data=[e[0] for e in euler_rots], index=euler_df.index
-                )
-                euler_df["%s_%srotation" % (joint, rot_order[1])] = pd.Series(
-                    data=[e[1] for e in euler_rots], index=euler_df.index
-                )
-                euler_df["%s_%srotation" % (joint, rot_order[2])] = pd.Series(
-                    data=[e[2] for e in euler_rots], index=euler_df.index
-                )
-
-            new_track = track.clone()
-            new_track.values = euler_df
-            Q.append(new_track)
-
-        return Q
-
-
-class Mirror(BaseEstimator, TransformerMixin):
-    def __init__(self, axis="X", append=True):
-        """
-        Mirrors the data
-        """
-        self.axis = axis
-        self.append = append
-
-    def fit(self, X, y=None):
-        return self
-
-    def transform(self, X, y=None):
-        print("Mirror: " + self.axis)
-        Q = []
-
-        if self.append:
-            for track in X:
-                Q.append(track)
-
-        for track in X:
-            channels = []
-            titles = []
-
-            if self.axis == "X":
-                signs = np.array([1, -1, -1])
-            if self.axis == "Y":
-                signs = np.array([-1, 1, -1])
-            if self.axis == "Z":
-                signs = np.array([-1, -1, 1])
-
-            euler_df = track.values
-
-            # Create a new DataFrame to store the exponential map rep
-            new_df = pd.DataFrame(index=euler_df.index)
-
-            # Copy the root positions into the new DataFrame
-            rxp = "%s_Xposition" % track.root_name
-            ryp = "%s_Yposition" % track.root_name
-            rzp = "%s_Zposition" % track.root_name
-            new_df[rxp] = pd.Series(data=-signs[0] * euler_df[rxp], index=new_df.index)
-            new_df[ryp] = pd.Series(data=-signs[1] * euler_df[ryp], index=new_df.index)
-            new_df[rzp] = pd.Series(data=-signs[2] * euler_df[rzp], index=new_df.index)
-
-            # List the columns that contain rotation channels
-            rots = [c for c in euler_df.columns if ("rotation" in c and "Nub" not in c)]
-            # lft_rots = [c for c in euler_df.columns if ('Left' in c and 'rotation' in c and 'Nub' not in c)]
-            # rgt_rots = [c for c in euler_df.columns if ('Right' in c and 'rotation' in c and 'Nub' not in c)]
-            lft_joints = (joint for joint in track.skeleton if "Left" in joint and "Nub" not in joint)
-            rgt_joints = (joint for joint in track.skeleton if "Right" in joint and "Nub" not in joint)
-
-            new_track = track.clone()
-
-            for lft_joint in lft_joints:
-                # lr = euler_df[[c for c in rots if lft_joint + "_" in c]]
-                # rot_order = track.skeleton[lft_joint]['order']
-                # lft_eulers = [[f[1]['%s_Xrotation'%lft_joint], f[1]['%s_Yrotation'%lft_joint], f[1]['%s_Zrotation'%lft_joint]] for f in lr.iterrows()]
-
-                rgt_joint = lft_joint.replace("Left", "Right")
-                # rr = euler_df[[c for c in rots if rgt_joint + "_" in c]]
-                # rot_order = track.skeleton[rgt_joint]['order']
-                #                rgt_eulers = [[f[1]['%s_Xrotation'%rgt_joint], f[1]['%s_Yrotation'%rgt_joint], f[1]['%s_Zrotation'%rgt_joint]] for f in rr.iterrows()]
-
-                # Create the corresponding columns in the new DataFrame
-
-                new_df["%s_Xrotation" % lft_joint] = pd.Series(
-                    data=signs[0] * track.values["%s_Xrotation" % rgt_joint], index=new_df.index
-                )
-                new_df["%s_Yrotation" % lft_joint] = pd.Series(
-                    data=signs[1] * track.values["%s_Yrotation" % rgt_joint], index=new_df.index
-                )
-                new_df["%s_Zrotation" % lft_joint] = pd.Series(
-                    data=signs[2] * track.values["%s_Zrotation" % rgt_joint], index=new_df.index
-                )
-
-                new_df["%s_Xrotation" % rgt_joint] = pd.Series(
-                    data=signs[0] * track.values["%s_Xrotation" % lft_joint], index=new_df.index
-                )
-                new_df["%s_Yrotation" % rgt_joint] = pd.Series(
-                    data=signs[1] * track.values["%s_Yrotation" % lft_joint], index=new_df.index
-                )
-                new_df["%s_Zrotation" % rgt_joint] = pd.Series(
-                    data=signs[2] * track.values["%s_Zrotation" % lft_joint], index=new_df.index
-                )
-
-            # List the joints that are not left or right, i.e. are on the trunk
-            joints = (
-                joint for joint in track.skeleton if "Nub" not in joint and "Left" not in joint and "Right" not in joint
-            )
-
-            for joint in joints:
-                # r = euler_df[[c for c in rots if joint in c]] # Get the columns that belong to this joint
-                # rot_order = track.skeleton[joint]['order']
-
-                # eulers = [[f[1]['%s_Xrotation'%joint], f[1]['%s_Yrotation'%joint], f[1]['%s_Zrotation'%joint]] for f in r.iterrows()]
-
-                # Create the corresponding columns in the new DataFrame
-                new_df["%s_Xrotation" % joint] = pd.Series(
-                    data=signs[0] * track.values["%s_Xrotation" % joint], index=new_df.index
-                )
-                new_df["%s_Yrotation" % joint] = pd.Series(
-                    data=signs[1] * track.values["%s_Yrotation" % joint], index=new_df.index
-                )
-                new_df["%s_Zrotation" % joint] = pd.Series(
-                    data=signs[2] * track.values["%s_Zrotation" % joint], index=new_df.index
-                )
-
-            new_track.values = new_df
-            Q.append(new_track)
-
-        return Q
-
-    def inverse_transform(self, X, copy=None, start_pos=None):
-        return X
-
-
-class EulerReorder(BaseEstimator, TransformerMixin):
-    def __init__(self, new_order):
-        """
-        Add a
-        """
-        self.new_order = new_order
-
-    def fit(self, X, y=None):
-        self.orig_skeleton = copy.deepcopy(X[0].skeleton)
-        print(self.orig_skeleton)
-        return self
-
-    def transform(self, X, y=None):
-        print("EulerReorder")
-        Q = []
-
-        for track in X:
-            channels = []
-            titles = []
-            euler_df = track.values
-
-            # Create a new DataFrame to store the exponential map rep
-            # new_df = pd.DataFrame(index=euler_df.index)
-            new_df = euler_df.copy()
-
-            # Copy the root positions into the new DataFrame
-            rxp = "%s_Xposition" % track.root_name
-            ryp = "%s_Yposition" % track.root_name
-            rzp = "%s_Zposition" % track.root_name
-            new_df[rxp] = pd.Series(data=euler_df[rxp], index=new_df.index)
-            new_df[ryp] = pd.Series(data=euler_df[ryp], index=new_df.index)
-            new_df[rzp] = pd.Series(data=euler_df[rzp], index=new_df.index)
-
-            # List the columns that contain rotation channels
-            rots = [c for c in euler_df.columns if ("rotation" in c and "Nub" not in c)]
-
-            # List the joints that are not end sites, i.e., have channels
-            joints = (joint for joint in track.skeleton if "Nub" not in joint)
-
-            new_track = track.clone()
-            for joint in joints:
-                r = euler_df[[c for c in rots if joint in c]]  # Get the columns that belong to this joint
-                rot_order = track.skeleton[joint]["order"]
-                r1_col = "%s_%srotation" % (joint, rot_order[0])
-                r2_col = "%s_%srotation" % (joint, rot_order[1])
-                r3_col = "%s_%srotation" % (joint, rot_order[2])
-                euler = [[f[1][r1_col], f[1][r2_col], f[1][r3_col]] for f in r.iterrows()]
-
-                # euler = [[f[1]['%s_Xrotation'%(joint)], f[1]['%s_Yrotation'%(joint)], f[1]['%s_Zrotation'%(joint)]] for f in r.iterrows()]
-                new_euler = [euler_reorder(f, rot_order, self.new_order, True) for f in euler]
-                # new_euler = euler_reorder2(np.array(euler), rot_order, self.new_order, True)
-
-                # Create the corresponding columns in the new DataFrame
-                new_df["%s_%srotation" % (joint, self.new_order[0])] = pd.Series(
-                    data=[e[0] for e in new_euler], index=new_df.index
-                )
-                new_df["%s_%srotation" % (joint, self.new_order[1])] = pd.Series(
-                    data=[e[1] for e in new_euler], index=new_df.index
-                )
-                new_df["%s_%srotation" % (joint, self.new_order[2])] = pd.Series(
-                    data=[e[2] for e in new_euler], index=new_df.index
-                )
-
-                new_track.skeleton[joint]["order"] = self.new_order
-
-            new_track.values = new_df
-            Q.append(new_track)
-
-        return Q
-
-    def inverse_transform(self, X, copy=None, start_pos=None):
-        return X
-
-
-class JointSelector(BaseEstimator, TransformerMixin):
-    """
-    Allows for filtering the mocap data to include only the selected joints
-    """
-
-    def __init__(self, joints, include_root=False):
-        self.joints = joints
-        self.include_root = include_root
-
-    def fit(self, X, y=None):
-        selected_joints = []
-        selected_channels = []
-
-        if self.include_root:
-            selected_joints.append(X[0].root_name)
-
-        selected_joints.extend(self.joints)
-
-        for joint_name in selected_joints:
-            selected_channels.extend([o for o in X[0].values.columns if (joint_name + "_") in o and "Nub" not in o])
-
-        self.selected_joints = selected_joints
-        self.selected_channels = selected_channels
-        self.not_selected = X[0].values.columns.difference(selected_channels)
-        self.not_selected_values = {c: X[0].values[c].values[0] for c in self.not_selected}
-
-        self.orig_skeleton = X[0].skeleton
-        return self
-
-    def transform(self, X, y=None):
-        print("JointSelector")
-        Q = []
-        for track in X:
-            t2 = track.clone()
-            for key in track.skeleton.keys():
-                if key not in self.selected_joints:
-                    t2.skeleton.pop(key)
-            t2.values = track.values[self.selected_channels]
-
-            for key in t2.skeleton.keys():
-                to_remove = list(set(t2.skeleton[key]["children"]) - set(self.selected_joints))
-                [t2.skeleton[key]["children"].remove(c) for c in to_remove]
-
-            Q.append(t2)
-
-        return Q
-
-    def inverse_transform(self, X, copy=None):
-        Q = []
-
-        for track in X:
-            t2 = track.clone()
-            t2.skeleton = self.orig_skeleton
-            for d in self.not_selected:
-                t2.values[d] = self.not_selected_values[d]
-            Q.append(t2)
-
-        return Q
-
-
-class Numpyfier(BaseEstimator, TransformerMixin):
-    """
-    Just converts the values in a MocapData object into a numpy array
-    Useful for the final stage of a pipeline before training
-    """
-
-    def __init__(self):
-        pass
-
-    def fit(self, X, y=None):
-        self.org_mocap_ = X[0].clone()
-        self.org_mocap_.values.drop(self.org_mocap_.values.index, inplace=True)
-
-        return self
-
-    def transform(self, X, y=None):
-        print("Numpyfier")
-        Q = []
-
-        for track in X:
-            Q.append(track.values.values)
-            # print("Numpyfier:" + str(track.values.columns))
-
-        return np.array(Q)
-
-    def inverse_transform(self, X, copy=None):
-        Q = []
-
-        for track in X:
-            new_mocap = self.org_mocap_.clone()
-            time_index = pd.to_timedelta([f for f in range(track.shape[0])], unit="s")
-
-            new_df = pd.DataFrame(data=track, index=time_index, columns=self.org_mocap_.values.columns)
-
-            new_mocap.values = new_df
-
-            Q.append(new_mocap)
-
-        return Q
-
-
-class Slicer(BaseEstimator, TransformerMixin):
-    """
-    Slice the data into intervals of equal size
-    """
-
-    def __init__(self, window_size, overlap=0.5):
-        self.window_size = window_size
-        self.overlap = overlap
-        pass
-
-    def fit(self, X, y=None):
-        self.org_mocap_ = X[0].clone()
-        self.org_mocap_.values.drop(self.org_mocap_.values.index, inplace=True)
-
-        return self
-
-    def transform(self, X, y=None):
-        print("Slicer")
-        Q = []
-
-        for track in X:
-            vals = track.values.values
-            nframes = vals.shape[0]
-            overlap_frames = (int)(self.overlap * self.window_size)
-
-            n_sequences = (nframes - overlap_frames) // (self.window_size - overlap_frames)
-
-            if n_sequences > 0:
-                y = np.zeros((n_sequences, self.window_size, vals.shape[1]))
-
-                # extract sequences from the input data
-                for i in range(0, n_sequences):
-                    frameIdx = (self.window_size - overlap_frames) * i
-                    Q.append(vals[frameIdx : frameIdx + self.window_size, :])
-
-        return np.array(Q)
-
-    def inverse_transform(self, X, copy=None):
-        Q = []
-
-        for track in X:
-            new_mocap = self.org_mocap_.clone()
-            time_index = pd.to_timedelta([f for f in range(track.shape[0])], unit="s")
-
-            new_df = pd.DataFrame(data=track, index=time_index, columns=self.org_mocap_.values.columns)
-
-            new_mocap.values = new_df
-
-            Q.append(new_mocap)
-
-        return Q
-
-
-class RootTransformer(BaseEstimator, TransformerMixin):
-    def __init__(self, method, hips_axis_order="XYZ", position_smoothing=0, rotation_smoothing=0, separate_root=True):
-        """
-        Accepted methods:
-            abdolute_translation_deltas
-            pos_rot_deltas
-        """
-        self.method = method
-        self.position_smoothing = position_smoothing
-        self.rotation_smoothing = rotation_smoothing
-        self.separate_root = separate_root
-
-        # relative rotation from the hips awis the the x-side, y-up, z-forward convention
-        rot_mat = np.zeros((3, 3))
-        for i in range(3):
-            ax_i = ord(hips_axis_order[i]) - ord("X")
-            rot_mat[i, ax_i] = 1
-        self.root_rotation_offset = Quaternions.from_transforms(rot_mat[np.newaxis, :, :])
-        self.hips_side_axis = -rot_mat[0, :]
-
-    #        self.hips_forward_axis = ord(hips_forward_axis)-ord('X')
-
-    def fit(self, X, y=None):
-        return self
-
-    def transform(self, X, y=None):
-        print("RootTransformer")
-        Q = []
-
-        for track in X:
-            if self.method == "abdolute_translation_deltas":
-                new_df = track.values.copy()
-                xpcol = "%s_Xposition" % track.root_name
-                ypcol = "%s_Yposition" % track.root_name
-                zpcol = "%s_Zposition" % track.root_name
-
-                dxpcol = "%s_dXposition" % track.root_name
-                dzpcol = "%s_dZposition" % track.root_name
-
-                x = track.values[xpcol].copy()
-                z = track.values[zpcol].copy()
-
-                if self.position_smoothing > 0:
-                    x_sm = filters.gaussian_filter1d(x, self.position_smoothing, axis=0, mode="nearest")
-                    z_sm = filters.gaussian_filter1d(z, self.position_smoothing, axis=0, mode="nearest")
-                    dx = pd.Series(data=x_sm, index=new_df.index).diff()
-                    dz = pd.Series(data=z_sm, index=new_df.index).diff()
-                    new_df[xpcol] = x - x_sm
-                    new_df[zpcol] = z - z_sm
-                else:
-                    dx = x.diff()
-                    dz = z.diff()
-                    new_df.drop([xpcol, zpcol], axis=1, inplace=True)
-
-                dx[0] = dx[1]
-                dz[0] = dz[1]
-
-                new_df[dxpcol] = dx
-                new_df[dzpcol] = dz
-
-                new_track = track.clone()
-                new_track.values = new_df
-            # end of abdolute_translation_deltas
-
-            elif self.method == "pos_rot_deltas":
-                new_track = track.clone()
-
-                # Absolute columns
-                xp_col = "%s_Xposition" % track.root_name
-                yp_col = "%s_Yposition" % track.root_name
-                zp_col = "%s_Zposition" % track.root_name
-
-                # rot_order = track.skeleton[track.root_name]['order']
-                # %(joint, rot_order[0])
-
-                rot_order = track.skeleton[track.root_name]["order"]
-                r1_col = "%s_%srotation" % (track.root_name, rot_order[0])
-                r2_col = "%s_%srotation" % (track.root_name, rot_order[1])
-                r3_col = "%s_%srotation" % (track.root_name, rot_order[2])
-
-                # Delta columns
-                # dxp_col = '%s_dXposition'%track.root_name
-                # dzp_col = '%s_dZposition'%track.root_name
-
-                # dxr_col = '%s_dXrotation'%track.root_name
-                # dyr_col = '%s_dYrotation'%track.root_name
-                # dzr_col = '%s_dZrotation'%track.root_name
-                dxp_col = "reference_dXposition"
-                dzp_col = "reference_dZposition"
-                dxr_col = "reference_dXrotation"
-                dyr_col = "reference_dYrotation"
-                dzr_col = "reference_dZrotation"
-
-                positions = np.transpose(np.array([track.values[xp_col], track.values[yp_col], track.values[zp_col]]))
-                rotations = (
-                    np.pi
-                    / 180.0
-                    * np.transpose(np.array([track.values[r1_col], track.values[r2_col], track.values[r3_col]]))
-                )
-
-                """ Get Trajectory and smooth it"""
-                trajectory_filterwidth = self.position_smoothing
-                reference = positions.copy() * np.array([1, 0, 1])
-                if trajectory_filterwidth > 0:
-                    reference = filters.gaussian_filter1d(reference, trajectory_filterwidth, axis=0, mode="nearest")
-
-                """ Get Root Velocity """
-                velocity = np.diff(reference, axis=0)
-                velocity = np.vstack((velocity[0, :], velocity))
-
-                """ Remove Root Translation """
-                positions = positions - reference
-
-                """ Get Forward Direction along the x-z plane, assuming character is facig z-forward """
-                # forward = [Rotation(f, 'euler', from_deg=True, order=rot_order).rotmat[:,2] for f in rotations] # get the z-axis of the rotation matrix, assuming character is facig z-forward
-                # print("order:" + rot_order.lower())
-                quats = Quaternions.from_euler(rotations, order=rot_order.lower(), world=False)
-                # forward = quats*np.array([[0,0,1]])
-                # forward[:,1] = 0
-                side_dirs = quats * self.hips_side_axis
-                forward = np.cross(np.array([[0, 1, 0]]), side_dirs)
-
-                """ Smooth Forward Direction """
-                direction_filterwidth = self.rotation_smoothing
-                if direction_filterwidth > 0:
-                    forward = filters.gaussian_filter1d(forward, direction_filterwidth, axis=0, mode="nearest")
-
-                forward = forward / np.sqrt((forward**2).sum(axis=-1))[..., np.newaxis]
-
-                """ Remove Y Rotation """
-                target = np.array([[0, 0, 1]]).repeat(len(forward), axis=0)
-                rotation = Quaternions.between(target, forward)[:, np.newaxis]
-                positions = (-rotation[:, 0]) * positions
-                # new_rotations = (-rotation[:,0]) * quats
-                new_rotations = (-self.root_rotation_offset) * (-rotation[:, 0]) * quats
-
-                """ Get Root Rotation """
-                # print(rotation[:,0])
-                velocity = (-rotation[:, 0]) * velocity
-                rvelocity = Pivots.from_quaternions(rotation[1:] * -rotation[:-1]).ps
-                rvelocity = np.vstack((rvelocity[0], rvelocity))
-
-                eulers = (
-                    np.array(
-                        [t3d.euler.quat2euler(q, axes=("s" + rot_order.lower()[::-1]))[::-1] for q in new_rotations]
-                    )
-                    * 180.0
-                    / np.pi
-                )
-
-                new_df = track.values.copy()
-
-                root_pos_x = pd.Series(data=positions[:, 0], index=new_df.index)
-                root_pos_y = pd.Series(data=positions[:, 1], index=new_df.index)
-                root_pos_z = pd.Series(data=positions[:, 2], index=new_df.index)
-                root_pos_x_diff = pd.Series(data=velocity[:, 0], index=new_df.index)
-                root_pos_z_diff = pd.Series(data=velocity[:, 2], index=new_df.index)
-
-                root_rot_1 = pd.Series(data=eulers[:, 0], index=new_df.index)
-                root_rot_2 = pd.Series(data=eulers[:, 1], index=new_df.index)
-                root_rot_3 = pd.Series(data=eulers[:, 2], index=new_df.index)
-                root_rot_y_diff = pd.Series(data=rvelocity[:, 0], index=new_df.index)
-
-                # new_df.drop([xr_col, yr_col, zr_col, xp_col, zp_col], axis=1, inplace=True)
-
-                new_df[xp_col] = root_pos_x
-                new_df[yp_col] = root_pos_y
-                new_df[zp_col] = root_pos_z
-                new_df[dxp_col] = root_pos_x_diff
-                new_df[dzp_col] = root_pos_z_diff
-
-                new_df[r1_col] = root_rot_1
-                new_df[r2_col] = root_rot_2
-                new_df[r3_col] = root_rot_3
-                # new_df[dxr_col] = root_rot_x_diff
-                new_df[dyr_col] = root_rot_y_diff
-                # new_df[dzr_col] = root_rot_z_diff
-
-                new_track.values = new_df
-            elif self.method == "pos_xyz_rot_deltas":
-                new_track = track.clone()
-
-                # Absolute columns
-                xp_col = "%s_Xposition" % track.root_name
-                yp_col = "%s_Yposition" % track.root_name
-                zp_col = "%s_Zposition" % track.root_name
-
-                # rot_order = track.skeleton[track.root_name]['order']
-                # %(joint, rot_order[0])
-
-                rot_order = track.skeleton[track.root_name]["order"]
-                r1_col = "%s_%srotation" % (track.root_name, rot_order[0])
-                r2_col = "%s_%srotation" % (track.root_name, rot_order[1])
-                r3_col = "%s_%srotation" % (track.root_name, rot_order[2])
-
-                # Delta columns
-                # dxp_col = '%s_dXposition'%track.root_name
-                # dzp_col = '%s_dZposition'%track.root_name
-
-                # dxr_col = '%s_dXrotation'%track.root_name
-                # dyr_col = '%s_dYrotation'%track.root_name
-                # dzr_col = '%s_dZrotation'%track.root_name
-                dxp_col = "reference_dXposition"
-                dyp_col = "reference_dYposition"
-                dzp_col = "reference_dZposition"
-                dxr_col = "reference_dXrotation"
-                dyr_col = "reference_dYrotation"
-                dzr_col = "reference_dZrotation"
-
-                positions = np.transpose(np.array([track.values[xp_col], track.values[yp_col], track.values[zp_col]]))
-                rotations = (
-                    np.pi
-                    / 180.0
-                    * np.transpose(np.array([track.values[r1_col], track.values[r2_col], track.values[r3_col]]))
-                )
-
-                """ Get Trajectory and smooth it"""
-                trajectory_filterwidth = self.position_smoothing
-                # reference = positions.copy()*np.array([1,0,1])
-                if trajectory_filterwidth > 0:
-                    reference = filters.gaussian_filter1d(positions, trajectory_filterwidth, axis=0, mode="nearest")
-
-                """ Get Root Velocity """
-                velocity = np.diff(reference, axis=0)
-                velocity = np.vstack((velocity[0, :], velocity))
-
-                """ Remove Root Translation """
-                positions = positions - reference
-
-                """ Get Forward Direction along the x-z plane, assuming character is facig z-forward """
-                # forward = [Rotation(f, 'euler', from_deg=True, order=rot_order).rotmat[:,2] for f in rotations] # get the z-axis of the rotation matrix, assuming character is facig z-forward
-                # print("order:" + rot_order.lower())
-                quats = Quaternions.from_euler(rotations, order=rot_order.lower(), world=False)
-
-                # calculate the hips forward directions given in global cordinates
-                # side_ax = np.zeros((1,3))
-                # side_ax[0,self.hips_side_axis]=1
-                # side_dirs = quats*side_ax
-                side_dirs = quats * self.hips_side_axis
-                forward = np.cross(np.array([[0, 1, 0]]), side_dirs)
-
-                """ Smooth Forward Direction """
-                direction_filterwidth = self.rotation_smoothing
-                if direction_filterwidth > 0:
-                    forward = filters.gaussian_filter1d(forward, direction_filterwidth, axis=0, mode="nearest")
-
-                # make unit vector
-                forward = forward / np.sqrt((forward**2).sum(axis=-1))[..., np.newaxis]
-
-                """ Remove Y Rotation """
-                target = np.array([[0, 0, 1]]).repeat(len(forward), axis=0)
-                rotation = Quaternions.between(target, forward)[:, np.newaxis]
-                positions = (-rotation[:, 0]) * positions
-                new_rotations = (-self.root_rotation_offset) * (-rotation[:, 0]) * quats
-
-                """ Get Root Rotation """
-                # print(rotation[:,0])
-                velocity = (-rotation[:, 0]) * velocity
-                rvelocity = Pivots.from_quaternions(rotation[1:] * -rotation[:-1]).ps
-                rvelocity = np.vstack((rvelocity[0], rvelocity))
-
-                eulers = (
-                    np.array(
-                        [t3d.euler.quat2euler(q, axes=("s" + rot_order.lower()[::-1]))[::-1] for q in new_rotations]
-                    )
-                    * 180.0
-                    / np.pi
-                )
-
-                new_df = track.values.copy()
-
-                root_pos_x = pd.Series(data=positions[:, 0], index=new_df.index)
-                root_pos_y = pd.Series(data=positions[:, 1], index=new_df.index)
-                root_pos_z = pd.Series(data=positions[:, 2], index=new_df.index)
-                root_pos_x_diff = pd.Series(data=velocity[:, 0], index=new_df.index)
-                root_pos_y_diff = pd.Series(data=velocity[:, 1], index=new_df.index)
-                root_pos_z_diff = pd.Series(data=velocity[:, 2], index=new_df.index)
-
-                root_rot_1 = pd.Series(data=eulers[:, 0], index=new_df.index)
-                root_rot_2 = pd.Series(data=eulers[:, 1], index=new_df.index)
-                root_rot_3 = pd.Series(data=eulers[:, 2], index=new_df.index)
-                root_rot_y_diff = pd.Series(data=rvelocity[:, 0], index=new_df.index)
-
-                # new_df.drop([xr_col, yr_col, zr_col, xp_col, zp_col], axis=1, inplace=True)
-
-                new_df[xp_col] = root_pos_x
-                new_df[yp_col] = root_pos_y
-                new_df[zp_col] = root_pos_z
-                new_df[dxp_col] = root_pos_x_diff
-                new_df[dyp_col] = root_pos_y_diff
-                new_df[dzp_col] = root_pos_z_diff
-
-                new_df[r1_col] = root_rot_1
-                new_df[r2_col] = root_rot_2
-                new_df[r3_col] = root_rot_3
-                # new_df[dxr_col] = root_rot_x_diff
-                new_df[dyr_col] = root_rot_y_diff
-                # new_df[dzr_col] = root_rot_z_diff
-
-                new_track.values = new_df
-
-            elif self.method == "hip_centric":
-                new_track = track.clone()
-
-                # Absolute columns
-                xp_col = "%s_Xposition" % track.root_name
-                yp_col = "%s_Yposition" % track.root_name
-                zp_col = "%s_Zposition" % track.root_name
-
-                xr_col = "%s_Xrotation" % track.root_name
-                yr_col = "%s_Yrotation" % track.root_name
-                zr_col = "%s_Zrotation" % track.root_name
-
-                new_df = track.values.copy()
-
-                all_zeros = np.zeros(track.values[xp_col].values.shape)
-
-                new_df[xp_col] = pd.Series(data=all_zeros, index=new_df.index)
-                new_df[yp_col] = pd.Series(data=all_zeros, index=new_df.index)
-                new_df[zp_col] = pd.Series(data=all_zeros, index=new_df.index)
-
-                new_df[xr_col] = pd.Series(data=all_zeros, index=new_df.index)
-                new_df[yr_col] = pd.Series(data=all_zeros, index=new_df.index)
-                new_df[zr_col] = pd.Series(data=all_zeros, index=new_df.index)
-
-                new_track.values = new_df
-
-            # print(new_track.values.columns)
-            Q.append(new_track)
-
-        return Q
-
-    def inverse_transform(self, X, copy=None, start_pos=None):
-        Q = []
-
-        # TODO: simplify this implementation
-
-        startx = 0
-        startz = 0
-
-        if start_pos is not None:
-            startx, startz = start_pos
-
-        for track in X:
-            new_track = track.clone()
-            if self.method == "abdolute_translation_deltas":
-                new_df = new_track.values
-                xpcol = "%s_Xposition" % track.root_name
-                ypcol = "%s_Yposition" % track.root_name
-                zpcol = "%s_Zposition" % track.root_name
-
-                dxpcol = "%s_dXposition" % track.root_name
-                dzpcol = "%s_dZposition" % track.root_name
-
-                dx = track.values[dxpcol].values
-                dz = track.values[dzpcol].values
-
-                recx = [startx]
-                recz = [startz]
-
-                for i in range(dx.shape[0] - 1):
-                    recx.append(recx[i] + dx[i + 1])
-                    recz.append(recz[i] + dz[i + 1])
-
-                # recx = [recx[i]+dx[i+1] for i in range(dx.shape[0]-1)]
-                # recz = [recz[i]+dz[i+1] for i in range(dz.shape[0]-1)]
-                # recx = dx[:-1] + dx[1:]
-                # recz = dz[:-1] + dz[1:]
-                if self.position_smoothing > 0:
-                    new_df[xpcol] = pd.Series(data=new_df[xpcol] + recx, index=new_df.index)
-                    new_df[zpcol] = pd.Series(data=new_df[zpcol] + recz, index=new_df.index)
-                else:
-                    new_df[xpcol] = pd.Series(data=recx, index=new_df.index)
-                    new_df[zpcol] = pd.Series(data=recz, index=new_df.index)
-
-                new_df.drop([dxpcol, dzpcol], axis=1, inplace=True)
-
-                new_track.values = new_df
-            # end of abdolute_translation_deltas
-
-            elif self.method == "pos_rot_deltas":
-                # Absolute columns
-                rot_order = track.skeleton[track.root_name]["order"]
-                xp_col = "%s_Xposition" % track.root_name
-                yp_col = "%s_Yposition" % track.root_name
-                zp_col = "%s_Zposition" % track.root_name
-
-                xr_col = "%s_Xrotation" % track.root_name
-                yr_col = "%s_Yrotation" % track.root_name
-                zr_col = "%s_Zrotation" % track.root_name
-                r1_col = "%s_%srotation" % (track.root_name, rot_order[0])
-                r2_col = "%s_%srotation" % (track.root_name, rot_order[1])
-                r3_col = "%s_%srotation" % (track.root_name, rot_order[2])
-
-                # Delta columns
-                # dxp_col = '%s_dXposition'%track.root_name
-                # dzp_col = '%s_dZposition'%track.root_name
-                # dyr_col = '%s_dYrotation'%track.root_name
-                dxp_col = "reference_dXposition"
-                dzp_col = "reference_dZposition"
-                dyr_col = "reference_dYrotation"
-
-                positions = np.transpose(np.array([track.values[xp_col], track.values[yp_col], track.values[zp_col]]))
-                rotations = (
-                    np.pi
-                    / 180.0
-                    * np.transpose(np.array([track.values[r1_col], track.values[r2_col], track.values[r3_col]]))
-                )
-                quats = Quaternions.from_euler(rotations, order=rot_order.lower(), world=False)
-
-                new_df = track.values.copy()
-
-                dx = track.values[dxp_col].values
-                dz = track.values[dzp_col].values
-
-                dry = track.values[dyr_col].values
-
-                # rec_p = np.array([startx, 0, startz])+positions[0,:]
-                rec_ry = Quaternions.id(quats.shape[0])
-                rec_xp = [0]
-                rec_zp = [0]
-
-                # rec_r = Quaternions.id(quats.shape[0])
-
-                for i in range(dx.shape[0] - 1):
-                    # print(dry[i])
-                    q_y = Quaternions.from_angle_axis(np.array(dry[i + 1]), np.array([0, 1, 0]))
-                    rec_ry[i + 1] = q_y * rec_ry[i]
-                    # print("dx: + " + str(dx[i+1]))
-                    dp = rec_ry[i + 1] * np.array([dx[i + 1], 0, dz[i + 1]])
-                    rec_xp.append(rec_xp[i] + dp[0, 0])
-                    rec_zp.append(rec_zp[i] + dp[0, 2])
-
-                if self.separate_root:
-                    qq = quats
-                    xx = positions[:, 0]
-                    zz = positions[:, 2]
-                else:
-                    qq = rec_ry * self.root_rotation_offset * quats
-                    pp = rec_ry * positions
-                    xx = rec_xp + pp[:, 0]
-                    zz = rec_zp + pp[:, 2]
-
-                eulers = (
-                    np.array([t3d.euler.quat2euler(q, axes=("s" + rot_order.lower()[::-1]))[::-1] for q in qq])
-                    * 180.0
-                    / np.pi
-                )
-
-                new_df = track.values.copy()
-
-                root_rot_1 = pd.Series(data=eulers[:, 0], index=new_df.index)
-                root_rot_2 = pd.Series(data=eulers[:, 1], index=new_df.index)
-                root_rot_3 = pd.Series(data=eulers[:, 2], index=new_df.index)
-
-                new_df[xp_col] = pd.Series(data=xx, index=new_df.index)
-                new_df[zp_col] = pd.Series(data=zz, index=new_df.index)
-
-                new_df[r1_col] = pd.Series(data=root_rot_1, index=new_df.index)
-                new_df[r2_col] = pd.Series(data=root_rot_2, index=new_df.index)
-                new_df[r3_col] = pd.Series(data=root_rot_3, index=new_df.index)
-
-                if self.separate_root:
-                    new_df["reference_Xposition"] = pd.Series(data=rec_xp, index=new_df.index)
-                    new_df["reference_Zposition"] = pd.Series(data=rec_zp, index=new_df.index)
-                    eulers_ry = (
-                        np.array([t3d.euler.quat2euler(q, axes=("s" + rot_order.lower()[::-1]))[::-1] for q in rec_ry])
-                        * 180.0
-                        / np.pi
-                    )
-                    new_df["reference_Yrotation"] = pd.Series(
-                        data=eulers_ry[:, rot_order.find("Y")], index=new_df.index
-                    )
-
-                new_df.drop([dyr_col, dxp_col, dzp_col], axis=1, inplace=True)
-
-                new_track.values = new_df
-
-            elif self.method == "pos_xyz_rot_deltas":
-                # Absolute columns
-                rot_order = track.skeleton[track.root_name]["order"]
-                xp_col = "%s_Xposition" % track.root_name
-                yp_col = "%s_Yposition" % track.root_name
-                zp_col = "%s_Zposition" % track.root_name
-
-                xr_col = "%s_Xrotation" % track.root_name
-                yr_col = "%s_Yrotation" % track.root_name
-                zr_col = "%s_Zrotation" % track.root_name
-                r1_col = "%s_%srotation" % (track.root_name, rot_order[0])
-                r2_col = "%s_%srotation" % (track.root_name, rot_order[1])
-                r3_col = "%s_%srotation" % (track.root_name, rot_order[2])
-
-                # Delta columns
-                # dxp_col = '%s_dXposition'%track.root_name
-                # dzp_col = '%s_dZposition'%track.root_name
-                # dyr_col = '%s_dYrotation'%track.root_name
-                dxp_col = "reference_dXposition"
-                dyp_col = "reference_dYposition"
-                dzp_col = "reference_dZposition"
-                dyr_col = "reference_dYrotation"
-
-                positions = np.transpose(np.array([track.values[xp_col], track.values[yp_col], track.values[zp_col]]))
-                rotations = (
-                    np.pi
-                    / 180.0
-                    * np.transpose(np.array([track.values[r1_col], track.values[r2_col], track.values[r3_col]]))
-                )
-                quats = Quaternions.from_euler(rotations, order=rot_order.lower(), world=False)
-
-                new_df = track.values.copy()
-
-                dx = track.values[dxp_col].values
-                dy = track.values[dyp_col].values
-                dz = track.values[dzp_col].values
-
-                dry = track.values[dyr_col].values
-
-                # rec_p = np.array([startx, 0, startz])+positions[0,:]
-                rec_ry = Quaternions.id(quats.shape[0])
-                rec_xp = [0]
-                rec_yp = [0]
-                rec_zp = [0]
-
-                # rec_r = Quaternions.id(quats.shape[0])
-
-                for i in range(dx.shape[0] - 1):
-                    # print(dry[i])
-                    q_y = Quaternions.from_angle_axis(np.array(dry[i + 1]), np.array([0, 1, 0]))
-                    rec_ry[i + 1] = q_y * rec_ry[i]
-                    # print("dx: + " + str(dx[i+1]))
-                    dp = rec_ry[i + 1] * np.array([dx[i + 1], dy[i + 1], dz[i + 1]])
-                    rec_xp.append(rec_xp[i] + dp[0, 0])
-                    rec_yp.append(rec_yp[i] + dp[0, 1])
-                    rec_zp.append(rec_zp[i] + dp[0, 2])
-
-                if self.separate_root:
-                    qq = quats
-                    xx = positions[:, 0]
-                    yy = positions[:, 1]
-                    zz = positions[:, 2]
-                else:
-                    qq = rec_ry * self.root_rotation_offset * quats
-                    pp = rec_ry * positions
-                    xx = rec_xp + pp[:, 0]
-                    yy = rec_yp + pp[:, 1]
-                    zz = rec_zp + pp[:, 2]
-
-                eulers = (
-                    np.array([t3d.euler.quat2euler(q, axes=("s" + rot_order.lower()[::-1]))[::-1] for q in qq])
-                    * 180.0
-                    / np.pi
-                )
-
-                new_df = track.values.copy()
-
-                root_rot_1 = pd.Series(data=eulers[:, 0], index=new_df.index)
-                root_rot_2 = pd.Series(data=eulers[:, 1], index=new_df.index)
-                root_rot_3 = pd.Series(data=eulers[:, 2], index=new_df.index)
-
-                new_df[xp_col] = pd.Series(data=xx, index=new_df.index)
-                new_df[yp_col] = pd.Series(data=yy, index=new_df.index)
-                new_df[zp_col] = pd.Series(data=zz, index=new_df.index)
-
-                new_df[r1_col] = pd.Series(data=root_rot_1, index=new_df.index)
-                new_df[r2_col] = pd.Series(data=root_rot_2, index=new_df.index)
-                new_df[r3_col] = pd.Series(data=root_rot_3, index=new_df.index)
-
-                if self.separate_root:
-                    new_df["reference_Xposition"] = pd.Series(data=rec_xp, index=new_df.index)
-                    new_df["reference_Yposition"] = pd.Series(data=rec_yp, index=new_df.index)
-                    new_df["reference_Zposition"] = pd.Series(data=rec_zp, index=new_df.index)
-                    eulers_ry = (
-                        np.array([t3d.euler.quat2euler(q, axes=("s" + rot_order.lower()[::-1]))[::-1] for q in rec_ry])
-                        * 180.0
-                        / np.pi
-                    )
-                    new_df["reference_Yrotation"] = pd.Series(
-                        data=eulers_ry[:, rot_order.find("Y")], index=new_df.index
-                    )
-
-                new_df.drop([dyr_col, dxp_col, dyp_col, dzp_col], axis=1, inplace=True)
-
-                new_track.values = new_df
-
-            # print(new_track.values.columns)
-            Q.append(new_track)
-
-        return Q
-
-
-class RootCentricPositionNormalizer(BaseEstimator, TransformerMixin):
-    def __init__(self):
-        pass
-
-    def fit(self, X, y=None):
-        return self
-
-    def transform(self, X, y=None):
-        Q = []
-
-        for track in X:
-            new_track = track.clone()
-
-            rxp = "%s_Xposition" % track.root_name
-            ryp = "%s_Yposition" % track.root_name
-            rzp = "%s_Zposition" % track.root_name
-
-            projected_root_pos = track.values[[rxp, ryp, rzp]]
-
-            projected_root_pos.loc[:, ryp] = 0  # we want the root's projection on the floor plane as the ref
-
-            new_df = pd.DataFrame(index=track.values.index)
-
-            all_but_root = [joint for joint in track.skeleton if track.root_name not in joint]
-            # all_but_root = [joint for joint in track.skeleton]
-            for joint in all_but_root:
-                new_df["%s_Xposition" % joint] = pd.Series(
-                    data=track.values["%s_Xposition" % joint] - projected_root_pos[rxp], index=new_df.index
-                )
-                new_df["%s_Yposition" % joint] = pd.Series(
-                    data=track.values["%s_Yposition" % joint] - projected_root_pos[ryp], index=new_df.index
-                )
-                new_df["%s_Zposition" % joint] = pd.Series(
-                    data=track.values["%s_Zposition" % joint] - projected_root_pos[rzp], index=new_df.index
-                )
-
-            # keep the root as it is now
-            new_df[rxp] = track.values[rxp]
-            new_df[ryp] = track.values[ryp]
-            new_df[rzp] = track.values[rzp]
-
-            new_track.values = new_df
-
-            Q.append(new_track)
-
-        return Q
-
-    def inverse_transform(self, X, copy=None):
-        Q = []
-
-        for track in X:
-            new_track = track.clone()
-
-            rxp = "%s_Xposition" % track.root_name
-            ryp = "%s_Yposition" % track.root_name
-            rzp = "%s_Zposition" % track.root_name
-
-            projected_root_pos = track.values[[rxp, ryp, rzp]]
-
-            projected_root_pos.loc[:, ryp] = 0  # we want the root's projection on the floor plane as the ref
-
-            new_df = pd.DataFrame(index=track.values.index)
-
-            for joint in track.skeleton:
-                new_df["%s_Xposition" % joint] = pd.Series(
-                    data=track.values["%s_Xposition" % joint] + projected_root_pos[rxp], index=new_df.index
-                )
-                new_df["%s_Yposition" % joint] = pd.Series(
-                    data=track.values["%s_Yposition" % joint] + projected_root_pos[ryp], index=new_df.index
-                )
-                new_df["%s_Zposition" % joint] = pd.Series(
-                    data=track.values["%s_Zposition" % joint] + projected_root_pos[rzp], index=new_df.index
-                )
-
-            new_track.values = new_df
-
-            Q.append(new_track)
-
-        return Q
-
-
-class Flattener(BaseEstimator, TransformerMixin):
-    def __init__(self):
-        pass
-
-    def fit(self, X, y=None):
-        return self
-
-    def transform(self, X, y=None):
-        return np.concatenate(X, axis=0)
-
-
-class ConstantsRemover(BaseEstimator, TransformerMixin):
-    """
-    For now it just looks at the first track
-    """
-
-    def __init__(self, eps=1e-4):
-        self.eps = eps
-
-    def fit(self, X, y=None):
-        stds = X[0].values.std()
-        cols = X[0].values.columns.values
-        self.const_dims_ = [c for c in cols if (stds[c] < self.eps).any()]
-        self.const_values_ = {c: X[0].values[c].values[0] for c in cols if (stds[c] < self.eps).any()}
-        return self
-
-    def transform(self, X, y=None):
-        Q = []
-
-        for track in X:
-            t2 = track.clone()
-            # for key in t2.skeleton.keys():
-            #    if key in self.ConstDims_:
-            #        t2.skeleton.pop(key)
-            # print(track.values.columns.difference(self.const_dims_))
-            t2.values.drop(self.const_dims_, axis=1, inplace=True)
-            # t2.values = track.values[track.values.columns.difference(self.const_dims_)]
-            Q.append(t2)
-
-        return Q
-
-    def inverse_transform(self, X, copy=None):
-        Q = []
-
-        for track in X:
-            t2 = track.clone()
-            for d in self.const_dims_:
-                t2.values[d] = self.const_values_[d]
-            #                t2.values.assign(d=pd.Series(data=self.const_values_[d], index = t2.values.index))
-            Q.append(t2)
-
-        return Q
-
-
-class FeatureSelector(BaseEstimator, TransformerMixin):
-    """
-    Selects features and not joints
-    """
-
-    def __init__(self, cols):
-        self.selected_cols = cols
-
-    def fit(self, X, y=None):
-        self.unselected_cols = [c for c in X[0].values.columns.values if c not in self.selected_cols]
-        self.unselected_values_ = {c: X[0].values[c].values[0] for c in self.unselected_cols}
-        return self
-
-    def transform(self, X, y=None):
-        Q = []
-
-        for track in X:
-            t2 = track.clone()
-            # for key in t2.skeleton.keys():
-            #    if key in self.ConstDims_:
-            #        t2.skeleton.pop(key)
-            # print(track.values.columns.difference(self.const_dims_))
-            t2.values.drop(self.unselected_cols, axis=1, inplace=True)
-            # t2.values = track.values[track.values.columns.difference(self.const_dims_)]
-            Q.append(t2)
-
-        return Q
-
-    def inverse_transform(self, X, copy=None):
-        Q = []
-
-        for track in X:
-            t2 = track.clone()
-            for d in self.unselected_cols:
-                t2.values[d] = self.unselected_values_[d]
-            #                t2.values.assign(d=pd.Series(data=self.const_values_[d], index = t2.values.index))
-            Q.append(t2)
-
-        return Q
-
-
-class ListStandardScaler(BaseEstimator, TransformerMixin):
-    def __init__(self, is_DataFrame=False):
-        self.is_DataFrame = is_DataFrame
-
-    def fit(self, X, y=None):
-        if self.is_DataFrame:
-            X_train_flat = np.concatenate([m.values for m in X], axis=0)
-        else:
-            X_train_flat = np.concatenate([m for m in X], axis=0)
-
-        self.data_mean_ = np.mean(X_train_flat, axis=0)
-        self.data_std_ = np.std(X_train_flat, axis=0)
-
-        return self
-
-    def transform(self, X, y=None):
-        Q = []
-
-        for track in X:
-            if self.is_DataFrame:
-                normalized_track = track.copy()
-                normalized_track.values = (track.values - self.data_mean_) / self.data_std_
-            else:
-                normalized_track = (track - self.data_mean_) / self.data_std_
-
-            Q.append(normalized_track)
-
-        if self.is_DataFrame:
-            return Q
-        else:
-            return np.array(Q)
-
-    def inverse_transform(self, X, copy=None):
-        Q = []
-
-        for track in X:
-            if self.is_DataFrame:
-                unnormalized_track = track.copy()
-                unnormalized_track.values = (track.values * self.data_std_) + self.data_mean_
-            else:
-                unnormalized_track = (track * self.data_std_) + self.data_mean_
-
-            Q.append(unnormalized_track)
-
-        if self.is_DataFrame:
-            return Q
-        else:
-            return np.array(Q)
-
-
-class ListMinMaxScaler(BaseEstimator, TransformerMixin):
-    def __init__(self, is_DataFrame=False):
-        self.is_DataFrame = is_DataFrame
-
-    def fit(self, X, y=None):
-        if self.is_DataFrame:
-            X_train_flat = np.concatenate([m.values for m in X], axis=0)
-        else:
-            X_train_flat = np.concatenate([m for m in X], axis=0)
-
-        self.data_max_ = np.max(X_train_flat, axis=0)
-        self.data_min_ = np.min(X_train_flat, axis=0)
-
-        return self
-
-    def transform(self, X, y=None):
-        Q = []
-
-        for track in X:
-            if self.is_DataFrame:
-                normalized_track = track.copy()
-                normalized_track.values = (track.values - self.data_min_) / (self.data_max_ - self.data_min_)
-            else:
-                normalized_track = (track - self.data_min_) / (self.data_max_ - self.data_min_)
-
-            Q.append(normalized_track)
-
-        if self.is_DataFrame:
-            return Q
-        else:
-            return np.array(Q)
-
-    def inverse_transform(self, X, copy=None):
-        Q = []
-
-        for track in X:
-            if self.is_DataFrame:
-                unnormalized_track = track.copy()
-                unnormalized_track.values = (track.values * (self.data_max_ - self.data_min_)) + self.data_min_
-            else:
-                unnormalized_track = (track * (self.data_max_ - self.data_min_)) + self.data_min_
-
-            Q.append(unnormalized_track)
-
-        if self.is_DataFrame:
-            return Q
-        else:
-            return np.array(Q)
-
-
-class DownSampler(BaseEstimator, TransformerMixin):
-    def __init__(self, tgt_fps, keep_all=False):
-        self.tgt_fps = tgt_fps
-        self.keep_all = keep_all
-
-    def fit(self, X, y=None):
-        return self
-
-    def transform(self, X, y=None):
-        Q = []
-
-        for track in X:
-            orig_fps = round(1.0 / track.framerate)
-            rate = orig_fps // self.tgt_fps
-            if orig_fps % self.tgt_fps != 0:
-                print(
-                    "error orig_fps (" + str(orig_fps) + ") is not dividable with tgt_fps (" + str(self.tgt_fps) + ")"
-                )
-            else:
-                print("downsampling with rate: " + str(rate))
-
-            # print(track.values.size)
-            for ii in range(0, rate):
-                new_track = track.clone()
-                new_track.values = track.values[ii:-1:rate].copy()
-                # print(new_track.values.size)
-                # new_track = track[0:-1:self.rate]
-                new_track.framerate = 1.0 / self.tgt_fps
-                Q.append(new_track)
-                if not self.keep_all:
-                    break
-
-        return Q
-
-    def inverse_transform(self, X, copy=None):
-        return X
-
-
-class ReverseTime(BaseEstimator, TransformerMixin):
-    def __init__(self, append=True):
-        self.append = append
-
-    def fit(self, X, y=None):
-        return self
-
-    def transform(self, X, y=None):
-        print("ReverseTime")
-        Q = []
-        if self.append:
-            for track in X:
-                Q.append(track)
-        for track in X:
-            new_track = track.clone()
-            new_track.values = track.values[-1::-1]
-            new_track.values.index = new_track.values.index[0] - new_track.values.index
-            Q.append(new_track)
-
-        return Q
-
-    def inverse_transform(self, X, copy=None):
-        return X
-
-
-class ListFeatureUnion(BaseEstimator, TransformerMixin):
-    def __init__(self, processors):
-        self.processors = processors
-
-    def fit(self, X, y=None):
-        assert y is None
-        for proc in self.processors:
-            if isinstance(proc, Pipeline):
-                # Loop steps and run fit on each. This is necessary since
-                # running fit on a Pipeline runs fit_transform on all steps
-                # and not only fit.
-                for step in proc.steps:
-                    step[1].fit(X)
-            else:
-                proc.fit(X)
-        return self
-
-    def transform(self, X, y=None):
-        assert y is None
-        print("ListFeatureUnion")
-
-        Q = []
-
-        idx = 0
-        for proc in self.processors:
-            Z = proc.transform(X)
-            if idx == 0:
-                Q = Z
-            else:
-                assert len(Q) == len(Z)
-                for idx2, track in enumerate(Z):
-                    Q[idx2].values = pd.concat([Q[idx2].values, Z[idx2].values], axis=1)
-            idx += 1
-
-        return Q
-
-    def inverse_transform(self, X, y=None):
-        return X
-
-
-class RollingStatsCalculator(BaseEstimator, TransformerMixin):
-    """
-    Creates a causal mean and std filter with a rolling window of length win (based on using prev and current values)
-    """
-
-    def __init__(self, win):
-        self.win = win
-
-    def fit(self, X, y=None):
-        return self
-
-    def transform(self, X, y=None):
-        print("RollingStatsCalculator: " + str(self.win))
-
-        Q = []
-        for track in X:
-            new_track = track.clone()
-            mean_df = track.values.rolling(window=self.win).mean()
-            std_df = track.values.rolling(window=self.win).std()
-            # rolling.mean results in Nans in start seq. Here we fill these
-            win = min(self.win, new_track.values.shape[0])
-            for i in range(1, win):
-                mm = track.values[:i].rolling(window=i).mean()
-                ss = track.values[:i].rolling(window=i).std()
-                mean_df.iloc[i - 1] = mm.iloc[i - 1]
-                std_df.iloc[i - 1] = ss.iloc[i - 1]
-
-            std_df.iloc[0] = std_df.iloc[1]
-            # Append to
-            new_track.values = pd.concat([mean_df.add_suffix("_mean"), std_df.add_suffix("_std")], axis=1)
-            Q.append(new_track)
-        return Q
-
-    def inverse_transform(self, X, copy=None):
-        return X
-
-
-class FeatureCounter(BaseEstimator, TransformerMixin):
-    def __init__(self):
-        pass
-
-    def fit(self, X, y=None):
-        self.n_features = len(X[0].values.columns)
-
-        return self
-
-    def transform(self, X, y=None):
-        return X
-
-    def inverse_transform(self, X, copy=None):
-        return X
-
-
-# TODO: JointsSelector (x)
-# TODO: SegmentMaker
-# TODO: DynamicFeaturesAdder
-# TODO: ShapeFeaturesAdder
-# TODO: DataFrameNumpier (x)
-
-
-class TemplateTransform(BaseEstimator, TransformerMixin):
-    def __init__(self):
-        pass
-
-    def fit(self, X, y=None):
-        return self
-
-    def transform(self, X, y=None):
-        return X
diff --git a/spaces/shriarul5273/Yolov7/app.py b/spaces/shriarul5273/Yolov7/app.py
deleted file mode 100644
index 5e2a1da637389546b421b89bff13e637615bc4ad..0000000000000000000000000000000000000000
--- a/spaces/shriarul5273/Yolov7/app.py
+++ /dev/null
@@ -1,180 +0,0 @@
-import torch
-import gradio as gr
-import cv2
-import numpy as np
-import random
-import numpy as np
-from models.experimental import attempt_load
-from utils.general import check_img_size, non_max_suppression, \
-    scale_coords
-from utils.plots import plot_one_box
-from utils.torch_utils import time_synchronized
-import time
-
-
-
-def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleup=True, stride=32):
-    # Resize and pad image while meeting stride-multiple constraints
-    shape = im.shape[:2]  # current shape [height, width]
-    if isinstance(new_shape, int):
-        new_shape = (new_shape, new_shape)
-
-    # Scale ratio (new / old)
-    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-    if not scaleup:  # only scale down, do not scale up (for better val mAP)
-        r = min(r, 1.0)
-
-    # Compute padding
-    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
-    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
-
-    if auto:  # minimum rectangle
-        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
-
-    dw /= 2  # divide padding into 2 sides
-    dh /= 2
-
-    if shape[::-1] != new_unpad:  # resize
-        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
-    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
-    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
-    return im, r, (dw, dh)
-
-names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 
-         'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 
-         'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 
-         'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 
-         'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 
-         'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 
-         'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 
-         'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 
-         'hair drier', 'toothbrush']
-
-
-colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
-
-
-def detect(img,model,device,iou_threshold=0.45,confidence_threshold=0.25):   
-    imgsz = 640
-    img = np.array(img)
-    stride = int(model.stride.max())  # model stride
-    imgsz = check_img_size(imgsz, s=stride)  # check img_size
-
-    # Get names and colors
-    names = model.module.names if hasattr(model, 'module') else model.names
-
-    # Run inference
-    imgs = img.copy()  # for NMS
-    
-    image, ratio, dwdh = letterbox(img, auto=False)
-    image = image.transpose((2, 0, 1))
-    img = torch.from_numpy(image).to(device)
-    img = img.float()  # uint8 to fp16/32
-    img /= 255.0  # 0 - 255 to 0.0 - 1.0
-    if img.ndimension() == 3:
-        img = img.unsqueeze(0)
-
-
-    # Inference
-    t1 = time_synchronized()
-    start = time.time()
-    with torch.no_grad():   # Calculating gradients would cause a GPU memory leak
-        pred = model(img,augment=True)[0]
-    fps_inference = 1/(time.time()-start)
-    t2 = time_synchronized()
-
-    # Apply NMS
-    pred = non_max_suppression(pred, confidence_threshold, iou_threshold, classes=None, agnostic=True)
-    t3 = time_synchronized()
-
-    for i, det in enumerate(pred):  # detections per image
-        if len(det):
-            # Rescale boxes from img_size to im0 size
-            det[:, :4] = scale_coords(img.shape[2:], det[:, :4], imgs.shape).round()
-
-
-            # Write results
-            for *xyxy, conf, cls in reversed(det):
-                label = f'{names[int(cls)]} {conf:.2f}'
-                plot_one_box(xyxy, imgs, label=label, color=colors[int(cls)], line_thickness=2)
-
-    return imgs,fps_inference
-
-def inference(img,model_link,iou_threshold,confidence_threshold):
-    print(model_link)
-    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    # Load model
-    model_path = 'weights/'+str(model_link)+'.pt'
-    model = attempt_load(model_path, map_location=device) 
-    return detect(img,model,device,iou_threshold,confidence_threshold)
-
-
-def inference2(video,model_link,iou_threshold,confidence_threshold):
-    print(model_link)
-    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    # Load model
-    model_path = 'weights/'+str(model_link)+'.pt'
-    model = attempt_load(model_path, map_location=device) 
-    frames = cv2.VideoCapture(video)
-    fps = frames.get(cv2.CAP_PROP_FPS)
-    image_size = (int(frames.get(cv2.CAP_PROP_FRAME_WIDTH)),int(frames.get(cv2.CAP_PROP_FRAME_HEIGHT)))
-    finalVideo = cv2.VideoWriter('output.mp4',cv2.VideoWriter_fourcc(*'VP90'), fps, image_size)
-    fps_video = []
-    while frames.isOpened():
-        ret,frame = frames.read()
-        if not ret:
-            break
-        frame,fps = detect(frame,model,device,iou_threshold,confidence_threshold)
-        fps_video.append[fps]
-        finalVideo.write(frame)
-    frames.release()
-    finalVideo.release()
-    return 'output.mp4',np.mean(fps_video)
-
-
-
-examples_images = ['data/images/horses.jpg',
-            'data/images/bus.jpg',
-            'data/images/zidane.jpg']
-examples_videos = ['data/video/input_0.mp4','data/video/input_1.mp4'] 
-
-models = ['yolov7','yolov7x','yolov7-w6','yolov7-d6','yolov7-e6e']
-
-with gr.Blocks() as demo:
-    gr.Markdown("## YOLOv7 Inference")
-    with gr.Tab("Image"):
-        gr.Markdown("## YOLOv7 Inference on Image")
-        with gr.Row():
-            image_input = gr.Image(type='pil', label="Input Image", source="upload")
-            image_output = gr.Image(type='pil', label="Output Image", source="upload")
-        fps_image = gr.Number(0,label='FPS')
-        image_drop = gr.Dropdown(choices=models,value=models[0])
-        image_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
-        image_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
-        gr.Examples(examples=examples_images,inputs=image_input,outputs=image_output)
-        text_button = gr.Button("Detect")
-    with gr.Tab("Video"):
-        gr.Markdown("## YOLOv7 Inference on Video")
-        with gr.Row():
-            video_input = gr.Video(type='pil', label="Input Image", source="upload")
-            video_output = gr.Video(type="pil", label="Output Image",format="mp4")
-        fps_video = gr.Number(0,label='FPS')
-        video_drop = gr.Dropdown(choices=models,value=models[0])
-        video_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
-        video_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
-        gr.Examples(examples=examples_videos,inputs=video_input,outputs=video_output)
-        video_button = gr.Button("Detect")
-    
-    with gr.Tab("Webcam Video"):
-        gr.Markdown("## YOLOv7 Inference on Webcam Video")
-        gr.Markdown("Coming Soon")
-
-    text_button.click(inference, inputs=[image_input,image_drop,
-                                         image_iou_threshold,image_conf_threshold],
-                                        outputs=[image_output,fps_image])
-    video_button.click(inference2, inputs=[video_input,video_drop,
-                                           video_iou_threshold,video_conf_threshold],            
-                                        outputs=[video_output,fps_video])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/silencewing/server/youyou/.history/game_20230613230242.html b/spaces/silencewing/server/youyou/.history/game_20230613230242.html
deleted file mode 100644
index ea821291bececa0c7e1e5327e380df34baa6dbd0..0000000000000000000000000000000000000000
--- a/spaces/silencewing/server/youyou/.history/game_20230613230242.html
+++ /dev/null
@@ -1,351 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-  <meta charset="UTF-8">
-  <meta http-equiv="X-UA-Compatible" content="IE=edge">
-  <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  <title>转盘抽奖</title>
-  <style>
-    *,
-    *::before,
-    *::after {
-      margin: 0;
-      padding: 0;
-      box-sizing: border-box;
-    }
-
-    html {
-      height: 100%;
-      background: #264563;
-    }
-
-    .luckdraw {
-      width: 300px;
-      height: 300px;
-      position: relative;
-      margin: 100px auto;
-      border-radius: 50%;
-      overflow: hidden;
-    }
-
-    .luckpanel {
-      position: relative;
-      width: 100%;
-      height: 100%;
-
-      transition: transform 3s ease-in-out;
-    }
-
-    #canvas {
-      position: relative;
-      width: 100%;
-      height: 100%;
-    }
-
-    #canvas .sector-item {
-      position: absolute;
-      top: 0;
-      left: 0;
-      width: 50%;
-      height: 50%;
-      transform-origin: right bottom;
-      overflow: hidden;
-    }
-
-    /* #canvas .sector-item:nth-child(odd) {
-      background: pink;
-    }
-
-    #canvas .sector-item:nth-child(even) {
-      background: skyblue;
-    } */
-
-
-    .prize {
-      position: absolute;
-      top: 0;
-      left: 50%;
-      width: 50%;
-      height: 50%;
-      transform: translateX(-50%);
-    }
-
-    .prize-item {
-      position: absolute;
-      top: 0;
-      left: 0;
-      width: 100%;
-      height: 100%;
-      transform-origin: center bottom;
-    }
-
-    .prize-item__name {
-      position: absolute;
-      top: 20px;
-      left: 10px;
-      width: calc(100% - 20px);
-      font-size: 12px;
-      text-align: center;
-      color: #ff572f;
-    }
-
-    .prize-item__img {
-      position: absolute;
-      top: 50px;
-      left: calc(50% - 30px /2);
-      width: 30px;
-      height: 30px;
-    }
-
-    .prize-item__img img {
-      width: 100%;
-      height: 100%;
-      vertical-align: bottom;
-    }
-
-    /* 抽奖 */
-    .pointer {
-      position: absolute;
-      left: 50%;
-      top: 50%;
-      transform: translate(-50%, -50%);
-      width: 90px;
-      height: 90px;
-      background-color: orange;
-      border-radius: 50%;
-      display: flex;
-      justify-content: center;
-      align-items: center;
-      font-size: 30px;
-      font-weight: bold;
-      cursor: pointer;
-      user-select: none;
-    }
-
-    .pointer::after {
-      content: '';
-      position: absolute;
-      top: -70px;
-      border: 40px solid orange;
-      border-left-width: 10px;
-      border-right-width: 10px;
-      border-left-color: transparent;
-      border-top-color: transparent;
-      border-right-color: transparent;
-    }
-  </style>
-</head>
-
-<body>
-  <div class="luckdraw">
-    <div class="luckpanel">
-      <div id="canvas"></div>
-      <div class="prize">
-        <!-- <div class="prize-item">
-          <div class="prize-item__name">奖品</div>
-          <div class="prize-item__img">
-            <img
-              src="https://gimg2.baidu.com/image_search/src=http%3A%2F%2Fimage.it168.com%2Fn%2F640x480%2F6%2F6414%2F6414197.jpg&refer=http%3A%2F%2Fimage.it168.com&app=2002&size=f9999,10000&q=a80&n=0&g=0n&fmt=jpeg?sec=1640667263&t=18bb80a694a5ff966713191b4a81745d"
-              alt="">
-          </div>
-        </div> -->
-      </div>
-    </div>
-    <div class="pointer">抽奖</div>
-  </div>
-  <script>
-    const consts = {
-      // 奖品清单
-      prizeList: [
-        // {
-        //   prizeName: '休息多5分钟',
-        //   // prizeImg: 'https://gimg2.baidu.com/image_search/src=http%3A%2F%2Fimage.it168.com%2Fn%2F640x480%2F6%2F6414%2F6414197.jpg&refer=http%3A%2F%2Fimage.it168.com&app=2002&size=f9999,10000&q=a80&n=0&g=0n&fmt=jpeg?sec=1640667263&t=18bb80a694a5ff966713191b4a81745d',
-        //   count: 10,
-        // },
-        // {
-        //   prizeName: '休息多10分钟',
-        //   // prizeImg: 'https://gimg2.baidu.com/image_search/src=http%3A%2F%2Fimage.it168.com%2Fn%2F640x480%2F6%2F6414%2F6414197.jpg&refer=http%3A%2F%2Fimage.it168.com&app=2002&size=f9999,10000&q=a80&n=0&g=0n&fmt=jpeg?sec=1640667263&t=18bb80a694a5ff966713191b4a81745d',
-        //   count: 5,
-        // },
-        {
-          prizeName: '托马斯1集',
-          // prizeImg: 'https://gimg2.baidu.com/image_search/src=http%3A%2F%2Fimage.it168.com%2Fn%2F640x480%2F6%2F6414%2F6414197.jpg&refer=http%3A%2F%2Fimage.it168.com&app=2002&size=f9999,10000&q=a80&n=0&g=0n&fmt=jpeg?sec=1640667263&t=18bb80a694a5ff966713191b4a81745d',
-          count: 6,
-        },
-        {
-          prizeName: '丁丁1集',
-          // prizeImg: 'https://gimg2.baidu.com/image_search/src=http%3A%2F%2Fimage.it168.com%2Fn%2F640x480%2F6%2F6414%2F6414197.jpg&refer=http%3A%2F%2Fimage.it168.com&app=2002&size=f9999,10000&q=a80&n=0&g=0n&fmt=jpeg?sec=1640667263&t=18bb80a694a5ff966713191b4a81745d',
-          count: 3,
-        },
-        {
-          prizeName: '小熊15分钟',
-          // prizeImg: 'https://gimg2.baidu.com/image_search/src=http%3A%2F%2Fimage.it168.com%2Fn%2F640x480%2F6%2F6414%2F6414197.jpg&refer=http%3A%2F%2Fimage.it168.com&app=2002&size=f9999,10000&q=a80&n=0&g=0n&fmt=jpeg?sec=1640667263&t=18bb80a694a5ff966713191b4a81745d',
-          count: 5,
-        },
-        {
-          prizeName: '读书写字画画10分钟',
-          // prizeImg: 'https://gimg2.baidu.com/image_search/src=http%3A%2F%2Fimage.it168.com%2Fn%2F640x480%2F6%2F6414%2F6414197.jpg&refer=http%3A%2F%2Fimage.it168.com&app=2002&size=f9999,10000&q=a80&n=0&g=0n&fmt=jpeg?sec=1640667263&t=18bb80a694a5ff966713191b4a81745d',
-          count: 2,
-        }
-      ],
-      // 每一块扇形的背景色
-      prizeBgColors: [
-        'rgb(255,231,149)',
-        'rgb(255,247,223)',
-        'rgb(255,231,149)',
-        'rgb(255,247,223)',
-        'rgb(255,231,149)',
-        'rgb(255,247,223)',
-        'rgb(255,231,149)',
-        'rgb(255,247,223)',
-      ],
-      // 每一块扇形的外边框颜色
-      borderColor: '#ff9800'
-    }
-
-    const prizeNum = consts.prizeList.length
-    let itemAngle = 360 / prizeNum     // 每个扇形区域的角度
-    const offsetAngle = itemAngle / 2
-    let isRotating = false
-    const circleCount = 3 // 旋转圈数
-    const rotateDuration = 3 // 持续时间
-    const panel = document.querySelector('.luckpanel')
-
-    // 画出扇形骨架
-    function drawPanel() {
-      let fragMent = document.createDocumentFragment()
-      consts.prizeList.forEach((item, index) => {
-        let itemDom = document.createElement('div')
-        itemDom.setAttribute('class', 'sector-item')
-        itemDom.style.background = `${consts.prizeBgColors[index]}`
-        itemDom.style.borderBottom = `1px solid ${consts.borderColor}`
-        itemDom.style.transform = `rotate(${itemAngle * (index + 1)}deg) skewY(${90 - itemAngle}deg)`
-        fragMent.appendChild(itemDom)
-      })
-      document.getElementById('canvas').appendChild(fragMent)
-    }
-
-    function getPrizeItem({ name, src }) {
-      const el = document.createElement('div')
-      let tpl = ''
-      if(src){
-        tpl = `
-          <div class="prize-item">
-            <div class="prize-item__name">${name}</div>
-            <div class="prize-item__img">
-              <img
-                src="${src}"
-                alt="">
-            </div>
-          </div>
-        `
-      }
-      else{
-        tpl = `
-          <div class="prize-item">
-            <div class="prize-item__name">${name}</div>
-            <div class="prize-item__img">
-            </div>
-          </div>
-        `
-      }
-      el.innerHTML = tpl
-      return el.firstElementChild
-    }
-    // 填充奖品内容
-    function fillPrize() {
-      const container = document.querySelector('.prize')
-      consts.prizeList.forEach((item, i) => {
-        const el = getPrizeItem({
-          name: item.prizeName,
-          src: item.prizeImg
-        })
-        // 旋转
-        const currentAngle = itemAngle * i + offsetAngle
-        el.style.transform = `rotate(${currentAngle}deg)`
-        container.appendChild(el)
-      })
-    }
-    // 获得哪一个奖品
-    function getPrizeIndex(prizeList) {
-      // 中奖的是哪一个奖品
-      let index = 0
-      // 当前奖品总数量
-      let prizeTotalNum = 0
-      for (let i = 0; i < prizeList.length; i++) {
-        prizeTotalNum += prizeList[i].count
-      }
-      if (prizeTotalNum === 0) {
-        alert('奖品已抽完');
-        return index = -1
-      }
-      // 产生一个随机数 0-总数
-      let random = parseInt(Math.random() * prizeTotalNum)
-      // 当前奖品的概率区间
-      let currentWeight = 0
-      for (let i = 0; i < prizeList.length; i++) {
-        currentWeight += prizeList[i].count
-        if (random < currentWeight) {
-          index = i
-          prizeList[i].count--
-          break
-        }
-      }
-      return index
-    }
-    // 抽奖事件
-    function bindEvent() {
-      document.querySelector('.pointer').addEventListener('click', function () {
-        if (isRotating) {
-          return
-        } else {
-          isRotating = true
-        }
-        const index = getPrizeIndex(consts.prizeList)
-        console.log('index', index);
-        if (index === -1) {
-          isRotating = false
-        } else {
-          console.log('奖品名称', consts.prizeList[index].prizeName);
-          rotate(index)
-        }
-      })
-    }
-    // 旋转转盘
-    let statrtRotatAngle = 0
-    function rotate(index) {
-      // statrtRotatAngle % 360 上一次旋转到index的度数
-      // statrtRotatAngle - statrtRotatAngle % 360  得到一个度数为 360*n
-      // 旋转到索引index商品的度数
-      let angleToIndex = 360 - (offsetAngle + itemAngle * index)
-      const rotateAngle = statrtRotatAngle - statrtRotatAngle % 360 + circleCount * 360 + angleToIndex
-      statrtRotatAngle = rotateAngle
-      panel.style.transform = `rotate(${rotateAngle}deg)`
-      panel.style.transitionDuration = `${rotateDuration}s`
-      setTimeout(() => {
-        isRotating = false
-      }, 1000)
-    }
-
-
-    function init() {
-      // 画出扇形骨架
-      drawPanel()
-      // 填充奖品内容
-      fillPrize()
-      // 抽奖事件
-      bindEvent()
-
-      document.onkeydown = function (event) {
-        if(event.key === 'm'){
-          window.location.href = 'math.html'
-        }
-      }
-    }
-    document.addEventListener('DOMContentLoaded', init)
-
-  </script>
-</body>
-
-</html>
\ No newline at end of file
diff --git a/spaces/simonduerr/ProteinMPNN/ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_2.sh b/spaces/simonduerr/ProteinMPNN/ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_2.sh
deleted file mode 100644
index b001a4eb9625d8a8a83192364f9ad6ff07c4dddf..0000000000000000000000000000000000000000
--- a/spaces/simonduerr/ProteinMPNN/ProteinMPNN/vanilla_proteinmpnn/examples/submit_example_2.sh
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/bin/bash
-#SBATCH -p gpu
-#SBATCH --mem=32g
-#SBATCH --gres=gpu:rtx2080:1
-#SBATCH -c 2
-#SBATCH --output=example_2.out
-
-source activate mlfold
-
-folder_with_pdbs="../PDB_complexes/pdbs/"
-
-output_dir="../PDB_complexes/example_2_outputs"
-if [ ! -d $output_dir ] 
-then
-    mkdir -p $output_dir
-fi
-
-path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
-path_for_assigned_chains=$output_dir"/assigned_pdbs.jsonl"
-chains_to_design="A B"
-
-python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
-
-python ../helper_scripts/assign_fixed_chains.py --input_path=$path_for_parsed_chains --output_path=$path_for_assigned_chains --chain_list "$chains_to_design"
-
-python ../protein_mpnn_run.py \
-        --jsonl_path $path_for_parsed_chains \
-        --chain_id_jsonl $path_for_assigned_chains \
-        --out_folder $output_dir \
-        --num_seq_per_target 2 \
-        --sampling_temp "0.1" \
-        --batch_size 1
diff --git a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/model/tf/utils.py b/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/model/tf/utils.py
deleted file mode 100644
index fc40a2ceb2de1c2d56c17697393713804d7da350..0000000000000000000000000000000000000000
--- a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/model/tf/utils.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright 2021 DeepMind Technologies Limited
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Shared utilities for various components."""
-import tensorflow.compat.v1 as tf
-
-
-def tf_combine_mask(*masks):
-  """Take the intersection of float-valued masks."""
-  ret = 1
-  for m in masks:
-    ret *= m
-  return ret
-
-
-class SeedMaker(object):
-  """Return unique seeds."""
-
-  def __init__(self, initial_seed=0):
-    self.next_seed = initial_seed
-
-  def __call__(self):
-    i = self.next_seed
-    self.next_seed += 1
-    return i
-
-seed_maker = SeedMaker()
-
-
-def make_random_seed():
-  return tf.random.uniform([2],
-                           tf.int32.min,
-                           tf.int32.max,
-                           tf.int32,
-                           seed=seed_maker())
-
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/ARK Survival Evolved - How to Enjoy the Ultimate Survival and Adventure Game with Unlimited Amber MOD APK 2.0.28.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/ARK Survival Evolved - How to Enjoy the Ultimate Survival and Adventure Game with Unlimited Amber MOD APK 2.0.28.md
deleted file mode 100644
index 8f34a74e6000d4bc3982e8c593fc773b255e5941..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/ARK Survival Evolved - How to Enjoy the Ultimate Survival and Adventure Game with Unlimited Amber MOD APK 2.0.28.md	
+++ /dev/null
@@ -1,99 +0,0 @@
-
-<h1>ARK Survival Evolved APK Unlimited Amber: How to Download and Play</h1>
-<p>Are you looking for the ultimate survival and adventure gameplay on your mobile devices? Are you intrigued by the magnificent dinosaurs and other prehistoric creatures? Do you want to enjoy unlimited resources and features in your game? If you answered yes to any of these questions, then you should try ARK Survival Evolved APK Unlimited Amber. This is a modded version of the popular game ARK Survival Evolved that gives you access to unlimited amber, the premium currency in the game. In this article, we will tell you what ARK Survival Evolved is, why you need unlimited amber, how to download and install the modded APK file, and what are the benefits of playing this game. Let's get started!</p>
-<h2>ark survival evolved apk unlimited amber</h2><br /><p><b><b>Download File</b> ->>> <a href="https://ssurll.com/2uNWTm">https://ssurll.com/2uNWTm</a></b></p><br /><br />
- <h2>What is ARK Survival Evolved?</h2>
-<h3>A survival and adventure game with dinosaurs and prehistoric creatures</h3>
-<p>ARK Survival Evolved is a game that lets you experience a thrilling adventure in a world where dinosaurs and other prehistoric creatures roam freely. You start as a naked and stranded survivor on a mysterious island called ARK, where you have to craft, build, hunt, harvest, and survive. You can also tame, breed, and ride over 80 different dinosaurs and other creatures, from the fearsome Tyrannosaurus Rex to the majestic Pteranodon. You can explore a vast open world with stunning graphics and realistic physics, where you will encounter various challenges and threats, such as weather changes, natural disasters, hostile wildlife, enemy tribes, and more.</p>
- <h3>A mobile version with modded features and unlimited resources</h3>
-<p>ARK Survival Evolved is also available as a mobile version for Android and iOS devices. The mobile version has some differences from the PC and console versions, such as simplified controls, reduced graphics quality, smaller map size, fewer creatures, and limited multiplayer options. However, it still offers a fun and immersive gameplay that can keep you hooked for hours. Moreover, there are modded versions of the mobile game that offer additional features and resources that are not available in the original game. One of these modded versions is ARK Survival Evolved APK Unlimited Amber, which gives you access to unlimited amber, the premium currency in the game.</p>
- <h2>Why do you need unlimited amber in ARK Survival Evolved?</h2>
-<h3>Amber is the premium currency in the game</h3>
-<p>Amber is a rare and valuable resource that can be used for various purposes in ARK Survival Evolved. You can obtain amber by watching ads, completing offers, finding it in chests or caves, or buying it with real money. However, amber is very scarce and expensive, so you may not be able to get enough of it to enjoy all the benefits it offers.</p>
- <h3>Amber can be used to buy items, upgrade structures, revive creatures, and more</h3>
-<p>With amber, you can buy items such as weapons, armor, tools, consumables, skins, hairstyles, emotes, and more. You can also use amber to upgrade your structures such as the installation. Tap on Install or Next to continue.</li>
-<li>Wait for the installation process to finish. You can check the progress bar or notification panel to see how much time is left.</li>
-<li>Once the installation process is finished, you can launch the game by tapping on Open or by finding the game icon on your device home screen or app drawer.</li>
-</ul>
-<p>Congratulations! You have successfully downloaded and installed ARK Survival Evolved APK Unlimited Amber on your device. You can now enjoy unlimited amber and other modded features in your game.</p>
- <h2>What are the benefits of playing ARK Survival Evolved APK Unlimited Amber?</h2>
-<h3>Explore a vast open world with stunning graphics and realistic physics</h3>
-<p>One of the benefits of playing ARK Survival Evolved APK Unlimited Amber is that you can explore a vast open world with stunning graphics and realistic physics. The game offers a beautiful and immersive environment that will make you feel like you are in a real prehistoric world. You can see the sun, moon, stars, clouds, rain, snow, fog, and other weather effects. You can also interact with the terrain, water, plants, rocks, trees, and other objects. You can also witness the day and night cycle, the seasons, and the lunar phases. The game also has a dynamic lighting and shadow system that creates realistic effects and atmosphere. The game also has a realistic physics system that affects the movement and behavior of the creatures and objects in the game. You can see how they react to gravity, inertia, momentum, friction, collision, and other forces.</p>
- <h3>Craft, build, and survive in a harsh environment with various challenges and threats</h3>
-<p>Another benefit of playing ARK Survival Evolved APK Unlimited Amber is that you can craft, build, and survive in a harsh environment with various challenges and threats. The game offers a challenging and rewarding gameplay that will test your skills and creativity. You have to craft items such as weapons, armor, tools, consumables, and more using the resources you gather from the environment or from the creatures you kill. You have to build structures such as shelters, bases, farms, traps, defenses, and more using the materials you collect from the environment or from the creatures you tame. You have to survive in a harsh environment that has various hazards such as hunger, thirst, temperature, radiation, diseases, poisons, and more. You have to face various threats such as hostile wildlife, enemy tribes, natural disasters, and more.</p>
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- <h3>Tame, breed, and ride over 80 different dinosaurs and other creatures</h3>
-<p>A third benefit of playing ARK Survival Evolved APK Unlimited Amber is that you can tame, breed, and ride over 80 different dinosaurs and other creatures. The game offers a unique and fun feature that allows you to interact with the magnificent dinosaurs and other prehistoric creatures that roam freely in the game world. You can tame them by feeding them their favorite food or by knocking them out and placing a saddle on them. You can breed them by mating them with another creature of the same species or by using an incubator or a hatchery. You can ride them by mounting them on their back or by using a platform saddle that allows you to build structures on them. You can also use them for various purposes such as combat, transportation, hunting, harvesting, and more. You can also customize them with skins, colors, accessories, and more.</p>
- <h3>Join a tribe and cooperate or compete with other players online</h3>
-<p>A fourth benefit of playing ARK Survival Evolved APK Unlimited Amber is that you can join a tribe and cooperate or compete with other players online. The game offers a multiplayer mode that allows you to play with or against other players from around the world. You can join a tribe, which is a group of players that share resources, structures, creatures, and chat. You can cooperate with your tribe members to survive, build, tame, breed, and fight together. You can also compete with other tribes for territory, resources, creatures, and glory. You can engage in PvP battles, raids, wars, alliances, and more. You can also communicate with other players using voice chat or text chat.</p>
- <h2>Conclusion</h2>
-<p>ARK Survival Evolved APK Unlimited Amber is a modded version of the popular game ARK Survival Evolved that gives you access to unlimited amber, the premium currency in the game. With unlimited amber, you can enjoy various benefits such as buying items, upgrading structures, reviving creatures, speeding up processes, and more. You can also explore a vast open world with stunning graphics and realistic physics, craft, build, and survive in a harsh environment with various challenges and threats, tame, breed, and ride over 80 different dinosaurs and other creatures, and join a tribe and cooperate or compete with other players online. To download and install ARK Survival Evolved APK Unlimited Amber on your device, you need to find a reliable source for the modded APK file, enable unknown sources on your device settings, and download and install the APK file. If you are looking for the ultimate survival and adventure gameplay on your mobile devices, you should try ARK Survival Evolved APK Unlimited Amber today!</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>Is ARK Survival Evolved APK Unlimited Amber safe to use?</b></li>
-<p>ARK Survival Evolved APK Unlimited Amber is safe to use as long as you download it from a reliable source that does not contain any viruses, malware, spyware, or other harmful software. However, you should be aware that using modded versions of the game may violate the terms of service of the original game developers and may result in bans or penalties. Therefore, you should use ARK Survival Evolved APK Unlimited Amber at your own risk and discretion.</p>
-<li><b>Is ARK Survival Evolved APK Unlimited Amber compatible with my device?</b></li>
-<p>ARK Survival Evolved APK Unlimited Amber is compatible with most Android devices that have Android 7.0 or higher operating system. However, some devices may not support the modded features or may experience performance issues due to the high graphics quality and resource consumption of the game. Therefore, you should check the compatibility and requirements of the modded APK file before downloading and installing it on your device.</p>
-<li><b>Can I play ARK Survival Evolved APK Unlimited Amber offline?</b></li>
-<p>ARK Survival Evolved APK Unlimited Amber can be played offline in single-player mode or local multiplayer mode. However, some features such as online multiplayer mode, cloud saving, online chat, and online updates may not be available or may not work properly in offline mode. Therefore, you may need an internet connection to access and enjoy all the features and functions of the game.</p>
-<li><b>How can I update ARK Survival Evolved APK Unlimited Amber?</b></li>
-<p>ARK Survival Evolved APK Unlimited Amber may not receive automatic updates from the official game developers, so you may need to manually update it from the source where you downloaded it. To update ARK Survival Evolved APK Unlimited Amber, you need to follow these steps:</p>
-<ul>
-<li>Go to the website or platform where you downloaded the modded APK file.</li>
-<li>Look for the latest version of the modded APK file and tap on the download button or link.</li>
-<li>Follow the same steps as downloading and installing the modded APK file.</li>
-<li>You may need to uninstall the previous version of the modded APK file before installing the new one.</li>
-<li>You may also need to back up your game data before updating to avoid losing your progress and settings.</li>
-</ul>
-<p>Alternatively, you can also check for updates from within the game by tapping on the settings icon and then on the check for updates button. However, this may not work for some modded versions of the game.</p>
-<li><b>Can I play ARK Survival Evolved APK Unlimited Amber with my friends?</b></li>
-<p>Yes, you can play ARK Survival Evolved APK Unlimited Amber with your friends in multiplayer mode. You can either join an existing online server or create your own private server. You can also play with your friends in local multiplayer mode by using a Wi-Fi or Bluetooth connection. However, you and your friends may need to have the same version of the modded APK file to play together. You may also encounter some issues or errors when playing with other players who have different versions of the game or who have not installed the modded APK file.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
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+++ /dev/null
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-<br />
-<h1>DJMAX Technika Q: How to Unlock All Songs with APK Mod</h1>
-<p>If you are a fan of rhythm games, you might have heard of DJMAX Technika Q, a mobile game that lets you enjoy the music and gameplay of the popular arcade series DJMAX. In this article, we will show you how to unlock all songs in DJMAX Technika Q with an APK mod, so you can play any song you want without spending money or waiting for updates.</p>
- <h2>What is DJMAX Technika Q?</h2>
-<p>DJMAX Technika Q is a rhythm game developed by NEOWIZ and Planetboom, based on the arcade game DJMAX Technika. It was released for Android and iOS devices in 2014, and has been updated regularly with new songs and features. The game has over 200 songs from various genres, such as pop, rock, electronic, classical, jazz, and more. You can also customize your gameplay with different skins, notes, effects, and modes.</p>
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-<p>The game uses a touch screen interface, where you have to tap, slide, hold, or flick notes that appear on the screen according to the music. The game has four difficulty levels: Pop Mixing, Club Mixing, Crew Challenge, and Freestyle. You can also compete with other players online or challenge yourself with missions and achievements.</p>
- <h2>Why do you need an APK mod to unlock all songs?</h2>
-<p>While DJMAX Technika Q is free to download and play, it has some limitations that might frustrate some players. For example, you can only play a limited number of songs per day, unless you pay for premium tickets or watch ads. You also have to wait for new songs to be added to the game through updates, which might take a long time or never happen at all. Some songs are also exclusive to certain regions or events, making them inaccessible to most players.</p>
-<p>That's why some players prefer to use an APK mod for DJMAX Technika Q, which is a modified version of the game that bypasses these restrictions and gives you access to all songs in the game. With an APK mod, you can play any song you want without spending money or waiting for updates. You can also enjoy some extra features that are not available in the official version, such as offline mode, custom playlists, song previews, and more.</p>
- <h2>How to download and install the APK mod for DJMAX Technika Q?</h2>
-<p>If you want to try out the APK mod for DJMAX Technika Q, you will need an Android device that can run the game and a file manager app that can install APK files. You will also need to enable unknown sources in your device settings, so you can install apps from outside the Google Play Store. Here are the steps to download and install the APK mod for DJMAX Technika Q:</p>
-<ol>
-<li>Go to this link and download the latest version of the APK mod for DJMAX Technika Q. The file size is about 500 MB.</li>
-<li>Open your file manager app and locate the downloaded APK file. Tap on it and follow the instructions to install it on your device.</li>
-<li>Once the installation is complete, launch the app and grant it any permissions it asks for.</li>
-<li>You will see a screen with some options and settings for the APK mod. You can adjust them according to your preferences or leave them as they are.</li>
-<li>Tap on Start Game and enjoy all songs in DJMAX Technika Q.</li>
-</ol>
- <h2>How to use the APK mod to access all songs in DJMAX Technika Q?</h2>
-<p>After you have installed and launched the APK mod for DJMAX Technika Q, you will be able to access all songs in the game without any restrictions. Here are some tips on how to use the APK mod to enjoy the game:</p>
-<ul>
-<li>To play a song, tap on the Music icon on the bottom left corner of the screen. You will see a list of all songs in the game, sorted by genre, difficulty, or alphabetically. You can also use the search bar to find a specific song by name or artist.</li>
-<li>To change the difficulty level, tap on the Difficulty icon on the bottom right corner of the screen. You can choose from Pop Mixing, Club Mixing, Crew Challenge, or Freestyle. You can also adjust the speed and note size of the game.</li>
-<li>To create a custom playlist, tap on the Playlist icon on the top right corner of the screen. You can add up to 10 songs to your playlist and play them in order or shuffle them. You can also save your playlist and load it later.</li>
-<li>To preview a song before playing it, tap on the Preview icon on the top left corner of the screen. You can listen to a short clip of the song and see its information, such as title, artist, genre, difficulty, and BPM.</li>
-<li>To play offline, tap on the Offline icon on the top center of the screen. You can download any song you want to your device and play it without an internet connection. You can also delete any song you don't want to save space.</li>
-</ul>
- <h2>What are some of the best songs in DJMAX Technika Q?</h2>
-<p>With over 200 songs in DJMAX Technika Q, you might be wondering which ones are worth playing. Of course, this depends on your personal taste and preference, but here are some of the best songs in DJMAX Technika Q according to various criteria:</p>
- <table>
-<tr>
-<th>Criteria</th>
-<th>Song</th>
-<th>Artist</th>
-<th>Genre</th>
-<th>Difficulty</th>
-</tr>
-<tr>
-<td>Most popular</td>
-<td>Ask to Wind</td>
-<td>Forte Escape</td>
-<td>Trance Pop</td>
-<td>6/8/10/12</td>
-</tr>
-<tr>
-<td>Most challenging</td>
-<td>Fermion</td>
-<td>Makou</td>
-<td>Nu Skool Breaks</td>
-<td>7/9/12/14</td>
-</tr>
-<tr>
-<td>Most catchy</td>
-<td>Sunny Side (Remix)</td>
-<td>Croove feat. Kim Tae Hyeon</td>
-<td>K-Pop Dance Remix</td>
-<td>5/7/9/11</td>
-</tr>
-<tr>
-<td>Most relaxing</td>
-<td>Landscape</td>
-<td>Tsukasa</td>
-<td>New Age Piano</td>
-<td>3/5/7/9</td>
-</tr>
-<tr>
-<td>Most unique</td>
-<td>Blythe</td>
-<td>M2U feat. Nicode & Lucyana Zanetta</td>
-<td>Symphonic Rave Rock</td>
-<td>6/8/10/12</td> </tr>
-</table>
- <h2>Conclusion</h2>
-<p>DJMAX Technika Q is a fun and addictive rhythm game that offers a variety of songs and gameplay options. However, if you want to unlock all songs in the game without spending money or waiting for updates, you might want to try the APK mod for DJMAX Technika Q. This mod allows you to access all songs in the game and enjoy some extra features that are not available in the official version. You can download and install the APK mod easily by following the steps in this article. You can also use the APK mod to play offline, create custom playlists, preview songs, and more. With the APK mod, you can enjoy DJMAX Technika Q to the fullest and discover some of the best songs in the game.</p>
-<p>If you liked this article, please share it with your friends and leave a comment below. You can also check out our other articles on similar topics, such as how to play DJMAX Respect on PC, how to download DJMAX Trilogy for free, and how to get DJMAX Portable 3 on PSP. Thanks for reading and happy gaming!</p>
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- <h2>FAQs</h2>
-<ul>
-<li>Q: Is the APK mod for DJMAX Technika Q safe to use?</li>
-<li>A: The APK mod for DJMAX Technika Q is safe to use as long as you download it from a trusted source and scan it with an antivirus app before installing it. However, you should be aware that using the APK mod might violate the terms of service of the game and result in a ban or suspension of your account. Use the APK mod at your own risk and discretion.</li>
-<li>Q: Can I play DJMAX Technika Q on iOS devices?</li>
-<li>A: Yes, you can play DJMAX Technika Q on iOS devices by downloading it from the App Store. However, you will not be able to use the APK mod on iOS devices, as it is only compatible with Android devices. If you want to unlock all songs on iOS devices, you will have to pay for premium tickets or watch ads.</li>
-<li>Q: Can I play DJMAX Technika Q with a controller or keyboard?</li>
-<li>A: No, you cannot play DJMAX Technika Q with a controller or keyboard, as it is designed for touch screen devices only. You will have to use your fingers to tap, slide, hold, or flick notes on the screen according to the music.</li>
-<li>Q: How can I update DJMAX Technika Q to the latest version?</li>
-<li>A: If you are using the official version of DJMAX Technika Q, you can update it by going to the Google Play Store and tapping on Update. If you are using the APK mod for DJMAX Technika Q, you will have to download and install the latest version of the APK mod from this link whenever there is a new update.</li>
-<li>Q: How can I contact the developers of DJMAX Technika Q?</li>
-<li>A: You can contact the developers of DJMAX Technika Q by visiting their official website, Facebook page, Twitter account, or YouTube channel. You can also send them an email at support@neowiz.com or leave a review on the Google Play Store or App Store.</li>
-</ul></p> 401be4b1e0<br />
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-
-<h1>Among Us APK Yükle: How to Download and Install Among Us on Your Android Device</h1>
-<p>Among Us is a multiplayer social deduction game that has taken the gaming world by storm. It is a fun and addictive game where you have to work together with other crewmates to prepare your spaceship for departure, but watch out for the impostors who are trying to kill everyone. You can play online or over local WiFi with up to 15 players, each with their own roles, tasks, and customization options. You can also choose between different game modes, maps, and settings to spice up your gameplay.</p>
-<p>But what if you want to play Among Us on your Android device and you can't find it on the Google Play Store? Or what if you want to get the latest version of the game before it is officially released? Or what if you want to install the game on a device that doesn't have access to the Play Store? In that case, you will need to download and install an APK file.</p>
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-<p>An APK file is an application package file that contains all the files and data needed to run an Android app. It is similar to an EXE file on Windows or a DMG file on Mac. You can download APK files from various websites that offer them, but you have to be careful about the source and the content of the file. Some APK files may contain malware or viruses that can harm your device or steal your personal information. That's why you should only download APK files from reputable sources that are verified by other users.</p>
-<p>In this article, we will show you how to download and install Among Us APK file on your Android device safely and easily. We will also tell you about some of the features of the game that make it so popular and fun. Let's get started!</p>
- <h2>How to Download Among Us APK File from a Reputable Source</h2>
-<p>The first step to install Among Us on your Android device is to download the APK file from a reputable source. There are many websites that offer APK files for various apps and games, but not all of them are trustworthy. Some of them may have outdated or modified versions of the apps that may not work properly or may contain malware. That's why you should always check the reviews, ratings, and comments of other users before downloading any APK file.</p>
-<p>One of the most reliable sources for downloading APK files is <a href="(^1^)">APKCombo</a>. This website has a large collection of APK files for different apps and games, including Among Us. You can find the latest version of Among Us APK file on this website, as well as older versions if you prefer. You can also see the details of each version, such as the size, date, developer, permissions, and changelog. You can also scan the QR code or use the direct link to download the APK file to your device.</p>
-<p>To download Among Us APK file from APKCombo, follow these steps:</p>
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-<ol>
-<li>Go to <a href="(^1^)">https://apkcombo.com/among-us/com.innersloth.spacemafia/</a> on your browser.</ <li>Select the version of Among Us APK file that you want to download. You can choose the latest version or any previous version that you like.</li>
-<li>Tap on the "Download APK" button and wait for the file to be downloaded to your device. You can see the progress of the download on the notification bar.</li>
-<li>Once the download is complete, you can proceed to install the APK file on your device.</li>
-</ol>
-<p>You can also download Among Us APK file from other trusted websites, such as <a href="">APKPure</a>, <a href="">APKMody</a>, or <a href="">Uptodown</a>. Just make sure to check the reviews and ratings of the websites and the files before downloading them.</p>
- <h2>How to Install Among Us APK File on Your Android Device</h2>
-<p>The next step to install Among Us on your Android device is to install the APK file that you have downloaded. However, before you can do that, you need to enable the option to allow unknown apps on your device. This option lets you install apps from sources other than the Google Play Store. To enable this option, follow these steps:</p>
-<ol>
-<li>Go to your device's settings and tap on "Apps and notifications" or "Security and privacy" depending on your device model.</li>
-<li>Tap on "Advanced" or "More settings" and then tap on "Special app access" or "Install unknown apps".</li>
-<li>Find and tap on the browser app that you used to download the APK file, such as Chrome, Firefox, or Opera.</li>
-<li>Toggle on the switch that says "Allow from this source" or "Install unknown apps".</li>
-</ol>
-<p>Now you are ready to install the APK file on your device. To do that, follow these steps:</p>
-<ol>
-<li>Open the file manager app that you have installed on your device, such as Files, ES File Explorer, or Solid Explorer.</li>
-<li>Navigate to the folder where you have saved the downloaded APK file. It is usually in the "Downloads" folder.</li>
-<li>Tap on the APK file and confirm the installation by tapping on "Install" or "Yes".</li>
-<li>Wait for the installation to finish and then tap on "Open" or "Done".</li>
-</ol>
-<p>Congratulations! You have successfully installed Among Us on your Android device. You can now launch the game and enjoy playing with your friends.</p>
- <h2>Features of Among Us Game</h2>
-<p>Among Us is a game that offers a lot of features and options to make your gameplay more fun and exciting. Here are some of the features that you can enjoy in Among Us:</p>
- <h3>Customization: Pick your color, hat, visor, skin, and pet</h3>
-<p>You can customize your character in Among Us by choosing from different colors, hats, visors, skins, and pets. You can change your appearance in the lobby before starting a game or in between games. You can also buy more hats, skins, and pets with real money or by watching ads.</p>
- <h3>Game options: Add more impostors, tasks, roles, and more</h3>
-<p>You can also customize your game settings in Among Us by changing various options, such as the number of impostors, tasks, roles, speed, vision, kill cooldown, voting time, emergency meetings, and more. You can adjust these options in the lobby before starting a game or in between games. You can also create your own rules and modes with your friends for more fun.</p>
- <h3>Game modes: Choose between classic or hide and seek mode</h3>
-<p>You can also choose between two different game modes in Among Us: classic or hide and seek. In classic mode, you have to find out who are the impostors among you and vote them out before they kill everyone. In hide and seek mode, one impostor has low vision and has to find and kill all the crewmates who have high vision and no tasks. You can switch between these modes in the lobby before starting a game or in between games.</p>
- <h3>Maps: Play in four different maps with different layouts and tasks</h3>
-<p>You can also play in four different maps in Among Us: The Skeld, Mira HQ, Polus, and The Airship. Each map has a different layout, design, theme, and tasks. You can explore different rooms, vents, sabotages, doors, ladders, platforms, cameras, logs, vitals, admin table, and more. You can select your preferred map in the lobby before starting a game or in between games.</p>
- <h3>Online and local multiplayer: Play with up to 15 players online or over local WiFi</h3>
-<p>You can also play with up to 15 players in Among Us, either online or over local WiFi. You can join a public game with random players from around the world, or create a private game with your friends using a code. You can also host a local game with your friends who are connected to the same WiFi network as you. You can chat with other players using text or voice chat, or use external apps like Discord or Zoom.</p>
- <h3>In-game chat and voice chat: Communicate with other players or deceive them</h3>
-<p>You can also communicate with other players in Among Us using in-game chat and voice chat. You can use the chat feature to send messages to other players during meetings or when you are dead. You can also use the voice chat feature to talk to other players in real time, but only if you are in the same room or near each other. You can use these features to share information, accuse, defend, lie, or trick other players.</p>
- <h2>Conclusion</h2>
-<p>Among Us is a fun and addictive game that you can play on your Android device by downloading and installing an APK file. You can customize your character, game settings, game modes, and maps to make your gameplay more enjoyable. You can also play with up to 15 players online or over local WiFi, and communicate with them using in-game chat and voice chat. You can have a lot of fun playing as a crewmate or an impostor, and experience different scenarios and outcomes every time.</p>
-<p>If you want to play Among Us on your Android device, follow these steps:</p>
-<ul>
-<li>Enable the option to allow unknown apps on your device.</li>
-<li>Install a file manager app on your device.</li>
-<li>Download the Among Us APK file from a reputable source.</li>
-<li>Install the APK file on your device.</li>
-<li>Launch the game and enjoy playing with your friends.</li>
-</ul>
-<p>Here are some tips and tricks for playing Among Us:</p>
-<ul>
-<li>Use common sense and logic to find out who are the impostors.</li>
-<li>Pay attention to the tasks, vents, sabotages, bodies, and alibis of other players.</li>
-<li>Be careful about who you trust and who you accuse.</li>
-<li>Use your skills of deception, persuasion, and manipulation if you are an impostor.</li>
-<li>Have fun and don't take the game too seriously.</li>
-</ul>
-<p>We hope you found this article helpful and informative. If you have any questions or feedback, please let us know in the comments below. And if you liked this article, please share it with your friends who might be interested in playing Among Us on their Android devices. Thank you for reading!</p>
- <h2>Frequently Asked Questions</h2>
-<h3>Q: Is Among Us free to play on Android?</h3>
-<p>A: Yes, Among Us is free to play on Android devices. However, you may have to watch ads after every game or pay a small fee to remove them. You may also have to pay for some hats, skins, and pets in the game.</p>
- <h3>Q: Is Among Us safe to download and install on Android?</h3>
-<p>A: Yes, Among Us is safe to download and install on Android devices as long as you download it from a reputable source. You should always check the reviews, ratings, and comments of other users before downloading any APK file. You should also scan the APK file with an antivirus app before installing it on your device.</p>
- <h3>Q: Can I play Among Us on PC or iOS devices?</h3>
-<p>A: Yes, you can play Among Us on PC or iOS devices as well. You can download Among Us from Steam for PC or from the App Store for iOS devices. However, you may have to pay a small fee to download the game on these platforms.</p>
- <h3>Q: Can I play Among Us with cross-platform compatibility?</h3>
-<p>A: Yes, you can play Among Us with cross-platform compatibility. This means that you can play with other players who are using different devices or platforms, such as PC, Android, or iOS. You just need to join the same game code or host a local game over WiFi.</p>
- <h3>Q: How often is Among Us updated?</h3>
-<p>A: Among Us is updated regularly by the developers to fix bugs, add new features, improve performance, and enhance user experience. You can check the changelog of each version on the website where you download the APK file. You can also follow the official social media accounts of Among Us to get the latest news and updates about the game.</p> 401be4b1e0<br />
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-<h1>How to Download Bleach vs Naruto Original - A Guide for Anime Fans</h1>
- <p>If you are a fan of anime, you have probably heard of <strong>Bleach</strong> and <strong>Naruto</strong>, two of the most popular and influential manga and anime series of all time. But have you ever wondered what would happen if these two worlds collided? Well, you don't have to imagine anymore, because there is a game that lets you experience this epic crossover. It's called <strong>Bleach vs Naruto</strong>, and it's a flash game that you can play on your PC or Android device. In this article, we will show you how to download Bleach vs Naruto original, the unmodified version of the game, and give you some tips and tricks for playing it.</p>
- <h2>What is Bleach vs Naruto?</h2>
- <p>Bleach vs Naruto is a <strong>crossover fighting game</strong> featuring characters from Bleach and Naruto. You can choose from over 40 heroes, each with their own unique style and fighting technique, and battle against other characters or against your friends in various modes. You can also unlock new characters, stages, and transformations as you progress through the game.</p>
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- <p>Bleach vs Naruto is a <strong>popular flash game</strong> that has been developed by the Chinese company 5Dplay since 2011. It has been updated regularly with new versions and mods, adding more content and features to the game. The latest version of the game is Bleach vs Naruto 3.5, which was released in 2020.</p>
- <p>Bleach vs Naruto is a <strong>fun and challenging game</strong> for anime lovers. It has amazing graphics, sound effects, and animations that capture the essence of both series. It also has a simple but addictive gameplay that will keep you hooked for hours. Whether you are a fan of Bleach, Naruto, or both, you will surely enjoy playing this game.</p>
- <h2>Why Download Bleach vs Naruto Original?</h2>
- <p>While there are many versions and mods of Bleach vs Naruto available online, some of you may prefer to play the original version of the game, with no modifications or additions. Here are some reasons why you may want to download Bleach vs Naruto original:</p>
- <p>- To experience the <strong>original version</strong> of the game with no modifications. If you want to play the game as it was first released, without any changes or enhancements, then downloading Bleach vs Naruto original is the way to go. You will be able to see how the game evolved over time and appreciate its original charm.</p>
- <p>- To enjoy the <strong>classic gameplay and graphics</strong>. If you are nostalgic for the old-school flash games, then downloading Bleach vs Naruto original is the best option for you. You will be able to enjoy the classic gameplay and graphics that made the game so popular in the first place. You will also be able to appreciate the original design and art style of the characters and stages.</p>
- <p>- To play <strong>offline or online</strong> with friends. If you want to play Bleach vs Naruto original without any internet connection, or if you want to play online with your friends, then downloading the game is a must. You will be able to play the game anytime and anywhere, without any lag or interruption. You will also be able to challenge your friends and see who is the best fighter among you.</p>
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- <h2>How to Download Bleach vs Naruto Original for PC?</h2>
- <p>If you want to play Bleach vs Naruto original on your PC, here are the steps you need to follow:</p>
- <ol>
-<li>Find a <strong>reliable website</strong> that offers the game for download. There are many websites that claim to have the game, but some of them may be unsafe or contain viruses. To avoid any risk, we recommend you to use this website, which is trusted and verified by many users.</li>
-<li>Click on the <strong>download link</strong> and save the file to your computer. The file size is about 40 MB, so it should not take too long to download. Make sure you have enough space on your hard drive before downloading.</li>
-<li>Extract the file using a software like <strong>WinRAR or 7-Zip</strong>. The file is compressed in a .rar format, so you need a software that can extract it. You can download WinRAR or 7-Zip for free from their official websites.</li>
-<li>Run the game by double-clicking on the <strong>.exe file</strong>. Once you have extracted the file, you will see a folder named "Bleach vs Naruto". Inside this folder, you will find a file named "Bleach vs Naruto.exe". This is the executable file that runs the game. Double-click on it and enjoy playing.</li>
-</ol>
- <h2>How to Download Bleach vs Naruto Original for Android?</h2>
- <p>If you want to play Bleach vs Naruto original on your Android device, here are the steps you need to follow:</p>
- <ol>
-<li>Find a <strong>trustworthy website</strong> that provides the game for download. As mentioned before, there are many websites that claim to have the game, but some of them may be harmful or contain malware. To avoid any risk, we suggest you to use this website, which is safe and verified by many users.</li>
-<li>Tap on the <strong>download link</strong> and save the file to your device. The file size is about 50 MB, so it should not take too long to download. Make sure you have enough space on your device before downloading.</li>
-<li>Enable <strong>unknown sources</strong> in your settings to install the game. Since the game is not from the Google Play Store, you need to enable unknown sources in your settings to allow the installation. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Open the game and enjoy playing. Once you have downloaded and installed the game, you will see an icon named "Bleach vs Naruto" on your home screen or app drawer. Tap on it and start playing.</li>
-</ol>
- <h2>Tips and Tricks for Playing Bleach vs Naruto Original</h2>
- <p>Bleach vs Naruto original is a fun and challenging game that requires skill and strategy. Here are some tips and tricks that can help you improve your performance and enjoy the game more:</p>
- <ul>
-<li><strong>Learn the controls and combos of each character</strong>. Each character has different controls and combos that can make them more effective in battle. You can check the controls and combos of each character by pressing P on your keyboard (for PC) or tapping on Menu > Help (for Android). You can also practice them in Training mode before playing against other opponents.</li>
-<li><strong>Use the space bar to activate special moves and transformations</strong>. The space bar is a very important key in this game, as it allows you to activate special moves and transformations that can give you an edge in combat. For example, you can use it to unleash Bankai (for Bleach characters) or Chakra Mode (for Naruto characters). However, be careful not to waste it, as it takes time to recharge.</li>
-<li><strong>Experiment with different modes and settings</strong>. The game offers various modes and settings that can make it more fun and challenging. You can choose from Single Player, Two Player, Survival, Arcade, VS CPU, and Online modes, and adjust the difficulty, time limit, and number of rounds to your liking. You can also change the background music and sound effects in the Options menu.</li>
-<li><strong>Challenge yourself with different difficulty levels and opponents</strong>. The game offers different difficulty levels, ranging from Easy to Very Hard, that can test your skills and reflexes. You can also face different opponents, from random characters to specific ones, that can challenge your strategy and tactics. You can also play against your friends or other players online and see who is the best fighter among you.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Bleach vs Naruto original is a game that every anime fan should try. It is a game that combines two of the most popular and beloved manga and anime series of all time, and lets you experience an epic crossover that you will never forget. It is a game that offers a simple but addictive gameplay, amazing graphics and animations, and a variety of characters and modes to choose from. It is a game that you can play on your PC or Android device, offline or online, alone or with friends. It is a game that will keep you entertained for hours and hours.</p>
- <p>If you want to download Bleach vs Naruto original, just follow the steps we have provided in this article, and you will be able to enjoy playing this game in no time. And if you want to improve your performance and have more fun playing this game, just follow the tips and tricks we have shared with you, and you will be able to master this game like a pro.</p>
- <p>So what are you waiting for? Download Bleach vs Naruto original now and join the ultimate anime battle!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Bleach vs Naruto original:</p>
- <ol>
-<li><strong>What are the differences between Bleach vs Naruto original and other versions or mods?</strong></li>
-<p>Bleach vs Naruto original is the unmodified version of the game, with no changes or additions to the content or features. Other versions or mods may have different characters, stages, modes, graphics, or gameplay elements that are not present in the original version.</p>
-<li><strong>Is Bleach vs Naruto original safe to download?</strong></li>
-<p>Yes, Bleach vs Naruto original is safe to download, as long as you use a reliable and trustworthy website that offers the game for download. We recommend you to use this website, which is verified by many users and has no viruses or malware.</p>
-<li><strong>Can I play Bleach vs Naruto original on other devices besides PC or Android?</strong></li>
-<p>No, Bleach vs Naruto original is only available for PC or Android devices. You cannot play it on other devices such as iOS, Mac, or Linux.</p>
-<li><strong>How can I update Bleach vs Naruto original?</strong></li>
-<p>Bleach vs Naruto original does not have any updates, as it is the original version of the game. If you want to play the latest version of the game, which is Bleach vs Naruto 3.5, you need to download it separately from another website.</p>
-<li><strong>Where can I find more information about Bleach vs Naruto original?</strong></li>
-<p>You can find more information about Bleach vs Naruto original on this website, which has a detailed guide on how to play the game, as well as a forum where you can interact with other players and fans.</p>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy the New Features of Clash of Clans Server 2 v14 0.1 APK on Android.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy the New Features of Clash of Clans Server 2 v14 0.1 APK on Android.md
deleted file mode 100644
index e6408bada185a378f3d8eae6fb47263fc0ba6f7e..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy the New Features of Clash of Clans Server 2 v14 0.1 APK on Android.md	
+++ /dev/null
@@ -1,74 +0,0 @@
-<br />
-<h1>Clash of Clans Server 2 v14 0.1 Android APK: Everything You Need to Know</h1>
-<p>If you are a fan of strategy games, you have probably heard of Clash of Clans, one of the most popular and addictive games on mobile devices. But did you know that there is a way to enjoy the game even more, with unlimited resources, custom buildings, and faster performance? In this article, we will tell you everything you need to know about Clash of Clans Server 2 v14 0.1 Android APK, a private server that lets you play the game with more freedom and fun.</p>
-<h2>What is Clash of Clans?</h2>
-<p>Clash of Clans is a multiplayer online strategy game developed by Supercell, a Finnish company that also created other hit games like Hay Day, Boom Beach, and Brawl Stars. The game was released in 2012 for iOS devices and in 2013 for Android devices. Since then, it has become one of the most downloaded and highest-grossing games in the world, with over 500 million downloads and billions of dollars in revenue.</p>
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-<p>The game is set in a fantasy world where you have to build your own village, train your troops, and fight against other players or computer-controlled enemies. You can join or create a clan with other players and participate in clan wars, clan games, and other events. You can also upgrade your buildings, troops, spells, heroes, and defenses with various resources like gold, elixir, dark elixir, and gems.</p>
-<h2>What is Clash of Clans Server 2?</h2>
-<p>Clash of Clans Server 2 is a private server that runs a modified version of the original game. A private server is a separate entity from the official server, which means that it has its own rules, features, and players. You can access a private server by downloading and installing an APK file on your Android device.</p>
-<p>The main advantage of playing on a private server is that you can enjoy the game without any limitations or restrictions. You can have unlimited resources, gems, custom buildings, troops, spells, heroes, and more. You can also play with other players who use the same private server and have fun together.</p>
-<h4>How to download and install Clash of Clans Server 2 v14 0.1 Android APK</h4>
-<p>To download and install Clash of Clans Server 2 v14 0.1 Android APK, you need to follow these simple steps:</p>
-<ol>
-<li>Go to [this link](^1^) or [this link](^2^) and download the APK file on your device.</li>
-<li>Go to your device settings and enable the option to install apps from unknown sources.</li>
-<li>Locate the downloaded APK file on your device and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Launch the app and enjoy playing Clash of Clans Server 2 v14 0.1 Android APK.</li>
-</ol>
-<h4>How to play Clash of Clans Server 2 v14 0.1 Android APK</h4>
-<p>To play Clash of Clans Server 2 v14 0.1 Android APK, you need to follow these simple steps:</p> <ol start="6">
-<li>Choose a name for your village and start building it with the unlimited resources and gems you have.</li>
-<li>Train your troops and heroes with the custom options and levels you have.</li>
-<li>Attack other players or computer enemies with the custom spells and strategies you have.</li>
-<li>Join or create a clan with other players who use the same private server and chat with them.</li>
-<li>Participate in clan wars, clan games, and other events with your clan mates and win rewards.</li>
-</ol>
-<h2>What are the features of Clash of Clans Server 2 v14 0.1 Android APK?</h2>
-<p>Clash of Clans Server 2 v14 0.1 Android APK has many features that make it different from the original game. Here are some of the main features and benefits of the private server:</p>
-<h4>Unlimited resources and gems</h4>
-<p>One of the most appealing features of Clash of Clans Server 2 v14 0.1 Android APK is that you can have unlimited resources and gems to build your village, train your troops, upgrade your buildings, and buy anything you want. You don't have to worry about running out of resources or gems, or spending real money to buy them. You can enjoy the game without any limitations or restrictions.</p>
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-<h4>Custom buildings and troops</h4>
-<p>Another feature of Clash of Clans Server 2 v14 0.1 Android APK is that you can have custom buildings and troops that are not available in the original game. You can build unique structures like dragon towers, witch towers, barbarian king towers, and more. You can also train special troops like goblins, giants, dragons, witches, and more. You can mix and match different buildings and troops to create your own strategies and combinations.</p>
-<h4>Fast and stable performance</h4>
-<p>Clash of Clans Server 2 v14 0.1 Android APK also offers fast and stable performance for your gaming experience. The private server is hosted on a powerful server that can handle thousands of players at the same time. You don't have to worry about lagging, crashing, or disconnecting from the game. You can play smoothly and seamlessly without any interruptions or errors.</p>
-<h4>Regular updates and support</h4>
-<p>Finally, Clash of Clans Server 2 v14 0.1 Android APK also provides regular updates and support for its users. The private server is updated frequently to match the latest version of the original game, as well as to add new features and improvements. You can also contact the support team if you have any questions or issues with the private server. They will respond to you as soon as possible and help you solve your problems.</p>
-<h2>Conclusion</h2>
-<p>In conclusion, Clash of Clans Server 2 v14 0.1 Android APK is a private server that lets you play Clash of Clans with more freedom and fun. You can have unlimited resources, gems, custom buildings, troops, spells, heroes, and more. You can also play with other players who use the same private server and join or create a clan with them. You can also enjoy fast and stable performance, regular updates, and support from the private server team.</p>
-<p>If you are looking for a way to spice up your Clash of Clans gaming experience, you should definitely try Clash of Clans Server 2 v14 0.1 Android APK. It is easy to download and install, and it is compatible with most Android devices. You will not regret it!</p>
-<p>To download Clash of Clans Server 2 v14 0.1 Android APK, click on [this link] or [this link] now!</p>
- FAQs Q: Is Clash of Clans Server 2 v14 0.1 Android APK safe to use? A: Yes, Clash of Clans Server 2 v14 0.1 Android APK is safe to use. It does not contain any viruses, malware, or spyware that can harm your device or data. However, you should always download it from a trusted source like [this link] or [this link] to avoid any fake or malicious files. Q: Is Clash of Clans Server 2 v14 0.1 Android APK legal to use? A: Yes, Clash of Clans Server 2 v14 0.1 Android APK is legal to use. It does not violate any laws or regulations that govern the original game or its developer Supercell. However, you should be aware that using a private server is against the terms of service of the original game, which means that you may face some risks or consequences if you use it. For example, you may lose your account or progress on the official server, or you may get banned or suspended from the game. Therefore, you should use Clash of Clans Server 2 v14 0.1 Android APK at your own risk and discretion. Q: Can I play Clash of Clans Server 2 v14 0.1 Android APK with my friends who use the official server? A: No, you cannot play Clash of Clans Server 2 v14 0.1 Android APK with your friends who use the official server. The private server and the official server are separate entities that do not interact with each other. You can only play with other players who use the same private server as you. Q: How can I switch between Clash of Clans Server 2 v14 0.1 Android APK and the original game? A: To switch between Clash of Clans Server 2 v14 0.1 Android APK and the original game, you need to uninstall one app and install the other app on your device. You cannot have both apps installed on your device at the same time, as they will conflict with each other and cause errors. You should also make sure that you backup your data on both apps before switching, as you may lose your progress or account if you do not. Q: What are some other private servers for Clash of Clans that I can try? A: There are many other private servers for Clash of Clans that you can try, such as Clash of Lights, Clash of Magic, Clash of Souls, and more. Each private server has its own features and advantages, so you can choose the one that suits your preferences and needs. However, you should always be careful when downloading and installing any private server, as some of them may contain harmful or malicious files that can damage your device or data.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sitong608/bingAI/README.md b/spaces/sitong608/bingAI/README.md
deleted file mode 100644
index 6b7f51bed71ed162d4433df8695734d20b38b1c1..0000000000000000000000000000000000000000
--- a/spaces/sitong608/bingAI/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: BingAI
-emoji: 🌍
-colorFrom: pink
-colorTo: purple
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sqc1729/bingi/tests/parse.ts b/spaces/sqc1729/bingi/tests/parse.ts
deleted file mode 100644
index 92940fe6315f1d7cb2b267ba5e5a7e26460a1de3..0000000000000000000000000000000000000000
--- a/spaces/sqc1729/bingi/tests/parse.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-import { promises as fs } from 'fs'
-import { join } from 'path'
-import { parseHeadersFromCurl } from '@/lib/utils'
-
-(async () => {
-  const content = await fs.readFile(join(__dirname, './fixtures/curl.txt'), 'utf-8')
-  const headers = parseHeadersFromCurl(content)
-  console.log(headers)
-
-  const cmdContent = await fs.readFile(join(__dirname, './fixtures/cmd.txt'), 'utf-8')
-  const cmdHeaders = parseHeadersFromCurl(cmdContent)
-  console.log(cmdHeaders)
-})()
diff --git a/spaces/srijan2024/SentimentAnalysis/main.py b/spaces/srijan2024/SentimentAnalysis/main.py
deleted file mode 100644
index 0aaea1cea978650df62030c934bb1a68a1d5a6b1..0000000000000000000000000000000000000000
--- a/spaces/srijan2024/SentimentAnalysis/main.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import pandas as pd
-import numpy as np
-import string
-import flask
-from tensorflow.keras.preprocessing.text import Tokenizer
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from flask import Flask, request, jsonify, render_template
-import nltk
-import json
-from tensorflow.keras.models import load_model
-import tensorflow as tf
-
-model = load_model("E:\CODES\pythonProject7\_best_model_LSTM.hdf5")
-
-app = Flask(__name__)
-
-
-from tensorflow.keras.preprocessing.text import tokenizer_from_json
-
-import json
-
-with open("D:/Analysis_sentiment/tokenizer.json", 'r') as f:
-    tokenizer_data = f.read()
-
-
-tokenizer_config = json.loads(tokenizer_data)
-tokenizer = tokenizer_from_json(tokenizer_config)
-
-print(tokenizer)
-@app.route('/')
-def home():
-    return render_template('index.html')
-
-@app.route('/predict',methods=['POST'])
-def predict():
-    new_review = [str(x) for x in request.form.values()]
-    print(new_review)
-    # data = pd.DataFrame(new_review)
-    # data.columns = ['new_review']
-    sequences = tokenizer.texts_to_sequences(new_review)
-    print(sequences)
-    padded_sequences = pad_sequences(sequences, maxlen=200,padding='post', value=0)
-
-    predictions = model.predict(padded_sequences)
-    print(predictions)
-    if predictions<.5:
-        return render_template('index.html', prediction_text='Negative')
-    else:
-        return render_template('index.html', prediction_text='Positive')
-
-
-if __name__ == "__main__":
-    app.run(host='0.0.0.0',port=8080)
\ No newline at end of file
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/models/ema/__init__.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/models/ema/__init__.py
deleted file mode 100644
index 503ceaa609b092e48bd32a0031f4e2ffb875483f..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/models/ema/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import importlib
-import os
-
-from .ema import EMA
-
-
-def build_ema(model, cfg, device):
-    return EMA(model, cfg, device)
-
-
-# automatically import any Python files in the models/ema/ directory
-for file in sorted(os.listdir(os.path.dirname(__file__))):
-    if file.endswith(".py") and not file.startswith("_"):
-        file_name = file[: file.find(".py")]
-        importlib.import_module("fairseq.models.ema." + file_name)
diff --git a/spaces/stomexserde/gpt4-ui/Examples/AutoCAD Electrical 2019 Free [UPDATED] Download.md b/spaces/stomexserde/gpt4-ui/Examples/AutoCAD Electrical 2019 Free [UPDATED] Download.md
deleted file mode 100644
index 5242bf1a8d2c045cb5036a370f927e6f28a3beb6..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/AutoCAD Electrical 2019 Free [UPDATED] Download.md	
+++ /dev/null
@@ -1,124 +0,0 @@
-
-<h1>How to Download and Install AutoCAD Electrical 2019 for Free</h1>
-<p>AutoCAD Electrical 2019 is a powerful software for designing electrical circuits and systems. It is part of the AutoCAD family of products, which includes specialized toolsets and apps for various industries and disciplines. AutoCAD Electrical 2019 helps you create accurate and efficient electrical drawings, schematics, panel layouts, and reports.</p>
-<h2>AutoCAD Electrical 2019 Free Download</h2><br /><p><b><b>Download</b> &#8230; <a href="https://urlgoal.com/2uI5D2">https://urlgoal.com/2uI5D2</a></b></p><br /><br />
-<p>If you want to try AutoCAD Electrical 2019 for free, you can download a free trial from Autodesk's official website. The free trial lasts for 30 days and gives you access to all the features and functions of the software. You can also use the free trial to learn how to use AutoCAD Electrical 2019 with online tutorials, videos, and documentation.</p>
-<p>To download and install AutoCAD Electrical 2019 for free, follow these steps:</p>
-<ol>
-<li>Go to <a href="https://www.autodesk.com/free-trials">https://www.autodesk.com/free-trials</a> and find AutoCAD Electrical 2019 in the list of products. Click on "Download free trial".[^1^]</li>
-<li>Select your operating system, language, and version. You can also choose to download additional toolsets and apps that are compatible with AutoCAD Electrical 2019.</li>
-<li>Sign in with your Autodesk account or create one if you don't have one. You will need an Autodesk account to activate your free trial.</li>
-<li>Click on "Download now" and follow the instructions to install the software on your computer.</li>
-<li>Launch AutoCAD Electrical 2019 and sign in with your Autodesk account. You will see a screen that shows how many days are left in your free trial. You can start using the software right away or explore the learning resources available on the Autodesk website.</li>
-</ol>
-<p>If you want to use AutoCAD Electrical 2019 offline, you can download and install the offline help files from <a href="https://www.autodesk.com/support/technical/article/caas/tsarticles/ts/3fASYuDVRDtBjnkzfVXUxQ.html">https://www.autodesk.com/support/technical/article/caas/tsarticles/ts/3fASYuDVRDtBjnkzfVXUxQ.html</a>.[^2^] You can choose from different languages and download the executable files that match your system requirements.</p>
-<p>AutoCAD Electrical 2019 is a great software for electrical engineers, designers, and technicians. It helps you create professional and accurate electrical drawings and documentation. With the free trial, you can test the software before buying it or use it for educational purposes. If you are a student or an educator, you can also get free access to AutoCAD Electrical 2019 and other Autodesk products through the Autodesk Education Community. To learn more, visit <a href="https://www.autodesk.com/education/edu-software/overview">https://www.autodesk.com/education/edu-software/overview</a>.[^3^]</p>
-
-<p>In this article, we will show you some of the features and benefits of AutoCAD Electrical 2019. We will also give you some tips and tricks on how to use the software more efficiently and effectively.</p>
-<h2>Features and Benefits of AutoCAD Electrical 2019</h2>
-<p>AutoCAD Electrical 2019 is a comprehensive software for electrical design and documentation. It has many features and benefits that make it a powerful and versatile tool for electrical engineers and designers. Some of the main features and benefits are:</p>
-<p></p>
-<ul>
-<li>It supports industry standards and formats, such as IEC, IEEE, NFPA, GB, and JIC. You can easily create and edit electrical drawings that comply with these standards and formats.</li>
-<li>It has a large library of symbols, components, wires, cables, and connectors. You can drag and drop these elements into your drawings and customize them according to your needs. You can also create your own symbols and components and add them to the library.</li>
-<li>It has a smart panel layout feature that helps you design and arrange electrical panels in a realistic and efficient way. You can insert components from the library or from your schematic drawings into the panel layout. You can also use automatic wire numbering and tagging to simplify the wiring process.</li>
-<li>It has a schematic design feature that helps you create and edit electrical schematics with ease. You can use tools such as copy, move, rotate, align, mirror, stretch, trim, extend, break, join, and edit to modify your schematic drawings. You can also use tools such as insert wires, insert components, insert connectors, insert terminals, insert PLC modules, insert ladder diagrams, insert circuit breakers, insert fuses, insert switches, insert relays, insert contactors, insert motors, insert transformers, insert generators, insert batteries, insert resistors, insert capacitors, insert inductors, insert diodes, insert transistors, insert LEDs, insert sensors, insert actuators, insert logic gates, insert timers, insert counters, insert encoders, insert decoders, insert multiplexers, insert demultiplexers, insert flip-flops, insert registers, insert memory devices,
-insert arithmetic units,
-insert comparators,
-insert converters,
-insert amplifiers,
-insert filters,
-insert oscillators,
-insert modulators,
-insert demodulators,
-insert antennas,
-insert radio frequency devices,
-and more to create complex electrical circuits.</li>
-<li>It has a report generation feature that helps you create and export various types of reports from your electrical drawings. You can generate reports such as bill of materials (BOM), wire list,
-cable list,
-connector list,
-terminal list,
-PLC I/O list,
-panel layout list,
-schematic list,
-and more. You can also customize the format and content of your reports according to your preferences.</li>
-<li>It has a collaboration feature that helps you share and review your electrical drawings with other stakeholders. You can use tools such as DWG compare,
-DWG history,
-DWG references,
-DWG xref manager,
-DWG purge,
-DWG recover,
-DWG audit,
-and more to manage your drawing files. You can also use tools such as cloud storage,
-cloud collaboration,
-cloud rendering,
-cloud simulation,
-cloud analysis,
-and more to access and work on your drawings online or on mobile devices.</li>
-</ul>
-<h2>Tips and Tricks for Using AutoCAD Electrical 2019</h2>
-<p>AutoCAD Electrical 2019 is a user-friendly software that has many tips and tricks to help you use it more efficiently and effectively. Some of the tips and tricks are:</p>
-<ul>
-<li>You can use keyboard shortcuts to perform common commands faster. For example,
-
-CTRL+C to copy,
-
-CTRL+V to paste,
-
-CTRL+Z to undo,
-
-CTRL+Y to redo,
-
-CTRL+F to find,
-
-CTRL+H to replace,
-
-CTRL+A to select all,
-
-CTRL+P to print,
-
-CTRL+S to save,
-
-CTRL+O to open,
-
-CTRL+N to create a new drawing,
-
-and more.</li>
-<li>You can use command aliases to type commands faster. For example,
-
-C for circle,
-
-L for line,
-
-PL for polyline,
-
-TR for trim,
-
-EX for extend,
-
-BR for break,
-
-J for join,
-
-M for move,
-
-RO for rotate,
-
-MI for mirror,
-
-SC for scale,
-
-A for arc,
-
-E for erase,
-
-and more.</li>
-<li>You can use dynamic input to enter commands and values directly on the drawing area. For example,
-
-when you draw a line,
-
-you can see the length and angle of the line on the screen.
-
-You can also type values or expressions to change the length or angle</p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Constitutional And Political History Of Pakistan B Exbox Sobredosis Mai.md b/spaces/stomexserde/gpt4-ui/Examples/Constitutional And Political History Of Pakistan B Exbox Sobredosis Mai.md
deleted file mode 100644
index 4ca80708d72626677837addb6a777499d4a06ab1..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Constitutional And Political History Of Pakistan B Exbox Sobredosis Mai.md	
+++ /dev/null
@@ -1,14 +0,0 @@
-<br />
-<h1>Constitutional And Political History Of Pakistan: A Review of Hamid Khan's Book</h1>
-<p>Hamid Khan is a senior advocate of the Supreme Court of Pakistan and a founding partner of a leading Pakistani law firm. He is also the author of <em>Constitutional and Political History of Pakistan</em>, a book that analyses constitutional development in Pakistan from its inception to present times. The book provides a case-by-case account of constitution-making in Pakistan, with the inclusion of all pertinent documentation. Constitutional developments have been explained in the context of social and political events that shaped them.</p>
-<p>The book covers various topics such as the partition of India and Pakistan, the Objectives Resolution, the first and second Constituent Assemblies, the 1956, 1962, and 1973 constitutions, the martial law regimes of Ayub Khan, Yahya Khan, Zia-ul-Haq, and Pervez Musharraf, the judicial activism of the Supreme Court, the constitutional amendments and reforms, and the current challenges and prospects for constitutional democracy in Pakistan. The book also includes a liberal humanitarian reading of the travails of lawmakers and the role of generals, judges, politicians, and bureaucrats in the implementation of law.</p>
-<h2>Constitutional And Political History Of Pakistan B exbox sobredosis mai</h2><br /><p><b><b>Download</b> &mdash; <a href="https://urlgoal.com/2uI8Hn">https://urlgoal.com/2uI8Hn</a></b></p><br /><br />
-<p>The book is intended for students of law, political science, and history, as well as lawyers, judges, and professors. It is also of interest to the general reader who wants to learn more about the constitutional and political history of Pakistan. The book is based on extensive research and citation of primary and secondary sources. It is written in a clear and concise style that makes it accessible to a wide audience. The book is updated to cover the constitutional and political developments up until 2013.</p>
-<p><em>Constitutional and Political History of Pakistan</em> is a valuable contribution to the literature on constitutional law and politics in Pakistan. It offers a comprehensive and critical overview of the historical evolution and current state of constitutionalism in Pakistan. It also highlights the challenges and opportunities for strengthening constitutional democracy in Pakistan in the future.</p>
-
-<p>The book also discusses the role of the judiciary in interpreting and upholding the Constitution. It examines the landmark cases and judgments that have shaped the constitutional jurisprudence of Pakistan. It also critically evaluates the judicial activism and interventionism that have often challenged the constitutional balance of powers and legitimacy of elected governments. The book argues that the judiciary should act as a guardian of the Constitution and not as a usurper of political power.</p>
-<p>Another theme that the book explores is the relationship between Islam and the Constitution. It traces the historical debates and controversies over the place of Islam in the constitutional framework of Pakistan. It analyzes the various constitutional provisions and amendments that have sought to define and implement Islamic principles and values in Pakistan's legal and political system. It also assesses the impact of Islamization policies and laws on the rights and freedoms of citizens, especially women and minorities.</p>
-<p>The book concludes with a critical appraisal of the current state of constitutional democracy in Pakistan. It identifies the major challenges and threats that confront the constitutional order and democratic process in Pakistan, such as political instability, civil-military imbalance, corruption, terrorism, ethnic conflicts, and external pressures. It also suggests some possible ways and means to overcome these challenges and strengthen constitutional democracy in Pakistan.</p>
-<p></p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Evolve TV V0.7.7 [Mod AdFree V2] [Latest].md b/spaces/stomexserde/gpt4-ui/Examples/Evolve TV V0.7.7 [Mod AdFree V2] [Latest].md
deleted file mode 100644
index 04fdb4a84d3261d5cfde3e755fb6e32d44501cde..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Evolve TV V0.7.7 [Mod AdFree V2] [Latest].md	
+++ /dev/null
@@ -1,18 +0,0 @@
-<br />
-<h1>Evolve TV v0.7.7 [Mod AdFree v2] [Latest]: A Free Streaming App for Android</h1>
-<p>Evolve TV is a free streaming app that lets you watch live TV channels from around the world on your Android device. You can choose from over 1100 channels in various categories, such as sports, movies, news, entertainment, and more. Evolve TV also supports external media players, such as MX Player and VLC Player, for a better viewing experience.</p>
-<h2>Evolve TV v0.7.7 [Mod AdFree v2] [Latest]</h2><br /><p><b><b>Download Zip</b> &rarr;&rarr;&rarr; <a href="https://urlgoal.com/2uIb1T">https://urlgoal.com/2uIb1T</a></b></p><br /><br />
-<p>However, the original version of Evolve TV contains ads that may interrupt your streaming. That's why some modders have created a modified version of Evolve TV that removes all the ads and makes the app more user-friendly. One of these mods is Evolve TV v0.7.7 [Mod AdFree v2] [Latest], which was created by canary and posted on Reddit[^1^]. This mod disables all the ads and also fixes some bugs and errors that were present in the previous version.</p>
-<p>If you want to download and install Evolve TV v0.7.7 [Mod AdFree v2] [Latest], you can follow these steps:</p>
-<ol>
-<li>Download the APK file from one of the links provided by canary[^1^]. Make sure you have enough storage space on your device.</li>
-<li>Enable unknown sources on your device settings. This will allow you to install apps from sources other than the Google Play Store.</li>
-<li>Locate the downloaded APK file and tap on it to start the installation process. Follow the instructions on the screen.</li>
-<li>Once the installation is complete, launch the app and enjoy watching live TV channels without any ads.</li>
-</ol>
-<p>Note: This mod is not affiliated with or endorsed by the official developers of Evolve TV. Use it at your own risk and discretion. We are not responsible for any damages or issues that may arise from using this mod.</p><p>Evolve TV v0.7.7 [Mod AdFree v2] [Latest] is one of the best free streaming apps for Android users who want to watch live TV channels from different countries and genres. You can find channels from the USA, UK, Canada, India, Pakistan, France, Germany, Spain, Italy, and many more. You can also watch sports channels, such as ESPN, Fox Sports, Sky Sports, BT Sports, and others. Whether you want to watch the latest movies, TV shows, news, documentaries, or music videos, Evolve TV has something for everyone.</p>
-<p>One of the advantages of Evolve TV is that it supports multiple media players. You can choose the default player or use an external player of your choice. Some of the supported players are MX Player, VLC Player, Wuffy Player, XPlayer, and Yes Player. You can also change the video quality and resolution according to your preference and internet speed. Evolve TV also has a simple and intuitive interface that makes it easy to navigate and use.</p>
-<p></p>
-<p>Evolve TV v0.7.7 [Mod AdFree v2] [Latest] is a must-have app for anyone who loves watching live TV on their Android device. It offers a wide range of channels and content without any annoying ads or interruptions. You can download it from the links provided by canary and enjoy unlimited streaming for free.</p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/subhc/Guess-What-Moves/utils/vit_extractor.py b/spaces/subhc/Guess-What-Moves/utils/vit_extractor.py
deleted file mode 100644
index 3fa42d9a10d5580af96d9ae3b379018a332dd945..0000000000000000000000000000000000000000
--- a/spaces/subhc/Guess-What-Moves/utils/vit_extractor.py
+++ /dev/null
@@ -1,364 +0,0 @@
-import argparse
-import math
-import types
-from pathlib import Path
-from typing import Union, List, Tuple
-
-import timm
-import torch
-import torch.nn.modules.utils as nn_utils
-from PIL import Image
-from torch import nn
-from torchvision import transforms
-
-
-class ViTExtractor:
-    """ This class facilitates extraction of features, descriptors, and saliency maps from a ViT.
-
-    We use the following notation in the documentation of the module's methods:
-    B - batch size
-    h - number of heads. usually takes place of the channel dimension in pytorch's convention BxCxHxW
-    p - patch size of the ViT. either 8 or 16.
-    t - number of tokens. equals the number of patches + 1, e.g. HW / p**2 + 1. Where H and W are the height and width
-    of the input image.
-    d - the embedding dimension in the ViT.
-    """
-
-    def __init__(self, model_type: str = 'dino_vits8', stride: int = 4, model: nn.Module = None, device: str = 'cuda'):
-        """
-        :param model_type: A string specifying the type of model to extract from.
-                          [dino_vits8 | dino_vits16 | dino_vitb8 | dino_vitb16 | vit_small_patch8_224 |
-                          vit_small_patch16_224 | vit_base_patch8_224 | vit_base_patch16_224]
-        :param stride: stride of first convolution layer. small stride -> higher resolution.
-        :param model: Optional parameter. The nn.Module to extract from instead of creating a new one in ViTExtractor.
-                      should be compatible with model_type.
-        """
-        self.model_type = model_type
-        self.device = device
-        if model is not None:
-            self.model = model
-        else:
-            self.model = ViTExtractor.create_model(model_type)
-
-        self.model = ViTExtractor.patch_vit_resolution(self.model, stride=stride)
-        self.model.eval()
-        self.model.to(self.device)
-        self.p = self.model.patch_embed.patch_size
-        self.stride = self.model.patch_embed.proj.stride
-
-        self.mean = (0.485, 0.456, 0.406) if "dino" in self.model_type else (0.5, 0.5, 0.5)
-        self.std = (0.229, 0.224, 0.225) if "dino" in self.model_type else (0.5, 0.5, 0.5)
-
-        self._feats = []
-        self.hook_handlers = []
-        self.load_size = None
-        self.num_patches = None
-
-    @staticmethod
-    def create_model(model_type: str) -> nn.Module:
-        """
-        :param model_type: a string specifying which model to load. [dino_vits8 | dino_vits16 | dino_vitb8 |
-                           dino_vitb16 | vit_small_patch8_224 | vit_small_patch16_224 | vit_base_patch8_224 |
-                           vit_base_patch16_224]
-        :return: the model
-        """
-        if 'dino' in model_type:
-            model = torch.hub.load('facebookresearch/dino:main', model_type)
-        else:  # model from timm -- load weights from timm to dino model (enables working on arbitrary size images).
-            temp_model = timm.create_model(model_type, pretrained=True)
-            model_type_dict = {
-                'vit_small_patch16_224': 'dino_vits16',
-                'vit_small_patch8_224': 'dino_vits8',
-                'vit_base_patch16_224': 'dino_vitb16',
-                'vit_base_patch8_224': 'dino_vitb8'
-            }
-            model = torch.hub.load('facebookresearch/dino:main', model_type_dict[model_type])
-            temp_state_dict = temp_model.state_dict()
-            del temp_state_dict['head.weight']
-            del temp_state_dict['head.bias']
-            model.load_state_dict(temp_state_dict)
-        return model
-
-    @staticmethod
-    def _fix_pos_enc(patch_size: int, stride_hw: Tuple[int, int]):
-        """
-        Creates a method for position encoding interpolation.
-        :param patch_size: patch size of the model.
-        :param stride_hw: A tuple containing the new height and width stride respectively.
-        :return: the interpolation method
-        """
-
-        def interpolate_pos_encoding(self, x: torch.Tensor, w: int, h: int) -> torch.Tensor:
-            npatch = x.shape[1] - 1
-            N = self.pos_embed.shape[1] - 1
-            if npatch == N and w == h:
-                return self.pos_embed
-            class_pos_embed = self.pos_embed[:, 0]
-            patch_pos_embed = self.pos_embed[:, 1:]
-            dim = x.shape[-1]
-            # compute number of tokens taking stride into account
-            w0 = 1 + (w - patch_size) // stride_hw[1]
-            h0 = 1 + (h - patch_size) // stride_hw[0]
-            assert (w0 * h0 == npatch), f"""got wrong grid size for {h}x{w} with patch_size {patch_size} and 
-                                            stride {stride_hw} got {h0}x{w0}={h0 * w0} expecting {npatch}"""
-            # we add a small number to avoid floating point error in the interpolation
-            # see discussion at https://github.com/facebookresearch/dino/issues/8
-            w0, h0 = w0 + 0.1, h0 + 0.1
-            patch_pos_embed = nn.functional.interpolate(
-                patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
-                scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
-                mode='bicubic',
-                align_corners=False, recompute_scale_factor=False
-            )
-            assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1]
-            patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
-            return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
-
-        return interpolate_pos_encoding
-
-    @staticmethod
-    def patch_vit_resolution(model: nn.Module, stride: int) -> nn.Module:
-        """
-        change resolution of model output by changing the stride of the patch extraction.
-        :param model: the model to change resolution for.
-        :param stride: the new stride parameter.
-        :return: the adjusted model
-        """
-        patch_size = model.patch_embed.patch_size
-        if stride == patch_size:  # nothing to do
-            return model
-
-        stride = nn_utils._pair(stride)
-        assert all([(patch_size // s_) * s_ == patch_size for s_ in
-                    stride]), f'stride {stride} should divide patch_size {patch_size}'
-
-        # fix the stride
-        model.patch_embed.proj.stride = stride
-        # fix the positional encoding code
-        model.interpolate_pos_encoding = types.MethodType(ViTExtractor._fix_pos_enc(patch_size, stride), model)
-        return model
-
-    def preprocess(self, image_path: Union[str, Path],
-                   load_size: Union[int, Tuple[int, int]] = None) -> Tuple[torch.Tensor, Image.Image]:
-        """
-        Preprocesses an image before extraction.
-        :param image_path: path to image to be extracted.
-        :param load_size: optional. Size to resize image before the rest of preprocessing.
-        :return: a tuple containing:
-                    (1) the preprocessed image as a tensor to insert the model of shape BxCxHxW.
-                    (2) the pil image in relevant dimensions
-        """
-        pil_image = Image.open(image_path).convert('RGB')
-        if load_size is not None:
-            pil_image = transforms.Resize(load_size, interpolation=transforms.InterpolationMode.LANCZOS)(pil_image)
-        prep = transforms.Compose([
-            transforms.ToTensor(),
-            transforms.Normalize(mean=self.mean, std=self.std)
-        ])
-        prep_img = prep(pil_image)[None, ...]
-        return prep_img, pil_image
-
-    def _get_hook(self, facet: str):
-        """
-        generate a hook method for a specific block and facet.
-        """
-        if facet in ['attn', 'token']:
-            def _hook(model, input, output):
-                self._feats.append(output)
-
-            return _hook
-
-        if facet == 'query':
-            facet_idx = 0
-        elif facet == 'key':
-            facet_idx = 1
-        elif facet == 'value':
-            facet_idx = 2
-        else:
-            raise TypeError(f"{facet} is not a supported facet.")
-
-        def _inner_hook(module, input, output):
-            input = input[0]
-            B, N, C = input.shape
-            qkv = module.qkv(input).reshape(B, N, 3, module.num_heads, C // module.num_heads).permute(2, 0, 3, 1, 4)
-            self._feats.append(qkv[facet_idx])  # Bxhxtxd
-
-        return _inner_hook
-
-    def _register_hooks(self, layers: List[int], facet: str) -> None:
-        """
-        register hook to extract features.
-        :param layers: layers from which to extract features.
-        :param facet: facet to extract. One of the following options: ['key' | 'query' | 'value' | 'token' | 'attn']
-        """
-        for block_idx, block in enumerate(self.model.blocks):
-            if block_idx in layers:
-                if facet == 'token':
-                    self.hook_handlers.append(block.register_forward_hook(self._get_hook(facet)))
-                elif facet == 'attn':
-                    self.hook_handlers.append(block.attn.attn_drop.register_forward_hook(self._get_hook(facet)))
-                elif facet in ['key', 'query', 'value']:
-                    self.hook_handlers.append(block.attn.register_forward_hook(self._get_hook(facet)))
-                else:
-                    raise TypeError(f"{facet} is not a supported facet.")
-
-    def _unregister_hooks(self) -> None:
-        """
-        unregisters the hooks. should be called after feature extraction.
-        """
-        for handle in self.hook_handlers:
-            handle.remove()
-        self.hook_handlers = []
-
-    def _extract_features(self, batch: torch.Tensor, layers: List[int] = 11, facet: str = 'key') -> List[torch.Tensor]:
-        """
-        extract features from the model
-        :param batch: batch to extract features for. Has shape BxCxHxW.
-        :param layers: layer to extract. A number between 0 to 11.
-        :param facet: facet to extract. One of the following options: ['key' | 'query' | 'value' | 'token' | 'attn']
-        :return : tensor of features.
-                  if facet is 'key' | 'query' | 'value' has shape Bxhxtxd
-                  if facet is 'attn' has shape Bxhxtxt
-                  if facet is 'token' has shape Bxtxd
-        """
-        B, C, H, W = batch.shape
-        self._feats = []
-        self._register_hooks(layers, facet)
-        _ = self.model(batch)
-        self._unregister_hooks()
-        self.load_size = (H, W)
-        self.num_patches = (1 + (H - self.p) // self.stride[0], 1 + (W - self.p) // self.stride[1])
-        return self._feats
-
-    def _log_bin(self, x: torch.Tensor, hierarchy: int = 2) -> torch.Tensor:
-        """
-        create a log-binned descriptor.
-        :param x: tensor of features. Has shape Bxhxtxd.
-        :param hierarchy: how many bin hierarchies to use.
-        """
-        B = x.shape[0]
-        num_bins = 1 + 8 * hierarchy
-
-        bin_x = x.permute(0, 2, 3, 1).flatten(start_dim=-2, end_dim=-1)  # Bx(t-1)x(dxh)
-        bin_x = bin_x.permute(0, 2, 1)
-        bin_x = bin_x.reshape(B, bin_x.shape[1], self.num_patches[0], self.num_patches[1])
-        # Bx(dxh)xnum_patches[0]xnum_patches[1]
-        sub_desc_dim = bin_x.shape[1]
-
-        avg_pools = []
-        # compute bins of all sizes for all spatial locations.
-        for k in range(0, hierarchy):
-            # avg pooling with kernel 3**kx3**k
-            win_size = 3 ** k
-            avg_pool = torch.nn.AvgPool2d(win_size, stride=1, padding=win_size // 2, count_include_pad=False)
-            avg_pools.append(avg_pool(bin_x))
-
-        bin_x = torch.zeros((B, sub_desc_dim * num_bins, self.num_patches[0], self.num_patches[1])).to(self.device)
-        for y in range(self.num_patches[0]):
-            for x in range(self.num_patches[1]):
-                part_idx = 0
-                # fill all bins for a spatial location (y, x)
-                for k in range(0, hierarchy):
-                    kernel_size = 3 ** k
-                    for i in range(y - kernel_size, y + kernel_size + 1, kernel_size):
-                        for j in range(x - kernel_size, x + kernel_size + 1, kernel_size):
-                            if i == y and j == x and k != 0:
-                                continue
-                            if 0 <= i < self.num_patches[0] and 0 <= j < self.num_patches[1]:
-                                bin_x[:, part_idx * sub_desc_dim: (part_idx + 1) * sub_desc_dim, y, x] = avg_pools[k][
-                                                                                                         :, :, i, j]
-                            else:  # handle padding in a more delicate way than zero padding
-                                temp_i = max(0, min(i, self.num_patches[0] - 1))
-                                temp_j = max(0, min(j, self.num_patches[1] - 1))
-                                bin_x[:, part_idx * sub_desc_dim: (part_idx + 1) * sub_desc_dim, y, x] = avg_pools[k][
-                                                                                                         :, :, temp_i,
-                                                                                                         temp_j]
-                            part_idx += 1
-        bin_x = bin_x.flatten(start_dim=-2, end_dim=-1).permute(0, 2, 1).unsqueeze(dim=1)
-        # Bx1x(t-1)x(dxh)
-        return bin_x
-
-    def extract_descriptors(self, batch: torch.Tensor, layer: int = 11, facet: str = 'key',
-                            bin: bool = False, include_cls: bool = False) -> torch.Tensor:
-        """
-        extract descriptors from the model
-        :param batch: batch to extract descriptors for. Has shape BxCxHxW.
-        :param layers: layer to extract. A number between 0 to 11.
-        :param facet: facet to extract. One of the following options: ['key' | 'query' | 'value' | 'token']
-        :param bin: apply log binning to the descriptor. default is False.
-        :return: tensor of descriptors. Bx1xtxd' where d' is the dimension of the descriptors.
-        """
-        assert facet in ['key', 'query', 'value', 'token'], f"""{facet} is not a supported facet for descriptors. 
-                                                             choose from ['key' | 'query' | 'value' | 'token'] """
-        self._extract_features(batch, [layer], facet)
-        x = self._feats[0]
-        if facet == 'token':
-            x.unsqueeze_(dim=1)  # Bx1xtxd
-        if not include_cls:
-            x = x[:, :, 1:, :]  # remove cls token
-        else:
-            assert not bin, "bin = True and include_cls = True are not supported together, set one of them False."
-        if not bin:
-            desc = x.permute(0, 2, 3, 1).flatten(start_dim=-2, end_dim=-1).unsqueeze(dim=1)  # Bx1xtx(dxh)
-        else:
-            desc = self._log_bin(x)
-        return desc
-
-    def extract_saliency_maps(self, batch: torch.Tensor) -> torch.Tensor:
-        """
-        extract saliency maps. The saliency maps are extracted by averaging several attention heads from the last layer
-        in of the CLS token. All values are then normalized to range between 0 and 1.
-        :param batch: batch to extract saliency maps for. Has shape BxCxHxW.
-        :return: a tensor of saliency maps. has shape Bxt-1
-        """
-        assert self.model_type == "dino_vits8", f"saliency maps are supported only for dino_vits model_type."
-        self._extract_features(batch, [11], 'attn')
-        head_idxs = [0, 2, 4, 5]
-        curr_feats = self._feats[0]  # Bxhxtxt
-        cls_attn_map = curr_feats[:, head_idxs, 0, 1:].mean(dim=1)  # Bx(t-1)
-        temp_mins, temp_maxs = cls_attn_map.min(dim=1)[0], cls_attn_map.max(dim=1)[0]
-        cls_attn_maps = (cls_attn_map - temp_mins) / (temp_maxs - temp_mins)  # normalize to range [0,1]
-        return cls_attn_maps
-
-
-""" taken from https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse"""
-
-
-def str2bool(v):
-    if isinstance(v, bool):
-        return v
-    if v.lower() in ('yes', 'true', 't', 'y', '1'):
-        return True
-    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
-        return False
-    else:
-        raise argparse.ArgumentTypeError('Boolean value expected.')
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='Facilitate ViT Descriptor extraction.')
-    parser.add_argument('--image_path', type=str, required=True, help='path of the extracted image.')
-    parser.add_argument('--output_path', type=str, required=True, help='path to file containing extracted descriptors.')
-    parser.add_argument('--load_size', default=224, type=int, help='load size of the input image.')
-    parser.add_argument('--stride', default=4, type=int, help="""stride of first convolution layer. 
-                                                              small stride -> higher resolution.""")
-    parser.add_argument('--model_type', default='dino_vits8', type=str,
-                        help="""type of model to extract. 
-                        Choose from [dino_vits8 | dino_vits16 | dino_vitb8 | dino_vitb16 | vit_small_patch8_224 | 
-                        vit_small_patch16_224 | vit_base_patch8_224 | vit_base_patch16_224]""")
-    parser.add_argument('--facet', default='key', type=str, help="""facet to create descriptors from. 
-                                                                    options: ['key' | 'query' | 'value' | 'token']""")
-    parser.add_argument('--layer', default=11, type=int, help="layer to create descriptors from.")
-    parser.add_argument('--bin', default='False', type=str2bool, help="create a binned descriptor if True.")
-
-    args = parser.parse_args()
-
-    with torch.no_grad():
-        device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        extractor = ViTExtractor(args.model_type, args.stride, device=device)
-        image_batch, image_pil = extractor.preprocess(args.image_path, args.load_size)
-        print(f"Image {args.image_path} is preprocessed to tensor of size {image_batch.shape}.")
-        descriptors = extractor.extract_descriptors(image_batch.to(device), args.layer, args.facet, args.bin)
-        print(f"Descriptors are of size: {descriptors.shape}")
-        torch.save(descriptors, args.output_path)
-        print(f"Descriptors saved to: {args.output_path}")
diff --git "a/spaces/suchun/chatGPT_acdemic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243pdfminer.py" "b/spaces/suchun/chatGPT_acdemic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243pdfminer.py"
deleted file mode 100644
index ffbb05599ef09c9de25334ebeca2eef8022b9aaf..0000000000000000000000000000000000000000
--- "a/spaces/suchun/chatGPT_acdemic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243pdfminer.py"
+++ /dev/null
@@ -1,160 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-fast_debug = False
-
-def readPdf(pdfPath):
-    """
-    读取pdf文件，返回文本内容
-    """
-    import pdfminer
-    from pdfminer.pdfparser import PDFParser
-    from pdfminer.pdfdocument import PDFDocument
-    from pdfminer.pdfpage import PDFPage, PDFTextExtractionNotAllowed
-    from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
-    from pdfminer.pdfdevice import PDFDevice
-    from pdfminer.layout import LAParams
-    from pdfminer.converter import PDFPageAggregator
-
-    fp = open(pdfPath, 'rb')
-
-    # Create a PDF parser object associated with the file object
-    parser = PDFParser(fp)
-
-    # Create a PDF document object that stores the document structure.
-    # Password for initialization as 2nd parameter
-    document = PDFDocument(parser)
-    # Check if the document allows text extraction. If not, abort.
-    if not document.is_extractable:
-        raise PDFTextExtractionNotAllowed
-
-    # Create a PDF resource manager object that stores shared resources.
-    rsrcmgr = PDFResourceManager()
-
-    # Create a PDF device object.
-    # device = PDFDevice(rsrcmgr)
-
-    # BEGIN LAYOUT ANALYSIS.
-    # Set parameters for analysis.
-    laparams = LAParams(
-        char_margin=10.0,
-        line_margin=0.2,
-        boxes_flow=0.2,
-        all_texts=False,
-    )
-    # Create a PDF page aggregator object.
-    device = PDFPageAggregator(rsrcmgr, laparams=laparams)
-    # Create a PDF interpreter object.
-    interpreter = PDFPageInterpreter(rsrcmgr, device)
-
-    # loop over all pages in the document
-    outTextList = []
-    for page in PDFPage.create_pages(document):
-        # read the page into a layout object
-        interpreter.process_page(page)
-        layout = device.get_result()
-        for obj in layout._objs:
-            if isinstance(obj, pdfminer.layout.LTTextBoxHorizontal):
-                # print(obj.get_text())
-                outTextList.append(obj.get_text())
-
-    return outTextList
-
-
-def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, glob, os
-    from bs4 import BeautifulSoup
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        if ".tex" in fp:
-            with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-                file_content = f.read()
-        if ".pdf" in fp.lower():
-            file_content = readPdf(fp)
-            file_content = BeautifulSoup(''.join(file_content), features="lxml").body.text.encode('gbk', 'ignore').decode('gbk')
-
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
-        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug:
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                inputs=i_say, 
-                inputs_show_user=i_say_show_user, 
-                llm_kwargs=llm_kwargs,
-                chatbot=chatbot, 
-                history=[],
-                sys_prompt="总结文章。"
-            )  # 带超时倒计时
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if not fast_debug:
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=i_say, 
-            inputs_show_user=i_say, 
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, 
-            history=history,
-            sys_prompt="总结文章。"
-        )  # 带超时倒计时
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-
-@CatchException
-def 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量总结PDF文档，此版本使用pdfminer插件，带token约简功能。函数插件贡献者: Euclid-Jie。"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import pdfminer, bs4
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
-                    [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
-
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Dmelect 2012 Descargar.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Dmelect 2012 Descargar.md
deleted file mode 100644
index 2f3f1f8444ea71dd1e93c9c3d0b5b3c903542215..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Dmelect 2012 Descargar.md	
+++ /dev/null
@@ -1,11 +0,0 @@
-<br />
-<p>https://coub.com/stories/3042501-dmelect-20111-descargar-5-peaks. Descargar Dmelect Windows 10 epub. Inadvertently listed in order of the original product name, we are displaying the latest products here. Games. The single speaker-independent speaker. A music app. </p>
-<p> 1:14 PM on May 13, 2011. Dmelect 2012 Descargar. No downloads have been recorded yet! Some players are able to change the media transfer settings from ‘on’ to ‘off’ and still the video transfer will continue. Microsoft downloads his own applications for Windows 7, you can block accounts that has been given the ability to upgrade your computer. Be very careful with the instruction the media files. </p>
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-<p>Descargar Dmelect Windows 10 epub. It comes with a convenient and straightforward interface that does not need any help to use it. It’s even possible to set Dmelect 2010 9. The media experts. There is much more. </p>
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-<p></p> 899543212b<br />
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\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Julun Yeti Reshimgathi Instrumental Song Download PATCHED.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Julun Yeti Reshimgathi Instrumental Song Download PATCHED.md
deleted file mode 100644
index 7b25d1dfb7918dd8527a7ba8bfdb948621a828f6..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Julun Yeti Reshimgathi Instrumental Song Download PATCHED.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-
-<h1>How to Download Julun Yeti Reshimgathi Instrumental Song for Free</h1>
-<p>Julun Yeti Reshimgathi is a popular Marathi TV serial that aired on Zee Marathi from 2013 to 2015. The show featured a romantic story of Aditya and Meghana, two young professionals who fall in love despite their different backgrounds and personalities. The show also had a catchy title song that captured the essence of their relationship and became a hit among the viewers.</p>
-<p>If you are a fan of Julun Yeti Reshimgathi and want to download the instrumental version of the title song for free, you have come to the right place. In this article, we will show you how to find and download the song from various online sources. You can use the song as your ringtone, background music, or just enjoy listening to it.</p>
-<h2>Julun Yeti Reshimgathi Instrumental Song Download</h2><br /><p><b><b>DOWNLOAD</b> &#10002; &#10002; &#10002; <a href="https://cinurl.com/2uEY22">https://cinurl.com/2uEY22</a></b></p><br /><br />
-<h2>Method 1: SoundCloud</h2>
-<p>SoundCloud is a popular online platform for streaming and downloading music from various artists and genres. You can find many songs from Marathi TV serials on SoundCloud, including Julun Yeti Reshimgathi. Here are the steps to download the instrumental song from SoundCloud:</p>
-<ol>
-<li>Go to <a href="https://soundcloud.com/xhekkhar-kumar/julun-yeti-reshimgathi-full">this link</a>, which is the original upload of the full song by Xhekkhar Maghade[^1^]. You can also search for "Julun Yeti Reshimgathi Full Song" on SoundCloud.</li>
-<li>Click on the "More" button below the song title and select "Download file". You may need to sign in or create an account on SoundCloud to download the file.</li>
-<li>Save the file on your device. The file format is MP3 and the size is 4.9 MB.</li>
-<li>If you want to download only the instrumental part of the song, you can use an online audio editor like <a href="https://audiotrimmer.com/">AudioTrimmer</a> or <a href="https://mp3cut.net/">MP3Cut</a> to cut out the vocal part. You can also adjust the volume, speed, pitch, and other parameters of the song.</li>
-</ol>
-<h2>Method 2: YouTube</h2>
-<p>YouTube is another popular online platform for watching and downloading videos from various categories and topics. You can find many videos related to Julun Yeti Reshimgathi on YouTube, including the instrumental version of the title song. Here are the steps to download the instrumental song from YouTube:</p>
-<ol>
-<li>Go to <a href="https://www.youtube.com/watch?v=8Ok4TE-vDHs">this link</a>, which is a video of the background music track of Julun Yeti Reshimgathi[^3^]. You can also search for "Julun Yeti Reshimgathi Background Music Track" on YouTube.</li>
-<li>Copy the URL of the video from the address bar of your browser.</li>
-<li>Paste the URL into an online YouTube downloader like <a href="https://ytmp3.cc/en13/">YTMP3</a> or <a href="https://y2mate.com/">Y2Mate</a>. Choose MP3 as the output format and click on "Convert" or "Download".</li>
-<li>Save the file on your device. The file format is MP3 and the size is 2.6 MB.</li>
-<li>If you want to edit or trim the song, you can use an online audio editor like <a href="https://audiotrimmer.com/">AudioTrimmer</a> or <a href="https://mp3cut.net/">MP3Cut</a>.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to download Julun Yeti Reshimgathi instrumental song for free from SoundCloud and YouTube. You can use these methods to download other songs from Marathi TV serials as well. We hope you enjoy listening to Julun Yeti Reshimgathi instrumental song and relive</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Mp3gain Pro 107 Keygen BEST 75.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Mp3gain Pro 107 Keygen BEST 75.md
deleted file mode 100644
index c650b83d1eb3ea367bb8a0a51922cf87af2244d1..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Mp3gain Pro 107 Keygen BEST 75.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/szukevin/VISOR-GPT/train/tencentpretrain/embeddings/dual_embedding.py b/spaces/szukevin/VISOR-GPT/train/tencentpretrain/embeddings/dual_embedding.py
deleted file mode 100644
index 99723a88e060ae814e92849020bbade2f9694626..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/tencentpretrain/embeddings/dual_embedding.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from argparse import Namespace
-import torch.nn as nn
-import copy
-from tencentpretrain.layers.layer_norm import LayerNorm
-
-
-class DualEmbedding(nn.Module):
-    """
-    """
-    def __init__(self, args, vocab_size):
-        super(DualEmbedding, self).__init__()
-        from tencentpretrain.embeddings import str2embedding
-        from tencentpretrain.embeddings.embedding import Embedding
-
-        stream_0_args = copy.deepcopy(vars(args))
-        stream_0_args.update(args.stream_0)
-        stream_0_args = Namespace(**stream_0_args)
-        self.embedding_0 = Embedding(stream_0_args)
-        for embedding_name in stream_0_args.embedding:
-            self.embedding_0.update(str2embedding[embedding_name](stream_0_args, vocab_size), embedding_name)
-        self.stream_0_remove_embedding_layernorm = stream_0_args.remove_embedding_layernorm
-        if not self.stream_0_remove_embedding_layernorm:
-            self.stream_0_layer_norm = LayerNorm(stream_0_args.emb_size)
-
-        stream_1_args = copy.deepcopy(vars(args))
-        stream_1_args.update(args.stream_1)
-        stream_1_args = Namespace(**stream_1_args)
-        self.embedding_1 = Embedding(stream_1_args)
-        for embedding_name in stream_1_args.embedding:
-            self.embedding_1.update(str2embedding[embedding_name](stream_1_args, vocab_size), embedding_name)
-        self.stream_1_remove_embedding_layernorm = stream_1_args.remove_embedding_layernorm
-        if not self.stream_1_remove_embedding_layernorm:
-            self.stream_1_layer_norm = LayerNorm(stream_1_args.emb_size)
-        self.dropout = nn.Dropout(args.dropout)
-
-        if args.tie_weights:
-            self.embedding_0 = self.embedding_1
-
-    def forward(self, src, seg):
-        """
-        Args:
-            src: ([batch_size x seq_length], [batch_size x seq_length])
-            seg: ([batch_size x seq_length], [batch_size x seq_length])
-        Returns:
-            emb_0: [batch_size x seq_length x hidden_size]
-            emb_1: [batch_size x seq_length x hidden_size]
-        """
-        emb_0 = self.get_embedding_0(src[0], seg[0])
-        emb_1 = self.get_embedding_1(src[1], seg[1])
-
-        emb_0 = self.dropout(emb_0)
-        emb_1 = self.dropout(emb_1)
-
-        return emb_0, emb_1
-
-    def get_embedding_0(self, src, seg):
-        emb = self.embedding_0(src, seg)
-        if not self.stream_0_remove_embedding_layernorm:
-            emb = self.stream_0_layer_norm(emb)
-        return emb
-
-    def get_embedding_1(self, src, seg):
-        emb = self.embedding_1(src, seg)
-        if not self.stream_1_remove_embedding_layernorm:
-            emb = self.stream_1_layer_norm(emb)
-        return emb
diff --git a/spaces/taesiri/ChatGPT-ImageCaptioner/detic/modeling/debug.py b/spaces/taesiri/ChatGPT-ImageCaptioner/detic/modeling/debug.py
deleted file mode 100644
index 9c7c442eb8aa9474c8874ac1dc75659371e8c894..0000000000000000000000000000000000000000
--- a/spaces/taesiri/ChatGPT-ImageCaptioner/detic/modeling/debug.py
+++ /dev/null
@@ -1,334 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-import os
-
-COLORS = ((np.random.rand(1300, 3) * 0.4 + 0.6) * 255).astype(
-  np.uint8).reshape(1300, 1, 1, 3)
-
-def _get_color_image(heatmap):
-  heatmap = heatmap.reshape(
-    heatmap.shape[0], heatmap.shape[1], heatmap.shape[2], 1)
-  if heatmap.shape[0] == 1:
-      color_map = (heatmap * np.ones((1, 1, 1, 3), np.uint8) * 255).max(
-          axis=0).astype(np.uint8) # H, W, 3
-  else:
-      color_map = (heatmap * COLORS[:heatmap.shape[0]]).max(axis=0).astype(np.uint8) # H, W, 3
-
-  return color_map
-
-def _blend_image(image, color_map, a=0.7):
-  color_map = cv2.resize(color_map, (image.shape[1], image.shape[0]))
-  ret = np.clip(image * (1 - a) + color_map * a, 0, 255).astype(np.uint8)
-  return ret
-
-def _blend_image_heatmaps(image, color_maps, a=0.7):
-    merges = np.zeros((image.shape[0], image.shape[1], 3), np.float32)
-    for color_map in color_maps:
-        color_map = cv2.resize(color_map, (image.shape[1], image.shape[0]))
-        merges = np.maximum(merges, color_map)
-    ret = np.clip(image * (1 - a) + merges * a, 0, 255).astype(np.uint8)
-    return ret
-
-def _decompose_level(x, shapes_per_level, N):
-    '''
-    x: LNHiWi x C
-    '''
-    x = x.view(x.shape[0], -1)
-    ret = []
-    st = 0
-    for l in range(len(shapes_per_level)):
-        ret.append([])
-        h = shapes_per_level[l][0].int().item()
-        w = shapes_per_level[l][1].int().item()
-        for i in range(N):
-            ret[l].append(x[st + h * w * i:st + h * w * (i + 1)].view(
-                h, w, -1).permute(2, 0, 1))
-        st += h * w * N
-    return ret
-
-def _imagelist_to_tensor(images):
-    images = [x for x in images]
-    image_sizes = [x.shape[-2:] for x in images]
-    h = max([size[0] for size in image_sizes])
-    w = max([size[1] for size in image_sizes])
-    S = 32
-    h, w = ((h - 1) // S + 1) * S, ((w - 1) // S + 1) * S
-    images = [F.pad(x, (0, w - x.shape[2], 0, h - x.shape[1], 0, 0)) \
-        for x in images]
-    images = torch.stack(images)
-    return images
-
-
-def _ind2il(ind, shapes_per_level, N):
-    r = ind
-    l = 0
-    S = 0
-    while r - S >= N * shapes_per_level[l][0] * shapes_per_level[l][1]:
-        S += N * shapes_per_level[l][0] * shapes_per_level[l][1]
-        l += 1
-    i = (r - S) // (shapes_per_level[l][0] * shapes_per_level[l][1])
-    return i, l
-
-def debug_train(
-    images, gt_instances, flattened_hms, reg_targets, labels, pos_inds,
-    shapes_per_level, locations, strides):
-    '''
-    images: N x 3 x H x W
-    flattened_hms: LNHiWi x C
-    shapes_per_level: L x 2 [(H_i, W_i)]
-    locations: LNHiWi x 2
-    '''
-    reg_inds = torch.nonzero(
-        reg_targets.max(dim=1)[0] > 0).squeeze(1)
-    N = len(images)
-    images = _imagelist_to_tensor(images)
-    repeated_locations = [torch.cat([loc] * N, dim=0) \
-        for loc in locations]
-    locations = torch.cat(repeated_locations, dim=0)
-    gt_hms = _decompose_level(flattened_hms, shapes_per_level, N)
-    masks = flattened_hms.new_zeros((flattened_hms.shape[0], 1))
-    masks[pos_inds] = 1
-    masks = _decompose_level(masks, shapes_per_level, N)
-    for i in range(len(images)):
-        image = images[i].detach().cpu().numpy().transpose(1, 2, 0)
-        color_maps = []
-        for l in range(len(gt_hms)):
-            color_map = _get_color_image(
-                gt_hms[l][i].detach().cpu().numpy())
-            color_maps.append(color_map)
-            cv2.imshow('gthm_{}'.format(l), color_map)
-        blend = _blend_image_heatmaps(image.copy(), color_maps)
-        if gt_instances is not None:
-            bboxes = gt_instances[i].gt_boxes.tensor
-            for j in range(len(bboxes)):
-                bbox = bboxes[j]
-                cv2.rectangle(
-                    blend, 
-                    (int(bbox[0]), int(bbox[1])),
-                    (int(bbox[2]), int(bbox[3])),
-                    (0, 0, 255), 3, cv2.LINE_AA)
-    
-        for j in range(len(pos_inds)):
-            image_id, l = _ind2il(pos_inds[j], shapes_per_level, N)
-            if image_id != i:
-                continue
-            loc = locations[pos_inds[j]]
-            cv2.drawMarker(
-                blend, (int(loc[0]), int(loc[1])), (0, 255, 255),
-                markerSize=(l + 1) * 16)
-        
-        for j in range(len(reg_inds)):
-            image_id, l = _ind2il(reg_inds[j], shapes_per_level, N)
-            if image_id != i:
-                continue
-            ltrb = reg_targets[reg_inds[j]]
-            ltrb *= strides[l]
-            loc = locations[reg_inds[j]]
-            bbox = [(loc[0] - ltrb[0]), (loc[1] - ltrb[1]),
-                    (loc[0] + ltrb[2]), (loc[1] + ltrb[3])]
-            cv2.rectangle(
-                blend, 
-                (int(bbox[0]), int(bbox[1])),
-                (int(bbox[2]), int(bbox[3])),
-                (255, 0, 0), 1, cv2.LINE_AA)  
-            cv2.circle(blend, (int(loc[0]), int(loc[1])), 2, (255, 0, 0), -1)
-
-        cv2.imshow('blend', blend)
-        cv2.waitKey()
-
-
-def debug_test(
-    images, logits_pred, reg_pred, agn_hm_pred=[], preds=[], 
-    vis_thresh=0.3, debug_show_name=False, mult_agn=False):
-    '''
-    images: N x 3 x H x W
-    class_target: LNHiWi x C
-    cat_agn_heatmap: LNHiWi
-    shapes_per_level: L x 2 [(H_i, W_i)]
-    '''
-    N = len(images)
-    for i in range(len(images)):
-        image = images[i].detach().cpu().numpy().transpose(1, 2, 0)
-        result = image.copy().astype(np.uint8)
-        pred_image = image.copy().astype(np.uint8)
-        color_maps = []
-        L = len(logits_pred)
-        for l in range(L):
-            if logits_pred[0] is not None:
-                stride = min(image.shape[0], image.shape[1]) / min(
-                    logits_pred[l][i].shape[1], logits_pred[l][i].shape[2])
-            else:
-                stride = min(image.shape[0], image.shape[1]) / min(
-                    agn_hm_pred[l][i].shape[1], agn_hm_pred[l][i].shape[2])
-            stride = stride if stride < 60 else 64 if stride < 100 else 128
-            if logits_pred[0] is not None:
-                if mult_agn:
-                    logits_pred[l][i] = logits_pred[l][i] * agn_hm_pred[l][i]
-                color_map = _get_color_image(
-                    logits_pred[l][i].detach().cpu().numpy())
-                color_maps.append(color_map)
-                cv2.imshow('predhm_{}'.format(l), color_map)
-
-            if debug_show_name:
-                from detectron2.data.datasets.lvis_v1_categories import LVIS_CATEGORIES 
-                cat2name = [x['name'] for x in LVIS_CATEGORIES]
-            for j in range(len(preds[i].scores) if preds is not None else 0):
-                if preds[i].scores[j] > vis_thresh:
-                    bbox = preds[i].proposal_boxes[j] \
-                        if preds[i].has('proposal_boxes') else \
-                        preds[i].pred_boxes[j]
-                    bbox = bbox.tensor[0].detach().cpu().numpy().astype(np.int32)
-                    cat = int(preds[i].pred_classes[j]) \
-                        if preds[i].has('pred_classes') else 0
-                    cl = COLORS[cat, 0, 0]
-                    cv2.rectangle(
-                        pred_image, (int(bbox[0]), int(bbox[1])), 
-                        (int(bbox[2]), int(bbox[3])), 
-                        (int(cl[0]), int(cl[1]), int(cl[2])), 2, cv2.LINE_AA)
-                    if debug_show_name:
-                        txt = '{}{:.1f}'.format(
-                            cat2name[cat] if cat > 0 else '', 
-                            preds[i].scores[j])
-                        font = cv2.FONT_HERSHEY_SIMPLEX
-                        cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0]
-                        cv2.rectangle(
-                            pred_image,
-                            (int(bbox[0]), int(bbox[1] - cat_size[1] - 2)),
-                            (int(bbox[0] + cat_size[0]), int(bbox[1] - 2)), 
-                            (int(cl[0]), int(cl[1]), int(cl[2])), -1)
-                        cv2.putText(
-                            pred_image, txt, (int(bbox[0]), int(bbox[1] - 2)), 
-                            font, 0.5, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA)
-
-
-            if agn_hm_pred[l] is not None:
-                agn_hm_ = agn_hm_pred[l][i, 0, :, :, None].detach().cpu().numpy()
-                agn_hm_ = (agn_hm_ * np.array([255, 255, 255]).reshape(
-                    1, 1, 3)).astype(np.uint8)
-                cv2.imshow('agn_hm_{}'.format(l), agn_hm_)
-        blend = _blend_image_heatmaps(image.copy(), color_maps)
-        cv2.imshow('blend', blend)
-        cv2.imshow('preds', pred_image)
-        cv2.waitKey()
-
-global cnt
-cnt = 0
-
-def debug_second_stage(images, instances, proposals=None, vis_thresh=0.3, 
-    save_debug=False, debug_show_name=False, image_labels=[],
-    save_debug_path='output/save_debug/',
-    bgr=False):
-    images = _imagelist_to_tensor(images)
-    if 'COCO' in save_debug_path:
-        from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
-        cat2name = [x['name'] for x in COCO_CATEGORIES]
-    else:
-        from detectron2.data.datasets.lvis_v1_categories import LVIS_CATEGORIES
-        cat2name = ['({}){}'.format(x['frequency'], x['name']) \
-            for x in LVIS_CATEGORIES]
-    for i in range(len(images)):
-        image = images[i].detach().cpu().numpy().transpose(1, 2, 0).astype(np.uint8).copy()
-        if bgr:
-            image = image[:, :, ::-1].copy()
-        if instances[i].has('gt_boxes'):
-            bboxes = instances[i].gt_boxes.tensor.cpu().numpy()
-            scores = np.ones(bboxes.shape[0])
-            cats = instances[i].gt_classes.cpu().numpy()
-        else:
-            bboxes = instances[i].pred_boxes.tensor.cpu().numpy()
-            scores = instances[i].scores.cpu().numpy()
-            cats = instances[i].pred_classes.cpu().numpy()
-        for j in range(len(bboxes)):
-            if scores[j] > vis_thresh:
-                bbox = bboxes[j]
-                cl = COLORS[cats[j], 0, 0]
-                cl = (int(cl[0]), int(cl[1]), int(cl[2]))
-                cv2.rectangle(
-                    image, 
-                    (int(bbox[0]), int(bbox[1])),
-                    (int(bbox[2]), int(bbox[3])),
-                    cl, 2, cv2.LINE_AA)
-                if debug_show_name:
-                    cat = cats[j]
-                    txt = '{}{:.1f}'.format(
-                        cat2name[cat] if cat > 0 else '', 
-                        scores[j])
-                    font = cv2.FONT_HERSHEY_SIMPLEX
-                    cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0]
-                    cv2.rectangle(
-                        image,
-                        (int(bbox[0]), int(bbox[1] - cat_size[1] - 2)),
-                        (int(bbox[0] + cat_size[0]), int(bbox[1] - 2)), 
-                        (int(cl[0]), int(cl[1]), int(cl[2])), -1)
-                    cv2.putText(
-                        image, txt, (int(bbox[0]), int(bbox[1] - 2)), 
-                        font, 0.5, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA)
-        if proposals is not None:
-            proposal_image = images[i].detach().cpu().numpy().transpose(1, 2, 0).astype(np.uint8).copy()
-            if bgr:
-                proposal_image = proposal_image.copy()
-            else:
-                proposal_image = proposal_image[:, :, ::-1].copy()
-            bboxes = proposals[i].proposal_boxes.tensor.cpu().numpy()
-            if proposals[i].has('scores'):
-                scores = proposals[i].scores.detach().cpu().numpy()
-            else:
-                scores = proposals[i].objectness_logits.detach().cpu().numpy()
-            # selected = -1
-            # if proposals[i].has('image_loss'):
-            #     selected = proposals[i].image_loss.argmin()
-            if proposals[i].has('selected'):
-                selected = proposals[i].selected
-            else:
-                selected = [-1 for _ in range(len(bboxes))]
-            for j in range(len(bboxes)):
-                if scores[j] > vis_thresh or selected[j] >= 0:
-                    bbox = bboxes[j]
-                    cl = (209, 159, 83)
-                    th = 2
-                    if selected[j] >= 0:
-                        cl = (0, 0, 0xa4)
-                        th = 4
-                    cv2.rectangle(
-                        proposal_image, 
-                        (int(bbox[0]), int(bbox[1])),
-                        (int(bbox[2]), int(bbox[3])),
-                        cl, th, cv2.LINE_AA)
-                    if selected[j] >= 0 and debug_show_name:
-                        cat = selected[j].item()
-                        txt = '{}'.format(cat2name[cat])
-                        font = cv2.FONT_HERSHEY_SIMPLEX
-                        cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0]
-                        cv2.rectangle(
-                            proposal_image,
-                            (int(bbox[0]), int(bbox[1] - cat_size[1] - 2)),
-                            (int(bbox[0] + cat_size[0]), int(bbox[1] - 2)), 
-                            (int(cl[0]), int(cl[1]), int(cl[2])), -1)
-                        cv2.putText(
-                            proposal_image, txt, 
-                            (int(bbox[0]), int(bbox[1] - 2)), 
-                            font, 0.5, (0, 0, 0), thickness=1, 
-                            lineType=cv2.LINE_AA)
-
-        if save_debug:
-            global cnt
-            cnt = (cnt + 1) % 5000
-            if not os.path.exists(save_debug_path):
-                os.mkdir(save_debug_path)
-            save_name = '{}/{:05d}.jpg'.format(save_debug_path, cnt)
-            if i < len(image_labels):
-                image_label = image_labels[i]
-                save_name = '{}/{:05d}'.format(save_debug_path, cnt)
-                for x in image_label:
-                    class_name = cat2name[x]
-                    save_name = save_name + '|{}'.format(class_name)
-                save_name = save_name + '.jpg'
-            cv2.imwrite(save_name, proposal_image)
-        else:
-            cv2.imshow('image', image)
-            if proposals is not None:
-                cv2.imshow('proposals', proposal_image)
-            cv2.waitKey()
\ No newline at end of file
diff --git a/spaces/taskswithcode/semantic_similarity/app.py b/spaces/taskswithcode/semantic_similarity/app.py
deleted file mode 100644
index 25150f79e71293da463cb677da68c741dc74f43d..0000000000000000000000000000000000000000
--- a/spaces/taskswithcode/semantic_similarity/app.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import time
-import sys
-import streamlit as st
-import string
-from io import StringIO 
-import pdb
-import json
-from twc_embeddings import HFModel,SimCSEModel,SGPTModel
-from twc_openai_embeddings import OpenAIModel
-import torch
-import requests
-import socket
-
-
-MAX_INPUT = 5000
-
-SEM_SIMILARITY="1"
-DOC_RETRIEVAL="2"
-CLUSTERING="3"
-
-
-use_case = {"1":"Finding similar phrases/sentences","2":"Retrieving semantically matching information to a query. It may not be a factual match","3":"Clustering"}
-use_case_url = {"1":"https://huggingface.co/spaces/taskswithcode/semantic_similarity","2":"https://huggingface.co/spaces/taskswithcode/semantic_search","3":"https://huggingface.co/spaces/taskswithcode/semantic_clustering"}
-
-
-APP_NAME = "hf/semantic_similarity"
-INFO_URL = "https://www.taskswithcode.com/stats/"
-
-
-from transformers import BertTokenizer, BertForMaskedLM
-
-
-
-
-def get_views(action):
-    #print(f"in get views:outer:{action}")
-    ret_val = 0
-    hostname = socket.gethostname()
-    ip_address = socket.gethostbyname(hostname)
-    if ("view_count" not in st.session_state):
-        try:
-           #print("inside get views:api request")
-           app_info = {'name': APP_NAME,"action":action,"host":hostname,"ip":ip_address}
-           res = requests.post(INFO_URL, json = app_info).json()
-           print(res)
-           data = res["count"]
-        except:
-           data = 0
-        ret_val = data
-        st.session_state["view_count"] = data
-    else:
-        ret_val = st.session_state["view_count"]
-        if (action != "init"):
-           #print("non init:api request")
-           app_info = {'name': APP_NAME,"action":action,"host":hostname,"ip":ip_address}
-           res = requests.post(INFO_URL, json = app_info).json()
-    return "{:,}".format(ret_val)
-        
-        
-
-
-def construct_model_info_for_display(model_names):
-    options_arr  = []
-    markdown_str = f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\"><br/><b>Models evaluated ({len(model_names)})</b><br/><i>The selected models satisfy one or more of the following (1) state-of-the-art (2) the most downloaded models on Hugging Face (3) Large Language Models (e.g. GPT-3)</i></div>"
-    markdown_str += f"<div style=\"font-size:2px; color: #2f2f2f; text-align: left\"><br/></div>"
-    for node in model_names:
-        options_arr .append(node["name"])
-        if (node["mark"] == "True"):
-            markdown_str += f"<div style=\"font-size:16px; color: #5f5f5f; text-align: left\">&nbsp;•&nbsp;Model:&nbsp;<a href=\'{node['paper_url']}\' target='_blank'>{node['name']}</a><br/>&nbsp;&nbsp;&nbsp;&nbsp;Code released by:&nbsp;<a href=\'{node['orig_author_url']}\' target='_blank'>{node['orig_author']}</a><br/>&nbsp;&nbsp;&nbsp;&nbsp;Model info:&nbsp;<a href=\'{node['sota_info']['sota_link']}\' target='_blank'>{node['sota_info']['task']}</a></div>"
-            if ("Note" in node):
-                markdown_str += f"<div style=\"font-size:16px; color: #a91212; text-align: left\">&nbsp;&nbsp;&nbsp;&nbsp;{node['Note']}<a href=\'{node['alt_url']}\' target='_blank'>link</a></div>"
-            markdown_str += "<div style=\"font-size:16px; color: #5f5f5f; text-align: left\"><br/></div>"
-        
-    markdown_str += "<div style=\"font-size:12px; color: #9f9f9f; text-align: left\"><b>Note:</b><br/>•&nbsp;Uploaded files are loaded into non-persistent memory for the duration of the computation. They are not cached</div>"
-    limit = "{:,}".format(MAX_INPUT)
-    markdown_str += f"<div style=\"font-size:12px; color: #9f9f9f; text-align: left\">•&nbsp;User uploaded file has a maximum limit of {limit} sentences.</div>"
-    return options_arr,markdown_str
-
-
-st.set_page_config(page_title='TWC - Compare popular/state-of-the-art models for sentence similarity using sentence embeddings', page_icon="logo.jpg", layout='centered', initial_sidebar_state='auto',
-            menu_items={
-             'About': 'This app was created by taskswithcode. http://taskswithcode.com'
-             
-              })
-col,pad = st.columns([85,15])
-
-with col:
-    st.image("long_form_logo_with_icon.png")
-
-
-@st.experimental_memo
-def load_model(model_name,model_class,load_model_name):
-    try:
-        ret_model = None
-        obj_class = globals()[model_class]
-        ret_model = obj_class()
-        ret_model.init_model(load_model_name)
-        assert(ret_model is not None)
-    except Exception as e:
-        st.error(f"Unable to load model class:{model_class} model_name: {model_name} load_model_name: {load_model_name}   {str(e)}")
-        pass
-    return ret_model
-
-  
-@st.experimental_memo
-def cached_compute_similarity(input_file_name,sentences,_model,model_name,main_index):
-    texts,embeddings = _model.compute_embeddings(input_file_name,sentences,is_file=False)
-    results = _model.output_results(None,texts,embeddings,main_index)
-    return results
-
-
-def uncached_compute_similarity(input_file_name,sentences,_model,model_name,main_index):
-    with st.spinner('Computing vectors for sentences'):
-        texts,embeddings = _model.compute_embeddings(input_file_name,sentences,is_file=False)
-        results = _model.output_results(None,texts,embeddings,main_index)
-    #st.success("Similarity computation complete")
-    return results
-
-DEFAULT_HF_MODEL = "sentence-transformers/paraphrase-MiniLM-L6-v2"
-def get_model_info(model_names,model_name):
-    for node in model_names:
-        if (model_name == node["name"]):
-            return node,model_name
-    return get_model_info(model_names,DEFAULT_HF_MODEL)
-
-def run_test(model_names,model_name,input_file_name,sentences,display_area,main_index,user_uploaded,custom_model):
-    display_area.text("Loading model:" + model_name)
-    #Note. model_name may get mapped to new name in the call below for custom models
-    orig_model_name = model_name
-    model_info,model_name = get_model_info(model_names,model_name)
-    if (model_name != orig_model_name):
-        load_model_name  = orig_model_name
-    else:
-        load_model_name = model_info["model"]
-    if ("Note" in model_info):
-        fail_link = f"{model_info['Note']} [link]({model_info['alt_url']})"
-        display_area.write(fail_link)
-    if (user_uploaded and "custom_load" in model_info and model_info["custom_load"] == "False"):
-        fail_link = f"{model_info['Note']} [link]({model_info['alt_url']})"
-        display_area.write(fail_link)
-        return {"error":fail_link}
-    model = load_model(model_name,model_info["class"],load_model_name)
-    display_area.text("Model " + model_name  + " load complete")
-    try:
-            if (user_uploaded):
-                results = uncached_compute_similarity(input_file_name,sentences,model,model_name,main_index)
-            else:
-                display_area.text("Computing vectors for sentences")
-                results = cached_compute_similarity(input_file_name,sentences,model,model_name,main_index)
-                display_area.text("Similarity computation complete")
-            return results
-            
-    except Exception as e:
-        st.error("Some error occurred during prediction" + str(e))
-        st.stop()
-    return {}
-
-
-
-    
-
-def display_results(orig_sentences,main_index,results,response_info,app_mode,model_name):
-    main_sent = f"<div style=\"font-size:14px; color: #2f2f2f; text-align: left\">{response_info}<br/><br/></div>"
-    main_sent += f"<div style=\"font-size:14px; color: #2f2f2f; text-align: left\">Showing results for model:&nbsp;<b>{model_name}</b></div>"
-    score_text = "cosine distance" if app_mode == SEM_SIMILARITY else "cosine distance/score"
-    pivot_name = "main sentence" if app_mode == SEM_SIMILARITY else "query"
-    main_sent += f"<div style=\"font-size:14px; color: #6f6f6f; text-align: left\">Results sorted by {score_text}. Closest to furthest away from {pivot_name}</div>"
-    pivot_name = pivot_name[0].upper() + pivot_name[1:]
-    main_sent += f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\"><b>{pivot_name}:</b>&nbsp;&nbsp;{orig_sentences[main_index]}</div>"
-    body_sent = []
-    download_data = {}
-    first = True
-    for key in results:
-        if (app_mode == DOC_RETRIEVAL and first):
-            first = False
-            continue
-        index = orig_sentences.index(key) + 1
-        body_sent.append(f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\">{index}]&nbsp;{key}&nbsp;&nbsp;&nbsp;<b>{results[key]:.2f}</b></div>")
-        download_data[key] =  f"{results[key]:.2f}" 
-    main_sent = main_sent + "\n" + '\n'.join(body_sent)
-    st.markdown(main_sent,unsafe_allow_html=True)
-    st.session_state["download_ready"] = json.dumps(download_data,indent=4)
-    get_views("submit")
-    
-
-
-def init_session():
-    if ("model_name" not in st.session_state):
-        print("Performing init session")
-        st.session_state["download_ready"] = None    
-        st.session_state["model_name"] = "ss_test"
-        st.session_state["main_index"] = 1
-        st.session_state["file_name"] = "default"
-    else:
-        print("****Skipping init session")
- 
-def app_main(app_mode,example_files,model_name_files):
-  init_session()
-  with open(example_files) as fp:
-        example_file_names = json.load(fp) 
-  with open(model_name_files) as fp:
-        model_names = json.load(fp)
-  curr_use_case = use_case[app_mode].split(".")[0]
-  st.markdown("<h5 style='text-align: center;'>Compare state-of-the-art/popular models for sentence similarity using sentence embeddings</h5>", unsafe_allow_html=True)
-  st.markdown(f"<p style='font-size:14px; color: #4f4f4f; text-align: center'><i>Or compare your own model with state-of-the-art/popular models</p>", unsafe_allow_html=True)
-  st.markdown(f"<div style='color: #4f4f4f; text-align: left'>Use cases for sentence embeddings<br/>&nbsp;&nbsp;&nbsp;•&nbsp;&nbsp;{use_case['1']}<br/>&nbsp;&nbsp;&nbsp;•&nbsp;&nbsp;<a href=\'{use_case_url['2']}\' target='_blank'>{use_case['2']}</a><br/>&nbsp;&nbsp;&nbsp;•&nbsp;&nbsp;<a href=\'{use_case_url['3']}\' target='_blank'>{use_case['3']}</a><br/><i>This app illustrates <b>'{curr_use_case}'</b> use case</i></div>", unsafe_allow_html=True)
-  st.markdown(f"<div style='color: #9f9f9f; text-align: right'>views:&nbsp;{get_views('init')}</div>", unsafe_allow_html=True)
-
-
-  try:
-      
-      
-      with st.form('twc_form'):
-
-        step1_line = "Upload text file(one sentence in a line) or choose an example text file below"
-        if (app_mode ==  DOC_RETRIEVAL):
-            step1_line += ". The first line is treated as the query"
-        uploaded_file = st.file_uploader(step1_line, type=".txt")
-
-        selected_file_index = st.selectbox(label=f'Example files ({len(example_file_names)})',  
-                    options = list(dict.keys(example_file_names)), index=0,  key = "twc_file")
-        st.write("")
-        options_arr,markdown_str = construct_model_info_for_display(model_names)
-        selection_label = 'Select Model'
-        selected_model = st.selectbox(label=selection_label,  
-                    options = options_arr, index=0,  key = "twc_model")
-        st.write("")
-        custom_model_selection = st.text_input("Model not listed above? Type any Hugging Face sentence similarity model name ", "",key="custom_model")
-        hf_link_str = "<div style=\"font-size:12px; color: #9f9f9f; text-align: left\"><a href='https://huggingface.co/models?pipeline_tag=sentence-similarity' target = '_blank'>List of Hugging Face sentence similarity models</a><br/><br/><br/></div>"
-        st.markdown(hf_link_str, unsafe_allow_html=True)
-        if (app_mode == SEM_SIMILARITY):
-            main_index = st.number_input('Enter index of sentence in file to make it the main sentence',value=1,min_value = 1)
-        else:
-            main_index = 1
-        st.write("")
-        submit_button = st.form_submit_button('Run')
-
-        
-        input_status_area = st.empty()
-        display_area = st.empty()
-        if submit_button:
-            print("Inside submit")
-            start = time.time()
-            if uploaded_file is not None:
-                st.session_state["file_name"]  = uploaded_file.name
-                sentences = StringIO(uploaded_file.getvalue().decode("utf-8")).read()
-            else:
-                st.session_state["file_name"]  = example_file_names[selected_file_index]["name"]
-                sentences = open(example_file_names[selected_file_index]["name"]).read()
-            sentences = sentences.split("\n")[:-1]
-            if (len(sentences) < main_index):
-                main_index = len(sentences)
-                st.info("Selected sentence index is larger than number of sentences in file. Truncating to " + str(main_index)) 
-            if (len(sentences) > MAX_INPUT):
-                st.info(f"Input sentence count exceeds maximum sentence limit. First {MAX_INPUT} out of {len(sentences)} sentences chosen")
-                sentences = sentences[:MAX_INPUT]
-            if (len(custom_model_selection) != 0):
-                run_model = custom_model_selection
-            else:
-                run_model = selected_model
-            st.session_state["model_name"] = run_model
-            st.session_state["main_index"] = main_index
-                  
-            results = run_test(model_names,run_model,st.session_state["file_name"],sentences,display_area,main_index - 1,(uploaded_file is not None),(len(custom_model_selection) != 0))
-            display_area.empty()
-            with display_area.container():
-                if ("error" in results):
-                    st.error(results["error"])
-                else:
-                    device = 'GPU' if torch.cuda.is_available() else 'CPU'
-                    response_info = f"Computation time on {device}: {time.time() - start:.2f} secs for {len(sentences)} sentences"
-                    if (len(custom_model_selection) != 0):
-                        st.info("Custom model overrides model selection in step 2 above. So please clear the custom model text box to choose models from step 2")
-                    display_results(sentences,main_index - 1,results,response_info,app_mode,run_model)
-                    #st.json(results)
-      st.download_button(
-         label="Download results as json",
-         data= st.session_state["download_ready"] if st.session_state["download_ready"] != None else "",
-         disabled = False if st.session_state["download_ready"] != None else True,
-         file_name= (st.session_state["model_name"] + "_" +  str(st.session_state["main_index"]) + "_" + '_'.join(st.session_state["file_name"].split(".")[:-1]) + ".json").replace("/","_"),
-         mime='text/json',
-         key ="download" 
-        )
-      
-      
-
-  except Exception as e:
-    st.error("Some error occurred during loading" + str(e))
-    st.stop()  
-	
-  st.markdown(markdown_str, unsafe_allow_html=True)
-  
- 
-
-if __name__ == "__main__":
-   #print("comand line input:",len(sys.argv),str(sys.argv))
-   #app_main(sys.argv[1],sys.argv[2],sys.argv[3])
-   app_main("1","sim_app_examples.json","sim_app_models.json")
-   #app_main("2","doc_app_examples.json","doc_app_models.json")
-
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Fly Emirates Font EK2003Regularzip.md b/spaces/terfces0erbo/CollegeProjectV2/Fly Emirates Font EK2003Regularzip.md
deleted file mode 100644
index 2f66ed579b4e89f65df43c164eef7c172eaaf810..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Fly Emirates Font EK2003Regularzip.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Fly Emirates Font EK2003Regularzip</h2><br /><p><b><b>DOWNLOAD</b> &#10022; <a href="https://bytlly.com/2uGlzx">https://bytlly.com/2uGlzx</a></b></p><br /><br />
-
-Welcome to the third edition of this popular information technology website. Nowadays you can find most of the information on the Internet through various search engines like Google, Yahoo and Bing. She has also been featured on technology websites such as eHow and eWEEK. I have chosen the top 31 sites of all time that have helped me through different stages of my life. 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/test12356/SUI-svc-3.0/models.py b/spaces/test12356/SUI-svc-3.0/models.py
deleted file mode 100644
index 5d8f154887a43a5c5f67cf6340f74268398e32d5..0000000000000000000000000000000000000000
--- a/spaces/test12356/SUI-svc-3.0/models.py
+++ /dev/null
@@ -1,351 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import attentions
-import commons
-import modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-from vdecoder.hifigan.models import Generator
-from utils import f0_to_coarse
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class Encoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    # print(x.shape,x_lengths.shape)
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0,
-      filter_channels=None,
-      n_heads=None,
-      p_dropout=None):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    self.f0_emb = nn.Embedding(256, hidden_channels)
-
-    self.enc_ =  attentions.Encoder(
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-
-  def forward(self, x, x_lengths, f0=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = x + self.f0_emb(f0).transpose(1,2)
-    x = self.enc_(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-
-    return z, m, logs, x_mask
-
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-        
-        
-class SpeakerEncoder(torch.nn.Module):
-    def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
-        super(SpeakerEncoder, self).__init__()
-        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
-        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
-        self.relu = nn.ReLU()
-
-    def forward(self, mels):
-        self.lstm.flatten_parameters()
-        _, (hidden, _) = self.lstm(mels)
-        embeds_raw = self.relu(self.linear(hidden[-1]))
-        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-        
-    def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
-        mel_slices = []
-        for i in range(0, total_frames-partial_frames, partial_hop):
-            mel_range = torch.arange(i, i+partial_frames)
-            mel_slices.append(mel_range)
-            
-        return mel_slices
-    
-    def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
-        mel_len = mel.size(1)
-        last_mel = mel[:,-partial_frames:]
-        
-        if mel_len > partial_frames:
-            mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
-            mels = list(mel[:,s] for s in mel_slices)
-            mels.append(last_mel)
-            mels = torch.stack(tuple(mels), 0).squeeze(1)
-        
-            with torch.no_grad():
-                partial_embeds = self(mels)
-            embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
-            #embed = embed / torch.linalg.norm(embed, 2)
-        else:
-            with torch.no_grad():
-                embed = self(last_mel)
-        
-        return embed
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    gin_channels,
-    ssl_dim,
-    n_speakers,
-    **kwargs):
-
-    super().__init__()
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.gin_channels = gin_channels
-    self.ssl_dim = ssl_dim
-    self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-    self.enc_p_ = TextEncoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16,0, filter_channels, n_heads, p_dropout)
-    hps = {
-        "sampling_rate": 48000,
-        "inter_channels": 192,
-        "resblock": "1",
-        "resblock_kernel_sizes": [3, 7, 11],
-        "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-        "upsample_rates": [10, 8, 2, 2],
-        "upsample_initial_channel": 512,
-        "upsample_kernel_sizes": [16, 16, 4, 4],
-        "gin_channels": 256,
-    }
-    self.dec = Generator(h=hps)
-    self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-  def forward(self, c, f0, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
-    if c_lengths == None:
-      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
-    if spec_lengths == None:
-      spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
-
-    g = self.emb_g(g).transpose(1,2)
-
-    z_ptemp, m_p, logs_p, _ = self.enc_p_(c, c_lengths, f0=f0_to_coarse(f0))
-    z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) 
-
-    z_p = self.flow(z, spec_mask, g=g)
-    z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, f0, spec_lengths, self.segment_size)
-
-    # o = self.dec(z_slice, g=g)
-    o = self.dec(z_slice, g=g, f0=pitch_slice)
-
-    return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, c, f0, g=None, mel=None, c_lengths=None):
-    if c_lengths == None:
-      c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
-    g = self.emb_g(g).transpose(1,2)
-
-    z_p, m_p, logs_p, c_mask = self.enc_p_(c, c_lengths, f0=f0_to_coarse(f0))
-    z = self.flow(z_p, c_mask, g=g, reverse=True)
-
-    o = self.dec(z * c_mask, g=g, f0=f0)
-
-    return o
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Greys Anatomy Season 1 720p Download.md b/spaces/tialenAdioni/chat-gpt-api/logs/Greys Anatomy Season 1 720p Download.md
deleted file mode 100644
index a1f854c7b5c5b1fe93d1e9ed9d6927121b43c2ba..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Greys Anatomy Season 1 720p Download.md	
+++ /dev/null
@@ -1,29 +0,0 @@
-
-Here is a possible title and article for the keyword "Grey's Anatomy Season 1 720p Download":
-
-<h1>How to Download Grey's Anatomy Season 1 in High Quality</h1>
-<p>If you are a fan of medical dramas, you might be interested in watching <em>Grey's Anatomy</em>, one of the most popular and longest-running shows in the genre. The first season of <em>Grey's Anatomy</em> introduces us to the lives and challenges of a group of surgical interns at Seattle Grace Hospital, led by the brilliant but troubled Dr. Meredith Grey.</p>
-<h2>Grey's Anatomy Season 1 720p Download</h2><br /><p><b><b>Download Zip</b>  <a href="https://urlcod.com/2uK9Yr">https://urlcod.com/2uK9Yr</a></b></p><br /><br />
-<p>In this article, we will show you how to download <em>Grey's Anatomy Season 1</em> in high quality (720p) for free and legally. You will also learn more about the plot, the cast, and the reviews of the first season of this acclaimed show.</p>
-
-<h2>Why Watch Grey's Anatomy Season 1?</h2>
-<p><em>Grey's Anatomy Season 1</em> consists of nine episodes that aired from March to May 2005 on ABC. The season received positive reviews from critics and audiences alike, who praised the writing, the acting, and the realism of the medical cases. The season also won several awards, including a Golden Globe for Best Television Series - Drama and an Emmy for Outstanding Casting for a Drama Series.</p>
-<p>The season follows the first year of residency for five interns: Meredith Grey (Ellen Pompeo), Cristina Yang (Sandra Oh), Izzie Stevens (Katherine Heigl), George O'Malley (T.R. Knight), and Alex Karev (Justin Chambers). They have to deal with the demands and pressures of their profession, as well as their personal relationships and secrets. Along the way, they encounter various patients, mentors, and colleagues, such as Dr. Derek Shepherd (Patrick Dempsey), Dr. Miranda Bailey (Chandra Wilson), Dr. Richard Webber (James Pickens Jr.), and Dr. Addison Montgomery (Kate Walsh).</p>
-<p>Some of the memorable episodes from <em>Grey's Anatomy Season 1</em> include:</p>
-<ul>
-<li><strong>A Hard Day's Night</strong>: The pilot episode introduces us to the main characters and their first day as interns at Seattle Grace Hospital.</li>
-<li><strong>The First Cut Is the Deepest</strong>: Meredith treats a rape victim who has part of her attacker's penis inside her, while Izzie struggles with a language barrier with a patient who needs a heart transplant.</li>
-<li><strong>If Tomorrow Never Comes</strong>: George performs his first surgery on a patient who needs a new liver, while Meredith discovers that her mother has Alzheimer's disease.</li>
-<li><strong>The Self-Destruct Button</strong>: Izzie falls in love with a patient who has a brain tumor, while Cristina finds out that she is pregnant with Burke's baby.</li>
-<li><strong>Who's Zoomin' Who?</strong>: The season finale reveals that Derek is married to Addison, who arrives at Seattle Grace to confront him and Meredith.</li>
-</ul>
-
-<h2>How to Download Grey's Anatomy Season 1 in 720p?</h2>
-<p>If you want to watch <em>Grey's Anatomy Season 1</em> in high quality (720p), you have several options to download it for free and legally. Here are some of them:</p>
-<p></p>
-<ul>
-<li><strong>ABC.com</strong>: You can stream all the episodes of <em>Grey's Anatomy Season 1</em> on the official website of ABC[^2^]. You will need to sign in with your TV provider or create an account to access the full episodes. You can also download the ABC app on your mobile device or smart TV and watch them there.</li>
-<li><strong>JustWatch.com</strong>: You can use this website to find out where you can watch or download <em>Grey's Anatomy Season 1</em> online[^3^]. You can filter by price, quality, genre, and more. You can also see which streaming services offer <em>Grey's Anatomy Season 1</em>, such as Netflix, Hulu, Amazon Prime Video, Disney+, etc.</li>
-<li><strong>Torrent sites</strong>: You can also use torrent sites to download <em>Grey's Anatomy Season 1</em> in 720p. However, this method is not recommended as it may be illegal or unsafe. You may encounter viruses, malware, or copyright infringement</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/timqian/like-history/static/css/main.3cb50c92.css b/spaces/timqian/like-history/static/css/main.3cb50c92.css
deleted file mode 100644
index e3d091584a17c82cb28d2861768dab92f5947ad0..0000000000000000000000000000000000000000
--- a/spaces/timqian/like-history/static/css/main.3cb50c92.css
+++ /dev/null
@@ -1,4 +0,0 @@
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Archicad11freedownloadcrack WORK.md b/spaces/tioseFevbu/cartoon-converter/scripts/Archicad11freedownloadcrack WORK.md
deleted file mode 100644
index bf0a5c0571021b7217ffa601722e0e3bbafa1147..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Archicad11freedownloadcrack WORK.md	
+++ /dev/null
@@ -1,101 +0,0 @@
-<br />
-<h1>Archicad 11 Free Download Crack: How to Get the Best 3D CAD Software for Architects</h1>
- <p>If you are an architect or a designer, you know how important it is to have a powerful and versatile software that can help you create stunning 3D models, renderings, and animations of your projects. You also know how expensive such software can be, especially if you want to use the latest version with all the features and updates.</p>
- <p>That's why you might be interested in finding out how to download Archicad 11 for free with crack. Archicad 11 is one of the best 3D CAD software for architects, developed by Graphisoft, a Hungarian company that specializes in architectural design software. Archicad 11 was released in 2007 and it offers many advantages over other CAD software, such as:</p>
-<h2>archicad11freedownloadcrack</h2><br /><p><b><b>Download Zip</b> &#9733; <a href="https://urlcod.com/2uHy2V">https://urlcod.com/2uHy2V</a></b></p><br /><br />
- <ul>
-<li>It has a user-friendly interface that allows you to work faster and easier.</li>
-<li>It supports parametric design, which means you can change any aspect of your model without affecting the rest.</li>
-<li>It has a built-in BIM (Building Information Modeling) system, which means you can store and manage all the information related to your project in one place.</li>
-<li>It has a powerful rendering engine that can produce realistic and high-quality images and animations of your project.</li>
-<li>It has a wide range of tools and features that can help you with every stage of your project, from conceptual design to construction documentation.</li>
-</ul>
- <p>In this article, we will show you how to download Archicad 11 for free with crack, so you can enjoy all these benefits without spending a fortune. We will guide you through three simple steps:</p>
- <ol>
-<li>Download Archicad 11 from a reliable source.</li>
-<li>Install Archicad 11 on your computer.</li>
-<li>Apply the crack to activate Archicad 11.</li>
-</ol>
- <p>By following these steps, you will be able to get Archicad 11 for free with crack in no time. However, before we start, we want to warn you that downloading cracked software is illegal and risky. You might face legal consequences if you get caught by the authorities or by Graphisoft. You might also expose your computer to viruses and malware that can damage your system or steal your personal data. Therefore, we do not recommend or endorse downloading cracked software. We only provide this information for educational purposes only. If you want to use Archicad 11 legally and safely, we suggest you buy it from Graphisoft's official website or from an authorized reseller.</p>
- <h2>Step 1: Download Archicad 11 from a reliable source</h2>
- <p>The first step to get Archicad 11 for free with crack is to download the setup file from a reliable source. There are many websites that claim to offer free downloads of Archicad 11, but not all of them are trustworthy. Some of them might contain fake or corrupted files, or worse, malicious software that can harm your computer or steal your data. Therefore, you need to be careful and choose a website that has a good reputation and positive reviews from other users. Here are some tips on how to choose a trustworthy website to download Archicad 11:</p>
- <ul>
-<li>Check the domain name and the URL of the website. Avoid websites that have suspicious or unfamiliar domain names, such as .ru, .cn, .tk, .biz, etc. Also, avoid websites that have long or complicated URLs that contain random letters and numbers, such as https://www.archicad11freedownloadcrack.xyz/1234567890abcdefg.</li>
-<li>Check the design and the content of the website. Avoid websites that have poor or outdated design, such as low-quality images, broken links, misspelled words, etc. Also, avoid websites that have irrelevant or spammy content, such as ads, pop-ups, surveys, etc.</li>
-<li>Check the download link and the file size of Archicad 11. Avoid websites that have suspicious or hidden download links, such as links that redirect you to another website, links that require you to complete a survey or an offer, links that ask you to enter your personal information, etc. Also, avoid websites that have unrealistic or inaccurate file sizes of Archicad 11, such as too small (less than 1 GB) or too large (more than 10 GB).</li>
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- <p>Based on these criteria, we have found a website that seems to be reliable and safe to download Archicad 11 for free with crack. The website is called [Archicad Crack] and it has the following features:</p>
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-<li>It has a simple and clean domain name and URL: https://archicadcrack.com/.</li>
-<li>It has a modern and professional design and content: it has high-quality images, clear instructions, relevant information, and no ads or pop-ups.</li>
-<li>It has a direct and visible download link and a reasonable file size of Archicad 11: it has a button that says "Download Archicad 11 Crack" and the file size is about 4 GB.</li>
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- <p>Therefore, we recommend you to use this website to download Archicad 11 for free with crack. However, we still advise you to be cautious and scan the file with an antivirus software before opening it.</p>
- <h2>Step 2: Install Archicad 11 on your computer</h2>
- <p>The second step to get Archicad 11 for free with crack is to install the setup file on your computer. To do this, you need to follow these steps:</p>
- <ol>
-<li>Locate the downloaded file on your computer. It should be in a ZIP or RAR format, which means it is compressed and needs to be extracted first. To extract it, you need to use a software like WinRAR or 7-Zip. Right-click on the file and select "Extract here" or "Extract to folder". This will create a new folder with the same name as the file.</li>
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-<li>Follow the installation wizard that will guide you through the installation process. You will need to accept the license agreement, choose the destination folder, select the components you want to install, etc. You can use the default options or customize them according to your preferences.</li>
-<li>Wait for the installation to finish. It might take several minutes depending on your computer's speed and performance. When it is done, you will see a message that says "Installation completed successfully". Click on "Finish" to exit the wizard.</li>
-</ol>
- <p>Congratulations! You have successfully installed Archicad 11 on your computer. However, you are not done yet. You still need to apply the crack to activate Archicad 11.</p>
- <h2>Step 3: Apply the crack to activate Archicad 11</ <p>The third and final step to get Archicad 11 for free with crack is to apply the crack to activate Archicad 11. The crack is a file that modifies or replaces some of the original files of Archicad 11, so that it bypasses the activation process and makes it think that it is a licensed version. To apply the crack, you need to follow these steps:</p>
- <ol>
-<li>Go back to the folder where you extracted the downloaded file of Archicad 11. Look for another ZIP or RAR file that contains the crack. It should have a name like "Archicad-11-Crack.zip" or "Archicad-11-Crack.rar". Extract it using WinRAR or 7-Zip, just like you did before.</li>
-<li>Open the extracted folder and look for the crack file of Archicad 11. It should be an EXE or DLL file with a name like "Archicad-11-Crack.exe" or "Archicad-11-Crack.dll". Copy it by right-clicking on it and selecting "Copy".</li>
-<li>Go to the folder where you installed Archicad 11 on your computer. It should be in a location like "C:\Program Files\Graphisoft\Archicad 11". Paste the crack file by right-clicking on an empty space and selecting "Paste". You will see a message that asks you if you want to replace the existing file. Click on "Yes" to confirm.</li>
-<li>Run Archicad 11 by clicking on its shortcut on your desktop or in your start menu. You will see a message that says "Archicad 11 has been successfully activated". Click on "OK" to continue.</li>
-</ol>
- <p>Congratulations! You have successfully applied the crack and activated Archicad 11. You can now use Archicad 11 for free with all its features and updates.</p>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to download Archicad 11 for free with crack, so you can get the best 3D CAD software for architects without spending a fortune. We have guided you through three simple steps:</p>
- <ol>
-<li>Download Archicad 11 from a reliable source.</li>
-<li>Install Archicad 11 on your computer.</li>
-<li>Apply the crack to activate Archicad 11.</li>
-</ol>
- <p>By following these steps, you will be able to get Archicad 11 for free with crack in no time. However, we want to remind you that downloading cracked software is illegal and risky. You might face legal consequences if you get caught by the authorities or by Graphisoft. You might also expose your computer to viruses and malware that can damage your system or steal your personal data. Therefore, we do not recommend or endorse downloading cracked software. We only provide this information for educational purposes only. If you want to use Archicad 11 legally and safely, we suggest you buy it from Graphisoft's official website or from an authorized reseller.</p>
- <p>We hope you found this article helpful and informative. If you have any questions or comments, please feel free to leave them below. We would love to hear from you and help you with any issues you might have. Thank you for reading and happy designing!</p>
- <h2>Frequently Asked Questions</h2>
- <p>Here are some of the most common questions that people ask about Archicad 11 free download crack:</p>
- <h3>Q: Is Archicad 11 compatible with Windows 10?</h3>
- <p>A: Yes, Archicad 11 is compatible with Windows 10, as well as Windows XP, Vista, 7, and 8. However, you might need to run it in compatibility mode or as an administrator if you encounter any problems.</p>
- <h3>Q: Is Archicad 11 compatible with Mac OS?</h3>
- <p>A: No, Archicad 11 is not compatible with Mac OS. It is only available for Windows platforms. If you want to use Archicad on Mac OS, you need to use a newer version of Archicad, such as Archicad 24.</p>
- <h3>Q: What are the system requirements for Archicad 11?</h3>
- <p>A: The minimum system requirements for Archicad 11 are:</p>
- <ul>
-<li>Processor: Intel Pentium IV or compatible (1 GHz or higher)</li>
-<li>Memory: 512 MB RAM (1 GB recommended)</li>
-<li>Hard disk space: 1 GB free space (2 GB recommended)</li>
-<li>Video card: OpenGL compatible graphics card (128 MB or higher)</li>
-<li>Display: 1024 x 768 resolution (1280 x 1024 recommended <li>Operating system: Windows XP, Vista, 7, 8, or 10</li>
-</ul>
- <p>The recommended system requirements for Archicad 11 are:</p>
- <ul>
-<li>Processor: Intel Core 2 Duo or compatible (2 GHz or higher)</li>
-<li>Memory: 2 GB RAM (4 GB or higher recommended)</li>
-<li>Hard disk space: 2 GB free space (4 GB or higher recommended)</li>
-<li>Video card: OpenGL compatible graphics card (256 MB or higher)</li>
-<li>Display: 1280 x 1024 resolution (1600 x 1200 or higher recommended)</li>
-<li>Operating system: Windows XP, Vista, 7, 8, or 10</li>
-</ul>
- <h3>Q: How can I update Archicad 11 to the latest version?</h3>
- <p>A: You can update Archicad 11 to the latest version by downloading and installing the latest patch from Graphisoft's website. The latest patch for Archicad 11 is version 11.0.9, which was released in 2009. You can find it here: [Archicad 11 Patch] . However, you should be aware that updating Archicad 11 might affect the crack and cause it to stop working. Therefore, you might need to reapply the crack after updating Archicad 11.</p>
- <h3>Q: How can I uninstall Archicad 11 from my computer?</h3>
- <p>A: You can uninstall Archicad 11 from your computer by following these steps:</p>
- <ol>
-<li>Go to the Control Panel and select "Programs and Features" or "Add or Remove Programs".</li>
-<li>Find and select "Archicad 11" from the list of programs and click on "Uninstall" or "Remove".</li>
-<li>Follow the uninstallation wizard that will guide you through the uninstallation process. You will need to confirm that you want to uninstall Archicad 11 and choose whether you want to keep or delete your user data and preferences.</li>
-<li>Wait for the uninstallation to finish. It might take a few minutes depending on your computer's speed and performance. When it is done, you will see a message that says "Uninstallation completed successfully". Click on "Finish" to exit the wizard.</li>
-</ol>
- <p>You have successfully uninstalled Archicad 11 from your computer. You can also delete the downloaded and extracted files of Archicad 11 and the crack if you want to free up some space on your hard drive.</p>
- <h3>Q: Where can I find more information and support for Archicad 11?</h3>
- <p>A: You can find more information and support for Archicad 11 on Graphisoft's website, where you can access the user manual, the online help, the tutorials, the forums, the FAQs, and the contact details. You can visit Graphisoft's website here: [Graphisoft] . However, you should be careful not to reveal that you are using a cracked version of Archicad 11, as this might get you in trouble with Graphisoft or the authorities.</p> b2dd77e56b<br />
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-<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Avid.First.AIR.Instruments.Bundle.12.0.x64.AAX-AudioUTOPiA.[oddsox] ((FREE)).md b/spaces/tioseFevbu/cartoon-converter/scripts/Avid.First.AIR.Instruments.Bundle.12.0.x64.AAX-AudioUTOPiA.[oddsox] ((FREE)).md
deleted file mode 100644
index b83aba57d8a6df3853ae95f24da9001357505f44..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Avid.First.AIR.Instruments.Bundle.12.0.x64.AAX-AudioUTOPiA.[oddsox] ((FREE)).md	
+++ /dev/null
@@ -1,31 +0,0 @@
-
-<h1>Avid First AIR Instruments Bundle: A Review of the Five Stunning Plugins</h1>
-<p>If you are looking for a way to spice up your music production with some amazing virtual instruments and effects, you might want to check out the Avid First AIR Instruments Bundle. This bundle is designed only for Pro Tools | First, the free version of the industry-standard DAW, and it includes five stunning plugins that cover a wide range of sounds and styles.</p>
-<p>The Avid First AIR Instruments Bundle is a collection of five plugins from AIR Music Technology, one of the pioneers in virtual instruments and effects. The plugins are:</p>
-<h2>Avid.First.AIR.Instruments.Bundle.12.0.x64.AAX-AudioUTOPiA.[oddsox]</h2><br /><p><b><b>Download</b> &ndash;&ndash;&ndash; <a href="https://urlcod.com/2uHx7H">https://urlcod.com/2uHx7H</a></b></p><br /><br />
-<ul>
-<li><strong>Boom</strong>: A drum computer with pattern sequencer that lets you create realistic and electronic drum tracks with ease.</li>
-<li><strong>DB-33</strong>: A tonewheel organ simulator that emulates the classic sound of Hammond organs with rotary speaker effects.</li>
-<li><strong>Vacuum</strong>: An analog tube emulation synthesizer that recreates the warm and gritty sound of vintage synths.</li>
-<li><strong>Mini Grand</strong>: An acoustic grand piano that offers seven different piano models with realistic resonance and dynamics.</li>
-<li><strong>Structure Free</strong>: A sample player that allows you to load and play any WAV or AIFF file with a simple interface.</li>
-</ul>
-<p>Each plugin has its own unique features and parameters that let you tweak and customize your sound to your liking. You can also use them together to create complex and layered sounds that will inspire your creativity.</p>
-<p>The Avid First AIR Instruments Bundle is a great way to expand your sonic palette and add some professional quality sounds to your Pro Tools | First projects. You can get this bundle as an annual subscription for only $4.99 per month, which is a bargain considering the value and versatility of these plugins.</p>
-<p>If you want to learn more about the Avid First AIR Instruments Bundle, you can visit the official website[^1^] or watch some video tutorials on YouTube. You can also download a free trial version of Pro Tools | First and try out these plugins for yourself.</p>
-<p>Don't miss this opportunity to get your hands on some legendary instruments at your fingertips. The Avid First AIR Instruments Bundle will take your music production to the next level.</p>
-
-<p>In this article, we will take a closer look at each plugin in the Avid First AIR Instruments Bundle and see what they can do for your music.</p>
-<h2>Boom: The Ultimate Drum Machine</h2>
-<p>Boom is a drum computer with pattern sequencer that lets you create realistic and electronic drum tracks with ease. You can choose from 10 different drum kits, each with 10 sounds that cover various genres and styles. You can also mix and match sounds from different kits to create your own custom drum kit.</p>
-<p>Boom has a simple and intuitive interface that lets you program your drum patterns using the 16-step sequencer. You can adjust the tempo, swing, volume, pan, and mute/solo for each sound. You can also use the global effects section to add some compression, distortion, EQ, and reverb to your drum track.</p>
-<p></p>
-<p>Boom also has a built-in mixer that lets you control the level and output of each sound. You can route each sound to one of four outputs for further processing in Pro Tools | First. You can also export your drum patterns as MIDI or audio files for use in other applications.</p>
-<p>Boom is a powerful and versatile drum machine that will give you the perfect beat for any song. Whether you need a rock, pop, hip hop, or techno drum track, Boom has you covered.</p>
-<h2>DB-33: The Classic Organ Sound</h2>
-<p>DB-33 is a tonewheel organ simulator that emulates the classic sound of Hammond organs with rotary speaker effects. You can choose from 122 presets that cover various organ sounds, from gospel to jazz to rock. You can also create your own organ sound by adjusting the drawbars, percussion, vibrato/chorus, and key click parameters.</p>
-<p>DB-33 has a realistic and responsive keyboard that lets you play with expression and dynamics. You can use the modulation wheel to control the speed of the rotary speaker effect, which adds depth and movement to your organ sound. You can also use the pitch bend wheel to create some glissando effects.</p>
-<p>DB-33 also has a built-in tube amplifier that lets you add some warmth and grit to your organ sound. You can adjust the drive, tone, and volume of the amp to suit your taste. You can also use the global effects section to add some reverb or delay to your organ track.</p>
-<p>DB-33 is a faithful and flexible organ simulator that will bring some soul and groove to your music. Whether you need a smooth or funky organ sound, DB-33 has you covered.</p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Endnote X9.1 Build 12691 Portable Mac ? ((INSTALL)).md b/spaces/tioseFevbu/cartoon-converter/scripts/Endnote X9.1 Build 12691 Portable Mac ? ((INSTALL)).md
deleted file mode 100644
index 3b64340c286d18866f23e47ab4b2304f719b943a..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Endnote X9.1 Build 12691 Portable Mac ? ((INSTALL)).md	
+++ /dev/null
@@ -1,27 +0,0 @@
-
-<h1>Endnote X9.1 Build 12691 Portable Mac – A Powerful Tool for Research and Citation Management</h1>
-<p>Endnote is a popular software that helps researchers organize their references, create bibliographies, and collaborate with others. Endnote X9.1 is the latest version of the software, which offers several improvements and new features for Windows and Mac users.</p>
-<h2>Endnote X9.1 Build 12691 Portable Mac –</h2><br /><p><b><b>Download Zip</b> &middot; <a href="https://urlcod.com/2uHyRy">https://urlcod.com/2uHyRy</a></b></p><br /><br />
-<p>One of the most notable features of Endnote X9.1 is the ability to run it as a portable application on a Mac computer. This means that you can use Endnote without installing it on your hard drive, and carry it with you on a USB flash drive or an external hard drive. This way, you can access your Endnote library and work on your research projects from any Mac computer, without worrying about compatibility issues or license restrictions.</p>
-<p>To use Endnote X9.1 as a portable application on a Mac, you need to download the Endnote X9.1 Build 12691 Portable Mac file from the official website or a trusted source. Then, you need to extract the file to a folder on your removable device. After that, you can launch Endnote by double-clicking on the Endnote X9 icon in the folder. You can also create a shortcut to the icon on your desktop or dock for easy access.</p>
-<p>Using Endnote X9.1 as a portable application on a Mac has many benefits. You can save space on your hard drive, avoid installation errors, and work on your research projects from different computers. You can also sync your Endnote library with your online account, so you can access it from any device with an internet connection. Moreover, you can use Endnote X9.1 with Microsoft Word, PowerPoint, and other applications that support Cite While You Write (CWYW), a feature that allows you to insert citations and references into your documents as you write.</p>
-<p></p>
-<p>Endnote X9.1 Build 12691 Portable Mac is a powerful tool for research and citation management that offers flexibility and convenience for Mac users. If you are looking for a way to use Endnote without installing it on your computer, you should give it a try.</p>
-
-<h2>How to Use Endnote X9.1 for Research and Citation Management</h2>
-<p>Endnote X9.1 is a versatile software that can help you with various aspects of your research and citation management. Here are some of the main features and functions of Endnote X9.1 that you can use to enhance your research workflow:</p>
-<ul>
-<li><b>Search online databases and import references.</b> You can use Endnote X9.1 to search online databases such as Web of Science, PubMed, Google Scholar, and more, and import references directly into your Endnote library. You can also import references from other sources, such as PDF files, websites, or other citation managers.</li>
-<li><b>Organize and manage your references.</b> You can use Endnote X9.1 to organize your references into groups, group sets, smart groups, or combination groups, based on various criteria such as keywords, authors, journals, or custom fields. You can also use Endnote X9.1 to find and remove duplicate references, find and update outdated references, attach files and notes to your references, and create custom fields and labels for your references.</li>
-<li><b>Cite while you write and create bibliographies.</b> You can use Endnote X9.1 to insert citations and references into your documents using the Cite While You Write (CWYW) feature, which is compatible with Microsoft Word, PowerPoint, and other applications. You can also use Endnote X9.1 to create bibliographies in various output styles, such as APA, MLA, Chicago, or custom styles. You can also use Endnote X9.1 to create category bibliographies or subject bibliographies for different sections or topics in your document.</li>
-<li><b>Collaborate and share your research.</b> You can use Endnote X9.1 to collaborate and share your research with other Endnote users. You can share your entire library or specific groups with up to 100 people, with read-only or read-and-write access. You can also sync your library across multiple devices using your Endnote online account, which offers unlimited storage space and unlimited number of references. You can also use Endnote X9.1 to access your library from the web or from the Endnote iPad app.</li>
-</ul>
-<h2>Why Choose Endnote X9.1 for Research and Citation Management</h2>
-<p>Endnote X9.1 is a powerful tool for research and citation management that offers many benefits for researchers of all disciplines and levels. Here are some of the reasons why you should choose Endnote X9.1 for your research needs:</p>
-<ul>
-<li><b>It saves you time and effort.</b> Endnote X9.1 automates many tasks that would otherwise take a lot of time and effort, such as searching online databases, importing references, formatting citations and bibliographies, updating references, finding full text articles, and more. With Endnote X9.1, you can focus more on your research content and less on the technical details.</li>
-<li><b>It improves your research quality and accuracy.</b> Endnote X9.1 helps you avoid errors and inconsistencies in your citations and references, by using reliable sources, standardized output styles, and advanced features such as deduplication by DOI or PMCID, extraction of metadata from PDFs, reference updates from online databases, and more. With Endnote X9.1, you can ensure that your research is credible and trustworthy.</li>
-<li><b>It enhances your research impact and visibility.</b> Endnote X9.1 helps you increase your research impact and visibility by enabling you to discover relevant literature, find potential collaborators, share your research with others, and publish your work in high-quality journals. With Endnote X9.1, you can also use the Manuscript Matcher feature to find the best journals for your manuscript based on your title, abstract, and references.</li>
-</ul></p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Grays Anatomy 38th Edition Pdf Download !LINK!.md b/spaces/tioseFevbu/cartoon-converter/scripts/Grays Anatomy 38th Edition Pdf Download !LINK!.md
deleted file mode 100644
index b04df2a2162149055c3ffc0cc691895e7578ee67..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Grays Anatomy 38th Edition Pdf Download !LINK!.md	
+++ /dev/null
@@ -1,28 +0,0 @@
-
-<h1>How to Download Gray's Anatomy 38th Edition Pdf for Free</h1>
-<p>Gray's Anatomy is one of the most authoritative and comprehensive books on human anatomy, with a history of over 160 years. The 38th edition, published in 1995, was the last one to be edited by British anatomists before the editorial team became international. It contains over 1900 illustrations and covers all aspects of anatomy, from embryology to neuroanatomy, with clinical correlations and updated information on new imaging modalities.</p>
-<h2>Gray's Anatomy 38th Edition Pdf Download</h2><br /><p><b><b>DOWNLOAD</b> &bull; <a href="https://urlcod.com/2uHyei">https://urlcod.com/2uHyei</a></b></p><br /><br />
-<p>If you are looking for a free pdf download of Gray's Anatomy 38th edition, you may be disappointed to find out that it is not legally available online. The book is protected by copyright laws and any unauthorized distribution or reproduction is prohibited. However, there are some ways you can access the book without breaking the law or spending a fortune.</p>
-<ul>
-<li>One option is to borrow the book from a library or a friend who owns a copy. You can then scan the pages you need or take photos with your smartphone. This may be time-consuming and inconvenient, but it is a legal and ethical way to use the book for personal study.</li>
-<li>Another option is to buy a used copy of the book from online platforms such as Amazon or eBay. You can find some sellers who offer the book at a reasonable price, especially if it is in good condition. You can then either keep the book for future reference or resell it after you are done with it.</li>
-<li>A third option is to subscribe to an online service that provides access to digital versions of medical books, such as Elsevier's ClinicalKey or Wolters Kluwer's Ovid. These services require a monthly or annual fee, but they offer unlimited access to thousands of books and journals, including Gray's Anatomy 38th edition. You can read the book online or download it as a pdf file for offline use.</li>
-</ul>
-<p>Whichever option you choose, make sure you respect the intellectual property rights of the authors and publishers of Gray's Anatomy 38th edition. The book is a valuable resource for medical students, doctors, and researchers, and it deserves to be treated with care and appreciation.</p>
-
-<p>Why should you download Gray's Anatomy 38th edition pdf? There are many reasons why this book is a valuable resource for anyone interested in human anatomy. Here are some of them:</p>
-<p></p>
-<ol>
-<li>It is comprehensive and authoritative. Gray's Anatomy 38th edition covers all aspects of anatomy, from embryology to neuroanatomy, with clinical correlations and updated information on new imaging modalities. It is written by experts in their fields and edited by British anatomists who have maintained the high standards of the original work.</li>
-<li>It is well-illustrated and clear. Gray's Anatomy 38th edition contains over 1900 illustrations, including diagrams, photographs, radiographs, CT scans, MR images, and ultrasonic images. The illustrations are carefully selected and arranged to complement the text and enhance the understanding of the structures and functions of the human body.</li>
-<li>It is historical and cultural. Gray's Anatomy 38th edition is the last one to be edited by British anatomists before the editorial team became international. It is also the last one to use the traditional Latin terminology for anatomical structures, which has been replaced by the more standardized Terminologia Anatomica in later editions. The book thus preserves a historical and cultural legacy of anatomy that is worth appreciating and studying.</li>
-</ol>
-<p>How can you use Gray's Anatomy 38th edition pdf effectively? There are many ways you can use this book to enhance your knowledge and skills in anatomy. Here are some tips:</p>
-<ul>
-<li>Use it as a reference book. You can consult Gray's Anatomy 38th edition whenever you need to clarify a concept, check a fact, or review a topic related to anatomy. The book has a detailed table of contents, an index, and cross-references that make it easy to find the information you need.</li>
-<li>Use it as a study guide. You can use Gray's Anatomy 38th edition to prepare for exams, quizzes, or assignments related to anatomy. The book has a clear and logical organization that follows the regional approach to anatomy. You can also use the accompanying CD-ROMs that contain anatomy quizzes and interactive images to test your knowledge and understanding.</li>
-<li>Use it as a source of inspiration. You can use Gray's Anatomy 38th edition to explore the beauty and complexity of the human body. The book has many fascinating facts, anecdotes, and historical notes that reveal the wonders of anatomy and its relevance to medicine and society. You can also use the book to appreciate the artistry and skill of the illustrators who have contributed to its success.</li>
-</ul>
-<p>In conclusion, Gray's Anatomy 38th edition pdf is a valuable resource for anyone interested in human anatomy. It is comprehensive, authoritative, well-illustrated, clear, historical, cultural, and inspiring. It is also available for free download online if you know where to look and how to do it legally and ethically.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/monkey.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/monkey.py
deleted file mode 100644
index fb36dc1a97a9f1f2a52c25fb6b872a7afa640be7..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/monkey.py
+++ /dev/null
@@ -1,177 +0,0 @@
-"""
-Monkey patching of distutils.
-"""
-
-import sys
-import distutils.filelist
-import platform
-import types
-import functools
-from importlib import import_module
-import inspect
-
-import setuptools
-
-__all__ = []
-"""
-Everything is private. Contact the project team
-if you think you need this functionality.
-"""
-
-
-def _get_mro(cls):
-    """
-    Returns the bases classes for cls sorted by the MRO.
-
-    Works around an issue on Jython where inspect.getmro will not return all
-    base classes if multiple classes share the same name. Instead, this
-    function will return a tuple containing the class itself, and the contents
-    of cls.__bases__. See https://github.com/pypa/setuptools/issues/1024.
-    """
-    if platform.python_implementation() == "Jython":
-        return (cls,) + cls.__bases__
-    return inspect.getmro(cls)
-
-
-def get_unpatched(item):
-    lookup = (
-        get_unpatched_class if isinstance(item, type) else
-        get_unpatched_function if isinstance(item, types.FunctionType) else
-        lambda item: None
-    )
-    return lookup(item)
-
-
-def get_unpatched_class(cls):
-    """Protect against re-patching the distutils if reloaded
-
-    Also ensures that no other distutils extension monkeypatched the distutils
-    first.
-    """
-    external_bases = (
-        cls
-        for cls in _get_mro(cls)
-        if not cls.__module__.startswith('setuptools')
-    )
-    base = next(external_bases)
-    if not base.__module__.startswith('distutils'):
-        msg = "distutils has already been patched by %r" % cls
-        raise AssertionError(msg)
-    return base
-
-
-def patch_all():
-    # we can't patch distutils.cmd, alas
-    distutils.core.Command = setuptools.Command
-
-    has_issue_12885 = sys.version_info <= (3, 5, 3)
-
-    if has_issue_12885:
-        # fix findall bug in distutils (http://bugs.python.org/issue12885)
-        distutils.filelist.findall = setuptools.findall
-
-    needs_warehouse = (
-        sys.version_info < (2, 7, 13)
-        or
-        (3, 4) < sys.version_info < (3, 4, 6)
-        or
-        (3, 5) < sys.version_info <= (3, 5, 3)
-    )
-
-    if needs_warehouse:
-        warehouse = 'https://upload.pypi.org/legacy/'
-        distutils.config.PyPIRCCommand.DEFAULT_REPOSITORY = warehouse
-
-    _patch_distribution_metadata()
-
-    # Install Distribution throughout the distutils
-    for module in distutils.dist, distutils.core, distutils.cmd:
-        module.Distribution = setuptools.dist.Distribution
-
-    # Install the patched Extension
-    distutils.core.Extension = setuptools.extension.Extension
-    distutils.extension.Extension = setuptools.extension.Extension
-    if 'distutils.command.build_ext' in sys.modules:
-        sys.modules['distutils.command.build_ext'].Extension = (
-            setuptools.extension.Extension
-        )
-
-    patch_for_msvc_specialized_compiler()
-
-
-def _patch_distribution_metadata():
-    """Patch write_pkg_file and read_pkg_file for higher metadata standards"""
-    for attr in ('write_pkg_file', 'read_pkg_file', 'get_metadata_version'):
-        new_val = getattr(setuptools.dist, attr)
-        setattr(distutils.dist.DistributionMetadata, attr, new_val)
-
-
-def patch_func(replacement, target_mod, func_name):
-    """
-    Patch func_name in target_mod with replacement
-
-    Important - original must be resolved by name to avoid
-    patching an already patched function.
-    """
-    original = getattr(target_mod, func_name)
-
-    # set the 'unpatched' attribute on the replacement to
-    # point to the original.
-    vars(replacement).setdefault('unpatched', original)
-
-    # replace the function in the original module
-    setattr(target_mod, func_name, replacement)
-
-
-def get_unpatched_function(candidate):
-    return getattr(candidate, 'unpatched')
-
-
-def patch_for_msvc_specialized_compiler():
-    """
-    Patch functions in distutils to use standalone Microsoft Visual C++
-    compilers.
-    """
-    # import late to avoid circular imports on Python < 3.5
-    msvc = import_module('setuptools.msvc')
-
-    if platform.system() != 'Windows':
-        # Compilers only available on Microsoft Windows
-        return
-
-    def patch_params(mod_name, func_name):
-        """
-        Prepare the parameters for patch_func to patch indicated function.
-        """
-        repl_prefix = 'msvc9_' if 'msvc9' in mod_name else 'msvc14_'
-        repl_name = repl_prefix + func_name.lstrip('_')
-        repl = getattr(msvc, repl_name)
-        mod = import_module(mod_name)
-        if not hasattr(mod, func_name):
-            raise ImportError(func_name)
-        return repl, mod, func_name
-
-    # Python 2.7 to 3.4
-    msvc9 = functools.partial(patch_params, 'distutils.msvc9compiler')
-
-    # Python 3.5+
-    msvc14 = functools.partial(patch_params, 'distutils._msvccompiler')
-
-    try:
-        # Patch distutils.msvc9compiler
-        patch_func(*msvc9('find_vcvarsall'))
-        patch_func(*msvc9('query_vcvarsall'))
-    except ImportError:
-        pass
-
-    try:
-        # Patch distutils._msvccompiler._get_vc_env
-        patch_func(*msvc14('_get_vc_env'))
-    except ImportError:
-        pass
-
-    try:
-        # Patch distutils._msvccompiler.gen_lib_options for Numpy
-        patch_func(*msvc14('gen_lib_options'))
-    except ImportError:
-        pass
diff --git a/spaces/tomemojo/customerservice/README.md b/spaces/tomemojo/customerservice/README.md
deleted file mode 100644
index b1ff0454a350ea42771bce3f80c76b319722923b..0000000000000000000000000000000000000000
--- a/spaces/tomemojo/customerservice/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Customerservice
-emoji: 💩
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/tomg-group-umd/pez-dispenser/open_clip/transform.py b/spaces/tomg-group-umd/pez-dispenser/open_clip/transform.py
deleted file mode 100644
index 3aee70ff0059068330e81f2c9ee03eeb2ebabc76..0000000000000000000000000000000000000000
--- a/spaces/tomg-group-umd/pez-dispenser/open_clip/transform.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from typing import Optional, Sequence, Tuple
-
-import torch
-import torch.nn as nn
-import torchvision.transforms.functional as F
-
-from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
-    CenterCrop
-
-from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
-
-
-class ResizeMaxSize(nn.Module):
-
-    def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0):
-        super().__init__()
-        if not isinstance(max_size, int):
-            raise TypeError(f"Size should be int. Got {type(max_size)}")
-        self.max_size = max_size
-        self.interpolation = interpolation
-        self.fn = min if fn == 'min' else min
-        self.fill = fill
-
-    def forward(self, img):
-        if isinstance(img, torch.Tensor):
-            height, width = img.shape[:2]
-        else:
-            width, height = img.size
-        scale = self.max_size / float(max(height, width))
-        if scale != 1.0:
-            new_size = tuple(round(dim * scale) for dim in (height, width))
-            img = F.resize(img, new_size, self.interpolation)
-            pad_h = self.max_size - new_size[0]
-            pad_w = self.max_size - new_size[1]
-            img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill)
-        return img
-
-
-def _convert_to_rgb(image):
-    return image.convert('RGB')
-
-
-def image_transform(
-        image_size: int,
-        is_train: bool,
-        mean: Optional[Tuple[float, ...]] = None,
-        std: Optional[Tuple[float, ...]] = None,
-        resize_longest_max: bool = False,
-        fill_color: int = 0,
-):
-    mean = mean or OPENAI_DATASET_MEAN
-    if not isinstance(mean, (list, tuple)):
-        mean = (mean,) * 3
-
-    std = std or OPENAI_DATASET_STD
-    if not isinstance(std, (list, tuple)):
-        std = (std,) * 3
-
-    if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]:
-        # for square size, pass size as int so that Resize() uses aspect preserving shortest edge
-        image_size = image_size[0]
-
-    normalize = Normalize(mean=mean, std=std)
-    if is_train:
-        return Compose([
-            RandomResizedCrop(image_size, scale=(0.9, 1.0), interpolation=InterpolationMode.BICUBIC),
-            _convert_to_rgb,
-            ToTensor(),
-            normalize,
-        ])
-    else:
-        if resize_longest_max:
-            transforms = [
-                ResizeMaxSize(image_size, fill=fill_color)
-            ]
-        else:
-            transforms = [
-                Resize(image_size, interpolation=InterpolationMode.BICUBIC),
-                CenterCrop(image_size),
-            ]
-        transforms.extend([
-            _convert_to_rgb,
-            ToTensor(),
-            normalize,
-        ])
-        return Compose(transforms)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/centripetalnet/README.md b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/centripetalnet/README.md
deleted file mode 100644
index bc9a4b177bd0fdc5bf7be99453de261d83f6c762..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/centripetalnet/README.md
+++ /dev/null
@@ -1,26 +0,0 @@
-# CentripetalNet
-
-## Introduction
-
-<!-- [ALGORITHM] -->
-
-```latex
-@InProceedings{Dong_2020_CVPR,
-author = {Dong, Zhiwei and Li, Guoxuan and Liao, Yue and Wang, Fei and Ren, Pengju and Qian, Chen},
-title = {CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection},
-booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-month = {June},
-year = {2020}
-}
-```
-
-## Results and models
-
-| Backbone        | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Config | Download |
-| :-------------: | :--------: |:----------------: | :------: | :------------: | :----: | :------: | :--------: |
-| HourglassNet-104 | [16 x 6](./centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | 190/210 | 16.7 | 3.7 | 44.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth) &#124; [log](http://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804.log.json) |
-
-Note:
-
-- TTA setting is single-scale and `flip=True`.
-- The model we released is the best checkpoint rather than the latest checkpoint (box AP 44.8 vs 44.6 in our experiment).
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docs/robustness_benchmarking.md b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docs/robustness_benchmarking.md
deleted file mode 100644
index 5be16dfae2ebd42c75b0f886efa5459ab97afe26..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docs/robustness_benchmarking.md
+++ /dev/null
@@ -1,110 +0,0 @@
-# Corruption Benchmarking
-
-## Introduction
-
-We provide tools to test object detection and instance segmentation models on the image corruption benchmark defined in [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://arxiv.org/abs/1907.07484).
-This page provides basic tutorials how to use the benchmark.
-
-```latex
-@article{michaelis2019winter,
-  title={Benchmarking Robustness in Object Detection:
-    Autonomous Driving when Winter is Coming},
-  author={Michaelis, Claudio and Mitzkus, Benjamin and
-    Geirhos, Robert and Rusak, Evgenia and
-    Bringmann, Oliver and Ecker, Alexander S. and
-    Bethge, Matthias and Brendel, Wieland},
-  journal={arXiv:1907.07484},
-  year={2019}
-}
-```
-
-![image corruption example](../resources/corruptions_sev_3.png)
-
-## About the benchmark
-
-To submit results to the benchmark please visit the [benchmark homepage](https://github.com/bethgelab/robust-detection-benchmark)
-
-The benchmark is modelled after the [imagenet-c benchmark](https://github.com/hendrycks/robustness) which was originally
-published in [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/abs/1903.12261) (ICLR 2019) by Dan Hendrycks and Thomas Dietterich.
-
-The image corruption functions are included in this library but can be installed separately using:
-
-```shell
-pip install imagecorruptions
-```
-
-Compared to imagenet-c a few changes had to be made to handle images of arbitrary size and greyscale images.
-We also modfied the 'motion blur' and 'snow' corruptions to remove dependency from a linux specific library,
-which would have to be installed separately otherwise. For details please refer to the [imagecorruptions repository](https://github.com/bethgelab/imagecorruptions).
-
-## Inference with pretrained models
-
-We provide a testing script to evaluate a models performance on any combination of the corruptions provided in the benchmark.
-
-### Test a dataset
-
-- [x] single GPU testing
-- [ ] multiple GPU testing
-- [ ] visualize detection results
-
-You can use the following commands to test a models performance under the 15 corruptions used in the benchmark.
-
-```shell
-# single-gpu testing
-python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
-```
-
-Alternatively different group of corruptions can be selected.
-
-```shell
-# noise
-python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions noise
-
-# blur
-python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions blur
-
-# wetaher
-python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions weather
-
-# digital
-python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions digital
-```
-
-Or a costom set of corruptions e.g.:
-
-```shell
-# gaussian noise, zoom blur and snow
-python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions gaussian_noise zoom_blur snow
-```
-
-Finally the corruption severities to evaluate can be chosen.
-Severity 0 corresponds to clean data and the effect increases from 1 to 5.
-
-```shell
-# severity 1
-python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 1
-
-# severities 0,2,4
-python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 0 2 4
-```
-
-## Results for modelzoo models
-
-The results on COCO 2017val are shown in the below table.
-
-Model  | Backbone  | Style   | Lr schd | box AP clean | box AP corr. | box % | mask AP clean | mask AP corr. | mask % |
-:-----:|:---------:|:-------:|:-------:|:------------:|:------------:|:-----:|:-------------:|:-------------:|:------:|
-Faster R-CNN | R-50-FPN  | pytorch | 1x      | 36.3   | 18.2         | 50.2  | -             | -             | -      |
-Faster R-CNN | R-101-FPN | pytorch | 1x      | 38.5   | 20.9         | 54.2  | -             | -             | -      |
-Faster R-CNN | X-101-32x4d-FPN | pytorch |1x | 40.1   | 22.3         | 55.5  | -             | -             | -      |
-Faster R-CNN | X-101-64x4d-FPN | pytorch |1x | 41.3   | 23.4         | 56.6  | -             | -             | -      |
-Faster R-CNN | R-50-FPN-DCN | pytorch | 1x   | 40.0   | 22.4         | 56.1  | -             | -             | -      |
-Faster R-CNN | X-101-32x4d-FPN-DCN | pytorch | 1x | 43.4 | 26.7      | 61.6  | -             | -             | -      |
-Mask R-CNN   | R-50-FPN  | pytorch | 1x      | 37.3   | 18.7         | 50.1  | 34.2          | 16.8          | 49.1   |
-Mask R-CNN   | R-50-FPN-DCN | pytorch | 1x   | 41.1   | 23.3         | 56.7  | 37.2          | 20.7          | 55.7   |
-Cascade R-CNN | R-50-FPN  | pytorch | 1x     | 40.4   | 20.1         | 49.7  | -             | -             | -      |
-Cascade Mask R-CNN | R-50-FPN  | pytorch | 1x| 41.2   | 20.7         | 50.2  | 35.7          | 17.6          | 49.3   |
-RetinaNet    | R-50-FPN  | pytorch | 1x      | 35.6   | 17.8         | 50.1  | -             | -             | -      |
-Hybrid Task Cascade | X-101-64x4d-FPN-DCN | pytorch | 1x | 50.6 | 32.7 | 64.7 | 43.8         | 28.1          | 64.0   |
-
-Results may vary slightly due to the stochastic application of the corruptions.
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/base_roi_head.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/base_roi_head.py
deleted file mode 100644
index 423af25c24657f8d4833f35d0fb4142df38adf35..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/roi_heads/base_roi_head.py
+++ /dev/null
@@ -1,102 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-from mmcv.runner import BaseModule
-
-from ..builder import build_shared_head
-
-
-class BaseRoIHead(BaseModule, metaclass=ABCMeta):
-    """Base class for RoIHeads."""
-
-    def __init__(self,
-                 bbox_roi_extractor=None,
-                 bbox_head=None,
-                 mask_roi_extractor=None,
-                 mask_head=None,
-                 shared_head=None,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None,
-                 init_cfg=None):
-        super(BaseRoIHead, self).__init__(init_cfg)
-        self.train_cfg = train_cfg
-        self.test_cfg = test_cfg
-        if shared_head is not None:
-            shared_head.pretrained = pretrained
-            self.shared_head = build_shared_head(shared_head)
-
-        if bbox_head is not None:
-            self.init_bbox_head(bbox_roi_extractor, bbox_head)
-
-        if mask_head is not None:
-            self.init_mask_head(mask_roi_extractor, mask_head)
-
-        self.init_assigner_sampler()
-
-    @property
-    def with_bbox(self):
-        """bool: whether the RoI head contains a `bbox_head`"""
-        return hasattr(self, 'bbox_head') and self.bbox_head is not None
-
-    @property
-    def with_mask(self):
-        """bool: whether the RoI head contains a `mask_head`"""
-        return hasattr(self, 'mask_head') and self.mask_head is not None
-
-    @property
-    def with_shared_head(self):
-        """bool: whether the RoI head contains a `shared_head`"""
-        return hasattr(self, 'shared_head') and self.shared_head is not None
-
-    @abstractmethod
-    def init_bbox_head(self):
-        """Initialize ``bbox_head``"""
-        pass
-
-    @abstractmethod
-    def init_mask_head(self):
-        """Initialize ``mask_head``"""
-        pass
-
-    @abstractmethod
-    def init_assigner_sampler(self):
-        """Initialize assigner and sampler."""
-        pass
-
-    @abstractmethod
-    def forward_train(self,
-                      x,
-                      img_meta,
-                      proposal_list,
-                      gt_bboxes,
-                      gt_labels,
-                      gt_bboxes_ignore=None,
-                      gt_masks=None,
-                      **kwargs):
-        """Forward function during training."""
-
-    async def async_simple_test(self,
-                                x,
-                                proposal_list,
-                                img_metas,
-                                proposals=None,
-                                rescale=False,
-                                **kwargs):
-        """Asynchronized test function."""
-        raise NotImplementedError
-
-    def simple_test(self,
-                    x,
-                    proposal_list,
-                    img_meta,
-                    proposals=None,
-                    rescale=False,
-                    **kwargs):
-        """Test without augmentation."""
-
-    def aug_test(self, x, proposal_list, img_metas, rescale=False, **kwargs):
-        """Test with augmentations.
-
-        If rescale is False, then returned bboxes and masks will fit the scale
-        of imgs[0].
-        """
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_backbones/test_trident_resnet.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_backbones/test_trident_resnet.py
deleted file mode 100644
index ebb4415bf3c4af34f77112d75d02aba6936ef497..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tests/test_models/test_backbones/test_trident_resnet.py
+++ /dev/null
@@ -1,180 +0,0 @@
-import pytest
-import torch
-
-from mmdet.models.backbones import TridentResNet
-from mmdet.models.backbones.trident_resnet import TridentBottleneck
-
-
-def test_trident_resnet_bottleneck():
-    trident_dilations = (1, 2, 3)
-    test_branch_idx = 1
-    concat_output = True
-    trident_build_config = (trident_dilations, test_branch_idx, concat_output)
-
-    with pytest.raises(AssertionError):
-        # Style must be in ['pytorch', 'caffe']
-        TridentBottleneck(
-            *trident_build_config, inplanes=64, planes=64, style='tensorflow')
-
-    with pytest.raises(AssertionError):
-        # Allowed positions are 'after_conv1', 'after_conv2', 'after_conv3'
-        plugins = [
-            dict(
-                cfg=dict(type='ContextBlock', ratio=1. / 16),
-                position='after_conv4')
-        ]
-        TridentBottleneck(
-            *trident_build_config, inplanes=64, planes=16, plugins=plugins)
-
-    with pytest.raises(AssertionError):
-        # Need to specify different postfix to avoid duplicate plugin name
-        plugins = [
-            dict(
-                cfg=dict(type='ContextBlock', ratio=1. / 16),
-                position='after_conv3'),
-            dict(
-                cfg=dict(type='ContextBlock', ratio=1. / 16),
-                position='after_conv3')
-        ]
-        TridentBottleneck(
-            *trident_build_config, inplanes=64, planes=16, plugins=plugins)
-
-    with pytest.raises(KeyError):
-        # Plugin type is not supported
-        plugins = [dict(cfg=dict(type='WrongPlugin'), position='after_conv3')]
-        TridentBottleneck(
-            *trident_build_config, inplanes=64, planes=16, plugins=plugins)
-
-    # Test Bottleneck with checkpoint forward
-    block = TridentBottleneck(
-        *trident_build_config, inplanes=64, planes=16, with_cp=True)
-    assert block.with_cp
-    x = torch.randn(1, 64, 56, 56)
-    x_out = block(x)
-    assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56])
-
-    # Test Bottleneck style
-    block = TridentBottleneck(
-        *trident_build_config,
-        inplanes=64,
-        planes=64,
-        stride=2,
-        style='pytorch')
-    assert block.conv1.stride == (1, 1)
-    assert block.conv2.stride == (2, 2)
-    block = TridentBottleneck(
-        *trident_build_config, inplanes=64, planes=64, stride=2, style='caffe')
-    assert block.conv1.stride == (2, 2)
-    assert block.conv2.stride == (1, 1)
-
-    # Test Bottleneck forward
-    block = TridentBottleneck(*trident_build_config, inplanes=64, planes=16)
-    x = torch.randn(1, 64, 56, 56)
-    x_out = block(x)
-    assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56])
-
-    # Test Bottleneck with 1 ContextBlock after conv3
-    plugins = [
-        dict(
-            cfg=dict(type='ContextBlock', ratio=1. / 16),
-            position='after_conv3')
-    ]
-    block = TridentBottleneck(
-        *trident_build_config, inplanes=64, planes=16, plugins=plugins)
-    assert block.context_block.in_channels == 64
-    x = torch.randn(1, 64, 56, 56)
-    x_out = block(x)
-    assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56])
-
-    # Test Bottleneck with 1 GeneralizedAttention after conv2
-    plugins = [
-        dict(
-            cfg=dict(
-                type='GeneralizedAttention',
-                spatial_range=-1,
-                num_heads=8,
-                attention_type='0010',
-                kv_stride=2),
-            position='after_conv2')
-    ]
-    block = TridentBottleneck(
-        *trident_build_config, inplanes=64, planes=16, plugins=plugins)
-    assert block.gen_attention_block.in_channels == 16
-    x = torch.randn(1, 64, 56, 56)
-    x_out = block(x)
-    assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56])
-
-    # Test Bottleneck with 1 GeneralizedAttention after conv2, 1 NonLocal2D
-    # after conv2, 1 ContextBlock after conv3
-    plugins = [
-        dict(
-            cfg=dict(
-                type='GeneralizedAttention',
-                spatial_range=-1,
-                num_heads=8,
-                attention_type='0010',
-                kv_stride=2),
-            position='after_conv2'),
-        dict(cfg=dict(type='NonLocal2d'), position='after_conv2'),
-        dict(
-            cfg=dict(type='ContextBlock', ratio=1. / 16),
-            position='after_conv3')
-    ]
-    block = TridentBottleneck(
-        *trident_build_config, inplanes=64, planes=16, plugins=plugins)
-    assert block.gen_attention_block.in_channels == 16
-    assert block.nonlocal_block.in_channels == 16
-    assert block.context_block.in_channels == 64
-    x = torch.randn(1, 64, 56, 56)
-    x_out = block(x)
-    assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56])
-
-    # Test Bottleneck with 1 ContextBlock after conv2, 2 ContextBlock after
-    # conv3
-    plugins = [
-        dict(
-            cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1),
-            position='after_conv2'),
-        dict(
-            cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2),
-            position='after_conv3'),
-        dict(
-            cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=3),
-            position='after_conv3')
-    ]
-    block = TridentBottleneck(
-        *trident_build_config, inplanes=64, planes=16, plugins=plugins)
-    assert block.context_block1.in_channels == 16
-    assert block.context_block2.in_channels == 64
-    assert block.context_block3.in_channels == 64
-    x = torch.randn(1, 64, 56, 56)
-    x_out = block(x)
-    assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56])
-
-
-def test_trident_resnet_backbone():
-    tridentresnet_config = dict(
-        num_branch=3,
-        test_branch_idx=1,
-        strides=(1, 2, 2),
-        dilations=(1, 1, 1),
-        trident_dilations=(1, 2, 3),
-        out_indices=(2, ),
-    )
-    """Test tridentresnet backbone."""
-    with pytest.raises(AssertionError):
-        # TridentResNet depth should be in [50, 101, 152]
-        TridentResNet(18, **tridentresnet_config)
-
-    with pytest.raises(AssertionError):
-        # In TridentResNet: num_stages == 3
-        TridentResNet(50, num_stages=4, **tridentresnet_config)
-
-    model = TridentResNet(50, num_stages=3, **tridentresnet_config)
-    model.init_weights()
-    model.train()
-
-    imgs = torch.randn(1, 3, 224, 224)
-    feat = model(imgs)
-    assert len(feat) == 1
-    assert feat[0].shape == torch.Size([3, 1024, 14, 14])
diff --git a/spaces/udion/BayesCap/networks_T1toT2.py b/spaces/udion/BayesCap/networks_T1toT2.py
deleted file mode 100644
index 0a4957071e817fb551bc1fc86fe1cc5dc4e75cfe..0000000000000000000000000000000000000000
--- a/spaces/udion/BayesCap/networks_T1toT2.py
+++ /dev/null
@@ -1,477 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import functools
-
-### components
-class ResConv(nn.Module):
-    """
-    Residual convolutional block, where
-    convolutional block consists: (convolution => [BN] => ReLU) * 3
-    residual connection adds the input to the output
-    """
-    def __init__(self, in_channels, out_channels, mid_channels=None):
-        super().__init__()
-        if not mid_channels:
-            mid_channels = out_channels
-        self.double_conv = nn.Sequential(
-            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
-            nn.BatchNorm2d(mid_channels),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(mid_channels, mid_channels, kernel_size=3, padding=1),
-            nn.BatchNorm2d(mid_channels),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True)
-        )
-        self.double_conv1 = nn.Sequential(
-            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(inplace=True),
-        )
-    def forward(self, x):
-        x_in = self.double_conv1(x)
-        x1 = self.double_conv(x)
-        return self.double_conv(x) + x_in
-
-class Down(nn.Module):
-    """Downscaling with maxpool then Resconv"""
-    def __init__(self, in_channels, out_channels):
-        super().__init__()
-        self.maxpool_conv = nn.Sequential(
-            nn.MaxPool2d(2),
-            ResConv(in_channels, out_channels)
-        )
-    def forward(self, x):
-        return self.maxpool_conv(x)
-
-class Up(nn.Module):
-	"""Upscaling then double conv"""
-	def __init__(self, in_channels, out_channels, bilinear=True):
-		super().__init__()
-		# if bilinear, use the normal convolutions to reduce the number of channels
-		if bilinear:
-			self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
-			self.conv = ResConv(in_channels, out_channels, in_channels // 2)
-		else:
-			self.up = nn.ConvTranspose2d(in_channels , in_channels // 2, kernel_size=2, stride=2)
-			self.conv = ResConv(in_channels, out_channels)
-	def forward(self, x1, x2):
-		x1 = self.up(x1)
-		# input is CHW
-		diffY = x2.size()[2] - x1.size()[2]
-		diffX = x2.size()[3] - x1.size()[3]
-		x1 = F.pad(
-			x1, 
-			[
-				diffX // 2, diffX - diffX // 2,
-				diffY // 2, diffY - diffY // 2
-			]
-		)
-		# if you have padding issues, see
-		# https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
-		# https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
-		x = torch.cat([x2, x1], dim=1)
-		return self.conv(x)
-
-class OutConv(nn.Module):
-	def __init__(self, in_channels, out_channels):
-		super(OutConv, self).__init__()
-		self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
-	def forward(self, x):
-		# return F.relu(self.conv(x))
-		return self.conv(x)
-
-##### The composite networks
-class UNet(nn.Module):
-	def __init__(self, n_channels, out_channels, bilinear=True):
-		super(UNet, self).__init__()
-		self.n_channels = n_channels
-		self.out_channels = out_channels
-		self.bilinear = bilinear
-		####
-		self.inc = ResConv(n_channels, 64)
-		self.down1 = Down(64, 128)
-		self.down2 = Down(128, 256)
-		self.down3 = Down(256, 512)
-		factor = 2 if bilinear else 1
-		self.down4 = Down(512, 1024 // factor)
-		self.up1 = Up(1024, 512 // factor, bilinear)
-		self.up2 = Up(512, 256 // factor, bilinear)
-		self.up3 = Up(256, 128 // factor, bilinear)
-		self.up4 = Up(128, 64, bilinear)
-		self.outc = OutConv(64, out_channels)
-	def forward(self, x):
-		x1 = self.inc(x)
-		x2 = self.down1(x1)
-		x3 = self.down2(x2)
-		x4 = self.down3(x3)
-		x5 = self.down4(x4)
-		x = self.up1(x5, x4)
-		x = self.up2(x, x3)
-		x = self.up3(x, x2)
-		x = self.up4(x, x1)
-		y = self.outc(x)
-		return y
-
-class CasUNet(nn.Module):
-	def __init__(self, n_unet, io_channels, bilinear=True):
-		super(CasUNet, self).__init__()
-		self.n_unet = n_unet
-		self.io_channels = io_channels
-		self.bilinear = bilinear
-		####
-		self.unet_list = nn.ModuleList()
-		for i in range(self.n_unet):
-			self.unet_list.append(UNet(self.io_channels, self.io_channels, self.bilinear))
-	def forward(self, x, dop=None):
-		y = x
-		for i in range(self.n_unet):
-			if i==0:
-				if dop is not None:
-					y = F.dropout2d(self.unet_list[i](y), p=dop)
-				else:
-					y = self.unet_list[i](y)
-			else:
-				y = self.unet_list[i](y+x)
-		return y
-
-class CasUNet_2head(nn.Module):
-	def __init__(self, n_unet, io_channels, bilinear=True):
-		super(CasUNet_2head, self).__init__()
-		self.n_unet = n_unet
-		self.io_channels = io_channels
-		self.bilinear = bilinear
-		####
-		self.unet_list = nn.ModuleList()
-		for i in range(self.n_unet):
-			if i != self.n_unet-1:
-				self.unet_list.append(UNet(self.io_channels, self.io_channels, self.bilinear))
-			else:
-				self.unet_list.append(UNet_2head(self.io_channels, self.io_channels, self.bilinear))
-	def forward(self, x):
-		y = x
-		for i in range(self.n_unet):
-			if i==0:
-				y = self.unet_list[i](y)
-			else:
-				y = self.unet_list[i](y+x)
-		y_mean, y_sigma = y[0], y[1]
-		return y_mean, y_sigma
-
-class CasUNet_3head(nn.Module):
-	def __init__(self, n_unet, io_channels, bilinear=True):
-		super(CasUNet_3head, self).__init__()
-		self.n_unet = n_unet
-		self.io_channels = io_channels
-		self.bilinear = bilinear
-		####
-		self.unet_list = nn.ModuleList()
-		for i in range(self.n_unet):
-			if i != self.n_unet-1:
-				self.unet_list.append(UNet(self.io_channels, self.io_channels, self.bilinear))
-			else:
-				self.unet_list.append(UNet_3head(self.io_channels, self.io_channels, self.bilinear))
-	def forward(self, x):
-		y = x
-		for i in range(self.n_unet):
-			if i==0:
-				y = self.unet_list[i](y)
-			else:
-				y = self.unet_list[i](y+x)
-		y_mean, y_alpha, y_beta = y[0], y[1], y[2]
-		return y_mean, y_alpha, y_beta
-
-class UNet_2head(nn.Module):
-	def __init__(self, n_channels, out_channels, bilinear=True):
-		super(UNet_2head, self).__init__()
-		self.n_channels = n_channels
-		self.out_channels = out_channels
-		self.bilinear = bilinear
-		####
-		self.inc = ResConv(n_channels, 64)
-		self.down1 = Down(64, 128)
-		self.down2 = Down(128, 256)
-		self.down3 = Down(256, 512)
-		factor = 2 if bilinear else 1
-		self.down4 = Down(512, 1024 // factor)
-		self.up1 = Up(1024, 512 // factor, bilinear)
-		self.up2 = Up(512, 256 // factor, bilinear)
-		self.up3 = Up(256, 128 // factor, bilinear)
-		self.up4 = Up(128, 64, bilinear)
-		#per pixel multiple channels may exist
-		self.out_mean = OutConv(64, out_channels)
-		#variance will always be a single number for a pixel
-		self.out_var = nn.Sequential(
-			OutConv(64, 128),
-			OutConv(128, 1),
-		)
-	def forward(self, x):
-		x1 = self.inc(x)
-		x2 = self.down1(x1)
-		x3 = self.down2(x2)
-		x4 = self.down3(x3)
-		x5 = self.down4(x4)
-		x = self.up1(x5, x4)
-		x = self.up2(x, x3)
-		x = self.up3(x, x2)
-		x = self.up4(x, x1)
-		y_mean, y_var = self.out_mean(x), self.out_var(x)
-		return y_mean, y_var
-
-class UNet_3head(nn.Module):
-	def __init__(self, n_channels, out_channels, bilinear=True):
-		super(UNet_3head, self).__init__()
-		self.n_channels = n_channels
-		self.out_channels = out_channels
-		self.bilinear = bilinear
-		####
-		self.inc = ResConv(n_channels, 64)
-		self.down1 = Down(64, 128)
-		self.down2 = Down(128, 256)
-		self.down3 = Down(256, 512)
-		factor = 2 if bilinear else 1
-		self.down4 = Down(512, 1024 // factor)
-		self.up1 = Up(1024, 512 // factor, bilinear)
-		self.up2 = Up(512, 256 // factor, bilinear)
-		self.up3 = Up(256, 128 // factor, bilinear)
-		self.up4 = Up(128, 64, bilinear)
-		#per pixel multiple channels may exist
-		self.out_mean = OutConv(64, out_channels)
-		#variance will always be a single number for a pixel
-		self.out_alpha = nn.Sequential(
-			OutConv(64, 128),
-			OutConv(128, 1),
-			nn.ReLU()
-		)
-		self.out_beta = nn.Sequential(
-			OutConv(64, 128),
-			OutConv(128, 1),
-			nn.ReLU()
-		)
-	def forward(self, x):
-		x1 = self.inc(x)
-		x2 = self.down1(x1)
-		x3 = self.down2(x2)
-		x4 = self.down3(x3)
-		x5 = self.down4(x4)
-		x = self.up1(x5, x4)
-		x = self.up2(x, x3)
-		x = self.up3(x, x2)
-		x = self.up4(x, x1)
-		y_mean, y_alpha, y_beta = self.out_mean(x), \
-		self.out_alpha(x), self.out_beta(x)
-		return y_mean, y_alpha, y_beta
-
-class ResidualBlock(nn.Module):
-    def __init__(self, in_features):
-        super(ResidualBlock, self).__init__()
-        conv_block = [  
-			nn.ReflectionPad2d(1),
-			nn.Conv2d(in_features, in_features, 3),
-			nn.InstanceNorm2d(in_features),
-			nn.ReLU(inplace=True),
-			nn.ReflectionPad2d(1),
-			nn.Conv2d(in_features, in_features, 3),
-			nn.InstanceNorm2d(in_features)
-		]
-        self.conv_block = nn.Sequential(*conv_block)
-    def forward(self, x):
-        return x + self.conv_block(x)
-
-class Generator(nn.Module):
-    def __init__(self, input_nc, output_nc, n_residual_blocks=9):
-        super(Generator, self).__init__()
-        # Initial convolution block       
-        model = [
-			nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, 7),
-            nn.InstanceNorm2d(64), nn.ReLU(inplace=True)
-		]
-        # Downsampling
-        in_features = 64
-        out_features = in_features*2
-        for _ in range(2):
-            model += [  
-				nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
-                nn.InstanceNorm2d(out_features),
-                nn.ReLU(inplace=True) 
-			]
-            in_features = out_features
-            out_features = in_features*2
-        # Residual blocks
-        for _ in range(n_residual_blocks):
-            model += [ResidualBlock(in_features)]
-        # Upsampling
-        out_features = in_features//2
-        for _ in range(2):
-            model += [  
-				nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
-				nn.InstanceNorm2d(out_features),
-                nn.ReLU(inplace=True)
-			]
-            in_features = out_features
-            out_features = in_features//2
-        # Output layer
-        model += [nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, 7), nn.Tanh()]
-        self.model = nn.Sequential(*model)
-    def forward(self, x):
-        return self.model(x)
-    
-    
-class ResnetGenerator(nn.Module):
-    """Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
-    We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
-    """
-
-    def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
-        """Construct a Resnet-based generator
-        Parameters:
-            input_nc (int)      -- the number of channels in input images
-            output_nc (int)     -- the number of channels in output images
-            ngf (int)           -- the number of filters in the last conv layer
-            norm_layer          -- normalization layer
-            use_dropout (bool)  -- if use dropout layers
-            n_blocks (int)      -- the number of ResNet blocks
-            padding_type (str)  -- the name of padding layer in conv layers: reflect | replicate | zero
-        """
-        assert(n_blocks >= 0)
-        super(ResnetGenerator, self).__init__()
-        if type(norm_layer) == functools.partial:
-            use_bias = norm_layer.func == nn.InstanceNorm2d
-        else:
-            use_bias = norm_layer == nn.InstanceNorm2d
-
-        model = [nn.ReflectionPad2d(3),
-                 nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
-                 norm_layer(ngf),
-                 nn.ReLU(True)]
-
-        n_downsampling = 2
-        for i in range(n_downsampling):  # add downsampling layers
-            mult = 2 ** i
-            model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),
-                      norm_layer(ngf * mult * 2),
-                      nn.ReLU(True)]
-
-        mult = 2 ** n_downsampling
-        for i in range(n_blocks):       # add ResNet blocks
-
-            model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
-
-        for i in range(n_downsampling):  # add upsampling layers
-            mult = 2 ** (n_downsampling - i)
-            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
-                                         kernel_size=3, stride=2,
-                                         padding=1, output_padding=1,
-                                         bias=use_bias),
-                      norm_layer(int(ngf * mult / 2)),
-                      nn.ReLU(True)]
-        model += [nn.ReflectionPad2d(3)]
-        model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
-        model += [nn.Tanh()]
-
-        self.model = nn.Sequential(*model)
-
-    def forward(self, input):
-        """Standard forward"""
-        return self.model(input)
-
-
-class ResnetBlock(nn.Module):
-    """Define a Resnet block"""
-
-    def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
-        """Initialize the Resnet block
-        A resnet block is a conv block with skip connections
-        We construct a conv block with build_conv_block function,
-        and implement skip connections in <forward> function.
-        Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
-        """
-        super(ResnetBlock, self).__init__()
-        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)
-
-    def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
-        """Construct a convolutional block.
-        Parameters:
-            dim (int)           -- the number of channels in the conv layer.
-            padding_type (str)  -- the name of padding layer: reflect | replicate | zero
-            norm_layer          -- normalization layer
-            use_dropout (bool)  -- if use dropout layers.
-            use_bias (bool)     -- if the conv layer uses bias or not
-        Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
-        """
-        conv_block = []
-        p = 0
-        if padding_type == 'reflect':
-            conv_block += [nn.ReflectionPad2d(1)]
-        elif padding_type == 'replicate':
-            conv_block += [nn.ReplicationPad2d(1)]
-        elif padding_type == 'zero':
-            p = 1
-        else:
-            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
-
-        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)]
-        if use_dropout:
-            conv_block += [nn.Dropout(0.5)]
-
-        p = 0
-        if padding_type == 'reflect':
-            conv_block += [nn.ReflectionPad2d(1)]
-        elif padding_type == 'replicate':
-            conv_block += [nn.ReplicationPad2d(1)]
-        elif padding_type == 'zero':
-            p = 1
-        else:
-            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
-        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)]
-
-        return nn.Sequential(*conv_block)
-
-    def forward(self, x):
-        """Forward function (with skip connections)"""
-        out = x + self.conv_block(x)  # add skip connections
-        return out
-
-### discriminator
-class NLayerDiscriminator(nn.Module):
-    """Defines a PatchGAN discriminator"""
-    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d):
-        """Construct a PatchGAN discriminator
-        Parameters:
-            input_nc (int)  -- the number of channels in input images
-            ndf (int)       -- the number of filters in the last conv layer
-            n_layers (int)  -- the number of conv layers in the discriminator
-            norm_layer      -- normalization layer
-        """
-        super(NLayerDiscriminator, self).__init__()
-        if type(norm_layer) == functools.partial:  # no need to use bias as BatchNorm2d has affine parameters
-            use_bias = norm_layer.func == nn.InstanceNorm2d
-        else:
-            use_bias = norm_layer == nn.InstanceNorm2d
-        kw = 4
-        padw = 1
-        sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
-        nf_mult = 1
-        nf_mult_prev = 1
-        for n in range(1, n_layers):  # gradually increase the number of filters
-            nf_mult_prev = nf_mult
-            nf_mult = min(2 ** n, 8)
-            sequence += [
-                nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
-                norm_layer(ndf * nf_mult),
-                nn.LeakyReLU(0.2, True)
-            ]
-        nf_mult_prev = nf_mult
-        nf_mult = min(2 ** n_layers, 8)
-        sequence += [
-            nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
-            norm_layer(ndf * nf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-        sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)]  # output 1 channel prediction map
-        self.model = nn.Sequential(*sequence)
-    def forward(self, input):
-        """Standard forward."""
-        return self.model(input)
\ No newline at end of file
diff --git a/spaces/ulysses115/Nogizaka46-so/onnx/model_onnx.py b/spaces/ulysses115/Nogizaka46-so/onnx/model_onnx.py
deleted file mode 100644
index 1567d28875c8a6620d5db8114daa0f073ddb145c..0000000000000000000000000000000000000000
--- a/spaces/ulysses115/Nogizaka46-so/onnx/model_onnx.py
+++ /dev/null
@@ -1,328 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import modules.attentions as attentions
-import modules.commons as commons
-import modules.modules as modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from modules.commons import init_weights, get_padding
-from vdecoder.hifigan.models import Generator
-from utils import f0_to_coarse
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class Encoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    # print(x.shape,x_lengths.shape)
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0,
-      filter_channels=None,
-      n_heads=None,
-      p_dropout=None):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-    self.f0_emb = nn.Embedding(256, hidden_channels)
-
-    self.enc_ =  attentions.Encoder(
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-
-  def forward(self, x, x_lengths, f0=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = x + self.f0_emb(f0.long()).transpose(1,2)
-    x = self.enc_(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-
-    return z, m, logs, x_mask
-
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-        
-        
-class SpeakerEncoder(torch.nn.Module):
-    def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
-        super(SpeakerEncoder, self).__init__()
-        self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
-        self.linear = nn.Linear(model_hidden_size, model_embedding_size)
-        self.relu = nn.ReLU()
-
-    def forward(self, mels):
-        self.lstm.flatten_parameters()
-        _, (hidden, _) = self.lstm(mels)
-        embeds_raw = self.relu(self.linear(hidden[-1]))
-        return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-        
-    def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
-        mel_slices = []
-        for i in range(0, total_frames-partial_frames, partial_hop):
-            mel_range = torch.arange(i, i+partial_frames)
-            mel_slices.append(mel_range)
-            
-        return mel_slices
-    
-    def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
-        mel_len = mel.size(1)
-        last_mel = mel[:,-partial_frames:]
-        
-        if mel_len > partial_frames:
-            mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
-            mels = list(mel[:,s] for s in mel_slices)
-            mels.append(last_mel)
-            mels = torch.stack(tuple(mels), 0).squeeze(1)
-        
-            with torch.no_grad():
-                partial_embeds = self(mels)
-            embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
-            #embed = embed / torch.linalg.norm(embed, 2)
-        else:
-            with torch.no_grad():
-                embed = self(last_mel)
-        
-        return embed
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    gin_channels,
-    ssl_dim,
-    n_speakers,
-    **kwargs):
-
-    super().__init__()
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.gin_channels = gin_channels
-    self.ssl_dim = ssl_dim
-    self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-    self.enc_p_ = TextEncoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16,0, filter_channels, n_heads, p_dropout)
-    hps = {
-        "sampling_rate": 32000,
-        "inter_channels": 192,
-        "resblock": "1",
-        "resblock_kernel_sizes": [3, 7, 11],
-        "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
-        "upsample_rates": [10, 8, 2, 2],
-        "upsample_initial_channel": 512,
-        "upsample_kernel_sizes": [16, 16, 4, 4],
-        "gin_channels": 256,
-    }
-    self.dec = Generator(h=hps)
-    self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-  def forward(self, c, c_lengths, f0, g=None):
-    g = self.emb_g(g.unsqueeze(0)).transpose(1,2)
-    z_p, m_p, logs_p, c_mask = self.enc_p_(c.transpose(1,2), c_lengths, f0=f0_to_coarse(f0))
-    z = self.flow(z_p, c_mask, g=g, reverse=True)
-    o = self.dec(z * c_mask, g=g, f0=f0.float())
-    return o
-    
diff --git a/spaces/upstage/open-ko-llm-leaderboard/models_backlinks.py b/spaces/upstage/open-ko-llm-leaderboard/models_backlinks.py
deleted file mode 100644
index 23446f2959485b29b9c3367b9bf890a8652433f9..0000000000000000000000000000000000000000
--- a/spaces/upstage/open-ko-llm-leaderboard/models_backlinks.py
+++ /dev/null
@@ -1 +0,0 @@
-models = ['upstage/Llama-2-70b-instruct-v2', 'upstage/Llama-2-70b-instruct', 'upstage/llama-65b-instruct', 'upstage/llama-65b-instruct', 'upstage/llama-30b-instruct-2048', 'upstage/llama-30b-instruct', 'baseline']
diff --git a/spaces/uragankatrrin/MHN-React/mhnreact/view.py b/spaces/uragankatrrin/MHN-React/mhnreact/view.py
deleted file mode 100644
index 714f70ae36c6936767f8370eeb8bdd1356296668..0000000000000000000000000000000000000000
--- a/spaces/uragankatrrin/MHN-React/mhnreact/view.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Author: Philipp Seidl
-        ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning
-        Johannes Kepler University Linz
-Contact: seidl@ml.jku.at
-
-Loading log-files from training
-"""
-
-from pathlib import Path
-import os
-import datetime
-import pandas as pd
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-
-def load_experiments(EXP_DIR = Path('data/experiments/')):
-    dfs = []
-    for fn in os.listdir(EXP_DIR):
-        print(fn, end='\r')
-        if fn.split('.')[-1]=='tsv':
-            df = pd.read_csv(EXP_DIR/fn, sep='\t', index_col=0)
-            try:
-                with open(df['fn_hist'][0]) as f:
-                    hist = eval(f.readlines()[0] )
-                df['hist'] = [hist]
-                df['fn'] = fn
-            except:
-                print('err')
-                #print(df['fn_hist'])
-            dfs.append( df )
-    df = pd.concat(dfs,ignore_index=True)
-    return df
-
-def get_x(k, kw, operation='max', index=None):
-    operation = getattr(np,operation)
-    try:
-        if index is not None:
-            return k[kw][index]
-
-        return operation(k[kw])
-    except:
-        return 0
-
-def get_min_val_loss_idx(k):
-    return get_x(k, 'loss_valid', 'argmin') #changed from argmax to argmin!!
-
-def get_tauc(hist):
-    idx = get_min_val_loss_idx(hist)
-    # takes max TODO take idx
-    return np.mean([get_x(hist, f't100_acc_nte_{nt}') for nt in [*range(11),'>10']])
-
-def get_stats_from_hist(df):
-    df['0shot_acc'] = df['hist'].apply(lambda k: get_x(k, 't100_acc_nte_0'))
-    df['1shot_acc'] = df['hist'].apply(lambda k: get_x(k, 't100_acc_nte_1'))
-    df['>49shot_acc'] = df['hist'].apply(lambda k: get_x(k, 't100_acc_nte_>49'))
-    df['min_loss_valid'] = df['hist'].apply(lambda k: get_x(k, 'loss_valid', 'min'))
-    return df
\ No newline at end of file
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Catedralmaisdoqueimagineirar is a Portuguese phrase that means Cathedral more than I imagined..md b/spaces/usbethFlerru/sovits-modelsV2/example/Catedralmaisdoqueimagineirar is a Portuguese phrase that means Cathedral more than I imagined..md
deleted file mode 100644
index 2d84a901e63fc9c472c30e6df58f3ee5877b36a4..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Catedralmaisdoqueimagineirar is a Portuguese phrase that means Cathedral more than I imagined..md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>catedralmaisdoqueimagineirar</h2><br /><p><b><b>DOWNLOAD</b> ->->->-> <a href="https://urlcod.com/2uyXGN">https://urlcod.com/2uyXGN</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Code Composer Studio V33 Free Download The Best IDE for TI Embedded Systems Development.md b/spaces/usbethFlerru/sovits-modelsV2/example/Code Composer Studio V33 Free Download The Best IDE for TI Embedded Systems Development.md
deleted file mode 100644
index cc2b709f2ed4b5641f45130179da17c4ffc71e2f..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Code Composer Studio V33 Free Download The Best IDE for TI Embedded Systems Development.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Code Composer Studio V33 Free Download</h2><br /><p><b><b>Download File</b> &raquo;&raquo;&raquo; <a href="https://urlcod.com/2uyU4q">https://urlcod.com/2uyU4q</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/user238921933/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py b/spaces/user238921933/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py
deleted file mode 100644
index 9d16fc11b8fc0678c36dadc9cca0de7122f47cee..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py
+++ /dev/null
@@ -1,357 +0,0 @@
-from collections import deque
-import torch
-import inspect
-import einops
-import k_diffusion.sampling
-from modules import prompt_parser, devices, sd_samplers_common
-
-from modules.shared import opts, state
-import modules.shared as shared
-from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
-from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
-
-samplers_k_diffusion = [
-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
-    ('Euler', 'sample_euler', ['k_euler'], {}),
-    ('LMS', 'sample_lms', ['k_lms'], {}),
-    ('Heun', 'sample_heun', ['k_heun'], {}),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
-    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
-    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
-    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
-    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
-    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
-    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
-    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
-    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
-    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
-    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
-    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
-]
-
-samplers_data_k_diffusion = [
-    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
-    for label, funcname, aliases, options in samplers_k_diffusion
-    if hasattr(k_diffusion.sampling, funcname)
-]
-
-sampler_extra_params = {
-    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-}
-
-
-class CFGDenoiser(torch.nn.Module):
-    """
-    Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
-    that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
-    instead of one. Originally, the second prompt is just an empty string, but we use non-empty
-    negative prompt.
-    """
-
-    def __init__(self, model):
-        super().__init__()
-        self.inner_model = model
-        self.mask = None
-        self.nmask = None
-        self.init_latent = None
-        self.step = 0
-        self.image_cfg_scale = None
-
-    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
-        denoised_uncond = x_out[-uncond.shape[0]:]
-        denoised = torch.clone(denoised_uncond)
-
-        for i, conds in enumerate(conds_list):
-            for cond_index, weight in conds:
-                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
-
-        return denoised
-
-    def combine_denoised_for_edit_model(self, x_out, cond_scale):
-        out_cond, out_img_cond, out_uncond = x_out.chunk(3)
-        denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
-
-        return denoised
-
-    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
-        if state.interrupted or state.skipped:
-            raise sd_samplers_common.InterruptedException
-
-        # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
-        # so is_edit_model is set to False to support AND composition.
-        is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
-
-        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
-
-        assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
-
-        batch_size = len(conds_list)
-        repeats = [len(conds_list[i]) for i in range(batch_size)]
-
-        if not is_edit_model:
-            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
-            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
-        else:
-            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
-            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
-            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)])
-
-        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
-        cfg_denoiser_callback(denoiser_params)
-        x_in = denoiser_params.x
-        image_cond_in = denoiser_params.image_cond
-        sigma_in = denoiser_params.sigma
-
-        if tensor.shape[1] == uncond.shape[1]:
-            if not is_edit_model:
-                cond_in = torch.cat([tensor, uncond])
-            else:
-                cond_in = torch.cat([tensor, uncond, uncond])
-
-            if shared.batch_cond_uncond:
-                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
-            else:
-                x_out = torch.zeros_like(x_in)
-                for batch_offset in range(0, x_out.shape[0], batch_size):
-                    a = batch_offset
-                    b = a + batch_size
-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
-        else:
-            x_out = torch.zeros_like(x_in)
-            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
-            for batch_offset in range(0, tensor.shape[0], batch_size):
-                a = batch_offset
-                b = min(a + batch_size, tensor.shape[0])
-
-                if not is_edit_model:
-                    c_crossattn = [tensor[a:b]]
-                else:
-                    c_crossattn = torch.cat([tensor[a:b]], uncond)
-
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]})
-
-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
-
-        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
-        cfg_denoised_callback(denoised_params)
-
-        devices.test_for_nans(x_out, "unet")
-
-        if opts.live_preview_content == "Prompt":
-            sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
-        elif opts.live_preview_content == "Negative prompt":
-            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
-
-        if not is_edit_model:
-            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
-        else:
-            denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
-
-        if self.mask is not None:
-            denoised = self.init_latent * self.mask + self.nmask * denoised
-
-        self.step += 1
-
-        return denoised
-
-
-class TorchHijack:
-    def __init__(self, sampler_noises):
-        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
-        # implementation.
-        self.sampler_noises = deque(sampler_noises)
-
-    def __getattr__(self, item):
-        if item == 'randn_like':
-            return self.randn_like
-
-        if hasattr(torch, item):
-            return getattr(torch, item)
-
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
-
-    def randn_like(self, x):
-        if self.sampler_noises:
-            noise = self.sampler_noises.popleft()
-            if noise.shape == x.shape:
-                return noise
-
-        if x.device.type == 'mps':
-            return torch.randn_like(x, device=devices.cpu).to(x.device)
-        else:
-            return torch.randn_like(x)
-
-
-class KDiffusionSampler:
-    def __init__(self, funcname, sd_model):
-        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-
-        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
-        self.funcname = funcname
-        self.func = getattr(k_diffusion.sampling, self.funcname)
-        self.extra_params = sampler_extra_params.get(funcname, [])
-        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
-        self.sampler_noises = None
-        self.stop_at = None
-        self.eta = None
-        self.config = None
-        self.last_latent = None
-
-        self.conditioning_key = sd_model.model.conditioning_key
-
-    def callback_state(self, d):
-        step = d['i']
-        latent = d["denoised"]
-        if opts.live_preview_content == "Combined":
-            sd_samplers_common.store_latent(latent)
-        self.last_latent = latent
-
-        if self.stop_at is not None and step > self.stop_at:
-            raise sd_samplers_common.InterruptedException
-
-        state.sampling_step = step
-        shared.total_tqdm.update()
-
-    def launch_sampling(self, steps, func):
-        state.sampling_steps = steps
-        state.sampling_step = 0
-
-        try:
-            return func()
-        except sd_samplers_common.InterruptedException:
-            return self.last_latent
-
-    def number_of_needed_noises(self, p):
-        return p.steps
-
-    def initialize(self, p):
-        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
-        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
-        self.model_wrap_cfg.step = 0
-        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
-        self.eta = p.eta if p.eta is not None else opts.eta_ancestral
-
-        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
-
-        extra_params_kwargs = {}
-        for param_name in self.extra_params:
-            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
-                extra_params_kwargs[param_name] = getattr(p, param_name)
-
-        if 'eta' in inspect.signature(self.func).parameters:
-            if self.eta != 1.0:
-                p.extra_generation_params["Eta"] = self.eta
-
-            extra_params_kwargs['eta'] = self.eta
-
-        return extra_params_kwargs
-
-    def get_sigmas(self, p, steps):
-        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
-        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
-            discard_next_to_last_sigma = True
-            p.extra_generation_params["Discard penultimate sigma"] = True
-
-        steps += 1 if discard_next_to_last_sigma else 0
-
-        if p.sampler_noise_scheduler_override:
-            sigmas = p.sampler_noise_scheduler_override(steps)
-        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
-        else:
-            sigmas = self.model_wrap.get_sigmas(steps)
-
-        if discard_next_to_last_sigma:
-            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-
-        return sigmas
-
-    def create_noise_sampler(self, x, sigmas, p):
-        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
-        if shared.opts.no_dpmpp_sde_batch_determinism:
-            return None
-
-        from k_diffusion.sampling import BrownianTreeNoiseSampler
-        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
-        current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
-        return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
-
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
-
-        sigmas = self.get_sigmas(p, steps)
-
-        sigma_sched = sigmas[steps - t_enc - 1:]
-        xi = x + noise * sigma_sched[0]
-        
-        extra_params_kwargs = self.initialize(p)
-        parameters = inspect.signature(self.func).parameters
-
-        if 'sigma_min' in parameters:
-            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
-            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
-        if 'sigma_max' in parameters:
-            extra_params_kwargs['sigma_max'] = sigma_sched[0]
-        if 'n' in parameters:
-            extra_params_kwargs['n'] = len(sigma_sched) - 1
-        if 'sigma_sched' in parameters:
-            extra_params_kwargs['sigma_sched'] = sigma_sched
-        if 'sigmas' in parameters:
-            extra_params_kwargs['sigmas'] = sigma_sched
-
-        if self.funcname == 'sample_dpmpp_sde':
-            noise_sampler = self.create_noise_sampler(x, sigmas, p)
-            extra_params_kwargs['noise_sampler'] = noise_sampler
-
-        self.model_wrap_cfg.init_latent = x
-        self.last_latent = x
-        extra_args={
-            'cond': conditioning, 
-            'image_cond': image_conditioning, 
-            'uncond': unconditional_conditioning, 
-            'cond_scale': p.cfg_scale,
-        }
-
-        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
-        return samples
-
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps = steps or p.steps
-
-        sigmas = self.get_sigmas(p, steps)
-
-        x = x * sigmas[0]
-
-        extra_params_kwargs = self.initialize(p)
-        parameters = inspect.signature(self.func).parameters
-
-        if 'sigma_min' in parameters:
-            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
-            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
-            if 'n' in parameters:
-                extra_params_kwargs['n'] = steps
-        else:
-            extra_params_kwargs['sigmas'] = sigmas
-
-        if self.funcname == 'sample_dpmpp_sde':
-            noise_sampler = self.create_noise_sampler(x, sigmas, p)
-            extra_params_kwargs['noise_sampler'] = noise_sampler
-
-        self.last_latent = x
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
-            'cond': conditioning, 
-            'image_cond': image_conditioning, 
-            'uncond': unconditional_conditioning, 
-            'cond_scale': p.cfg_scale
-        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
-        return samples
-
diff --git a/spaces/valeriylo/rag_demo/htmlTemplates.py b/spaces/valeriylo/rag_demo/htmlTemplates.py
deleted file mode 100644
index 9f0e6496058299100f75cb3b121be84c077e723e..0000000000000000000000000000000000000000
--- a/spaces/valeriylo/rag_demo/htmlTemplates.py
+++ /dev/null
@@ -1,44 +0,0 @@
-css = '''
-<style>
-.chat-message {
-    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
-}
-.chat-message.user {
-    background-color: #2b313e
-}
-.chat-message.bot {
-    background-color: #475063
-}
-.chat-message .avatar {
-  width: 20%;
-}
-.chat-message .avatar img {
-  max-width: 78px;
-  max-height: 78px;
-  border-radius: 50%;
-  object-fit: cover;
-}
-.chat-message .message {
-  width: 80%;
-  padding: 0 1.5rem;
-  color: #fff;
-}
-'''
-
-bot_template = '''
-<div class="chat-message bot">
-    <div class="avatar">
-        <img src="https://i.ibb.co/cN0nmSj/Screenshot-2023-05-28-at-02-37-21.png" style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;">
-    </div>
-    <div class="message">{{MSG}}</div>
-</div>
-'''
-
-user_template = '''
-<div class="chat-message user">
-    <div class="avatar">
-        <img src="https://i.ibb.co/rdZC7LZ/Photo-logo-1.png">
-    </div>    
-    <div class="message">{{MSG}}</div>
-</div>
-'''
diff --git a/spaces/vibhorvats/Joeythemonster-anything-midjourney-v-4-1/README.md b/spaces/vibhorvats/Joeythemonster-anything-midjourney-v-4-1/README.md
deleted file mode 100644
index 97b2a34023dbd2d827cffd43927066e9b639b08c..0000000000000000000000000000000000000000
--- a/spaces/vibhorvats/Joeythemonster-anything-midjourney-v-4-1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Joeythemonster Anything Midjourney V 4 1
-emoji: 💩
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/victor/spaces-collection/README.md b/spaces/victor/spaces-collection/README.md
deleted file mode 100644
index d7cd317ccc8a17339455de5c40cb55bcd3fd495d..0000000000000000000000000000000000000000
--- a/spaces/victor/spaces-collection/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Spaces Collection
-emoji: 💻
-colorFrom: blue
-colorTo: blue
-sdk: static
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/videfikri/aicover/train/utils.py b/spaces/videfikri/aicover/train/utils.py
deleted file mode 100644
index f0b9907a9aa8b6a47bc908c4966a525fb2079b77..0000000000000000000000000000000000000000
--- a/spaces/videfikri/aicover/train/utils.py
+++ /dev/null
@@ -1,471 +0,0 @@
-import os, traceback
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-
-    ##################
-    def go(model, bkey):
-        saved_state_dict = checkpoint_dict[bkey]
-        if hasattr(model, "module"):
-            state_dict = model.module.state_dict()
-        else:
-            state_dict = model.state_dict()
-        new_state_dict = {}
-        for k, v in state_dict.items():  # 模型需要的shape
-            try:
-                new_state_dict[k] = saved_state_dict[k]
-                if saved_state_dict[k].shape != state_dict[k].shape:
-                    print(
-                        "shape-%s-mismatch|need-%s|get-%s"
-                        % (k, state_dict[k].shape, saved_state_dict[k].shape)
-                    )  #
-                    raise KeyError
-            except:
-                # logger.info(traceback.format_exc())
-                logger.info("%s is not in the checkpoint" % k)  # pretrain缺失的
-                new_state_dict[k] = v  # 模型自带的随机值
-        if hasattr(model, "module"):
-            model.module.load_state_dict(new_state_dict, strict=False)
-        else:
-            model.load_state_dict(new_state_dict, strict=False)
-
-    go(combd, "combd")
-    go(sbd, "sbd")
-    #############
-    logger.info("Loaded model weights")
-
-    iteration = checkpoint_dict["iteration"]
-    learning_rate = checkpoint_dict["learning_rate"]
-    if (
-        optimizer is not None and load_opt == 1
-    ):  ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
-        #   try:
-        optimizer.load_state_dict(checkpoint_dict["optimizer"])
-    #   except:
-    #     traceback.print_exc()
-    logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-# def load_checkpoint(checkpoint_path, model, optimizer=None):
-#   assert os.path.isfile(checkpoint_path)
-#   checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-#   iteration = checkpoint_dict['iteration']
-#   learning_rate = checkpoint_dict['learning_rate']
-#   if optimizer is not None:
-#     optimizer.load_state_dict(checkpoint_dict['optimizer'])
-#   # print(1111)
-#   saved_state_dict = checkpoint_dict['model']
-#   # print(1111)
-#
-#   if hasattr(model, 'module'):
-#     state_dict = model.module.state_dict()
-#   else:
-#     state_dict = model.state_dict()
-#   new_state_dict= {}
-#   for k, v in state_dict.items():
-#     try:
-#       new_state_dict[k] = saved_state_dict[k]
-#     except:
-#       logger.info("%s is not in the checkpoint" % k)
-#       new_state_dict[k] = v
-#   if hasattr(model, 'module'):
-#     model.module.load_state_dict(new_state_dict)
-#   else:
-#     model.load_state_dict(new_state_dict)
-#   logger.info("Loaded checkpoint '{}' (epoch {})" .format(
-#     checkpoint_path, iteration))
-#   return model, optimizer, learning_rate, iteration
-def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
-    assert os.path.isfile(checkpoint_path)
-    checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
-
-    saved_state_dict = checkpoint_dict["model"]
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    new_state_dict = {}
-    for k, v in state_dict.items():  # 模型需要的shape
-        try:
-            new_state_dict[k] = saved_state_dict[k]
-            if saved_state_dict[k].shape != state_dict[k].shape:
-                print(
-                    "shape-%s-mismatch|need-%s|get-%s"
-                    % (k, state_dict[k].shape, saved_state_dict[k].shape)
-                )  #
-                raise KeyError
-        except:
-            # logger.info(traceback.format_exc())
-            logger.info("%s is not in the checkpoint" % k)  # pretrain缺失的
-            new_state_dict[k] = v  # 模型自带的随机值
-    if hasattr(model, "module"):
-        model.module.load_state_dict(new_state_dict, strict=False)
-    else:
-        model.load_state_dict(new_state_dict, strict=False)
-    logger.info("Loaded model weights")
-
-    iteration = checkpoint_dict["iteration"]
-    learning_rate = checkpoint_dict["learning_rate"]
-    if (
-        optimizer is not None and load_opt == 1
-    ):  ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
-        #   try:
-        optimizer.load_state_dict(checkpoint_dict["optimizer"])
-    #   except:
-    #     traceback.print_exc()
-    logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info(
-        "Saving model and optimizer state at epoch {} to {}".format(
-            iteration, checkpoint_path
-        )
-    )
-    if hasattr(model, "module"):
-        state_dict = model.module.state_dict()
-    else:
-        state_dict = model.state_dict()
-    torch.save(
-        {
-            "model": state_dict,
-            "iteration": iteration,
-            "optimizer": optimizer.state_dict(),
-            "learning_rate": learning_rate,
-        },
-        checkpoint_path,
-    )
-
-
-def save_checkpoint_d(combd, sbd, optimizer, learning_rate, iteration, checkpoint_path):
-    logger.info(
-        "Saving model and optimizer state at epoch {} to {}".format(
-            iteration, checkpoint_path
-        )
-    )
-    if hasattr(combd, "module"):
-        state_dict_combd = combd.module.state_dict()
-    else:
-        state_dict_combd = combd.state_dict()
-    if hasattr(sbd, "module"):
-        state_dict_sbd = sbd.module.state_dict()
-    else:
-        state_dict_sbd = sbd.state_dict()
-    torch.save(
-        {
-            "combd": state_dict_combd,
-            "sbd": state_dict_sbd,
-            "iteration": iteration,
-            "optimizer": optimizer.state_dict(),
-            "learning_rate": learning_rate,
-        },
-        checkpoint_path,
-    )
-
-
-def summarize(
-    writer,
-    global_step,
-    scalars={},
-    histograms={},
-    images={},
-    audios={},
-    audio_sampling_rate=22050,
-):
-    for k, v in scalars.items():
-        writer.add_scalar(k, v, global_step)
-    for k, v in histograms.items():
-        writer.add_histogram(k, v, global_step)
-    for k, v in images.items():
-        writer.add_image(k, v, global_step, dataformats="HWC")
-    for k, v in audios.items():
-        writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-    f_list = glob.glob(os.path.join(dir_path, regex))
-    f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-    x = f_list[-1]
-    print(x)
-    return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger("matplotlib")
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
-    plt.colorbar(im, ax=ax)
-    plt.xlabel("Frames")
-    plt.ylabel("Channels")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-    global MATPLOTLIB_FLAG
-    if not MATPLOTLIB_FLAG:
-        import matplotlib
-
-        matplotlib.use("Agg")
-        MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger("matplotlib")
-        mpl_logger.setLevel(logging.WARNING)
-    import matplotlib.pylab as plt
-    import numpy as np
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    im = ax.imshow(
-        alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
-    )
-    fig.colorbar(im, ax=ax)
-    xlabel = "Decoder timestep"
-    if info is not None:
-        xlabel += "\n\n" + info
-    plt.xlabel(xlabel)
-    plt.ylabel("Encoder timestep")
-    plt.tight_layout()
-
-    fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    plt.close()
-    return data
-
-
-def load_wav_to_torch(full_path):
-    sampling_rate, data = read(full_path)
-    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-    with open(filename, encoding="utf-8") as f:
-        filepaths_and_text = [line.strip().split(split) for line in f]
-    return filepaths_and_text
-
-
-def get_hparams(init=True):
-    """
-    todo:
-      结尾七人组：
-        保存频率、总epoch                     done
-        bs                                    done
-        pretrainG、pretrainD                  done
-        卡号：os.en["CUDA_VISIBLE_DEVICES"]   done
-        if_latest                             todo
-      模型：if_f0                             todo
-      采样率：自动选择config                  done
-      是否缓存数据集进GPU:if_cache_data_in_gpu done
-
-      -m:
-        自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files    done
-      -c不要了
-    """
-    parser = argparse.ArgumentParser()
-    # parser.add_argument('-c', '--config', type=str, default="configs/40k.json",help='JSON file for configuration')
-    parser.add_argument(
-        "-se",
-        "--save_every_epoch",
-        type=int,
-        required=True,
-        help="checkpoint save frequency (epoch)",
-    )
-    parser.add_argument(
-        "-te", "--total_epoch", type=int, required=True, help="total_epoch"
-    )
-    parser.add_argument(
-        "-pg", "--pretrainG", type=str, default="", help="Pretrained Discriminator path"
-    )
-    parser.add_argument(
-        "-pd", "--pretrainD", type=str, default="", help="Pretrained Generator path"
-    )
-    parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -")
-    parser.add_argument(
-        "-bs", "--batch_size", type=int, required=True, help="batch size"
-    )
-    parser.add_argument(
-        "-e", "--experiment_dir", type=str, required=True, help="experiment dir"
-    )  # -m
-    parser.add_argument(
-        "-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
-    )
-    parser.add_argument(
-        "-f0",
-        "--if_f0",
-        type=int,
-        required=True,
-        help="use f0 as one of the inputs of the model, 1 or 0",
-    )
-    parser.add_argument(
-        "-l",
-        "--if_latest",
-        type=int,
-        required=True,
-        help="if only save the latest G/D pth file, 1 or 0",
-    )
-    parser.add_argument(
-        "-c",
-        "--if_cache_data_in_gpu",
-        type=int,
-        required=True,
-        help="if caching the dataset in GPU memory, 1 or 0",
-    )
-
-    args = parser.parse_args()
-    name = args.experiment_dir
-    experiment_dir = os.path.join("./logs", args.experiment_dir)
-
-    if not os.path.exists(experiment_dir):
-        os.makedirs(experiment_dir)
-
-    config_path = "configs/%s.json" % args.sample_rate
-    config_save_path = os.path.join(experiment_dir, "config.json")
-    if init:
-        with open(config_path, "r") as f:
-            data = f.read()
-        with open(config_save_path, "w") as f:
-            f.write(data)
-    else:
-        with open(config_save_path, "r") as f:
-            data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = hparams.experiment_dir = experiment_dir
-    hparams.save_every_epoch = args.save_every_epoch
-    hparams.name = name
-    hparams.total_epoch = args.total_epoch
-    hparams.pretrainG = args.pretrainG
-    hparams.pretrainD = args.pretrainD
-    hparams.gpus = args.gpus
-    hparams.train.batch_size = args.batch_size
-    hparams.sample_rate = args.sample_rate
-    hparams.if_f0 = args.if_f0
-    hparams.if_latest = args.if_latest
-    hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
-    hparams.data.training_files = "%s/filelist.txt" % experiment_dir
-    return hparams
-
-
-def get_hparams_from_dir(model_dir):
-    config_save_path = os.path.join(model_dir, "config.json")
-    with open(config_save_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    hparams.model_dir = model_dir
-    return hparams
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def check_git_hash(model_dir):
-    source_dir = os.path.dirname(os.path.realpath(__file__))
-    if not os.path.exists(os.path.join(source_dir, ".git")):
-        logger.warn(
-            "{} is not a git repository, therefore hash value comparison will be ignored.".format(
-                source_dir
-            )
-        )
-        return
-
-    cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-    path = os.path.join(model_dir, "githash")
-    if os.path.exists(path):
-        saved_hash = open(path).read()
-        if saved_hash != cur_hash:
-            logger.warn(
-                "git hash values are different. {}(saved) != {}(current)".format(
-                    saved_hash[:8], cur_hash[:8]
-                )
-            )
-    else:
-        open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-    global logger
-    logger = logging.getLogger(os.path.basename(model_dir))
-    logger.setLevel(logging.DEBUG)
-
-    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-    h = logging.FileHandler(os.path.join(model_dir, filename))
-    h.setLevel(logging.DEBUG)
-    h.setFormatter(formatter)
-    logger.addHandler(h)
-    return logger
-
-
-class HParams:
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
diff --git a/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/__init__.py b/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/__init__.py
deleted file mode 100644
index 76e4b272b479a26c63d120c818c140870cd8c287..0000000000000000000000000000000000000000
--- a/spaces/vinay123/panoptic-segment-anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .backbone import build_backbone
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/checkpoint.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/checkpoint.py
deleted file mode 100644
index b29ca320679164432f446adad893e33fb2b4b29e..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/checkpoint.py
+++ /dev/null
@@ -1,707 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import io
-import os
-import os.path as osp
-import pkgutil
-import re
-import time
-import warnings
-from collections import OrderedDict
-from importlib import import_module
-from tempfile import TemporaryDirectory
-
-import torch
-import torchvision
-from torch.optim import Optimizer
-from torch.utils import model_zoo
-
-import annotator.uniformer.mmcv as mmcv
-from ..fileio import FileClient
-from ..fileio import load as load_file
-from ..parallel import is_module_wrapper
-from ..utils import mkdir_or_exist
-from .dist_utils import get_dist_info
-
-ENV_MMCV_HOME = 'MMCV_HOME'
-ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
-DEFAULT_CACHE_DIR = '~/.cache'
-
-
-def _get_mmcv_home():
-    mmcv_home = os.path.expanduser(
-        os.getenv(
-            ENV_MMCV_HOME,
-            os.path.join(
-                os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv')))
-
-    mkdir_or_exist(mmcv_home)
-    return mmcv_home
-
-
-def load_state_dict(module, state_dict, strict=False, logger=None):
-    """Load state_dict to a module.
-
-    This method is modified from :meth:`torch.nn.Module.load_state_dict`.
-    Default value for ``strict`` is set to ``False`` and the message for
-    param mismatch will be shown even if strict is False.
-
-    Args:
-        module (Module): Module that receives the state_dict.
-        state_dict (OrderedDict): Weights.
-        strict (bool): whether to strictly enforce that the keys
-            in :attr:`state_dict` match the keys returned by this module's
-            :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
-        logger (:obj:`logging.Logger`, optional): Logger to log the error
-            message. If not specified, print function will be used.
-    """
-    unexpected_keys = []
-    all_missing_keys = []
-    err_msg = []
-
-    metadata = getattr(state_dict, '_metadata', None)
-    state_dict = state_dict.copy()
-    if metadata is not None:
-        state_dict._metadata = metadata
-
-    # use _load_from_state_dict to enable checkpoint version control
-    def load(module, prefix=''):
-        # recursively check parallel module in case that the model has a
-        # complicated structure, e.g., nn.Module(nn.Module(DDP))
-        if is_module_wrapper(module):
-            module = module.module
-        local_metadata = {} if metadata is None else metadata.get(
-            prefix[:-1], {})
-        module._load_from_state_dict(state_dict, prefix, local_metadata, True,
-                                     all_missing_keys, unexpected_keys,
-                                     err_msg)
-        for name, child in module._modules.items():
-            if child is not None:
-                load(child, prefix + name + '.')
-
-    load(module)
-    load = None  # break load->load reference cycle
-
-    # ignore "num_batches_tracked" of BN layers
-    missing_keys = [
-        key for key in all_missing_keys if 'num_batches_tracked' not in key
-    ]
-
-    if unexpected_keys:
-        err_msg.append('unexpected key in source '
-                       f'state_dict: {", ".join(unexpected_keys)}\n')
-    if missing_keys:
-        err_msg.append(
-            f'missing keys in source state_dict: {", ".join(missing_keys)}\n')
-
-    rank, _ = get_dist_info()
-    if len(err_msg) > 0 and rank == 0:
-        err_msg.insert(
-            0, 'The model and loaded state dict do not match exactly\n')
-        err_msg = '\n'.join(err_msg)
-        if strict:
-            raise RuntimeError(err_msg)
-        elif logger is not None:
-            logger.warning(err_msg)
-        else:
-            print(err_msg)
-
-
-def get_torchvision_models():
-    model_urls = dict()
-    for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__):
-        if ispkg:
-            continue
-        _zoo = import_module(f'torchvision.models.{name}')
-        if hasattr(_zoo, 'model_urls'):
-            _urls = getattr(_zoo, 'model_urls')
-            model_urls.update(_urls)
-    return model_urls
-
-
-def get_external_models():
-    mmcv_home = _get_mmcv_home()
-    default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json')
-    default_urls = load_file(default_json_path)
-    assert isinstance(default_urls, dict)
-    external_json_path = osp.join(mmcv_home, 'open_mmlab.json')
-    if osp.exists(external_json_path):
-        external_urls = load_file(external_json_path)
-        assert isinstance(external_urls, dict)
-        default_urls.update(external_urls)
-
-    return default_urls
-
-
-def get_mmcls_models():
-    mmcls_json_path = osp.join(mmcv.__path__[0], 'model_zoo/mmcls.json')
-    mmcls_urls = load_file(mmcls_json_path)
-
-    return mmcls_urls
-
-
-def get_deprecated_model_names():
-    deprecate_json_path = osp.join(mmcv.__path__[0],
-                                   'model_zoo/deprecated.json')
-    deprecate_urls = load_file(deprecate_json_path)
-    assert isinstance(deprecate_urls, dict)
-
-    return deprecate_urls
-
-
-def _process_mmcls_checkpoint(checkpoint):
-    state_dict = checkpoint['state_dict']
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k.startswith('backbone.'):
-            new_state_dict[k[9:]] = v
-    new_checkpoint = dict(state_dict=new_state_dict)
-
-    return new_checkpoint
-
-
-class CheckpointLoader:
-    """A general checkpoint loader to manage all schemes."""
-
-    _schemes = {}
-
-    @classmethod
-    def _register_scheme(cls, prefixes, loader, force=False):
-        if isinstance(prefixes, str):
-            prefixes = [prefixes]
-        else:
-            assert isinstance(prefixes, (list, tuple))
-        for prefix in prefixes:
-            if (prefix not in cls._schemes) or force:
-                cls._schemes[prefix] = loader
-            else:
-                raise KeyError(
-                    f'{prefix} is already registered as a loader backend, '
-                    'add "force=True" if you want to override it')
-        # sort, longer prefixes take priority
-        cls._schemes = OrderedDict(
-            sorted(cls._schemes.items(), key=lambda t: t[0], reverse=True))
-
-    @classmethod
-    def register_scheme(cls, prefixes, loader=None, force=False):
-        """Register a loader to CheckpointLoader.
-
-        This method can be used as a normal class method or a decorator.
-
-        Args:
-            prefixes (str or list[str] or tuple[str]):
-            The prefix of the registered loader.
-            loader (function, optional): The loader function to be registered.
-                When this method is used as a decorator, loader is None.
-                Defaults to None.
-            force (bool, optional): Whether to override the loader
-                if the prefix has already been registered. Defaults to False.
-        """
-
-        if loader is not None:
-            cls._register_scheme(prefixes, loader, force=force)
-            return
-
-        def _register(loader_cls):
-            cls._register_scheme(prefixes, loader_cls, force=force)
-            return loader_cls
-
-        return _register
-
-    @classmethod
-    def _get_checkpoint_loader(cls, path):
-        """Finds a loader that supports the given path. Falls back to the local
-        loader if no other loader is found.
-
-        Args:
-            path (str): checkpoint path
-
-        Returns:
-            loader (function): checkpoint loader
-        """
-
-        for p in cls._schemes:
-            if path.startswith(p):
-                return cls._schemes[p]
-
-    @classmethod
-    def load_checkpoint(cls, filename, map_location=None, logger=None):
-        """load checkpoint through URL scheme path.
-
-        Args:
-            filename (str): checkpoint file name with given prefix
-            map_location (str, optional): Same as :func:`torch.load`.
-                Default: None
-            logger (:mod:`logging.Logger`, optional): The logger for message.
-                Default: None
-
-        Returns:
-            dict or OrderedDict: The loaded checkpoint.
-        """
-
-        checkpoint_loader = cls._get_checkpoint_loader(filename)
-        class_name = checkpoint_loader.__name__
-        mmcv.print_log(
-            f'load checkpoint from {class_name[10:]} path: {filename}', logger)
-        return checkpoint_loader(filename, map_location)
-
-
-@CheckpointLoader.register_scheme(prefixes='')
-def load_from_local(filename, map_location):
-    """load checkpoint by local file path.
-
-    Args:
-        filename (str): local checkpoint file path
-        map_location (str, optional): Same as :func:`torch.load`.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-
-    if not osp.isfile(filename):
-        raise IOError(f'{filename} is not a checkpoint file')
-    checkpoint = torch.load(filename, map_location=map_location)
-    return checkpoint
-
-
-@CheckpointLoader.register_scheme(prefixes=('http://', 'https://'))
-def load_from_http(filename, map_location=None, model_dir=None):
-    """load checkpoint through HTTP or HTTPS scheme path. In distributed
-    setting, this function only download checkpoint at local rank 0.
-
-    Args:
-        filename (str): checkpoint file path with modelzoo or
-            torchvision prefix
-        map_location (str, optional): Same as :func:`torch.load`.
-        model_dir (string, optional): directory in which to save the object,
-            Default: None
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    if rank == 0:
-        checkpoint = model_zoo.load_url(
-            filename, model_dir=model_dir, map_location=map_location)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            checkpoint = model_zoo.load_url(
-                filename, model_dir=model_dir, map_location=map_location)
-    return checkpoint
-
-
-@CheckpointLoader.register_scheme(prefixes='pavi://')
-def load_from_pavi(filename, map_location=None):
-    """load checkpoint through the file path prefixed with pavi. In distributed
-    setting, this function download ckpt at all ranks to different temporary
-    directories.
-
-    Args:
-        filename (str): checkpoint file path with pavi prefix
-        map_location (str, optional): Same as :func:`torch.load`.
-          Default: None
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    assert filename.startswith('pavi://'), \
-        f'Expected filename startswith `pavi://`, but get {filename}'
-    model_path = filename[7:]
-
-    try:
-        from pavi import modelcloud
-    except ImportError:
-        raise ImportError(
-            'Please install pavi to load checkpoint from modelcloud.')
-
-    model = modelcloud.get(model_path)
-    with TemporaryDirectory() as tmp_dir:
-        downloaded_file = osp.join(tmp_dir, model.name)
-        model.download(downloaded_file)
-        checkpoint = torch.load(downloaded_file, map_location=map_location)
-    return checkpoint
-
-
-@CheckpointLoader.register_scheme(prefixes='s3://')
-def load_from_ceph(filename, map_location=None, backend='petrel'):
-    """load checkpoint through the file path prefixed with s3.  In distributed
-    setting, this function download ckpt at all ranks to different temporary
-    directories.
-
-    Args:
-        filename (str): checkpoint file path with s3 prefix
-        map_location (str, optional): Same as :func:`torch.load`.
-        backend (str, optional): The storage backend type. Options are 'ceph',
-            'petrel'. Default: 'petrel'.
-
-    .. warning::
-        :class:`mmcv.fileio.file_client.CephBackend` will be deprecated,
-        please use :class:`mmcv.fileio.file_client.PetrelBackend` instead.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    allowed_backends = ['ceph', 'petrel']
-    if backend not in allowed_backends:
-        raise ValueError(f'Load from Backend {backend} is not supported.')
-
-    if backend == 'ceph':
-        warnings.warn(
-            'CephBackend will be deprecated, please use PetrelBackend instead')
-
-    # CephClient and PetrelBackend have the same prefix 's3://' and the latter
-    # will be chosen as default. If PetrelBackend can not be instantiated
-    # successfully, the CephClient will be chosen.
-    try:
-        file_client = FileClient(backend=backend)
-    except ImportError:
-        allowed_backends.remove(backend)
-        file_client = FileClient(backend=allowed_backends[0])
-
-    with io.BytesIO(file_client.get(filename)) as buffer:
-        checkpoint = torch.load(buffer, map_location=map_location)
-    return checkpoint
-
-
-@CheckpointLoader.register_scheme(prefixes=('modelzoo://', 'torchvision://'))
-def load_from_torchvision(filename, map_location=None):
-    """load checkpoint through the file path prefixed with modelzoo or
-    torchvision.
-
-    Args:
-        filename (str): checkpoint file path with modelzoo or
-            torchvision prefix
-        map_location (str, optional): Same as :func:`torch.load`.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    model_urls = get_torchvision_models()
-    if filename.startswith('modelzoo://'):
-        warnings.warn('The URL scheme of "modelzoo://" is deprecated, please '
-                      'use "torchvision://" instead')
-        model_name = filename[11:]
-    else:
-        model_name = filename[14:]
-    return load_from_http(model_urls[model_name], map_location=map_location)
-
-
-@CheckpointLoader.register_scheme(prefixes=('open-mmlab://', 'openmmlab://'))
-def load_from_openmmlab(filename, map_location=None):
-    """load checkpoint through the file path prefixed with open-mmlab or
-    openmmlab.
-
-    Args:
-        filename (str): checkpoint file path with open-mmlab or
-        openmmlab prefix
-        map_location (str, optional): Same as :func:`torch.load`.
-          Default: None
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-
-    model_urls = get_external_models()
-    prefix_str = 'open-mmlab://'
-    if filename.startswith(prefix_str):
-        model_name = filename[13:]
-    else:
-        model_name = filename[12:]
-        prefix_str = 'openmmlab://'
-
-    deprecated_urls = get_deprecated_model_names()
-    if model_name in deprecated_urls:
-        warnings.warn(f'{prefix_str}{model_name} is deprecated in favor '
-                      f'of {prefix_str}{deprecated_urls[model_name]}')
-        model_name = deprecated_urls[model_name]
-    model_url = model_urls[model_name]
-    # check if is url
-    if model_url.startswith(('http://', 'https://')):
-        checkpoint = load_from_http(model_url, map_location=map_location)
-    else:
-        filename = osp.join(_get_mmcv_home(), model_url)
-        if not osp.isfile(filename):
-            raise IOError(f'{filename} is not a checkpoint file')
-        checkpoint = torch.load(filename, map_location=map_location)
-    return checkpoint
-
-
-@CheckpointLoader.register_scheme(prefixes='mmcls://')
-def load_from_mmcls(filename, map_location=None):
-    """load checkpoint through the file path prefixed with mmcls.
-
-    Args:
-        filename (str): checkpoint file path with mmcls prefix
-        map_location (str, optional): Same as :func:`torch.load`.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-
-    model_urls = get_mmcls_models()
-    model_name = filename[8:]
-    checkpoint = load_from_http(
-        model_urls[model_name], map_location=map_location)
-    checkpoint = _process_mmcls_checkpoint(checkpoint)
-    return checkpoint
-
-
-def _load_checkpoint(filename, map_location=None, logger=None):
-    """Load checkpoint from somewhere (modelzoo, file, url).
-
-    Args:
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str, optional): Same as :func:`torch.load`.
-           Default: None.
-        logger (:mod:`logging.Logger`, optional): The logger for error message.
-           Default: None
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint. It can be either an
-           OrderedDict storing model weights or a dict containing other
-           information, which depends on the checkpoint.
-    """
-    return CheckpointLoader.load_checkpoint(filename, map_location, logger)
-
-
-def _load_checkpoint_with_prefix(prefix, filename, map_location=None):
-    """Load partial pretrained model with specific prefix.
-
-    Args:
-        prefix (str): The prefix of sub-module.
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str | None): Same as :func:`torch.load`. Default: None.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-
-    checkpoint = _load_checkpoint(filename, map_location=map_location)
-
-    if 'state_dict' in checkpoint:
-        state_dict = checkpoint['state_dict']
-    else:
-        state_dict = checkpoint
-    if not prefix.endswith('.'):
-        prefix += '.'
-    prefix_len = len(prefix)
-
-    state_dict = {
-        k[prefix_len:]: v
-        for k, v in state_dict.items() if k.startswith(prefix)
-    }
-
-    assert state_dict, f'{prefix} is not in the pretrained model'
-    return state_dict
-
-
-def load_checkpoint(model,
-                    filename,
-                    map_location=None,
-                    strict=False,
-                    logger=None,
-                    revise_keys=[(r'^module\.', '')]):
-    """Load checkpoint from a file or URI.
-
-    Args:
-        model (Module): Module to load checkpoint.
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str): Same as :func:`torch.load`.
-        strict (bool): Whether to allow different params for the model and
-            checkpoint.
-        logger (:mod:`logging.Logger` or None): The logger for error message.
-        revise_keys (list): A list of customized keywords to modify the
-            state_dict in checkpoint. Each item is a (pattern, replacement)
-            pair of the regular expression operations. Default: strip
-            the prefix 'module.' by [(r'^module\\.', '')].
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    checkpoint = _load_checkpoint(filename, map_location, logger)
-    # OrderedDict is a subclass of dict
-    if not isinstance(checkpoint, dict):
-        raise RuntimeError(
-            f'No state_dict found in checkpoint file {filename}')
-    # get state_dict from checkpoint
-    if 'state_dict' in checkpoint:
-        state_dict = checkpoint['state_dict']
-    else:
-        state_dict = checkpoint
-
-    # strip prefix of state_dict
-    metadata = getattr(state_dict, '_metadata', OrderedDict())
-    for p, r in revise_keys:
-        state_dict = OrderedDict(
-            {re.sub(p, r, k): v
-             for k, v in state_dict.items()})
-    # Keep metadata in state_dict
-    state_dict._metadata = metadata
-
-    # load state_dict
-    load_state_dict(model, state_dict, strict, logger)
-    return checkpoint
-
-
-def weights_to_cpu(state_dict):
-    """Copy a model state_dict to cpu.
-
-    Args:
-        state_dict (OrderedDict): Model weights on GPU.
-
-    Returns:
-        OrderedDict: Model weights on GPU.
-    """
-    state_dict_cpu = OrderedDict()
-    for key, val in state_dict.items():
-        state_dict_cpu[key] = val.cpu()
-    # Keep metadata in state_dict
-    state_dict_cpu._metadata = getattr(state_dict, '_metadata', OrderedDict())
-    return state_dict_cpu
-
-
-def _save_to_state_dict(module, destination, prefix, keep_vars):
-    """Saves module state to `destination` dictionary.
-
-    This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.
-
-    Args:
-        module (nn.Module): The module to generate state_dict.
-        destination (dict): A dict where state will be stored.
-        prefix (str): The prefix for parameters and buffers used in this
-            module.
-    """
-    for name, param in module._parameters.items():
-        if param is not None:
-            destination[prefix + name] = param if keep_vars else param.detach()
-    for name, buf in module._buffers.items():
-        # remove check of _non_persistent_buffers_set to allow nn.BatchNorm2d
-        if buf is not None:
-            destination[prefix + name] = buf if keep_vars else buf.detach()
-
-
-def get_state_dict(module, destination=None, prefix='', keep_vars=False):
-    """Returns a dictionary containing a whole state of the module.
-
-    Both parameters and persistent buffers (e.g. running averages) are
-    included. Keys are corresponding parameter and buffer names.
-
-    This method is modified from :meth:`torch.nn.Module.state_dict` to
-    recursively check parallel module in case that the model has a complicated
-    structure, e.g., nn.Module(nn.Module(DDP)).
-
-    Args:
-        module (nn.Module): The module to generate state_dict.
-        destination (OrderedDict): Returned dict for the state of the
-            module.
-        prefix (str): Prefix of the key.
-        keep_vars (bool): Whether to keep the variable property of the
-            parameters. Default: False.
-
-    Returns:
-        dict: A dictionary containing a whole state of the module.
-    """
-    # recursively check parallel module in case that the model has a
-    # complicated structure, e.g., nn.Module(nn.Module(DDP))
-    if is_module_wrapper(module):
-        module = module.module
-
-    # below is the same as torch.nn.Module.state_dict()
-    if destination is None:
-        destination = OrderedDict()
-        destination._metadata = OrderedDict()
-    destination._metadata[prefix[:-1]] = local_metadata = dict(
-        version=module._version)
-    _save_to_state_dict(module, destination, prefix, keep_vars)
-    for name, child in module._modules.items():
-        if child is not None:
-            get_state_dict(
-                child, destination, prefix + name + '.', keep_vars=keep_vars)
-    for hook in module._state_dict_hooks.values():
-        hook_result = hook(module, destination, prefix, local_metadata)
-        if hook_result is not None:
-            destination = hook_result
-    return destination
-
-
-def save_checkpoint(model,
-                    filename,
-                    optimizer=None,
-                    meta=None,
-                    file_client_args=None):
-    """Save checkpoint to file.
-
-    The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
-    ``optimizer``. By default ``meta`` will contain version and time info.
-
-    Args:
-        model (Module): Module whose params are to be saved.
-        filename (str): Checkpoint filename.
-        optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
-        meta (dict, optional): Metadata to be saved in checkpoint.
-        file_client_args (dict, optional): Arguments to instantiate a
-            FileClient. See :class:`mmcv.fileio.FileClient` for details.
-            Default: None.
-            `New in version 1.3.16.`
-    """
-    if meta is None:
-        meta = {}
-    elif not isinstance(meta, dict):
-        raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
-    meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
-
-    if is_module_wrapper(model):
-        model = model.module
-
-    if hasattr(model, 'CLASSES') and model.CLASSES is not None:
-        # save class name to the meta
-        meta.update(CLASSES=model.CLASSES)
-
-    checkpoint = {
-        'meta': meta,
-        'state_dict': weights_to_cpu(get_state_dict(model))
-    }
-    # save optimizer state dict in the checkpoint
-    if isinstance(optimizer, Optimizer):
-        checkpoint['optimizer'] = optimizer.state_dict()
-    elif isinstance(optimizer, dict):
-        checkpoint['optimizer'] = {}
-        for name, optim in optimizer.items():
-            checkpoint['optimizer'][name] = optim.state_dict()
-
-    if filename.startswith('pavi://'):
-        if file_client_args is not None:
-            raise ValueError(
-                'file_client_args should be "None" if filename starts with'
-                f'"pavi://", but got {file_client_args}')
-        try:
-            from pavi import modelcloud
-            from pavi import exception
-        except ImportError:
-            raise ImportError(
-                'Please install pavi to load checkpoint from modelcloud.')
-        model_path = filename[7:]
-        root = modelcloud.Folder()
-        model_dir, model_name = osp.split(model_path)
-        try:
-            model = modelcloud.get(model_dir)
-        except exception.NodeNotFoundError:
-            model = root.create_training_model(model_dir)
-        with TemporaryDirectory() as tmp_dir:
-            checkpoint_file = osp.join(tmp_dir, model_name)
-            with open(checkpoint_file, 'wb') as f:
-                torch.save(checkpoint, f)
-                f.flush()
-            model.create_file(checkpoint_file, name=model_name)
-    else:
-        file_client = FileClient.infer_client(file_client_args, filename)
-        with io.BytesIO() as f:
-            torch.save(checkpoint, f)
-            file_client.put(f.getvalue(), filename)
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/datasets/pascal_context.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/datasets/pascal_context.py
deleted file mode 100644
index 541a63c66a13fb16fd52921e755715ad8d078fdd..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/datasets/pascal_context.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import os.path as osp
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class PascalContextDataset(CustomDataset):
-    """PascalContext dataset.
-
-    In segmentation map annotation for PascalContext, 0 stands for background,
-    which is included in 60 categories. ``reduce_zero_label`` is fixed to
-    False. The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is
-    fixed to '.png'.
-
-    Args:
-        split (str): Split txt file for PascalContext.
-    """
-
-    CLASSES = ('background', 'aeroplane', 'bag', 'bed', 'bedclothes', 'bench',
-               'bicycle', 'bird', 'boat', 'book', 'bottle', 'building', 'bus',
-               'cabinet', 'car', 'cat', 'ceiling', 'chair', 'cloth',
-               'computer', 'cow', 'cup', 'curtain', 'dog', 'door', 'fence',
-               'floor', 'flower', 'food', 'grass', 'ground', 'horse',
-               'keyboard', 'light', 'motorbike', 'mountain', 'mouse', 'person',
-               'plate', 'platform', 'pottedplant', 'road', 'rock', 'sheep',
-               'shelves', 'sidewalk', 'sign', 'sky', 'snow', 'sofa', 'table',
-               'track', 'train', 'tree', 'truck', 'tvmonitor', 'wall', 'water',
-               'window', 'wood')
-
-    PALETTE = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
-               [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
-               [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
-               [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
-               [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
-               [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
-               [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
-               [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
-               [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
-               [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
-               [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
-               [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
-               [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
-               [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
-               [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255]]
-
-    def __init__(self, split, **kwargs):
-        super(PascalContextDataset, self).__init__(
-            img_suffix='.jpg',
-            seg_map_suffix='.png',
-            split=split,
-            reduce_zero_label=False,
-            **kwargs)
-        assert osp.exists(self.img_dir) and self.split is not None
-
-
-@DATASETS.register_module()
-class PascalContextDataset59(CustomDataset):
-    """PascalContext dataset.
-
-    In segmentation map annotation for PascalContext, 0 stands for background,
-    which is included in 60 categories. ``reduce_zero_label`` is fixed to
-    False. The ``img_suffix`` is fixed to '.jpg' and ``seg_map_suffix`` is
-    fixed to '.png'.
-
-    Args:
-        split (str): Split txt file for PascalContext.
-    """
-
-    CLASSES = ('aeroplane', 'bag', 'bed', 'bedclothes', 'bench', 'bicycle',
-               'bird', 'boat', 'book', 'bottle', 'building', 'bus', 'cabinet',
-               'car', 'cat', 'ceiling', 'chair', 'cloth', 'computer', 'cow',
-               'cup', 'curtain', 'dog', 'door', 'fence', 'floor', 'flower',
-               'food', 'grass', 'ground', 'horse', 'keyboard', 'light',
-               'motorbike', 'mountain', 'mouse', 'person', 'plate', 'platform',
-               'pottedplant', 'road', 'rock', 'sheep', 'shelves', 'sidewalk',
-               'sign', 'sky', 'snow', 'sofa', 'table', 'track', 'train',
-               'tree', 'truck', 'tvmonitor', 'wall', 'water', 'window', 'wood')
-
-    PALETTE = [[180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3],
-               [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230],
-               [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61],
-               [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140],
-               [204, 255, 4], [255, 51, 7], [204, 70, 3], [0, 102, 200],
-               [61, 230, 250], [255, 6, 51], [11, 102, 255], [255, 7, 71],
-               [255, 9, 224], [9, 7, 230], [220, 220, 220], [255, 9, 92],
-               [112, 9, 255], [8, 255, 214], [7, 255, 224], [255, 184, 6],
-               [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8],
-               [102, 8, 255], [255, 61, 6], [255, 194, 7], [255, 122, 8],
-               [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255],
-               [235, 12, 255], [160, 150, 20], [0, 163, 255], [140, 140, 140],
-               [250, 10, 15], [20, 255, 0], [31, 255, 0], [255, 31, 0],
-               [255, 224, 0], [153, 255, 0], [0, 0, 255], [255, 71, 0],
-               [0, 235, 255], [0, 173, 255], [31, 0, 255]]
-
-    def __init__(self, split, **kwargs):
-        super(PascalContextDataset59, self).__init__(
-            img_suffix='.jpg',
-            seg_map_suffix='.png',
-            split=split,
-            reduce_zero_label=True,
-            **kwargs)
-        assert osp.exists(self.img_dir) and self.split is not None
diff --git a/spaces/wendys-llc/panoptic-segment-anything/segment_anything/segment_anything/modeling/common.py b/spaces/wendys-llc/panoptic-segment-anything/segment_anything/segment_anything/modeling/common.py
deleted file mode 100644
index 2bf15236a3eb24d8526073bc4fa2b274cccb3f96..0000000000000000000000000000000000000000
--- a/spaces/wendys-llc/panoptic-segment-anything/segment_anything/segment_anything/modeling/common.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-
-from typing import Type
-
-
-class MLPBlock(nn.Module):
-    def __init__(
-        self,
-        embedding_dim: int,
-        mlp_dim: int,
-        act: Type[nn.Module] = nn.GELU,
-    ) -> None:
-        super().__init__()
-        self.lin1 = nn.Linear(embedding_dim, mlp_dim)
-        self.lin2 = nn.Linear(mlp_dim, embedding_dim)
-        self.act = act()
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.lin2(self.act(self.lin1(x)))
-
-
-# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa
-# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119  # noqa
-class LayerNorm2d(nn.Module):
-    def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(num_channels))
-        self.bias = nn.Parameter(torch.zeros(num_channels))
-        self.eps = eps
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        u = x.mean(1, keepdim=True)
-        s = (x - u).pow(2).mean(1, keepdim=True)
-        x = (x - u) / torch.sqrt(s + self.eps)
-        x = self.weight[:, None, None] * x + self.bias[:, None, None]
-        return x
diff --git a/spaces/whispy/Whisper-Ita-V2/app.py b/spaces/whispy/Whisper-Ita-V2/app.py
deleted file mode 100644
index 3e037718b5d1d9f30ca418bdd3c6bdecf29b8416..0000000000000000000000000000000000000000
--- a/spaces/whispy/Whisper-Ita-V2/app.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import torch
-
-import gradio as gr
-import pytube as pt
-from transformers import pipeline
-
-
-MODEL_NAME = "whispy/whisper_italian"
-
-device = 0 if torch.cuda.is_available() else "cpu"
-
-summarizer = pipeline(
-    "summarization",
-    model="it5/it5-efficient-small-el32-news-summarization",
-)
-
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device=device,
-)
-
-def transcribe(microphone, file_upload):
-    warn_output = ""
-    if (microphone is not None) and (file_upload is not None):
-        warn_output = (
-            "WARNING: You've uploaded an audio file and used the microphone. "
-            "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
-        )
-
-    elif (microphone is None) and (file_upload is None):
-        return "ERROR: You have to either use the microphone or upload an audio file"
-
-    file = microphone if microphone is not None else file_upload
-
-    text = pipe(file)["text"]
-
-    return warn_output + text
-
-
-def _return_yt_html_embed(yt_url):
-    video_id = yt_url.split("?v=")[-1]
-    HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-        " </center>"
-    )
-    return HTML_str
-
-
-def yt_transcribe(yt_url):
-    yt = pt.YouTube(yt_url)
-    html_embed_str = _return_yt_html_embed(yt_url)
-    stream = yt.streams.filter(only_audio=True)[0]
-    stream.download(filename="audio.mp3")
-
-    text = pipe("audio.mp3")["text"]
-    summary = summarizer(text)
-    summary = summary[0]["summary_text"]
-
-    return html_embed_str, text, summary
-
-demo = gr.Blocks()
-
-mf_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
-        gr.inputs.Audio(source="upload", type="filepath", optional=True),
-    ],
-    outputs="text",
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Demo: Transcribe Audio",
-    description=(
-        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned"
-        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
-        " of arbitrary length."
-    ),
-    allow_flagging="never",
-)
-
-yt_transcribe = gr.Interface(
-    fn=yt_transcribe,
-    inputs=[gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")],
-    outputs=["html", "text", "text"],
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Demo: Transcribe YouTube",
-    description=(
-        "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:"
-        f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of"
-        " arbitrary length."
-    ),
-    allow_flagging="never",
-)
-
-with demo:
-    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
-
-demo.launch(enable_queue=True)
diff --git a/spaces/wilson1/bingo/src/lib/bots/bing/utils.ts b/spaces/wilson1/bingo/src/lib/bots/bing/utils.ts
deleted file mode 100644
index 64b4b96452d125346b0fc4436b5f7c18c962df0b..0000000000000000000000000000000000000000
--- a/spaces/wilson1/bingo/src/lib/bots/bing/utils.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-import { ChatResponseMessage, BingChatResponse } from './types'
-
-export function convertMessageToMarkdown(message: ChatResponseMessage): string {
-  if (message.messageType === 'InternalSearchQuery') {
-    return message.text
-  }
-  for (const card of message.adaptiveCards??[]) {
-    for (const block of card.body) {
-      if (block.type === 'TextBlock') {
-        return block.text
-      }
-    }
-  }
-  return ''
-}
-
-const RecordSeparator = String.fromCharCode(30)
-
-export const websocketUtils = {
-  packMessage(data: any) {
-    return `${JSON.stringify(data)}${RecordSeparator}`
-  },
-  unpackMessage(data: string | ArrayBuffer | Blob) {
-    if (!data) return {}
-    return data
-      .toString()
-      .split(RecordSeparator)
-      .filter(Boolean)
-      .map((s) => {
-        try {
-          return JSON.parse(s)
-        } catch (e) {
-          return {}
-        }
-      })
-  },
-}
-
-export async function createImage(prompt: string, id: string, headers: HeadersInit): Promise<string | undefined> {
-  const { headers: responseHeaders } = await fetch(`https://www.bing.com/images/create?partner=sydney&re=1&showselective=1&sude=1&kseed=7000&SFX=&q=${encodeURIComponent(prompt)}&iframeid=${id}`,
-    {
-      method: 'HEAD',
-      headers,
-      redirect: 'manual'
-    },
-  );
-
-  if (!/&id=([^&]+)$/.test(responseHeaders.get('location') || '')) {
-    throw new Error('请求异常，请检查 cookie 是否有效')
-  }
-
-  const resultId = RegExp.$1;
-  let count = 0
-  const imageThumbUrl = `https://www.bing.com/images/create/async/results/${resultId}?q=${encodeURIComponent(prompt)}&partner=sydney&showselective=1&IID=images.as`;
-
-  do {
-    await sleep(3000);
-    const content = await fetch(imageThumbUrl, { headers, method: 'GET' })
-
-    // @ts-ignore
-    if (content.headers.get('content-length') > 1) {
-      const text = await content.text()
-        return (text?.match(/<img class="mimg"((?!src).)+src="[^"]+/mg)??[])
-          .map(target => target?.split('src="').pop()?.replace(/&amp;/g, '&'))
-          .map(img => `![${prompt}](${img})`).join(' ')
-    }
-  } while(count ++ < 10);
-}
-
-
-export async function* streamAsyncIterable(stream: ReadableStream) {
-  const reader = stream.getReader()
-  try {
-    while (true) {
-      const { done, value } = await reader.read()
-      if (done) {
-        return
-      }
-      yield value
-    }
-  } finally {
-    reader.releaseLock()
-  }
-}
-
-export const sleep = (ms: number) => new Promise(resolve => setTimeout(resolve, ms))
-
diff --git a/spaces/wydgg/bingo-wyd-ai/src/components/ui/codeblock.tsx b/spaces/wydgg/bingo-wyd-ai/src/components/ui/codeblock.tsx
deleted file mode 100644
index aabda4e3b59f4e36b6ab79feb19d8d18b70e881b..0000000000000000000000000000000000000000
--- a/spaces/wydgg/bingo-wyd-ai/src/components/ui/codeblock.tsx
+++ /dev/null
@@ -1,142 +0,0 @@
-'use client'
-
-import { FC, memo } from 'react'
-import { Prism as SyntaxHighlighter } from 'react-syntax-highlighter'
-import { coldarkDark } from 'react-syntax-highlighter/dist/cjs/styles/prism'
-
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-import { IconCheck, IconCopy, IconDownload } from '@/components/ui/icons'
-import { Button } from '@/components/ui/button'
-
-interface Props {
-  language: string
-  value: string
-}
-
-interface languageMap {
-  [key: string]: string | undefined
-}
-
-export const programmingLanguages: languageMap = {
-  javascript: '.js',
-  python: '.py',
-  java: '.java',
-  c: '.c',
-  cpp: '.cpp',
-  'c++': '.cpp',
-  'c#': '.cs',
-  ruby: '.rb',
-  php: '.php',
-  swift: '.swift',
-  'objective-c': '.m',
-  kotlin: '.kt',
-  typescript: '.ts',
-  go: '.go',
-  perl: '.pl',
-  rust: '.rs',
-  scala: '.scala',
-  haskell: '.hs',
-  lua: '.lua',
-  shell: '.sh',
-  sql: '.sql',
-  html: '.html',
-  css: '.css'
-  // add more file extensions here, make sure the key is same as language prop in CodeBlock.tsx component
-}
-
-export const generateRandomString = (length: number, lowercase = false) => {
-  const chars = 'ABCDEFGHJKLMNPQRSTUVWXY3456789' // excluding similar looking characters like Z, 2, I, 1, O, 0
-  let result = ''
-  for (let i = 0; i < length; i++) {
-    result += chars.charAt(Math.floor(Math.random() * chars.length))
-  }
-  return lowercase ? result.toLowerCase() : result
-}
-
-const CodeBlock: FC<Props> = memo(({ language, value }) => {
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-
-  const downloadAsFile = () => {
-    if (typeof window === 'undefined') {
-      return
-    }
-    const fileExtension = programmingLanguages[language] || '.file'
-    const suggestedFileName = `file-${generateRandomString(
-      3,
-      true
-    )}${fileExtension}`
-    const fileName = window.prompt('Enter file name' || '', suggestedFileName)
-
-    if (!fileName) {
-      // User pressed cancel on prompt.
-      return
-    }
-
-    const blob = new Blob([value], { type: 'text/plain' })
-    const url = URL.createObjectURL(blob)
-    const link = document.createElement('a')
-    link.download = fileName
-    link.href = url
-    link.style.display = 'none'
-    document.body.appendChild(link)
-    link.click()
-    document.body.removeChild(link)
-    URL.revokeObjectURL(url)
-  }
-
-  const onCopy = () => {
-    if (isCopied) return
-    copyToClipboard(value)
-  }
-
-  return (
-    <div className="codeblock relative w-full bg-zinc-950 font-sans">
-      <div className="flex w-full items-center justify-between bg-zinc-800 px-6 py-2 pr-4 text-zinc-100">
-        <span className="text-xs lowercase">{language}</span>
-        <div className="flex items-center space-x-1">
-          <Button
-            variant="ghost"
-            className="hover:bg-zinc-800 focus-visible:ring-1 focus-visible:ring-slate-700 focus-visible:ring-offset-0"
-            onClick={downloadAsFile}
-            size="icon"
-          >
-            <IconDownload />
-            <span className="sr-only">Download</span>
-          </Button>
-          <Button
-            variant="ghost"
-            size="icon"
-            className="text-xs hover:bg-zinc-800 focus-visible:ring-1 focus-visible:ring-slate-700 focus-visible:ring-offset-0"
-            onClick={onCopy}
-          >
-            {isCopied ? <IconCheck /> : <IconCopy />}
-            <span className="sr-only">Copy code</span>
-          </Button>
-        </div>
-      </div>
-      <SyntaxHighlighter
-        language={language}
-        style={coldarkDark}
-        PreTag="div"
-        showLineNumbers
-        customStyle={{
-          margin: 0,
-          width: '100%',
-          background: 'transparent',
-          padding: '1.5rem 1rem'
-        }}
-        codeTagProps={{
-          style: {
-            fontSize: '0.9rem',
-            fontFamily: 'var(--font-mono)'
-          }
-        }}
-      >
-        {value}
-      </SyntaxHighlighter>
-    </div>
-  )
-})
-CodeBlock.displayName = 'CodeBlock'
-
-export { CodeBlock }
diff --git a/spaces/xcchen/vits-uma-genshin-honkai/attentions.py b/spaces/xcchen/vits-uma-genshin-honkai/attentions.py
deleted file mode 100644
index 86bc73b5fe98cc7b443e9078553920346c996707..0000000000000000000000000000000000000000
--- a/spaces/xcchen/vits-uma-genshin-honkai/attentions.py
+++ /dev/null
@@ -1,300 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-from modules import LayerNorm
-   
-
-class Encoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/xfys/yolov5_tracking/val_utils/trackeval/datasets/person_path_22.py b/spaces/xfys/yolov5_tracking/val_utils/trackeval/datasets/person_path_22.py
deleted file mode 100644
index 177954a82009d68e7e4a68a2087255fc5dcac42e..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/val_utils/trackeval/datasets/person_path_22.py
+++ /dev/null
@@ -1,452 +0,0 @@
-import os
-import csv
-import configparser
-import numpy as np
-from scipy.optimize import linear_sum_assignment
-from ._base_dataset import _BaseDataset
-from .. import utils
-from .. import _timing
-from ..utils import TrackEvalException
-
-class PersonPath22(_BaseDataset):
-    """Dataset class for MOT Challenge 2D bounding box tracking"""
-
-    @staticmethod
-    def get_default_dataset_config():
-        """Default class config values"""
-        code_path = utils.get_code_path()
-        default_config = {
-            'GT_FOLDER': os.path.join(code_path, 'data/gt/person_path_22/'),  # Location of GT data
-            'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/person_path_22/'),  # Trackers location
-            'OUTPUT_FOLDER': None,  # Where to save eval results (if None, same as TRACKERS_FOLDER)
-            'TRACKERS_TO_EVAL': None,  # Filenames of trackers to eval (if None, all in folder)
-            'CLASSES_TO_EVAL': ['pedestrian'],  # Valid: ['pedestrian']
-            'BENCHMARK': 'person_path_22',  # Valid: 'person_path_22'
-            'SPLIT_TO_EVAL': 'test',  # Valid: 'train', 'test', 'all'
-            'INPUT_AS_ZIP': False,  # Whether tracker input files are zipped
-            'PRINT_CONFIG': True,  # Whether to print current config
-            'DO_PREPROC': True,  # Whether to perform preprocessing
-            'TRACKER_SUB_FOLDER': 'data',  # Tracker files are in TRACKER_FOLDER/tracker_name/TRACKER_SUB_FOLDER
-            'OUTPUT_SUB_FOLDER': '',  # Output files are saved in OUTPUT_FOLDER/tracker_name/OUTPUT_SUB_FOLDER
-            'TRACKER_DISPLAY_NAMES': None,  # Names of trackers to display, if None: TRACKERS_TO_EVAL
-            'SEQMAP_FOLDER': None,  # Where seqmaps are found (if None, GT_FOLDER/seqmaps)
-            'SEQMAP_FILE': None,  # Directly specify seqmap file (if none use seqmap_folder/benchmark-split_to_eval)
-            'SEQ_INFO': None,  # If not None, directly specify sequences to eval and their number of timesteps
-            'GT_LOC_FORMAT': '{gt_folder}/{seq}/gt/gt.txt',  # '{gt_folder}/{seq}/gt/gt.txt'
-            'SKIP_SPLIT_FOL': False,  # If False, data is in GT_FOLDER/BENCHMARK-SPLIT_TO_EVAL/ and in
-                                      # TRACKERS_FOLDER/BENCHMARK-SPLIT_TO_EVAL/tracker/
-                                      # If True, then the middle 'benchmark-split' folder is skipped for both.
-        }
-        return default_config
-
-    def __init__(self, config=None):
-        """Initialise dataset, checking that all required files are present"""
-        super().__init__()
-        # Fill non-given config values with defaults
-        self.config = utils.init_config(config, self.get_default_dataset_config(), self.get_name())
-
-        self.benchmark = self.config['BENCHMARK']
-        gt_set = self.config['BENCHMARK'] + '-' + self.config['SPLIT_TO_EVAL']
-        self.gt_set = gt_set
-        if not self.config['SKIP_SPLIT_FOL']:
-            split_fol = gt_set
-        else:
-            split_fol = ''
-        self.gt_fol = os.path.join(self.config['GT_FOLDER'], split_fol)
-        self.tracker_fol = os.path.join(self.config['TRACKERS_FOLDER'], split_fol)
-        self.should_classes_combine = False
-        self.use_super_categories = False
-        self.data_is_zipped = self.config['INPUT_AS_ZIP']
-        self.do_preproc = self.config['DO_PREPROC']
-
-        self.output_fol = self.config['OUTPUT_FOLDER']
-        if self.output_fol is None:
-            self.output_fol = self.tracker_fol
-
-        self.tracker_sub_fol = self.config['TRACKER_SUB_FOLDER']
-        self.output_sub_fol = self.config['OUTPUT_SUB_FOLDER']
-
-        # Get classes to eval
-        self.valid_classes = ['pedestrian']
-        self.class_list = [cls.lower() if cls.lower() in self.valid_classes else None
-                           for cls in self.config['CLASSES_TO_EVAL']]
-        if not all(self.class_list):
-            raise TrackEvalException('Attempted to evaluate an invalid class. Only pedestrian class is valid.')
-        self.class_name_to_class_id = {'pedestrian': 1, 'person_on_vehicle': 2, 'car': 3, 'bicycle': 4, 'motorbike': 5,
-                                       'non_mot_vehicle': 6, 'static_person': 7, 'distractor': 8, 'occluder': 9,
-                                       'occluder_on_ground': 10, 'occluder_full': 11, 'reflection': 12, 'crowd': 13}
-        self.valid_class_numbers = list(self.class_name_to_class_id.values())
-
-        # Get sequences to eval and check gt files exist
-        self.seq_list, self.seq_lengths = self._get_seq_info()
-        if len(self.seq_list) < 1:
-            raise TrackEvalException('No sequences are selected to be evaluated.')
-
-        # Check gt files exist
-        for seq in self.seq_list:
-            if not self.data_is_zipped:
-                curr_file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
-                if not os.path.isfile(curr_file):
-                    print('GT file not found ' + curr_file)
-                    raise TrackEvalException('GT file not found for sequence: ' + seq)
-        if self.data_is_zipped:
-            curr_file = os.path.join(self.gt_fol, 'data.zip')
-            if not os.path.isfile(curr_file):
-                print('GT file not found ' + curr_file)
-                raise TrackEvalException('GT file not found: ' + os.path.basename(curr_file))
-
-        # Get trackers to eval
-        if self.config['TRACKERS_TO_EVAL'] is None:
-            self.tracker_list = os.listdir(self.tracker_fol)
-        else:
-            self.tracker_list = self.config['TRACKERS_TO_EVAL']
-
-        if self.config['TRACKER_DISPLAY_NAMES'] is None:
-            self.tracker_to_disp = dict(zip(self.tracker_list, self.tracker_list))
-        elif (self.config['TRACKERS_TO_EVAL'] is not None) and (
-                len(self.config['TRACKER_DISPLAY_NAMES']) == len(self.tracker_list)):
-            self.tracker_to_disp = dict(zip(self.tracker_list, self.config['TRACKER_DISPLAY_NAMES']))
-        else:
-            raise TrackEvalException('List of tracker files and tracker display names do not match.')
-
-        for tracker in self.tracker_list:
-            if self.data_is_zipped:
-                curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
-                if not os.path.isfile(curr_file):
-                    print('Tracker file not found: ' + curr_file)
-                    raise TrackEvalException('Tracker file not found: ' + tracker + '/' + os.path.basename(curr_file))
-            else:
-                for seq in self.seq_list:
-                    curr_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')
-                    if not os.path.isfile(curr_file):
-                        print('Tracker file not found: ' + curr_file)
-                        raise TrackEvalException(
-                            'Tracker file not found: ' + tracker + '/' + self.tracker_sub_fol + '/' + os.path.basename(
-                                curr_file))
-
-    def get_display_name(self, tracker):
-        return self.tracker_to_disp[tracker]
-
-    def _get_seq_info(self):
-        seq_list = []
-        seq_lengths = {}
-        if self.config["SEQ_INFO"]:
-            seq_list = list(self.config["SEQ_INFO"].keys())
-            seq_lengths = self.config["SEQ_INFO"]
-
-            # If sequence length is 'None' tries to read sequence length from .ini files.
-            for seq, seq_length in seq_lengths.items():
-                if seq_length is None:
-                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')
-                    if not os.path.isfile(ini_file):
-                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))
-                    ini_data = configparser.ConfigParser()
-                    ini_data.read(ini_file)
-                    seq_lengths[seq] = int(ini_data['Sequence']['seqLength'])
-
-        else:
-            if self.config["SEQMAP_FILE"]:
-                seqmap_file = self.config["SEQMAP_FILE"]
-            else:
-                if self.config["SEQMAP_FOLDER"] is None:
-                    seqmap_file = os.path.join(self.config['GT_FOLDER'], 'seqmaps', self.gt_set + '.txt')
-                else:
-                    seqmap_file = os.path.join(self.config["SEQMAP_FOLDER"], self.gt_set + '.txt')
-            if not os.path.isfile(seqmap_file):
-                print('no seqmap found: ' + seqmap_file)
-                raise TrackEvalException('no seqmap found: ' + os.path.basename(seqmap_file))
-            with open(seqmap_file) as fp:
-                reader = csv.reader(fp)
-                for i, row in enumerate(reader):
-                    if i == 0 or row[0] == '':
-                        continue
-                    seq = row[0]
-                    seq_list.append(seq)
-                    ini_file = os.path.join(self.gt_fol, seq, 'seqinfo.ini')
-                    if not os.path.isfile(ini_file):
-                        raise TrackEvalException('ini file does not exist: ' + seq + '/' + os.path.basename(ini_file))
-                    ini_data = configparser.ConfigParser()
-                    ini_data.read(ini_file)
-                    seq_lengths[seq] = int(ini_data['Sequence']['seqLength'])
-        return seq_list, seq_lengths
-
-    def _load_raw_file(self, tracker, seq, is_gt):
-        """Load a file (gt or tracker) in the MOT Challenge 2D box format
-
-        If is_gt, this returns a dict which contains the fields:
-        [gt_ids, gt_classes] : list (for each timestep) of 1D NDArrays (for each det).
-        [gt_dets, gt_crowd_ignore_regions]: list (for each timestep) of lists of detections.
-        [gt_extras] : list (for each timestep) of dicts (for each extra) of 1D NDArrays (for each det).
-
-        if not is_gt, this returns a dict which contains the fields:
-        [tracker_ids, tracker_classes, tracker_confidences] : list (for each timestep) of 1D NDArrays (for each det).
-        [tracker_dets]: list (for each timestep) of lists of detections.
-        """
-        # File location
-        if self.data_is_zipped:
-            if is_gt:
-                zip_file = os.path.join(self.gt_fol, 'data.zip')
-            else:
-                zip_file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol + '.zip')
-            file = seq + '.txt'
-        else:
-            zip_file = None
-            if is_gt:
-                file = self.config["GT_LOC_FORMAT"].format(gt_folder=self.gt_fol, seq=seq)
-            else:
-                file = os.path.join(self.tracker_fol, tracker, self.tracker_sub_fol, seq + '.txt')
-
-        # Ignore regions
-        if is_gt:
-            crowd_ignore_filter = {7: ['13']}
-        else:
-            crowd_ignore_filter = None
-
-        # Load raw data from text file
-        read_data, ignore_data = self._load_simple_text_file(file, is_zipped=self.data_is_zipped, zip_file=zip_file, crowd_ignore_filter=crowd_ignore_filter)
-
-        # Convert data to required format
-        num_timesteps = self.seq_lengths[seq]
-        data_keys = ['ids', 'classes', 'dets']
-        if is_gt:
-            data_keys += ['gt_crowd_ignore_regions', 'gt_extras']
-        else:
-            data_keys += ['tracker_confidences']
-        raw_data = {key: [None] * num_timesteps for key in data_keys}
-
-        # Check for any extra time keys
-        current_time_keys = [str( t+ 1) for t in range(num_timesteps)]
-        extra_time_keys = [x for x in read_data.keys() if x not in current_time_keys]
-        if len(extra_time_keys) > 0:
-            if is_gt:
-                text = 'Ground-truth'
-            else:
-                text = 'Tracking'
-            raise TrackEvalException(
-                text + ' data contains the following invalid timesteps in seq %s: ' % seq + ', '.join(
-                    [str(x) + ', ' for x in extra_time_keys]))
-
-        for t in range(num_timesteps):
-            time_key = str(t+1)
-            if time_key in read_data.keys():
-                try:
-                    time_data = np.asarray(read_data[time_key], dtype=np.float)
-                except ValueError:
-                    if is_gt:
-                        raise TrackEvalException(
-                            'Cannot convert gt data for sequence %s to float. Is data corrupted?' % seq)
-                    else:
-                        raise TrackEvalException(
-                            'Cannot convert tracking data from tracker %s, sequence %s to float. Is data corrupted?' % (
-                                tracker, seq))
-                try:
-                    raw_data['dets'][t] = np.atleast_2d(time_data[:, 2:6])
-                    raw_data['ids'][t] = np.atleast_1d(time_data[:, 1]).astype(int)
-                except IndexError:
-                    if is_gt:
-                        err = 'Cannot load gt data from sequence %s, because there is not enough ' \
-                              'columns in the data.' % seq
-                        raise TrackEvalException(err)
-                    else:
-                        err = 'Cannot load tracker data from tracker %s, sequence %s, because there is not enough ' \
-                              'columns in the data.' % (tracker, seq)
-                        raise TrackEvalException(err)
-                if time_data.shape[1] >= 8:
-                    raw_data['classes'][t] = np.atleast_1d(time_data[:, 7]).astype(int)
-                else:
-                    if not is_gt:
-                        raw_data['classes'][t] = np.ones_like(raw_data['ids'][t])
-                    else:
-                        raise TrackEvalException(
-                            'GT data is not in a valid format, there is not enough rows in seq %s, timestep %i.' % (
-                                seq, t))
-                if is_gt:
-                    gt_extras_dict = {'zero_marked': np.atleast_1d(time_data[:, 6].astype(int))}
-                    raw_data['gt_extras'][t] = gt_extras_dict
-                else:
-                    raw_data['tracker_confidences'][t] = np.atleast_1d(time_data[:, 6])
-            else:
-                raw_data['dets'][t] = np.empty((0, 4))
-                raw_data['ids'][t] = np.empty(0).astype(int)
-                raw_data['classes'][t] = np.empty(0).astype(int)
-                if is_gt:
-                    gt_extras_dict = {'zero_marked': np.empty(0)}
-                    raw_data['gt_extras'][t] = gt_extras_dict
-                else:
-                    raw_data['tracker_confidences'][t] = np.empty(0)
-            if is_gt:
-                if time_key in ignore_data.keys():
-                    time_ignore = np.asarray(ignore_data[time_key], dtype=np.float)
-                    raw_data['gt_crowd_ignore_regions'][t] = np.atleast_2d(time_ignore[:, 2:6])
-                else:
-                    raw_data['gt_crowd_ignore_regions'][t] = np.empty((0, 4))
-
-        if is_gt:
-            key_map = {'ids': 'gt_ids',
-                       'classes': 'gt_classes',
-                       'dets': 'gt_dets'}
-        else:
-            key_map = {'ids': 'tracker_ids',
-                       'classes': 'tracker_classes',
-                       'dets': 'tracker_dets'}
-        for k, v in key_map.items():
-            raw_data[v] = raw_data.pop(k)
-        raw_data['num_timesteps'] = num_timesteps
-        raw_data['seq'] = seq
-        return raw_data
-
-    @_timing.time
-    def get_preprocessed_seq_data(self, raw_data, cls):
-        """ Preprocess data for a single sequence for a single class ready for evaluation.
-        Inputs:
-             - raw_data is a dict containing the data for the sequence already read in by get_raw_seq_data().
-             - cls is the class to be evaluated.
-        Outputs:
-             - data is a dict containing all of the information that metrics need to perform evaluation.
-                It contains the following fields:
-                    [num_timesteps, num_gt_ids, num_tracker_ids, num_gt_dets, num_tracker_dets] : integers.
-                    [gt_ids, tracker_ids, tracker_confidences]: list (for each timestep) of 1D NDArrays (for each det).
-                    [gt_dets, tracker_dets]: list (for each timestep) of lists of detections.
-                    [similarity_scores]: list (for each timestep) of 2D NDArrays.
-        Notes:
-            General preprocessing (preproc) occurs in 4 steps. Some datasets may not use all of these steps.
-                1) Extract only detections relevant for the class to be evaluated (including distractor detections).
-                2) Match gt dets and tracker dets. Remove tracker dets that are matched to a gt det that is of a
-                    distractor class, or otherwise marked as to be removed.
-                3) Remove unmatched tracker dets if they fall within a crowd ignore region or don't meet a certain
-                    other criteria (e.g. are too small).
-                4) Remove gt dets that were only useful for preprocessing and not for actual evaluation.
-            After the above preprocessing steps, this function also calculates the number of gt and tracker detections
-                and unique track ids. It also relabels gt and tracker ids to be contiguous and checks that ids are
-                unique within each timestep.
-
-        MOT Challenge:
-            In MOT Challenge, the 4 preproc steps are as follow:
-                1) There is only one class (pedestrian) to be evaluated, but all other classes are used for preproc.
-                2) Predictions are matched against all gt boxes (regardless of class), those matching with distractor
-                    objects are removed.
-                3) There is no crowd ignore regions.
-                4) All gt dets except pedestrian are removed, also removes pedestrian gt dets marked with zero_marked.
-        """
-        # Check that input data has unique ids
-        self._check_unique_ids(raw_data)
-
-        distractor_class_names = ['person_on_vehicle', 'static_person', 'distractor', 'reflection']
-        if self.benchmark == 'MOT20':
-            distractor_class_names.append('non_mot_vehicle')
-        distractor_classes = [self.class_name_to_class_id[x] for x in distractor_class_names]
-        cls_id = self.class_name_to_class_id[cls]
-
-        data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'tracker_confidences', 'similarity_scores']
-        data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}
-        unique_gt_ids = []
-        unique_tracker_ids = []
-        num_gt_dets = 0
-        num_tracker_dets = 0
-        for t in range(raw_data['num_timesteps']):
-
-            # Get all data
-            gt_ids = raw_data['gt_ids'][t]
-            gt_dets = raw_data['gt_dets'][t]
-            gt_classes = raw_data['gt_classes'][t]
-            gt_zero_marked = raw_data['gt_extras'][t]['zero_marked']
-
-            tracker_ids = raw_data['tracker_ids'][t]
-            tracker_dets = raw_data['tracker_dets'][t]
-            tracker_classes = raw_data['tracker_classes'][t]
-            tracker_confidences = raw_data['tracker_confidences'][t]
-            similarity_scores = raw_data['similarity_scores'][t]
-            crowd_ignore_regions = raw_data['gt_crowd_ignore_regions'][t]
-
-            # Evaluation is ONLY valid for pedestrian class
-            if len(tracker_classes) > 0 and np.max(tracker_classes) > 1:
-                raise TrackEvalException(
-                    'Evaluation is only valid for pedestrian class. Non pedestrian class (%i) found in sequence %s at '
-                    'timestep %i.' % (np.max(tracker_classes), raw_data['seq'], t))
-
-            # Match tracker and gt dets (with hungarian algorithm) and remove tracker dets which match with gt dets
-            # which are labeled as belonging to a distractor class.
-            to_remove_tracker = np.array([], np.int)
-            if self.do_preproc and self.benchmark != 'MOT15' and (gt_ids.shape[0] > 0 or len(crowd_ignore_regions) > 0) and tracker_ids.shape[0] > 0:
-
-                # Check all classes are valid:
-                invalid_classes = np.setdiff1d(np.unique(gt_classes), self.valid_class_numbers)
-                if len(invalid_classes) > 0:
-                    print(' '.join([str(x) for x in invalid_classes]))
-                    raise(TrackEvalException('Attempting to evaluate using invalid gt classes. '
-                                             'This warning only triggers if preprocessing is performed, '
-                                             'e.g. not for MOT15 or where prepropressing is explicitly disabled. '
-                                             'Please either check your gt data, or disable preprocessing. '
-                                             'The following invalid classes were found in timestep ' + str(t) + ': ' +
-                                             ' '.join([str(x) for x in invalid_classes])))
-
-                matching_scores = similarity_scores.copy()
-                matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0
-                match_rows, match_cols = linear_sum_assignment(-matching_scores)
-                actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps
-                match_rows = match_rows[actually_matched_mask]
-                match_cols = match_cols[actually_matched_mask]
-
-                is_distractor_class = np.isin(gt_classes[match_rows], distractor_classes)
-                to_remove_tracker = match_cols[is_distractor_class]
-
-                # remove bounding boxes that overlap with crowd ignore region.
-                intersection_with_ignore_region = self._calculate_box_ious(tracker_dets, crowd_ignore_regions, box_format='xywh', do_ioa=True)
-                is_within_crowd_ignore_region = np.any(intersection_with_ignore_region > 0.95 + np.finfo('float').eps, axis=1)
-                to_remove_tracker = np.unique(np.concatenate([to_remove_tracker, np.where(is_within_crowd_ignore_region)[0]]))
-
-            # Apply preprocessing to remove all unwanted tracker dets.
-            data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)
-            data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)
-            data['tracker_confidences'][t] = np.delete(tracker_confidences, to_remove_tracker, axis=0)
-            similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)
-
-            # Remove gt detections marked as to remove (zero marked), and also remove gt detections not in pedestrian
-            # class (not applicable for MOT15)
-            if self.do_preproc and self.benchmark != 'MOT15':
-                gt_to_keep_mask = (np.not_equal(gt_zero_marked, 0)) & \
-                                  (np.equal(gt_classes, cls_id))
-            else:
-                # There are no classes for MOT15
-                gt_to_keep_mask = np.not_equal(gt_zero_marked, 0)
-            data['gt_ids'][t] = gt_ids[gt_to_keep_mask]
-            data['gt_dets'][t] = gt_dets[gt_to_keep_mask, :]
-            data['similarity_scores'][t] = similarity_scores[gt_to_keep_mask]
-
-            unique_gt_ids += list(np.unique(data['gt_ids'][t]))
-            unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))
-            num_tracker_dets += len(data['tracker_ids'][t])
-            num_gt_dets += len(data['gt_ids'][t])
-
-        # Re-label IDs such that there are no empty IDs
-        if len(unique_gt_ids) > 0:
-            unique_gt_ids = np.unique(unique_gt_ids)
-            gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))
-            gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))
-            for t in range(raw_data['num_timesteps']):
-                if len(data['gt_ids'][t]) > 0:
-                    data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)
-        if len(unique_tracker_ids) > 0:
-            unique_tracker_ids = np.unique(unique_tracker_ids)
-            tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))
-            tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))
-            for t in range(raw_data['num_timesteps']):
-                if len(data['tracker_ids'][t]) > 0:
-                    data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)
-
-        # Record overview statistics.
-        data['num_tracker_dets'] = num_tracker_dets
-        data['num_gt_dets'] = num_gt_dets
-        data['num_tracker_ids'] = len(unique_tracker_ids)
-        data['num_gt_ids'] = len(unique_gt_ids)
-        data['num_timesteps'] = raw_data['num_timesteps']
-        data['seq'] = raw_data['seq']
-
-        # Ensure again that ids are unique per timestep after preproc.
-        self._check_unique_ids(data, after_preproc=True)
-
-        return data
-
-    def _calculate_similarities(self, gt_dets_t, tracker_dets_t):
-        similarity_scores = self._calculate_box_ious(gt_dets_t, tracker_dets_t, box_format='xywh')
-        return similarity_scores
diff --git a/spaces/xfys/yolov5_tracking/val_utils/trackeval/metrics/track_map.py b/spaces/xfys/yolov5_tracking/val_utils/trackeval/metrics/track_map.py
deleted file mode 100644
index 039f89084c8defd683c7f7d26cdd77834ecc2f23..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/val_utils/trackeval/metrics/track_map.py
+++ /dev/null
@@ -1,462 +0,0 @@
-import numpy as np
-from ._base_metric import _BaseMetric
-from .. import _timing
-from functools import partial
-from .. import utils
-from ..utils import TrackEvalException
-
-
-class TrackMAP(_BaseMetric):
-    """Class which implements the TrackMAP metrics"""
-
-    @staticmethod
-    def get_default_metric_config():
-        """Default class config values"""
-        default_config = {
-            'USE_AREA_RANGES': True,  # whether to evaluate for certain area ranges
-            'AREA_RANGES': [[0 ** 2, 32 ** 2],  # additional area range sets for which TrackMAP is evaluated
-                            [32 ** 2, 96 ** 2],  # (all area range always included), default values for TAO
-                            [96 ** 2, 1e5 ** 2]],  # evaluation
-            'AREA_RANGE_LABELS': ["area_s", "area_m", "area_l"],  # the labels for the area ranges
-            'USE_TIME_RANGES': True,  # whether to evaluate for certain time ranges (length of tracks)
-            'TIME_RANGES': [[0, 3], [3, 10], [10, 1e5]],  # additional time range sets for which TrackMAP is evaluated
-            # (all time range always included) , default values for TAO evaluation
-            'TIME_RANGE_LABELS': ["time_s", "time_m", "time_l"],  # the labels for the time ranges
-            'IOU_THRESHOLDS': np.arange(0.5, 0.96, 0.05),  # the IoU thresholds
-            'RECALL_THRESHOLDS': np.linspace(0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01) + 1), endpoint=True),
-            # recall thresholds at which precision is evaluated
-            'MAX_DETECTIONS': 0,  # limit the maximum number of considered tracks per sequence (0 for unlimited)
-            'PRINT_CONFIG': True
-        }
-        return default_config
-
-    def __init__(self, config=None):
-        super().__init__()
-        self.config = utils.init_config(config, self.get_default_metric_config(), self.get_name())
-
-        self.num_ig_masks = 1
-        self.lbls = ['all']
-        self.use_area_rngs = self.config['USE_AREA_RANGES']
-        if self.use_area_rngs:
-            self.area_rngs = self.config['AREA_RANGES']
-            self.area_rng_lbls = self.config['AREA_RANGE_LABELS']
-            self.num_ig_masks += len(self.area_rng_lbls)
-            self.lbls += self.area_rng_lbls
-
-        self.use_time_rngs = self.config['USE_TIME_RANGES']
-        if self.use_time_rngs:
-            self.time_rngs = self.config['TIME_RANGES']
-            self.time_rng_lbls = self.config['TIME_RANGE_LABELS']
-            self.num_ig_masks += len(self.time_rng_lbls)
-            self.lbls += self.time_rng_lbls
-
-        self.array_labels = self.config['IOU_THRESHOLDS']
-        self.rec_thrs = self.config['RECALL_THRESHOLDS']
-
-        self.maxDet = self.config['MAX_DETECTIONS']
-        self.float_array_fields = ['AP_' + lbl for lbl in self.lbls] + ['AR_' + lbl for lbl in self.lbls]
-        self.fields = self.float_array_fields
-        self.summary_fields = self.float_array_fields
-
-    @_timing.time
-    def eval_sequence(self, data):
-        """Calculates GT and Tracker matches for one sequence for TrackMAP metrics. Adapted from
-        https://github.com/TAO-Dataset/"""
-
-        # Initialise results to zero for each sequence as the fields are only defined over the set of all sequences
-        res = {}
-        for field in self.fields:
-            res[field] = [0 for _ in self.array_labels]
-
-        gt_ids, dt_ids = data['gt_track_ids'], data['dt_track_ids']
-
-        if len(gt_ids) == 0 and len(dt_ids) == 0:
-            for idx in range(self.num_ig_masks):
-                res[idx] = None
-            return res
-
-        # get track data
-        gt_tr_areas = data.get('gt_track_areas', None) if self.use_area_rngs else None
-        gt_tr_lengths = data.get('gt_track_lengths', None) if self.use_time_rngs else None
-        gt_tr_iscrowd = data.get('gt_track_iscrowd', None)
-        dt_tr_areas = data.get('dt_track_areas', None) if self.use_area_rngs else None
-        dt_tr_lengths = data.get('dt_track_lengths', None) if self.use_time_rngs else None
-        is_nel = data.get('not_exhaustively_labeled', False)
-
-        # compute ignore masks for different track sets to eval
-        gt_ig_masks = self._compute_track_ig_masks(len(gt_ids), track_lengths=gt_tr_lengths, track_areas=gt_tr_areas,
-                                                   iscrowd=gt_tr_iscrowd)
-        dt_ig_masks = self._compute_track_ig_masks(len(dt_ids), track_lengths=dt_tr_lengths, track_areas=dt_tr_areas,
-                                                   is_not_exhaustively_labeled=is_nel, is_gt=False)
-
-        boxformat = data.get('boxformat', 'xywh')
-        ious = self._compute_track_ious(data['dt_tracks'], data['gt_tracks'], iou_function=data['iou_type'],
-                                        boxformat=boxformat)
-
-        for mask_idx in range(self.num_ig_masks):
-            gt_ig_mask = gt_ig_masks[mask_idx]
-
-            # Sort gt ignore last
-            gt_idx = np.argsort([g for g in gt_ig_mask], kind="mergesort")
-            gt_ids = [gt_ids[i] for i in gt_idx]
-
-            ious_sorted = ious[:, gt_idx] if len(ious) > 0 else ious
-
-            num_thrs = len(self.array_labels)
-            num_gt = len(gt_ids)
-            num_dt = len(dt_ids)
-
-            # Array to store the "id" of the matched dt/gt
-            gt_m = np.zeros((num_thrs, num_gt)) - 1
-            dt_m = np.zeros((num_thrs, num_dt)) - 1
-
-            gt_ig = np.array([gt_ig_mask[idx] for idx in gt_idx])
-            dt_ig = np.zeros((num_thrs, num_dt))
-
-            for iou_thr_idx, iou_thr in enumerate(self.array_labels):
-                if len(ious_sorted) == 0:
-                    break
-
-                for dt_idx, _dt in enumerate(dt_ids):
-                    iou = min([iou_thr, 1 - 1e-10])
-                    # information about best match so far (m=-1 -> unmatched)
-                    # store the gt_idx which matched for _dt
-                    m = -1
-                    for gt_idx, _ in enumerate(gt_ids):
-                        # if this gt already matched continue
-                        if gt_m[iou_thr_idx, gt_idx] > 0:
-                            continue
-                        # if _dt matched to reg gt, and on ignore gt, stop
-                        if m > -1 and gt_ig[m] == 0 and gt_ig[gt_idx] == 1:
-                            break
-                        # continue to next gt unless better match made
-                        if ious_sorted[dt_idx, gt_idx] < iou - np.finfo('float').eps:
-                            continue
-                        # if match successful and best so far, store appropriately
-                        iou = ious_sorted[dt_idx, gt_idx]
-                        m = gt_idx
-
-                    # No match found for _dt, go to next _dt
-                    if m == -1:
-                        continue
-
-                    # if gt to ignore for some reason update dt_ig.
-                    # Should not be used in evaluation.
-                    dt_ig[iou_thr_idx, dt_idx] = gt_ig[m]
-                    # _dt match found, update gt_m, and dt_m with "id"
-                    dt_m[iou_thr_idx, dt_idx] = gt_ids[m]
-                    gt_m[iou_thr_idx, m] = _dt
-
-            dt_ig_mask = dt_ig_masks[mask_idx]
-
-            dt_ig_mask = np.array(dt_ig_mask).reshape((1, num_dt))  # 1 X num_dt
-            dt_ig_mask = np.repeat(dt_ig_mask, num_thrs, 0)  # num_thrs X num_dt
-
-            # Based on dt_ig_mask ignore any unmatched detection by updating dt_ig
-            dt_ig = np.logical_or(dt_ig, np.logical_and(dt_m == -1, dt_ig_mask))
-            # store results for given video and category
-            res[mask_idx] = {
-                "dt_ids": dt_ids,
-                "gt_ids": gt_ids,
-                "dt_matches": dt_m,
-                "gt_matches": gt_m,
-                "dt_scores": data['dt_track_scores'],
-                "gt_ignore": gt_ig,
-                "dt_ignore": dt_ig,
-            }
-
-        return res
-
-    def combine_sequences(self, all_res):
-        """Combines metrics across all sequences. Computes precision and recall values based on track matches.
-        Adapted from https://github.com/TAO-Dataset/
-        """
-        num_thrs = len(self.array_labels)
-        num_recalls = len(self.rec_thrs)
-
-        # -1 for absent categories
-        precision = -np.ones(
-            (num_thrs, num_recalls, self.num_ig_masks)
-        )
-        recall = -np.ones((num_thrs, self.num_ig_masks))
-
-        for ig_idx in range(self.num_ig_masks):
-            ig_idx_results = [res[ig_idx] for res in all_res.values() if res[ig_idx] is not None]
-
-            # Remove elements which are None
-            if len(ig_idx_results) == 0:
-                continue
-
-            # Append all scores: shape (N,)
-            # limit considered tracks for each sequence if maxDet > 0
-            if self.maxDet == 0:
-                dt_scores = np.concatenate([res["dt_scores"] for res in ig_idx_results], axis=0)
-
-                dt_idx = np.argsort(-dt_scores, kind="mergesort")
-
-                dt_m = np.concatenate([e["dt_matches"] for e in ig_idx_results],
-                                      axis=1)[:, dt_idx]
-                dt_ig = np.concatenate([e["dt_ignore"] for e in ig_idx_results],
-                                       axis=1)[:, dt_idx]
-            elif self.maxDet > 0:
-                dt_scores = np.concatenate([res["dt_scores"][0:self.maxDet] for res in ig_idx_results], axis=0)
-
-                dt_idx = np.argsort(-dt_scores, kind="mergesort")
-
-                dt_m = np.concatenate([e["dt_matches"][:, 0:self.maxDet] for e in ig_idx_results],
-                                      axis=1)[:, dt_idx]
-                dt_ig = np.concatenate([e["dt_ignore"][:, 0:self.maxDet] for e in ig_idx_results],
-                                       axis=1)[:, dt_idx]
-            else:
-                raise Exception("Number of maximum detections must be >= 0, but is set to %i" % self.maxDet)
-
-            gt_ig = np.concatenate([res["gt_ignore"] for res in ig_idx_results])
-            # num gt anns to consider
-            num_gt = np.count_nonzero(gt_ig == 0)
-
-            if num_gt == 0:
-                continue
-
-            tps = np.logical_and(dt_m != -1, np.logical_not(dt_ig))
-            fps = np.logical_and(dt_m == -1, np.logical_not(dt_ig))
-
-            tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
-            fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
-
-            for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
-                tp = np.array(tp)
-                fp = np.array(fp)
-                num_tp = len(tp)
-                rc = tp / num_gt
-                if num_tp:
-                    recall[iou_thr_idx, ig_idx] = rc[-1]
-                else:
-                    recall[iou_thr_idx, ig_idx] = 0
-
-                # np.spacing(1) ~= eps
-                pr = tp / (fp + tp + np.spacing(1))
-                pr = pr.tolist()
-
-                # Ensure precision values are monotonically decreasing
-                for i in range(num_tp - 1, 0, -1):
-                    if pr[i] > pr[i - 1]:
-                        pr[i - 1] = pr[i]
-
-                # find indices at the predefined recall values
-                rec_thrs_insert_idx = np.searchsorted(rc, self.rec_thrs, side="left")
-
-                pr_at_recall = [0.0] * num_recalls
-
-                try:
-                    for _idx, pr_idx in enumerate(rec_thrs_insert_idx):
-                        pr_at_recall[_idx] = pr[pr_idx]
-                except IndexError:
-                    pass
-
-                precision[iou_thr_idx, :, ig_idx] = (np.array(pr_at_recall))
-
-        res = {'precision': precision, 'recall': recall}
-
-        # compute the precision and recall averages for the respective alpha thresholds and ignore masks
-        for lbl in self.lbls:
-            res['AP_' + lbl] = np.zeros((len(self.array_labels)), dtype=np.float)
-            res['AR_' + lbl] = np.zeros((len(self.array_labels)), dtype=np.float)
-
-        for a_id, alpha in enumerate(self.array_labels):
-            for lbl_idx, lbl in enumerate(self.lbls):
-                p = precision[a_id, :, lbl_idx]
-                if len(p[p > -1]) == 0:
-                    mean_p = -1
-                else:
-                    mean_p = np.mean(p[p > -1])
-                res['AP_' + lbl][a_id] = mean_p
-                res['AR_' + lbl][a_id] = recall[a_id, lbl_idx]
-
-        return res
-
-    def combine_classes_class_averaged(self, all_res, ignore_empty_classes=True):
-        """Combines metrics across all classes by averaging over the class values
-        Note mAP is not well defined for 'empty classes' so 'ignore empty classes' is always true here.
-        """
-        res = {}
-        for field in self.fields:
-            res[field] = np.zeros((len(self.array_labels)), dtype=np.float)
-            field_stacked = np.array([res[field] for res in all_res.values()])
-
-            for a_id, alpha in enumerate(self.array_labels):
-                values = field_stacked[:, a_id]
-                if len(values[values > -1]) == 0:
-                    mean = -1
-                else:
-                    mean = np.mean(values[values > -1])
-                res[field][a_id] = mean
-        return res
-
-    def combine_classes_det_averaged(self, all_res):
-        """Combines metrics across all classes by averaging over the detection values"""
-
-        res = {}
-        for field in self.fields:
-            res[field] = np.zeros((len(self.array_labels)), dtype=np.float)
-            field_stacked = np.array([res[field] for res in all_res.values()])
-
-            for a_id, alpha in enumerate(self.array_labels):
-                values = field_stacked[:, a_id]
-                if len(values[values > -1]) == 0:
-                    mean = -1
-                else:
-                    mean = np.mean(values[values > -1])
-                res[field][a_id] = mean
-        return res
-
-    def _compute_track_ig_masks(self, num_ids, track_lengths=None, track_areas=None, iscrowd=None,
-                                is_not_exhaustively_labeled=False, is_gt=True):
-        """
-        Computes ignore masks for different track sets to evaluate
-        :param num_ids: the number of track IDs
-        :param track_lengths: the lengths of the tracks (number of timesteps)
-        :param track_areas: the average area of a track
-        :param iscrowd: whether a track is marked as crowd
-        :param is_not_exhaustively_labeled: whether the track category is not exhaustively labeled
-        :param is_gt: whether it is gt
-        :return: the track ignore masks
-        """
-        # for TAO tracks for classes which are not exhaustively labeled are not evaluated
-        if not is_gt and is_not_exhaustively_labeled:
-            track_ig_masks = [[1 for _ in range(num_ids)] for i in range(self.num_ig_masks)]
-        else:
-            # consider all tracks
-            track_ig_masks = [[0 for _ in range(num_ids)]]
-
-            # consider tracks with certain area
-            if self.use_area_rngs:
-                for rng in self.area_rngs:
-                    track_ig_masks.append([0 if rng[0] - np.finfo('float').eps <= area <= rng[1] + np.finfo('float').eps
-                                           else 1 for area in track_areas])
-
-            # consider tracks with certain duration
-            if self.use_time_rngs:
-                for rng in self.time_rngs:
-                    track_ig_masks.append([0 if rng[0] - np.finfo('float').eps <= length
-                                                <= rng[1] + np.finfo('float').eps else 1 for length in track_lengths])
-
-        # for YouTubeVIS evaluation tracks with crowd tag are not evaluated
-        if is_gt and iscrowd:
-            track_ig_masks = [np.logical_or(mask, iscrowd) for mask in track_ig_masks]
-
-        return track_ig_masks
-
-    @staticmethod
-    def _compute_bb_track_iou(dt_track, gt_track, boxformat='xywh'):
-        """
-        Calculates the track IoU for one detected track and one ground truth track for bounding boxes
-        :param dt_track: the detected track (format: dictionary with frame index as keys and
-                            numpy arrays as values)
-        :param gt_track: the ground truth track (format: dictionary with frame index as keys and
-                        numpy array as values)
-        :param boxformat: the format of the boxes
-        :return: the track IoU
-        """
-        intersect = 0
-        union = 0
-        image_ids = set(gt_track.keys()) | set(dt_track.keys())
-        for image in image_ids:
-            g = gt_track.get(image, None)
-            d = dt_track.get(image, None)
-            if boxformat == 'xywh':
-                if d is not None and g is not None:
-                    dx, dy, dw, dh = d
-                    gx, gy, gw, gh = g
-                    w = max(min(dx + dw, gx + gw) - max(dx, gx), 0)
-                    h = max(min(dy + dh, gy + gh) - max(dy, gy), 0)
-                    i = w * h
-                    u = dw * dh + gw * gh - i
-                    intersect += i
-                    union += u
-                elif d is None and g is not None:
-                    union += g[2] * g[3]
-                elif d is not None and g is None:
-                    union += d[2] * d[3]
-            elif boxformat == 'x0y0x1y1':
-                if d is not None and g is not None:
-                    dx0, dy0, dx1, dy1 = d
-                    gx0, gy0, gx1, gy1 = g
-                    w = max(min(dx1, gx1) - max(dx0, gx0), 0)
-                    h = max(min(dy1, gy1) - max(dy0, gy0), 0)
-                    i = w * h
-                    u = (dx1 - dx0) * (dy1 - dy0) + (gx1 - gx0) * (gy1 - gy0) - i
-                    intersect += i
-                    union += u
-                elif d is None and g is not None:
-                    union += (g[2] - g[0]) * (g[3] - g[1])
-                elif d is not None and g is None:
-                    union += (d[2] - d[0]) * (d[3] - d[1])
-            else:
-                raise TrackEvalException('BoxFormat not implemented')
-        if intersect > union:
-            raise TrackEvalException("Intersection value > union value. Are the box values corrupted?")
-        return intersect / union if union > 0 else 0
-
-    @staticmethod
-    def _compute_mask_track_iou(dt_track, gt_track):
-        """
-        Calculates the track IoU for one detected track and one ground truth track for segmentation masks
-        :param dt_track: the detected track (format: dictionary with frame index as keys and
-                            pycocotools rle encoded masks as values)
-        :param gt_track: the ground truth track (format: dictionary with frame index as keys and
-                            pycocotools rle encoded masks as values)
-        :return: the track IoU
-        """
-        # only loaded when needed to reduce minimum requirements
-        from pycocotools import mask as mask_utils
-
-        intersect = .0
-        union = .0
-        image_ids = set(gt_track.keys()) | set(dt_track.keys())
-        for image in image_ids:
-            g = gt_track.get(image, None)
-            d = dt_track.get(image, None)
-            if d and g:
-                intersect += mask_utils.area(mask_utils.merge([d, g], True))
-                union += mask_utils.area(mask_utils.merge([d, g], False))
-            elif not d and g:
-                union += mask_utils.area(g)
-            elif d and not g:
-                union += mask_utils.area(d)
-        if union < 0.0 - np.finfo('float').eps:
-            raise TrackEvalException("Union value < 0. Are the segmentaions corrupted?")
-        if intersect > union:
-            raise TrackEvalException("Intersection value > union value. Are the segmentations corrupted?")
-        iou = intersect / union if union > 0.0 + np.finfo('float').eps else 0.0
-        return iou
-
-    @staticmethod
-    def _compute_track_ious(dt, gt, iou_function='bbox', boxformat='xywh'):
-        """
-        Calculate track IoUs for a set of ground truth tracks and a set of detected tracks
-        """
-
-        if len(gt) == 0 and len(dt) == 0:
-            return []
-
-        if iou_function == 'bbox':
-            track_iou_function = partial(TrackMAP._compute_bb_track_iou, boxformat=boxformat)
-        elif iou_function == 'mask':
-            track_iou_function = partial(TrackMAP._compute_mask_track_iou)
-        else:
-            raise Exception('IoU function not implemented')
-
-        ious = np.zeros([len(dt), len(gt)])
-        for i, j in np.ndindex(ious.shape):
-            ious[i, j] = track_iou_function(dt[i], gt[j])
-        return ious
-
-    @staticmethod
-    def _row_print(*argv):
-        """Prints results in an evenly spaced rows, with more space in first row"""
-        if len(argv) == 1:
-            argv = argv[0]
-        to_print = '%-40s' % argv[0]
-        for v in argv[1:]:
-            to_print += '%-12s' % str(v)
-        print(to_print)
diff --git a/spaces/xfys/yolov5_tracking/yolov5/train.py b/spaces/xfys/yolov5_tracking/yolov5/train.py
deleted file mode 100644
index 216da6399028bd3f6ed399bfdd377d25e3356a6e..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/yolov5/train.py
+++ /dev/null
@@ -1,642 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Train a YOLOv5 model on a custom dataset.
-Models and datasets download automatically from the latest YOLOv5 release.
-
-Usage - Single-GPU training:
-    $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640  # from pretrained (recommended)
-    $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640  # from scratch
-
-Usage - Multi-GPU DDP training:
-    $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3
-
-Models:     https://github.com/ultralytics/yolov5/tree/master/models
-Datasets:   https://github.com/ultralytics/yolov5/tree/master/data
-Tutorial:   https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
-"""
-
-import argparse
-import math
-import os
-import random
-import subprocess
-import sys
-import time
-from copy import deepcopy
-from datetime import datetime
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-import yaml
-from torch.optim import lr_scheduler
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-import val as validate  # for end-of-epoch mAP
-from models.experimental import attempt_load
-from models.yolo import Model
-from utils.autoanchor import check_anchors
-from utils.autobatch import check_train_batch_size
-from utils.callbacks import Callbacks
-from utils.dataloaders import create_dataloader
-from utils.downloads import attempt_download, is_url
-from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info,
-                           check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr,
-                           get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
-                           labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer,
-                           yaml_save)
-from utils.loggers import Loggers
-from utils.loggers.comet.comet_utils import check_comet_resume
-from utils.loss import ComputeLoss
-from utils.metrics import fitness
-from utils.plots import plot_evolve
-from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer,
-                               smart_resume, torch_distributed_zero_first)
-
-LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
-RANK = int(os.getenv('RANK', -1))
-WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
-GIT_INFO = check_git_info()
-
-
-def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictionary
-    save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \
-        Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
-        opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
-    callbacks.run('on_pretrain_routine_start')
-
-    # Directories
-    w = save_dir / 'weights'  # weights dir
-    (w.parent if evolve else w).mkdir(parents=True, exist_ok=True)  # make dir
-    last, best = w / 'last.pt', w / 'best.pt'
-
-    # Hyperparameters
-    if isinstance(hyp, str):
-        with open(hyp, errors='ignore') as f:
-            hyp = yaml.safe_load(f)  # load hyps dict
-    LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
-    opt.hyp = hyp.copy()  # for saving hyps to checkpoints
-
-    # Save run settings
-    if not evolve:
-        yaml_save(save_dir / 'hyp.yaml', hyp)
-        yaml_save(save_dir / 'opt.yaml', vars(opt))
-
-    # Loggers
-    data_dict = None
-    if RANK in {-1, 0}:
-        loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance
-
-        # Register actions
-        for k in methods(loggers):
-            callbacks.register_action(k, callback=getattr(loggers, k))
-
-        # Process custom dataset artifact link
-        data_dict = loggers.remote_dataset
-        if resume:  # If resuming runs from remote artifact
-            weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size
-
-    # Config
-    plots = not evolve and not opt.noplots  # create plots
-    cuda = device.type != 'cpu'
-    init_seeds(opt.seed + 1 + RANK, deterministic=True)
-    with torch_distributed_zero_first(LOCAL_RANK):
-        data_dict = data_dict or check_dataset(data)  # check if None
-    train_path, val_path = data_dict['train'], data_dict['val']
-    nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
-    names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
-    is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt')  # COCO dataset
-
-    # Model
-    check_suffix(weights, '.pt')  # check weights
-    pretrained = weights.endswith('.pt')
-    if pretrained:
-        with torch_distributed_zero_first(LOCAL_RANK):
-            weights = attempt_download(weights)  # download if not found locally
-        ckpt = torch.load(weights, map_location='cpu')  # load checkpoint to CPU to avoid CUDA memory leak
-        model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
-        exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else []  # exclude keys
-        csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
-        csd = intersect_dicts(csd, model.state_dict(), exclude=exclude)  # intersect
-        model.load_state_dict(csd, strict=False)  # load
-        LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}')  # report
-    else:
-        model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
-    amp = check_amp(model)  # check AMP
-
-    # Freeze
-    freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))]  # layers to freeze
-    for k, v in model.named_parameters():
-        v.requires_grad = True  # train all layers
-        # v.register_hook(lambda x: torch.nan_to_num(x))  # NaN to 0 (commented for erratic training results)
-        if any(x in k for x in freeze):
-            LOGGER.info(f'freezing {k}')
-            v.requires_grad = False
-
-    # Image size
-    gs = max(int(model.stride.max()), 32)  # grid size (max stride)
-    imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2)  # verify imgsz is gs-multiple
-
-    # Batch size
-    if RANK == -1 and batch_size == -1:  # single-GPU only, estimate best batch size
-        batch_size = check_train_batch_size(model, imgsz, amp)
-        loggers.on_params_update({'batch_size': batch_size})
-
-    # Optimizer
-    nbs = 64  # nominal batch size
-    accumulate = max(round(nbs / batch_size), 1)  # accumulate loss before optimizing
-    hyp['weight_decay'] *= batch_size * accumulate / nbs  # scale weight_decay
-    optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay'])
-
-    # Scheduler
-    if opt.cos_lr:
-        lf = one_cycle(1, hyp['lrf'], epochs)  # cosine 1->hyp['lrf']
-    else:
-        lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf']  # linear
-    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)  # plot_lr_scheduler(optimizer, scheduler, epochs)
-
-    # EMA
-    ema = ModelEMA(model) if RANK in {-1, 0} else None
-
-    # Resume
-    best_fitness, start_epoch = 0.0, 0
-    if pretrained:
-        if resume:
-            best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)
-        del ckpt, csd
-
-    # DP mode
-    if cuda and RANK == -1 and torch.cuda.device_count() > 1:
-        LOGGER.warning(
-            'WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
-            'See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started.'
-        )
-        model = torch.nn.DataParallel(model)
-
-    # SyncBatchNorm
-    if opt.sync_bn and cuda and RANK != -1:
-        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
-        LOGGER.info('Using SyncBatchNorm()')
-
-    # Trainloader
-    train_loader, dataset = create_dataloader(train_path,
-                                              imgsz,
-                                              batch_size // WORLD_SIZE,
-                                              gs,
-                                              single_cls,
-                                              hyp=hyp,
-                                              augment=True,
-                                              cache=None if opt.cache == 'val' else opt.cache,
-                                              rect=opt.rect,
-                                              rank=LOCAL_RANK,
-                                              workers=workers,
-                                              image_weights=opt.image_weights,
-                                              quad=opt.quad,
-                                              prefix=colorstr('train: '),
-                                              shuffle=True,
-                                              seed=opt.seed)
-    labels = np.concatenate(dataset.labels, 0)
-    mlc = int(labels[:, 0].max())  # max label class
-    assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
-
-    # Process 0
-    if RANK in {-1, 0}:
-        val_loader = create_dataloader(val_path,
-                                       imgsz,
-                                       batch_size // WORLD_SIZE * 2,
-                                       gs,
-                                       single_cls,
-                                       hyp=hyp,
-                                       cache=None if noval else opt.cache,
-                                       rect=True,
-                                       rank=-1,
-                                       workers=workers * 2,
-                                       pad=0.5,
-                                       prefix=colorstr('val: '))[0]
-
-        if not resume:
-            if not opt.noautoanchor:
-                check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)  # run AutoAnchor
-            model.half().float()  # pre-reduce anchor precision
-
-        callbacks.run('on_pretrain_routine_end', labels, names)
-
-    # DDP mode
-    if cuda and RANK != -1:
-        model = smart_DDP(model)
-
-    # Model attributes
-    nl = de_parallel(model).model[-1].nl  # number of detection layers (to scale hyps)
-    hyp['box'] *= 3 / nl  # scale to layers
-    hyp['cls'] *= nc / 80 * 3 / nl  # scale to classes and layers
-    hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl  # scale to image size and layers
-    hyp['label_smoothing'] = opt.label_smoothing
-    model.nc = nc  # attach number of classes to model
-    model.hyp = hyp  # attach hyperparameters to model
-    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc  # attach class weights
-    model.names = names
-
-    # Start training
-    t0 = time.time()
-    nb = len(train_loader)  # number of batches
-    nw = max(round(hyp['warmup_epochs'] * nb), 100)  # number of warmup iterations, max(3 epochs, 100 iterations)
-    # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
-    last_opt_step = -1
-    maps = np.zeros(nc)  # mAP per class
-    results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
-    scheduler.last_epoch = start_epoch - 1  # do not move
-    scaler = torch.cuda.amp.GradScaler(enabled=amp)
-    stopper, stop = EarlyStopping(patience=opt.patience), False
-    compute_loss = ComputeLoss(model)  # init loss class
-    callbacks.run('on_train_start')
-    LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
-                f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
-                f"Logging results to {colorstr('bold', save_dir)}\n"
-                f'Starting training for {epochs} epochs...')
-    for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
-        callbacks.run('on_train_epoch_start')
-        model.train()
-
-        # Update image weights (optional, single-GPU only)
-        if opt.image_weights:
-            cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc  # class weights
-            iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights
-            dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx
-
-        # Update mosaic border (optional)
-        # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
-        # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders
-
-        mloss = torch.zeros(3, device=device)  # mean losses
-        if RANK != -1:
-            train_loader.sampler.set_epoch(epoch)
-        pbar = enumerate(train_loader)
-        LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size'))
-        if RANK in {-1, 0}:
-            pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT)  # progress bar
-        optimizer.zero_grad()
-        for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
-            callbacks.run('on_train_batch_start')
-            ni = i + nb * epoch  # number integrated batches (since train start)
-            imgs = imgs.to(device, non_blocking=True).float() / 255  # uint8 to float32, 0-255 to 0.0-1.0
-
-            # Warmup
-            if ni <= nw:
-                xi = [0, nw]  # x interp
-                # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0])  # iou loss ratio (obj_loss = 1.0 or iou)
-                accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
-                for j, x in enumerate(optimizer.param_groups):
-                    # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
-                    x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)])
-                    if 'momentum' in x:
-                        x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
-
-            # Multi-scale
-            if opt.multi_scale:
-                sz = random.randrange(int(imgsz * 0.5), int(imgsz * 1.5) + gs) // gs * gs  # size
-                sf = sz / max(imgs.shape[2:])  # scale factor
-                if sf != 1:
-                    ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]]  # new shape (stretched to gs-multiple)
-                    imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
-
-            # Forward
-            with torch.cuda.amp.autocast(amp):
-                pred = model(imgs)  # forward
-                loss, loss_items = compute_loss(pred, targets.to(device))  # loss scaled by batch_size
-                if RANK != -1:
-                    loss *= WORLD_SIZE  # gradient averaged between devices in DDP mode
-                if opt.quad:
-                    loss *= 4.
-
-            # Backward
-            scaler.scale(loss).backward()
-
-            # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
-            if ni - last_opt_step >= accumulate:
-                scaler.unscale_(optimizer)  # unscale gradients
-                torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0)  # clip gradients
-                scaler.step(optimizer)  # optimizer.step
-                scaler.update()
-                optimizer.zero_grad()
-                if ema:
-                    ema.update(model)
-                last_opt_step = ni
-
-            # Log
-            if RANK in {-1, 0}:
-                mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
-                mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
-                pbar.set_description(('%11s' * 2 + '%11.4g' * 5) %
-                                     (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
-                callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths, list(mloss))
-                if callbacks.stop_training:
-                    return
-            # end batch ------------------------------------------------------------------------------------------------
-
-        # Scheduler
-        lr = [x['lr'] for x in optimizer.param_groups]  # for loggers
-        scheduler.step()
-
-        if RANK in {-1, 0}:
-            # mAP
-            callbacks.run('on_train_epoch_end', epoch=epoch)
-            ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
-            final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
-            if not noval or final_epoch:  # Calculate mAP
-                results, maps, _ = validate.run(data_dict,
-                                                batch_size=batch_size // WORLD_SIZE * 2,
-                                                imgsz=imgsz,
-                                                half=amp,
-                                                model=ema.ema,
-                                                single_cls=single_cls,
-                                                dataloader=val_loader,
-                                                save_dir=save_dir,
-                                                plots=False,
-                                                callbacks=callbacks,
-                                                compute_loss=compute_loss)
-
-            # Update best mAP
-            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
-            stop = stopper(epoch=epoch, fitness=fi)  # early stop check
-            if fi > best_fitness:
-                best_fitness = fi
-            log_vals = list(mloss) + list(results) + lr
-            callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)
-
-            # Save model
-            if (not nosave) or (final_epoch and not evolve):  # if save
-                ckpt = {
-                    'epoch': epoch,
-                    'best_fitness': best_fitness,
-                    'model': deepcopy(de_parallel(model)).half(),
-                    'ema': deepcopy(ema.ema).half(),
-                    'updates': ema.updates,
-                    'optimizer': optimizer.state_dict(),
-                    'opt': vars(opt),
-                    'git': GIT_INFO,  # {remote, branch, commit} if a git repo
-                    'date': datetime.now().isoformat()}
-
-                # Save last, best and delete
-                torch.save(ckpt, last)
-                if best_fitness == fi:
-                    torch.save(ckpt, best)
-                if opt.save_period > 0 and epoch % opt.save_period == 0:
-                    torch.save(ckpt, w / f'epoch{epoch}.pt')
-                del ckpt
-                callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)
-
-        # EarlyStopping
-        if RANK != -1:  # if DDP training
-            broadcast_list = [stop if RANK == 0 else None]
-            dist.broadcast_object_list(broadcast_list, 0)  # broadcast 'stop' to all ranks
-            if RANK != 0:
-                stop = broadcast_list[0]
-        if stop:
-            break  # must break all DDP ranks
-
-        # end epoch ----------------------------------------------------------------------------------------------------
-    # end training -----------------------------------------------------------------------------------------------------
-    if RANK in {-1, 0}:
-        LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
-        for f in last, best:
-            if f.exists():
-                strip_optimizer(f)  # strip optimizers
-                if f is best:
-                    LOGGER.info(f'\nValidating {f}...')
-                    results, _, _ = validate.run(
-                        data_dict,
-                        batch_size=batch_size // WORLD_SIZE * 2,
-                        imgsz=imgsz,
-                        model=attempt_load(f, device).half(),
-                        iou_thres=0.65 if is_coco else 0.60,  # best pycocotools at iou 0.65
-                        single_cls=single_cls,
-                        dataloader=val_loader,
-                        save_dir=save_dir,
-                        save_json=is_coco,
-                        verbose=True,
-                        plots=plots,
-                        callbacks=callbacks,
-                        compute_loss=compute_loss)  # val best model with plots
-                    if is_coco:
-                        callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
-
-        callbacks.run('on_train_end', last, best, epoch, results)
-
-    torch.cuda.empty_cache()
-    return results
-
-
-def parse_opt(known=False):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
-    parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
-    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
-    parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
-    parser.add_argument('--epochs', type=int, default=100, help='total training epochs')
-    parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
-    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
-    parser.add_argument('--rect', action='store_true', help='rectangular training')
-    parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
-    parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
-    parser.add_argument('--noval', action='store_true', help='only validate final epoch')
-    parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
-    parser.add_argument('--noplots', action='store_true', help='save no plot files')
-    parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
-    parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
-    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk')
-    parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
-    parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
-    parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
-    parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
-    parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
-    parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
-    parser.add_argument('--name', default='exp', help='save to project/name')
-    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
-    parser.add_argument('--quad', action='store_true', help='quad dataloader')
-    parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
-    parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
-    parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
-    parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
-    parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
-    parser.add_argument('--seed', type=int, default=0, help='Global training seed')
-    parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
-
-    # Logger arguments
-    parser.add_argument('--entity', default=None, help='Entity')
-    parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='Upload data, "val" option')
-    parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval')
-    parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use')
-
-    return parser.parse_known_args()[0] if known else parser.parse_args()
-
-
-def main(opt, callbacks=Callbacks()):
-    # Checks
-    if RANK in {-1, 0}:
-        print_args(vars(opt))
-        check_git_status()
-        check_requirements()
-
-    # Resume (from specified or most recent last.pt)
-    if opt.resume and not check_comet_resume(opt) and not opt.evolve:
-        last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
-        opt_yaml = last.parent.parent / 'opt.yaml'  # train options yaml
-        opt_data = opt.data  # original dataset
-        if opt_yaml.is_file():
-            with open(opt_yaml, errors='ignore') as f:
-                d = yaml.safe_load(f)
-        else:
-            d = torch.load(last, map_location='cpu')['opt']
-        opt = argparse.Namespace(**d)  # replace
-        opt.cfg, opt.weights, opt.resume = '', str(last), True  # reinstate
-        if is_url(opt_data):
-            opt.data = check_file(opt_data)  # avoid HUB resume auth timeout
-    else:
-        opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \
-            check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project)  # checks
-        assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
-        if opt.evolve:
-            if opt.project == str(ROOT / 'runs/train'):  # if default project name, rename to runs/evolve
-                opt.project = str(ROOT / 'runs/evolve')
-            opt.exist_ok, opt.resume = opt.resume, False  # pass resume to exist_ok and disable resume
-        if opt.name == 'cfg':
-            opt.name = Path(opt.cfg).stem  # use model.yaml as name
-        opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
-
-    # DDP mode
-    device = select_device(opt.device, batch_size=opt.batch_size)
-    if LOCAL_RANK != -1:
-        msg = 'is not compatible with YOLOv5 Multi-GPU DDP training'
-        assert not opt.image_weights, f'--image-weights {msg}'
-        assert not opt.evolve, f'--evolve {msg}'
-        assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size'
-        assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
-        assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
-        torch.cuda.set_device(LOCAL_RANK)
-        device = torch.device('cuda', LOCAL_RANK)
-        dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo')
-
-    # Train
-    if not opt.evolve:
-        train(opt.hyp, opt, device, callbacks)
-
-    # Evolve hyperparameters (optional)
-    else:
-        # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
-        meta = {
-            'lr0': (1, 1e-5, 1e-1),  # initial learning rate (SGD=1E-2, Adam=1E-3)
-            'lrf': (1, 0.01, 1.0),  # final OneCycleLR learning rate (lr0 * lrf)
-            'momentum': (0.3, 0.6, 0.98),  # SGD momentum/Adam beta1
-            'weight_decay': (1, 0.0, 0.001),  # optimizer weight decay
-            'warmup_epochs': (1, 0.0, 5.0),  # warmup epochs (fractions ok)
-            'warmup_momentum': (1, 0.0, 0.95),  # warmup initial momentum
-            'warmup_bias_lr': (1, 0.0, 0.2),  # warmup initial bias lr
-            'box': (1, 0.02, 0.2),  # box loss gain
-            'cls': (1, 0.2, 4.0),  # cls loss gain
-            'cls_pw': (1, 0.5, 2.0),  # cls BCELoss positive_weight
-            'obj': (1, 0.2, 4.0),  # obj loss gain (scale with pixels)
-            'obj_pw': (1, 0.5, 2.0),  # obj BCELoss positive_weight
-            'iou_t': (0, 0.1, 0.7),  # IoU training threshold
-            'anchor_t': (1, 2.0, 8.0),  # anchor-multiple threshold
-            'anchors': (2, 2.0, 10.0),  # anchors per output grid (0 to ignore)
-            'fl_gamma': (0, 0.0, 2.0),  # focal loss gamma (efficientDet default gamma=1.5)
-            'hsv_h': (1, 0.0, 0.1),  # image HSV-Hue augmentation (fraction)
-            'hsv_s': (1, 0.0, 0.9),  # image HSV-Saturation augmentation (fraction)
-            'hsv_v': (1, 0.0, 0.9),  # image HSV-Value augmentation (fraction)
-            'degrees': (1, 0.0, 45.0),  # image rotation (+/- deg)
-            'translate': (1, 0.0, 0.9),  # image translation (+/- fraction)
-            'scale': (1, 0.0, 0.9),  # image scale (+/- gain)
-            'shear': (1, 0.0, 10.0),  # image shear (+/- deg)
-            'perspective': (0, 0.0, 0.001),  # image perspective (+/- fraction), range 0-0.001
-            'flipud': (1, 0.0, 1.0),  # image flip up-down (probability)
-            'fliplr': (0, 0.0, 1.0),  # image flip left-right (probability)
-            'mosaic': (1, 0.0, 1.0),  # image mixup (probability)
-            'mixup': (1, 0.0, 1.0),  # image mixup (probability)
-            'copy_paste': (1, 0.0, 1.0)}  # segment copy-paste (probability)
-
-        with open(opt.hyp, errors='ignore') as f:
-            hyp = yaml.safe_load(f)  # load hyps dict
-            if 'anchors' not in hyp:  # anchors commented in hyp.yaml
-                hyp['anchors'] = 3
-        if opt.noautoanchor:
-            del hyp['anchors'], meta['anchors']
-        opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir)  # only val/save final epoch
-        # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices
-        evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'
-        if opt.bucket:
-            # download evolve.csv if exists
-            subprocess.run([
-                'gsutil',
-                'cp',
-                f'gs://{opt.bucket}/evolve.csv',
-                str(evolve_csv),])
-
-        for _ in range(opt.evolve):  # generations to evolve
-            if evolve_csv.exists():  # if evolve.csv exists: select best hyps and mutate
-                # Select parent(s)
-                parent = 'single'  # parent selection method: 'single' or 'weighted'
-                x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
-                n = min(5, len(x))  # number of previous results to consider
-                x = x[np.argsort(-fitness(x))][:n]  # top n mutations
-                w = fitness(x) - fitness(x).min() + 1E-6  # weights (sum > 0)
-                if parent == 'single' or len(x) == 1:
-                    # x = x[random.randint(0, n - 1)]  # random selection
-                    x = x[random.choices(range(n), weights=w)[0]]  # weighted selection
-                elif parent == 'weighted':
-                    x = (x * w.reshape(n, 1)).sum(0) / w.sum()  # weighted combination
-
-                # Mutate
-                mp, s = 0.8, 0.2  # mutation probability, sigma
-                npr = np.random
-                npr.seed(int(time.time()))
-                g = np.array([meta[k][0] for k in hyp.keys()])  # gains 0-1
-                ng = len(meta)
-                v = np.ones(ng)
-                while all(v == 1):  # mutate until a change occurs (prevent duplicates)
-                    v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
-                for i, k in enumerate(hyp.keys()):  # plt.hist(v.ravel(), 300)
-                    hyp[k] = float(x[i + 7] * v[i])  # mutate
-
-            # Constrain to limits
-            for k, v in meta.items():
-                hyp[k] = max(hyp[k], v[1])  # lower limit
-                hyp[k] = min(hyp[k], v[2])  # upper limit
-                hyp[k] = round(hyp[k], 5)  # significant digits
-
-            # Train mutation
-            results = train(hyp.copy(), opt, device, callbacks)
-            callbacks = Callbacks()
-            # Write mutation results
-            keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss',
-                    'val/obj_loss', 'val/cls_loss')
-            print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket)
-
-        # Plot results
-        plot_evolve(evolve_csv)
-        LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n'
-                    f"Results saved to {colorstr('bold', save_dir)}\n"
-                    f'Usage example: $ python train.py --hyp {evolve_yaml}')
-
-
-def run(**kwargs):
-    # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
-    opt = parse_opt(True)
-    for k, v in kwargs.items():
-        setattr(opt, k, v)
-    main(opt)
-    return opt
-
-
-if __name__ == '__main__':
-    opt = parse_opt()
-    main(opt)
diff --git a/spaces/xiaoti/Real-CUGAN/upcunet_v3.py b/spaces/xiaoti/Real-CUGAN/upcunet_v3.py
deleted file mode 100644
index f7919a6cc9efe3b8af73a73e30825a4c7d7d76da..0000000000000000000000000000000000000000
--- a/spaces/xiaoti/Real-CUGAN/upcunet_v3.py
+++ /dev/null
@@ -1,714 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-import os, sys
-import numpy as np
-
-root_path = os.path.abspath('.')
-sys.path.append(root_path)
-
-
-class SEBlock(nn.Module):
-    def __init__(self, in_channels, reduction=8, bias=False):
-        super(SEBlock, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias)
-        self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias)
-
-    def forward(self, x):
-        if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-            x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half()
-        else:
-            x0 = torch.mean(x, dim=(2, 3), keepdim=True)
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-    def forward_mean(self, x, x0):
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-
-class UNetConv(nn.Module):
-    def __init__(self, in_channels, mid_channels, out_channels, se):
-        super(UNetConv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(in_channels, mid_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-            nn.Conv2d(mid_channels, out_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-        )
-        if se:
-            self.seblock = SEBlock(out_channels, reduction=8, bias=True)
-        else:
-            self.seblock = None
-
-    def forward(self, x):
-        z = self.conv(x)
-        if self.seblock is not None:
-            z = self.seblock(z)
-        return z
-
-
-class UNet1(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet1x3(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1x3, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet2(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet2, self).__init__()
-
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 64, 128, se=True)
-        self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0)
-        self.conv3 = UNetConv(128, 256, 128, se=True)
-        self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0)
-        self.conv4 = UNetConv(128, 64, 64, se=True)
-        self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv5 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3(x3)
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4(x2 + x3)
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-    def forward_a(self, x):  # conv234结尾有se
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x2):  # conv234结尾有se
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3.conv(x3)
-        return x3
-
-    def forward_c(self, x2, x3):  # conv234结尾有se
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4.conv(x2 + x3)
-        return x4
-
-    def forward_d(self, x1, x4):  # conv234结尾有se
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-
-class UpCunet2x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet2x, self).__init__()
-        self.unet1 = UNet1(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 36, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 36, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2]
-        return res  #
-
-
-class UpCunet3x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet3x, self).__init__()
-        self.unet1 = UNet1x3(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 4 + 1) * 4
-            pw = ((w0 - 1) // 4 + 1) * 4
-            x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_h = (h0 - 1) // 4 * 4 + 4  # 能被4整除
-            else:
-                crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_w = (w0 - 1) // 4 * 4 + 4  # 能被4整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 28, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 28, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop  #
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3]
-        return res
-
-
-class UpCunet4x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet4x, self).__init__()
-        self.unet1 = UNet1(in_channels, 64, deconv=True)
-        self.unet2 = UNet2(64, 64, deconv=False)
-        self.ps = nn.PixelShuffle(2)
-        self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True)
-
-    def forward(self, x, tile_mode):
-        n, c, h0, w0 = x.shape
-        x00 = x
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            x = self.conv_final(x)
-            x = F.pad(x, (-1, -1, -1, -1))
-            x = self.ps(x)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4]
-            x += F.interpolate(x00, scale_factor=4, mode='nearest')
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.1G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 38, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 38, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                x_crop = self.conv_final(x_crop)
-                x_crop = F.pad(x_crop, (-1, -1, -1, -1))
-                x_crop = self.ps(x_crop)
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape)
-                res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4]
-        res += F.interpolate(x00, scale_factor=4, mode='nearest')
-        return res  #
-
-
-class RealWaifuUpScaler(object):
-    def __init__(self, scale, weight_path, half, device):
-        weight = torch.load(weight_path, map_location="cpu")
-        self.model = eval("UpCunet%sx" % scale)()
-        if (half == True):
-            self.model = self.model.half().to(device)
-        else:
-            self.model = self.model.to(device)
-        self.model.load_state_dict(weight, strict=True)
-        self.model.eval()
-        self.half = half
-        self.device = device
-
-    def np2tensor(self, np_frame):
-        if (self.half == False):
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255
-        else:
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255
-
-    def tensor2np(self, tensor):
-        if (self.half == False):
-            return (
-                np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0)))
-        else:
-            return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(),
-                                 (1, 2, 0)))
-
-    def __call__(self, frame, tile_mode):
-        with torch.no_grad():
-            tensor = self.np2tensor(frame)
-            result = self.tensor2np(self.model(tensor, tile_mode))
-        return result
-
-
-if __name__ == "__main__":
-    ###########inference_img
-    import time, cv2, sys
-    from time import time as ttime
-
-    for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3),
-                               ("weights_v3/up4x-latest-denoise3x.pth", 4)]:
-        for tile_mode in [0, 1, 2, 3, 4]:
-            upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0")
-            input_dir = "%s/input_dir1" % root_path
-            output_dir = "%s/opt-dir-all-test" % root_path
-            os.makedirs(output_dir, exist_ok=True)
-            for name in os.listdir(input_dir):
-                print(name)
-                tmp = name.split(".")
-                inp_path = os.path.join(input_dir, name)
-                suffix = tmp[-1]
-                prefix = ".".join(tmp[:-1])
-                tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                print(inp_path, tmp_path)
-                # 支持中文路径
-                # os.link(inp_path, tmp_path)#win用硬链接
-                os.symlink(inp_path, tmp_path)  # linux用软链接
-                frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]]
-                t0 = ttime()
-                result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1]
-                t1 = ttime()
-                print(prefix, "done", t1 - t0)
-                tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                cv2.imwrite(tmp_opt_path, result)
-                n = 0
-                while (1):
-                    if (n == 0):
-                        suffix = "_%sx_tile%s.png" % (scale, tile_mode)
-                    else:
-                        suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n)  #
-                    if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False):
-                        break
-                    else:
-                        n += 1
-                final_opt_path = os.path.join(output_dir, prefix + suffix)
-                os.rename(tmp_opt_path, final_opt_path)
-                os.remove(tmp_path)
diff --git a/spaces/xiaoyeAI/clewd/update.sh b/spaces/xiaoyeAI/clewd/update.sh
deleted file mode 100644
index c0f478936d289ccbd2df5e8f6071d7dbc5fa4380..0000000000000000000000000000000000000000
--- a/spaces/xiaoyeAI/clewd/update.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/bin/bash
-
-if ! [ -x "$(command -v git)" ]
-then
-	echo "Install git to update"
-	exit
-fi
-
-if [ -x "$(command -v git)" ]
-then
-    if [ -d ".git" ]
-	then
-		git config --local url."https://".insteadOf git://
-		git config --local url."https://github.com/".insteadOf git@github.com:
-		git config --local url."https://".insteadOf ssh://
-		git pull --rebase --autostash
-	else
-		echo "Only able to update if you clone the repository (git clone https://github.com/teralomaniac/clewd.git)"
-	fi
-fi
\ No newline at end of file
diff --git a/spaces/xuyaxiong/HandwrittenDigits/app.py b/spaces/xuyaxiong/HandwrittenDigits/app.py
deleted file mode 100644
index 3886a786e0223fef31f0269632374f20be59bd1b..0000000000000000000000000000000000000000
--- a/spaces/xuyaxiong/HandwrittenDigits/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from fastai.vision.all import *
-import gradio as gr
-from gradio.components import Image, Label
-import pathlib
-
-plt = platform.system()
-if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
-
-modelPath = Path('model.pkl')
-learn = load_learner(modelPath)
-categories = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9')
-
-def classify_image(img):
-    pred,idx,probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-image = Image(shape=(192,192))
-label = Label()
-examples = ['img_0.jpg', 'img_1.jpg', 'img_2.jpg', 'img_4.jpg', 'img_7.jpg']
-
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)
\ No newline at end of file
diff --git "a/spaces/xxccc/gpt-academic/crazy_functions/Latex\345\205\250\346\226\207\347\277\273\350\257\221.py" "b/spaces/xxccc/gpt-academic/crazy_functions/Latex\345\205\250\346\226\207\347\277\273\350\257\221.py"
deleted file mode 100644
index 554c485aa0891f74c57cacfcbe076febe7a11029..0000000000000000000000000000000000000000
--- "a/spaces/xxccc/gpt-academic/crazy_functions/Latex\345\205\250\346\226\207\347\277\273\350\257\221.py"
+++ /dev/null
@@ -1,175 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-fast_debug = False
-
-class PaperFileGroup():
-    def __init__(self):
-        self.file_paths = []
-        self.file_contents = []
-        self.sp_file_contents = []
-        self.sp_file_index = []
-        self.sp_file_tag = []
-
-        # count_token
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        self.get_token_num = get_token_num
-
-    def run_file_split(self, max_token_limit=1900):
-        """
-        将长文本分离开来
-        """
-        for index, file_content in enumerate(self.file_contents):
-            if self.get_token_num(file_content) < max_token_limit:
-                self.sp_file_contents.append(file_content)
-                self.sp_file_index.append(index)
-                self.sp_file_tag.append(self.file_paths[index])
-            else:
-                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
-                for j, segment in enumerate(segments):
-                    self.sp_file_contents.append(segment)
-                    self.sp_file_index.append(index)
-                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
-
-        print('Segmentation: done')
-
-def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
-    import time, os, re
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-
-    #  <-------- 读取Latex文件，删除其中的所有注释 ----------> 
-    pfg = PaperFileGroup()
-
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-            # 定义注释的正则表达式
-            comment_pattern = r'(?<!\\)%.*'
-            # 使用正则表达式查找注释，并替换为空字符串
-            clean_tex_content = re.sub(comment_pattern, '', file_content)
-            # 记录删除注释后的文本
-            pfg.file_paths.append(fp)
-            pfg.file_contents.append(clean_tex_content)
-
-    #  <-------- 拆分过长的latex文件 ----------> 
-    pfg.run_file_split(max_token_limit=1024)
-    n_split = len(pfg.sp_file_contents)
-
-    #  <-------- 抽取摘要 ----------> 
-    # if language == 'en':
-    #     abs_extract_inputs = f"Please write an abstract for this paper"
-
-    # # 单线，获取文章meta信息
-    # paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-    #     inputs=abs_extract_inputs,
-    #     inputs_show_user=f"正在抽取摘要信息。",
-    #     llm_kwargs=llm_kwargs,
-    #     chatbot=chatbot, history=[],
-    #     sys_prompt="Your job is to collect information from materials。",
-    # )
-
-    #  <-------- 多线程润色开始 ----------> 
-    if language == 'en->zh':
-        inputs_array = ["Below is a section from an English academic paper, translate it into Chinese, do not modify any latex command such as \section, \cite and equations:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-    elif language == 'zh->en':
-        inputs_array = [f"Below is a section from a Chinese academic paper, translate it into English, do not modify any latex command such as \section, \cite and equations:" + 
-                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
-        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
-        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
-
-    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-        inputs_array=inputs_array,
-        inputs_show_user_array=inputs_show_user_array,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history_array=[[""] for _ in range(n_split)],
-        sys_prompt_array=sys_prompt_array,
-        # max_workers=5,  # OpenAI所允许的最大并行过载
-        scroller_max_len = 80
-    )
-
-    #  <-------- 整理结果，退出 ----------> 
-    create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"
-    res = write_results_to_file(gpt_response_collection, file_name=create_report_file_name)
-    history = gpt_response_collection
-    chatbot.append((f"{fp}完成了吗？", res))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-
-
-
-@CatchException
-def Latex英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
-
-
-
-
-
-@CatchException
-def Latex中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')
\ No newline at end of file
diff --git a/spaces/yanli01/gpt01/modules/presets.py b/spaces/yanli01/gpt01/modules/presets.py
deleted file mode 100644
index cbf63e0532bab9599affe6f1e2e8341fdaffddce..0000000000000000000000000000000000000000
--- a/spaces/yanli01/gpt01/modules/presets.py
+++ /dev/null
@@ -1,219 +0,0 @@
-# -*- coding:utf-8 -*-
-import os
-from pathlib import Path
-import gradio as gr
-from .webui_locale import I18nAuto
-
-i18n = I18nAuto()  # internationalization
-
-CHATGLM_MODEL = None
-CHATGLM_TOKENIZER = None
-LLAMA_MODEL = None
-LLAMA_INFERENCER = None
-
-# ChatGPT 设置
-INITIAL_SYSTEM_PROMPT = "You are a helpful assistant."
-API_HOST = "api.openai.com"
-COMPLETION_URL = "https://api.openai.com/v1/chat/completions"
-BALANCE_API_URL="https://api.openai.com/dashboard/billing/credit_grants"
-USAGE_API_URL="https://api.openai.com/dashboard/billing/usage"
-HISTORY_DIR = Path("history")
-HISTORY_DIR = "history"
-TEMPLATES_DIR = "templates"
-
-# 错误信息
-STANDARD_ERROR_MSG = i18n("☹️发生了错误：")  # 错误信息的标准前缀
-GENERAL_ERROR_MSG = i18n("获取对话时发生错误，请查看后台日志")
-ERROR_RETRIEVE_MSG = i18n("请检查网络连接，或者API-Key是否有效。")
-CONNECTION_TIMEOUT_MSG = i18n("连接超时，无法获取对话。")  # 连接超时
-READ_TIMEOUT_MSG = i18n("读取超时，无法获取对话。")  # 读取超时
-PROXY_ERROR_MSG = i18n("代理错误，无法获取对话。")  # 代理错误
-SSL_ERROR_PROMPT = i18n("SSL错误，无法获取对话。")  # SSL 错误
-NO_APIKEY_MSG = i18n("API key为空，请检查是否输入正确。")  # API key 长度不足 51 位
-NO_INPUT_MSG = i18n("请输入对话内容。")  # 未输入对话内容
-BILLING_NOT_APPLICABLE_MSG = i18n("账单信息不适用") # 本地运行的模型返回的账单信息
-
-TIMEOUT_STREAMING = 60  # 流式对话时的超时时间
-TIMEOUT_ALL = 200  # 非流式对话时的超时时间
-ENABLE_STREAMING_OPTION = True  # 是否启用选择选择是否实时显示回答的勾选框
-HIDE_MY_KEY = False  # 如果你想在UI中隐藏你的 API 密钥，将此值设置为 True
-CONCURRENT_COUNT = 100 # 允许同时使用的用户数量
-
-SIM_K = 5
-INDEX_QUERY_TEMPRATURE = 1.0
-
-CHUANHU_TITLE = i18n("无人问津GPT🚀")
-
-CHUANHU_DESCRIPTION = i18n(" 主站[无人问津小站](https://wrwj.top)小站完全公益开放给大家使用，维护不易，大家可提前加入[qq群](https://jq.qq.com/?_wv=1027&k=gK2Mq2xI),里面不时有API通道，优先体验新功能， 为节约资源请大家合理使用！由衷感谢川虎大佬项目！")
-FOOTER = """<div class="versions">{versions}</div>"""
-
-APPEARANCE_SWITCHER = """
-<div style="display: flex; justify-content: space-between;">
-<span style="margin-top: 4px !important;">"""+ i18n("切换亮暗色主题")  + """</span>
-<span><label class="apSwitch" for="checkbox">
-    <input type="checkbox" id="checkbox">
-    <div class="apSlider"></div>
-</label></span>
-</div>
-"""
-
-SUMMARIZE_PROMPT = "你是谁？我们刚才聊了什么？"  # 总结对话时的 prompt
-
-ONLINE_MODELS = [
-    "gpt-3.5-turbo",
-    "gpt-3.5-turbo-0301",
-    "gpt-4",
-    "gpt-4-0314",
-    "gpt-4-32k",
-    "gpt-4-32k-0314",
-    "xmbot",
-]
-
-LOCAL_MODELS = [
-    "chatglm-6b",
-    "chatglm-6b-int4",
-    "chatglm-6b-int4-qe",
-    "llama-7b-hf",
-    "llama-13b-hf",
-    "llama-30b-hf",
-    "llama-65b-hf"
-]
-
-if os.environ.get('HIDE_LOCAL_MODELS', 'false') == 'true':
-    MODELS = ONLINE_MODELS
-else:
-    MODELS = ONLINE_MODELS + LOCAL_MODELS
-
-DEFAULT_MODEL = 0
-
-os.makedirs("models", exist_ok=True)
-os.makedirs("lora", exist_ok=True)
-os.makedirs("history", exist_ok=True)
-for dir_name in os.listdir("models"):
-    if os.path.isdir(os.path.join("models", dir_name)):
-        if dir_name not in MODELS:
-            MODELS.append(dir_name)
-
-MODEL_TOKEN_LIMIT = {
-    "gpt-3.5-turbo": 4096,
-    "gpt-3.5-turbo-0301": 4096,
-    "gpt-4": 8192,
-    "gpt-4-0314": 8192,
-    "gpt-4-32k": 32768,
-    "gpt-4-32k-0314": 32768
-}
-
-TOKEN_OFFSET = 1000 # 模型的token上限减去这个值，得到软上限。到达软上限之后，自动尝试减少token占用。
-DEFAULT_TOKEN_LIMIT = 3000 # 默认的token上限
-REDUCE_TOKEN_FACTOR = 0.5 # 与模型token上限想乘，得到目标token数。减少token占用时，将token占用减少到目标token数以下。
-
-REPLY_LANGUAGES = [
-    "简体中文",
-    "繁體中文",
-    "English",
-    "日本語",
-    "Español",
-    "Français",
-    "Deutsch",
-    "跟随问题语言（不稳定）"
-]
-
-
-WEBSEARCH_PTOMPT_TEMPLATE = """\
-Web search results:
-{web_results}
-Current date: {current_date}
-Instructions: Using the provided web search results, write a comprehensive reply to the given query. Make sure to cite results using [[number](URL)] notation after the reference. If the provided search results refer to multiple subjects with the same name, write separate answers for each subject.
-Query: {query}
-Reply in {reply_language}
-"""
-
-PROMPT_TEMPLATE = """\
-Context information is below.
----------------------
-{context_str}
----------------------
-Current date: {current_date}.
-Using the provided context information, write a comprehensive reply to the given query.
-Make sure to cite results using [number] notation after the reference.
-If the provided context information refer to multiple subjects with the same name, write separate answers for each subject.
-Use prior knowledge only if the given context didn't provide enough information.
-Answer the question: {query_str}
-Reply in {reply_language}
-"""
-
-REFINE_TEMPLATE = """\
-The original question is as follows: {query_str}
-We have provided an existing answer: {existing_answer}
-We have the opportunity to refine the existing answer
-(only if needed) with some more context below.
-------------
-{context_msg}
-------------
-Given the new context, refine the original answer to better
-Reply in {reply_language}
-If the context isn't useful, return the original answer.
-"""
-
-ALREADY_CONVERTED_MARK = "<!-- ALREADY CONVERTED BY PARSER. -->"
-
-small_and_beautiful_theme = gr.themes.Soft(
-        primary_hue=gr.themes.Color(
-            c50="#02C160",
-            c100="rgba(2, 193, 96, 0.2)",
-            c200="#02C160",
-            c300="rgba(2, 193, 96, 0.32)",
-            c400="rgba(2, 193, 96, 0.32)",
-            c500="rgba(2, 193, 96, 1.0)",
-            c600="rgba(2, 193, 96, 1.0)",
-            c700="rgba(2, 193, 96, 0.32)",
-            c800="rgba(2, 193, 96, 0.32)",
-            c900="#02C160",
-            c950="#02C160",
-        ),
-        secondary_hue=gr.themes.Color(
-            c50="#576b95",
-            c100="#576b95",
-            c200="#576b95",
-            c300="#576b95",
-            c400="#576b95",
-            c500="#576b95",
-            c600="#576b95",
-            c700="#576b95",
-            c800="#576b95",
-            c900="#576b95",
-            c950="#576b95",
-        ),
-        neutral_hue=gr.themes.Color(
-            name="gray",
-            c50="#f9fafb",
-            c100="#f3f4f6",
-            c200="#e5e7eb",
-            c300="#d1d5db",
-            c400="#B2B2B2",
-            c500="#808080",
-            c600="#636363",
-            c700="#515151",
-            c800="#393939",
-            c900="#272727",
-            c950="#171717",
-        ),
-        radius_size=gr.themes.sizes.radius_sm,
-    ).set(
-        button_primary_background_fill="#06AE56",
-        button_primary_background_fill_dark="#06AE56",
-        button_primary_background_fill_hover="#07C863",
-        button_primary_border_color="#06AE56",
-        button_primary_border_color_dark="#06AE56",
-        button_primary_text_color="#FFFFFF",
-        button_primary_text_color_dark="#FFFFFF",
-        button_secondary_background_fill="#F2F2F2",
-        button_secondary_background_fill_dark="#2B2B2B",
-        button_secondary_text_color="#393939",
-        button_secondary_text_color_dark="#FFFFFF",
-        # background_fill_primary="#F7F7F7",
-        # background_fill_primary_dark="#1F1F1F",
-        block_title_text_color="*primary_500",
-        block_title_background_fill="*primary_100",
-        input_background_fill="#F6F6F6",
-    )
diff --git a/spaces/yderre-aubay/midi-player-demo/src/common/geometry/Rect.ts b/spaces/yderre-aubay/midi-player-demo/src/common/geometry/Rect.ts
deleted file mode 100644
index 8edf2d890758e8f10950834c4b2410b3c2326029..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/common/geometry/Rect.ts
+++ /dev/null
@@ -1,77 +0,0 @@
-import { IPoint } from "../geometry"
-
-export interface IRect extends IPoint {
-  width: number
-  height: number
-}
-
-export function containsPoint(rect: IRect, point: IPoint) {
-  return (
-    point.x >= rect.x &&
-    point.x <= rect.x + rect.width &&
-    point.y >= rect.y &&
-    point.y <= rect.y + rect.height
-  )
-}
-
-export function right(rect: IRect) {
-  return rect.x + rect.width
-}
-
-export function bottom(rect: IRect) {
-  return rect.y + rect.height
-}
-
-export function intersects(rectA: IRect, rectB: IRect) {
-  return (
-    right(rectA) > rectB.x &&
-    right(rectB) > rectA.x &&
-    bottom(rectA) > rectB.y &&
-    bottom(rectB) > rectA.y
-  )
-}
-
-export function containsRect(rectA: IRect, rectB: IRect) {
-  return containsPoint(rectA, rectB) && containsPoint(rectA, br(rectB))
-}
-
-export function br(rect: IRect): IPoint {
-  return {
-    x: right(rect),
-    y: bottom(rect),
-  }
-}
-
-export function fromPoints(pointA: IPoint, pointB: IPoint): IRect {
-  const x1 = Math.min(pointA.x, pointB.x)
-  const x2 = Math.max(pointA.x, pointB.x)
-  const y1 = Math.min(pointA.y, pointB.y)
-  const y2 = Math.max(pointA.y, pointB.y)
-
-  return {
-    x: x1,
-    y: y1,
-    width: x2 - x1,
-    height: y2 - y1,
-  }
-}
-
-export function scale(rect: IRect, scaleX: number, scaleY: number): IRect {
-  return {
-    x: rect.x * scaleX,
-    y: rect.y * scaleY,
-    width: rect.width * scaleX,
-    height: rect.height * scaleY,
-  }
-}
-
-export const zeroRect: IRect = { x: 0, y: 0, width: 0, height: 0 }
-
-export function moveRect(rect: IRect, p: IPoint): IRect {
-  return {
-    x: rect.x + p.x,
-    y: rect.y + p.y,
-    width: rect.width,
-    height: rect.height,
-  }
-}
diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/components/PianoRoll/PianoRollCanvas/Notes.tsx b/spaces/yderre-aubay/midi-player-demo/src/main/components/PianoRoll/PianoRollCanvas/Notes.tsx
deleted file mode 100644
index 75dee6ba57c06c7529408bf4757a1d397820d51f..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/main/components/PianoRoll/PianoRollCanvas/Notes.tsx
+++ /dev/null
@@ -1,54 +0,0 @@
-import Color from "color"
-import { partition } from "lodash"
-import { observer } from "mobx-react-lite"
-import { FC } from "react"
-import { trackColorToCSSColor } from "../../../../common/track/TrackColor"
-import { colorToVec4 } from "../../../gl/color"
-import { useStores } from "../../../hooks/useStores"
-import { useTheme } from "../../../hooks/useTheme"
-import { PianoNoteItem } from "../../../stores/PianoRollStore"
-import { NoteCircles } from "./NoteCircles"
-import { NoteRectangles } from "./NoteRectangles"
-
-export const Notes: FC<{ zIndex: number }> = observer(({ zIndex }) => {
-  const {
-    pianoRollStore: { notes, selectedTrack },
-  } = useStores()
-  const theme = useTheme()
-
-  if (selectedTrack === undefined) {
-    return <></>
-  }
-
-  const [drumNotes, normalNotes] = partition(notes, (n) => n.isDrum)
-  const baseColor = Color(
-    selectedTrack.color !== undefined
-      ? trackColorToCSSColor(selectedTrack.color)
-      : theme.themeColor,
-  )
-  const borderColor = colorToVec4(baseColor.lighten(0.3))
-  const selectedColor = colorToVec4(baseColor.lighten(0.7))
-  const backgroundColor = Color(theme.backgroundColor)
-
-  const colorize = (item: PianoNoteItem) => ({
-    ...item,
-    color: item.isSelected
-      ? selectedColor
-      : colorToVec4(baseColor.mix(backgroundColor, 1 - item.velocity / 127)),
-  })
-
-  return (
-    <>
-      <NoteCircles
-        strokeColor={borderColor}
-        rects={drumNotes.map(colorize)}
-        zIndex={zIndex}
-      />
-      <NoteRectangles
-        strokeColor={borderColor}
-        rects={normalNotes.map(colorize)}
-        zIndex={zIndex + 0.1}
-      />
-    </>
-  )
-})
diff --git a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/training/lpips/pretrained_networks.py b/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/training/lpips/pretrained_networks.py
deleted file mode 100644
index a70ebbeab1618da4fe2538833f049dc569f1eea1..0000000000000000000000000000000000000000
--- a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/GPEN/training/lpips/pretrained_networks.py
+++ /dev/null
@@ -1,180 +0,0 @@
-from collections import namedtuple
-import torch
-from torchvision import models as tv
-
-class squeezenet(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(squeezenet, self).__init__()
-        pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.slice6 = torch.nn.Sequential()
-        self.slice7 = torch.nn.Sequential()
-        self.N_slices = 7
-        for x in range(2):
-            self.slice1.add_module(str(x), pretrained_features[x])
-        for x in range(2,5):
-            self.slice2.add_module(str(x), pretrained_features[x])
-        for x in range(5, 8):
-            self.slice3.add_module(str(x), pretrained_features[x])
-        for x in range(8, 10):
-            self.slice4.add_module(str(x), pretrained_features[x])
-        for x in range(10, 11):
-            self.slice5.add_module(str(x), pretrained_features[x])
-        for x in range(11, 12):
-            self.slice6.add_module(str(x), pretrained_features[x])
-        for x in range(12, 13):
-            self.slice7.add_module(str(x), pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1 = h
-        h = self.slice2(h)
-        h_relu2 = h
-        h = self.slice3(h)
-        h_relu3 = h
-        h = self.slice4(h)
-        h_relu4 = h
-        h = self.slice5(h)
-        h_relu5 = h
-        h = self.slice6(h)
-        h_relu6 = h
-        h = self.slice7(h)
-        h_relu7 = h
-        vgg_outputs = namedtuple("SqueezeOutputs", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7'])
-        out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7)
-
-        return out
-
-
-class alexnet(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(alexnet, self).__init__()
-        alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.N_slices = 5
-        for x in range(2):
-            self.slice1.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(2, 5):
-            self.slice2.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(5, 8):
-            self.slice3.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(8, 10):
-            self.slice4.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(10, 12):
-            self.slice5.add_module(str(x), alexnet_pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1 = h
-        h = self.slice2(h)
-        h_relu2 = h
-        h = self.slice3(h)
-        h_relu3 = h
-        h = self.slice4(h)
-        h_relu4 = h
-        h = self.slice5(h)
-        h_relu5 = h
-        alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])
-        out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
-
-        return out
-
-class vgg16(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(vgg16, self).__init__()
-        vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.N_slices = 5
-        for x in range(4):
-            self.slice1.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(4, 9):
-            self.slice2.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(9, 16):
-            self.slice3.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(16, 23):
-            self.slice4.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(23, 30):
-            self.slice5.add_module(str(x), vgg_pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1_2 = h
-        h = self.slice2(h)
-        h_relu2_2 = h
-        h = self.slice3(h)
-        h_relu3_3 = h
-        h = self.slice4(h)
-        h_relu4_3 = h
-        h = self.slice5(h)
-        h_relu5_3 = h
-        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
-        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
-
-        return out
-
-
-
-class resnet(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True, num=18):
-        super(resnet, self).__init__()
-        if(num==18):
-            self.net = tv.resnet18(pretrained=pretrained)
-        elif(num==34):
-            self.net = tv.resnet34(pretrained=pretrained)
-        elif(num==50):
-            self.net = tv.resnet50(pretrained=pretrained)
-        elif(num==101):
-            self.net = tv.resnet101(pretrained=pretrained)
-        elif(num==152):
-            self.net = tv.resnet152(pretrained=pretrained)
-        self.N_slices = 5
-
-        self.conv1 = self.net.conv1
-        self.bn1 = self.net.bn1
-        self.relu = self.net.relu
-        self.maxpool = self.net.maxpool
-        self.layer1 = self.net.layer1
-        self.layer2 = self.net.layer2
-        self.layer3 = self.net.layer3
-        self.layer4 = self.net.layer4
-
-    def forward(self, X):
-        h = self.conv1(X)
-        h = self.bn1(h)
-        h = self.relu(h)
-        h_relu1 = h
-        h = self.maxpool(h)
-        h = self.layer1(h)
-        h_conv2 = h
-        h = self.layer2(h)
-        h_conv3 = h
-        h = self.layer3(h)
-        h_conv4 = h
-        h = self.layer4(h)
-        h_conv5 = h
-
-        outputs = namedtuple("Outputs", ['relu1','conv2','conv3','conv4','conv5'])
-        out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)
-
-        return out
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/utils.py b/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/utils.py
deleted file mode 100644
index 5bd18f70225e12b2e27fdb4eabcde91d959f8e31..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/models/GroundingDINO/utils.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-
-import copy
-import math
-
-import torch
-import torch.nn.functional as F
-from torch import Tensor, nn
-
-
-def _get_clones(module, N, layer_share=False):
-    # import ipdb; ipdb.set_trace()
-    if layer_share:
-        return nn.ModuleList([module for i in range(N)])
-    else:
-        return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
-
-
-def get_sine_pos_embed(
-    pos_tensor: torch.Tensor,
-    num_pos_feats: int = 128,
-    temperature: int = 10000,
-    exchange_xy: bool = True,
-):
-    """generate sine position embedding from a position tensor
-    Args:
-        pos_tensor (torch.Tensor): shape: [..., n].
-        num_pos_feats (int): projected shape for each float in the tensor.
-        temperature (int): temperature in the sine/cosine function.
-        exchange_xy (bool, optional): exchange pos x and pos y. \
-            For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.
-    Returns:
-        pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].
-    """
-    scale = 2 * math.pi
-    dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
-    dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
-
-    def sine_func(x: torch.Tensor):
-        sin_x = x * scale / dim_t
-        sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
-        return sin_x
-
-    pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]
-    if exchange_xy:
-        pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
-    pos_res = torch.cat(pos_res, dim=-1)
-    return pos_res
-
-
-def gen_encoder_output_proposals(
-    memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None
-):
-    """
-    Input:
-        - memory: bs, \sum{hw}, d_model
-        - memory_padding_mask: bs, \sum{hw}
-        - spatial_shapes: nlevel, 2
-        - learnedwh: 2
-    Output:
-        - output_memory: bs, \sum{hw}, d_model
-        - output_proposals: bs, \sum{hw}, 4
-    """
-    N_, S_, C_ = memory.shape
-    proposals = []
-    _cur = 0
-    for lvl, (H_, W_) in enumerate(spatial_shapes):
-        mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1)
-        valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
-        valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
-
-        # import ipdb; ipdb.set_trace()
-
-        grid_y, grid_x = torch.meshgrid(
-            torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
-            torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device),
-        )
-        grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)  # H_, W_, 2
-
-        scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
-        grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
-
-        if learnedwh is not None:
-            # import ipdb; ipdb.set_trace()
-            wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl)
-        else:
-            wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)
-
-        # scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1)
-        # grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
-        # wh = torch.ones_like(grid) / scale
-        proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
-        proposals.append(proposal)
-        _cur += H_ * W_
-    # import ipdb; ipdb.set_trace()
-    output_proposals = torch.cat(proposals, 1)
-    output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(
-        -1, keepdim=True
-    )
-    output_proposals = torch.log(output_proposals / (1 - output_proposals))  # unsigmoid
-    output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf"))
-    output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
-
-    output_memory = memory
-    output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
-    output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
-
-    # output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
-    # output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf'))
-
-    return output_memory, output_proposals
-
-
-class RandomBoxPerturber:
-    def __init__(
-        self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2
-    ) -> None:
-        self.noise_scale = torch.Tensor(
-            [x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale]
-        )
-
-    def __call__(self, refanchors: Tensor) -> Tensor:
-        nq, bs, query_dim = refanchors.shape
-        device = refanchors.device
-
-        noise_raw = torch.rand_like(refanchors)
-        noise_scale = self.noise_scale.to(device)[:query_dim]
-
-        new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale)
-        return new_refanchors.clamp_(0, 1)
-
-
-def sigmoid_focal_loss(
-    inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False
-):
-    """
-    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
-    Args:
-        inputs: A float tensor of arbitrary shape.
-                The predictions for each example.
-        targets: A float tensor with the same shape as inputs. Stores the binary
-                 classification label for each element in inputs
-                (0 for the negative class and 1 for the positive class).
-        alpha: (optional) Weighting factor in range (0,1) to balance
-                positive vs negative examples. Default = -1 (no weighting).
-        gamma: Exponent of the modulating factor (1 - p_t) to
-               balance easy vs hard examples.
-    Returns:
-        Loss tensor
-    """
-    prob = inputs.sigmoid()
-    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
-    p_t = prob * targets + (1 - prob) * (1 - targets)
-    loss = ce_loss * ((1 - p_t) ** gamma)
-
-    if alpha >= 0:
-        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
-        loss = alpha_t * loss
-
-    if no_reduction:
-        return loss
-
-    return loss.mean(1).sum() / num_boxes
-
-
-class MLP(nn.Module):
-    """Very simple multi-layer perceptron (also called FFN)"""
-
-    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
-        super().__init__()
-        self.num_layers = num_layers
-        h = [hidden_dim] * (num_layers - 1)
-        self.layers = nn.ModuleList(
-            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
-        )
-
-    def forward(self, x):
-        for i, layer in enumerate(self.layers):
-            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
-        return x
-
-
-def _get_activation_fn(activation, d_model=256, batch_dim=0):
-    """Return an activation function given a string"""
-    if activation == "relu":
-        return F.relu
-    if activation == "gelu":
-        return F.gelu
-    if activation == "glu":
-        return F.glu
-    if activation == "prelu":
-        return nn.PReLU()
-    if activation == "selu":
-        return F.selu
-
-    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
-
-
-def gen_sineembed_for_position(pos_tensor):
-    # n_query, bs, _ = pos_tensor.size()
-    # sineembed_tensor = torch.zeros(n_query, bs, 256)
-    scale = 2 * math.pi
-    dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
-    dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / 128)
-    x_embed = pos_tensor[:, :, 0] * scale
-    y_embed = pos_tensor[:, :, 1] * scale
-    pos_x = x_embed[:, :, None] / dim_t
-    pos_y = y_embed[:, :, None] / dim_t
-    pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
-    pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
-    if pos_tensor.size(-1) == 2:
-        pos = torch.cat((pos_y, pos_x), dim=2)
-    elif pos_tensor.size(-1) == 4:
-        w_embed = pos_tensor[:, :, 2] * scale
-        pos_w = w_embed[:, :, None] / dim_t
-        pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
-
-        h_embed = pos_tensor[:, :, 3] * scale
-        pos_h = h_embed[:, :, None] / dim_t
-        pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)
-
-        pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
-    else:
-        raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
-    return pos
-
-
-class ContrastiveEmbed(nn.Module):
-    def __init__(self, max_text_len=256):
-        """
-        Args:
-            max_text_len: max length of text.
-        """
-        super().__init__()
-        self.max_text_len = max_text_len
-
-    def forward(self, x, text_dict):
-        """_summary_
-
-        Args:
-            x (_type_): _description_
-            text_dict (_type_): _description_
-            {
-                'encoded_text': encoded_text, # bs, 195, d_model
-                'text_token_mask': text_token_mask, # bs, 195
-                        # True for used tokens. False for padding tokens
-            }
-        Returns:
-            _type_: _description_
-        """
-        assert isinstance(text_dict, dict)
-
-        y = text_dict["encoded_text"]
-        text_token_mask = text_dict["text_token_mask"]
-
-        res = x @ y.transpose(-1, -2)
-        res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
-
-        # padding to max_text_len
-        new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
-        new_res[..., : res.shape[-1]] = res
-
-        return new_res
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/data2vec/modeling_data2vec_audio.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/data2vec/modeling_data2vec_audio.py
deleted file mode 100644
index b886c6ad48ce98085e4d69b5612f66e7d6a06891..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/data2vec/modeling_data2vec_audio.py
+++ /dev/null
@@ -1,1523 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch Data2VecAudio model."""
-
-import math
-import warnings
-from typing import Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-
-from ...activations import ACT2FN
-from ...integrations.deepspeed import is_deepspeed_zero3_enabled
-from ...modeling_outputs import (
-    BaseModelOutput,
-    CausalLMOutput,
-    SequenceClassifierOutput,
-    TokenClassifierOutput,
-    Wav2Vec2BaseModelOutput,
-    XVectorOutput,
-)
-from ...modeling_utils import PreTrainedModel
-from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
-from .configuration_data2vec_audio import Data2VecAudioConfig
-
-
-logger = logging.get_logger(__name__)
-
-
-_HIDDEN_STATES_START_POSITION = 2
-
-# General docstring
-_CONFIG_FOR_DOC = "Data2VecAudioConfig"
-
-# Base docstring
-_CHECKPOINT_FOR_DOC = "facebook/data2vec-audio-base-960h"
-_EXPECTED_OUTPUT_SHAPE = [1, 292, 768]
-
-# CTC docstring
-_CTC_EXPECTED_OUTPUT = "'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'"
-_CTC_EXPECTED_LOSS = 66.95
-
-
-DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "facebook/data2vec-audio-base",
-    "facebook/data2vec-audio-base-10m",
-    "facebook/data2vec-audio-base-100h",
-    "facebook/data2vec-audio-base-960h",
-    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
-]
-
-
-# Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices
-def _compute_mask_indices(
-    shape: Tuple[int, int],
-    mask_prob: float,
-    mask_length: int,
-    attention_mask: Optional[torch.LongTensor] = None,
-    min_masks: int = 0,
-) -> np.ndarray:
-    """
-    Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for
-    ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on
-    CPU as part of the preprocessing during training.
-
-    Args:
-        shape: The shape for which to compute masks. This should be of a tuple of size 2 where
-               the first element is the batch size and the second element is the length of the axis to span.
-        mask_prob:  The percentage of the whole axis (between 0 and 1) which will be masked. The number of
-                    independently generated mask spans of length `mask_length` is computed by
-                    `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the
-                    actual percentage will be smaller.
-        mask_length: size of the mask
-        min_masks: minimum number of masked spans
-        attention_mask: A (right-padded) attention mask which independently shortens the feature axis of
-                        each batch dimension.
-    """
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}"
-            f" and `sequence_length`: {sequence_length}`"
-        )
-
-    # epsilon is used for probabilistic rounding
-    epsilon = np.random.rand(1).item()
-
-    def compute_num_masked_span(input_length):
-        """Given input length, compute how many spans should be masked"""
-        num_masked_span = int(mask_prob * input_length / mask_length + epsilon)
-        num_masked_span = max(num_masked_span, min_masks)
-
-        # make sure num masked span <= sequence_length
-        if num_masked_span * mask_length > sequence_length:
-            num_masked_span = sequence_length // mask_length
-
-        # make sure num_masked span is also <= input_length - (mask_length - 1)
-        if input_length - (mask_length - 1) < num_masked_span:
-            num_masked_span = max(input_length - (mask_length - 1), 0)
-
-        return num_masked_span
-
-    # compute number of masked spans in batch
-    input_lengths = (
-        attention_mask.sum(-1).detach().tolist()
-        if attention_mask is not None
-        else [sequence_length for _ in range(batch_size)]
-    )
-
-    # SpecAugment mask to fill
-    spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
-    spec_aug_mask_idxs = []
-
-    max_num_masked_span = compute_num_masked_span(sequence_length)
-
-    if max_num_masked_span == 0:
-        return spec_aug_mask
-
-    for input_length in input_lengths:
-        # compute num of masked spans for this input
-        num_masked_span = compute_num_masked_span(input_length)
-
-        # get random indices to mask
-        spec_aug_mask_idx = np.random.choice(
-            np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False
-        )
-
-        # pick first sampled index that will serve as a dummy index to pad vector
-        # to ensure same dimension for all batches due to probabilistic rounding
-        # Picking first sample just pads those vectors twice.
-        if len(spec_aug_mask_idx) == 0:
-            # this case can only happen if `input_length` is strictly smaller then
-            # `sequence_length` in which case the last token has to be a padding
-            # token which we can use as a dummy mask id
-            dummy_mask_idx = sequence_length - 1
-        else:
-            dummy_mask_idx = spec_aug_mask_idx[0]
-
-        spec_aug_mask_idx = np.concatenate(
-            [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx]
-        )
-        spec_aug_mask_idxs.append(spec_aug_mask_idx)
-
-    spec_aug_mask_idxs = np.array(spec_aug_mask_idxs)
-
-    # expand masked indices to masked spans
-    spec_aug_mask_idxs = np.broadcast_to(
-        spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length)
-    )
-    spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length)
-
-    # add offset to the starting indexes so that indexes now create a span
-    offsets = np.arange(mask_length)[None, None, :]
-    offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape(
-        batch_size, max_num_masked_span * mask_length
-    )
-    spec_aug_mask_idxs = spec_aug_mask_idxs + offsets
-
-    # ensure that we cannot have indices larger than sequence_length
-    if spec_aug_mask_idxs.max() > sequence_length - 1:
-        spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1
-
-    # scatter indices to mask
-    np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)
-
-    return spec_aug_mask
-
-
-class Data2VecAudioConvLayer(nn.Module):
-    def __init__(self, config, layer_id=0):
-        super().__init__()
-        self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
-        self.out_conv_dim = config.conv_dim[layer_id]
-
-        self.conv = nn.Conv1d(
-            self.in_conv_dim,
-            self.out_conv_dim,
-            kernel_size=config.conv_kernel[layer_id],
-            stride=config.conv_stride[layer_id],
-            bias=config.conv_bias,
-        )
-        self.layer_norm = nn.LayerNorm(self.out_conv_dim, elementwise_affine=True)
-        self.activation = ACT2FN[config.feat_extract_activation]
-
-    def forward(self, hidden_states):
-        hidden_states = self.conv(hidden_states)
-
-        hidden_states = hidden_states.transpose(-2, -1)
-        hidden_states = self.layer_norm(hidden_states)
-        hidden_states = hidden_states.transpose(-2, -1)
-
-        hidden_states = self.activation(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2SamePadLayer with Wav2Vec2->Data2VecAudio
-class Data2VecAudioPadLayer(nn.Module):
-    def __init__(self, num_conv_pos_embeddings):
-        super().__init__()
-        self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
-
-    def forward(self, hidden_states):
-        if self.num_pad_remove > 0:
-            hidden_states = hidden_states[:, :, : -self.num_pad_remove]
-        return hidden_states
-
-
-class Data2VecAudioPositionalConvLayer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            config.hidden_size,
-            config.hidden_size,
-            kernel_size=config.conv_pos_kernel_size,
-            padding=config.conv_pos_kernel_size // 2,
-            groups=config.num_conv_pos_embedding_groups,
-        )
-
-        self.padding = Data2VecAudioPadLayer(config.conv_pos_kernel_size)
-        self.activation = ACT2FN[config.feat_extract_activation]
-        # no learnable parameters
-        self.layer_norm = nn.LayerNorm(config.hidden_size, elementwise_affine=False)
-
-    def forward(self, hidden_states):
-        hidden_states = self.conv(hidden_states)
-        hidden_states = self.padding(hidden_states)
-
-        hidden_states = hidden_states.transpose(1, 2)
-        hidden_states = self.layer_norm(hidden_states)
-        hidden_states = hidden_states.transpose(1, 2)
-        hidden_states = self.activation(hidden_states)
-        return hidden_states
-
-
-class Data2VecAudioPositionalConvEmbedding(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.layers = nn.ModuleList(
-            [Data2VecAudioPositionalConvLayer(config) for _ in range(config.num_conv_pos_embeddings)]
-        )
-
-    def forward(self, hidden_states):
-        hidden_states = hidden_states.transpose(1, 2)
-        for layer in self.layers:
-            hidden_states = layer(hidden_states)
-        hidden_states = hidden_states.transpose(1, 2)
-        return hidden_states
-
-
-class Data2VecAudioFeatureEncoder(nn.Module):
-    """Construct the features from raw audio waveform"""
-
-    def __init__(self, config):
-        super().__init__()
-        self.conv_layers = nn.ModuleList(
-            [Data2VecAudioConvLayer(config, layer_id=i) for i in range(config.num_feat_extract_layers)]
-        )
-        self.gradient_checkpointing = False
-        self._requires_grad = True
-
-    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureEncoder._freeze_parameters
-    def _freeze_parameters(self):
-        for param in self.parameters():
-            param.requires_grad = False
-        self._requires_grad = False
-
-    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureEncoder.forward
-    def forward(self, input_values):
-        hidden_states = input_values[:, None]
-
-        # make sure hidden_states require grad for gradient_checkpointing
-        if self._requires_grad and self.training:
-            hidden_states.requires_grad = True
-
-        for conv_layer in self.conv_layers:
-            if self._requires_grad and self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(conv_layer),
-                    hidden_states,
-                )
-            else:
-                hidden_states = conv_layer(hidden_states)
-
-        return hidden_states
-
-
-# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureProjection with Wav2Vec2->Data2VecAudio
-class Data2VecAudioFeatureProjection(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
-        self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
-        self.dropout = nn.Dropout(config.feat_proj_dropout)
-
-    def forward(self, hidden_states):
-        # non-projected hidden states are needed for quantization
-        norm_hidden_states = self.layer_norm(hidden_states)
-        hidden_states = self.projection(norm_hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        return hidden_states, norm_hidden_states
-
-
-# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Data2VecAudio
-class Data2VecAudioAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(
-        self,
-        embed_dim: int,
-        num_heads: int,
-        dropout: float = 0.0,
-        is_decoder: bool = False,
-        bias: bool = True,
-    ):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-
-        if (self.head_dim * num_heads) != self.embed_dim:
-            raise ValueError(
-                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
-                f" and `num_heads`: {num_heads})."
-            )
-        self.scaling = self.head_dim**-0.5
-        self.is_decoder = is_decoder
-
-        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        key_value_states: Optional[torch.Tensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        layer_head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        """Input shape: Batch x Time x Channel"""
-
-        # if key_value_states are provided this layer is used as a cross-attention layer
-        # for the decoder
-        is_cross_attention = key_value_states is not None
-
-        bsz, tgt_len, _ = hidden_states.size()
-
-        # get query proj
-        query_states = self.q_proj(hidden_states) * self.scaling
-        # get key, value proj
-        # `past_key_value[0].shape[2] == key_value_states.shape[1]`
-        # is checking that the `sequence_length` of the `past_key_value` is the same as
-        # the provided `key_value_states` to support prefix tuning
-        if (
-            is_cross_attention
-            and past_key_value is not None
-            and past_key_value[0].shape[2] == key_value_states.shape[1]
-        ):
-            # reuse k,v, cross_attentions
-            key_states = past_key_value[0]
-            value_states = past_key_value[1]
-        elif is_cross_attention:
-            # cross_attentions
-            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
-            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
-        elif past_key_value is not None:
-            # reuse k, v, self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-            key_states = torch.cat([past_key_value[0], key_states], dim=2)
-            value_states = torch.cat([past_key_value[1], value_states], dim=2)
-        else:
-            # self_attention
-            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
-            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
-
-        if self.is_decoder:
-            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
-            # Further calls to cross_attention layer can then reuse all cross-attention
-            # key/value_states (first "if" case)
-            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
-            # all previous decoder key/value_states. Further calls to uni-directional self-attention
-            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
-            # if encoder bi-directional self-attention `past_key_value` is always `None`
-            past_key_value = (key_states, value_states)
-
-        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
-        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
-        key_states = key_states.reshape(*proj_shape)
-        value_states = value_states.reshape(*proj_shape)
-
-        src_len = key_states.size(1)
-        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
-
-        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
-
-        if layer_head_mask is not None:
-            if layer_head_mask.size() != (self.num_heads,):
-                raise ValueError(
-                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
-                    f" {layer_head_mask.size()}"
-                )
-            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        if output_attentions:
-            # this operation is a bit awkward, but it's required to
-            # make sure that attn_weights keeps its gradient.
-            # In order to do so, attn_weights have to be reshaped
-            # twice and have to be reused in the following
-            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
-        else:
-            attn_weights_reshaped = None
-
-        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-
-        attn_output = torch.bmm(attn_probs, value_states)
-
-        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
-        attn_output = attn_output.transpose(1, 2)
-
-        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
-        # partitioned across GPUs when using tensor-parallelism.
-        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped, past_key_value
-
-
-# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeedForward with Wav2Vec2->Data2VecAudio
-class Data2VecAudioFeedForward(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.intermediate_dropout = nn.Dropout(config.activation_dropout)
-
-        self.intermediate_dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-        self.output_dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.output_dropout = nn.Dropout(config.hidden_dropout)
-
-    def forward(self, hidden_states):
-        hidden_states = self.intermediate_dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        hidden_states = self.intermediate_dropout(hidden_states)
-
-        hidden_states = self.output_dense(hidden_states)
-        hidden_states = self.output_dropout(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2EncoderLayer with Wav2Vec2->Data2VecAudio
-class Data2VecAudioEncoderLayer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.attention = Data2VecAudioAttention(
-            embed_dim=config.hidden_size,
-            num_heads=config.num_attention_heads,
-            dropout=config.attention_dropout,
-            is_decoder=False,
-        )
-        self.dropout = nn.Dropout(config.hidden_dropout)
-        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.feed_forward = Data2VecAudioFeedForward(config)
-        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-    def forward(self, hidden_states, attention_mask=None, output_attentions=False):
-        attn_residual = hidden_states
-        hidden_states, attn_weights, _ = self.attention(
-            hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
-        )
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = attn_residual + hidden_states
-
-        hidden_states = self.layer_norm(hidden_states)
-        hidden_states = hidden_states + self.feed_forward(hidden_states)
-        hidden_states = self.final_layer_norm(hidden_states)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (attn_weights,)
-
-        return outputs
-
-
-# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Encoder with Wav2Vec2->Data2VecAudio
-class Data2VecAudioEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.pos_conv_embed = Data2VecAudioPositionalConvEmbedding(config)
-        self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout)
-        self.layers = nn.ModuleList([Data2VecAudioEncoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ):
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-
-        if attention_mask is not None:
-            # make sure padded tokens output 0
-            expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2])
-            hidden_states[~expand_attention_mask] = 0
-
-            # extend attention_mask
-            attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype)
-            attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min
-            attention_mask = attention_mask.expand(
-                attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]
-            )
-
-        position_embeddings = self.pos_conv_embed(hidden_states)
-        hidden_states = hidden_states + position_embeddings
-        hidden_states = self.layer_norm(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-
-        deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()
-
-        for layer in self.layers:
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
-            dropout_probability = torch.rand([])
-
-            skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
-            if not skip_the_layer or deepspeed_zero3_is_enabled:
-                # under deepspeed zero3 all gpus must run in sync
-                if self.gradient_checkpointing and self.training:
-                    # create gradient checkpointing function
-                    def create_custom_forward(module):
-                        def custom_forward(*inputs):
-                            return module(*inputs, output_attentions)
-
-                        return custom_forward
-
-                    layer_outputs = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(layer),
-                        hidden_states,
-                        attention_mask,
-                    )
-                else:
-                    layer_outputs = layer(
-                        hidden_states, attention_mask=attention_mask, output_attentions=output_attentions
-                    )
-                hidden_states = layer_outputs[0]
-
-            if skip_the_layer:
-                layer_outputs = (None, None)
-
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-        )
-
-
-# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Adapter with Wav2Vec2->Data2VecAudio
-class Data2VecAudioAdapter(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-
-        # feature dim might need to be down-projected
-        if config.output_hidden_size != config.hidden_size:
-            self.proj = nn.Linear(config.hidden_size, config.output_hidden_size)
-            self.proj_layer_norm = nn.LayerNorm(config.output_hidden_size)
-        else:
-            self.proj = self.proj_layer_norm = None
-
-        self.layers = nn.ModuleList(Data2VecAudioAdapterLayer(config) for _ in range(config.num_adapter_layers))
-        self.layerdrop = config.layerdrop
-
-    def forward(self, hidden_states):
-        # down project hidden_states if necessary
-        if self.proj is not None and self.proj_layer_norm is not None:
-            hidden_states = self.proj(hidden_states)
-            hidden_states = self.proj_layer_norm(hidden_states)
-
-        hidden_states = hidden_states.transpose(1, 2)
-
-        for layer in self.layers:
-            layerdrop_prob = np.random.random()
-            if not self.training or (layerdrop_prob > self.layerdrop):
-                hidden_states = layer(hidden_states)
-
-        hidden_states = hidden_states.transpose(1, 2)
-        return hidden_states
-
-
-# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2AdapterLayer with Wav2Vec2->Data2VecAudio
-class Data2VecAudioAdapterLayer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            config.output_hidden_size,
-            2 * config.output_hidden_size,
-            config.adapter_kernel_size,
-            stride=config.adapter_stride,
-            padding=1,
-        )
-
-    def forward(self, hidden_states):
-        hidden_states = self.conv(hidden_states)
-        hidden_states = nn.functional.glu(hidden_states, dim=1)
-
-        return hidden_states
-
-
-class Data2VecAudioPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = Data2VecAudioConfig
-    base_model_prefix = "data2vec_audio"
-    main_input_name = "input_values"
-    supports_gradient_checkpointing = True
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        if isinstance(module, Data2VecAudioFeatureProjection):
-            k = math.sqrt(1 / module.projection.in_features)
-            nn.init.uniform_(module.projection.weight, a=-k, b=k)
-            nn.init.uniform_(module.projection.bias, a=-k, b=k)
-        elif isinstance(module, Data2VecAudioPositionalConvLayer):
-            nn.init.constant_(module.conv.bias, 0)
-        elif isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
-            if module.bias is not None:
-                module.bias.data.zero_()
-            if module.weight is not None:
-                module.weight.data.fill_(1.0)
-        elif isinstance(module, nn.Conv1d):
-            nn.init.kaiming_normal_(module.weight)
-
-            if module.bias is not None:
-                k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0]))
-                nn.init.uniform_(module.bias, a=-k, b=k)
-
-    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2PreTrainedModel._get_feat_extract_output_lengths with
-    def _get_feat_extract_output_lengths(
-        self, input_lengths: Union[torch.LongTensor, int], add_adapter: Optional[bool] = None
-    ):
-        """
-        Computes the output length of the convolutional layers
-        """
-
-        add_adapter = self.config.add_adapter if add_adapter is None else add_adapter
-
-        def _conv_out_length(input_length, kernel_size, stride):
-            # 1D convolutional layer output length formula taken
-            # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
-            return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1
-
-        for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
-            input_lengths = _conv_out_length(input_lengths, kernel_size, stride)
-
-        if add_adapter:
-            for _ in range(self.config.num_adapter_layers):
-                input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride)
-
-        return input_lengths
-
-    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2PreTrainedModel._get_feature_vector_attention_mask
-    def _get_feature_vector_attention_mask(
-        self, feature_vector_length: int, attention_mask: torch.LongTensor, add_adapter=None
-    ):
-        # Effectively attention_mask.sum(-1), but not inplace to be able to run
-        # on inference mode.
-        non_padded_lengths = attention_mask.cumsum(dim=-1)[:, -1]
-
-        output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths, add_adapter=add_adapter)
-        output_lengths = output_lengths.to(torch.long)
-
-        batch_size = attention_mask.shape[0]
-
-        attention_mask = torch.zeros(
-            (batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
-        )
-        # these two operations makes sure that all values before the output lengths idxs are attended to
-        attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1
-        attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
-        return attention_mask
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (Data2VecAudioEncoder, Data2VecAudioFeatureEncoder)):
-            module.gradient_checkpointing = value
-
-
-DATA2VEC_AUDIO_START_DOCSTRING = r"""
-    Data2VecAudio was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
-    Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and
-    Michael Auli.
-
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving etc.).
-
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
-    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
-    behavior.
-
-    Parameters:
-        config ([`Data2VecAudioConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-DATA2VEC_AUDIO_INPUTS_DOCSTRING = r"""
-    Args:
-        input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
-            Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file
-            into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install
-            soundfile*). To prepare the array into *input_values*, the [`AutoProcessor`] should be used for padding and
-            conversion into a tensor of type *torch.FloatTensor*. See [`Wav2Vec2Processor.__call__`] for details.
-        attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0,
-            1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-
-            <Tip warning={true}>
-
-            `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask ==
-            True`. For all models whose processor has `config.return_attention_mask == False`, such as
-            [data2vec-audio-base](https://huggingface.co/facebook/data2vec-audio-base-960h), `attention_mask` should
-            **not** be passed to avoid degraded performance when doing batched inference. For such models
-            `input_values` should simply be padded with 0 and passed without `attention_mask`. Be aware that these
-            models also yield slightly different results depending on whether `input_values` is padded or not.
-
-            </Tip>
-
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare Data2VecAudio Model transformer outputting raw hidden-states without any specific head on top.",
-    DATA2VEC_AUDIO_START_DOCSTRING,
-)
-class Data2VecAudioModel(Data2VecAudioPreTrainedModel):
-    def __init__(self, config: Data2VecAudioConfig):
-        super().__init__(config)
-        self.config = config
-        self.feature_extractor = Data2VecAudioFeatureEncoder(config)
-        self.feature_projection = Data2VecAudioFeatureProjection(config)
-
-        # model only needs masking vector if mask prob is > 0.0
-        if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
-            self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
-
-        self.encoder = Data2VecAudioEncoder(config)
-
-        self.adapter = Data2VecAudioAdapter(config) if config.add_adapter else None
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def freeze_feature_encoder(self):
-        """
-        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
-        not be updated during training.
-        """
-        self.feature_extractor._freeze_parameters()
-
-    def _mask_hidden_states(
-        self,
-        hidden_states: torch.FloatTensor,
-        mask_time_indices: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.LongTensor] = None,
-    ):
-        """
-        Masks extracted features along time axis and/or along feature axis according to
-        [SpecAugment](https://arxiv.org/abs/1904.08779).
-        """
-
-        # `config.apply_spec_augment` can set masking to False
-        if not getattr(self.config, "apply_spec_augment", True):
-            return hidden_states
-
-        # generate indices & apply SpecAugment along time axis
-        batch_size, sequence_length, hidden_size = hidden_states.size()
-
-        if mask_time_indices is not None:
-            # apply SpecAugment along time axis with given mask_time_indices
-            hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
-        elif self.config.mask_time_prob > 0 and self.training:
-            mask_time_indices = _compute_mask_indices(
-                (batch_size, sequence_length),
-                mask_prob=self.config.mask_time_prob,
-                mask_length=self.config.mask_time_length,
-                attention_mask=attention_mask,
-                min_masks=self.config.mask_time_min_masks,
-            )
-            mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool)
-            hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype)
-
-        if self.config.mask_feature_prob > 0 and self.training:
-            # generate indices & apply SpecAugment along feature axis
-            mask_feature_indices = _compute_mask_indices(
-                (batch_size, hidden_size),
-                mask_prob=self.config.mask_feature_prob,
-                mask_length=self.config.mask_feature_length,
-                min_masks=self.config.mask_feature_min_masks,
-            )
-            mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool)
-            mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1)
-            hidden_states[mask_feature_indices] = 0
-
-        return hidden_states
-
-    @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=Wav2Vec2BaseModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-        modality="audio",
-        expected_output=_EXPECTED_OUTPUT_SHAPE,
-    )
-    def forward(
-        self,
-        input_values: Optional[torch.Tensor],
-        attention_mask: Optional[torch.Tensor] = None,
-        mask_time_indices: Optional[torch.FloatTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, Wav2Vec2BaseModelOutput]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        extract_features = self.feature_extractor(input_values)
-        extract_features = extract_features.transpose(1, 2)
-
-        if attention_mask is not None:
-            # compute reduced attention_mask corresponding to feature vectors
-            attention_mask = self._get_feature_vector_attention_mask(
-                extract_features.shape[1], attention_mask, add_adapter=False
-            )
-
-        hidden_states, extract_features = self.feature_projection(extract_features)
-        hidden_states = self._mask_hidden_states(
-            hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
-        )
-
-        encoder_outputs = self.encoder(
-            hidden_states,
-            attention_mask=attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = encoder_outputs[0]
-
-        if self.adapter is not None:
-            hidden_states = self.adapter(hidden_states)
-
-        if not return_dict:
-            return (hidden_states, extract_features) + encoder_outputs[1:]
-
-        return Wav2Vec2BaseModelOutput(
-            last_hidden_state=hidden_states,
-            extract_features=extract_features,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """Data2VecAudio Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
-    DATA2VEC_AUDIO_START_DOCSTRING,
-)
-class Data2VecAudioForCTC(Data2VecAudioPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.data2vec_audio = Data2VecAudioModel(config)
-        self.dropout = nn.Dropout(config.final_dropout)
-
-        if config.vocab_size is None:
-            raise ValueError(
-                f"You are trying to instantiate {self.__class__} with a configuration that "
-                "does not define the vocabulary size of the language model head. Please "
-                "instantiate the model as follows: `Data2VecAudioForCTC.from_pretrained(..., vocab_size=vocab_size)`. "
-                "or define `vocab_size` of your model's configuration."
-            )
-        output_hidden_size = (
-            config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size
-        )
-        self.lm_head = nn.Linear(output_hidden_size, config.vocab_size)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def freeze_feature_extractor(self):
-        """
-        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
-        not be updated during training.
-        """
-        warnings.warn(
-            "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5."
-            "Please use the equivalent `freeze_feature_encoder` method instead.",
-            FutureWarning,
-        )
-        self.freeze_feature_encoder()
-
-    def freeze_feature_encoder(self):
-        """
-        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
-        not be updated during training.
-        """
-        self.data2vec_audio.feature_extractor._freeze_parameters()
-
-    @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=CausalLMOutput,
-        config_class=_CONFIG_FOR_DOC,
-        expected_output=_CTC_EXPECTED_OUTPUT,
-        expected_loss=_CTC_EXPECTED_LOSS,
-    )
-    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.forward with wav2vec2->data2vec_audio
-    def forward(
-        self,
-        input_values: Optional[torch.Tensor],
-        attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        labels: Optional[torch.Tensor] = None,
-    ) -> Union[Tuple, CausalLMOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
-            Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
-            the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
-            All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
-            config.vocab_size - 1]`.
-        """
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        outputs = self.data2vec_audio(
-            input_values,
-            attention_mask=attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = outputs[0]
-        hidden_states = self.dropout(hidden_states)
-
-        logits = self.lm_head(hidden_states)
-
-        loss = None
-        if labels is not None:
-            if labels.max() >= self.config.vocab_size:
-                raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
-
-            # retrieve loss input_lengths from attention_mask
-            attention_mask = (
-                attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long)
-            )
-            input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
-
-            # assuming that padded tokens are filled with -100
-            # when not being attended to
-            labels_mask = labels >= 0
-            target_lengths = labels_mask.sum(-1)
-            flattened_targets = labels.masked_select(labels_mask)
-
-            # ctc_loss doesn't support fp16
-            log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
-
-            with torch.backends.cudnn.flags(enabled=False):
-                loss = nn.functional.ctc_loss(
-                    log_probs,
-                    flattened_targets,
-                    input_lengths,
-                    target_lengths,
-                    blank=self.config.pad_token_id,
-                    reduction=self.config.ctc_loss_reduction,
-                    zero_infinity=self.config.ctc_zero_infinity,
-                )
-
-        if not return_dict:
-            output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
-            return ((loss,) + output) if loss is not None else output
-
-        return CausalLMOutput(
-            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
-        )
-
-
-@add_start_docstrings(
-    """
-    Data2VecAudio Model with a sequence classification head on top (a linear layer over the pooled output) for tasks
-    like SUPERB Keyword Spotting.
-    """,
-    DATA2VEC_AUDIO_START_DOCSTRING,
-)
-class Data2VecAudioForSequenceClassification(Data2VecAudioPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        if hasattr(config, "add_adapter") and config.add_adapter:
-            raise ValueError(
-                "Sequence classification does not support the use of Data2VecAudio adapters (config.add_adapter=True)"
-            )
-        self.data2vec_audio = Data2VecAudioModel(config)
-        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
-        if config.use_weighted_layer_sum:
-            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
-        self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size)
-        self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def freeze_feature_extractor(self):
-        """
-        Calling this function will disable the gradient computation for the feature encoder so that its parameters will
-        not be updated during training.
-        """
-        warnings.warn(
-            "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5."
-            "Please use the equivalent `freeze_feature_encoder` method instead.",
-            FutureWarning,
-        )
-        self.freeze_feature_encoder()
-
-    def freeze_feature_encoder(self):
-        """
-        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
-        not be updated during training.
-        """
-        self.data2vec_audio.feature_extractor._freeze_parameters()
-
-    def freeze_base_model(self):
-        """
-        Calling this function will disable the gradient computation for the base model so that its parameters will not
-        be updated during training. Only the classification head will be updated.
-        """
-        for param in self.data2vec_audio.parameters():
-            param.requires_grad = False
-
-    @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=SequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-        modality="audio",
-    )
-    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.forward with wav2vec2->data2vec_audio
-    def forward(
-        self,
-        input_values: Optional[torch.Tensor],
-        attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        labels: Optional[torch.Tensor] = None,
-    ) -> Union[Tuple, SequenceClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
-
-        outputs = self.data2vec_audio(
-            input_values,
-            attention_mask=attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        if self.config.use_weighted_layer_sum:
-            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
-            hidden_states = torch.stack(hidden_states, dim=1)
-            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
-            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
-        else:
-            hidden_states = outputs[0]
-
-        hidden_states = self.projector(hidden_states)
-        if attention_mask is None:
-            pooled_output = hidden_states.mean(dim=1)
-        else:
-            padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask)
-            hidden_states[~padding_mask] = 0.0
-            pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1)
-
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
-
-        if not return_dict:
-            output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    Data2VecAudio Model with a frame classification head on top for tasks like Speaker Diarization.
-    """,
-    DATA2VEC_AUDIO_START_DOCSTRING,
-)
-class Data2VecAudioForAudioFrameClassification(Data2VecAudioPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        if hasattr(config, "add_adapter") and config.add_adapter:
-            raise ValueError(
-                "Audio frame classification does not support the use of Data2VecAudio adapters"
-                " (config.add_adapter=True)"
-            )
-        self.data2vec_audio = Data2VecAudioModel(config)
-        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
-        if config.use_weighted_layer_sum:
-            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-        self.num_labels = config.num_labels
-
-        self.init_weights()
-
-    def freeze_feature_extractor(self):
-        """
-        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
-        not be updated during training.
-        """
-        warnings.warn(
-            "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5."
-            "Please use the equivalent `freeze_feature_encoder` method instead.",
-            FutureWarning,
-        )
-        self.freeze_feature_encoder()
-
-    def freeze_feature_encoder(self):
-        """
-        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
-        not be updated during training.
-        """
-        self.data2vec_audio.feature_extractor._freeze_parameters()
-
-    def freeze_base_model(self):
-        """
-        Calling this function will disable the gradient computation for the base model so that its parameters will not
-        be updated during training. Only the classification head will be updated.
-        """
-        for param in self.data2vec_audio.parameters():
-            param.requires_grad = False
-
-    @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-        modality="audio",
-    )
-    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification.forward with wav2vec2->data2vec_audio
-    def forward(
-        self,
-        input_values: Optional[torch.Tensor],
-        attention_mask: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, TokenClassifierOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
-
-        outputs = self.data2vec_audio(
-            input_values,
-            attention_mask=attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        if self.config.use_weighted_layer_sum:
-            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
-            hidden_states = torch.stack(hidden_states, dim=1)
-            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
-            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
-        else:
-            hidden_states = outputs[0]
-
-        logits = self.classifier(hidden_states)
-
-        loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(logits.view(-1, self.num_labels), torch.argmax(labels.view(-1, self.num_labels), axis=1))
-
-        if not return_dict:
-            output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
-            return output
-
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-# Copied from transformers.models.wav2vec2.modeling_wav2vec2.AMSoftmaxLoss
-class AMSoftmaxLoss(nn.Module):
-    def __init__(self, input_dim, num_labels, scale=30.0, margin=0.4):
-        super(AMSoftmaxLoss, self).__init__()
-        self.scale = scale
-        self.margin = margin
-        self.num_labels = num_labels
-        self.weight = nn.Parameter(torch.randn(input_dim, num_labels), requires_grad=True)
-        self.loss = nn.CrossEntropyLoss()
-
-    def forward(self, hidden_states, labels):
-        labels = labels.flatten()
-        weight = nn.functional.normalize(self.weight, dim=0)
-        hidden_states = nn.functional.normalize(hidden_states, dim=1)
-        cos_theta = torch.mm(hidden_states, weight)
-        psi = cos_theta - self.margin
-
-        onehot = nn.functional.one_hot(labels, self.num_labels)
-        logits = self.scale * torch.where(onehot.bool(), psi, cos_theta)
-        loss = self.loss(logits, labels)
-
-        return loss
-
-
-# Copied from transformers.models.wav2vec2.modeling_wav2vec2.TDNNLayer
-class TDNNLayer(nn.Module):
-    def __init__(self, config, layer_id=0):
-        super().__init__()
-        self.in_conv_dim = config.tdnn_dim[layer_id - 1] if layer_id > 0 else config.tdnn_dim[layer_id]
-        self.out_conv_dim = config.tdnn_dim[layer_id]
-        self.kernel_size = config.tdnn_kernel[layer_id]
-        self.dilation = config.tdnn_dilation[layer_id]
-
-        self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim)
-        self.activation = nn.ReLU()
-
-    def forward(self, hidden_states):
-        hidden_states = hidden_states.unsqueeze(1)
-        hidden_states = nn.functional.unfold(
-            hidden_states,
-            (self.kernel_size, self.in_conv_dim),
-            stride=(1, self.in_conv_dim),
-            dilation=(self.dilation, 1),
-        )
-        hidden_states = hidden_states.transpose(1, 2)
-        hidden_states = self.kernel(hidden_states)
-
-        hidden_states = self.activation(hidden_states)
-        return hidden_states
-
-
-@add_start_docstrings(
-    """
-    Data2VecAudio Model with an XVector feature extraction head on top for tasks like Speaker Verification.
-    """,
-    DATA2VEC_AUDIO_START_DOCSTRING,
-)
-class Data2VecAudioForXVector(Data2VecAudioPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.data2vec_audio = Data2VecAudioModel(config)
-        num_layers = config.num_hidden_layers + 1  # transformer layers + input embeddings
-        if config.use_weighted_layer_sum:
-            self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers)
-        self.projector = nn.Linear(config.hidden_size, config.tdnn_dim[0])
-
-        tdnn_layers = [TDNNLayer(config, i) for i in range(len(config.tdnn_dim))]
-        self.tdnn = nn.ModuleList(tdnn_layers)
-
-        self.feature_extractor = nn.Linear(config.tdnn_dim[-1] * 2, config.xvector_output_dim)
-        self.classifier = nn.Linear(config.xvector_output_dim, config.xvector_output_dim)
-
-        self.objective = AMSoftmaxLoss(config.xvector_output_dim, config.num_labels)
-
-        self.init_weights()
-
-    def freeze_feature_extractor(self):
-        """
-        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
-        not be updated during training.
-        """
-        warnings.warn(
-            "The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5."
-            "Please use the equivalent `freeze_feature_encoder` method instead.",
-            FutureWarning,
-        )
-        self.freeze_feature_encoder()
-
-    def freeze_feature_encoder(self):
-        """
-        Calling this function will disable the gradient computation for the feature encoder so that its parameter will
-        not be updated during training.
-        """
-        self.data2vec_audio.feature_extractor._freeze_parameters()
-
-    def freeze_base_model(self):
-        """
-        Calling this function will disable the gradient computation for the base model so that its parameters will not
-        be updated during training. Only the classification head will be updated.
-        """
-        for param in self.data2vec_audio.parameters():
-            param.requires_grad = False
-
-    def _get_tdnn_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
-        """
-        Computes the output length of the TDNN layers
-        """
-
-        def _conv_out_length(input_length, kernel_size, stride):
-            # 1D convolutional layer output length formula taken
-            # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
-            return (input_length - kernel_size) // stride + 1
-
-        for kernel_size in self.config.tdnn_kernel:
-            input_lengths = _conv_out_length(input_lengths, kernel_size, 1)
-
-        return input_lengths
-
-    @add_start_docstrings_to_model_forward(DATA2VEC_AUDIO_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=XVectorOutput,
-        config_class=_CONFIG_FOR_DOC,
-        modality="audio",
-    )
-    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForXVector.forward with wav2vec2->data2vec_audio
-    def forward(
-        self,
-        input_values: Optional[torch.Tensor],
-        attention_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        labels: Optional[torch.Tensor] = None,
-    ) -> Union[Tuple, XVectorOutput]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states
-
-        outputs = self.data2vec_audio(
-            input_values,
-            attention_mask=attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        if self.config.use_weighted_layer_sum:
-            hidden_states = outputs[_HIDDEN_STATES_START_POSITION]
-            hidden_states = torch.stack(hidden_states, dim=1)
-            norm_weights = nn.functional.softmax(self.layer_weights, dim=-1)
-            hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1)
-        else:
-            hidden_states = outputs[0]
-
-        hidden_states = self.projector(hidden_states)
-
-        for tdnn_layer in self.tdnn:
-            hidden_states = tdnn_layer(hidden_states)
-
-        # Statistic Pooling
-        if attention_mask is None:
-            mean_features = hidden_states.mean(dim=1)
-            std_features = hidden_states.std(dim=1)
-        else:
-            feat_extract_output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(dim=1))
-            tdnn_output_lengths = self._get_tdnn_output_lengths(feat_extract_output_lengths)
-            mean_features = []
-            std_features = []
-            for i, length in enumerate(tdnn_output_lengths):
-                mean_features.append(hidden_states[i, :length].mean(dim=0))
-                std_features.append(hidden_states[i, :length].std(dim=0))
-            mean_features = torch.stack(mean_features)
-            std_features = torch.stack(std_features)
-        statistic_pooling = torch.cat([mean_features, std_features], dim=-1)
-
-        output_embeddings = self.feature_extractor(statistic_pooling)
-        logits = self.classifier(output_embeddings)
-
-        loss = None
-        if labels is not None:
-            loss = self.objective(logits, labels)
-
-        if not return_dict:
-            output = (logits, output_embeddings) + outputs[_HIDDEN_STATES_START_POSITION:]
-            return ((loss,) + output) if loss is not None else output
-
-        return XVectorOutput(
-            loss=loss,
-            logits=logits,
-            embeddings=output_embeddings,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deformable_detr/image_processing_deformable_detr.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deformable_detr/image_processing_deformable_detr.py
deleted file mode 100644
index ae35a07e43d84838aad9c05c6a2256e220dddc12..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/deformable_detr/image_processing_deformable_detr.py
+++ /dev/null
@@ -1,1449 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Image processor class for Deformable DETR."""
-
-import io
-import pathlib
-from collections import defaultdict
-from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
-
-import numpy as np
-
-from ...feature_extraction_utils import BatchFeature
-from ...image_processing_utils import BaseImageProcessor, get_size_dict
-from ...image_transforms import (
-    PaddingMode,
-    center_to_corners_format,
-    corners_to_center_format,
-    id_to_rgb,
-    pad,
-    rescale,
-    resize,
-    rgb_to_id,
-    to_channel_dimension_format,
-)
-from ...image_utils import (
-    IMAGENET_DEFAULT_MEAN,
-    IMAGENET_DEFAULT_STD,
-    ChannelDimension,
-    ImageInput,
-    PILImageResampling,
-    get_image_size,
-    infer_channel_dimension_format,
-    is_scaled_image,
-    make_list_of_images,
-    to_numpy_array,
-    valid_coco_detection_annotations,
-    valid_coco_panoptic_annotations,
-    valid_images,
-)
-from ...utils import (
-    ExplicitEnum,
-    TensorType,
-    is_flax_available,
-    is_jax_tensor,
-    is_scipy_available,
-    is_tf_available,
-    is_tf_tensor,
-    is_torch_available,
-    is_torch_tensor,
-    is_vision_available,
-    logging,
-)
-
-
-if is_torch_available():
-    import torch
-    from torch import nn
-
-
-if is_vision_available():
-    import PIL
-
-if is_scipy_available():
-    import scipy.special
-    import scipy.stats
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-AnnotationType = Dict[str, Union[int, str, List[Dict]]]
-
-
-class AnnotionFormat(ExplicitEnum):
-    COCO_DETECTION = "coco_detection"
-    COCO_PANOPTIC = "coco_panoptic"
-
-
-SUPPORTED_ANNOTATION_FORMATS = (AnnotionFormat.COCO_DETECTION, AnnotionFormat.COCO_PANOPTIC)
-
-
-# Copied from transformers.models.detr.image_processing_detr.get_size_with_aspect_ratio
-def get_size_with_aspect_ratio(image_size, size, max_size=None) -> Tuple[int, int]:
-    """
-    Computes the output image size given the input image size and the desired output size.
-
-    Args:
-        image_size (`Tuple[int, int]`):
-            The input image size.
-        size (`int`):
-            The desired output size.
-        max_size (`int`, *optional*):
-            The maximum allowed output size.
-    """
-    height, width = image_size
-    if max_size is not None:
-        min_original_size = float(min((height, width)))
-        max_original_size = float(max((height, width)))
-        if max_original_size / min_original_size * size > max_size:
-            size = int(round(max_size * min_original_size / max_original_size))
-
-    if (height <= width and height == size) or (width <= height and width == size):
-        return height, width
-
-    if width < height:
-        ow = size
-        oh = int(size * height / width)
-    else:
-        oh = size
-        ow = int(size * width / height)
-    return (oh, ow)
-
-
-# Copied from transformers.models.detr.image_processing_detr.get_resize_output_image_size
-def get_resize_output_image_size(
-    input_image: np.ndarray,
-    size: Union[int, Tuple[int, int], List[int]],
-    max_size: Optional[int] = None,
-    input_data_format: Optional[Union[str, ChannelDimension]] = None,
-) -> Tuple[int, int]:
-    """
-    Computes the output image size given the input image size and the desired output size. If the desired output size
-    is a tuple or list, the output image size is returned as is. If the desired output size is an integer, the output
-    image size is computed by keeping the aspect ratio of the input image size.
-
-    Args:
-        image_size (`Tuple[int, int]`):
-            The input image size.
-        size (`int`):
-            The desired output size.
-        max_size (`int`, *optional*):
-            The maximum allowed output size.
-        input_data_format (`ChannelDimension` or `str`, *optional*):
-            The channel dimension format of the input image. If not provided, it will be inferred from the input image.
-    """
-    image_size = get_image_size(input_image, input_data_format)
-    if isinstance(size, (list, tuple)):
-        return size
-
-    return get_size_with_aspect_ratio(image_size, size, max_size)
-
-
-# Copied from transformers.models.detr.image_processing_detr.get_numpy_to_framework_fn
-def get_numpy_to_framework_fn(arr) -> Callable:
-    """
-    Returns a function that converts a numpy array to the framework of the input array.
-
-    Args:
-        arr (`np.ndarray`): The array to convert.
-    """
-    if isinstance(arr, np.ndarray):
-        return np.array
-    if is_tf_available() and is_tf_tensor(arr):
-        import tensorflow as tf
-
-        return tf.convert_to_tensor
-    if is_torch_available() and is_torch_tensor(arr):
-        import torch
-
-        return torch.tensor
-    if is_flax_available() and is_jax_tensor(arr):
-        import jax.numpy as jnp
-
-        return jnp.array
-    raise ValueError(f"Cannot convert arrays of type {type(arr)}")
-
-
-# Copied from transformers.models.detr.image_processing_detr.safe_squeeze
-def safe_squeeze(arr: np.ndarray, axis: Optional[int] = None) -> np.ndarray:
-    """
-    Squeezes an array, but only if the axis specified has dim 1.
-    """
-    if axis is None:
-        return arr.squeeze()
-
-    try:
-        return arr.squeeze(axis=axis)
-    except ValueError:
-        return arr
-
-
-# Copied from transformers.models.detr.image_processing_detr.normalize_annotation
-def normalize_annotation(annotation: Dict, image_size: Tuple[int, int]) -> Dict:
-    image_height, image_width = image_size
-    norm_annotation = {}
-    for key, value in annotation.items():
-        if key == "boxes":
-            boxes = value
-            boxes = corners_to_center_format(boxes)
-            boxes /= np.asarray([image_width, image_height, image_width, image_height], dtype=np.float32)
-            norm_annotation[key] = boxes
-        else:
-            norm_annotation[key] = value
-    return norm_annotation
-
-
-# Copied from transformers.models.detr.image_processing_detr.max_across_indices
-def max_across_indices(values: Iterable[Any]) -> List[Any]:
-    """
-    Return the maximum value across all indices of an iterable of values.
-    """
-    return [max(values_i) for values_i in zip(*values)]
-
-
-# Copied from transformers.models.detr.image_processing_detr.get_max_height_width
-def get_max_height_width(
-    images: List[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None
-) -> List[int]:
-    """
-    Get the maximum height and width across all images in a batch.
-    """
-    if input_data_format is None:
-        input_data_format = infer_channel_dimension_format(images[0])
-
-    if input_data_format == ChannelDimension.FIRST:
-        _, max_height, max_width = max_across_indices([img.shape for img in images])
-    elif input_data_format == ChannelDimension.LAST:
-        max_height, max_width, _ = max_across_indices([img.shape for img in images])
-    else:
-        raise ValueError(f"Invalid channel dimension format: {input_data_format}")
-    return (max_height, max_width)
-
-
-# Copied from transformers.models.detr.image_processing_detr.make_pixel_mask
-def make_pixel_mask(
-    image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None
-) -> np.ndarray:
-    """
-    Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
-
-    Args:
-        image (`np.ndarray`):
-            Image to make the pixel mask for.
-        output_size (`Tuple[int, int]`):
-            Output size of the mask.
-    """
-    input_height, input_width = get_image_size(image, channel_dim=input_data_format)
-    mask = np.zeros(output_size, dtype=np.int64)
-    mask[:input_height, :input_width] = 1
-    return mask
-
-
-# Copied from transformers.models.detr.image_processing_detr.convert_coco_poly_to_mask
-def convert_coco_poly_to_mask(segmentations, height: int, width: int) -> np.ndarray:
-    """
-    Convert a COCO polygon annotation to a mask.
-
-    Args:
-        segmentations (`List[List[float]]`):
-            List of polygons, each polygon represented by a list of x-y coordinates.
-        height (`int`):
-            Height of the mask.
-        width (`int`):
-            Width of the mask.
-    """
-    try:
-        from pycocotools import mask as coco_mask
-    except ImportError:
-        raise ImportError("Pycocotools is not installed in your environment.")
-
-    masks = []
-    for polygons in segmentations:
-        rles = coco_mask.frPyObjects(polygons, height, width)
-        mask = coco_mask.decode(rles)
-        if len(mask.shape) < 3:
-            mask = mask[..., None]
-        mask = np.asarray(mask, dtype=np.uint8)
-        mask = np.any(mask, axis=2)
-        masks.append(mask)
-    if masks:
-        masks = np.stack(masks, axis=0)
-    else:
-        masks = np.zeros((0, height, width), dtype=np.uint8)
-
-    return masks
-
-
-# Copied from transformers.models.detr.image_processing_detr.prepare_coco_detection_annotation with DETR->DeformableDetr
-def prepare_coco_detection_annotation(
-    image,
-    target,
-    return_segmentation_masks: bool = False,
-    input_data_format: Optional[Union[ChannelDimension, str]] = None,
-):
-    """
-    Convert the target in COCO format into the format expected by DeformableDetr.
-    """
-    image_height, image_width = get_image_size(image, channel_dim=input_data_format)
-
-    image_id = target["image_id"]
-    image_id = np.asarray([image_id], dtype=np.int64)
-
-    # Get all COCO annotations for the given image.
-    annotations = target["annotations"]
-    annotations = [obj for obj in annotations if "iscrowd" not in obj or obj["iscrowd"] == 0]
-
-    classes = [obj["category_id"] for obj in annotations]
-    classes = np.asarray(classes, dtype=np.int64)
-
-    # for conversion to coco api
-    area = np.asarray([obj["area"] for obj in annotations], dtype=np.float32)
-    iscrowd = np.asarray([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in annotations], dtype=np.int64)
-
-    boxes = [obj["bbox"] for obj in annotations]
-    # guard against no boxes via resizing
-    boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4)
-    boxes[:, 2:] += boxes[:, :2]
-    boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=image_width)
-    boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=image_height)
-
-    keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
-
-    new_target = {}
-    new_target["image_id"] = image_id
-    new_target["class_labels"] = classes[keep]
-    new_target["boxes"] = boxes[keep]
-    new_target["area"] = area[keep]
-    new_target["iscrowd"] = iscrowd[keep]
-    new_target["orig_size"] = np.asarray([int(image_height), int(image_width)], dtype=np.int64)
-
-    if annotations and "keypoints" in annotations[0]:
-        keypoints = [obj["keypoints"] for obj in annotations]
-        keypoints = np.asarray(keypoints, dtype=np.float32)
-        num_keypoints = keypoints.shape[0]
-        keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints
-        new_target["keypoints"] = keypoints[keep]
-
-    if return_segmentation_masks:
-        segmentation_masks = [obj["segmentation"] for obj in annotations]
-        masks = convert_coco_poly_to_mask(segmentation_masks, image_height, image_width)
-        new_target["masks"] = masks[keep]
-
-    return new_target
-
-
-# Copied from transformers.models.detr.image_processing_detr.masks_to_boxes
-def masks_to_boxes(masks: np.ndarray) -> np.ndarray:
-    """
-    Compute the bounding boxes around the provided panoptic segmentation masks.
-
-    Args:
-        masks: masks in format `[number_masks, height, width]` where N is the number of masks
-
-    Returns:
-        boxes: bounding boxes in format `[number_masks, 4]` in xyxy format
-    """
-    if masks.size == 0:
-        return np.zeros((0, 4))
-
-    h, w = masks.shape[-2:]
-    y = np.arange(0, h, dtype=np.float32)
-    x = np.arange(0, w, dtype=np.float32)
-    # see https://github.com/pytorch/pytorch/issues/50276
-    y, x = np.meshgrid(y, x, indexing="ij")
-
-    x_mask = masks * np.expand_dims(x, axis=0)
-    x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1)
-    x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool)))
-    x_min = x.filled(fill_value=1e8)
-    x_min = x_min.reshape(x_min.shape[0], -1).min(-1)
-
-    y_mask = masks * np.expand_dims(y, axis=0)
-    y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1)
-    y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool)))
-    y_min = y.filled(fill_value=1e8)
-    y_min = y_min.reshape(y_min.shape[0], -1).min(-1)
-
-    return np.stack([x_min, y_min, x_max, y_max], 1)
-
-
-# Copied from transformers.models.detr.image_processing_detr.prepare_coco_panoptic_annotation with DETR->DeformableDetr
-def prepare_coco_panoptic_annotation(
-    image: np.ndarray,
-    target: Dict,
-    masks_path: Union[str, pathlib.Path],
-    return_masks: bool = True,
-    input_data_format: Union[ChannelDimension, str] = None,
-) -> Dict:
-    """
-    Prepare a coco panoptic annotation for DeformableDetr.
-    """
-    image_height, image_width = get_image_size(image, channel_dim=input_data_format)
-    annotation_path = pathlib.Path(masks_path) / target["file_name"]
-
-    new_target = {}
-    new_target["image_id"] = np.asarray([target["image_id"] if "image_id" in target else target["id"]], dtype=np.int64)
-    new_target["size"] = np.asarray([image_height, image_width], dtype=np.int64)
-    new_target["orig_size"] = np.asarray([image_height, image_width], dtype=np.int64)
-
-    if "segments_info" in target:
-        masks = np.asarray(PIL.Image.open(annotation_path), dtype=np.uint32)
-        masks = rgb_to_id(masks)
-
-        ids = np.array([segment_info["id"] for segment_info in target["segments_info"]])
-        masks = masks == ids[:, None, None]
-        masks = masks.astype(np.uint8)
-        if return_masks:
-            new_target["masks"] = masks
-        new_target["boxes"] = masks_to_boxes(masks)
-        new_target["class_labels"] = np.array(
-            [segment_info["category_id"] for segment_info in target["segments_info"]], dtype=np.int64
-        )
-        new_target["iscrowd"] = np.asarray(
-            [segment_info["iscrowd"] for segment_info in target["segments_info"]], dtype=np.int64
-        )
-        new_target["area"] = np.asarray(
-            [segment_info["area"] for segment_info in target["segments_info"]], dtype=np.float32
-        )
-
-    return new_target
-
-
-# Copied from transformers.models.detr.image_processing_detr.get_segmentation_image
-def get_segmentation_image(
-    masks: np.ndarray, input_size: Tuple, target_size: Tuple, stuff_equiv_classes, deduplicate=False
-):
-    h, w = input_size
-    final_h, final_w = target_size
-
-    m_id = scipy.special.softmax(masks.transpose(0, 1), -1)
-
-    if m_id.shape[-1] == 0:
-        # We didn't detect any mask :(
-        m_id = np.zeros((h, w), dtype=np.int64)
-    else:
-        m_id = m_id.argmax(-1).reshape(h, w)
-
-    if deduplicate:
-        # Merge the masks corresponding to the same stuff class
-        for equiv in stuff_equiv_classes.values():
-            for eq_id in equiv:
-                m_id[m_id == eq_id] = equiv[0]
-
-    seg_img = id_to_rgb(m_id)
-    seg_img = resize(seg_img, (final_w, final_h), resample=PILImageResampling.NEAREST)
-    return seg_img
-
-
-# Copied from transformers.models.detr.image_processing_detr.get_mask_area
-def get_mask_area(seg_img: np.ndarray, target_size: Tuple[int, int], n_classes: int) -> np.ndarray:
-    final_h, final_w = target_size
-    np_seg_img = seg_img.astype(np.uint8)
-    np_seg_img = np_seg_img.reshape(final_h, final_w, 3)
-    m_id = rgb_to_id(np_seg_img)
-    area = [(m_id == i).sum() for i in range(n_classes)]
-    return area
-
-
-# Copied from transformers.models.detr.image_processing_detr.score_labels_from_class_probabilities
-def score_labels_from_class_probabilities(logits: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
-    probs = scipy.special.softmax(logits, axis=-1)
-    labels = probs.argmax(-1, keepdims=True)
-    scores = np.take_along_axis(probs, labels, axis=-1)
-    scores, labels = scores.squeeze(-1), labels.squeeze(-1)
-    return scores, labels
-
-
-# Copied from transformers.models.detr.image_processing_detr.post_process_panoptic_sample
-def post_process_panoptic_sample(
-    out_logits: np.ndarray,
-    masks: np.ndarray,
-    boxes: np.ndarray,
-    processed_size: Tuple[int, int],
-    target_size: Tuple[int, int],
-    is_thing_map: Dict,
-    threshold=0.85,
-) -> Dict:
-    """
-    Converts the output of [`DetrForSegmentation`] into panoptic segmentation predictions for a single sample.
-
-    Args:
-        out_logits (`torch.Tensor`):
-            The logits for this sample.
-        masks (`torch.Tensor`):
-            The predicted segmentation masks for this sample.
-        boxes (`torch.Tensor`):
-            The prediced bounding boxes for this sample. The boxes are in the normalized format `(center_x, center_y,
-            width, height)` and values between `[0, 1]`, relative to the size the image (disregarding padding).
-        processed_size (`Tuple[int, int]`):
-            The processed size of the image `(height, width)`, as returned by the preprocessing step i.e. the size
-            after data augmentation but before batching.
-        target_size (`Tuple[int, int]`):
-            The target size of the image, `(height, width)` corresponding to the requested final size of the
-            prediction.
-        is_thing_map (`Dict`):
-            A dictionary mapping class indices to a boolean value indicating whether the class is a thing or not.
-        threshold (`float`, *optional*, defaults to 0.85):
-            The threshold used to binarize the segmentation masks.
-    """
-    # we filter empty queries and detection below threshold
-    scores, labels = score_labels_from_class_probabilities(out_logits)
-    keep = (labels != out_logits.shape[-1] - 1) & (scores > threshold)
-
-    cur_scores = scores[keep]
-    cur_classes = labels[keep]
-    cur_boxes = center_to_corners_format(boxes[keep])
-
-    if len(cur_boxes) != len(cur_classes):
-        raise ValueError("Not as many boxes as there are classes")
-
-    cur_masks = masks[keep]
-    cur_masks = resize(cur_masks[:, None], processed_size, resample=PILImageResampling.BILINEAR)
-    cur_masks = safe_squeeze(cur_masks, 1)
-    b, h, w = cur_masks.shape
-
-    # It may be that we have several predicted masks for the same stuff class.
-    # In the following, we track the list of masks ids for each stuff class (they are merged later on)
-    cur_masks = cur_masks.reshape(b, -1)
-    stuff_equiv_classes = defaultdict(list)
-    for k, label in enumerate(cur_classes):
-        if not is_thing_map[label]:
-            stuff_equiv_classes[label].append(k)
-
-    seg_img = get_segmentation_image(cur_masks, processed_size, target_size, stuff_equiv_classes, deduplicate=True)
-    area = get_mask_area(cur_masks, processed_size, n_classes=len(cur_scores))
-
-    # We filter out any mask that is too small
-    if cur_classes.size() > 0:
-        # We know filter empty masks as long as we find some
-        filtered_small = np.array([a <= 4 for a in area], dtype=bool)
-        while filtered_small.any():
-            cur_masks = cur_masks[~filtered_small]
-            cur_scores = cur_scores[~filtered_small]
-            cur_classes = cur_classes[~filtered_small]
-            seg_img = get_segmentation_image(cur_masks, (h, w), target_size, stuff_equiv_classes, deduplicate=True)
-            area = get_mask_area(seg_img, target_size, n_classes=len(cur_scores))
-            filtered_small = np.array([a <= 4 for a in area], dtype=bool)
-    else:
-        cur_classes = np.ones((1, 1), dtype=np.int64)
-
-    segments_info = [
-        {"id": i, "isthing": is_thing_map[cat], "category_id": int(cat), "area": a}
-        for i, (cat, a) in enumerate(zip(cur_classes, area))
-    ]
-    del cur_classes
-
-    with io.BytesIO() as out:
-        PIL.Image.fromarray(seg_img).save(out, format="PNG")
-        predictions = {"png_string": out.getvalue(), "segments_info": segments_info}
-
-    return predictions
-
-
-# Copied from transformers.models.detr.image_processing_detr.resize_annotation
-def resize_annotation(
-    annotation: Dict[str, Any],
-    orig_size: Tuple[int, int],
-    target_size: Tuple[int, int],
-    threshold: float = 0.5,
-    resample: PILImageResampling = PILImageResampling.NEAREST,
-):
-    """
-    Resizes an annotation to a target size.
-
-    Args:
-        annotation (`Dict[str, Any]`):
-            The annotation dictionary.
-        orig_size (`Tuple[int, int]`):
-            The original size of the input image.
-        target_size (`Tuple[int, int]`):
-            The target size of the image, as returned by the preprocessing `resize` step.
-        threshold (`float`, *optional*, defaults to 0.5):
-            The threshold used to binarize the segmentation masks.
-        resample (`PILImageResampling`, defaults to `PILImageResampling.NEAREST`):
-            The resampling filter to use when resizing the masks.
-    """
-    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(target_size, orig_size))
-    ratio_height, ratio_width = ratios
-
-    new_annotation = {}
-    new_annotation["size"] = target_size
-
-    for key, value in annotation.items():
-        if key == "boxes":
-            boxes = value
-            scaled_boxes = boxes * np.asarray([ratio_width, ratio_height, ratio_width, ratio_height], dtype=np.float32)
-            new_annotation["boxes"] = scaled_boxes
-        elif key == "area":
-            area = value
-            scaled_area = area * (ratio_width * ratio_height)
-            new_annotation["area"] = scaled_area
-        elif key == "masks":
-            masks = value[:, None]
-            masks = np.array([resize(mask, target_size, resample=resample) for mask in masks])
-            masks = masks.astype(np.float32)
-            masks = masks[:, 0] > threshold
-            new_annotation["masks"] = masks
-        elif key == "size":
-            new_annotation["size"] = target_size
-        else:
-            new_annotation[key] = value
-
-    return new_annotation
-
-
-# Copied from transformers.models.detr.image_processing_detr.binary_mask_to_rle
-def binary_mask_to_rle(mask):
-    """
-    Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format.
-
-    Args:
-        mask (`torch.Tensor` or `numpy.array`):
-            A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
-            segment_id or class_id.
-    Returns:
-        `List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
-        format.
-    """
-    if is_torch_tensor(mask):
-        mask = mask.numpy()
-
-    pixels = mask.flatten()
-    pixels = np.concatenate([[0], pixels, [0]])
-    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
-    runs[1::2] -= runs[::2]
-    return list(runs)
-
-
-# Copied from transformers.models.detr.image_processing_detr.convert_segmentation_to_rle
-def convert_segmentation_to_rle(segmentation):
-    """
-    Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format.
-
-    Args:
-        segmentation (`torch.Tensor` or `numpy.array`):
-            A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
-    Returns:
-        `List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id.
-    """
-    segment_ids = torch.unique(segmentation)
-
-    run_length_encodings = []
-    for idx in segment_ids:
-        mask = torch.where(segmentation == idx, 1, 0)
-        rle = binary_mask_to_rle(mask)
-        run_length_encodings.append(rle)
-
-    return run_length_encodings
-
-
-# Copied from transformers.models.detr.image_processing_detr.remove_low_and_no_objects
-def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
-    """
-    Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
-    `labels`.
-
-    Args:
-        masks (`torch.Tensor`):
-            A tensor of shape `(num_queries, height, width)`.
-        scores (`torch.Tensor`):
-            A tensor of shape `(num_queries)`.
-        labels (`torch.Tensor`):
-            A tensor of shape `(num_queries)`.
-        object_mask_threshold (`float`):
-            A number between 0 and 1 used to binarize the masks.
-    Raises:
-        `ValueError`: Raised when the first dimension doesn't match in all input tensors.
-    Returns:
-        `Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
-        < `object_mask_threshold`.
-    """
-    if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
-        raise ValueError("mask, scores and labels must have the same shape!")
-
-    to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
-
-    return masks[to_keep], scores[to_keep], labels[to_keep]
-
-
-# Copied from transformers.models.detr.image_processing_detr.check_segment_validity
-def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8):
-    # Get the mask associated with the k class
-    mask_k = mask_labels == k
-    mask_k_area = mask_k.sum()
-
-    # Compute the area of all the stuff in query k
-    original_area = (mask_probs[k] >= mask_threshold).sum()
-    mask_exists = mask_k_area > 0 and original_area > 0
-
-    # Eliminate disconnected tiny segments
-    if mask_exists:
-        area_ratio = mask_k_area / original_area
-        if not area_ratio.item() > overlap_mask_area_threshold:
-            mask_exists = False
-
-    return mask_exists, mask_k
-
-
-# Copied from transformers.models.detr.image_processing_detr.compute_segments
-def compute_segments(
-    mask_probs,
-    pred_scores,
-    pred_labels,
-    mask_threshold: float = 0.5,
-    overlap_mask_area_threshold: float = 0.8,
-    label_ids_to_fuse: Optional[Set[int]] = None,
-    target_size: Tuple[int, int] = None,
-):
-    height = mask_probs.shape[1] if target_size is None else target_size[0]
-    width = mask_probs.shape[2] if target_size is None else target_size[1]
-
-    segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device)
-    segments: List[Dict] = []
-
-    if target_size is not None:
-        mask_probs = nn.functional.interpolate(
-            mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False
-        )[0]
-
-    current_segment_id = 0
-
-    # Weigh each mask by its prediction score
-    mask_probs *= pred_scores.view(-1, 1, 1)
-    mask_labels = mask_probs.argmax(0)  # [height, width]
-
-    # Keep track of instances of each class
-    stuff_memory_list: Dict[str, int] = {}
-    for k in range(pred_labels.shape[0]):
-        pred_class = pred_labels[k].item()
-        should_fuse = pred_class in label_ids_to_fuse
-
-        # Check if mask exists and large enough to be a segment
-        mask_exists, mask_k = check_segment_validity(
-            mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold
-        )
-
-        if mask_exists:
-            if pred_class in stuff_memory_list:
-                current_segment_id = stuff_memory_list[pred_class]
-            else:
-                current_segment_id += 1
-
-            # Add current object segment to final segmentation map
-            segmentation[mask_k] = current_segment_id
-            segment_score = round(pred_scores[k].item(), 6)
-            segments.append(
-                {
-                    "id": current_segment_id,
-                    "label_id": pred_class,
-                    "was_fused": should_fuse,
-                    "score": segment_score,
-                }
-            )
-            if should_fuse:
-                stuff_memory_list[pred_class] = current_segment_id
-
-    return segmentation, segments
-
-
-class DeformableDetrImageProcessor(BaseImageProcessor):
-    r"""
-    Constructs a Deformable DETR image processor.
-
-    Args:
-        format (`str`, *optional*, defaults to `"coco_detection"`):
-            Data format of the annotations. One of "coco_detection" or "coco_panoptic".
-        do_resize (`bool`, *optional*, defaults to `True`):
-            Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be
-            overridden by the `do_resize` parameter in the `preprocess` method.
-        size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 800, "longest_edge": 1333}`):
-            Size of the image's (height, width) dimensions after resizing. Can be overridden by the `size` parameter in
-            the `preprocess` method.
-        resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
-            Resampling filter to use if resizing the image.
-        do_rescale (`bool`, *optional*, defaults to `True`):
-            Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
-            `do_rescale` parameter in the `preprocess` method.
-        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
-            Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
-            `preprocess` method.
-        do_normalize:
-            Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
-            `preprocess` method.
-        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
-            Mean values to use when normalizing the image. Can be a single value or a list of values, one for each
-            channel. Can be overridden by the `image_mean` parameter in the `preprocess` method.
-        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
-            Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one
-            for each channel. Can be overridden by the `image_std` parameter in the `preprocess` method.
-        do_pad (`bool`, *optional*, defaults to `True`):
-            Controls whether to pad the image to the largest image in a batch and create a pixel mask. Can be
-            overridden by the `do_pad` parameter in the `preprocess` method.
-    """
-
-    model_input_names = ["pixel_values", "pixel_mask"]
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.__init__
-    def __init__(
-        self,
-        format: Union[str, AnnotionFormat] = AnnotionFormat.COCO_DETECTION,
-        do_resize: bool = True,
-        size: Dict[str, int] = None,
-        resample: PILImageResampling = PILImageResampling.BILINEAR,
-        do_rescale: bool = True,
-        rescale_factor: Union[int, float] = 1 / 255,
-        do_normalize: bool = True,
-        image_mean: Union[float, List[float]] = None,
-        image_std: Union[float, List[float]] = None,
-        do_pad: bool = True,
-        **kwargs,
-    ) -> None:
-        if "pad_and_return_pixel_mask" in kwargs:
-            do_pad = kwargs.pop("pad_and_return_pixel_mask")
-
-        if "max_size" in kwargs:
-            logger.warning_once(
-                "The `max_size` parameter is deprecated and will be removed in v4.26. "
-                "Please specify in `size['longest_edge'] instead`.",
-            )
-            max_size = kwargs.pop("max_size")
-        else:
-            max_size = None if size is None else 1333
-
-        size = size if size is not None else {"shortest_edge": 800, "longest_edge": 1333}
-        size = get_size_dict(size, max_size=max_size, default_to_square=False)
-
-        super().__init__(**kwargs)
-        self.format = format
-        self.do_resize = do_resize
-        self.size = size
-        self.resample = resample
-        self.do_rescale = do_rescale
-        self.rescale_factor = rescale_factor
-        self.do_normalize = do_normalize
-        self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
-        self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
-        self.do_pad = do_pad
-
-    @classmethod
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.from_dict with Detr->DeformableDetr
-    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
-        """
-        Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
-        created using from_dict and kwargs e.g. `DeformableDetrImageProcessor.from_pretrained(checkpoint, size=600,
-        max_size=800)`
-        """
-        image_processor_dict = image_processor_dict.copy()
-        if "max_size" in kwargs:
-            image_processor_dict["max_size"] = kwargs.pop("max_size")
-        if "pad_and_return_pixel_mask" in kwargs:
-            image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask")
-        return super().from_dict(image_processor_dict, **kwargs)
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_annotation with DETR->DeformableDetr
-    def prepare_annotation(
-        self,
-        image: np.ndarray,
-        target: Dict,
-        format: Optional[AnnotionFormat] = None,
-        return_segmentation_masks: bool = None,
-        masks_path: Optional[Union[str, pathlib.Path]] = None,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-    ) -> Dict:
-        """
-        Prepare an annotation for feeding into DeformableDetr model.
-        """
-        format = format if format is not None else self.format
-
-        if format == AnnotionFormat.COCO_DETECTION:
-            return_segmentation_masks = False if return_segmentation_masks is None else return_segmentation_masks
-            target = prepare_coco_detection_annotation(
-                image, target, return_segmentation_masks, input_data_format=input_data_format
-            )
-        elif format == AnnotionFormat.COCO_PANOPTIC:
-            return_segmentation_masks = True if return_segmentation_masks is None else return_segmentation_masks
-            target = prepare_coco_panoptic_annotation(
-                image,
-                target,
-                masks_path=masks_path,
-                return_masks=return_segmentation_masks,
-                input_data_format=input_data_format,
-            )
-        else:
-            raise ValueError(f"Format {format} is not supported.")
-        return target
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare
-    def prepare(self, image, target, return_segmentation_masks=None, masks_path=None):
-        logger.warning_once(
-            "The `prepare` method is deprecated and will be removed in a v4.33. "
-            "Please use `prepare_annotation` instead. Note: the `prepare_annotation` method "
-            "does not return the image anymore.",
-        )
-        target = self.prepare_annotation(image, target, return_segmentation_masks, masks_path, self.format)
-        return image, target
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask
-    def convert_coco_poly_to_mask(self, *args, **kwargs):
-        logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ")
-        return convert_coco_poly_to_mask(*args, **kwargs)
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection
-    def prepare_coco_detection(self, *args, **kwargs):
-        logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ")
-        return prepare_coco_detection_annotation(*args, **kwargs)
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic
-    def prepare_coco_panoptic(self, *args, **kwargs):
-        logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ")
-        return prepare_coco_panoptic_annotation(*args, **kwargs)
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize
-    def resize(
-        self,
-        image: np.ndarray,
-        size: Dict[str, int],
-        resample: PILImageResampling = PILImageResampling.BILINEAR,
-        data_format: Optional[ChannelDimension] = None,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-        **kwargs,
-    ) -> np.ndarray:
-        """
-        Resize the image to the given size. Size can be `min_size` (scalar) or `(height, width)` tuple. If size is an
-        int, smaller edge of the image will be matched to this number.
-
-        Args:
-            image (`np.ndarray`):
-                Image to resize.
-            size (`Dict[str, int]`):
-                Dictionary containing the size to resize to. Can contain the keys `shortest_edge` and `longest_edge` or
-                `height` and `width`.
-            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
-                Resampling filter to use if resizing the image.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format for the output image. If unset, the channel dimension format of the input
-                image is used.
-            input_data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format of the input image. If not provided, it will be inferred.
-        """
-        if "max_size" in kwargs:
-            logger.warning_once(
-                "The `max_size` parameter is deprecated and will be removed in v4.26. "
-                "Please specify in `size['longest_edge'] instead`.",
-            )
-            max_size = kwargs.pop("max_size")
-        else:
-            max_size = None
-        size = get_size_dict(size, max_size=max_size, default_to_square=False)
-        if "shortest_edge" in size and "longest_edge" in size:
-            size = get_resize_output_image_size(
-                image, size["shortest_edge"], size["longest_edge"], input_data_format=input_data_format
-            )
-        elif "height" in size and "width" in size:
-            size = (size["height"], size["width"])
-        else:
-            raise ValueError(
-                "Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
-                f" {size.keys()}."
-            )
-        image = resize(
-            image, size=size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs
-        )
-        return image
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize_annotation
-    def resize_annotation(
-        self,
-        annotation,
-        orig_size,
-        size,
-        resample: PILImageResampling = PILImageResampling.NEAREST,
-    ) -> Dict:
-        """
-        Resize the annotation to match the resized image. If size is an int, smaller edge of the mask will be matched
-        to this number.
-        """
-        return resize_annotation(annotation, orig_size=orig_size, target_size=size, resample=resample)
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.rescale
-    def rescale(
-        self,
-        image: np.ndarray,
-        rescale_factor: float,
-        data_format: Optional[Union[str, ChannelDimension]] = None,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-    ) -> np.ndarray:
-        """
-        Rescale the image by the given factor. image = image * rescale_factor.
-
-        Args:
-            image (`np.ndarray`):
-                Image to rescale.
-            rescale_factor (`float`):
-                The value to use for rescaling.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format for the output image. If unset, the channel dimension format of the input
-                image is used. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-            input_data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format for the input image. If unset, is inferred from the input image. Can be
-                one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-        """
-        return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format)
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.normalize_annotation
-    def normalize_annotation(self, annotation: Dict, image_size: Tuple[int, int]) -> Dict:
-        """
-        Normalize the boxes in the annotation from `[top_left_x, top_left_y, bottom_right_x, bottom_right_y]` to
-        `[center_x, center_y, width, height]` format.
-        """
-        return normalize_annotation(annotation, image_size=image_size)
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._pad_image
-    def _pad_image(
-        self,
-        image: np.ndarray,
-        output_size: Tuple[int, int],
-        constant_values: Union[float, Iterable[float]] = 0,
-        data_format: Optional[ChannelDimension] = None,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-    ) -> np.ndarray:
-        """
-        Pad an image with zeros to the given size.
-        """
-        input_height, input_width = get_image_size(image, channel_dim=input_data_format)
-        output_height, output_width = output_size
-
-        pad_bottom = output_height - input_height
-        pad_right = output_width - input_width
-        padding = ((0, pad_bottom), (0, pad_right))
-        padded_image = pad(
-            image,
-            padding,
-            mode=PaddingMode.CONSTANT,
-            constant_values=constant_values,
-            data_format=data_format,
-            input_data_format=input_data_format,
-        )
-        return padded_image
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.pad
-    def pad(
-        self,
-        images: List[np.ndarray],
-        constant_values: Union[float, Iterable[float]] = 0,
-        return_pixel_mask: bool = True,
-        return_tensors: Optional[Union[str, TensorType]] = None,
-        data_format: Optional[ChannelDimension] = None,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-    ) -> BatchFeature:
-        """
-        Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
-        in the batch and optionally returns their corresponding pixel mask.
-
-        Args:
-            image (`np.ndarray`):
-                Image to pad.
-            constant_values (`float` or `Iterable[float]`, *optional*):
-                The value to use for the padding if `mode` is `"constant"`.
-            return_pixel_mask (`bool`, *optional*, defaults to `True`):
-                Whether to return a pixel mask.
-            return_tensors (`str` or `TensorType`, *optional*):
-                The type of tensors to return. Can be one of:
-                    - Unset: Return a list of `np.ndarray`.
-                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
-                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
-                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
-                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
-            data_format (`str` or `ChannelDimension`, *optional*):
-                The channel dimension format of the image. If not provided, it will be the same as the input image.
-            input_data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format of the input image. If not provided, it will be inferred.
-        """
-        pad_size = get_max_height_width(images, input_data_format=input_data_format)
-
-        padded_images = [
-            self._pad_image(
-                image,
-                pad_size,
-                constant_values=constant_values,
-                data_format=data_format,
-                input_data_format=input_data_format,
-            )
-            for image in images
-        ]
-        data = {"pixel_values": padded_images}
-
-        if return_pixel_mask:
-            masks = [
-                make_pixel_mask(image=image, output_size=pad_size, input_data_format=input_data_format)
-                for image in images
-            ]
-            data["pixel_mask"] = masks
-
-        return BatchFeature(data=data, tensor_type=return_tensors)
-
-    # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.preprocess
-    def preprocess(
-        self,
-        images: ImageInput,
-        annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None,
-        return_segmentation_masks: bool = None,
-        masks_path: Optional[Union[str, pathlib.Path]] = None,
-        do_resize: Optional[bool] = None,
-        size: Optional[Dict[str, int]] = None,
-        resample=None,  # PILImageResampling
-        do_rescale: Optional[bool] = None,
-        rescale_factor: Optional[Union[int, float]] = None,
-        do_normalize: Optional[bool] = None,
-        image_mean: Optional[Union[float, List[float]]] = None,
-        image_std: Optional[Union[float, List[float]]] = None,
-        do_pad: Optional[bool] = None,
-        format: Optional[Union[str, AnnotionFormat]] = None,
-        return_tensors: Optional[Union[TensorType, str]] = None,
-        data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
-        input_data_format: Optional[Union[str, ChannelDimension]] = None,
-        **kwargs,
-    ) -> BatchFeature:
-        """
-        Preprocess an image or a batch of images so that it can be used by the model.
-
-        Args:
-            images (`ImageInput`):
-                Image or batch of images to preprocess. Expects a single or batch of images with pixel values ranging
-                from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`.
-            annotations (`AnnotationType` or `List[AnnotationType]`, *optional*):
-                List of annotations associated with the image or batch of images. If annotation is for object
-                detection, the annotations should be a dictionary with the following keys:
-                - "image_id" (`int`): The image id.
-                - "annotations" (`List[Dict]`): List of annotations for an image. Each annotation should be a
-                  dictionary. An image can have no annotations, in which case the list should be empty.
-                If annotation is for segmentation, the annotations should be a dictionary with the following keys:
-                - "image_id" (`int`): The image id.
-                - "segments_info" (`List[Dict]`): List of segments for an image. Each segment should be a dictionary.
-                  An image can have no segments, in which case the list should be empty.
-                - "file_name" (`str`): The file name of the image.
-            return_segmentation_masks (`bool`, *optional*, defaults to self.return_segmentation_masks):
-                Whether to return segmentation masks.
-            masks_path (`str` or `pathlib.Path`, *optional*):
-                Path to the directory containing the segmentation masks.
-            do_resize (`bool`, *optional*, defaults to self.do_resize):
-                Whether to resize the image.
-            size (`Dict[str, int]`, *optional*, defaults to self.size):
-                Size of the image after resizing.
-            resample (`PILImageResampling`, *optional*, defaults to self.resample):
-                Resampling filter to use when resizing the image.
-            do_rescale (`bool`, *optional*, defaults to self.do_rescale):
-                Whether to rescale the image.
-            rescale_factor (`float`, *optional*, defaults to self.rescale_factor):
-                Rescale factor to use when rescaling the image.
-            do_normalize (`bool`, *optional*, defaults to self.do_normalize):
-                Whether to normalize the image.
-            image_mean (`float` or `List[float]`, *optional*, defaults to self.image_mean):
-                Mean to use when normalizing the image.
-            image_std (`float` or `List[float]`, *optional*, defaults to self.image_std):
-                Standard deviation to use when normalizing the image.
-            do_pad (`bool`, *optional*, defaults to self.do_pad):
-                Whether to pad the image.
-            format (`str` or `AnnotionFormat`, *optional*, defaults to self.format):
-                Format of the annotations.
-            return_tensors (`str` or `TensorType`, *optional*, defaults to self.return_tensors):
-                Type of tensors to return. If `None`, will return the list of images.
-            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
-                The channel dimension format for the output image. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-                - Unset: Use the channel dimension format of the input image.
-            input_data_format (`ChannelDimension` or `str`, *optional*):
-                The channel dimension format for the input image. If unset, the channel dimension format is inferred
-                from the input image. Can be one of:
-                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
-                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
-                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
-        """
-        if "pad_and_return_pixel_mask" in kwargs:
-            logger.warning_once(
-                "The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, "
-                "use `do_pad` instead."
-            )
-            do_pad = kwargs.pop("pad_and_return_pixel_mask")
-
-        max_size = None
-        if "max_size" in kwargs:
-            logger.warning_once(
-                "The `max_size` argument is deprecated and will be removed in a future version, use"
-                " `size['longest_edge']` instead."
-            )
-            size = kwargs.pop("max_size")
-
-        do_resize = self.do_resize if do_resize is None else do_resize
-        size = self.size if size is None else size
-        size = get_size_dict(size=size, max_size=max_size, default_to_square=False)
-        resample = self.resample if resample is None else resample
-        do_rescale = self.do_rescale if do_rescale is None else do_rescale
-        rescale_factor = self.rescale_factor if rescale_factor is None else rescale_factor
-        do_normalize = self.do_normalize if do_normalize is None else do_normalize
-        image_mean = self.image_mean if image_mean is None else image_mean
-        image_std = self.image_std if image_std is None else image_std
-        do_pad = self.do_pad if do_pad is None else do_pad
-        format = self.format if format is None else format
-
-        if do_resize is not None and size is None:
-            raise ValueError("Size and max_size must be specified if do_resize is True.")
-
-        if do_rescale is not None and rescale_factor is None:
-            raise ValueError("Rescale factor must be specified if do_rescale is True.")
-
-        if do_normalize is not None and (image_mean is None or image_std is None):
-            raise ValueError("Image mean and std must be specified if do_normalize is True.")
-
-        images = make_list_of_images(images)
-        if annotations is not None and isinstance(annotations, dict):
-            annotations = [annotations]
-
-        if annotations is not None and len(images) != len(annotations):
-            raise ValueError(
-                f"The number of images ({len(images)}) and annotations ({len(annotations)}) do not match."
-            )
-
-        if not valid_images(images):
-            raise ValueError(
-                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
-                "torch.Tensor, tf.Tensor or jax.ndarray."
-            )
-
-        format = AnnotionFormat(format)
-        if annotations is not None:
-            if format == AnnotionFormat.COCO_DETECTION and not valid_coco_detection_annotations(annotations):
-                raise ValueError(
-                    "Invalid COCO detection annotations. Annotations must a dict (single image) of list of dicts"
-                    "(batch of images) with the following keys: `image_id` and `annotations`, with the latter "
-                    "being a list of annotations in the COCO format."
-                )
-            elif format == AnnotionFormat.COCO_PANOPTIC and not valid_coco_panoptic_annotations(annotations):
-                raise ValueError(
-                    "Invalid COCO panoptic annotations. Annotations must a dict (single image) of list of dicts "
-                    "(batch of images) with the following keys: `image_id`, `file_name` and `segments_info`, with "
-                    "the latter being a list of annotations in the COCO format."
-                )
-            elif format not in SUPPORTED_ANNOTATION_FORMATS:
-                raise ValueError(
-                    f"Unsupported annotation format: {format} must be one of {SUPPORTED_ANNOTATION_FORMATS}"
-                )
-
-        if (
-            masks_path is not None
-            and format == AnnotionFormat.COCO_PANOPTIC
-            and not isinstance(masks_path, (pathlib.Path, str))
-        ):
-            raise ValueError(
-                "The path to the directory containing the mask PNG files should be provided as a"
-                f" `pathlib.Path` or string object, but is {type(masks_path)} instead."
-            )
-
-        # All transformations expect numpy arrays
-        images = [to_numpy_array(image) for image in images]
-
-        if is_scaled_image(images[0]) and do_rescale:
-            logger.warning_once(
-                "It looks like you are trying to rescale already rescaled images. If the input"
-                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
-            )
-
-        if input_data_format is None:
-            # We assume that all images have the same channel dimension format.
-            input_data_format = infer_channel_dimension_format(images[0])
-
-        # prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image)
-        if annotations is not None:
-            prepared_images = []
-            prepared_annotations = []
-            for image, target in zip(images, annotations):
-                target = self.prepare_annotation(
-                    image,
-                    target,
-                    format,
-                    return_segmentation_masks=return_segmentation_masks,
-                    masks_path=masks_path,
-                    input_data_format=input_data_format,
-                )
-                prepared_images.append(image)
-                prepared_annotations.append(target)
-            images = prepared_images
-            annotations = prepared_annotations
-            del prepared_images, prepared_annotations
-
-        # transformations
-        if do_resize:
-            if annotations is not None:
-                resized_images, resized_annotations = [], []
-                for image, target in zip(images, annotations):
-                    orig_size = get_image_size(image, input_data_format)
-                    resized_image = self.resize(
-                        image, size=size, max_size=max_size, resample=resample, input_data_format=input_data_format
-                    )
-                    resized_annotation = self.resize_annotation(
-                        target, orig_size, get_image_size(resized_image, input_data_format)
-                    )
-                    resized_images.append(resized_image)
-                    resized_annotations.append(resized_annotation)
-                images = resized_images
-                annotations = resized_annotations
-                del resized_images, resized_annotations
-            else:
-                images = [
-                    self.resize(image, size=size, resample=resample, input_data_format=input_data_format)
-                    for image in images
-                ]
-
-        if do_rescale:
-            images = [self.rescale(image, rescale_factor, input_data_format=input_data_format) for image in images]
-
-        if do_normalize:
-            images = [
-                self.normalize(image, image_mean, image_std, input_data_format=input_data_format) for image in images
-            ]
-            if annotations is not None:
-                annotations = [
-                    self.normalize_annotation(annotation, get_image_size(image, input_data_format))
-                    for annotation, image in zip(annotations, images)
-                ]
-
-        if do_pad:
-            # Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...}
-            data = self.pad(
-                images, return_pixel_mask=True, data_format=data_format, input_data_format=input_data_format
-            )
-        else:
-            images = [
-                to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
-                for image in images
-            ]
-            data = {"pixel_values": images}
-
-        encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
-        if annotations is not None:
-            encoded_inputs["labels"] = [
-                BatchFeature(annotation, tensor_type=return_tensors) for annotation in annotations
-            ]
-
-        return encoded_inputs
-
-    # POSTPROCESSING METHODS - TODO: add support for other frameworks
-    def post_process(self, outputs, target_sizes):
-        """
-        Converts the raw output of [`DeformableDetrForObjectDetection`] into final bounding boxes in (top_left_x,
-        top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch.
-
-        Args:
-            outputs ([`DeformableDetrObjectDetectionOutput`]):
-                Raw outputs of the model.
-            target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
-                Tensor containing the size (height, width) of each image of the batch. For evaluation, this must be the
-                original image size (before any data augmentation). For visualization, this should be the image size
-                after data augment, but before padding.
-        Returns:
-            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
-            in the batch as predicted by the model.
-        """
-        logger.warning_once(
-            "`post_process` is deprecated and will be removed in v5 of Transformers, please use"
-            " `post_process_object_detection` instead, with `threshold=0.` for equivalent results.",
-        )
-
-        out_logits, out_bbox = outputs.logits, outputs.pred_boxes
-
-        if len(out_logits) != len(target_sizes):
-            raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
-        if target_sizes.shape[1] != 2:
-            raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
-
-        prob = out_logits.sigmoid()
-        topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1)
-        scores = topk_values
-        topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode="floor")
-        labels = topk_indexes % out_logits.shape[2]
-        boxes = center_to_corners_format(out_bbox)
-        boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
-
-        # and from relative [0, 1] to absolute [0, height] coordinates
-        img_h, img_w = target_sizes.unbind(1)
-        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
-        boxes = boxes * scale_fct[:, None, :]
-
-        results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
-
-        return results
-
-    def post_process_object_detection(
-        self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None, top_k: int = 100
-    ):
-        """
-        Converts the raw output of [`DeformableDetrForObjectDetection`] into final bounding boxes in (top_left_x,
-        top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch.
-
-        Args:
-            outputs ([`DetrObjectDetectionOutput`]):
-                Raw outputs of the model.
-            threshold (`float`, *optional*):
-                Score threshold to keep object detection predictions.
-            target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*):
-                Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
-                (height, width) of each image in the batch. If left to None, predictions will not be resized.
-            top_k (`int`, *optional*, defaults to 100):
-                Keep only top k bounding boxes before filtering by thresholding.
-
-        Returns:
-            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
-            in the batch as predicted by the model.
-        """
-        out_logits, out_bbox = outputs.logits, outputs.pred_boxes
-
-        if target_sizes is not None:
-            if len(out_logits) != len(target_sizes):
-                raise ValueError(
-                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
-                )
-
-        prob = out_logits.sigmoid()
-        prob = prob.view(out_logits.shape[0], -1)
-        k_value = min(top_k, prob.size(1))
-        topk_values, topk_indexes = torch.topk(prob, k_value, dim=1)
-        scores = topk_values
-        topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode="floor")
-        labels = topk_indexes % out_logits.shape[2]
-        boxes = center_to_corners_format(out_bbox)
-        boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))
-
-        # and from relative [0, 1] to absolute [0, height] coordinates
-        if isinstance(target_sizes, List):
-            img_h = torch.Tensor([i[0] for i in target_sizes])
-            img_w = torch.Tensor([i[1] for i in target_sizes])
-        else:
-            img_h, img_w = target_sizes.unbind(1)
-        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
-        boxes = boxes * scale_fct[:, None, :]
-
-        results = []
-        for s, l, b in zip(scores, labels, boxes):
-            score = s[s > threshold]
-            label = l[s > threshold]
-            box = b[s > threshold]
-            results.append({"scores": score, "labels": label, "boxes": box})
-
-        return results
diff --git a/spaces/yl12053/so-vits-4.1-Grass-Wonder/inference/slicer.py b/spaces/yl12053/so-vits-4.1-Grass-Wonder/inference/slicer.py
deleted file mode 100644
index b05840bcf6bdced0b6e2adbecb1a1dd5b3dee462..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Grass-Wonder/inference/slicer.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import librosa
-import torch
-import torchaudio
-
-
-class Slicer:
-    def __init__(self,
-                 sr: int,
-                 threshold: float = -40.,
-                 min_length: int = 5000,
-                 min_interval: int = 300,
-                 hop_size: int = 20,
-                 max_sil_kept: int = 5000):
-        if not min_length >= min_interval >= hop_size:
-            raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size')
-        if not max_sil_kept >= hop_size:
-            raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size')
-        min_interval = sr * min_interval / 1000
-        self.threshold = 10 ** (threshold / 20.)
-        self.hop_size = round(sr * hop_size / 1000)
-        self.win_size = min(round(min_interval), 4 * self.hop_size)
-        self.min_length = round(sr * min_length / 1000 / self.hop_size)
-        self.min_interval = round(min_interval / self.hop_size)
-        self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
-
-    def _apply_slice(self, waveform, begin, end):
-        if len(waveform.shape) > 1:
-            return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
-        else:
-            return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
-
-    # @timeit
-    def slice(self, waveform):
-        if len(waveform.shape) > 1:
-            samples = librosa.to_mono(waveform)
-        else:
-            samples = waveform
-        if samples.shape[0] <= self.min_length:
-            return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
-        rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
-        sil_tags = []
-        silence_start = None
-        clip_start = 0
-        for i, rms in enumerate(rms_list):
-            # Keep looping while frame is silent.
-            if rms < self.threshold:
-                # Record start of silent frames.
-                if silence_start is None:
-                    silence_start = i
-                continue
-            # Keep looping while frame is not silent and silence start has not been recorded.
-            if silence_start is None:
-                continue
-            # Clear recorded silence start if interval is not enough or clip is too short
-            is_leading_silence = silence_start == 0 and i > self.max_sil_kept
-            need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
-            if not is_leading_silence and not need_slice_middle:
-                silence_start = None
-                continue
-            # Need slicing. Record the range of silent frames to be removed.
-            if i - silence_start <= self.max_sil_kept:
-                pos = rms_list[silence_start: i + 1].argmin() + silence_start
-                if silence_start == 0:
-                    sil_tags.append((0, pos))
-                else:
-                    sil_tags.append((pos, pos))
-                clip_start = pos
-            elif i - silence_start <= self.max_sil_kept * 2:
-                pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin()
-                pos += i - self.max_sil_kept
-                pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
-                pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
-                if silence_start == 0:
-                    sil_tags.append((0, pos_r))
-                    clip_start = pos_r
-                else:
-                    sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
-                    clip_start = max(pos_r, pos)
-            else:
-                pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
-                pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
-                if silence_start == 0:
-                    sil_tags.append((0, pos_r))
-                else:
-                    sil_tags.append((pos_l, pos_r))
-                clip_start = pos_r
-            silence_start = None
-        # Deal with trailing silence.
-        total_frames = rms_list.shape[0]
-        if silence_start is not None and total_frames - silence_start >= self.min_interval:
-            silence_end = min(total_frames, silence_start + self.max_sil_kept)
-            pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
-            sil_tags.append((pos, total_frames + 1))
-        # Apply and return slices.
-        if len(sil_tags) == 0:
-            return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
-        else:
-            chunks = []
-            # 第一段静音并非从头开始，补上有声片段
-            if sil_tags[0][0]:
-                chunks.append(
-                    {"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"})
-            for i in range(0, len(sil_tags)):
-                # 标识有声片段（跳过第一段）
-                if i:
-                    chunks.append({"slice": False,
-                                   "split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"})
-                # 标识所有静音片段
-                chunks.append({"slice": True,
-                               "split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"})
-            # 最后一段静音并非结尾，补上结尾片段
-            if sil_tags[-1][1] * self.hop_size < len(waveform):
-                chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"})
-            chunk_dict = {}
-            for i in range(len(chunks)):
-                chunk_dict[str(i)] = chunks[i]
-            return chunk_dict
-
-
-def cut(audio_path, db_thresh=-30, min_len=5000):
-    audio, sr = librosa.load(audio_path, sr=None)
-    slicer = Slicer(
-        sr=sr,
-        threshold=db_thresh,
-        min_length=min_len
-    )
-    chunks = slicer.slice(audio)
-    return chunks
-
-
-def chunks2audio(audio_path, chunks):
-    chunks = dict(chunks)
-    audio, sr = torchaudio.load(audio_path)
-    if len(audio.shape) == 2 and audio.shape[1] >= 2:
-        audio = torch.mean(audio, dim=0).unsqueeze(0)
-    audio = audio.cpu().numpy()[0]
-    result = []
-    for k, v in chunks.items():
-        tag = v["split_time"].split(",")
-        if tag[0] != tag[1]:
-            result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
-    return result, sr
diff --git a/spaces/yl12053/so-vits-4.1-Kitasan-Black/modules/F0Predictor/crepe.py b/spaces/yl12053/so-vits-4.1-Kitasan-Black/modules/F0Predictor/crepe.py
deleted file mode 100644
index c6fb45c79bcd306202a2c0282b3d73a8074ced5d..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Kitasan-Black/modules/F0Predictor/crepe.py
+++ /dev/null
@@ -1,340 +0,0 @@
-from typing import Optional,Union
-try:
-    from typing import Literal
-except Exception as e:
-    from typing_extensions import Literal
-import numpy as np
-import torch
-import torchcrepe
-from torch import nn
-from torch.nn import functional as F
-import scipy
-
-#from:https://github.com/fishaudio/fish-diffusion
-
-def repeat_expand(
-    content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest"
-):
-    """Repeat content to target length.
-    This is a wrapper of torch.nn.functional.interpolate.
-
-    Args:
-        content (torch.Tensor): tensor
-        target_len (int): target length
-        mode (str, optional): interpolation mode. Defaults to "nearest".
-
-    Returns:
-        torch.Tensor: tensor
-    """
-
-    ndim = content.ndim
-
-    if content.ndim == 1:
-        content = content[None, None]
-    elif content.ndim == 2:
-        content = content[None]
-
-    assert content.ndim == 3
-
-    is_np = isinstance(content, np.ndarray)
-    if is_np:
-        content = torch.from_numpy(content)
-
-    results = torch.nn.functional.interpolate(content, size=target_len, mode=mode)
-
-    if is_np:
-        results = results.numpy()
-
-    if ndim == 1:
-        return results[0, 0]
-    elif ndim == 2:
-        return results[0]
-
-
-class BasePitchExtractor:
-    def __init__(
-        self,
-        hop_length: int = 512,
-        f0_min: float = 50.0,
-        f0_max: float = 1100.0,
-        keep_zeros: bool = True,
-    ):
-        """Base pitch extractor.
-
-        Args:
-            hop_length (int, optional): Hop length. Defaults to 512.
-            f0_min (float, optional): Minimum f0. Defaults to 50.0.
-            f0_max (float, optional): Maximum f0. Defaults to 1100.0.
-            keep_zeros (bool, optional): Whether keep zeros in pitch. Defaults to True.
-        """
-
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.keep_zeros = keep_zeros
-
-    def __call__(self, x, sampling_rate=44100, pad_to=None):
-        raise NotImplementedError("BasePitchExtractor is not callable.")
-
-    def post_process(self, x, sampling_rate, f0, pad_to):
-        if isinstance(f0, np.ndarray):
-            f0 = torch.from_numpy(f0).float().to(x.device)
-
-        if pad_to is None:
-            return f0
-
-        f0 = repeat_expand(f0, pad_to)
-
-        if self.keep_zeros:
-            return f0
-        
-        vuv_vector = torch.zeros_like(f0)
-        vuv_vector[f0 > 0.0] = 1.0
-        vuv_vector[f0 <= 0.0] = 0.0
-        
-        # 去掉0频率, 并线性插值
-        nzindex = torch.nonzero(f0).squeeze()
-        f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
-        time_org = self.hop_length / sampling_rate * nzindex.cpu().numpy()
-        time_frame = np.arange(pad_to) * self.hop_length / sampling_rate
-
-        if f0.shape[0] <= 0:
-            return torch.zeros(pad_to, dtype=torch.float, device=x.device),torch.zeros(pad_to, dtype=torch.float, device=x.device)
-
-        if f0.shape[0] == 1:
-            return torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[0],torch.ones(pad_to, dtype=torch.float, device=x.device)
-    
-        # 大概可以用 torch 重写?
-        f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
-        vuv_vector = vuv_vector.cpu().numpy()
-        vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0))
-        
-        return f0,vuv_vector
-
-
-class MaskedAvgPool1d(nn.Module):
-    def __init__(
-        self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
-    ):
-        """An implementation of mean pooling that supports masked values.
-
-        Args:
-            kernel_size (int): The size of the median pooling window.
-            stride (int, optional): The stride of the median pooling window. Defaults to None.
-            padding (int, optional): The padding of the median pooling window. Defaults to 0.
-        """
-
-        super(MaskedAvgPool1d, self).__init__()
-        self.kernel_size = kernel_size
-        self.stride = stride or kernel_size
-        self.padding = padding
-
-    def forward(self, x, mask=None):
-        ndim = x.dim()
-        if ndim == 2:
-            x = x.unsqueeze(1)
-
-        assert (
-            x.dim() == 3
-        ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
-
-        # Apply the mask by setting masked elements to zero, or make NaNs zero
-        if mask is None:
-            mask = ~torch.isnan(x)
-
-        # Ensure mask has the same shape as the input tensor
-        assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
-
-        masked_x = torch.where(mask, x, torch.zeros_like(x))
-        # Create a ones kernel with the same number of channels as the input tensor
-        ones_kernel = torch.ones(x.size(1), 1, self.kernel_size, device=x.device)
-
-        # Perform sum pooling
-        sum_pooled = nn.functional.conv1d(
-            masked_x,
-            ones_kernel,
-            stride=self.stride,
-            padding=self.padding,
-            groups=x.size(1),
-        )
-
-        # Count the non-masked (valid) elements in each pooling window
-        valid_count = nn.functional.conv1d(
-            mask.float(),
-            ones_kernel,
-            stride=self.stride,
-            padding=self.padding,
-            groups=x.size(1),
-        )
-        valid_count = valid_count.clamp(min=1)  # Avoid division by zero
-
-        # Perform masked average pooling
-        avg_pooled = sum_pooled / valid_count
-
-        # Fill zero values with NaNs
-        avg_pooled[avg_pooled == 0] = float("nan")
-
-        if ndim == 2:
-            return avg_pooled.squeeze(1)
-
-        return avg_pooled
-
-
-class MaskedMedianPool1d(nn.Module):
-    def __init__(
-        self, kernel_size: int, stride: Optional[int] = None, padding: Optional[int] = 0
-    ):
-        """An implementation of median pooling that supports masked values.
-
-        This implementation is inspired by the median pooling implementation in
-        https://gist.github.com/rwightman/f2d3849281624be7c0f11c85c87c1598
-
-        Args:
-            kernel_size (int): The size of the median pooling window.
-            stride (int, optional): The stride of the median pooling window. Defaults to None.
-            padding (int, optional): The padding of the median pooling window. Defaults to 0.
-        """
-
-        super(MaskedMedianPool1d, self).__init__()
-        self.kernel_size = kernel_size
-        self.stride = stride or kernel_size
-        self.padding = padding
-
-    def forward(self, x, mask=None):
-        ndim = x.dim()
-        if ndim == 2:
-            x = x.unsqueeze(1)
-
-        assert (
-            x.dim() == 3
-        ), "Input tensor must have 2 or 3 dimensions (batch_size, channels, width)"
-
-        if mask is None:
-            mask = ~torch.isnan(x)
-
-        assert x.shape == mask.shape, "Input tensor and mask must have the same shape"
-
-        masked_x = torch.where(mask, x, torch.zeros_like(x))
-
-        x = F.pad(masked_x, (self.padding, self.padding), mode="reflect")
-        mask = F.pad(
-            mask.float(), (self.padding, self.padding), mode="constant", value=0
-        )
-
-        x = x.unfold(2, self.kernel_size, self.stride)
-        mask = mask.unfold(2, self.kernel_size, self.stride)
-
-        x = x.contiguous().view(x.size()[:3] + (-1,))
-        mask = mask.contiguous().view(mask.size()[:3] + (-1,)).to(x.device)
-
-        # Combine the mask with the input tensor
-        #x_masked = torch.where(mask.bool(), x, torch.fill_(torch.zeros_like(x),float("inf")))
-        x_masked = torch.where(mask.bool(), x, torch.FloatTensor([float("inf")]).to(x.device))
-
-        # Sort the masked tensor along the last dimension
-        x_sorted, _ = torch.sort(x_masked, dim=-1)
-
-        # Compute the count of non-masked (valid) values
-        valid_count = mask.sum(dim=-1)
-
-        # Calculate the index of the median value for each pooling window
-        median_idx = (torch.div((valid_count - 1), 2, rounding_mode='trunc')).clamp(min=0)
-
-        # Gather the median values using the calculated indices
-        median_pooled = x_sorted.gather(-1, median_idx.unsqueeze(-1).long()).squeeze(-1)
-
-        # Fill infinite values with NaNs
-        median_pooled[torch.isinf(median_pooled)] = float("nan")
-        
-        if ndim == 2:
-            return median_pooled.squeeze(1)
-
-        return median_pooled
-
-
-class CrepePitchExtractor(BasePitchExtractor):
-    def __init__(
-        self,
-        hop_length: int = 512,
-        f0_min: float = 50.0,
-        f0_max: float = 1100.0,
-        threshold: float = 0.05,
-        keep_zeros: bool = False,
-        device = None,
-        model: Literal["full", "tiny"] = "full",
-        use_fast_filters: bool = True,
-        decoder="viterbi"
-    ):
-        super().__init__(hop_length, f0_min, f0_max, keep_zeros)
-        if decoder == "viterbi":
-            self.decoder = torchcrepe.decode.viterbi
-        elif decoder == "argmax":
-            self.decoder = torchcrepe.decode.argmax
-        elif decoder == "weighted_argmax":
-            self.decoder = torchcrepe.decode.weighted_argmax
-        else:
-            raise "Unknown decoder"
-        self.threshold = threshold
-        self.model = model
-        self.use_fast_filters = use_fast_filters
-        self.hop_length = hop_length
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        if self.use_fast_filters:
-            self.median_filter = MaskedMedianPool1d(3, 1, 1).to(device)
-            self.mean_filter = MaskedAvgPool1d(3, 1, 1).to(device)
-
-    def __call__(self, x, sampling_rate=44100, pad_to=None):
-        """Extract pitch using crepe.
-
-
-        Args:
-            x (torch.Tensor): Audio signal, shape (1, T).
-            sampling_rate (int, optional): Sampling rate. Defaults to 44100.
-            pad_to (int, optional): Pad to length. Defaults to None.
-
-        Returns:
-            torch.Tensor: Pitch, shape (T // hop_length,).
-        """
-
-        assert x.ndim == 2, f"Expected 2D tensor, got {x.ndim}D tensor."
-        assert x.shape[0] == 1, f"Expected 1 channel, got {x.shape[0]} channels."
-
-        x = x.to(self.dev)
-        f0, pd = torchcrepe.predict(
-            x,
-            sampling_rate,
-            self.hop_length,
-            self.f0_min,
-            self.f0_max,
-            pad=True,
-            model=self.model,
-            batch_size=1024,
-            device=x.device,
-            return_periodicity=True,
-            decoder=self.decoder
-        )
-
-        # Filter, remove silence, set uv threshold, refer to the original warehouse readme
-        if self.use_fast_filters:
-            pd = self.median_filter(pd)
-        else:
-            pd = torchcrepe.filter.median(pd, 3)
-
-        pd = torchcrepe.threshold.Silence(-60.0)(pd, x, sampling_rate, 512)
-        f0 = torchcrepe.threshold.At(self.threshold)(f0, pd)
-        
-        if self.use_fast_filters:
-            f0 = self.mean_filter(f0)
-        else:
-            f0 = torchcrepe.filter.mean(f0, 3)
-
-        f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)[0]
-
-        if torch.all(f0 == 0):
-            rtn = f0.cpu().numpy() if pad_to==None else np.zeros(pad_to)
-            return rtn,rtn
-        
-        return self.post_process(x, sampling_rate, f0, pad_to)
diff --git a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/flex-shrink.js b/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/flex-shrink.js
deleted file mode 100644
index fbd0e82b679985404975005761083a2d27d6a671..0000000000000000000000000000000000000000
--- a/spaces/younker/chatgpt-turbo/client/node_modules/autoprefixer/lib/hacks/flex-shrink.js
+++ /dev/null
@@ -1,39 +0,0 @@
-let flexSpec = require('./flex-spec')
-let Declaration = require('../declaration')
-
-class FlexShrink extends Declaration {
-  /**
-   * Return property name by final spec
-   */
-  normalize() {
-    return 'flex-shrink'
-  }
-
-  /**
-   * Return flex property for 2012 spec
-   */
-  prefixed(prop, prefix) {
-    let spec
-    ;[spec, prefix] = flexSpec(prefix)
-    if (spec === 2012) {
-      return prefix + 'flex-negative'
-    }
-    return super.prefixed(prop, prefix)
-  }
-
-  /**
-   * Ignore 2009 spec and use flex property for 2012
-   */
-  set(decl, prefix) {
-    let spec
-    ;[spec, prefix] = flexSpec(prefix)
-    if (spec === 2012 || spec === 'final') {
-      return super.set(decl, prefix)
-    }
-    return undefined
-  }
-}
-
-FlexShrink.names = ['flex-shrink', 'flex-negative']
-
-module.exports = FlexShrink
diff --git a/spaces/ysharma/ChatGPTwithAPI/app.py b/spaces/ysharma/ChatGPTwithAPI/app.py
deleted file mode 100644
index 374bd42fdba4b848e43a0c6f2243b8e766e9a6cf..0000000000000000000000000000000000000000
--- a/spaces/ysharma/ChatGPTwithAPI/app.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import gradio as gr
-import os 
-import json 
-import requests
-
-#Streaming endpoint 
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-#Testing with my Open AI Key 
-#OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
-
-def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[], history=[]):  #repetition_penalty, top_k
-
-    payload = {
-    "model": "gpt-3.5-turbo",
-    "messages": [{"role": "user", "content": f"{inputs}"}],
-    "temperature" : 1.0,
-    "top_p":1.0,
-    "n" : 1,
-    "stream": True,
-    "presence_penalty":0,
-    "frequency_penalty":0,
-    }
-
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {openai_api_key}"
-    }
-
-    print(f"chat_counter - {chat_counter}")
-    if chat_counter != 0 :
-        messages=[]
-        for data in chatbot:
-          temp1 = {}
-          temp1["role"] = "user" 
-          temp1["content"] = data[0] 
-          temp2 = {}
-          temp2["role"] = "assistant" 
-          temp2["content"] = data[1]
-          messages.append(temp1)
-          messages.append(temp2)
-        temp3 = {}
-        temp3["role"] = "user" 
-        temp3["content"] = inputs
-        messages.append(temp3)
-        #messages
-        payload = {
-        "model": "gpt-3.5-turbo",
-        "messages": messages, #[{"role": "user", "content": f"{inputs}"}],
-        "temperature" : temperature, #1.0,
-        "top_p": top_p, #1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,
-        }
-
-    chat_counter+=1
-
-    history.append(inputs)
-    print(f"payload is - {payload}")
-    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    #response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    token_counter = 0 
-    partial_words = "" 
-
-    counter=0
-    for chunk in response.iter_lines():
-        #Skipping first chunk
-        if counter == 0:
-          counter+=1
-          continue
-        #counter+=1
-        # check whether each line is non-empty
-        if chunk.decode() :
-          chunk = chunk.decode()
-          # decode each line as response data is in bytes
-          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              #if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
-              #  break
-              partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
-              if token_counter == 0:
-                history.append(" " + partial_words)
-              else:
-                history[-1] = partial_words
-              chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
-              token_counter+=1
-              yield chat, history, chat_counter  # resembles {chatbot: chat, state: history}  
-                   
-
-def reset_textbox():
-    return gr.update(value='')
-
-title = """<h1 align="center">🔥ChatGPT API 🚀Streaming🚀</h1>"""
-description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
-```
-User: <utterance>
-Assistant: <utterance>
-User: <utterance>
-Assistant: <utterance>
-...
-```
-In this app, you can explore the outputs of a gpt-3.5-turbo LLM.
-"""
-                
-with gr.Blocks(css = """#col_container {width: 1000px; margin-left: auto; margin-right: auto;}
-                #chatbot {height: 520px; overflow: auto;}""") as demo:
-    gr.HTML(title)
-    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPTwithAPI?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
-    with gr.Column(elem_id = "col_container"):
-        openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here")
-        chatbot = gr.Chatbot(elem_id='chatbot') #c
-        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t
-        state = gr.State([]) #s
-        b1 = gr.Button()
-    
-        #inputs, top_p, temperature, top_k, repetition_penalty
-        with gr.Accordion("Parameters", open=False):
-            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
-            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
-            #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
-            #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
-            chat_counter = gr.Number(value=0, visible=False, precision=0)
-
-    inputs.submit( predict, [inputs, top_p, temperature, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],)
-    b1.click( predict, [inputs, top_p, temperature, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],)
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-                    
-    #gr.Markdown(description)
-    demo.queue().launch(debug=True)
diff --git a/spaces/yuan1615/EmpathyVC/losses.py b/spaces/yuan1615/EmpathyVC/losses.py
deleted file mode 100644
index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000
--- a/spaces/yuan1615/EmpathyVC/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch 
-from torch.nn import functional as F
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
-  loss = 0
-  for dr, dg in zip(fmap_r, fmap_g):
-    for rl, gl in zip(dr, dg):
-      rl = rl.float().detach()
-      gl = gl.float()
-      loss += torch.mean(torch.abs(rl - gl))
-
-  return loss * 2 
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-  loss = 0
-  r_losses = []
-  g_losses = []
-  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-    dr = dr.float()
-    dg = dg.float()
-    r_loss = torch.mean((1-dr)**2)
-    g_loss = torch.mean(dg**2)
-    loss += (r_loss + g_loss)
-    r_losses.append(r_loss.item())
-    g_losses.append(g_loss.item())
-
-  return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-  loss = 0
-  gen_losses = []
-  for dg in disc_outputs:
-    dg = dg.float()
-    l = torch.mean((1-dg)**2)
-    gen_losses.append(l)
-    loss += l
-
-  return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-  """
-  z_p, logs_q: [b, h, t_t]
-  m_p, logs_p: [b, h, t_t]
-  """
-  z_p = z_p.float()
-  logs_q = logs_q.float()
-  m_p = m_p.float()
-  logs_p = logs_p.float()
-  z_mask = z_mask.float()
-
-  kl = logs_p - logs_q - 0.5
-  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
-  kl = torch.sum(kl * z_mask)
-  l = kl / torch.sum(z_mask)
-  return l
diff --git a/spaces/yuchenlin/Rebiber/README.md b/spaces/yuchenlin/Rebiber/README.md
deleted file mode 100644
index ed5bd03345be1b5db45b1895353e648cfd33139b..0000000000000000000000000000000000000000
--- a/spaces/yuchenlin/Rebiber/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Rebiber
-emoji: 📚
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git "a/spaces/yunfei0710/gpt-academic/crazy_functions/\346\211\271\351\207\217\347\277\273\350\257\221PDF\346\226\207\346\241\243_\345\244\232\347\272\277\347\250\213.py" "b/spaces/yunfei0710/gpt-academic/crazy_functions/\346\211\271\351\207\217\347\277\273\350\257\221PDF\346\226\207\346\241\243_\345\244\232\347\272\277\347\250\213.py"
deleted file mode 100644
index 06d8a5a7f4459d9620f33fa2b96e28e8c27abbc7..0000000000000000000000000000000000000000
--- "a/spaces/yunfei0710/gpt-academic/crazy_functions/\346\211\271\351\207\217\347\277\273\350\257\221PDF\346\226\207\346\241\243_\345\244\232\347\272\277\347\250\213.py"
+++ /dev/null
@@ -1,216 +0,0 @@
-from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-from .crazy_utils import read_and_clean_pdf_text
-from colorful import *
-
-@CatchException
-def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
-    import glob
-    import os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-        import tiktoken
-    except:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}",
-                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(
-        f'{project_folder}/**/*.pdf', recursive=True)]
-
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 开始正式执行任务
-    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt)
-
-
-def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt):
-    import os
-    import copy
-    import tiktoken
-    TOKEN_LIMIT_PER_FRAGMENT = 1280
-    generated_conclusion_files = []
-    generated_html_files = []
-    for index, fp in enumerate(file_manifest):
-
-        # 读取PDF文件
-        file_content, page_one = read_and_clean_pdf_text(fp)
-        file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-        page_one = str(page_one).encode('utf-8', 'ignore').decode()      # avoid reading non-utf8 chars
-        # 递归地切割PDF文件
-        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        from request_llm.bridge_all import model_info
-        enc = model_info["gpt-3.5-turbo"]['tokenizer']
-        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
-        page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-            txt=page_one, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
-
-        # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-        
-        # 单线，获取文章meta信息
-        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
-            inputs=f"以下是一篇学术论文的基础信息，请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出，最后用中文翻译摘要部分。请提取：{paper_meta}",
-            inputs_show_user=f"请从{fp}中提取出“标题”、“收录会议或期刊”等基本信息。",
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot, history=[],
-            sys_prompt="Your job is to collect information from materials。",
-        )
-
-        # 多线，翻译
-        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
-            inputs_array=[
-                f"你需要翻译以下内容：\n{frag}" for frag in paper_fragments],
-            inputs_show_user_array=[f"\n---\n 原文： \n\n {frag.replace('#', '')}  \n---\n 翻译：\n " for frag in paper_fragments],
-            llm_kwargs=llm_kwargs,
-            chatbot=chatbot,
-            history_array=[[paper_meta] for _ in paper_fragments],
-            sys_prompt_array=[
-                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in paper_fragments],
-            # max_workers=5  # OpenAI所允许的最大并行过载
-        )
-        gpt_response_collection_md = copy.deepcopy(gpt_response_collection)
-        # 整理报告的格式
-        for i,k in enumerate(gpt_response_collection_md): 
-            if i%2==0:
-                gpt_response_collection_md[i] = f"\n\n---\n\n ## 原文[{i//2}/{len(gpt_response_collection_md)//2}]： \n\n {paper_fragments[i//2].replace('#', '')}  \n\n---\n\n ## 翻译[{i//2}/{len(gpt_response_collection_md)//2}]：\n "
-            else:
-                gpt_response_collection_md[i] = gpt_response_collection_md[i]
-        final = ["一、论文概况\n\n---\n\n", paper_meta_info.replace('# ', '### ') + '\n\n---\n\n', "二、论文翻译", ""]
-        final.extend(gpt_response_collection_md)
-        create_report_file_name = f"{os.path.basename(fp)}.trans.md"
-        res = write_results_to_file(final, file_name=create_report_file_name)
-
-        # 更新UI
-        generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}')
-        chatbot.append((f"{fp}完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        # write html
-        try:
-            ch = construct_html() 
-            orig = ""
-            trans = ""
-            gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
-            for i,k in enumerate(gpt_response_collection_html): 
-                if i%2==0:
-                    gpt_response_collection_html[i] = paper_fragments[i//2].replace('#', '')
-                else:
-                    gpt_response_collection_html[i] = gpt_response_collection_html[i]
-            final = ["论文概况", paper_meta_info.replace('# ', '### '),  "二、论文翻译",  ""]
-            final.extend(gpt_response_collection_html)
-            for i, k in enumerate(final): 
-                if i%2==0:
-                    orig = k
-                if i%2==1:
-                    trans = k
-                    ch.add_row(a=orig, b=trans)
-            create_report_file_name = f"{os.path.basename(fp)}.trans.html"
-            ch.save_file(create_report_file_name)
-            generated_html_files.append(f'./gpt_log/{create_report_file_name}')
-        except:
-            from toolbox import trimmed_format_exc
-            print('writing html result failed:', trimmed_format_exc())
-
-    # 准备文件的下载
-    import shutil
-    for pdf_path in generated_conclusion_files:
-        # 重命名文件
-        rename_file = f'./gpt_log/翻译-{os.path.basename(pdf_path)}'
-        if os.path.exists(rename_file):
-            os.remove(rename_file)
-        shutil.copyfile(pdf_path, rename_file)
-        if os.path.exists(pdf_path):
-            os.remove(pdf_path)
-    for html_path in generated_html_files:
-        # 重命名文件
-        rename_file = f'./gpt_log/翻译-{os.path.basename(html_path)}'
-        if os.path.exists(rename_file):
-            os.remove(rename_file)
-        shutil.copyfile(html_path, rename_file)
-        if os.path.exists(html_path):
-            os.remove(html_path)
-    chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-
-class construct_html():
-    def __init__(self) -> None:
-        self.css = """
-.row {
-  display: flex;
-  flex-wrap: wrap;
-}
-
-.column {
-  flex: 1;
-  padding: 10px;
-}
-
-.table-header {
-  font-weight: bold;
-  border-bottom: 1px solid black;
-}
-
-.table-row {
-  border-bottom: 1px solid lightgray;
-}
-
-.table-cell {
-  padding: 5px;
-}
-        """
-        self.html_string = f'<!DOCTYPE html><head><meta charset="utf-8"><title>翻译结果</title><style>{self.css}</style></head>'
-
-
-    def add_row(self, a, b):
-        tmp = """
-<div class="row table-row">
-    <div class="column table-cell">REPLACE_A</div>
-    <div class="column table-cell">REPLACE_B</div>
-</div>
-        """
-        from toolbox import markdown_convertion
-        tmp = tmp.replace('REPLACE_A', markdown_convertion(a))
-        tmp = tmp.replace('REPLACE_B', markdown_convertion(b))
-        self.html_string += tmp
-
-
-    def save_file(self, file_name):
-        with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
-            f.write(self.html_string.encode('utf-8', 'ignore').decode())
-
diff --git a/spaces/zhan66/vits-simple-api/bert_vits2/bert_vits2.py b/spaces/zhan66/vits-simple-api/bert_vits2/bert_vits2.py
deleted file mode 100644
index 445ff1ca7dc1e9468e0b3ccbb1a36a55cfa883c7..0000000000000000000000000000000000000000
--- a/spaces/zhan66/vits-simple-api/bert_vits2/bert_vits2.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import numpy as np
-import torch
-
-from bert_vits2 import commons
-from bert_vits2 import utils as bert_vits2_utils
-from bert_vits2.models import SynthesizerTrn
-from bert_vits2.text import *
-from bert_vits2.text.cleaner import clean_text
-from utils import classify_language, get_hparams_from_file, lang_dict
-from utils.sentence import sentence_split_and_markup, cut
-
-
-class Bert_VITS2:
-    def __init__(self, model, config, device=torch.device("cpu"), **kwargs):
-        self.hps_ms = get_hparams_from_file(config)
-        self.n_speakers = getattr(self.hps_ms.data, 'n_speakers', 0)
-        self.speakers = [item[0] for item in
-                         sorted(list(getattr(self.hps_ms.data, 'spk2id', {'0': 0}).items()), key=lambda x: x[1])]
-
-        self.legacy = getattr(self.hps_ms.data, 'legacy', False)
-        self.symbols = symbols_legacy if self.legacy else symbols
-        self._symbol_to_id = {s: i for i, s in enumerate(self.symbols)}
-
-        self.net_g = SynthesizerTrn(
-            len(self.symbols),
-            self.hps_ms.data.filter_length // 2 + 1,
-            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
-            n_speakers=self.hps_ms.data.n_speakers,
-            symbols=self.symbols,
-            **self.hps_ms.model).to(device)
-        _ = self.net_g.eval()
-        self.device = device
-        self.load_model(model)
-
-    def load_model(self, model):
-        bert_vits2_utils.load_checkpoint(model, self.net_g, None, skip_optimizer=True)
-
-    def get_speakers(self):
-        return self.speakers
-
-    def get_text(self, text, language_str, hps):
-        norm_text, phone, tone, word2ph = clean_text(text, language_str)
-        phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str, self._symbol_to_id)
-
-        if hps.data.add_blank:
-            phone = commons.intersperse(phone, 0)
-            tone = commons.intersperse(tone, 0)
-            language = commons.intersperse(language, 0)
-            for i in range(len(word2ph)):
-                word2ph[i] = word2ph[i] * 2
-            word2ph[0] += 1
-        bert = get_bert(norm_text, word2ph, language_str)
-        del word2ph
-        assert bert.shape[-1] == len(phone), phone
-
-        if language_str == "zh":
-            bert = bert
-            ja_bert = torch.zeros(768, len(phone))
-        elif language_str == "ja":
-            ja_bert = bert
-            bert = torch.zeros(1024, len(phone))
-        else:
-            bert = torch.zeros(1024, len(phone))
-            ja_bert = torch.zeros(768, len(phone))
-        assert bert.shape[-1] == len(
-            phone
-        ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
-        phone = torch.LongTensor(phone)
-        tone = torch.LongTensor(tone)
-        language = torch.LongTensor(language)
-        return bert, ja_bert, phone, tone, language
-
-    def infer(self, text, lang, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
-        bert, ja_bert, phones, tones, lang_ids = self.get_text(text, lang, self.hps_ms)
-        with torch.no_grad():
-            x_tst = phones.to(self.device).unsqueeze(0)
-            tones = tones.to(self.device).unsqueeze(0)
-            lang_ids = lang_ids.to(self.device).unsqueeze(0)
-            bert = bert.to(self.device).unsqueeze(0)
-            ja_bert = ja_bert.to(self.device).unsqueeze(0)
-            x_tst_lengths = torch.LongTensor([phones.size(0)]).to(self.device)
-            speakers = torch.LongTensor([int(sid)]).to(self.device)
-            audio = self.net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, bert, ja_bert, sdp_ratio=sdp_ratio
-                                     , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[
-                0][0, 0].data.cpu().float().numpy()
-
-        torch.cuda.empty_cache()
-        return audio
-
-    def get_audio(self, voice, auto_break=False):
-        text = voice.get("text", None)
-        lang = voice.get("lang", "auto")
-        sdp_ratio = voice.get("sdp_ratio", 0.2)
-        noise_scale = voice.get("noise", 0.5)
-        noise_scale_w = voice.get("noisew", 0.6)
-        length_scale = voice.get("length", 1)
-        sid = voice.get("id", 0)
-        max = voice.get("max", 50)
-        # sentence_list = sentence_split_and_markup(text, max, "ZH", ["zh"])
-        if lang == "auto":
-            lang = classify_language(text, target_languages=lang_dict["bert_vits2"])
-        sentence_list = cut(text, max)
-        audios = []
-        for sentence in sentence_list:
-            audio = self.infer(sentence, lang, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid)
-            audios.append(audio)
-        audio = np.concatenate(audios)
-        return audio
diff --git a/spaces/zhan66/vits-simple-api/vits/text/thai.py b/spaces/zhan66/vits-simple-api/vits/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/zhan66/vits-simple-api/vits/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'เอ'),
-    ('b','บี'),
-    ('c','ซี'),
-    ('d','ดี'),
-    ('e','อี'),
-    ('f','เอฟ'),
-    ('g','จี'),
-    ('h','เอช'),
-    ('i','ไอ'),
-    ('j','เจ'),
-    ('k','เค'),
-    ('l','แอล'),
-    ('m','เอ็ม'),
-    ('n','เอ็น'),
-    ('o','โอ'),
-    ('p','พี'),
-    ('q','คิว'),
-    ('r','แอร์'),
-    ('s','เอส'),
-    ('t','ที'),
-    ('u','ยู'),
-    ('v','วี'),
-    ('w','ดับเบิลยู'),
-    ('x','เอ็กซ์'),
-    ('y','วาย'),
-    ('z','ซี')
-]]
-
-
-def num_to_thai(text):
-    return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
-    for regex, replacement in _latin_to_thai:
-        text = re.sub(regex, replacement, text)
-    return text
diff --git a/spaces/zhang-wei-jian/docker/node_modules/nopt/lib/nopt.js b/spaces/zhang-wei-jian/docker/node_modules/nopt/lib/nopt.js
deleted file mode 100644
index ff802dafe3a8b3ed843b82dcd0b2bb4a67f247a1..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/nopt/lib/nopt.js
+++ /dev/null
@@ -1,552 +0,0 @@
-// info about each config option.
-
-var debug = process.env.DEBUG_NOPT || process.env.NOPT_DEBUG
-  ? function () { console.error.apply(console, arguments) }
-  : function () {}
-
-var url = require("url")
-  , path = require("path")
-  , Stream = require("stream").Stream
-  , abbrev = require("abbrev")
-
-module.exports = exports = nopt
-exports.clean = clean
-
-exports.typeDefs =
-  { String  : { type: String,  validate: validateString  }
-  , Boolean : { type: Boolean, validate: validateBoolean }
-  , url     : { type: url,     validate: validateUrl     }
-  , Number  : { type: Number,  validate: validateNumber  }
-  , path    : { type: path,    validate: validatePath    }
-  , Stream  : { type: Stream,  validate: validateStream  }
-  , Date    : { type: Date,    validate: validateDate    }
-  }
-
-function nopt (types, shorthands, args, slice) {
-  args = args || process.argv
-  types = types || {}
-  shorthands = shorthands || {}
-  if (typeof slice !== "number") slice = 2
-
-  debug(types, shorthands, args, slice)
-
-  args = args.slice(slice)
-  var data = {}
-    , key
-    , remain = []
-    , cooked = args
-    , original = args.slice(0)
-
-  parse(args, data, remain, types, shorthands)
-  // now data is full
-  clean(data, types, exports.typeDefs)
-  data.argv = {remain:remain,cooked:cooked,original:original}
-  data.argv.toString = function () {
-    return this.original.map(JSON.stringify).join(" ")
-  }
-  return data
-}
-
-function clean (data, types, typeDefs) {
-  typeDefs = typeDefs || exports.typeDefs
-  var remove = {}
-    , typeDefault = [false, true, null, String, Number]
-
-  Object.keys(data).forEach(function (k) {
-    if (k === "argv") return
-    var val = data[k]
-      , isArray = Array.isArray(val)
-      , type = types[k]
-    if (!isArray) val = [val]
-    if (!type) type = typeDefault
-    if (type === Array) type = typeDefault.concat(Array)
-    if (!Array.isArray(type)) type = [type]
-
-    debug("val=%j", val)
-    debug("types=", type)
-    val = val.map(function (val) {
-      // if it's an unknown value, then parse false/true/null/numbers/dates
-      if (typeof val === "string") {
-        debug("string %j", val)
-        val = val.trim()
-        if ((val === "null" && ~type.indexOf(null))
-            || (val === "true" &&
-               (~type.indexOf(true) || ~type.indexOf(Boolean)))
-            || (val === "false" &&
-               (~type.indexOf(false) || ~type.indexOf(Boolean)))) {
-          val = JSON.parse(val)
-          debug("jsonable %j", val)
-        } else if (~type.indexOf(Number) && !isNaN(val)) {
-          debug("convert to number", val)
-          val = +val
-        } else if (~type.indexOf(Date) && !isNaN(Date.parse(val))) {
-          debug("convert to date", val)
-          val = new Date(val)
-        }
-      }
-
-      if (!types.hasOwnProperty(k)) {
-        return val
-      }
-
-      // allow `--no-blah` to set 'blah' to null if null is allowed
-      if (val === false && ~type.indexOf(null) &&
-          !(~type.indexOf(false) || ~type.indexOf(Boolean))) {
-        val = null
-      }
-
-      var d = {}
-      d[k] = val
-      debug("prevalidated val", d, val, types[k])
-      if (!validate(d, k, val, types[k], typeDefs)) {
-        if (exports.invalidHandler) {
-          exports.invalidHandler(k, val, types[k], data)
-        } else if (exports.invalidHandler !== false) {
-          debug("invalid: "+k+"="+val, types[k])
-        }
-        return remove
-      }
-      debug("validated val", d, val, types[k])
-      return d[k]
-    }).filter(function (val) { return val !== remove })
-
-    if (!val.length) delete data[k]
-    else if (isArray) {
-      debug(isArray, data[k], val)
-      data[k] = val
-    } else data[k] = val[0]
-
-    debug("k=%s val=%j", k, val, data[k])
-  })
-}
-
-function validateString (data, k, val) {
-  data[k] = String(val)
-}
-
-function validatePath (data, k, val) {
-  data[k] = path.resolve(String(val))
-  return true
-}
-
-function validateNumber (data, k, val) {
-  debug("validate Number %j %j %j", k, val, isNaN(val))
-  if (isNaN(val)) return false
-  data[k] = +val
-}
-
-function validateDate (data, k, val) {
-  debug("validate Date %j %j %j", k, val, Date.parse(val))
-  var s = Date.parse(val)
-  if (isNaN(s)) return false
-  data[k] = new Date(val)
-}
-
-function validateBoolean (data, k, val) {
-  if (val instanceof Boolean) val = val.valueOf()
-  else if (typeof val === "string") {
-    if (!isNaN(val)) val = !!(+val)
-    else if (val === "null" || val === "false") val = false
-    else val = true
-  } else val = !!val
-  data[k] = val
-}
-
-function validateUrl (data, k, val) {
-  val = url.parse(String(val))
-  if (!val.host) return false
-  data[k] = val.href
-}
-
-function validateStream (data, k, val) {
-  if (!(val instanceof Stream)) return false
-  data[k] = val
-}
-
-function validate (data, k, val, type, typeDefs) {
-  // arrays are lists of types.
-  if (Array.isArray(type)) {
-    for (var i = 0, l = type.length; i < l; i ++) {
-      if (type[i] === Array) continue
-      if (validate(data, k, val, type[i], typeDefs)) return true
-    }
-    delete data[k]
-    return false
-  }
-
-  // an array of anything?
-  if (type === Array) return true
-
-  // NaN is poisonous.  Means that something is not allowed.
-  if (type !== type) {
-    debug("Poison NaN", k, val, type)
-    delete data[k]
-    return false
-  }
-
-  // explicit list of values
-  if (val === type) {
-    debug("Explicitly allowed %j", val)
-    // if (isArray) (data[k] = data[k] || []).push(val)
-    // else data[k] = val
-    data[k] = val
-    return true
-  }
-
-  // now go through the list of typeDefs, validate against each one.
-  var ok = false
-    , types = Object.keys(typeDefs)
-  for (var i = 0, l = types.length; i < l; i ++) {
-    debug("test type %j %j %j", k, val, types[i])
-    var t = typeDefs[types[i]]
-    if (t && type === t.type) {
-      var d = {}
-      ok = false !== t.validate(d, k, val)
-      val = d[k]
-      if (ok) {
-        // if (isArray) (data[k] = data[k] || []).push(val)
-        // else data[k] = val
-        data[k] = val
-        break
-      }
-    }
-  }
-  debug("OK? %j (%j %j %j)", ok, k, val, types[i])
-
-  if (!ok) delete data[k]
-  return ok
-}
-
-function parse (args, data, remain, types, shorthands) {
-  debug("parse", args, data, remain)
-
-  var key = null
-    , abbrevs = abbrev(Object.keys(types))
-    , shortAbbr = abbrev(Object.keys(shorthands))
-
-  for (var i = 0; i < args.length; i ++) {
-    var arg = args[i]
-    debug("arg", arg)
-
-    if (arg.match(/^-{2,}$/)) {
-      // done with keys.
-      // the rest are args.
-      remain.push.apply(remain, args.slice(i + 1))
-      args[i] = "--"
-      break
-    }
-    if (arg.charAt(0) === "-") {
-      if (arg.indexOf("=") !== -1) {
-        var v = arg.split("=")
-        arg = v.shift()
-        v = v.join("=")
-        args.splice.apply(args, [i, 1].concat([arg, v]))
-      }
-      // see if it's a shorthand
-      // if so, splice and back up to re-parse it.
-      var shRes = resolveShort(arg, shorthands, shortAbbr, abbrevs)
-      debug("arg=%j shRes=%j", arg, shRes)
-      if (shRes) {
-        debug(arg, shRes)
-        args.splice.apply(args, [i, 1].concat(shRes))
-        if (arg !== shRes[0]) {
-          i --
-          continue
-        }
-      }
-      arg = arg.replace(/^-+/, "")
-      var no = false
-      while (arg.toLowerCase().indexOf("no-") === 0) {
-        no = !no
-        arg = arg.substr(3)
-      }
-
-      if (abbrevs[arg]) arg = abbrevs[arg]
-
-      var isArray = types[arg] === Array ||
-        Array.isArray(types[arg]) && types[arg].indexOf(Array) !== -1
-
-      var val
-        , la = args[i + 1]
-
-      var isBool = no ||
-        types[arg] === Boolean ||
-        Array.isArray(types[arg]) && types[arg].indexOf(Boolean) !== -1 ||
-        (la === "false" &&
-         (types[arg] === null ||
-          Array.isArray(types[arg]) && ~types[arg].indexOf(null)))
-
-      if (isBool) {
-        // just set and move along
-        val = !no
-        // however, also support --bool true or --bool false
-        if (la === "true" || la === "false") {
-          val = JSON.parse(la)
-          la = null
-          if (no) val = !val
-          i ++
-        }
-
-        // also support "foo":[Boolean, "bar"] and "--foo bar"
-        if (Array.isArray(types[arg]) && la) {
-          if (~types[arg].indexOf(la)) {
-            // an explicit type
-            val = la
-            i ++
-          } else if ( la === "null" && ~types[arg].indexOf(null) ) {
-            // null allowed
-            val = null
-            i ++
-          } else if ( !la.match(/^-{2,}[^-]/) &&
-                      !isNaN(la) &&
-                      ~types[arg].indexOf(Number) ) {
-            // number
-            val = +la
-            i ++
-          } else if ( !la.match(/^-[^-]/) && ~types[arg].indexOf(String) ) {
-            // string
-            val = la
-            i ++
-          }
-        }
-
-        if (isArray) (data[arg] = data[arg] || []).push(val)
-        else data[arg] = val
-
-        continue
-      }
-
-      if (la && la.match(/^-{2,}$/)) {
-        la = undefined
-        i --
-      }
-
-      val = la === undefined ? true : la
-      if (isArray) (data[arg] = data[arg] || []).push(val)
-      else data[arg] = val
-
-      i ++
-      continue
-    }
-    remain.push(arg)
-  }
-}
-
-function resolveShort (arg, shorthands, shortAbbr, abbrevs) {
-  // handle single-char shorthands glommed together, like
-  // npm ls -glp, but only if there is one dash, and only if
-  // all of the chars are single-char shorthands, and it's
-  // not a match to some other abbrev.
-  arg = arg.replace(/^-+/, '')
-  if (abbrevs[arg] && !shorthands[arg]) {
-    return null
-  }
-  if (shortAbbr[arg]) {
-    arg = shortAbbr[arg]
-  } else {
-    var singles = shorthands.___singles
-    if (!singles) {
-      singles = Object.keys(shorthands).filter(function (s) {
-        return s.length === 1
-      }).reduce(function (l,r) { l[r] = true ; return l }, {})
-      shorthands.___singles = singles
-    }
-    var chrs = arg.split("").filter(function (c) {
-      return singles[c]
-    })
-    if (chrs.join("") === arg) return chrs.map(function (c) {
-      return shorthands[c]
-    }).reduce(function (l, r) {
-      return l.concat(r)
-    }, [])
-  }
-
-  if (shorthands[arg] && !Array.isArray(shorthands[arg])) {
-    shorthands[arg] = shorthands[arg].split(/\s+/)
-  }
-  return shorthands[arg]
-}
-
-if (module === require.main) {
-var assert = require("assert")
-  , util = require("util")
-
-  , shorthands =
-    { s : ["--loglevel", "silent"]
-    , d : ["--loglevel", "info"]
-    , dd : ["--loglevel", "verbose"]
-    , ddd : ["--loglevel", "silly"]
-    , noreg : ["--no-registry"]
-    , reg : ["--registry"]
-    , "no-reg" : ["--no-registry"]
-    , silent : ["--loglevel", "silent"]
-    , verbose : ["--loglevel", "verbose"]
-    , h : ["--usage"]
-    , H : ["--usage"]
-    , "?" : ["--usage"]
-    , help : ["--usage"]
-    , v : ["--version"]
-    , f : ["--force"]
-    , desc : ["--description"]
-    , "no-desc" : ["--no-description"]
-    , "local" : ["--no-global"]
-    , l : ["--long"]
-    , p : ["--parseable"]
-    , porcelain : ["--parseable"]
-    , g : ["--global"]
-    }
-
-  , types =
-    { aoa: Array
-    , nullstream: [null, Stream]
-    , date: Date
-    , str: String
-    , browser : String
-    , cache : path
-    , color : ["always", Boolean]
-    , depth : Number
-    , description : Boolean
-    , dev : Boolean
-    , editor : path
-    , force : Boolean
-    , global : Boolean
-    , globalconfig : path
-    , group : [String, Number]
-    , gzipbin : String
-    , logfd : [Number, Stream]
-    , loglevel : ["silent","win","error","warn","info","verbose","silly"]
-    , long : Boolean
-    , "node-version" : [false, String]
-    , npaturl : url
-    , npat : Boolean
-    , "onload-script" : [false, String]
-    , outfd : [Number, Stream]
-    , parseable : Boolean
-    , pre: Boolean
-    , prefix: path
-    , proxy : url
-    , "rebuild-bundle" : Boolean
-    , registry : url
-    , searchopts : String
-    , searchexclude: [null, String]
-    , shell : path
-    , t: [Array, String]
-    , tag : String
-    , tar : String
-    , tmp : path
-    , "unsafe-perm" : Boolean
-    , usage : Boolean
-    , user : String
-    , username : String
-    , userconfig : path
-    , version : Boolean
-    , viewer: path
-    , _exit : Boolean
-    }
-
-; [["-v", {version:true}, []]
-  ,["---v", {version:true}, []]
-  ,["ls -s --no-reg connect -d",
-    {loglevel:"info",registry:null},["ls","connect"]]
-  ,["ls ---s foo",{loglevel:"silent"},["ls","foo"]]
-  ,["ls --registry blargle", {}, ["ls"]]
-  ,["--no-registry", {registry:null}, []]
-  ,["--no-color true", {color:false}, []]
-  ,["--no-color false", {color:true}, []]
-  ,["--no-color", {color:false}, []]
-  ,["--color false", {color:false}, []]
-  ,["--color --logfd 7", {logfd:7,color:true}, []]
-  ,["--color=true", {color:true}, []]
-  ,["--logfd=10", {logfd:10}, []]
-  ,["--tmp=/tmp -tar=gtar",{tmp:"/tmp",tar:"gtar"},[]]
-  ,["--tmp=tmp -tar=gtar",
-    {tmp:path.resolve(process.cwd(), "tmp"),tar:"gtar"},[]]
-  ,["--logfd x", {}, []]
-  ,["a -true -- -no-false", {true:true},["a","-no-false"]]
-  ,["a -no-false", {false:false},["a"]]
-  ,["a -no-no-true", {true:true}, ["a"]]
-  ,["a -no-no-no-false", {false:false}, ["a"]]
-  ,["---NO-no-No-no-no-no-nO-no-no"+
-    "-No-no-no-no-no-no-no-no-no"+
-    "-no-no-no-no-NO-NO-no-no-no-no-no-no"+
-    "-no-body-can-do-the-boogaloo-like-I-do"
-   ,{"body-can-do-the-boogaloo-like-I-do":false}, []]
-  ,["we are -no-strangers-to-love "+
-    "--you-know the-rules --and so-do-i "+
-    "---im-thinking-of=a-full-commitment "+
-    "--no-you-would-get-this-from-any-other-guy "+
-    "--no-gonna-give-you-up "+
-    "-no-gonna-let-you-down=true "+
-    "--no-no-gonna-run-around false "+
-    "--desert-you=false "+
-    "--make-you-cry false "+
-    "--no-tell-a-lie "+
-    "--no-no-and-hurt-you false"
-   ,{"strangers-to-love":false
-    ,"you-know":"the-rules"
-    ,"and":"so-do-i"
-    ,"you-would-get-this-from-any-other-guy":false
-    ,"gonna-give-you-up":false
-    ,"gonna-let-you-down":false
-    ,"gonna-run-around":false
-    ,"desert-you":false
-    ,"make-you-cry":false
-    ,"tell-a-lie":false
-    ,"and-hurt-you":false
-    },["we", "are"]]
-  ,["-t one -t two -t three"
-   ,{t: ["one", "two", "three"]}
-   ,[]]
-  ,["-t one -t null -t three four five null"
-   ,{t: ["one", "null", "three"]}
-   ,["four", "five", "null"]]
-  ,["-t foo"
-   ,{t:["foo"]}
-   ,[]]
-  ,["--no-t"
-   ,{t:["false"]}
-   ,[]]
-  ,["-no-no-t"
-   ,{t:["true"]}
-   ,[]]
-  ,["-aoa one -aoa null -aoa 100"
-   ,{aoa:["one", null, 100]}
-   ,[]]
-  ,["-str 100"
-   ,{str:"100"}
-   ,[]]
-  ,["--color always"
-   ,{color:"always"}
-   ,[]]
-  ,["--no-nullstream"
-   ,{nullstream:null}
-   ,[]]
-  ,["--nullstream false"
-   ,{nullstream:null}
-   ,[]]
-  ,["--notadate 2011-01-25"
-   ,{notadate: "2011-01-25"}
-   ,[]]
-  ,["--date 2011-01-25"
-   ,{date: new Date("2011-01-25")}
-   ,[]]
-  ].forEach(function (test) {
-    var argv = test[0].split(/\s+/)
-      , opts = test[1]
-      , rem = test[2]
-      , actual = nopt(types, shorthands, argv, 0)
-      , parsed = actual.argv
-    delete actual.argv
-    console.log(util.inspect(actual, false, 2, true), parsed.remain)
-    for (var i in opts) {
-      var e = JSON.stringify(opts[i])
-        , a = JSON.stringify(actual[i] === undefined ? null : actual[i])
-      if (e && typeof e === "object") {
-        assert.deepEqual(e, a)
-      } else {
-        assert.equal(e, a)
-      }
-    }
-    assert.deepEqual(rem, parsed.remain)
-  })
-}
diff --git a/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/internal/re.js b/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/internal/re.js
deleted file mode 100644
index 0e8fb52897ef8b3e1f96f3e6e67658f1e047a4bf..0000000000000000000000000000000000000000
--- a/spaces/zhang-wei-jian/docker/node_modules/simple-update-notifier/node_modules/semver/internal/re.js
+++ /dev/null
@@ -1,179 +0,0 @@
-const { MAX_SAFE_COMPONENT_LENGTH } = require('./constants')
-const debug = require('./debug')
-exports = module.exports = {}
-
-// The actual regexps go on exports.re
-const re = exports.re = []
-const src = exports.src = []
-const t = exports.t = {}
-let R = 0
-
-const createToken = (name, value, isGlobal) => {
-  const index = R++
-  debug(index, value)
-  t[name] = index
-  src[index] = value
-  re[index] = new RegExp(value, isGlobal ? 'g' : undefined)
-}
-
-// The following Regular Expressions can be used for tokenizing,
-// validating, and parsing SemVer version strings.
-
-// ## Numeric Identifier
-// A single `0`, or a non-zero digit followed by zero or more digits.
-
-createToken('NUMERICIDENTIFIER', '0|[1-9]\\d*')
-createToken('NUMERICIDENTIFIERLOOSE', '[0-9]+')
-
-// ## Non-numeric Identifier
-// Zero or more digits, followed by a letter or hyphen, and then zero or
-// more letters, digits, or hyphens.
-
-createToken('NONNUMERICIDENTIFIER', '\\d*[a-zA-Z-][a-zA-Z0-9-]*')
-
-// ## Main Version
-// Three dot-separated numeric identifiers.
-
-createToken('MAINVERSION', `(${src[t.NUMERICIDENTIFIER]})\\.` +
-                   `(${src[t.NUMERICIDENTIFIER]})\\.` +
-                   `(${src[t.NUMERICIDENTIFIER]})`)
-
-createToken('MAINVERSIONLOOSE', `(${src[t.NUMERICIDENTIFIERLOOSE]})\\.` +
-                        `(${src[t.NUMERICIDENTIFIERLOOSE]})\\.` +
-                        `(${src[t.NUMERICIDENTIFIERLOOSE]})`)
-
-// ## Pre-release Version Identifier
-// A numeric identifier, or a non-numeric identifier.
-
-createToken('PRERELEASEIDENTIFIER', `(?:${src[t.NUMERICIDENTIFIER]
-}|${src[t.NONNUMERICIDENTIFIER]})`)
-
-createToken('PRERELEASEIDENTIFIERLOOSE', `(?:${src[t.NUMERICIDENTIFIERLOOSE]
-}|${src[t.NONNUMERICIDENTIFIER]})`)
-
-// ## Pre-release Version
-// Hyphen, followed by one or more dot-separated pre-release version
-// identifiers.
-
-createToken('PRERELEASE', `(?:-(${src[t.PRERELEASEIDENTIFIER]
-}(?:\\.${src[t.PRERELEASEIDENTIFIER]})*))`)
-
-createToken('PRERELEASELOOSE', `(?:-?(${src[t.PRERELEASEIDENTIFIERLOOSE]
-}(?:\\.${src[t.PRERELEASEIDENTIFIERLOOSE]})*))`)
-
-// ## Build Metadata Identifier
-// Any combination of digits, letters, or hyphens.
-
-createToken('BUILDIDENTIFIER', '[0-9A-Za-z-]+')
-
-// ## Build Metadata
-// Plus sign, followed by one or more period-separated build metadata
-// identifiers.
-
-createToken('BUILD', `(?:\\+(${src[t.BUILDIDENTIFIER]
-}(?:\\.${src[t.BUILDIDENTIFIER]})*))`)
-
-// ## Full Version String
-// A main version, followed optionally by a pre-release version and
-// build metadata.
-
-// Note that the only major, minor, patch, and pre-release sections of
-// the version string are capturing groups.  The build metadata is not a
-// capturing group, because it should not ever be used in version
-// comparison.
-
-createToken('FULLPLAIN', `v?${src[t.MAINVERSION]
-}${src[t.PRERELEASE]}?${
-  src[t.BUILD]}?`)
-
-createToken('FULL', `^${src[t.FULLPLAIN]}$`)
-
-// like full, but allows v1.2.3 and =1.2.3, which people do sometimes.
-// also, 1.0.0alpha1 (prerelease without the hyphen) which is pretty
-// common in the npm registry.
-createToken('LOOSEPLAIN', `[v=\\s]*${src[t.MAINVERSIONLOOSE]
-}${src[t.PRERELEASELOOSE]}?${
-  src[t.BUILD]}?`)
-
-createToken('LOOSE', `^${src[t.LOOSEPLAIN]}$`)
-
-createToken('GTLT', '((?:<|>)?=?)')
-
-// Something like "2.*" or "1.2.x".
-// Note that "x.x" is a valid xRange identifer, meaning "any version"
-// Only the first item is strictly required.
-createToken('XRANGEIDENTIFIERLOOSE', `${src[t.NUMERICIDENTIFIERLOOSE]}|x|X|\\*`)
-createToken('XRANGEIDENTIFIER', `${src[t.NUMERICIDENTIFIER]}|x|X|\\*`)
-
-createToken('XRANGEPLAIN', `[v=\\s]*(${src[t.XRANGEIDENTIFIER]})` +
-                   `(?:\\.(${src[t.XRANGEIDENTIFIER]})` +
-                   `(?:\\.(${src[t.XRANGEIDENTIFIER]})` +
-                   `(?:${src[t.PRERELEASE]})?${
-                     src[t.BUILD]}?` +
-                   `)?)?`)
-
-createToken('XRANGEPLAINLOOSE', `[v=\\s]*(${src[t.XRANGEIDENTIFIERLOOSE]})` +
-                        `(?:\\.(${src[t.XRANGEIDENTIFIERLOOSE]})` +
-                        `(?:\\.(${src[t.XRANGEIDENTIFIERLOOSE]})` +
-                        `(?:${src[t.PRERELEASELOOSE]})?${
-                          src[t.BUILD]}?` +
-                        `)?)?`)
-
-createToken('XRANGE', `^${src[t.GTLT]}\\s*${src[t.XRANGEPLAIN]}$`)
-createToken('XRANGELOOSE', `^${src[t.GTLT]}\\s*${src[t.XRANGEPLAINLOOSE]}$`)
-
-// Coercion.
-// Extract anything that could conceivably be a part of a valid semver
-createToken('COERCE', `${'(^|[^\\d])' +
-              '(\\d{1,'}${MAX_SAFE_COMPONENT_LENGTH}})` +
-              `(?:\\.(\\d{1,${MAX_SAFE_COMPONENT_LENGTH}}))?` +
-              `(?:\\.(\\d{1,${MAX_SAFE_COMPONENT_LENGTH}}))?` +
-              `(?:$|[^\\d])`)
-createToken('COERCERTL', src[t.COERCE], true)
-
-// Tilde ranges.
-// Meaning is "reasonably at or greater than"
-createToken('LONETILDE', '(?:~>?)')
-
-createToken('TILDETRIM', `(\\s*)${src[t.LONETILDE]}\\s+`, true)
-exports.tildeTrimReplace = '$1~'
-
-createToken('TILDE', `^${src[t.LONETILDE]}${src[t.XRANGEPLAIN]}$`)
-createToken('TILDELOOSE', `^${src[t.LONETILDE]}${src[t.XRANGEPLAINLOOSE]}$`)
-
-// Caret ranges.
-// Meaning is "at least and backwards compatible with"
-createToken('LONECARET', '(?:\\^)')
-
-createToken('CARETTRIM', `(\\s*)${src[t.LONECARET]}\\s+`, true)
-exports.caretTrimReplace = '$1^'
-
-createToken('CARET', `^${src[t.LONECARET]}${src[t.XRANGEPLAIN]}$`)
-createToken('CARETLOOSE', `^${src[t.LONECARET]}${src[t.XRANGEPLAINLOOSE]}$`)
-
-// A simple gt/lt/eq thing, or just "" to indicate "any version"
-createToken('COMPARATORLOOSE', `^${src[t.GTLT]}\\s*(${src[t.LOOSEPLAIN]})$|^$`)
-createToken('COMPARATOR', `^${src[t.GTLT]}\\s*(${src[t.FULLPLAIN]})$|^$`)
-
-// An expression to strip any whitespace between the gtlt and the thing
-// it modifies, so that `> 1.2.3` ==> `>1.2.3`
-createToken('COMPARATORTRIM', `(\\s*)${src[t.GTLT]
-}\\s*(${src[t.LOOSEPLAIN]}|${src[t.XRANGEPLAIN]})`, true)
-exports.comparatorTrimReplace = '$1$2$3'
-
-// Something like `1.2.3 - 1.2.4`
-// Note that these all use the loose form, because they'll be
-// checked against either the strict or loose comparator form
-// later.
-createToken('HYPHENRANGE', `^\\s*(${src[t.XRANGEPLAIN]})` +
-                   `\\s+-\\s+` +
-                   `(${src[t.XRANGEPLAIN]})` +
-                   `\\s*$`)
-
-createToken('HYPHENRANGELOOSE', `^\\s*(${src[t.XRANGEPLAINLOOSE]})` +
-                        `\\s+-\\s+` +
-                        `(${src[t.XRANGEPLAINLOOSE]})` +
-                        `\\s*$`)
-
-// Star ranges basically just allow anything at all.
-createToken('STAR', '(<|>)?=?\\s*\\*')
diff --git a/spaces/zhanghaohui/szu-gpt-academic/request_llm/bridge_jittorllms_pangualpha.py b/spaces/zhanghaohui/szu-gpt-academic/request_llm/bridge_jittorllms_pangualpha.py
deleted file mode 100644
index ad02565aef75ac056e0daa7396fb1c6ad7aae072..0000000000000000000000000000000000000000
--- a/spaces/zhanghaohui/szu-gpt-academic/request_llm/bridge_jittorllms_pangualpha.py
+++ /dev/null
@@ -1,178 +0,0 @@
-
-from transformers import AutoModel, AutoTokenizer
-import time
-import threading
-import importlib
-from toolbox import update_ui, get_conf
-from multiprocessing import Process, Pipe
-
-load_message = "jittorllms尚未加载，加载需要一段时间。注意，请避免混用多种jittor模型，否则可能导致显存溢出而造成卡顿，取决于`config.py`的配置，jittorllms消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
-
-#################################################################################
-class GetGLMHandle(Process):
-    def __init__(self):
-        super().__init__(daemon=True)
-        self.parent, self.child = Pipe()
-        self.jittorllms_model = None
-        self.info = ""
-        self.local_history = []
-        self.success = True
-        self.check_dependency()
-        self.start()
-        self.threadLock = threading.Lock()
-        
-    def check_dependency(self):
-        try:
-            import pandas
-            self.info = "依赖检测通过"
-            self.success = True
-        except:
-            from toolbox import trimmed_format_exc
-            self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
-                        r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
-                        r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc()
-            self.success = False
-
-    def ready(self):
-        return self.jittorllms_model is not None
-
-    def run(self):
-        # 子进程执行
-        # 第一次运行，加载参数
-        def validate_path():
-            import os, sys
-            dir_name = os.path.dirname(__file__)
-            env = os.environ.get("PATH", "")
-            os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
-            root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
-            os.chdir(root_dir_assume + '/request_llm/jittorllms')
-            sys.path.append(root_dir_assume + '/request_llm/jittorllms')
-        validate_path() # validate path so you can run from base directory
-
-        def load_model():
-            import types
-            try:
-                if self.jittorllms_model is None:
-                    device, = get_conf('LOCAL_MODEL_DEVICE')
-                    from .jittorllms.models import get_model
-                    # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
-                    args_dict = {'model': 'pangualpha'}
-                    print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))')
-                    self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))
-                    print('done get model')
-            except:
-                self.child.send('[Local Message] Call jittorllms fail 不能正常加载jittorllms的参数。')
-                raise RuntimeError("不能正常加载jittorllms的参数！")
-        print('load_model')
-        load_model()
-
-        # 进入任务等待状态
-        print('进入任务等待状态')
-        while True:
-            # 进入任务等待状态
-            kwargs = self.child.recv()
-            query = kwargs['query']
-            history = kwargs['history']
-            # 是否重置
-            if len(self.local_history) > 0 and len(history)==0:
-                print('触发重置')
-                self.jittorllms_model.reset()
-            self.local_history.append(query)
-
-            print('收到消息，开始请求')
-            try:
-                for response in self.jittorllms_model.stream_chat(query, history):
-                    print(response)
-                    self.child.send(response)
-            except:
-                from toolbox import trimmed_format_exc
-                print(trimmed_format_exc())
-                self.child.send('[Local Message] Call jittorllms fail.')
-            # 请求处理结束，开始下一个循环
-            self.child.send('[Finish]')
-
-    def stream_chat(self, **kwargs):
-        # 主进程执行
-        self.threadLock.acquire()
-        self.parent.send(kwargs)
-        while True:
-            res = self.parent.recv()
-            if res != '[Finish]':
-                yield res
-            else:
-                break
-        self.threadLock.release()
-    
-global pangu_glm_handle
-pangu_glm_handle = None
-#################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
-    """
-        多线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    global pangu_glm_handle
-    if pangu_glm_handle is None:
-        pangu_glm_handle = GetGLMHandle()
-        if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + pangu_glm_handle.info
-        if not pangu_glm_handle.success: 
-            error = pangu_glm_handle.info
-            pangu_glm_handle = None
-            raise RuntimeError(error)
-
-    # jittorllms 没有 sys_prompt 接口，因此把prompt加入 history
-    history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
-    response = ""
-    for response in pangu_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        print(response)
-        if len(observe_window) >= 1:  observe_window[0] = response
-        if len(observe_window) >= 2:  
-            if (time.time()-observe_window[1]) > watch_dog_patience:
-                raise RuntimeError("程序终止。")
-    return response
-
-
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-        单线程方法
-        函数的说明请见 request_llm/bridge_all.py
-    """
-    chatbot.append((inputs, ""))
-
-    global pangu_glm_handle
-    if pangu_glm_handle is None:
-        pangu_glm_handle = GetGLMHandle()
-        chatbot[-1] = (inputs, load_message + "\n\n" + pangu_glm_handle.info)
-        yield from update_ui(chatbot=chatbot, history=[])
-        if not pangu_glm_handle.success: 
-            pangu_glm_handle = None
-            return
-
-    if additional_fn is not None:
-        import core_functional
-        importlib.reload(core_functional)    # 热更新prompt
-        core_functional = core_functional.get_core_functions()
-        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
-        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-
-    # 处理历史信息
-    history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
-
-    # 开始接收jittorllms的回复
-    response = "[Local Message]: 等待jittorllms响应中 ..."
-    for response in pangu_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        chatbot[-1] = (inputs, response)
-        yield from update_ui(chatbot=chatbot, history=history)
-
-    # 总结输出
-    if response == "[Local Message]: 等待jittorllms响应中 ...":
-        response = "[Local Message]: jittorllms响应异常 ..."
-    history.extend([inputs, response])
-    yield from update_ui(chatbot=chatbot, history=history)
diff --git a/spaces/zideliu/styledrop/timm/utils/model.py b/spaces/zideliu/styledrop/timm/utils/model.py
deleted file mode 100644
index cfd42806c37e62bd1c8741c5a0cb934e813b2682..0000000000000000000000000000000000000000
--- a/spaces/zideliu/styledrop/timm/utils/model.py
+++ /dev/null
@@ -1,16 +0,0 @@
-""" Model / state_dict utils
-
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-from .model_ema import ModelEma
-
-
-def unwrap_model(model):
-    if isinstance(model, ModelEma):
-        return unwrap_model(model.ema)
-    else:
-        return model.module if hasattr(model, 'module') else model
-
-
-def get_state_dict(model, unwrap_fn=unwrap_model):
-    return unwrap_fn(model).state_dict()
diff --git a/spaces/ziguo/Real-ESRGAN/realesrgan/models/realesrnet_model.py b/spaces/ziguo/Real-ESRGAN/realesrgan/models/realesrnet_model.py
deleted file mode 100644
index d11668f3712bffcd062c57db14d22ca3a0e1e59d..0000000000000000000000000000000000000000
--- a/spaces/ziguo/Real-ESRGAN/realesrgan/models/realesrnet_model.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import numpy as np
-import random
-import torch
-from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
-from basicsr.data.transforms import paired_random_crop
-from basicsr.models.sr_model import SRModel
-from basicsr.utils import DiffJPEG, USMSharp
-from basicsr.utils.img_process_util import filter2D
-from basicsr.utils.registry import MODEL_REGISTRY
-from torch.nn import functional as F
-
-
-@MODEL_REGISTRY.register()
-class RealESRNetModel(SRModel):
-    """RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
-    It is trained without GAN losses.
-    It mainly performs:
-    1. randomly synthesize LQ images in GPU tensors
-    2. optimize the networks with GAN training.
-    """
-
-    def __init__(self, opt):
-        super(RealESRNetModel, self).__init__(opt)
-        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
-        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
-        self.queue_size = opt.get('queue_size', 180)
-
-    @torch.no_grad()
-    def _dequeue_and_enqueue(self):
-        """It is the training pair pool for increasing the diversity in a batch.
-
-        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
-        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
-        to increase the degradation diversity in a batch.
-        """
-        # initialize
-        b, c, h, w = self.lq.size()
-        if not hasattr(self, 'queue_lr'):
-            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
-            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
-            _, c, h, w = self.gt.size()
-            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
-            self.queue_ptr = 0
-        if self.queue_ptr == self.queue_size:  # the pool is full
-            # do dequeue and enqueue
-            # shuffle
-            idx = torch.randperm(self.queue_size)
-            self.queue_lr = self.queue_lr[idx]
-            self.queue_gt = self.queue_gt[idx]
-            # get first b samples
-            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
-            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
-            # update the queue
-            self.queue_lr[0:b, :, :, :] = self.lq.clone()
-            self.queue_gt[0:b, :, :, :] = self.gt.clone()
-
-            self.lq = lq_dequeue
-            self.gt = gt_dequeue
-        else:
-            # only do enqueue
-            self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
-            self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
-            self.queue_ptr = self.queue_ptr + b
-
-    @torch.no_grad()
-    def feed_data(self, data):
-        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
-        """
-        if self.is_train and self.opt.get('high_order_degradation', True):
-            # training data synthesis
-            self.gt = data['gt'].to(self.device)
-            # USM sharpen the GT images
-            if self.opt['gt_usm'] is True:
-                self.gt = self.usm_sharpener(self.gt)
-
-            self.kernel1 = data['kernel1'].to(self.device)
-            self.kernel2 = data['kernel2'].to(self.device)
-            self.sinc_kernel = data['sinc_kernel'].to(self.device)
-
-            ori_h, ori_w = self.gt.size()[2:4]
-
-            # ----------------------- The first degradation process ----------------------- #
-            # blur
-            out = filter2D(self.gt, self.kernel1)
-            # random resize
-            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
-            if updown_type == 'up':
-                scale = np.random.uniform(1, self.opt['resize_range'][1])
-            elif updown_type == 'down':
-                scale = np.random.uniform(self.opt['resize_range'][0], 1)
-            else:
-                scale = 1
-            mode = random.choice(['area', 'bilinear', 'bicubic'])
-            out = F.interpolate(out, scale_factor=scale, mode=mode)
-            # add noise
-            gray_noise_prob = self.opt['gray_noise_prob']
-            if np.random.uniform() < self.opt['gaussian_noise_prob']:
-                out = random_add_gaussian_noise_pt(
-                    out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
-            else:
-                out = random_add_poisson_noise_pt(
-                    out,
-                    scale_range=self.opt['poisson_scale_range'],
-                    gray_prob=gray_noise_prob,
-                    clip=True,
-                    rounds=False)
-            # JPEG compression
-            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
-            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
-            out = self.jpeger(out, quality=jpeg_p)
-
-            # ----------------------- The second degradation process ----------------------- #
-            # blur
-            if np.random.uniform() < self.opt['second_blur_prob']:
-                out = filter2D(out, self.kernel2)
-            # random resize
-            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
-            if updown_type == 'up':
-                scale = np.random.uniform(1, self.opt['resize_range2'][1])
-            elif updown_type == 'down':
-                scale = np.random.uniform(self.opt['resize_range2'][0], 1)
-            else:
-                scale = 1
-            mode = random.choice(['area', 'bilinear', 'bicubic'])
-            out = F.interpolate(
-                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
-            # add noise
-            gray_noise_prob = self.opt['gray_noise_prob2']
-            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
-                out = random_add_gaussian_noise_pt(
-                    out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
-            else:
-                out = random_add_poisson_noise_pt(
-                    out,
-                    scale_range=self.opt['poisson_scale_range2'],
-                    gray_prob=gray_noise_prob,
-                    clip=True,
-                    rounds=False)
-
-            # JPEG compression + the final sinc filter
-            # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
-            # as one operation.
-            # We consider two orders:
-            #   1. [resize back + sinc filter] + JPEG compression
-            #   2. JPEG compression + [resize back + sinc filter]
-            # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
-            if np.random.uniform() < 0.5:
-                # resize back + the final sinc filter
-                mode = random.choice(['area', 'bilinear', 'bicubic'])
-                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
-                out = filter2D(out, self.sinc_kernel)
-                # JPEG compression
-                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
-                out = torch.clamp(out, 0, 1)
-                out = self.jpeger(out, quality=jpeg_p)
-            else:
-                # JPEG compression
-                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
-                out = torch.clamp(out, 0, 1)
-                out = self.jpeger(out, quality=jpeg_p)
-                # resize back + the final sinc filter
-                mode = random.choice(['area', 'bilinear', 'bicubic'])
-                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
-                out = filter2D(out, self.sinc_kernel)
-
-            # clamp and round
-            self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.
-
-            # random crop
-            gt_size = self.opt['gt_size']
-            self.gt, self.lq = paired_random_crop(self.gt, self.lq, gt_size, self.opt['scale'])
-
-            # training pair pool
-            self._dequeue_and_enqueue()
-            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
-        else:
-            # for paired training or validation
-            self.lq = data['lq'].to(self.device)
-            if 'gt' in data:
-                self.gt = data['gt'].to(self.device)
-                self.gt_usm = self.usm_sharpener(self.gt)
-
-    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
-        # do not use the synthetic process during validation
-        self.is_train = False
-        super(RealESRNetModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
-        self.is_train = True
diff --git a/spaces/zixian/Zhenhuan-VITS/losses.py b/spaces/zixian/Zhenhuan-VITS/losses.py
deleted file mode 100644
index fb22a0e834dd87edaa37bb8190eee2c3c7abe0d5..0000000000000000000000000000000000000000
--- a/spaces/zixian/Zhenhuan-VITS/losses.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch 
-from torch.nn import functional as F
-
-import commons
-
-
-def feature_loss(fmap_r, fmap_g):
-  loss = 0
-  for dr, dg in zip(fmap_r, fmap_g):
-    for rl, gl in zip(dr, dg):
-      rl = rl.float().detach()
-      gl = gl.float()
-      loss += torch.mean(torch.abs(rl - gl))
-
-  return loss * 2 
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-  loss = 0
-  r_losses = []
-  g_losses = []
-  for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-    dr = dr.float()
-    dg = dg.float()
-    r_loss = torch.mean((1-dr)**2)
-    g_loss = torch.mean(dg**2)
-    loss += (r_loss + g_loss)
-    r_losses.append(r_loss.item())
-    g_losses.append(g_loss.item())
-
-  return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-  loss = 0
-  gen_losses = []
-  for dg in disc_outputs:
-    dg = dg.float()
-    l = torch.mean((1-dg)**2)
-    gen_losses.append(l)
-    loss += l
-
-  return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
-  """
-  z_p, logs_q: [b, h, t_t]
-  m_p, logs_p: [b, h, t_t]
-  """
-  z_p = z_p.float()
-  logs_q = logs_q.float()
-  m_p = m_p.float()
-  logs_p = logs_p.float()
-  z_mask = z_mask.float()
-
-  kl = logs_p - logs_q - 0.5
-  kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
-  kl = torch.sum(kl * z_mask)
-  l = kl / torch.sum(z_mask)
-  return l
diff --git a/spaces/zlc99/M4Singer/vocoders/__init__.py b/spaces/zlc99/M4Singer/vocoders/__init__.py
deleted file mode 100644
index 66c318857ce48048437dede7072901ad6471b8fc..0000000000000000000000000000000000000000
--- a/spaces/zlc99/M4Singer/vocoders/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from vocoders import hifigan
diff --git a/spaces/zwhe99/MAPS-mt/data/__init__.py b/spaces/zwhe99/MAPS-mt/data/__init__.py
deleted file mode 100644
index 4a0a152c4c2a5ab3129d416acef93200c438f70a..0000000000000000000000000000000000000000
--- a/spaces/zwhe99/MAPS-mt/data/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .format_ask_demo import demo_dict as demo_ex_dict
-from .format_ask_kw import demo_dict as kw_ex_dict
-from .format_ask_topic import demo_dict as topic_ex_dict
\ No newline at end of file
diff --git a/spaces/zzz666/ChuanhuChatGPT/modules/openai_func.py b/spaces/zzz666/ChuanhuChatGPT/modules/openai_func.py
deleted file mode 100644
index 284311bb11906e4bb5516cfcabf90bef4ec09b12..0000000000000000000000000000000000000000
--- a/spaces/zzz666/ChuanhuChatGPT/modules/openai_func.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import requests
-import logging
-from modules.presets import timeout_all, BALANCE_API_URL,standard_error_msg,connection_timeout_prompt,error_retrieve_prompt,read_timeout_prompt
-from modules import shared
-import os
-
-
-def get_usage_response(openai_api_key):
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {openai_api_key}",
-    }
-
-    timeout = timeout_all
-
-    # 获取环境变量中的代理设置
-    http_proxy = os.environ.get("HTTP_PROXY") or os.environ.get("http_proxy")
-    https_proxy = os.environ.get(
-        "HTTPS_PROXY") or os.environ.get("https_proxy")
-
-    # 如果存在代理设置，使用它们
-    proxies = {}
-    if http_proxy:
-        logging.info(f"使用 HTTP 代理: {http_proxy}")
-        proxies["http"] = http_proxy
-    if https_proxy:
-        logging.info(f"使用 HTTPS 代理: {https_proxy}")
-        proxies["https"] = https_proxy
-
-    # 如果有代理，使用代理发送请求，否则使用默认设置发送请求
-    """
-    暂不支持修改
-    if shared.state.balance_api_url != BALANCE_API_URL:
-        logging.info(f"使用自定义BALANCE API URL: {shared.state.balance_api_url}")
-    """
-    if proxies:
-        response = requests.get(
-            BALANCE_API_URL,
-            headers=headers,
-            timeout=timeout,
-            proxies=proxies,
-        )
-    else:
-        response = requests.get(
-            BALANCE_API_URL,
-            headers=headers,
-            timeout=timeout,
-        )
-    return response
-
-def get_usage(openai_api_key):
-    try:
-        response=get_usage_response(openai_api_key=openai_api_key)
-        logging.debug(response.json())
-        try:
-            balance = response.json().get("total_available") if response.json().get(
-                "total_available") else 0
-            total_used = response.json().get("total_used") if response.json().get(
-                "total_used") else 0
-        except Exception as e:
-            logging.error(f"API使用情况解析失败:"+str(e))
-            balance = 0
-            total_used=0
-        return f"**API使用情况**（已用/余额）\u3000{total_used}$ / {balance}$"
-    except requests.exceptions.ConnectTimeout:
-        status_text = standard_error_msg + connection_timeout_prompt + error_retrieve_prompt
-        return status_text
-    except requests.exceptions.ReadTimeout:
-        status_text = standard_error_msg + read_timeout_prompt + error_retrieve_prompt
-        return status_text