diff --git a/spaces/101-5/gpt4free/g4f/.v1/unfinished/openprompt/mail.py b/spaces/101-5/gpt4free/g4f/.v1/unfinished/openprompt/mail.py
deleted file mode 100644
index 1130e7df1d309fdf35e13380cf81a1d162d14ff5..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/.v1/unfinished/openprompt/mail.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import email
-
-import requests
-
-
-class MailClient:
-
-    def __init__(self):
-        self.username = None
-        self.token = None
-        self.raw = None
-        self.mailids = None
-        self.mails = None
-        self.mail = None
-
-    def create(self, force=False):
-        headers = {
-            'accept': 'application/json',
-        }
-
-        if self.username:
-            pass
-        else:
-            self.response = requests.put(
-                'https://www.developermail.com/api/v1/mailbox', headers=headers)
-            self.response = self.response.json()
-            self.username = self.response['result']['name']
-            self.token = self.response['result']['token']
-
-        return {'username': self.username, 'token': self.token}
-
-    def destroy(self):
-        headers = {
-            'accept': 'application/json',
-            'X-MailboxToken': self.token,
-        }
-        self.response = requests.delete(
-            f'https://www.developermail.com/api/v1/mailbox/{self.username}', headers=headers)
-        self.response = self.response.json()
-        self.username = None
-        self.token = None
-        return self.response
-
-    def newtoken(self):
-        headers = {
-            'accept': 'application/json',
-            'X-MailboxToken': self.token,
-        }
-        self.response = requests.put(
-            f'https://www.developermail.com/api/v1/mailbox/{self.username}/token', headers=headers)
-        self.response = self.response.json()
-        self.token = self.response['result']['token']
-        return {'username': self.username, 'token': self.token}
-
-    def getmailids(self):
-        headers = {
-            'accept': 'application/json',
-            'X-MailboxToken': self.token,
-        }
-
-        self.response = requests.get(
-            f'https://www.developermail.com/api/v1/mailbox/{self.username}', headers=headers)
-        self.response = self.response.json()
-        self.mailids = self.response['result']
-        return self.mailids
-
-    def getmails(self, mailids: list = None):
-        headers = {
-            'accept': 'application/json',
-            'X-MailboxToken': self.token,
-            'Content-Type': 'application/json',
-        }
-
-        if mailids is None:
-            mailids = self.mailids
-
-        data = str(mailids)
-
-        self.response = requests.post(
-            f'https://www.developermail.com/api/v1/mailbox/{self.username}/messages', headers=headers, data=data)
-        self.response = self.response.json()
-        self.mails = self.response['result']
-        return self.mails
-
-    def getmail(self, mailid: str, raw=False):
-        headers = {
-            'accept': 'application/json',
-            'X-MailboxToken': self.token,
-        }
-        self.response = requests.get(
-            f'https://www.developermail.com/api/v1/mailbox/{self.username}/messages/{mailid}', headers=headers)
-        self.response = self.response.json()
-        self.mail = self.response['result']
-        if raw is False:
-            self.mail = email.message_from_string(self.mail)
-        return self.mail
-
-    def delmail(self, mailid: str):
-        headers = {
-            'accept': 'application/json',
-            'X-MailboxToken': self.token,
-        }
-        self.response = requests.delete(
-            f'https://www.developermail.com/api/v1/mailbox/{self.username}/messages/{mailid}', headers=headers)
-        self.response = self.response.json()
-        return self.response
-
-
-client = MailClient()
-client.newtoken()
-print(client.getmails())
diff --git a/spaces/101-5/gpt4free/g4f/models.py b/spaces/101-5/gpt4free/g4f/models.py
deleted file mode 100644
index ecf18e6dffe029d6bbd651428094083c15b77283..0000000000000000000000000000000000000000
--- a/spaces/101-5/gpt4free/g4f/models.py
+++ /dev/null
@@ -1,201 +0,0 @@
-from g4f import Provider
-
-
-class Model:
-    class model:
-        name: str
-        base_provider: str
-        best_provider: str
-
-    class gpt_35_turbo:
-        name: str = 'gpt-3.5-turbo'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Forefront
-
-    class gpt_4:
-        name: str = 'gpt-4'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Bing
-        best_providers: list = [Provider.Bing, Provider.Lockchat]
-
-    class claude_instant_v1_100k:
-        name: str = 'claude-instant-v1-100k'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_instant_v1:
-        name: str = 'claude-instant-v1'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_v1_100k:
-        name: str = 'claude-v1-100k'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_v1:
-        name: str = 'claude-v1'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class alpaca_7b:
-        name: str = 'alpaca-7b'
-        base_provider: str = 'replicate'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class stablelm_tuned_alpha_7b:
-        name: str = 'stablelm-tuned-alpha-7b'
-        base_provider: str = 'replicate'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class bloom:
-        name: str = 'bloom'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class bloomz:
-        name: str = 'bloomz'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class flan_t5_xxl:
-        name: str = 'flan-t5-xxl'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class flan_ul2:
-        name: str = 'flan-ul2'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class gpt_neox_20b:
-        name: str = 'gpt-neox-20b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class oasst_sft_4_pythia_12b_epoch_35:
-        name: str = 'oasst-sft-4-pythia-12b-epoch-3.5'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class santacoder:
-        name: str = 'santacoder'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class command_medium_nightly:
-        name: str = 'command-medium-nightly'
-        base_provider: str = 'cohere'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class command_xlarge_nightly:
-        name: str = 'command-xlarge-nightly'
-        base_provider: str = 'cohere'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class code_cushman_001:
-        name: str = 'code-cushman-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class code_davinci_002:
-        name: str = 'code-davinci-002'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_ada_001:
-        name: str = 'text-ada-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_babbage_001:
-        name: str = 'text-babbage-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_curie_001:
-        name: str = 'text-curie-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_davinci_002:
-        name: str = 'text-davinci-002'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_davinci_003:
-        name: str = 'text-davinci-003'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-        
-    class palm:
-        name: str = 'palm'
-        base_provider: str = 'google'
-        best_provider: Provider.Provider = Provider.Bard
-        
-            
-    """    'falcon-40b': Model.falcon_40b,
-    'falcon-7b': Model.falcon_7b,
-    'llama-13b': Model.llama_13b,"""
-    
-    class falcon_40b:
-        name: str = 'falcon-40b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-    
-    class falcon_7b:
-        name: str = 'falcon-7b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-        
-    class llama_13b:
-        name: str = 'llama-13b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-    
-class ModelUtils:
-    convert: dict = {
-        'gpt-3.5-turbo': Model.gpt_35_turbo,
-        'gpt-4': Model.gpt_4,
-        
-        'claude-instant-v1-100k': Model.claude_instant_v1_100k,
-        'claude-v1-100k': Model.claude_v1_100k,
-        'claude-instant-v1': Model.claude_instant_v1,
-        'claude-v1': Model.claude_v1,
-        
-        'alpaca-7b': Model.alpaca_7b,
-        'stablelm-tuned-alpha-7b': Model.stablelm_tuned_alpha_7b,
-        
-        'bloom': Model.bloom,
-        'bloomz': Model.bloomz,
-        
-        'flan-t5-xxl': Model.flan_t5_xxl,
-        'flan-ul2': Model.flan_ul2,
-        
-        'gpt-neox-20b': Model.gpt_neox_20b,
-        'oasst-sft-4-pythia-12b-epoch-3.5': Model.oasst_sft_4_pythia_12b_epoch_35,
-        'santacoder': Model.santacoder,
-        
-        'command-medium-nightly': Model.command_medium_nightly,
-        'command-xlarge-nightly': Model.command_xlarge_nightly,
-        
-        'code-cushman-001': Model.code_cushman_001,
-        'code-davinci-002': Model.code_davinci_002,
-        
-        'text-ada-001': Model.text_ada_001,
-        'text-babbage-001': Model.text_babbage_001,
-        'text-curie-001': Model.text_curie_001,
-        'text-davinci-002': Model.text_davinci_002,
-        'text-davinci-003': Model.text_davinci_003,
-        
-        'palm2': Model.palm,
-        'palm': Model.palm,
-        'google': Model.palm,
-        'google-bard': Model.palm,
-        'google-palm': Model.palm,
-        'bard': Model.palm,
-        
-        'falcon-40b': Model.falcon_40b,
-        'falcon-7b': Model.falcon_7b,
-        'llama-13b': Model.llama_13b,
-    }
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Bluesoleil Activation Serial Number Free Download and Install Guide.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Bluesoleil Activation Serial Number Free Download and Install Guide.md
deleted file mode 100644
index e8b0e96f0239516b92d2bf8480ea4b30c2e2368a..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Bluesoleil Activation Serial Number Free Download and Install Guide.md	
+++ /dev/null
@@ -1,125 +0,0 @@
-<br />
-<h1>How to Get BlueSoleil Activation Serial Number for Free</h1>
-<p>Do you want to connect your Bluetooth devices with your computer without any hassle? Do you want to transfer data, make calls, listen to music, and more using Bluetooth technology? If yes, then you need BlueSoleil.</p>
-<h2>bluesoleil activation serial number free</h2><br /><p><b><b>Download</b> &#127379; <a href="https://byltly.com/2uKxPy">https://byltly.com/2uKxPy</a></b></p><br /><br />
-<h2>What is BlueSoleil and why do you need it?</h2>
-<h3>BlueSoleil is a Bluetooth software that allows you to connect your devices with your computer</h3>
-<p>BlueSoleil is a software that enables your computer to communicate with Bluetooth devices such as phones, headsets, keyboards, mice, printers, cameras, etc. It supports various Bluetooth protocols and profiles, such as A2DP, AVRCP, BIP, DUN, FTP, HID, OPP, PAN, PBAP, SPP, etc. It also supports multiple Bluetooth dongles and adapters.</p>
-<h3>You need BlueSoleil to transfer data, make calls, listen to music, and more</h3>
-<p>With BlueSoleil, you can do many things with your Bluetooth devices. For example:</p>
-<ul>
-<li>You can transfer files between your computer and your phone or other devices wirelessly.</li>
-<li>You can make or receive phone calls using your computer's microphone and speakers.</li>
-<li>You can listen to music from your phone or other devices using your computer's speakers or headphones.</li>
-<li>You can sync your contacts and messages with your phone or other devices.</li>
-<li>You can print documents or photos from your phone or other devices using a Bluetooth printer.</li>
-<li>You can control your computer remotely using a Bluetooth keyboard or mouse.</li>
-<li>You can share your internet connection with your phone or other devices using a Bluetooth network.</li>
-</ul>
-<p>And much more!</p>
-<h2>How to get BlueSoleil activation serial number for free?</h2>
-<p>BlueSoleil is not a free software. You need to buy a license key to activate it and use all its features. However, if you don't want to spend money on it, there are some ways to get BlueSoleil activation serial number for free. Here are two methods that you can try:</p>
-<h3>There are two ways to get BlueSoleil activation serial number for free: online and offline</h3>
-<h4>Online method: use a website that provides serial numbers for free</h4>
-<p>This method is very simple and fast. All you need to do is find a website that offers serial numbers for various software for free. For example:</p>
-<p>bluesoleil license key free download<br />
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-<code><a href="https://smartserials.com/serials/IVT-BlueSoleil-6.4.305.0-32199.htm">https://smartserials.com/serials/IVT-BlueSoleil-6.4.305.0-32199.htm</a></code>
-<code><a href="https://smartserials.com/serials/IVT-BlueSoleil-v10.0.417.0-34510.htm">https://smartserials.com/serials/IVT-BlueSoleil-v10.0.417.0-34510.htm</a></code>
-<p>On these websites, you can find serial numbers for different versions of BlueSoleil. Just copy one of them and paste it when you install or activate BlueSoleil on your computer.</p>
-<h4>Offline method: use a crack file that bypasses the activation process</h4>
-<p>This method is more complicated and slow. You need to download a crack file that modifies the original BlueSoleil program and makes it think that it is already activated. For example:</p>
-<code><a href="https://docslib.org/doc/10485325/bluesoleil-activation-serial-number-free">https://docslib.org/doc/10485325/bluesoleil-activation-serial-number-free</a></code>
-<code><a href="https://peatix.com/group/10135257">https://peatix.com/group/10135257</a></code>
-<p>On these websites, you can find crack files for different versions of BlueSoleil. Just download one of them and follow the instructions on how to run it on your computer.</p>
-<h2>What are the advantages and disadvantages of each method?</h2>
-<h3>Online method: easy, fast, but risky</h3>
-<h4>Advantages: you don't need to download anything, just copy and paste the serial number</h4>
-<p>This method is very convenient and quick. You don't have to waste time or space on downloading any files. You just have to visit a website and copy a serial number.</p>
-<h4>Disadvantages: you may get a fake or invalid serial number, or expose your computer to malware or viruses</h4>
-<p>This method is also very risky and unreliable. You may not get a valid serial number that works for your version of BlueSoleil. You may also get a fake serial number that contains malware or viruses that can harm your computer. You may also violate the terms of service of BlueSoleil by using an illegal serial number.</p>
-<h3>Offline method: complicated, slow, but safe</h3>
-<h4>Advantages: you don't need to rely on any website, just download and run the crack file</h4>
-<p>This method is more secure and reliable. You don't have to depend on any website that may be shady or fraudulent. You just have to download and run the crack file that modifies the original program and bypasses the activation process.</p>
- <h4>Disadvantages: you may need to disable your antivirus software, or face compatibility issues with your system</h4>
- <p>This method is also more complex and slow. You may have to disable your antivirus software to avoid false alarms or interference with the crack file. You may also face compatibility issues with your system, such as Windows version, architecture, or language. You may also violate the terms of service of BlueSoleil by using an illegal crack file.</p>
- <h2>How to use BlueSoleil after getting the activation serial number?</h2>
- <h3>Install BlueSoleil on your computer and enter the serial number when prompted</h3>
- <p>To use BlueSoleil, you first need to install it on your computer. You can download the latest version of BlueSoleil from its official website <code><a href="http://www.bluesoleil.com/">http://www.bluesoleil.com/</a></code>
-<p>Choose the version that matches your system and download it. Then run the setup file and follow the instructions to install it. When you are asked to enter the serial number, paste the one that you got from the online or offline method.</p>
-<h3>Connect your Bluetooth devices with your computer using BlueSoleil interface</h3>
-<p>After installing BlueSoleil, you can use its interface to connect your Bluetooth devices with your computer. You can find the BlueSoleil icon on your desktop or taskbar. Click on it to open the main window. You will see a red ball that represents your computer and some white balls that represent the Bluetooth devices in range. You can also see some service buttons at the top of the window that indicate the supported Bluetooth functions.</p>
-<p>To connect a device, you need to do the following steps:</p>
-<ol>
-<li>Double-click on a white ball to search for its supported services. The service buttons that match the device will be highlighted.</li>
-<li>Click on a service button that you want to use. For example, if you want to transfer files, click on the file transfer button.</li>
-<li>A service window will pop up. Follow the instructions on the window to complete the connection and operation. For example, if you want to transfer files, you can browse and select the files on your computer or device and drag and drop them to transfer.</li>
-</ol>
-<p>You can also right-click on a white ball to access more options, such as pairing, refreshing, disconnecting, etc.</p>
-<h3>Enjoy the features and functions of BlueSoleil</h3>
-<p>Once you have connected your Bluetooth devices with your computer using BlueSoleil, you can enjoy the features and functions of BlueSoleil. You can transfer data, make calls, listen to music, and more using Bluetooth technology. You can also manage your connections and devices using BlueSoleil interface. You can view the connection status, battery level, signal strength, etc. of your devices. You can also customize your settings, such as changing your Bluetooth name, enabling or disabling security mode, setting auto-connect options, etc.</p>
-<h2>Conclusion</h2>
-<p>BlueSoleil is a useful software that lets you connect your Bluetooth devices with your computer. You can get BlueSoleil activation serial number for free by using online or offline methods. Each method has its pros and cons, so choose wisely according to your needs and preferences. After getting the serial number, you can install BlueSoleil on your computer and enter it when prompted. Then you can connect your Bluetooth devices with your computer using BlueSoleil interface and enjoy its features and functions.</p>
- <h2>FAQs</h2>
- <ol>
-<li>Q: What are the system requirements for BlueSoleil?</li>
-<li>A: According to its official website , BlueSoleil supports Windows XP/Vista/7/8/10 (32-bit and 64-bit). It also requires a Bluetooth dongle or adapter that supports Bluetooth 2.0 or above.</li>
-<li>Q: How can I check if my Bluetooth dongle or adapter is compatible with BlueSoleil?</li>
-<li>A: You can check the compatibility list on its official website , or contact its customer service for more information.</li>
-<li>Q: How can I update BlueSoleil to the latest version?</li>
-<li>A: You can download the latest version of BlueSoleil from its official website , or use its built-in update function.</li>
-<li>Q: How can I get technical support for BlueSoleil?</li>
-<li>A: You can visit its official website  and access its online help center, forum, FAQ section, or contact its customer service via email or phone.</li>
-<li>Q: How can I uninstall BlueSoleil from my computer?</li>
-<li>A: You can uninstall BlueSoleil from your computer by using its built-in uninstall function, or by using Windows Control Panel.</li>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Chess Position Trainer 5 Crack A Comprehensive Review of the Features and Benefits of this Chess Software.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Chess Position Trainer 5 Crack A Comprehensive Review of the Features and Benefits of this Chess Software.md
deleted file mode 100644
index 799c26450c096e132186a59c34961b006947daa8..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Chess Position Trainer 5 Crack A Comprehensive Review of the Features and Benefits of this Chess Software.md	
+++ /dev/null
@@ -1,110 +0,0 @@
-
-<h1>Chess Position Trainer 5 Crack: How to Download and Use It</h1>
- <p>If you are a chess enthusiast who wants to improve your game, you might have heard of Chess Position Trainer 5, a professional chess software that helps you build your own repertoire, train your openings, tactics and endgames, and analyze your games and positions with a powerful engine. But what if you don't want to pay for the software? Is there a way to get it for free? In this article, we will show you how to download and use Chess Position Trainer 5 crack, as well as the risks and drawbacks of doing so.</p>
- <h2>What is Chess Position Trainer 5?</h2>
- <p>Chess Position Trainer 5 is a chess training software that was developed by chesspositiontrainer.com in 2019. It is designed to help chess players of all levels create, manage and study their chess opening repertoire. It also offers features such as flash-card based training, blindfold mode, position explorer, engine analysis, game import/export, statistics, and more. Chess Position Trainer 5 claims to be a major time saver for your daily work and make it fun to train your chess openings. It supports multiple languages such as English, Deutsch, Français, Nederlands, Italiano, Português.</p>
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- <h3>Features and benefits of Chess Position Trainer 5</h3>
- <p>Some of the features and benefits of Chess Position Trainer 5 are:</p>
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-<li>It has an innovative repertoire concept that allows you to create topics for managing endgame, middle-game and key positions, as well as creating new variations and detecting transpositions across openings.</li>
-<li>It has a rich formatting of position comments that lets you add diagrams, colors, fonts, symbols, links, etc. to your notes.</li>
-<li>It has an automatic re-ordering of candidate moves that helps you prioritize the most important moves in each position.</li>
-<li>It has a flash-card based training concept that tests your memory and understanding of your repertoire using spaced repetition.</li>
-<li>It has a blindfold mode that challenges you to visualize the board without seeing it.</li>
-<li>It has a position explorer that shows you all possible moves and statistics for each position in your repertoire.</li>
-<li>It has a min-max calculation that shows you the best and worst score for each move in your repertoire.</li>
-<li>It has a UCI engine support that allows you to analyze your positions and games with a powerful chess engine such as Stockfish or Komodo.</li>
-<li>It has a game import/export feature that lets you import games from PGN files or databases, or export games to PGN files or websites.</li>
-<li>It has a statistic module that shows you various data about your repertoire such as coverage, performance, progress, etc.</li>
-</ul>
- <h3>How to download Chess Position Trainer 5 for free</h3>
- <p>If you want to download Chess Position Trainer 5 for free, you will need to find a crack file that bypasses the software's protection and activation system. A crack file is a modified version of the original software file that allows you to use it without paying for it. However, finding a reliable source for downloading Chess Position Trainer 5 crack can be tricky and risky. Here are some steps you can follow:</p>
- <h4>Step 1: Find a reliable source</h4>
- <p>The first step is to find a website that offers Chess Position Trainer 5 crack for download. You can use search engines such as Google or Bing to look for keywords such as "Chess Position Trainer 5 crack", "Chess Position Trainer 5 free download", "Chess Position Trainer 5 torrent", etc. However, be careful not to click on any suspicious or malicious links that might harm your computer or steal your personal information. You can use tools such as VirusTotal or Web of Trust to check the reputation and safety of the websites before visiting them.</p>
- <h4>Step 2: Download the setup file and the crack file</h4>
- <p>The second step is to download the setup file and the crack file from the website you have chosen. The setup file is usually an executable file (.exe) that installs the software on your computer. The crack file is usually a DLL file (.dll) that replaces the original file in the software's folder. Sometimes, the crack file might also come with instructions on how to apply it. Make sure you scan both files with an antivirus program before opening them.</p>
- <h4>Step 3: Install the software and apply the crack</h4>
- <p>The third step is to install the software and apply the crack on your computer. To install the software, run the setup file and follow the instructions on the screen. To apply the crack, copy the crack file and paste it into the software's folder (usually located in C:\Program Files\ChessPositionTrainer). If prompted, overwrite or replace the existing file. You might also need to disable your antivirus program or firewall temporarily while applying the crack.</p>
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- <p>If you have successfully installed and cracked Chess Position Trainer 5 on your computer, you can start using it to improve your chess skills. Here are some steps you can follow:</p>
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- <h4>Step 1: Create and manage your own repertoire</h4>
- <p>The first step is to create and manage your own repertoire using Chess Position Trainer 5's innovative repertoire concept. You can create topics for different openings, endgames, middle-games or key positions that you want to study. You can also create new variations or edit existing ones using the position editor. You can add comments, diagrams, symbols or links to each position using the rich formatting feature. You can also reorder candidate moves according to their importance using the automatic re-ordering feature.</p>
- <h4>Step 2: Train your openings, tactics and endgames with flash cards</h4>
- <p>The second step is to train your openings, tactics and endgames with flash cards using Chess Position Trainer 5's flash-card based training concept. You can select which topics or positions you want to train from your repertoire using various criteria such as scheduled only, random, photo reading or systematic. You can also choose which side (white or black) you want to play from each position. You will be shown a position on the board and asked to enter the best move or moves according to your repertoire. You will get immediate feedback on whether you are correct or not. You can also review your mistakes or repeat difficult positions later.</p>
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- <p>The third step is to analyze your games and positions with a powerful engine using Chess Position Trainer 5's UCI engine support feature. You can import games from PGN files or databases into Chess Position Trainer 5 using the game import feature. You can also export games from Chess Position Trainer 5 into PGN files or websites using the game export feature. You can then analyze each move or position in your games using a UCI engine such as Stockfish or Komodo. You can see various information such as evaluation score, best move, depth Continuing the article: <h2>Risks and drawbacks of using Chess Position Trainer 5 crack</h2>
- <p>While using Chess Position Trainer 5 crack might seem like a good idea to save money and enjoy the software's features, it also comes with many risks and drawbacks that you should be aware of. Here are some of them:</p>
- <h3>Legal issues and ethical concerns</h3>
- <p>Using Chess Position Trainer 5 crack is illegal, as it violates the software's copyright law. You are essentially stealing from the software developers who spent time and money creating the product. You could face fines of up to $150,000 and up to five years in prison for each instance of using or distributing cracked software. You could also damage your reputation and credibility as a chess player or professional if you are caught using pirated software. Moreover, using Chess Position Trainer 5 crack is unethical, as it shows a lack of respect and appreciation for the work of the software creators. You are also depriving them of their rightful income and discouraging them from developing more quality products in the future.</p>
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- <p>Using Chess Position Trainer 5 crack is dangerous, as it exposes your computer to potential malware and viruses. The websites that offer cracked software are often shady and untrustworthy, and they might infect your computer with malicious programs that can harm your system, steal your personal information, or display unwanted ads. The crack files themselves might also contain hidden malware or viruses that can compromise your security and privacy. Even if you scan the files with an antivirus program, you might not detect all the threats, as some malware can evade detection or disable your antivirus program. You could end up losing your data, money, or identity by using cracked software.</p>
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- <p>Using Chess Position Trainer 5 crack is inconvenient, as it deprives you of updates and support from the software developers. The cracked software might not work properly or have bugs or errors that affect its functionality. You might not be able to access some features or options that are available in the original software. You might also miss out on new features or improvements that are added to the software through regular updates. Moreover, you will not be able to contact the software developers for any questions or issues that you encounter while using the software. You will have to rely on yourself or other users of cracked software for troubleshooting, which might not be effective or reliable.</p>
- <h2>Conclusion</h2>
- <p>Chess Position Trainer 5 is a professional chess software that can help you improve your chess skills by creating, managing and studying your chess opening repertoire. It also offers other features such as flash-card based training, blindfold mode, position explorer, engine analysis, game import/export, statistics, and more. However, using Chess Position Trainer 5 crack is not a wise decision, as it involves many risks and drawbacks such as legal issues, ethical concerns, potential malware and viruses, lack of updates and support. You should respect the software developers' rights and efforts and purchase the original software instead of using cracked software.</p>
- <h2>FAQs</h2>
- <ul>
-<li><b>Q: How much does Chess Position Trainer 5 cost?</b></li>
-<li>A: Chess Position Trainer 5 costs $39.95 for the Pro version and $29.95 for the Standard version. You can also download a free version with limited features from the official website.</li>
-<li><b>Q: How can I purchase Chess Position Trainer 5?</b></li>
-<li>A: You can purchase Chess Position Trainer 5 online through PayPal or credit card from the official website. You will receive an email with a download link and a license key after completing the payment.</li>
-<li><b>Q: How can I update Chess Position Trainer 5?</b></li>
-<li>A: You can update Chess Position Trainer 5 by downloading the latest version from the official website and installing it over your existing version. You don't need to uninstall your previous version or enter your license key again.</li>
-<li><b>Q: How can I contact Chess Position Trainer 5 support?</b></li>
-<li>A: You can contact Chess Position Trainer 5 support by sending an email to support@chesspositiontrainer.com or by filling out a contact form on the official website. You can also visit the blog or forum sections on the website for more information and tips.</li>
-<li><b>Q: How can I learn more about Chess Position Trainer 5?</b></li>
-<li>A: You can learn more about Chess Position Trainer 5 by visiting the official website where you can find features, screenshots, videos, manuals, testimonials, FAQs, and more.</li>
-</ul>
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diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download IVONA Text to Speech 1.6.63 with crack (All voices) and create amazing audio content.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download IVONA Text to Speech 1.6.63 with crack (All voices) and create amazing audio content.md
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-<tr><td>Marlene</td><td>German</td><</p> 0a6ba089eb<br />
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Be a Pro - Football Real Teams Real Players Real Action.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Be a Pro - Football Real Teams Real Players Real Action.md
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index f0ccd0d7b41e0fdae8da509d7cae93e2476df190..0000000000000000000000000000000000000000
--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Be a Pro - Football Real Teams Real Players Real Action.md	
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-<h3>Choose your team and players</h3>
-<p>When you launch the game, you will be asked to choose your team name, logo, and kit. You can also customize your players' names, appearances, and attributes. You can unlock more options as you progress in the game. You can also buy coins and gems with real money to get more items and features.</p>
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-<p>The main mode of Be a Pro - Football is the real-time 11v11 online PVP mode. In this mode, you can join matches with other players from around the world and compete for rankings and rewards. You can choose from different leagues and tournaments, such as Champions League, Europa League, World Cup, and more. You can also create or join clubs with your friends and play together.</p>
-<h3>Upgrade your skills and tactics</h3>
-<p>To win matches, you need to upgrade your skills and tactics. You can train your players to improve their attributes, such as speed, strength, stamina, shooting, passing, dribbling, tackling, and more. You can also change your formation, strategy, and style of play according to your opponents and situations. You can use different skills and items during matches, such as sprint, slide tackle, cross, shoot, pass, dribble, header, volley, chip shot, curve shot, free kick, corner kick, penalty kick, and more.</p>
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-<p>Be a Pro - Football is not just another football game. It is the best online football game that you can play on your mobile device. Here are some reasons why you should play it:</p>
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-<p>Be a Pro - Football offers competitive and diverse modes for you to enjoy. You can play in different leagues and tournaments with different rules and rewards. You can also play in idle mode where you can earn coins and gems even when you are offline. You can also challenge yourself in various missions and achievements.</p>
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-<p>A: You can join or create a club by tapping on the Club button on the main screen. You can then search for an existing club or create your own club. You can invite your friends to join your club and play together.</p>
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-<p>A: You can chat with other players by tapping on the Chat button on the main screen. You can then choose from different chat channels, such as Global, Club, Match, and Private. You can also use emojis and stickers to express yourself.</p>
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bus Simulator Ultimate MOD APK 1.5.3 Drive Your Way to Success with Unlimited Money and More.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bus Simulator Ultimate MOD APK 1.5.3 Drive Your Way to Success with Unlimited Money and More.md
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--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bus Simulator Ultimate MOD APK 1.5.3 Drive Your Way to Success with Unlimited Money and More.md	
+++ /dev/null
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-<p>Bus Simulator Ultimate is a realistic bus driving simulation game that lets you drive various types of buses across different countries and cities. You can choose from over 25 different buses, each with their own features and specifications, such as speed, capacity, fuel consumption, etc. You can also customize your buses with different skins, colors, logos, stickers, etc. You can drive in different weather conditions, day and night cycles, traffic situations, road types, etc. You can also interact with your passengers, who have their own personalities and behaviors. You can hear their conversations, complaints, compliments, etc.</p>
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-<p>Bus Simulator Ultimate has many features that make it one of the best bus driving games on the market. Here are some of them:</p>
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-<h4>Customizable buses and skins</h4>
-<p>The game has over 25 different buses that you can choose from. Each bus has its own features and specifications that affect its performance and fuel consumption. You can also customize your buses with different skins that change their appearance. You can choose from various colors, logos, stickers, etc. You can also create your own skins using the skin editor tool.</p>
-<h4>Online multiplayer and leaderboards</h4>
-<p>The game has an online multiplayer mode that lets you play with other players around the world. You can join or create online rooms and chat with other players. You can also race with them or cooperate with them in different missions and challenges. You can also check the leaderboards and see your rank and stats compared to other players.</p>
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-<p>Bus Simulator Ultimate is a fun and realistic bus driving simulation game that lets you drive various types of buses across different countries and cities. You can also start your own bus company and manage it as a business simulation game. You can also play online with other players and compete with them in different modes and challenges. And if you want to enjoy the game without any limitations, you can download Bus Simulator Ultimate Mod APK, which gives you unlimited money, gold, and access to all the buses and skins in the game.</p>
-<p>If you liked this article, please share it with your friends who love bus driving games. And if you have any questions or feedback about Bus Simulator Ultimate or its mod version, please leave a comment below. We would love to hear from you.</p>
- FAQs Q: Is Bus Simulator Ultimate Mod APK safe to download and install? A: Yes, Bus Simulator Ultimate Mod APK is safe to download and install on any Android device. It does not contain any viruses or malware that can harm your device or data. Q: Can I play Bus Simulator Ultimate offline? A: Yes, you can play Bus Simulator Ultimate offline in career mode or free mode. However, you need an internet connection to play online multiplayer mode. Q: How can I update Bus Simulator Ultimate Mod APK? A: To update Bus Simulator Ultimate Mod APK, you need to download the latest version of the mod APK file from the same source that you downloaded it from before. Then, you need to uninstall the previous version of the game from your device and install the new version of the mod APK file. You can also check the source website for any updates or notifications about the mod APK. Q: How can I contact the developers of Bus Simulator Ultimate? A: You can contact the developers of Bus Simulator Ultimate by visiting their official website: <a href="">Zuuks Games</a>. You can also follow them on their social media accounts, such as Facebook, Twitter, Instagram, etc. Q: What are some other bus driving games that I can play? A: Some other bus driving games that you can play are: - Bus Simulator 2021: A realistic bus driving game that lets you drive in various cities and countries, such as New York, London, Paris, Berlin, etc. You can also customize your buses and create your own routes. - Coach Bus Simulator: A bus driving game that lets you drive different types of coaches and transport passengers across Europe. You can also upgrade your coaches and hire new drivers. - World Bus Driving Simulator: A bus driving game that lets you drive in different countries and regions, such as Brazil, USA, Canada, etc. You can also choose from different bus models and customize them with various accessories.</p> 197e85843d<br />
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cubase 12 The Most Advanced and Reliable Music Production Software.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cubase 12 The Most Advanced and Reliable Music Production Software.md
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-
-<h1>Cubase 12: A Complete Guide for Beginners</h1>
- <p>If you are looking for a powerful and versatile music production software, you might want to check out Cubase 12. Cubase is one of the most popular and trusted DAWs (Digital Audio Workstations) in the world, used by professional composers, producers, engineers, and musicians across all genres and styles. Whether you want to create your own music, record and edit audio, mix and master your tracks, or collaborate with other artists, Cubase can help you achieve your musical goals.</p>
- <p>In this article, we will give you a complete overview of Cubase 12, its main features and benefits, and how to get started with it. We will also show you some tips and tricks on how to use some of the new and improved tools in Cubase 12, such as MIDI Remote integration, VariAudio and Scale Assistant, Sampler Track 2, MixConsole, and more. By the end of this article, you will have a better understanding of what Cubase 12 can do for you and how to use it effectively.</p>
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- <h2>MIDI Remote Integration</h2>
- <p>MIDI controllers are essential devices for any music production setup. They allow you to control various parameters and functions in your software with physical knobs, faders, buttons, pads, keys, or pedals. In Cubase 12, integrating your MIDI controller has become easier than ever. Thanks to the new concept of MIDI Remote Scripts, Cubase will automatically detect your device and map the controls accordingly. If there is no script for your device yet, you can easily create your own with the MIDI Controller Surface Editor. You can then conveniently connect controls and parameters with the Mapping Assistant.</p>
- <h3>How to connect and configure your MIDI controller with Cubase 12?</h3>
- <p>To connect your MIDI controller with Cubase 12, follow these steps:</p>
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- <ol>
-<li>Connect your MIDI controller to your computer via USB or MIDI cable.</li>
-<li>Launch Cubase 12 and create or open a project.</li>
-<li>Go to Studio > Studio Setup.</li>
-<li>Click on the + button in the top left corner and select your MIDI controller from the list.</li>
-<li>If there is a script available for your device, it will be automatically loaded. If not, you can create one using the MIDI Controller Surface Editor.</li>
-<li>Click on Apply and OK.</li>
-</ol>
- <h3>How to use MIDI Remote Scripts and Mapping Assistant to customize your control surface?</h3>
- <h3>How to record and edit MIDI data with Cubase 12?</h3>
- <p>To record and edit MIDI data with Cubase 12, follow these steps:</p>
- <ol>
-<li>Create a MIDI track and assign it to a VST instrument or an external MIDI device.</li>
-<li>Set the input and output routing for the MIDI track in the Inspector.</li>
-<li>Enable the Record Enable button on the MIDI track.</li>
-<li>Set the left and right locators to define the recording range.</li>
-<li>Click the Record button on the Transport panel or press * on the numeric keypad to start recording.</li>
-<li>Play your MIDI controller or use the On-Screen Keyboard to enter MIDI notes.</li>
-<li>Click the Stop button on the Transport panel or press 0 on the numeric keypad to stop recording.</li>
-<li>The recorded MIDI data will appear as a MIDI part on the MIDI track. You can double-click it to open it in the Key Editor, where you can edit the notes, velocities, lengths, and other parameters.</li>
-</ol>
- <h2>VariAudio and Scale Assistant</h2>
- <p>VariAudio is a powerful tool for editing and correcting the pitch of your vocals and monophonic instruments. It allows you to analyze, modify, and quantize the pitch of each note in an audio file, as well as adjust its formant, vibrato, and volume. You can also use VariAudio to create harmonies, melodies, or effects from your audio material. Scale Assistant is a new feature in Cubase 12 that helps you set and follow the scale of your project. It can automatically detect the scale of your audio or MIDI data, or let you choose one from a list of presets or custom scales. You can then use Scale Assistant to snap your notes to the scale, transpose them, or color-code them according to their harmonic function.</p>
- <h3>How to use VariAudio to edit and correct the pitch of your vocals and monophonic instruments?</h3>
- <p>To use VariAudio to edit and correct the pitch of your vocals and monophonic instruments, follow these steps:</p>
- <ol>
-<li>Select an audio event that contains vocals or monophonic instruments in the Project window.</li>
-<li>Open the Sample Editor by double-clicking the audio event.</li>
-<li>In the Inspector, click on the VariAudio tab.</li>
-<li>Click on Edit VariAudio to analyze the pitch of the audio event. The audio event will be divided into segments, each representing a note. The segments will be displayed as blocks on a piano roll grid.</li>
-<li>To correct the pitch of a segment, drag it up or down until it snaps to a semitone. You can also use the Quantize Pitch slider to automatically adjust all segments to the nearest semitone.</li>
-<li>To modify the pitch of a segment, drag its handles left or right to change its start or end position. You can also drag its center line up or down to bend its pitch curve.</li>
-<li>To adjust the formant of a segment, use the Formant Shift slider. This will change the timbre of the segment without affecting its pitch.</li>
-<li>To adjust the vibrato of a segment, use the Straighten Pitch slider. This will reduce or increase the amount of pitch variation within the segment.</li>
-<li>To adjust the volume of a segment, use the Volume slider. This will change the amplitude of the segment without affecting its dynamics.</li>
-</ol>
- <h3>How to use Scale Assistant to set and follow the scale of your project?</h3>
- <h3>How to use Scale Assistant to set and follow the scale of your project?</h3>
- <p>To use Scale Assistant to set and follow the scale of your project, follow these steps:</p>
- <ol>
-<li>Open the Key Editor by double-clicking a MIDI part or an audio event with VariAudio segments.</li>
-<li>In the Inspector, click on the Scale Assistant tab.</li>
-<li>To detect the scale of your data, click on Detect Scale. Cubase will analyze the notes or segments and suggest a scale that matches them. You can also choose a different scale from the drop-down menu.</li>
-<li>To snap your notes or segments to the scale, enable Snap Pitch Editing. This will prevent you from moving them outside the scale. You can also use the Snap Live Input button to snap the notes that you play on your MIDI controller to the scale.</li>
-<li>To transpose your notes or segments to a different key, use the Transpose slider. This will shift them up or down by semitones while keeping them in the scale.</li>
-<li>To color-code your notes or segments according to their harmonic function, enable Colorize Pitch Editing. This will assign different colors to tonic, dominant, subdominant, and other degrees of the scale.</li>
-</ol>
- <h2>Sampler Track 2</h2>
- <p>Sampler Track 2 is a new and improved version of the Sampler Track, a tool that allows you to create and use sampler tracks in Cubase 12. A sampler track is a track that contains a sample, which is a short audio file that can be played back at different pitches and speeds. You can use sampler tracks to create melodies, rhythms, effects, or sound design elements from any audio material. Sampler Track 2 introduces some new features and enhancements, such as Slice mode, LFOs, and Sample Library Browser.</p>
- <h3>How to create and use Sampler Tracks in Cubase 12?</h3>
- <p>To create and use Sampler Tracks in Cubase 12, follow these steps:</p>
- <ol>
-<li>Right-click on an empty space in the Project window and select Add Track > Sampler Track.</li>
-<li>A new sampler track will be created with an empty sampler zone. To load a sample into the sampler zone, drag and drop an audio file from the MediaBay, the Project window, or your file explorer.</li>
-<li>The sample will be mapped across the entire keyboard range. You can play it with your MIDI controller or use the On-Screen Keyboard to trigger it.</li>
-<li>You can adjust the pitch, volume, filter, envelope, and modulation settings of the sample in the Sampler Control section.</li>
-<li>You can also add effects to the sample in the Insert Effects section.</li>
-<li>To record MIDI data for the sampler track, follow the same steps as for recording MIDI data for any other track.</li>
-</ol>
- <h3>How to use the new Slice mode and LFOs to manipulate your samples?</h3>
- <p>To use the new Slice mode and LFOs to manipulate your samples, follow these steps:</p>
- <ol>
-<li>In the Sampler Control section, click on the Slice button to enable Slice mode. This will divide your sample into smaller segments based on its transients or beats.</li>
-<li>You can adjust the sensitivity of the slicing algorithm with the Threshold slider. You can also manually add or remove slices with the + and - buttons.</li>
-<li>Each slice will be assigned to a different key on your keyboard. You can play them individually or in any order you like.</li>
-<li>You can also edit each slice separately by clicking on it and adjusting its pitch, volume, filter, envelope, and modulation settings.</li>
-<li>To add LFOs (Low Frequency Oscillators) to your sample or slices, click on the LFO button in the Modulation section. This will open a new window where you can choose from different waveforms, rates, depths, and destinations for your LFOs.</li>
-<li>You can use LFOs to modulate various parameters of your sample or slices, such as pitch, volume, filter cutoff, pan, etc. This can create interesting rhythmic or dynamic effects.</li>
-</ol>
- <h3>How to use the new Sample Library Browser to find and load samples?</h3>
- <p>To use the new Sample Library Browser to find and load samples To use the new Sample Library Browser to find and load samples, follow these steps: <ol>
-<li>In the Sampler Control section, click on the Browse button to open the Sample Library Browser.</li>
-<li>The Sample Library Browser shows a list of categories and subcategories of samples that are available on your computer or online. You can also use the search field to find samples by name, keyword, or tag.</li>
-<li>To preview a sample, click on the Play button next to it. You can adjust the volume and pitch of the preview with the sliders below.</li>
-<li>To load a sample into the sampler zone, double-click on it or drag and drop it onto the sampler zone. The sample will replace the existing one in the sampler zone.</li>
-<li>You can also load multiple samples into different slices by selecting them and dragging and dropping them onto the sampler zone. This will create a new slice for each sample and assign them to consecutive keys on your keyboard.</li>
-</ol>
- <h2>MixConsole</h2>
- <p>MixConsole is the main mixing environment in Cubase 12. It allows you to control the level, pan, solo/mute status, routing, effects, and other parameters of your audio and MIDI channels. You can also use MixConsole to create groups, sends, inserts, and busses for your channels. MixConsole features a flexible and customizable layout that lets you adjust the size, position, and visibility of different sections and elements. You can also open multiple MixConsole windows with different configurations and views. MixConsole also introduces some new and improved features in Cubase 12, such as Spectral Comparison EQ, Channel Strip modules, Snapshots, History, and more.</p>
- <h3>How to use the MixConsole to mix your tracks and apply effects?</h3>
- <p>To use the MixConsole to mix your tracks and apply effects, follow these steps:</p>
- <ol>
-<li>Open the MixConsole window by pressing F3 or selecting Studio > MixConsole from the menu bar.</li>
-<li>The MixConsole window shows all the channels in your project in the same order as in the track list. You can use the scroll bar or the channel overview to navigate to different channels. You can also use the Visibility tab in the left zone to show or hide individual channels or channel types.</li>
-<li>To adjust the level of a channel, drag its fader up or down. To adjust the pan of a channel, drag its pan knob left or right. To solo or mute a channel, click on its S or M button. To enable or disable record for a channel, click on its R button.</li>
-<li>To set up the input and output routing for a channel, click on its input or output selector in the Inspector or in the Routing section. You can choose from different options depending on the type of channel. For example, you can route an audio channel to an audio input or output device, a group channel, an FX channel, a VCA fader, etc.</li>
-<li>To apply effects to a channel, you can use either inserts or sends. Inserts are effects that are applied directly to the signal of a channel. Sends are effects that are applied to a copy of the signal of a channel and then mixed with other signals in an FX channel. To add an insert effect to a channel, click on an empty slot in the Inserts section and select an effect from the list. To add a send effect to a channel, click on an empty slot in the Sends section and select an FX channel from the list. You can adjust the amount of send level with the knob next to the slot.</li>
-<h3>How to use the new Spectral Comparison EQ to clean up overlapping frequencies?</h3>
- <p>The Spectral Comparison EQ is a new feature in Cubase 12 that allows you to compare and adjust the frequency spectrum of two different channels. This can help you identify and eliminate any overlapping or clashing frequencies that might cause muddiness or interference in your mix. To use the Spectral Comparison EQ, follow these steps:</p>
- <ol>
-<li>Select a channel that you want to compare with another channel. This will be the reference channel.</li>
-<li>Open the Channel Settings window by clicking on the e button on the channel.</li>
-<li>In the Channel Settings window, click on the EQ tab to open the Equalizer section.</li>
-<li>In the Equalizer section, click on the Spectral Comparison button to enable it. This will open a new window where you can see the frequency spectrum of the reference channel and the comparison channel.</li>
-<li>To select a comparison channel, click on the drop-down menu next to the Spectral Comparison button and choose another channel from the list. You can also use the arrow buttons to cycle through different channels.</li>
-<li>You can adjust the opacity and color of the frequency spectrum of each channel with the sliders below. You can also switch between different views, such as Combined, Side by Side, or Overlay.</li>
-<li>To adjust the EQ of the reference channel, use the EQ bands and knobs as usual. You can also use the EQ Curve Display to drag and drop points on the curve. You will see how your adjustments affect the frequency spectrum of both channels in real time.</li>
-<li>To adjust the EQ of the comparison channel, click on the Swap button next to the Spectral Comparison button. This will switch the roles of the reference and comparison channels. You can then use the same steps as above to modify the EQ of the comparison channel.</li>
-</ol>
- <h3>How to use the Channel Strip modules to shape your sound?</h3>
- <p>The Channel Strip is a section in the Channel Settings window that contains various modules that can help you shape your sound. These modules include Gate, Compressor, Limiter, Envelope Shaper, Saturation, Magneto III, Standard Clipper, Tape Saturation, and Tube Saturation. You can use these modules to control the dynamics, tone, saturation, and distortion of your signal. To use the Channel Strip modules, follow these steps:</p>
- <ol>
-<li>Open the Channel Settings window by clicking on the e button on a channel.</li>
-<li>In the Channel Settings window, click on the Strip tab to open the Channel Strip section.</li>
-<li>In the Channel Strip section, you can see a list of modules that are available for your channel. You can enable or disable each module by clicking on its power button.</li>
-<h3>How to use the Channel Strip modules to shape your sound?</h3>
- <p>The Channel Strip is a section in the Channel Settings window that contains various modules that can help you shape your sound. These modules include Gate, Compressor, Limiter, Envelope Shaper, Saturation, Magneto III, Standard Clipper, Tape Saturation, and Tube Saturation. You can use these modules to control the dynamics, tone, saturation, and distortion of your signal. To use the Channel Strip modules, follow these steps:</p>
- <ol>
-<li>Open the Channel Settings window by clicking on the e button on a channel.</li>
-<li>In the Channel Settings window, click on the Strip tab to open the Channel Strip section.</li>
-<li>In the Channel Strip section, you can see a list of modules that are available for your channel. You can enable or disable each module by clicking on its power button.</li>
-<li>You can also reorder or replace each module by dragging and dropping it to a different slot or clicking on its name and selecting another module from the list.</li>
-<li>To adjust the settings of each module, use the knobs and buttons below its name. You can also use the Module Display to see a graphical representation of the module's parameters and curves.</li>
-<li>You can also use the Presets menu to load or save presets for each module or for the entire channel strip.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>Cubase 12 is a powerful and versatile music production software that offers a wide range of features and tools for creating, recording, editing, mixing, and mastering your music. Whether you are a beginner or a professional, Cubase 12 can help you achieve your musical goals with ease and efficiency. In this article, we have given you a complete overview of Cubase 12, its main features and benefits, and how to get started with it. We have also shown you some tips and tricks on how to use some of the new and improved tools in Cubase 12, such as MIDI Remote integration, VariAudio and Scale Assistant, Sampler Track 2, MixConsole, and more. We hope you have found this article useful and informative.</p>
- <p>If you want to learn more about Cubase 12, you can visit the official website of Steinberg, where you can find more tutorials, guides, videos, forums, and support. You can also download a free trial version of Cubase 12 and try it for yourself. Cubase 12 is available in three editions: Pro, Artist, and Elements. Each edition has different features and prices to suit your needs and budget. You can compare the editions and buy Cubase 12 online from Steinberg's online shop or from authorized dealers.</p>
- <p>Thank you for reading this article. We hope you have enjoyed it and learned something new. Now it's time for you to try Cubase 12 and unleash your creativity. Happy music making!</p>
- <h2>FAQs</h2>
- <h3>What are the system requirements for Cubase 12?</h3>
- <p>The system requirements for Cubase 12 are as follows:</p>
- <table>
-<tr>
-<th>Operating System</th>
-<th>Windows 10 (64-bit) or macOS Big Sur (11) / Catalina (10.15)</th>
-</tr>
-<tr>
-<th>CPU</th>
-<th>64-bit Intel or AMD multi-core processor (Intel i5 or faster recommended)</th>
-</tr>
-<tr>
-<th>RAM</th>
-<th>8 GB minimum (16 GB or more recommended)</th>
-</tr>
-<tr>
-<th>Hard Disk Space</th>
-<th>30 GB free space</th>
-</tr>
-<tr>
-<th>Graphics Card</th>
-<th>Graphics card with DirectX 10 and WDDM 1.1 support (Windows only)</th>
-</tr>
-<tr>
-<th>Audio Interface</th>
-<th>ASIO compatible audio hardware recommended</th>
-</tr>
-<tr>
-<th>MIDI Interface</th>
-<th>MIDI input/output device (optional)</th>
-</tr>
-<tr>
-<th>Internet Connection</th>
-<th>An internet connection is required for activation, account setup, access to manual and personal/product registration.</th>
-</tr>
-</table>
- <h3>What are the differences between Cubase Pro, Artist, and Elements?</h3>
- <h3>What are the differences between Cubase Pro, Artist, and Elements?</h3>
- <p>Cubase Pro is the most comprehensive edition of Cubase 12, offering all the features and tools that Cubase has to offer. It is designed for professional composers, producers, engineers, and musicians who need advanced functionality and flexibility. Cubase Pro supports up to 256 physical inputs and outputs, unlimited audio, instrument, and MIDI tracks, 8 VST instruments with over 3400 sounds, 75 audio effects, 18 MIDI effects, 5.1 surround sound, full score editing and printing, advanced audio editing and processing, VariAudio 3, Audio Alignment, ARA 2 support, SpectraLayers One integration, Sampler Track 2, Scale Assistant, Global Tracks, MixConsole Snapshots, Control Room, VCA faders, Channel Strip modules, Spectral Comparison EQ, Frequency 2 EQ, SuperVision metering suite, Video Export Render, Eucon support, and more.</p>
- <p>Cubase Artist is a more affordable edition of Cubase 12, offering most of the features and tools that Cubase Pro has to offer. It is designed for aspiring composers, producers, engineers, and musicians who need a reliable and versatile music production software. Cubase Artist supports up to 32 physical inputs and outputs, unlimited audio and MIDI tracks, 64 instrument tracks, 8 VST instruments with over 3400 sounds, 64 audio effects, 18 MIDI effects, full score editing and printing, basic audio editing and processing, VariAudio 3, Audio Alignment, ARA 2 support, Sampler Track 2, Scale Assistant, Global Tracks, MixConsole Snapshots, Control Room (limited), VCA faders (limited), Channel Strip modules (limited), Spectral Comparison EQ (limited), Frequency 2 EQ (limited), SuperVision metering suite (limited), Video Export Render (limited), Eucon support (limited), and more.</p>
- <p>Cubase Elements is the most basic edition of Cubase 12, Cubase Elements is the most basic edition of Cubase 12, offering the essential features and tools that Cubase has to offer. It is designed for beginners and hobbyists who want to learn and enjoy music production. Cubase Elements supports up to 24 physical inputs and outputs, 48 audio tracks, 64 MIDI tracks, 24 instrument tracks, 3 VST instruments with over 1000 sounds, 44 audio effects, 18 MIDI effects, basic score editing and printing, basic audio editing and processing, Sampler Track 2, Scale Assistant, MixConsole Snapshots (limited), Channel Strip modules (limited), Frequency 2 EQ (limited), SuperVision metering suite (limited), Video Export Render (limited), and more.</p>
- <p>As you can see, Cubase 12 offers different editions to suit your needs and budget. You can compare the editions and their features in more detail on the Steinberg website. You can also upgrade from one edition to another at any time by purchasing an update or upgrade license.</p>
- <h2>FAQs</h2>
- <h3>What are the system requirements for Cubase 12?</h3>
- <p>The system requirements for Cubase 12 are as follows:</p>
- <table>
-<tr>
-<th>Operating System</th>
-<th>Windows 10 (64-bit) or macOS Big Sur (11) / Catalina (10.15)</th>
-</tr>
-<tr>
-<th>CPU</th>
-<th>64-bit Intel or AMD multi-core processor (Intel i5 or faster recommended)</th>
-</tr>
-<tr>
-<th>RAM</th>
-<th>8 GB minimum (16 GB or more recommended)</th>
-</tr>
-<tr>
-<th>Hard Disk Space</th>
-<th>30 GB free space</th>
-</tr>
-<tr>
-<th>Graphics Card</th>
-<th>Graphics card with DirectX 10 and WDDM 1.1 support (Windows only)</th>
-</tr>
-<tr>
-<th>Audio Interface</th>
-<th>ASIO compatible audio hardware recommended</th>
-</tr>
-<tr>
-<th>MIDI Interface</th>
-<th>MIDI input/output device (optional)</th>
-</tr>
-<tr>
-<th>Internet Connection</th>
-<th>An internet connection is required for activation, account setup, access to manual and personal/product registration.</th>
-</tr>
-</table>
- <h3>What are the differences between Cubase Pro, Artist, and Elements?</h3>
- <h3>What are the differences between Cubase Pro, Artist, and Elements?</h3>
- <p>Cubase Pro is the most comprehensive edition of Cubase 12, offering all the features and tools that Cubase has to offer. It is designed for professional composers, producers, engineers, and musicians who need advanced functionality and flexibility. Cubase Pro supports up to 256 physical inputs and outputs, unlimited audio, instrument, and MIDI tracks, 8 VST instruments with over 3400 sounds, 75 audio effects, 18 MIDI effects, 5.1 surround sound, full score editing and printing, advanced audio editing and processing, VariAudio 3, Audio Alignment, ARA 2 support, SpectraLayers One integration, Sampler Track 2, Scale Assistant, Global Tracks, MixConsole Snapshots, Control Room, VCA faders, Channel Strip modules, Spectral Comparison EQ, Frequency 2 EQ, SuperVision metering suite, Video Export Render, Eucon support, and more.</p>
- <p>Cubase Artist is a more affordable edition of Cubase 12, offering most of the features and tools that Cubase Pro has to offer. It is designed for aspiring composers, producers, engineers, and musicians who need a reliable and versatile music production software. Cubase Artist supports up to 32 physical inputs and outputs, unlimited audio and MIDI tracks, 64 instrument tracks, 8 VST instruments with over 3400 sounds, 64 audio effects, 18 MIDI effects, full score editing and printing, basic audio editing and processing, VariAudio 3, Audio Alignment, ARA 2 support, Sampler Track 2, Scale Assistant, Global Tracks, MixConsole Snapshots, Control Room (limited), VCA faders (limited), Channel Strip modules (limited), Spectral Comparison EQ (limited), Frequency 2 EQ (limited), SuperVision metering suite (limited), Video Export Render (limited), Eucon support (limited), and more.</p>
- <p>Cubase Elements is the most basic edition of Cubase 12, offering the essential features and tools that Cubase has to offer. It is designed for beginners and hobbyists who want to learn and enjoy music production. Cubase Elements supports up to 24 physical inputs and outputs, 48 audio tracks, 64 MIDI tracks, 24 instrument tracks, 3 VST instruments with over 1000 sounds, 44 audio effects, 18 MIDI effects, basic score editing and printing, basic audio editing and processing, Sampler Track 2, Scale Assistant, MixConsole Snapshots (limited), Channel Strip modules (limited), Frequency 2 EQ (limited), SuperVision metering suite (limited), Video Export Render (limited), and more.</p>
- <p>As you can see, Cubase 12 offers different editions to suit your needs and budget. You can compare the editions and their features in more detail on the Steinberg website. You can also upgrade from one edition to another at any time by purchasing an update or upgrade license.</p>
- <h3>How much does Cubase 12 cost and where can I buy it?</h3>
- <p>The prices of Cubase 12 vary depending on the edition and the region. You can check the current prices on the Steinberg online shop or on authorized dealers' websites. Here are some approximate prices for reference:</p>
- <table>
-<tr>
-<th>Edition</th>
-<th>Price</th>
-</tr>
-<tr>
-<td>Cubase Pro</td>
-<td>$559.99 USD / €559 EUR / £499 GBP</td>
-</tr>
-<tr>
-<td>Cubase Artist</td>
-<td>$309.99 USD / €309 EUR / £269 GBP</td>
-</tr>
-<tr>
-<td>Cubase Elements</td>
-<td>$99.99 USD / €99 EUR / £85 GBP</td>
-</tr>
-</table>
- <p>You can buy Cubase 12 online from the Steinberg online shop or from authorized dealers. You can also buy a boxed version of Cubase 12 from some retailers. When you buy Cubase 12 online or in a box, you will receive a download access code that you can use to download the software from the Steinberg Download Assistant. You will also need a USB-eLicenser or a Soft-eLicenser to activate your license.</p>
- <h3>Where can I find more tutorials and guides for Cubase 12?</h3>
- <p>If you want to learn more about Cubase 12, you can find many tutorials and guides on the official website of Steinberg, where you can access the online help, the operation manual, the quick start guide, the video tutorials, the forums, and the support section. You can also find many tutorials and guides on YouTube, where you can watch videos from Steinberg's channel or from other users. You can also find many tutorials and guides on blogs, <h3>Where can I find more tutorials and guides for Cubase 12?</h3>
- <p>If you want to learn more about Cubase 12, you can find many tutorials and guides on the official website of Steinberg, where you can access the online help, the operation manual, the quick start guide, the video tutorials, the forums, and the support section. You can also find many tutorials and guides on YouTube, where you can watch videos from Steinberg's channel or from other users. You can also find many tutorials and guides on blogs, magazines, books, podcasts, or online courses that cover Cubase 12 or music production in general. Here are some examples of resources that you can check out:</p>
- <ul>
-<li>[Cubase 12 Tutorials]: A series of videos from Steinberg that introduce the new features and improvements in Cubase 12.</li>
-<li>[Cubase Academy]: An online course from Ask.Video that teaches you how to use Cubase 12 from beginner to advanced level.</li>
-<li>[Cubase 12 Explained]: A video tutorial from Groove3 that covers the basics and essentials of Cubase 12.</li>
-<li>[Cubase 12 Tips & Tricks]: A blog post from MusicTech that shares some useful tips and tricks for working with Cubase 12.</li>
-<li>[Cubase 12 Review]: A magazine article from Sound On Sound that gives an in-depth review of Cubase 12 and its features.</li>
-</ul>
- <h3>How can I contact Steinberg for support and feedback?</h3>
- <p>If you have any questions, issues, or feedback regarding Cubase 12 or any other Steinberg product, you can contact Steinberg for support and feedback through various channels. You can use the following methods to get in touch with Steinberg:</p>
- <ul>
-<li>[MySteinberg]: A personal online account where you can manage your licenses, products, downloads, updates, registration, and support requests.</li>
-<li>[Steinberg Support]: A section on the Steinberg website where you can find answers to frequently asked questions, submit support tickets, report bugs, request features, and access manuals and documentation.</li>
-<li>[Steinberg Forums]: A community platform where you can interact with other users and experts, ask questions, share tips, exchange ideas, and give feedback.</li>
-<li>[Steinberg YouTube Channel]: A video platform where you can watch tutorials, guides, demos, interviews, and live streams from Steinberg.</li>
-<li>[Steinberg Social Media]: A network of social media channels where you can follow Steinberg on Facebook, Twitter, Instagram, LinkedIn, and SoundCloud.</li>
-</ul></p> 197e85843d<br />
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-<br>
- <table>
-<tr>
-<td>
- <h1>Cookie Run: Kingdom APK 2022 - A Sweet Adventure Game for Android</h1>
- <h2>Introduction</h2>
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-<ol>
-<li><b>What are some tips and tricks for playing Cookie Run: Kingdom APK 2022?</b></li>
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-<ul>
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-<li><b>How can I contact the developers or report a bug in Cookie Run: Kingdom APK 2022?</b></li>
-<p>To contact the developers or report a bug in Cookie Run: Kingdom APK 2022, you can:</p>
-<ul>
-<li>Tap on the settings icon on the top right corner of the screen.</li>
-<li>Tap on the customer service button and choose the option that best suits your issue.</li>
-<li>Fill out the form with your details and your message, and attach any screenshots or videos if necessary.</li>
-<li>Submit the form and wait for a response from the developers.</li>
-</ul>
-<li><b>Is Cookie Run: Kingdom APK 2022 safe to download and install?</b></li>
-<p>Yes, Cookie Run: Kingdom APK 2022 is safe to download and install, as long as you download it from the official website or from the Google Play Store. You can also download the APK file from other sources, but make sure they are safe and reliable. You can check the reviews and ratings of other users before downloading. You can also scan the APK file with an antivirus software before installing it. However, you should always be careful when downloading and installing any app from unknown sources, as they may contain malware or viruses that can harm your device.</p>
- </td>
-</tr>
-</table></p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Hay Day Mod APK and Get Unlimited Coins and Diamonds.md b/spaces/1phancelerku/anime-remove-background/Download Hay Day Mod APK and Get Unlimited Coins and Diamonds.md
deleted file mode 100644
index 8d637b816bff435db12b3c8a5c8ea661bb695695..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Hay Day Mod APK and Get Unlimited Coins and Diamonds.md	
+++ /dev/null
@@ -1,136 +0,0 @@
-<br />
-<h1>Hay Day <h1>Hay Day Diamond Mod APK: A Guide for Beginners</h1>
- <p>Do you love farming games? Do you want to create your own dream farm and enjoy the rural life? If yes, then you should try Hay Day, one of the most popular and addictive farming simulators on mobile devices. Hay Day is a game where you can grow crops, raise animals, trade goods, and interact with other players. You can also customize your farm with various decorations and buildings. However, to do all these things, you need diamonds and coins, the two main currencies in the game. Diamonds are especially hard to come by, and they are used for speeding up processes, buying special items, and unlocking features. That's why many players look for ways to get more diamonds without spending real money. One of the ways is to use Hay Day Diamond Mod APK, a modified version of the game that gives you unlimited diamonds and coins. In this article, we will tell you everything you need to know about Hay Day Diamond Mod APK, including its features, how to download and install it, and its pros and cons. Let's get started!</p>
-<h2>hay day diamond mod apk</h2><br /><p><b><b>DOWNLOAD</b> &gt;&gt;&gt;&gt;&gt; <a href="https://jinyurl.com/2uNT8T">https://jinyurl.com/2uNT8T</a></b></p><br /><br />
- <h2>Hay Day Game Features</h2>
- <p>Before we dive into the details of Hay Day Diamond Mod APK, let's first take a look at the original game and its features. Hay Day is a game developed by Supercell, the same company behind other popular games like Clash of Clans and Brawl Stars. Hay Day was released in 2012 and has since gained millions of fans around the world. The game is available for both Android and iOS devices, and it is free to download and play. However, it also offers in-app purchases for some items and features.</p>
- <p>Hay Day is a game that simulates the life of a farmer. You start with a small piece of land and a few resources, and your goal is to turn it into a prosperous farm. You can do this by:</p>
- <h3>Build a Farm</h3>
- <p>One of the main aspects of Hay Day is building your farm. You can choose from a variety of buildings and structures to place on your land, such as barns, silos, mills, bakeries, dairy factories, sugar mills, popcorn pots, pie ovens, cake ovens, juice presses, ice cream makers, candy machines, jam makers, honey extractors, looms, sewing machines, tailors, smelters, jewelers, fishing boats, fishing nets, fishing lures, duck salons, duck traps, lobster pools, lobster tails, sushi bars, soup kitchens, salad bars, sandwich bars, barbecue grills, pizza ovens, pasta makers, and more. Each building has a different function and produces different goods. You can also upgrade your buildings to increase their capacity and efficiency. You can also decorate your farm with various items, such as fences, paths, trees, flowers, ponds, fountains, statues, benches, signs, and more. You can also change the layout of your farm anytime you want. Building and decorating your farm is a fun and creative way to express yourself and make your farm unique.</p>
- <h3>Crops to Harvest and Grow</h3>
- <p>Another important aspect of Hay Day is growing crops. You can plant various seeds on your fields, such as wheat, corn, carrots, soybeans, sugarcane, indigo, tomatoes, potatoes, strawberries, pumpkins, cotton, rice, chili peppers, cacao, coffee beans, tea leaves, and more. You can also grow fruits on your trees, such as apples, cherries, pears, plums, lemons, oranges, bananas, coconuts, olives, grapes, and more. You can also grow flowers on your bushes, such as roses, tulips, lilies, lavender, jasmine, and more. You can also grow mushrooms on your logs.</p>
- <p>Growing crops is not only a source of income but also a source of ingredients for your buildings. You can use your crops to make various products, such as bread, butter, cheese, cream, milk, sugar, flour, popcorn, pies, cakes, juices, ice cream, candy, jam, honey, wool, fabric, clothes, metal bars, jewelry, fish, lobster, sushi, soup, salad, sandwich, barbecue, pizza, pasta, and more. You can also sell your crops and products to other players or to the roadside shop for coins. You can also use them to fulfill orders from the truck or the boat for coins and experience points. You can also donate them to your neighborhood or to the town visitors for rewards.</p>
- <p>Growing crops is not only rewarding but also challenging. You have to water your crops regularly to make them grow faster. You also have to protect them from pests and diseases. You also have to harvest them before they wither and replant new seeds. You also have to manage your storage space and your resources wisely. Growing crops is a skill that requires patience and strategy.</p>
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- <h3>Animals</h3>
- <p>Another essential aspect of Hay Day is raising animals. You can keep various animals on your farm, such as chickens, cows, pigs, sheep, goats, horses, donkeys, rabbits, cats, dogs, and more. You can also keep bees in your beehives and catch fish in your fishing area. Each animal has a different function and produces different goods. You can also feed them and pet them to make them happy and productive.</p>
- <p>Raising animals is not only a source of income but also a source of fun. You can use your animal products to make various products, such as eggs, bacon, ham, wool, cheese, cream, milk, honey, fish fillets, lobster tails, and more. You can also sell your animal products to other players or to the roadside shop for coins. You can also use them to fulfill orders from the truck or the boat for coins and experience points. You can also donate them to your neighborhood or to the town visitors for rewards.</p>
- <p>Raising animals is not only fun but also challenging. You have to feed your animals regularly to make them produce goods. You also have to collect their goods before they expire and store them in your barn or silo. You also have to protect them from foxes and other predators. You also have to manage your storage space and your resources wisely. Raising animals is a skill that requires care and attention.</p>
- <h3>Places to Visit</h3>
- <p>Another exciting aspect of Hay Day is visiting other places. You can travel to different locations in the game world, such as the town, the mine, the valley, the derby, and more. Each place has a different purpose and offers different activities. You can also meet new characters and discover new stories.</p>
- <p>Visiting places is not only a source of adventure but also a source of rewards. You can use your places to earn coins, diamonds, vouchers, boosters, expansion materials, decoration materials, and more. You can also complete tasks and quests to earn rewards. You can also participate in events and competitions to earn rewards.</p>
- <p>Visiting places is not only rewarding but also challenging. You have to unlock new places by reaching certain levels or completing certain requirements. You also have to upgrade your places by spending coins or diamonds. You also have to maintain your places by repairing or cleaning them. You also have to manage your time and energy wisely. Visiting places is a skill that requires exploration and curiosity.</p>
- <h3>Play with Friends and Neighbors</h3>
- <p>Another enjoyable aspect of Hay Day is playing with friends and neighbors. You can connect with other players from around the world through various ways, such as Facebook, Game Center, Google Play Games, Supercell ID, or friend codes. You can also join or create a neighborhood with up to 30 members.</p>
- <p>Playing with friends and neighbors is not only a source of socialization but also a source of cooperation. You can chat with other players and share tips and tricks. You can also trade goods and resources with other players through the roadside shop or the newspaper. You can also help other players by reviving their crops or filling their crates. You can also request or donate goods or resources from your neighborhood members.</p>
- <p>Playing with friends and neighbors is not only cooperative but also competitive. You can compete with other players or neighborhoods in various ways, such as the derby, the valley, the global leaderboard, or the seasonal leaderboard. You can also earn trophies and badges for your achievements. You can also show off your farm and your products to other players.</p>
- <p>Playing with friends and neighbors is a skill that requires communication and collaboration.</p>
- <h2>Hay Day Diamond Mod APK Features</h2>
- <p>Now that we have covered the original game and its features, let's move on to Hay Day Diamond Mod APK and its features. Hay Day Diamond Mod APK is a modified version of the game that gives you unlimited diamonds and coins. It also removes ads from the game. It is not an official version of the game, but it is created by third-party developers who modify the original game files.</p>
- <p>Hay Day Diamond Mod APK has many features that make it different from the original game. Here are some of them:</p>
- <h3>Unlimited Diamonds</h3>
- <p>The most obvious feature of Hay Day Diamond Mod APK is unlimited diamonds. Diamonds are the premium currency in Hay Day, and they are very hard to get in the original game. You can only get them by leveling up, completing achievements, watching ads, or buying them with real money. However, in Hay Day Diamond Mod APK, you can get unlimited diamonds for free. You can use them to speed up processes, buy special items, unlock features, and more. You can also use them to buy coins, vouchers, boosters, expansion materials, decoration materials, and more. You can also use them to skip tasks and quests, or to retry events and competitions. With unlimited diamonds, you can enjoy the game without any limitations or restrictions.</p>
- <h3>Unlimited Coins</h3>
- <p>Another feature of Hay Day Diamond Mod APK is unlimited coins. Coins are the basic currency in Hay Day, and they are used for buying and selling goods and resources. You can earn coins by selling your crops and products to other players or to the roadside shop. You can also earn coins by fulfilling orders from the truck or the boat. You can also earn coins by helping other players or by completing tasks and quests. However, in Hay Day Diamond Mod APK, you can get unlimited coins for free. You can use them to buy more items and resources from the shop or from other players. You can also use them to upgrade your buildings and structures. You can also use them to expand your land and your fishing area. You can also use them to decorate your farm and your town. With unlimited coins, you can buy anything you want and need in the game.</p>
- <h3>No Ads</h3>
- <p>Another feature of Hay Day Diamond Mod APK is no ads. Ads are annoying and distracting, and they can ruin your gaming experience. In the original game, you have to watch ads to get some rewards, such as diamonds, vouchers, boosters, or expansion materials. However, in Hay Day Diamond Mod APK, you don't have to watch any ads at all. You can get all the rewards without wasting your time or data. You can also enjoy the game without any interruptions or pop-ups. With no ads, you can improve your gaming experience and focus on the fun.</p>
- <h2>How to Download and Install Hay Day Diamond Mod APK</h2>
- <p>Now that we have covered the features of Hay Day Diamond Mod APK, let's see how to download and install it on your device. Before we do that, let's first go over some requirements and precautions that you need to follow.</p>
- <h3>Requirements</h3>
- <p>To download and install Hay Day Diamond Mod APK on your device, you need to meet some minimum requirements. These are:</p>
- <ul>
-<li>Your device must have Android 4.1 or higher version.</li>
-<li>Your device must have at least 1 GB of RAM and 100 MB of free storage space.</li>
-<li>Your device must have a stable internet connection.</li>
-<li>Your device must allow installation of apps from unknown sources. To enable this option, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>You must uninstall the original Hay Day game from your device if you have it installed already.</li>
-</ul>
- <p>If you meet these requirements, you are ready to download and install Hay Day Diamond Mod APK on your device.</p>
- <h3>Download Link</h3>
- <p>To download Hay Day Diamond Mod APK on your device, you need to find a reliable and safe download link for it. There are many websites that claim to offer Hay Day Diamond Mod APK for free, but not all of them are trustworthy. Some of them may contain viruses or malware that can harm your device or steal your personal information. Some of them may also provide fake or outdated versions of the game that may not work properly or may cause errors.</p>
- <p>To avoid these risks, you should only download Hay Day Diamond Mod APK from a reputable source that has positive reviews and feedback from other users. One such source is [HayDayModAPK.com], a website that provides the latest version of Hay Day Diamond Mod APK for free. This website is secure and verified by Google Play Protect. It also has a user-friendly interface and fast download speed.</p>
- <p>To download Hay Day Diamond Mod APK from [HayDayModAPK.com], follow these steps:</p>
- <ol>
-<li>Go to [HayDayModAPK.com] using your browser.</li>
-<li>Click on the Download button on the homepage.</li>
-<li>Wait for the download to start automatically. If it doesn't start, click on the link that says "Click here if the download doesn't start automatically".</li>
-<li>Wait for the download to finish.</li>
-</ol>
- <p>Congratulations! You have successfully downloaded Hay Day Diamond Mod APK on your device.</p>
- <h3>Installation Process</h3>
- <p>To install Hay Day Diamond Mod APK on your device, follow these steps:</p>
- <ol>
-<li>Locate the downloaded file in your file manager or download folder. The file name should be HayDayModAPK.apk.</li>
-<li>Tap on the file to open it.</li>
-<li>A pop-up window will appear asking you to install the app. Tap on Install.</li>
-<li>Wait for the installation to complete.</li>
-<li>A pop-up window will appear asking you to open the app. Tap on Open.</li>
-<li>Allow the app to access your device's resources and permissions.</li>
-<li>Wait for the game to load and update.</li>
-<li>Enjoy playing Hay Day Diamond Mod APK with unlimited diamonds and coins!</li>
-</ol>
- <p>Congratulations! You have successfully installed Hay Day Diamond Mod APK on your device.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Hay Day Diamond Mod APK is a modified version of Hay Day that gives you unlimited diamonds and coins. It also removes ads from the game. It is not an official version of the game, but it is created by third-party developers who modify the original game files. It has many features that make it different from the original game, such as unlimited diamonds, unlimited coins, and no ads. It also has some requirements and precautions that you need to follow, such as uninstalling the original game, enabling unknown sources, finding a reliable download link, and following the installation process.</p>
- <p>Hay Day Diamond Mod APK is a great way to enjoy Hay Day without any limitations or restrictions. You can build your farm, grow crops, raise animals, visit places, play with friends and neighbors, and more. You can also buy anything you want and need in the game. You can also speed up processes, buy special items, unlock features, and more. You can also enjoy the game without any interruptions or pop-ups. Hay Day Diamond Mod APK is a fun and addictive farming simulator that you should try.</p>
- <p>However, Hay Day Diamond Mod APK also has some disadvantages that you should be aware of. For example, it is not an official version of the game, so it may not be compatible with some devices or updates. It may also cause errors or glitches in the game. It may also violate the terms and conditions of the original game, so it may result in a ban or a penalty from Supercell. It may also expose your device or personal information to viruses or malware from untrusted sources. Hay Day Diamond Mod APK is a risky and unofficial version of Hay Day that you should use at your own discretion.</p>
- <p>Hay Day Diamond Mod APK is a guide for beginners who want to know more about this modified version of Hay Day. We hope that this article has helped you understand what Hay Day Diamond Mod APK is, what are its features, how to download and install it, and what are its pros and cons. We hope that you have enjoyed reading this article and that you have learned something new. Thank you for your time and attention.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Hay Day Diamond Mod APK:</p>
- <h4>Q: Is Hay Day Diamond Mod APK safe to use?</h4>
- <p>A: Hay Day Diamond Mod APK is not an official version of Hay Day, so it may not be safe to use. It may contain viruses or malware that can harm your device or steal your personal information. It may also cause errors or glitches in the game. It may also violate the terms and conditions of the original game, so it may result in a ban or a penalty from Supercell. You should only download Hay Day Diamond Mod APK from a reputable source that has positive reviews and feedback from other users. You should also scan the file with an antivirus software before installing it. You should also backup your data before using it.</p>
- <h4>Q: How do I update Hay Day Diamond Mod APK?</h4>
- <p>A: Hay Day Diamond Mod APK may not be compatible with some updates of the original game, so it may not work properly or at all after an update. You should check the website where you downloaded Hay Day Diamond Mod APK for any updates or new versions of the mod apk. You should also uninstall the old version of Hay Day Diamond Mod APK before installing the new one. You should also backup your data before updating it.</p>
- <h4>Q: Can I play Hay Day Diamond Mod APK online?</h4>
- <p>A: Hay Day Diamond Mod APK requires an internet connection to run and update, so you can play it online. However, you may not be able to play with other players who are using the original game or other versions of the mod apk. You may also face some issues or difficulties while playing online, such as lagging, crashing, or disconnecting. You may also get detected by Supercell and get banned or penalized for using a modified version of the game.</p>
- <h4>Q: Can I use Hay Day Diamond Mod APK on iOS devices?</h4>
- <p>A A: Hay Day Diamond Mod APK is designed for Android devices only, so you cannot use it on iOS devices. If you want to play Hay Day with unlimited diamonds and coins on your iPhone or iPad, you need to use a different method, such as jailbreaking your device, using a third-party app store, or using a game emulator. However, these methods are also risky and unofficial, and they may damage your device or violate the terms and conditions of the original game. You should use them at your own discretion and responsibility.</p>
- <h4>Q: What are some alternatives to Hay Day Diamond Mod APK?</h4>
- <p>A: If you don't want to use Hay Day Diamond Mod APK or if you can't use it for some reason, you can try some alternatives that can also give you more diamonds and coins in Hay Day. Some of these alternatives are:</p>
- <ul>
-<li>Using cheats and hacks: There are some websites and apps that claim to offer cheats and hacks for Hay Day, such as generators, tools, scripts, or codes. These cheats and hacks promise to give you unlimited diamonds and coins in Hay Day for free. However, these cheats and hacks are also risky and unofficial, and they may not work properly or at all. They may also contain viruses or malware that can harm your device or steal your personal information. They may also cause errors or glitches in the game. They may also violate the terms and conditions of the original game, so they may result in a ban or a penalty from Supercell. You should only use cheats and hacks from a reputable source that has positive reviews and feedback from other users. You should also scan the file with an antivirus software before using it. You should also backup your data before using it.</li>
-<li>Using tips and tricks: There are some websites and blogs that offer tips and tricks for Hay Day, such as guides, tutorials, strategies, or secrets. These tips and tricks can help you earn more diamonds and coins in Hay Day by playing smartly and efficiently. They can also help you improve your skills and knowledge of the game. These tips and tricks are not risky or unofficial, and they can work well if you follow them correctly. However, these tips and tricks may not be enough to give you unlimited diamonds and coins in Hay Day, and they may take some time and effort to apply. You should only use tips and tricks from a reliable source that has accurate and updated information.</li>
-<li>Using real money: The simplest and safest way to get more diamonds and coins in Hay Day is to use real money. You can buy diamonds and coins from the in-game shop using your credit card, debit card, PayPal account, Google Play account, Apple ID account, or other payment methods. You can also buy gift cards or vouchers that can be redeemed for diamonds and coins in the game. Buying diamonds and coins with real money is not risky or unofficial, and it can give you instant access to any item or feature in the game. However, buying diamonds and coins with real money can be expensive and addictive, and it may not be affordable or feasible for everyone.</li>
-</ul>
- <p>These are some of the alternatives to Hay Day Diamond Mod APK that you can try if you want to get more diamonds and coins in Hay Day. However, none of them can match the convenience and benefits of Hay Day Diamond Mod APK, which gives you unlimited diamonds and coins for free. You can also enjoy the game without any ads or interruptions. Hay Day Diamond Mod APK is the best option for you if you want to have the ultimate farming experience in Hay Day.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/AI-ZTH-03-23/6.AI.Dashboard.Wiki.Chat.Cognitive.HTML5/README.md b/spaces/AI-ZTH-03-23/6.AI.Dashboard.Wiki.Chat.Cognitive.HTML5/README.md
deleted file mode 100644
index 5935744814668f85a7005ea6ba4e9540b216a1fa..0000000000000000000000000000000000000000
--- a/spaces/AI-ZTH-03-23/6.AI.Dashboard.Wiki.Chat.Cognitive.HTML5/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: 6.AI.Dashboard.Wiki.Chat.Cognitive.HTML5
-emoji: 🦀
-colorFrom: gray
-colorTo: gray
-sdk: static
-pinned: false
-license: mit
-duplicated_from: awacke1/AI.Dashboard.Wiki.Chat.Cognitive.HTML5
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AIFILMS/StyleGANEX/models/stylegan2/lpips/pretrained_networks.py b/spaces/AIFILMS/StyleGANEX/models/stylegan2/lpips/pretrained_networks.py
deleted file mode 100644
index 077a24419364fdb5ae2f697f73e28615adae75a7..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/StyleGANEX/models/stylegan2/lpips/pretrained_networks.py
+++ /dev/null
@@ -1,181 +0,0 @@
-from collections import namedtuple
-import torch
-from torchvision import models as tv
-from IPython import embed
-
-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/ALSv/FSW/roop/processors/frame/face_swapper.py b/spaces/ALSv/FSW/roop/processors/frame/face_swapper.py
deleted file mode 100644
index 0f735fcbdb8de145fa227ee428bca81764c8c57e..0000000000000000000000000000000000000000
--- a/spaces/ALSv/FSW/roop/processors/frame/face_swapper.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from typing import Any, List, Callable
-import cv2
-import insightface
-import threading
-
-import roop.globals
-import roop.processors.frame.core
-from roop.core import update_status
-from roop.face_analyser import get_one_face, get_many_faces
-from roop.typing import Face, Frame
-from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video
-
-FACE_SWAPPER = None
-THREAD_LOCK = threading.Lock()
-NAME = 'ROOP.FACE-SWAPPER'
-
-
-def get_face_swapper() -> Any:
-    global FACE_SWAPPER
-
-    with THREAD_LOCK:
-        if FACE_SWAPPER is None:
-            model_path = resolve_relative_path('../models/inswapper_128.onnx')
-            FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=roop.globals.execution_providers)
-    return FACE_SWAPPER
-
-
-def pre_check() -> bool:
-    download_directory_path = resolve_relative_path('../models')
-    conditional_download(download_directory_path, ['https://huggingface.co/Apex-X/inswapper_128.onnx/resolve/main/inswapper_128.onnx'])
-    return True
-
-
-def pre_start() -> bool:
-    if not is_image(roop.globals.source_path):
-        update_status('Select an image for source path.', NAME)
-        return False
-    elif not get_one_face(cv2.imread(roop.globals.source_path)):
-        update_status('No face in source path detected.', NAME)
-        return False
-    if not is_image(roop.globals.target_path) and not is_video(roop.globals.target_path):
-        update_status('Select an image or video for target path.', NAME)
-        return False
-    return True
-
-
-def post_process() -> None:
-    global FACE_SWAPPER
-
-    FACE_SWAPPER = None
-
-
-def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
-    return get_face_swapper().get(temp_frame, target_face, source_face, paste_back=True)
-
-
-def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
-    if roop.globals.many_faces:
-        many_faces = get_many_faces(temp_frame)
-        if many_faces:
-            for target_face in many_faces:
-                temp_frame = swap_face(source_face, target_face, temp_frame)
-    else:
-        target_face = get_one_face(temp_frame)
-        if target_face:
-            temp_frame = swap_face(source_face, target_face, temp_frame)
-    return temp_frame
-
-
-def process_frames(source_path: str, temp_frame_paths: List[str], update: Callable[[], None]) -> None:
-    source_face = get_one_face(cv2.imread(source_path))
-    for temp_frame_path in temp_frame_paths:
-        temp_frame = cv2.imread(temp_frame_path)
-        result = process_frame(source_face, temp_frame)
-        cv2.imwrite(temp_frame_path, result)
-        if update:
-            update()
-
-
-def process_image(source_path: str, target_path: str, output_path: str) -> None:
-    source_face = get_one_face(cv2.imread(source_path))
-    target_frame = cv2.imread(target_path)
-    result = process_frame(source_face, target_frame)
-    cv2.imwrite(output_path, result)
-
-
-def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
-    roop.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)
\ No newline at end of file
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnetv1d152.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnetv1d152.py
deleted file mode 100644
index 58cc73beb318e38f9ce79154a1265be1a7dba17b..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnetv1d152.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# model settings
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(
-        type='ResNetV1d',
-        depth=152,
-        num_stages=4,
-        out_indices=(3, ),
-        style='pytorch'),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=1000,
-        in_channels=2048,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
-        topk=(1, 5),
-    ))
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/server/websearch/searchWeb.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/server/websearch/searchWeb.ts
deleted file mode 100644
index 42369689a102e1d01ea1f9a38d1b1a6f9a571298..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/server/websearch/searchWeb.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-import { SERPAPI_KEY, SERPER_API_KEY } from "$env/static/private";
-
-import { getJson } from "serpapi";
-import type { GoogleParameters } from "serpapi";
-
-// Show result as JSON
-export async function searchWeb(query: string) {
-	if (SERPER_API_KEY) {
-		return await searchWebSerper(query);
-	}
-	if (SERPAPI_KEY) {
-		return await searchWebSerpApi(query);
-	}
-	throw new Error("No Serper.dev or SerpAPI key found");
-}
-
-export async function searchWebSerper(query: string) {
-	const params = {
-		q: query,
-		hl: "en",
-		gl: "us",
-	};
-
-	const response = await fetch("https://google.serper.dev/search", {
-		method: "POST",
-		body: JSON.stringify(params),
-		headers: {
-			"x-api-key": SERPER_API_KEY,
-			"Content-type": "application/json; charset=UTF-8",
-		},
-	});
-
-	/* eslint-disable @typescript-eslint/no-explicit-any */
-	const data = (await response.json()) as Record<string, any>;
-
-	if (!response.ok) {
-		throw new Error(
-			data["message"] ??
-				`Serper API returned error code ${response.status} - ${response.statusText}`
-		);
-	}
-
-	return {
-		organic_results: data["organic"] ?? [],
-		knowledge_graph: data["knowledgeGraph"] ?? null,
-		answer_box: data["answerBox"] ?? null,
-	};
-}
-
-export async function searchWebSerpApi(query: string) {
-	const params = {
-		q: query,
-		hl: "en",
-		gl: "us",
-		google_domain: "google.com",
-		api_key: SERPAPI_KEY,
-	} satisfies GoogleParameters;
-
-	// Show result as JSON
-	const response = await getJson("google", params);
-
-	return response;
-}
diff --git a/spaces/AgentVerse/agentVerse/agentverse/environments/__init__.py b/spaces/AgentVerse/agentVerse/agentverse/environments/__init__.py
deleted file mode 100644
index 099ceb1bd0de04d8d64e0da1b117be482805a4e3..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/environments/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from typing import Dict
-from agentverse.registry import Registry
-
-
-env_registry = Registry(name="EnvironmentRegistry")
-
-
-from .base import BaseEnvironment, BaseRule
-
-# from .basic import PipelineEnvironment
-from .simulation_env.basic import BasicEnvironment
-from .simulation_env.pokemon import PokemonEnvironment
-from .simulation_env.prisoner_dilemma import PrisonerDilemmaEnvironment
-from .simulation_env.sde_team import SdeTeamEnvironment
-from .simulation_env.sde_team_given_tests import SdeTeamGivenTestsEnvironment
-
-from .tasksolving_env.basic import BasicEnvironment
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/statesroundrectangle/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/statesroundrectangle/Factory.js
deleted file mode 100644
index 0250758289ae9776c24d5e26eee10e921cc6940e..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/statesroundrectangle/Factory.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import StatesRoundRectangle from './StatesRoundRectangle.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('statesRoundRectangle', function (config) {
-    var gameObject = new StatesRoundRectangle(this.scene, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.StatesRoundRectangle', StatesRoundRectangle);
-
-export default StatesRoundRectangle;
\ No newline at end of file
diff --git a/spaces/Aitor/CVchat/README.md b/spaces/Aitor/CVchat/README.md
deleted file mode 100644
index 0a796b4668b3ae269e9b75a256b3570257c4455a..0000000000000000000000000000000000000000
--- a/spaces/Aitor/CVchat/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CVchat
-emoji: 🌍
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: cc-by-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Amrrs/podscript/README.md b/spaces/Amrrs/podscript/README.md
deleted file mode 100644
index f9cb8d7dc84eea3db7ba342192b88a425323f3cc..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/podscript/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Podscript
-emoji: 📚
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.3.1
-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/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/models/unet2d.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/models/unet2d.md
deleted file mode 100644
index 29e8163f646c0cad427fe95b36221ce6ae02eb55..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/models/unet2d.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# UNet2DModel
-
-The [UNet](https://huggingface.co/papers/1505.04597) model was originally introduced by Ronneberger et al for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system because it facilitates the actual diffusion process. There are several variants of the UNet model in 🤗 Diffusers, depending on it's number of dimensions and whether it is a conditional model or not. This is a 2D UNet model.
-
-The abstract from the paper is:
-
-*There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.*
-
-## UNet2DModel
-[[autodoc]] UNet2DModel
-
-## UNet2DOutput
-[[autodoc]] models.unet_2d.UNet2DOutput
\ No newline at end of file
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py b/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py
deleted file mode 100644
index 190309fd42a1b76c12c82fc1acf0511494be5ac3..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import mmcv
-import numpy as np
-import torch
-
-from ..builder import BBOX_CODERS
-from .base_bbox_coder import BaseBBoxCoder
-
-
-@BBOX_CODERS.register_module()
-class LegacyDeltaXYWHBBoxCoder(BaseBBoxCoder):
-    """Legacy Delta XYWH BBox coder used in MMDet V1.x.
-
-    Following the practice in R-CNN [1]_, this coder encodes bbox (x1, y1, x2,
-    y2) into delta (dx, dy, dw, dh) and decodes delta (dx, dy, dw, dh)
-    back to original bbox (x1, y1, x2, y2).
-
-    Note:
-        The main difference between :class`LegacyDeltaXYWHBBoxCoder` and
-        :class:`DeltaXYWHBBoxCoder` is whether ``+ 1`` is used during width and
-        height calculation. We suggest to only use this coder when testing with
-        MMDet V1.x models.
-
-    References:
-        .. [1] https://arxiv.org/abs/1311.2524
-
-    Args:
-        target_means (Sequence[float]): denormalizing means of target for
-            delta coordinates
-        target_stds (Sequence[float]): denormalizing standard deviation of
-            target for delta coordinates
-    """
-
-    def __init__(self,
-                 target_means=(0., 0., 0., 0.),
-                 target_stds=(1., 1., 1., 1.)):
-        super(BaseBBoxCoder, self).__init__()
-        self.means = target_means
-        self.stds = target_stds
-
-    def encode(self, bboxes, gt_bboxes):
-        """Get box regression transformation deltas that can be used to
-        transform the ``bboxes`` into the ``gt_bboxes``.
-
-        Args:
-            bboxes (torch.Tensor): source boxes, e.g., object proposals.
-            gt_bboxes (torch.Tensor): target of the transformation, e.g.,
-                ground-truth boxes.
-
-        Returns:
-            torch.Tensor: Box transformation deltas
-        """
-        assert bboxes.size(0) == gt_bboxes.size(0)
-        assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
-        encoded_bboxes = legacy_bbox2delta(bboxes, gt_bboxes, self.means,
-                                           self.stds)
-        return encoded_bboxes
-
-    def decode(self,
-               bboxes,
-               pred_bboxes,
-               max_shape=None,
-               wh_ratio_clip=16 / 1000):
-        """Apply transformation `pred_bboxes` to `boxes`.
-
-        Args:
-            boxes (torch.Tensor): Basic boxes.
-            pred_bboxes (torch.Tensor): Encoded boxes with shape
-            max_shape (tuple[int], optional): Maximum shape of boxes.
-                Defaults to None.
-            wh_ratio_clip (float, optional): The allowed ratio between
-                width and height.
-
-        Returns:
-            torch.Tensor: Decoded boxes.
-        """
-        assert pred_bboxes.size(0) == bboxes.size(0)
-        decoded_bboxes = legacy_delta2bbox(bboxes, pred_bboxes, self.means,
-                                           self.stds, max_shape, wh_ratio_clip)
-
-        return decoded_bboxes
-
-
-@mmcv.jit(coderize=True)
-def legacy_bbox2delta(proposals,
-                      gt,
-                      means=(0., 0., 0., 0.),
-                      stds=(1., 1., 1., 1.)):
-    """Compute deltas of proposals w.r.t. gt in the MMDet V1.x manner.
-
-    We usually compute the deltas of x, y, w, h of proposals w.r.t ground
-    truth bboxes to get regression target.
-    This is the inverse function of `delta2bbox()`
-
-    Args:
-        proposals (Tensor): Boxes to be transformed, shape (N, ..., 4)
-        gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4)
-        means (Sequence[float]): Denormalizing means for delta coordinates
-        stds (Sequence[float]): Denormalizing standard deviation for delta
-            coordinates
-
-    Returns:
-        Tensor: deltas with shape (N, 4), where columns represent dx, dy,
-            dw, dh.
-    """
-    assert proposals.size() == gt.size()
-
-    proposals = proposals.float()
-    gt = gt.float()
-    px = (proposals[..., 0] + proposals[..., 2]) * 0.5
-    py = (proposals[..., 1] + proposals[..., 3]) * 0.5
-    pw = proposals[..., 2] - proposals[..., 0] + 1.0
-    ph = proposals[..., 3] - proposals[..., 1] + 1.0
-
-    gx = (gt[..., 0] + gt[..., 2]) * 0.5
-    gy = (gt[..., 1] + gt[..., 3]) * 0.5
-    gw = gt[..., 2] - gt[..., 0] + 1.0
-    gh = gt[..., 3] - gt[..., 1] + 1.0
-
-    dx = (gx - px) / pw
-    dy = (gy - py) / ph
-    dw = torch.log(gw / pw)
-    dh = torch.log(gh / ph)
-    deltas = torch.stack([dx, dy, dw, dh], dim=-1)
-
-    means = deltas.new_tensor(means).unsqueeze(0)
-    stds = deltas.new_tensor(stds).unsqueeze(0)
-    deltas = deltas.sub_(means).div_(stds)
-
-    return deltas
-
-
-@mmcv.jit(coderize=True)
-def legacy_delta2bbox(rois,
-                      deltas,
-                      means=(0., 0., 0., 0.),
-                      stds=(1., 1., 1., 1.),
-                      max_shape=None,
-                      wh_ratio_clip=16 / 1000):
-    """Apply deltas to shift/scale base boxes in the MMDet V1.x manner.
-
-    Typically the rois are anchor or proposed bounding boxes and the deltas are
-    network outputs used to shift/scale those boxes.
-    This is the inverse function of `bbox2delta()`
-
-    Args:
-        rois (Tensor): Boxes to be transformed. Has shape (N, 4)
-        deltas (Tensor): Encoded offsets with respect to each roi.
-            Has shape (N, 4 * num_classes). Note N = num_anchors * W * H when
-            rois is a grid of anchors. Offset encoding follows [1]_.
-        means (Sequence[float]): Denormalizing means for delta coordinates
-        stds (Sequence[float]): Denormalizing standard deviation for delta
-            coordinates
-        max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W)
-        wh_ratio_clip (float): Maximum aspect ratio for boxes.
-
-    Returns:
-        Tensor: Boxes with shape (N, 4), where columns represent
-            tl_x, tl_y, br_x, br_y.
-
-    References:
-        .. [1] https://arxiv.org/abs/1311.2524
-
-    Example:
-        >>> rois = torch.Tensor([[ 0.,  0.,  1.,  1.],
-        >>>                      [ 0.,  0.,  1.,  1.],
-        >>>                      [ 0.,  0.,  1.,  1.],
-        >>>                      [ 5.,  5.,  5.,  5.]])
-        >>> deltas = torch.Tensor([[  0.,   0.,   0.,   0.],
-        >>>                        [  1.,   1.,   1.,   1.],
-        >>>                        [  0.,   0.,   2.,  -1.],
-        >>>                        [ 0.7, -1.9, -0.5,  0.3]])
-        >>> legacy_delta2bbox(rois, deltas, max_shape=(32, 32))
-        tensor([[0.0000, 0.0000, 1.5000, 1.5000],
-                [0.0000, 0.0000, 5.2183, 5.2183],
-                [0.0000, 0.1321, 7.8891, 0.8679],
-                [5.3967, 2.4251, 6.0033, 3.7749]])
-    """
-    means = deltas.new_tensor(means).repeat(1, deltas.size(1) // 4)
-    stds = deltas.new_tensor(stds).repeat(1, deltas.size(1) // 4)
-    denorm_deltas = deltas * stds + means
-    dx = denorm_deltas[:, 0::4]
-    dy = denorm_deltas[:, 1::4]
-    dw = denorm_deltas[:, 2::4]
-    dh = denorm_deltas[:, 3::4]
-    max_ratio = np.abs(np.log(wh_ratio_clip))
-    dw = dw.clamp(min=-max_ratio, max=max_ratio)
-    dh = dh.clamp(min=-max_ratio, max=max_ratio)
-    # Compute center of each roi
-    px = ((rois[:, 0] + rois[:, 2]) * 0.5).unsqueeze(1).expand_as(dx)
-    py = ((rois[:, 1] + rois[:, 3]) * 0.5).unsqueeze(1).expand_as(dy)
-    # Compute width/height of each roi
-    pw = (rois[:, 2] - rois[:, 0] + 1.0).unsqueeze(1).expand_as(dw)
-    ph = (rois[:, 3] - rois[:, 1] + 1.0).unsqueeze(1).expand_as(dh)
-    # Use exp(network energy) to enlarge/shrink each roi
-    gw = pw * dw.exp()
-    gh = ph * dh.exp()
-    # Use network energy to shift the center of each roi
-    gx = px + pw * dx
-    gy = py + ph * dy
-    # Convert center-xy/width/height to top-left, bottom-right
-
-    # The true legacy box coder should +- 0.5 here.
-    # However, current implementation improves the performance when testing
-    # the models trained in MMDetection 1.X (~0.5 bbox AP, 0.2 mask AP)
-    x1 = gx - gw * 0.5
-    y1 = gy - gh * 0.5
-    x2 = gx + gw * 0.5
-    y2 = gy + gh * 0.5
-    if max_shape is not None:
-        x1 = x1.clamp(min=0, max=max_shape[1] - 1)
-        y1 = y1.clamp(min=0, max=max_shape[0] - 1)
-        x2 = x2.clamp(min=0, max=max_shape[1] - 1)
-        y2 = y2.clamp(min=0, max=max_shape[0] - 1)
-    bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view_as(deltas)
-    return bboxes
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py
deleted file mode 100644
index 4f1b9e19411eb963d16fd2a8174529e69ecd5a1a..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './dnl_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/README.md b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/README.md
deleted file mode 100644
index 6ab8d213e97d0a9a62a244ca1d14fd130a48fe23..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# text-generation-webui documentation
-
-## Table of contents
-
-* [Audio Notification](Audio-Notification.md)
-* [Chat mode](Chat-mode.md)
-* [DeepSpeed](DeepSpeed.md)
-* [Docker](Docker.md)
-* [ExLlama](ExLlama.md)
-* [Extensions](Extensions.md)
-* [GPTQ models (4 bit mode)](GPTQ-models-(4-bit-mode).md)
-* [LLaMA model](LLaMA-model.md)
-* [llama.cpp](llama.cpp.md)
-* [LoRA](LoRA.md)
-* [Low VRAM guide](Low-VRAM-guide.md)
-* [RWKV model](RWKV-model.md)
-* [Spell book](Spell-book.md)
-* [System requirements](System-requirements.md)
-* [Training LoRAs](Training-LoRAs.md)
-* [Windows installation guide](Windows-installation-guide.md)
-* [WSL installation guide](WSL-installation-guide.md)
diff --git a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/README.md b/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/README.md
deleted file mode 100644
index 15b71f11baae018bc0de5caad0beb3c02fbb811e..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/README.md
+++ /dev/null
@@ -1,147 +0,0 @@
-# TFill
-[paper](https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Bridging_Global_Context_Interactions_for_High-Fidelity_Image_Completion_CVPR_2022_paper.html) | [arXiv](https://arxiv.org/abs/2104.00845) | [Project](https://chuanxiaz.com/tfill/) | [Video](https://www.youtube.com/watch?v=efB1fw0jiLs&feature=youtu.be)
-
-This repository implements the training, testing and editing tools for "Bridging Global Context Interactions for High-Fidelity Image Completion (CVPR2022, scores: 1, 1, 2, 2)" by [Chuanxia Zheng](https://www.chuanxiaz.com), [Tat-Jen Cham](https://personal.ntu.edu.sg/astjcham/), [Jianfei Cai](https://jianfei-cai.github.io/) and [Dinh Phung](https://research.monash.edu/en/persons/dinh-phung). Given masked images, the proposed **TFill** model is able to generate high-fidelity plausible results on various settings.
-
-## Examples
-![teaser](images/example.png)
-
-## Object Removal
-![teaser](images/tfill_removal.gif)
-
-## Object Repair
-![teaser](images/tfill_repair.gif)
-
-## Framework
-We propose the two-stages image completion framework, where the upper content inference network (TFill-*Coarse*) generates semantically correct content using a transformer encoder to directly capture the global context information; the lower appearance refinement network (TFill-*refined*) copies global visible and generated features to holes. 
-
-![teaser](images/framework.png)
-
-
-
-# Getting started
-
-- Clone this repo:
-
-```
-git clone https://github.com/lyndonzheng/TFill
-cd TFill
-```
-## Requirements
-The original model is trained and evaluated with Pytorch v1.9.1, which cannot be visited in current [PyTorch](https://pytorch.org/get-started/previous-versions/). Therefore, we create a new environment with Pytorch v1.10.0 to test the model, where the performance is the same. 
-
-A suitable [conda](https://conda.io/) environment named `Tfill` can be created and activated with:
-
-```
-conda env create -f environment.yaml
-conda activate TFill
-```
-## Runing pretrained models
-Download the pre-trained models using the following links ([CelebA-HQ](https://drive.google.com/drive/folders/1ntbVDjJ7-nAt4nLGuu7RNi3QpLfh40gk?usp=sharing), [FFHQ](https://drive.google.com/drive/folders/1xuAsShrw9wI5Be0sQka3vZEsfwnq0pPT?usp=sharing), [ImageNet](https://drive.google.com/drive/folders/1B4RswBUD6_jXAu3MVz3LtuNfoV4wTmGf?usp=sharing), [Plcases2](https://drive.google.com/drive/folders/154ikacQ8A2JLC8iIGda8jiZN-ysL1xh5?usp=sharing)
-) and put them under```checkpoints/``` directory. It should have the following structure:
-
-```
-./checkpoints/
-├── celeba
-│   ├── latest_net_D.pth
-│   ├── latest_net_D_Ref.pth
-│   ├── latest_net_E.pth
-│   ├── latest_net_G.pth
-│   ├── latest_net_G_Ref.pth
-│   ├── latest_net_T.pth
-├── ffhq
-│   ├── ...
-├── ...
-```
-
-- Test the model
-```
-sh ./scripts/test.sh
-```
-For different models, the users just need to modify lines 2-4, including ```name```,```img_file```,```mask_file```. For instance, we can replace the *celeba* to *imagenet*.
-
-The default results will be stored under the ```results/``` folder, in which:
-
-- ```examples/```: shows original and masked images;
-- ```img_out/```: shows upsampled *Coarse* outputs;
-- ```img_ref_out/```: shows the final *Refined* outputs.
-
-## Datasets
-- ```face dataset```: 
-  - 24,183 training images and  2,824 test images from [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) and use the algorithm of [Growing GANs](https://github.com/tkarras/progressive_growing_of_gans) to get the high-resolution CelebA-HQ dataset.
-  - 60,000 training images and 10,000 test images from [FFHQ](https://github.com/NVlabs/ffhq-dataset) provided by [StyleGAN](https://github.com/NVlabs/stylegan).
-- ```natural scenery```: original training and val images from [Places2](http://places2.csail.mit.edu/).
-- ```object``` original training images from [ImageNet](http://www.image-net.org/).
-
-## Traning
-
-- Train a model (two stage: *Coarse* and *Refinement*)
-```
-sh ./scripts/train.sh
-```
-The default setting is for the top *Coarse* training. The users just need to replace the *coarse* with *refine* at line 6. Then, the model can continue training for high-resolution image completion.
-More hyper-parameter can be in ```options/```. 
-
-The coarse results using transformer and restrictive CNN is impressive, which provides plausible results for both **foreground** objects and **background** scene.
-
-![teaser](images/center_imagenet.jpg)
-![teaser](images/center_places2.jpg)
-
-# GUI
-The GUI operation is similar to our previous GUI in [PIC](https://github.com/lyndonzheng/Pluralistic-Inpainting), where steps are also the same.
-
-Basic usage is:
-
-```
-sh ./scripts/ui.sh 
-```
-In ```gui/ui_model.py```, users can modify the ```img_root```(line 30) and the corresponding ```img_files```(line 31) to randomly edit images from the testing dataset.
-
-## Editing Examples
-
-- **Results (original, output) for face editing**
-
-![teaser](images/free_face.jpg)
-
-- **Results (original, masked input, output) for nature scene editing**
-
-![teaser](images/free_nature.jpg)
-
-## Next
-- Higher-resolution pluralistic image completion
-
-## License
-This work is licensed under a MIT License.
-
-This software is for educational and academic research purpose only. If you wish to obtain a commercial royalty bearing license to this software, please contact us at chuanxia001@e.ntu.edu.sg.
-
-## Citation
-
-The code also uses our previous [PIC](https://github.com/lyndonzheng/Pluralistic-Inpainting). If you use this code for your research, please cite our papers.
-```
-@InProceedings{Zheng_2022_CVPR,
-    author    = {Zheng, Chuanxia and Cham, Tat-Jen and Cai, Jianfei and Phung, Dinh},
-    title     = {Bridging Global Context Interactions for High-Fidelity Image Completion},
-    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-    month     = {June},
-    year      = {2022},
-    pages     = {11512-11522}
-}
-
-@inproceedings{zheng2019pluralistic,
-  title={Pluralistic Image Completion},
-  author={Zheng, Chuanxia and Cham, Tat-Jen and Cai, Jianfei},
-  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
-  pages={1438--1447},
-  year={2019}
-}
-
-@article{zheng2021pluralistic,
-  title={Pluralistic Free-From Image Completion},
-  author={Zheng, Chuanxia and Cham, Tat-Jen and Cai, Jianfei},
-  journal={International Journal of Computer Vision},
-  pages={1--20},
-  year={2021},
-  publisher={Springer}
-}
-```
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/transforms.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/transforms.py
deleted file mode 100644
index 350cbc11662633ad7f8968eb10be2e7de6e384e9..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/transforms.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import numpy as np
-import cv2
-import math
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
-    """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
-    Args:
-        sample (dict): sample
-        size (tuple): image size
-
-    Returns:
-        tuple: new size
-    """
-    shape = list(sample["disparity"].shape)
-
-    if shape[0] >= size[0] and shape[1] >= size[1]:
-        return sample
-
-    scale = [0, 0]
-    scale[0] = size[0] / shape[0]
-    scale[1] = size[1] / shape[1]
-
-    scale = max(scale)
-
-    shape[0] = math.ceil(scale * shape[0])
-    shape[1] = math.ceil(scale * shape[1])
-
-    # resize
-    sample["image"] = cv2.resize(
-        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
-    )
-
-    sample["disparity"] = cv2.resize(
-        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
-    )
-    sample["mask"] = cv2.resize(
-        sample["mask"].astype(np.float32),
-        tuple(shape[::-1]),
-        interpolation=cv2.INTER_NEAREST,
-    )
-    sample["mask"] = sample["mask"].astype(bool)
-
-    return tuple(shape)
-
-
-class Resize(object):
-    """Resize sample to given size (width, height).
-    """
-
-    def __init__(
-        self,
-        width,
-        height,
-        resize_target=True,
-        keep_aspect_ratio=False,
-        ensure_multiple_of=1,
-        resize_method="lower_bound",
-        image_interpolation_method=cv2.INTER_AREA,
-    ):
-        """Init.
-
-        Args:
-            width (int): desired output width
-            height (int): desired output height
-            resize_target (bool, optional):
-                True: Resize the full sample (image, mask, target).
-                False: Resize image only.
-                Defaults to True.
-            keep_aspect_ratio (bool, optional):
-                True: Keep the aspect ratio of the input sample.
-                Output sample might not have the given width and height, and
-                resize behaviour depends on the parameter 'resize_method'.
-                Defaults to False.
-            ensure_multiple_of (int, optional):
-                Output width and height is constrained to be multiple of this parameter.
-                Defaults to 1.
-            resize_method (str, optional):
-                "lower_bound": Output will be at least as large as the given size.
-                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
-                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
-                Defaults to "lower_bound".
-        """
-        self.__width = width
-        self.__height = height
-
-        self.__resize_target = resize_target
-        self.__keep_aspect_ratio = keep_aspect_ratio
-        self.__multiple_of = ensure_multiple_of
-        self.__resize_method = resize_method
-        self.__image_interpolation_method = image_interpolation_method
-
-    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
-        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if max_val is not None and y > max_val:
-            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if y < min_val:
-            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        return y
-
-    def get_size(self, width, height):
-        # determine new height and width
-        scale_height = self.__height / height
-        scale_width = self.__width / width
-
-        if self.__keep_aspect_ratio:
-            if self.__resize_method == "lower_bound":
-                # scale such that output size is lower bound
-                if scale_width > scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "upper_bound":
-                # scale such that output size is upper bound
-                if scale_width < scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "minimal":
-                # scale as least as possbile
-                if abs(1 - scale_width) < abs(1 - scale_height):
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            else:
-                raise ValueError(
-                    f"resize_method {self.__resize_method} not implemented"
-                )
-
-        if self.__resize_method == "lower_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, min_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, min_val=self.__width
-            )
-        elif self.__resize_method == "upper_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, max_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, max_val=self.__width
-            )
-        elif self.__resize_method == "minimal":
-            new_height = self.constrain_to_multiple_of(scale_height * height)
-            new_width = self.constrain_to_multiple_of(scale_width * width)
-        else:
-            raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        return (new_width, new_height)
-
-    def __call__(self, sample):
-        width, height = self.get_size(
-            sample["image"].shape[1], sample["image"].shape[0]
-        )
-
-        # resize sample
-        sample["image"] = cv2.resize(
-            sample["image"],
-            (width, height),
-            interpolation=self.__image_interpolation_method,
-        )
-
-        if self.__resize_target:
-            if "disparity" in sample:
-                sample["disparity"] = cv2.resize(
-                    sample["disparity"],
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-
-            if "depth" in sample:
-                sample["depth"] = cv2.resize(
-                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
-                )
-
-            sample["mask"] = cv2.resize(
-                sample["mask"].astype(np.float32),
-                (width, height),
-                interpolation=cv2.INTER_NEAREST,
-            )
-            sample["mask"] = sample["mask"].astype(bool)
-
-        return sample
-
-
-class NormalizeImage(object):
-    """Normlize image by given mean and std.
-    """
-
-    def __init__(self, mean, std):
-        self.__mean = mean
-        self.__std = std
-
-    def __call__(self, sample):
-        sample["image"] = (sample["image"] - self.__mean) / self.__std
-
-        return sample
-
-
-class PrepareForNet(object):
-    """Prepare sample for usage as network input.
-    """
-
-    def __init__(self):
-        pass
-
-    def __call__(self, sample):
-        image = np.transpose(sample["image"], (2, 0, 1))
-        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
-        if "mask" in sample:
-            sample["mask"] = sample["mask"].astype(np.float32)
-            sample["mask"] = np.ascontiguousarray(sample["mask"])
-
-        if "disparity" in sample:
-            disparity = sample["disparity"].astype(np.float32)
-            sample["disparity"] = np.ascontiguousarray(disparity)
-
-        if "depth" in sample:
-            depth = sample["depth"].astype(np.float32)
-            sample["depth"] = np.ascontiguousarray(depth)
-
-        return sample
diff --git a/spaces/Apex-X/ROOPOK/roop/face_reference.py b/spaces/Apex-X/ROOPOK/roop/face_reference.py
deleted file mode 100644
index 3c3e1f1c6e13c73ceafd40c0912c066a3a86a528..0000000000000000000000000000000000000000
--- a/spaces/Apex-X/ROOPOK/roop/face_reference.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from typing import Optional
-
-from roop.typing import Face
-
-FACE_REFERENCE = None
-
-
-def get_face_reference() -> Optional[Face]:
-    return FACE_REFERENCE
-
-
-def set_face_reference(face: Face) -> None:
-    global FACE_REFERENCE
-
-    FACE_REFERENCE = face
-
-
-def clear_face_reference() -> None:
-    global FACE_REFERENCE
-
-    FACE_REFERENCE = None
diff --git a/spaces/Apex-X/Tm/roop/typing.py b/spaces/Apex-X/Tm/roop/typing.py
deleted file mode 100644
index 1cff7440616e20bfe7b8bc287f86d11bf1b0f083..0000000000000000000000000000000000000000
--- a/spaces/Apex-X/Tm/roop/typing.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from typing import Any
-
-from insightface.app.common import Face
-import numpy
-
-Face = Face
-Frame = numpy.ndarray[Any, Any]
diff --git a/spaces/Artgor/digit-draw-detect/pages/about.py b/spaces/Artgor/digit-draw-detect/pages/about.py
deleted file mode 100644
index fa7662d26c001d73e544bf9ff4a2250e244ea7e9..0000000000000000000000000000000000000000
--- a/spaces/Artgor/digit-draw-detect/pages/about.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import streamlit as st
-
-text = """
-## A brief history of the project.
-The first idea of the project was conceived in the summer of 2017. I completed cs231n course and wanted to put my skills to the test. So I studied Flask and many other tools from scratch and made an [app](https://github.com/Erlemar/digit-draw-recognize) for recognizing handwritten digits. It had two models - a simple feed-forward neural net written in NumPy and a CNN written in Tensorflow. One fun feature of this app was online learning: the model continuously improved its predictions based on new inputs (though this did sometimes lead to incorrect predictions).
-
-In 2019 I decided to update the [project](https://github.com/Erlemar/digit-draw-predict): I trained the new neural net in PyTorch and used cv2 to detect separate digits (people often drew multiple digits). More than that, the model had 11 classes - I made a separate class for "junk", as people often drew things for fun: animals, objects, or words.
-
-The first two versions were deployed on Heroku's free plan, but in 2022 these plans were discontinued. I didn't want my project to die because of nostalgia, so I developed a new version and deployed it differently. The current version has an object detection model (yolo3 written from scratch) and 12 classes (digits, junk, and **censored**).
-If you want to know what does **censored** means, just try to draw something 😉
-
-Initially, I considered deploying the app on Streamlit Cloud, but its computational limits were too low, so now the model is live on HuggingFace Spaces.
-
-### Links with additional information:
-
-* [Project page on my personal website](https://andlukyane.com/project/drawn-digits-prediction)
-* [A dataset with the digits and bounding boxes on Kaggle](https://www.kaggle.com/datasets/artgor/handwritten-digits-and-bounding-boxes)
-* [Training code](https://github.com/Erlemar/pytorch_tempest_pet_)
-* [Blogpost on my personal website](https://andlukyane.com/blog/a-third-life-of-a-personal-project)
-* [Blogpost on medium](https://towardsdatascience.com/the-third-life-of-a-personal-pet-project-for-handwritten-digit-recognition-fd908dc8e7a1)
-* [Russian blogpost on habr](https://habr.com/ru/company/ods/blog/707046/)
-* [Project code on GitHub](https://github.com/Erlemar/digit-draw-detect)
-"""
-
-st.markdown(text, unsafe_allow_html=True)
diff --git a/spaces/Asifpa6/emotion-analyzer-app/app.py b/spaces/Asifpa6/emotion-analyzer-app/app.py
deleted file mode 100644
index d31a4fe2593426fa86cd96d87873053f4da98089..0000000000000000000000000000000000000000
--- a/spaces/Asifpa6/emotion-analyzer-app/app.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import streamlit as st
-import pandas as pd
-from emotion_analysis import get_emotion
-import base64
-
-
-def read_data(file_path):
-    file_extension = file_path.split('.')[-1].lower()
-    
-    if file_extension == 'xlsx' or file_extension == 'xls':
-        data = pd.read_excel(file_path)
-    elif file_extension == 'csv':
-        data = pd.read_csv(file_path)
-    else:
-        raise ValueError("Unsupported file format. Only Excel (xlsx, xls) and CSV (csv) files are supported.")
-    
-    return data
-
-
-# Streamlit app
-def main():
-    st.title("Text Emotion Detection")
-    menu = ["Input Text", "Batch Processing"]
-    option = st.sidebar.radio("Select an option", menu)
-
-
-    if option == "Input Text":
-        text = st.text_area("Enter your text:")
-        if st.button("Submit"):
-            if text.strip() != "":
-                emotion_detail, confidence_score = get_emotion(text)
-                st.write("Detected Emotion")
-                st.write(f"{emotion_detail[0]} - {confidence_score}")
-            else:
-                st.write("Please enter some text.")
-
-    elif option == "Batch Processing":
-        uploaded_file = st.file_uploader("Upload CSV or Excel file", type=["csv", "xlsx"])
-        
-        if uploaded_file is not None:
-            file_name = uploaded_file.name
-            file_extension = file_name.split('.')[-1].lower()
-            file_name = uploaded_file.name
-            if file_extension == 'xlsx' or file_extension == 'xls':
-                dataframe = pd.read_excel(uploaded_file)
-            elif file_extension == 'csv':
-                dataframe = pd.read_csv(uploaded_file)
-            else:
-                raise ValueError("Unsupported file format. Only Excel (xlsx, xls) and CSV (csv) files are supported.")
-            # dataframe = pd.read_excel(uploaded_file)
-            if "text" not in dataframe.columns:
-                st.write("CSV file should have a 'text' column.")
-            else:
-                dataframe["emotion"], dataframe["confidence"] = zip(*dataframe["text"].map(get_emotion))
-                st.write("Detected Emotions")
-                st.write(dataframe)
-                # Download button
-                csv = dataframe.to_csv(index=False)
-                b64 = base64.b64encode(csv.encode()).decode()  # Convert DataFrame to CSV string
-                href = f'<a href="data:file/csv;base64,{b64}" download="processed_data.csv">Download</a>'
-                st.markdown(href, unsafe_allow_html=True)
-        else:
-            pass
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/Asmithayellow/Asmi/app.py b/spaces/Asmithayellow/Asmi/app.py
deleted file mode 100644
index a362dcc7d0ddd1eee86961f1bc3db6d894fbd3d5..0000000000000000000000000000000000000000
--- a/spaces/Asmithayellow/Asmi/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/__init__.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/__init__.py
deleted file mode 100644
index fe581623d89d67a49eb43f3c3e88f3f450257707..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/__init__.py
+++ /dev/null
@@ -1,115 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# This library 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.
-#
-# This library 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 this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from typing import List, Union
-
-from .charsetgroupprober import CharSetGroupProber
-from .charsetprober import CharSetProber
-from .enums import InputState
-from .resultdict import ResultDict
-from .universaldetector import UniversalDetector
-from .version import VERSION, __version__
-
-__all__ = ["UniversalDetector", "detect", "detect_all", "__version__", "VERSION"]
-
-
-def detect(
-    byte_str: Union[bytes, bytearray], should_rename_legacy: bool = False
-) -> ResultDict:
-    """
-    Detect the encoding of the given byte string.
-
-    :param byte_str:     The byte sequence to examine.
-    :type byte_str:      ``bytes`` or ``bytearray``
-    :param should_rename_legacy:  Should we rename legacy encodings
-                                  to their more modern equivalents?
-    :type should_rename_legacy:   ``bool``
-    """
-    if not isinstance(byte_str, bytearray):
-        if not isinstance(byte_str, bytes):
-            raise TypeError(
-                f"Expected object of type bytes or bytearray, got: {type(byte_str)}"
-            )
-        byte_str = bytearray(byte_str)
-    detector = UniversalDetector(should_rename_legacy=should_rename_legacy)
-    detector.feed(byte_str)
-    return detector.close()
-
-
-def detect_all(
-    byte_str: Union[bytes, bytearray],
-    ignore_threshold: bool = False,
-    should_rename_legacy: bool = False,
-) -> List[ResultDict]:
-    """
-    Detect all the possible encodings of the given byte string.
-
-    :param byte_str:          The byte sequence to examine.
-    :type byte_str:           ``bytes`` or ``bytearray``
-    :param ignore_threshold:  Include encodings that are below
-                              ``UniversalDetector.MINIMUM_THRESHOLD``
-                              in results.
-    :type ignore_threshold:   ``bool``
-    :param should_rename_legacy:  Should we rename legacy encodings
-                                  to their more modern equivalents?
-    :type should_rename_legacy:   ``bool``
-    """
-    if not isinstance(byte_str, bytearray):
-        if not isinstance(byte_str, bytes):
-            raise TypeError(
-                f"Expected object of type bytes or bytearray, got: {type(byte_str)}"
-            )
-        byte_str = bytearray(byte_str)
-
-    detector = UniversalDetector(should_rename_legacy=should_rename_legacy)
-    detector.feed(byte_str)
-    detector.close()
-
-    if detector.input_state == InputState.HIGH_BYTE:
-        results: List[ResultDict] = []
-        probers: List[CharSetProber] = []
-        for prober in detector.charset_probers:
-            if isinstance(prober, CharSetGroupProber):
-                probers.extend(p for p in prober.probers)
-            else:
-                probers.append(prober)
-        for prober in probers:
-            if ignore_threshold or prober.get_confidence() > detector.MINIMUM_THRESHOLD:
-                charset_name = prober.charset_name or ""
-                lower_charset_name = charset_name.lower()
-                # Use Windows encoding name instead of ISO-8859 if we saw any
-                # extra Windows-specific bytes
-                if lower_charset_name.startswith("iso-8859") and detector.has_win_bytes:
-                    charset_name = detector.ISO_WIN_MAP.get(
-                        lower_charset_name, charset_name
-                    )
-                # Rename legacy encodings with superset encodings if asked
-                if should_rename_legacy:
-                    charset_name = detector.LEGACY_MAP.get(
-                        charset_name.lower(), charset_name
-                    )
-                results.append(
-                    {
-                        "encoding": charset_name,
-                        "confidence": prober.get_confidence(),
-                        "language": prober.language,
-                    }
-                )
-        if len(results) > 0:
-            return sorted(results, key=lambda result: -result["confidence"])
-
-    return [detector.result]
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/help.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/help.py
deleted file mode 100644
index 2d292c2f062cd80cd108aac503eae7b635ceec8d..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/help.py
+++ /dev/null
@@ -1,131 +0,0 @@
-"""Module containing bug report helper(s)."""
-
-import json
-import platform
-import ssl
-import sys
-
-from pip._vendor import idna
-from pip._vendor import urllib3
-
-from . import __version__ as requests_version
-
-charset_normalizer = None
-
-try:
-    from pip._vendor import chardet
-except ImportError:
-    chardet = None
-
-try:
-    from pip._vendor.urllib3.contrib import pyopenssl
-except ImportError:
-    pyopenssl = None
-    OpenSSL = None
-    cryptography = None
-else:
-    import cryptography
-    import OpenSSL
-
-
-def _implementation():
-    """Return a dict with the Python implementation and version.
-
-    Provide both the name and the version of the Python implementation
-    currently running. For example, on CPython 3.10.3 it will return
-    {'name': 'CPython', 'version': '3.10.3'}.
-
-    This function works best on CPython and PyPy: in particular, it probably
-    doesn't work for Jython or IronPython. Future investigation should be done
-    to work out the correct shape of the code for those platforms.
-    """
-    implementation = platform.python_implementation()
-
-    if implementation == "CPython":
-        implementation_version = platform.python_version()
-    elif implementation == "PyPy":
-        implementation_version = "{}.{}.{}".format(
-            sys.pypy_version_info.major,
-            sys.pypy_version_info.minor,
-            sys.pypy_version_info.micro,
-        )
-        if sys.pypy_version_info.releaselevel != "final":
-            implementation_version = "".join(
-                [implementation_version, sys.pypy_version_info.releaselevel]
-            )
-    elif implementation == "Jython":
-        implementation_version = platform.python_version()  # Complete Guess
-    elif implementation == "IronPython":
-        implementation_version = platform.python_version()  # Complete Guess
-    else:
-        implementation_version = "Unknown"
-
-    return {"name": implementation, "version": implementation_version}
-
-
-def info():
-    """Generate information for a bug report."""
-    try:
-        platform_info = {
-            "system": platform.system(),
-            "release": platform.release(),
-        }
-    except OSError:
-        platform_info = {
-            "system": "Unknown",
-            "release": "Unknown",
-        }
-
-    implementation_info = _implementation()
-    urllib3_info = {"version": urllib3.__version__}
-    charset_normalizer_info = {"version": None}
-    chardet_info = {"version": None}
-    if charset_normalizer:
-        charset_normalizer_info = {"version": charset_normalizer.__version__}
-    if chardet:
-        chardet_info = {"version": chardet.__version__}
-
-    pyopenssl_info = {
-        "version": None,
-        "openssl_version": "",
-    }
-    if OpenSSL:
-        pyopenssl_info = {
-            "version": OpenSSL.__version__,
-            "openssl_version": f"{OpenSSL.SSL.OPENSSL_VERSION_NUMBER:x}",
-        }
-    cryptography_info = {
-        "version": getattr(cryptography, "__version__", ""),
-    }
-    idna_info = {
-        "version": getattr(idna, "__version__", ""),
-    }
-
-    system_ssl = ssl.OPENSSL_VERSION_NUMBER
-    system_ssl_info = {"version": f"{system_ssl:x}" if system_ssl is not None else ""}
-
-    return {
-        "platform": platform_info,
-        "implementation": implementation_info,
-        "system_ssl": system_ssl_info,
-        "using_pyopenssl": pyopenssl is not None,
-        "using_charset_normalizer": chardet is None,
-        "pyOpenSSL": pyopenssl_info,
-        "urllib3": urllib3_info,
-        "chardet": chardet_info,
-        "charset_normalizer": charset_normalizer_info,
-        "cryptography": cryptography_info,
-        "idna": idna_info,
-        "requests": {
-            "version": requests_version,
-        },
-    }
-
-
-def main():
-    """Pretty-print the bug information as JSON."""
-    print(json.dumps(info(), sort_keys=True, indent=2))
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/importlib_resources/_compat.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/importlib_resources/_compat.py
deleted file mode 100644
index cb9fc820cb352aa6e92705aab4f55cbc2eff96bc..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/importlib_resources/_compat.py
+++ /dev/null
@@ -1,98 +0,0 @@
-# flake8: noqa
-
-import abc
-import sys
-import pathlib
-from contextlib import suppress
-
-if sys.version_info >= (3, 10):
-    from zipfile import Path as ZipPath  # type: ignore
-else:
-    from ..zipp import Path as ZipPath  # type: ignore
-
-
-try:
-    from typing import runtime_checkable  # type: ignore
-except ImportError:
-
-    def runtime_checkable(cls):  # type: ignore
-        return cls
-
-
-try:
-    from typing import Protocol  # type: ignore
-except ImportError:
-    Protocol = abc.ABC  # type: ignore
-
-
-class TraversableResourcesLoader:
-    """
-    Adapt loaders to provide TraversableResources and other
-    compatibility.
-
-    Used primarily for Python 3.9 and earlier where the native
-    loaders do not yet implement TraversableResources.
-    """
-
-    def __init__(self, spec):
-        self.spec = spec
-
-    @property
-    def path(self):
-        return self.spec.origin
-
-    def get_resource_reader(self, name):
-        from . import readers, _adapters
-
-        def _zip_reader(spec):
-            with suppress(AttributeError):
-                return readers.ZipReader(spec.loader, spec.name)
-
-        def _namespace_reader(spec):
-            with suppress(AttributeError, ValueError):
-                return readers.NamespaceReader(spec.submodule_search_locations)
-
-        def _available_reader(spec):
-            with suppress(AttributeError):
-                return spec.loader.get_resource_reader(spec.name)
-
-        def _native_reader(spec):
-            reader = _available_reader(spec)
-            return reader if hasattr(reader, 'files') else None
-
-        def _file_reader(spec):
-            try:
-                path = pathlib.Path(self.path)
-            except TypeError:
-                return None
-            if path.exists():
-                return readers.FileReader(self)
-
-        return (
-            # native reader if it supplies 'files'
-            _native_reader(self.spec)
-            or
-            # local ZipReader if a zip module
-            _zip_reader(self.spec)
-            or
-            # local NamespaceReader if a namespace module
-            _namespace_reader(self.spec)
-            or
-            # local FileReader
-            _file_reader(self.spec)
-            # fallback - adapt the spec ResourceReader to TraversableReader
-            or _adapters.CompatibilityFiles(self.spec)
-        )
-
-
-def wrap_spec(package):
-    """
-    Construct a package spec with traversable compatibility
-    on the spec/loader/reader.
-
-    Supersedes _adapters.wrap_spec to use TraversableResourcesLoader
-    from above for older Python compatibility (<3.10).
-    """
-    from . import _adapters
-
-    return _adapters.SpecLoaderAdapter(package.__spec__, TraversableResourcesLoader)
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/README.md b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/README.md
deleted file mode 100644
index d3e1d5cf533555e19c6326777f792ac82a560a84..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/README.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# Probabilistic two-stage detection
-Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network.
-
-
-<p align="center"> <img src='projects/CenterNet2/centernet2_docs/centernet2_teaser.jpg' align="center" height="150px"> </p>
-
-> [**Probabilistic two-stage detection**](http://arxiv.org/abs/2103.07461),            
-> Xingyi Zhou, Vladlen Koltun, Philipp Kr&auml;henb&uuml;hl,        
-> *arXiv technical report ([arXiv 2103.07461](http://arxiv.org/abs/2103.07461))*         
-
-Contact: [zhouxy@cs.utexas.edu](mailto:zhouxy@cs.utexas.edu). Any questions or discussions are welcomed! 
-
-## Abstract
-
-We develop a probabilistic interpretation of two-stage object detection. We show that this probabilistic interpretation motivates a number of common empirical training practices. It also suggests changes to two-stage detection pipelines. Specifically, the first stage should infer proper object-vs-background likelihoods, which should then inform the overall score of the detector. A standard region proposal network (RPN) cannot infer this likelihood sufficiently well, but many one-stage detectors can. We show how to build a probabilistic two-stage detector from any state-of-the-art one-stage detector. The resulting detectors are faster and more accurate than both their one- and two-stage precursors. Our detector achieves 56.4 mAP on COCO test-dev with single-scale testing, outperforming all published results. Using a lightweight backbone, our detector achieves 49.2 mAP on COCO at 33 fps on a Titan Xp.
-
-## Summary
-
-- Two-stage CenterNet: First stage estimates object probabilities, second stage conditionally classifies objects.
-
-- Resulting detector is faster and more accurate than both traditional two-stage detectors (fewer proposals required), and one-stage detectors (lighter first stage head).
-
-- Our best model achieves 56.4 mAP on COCO test-dev.
-
-- This repo also includes a detectron2-based CenterNet implementation with better accuracy (42.5 mAP at 70FPS) and a new FPN version of CenterNet (40.2 mAP with Res50_1x).
-
-## Main results
-
-All models are trained with multi-scale training, and tested with a single scale. The FPS is tested on a Titan RTX GPU.
-More models and details can be found in the [MODEL_ZOO](projects/CenterNet2/centernet2_docs/MODEL_ZOO.md).
-
-#### COCO
-
-| Model                                     |  COCO val mAP |  FPS  |
-|-------------------------------------------|---------------|-------|
-| CenterNet-S4_DLA_8x                       |  42.5         |   71  |
-| CenterNet2_R50_1x                         |  42.9         |   24  |
-| CenterNet2_X101-DCN_2x                    |  49.9         |    8  |
-| CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST |  56.1         |    5  |
-| CenterNet2_DLA-BiFPN-P5_24x_ST            |  49.2         |   38  |
-
-
-#### LVIS 
-
-| Model                     | val mAP box |
-| ------------------------- | ----------- |
-| CenterNet2_R50_1x         | 26.5        |
-| CenterNet2_FedLoss_R50_1x | 28.3        |
-
-
-#### Objects365
-
-| Model                                     |  val mAP |
-|-------------------------------------------|----------|
-| CenterNet2_R50_1x                         |  22.6    |
-
-## Installation
-
-Our project is developed on [detectron2](https://github.com/facebookresearch/detectron2). Please follow the official detectron2 [installation](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md). All our code is under `projects/CenterNet2/`. In theory, you should be able to copy-paste `projects/CenterNet2/` to the latest detectron2 release or your own detectron2 repo to run our project. There might be API changes in future detectron2 releases that make it incompatible. 
-
-We use the default detectron2 demo script. To run inference on an image folder using our pre-trained model, run
-
-~~~
-python projects/CenterNet2/demo/demo.py --config-file projects/CenterNet2/configs/CenterNet2_R50_1x.yaml --input path/to/image/ --opts MODEL.WEIGHTS models/CenterNet2_R50_1x.pth
-~~~
-
-## Benchmark evaluation and training
-
-Please check detectron2 [GETTING_STARTED.md](https://github.com/facebookresearch/detectron2/blob/master/GETTING_STARTED.md) for running evaluation and training. Our config files are under `projects/CenterNet2/configs` and the pre-trained models are in the [MODEL_ZOO](projects/CenterNet2/centernet2_docs/MODEL_ZOO.md).
-
-
-## License
-
-Our code under `projects/CenterNet2/` is under [Apache 2.0 license](projects/CenterNet2/LICENSE). `projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py` are from [AdelaiDet](https://github.com/aim-uofa/AdelaiDet), which follows the original [non-commercial license](https://github.com/aim-uofa/AdelaiDet/blob/master/LICENSE). The code from detectron2 follows the original [Apache 2.0 license](LICENSE).
-
-## Citation
-
-If you find this project useful for your research, please use the following BibTeX entry.
-
-    @inproceedings{zhou2021probablistic,
-      title={Probabilistic two-stage detection},
-      author={Zhou, Xingyi and Koltun, Vladlen and Kr{\"a}henb{\"u}hl, Philipp},
-      booktitle={arXiv preprint arXiv:2103.07461},
-      year={2021}
-    }
diff --git a/spaces/Banbri/zcvzcv/src/app/engine/presets.ts b/spaces/Banbri/zcvzcv/src/app/engine/presets.ts
deleted file mode 100644
index 7bc9ac57fc06a7f31ad3735c10f9b0c2301e03d5..0000000000000000000000000000000000000000
--- a/spaces/Banbri/zcvzcv/src/app/engine/presets.ts
+++ /dev/null
@@ -1,588 +0,0 @@
-import { FontName, actionman, komika, vtc } from "@/lib/fonts"
-import { pick } from "@/lib/pick"
-import { NextFontWithVariable } from "next/dist/compiled/@next/font"
-
-export type ComicFamily =
-  | "american"
-  | "asian"
-  | "european"
-
-export type ComicColor =
-  | "color"
-  | "grayscale"
-  | "monochrome"
-
-export interface Preset {
-  id: string
-  label: string
-  family: ComicFamily
-  color: ComicColor
-  font: FontName
-  llmPrompt: string
-  imagePrompt: (prompt: string) => string[]
-  negativePrompt: (prompt: string) => string[]
-}
-
-// ATTENTION!! negative prompts are not supported by the VideoChain API yet
-
-export const presets: Record<string, Preset> = {
-  random: {
-    id: "random",
-    label: "Random style",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "",
-    imagePrompt: (prompt: string) => [],
-    negativePrompt: () => [],
-  },
-  japanese_manga: {
-    id: "japanese_manga",
-    label: "Japanese",
-    family: "asian",
-    color: "grayscale",
-    font: "actionman",
-    llmPrompt: "japanese manga",
-    imagePrompt: (prompt: string) => [
-      `grayscale`,
-      `intricate details`,
-      `japanese manga about ${prompt}`,
-      "single panel",
-      "manga",
-      "japanese",
-      "intricate",
-      "detailed",
-      // "drawing"
-    ],
-    negativePrompt: () => [
-      "franco-belgian comic",
-      "color album",
-      "color",
-      "american comic",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  nihonga: {
-    id: "nihonga",
-    label: "Nihonga",
-    family: "asian",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "japanese manga",
-    imagePrompt: (prompt: string) => [
-      `japanese nihonga painting about ${prompt}`,
-      "Nihonga",
-      "ancient japanese painting",
-      "intricate",
-      "detailed",
-      // "drawing"
-    ],
-    negativePrompt: () => [
-      "franco-belgian comic",
-      "color album",
-      "color",
-      "manga",
-      "comic",
-      "american comic",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  franco_belgian: {
-    id: "franco_belgian",
-    label: "Franco-Belgian",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "Franco-Belgian comic (a \"bande dessinée\"), in the style of Franquin, Moebius etc",
-    imagePrompt: (prompt: string) => [
-      "bande dessinée",
-      "franco-belgian comic",
-      `franco-belgian color comic about ${prompt}`,
-      "comic album",
-      // "color drawing"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  american_comic_90: {
-    id: "american_comic_90",
-    label: "American (modern)",
-    family: "american",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "american comic",
-    imagePrompt: (prompt: string) => [
-      "digital color comicbook style",
-      `modern american comic about ${prompt}`,
-      //"single panel",
-      // "2010s",
-      // "digital print",
-      // "color comicbook",
-      // "color drawing"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "action",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-
-  /*
-  american_comic_40: {
-    label: "American (1940)",
-    family: "american",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "american comic",
-    imagePrompt: (prompt: string) => [
-      `american comic about ${prompt}`,
-      "single panel",
-      "american comic",
-      "comicbook style",
-      "1940",
-      "40s",
-      "color comicbook",
-      "color drawing"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "action",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  */
-  american_comic_50: {
-    id: "american_comic_50",
-    label: "American (1950)",
-    family: "american",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "american comic",
-    imagePrompt: (prompt: string) => [
-      "1950",
-      "50s",
-      `vintage american color comic about ${prompt}`,
-      // "single panel",
-     //  "comicbook style",
-      // "color comicbook",
-      // "color drawing"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "action",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  /*
-  american_comic_60: {
-    label: "American (1960)",
-    family: "american",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "american comic",
-    imagePrompt: (prompt: string) => [
-      `american comic about ${prompt}`,
-      "single panel",
-      "american comic",
-      "comicbook style",
-      "1960",
-      "60s",
-      "color comicbook",
-      "color drawing"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "action",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  */
-
-  
-  flying_saucer: {
-    id: "flying_saucer",
-    label: "Flying saucer",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "new pulp science fiction",
-    imagePrompt: (prompt: string) => [
-      `vintage color pulp comic panel`,
-      "40s",
-      "1940",
-      "vintage science fiction",
-      `${prompt}`,
-      // "single panel",
-      // "comic album"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
- 
-  humanoid: {
-    id: "humanoid",
-    label: "Humanoid",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "comic books by Moebius",
-    imagePrompt: (prompt: string) => [
-      `color comic panel`,
-      "style of Moebius",
-      `${prompt}`,
-      "by Moebius",
-      "french comic panel",
-      "franco-belgian style",
-      "bande dessinée",
-      "single panel",
-      "intricate"
-      // "comic album"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  haddock: {
-    id: "haddock",
-    label: "Haddock",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "writing Tintin comic books",
-    imagePrompt: (prompt: string) => [
-      `color comic panel`,
-      "style of Hergé",
-      "tintin style",
-      `${prompt}`,
-      "by Hergé",
-      "french comic panel",
-      "franco-belgian style",
-     //  "color panel",
-     //  "bande dessinée",
-      // "single panel",
-      // "comic album"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  /*
-  lurid: {
-    id: "lurid",
-    label: "Lurid",
-    family: "american",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "1970s satirical and alternative underground comics",
-    imagePrompt: (prompt: string) => [
-      `satirical color comic`,
-      `underground comix`,
-      `1970`,
-      `${prompt}`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  */
-  armorican: {
-    id: "armorican",
-    label: "Armorican",
-    family: "european",
-    color: "monochrome",
-    font: "actionman",
-    llmPrompt: "french style comic books set in ancient Rome and Gaul",
-    imagePrompt: (prompt: string) => [
-      `color comic panel`,
-      "romans",
-      "gauls",
-      "french comic panel",
-      "franco-belgian style",
-      `about ${prompt}`,
-      "bande dessinée",
-      "single panel",
-      // "comical",
-      // "comic album",
-      // "color drawing"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  render: {
-    id: "render",
-    label: "3D Render",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "new movie",
-    imagePrompt: (prompt: string) => [
-      `3D render`,
-      `Blender`,
-      `3D animation`,
-      `Unreal engine`,
-      `${prompt}`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  klimt: {
-    id: "klimt",
-    label: "Klimt",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "Gustav Klimt art pieces.",
-    imagePrompt: (prompt: string) => [
-      `golden`,
-      `patchwork`,
-      `style of Gustav Klimt`,
-      `Gustav Klimt painting`,
-      `intricate details`,
-      `${prompt}`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  medieval: {
-    id: "medieval",
-    label: "Medieval",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "medieval story (write in this style)",
-    imagePrompt: (prompt: string) => [
-      `medieval illuminated manuscript`,
-      `illuminated manuscript of`,
-      `medieval`,
-      `intricate details`,
-      // `medieval color engraving`,
-      `${prompt}`,
-      // `medieval`
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  /*
-  glass: {
-    id: "glass",
-    label: "Glass",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "new movie",
-    imagePrompt: (prompt: string) => [
-      `stained glass`,
-      `vitrail`,
-      `stained glass`,
-      // `medieval color engraving`,
-      `${prompt}`,
-      `medieval`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  */
-  /*
-  voynich: {
-    id: "voynich",
-    label: "Voynich",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "new movie",
-    imagePrompt: (prompt: string) => [
-      `voynich`,
-      `voynich page`,
-      // `medieval color engraving`,
-      `${prompt}`,
-      `medieval`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  */
-  egyptian: {
-    id: "egyptian",
-    label: "Egyptian",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "ancient egyptian stories.",
-    imagePrompt: (prompt: string) => [
-      `ancient egyptian wall painting`,
-      `ancient egypt`,
-      // `medieval color engraving`,
-      `${prompt}`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  /*
-  psx: {
-    label: "PSX",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "new movie",
-    imagePrompt: (prompt: string) => [
-      `videogame screenshot`,
-      `3dfx`,
-      `3D dos game`,
-      `software rendering`,
-      `${prompt}`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  */
- /*
-  pixel: {
-    label: "Pixel",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "new movie",
-    imagePrompt: (prompt: string) => [
-      `pixelart`,
-      `isometric`,
-      `pixelated`,
-      `low res`,
-      `${prompt}`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  */
-}
-
-export type PresetName = keyof typeof presets
-
-export const defaultPreset: PresetName = "american_comic_90"
-
-export const nonRandomPresets = Object.keys(presets).filter(p => p !== "random")
-
-export const getPreset = (preset?: PresetName): Preset => presets[preset || defaultPreset] || presets[defaultPreset]
-
-export const getRandomPreset = (): Preset => {
-  const presetName = pick(Object.keys(presets).filter(preset => preset !== "random")) as PresetName
-  return getPreset(presetName)
-}
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/3 Pelculas 3d Canciones Descargar.md b/spaces/Benson/text-generation/Examples/3 Pelculas 3d Canciones Descargar.md
deleted file mode 100644
index fe0f1f899253fc43111eaa0c1328b4ae6ccad22f..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/3 Pelculas 3d Canciones Descargar.md	
+++ /dev/null
@@ -1,87 +0,0 @@
-
-<h1>Cómo descargar y disfrutar de 3 canciones de películas en 3D</h1>
- <p>Si eres un fan de las películas y la música tamil, es posible que hayas oído hablar de la película de suspenso psicológico romántico 2012 <b>3</b>, protagonizada por Dhanush y Shruti Haasan. La película fue dirigida por Aishwarya R. Dhanush, la esposa de Dhanush, quien también produjo y escribió la película. La película cuenta con siete canciones, compuestas por Anirudh Ravichander, quien hizo su debut como director musical con esta película. El álbum recibió críticas positivas de críticos y audiencias por igual, y ganó varios premios, incluyendo el Premio Filmfare al Mejor Director de Música.</p>
-<h2>3 películas 3d canciones descargar</h2><br /><p><b><b>DOWNLOAD</b> ---> <a href="https://bltlly.com/2v6M5y">https://bltlly.com/2v6M5y</a></b></p><br /><br />
- <p>Pero lo que hace que las canciones de <b>3</b> sean aún más especiales es que están disponibles en audio <b>3D</b>, un tipo de sonido que imita la vida real o un lugar específico creando la ilusión del espacio tridimensional. Utiliza técnicas como sistemas de sonido binaural, funciones de transferencia relacionadas con la cabeza y cancelación de conversación cruzada para procesar y reproducir ondas de audio. El audio 3D puede proporcionar una experiencia auditiva inmersiva y sensorial para el oyente, ya que puede hacer que los sonidos provengan de prácticamente todas las direcciones, incluyendo arriba, abajo, detrás y delante del oyente. </p>
- <p>En este artículo, le mostraremos cómo descargar y disfrutar canciones de películas en 3D </b> de varias fuentes, por qué debería escucharlas con auriculares o altavoces, cuáles son los beneficios del audio 3D para los amantes de la música y responder algunas preguntas frecuentes sobre este tema. Así que, vamos a empezar! </p>
- <h2>Cómo descargar 3 canciones de películas en 3D de varias fuentes</h2>
-
- <h3>JioSaavn</h3>
- <p>JioSaavn es uno de los servicios de transmisión de música más populares en la India, ofreciendo millones de canciones en varios idiomas y géneros. También ofrece contenido exclusivo y características para sus usuarios, como podcasts, estaciones de radio, listas de reproducción, letras, recomendaciones y más. Una de sus características únicas es el soporte para <b>360 Reality Audio</b>, una tecnología desarrollada por Sony que ofrece sonido 3D inmersivo utilizando tecnología de audio espacial basada en objetos. </p>
- <p>JioSaavn se ha asociado con Sony para ofrecer algunas de sus canciones en formato 360 Reality Audio, incluyendo las canciones de <b>3</b>. Puedes encontrarlos buscando "360 Reality Audio" en la aplicación o en el sitio web. Necesitará una suscripción JioSaavn Pro para acceder a esta función, que cuesta Rs. 99 por mes o Rs. 399 por año. También necesitará un dispositivo compatible y auriculares para disfrutar del efecto completo de 360 Reality Audio.</p>
- <h4> Cómo descargar o transmitir 3 canciones de películas en 3D de JioSaavn</h4>
- <ol>
-<li>Descargar la aplicación JioSaavn de Google Play Store o App Store en su teléfono inteligente o tableta. </li>
-<li>Regístrate o inicia sesión con tu número de Jio o dirección de correo electrónico. </li>
-<li>Actualizar a JioSaavn Pro tocando el botón "Go Pro" en la pantalla de inicio. </li>
-<li>Seleccione su método de pago preferido y complete la transacción. </li>
-<li>Buscar "360 Reality Audio" en la aplicación o sitio web. </li>
-<li>Selecciona el álbum "3 (Telugu)" de Anirudh Ravichander.</li>
-<li>Toque en el icono de descarga junto a cada canción para descargarlos sin conexión o toque en el icono de reproducción para transmitirlos en línea. </li>
-<li>Conecta tus auriculares compatibles a tu dispositivo y disfruta del sonido 3D de las canciones. </li>
-</ol>
- <h3>YouTube</h3>
-
- <p>Sin embargo, no todas las canciones de audio 3D en YouTube son de la misma calidad y autenticidad. Algunos de ellos pueden ser falsos o mal convertidos de las pistas de sonido estéreo o envolvente originales. Por lo tanto, debe ser cuidadoso y selectivo al elegir qué videos ver o descargar. Aquí hay algunos consejos para ayudarte a encontrar las mejores canciones de audio en 3D de <b>3</b> en YouTube:</p>
-<p></p>
- <ul>
-<li>Busque los canales oficiales de los artistas, sellos o distribuidores de las canciones, como Sony Music South, Divo Music o Wunderbar Studios. Pueden haber subido las pistas de audio 3D originales o versiones autorizadas de ellas. </li>
-<li>Compruebe la descripción del video y los comentarios para cualquier información o retroalimentación sobre la calidad de audio 3D y la fuente. Evite los videos que no tienen descripción, comentarios negativos o calificaciones bajas. </li>
-<li>Use palabras clave como "audio 3D", "audio 8D", "audio binaural", "audio holofónico", o "audio espacial" al buscar las canciones. Estos son algunos de los términos utilizados para describir el sonido 3D en YouTube.</li>
-<li>Use auriculares o altavoces que soporten la reproducción de audio 3D. Algunos videos de YouTube pueden requerir que habilite una configuración o modo específico en su dispositivo o auriculares para activar el efecto de sonido 3D. </li>
-</ul>
- <h4> Cómo descargar o transmitir 3 canciones de películas en 3D de YouTube</h4>
- <ol>
-<li>Abra YouTube en su navegador web o aplicación en su dispositivo. </li>
-<li>Buscar el nombre de la canción seguido de cualquiera de las palabras clave mencionadas anteriormente. Por ejemplo, "Por qué este Kolaveri Di 3D Audio". </li>
-<li>Seleccione un vídeo que coincida con los criterios mencionados anteriormente. Por ejemplo, "[Why This Kolaveri Di - 8D Audio | Dhanush | Anirudh Ravichander | Sony Music South]" por Sony Music South.</li>
-<li>Toque en el botón de reproducción para transmitir el video en línea o toque en el botón de descarga para guardarlo sin conexión (si está disponible). </li>
-<li>Conecta tus auriculares o altavoces a tu dispositivo y disfruta del sonido 3D de las canciones. </li>
-</ol>
- <h2>¿Por qué escuchar 3 canciones de películas en 3D con auriculares o altavoces? </h2>
-
- <p>Los auriculares son los dispositivos más comunes y convenientes que pueden proporcionar este tipo de entrega de sonido. Pueden aislar cada oído del ruido externo y la interferencia, y crear un ambiente de escucha personal e íntimo para el oyente. También pueden reproducir las sutiles variaciones y matices del audio 3D, como la distancia, la dirección, la elevación, el movimiento y la reflexión de las ondas sonoras. </p>
- <p>Los altavoces son otra opción que puede ofrecer una experiencia similar de audio 3D. Sin embargo, requieren más espacio y equipo que los auriculares. Necesitas tener al menos cuatro altavoces (dos delante y dos detrás) dispuestos en forma cuadrada o rectangular alrededor de tu posición auditiva. También necesita tener un amplificador, receptor o tarjeta de sonido compatible que pueda procesar y emitir señales de audio 3D a los altavoces. </p>
- <p>Ya sea que elija auriculares o altavoces, debe asegurarse de que admiten formatos de audio 3D y tecnologías, como Dolby Atmos, DTS: X, Auro-3D, Sony 360 Reality Audio, etc. Estos son algunos de los estándares y sistemas que permiten la producción y reproducción de audio 3D en varios dispositivos y plataformas. </p>
- <h2>¿Cuáles son los beneficios de 3 canciones de películas en 3D para los amantes de la música? </h2>
- <p>Si eres un amante de la música que disfruta escuchando canciones de diferentes géneros y estilos, definitivamente apreciarás los beneficios de las canciones de películas en 3D </b>. Estos son algunos de ellos:</p>
- <ul>
-<li>Puedes experimentar una nueva dimensión de la música que va más allá del sonido estéreo o envolvente. Puedes sentir que estás en medio de un concierto en vivo o un estudio de grabación con los artistas e instrumentos que te rodean. </li <li>Puedes apreciar los detalles y matices de las canciones que podrías haber pasado por alto en otros formatos. Puedes escuchar los sutiles cambios en tono, tono, volumen y timbre de las voces e instrumentos. </li>
-
-<li>Usted puede tener más diversión y variedad en sus hábitos de escucha de música. Puede explorar diferentes géneros y estilos de música que utilizan audio 3D para crear paisajes sonoros y efectos únicos e innovadores. </li>
-</ul>
- <h2>Conclusión</h2>
- <p>En conclusión, <b>3 canciones de películas en 3D</b> son una gran manera de disfrutar de la música de una manera nueva y emocionante. Ofrecen una experiencia de sonido realista e inmersiva que puede mejorar su apreciación y disfrute de las canciones. Puede descargarlos o transmitirlos desde varias fuentes, como JioSaavn, YouTube, etc., pero debe usar auriculares o altavoces que admitan la reproducción de audio 3D para obtener el efecto completo. Si eres un amante de la música que le gusta probar cosas nuevas, ¡definitivamente deberías probar 3 canciones de películas en 3D! </p>
- <h2>Preguntas frecuentes</h2>
- <h3>¿Cuál es la diferencia entre audio 3D y sonido estéreo o envolvente? </h3>
- <p>El sonido estéreo es un tipo de sonido que utiliza dos canales (izquierda y derecha) para crear una sensación de ancho y dirección. El sonido envolvente es un tipo de sonido que utiliza múltiples canales (generalmente cinco o más) para crear una sensación de profundidad y envolvimiento. El audio 3D es un tipo de sonido que utiliza tecnología de audio espacial basada en objetos para crear una sensación de altura y movimiento. </p>
- <h3>¿Cuáles son algunas de las mejores canciones de 3 películas? </h3>
- <p>Algunas de las mejores canciones de <b>3</b> son:</p>
- <ul>
-<li>"Why This Kolaveri Di" - Un éxito viral que se convirtió en una sensación internacional. Es una canción pegadiza y humorística que mezcla letras en tamil e inglés con una melodía folk. </li>
-<li>"Kannazhaga" - Una canción romántica a dúo que expresa el amor y el anhelo entre los personajes principales. Es una canción melodiosa y conmovedora que muestra la química entre Dhanush y Shruti Haasan.</li>
-<li>"Nee Paartha Vizhigal" - Una canción sentimental que representa el dolor y la separación de los personajes principales. Es una canción hermosa y emocional que toca las fibras del corazón de los oyentes. </li>
-</ul>
- <h3>¿Cómo puedo ver 3 películas online? </h3>
-
- <h3>¿Quiénes son los actores y actrices en la película 3? </h3>
- <p>Los principales actores y actrices de la película <b>3</b> son:</p>
- <ul>
-<li>Dhanush - Interpreta a Ram Kumar, un joven que se enamora de Janani, pero sufre de trastorno bipolar y tendencias suicidas. </li>
-<li>Shruti Haasan - Interpreta a Janani Kumar, una joven que se casa con Ram, pero lucha para hacer frente a su enfermedad mental y destino trágico. </li>
-<li>Sivakarthikeyan - Interpreta a Kumaran, el mejor amigo y colega de Ram, quien lo apoya a través de sus problemas. </li>
-<li>Prabhu - Interpreta al padre de Ram, que desaprueba su matrimonio con Janani, pero más tarde lamenta sus acciones. </li>
-<li>Bhanupriya - Ella interpreta a la madre de Janani, que ama mucho a su hija, pero se preocupa por su futuro con Ram.</li>
-</ul>
- <h3>¿Quiénes son los cantantes y compositores de 3 canciones de películas? </h3>
- <p>Los cantantes y compositores de <b>3</b> canciones de películas son:</p>
- <ul>
-<li>Anirudh Ravichander - Es el director musical, compositor, cantante y letrista de todas las canciones de la película. Hizo su debut con esta película y ganó varios premios por su trabajo. </li>
-<li>Dhanush - Es el actor, productor, escritor y cantante de algunas de las canciones de la película. Cantó "Why This Kolaveri Di", "Po Nee Po", "Idhazhin Oram", etc.</li>
-<li>Shruti Haasan - Ella es la actriz y cantante de algunas de las canciones de la película. Ella cantó "Kannazhaga", "Come On Girls", etc.</ <li>Ajeesh - Él es un cantante que ganó el reality show Airtel Super Singer. Cantó "Nee Paartha Vizhigal" y "Una vida llena de amor". </li>
-<li>Swetha Mohan - Es una cantante que ha cantado en varios idiomas y géneros. Cantó "Nee Paartha Vizhigal" y "Kannazhaga". </li>
-<li>Mohit Chauhan - Es un cantante que es conocido por sus canciones románticas y conmovedoras. Cantó "Po Nee Po (El dolor del amor)". </li>
-<li>Ananth Nag - Es un cantante que también es ingeniero de sonido y productor musical. Cantó "Idhazhin Oram (La inocencia del amor)". </li>
-</ul> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Betty Bayo 11a Hora Mp3 Descargar.md b/spaces/Benson/text-generation/Examples/Betty Bayo 11a Hora Mp3 Descargar.md
deleted file mode 100644
index ae9549aa804e1ed89f9640522ee293dda2a79b48..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Betty Bayo 11a Hora Mp3 Descargar.md	
+++ /dev/null
@@ -1,98 +0,0 @@
-<br />
-<h1>Betty Bayo 11a hora Mp3 Descargar</h1>
-<p>Si te gusta la música gospel, es posible que hayas oído hablar de <strong>Betty Bayo</strong>, una reconocida músico keniana que canta en su lengua materna, Agikuyu. Ella es mejor conocida por su canción <strong>11th Hour</strong>, que ha inspirado a muchas personas con su mensaje de esperanza y fe. En este artículo, te contaremos más sobre Betty Bayo, su canción 11th Hour, y dónde puedes descargarla. </p>
-<h2>betty bayo 11a hora mp3 descargar</h2><br /><p><b><b>Download</b> &#10037; <a href="https://bltlly.com/2v6K4W">https://bltlly.com/2v6K4W</a></b></p><br /><br />
- <h2>Biografía de Betty Bayo</h2>
-<p>Betty Bayo nació en Banana, condado de Kiambu, pero creció en Ol Kalou antes de mudarse a la ciudad de Kiambu. Ella viene de un entorno humilde y abandonó la escuela en el segundo grado debido a limitaciones financieras. Trabajó como mucama durante dos años antes de que una familia se ofreciera a pagar sus cuotas escolares. Luego persiguió su pasión por la música y lanzó su primer álbum en 2005. </p>
- <h3>Vida personal</h3>
-<p>Betty Bayo estuvo casada una vez con Victor Kanyari, un controvertido pastor que fue expuesto por estafar a sus seguidores al pedirles que plantaran una semilla de Ksh310. La pareja tuvo dos hijos, pero se separaron después del escándalo. Betty se distanció de los planes de su ex-marido y dijo que no recibió ningún apoyo de él. Más tarde reveló que estaba saliendo con un político, pero se separaron debido a su infidelidad. Ella dijo que estaba feliz y contenta como madre soltera y se centró en su música e hijos. </p>
- <h3>Blanqueamiento</h3>
-<p>Betty Bayo causó revuelo en las redes sociales cuando admitió que se había blanqueado la piel. Dijo que lo hizo por curiosidad y presión de los compañeros, pero se arrepintió más tarde. Dijo que se enfrentó a muchas críticas y burlas por su decisión, pero aprendió a aceptarse a sí misma y a amar su piel. Aconsejó a otras mujeres que no blanquearan su piel, sino que abrazaran su belleza natural. </p>
-<p></p>
- <h2>Canciones de Betty Bayo</h2>
-
-<tabla>
-<tr>
-<th>Canción</th>
-<th>Álbum</th>
-<th>Año</th>
-</tr>
-<tr>
-<td>11th Hour</td>
-<td>11th Hour</td>
-<td>2014</td>
-</tr>
-<tr>
-<td>La Ciudad de México</td>
-<td>La Ciudad de México</td>
-<td>2016</td>
-</tr>
-<tr>
-<td>Ni Nani Kama Wewe</td>
-<td>Ni Nani Kama Wewe</td>
-<td>2017</td>
-</tr>
-<tr>
-<td>Kikuyu Medley</td>
-<td>Kikuyu Medley</td>
-<td>2018</td>
-</tr>
-<tr>
-<td>Cheza Kama Wewe Remix</td>
-<td>Cheza Kama Wewe Remix</td>
-<td>2020</td>
-</tr>
-<tr>
-<td>La Ciudad de México</td>
-<td>La Ciudad de México</td>
-<td>2021</td>
-</tr>
-</tabla>
- <h3>Premios y reconocimiento</h3>
-<p>Betty Bayo ha recibido varios premios y nominaciones por su música, como:</p>
- - Groove Awards Artista Femenina del Año 2015 - Groove Awards Canción del Año 2015 por 11th Hour - Groove Awards Canción Central del Año 2016 por Siwezi Kukosa Mbingu - Groove Awards Canción Central del Año 2017 por Ni Nani Kama Wewe - Afrimma Awards Mejor Artista/Grupo/Dúo 2020 <h3>Colaboraciones</h3>
-<p>Betty Bayo también ha colaborado con otros artistas de la industria de la música gospel, como:</p>
- - Señor Seed en la canción Cheza Kama Wewe Remix, que es una melodía bailable que insta a los cristianos a disfrutar de la vida sin miedo ni culpa. - Rose Muhando en la canción Jemedari Wa Mbinguni, que es una canción de alabanza que reconoce a Dios como el comandante supremo del cielo y la tierra. - Ángel de la Guarda en la canción Nibariki, que es una canción de oración que pide a Dios que los bendiga a ellos y a sus familias. <h2>Betty Bayo 11th Hour Song</h2> <p>Una de las canciones más famosas de Betty Bayo es 11th Hour, que lanzó en 2014. La canción tiene un poderoso mensaje de esperanza y fe que resuena con muchas personas que están pasando por tiempos difíciles. </p>
- <h3>Significado</h3>
-
- <p>El coro de la canción va así:</p>
-<blockquote>
-<p>Mungu wangu ni wa 11th hour<br>
-(Mi Dios es un Dios de la hora 11) <br>
-Hata kama ni dakika ya mwisho<br>
-(Incluso si es el último minuto) <br>
-No se encontró la página
-(Él hará un camino en el desierto) <br>
-Atafanya njia kwa baharini<br>
-(Hará un camino en el mar) </p>
-</blockquote>
- <p>La canción anima a los oyentes a confiar en Dios y no renunciar a sus sueños y oraciones, porque Dios puede hacer cualquier cosa en cualquier momento. También les recuerda que Dios no está atado por las expectativas o cálculos humanos, sino que trabaja de maneras misteriosas que superan la comprensión humana. </p>
- <h3>Inspiración</h3>
-<p>Betty Bayo dijo que ella escribió y cantó la canción desde su propia experiencia personal. Dijo que estaba pasando por muchos desafíos y dificultades en su vida, especialmente después de su separación de su ex-marido. Ella dijo que se sentía como renunciar a todo, pero Dios le dio la fuerza y el valor para seguir adelante. Ella dijo que fue testigo de la intervención y el favor de Dios en su vida, incluso cuando menos lo esperaba. Dijo que quería compartir su testimonio e inspirar a otros que se enfrentan a situaciones similares. </p>
- <h3>Recepción</h3>
-<p>La canción fue bien recibida por fans y críticos, quienes elogiaron a Betty Bayo por su destreza vocal y su entrega emocional. La canción también tuvo un buen desempeño en varias listas y plataformas, como YouTube, donde tiene más de 2 millones de visitas en junio de 2023. La canción también ganó varios premios y nominaciones, como Groove Awards Canción del Año 2015 y Afrimma Awards Mejor Artista Gospel/ Grupo/ Duo 2020. </p>
- <h2>Betty Bayo 11a hora Mp3 Sitios de descarga</h2>
-<p>Si quieres descargar la canción de Betty Bayo’s 11th Hour, tienes dos opciones: sitios gratuitos o sitios de pago. Estos son algunos de los pros y contras de cada opción:</p>
- <h3>Sitios gratuitos</h3>
-
- - Tubidy: Este es un sitio móvil que te permite buscar y descargar archivos mp3 de varias fuentes, como YouTube, SoundCloud y Vimeo. También puede ver videos y escuchar música en línea. - Mdundo: Esta es una plataforma de música africana que ofrece descargas gratuitas de canciones de varios géneros y artistas, incluyendo música gospel. También puede transmitir música en línea y crear listas de reproducción. - Mp3juices: Este es un sitio sencillo y rápido que te permite buscar y descargar archivos mp3 de múltiples fuentes, como YouTube, SoundCloud y Archive. También puedes cortar y editar archivos mp3 en línea. <p>Los pros de los sitios gratuitos son:</p>
- - Son de fácil acceso y uso. - No requieren registro ni pago. - Ofrecen una amplia gama de canciones y géneros. <p>Los contras de los sitios libres son:</p>
- - Pueden tener audio de baja calidad o archivos incompletos. - Pueden tener anuncios emergentes o malware que pueden dañar su dispositivo o datos. - Pueden violar las leyes de copyright o los derechos de los artistas. <h3>Sitios de pago</h3>
-<p>Los sitios de pago son sitios web que requieren que usted pague una cuota o se suscriba a un servicio para descargar archivos mp3 de forma legal y segura. Algunos ejemplos de sitios de pago son:</p>
- - Spotify: Este es un servicio de streaming de música popular que ofrece millones de canciones y podcasts de varios artistas y géneros. También puede descargar canciones para escuchar sin conexión con una suscripción premium. - Apple Music: Este es otro servicio de streaming de música popular que ofrece millones de canciones y podcasts de varios artistas y géneros. También puedes descargar canciones para escuchar sin conexión con una suscripción de Apple Music. - Amazon Music: Este es otro servicio de streaming de música popular que ofrece millones de canciones y podcasts de varios artistas y géneros. También puede descargar canciones para escuchar sin conexión con una suscripción de Amazon Music o comprar canciones o álbumes individuales. <p>Los pros de los sitios de pago son:</p>
-
- - Requieren registro y pago. - Pueden tener disponibilidad limitada o compatibilidad con algunos dispositivos o regiones. - Pueden tener restricciones o condiciones sobre el uso o uso compartido de los archivos. <h2>Conclusión</h2>
-<p>En conclusión, Betty Bayo es una talentosa e inspiradora cantante de gospel que ha tocado muchas vidas con su canción 11th Hour. La canción es un testimonio de la gracia y la misericordia de Dios, que puede manifestarse en cualquier momento, incluso cuando las cosas parecen desesperadas. Si desea descargar la canción, puede elegir entre sitios gratuitos o sitios de pago, dependiendo de sus preferencias y necesidades. Esperamos que hayas disfrutado de este artículo y hayas aprendido algo nuevo sobre Betty Bayo y su música. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas y respuestas frecuentes sobre Betty Bayo y su canción 11th Hour:</p>
- <h3>Q: ¿Qué edad tiene Betty Bayo? </h3>
-<p>A: Betty Bayo nació el 1 de julio de 1985. Tiene 38 años desde junio de 2023. </p>
- <h3>Q: ¿Está casada Betty Bayo? </h3>
-<p>A: Betty Bayo no está casada. Una vez estuvo casada con Victor Kanyari, pero se divorciaron después de su escándalo. Más tarde salió con un político, pero se separaron debido a su infidelidad. Actualmente está soltera y feliz. </p>
- <h3>P: ¿Cuál es el nombre de la iglesia de Betty Bayo? </h3>
-<p>A: Betty Bayo no pertenece a ninguna iglesia específica. Dijo que asiste a diferentes iglesias dependiendo de su horario y ubicación. Ella dijo que cree en Dios y no en denominaciones. </p>
- <h3>Q: ¿Dónde puedo ver el video de la hora 11? </h3>
-<p>A: Puedes ver el video de la 11a Hora en YouTube, donde tiene más de 2 millones de visitas en junio de 2023. También puedes verlo en otras plataformas, como Vimeo o Dailymotion.</p>
- <h3>Q: ¿Cuáles son algunas de las otras canciones de Betty Bayo? </h3> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/docs/utils.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/docs/utils.py
deleted file mode 100644
index eb6cae145c843c1072e4f00856416a63cf912874..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/docs/utils.py
+++ /dev/null
@@ -1,222 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file 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 re
-from collections import namedtuple
-
-
-def py_type_name(type_name):
-    """Get the Python type name for a given model type.
-
-        >>> py_type_name('list')
-        'list'
-        >>> py_type_name('structure')
-        'dict'
-
-    :rtype: string
-    """
-    return {
-        'blob': 'bytes',
-        'character': 'string',
-        'double': 'float',
-        'long': 'integer',
-        'map': 'dict',
-        'structure': 'dict',
-        'timestamp': 'datetime',
-    }.get(type_name, type_name)
-
-
-def py_default(type_name):
-    """Get the Python default value for a given model type.
-
-        >>> py_default('string')
-        '\'string\''
-        >>> py_default('list')
-        '[...]'
-        >>> py_default('unknown')
-        '...'
-
-    :rtype: string
-    """
-    return {
-        'double': '123.0',
-        'long': '123',
-        'integer': '123',
-        'string': "'string'",
-        'blob': "b'bytes'",
-        'boolean': 'True|False',
-        'list': '[...]',
-        'map': '{...}',
-        'structure': '{...}',
-        'timestamp': 'datetime(2015, 1, 1)',
-    }.get(type_name, '...')
-
-
-def get_official_service_name(service_model):
-    """Generate the official name of an AWS Service
-
-    :param service_model: The service model representing the service
-    """
-    official_name = service_model.metadata.get('serviceFullName')
-    short_name = service_model.metadata.get('serviceAbbreviation', '')
-    if short_name.startswith('Amazon'):
-        short_name = short_name[7:]
-    if short_name.startswith('AWS'):
-        short_name = short_name[4:]
-    if short_name and short_name.lower() not in official_name.lower():
-        official_name += f' ({short_name})'
-    return official_name
-
-
-_DocumentedShape = namedtuple(
-    'DocumentedShape',
-    [
-        'name',
-        'type_name',
-        'documentation',
-        'metadata',
-        'members',
-        'required_members',
-    ],
-)
-
-
-class DocumentedShape(_DocumentedShape):
-    """Use this class to inject new shapes into a model for documentation"""
-
-    def __new__(
-        cls,
-        name,
-        type_name,
-        documentation,
-        metadata=None,
-        members=None,
-        required_members=None,
-    ):
-        if metadata is None:
-            metadata = []
-        if members is None:
-            members = []
-        if required_members is None:
-            required_members = []
-        return super().__new__(
-            cls,
-            name,
-            type_name,
-            documentation,
-            metadata,
-            members,
-            required_members,
-        )
-
-
-class AutoPopulatedParam:
-    def __init__(self, name, param_description=None):
-        self.name = name
-        self.param_description = param_description
-        if param_description is None:
-            self.param_description = (
-                'Please note that this parameter is automatically populated '
-                'if it is not provided. Including this parameter is not '
-                'required\n'
-            )
-
-    def document_auto_populated_param(self, event_name, section, **kwargs):
-        """Documents auto populated parameters
-
-        It will remove any required marks for the parameter, remove the
-        parameter from the example, and add a snippet about the parameter
-        being autopopulated in the description.
-        """
-        if event_name.startswith('docs.request-params'):
-            if self.name in section.available_sections:
-                section = section.get_section(self.name)
-                if 'is-required' in section.available_sections:
-                    section.delete_section('is-required')
-                description_section = section.get_section(
-                    'param-documentation'
-                )
-                description_section.writeln(self.param_description)
-        elif event_name.startswith('docs.request-example'):
-            section = section.get_section('structure-value')
-            if self.name in section.available_sections:
-                section.delete_section(self.name)
-
-
-class HideParamFromOperations:
-    """Hides a single parameter from multiple operations.
-
-    This method will remove a parameter from documentation and from
-    examples. This method is typically used for things that are
-    automatically populated because a user would be unable to provide
-    a value (e.g., a checksum of a serialized XML request body)."""
-
-    def __init__(self, service_name, parameter_name, operation_names):
-        """
-        :type service_name: str
-        :param service_name: Name of the service to modify.
-
-        :type parameter_name: str
-        :param parameter_name: Name of the parameter to modify.
-
-        :type operation_names: list
-        :param operation_names: Operation names to modify.
-        """
-        self._parameter_name = parameter_name
-        self._params_events = set()
-        self._example_events = set()
-        # Build up the sets of relevant event names.
-        param_template = 'docs.request-params.%s.%s.complete-section'
-        example_template = 'docs.request-example.%s.%s.complete-section'
-        for name in operation_names:
-            self._params_events.add(param_template % (service_name, name))
-            self._example_events.add(example_template % (service_name, name))
-
-    def hide_param(self, event_name, section, **kwargs):
-        if event_name in self._example_events:
-            # Modify the structure value for example events.
-            section = section.get_section('structure-value')
-        elif event_name not in self._params_events:
-            return
-        if self._parameter_name in section.available_sections:
-            section.delete_section(self._parameter_name)
-
-
-class AppendParamDocumentation:
-    """Appends documentation to a specific parameter"""
-
-    def __init__(self, parameter_name, doc_string):
-        self._parameter_name = parameter_name
-        self._doc_string = doc_string
-
-    def append_documentation(self, event_name, section, **kwargs):
-        if self._parameter_name in section.available_sections:
-            section = section.get_section(self._parameter_name)
-            description_section = section.get_section('param-documentation')
-            description_section.writeln(self._doc_string)
-
-
-_CONTROLS = {
-    '\n': '\\n',
-    '\r': '\\r',
-    '\t': '\\t',
-    '\b': '\\b',
-    '\f': '\\f',
-}
-# Combines all CONTROLS keys into a big or regular expression
-_ESCAPE_CONTROLS_RE = re.compile('|'.join(map(re.escape, _CONTROLS)))
-# Based on the match get the appropriate replacement from CONTROLS
-_CONTROLS_MATCH_HANDLER = lambda match: _CONTROLS[match.group(0)]
-
-
-def escape_controls(value):
-    return _ESCAPE_CONTROLS_RE.sub(_CONTROLS_MATCH_HANDLER, value)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/history.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/history.py
deleted file mode 100644
index 59d9481d7fb5ed48c737c6c835bd916a78aa3ff8..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/history.py
+++ /dev/null
@@ -1,55 +0,0 @@
-# Copyright 2017 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file 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 logging
-
-HISTORY_RECORDER = None
-logger = logging.getLogger(__name__)
-
-
-class BaseHistoryHandler:
-    def emit(self, event_type, payload, source):
-        raise NotImplementedError('emit()')
-
-
-class HistoryRecorder:
-    def __init__(self):
-        self._enabled = False
-        self._handlers = []
-
-    def enable(self):
-        self._enabled = True
-
-    def disable(self):
-        self._enabled = False
-
-    def add_handler(self, handler):
-        self._handlers.append(handler)
-
-    def record(self, event_type, payload, source='BOTOCORE'):
-        if self._enabled and self._handlers:
-            for handler in self._handlers:
-                try:
-                    handler.emit(event_type, payload, source)
-                except Exception:
-                    # Never let the process die because we had a failure in
-                    # a record collection handler.
-                    logger.debug(
-                        "Exception raised in %s.", handler, exc_info=True
-                    )
-
-
-def get_global_history_recorder():
-    global HISTORY_RECORDER
-    if HISTORY_RECORDER is None:
-        HISTORY_RECORDER = HistoryRecorder()
-    return HISTORY_RECORDER
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/util/ssl_match_hostname.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/util/ssl_match_hostname.py
deleted file mode 100644
index 1dd950c489607d06ecc5218292a1b55558b47be8..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/util/ssl_match_hostname.py
+++ /dev/null
@@ -1,159 +0,0 @@
-"""The match_hostname() function from Python 3.3.3, essential when using SSL."""
-
-# Note: This file is under the PSF license as the code comes from the python
-# stdlib.   http://docs.python.org/3/license.html
-
-import re
-import sys
-
-# ipaddress has been backported to 2.6+ in pypi.  If it is installed on the
-# system, use it to handle IPAddress ServerAltnames (this was added in
-# python-3.5) otherwise only do DNS matching.  This allows
-# util.ssl_match_hostname to continue to be used in Python 2.7.
-try:
-    import ipaddress
-except ImportError:
-    ipaddress = None
-
-__version__ = "3.5.0.1"
-
-
-class CertificateError(ValueError):
-    pass
-
-
-def _dnsname_match(dn, hostname, max_wildcards=1):
-    """Matching according to RFC 6125, section 6.4.3
-
-    http://tools.ietf.org/html/rfc6125#section-6.4.3
-    """
-    pats = []
-    if not dn:
-        return False
-
-    # Ported from python3-syntax:
-    # leftmost, *remainder = dn.split(r'.')
-    parts = dn.split(r".")
-    leftmost = parts[0]
-    remainder = parts[1:]
-
-    wildcards = leftmost.count("*")
-    if wildcards > max_wildcards:
-        # Issue #17980: avoid denials of service by refusing more
-        # than one wildcard per fragment.  A survey of established
-        # policy among SSL implementations showed it to be a
-        # reasonable choice.
-        raise CertificateError(
-            "too many wildcards in certificate DNS name: " + repr(dn)
-        )
-
-    # speed up common case w/o wildcards
-    if not wildcards:
-        return dn.lower() == hostname.lower()
-
-    # RFC 6125, section 6.4.3, subitem 1.
-    # The client SHOULD NOT attempt to match a presented identifier in which
-    # the wildcard character comprises a label other than the left-most label.
-    if leftmost == "*":
-        # When '*' is a fragment by itself, it matches a non-empty dotless
-        # fragment.
-        pats.append("[^.]+")
-    elif leftmost.startswith("xn--") or hostname.startswith("xn--"):
-        # RFC 6125, section 6.4.3, subitem 3.
-        # The client SHOULD NOT attempt to match a presented identifier
-        # where the wildcard character is embedded within an A-label or
-        # U-label of an internationalized domain name.
-        pats.append(re.escape(leftmost))
-    else:
-        # Otherwise, '*' matches any dotless string, e.g. www*
-        pats.append(re.escape(leftmost).replace(r"\*", "[^.]*"))
-
-    # add the remaining fragments, ignore any wildcards
-    for frag in remainder:
-        pats.append(re.escape(frag))
-
-    pat = re.compile(r"\A" + r"\.".join(pats) + r"\Z", re.IGNORECASE)
-    return pat.match(hostname)
-
-
-def _to_unicode(obj):
-    if isinstance(obj, str) and sys.version_info < (3,):
-        # ignored flake8 # F821 to support python 2.7 function
-        obj = unicode(obj, encoding="ascii", errors="strict")  # noqa: F821
-    return obj
-
-
-def _ipaddress_match(ipname, host_ip):
-    """Exact matching of IP addresses.
-
-    RFC 6125 explicitly doesn't define an algorithm for this
-    (section 1.7.2 - "Out of Scope").
-    """
-    # OpenSSL may add a trailing newline to a subjectAltName's IP address
-    # Divergence from upstream: ipaddress can't handle byte str
-    ip = ipaddress.ip_address(_to_unicode(ipname).rstrip())
-    return ip == host_ip
-
-
-def match_hostname(cert, hostname):
-    """Verify that *cert* (in decoded format as returned by
-    SSLSocket.getpeercert()) matches the *hostname*.  RFC 2818 and RFC 6125
-    rules are followed, but IP addresses are not accepted for *hostname*.
-
-    CertificateError is raised on failure. On success, the function
-    returns nothing.
-    """
-    if not cert:
-        raise ValueError(
-            "empty or no certificate, match_hostname needs a "
-            "SSL socket or SSL context with either "
-            "CERT_OPTIONAL or CERT_REQUIRED"
-        )
-    try:
-        # Divergence from upstream: ipaddress can't handle byte str
-        host_ip = ipaddress.ip_address(_to_unicode(hostname))
-    except (UnicodeError, ValueError):
-        # ValueError: Not an IP address (common case)
-        # UnicodeError: Divergence from upstream: Have to deal with ipaddress not taking
-        # byte strings.  addresses should be all ascii, so we consider it not
-        # an ipaddress in this case
-        host_ip = None
-    except AttributeError:
-        # Divergence from upstream: Make ipaddress library optional
-        if ipaddress is None:
-            host_ip = None
-        else:  # Defensive
-            raise
-    dnsnames = []
-    san = cert.get("subjectAltName", ())
-    for key, value in san:
-        if key == "DNS":
-            if host_ip is None and _dnsname_match(value, hostname):
-                return
-            dnsnames.append(value)
-        elif key == "IP Address":
-            if host_ip is not None and _ipaddress_match(value, host_ip):
-                return
-            dnsnames.append(value)
-    if not dnsnames:
-        # The subject is only checked when there is no dNSName entry
-        # in subjectAltName
-        for sub in cert.get("subject", ()):
-            for key, value in sub:
-                # XXX according to RFC 2818, the most specific Common Name
-                # must be used.
-                if key == "commonName":
-                    if _dnsname_match(value, hostname):
-                        return
-                    dnsnames.append(value)
-    if len(dnsnames) > 1:
-        raise CertificateError(
-            "hostname %r "
-            "doesn't match either of %s" % (hostname, ", ".join(map(repr, dnsnames)))
-        )
-    elif len(dnsnames) == 1:
-        raise CertificateError("hostname %r doesn't match %r" % (hostname, dnsnames[0]))
-    else:
-        raise CertificateError(
-            "no appropriate commonName or subjectAltName fields were found"
-        )
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/poolmanager.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/poolmanager.py
deleted file mode 100644
index ca4ec341184adb3d30f3cd825b49a81b87d29b08..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/poolmanager.py
+++ /dev/null
@@ -1,537 +0,0 @@
-from __future__ import absolute_import
-
-import collections
-import functools
-import logging
-
-from ._collections import RecentlyUsedContainer
-from .connectionpool import HTTPConnectionPool, HTTPSConnectionPool, port_by_scheme
-from .exceptions import (
-    LocationValueError,
-    MaxRetryError,
-    ProxySchemeUnknown,
-    ProxySchemeUnsupported,
-    URLSchemeUnknown,
-)
-from .packages import six
-from .packages.six.moves.urllib.parse import urljoin
-from .request import RequestMethods
-from .util.proxy import connection_requires_http_tunnel
-from .util.retry import Retry
-from .util.url import parse_url
-
-__all__ = ["PoolManager", "ProxyManager", "proxy_from_url"]
-
-
-log = logging.getLogger(__name__)
-
-SSL_KEYWORDS = (
-    "key_file",
-    "cert_file",
-    "cert_reqs",
-    "ca_certs",
-    "ssl_version",
-    "ca_cert_dir",
-    "ssl_context",
-    "key_password",
-    "server_hostname",
-)
-
-# All known keyword arguments that could be provided to the pool manager, its
-# pools, or the underlying connections. This is used to construct a pool key.
-_key_fields = (
-    "key_scheme",  # str
-    "key_host",  # str
-    "key_port",  # int
-    "key_timeout",  # int or float or Timeout
-    "key_retries",  # int or Retry
-    "key_strict",  # bool
-    "key_block",  # bool
-    "key_source_address",  # str
-    "key_key_file",  # str
-    "key_key_password",  # str
-    "key_cert_file",  # str
-    "key_cert_reqs",  # str
-    "key_ca_certs",  # str
-    "key_ssl_version",  # str
-    "key_ca_cert_dir",  # str
-    "key_ssl_context",  # instance of ssl.SSLContext or urllib3.util.ssl_.SSLContext
-    "key_maxsize",  # int
-    "key_headers",  # dict
-    "key__proxy",  # parsed proxy url
-    "key__proxy_headers",  # dict
-    "key__proxy_config",  # class
-    "key_socket_options",  # list of (level (int), optname (int), value (int or str)) tuples
-    "key__socks_options",  # dict
-    "key_assert_hostname",  # bool or string
-    "key_assert_fingerprint",  # str
-    "key_server_hostname",  # str
-)
-
-#: The namedtuple class used to construct keys for the connection pool.
-#: All custom key schemes should include the fields in this key at a minimum.
-PoolKey = collections.namedtuple("PoolKey", _key_fields)
-
-_proxy_config_fields = ("ssl_context", "use_forwarding_for_https")
-ProxyConfig = collections.namedtuple("ProxyConfig", _proxy_config_fields)
-
-
-def _default_key_normalizer(key_class, request_context):
-    """
-    Create a pool key out of a request context dictionary.
-
-    According to RFC 3986, both the scheme and host are case-insensitive.
-    Therefore, this function normalizes both before constructing the pool
-    key for an HTTPS request. If you wish to change this behaviour, provide
-    alternate callables to ``key_fn_by_scheme``.
-
-    :param key_class:
-        The class to use when constructing the key. This should be a namedtuple
-        with the ``scheme`` and ``host`` keys at a minimum.
-    :type  key_class: namedtuple
-    :param request_context:
-        A dictionary-like object that contain the context for a request.
-    :type  request_context: dict
-
-    :return: A namedtuple that can be used as a connection pool key.
-    :rtype:  PoolKey
-    """
-    # Since we mutate the dictionary, make a copy first
-    context = request_context.copy()
-    context["scheme"] = context["scheme"].lower()
-    context["host"] = context["host"].lower()
-
-    # These are both dictionaries and need to be transformed into frozensets
-    for key in ("headers", "_proxy_headers", "_socks_options"):
-        if key in context and context[key] is not None:
-            context[key] = frozenset(context[key].items())
-
-    # The socket_options key may be a list and needs to be transformed into a
-    # tuple.
-    socket_opts = context.get("socket_options")
-    if socket_opts is not None:
-        context["socket_options"] = tuple(socket_opts)
-
-    # Map the kwargs to the names in the namedtuple - this is necessary since
-    # namedtuples can't have fields starting with '_'.
-    for key in list(context.keys()):
-        context["key_" + key] = context.pop(key)
-
-    # Default to ``None`` for keys missing from the context
-    for field in key_class._fields:
-        if field not in context:
-            context[field] = None
-
-    return key_class(**context)
-
-
-#: A dictionary that maps a scheme to a callable that creates a pool key.
-#: This can be used to alter the way pool keys are constructed, if desired.
-#: Each PoolManager makes a copy of this dictionary so they can be configured
-#: globally here, or individually on the instance.
-key_fn_by_scheme = {
-    "http": functools.partial(_default_key_normalizer, PoolKey),
-    "https": functools.partial(_default_key_normalizer, PoolKey),
-}
-
-pool_classes_by_scheme = {"http": HTTPConnectionPool, "https": HTTPSConnectionPool}
-
-
-class PoolManager(RequestMethods):
-    """
-    Allows for arbitrary requests while transparently keeping track of
-    necessary connection pools for you.
-
-    :param num_pools:
-        Number of connection pools to cache before discarding the least
-        recently used pool.
-
-    :param headers:
-        Headers to include with all requests, unless other headers are given
-        explicitly.
-
-    :param \\**connection_pool_kw:
-        Additional parameters are used to create fresh
-        :class:`urllib3.connectionpool.ConnectionPool` instances.
-
-    Example::
-
-        >>> manager = PoolManager(num_pools=2)
-        >>> r = manager.request('GET', 'http://google.com/')
-        >>> r = manager.request('GET', 'http://google.com/mail')
-        >>> r = manager.request('GET', 'http://yahoo.com/')
-        >>> len(manager.pools)
-        2
-
-    """
-
-    proxy = None
-    proxy_config = None
-
-    def __init__(self, num_pools=10, headers=None, **connection_pool_kw):
-        RequestMethods.__init__(self, headers)
-        self.connection_pool_kw = connection_pool_kw
-        self.pools = RecentlyUsedContainer(num_pools, dispose_func=lambda p: p.close())
-
-        # Locally set the pool classes and keys so other PoolManagers can
-        # override them.
-        self.pool_classes_by_scheme = pool_classes_by_scheme
-        self.key_fn_by_scheme = key_fn_by_scheme.copy()
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.clear()
-        # Return False to re-raise any potential exceptions
-        return False
-
-    def _new_pool(self, scheme, host, port, request_context=None):
-        """
-        Create a new :class:`urllib3.connectionpool.ConnectionPool` based on host, port, scheme, and
-        any additional pool keyword arguments.
-
-        If ``request_context`` is provided, it is provided as keyword arguments
-        to the pool class used. This method is used to actually create the
-        connection pools handed out by :meth:`connection_from_url` and
-        companion methods. It is intended to be overridden for customization.
-        """
-        pool_cls = self.pool_classes_by_scheme[scheme]
-        if request_context is None:
-            request_context = self.connection_pool_kw.copy()
-
-        # Although the context has everything necessary to create the pool,
-        # this function has historically only used the scheme, host, and port
-        # in the positional args. When an API change is acceptable these can
-        # be removed.
-        for key in ("scheme", "host", "port"):
-            request_context.pop(key, None)
-
-        if scheme == "http":
-            for kw in SSL_KEYWORDS:
-                request_context.pop(kw, None)
-
-        return pool_cls(host, port, **request_context)
-
-    def clear(self):
-        """
-        Empty our store of pools and direct them all to close.
-
-        This will not affect in-flight connections, but they will not be
-        re-used after completion.
-        """
-        self.pools.clear()
-
-    def connection_from_host(self, host, port=None, scheme="http", pool_kwargs=None):
-        """
-        Get a :class:`urllib3.connectionpool.ConnectionPool` based on the host, port, and scheme.
-
-        If ``port`` isn't given, it will be derived from the ``scheme`` using
-        ``urllib3.connectionpool.port_by_scheme``. If ``pool_kwargs`` is
-        provided, it is merged with the instance's ``connection_pool_kw``
-        variable and used to create the new connection pool, if one is
-        needed.
-        """
-
-        if not host:
-            raise LocationValueError("No host specified.")
-
-        request_context = self._merge_pool_kwargs(pool_kwargs)
-        request_context["scheme"] = scheme or "http"
-        if not port:
-            port = port_by_scheme.get(request_context["scheme"].lower(), 80)
-        request_context["port"] = port
-        request_context["host"] = host
-
-        return self.connection_from_context(request_context)
-
-    def connection_from_context(self, request_context):
-        """
-        Get a :class:`urllib3.connectionpool.ConnectionPool` based on the request context.
-
-        ``request_context`` must at least contain the ``scheme`` key and its
-        value must be a key in ``key_fn_by_scheme`` instance variable.
-        """
-        scheme = request_context["scheme"].lower()
-        pool_key_constructor = self.key_fn_by_scheme.get(scheme)
-        if not pool_key_constructor:
-            raise URLSchemeUnknown(scheme)
-        pool_key = pool_key_constructor(request_context)
-
-        return self.connection_from_pool_key(pool_key, request_context=request_context)
-
-    def connection_from_pool_key(self, pool_key, request_context=None):
-        """
-        Get a :class:`urllib3.connectionpool.ConnectionPool` based on the provided pool key.
-
-        ``pool_key`` should be a namedtuple that only contains immutable
-        objects. At a minimum it must have the ``scheme``, ``host``, and
-        ``port`` fields.
-        """
-        with self.pools.lock:
-            # If the scheme, host, or port doesn't match existing open
-            # connections, open a new ConnectionPool.
-            pool = self.pools.get(pool_key)
-            if pool:
-                return pool
-
-            # Make a fresh ConnectionPool of the desired type
-            scheme = request_context["scheme"]
-            host = request_context["host"]
-            port = request_context["port"]
-            pool = self._new_pool(scheme, host, port, request_context=request_context)
-            self.pools[pool_key] = pool
-
-        return pool
-
-    def connection_from_url(self, url, pool_kwargs=None):
-        """
-        Similar to :func:`urllib3.connectionpool.connection_from_url`.
-
-        If ``pool_kwargs`` is not provided and a new pool needs to be
-        constructed, ``self.connection_pool_kw`` is used to initialize
-        the :class:`urllib3.connectionpool.ConnectionPool`. If ``pool_kwargs``
-        is provided, it is used instead. Note that if a new pool does not
-        need to be created for the request, the provided ``pool_kwargs`` are
-        not used.
-        """
-        u = parse_url(url)
-        return self.connection_from_host(
-            u.host, port=u.port, scheme=u.scheme, pool_kwargs=pool_kwargs
-        )
-
-    def _merge_pool_kwargs(self, override):
-        """
-        Merge a dictionary of override values for self.connection_pool_kw.
-
-        This does not modify self.connection_pool_kw and returns a new dict.
-        Any keys in the override dictionary with a value of ``None`` are
-        removed from the merged dictionary.
-        """
-        base_pool_kwargs = self.connection_pool_kw.copy()
-        if override:
-            for key, value in override.items():
-                if value is None:
-                    try:
-                        del base_pool_kwargs[key]
-                    except KeyError:
-                        pass
-                else:
-                    base_pool_kwargs[key] = value
-        return base_pool_kwargs
-
-    def _proxy_requires_url_absolute_form(self, parsed_url):
-        """
-        Indicates if the proxy requires the complete destination URL in the
-        request.  Normally this is only needed when not using an HTTP CONNECT
-        tunnel.
-        """
-        if self.proxy is None:
-            return False
-
-        return not connection_requires_http_tunnel(
-            self.proxy, self.proxy_config, parsed_url.scheme
-        )
-
-    def _validate_proxy_scheme_url_selection(self, url_scheme):
-        """
-        Validates that were not attempting to do TLS in TLS connections on
-        Python2 or with unsupported SSL implementations.
-        """
-        if self.proxy is None or url_scheme != "https":
-            return
-
-        if self.proxy.scheme != "https":
-            return
-
-        if six.PY2 and not self.proxy_config.use_forwarding_for_https:
-            raise ProxySchemeUnsupported(
-                "Contacting HTTPS destinations through HTTPS proxies "
-                "'via CONNECT tunnels' is not supported in Python 2"
-            )
-
-    def urlopen(self, method, url, redirect=True, **kw):
-        """
-        Same as :meth:`urllib3.HTTPConnectionPool.urlopen`
-        with custom cross-host redirect logic and only sends the request-uri
-        portion of the ``url``.
-
-        The given ``url`` parameter must be absolute, such that an appropriate
-        :class:`urllib3.connectionpool.ConnectionPool` can be chosen for it.
-        """
-        u = parse_url(url)
-        self._validate_proxy_scheme_url_selection(u.scheme)
-
-        conn = self.connection_from_host(u.host, port=u.port, scheme=u.scheme)
-
-        kw["assert_same_host"] = False
-        kw["redirect"] = False
-
-        if "headers" not in kw:
-            kw["headers"] = self.headers.copy()
-
-        if self._proxy_requires_url_absolute_form(u):
-            response = conn.urlopen(method, url, **kw)
-        else:
-            response = conn.urlopen(method, u.request_uri, **kw)
-
-        redirect_location = redirect and response.get_redirect_location()
-        if not redirect_location:
-            return response
-
-        # Support relative URLs for redirecting.
-        redirect_location = urljoin(url, redirect_location)
-
-        # RFC 7231, Section 6.4.4
-        if response.status == 303:
-            method = "GET"
-
-        retries = kw.get("retries")
-        if not isinstance(retries, Retry):
-            retries = Retry.from_int(retries, redirect=redirect)
-
-        # Strip headers marked as unsafe to forward to the redirected location.
-        # Check remove_headers_on_redirect to avoid a potential network call within
-        # conn.is_same_host() which may use socket.gethostbyname() in the future.
-        if retries.remove_headers_on_redirect and not conn.is_same_host(
-            redirect_location
-        ):
-            headers = list(six.iterkeys(kw["headers"]))
-            for header in headers:
-                if header.lower() in retries.remove_headers_on_redirect:
-                    kw["headers"].pop(header, None)
-
-        try:
-            retries = retries.increment(method, url, response=response, _pool=conn)
-        except MaxRetryError:
-            if retries.raise_on_redirect:
-                response.drain_conn()
-                raise
-            return response
-
-        kw["retries"] = retries
-        kw["redirect"] = redirect
-
-        log.info("Redirecting %s -> %s", url, redirect_location)
-
-        response.drain_conn()
-        return self.urlopen(method, redirect_location, **kw)
-
-
-class ProxyManager(PoolManager):
-    """
-    Behaves just like :class:`PoolManager`, but sends all requests through
-    the defined proxy, using the CONNECT method for HTTPS URLs.
-
-    :param proxy_url:
-        The URL of the proxy to be used.
-
-    :param proxy_headers:
-        A dictionary containing headers that will be sent to the proxy. In case
-        of HTTP they are being sent with each request, while in the
-        HTTPS/CONNECT case they are sent only once. Could be used for proxy
-        authentication.
-
-    :param proxy_ssl_context:
-        The proxy SSL context is used to establish the TLS connection to the
-        proxy when using HTTPS proxies.
-
-    :param use_forwarding_for_https:
-        (Defaults to False) If set to True will forward requests to the HTTPS
-        proxy to be made on behalf of the client instead of creating a TLS
-        tunnel via the CONNECT method. **Enabling this flag means that request
-        and response headers and content will be visible from the HTTPS proxy**
-        whereas tunneling keeps request and response headers and content
-        private.  IP address, target hostname, SNI, and port are always visible
-        to an HTTPS proxy even when this flag is disabled.
-
-    Example:
-        >>> proxy = urllib3.ProxyManager('http://localhost:3128/')
-        >>> r1 = proxy.request('GET', 'http://google.com/')
-        >>> r2 = proxy.request('GET', 'http://httpbin.org/')
-        >>> len(proxy.pools)
-        1
-        >>> r3 = proxy.request('GET', 'https://httpbin.org/')
-        >>> r4 = proxy.request('GET', 'https://twitter.com/')
-        >>> len(proxy.pools)
-        3
-
-    """
-
-    def __init__(
-        self,
-        proxy_url,
-        num_pools=10,
-        headers=None,
-        proxy_headers=None,
-        proxy_ssl_context=None,
-        use_forwarding_for_https=False,
-        **connection_pool_kw
-    ):
-
-        if isinstance(proxy_url, HTTPConnectionPool):
-            proxy_url = "%s://%s:%i" % (
-                proxy_url.scheme,
-                proxy_url.host,
-                proxy_url.port,
-            )
-        proxy = parse_url(proxy_url)
-
-        if proxy.scheme not in ("http", "https"):
-            raise ProxySchemeUnknown(proxy.scheme)
-
-        if not proxy.port:
-            port = port_by_scheme.get(proxy.scheme, 80)
-            proxy = proxy._replace(port=port)
-
-        self.proxy = proxy
-        self.proxy_headers = proxy_headers or {}
-        self.proxy_ssl_context = proxy_ssl_context
-        self.proxy_config = ProxyConfig(proxy_ssl_context, use_forwarding_for_https)
-
-        connection_pool_kw["_proxy"] = self.proxy
-        connection_pool_kw["_proxy_headers"] = self.proxy_headers
-        connection_pool_kw["_proxy_config"] = self.proxy_config
-
-        super(ProxyManager, self).__init__(num_pools, headers, **connection_pool_kw)
-
-    def connection_from_host(self, host, port=None, scheme="http", pool_kwargs=None):
-        if scheme == "https":
-            return super(ProxyManager, self).connection_from_host(
-                host, port, scheme, pool_kwargs=pool_kwargs
-            )
-
-        return super(ProxyManager, self).connection_from_host(
-            self.proxy.host, self.proxy.port, self.proxy.scheme, pool_kwargs=pool_kwargs
-        )
-
-    def _set_proxy_headers(self, url, headers=None):
-        """
-        Sets headers needed by proxies: specifically, the Accept and Host
-        headers. Only sets headers not provided by the user.
-        """
-        headers_ = {"Accept": "*/*"}
-
-        netloc = parse_url(url).netloc
-        if netloc:
-            headers_["Host"] = netloc
-
-        if headers:
-            headers_.update(headers)
-        return headers_
-
-    def urlopen(self, method, url, redirect=True, **kw):
-        "Same as HTTP(S)ConnectionPool.urlopen, ``url`` must be absolute."
-        u = parse_url(url)
-        if not connection_requires_http_tunnel(self.proxy, self.proxy_config, u.scheme):
-            # For connections using HTTP CONNECT, httplib sets the necessary
-            # headers on the CONNECT to the proxy. If we're not using CONNECT,
-            # we'll definitely need to set 'Host' at the very least.
-            headers = kw.get("headers", self.headers)
-            kw["headers"] = self._set_proxy_headers(url, headers)
-
-        return super(ProxyManager, self).urlopen(method, url, redirect=redirect, **kw)
-
-
-def proxy_from_url(url, **kw):
-    return ProxyManager(proxy_url=url, **kw)
diff --git a/spaces/BilalSardar/Like-Chatgpt-clone/app.py b/spaces/BilalSardar/Like-Chatgpt-clone/app.py
deleted file mode 100644
index 60ce1425fde524542514bb9ec270e8ef2fa64af5..0000000000000000000000000000000000000000
--- a/spaces/BilalSardar/Like-Chatgpt-clone/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate
-from langchain.chains.conversation.memory import ConversationBufferWindowMemory
-import os
-
-def chatgpt(background,key,prompts):
-    try:  
-      template = background+""" 
-      {history}
-      Human: {human_input}
-      Assistant:"""
-    
-      prompt = PromptTemplate(
-          input_variables=["history", "human_input"], 
-          template=template
-      )
-    
-    
-      chatgpt_chain = LLMChain(
-          llm=OpenAI(temperature=0,openai_api_key = (key)
-      ), 
-          prompt=prompt, 
-          verbose=True, 
-          memory=ConversationBufferWindowMemory(k=2),
-      )
-    
-      output = chatgpt_chain.predict(human_input=prompts)
-      #print(output)
-      return output
-    except:
-        return "Enter your key"
-
-import gradio as gr
-Backgorund_in = gr.inputs.Textbox(label="Enter Background")
-key_in =gr.inputs.Textbox(label="Enter Your API key")
-prompt = gr.inputs.Textbox(label="Enter what you want")
-output=gr.inputs.Textbox(label="Your Result")
-examples=[[""""Assistant is a large language model trained by OpenAI.
-
-Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
-
-Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
-
-Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.""","sk-IQHMkGxxxxxxxxx", """I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply wiht the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd."""]]
-
-demo=gr.Interface(fn=chatgpt,
-                  inputs=[Backgorund_in, key_in,prompt],
-                  outputs=output,
-                  examples=examples,
-                  title="Like-GPTClone",
-                  description="This space is a simple implementation of langChain pacakge github(https://github.com/hwchase17/langchain) "
-
-                  )
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/densepose/vis/base.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/densepose/vis/base.py
deleted file mode 100644
index b3f1ae5aff28375a98807982310f0fe69febe832..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/densepose/vis/base.py
+++ /dev/null
@@ -1,190 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import logging
-import numpy as np
-import cv2
-import torch
-
-Image = np.ndarray
-Boxes = torch.Tensor
-
-
-class MatrixVisualizer(object):
-    """
-    Base visualizer for matrix data
-    """
-
-    def __init__(
-        self,
-        inplace=True,
-        cmap=cv2.COLORMAP_PARULA,
-        val_scale=1.0,
-        alpha=0.7,
-        interp_method_matrix=cv2.INTER_LINEAR,
-        interp_method_mask=cv2.INTER_NEAREST,
-    ):
-        self.inplace = inplace
-        self.cmap = cmap
-        self.val_scale = val_scale
-        self.alpha = alpha
-        self.interp_method_matrix = interp_method_matrix
-        self.interp_method_mask = interp_method_mask
-
-    def visualize(self, image_bgr, mask, matrix, bbox_xywh):
-        self._check_image(image_bgr)
-        self._check_mask_matrix(mask, matrix)
-        if self.inplace:
-            image_target_bgr = image_bgr
-        else:
-            image_target_bgr = image_bgr * 0
-        x, y, w, h = [int(v) for v in bbox_xywh]
-        if w <= 0 or h <= 0:
-            return image_bgr
-        mask, matrix = self._resize(mask, matrix, w, h)
-        mask_bg = np.tile((mask == 0)[:, :, np.newaxis], [1, 1, 3])
-        matrix_scaled = matrix.astype(np.float32) * self.val_scale
-        _EPSILON = 1e-6
-        if np.any(matrix_scaled > 255 + _EPSILON):
-            logger = logging.getLogger(__name__)
-            logger.warning(
-                f"Matrix has values > {255 + _EPSILON} after " f"scaling, clipping to [0..255]"
-            )
-        matrix_scaled_8u = matrix_scaled.clip(0, 255).astype(np.uint8)
-        matrix_vis = cv2.applyColorMap(matrix_scaled_8u, self.cmap)
-        matrix_vis[mask_bg] = image_target_bgr[y : y + h, x : x + w, :][mask_bg]
-        image_target_bgr[y : y + h, x : x + w, :] = (
-            image_target_bgr[y : y + h, x : x + w, :] * (1.0 - self.alpha) + matrix_vis * self.alpha
-        )
-        return image_target_bgr.astype(np.uint8)
-
-    def _resize(self, mask, matrix, w, h):
-        if (w != mask.shape[1]) or (h != mask.shape[0]):
-            mask = cv2.resize(mask, (w, h), self.interp_method_mask)
-        if (w != matrix.shape[1]) or (h != matrix.shape[0]):
-            matrix = cv2.resize(matrix, (w, h), self.interp_method_matrix)
-        return mask, matrix
-
-    def _check_image(self, image_rgb):
-        assert len(image_rgb.shape) == 3
-        assert image_rgb.shape[2] == 3
-        assert image_rgb.dtype == np.uint8
-
-    def _check_mask_matrix(self, mask, matrix):
-        assert len(matrix.shape) == 2
-        assert len(mask.shape) == 2
-        assert mask.dtype == np.uint8
-
-
-class RectangleVisualizer(object):
-
-    _COLOR_GREEN = (18, 127, 15)
-
-    def __init__(self, color=_COLOR_GREEN, thickness=1):
-        self.color = color
-        self.thickness = thickness
-
-    def visualize(self, image_bgr, bbox_xywh, color=None, thickness=None):
-        x, y, w, h = bbox_xywh
-        color = color or self.color
-        thickness = thickness or self.thickness
-        cv2.rectangle(image_bgr, (int(x), int(y)), (int(x + w), int(y + h)), color, thickness)
-        return image_bgr
-
-
-class PointsVisualizer(object):
-
-    _COLOR_GREEN = (18, 127, 15)
-
-    def __init__(self, color_bgr=_COLOR_GREEN, r=5):
-        self.color_bgr = color_bgr
-        self.r = r
-
-    def visualize(self, image_bgr, pts_xy, colors_bgr=None, rs=None):
-        for j, pt_xy in enumerate(pts_xy):
-            x, y = pt_xy
-            color_bgr = colors_bgr[j] if colors_bgr is not None else self.color_bgr
-            r = rs[j] if rs is not None else self.r
-            cv2.circle(image_bgr, (x, y), r, color_bgr, -1)
-        return image_bgr
-
-
-class TextVisualizer(object):
-
-    _COLOR_GRAY = (218, 227, 218)
-    _COLOR_WHITE = (255, 255, 255)
-
-    def __init__(
-        self,
-        font_face=cv2.FONT_HERSHEY_SIMPLEX,
-        font_color_bgr=_COLOR_GRAY,
-        font_scale=0.35,
-        font_line_type=cv2.LINE_AA,
-        font_line_thickness=1,
-        fill_color_bgr=_COLOR_WHITE,
-        fill_color_transparency=1.0,
-        frame_color_bgr=_COLOR_WHITE,
-        frame_color_transparency=1.0,
-        frame_thickness=1,
-    ):
-        self.font_face = font_face
-        self.font_color_bgr = font_color_bgr
-        self.font_scale = font_scale
-        self.font_line_type = font_line_type
-        self.font_line_thickness = font_line_thickness
-        self.fill_color_bgr = fill_color_bgr
-        self.fill_color_transparency = fill_color_transparency
-        self.frame_color_bgr = frame_color_bgr
-        self.frame_color_transparency = frame_color_transparency
-        self.frame_thickness = frame_thickness
-
-    def visualize(self, image_bgr, txt, topleft_xy):
-        txt_w, txt_h = self.get_text_size_wh(txt)
-        topleft_xy = tuple(map(int, topleft_xy))
-        x, y = topleft_xy
-        if self.frame_color_transparency < 1.0:
-            t = self.frame_thickness
-            image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :] = (
-                image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :]
-                * self.frame_color_transparency
-                + np.array(self.frame_color_bgr) * (1.0 - self.frame_color_transparency)
-            ).astype(np.float)
-        if self.fill_color_transparency < 1.0:
-            image_bgr[y : y + txt_h, x : x + txt_w, :] = (
-                image_bgr[y : y + txt_h, x : x + txt_w, :] * self.fill_color_transparency
-                + np.array(self.fill_color_bgr) * (1.0 - self.fill_color_transparency)
-            ).astype(np.float)
-        cv2.putText(
-            image_bgr,
-            txt,
-            topleft_xy,
-            self.font_face,
-            self.font_scale,
-            self.font_color_bgr,
-            self.font_line_thickness,
-            self.font_line_type,
-        )
-        return image_bgr
-
-    def get_text_size_wh(self, txt):
-        ((txt_w, txt_h), _) = cv2.getTextSize(
-            txt, self.font_face, self.font_scale, self.font_line_thickness
-        )
-        return txt_w, txt_h
-
-
-class CompoundVisualizer(object):
-    def __init__(self, visualizers):
-        self.visualizers = visualizers
-
-    def visualize(self, image_bgr, data):
-        assert len(data) == len(self.visualizers), (
-            "The number of datas {} should match the number of visualizers"
-            " {}".format(len(data), len(self.visualizers))
-        )
-        image = image_bgr
-        for i, visualizer in enumerate(self.visualizers):
-            image = visualizer.visualize(image, data[i])
-        return image
-
-    def __str__(self):
-        visualizer_str = ", ".join([str(v) for v in self.visualizers])
-        return "Compound Visualizer [{}]".format(visualizer_str)
diff --git a/spaces/CVPR/LIVE/pydiffvg_tensorflow/color.py b/spaces/CVPR/LIVE/pydiffvg_tensorflow/color.py
deleted file mode 100644
index e0db61215407dfbee8e4021aa0b32e70df473ddb..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pydiffvg_tensorflow/color.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import tensorflow as tf
-
-class LinearGradient:
-    def __init__(self,
-                 begin = tf.constant([0.0, 0.0]),
-                 end = tf.constant([0.0, 0.0]),
-                 offsets = tf.constant([0.0]),
-                 stop_colors = tf.constant([0.0, 0.0, 0.0, 0.0])):
-        self.begin = begin
-        self.end = end
-        self.offsets = offsets
-        self.stop_colors = stop_colors
-
-class RadialGradient:
-    def __init__(self,
-                 center = tf.constant([0.0, 0.0]),
-                 radius = tf.constant([0.0, 0.0]),
-                 offsets = tf.constant([0.0]),
-                 stop_colors = tf.constant([0.0, 0.0, 0.0, 0.0])):
-        self.center = center
-        self.radius = radius
-        self.offsets = offsets
-        self.stop_colors = stop_colors
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/inner_product.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/inner_product.h
deleted file mode 100644
index 9423b1bdbf19deba9f7ec91d9ce4417b0c2f5145..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/inner_product.h
+++ /dev/null
@@ -1,44 +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 fill 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.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the inner_product.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch inner_product
-
-#include <thrust/system/detail/sequential/inner_product.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/inner_product.h>
-#include <thrust/system/cuda/detail/inner_product.h>
-#include <thrust/system/omp/detail/inner_product.h>
-#include <thrust/system/tbb/detail/inner_product.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_INNER_PRODUCT_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/inner_product.h>
-#include __THRUST_HOST_SYSTEM_INNER_PRODUCT_HEADER
-#undef __THRUST_HOST_SYSTEM_INNER_PRODUCT_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_INNER_PRODUCT_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/inner_product.h>
-#include __THRUST_DEVICE_SYSTEM_INNER_PRODUCT_HEADER
-#undef __THRUST_DEVICE_SYSTEM_INNER_PRODUCT_HEADER
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/fill.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/fill.h
deleted file mode 100644
index 20c636096c25e9a2d951ad2f50a4de72d0d1b968..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/fill.h
+++ /dev/null
@@ -1,22 +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.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system has no special fill functions
-
diff --git a/spaces/CVPR/Text2Human/Text2Human/utils/language_utils.py b/spaces/CVPR/Text2Human/Text2Human/utils/language_utils.py
deleted file mode 100644
index bb2ef69b3001f10b20069f40ec0141d28260482f..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Text2Human/Text2Human/utils/language_utils.py
+++ /dev/null
@@ -1,315 +0,0 @@
-from curses import A_ATTRIBUTES
-
-import numpy
-import torch
-from pip import main
-from sentence_transformers import SentenceTransformer, util
-
-# predefined shape text
-upper_length_text = [
-    'sleeveless', 'without sleeves', 'sleeves have been cut off', 'tank top',
-    'tank shirt', 'muscle shirt', 'short-sleeve', 'short sleeves',
-    'with short sleeves', 'medium-sleeve', 'medium sleeves',
-    'with medium sleeves', 'sleeves reach elbow', 'long-sleeve',
-    'long sleeves', 'with long sleeves'
-]
-upper_length_attr = {
-    'sleeveless': 0,
-    'without sleeves': 0,
-    'sleeves have been cut off': 0,
-    'tank top': 0,
-    'tank shirt': 0,
-    'muscle shirt': 0,
-    'short-sleeve': 1,
-    'with short sleeves': 1,
-    'short sleeves': 1,
-    'medium-sleeve': 2,
-    'with medium sleeves': 2,
-    'medium sleeves': 2,
-    'sleeves reach elbow': 2,
-    'long-sleeve': 3,
-    'long sleeves': 3,
-    'with long sleeves': 3
-}
-lower_length_text = [
-    'three-point', 'medium', 'short', 'covering knee', 'cropped',
-    'three-quarter', 'long', 'slack', 'of long length'
-]
-lower_length_attr = {
-    'three-point': 0,
-    'medium': 1,
-    'covering knee': 1,
-    'short': 1,
-    'cropped': 2,
-    'three-quarter': 2,
-    'long': 3,
-    'slack': 3,
-    'of long length': 3
-}
-socks_length_text = [
-    'socks', 'stocking', 'pantyhose', 'leggings', 'sheer hosiery'
-]
-socks_length_attr = {
-    'socks': 0,
-    'stocking': 1,
-    'pantyhose': 1,
-    'leggings': 1,
-    'sheer hosiery': 1
-}
-hat_text = ['hat', 'cap', 'chapeau']
-eyeglasses_text = ['sunglasses']
-belt_text = ['belt', 'with a dress tied around the waist']
-outer_shape_text = [
-    'with outer clothing open', 'with outer clothing unzipped',
-    'covering inner clothes', 'with outer clothing zipped'
-]
-outer_shape_attr = {
-    'with outer clothing open': 0,
-    'with outer clothing unzipped': 0,
-    'covering inner clothes': 1,
-    'with outer clothing zipped': 1
-}
-
-upper_types = [
-    'T-shirt', 'shirt', 'sweater', 'hoodie', 'tops', 'blouse', 'Basic Tee'
-]
-outer_types = [
-    'jacket', 'outer clothing', 'coat', 'overcoat', 'blazer', 'outerwear',
-    'duffle', 'cardigan'
-]
-skirt_types = ['skirt']
-dress_types = ['dress']
-pant_types = ['jeans', 'pants', 'trousers']
-rompers_types = ['rompers', 'bodysuit', 'jumpsuit']
-
-attr_names_list = [
-    'gender', 'hair length', '0 upper clothing length',
-    '1 lower clothing length', '2 socks', '3 hat', '4 eyeglasses', '5 belt',
-    '6 opening of outer clothing', '7 upper clothes', '8 outer clothing',
-    '9 skirt', '10 dress', '11 pants', '12 rompers'
-]
-
-
-def generate_shape_attributes(user_shape_texts):
-    model = SentenceTransformer('all-MiniLM-L6-v2')
-    parsed_texts = user_shape_texts.split(',')
-
-    text_num = len(parsed_texts)
-
-    human_attr = [0, 0]
-    attr = [1, 3, 0, 0, 0, 3, 1, 1, 0, 0, 0, 0, 0]
-
-    changed = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-    for text_id, text in enumerate(parsed_texts):
-        user_embeddings = model.encode(text)
-        if ('man' in text) and (text_id == 0):
-            human_attr[0] = 0
-            human_attr[1] = 0
-
-        if ('woman' in text or 'lady' in text) and (text_id == 0):
-            human_attr[0] = 1
-            human_attr[1] = 2
-
-        if (not changed[0]) and (text_id == 1):
-            # upper length
-            predefined_embeddings = model.encode(upper_length_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            attr[0] = upper_length_attr[upper_length_text[arg_idx]]
-            changed[0] = 1
-
-        if (not changed[1]) and ((text_num == 2 and text_id == 1) or
-                                 (text_num > 2 and text_id == 2)):
-            # lower length
-            predefined_embeddings = model.encode(lower_length_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            attr[1] = lower_length_attr[lower_length_text[arg_idx]]
-            changed[1] = 1
-
-        if (not changed[2]) and (text_id > 2):
-            # socks length
-            predefined_embeddings = model.encode(socks_length_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            if similarities[0][arg_idx] > 0.7:
-                attr[2] = arg_idx + 1
-                changed[2] = 1
-
-        if (not changed[3]) and (text_id > 2):
-            # hat
-            predefined_embeddings = model.encode(hat_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            if similarities[0][0] > 0.7:
-                attr[3] = 1
-                changed[3] = 1
-
-        if (not changed[4]) and (text_id > 2):
-            # glasses
-            predefined_embeddings = model.encode(eyeglasses_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            if similarities[0][arg_idx] > 0.7:
-                attr[4] = arg_idx + 1
-                changed[4] = 1
-
-        if (not changed[5]) and (text_id > 2):
-            # belt
-            predefined_embeddings = model.encode(belt_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            if similarities[0][arg_idx] > 0.7:
-                attr[5] = arg_idx + 1
-                changed[5] = 1
-
-        if (not changed[6]) and (text_id == 3):
-            # outer coverage
-            predefined_embeddings = model.encode(outer_shape_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            if similarities[0][arg_idx] > 0.7:
-                attr[6] = arg_idx
-                changed[6] = 1
-
-        if (not changed[10]) and (text_num == 2 and text_id == 1):
-            # dress_types
-            predefined_embeddings = model.encode(dress_types)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            similarity_skirt = util.dot_score(user_embeddings,
-                                              model.encode(skirt_types))
-            if similarities[0][0] > 0.5 and similarities[0][
-                    0] > similarity_skirt[0][0]:
-                attr[10] = 1
-                attr[7] = 0
-                attr[8] = 0
-                attr[9] = 0
-                attr[11] = 0
-                attr[12] = 0
-
-                changed[0] = 1
-                changed[10] = 1
-                changed[7] = 1
-                changed[8] = 1
-                changed[9] = 1
-                changed[11] = 1
-                changed[12] = 1
-
-        if (not changed[12]) and (text_num == 2 and text_id == 1):
-            # rompers_types
-            predefined_embeddings = model.encode(rompers_types)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            max_similarity = torch.max(similarities).item()
-            if max_similarity > 0.6:
-                attr[12] = 1
-                attr[7] = 0
-                attr[8] = 0
-                attr[9] = 0
-                attr[10] = 0
-                attr[11] = 0
-
-                changed[12] = 1
-                changed[7] = 1
-                changed[8] = 1
-                changed[9] = 1
-                changed[10] = 1
-                changed[11] = 1
-
-        if (not changed[7]) and (text_num > 2 and text_id == 1):
-            # upper_types
-            predefined_embeddings = model.encode(upper_types)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            max_similarity = torch.max(similarities).item()
-            if max_similarity > 0.6:
-                attr[7] = 1
-                changed[7] = 1
-
-        if (not changed[8]) and (text_id == 3):
-            # outer_types
-            predefined_embeddings = model.encode(outer_types)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            if similarities[0][arg_idx] > 0.7:
-                attr[6] = outer_shape_attr[outer_shape_text[arg_idx]]
-                attr[8] = 1
-                changed[8] = 1
-
-        if (not changed[9]) and (text_num > 2 and text_id == 2):
-            # skirt_types
-            predefined_embeddings = model.encode(skirt_types)
-            similarity_skirt = util.dot_score(user_embeddings,
-                                              predefined_embeddings)
-            similarity_dress = util.dot_score(user_embeddings,
-                                              model.encode(dress_types))
-            if similarity_skirt[0][0] > 0.7 and similarity_skirt[0][
-                    0] > similarity_dress[0][0]:
-                attr[9] = 1
-                attr[10] = 0
-                changed[9] = 1
-                changed[10] = 1
-
-        if (not changed[11]) and (text_num > 2 and text_id == 2):
-            # pant_types
-            predefined_embeddings = model.encode(pant_types)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            max_similarity = torch.max(similarities).item()
-            if max_similarity > 0.6:
-                attr[11] = 1
-                attr[9] = 0
-                attr[10] = 0
-                attr[12] = 0
-                changed[11] = 1
-                changed[9] = 1
-                changed[10] = 1
-                changed[12] = 1
-
-    return human_attr + attr
-
-
-def generate_texture_attributes(user_text):
-    parsed_texts = user_text.split(',')
-
-    attr = []
-    for text in parsed_texts:
-        if ('pure color' in text) or ('solid color' in text):
-            attr.append(4)
-        elif ('spline' in text) or ('stripe' in text):
-            attr.append(3)
-        elif ('plaid' in text) or ('lattice' in text):
-            attr.append(5)
-        elif 'floral' in text:
-            attr.append(1)
-        elif 'denim' in text:
-            attr.append(0)
-        else:
-            attr.append(17)
-
-    if len(attr) == 1:
-        attr.append(attr[0])
-        attr.append(17)
-
-    if len(attr) == 2:
-        attr.append(17)
-
-    return attr
-
-
-if __name__ == "__main__":
-    user_request = input('Enter your request: ')
-    while user_request != '\\q':
-        attr = generate_shape_attributes(user_request)
-        print(attr)
-        for attr_name, attr_value in zip(attr_names_list, attr):
-            print(attr_name, attr_value)
-        user_request = input('Enter your request: ')
diff --git a/spaces/CamCam17/Alexwww-davide-comic-book-characters/README.md b/spaces/CamCam17/Alexwww-davide-comic-book-characters/README.md
deleted file mode 100644
index 69e3efb33215d67b5c429ec8c1bb4a8f567a2188..0000000000000000000000000000000000000000
--- a/spaces/CamCam17/Alexwww-davide-comic-book-characters/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Livebook
-emoji: 📓
-colorFrom: pink
-colorTo: purple
-sdk: docker
-fullWidth: true
----
-
-You can install and run [Livebook](https://livebook.dev/) inside a Hugging Face Space. Here's [a tutorial](https://huggingface.co/docs/hub/spaces-sdks-docker-livebook) on how to do that.
\ No newline at end of file
diff --git a/spaces/Cpp4App/Cpp4App/CDM/detect_compo/deprecated/Block.py b/spaces/Cpp4App/Cpp4App/CDM/detect_compo/deprecated/Block.py
deleted file mode 100644
index 8cf301418704e0ac91f791126690100bbedc5a8b..0000000000000000000000000000000000000000
--- a/spaces/Cpp4App/Cpp4App/CDM/detect_compo/deprecated/Block.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import cv2
-from os.path import join as pjoin
-import time
-import numpy as np
-
-from CDM.detect_compo.lib_ip.Component import Component
-from CDM.config.CONFIG_UIED import Config
-C = Config()
-
-
-class Block(Component):
-    def __init__(self, region, image_shape):
-        super().__init__(region, image_shape)
-        self.category = 'Block'
-        self.parent = None
-        self.children = []
-        self.uicompo_ = None
-        self.top_or_botm = None
-        self.redundant = False
-
-    def block_is_uicompo(self, image_shape, max_compo_scale):
-        '''
-        Check the if the block is a ui component according to its relative size
-        '''
-        row, column = image_shape[:2]
-        # print(height, height / row, max_compo_scale[0], height / row > max_compo_scale[0])
-        # draw.draw_bounding_box(org, [corner], show=True)
-        # ignore atomic components
-        if self.bbox.height / row > max_compo_scale[0] or self.bbox.width / column > max_compo_scale[1]:
-            return False
-        return True
-
-    def block_is_top_or_bottom_bar(self, image_shape, top_bottom_height):
-        '''
-        Check if the block is top bar or bottom bar
-        '''
-        height, width = image_shape[:2]
-        (column_min, row_min, column_max, row_max) = self.bbox.put_bbox()
-        if column_min < 5 and row_min < 5 and \
-                width - column_max < 5 and row_max < height * top_bottom_height[0]:
-            self.uicompo_ = True
-            return True
-        if column_min < 5 and row_min > height * top_bottom_height[1] and \
-                width - column_max < 5 and height - row_max < 5:
-            self.uicompo_ = True
-            return True
-        return False
-
-    def block_erase_from_bin(self, binary, pad):
-        (column_min, row_min, column_max, row_max) = self.put_bbox()
-        column_min = max(column_min - pad, 0)
-        column_max = min(column_max + pad, binary.shape[1])
-        row_min = max(row_min - pad, 0)
-        row_max = min(row_max + pad, binary.shape[0])
-        cv2.rectangle(binary, (column_min, row_min), (column_max, row_max), (0), -1)
-
diff --git a/spaces/CyberHarem/find_my_waifu/huggingface.py b/spaces/CyberHarem/find_my_waifu/huggingface.py
deleted file mode 100644
index 3c357336b7d664810a027bbb4e01f7c7d0169eff..0000000000000000000000000000000000000000
--- a/spaces/CyberHarem/find_my_waifu/huggingface.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import math
-import os
-
-from gchar.utils import get_requests_session
-from huggingface_hub import configure_http_backend, HfApi, HfFileSystem
-
-_NUM_TAGS = [
-    ('n<1K', 0, 1_000),
-    ('1K<n<10K', 1_000, 10_000),
-    ('10K<n<100K', 10_000, 100_000),
-    ('100K<n<1M', 100_000, 1_000_000),
-    ('1M<n<10M', 1_000_000, 10_000_000),
-    ('10M<n<100M', 10_000_000, 100_000_000),
-    ('100M<n<1B', 100_000_000, 1_000_000_000),
-    ('1B<n<10B', 1_000_000_000, 10_000_000_000),
-    ('10B<n<100B', 10_000_000_000, 100_000_000_000),
-    ('100B<n<1T', 100_000_000_000, 1_000_000_000_000),
-    ('n>1T', 1_000_000_000_000, math.inf),
-]
-
-
-def number_to_tag(v):
-    for tag, min_, max_ in _NUM_TAGS:
-        if min_ <= v < max_:
-            return tag
-
-    raise ValueError(f'No tags found for {v!r}')
-
-
-configure_http_backend(get_requests_session)
-
-
-def get_hf_client() -> HfApi:
-    return HfApi(token=os.environ.get('HF_TOKEN'))
-
-
-def get_hf_fs() -> HfFileSystem:
-    return HfFileSystem(token=os.environ.get('HF_TOKEN'))
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GimpPaletteFile.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GimpPaletteFile.py
deleted file mode 100644
index d388928945a0f6711de2b1c8d1ed50ce192a8219..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GimpPaletteFile.py
+++ /dev/null
@@ -1,56 +0,0 @@
-#
-# Python Imaging Library
-# $Id$
-#
-# stuff to read GIMP palette files
-#
-# History:
-# 1997-08-23 fl     Created
-# 2004-09-07 fl     Support GIMP 2.0 palette files.
-#
-# Copyright (c) Secret Labs AB 1997-2004.  All rights reserved.
-# Copyright (c) Fredrik Lundh 1997-2004.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import re
-
-from ._binary import o8
-
-
-class GimpPaletteFile:
-    """File handler for GIMP's palette format."""
-
-    rawmode = "RGB"
-
-    def __init__(self, fp):
-        self.palette = [o8(i) * 3 for i in range(256)]
-
-        if fp.readline()[:12] != b"GIMP Palette":
-            msg = "not a GIMP palette file"
-            raise SyntaxError(msg)
-
-        for i in range(256):
-            s = fp.readline()
-            if not s:
-                break
-
-            # skip fields and comment lines
-            if re.match(rb"\w+:|#", s):
-                continue
-            if len(s) > 100:
-                msg = "bad palette file"
-                raise SyntaxError(msg)
-
-            v = tuple(map(int, s.split()[:3]))
-            if len(v) != 3:
-                msg = "bad palette entry"
-                raise ValueError(msg)
-
-            self.palette[i] = o8(v[0]) + o8(v[1]) + o8(v[2])
-
-        self.palette = b"".join(self.palette)
-
-    def getpalette(self):
-        return self.palette, self.rawmode
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_config.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_config.py
deleted file mode 100644
index f46a5bfe6ba6093688c7a91bd51de9d137840432..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_config.py
+++ /dev/null
@@ -1,369 +0,0 @@
-import logging
-import os
-import ssl
-import sys
-import typing
-from pathlib import Path
-
-import certifi
-
-from ._compat import set_minimum_tls_version_1_2
-from ._models import Headers
-from ._types import CertTypes, HeaderTypes, TimeoutTypes, URLTypes, VerifyTypes
-from ._urls import URL
-from ._utils import get_ca_bundle_from_env
-
-DEFAULT_CIPHERS = ":".join(
-    [
-        "ECDHE+AESGCM",
-        "ECDHE+CHACHA20",
-        "DHE+AESGCM",
-        "DHE+CHACHA20",
-        "ECDH+AESGCM",
-        "DH+AESGCM",
-        "ECDH+AES",
-        "DH+AES",
-        "RSA+AESGCM",
-        "RSA+AES",
-        "!aNULL",
-        "!eNULL",
-        "!MD5",
-        "!DSS",
-    ]
-)
-
-
-logger = logging.getLogger("httpx")
-
-
-class UnsetType:
-    pass  # pragma: no cover
-
-
-UNSET = UnsetType()
-
-
-def create_ssl_context(
-    cert: typing.Optional[CertTypes] = None,
-    verify: VerifyTypes = True,
-    trust_env: bool = True,
-    http2: bool = False,
-) -> ssl.SSLContext:
-    return SSLConfig(
-        cert=cert, verify=verify, trust_env=trust_env, http2=http2
-    ).ssl_context
-
-
-class SSLConfig:
-    """
-    SSL Configuration.
-    """
-
-    DEFAULT_CA_BUNDLE_PATH = Path(certifi.where())
-
-    def __init__(
-        self,
-        *,
-        cert: typing.Optional[CertTypes] = None,
-        verify: VerifyTypes = True,
-        trust_env: bool = True,
-        http2: bool = False,
-    ):
-        self.cert = cert
-        self.verify = verify
-        self.trust_env = trust_env
-        self.http2 = http2
-        self.ssl_context = self.load_ssl_context()
-
-    def load_ssl_context(self) -> ssl.SSLContext:
-        logger.debug(
-            "load_ssl_context verify=%r cert=%r trust_env=%r http2=%r",
-            self.verify,
-            self.cert,
-            self.trust_env,
-            self.http2,
-        )
-
-        if self.verify:
-            return self.load_ssl_context_verify()
-        return self.load_ssl_context_no_verify()
-
-    def load_ssl_context_no_verify(self) -> ssl.SSLContext:
-        """
-        Return an SSL context for unverified connections.
-        """
-        context = self._create_default_ssl_context()
-        context.check_hostname = False
-        context.verify_mode = ssl.CERT_NONE
-        self._load_client_certs(context)
-        return context
-
-    def load_ssl_context_verify(self) -> ssl.SSLContext:
-        """
-        Return an SSL context for verified connections.
-        """
-        if self.trust_env and self.verify is True:
-            ca_bundle = get_ca_bundle_from_env()
-            if ca_bundle is not None:
-                self.verify = ca_bundle
-
-        if isinstance(self.verify, ssl.SSLContext):
-            # Allow passing in our own SSLContext object that's pre-configured.
-            context = self.verify
-            self._load_client_certs(context)
-            return context
-        elif isinstance(self.verify, bool):
-            ca_bundle_path = self.DEFAULT_CA_BUNDLE_PATH
-        elif Path(self.verify).exists():
-            ca_bundle_path = Path(self.verify)
-        else:
-            raise IOError(
-                "Could not find a suitable TLS CA certificate bundle, "
-                "invalid path: {}".format(self.verify)
-            )
-
-        context = self._create_default_ssl_context()
-        context.verify_mode = ssl.CERT_REQUIRED
-        context.check_hostname = True
-
-        # Signal to server support for PHA in TLS 1.3. Raises an
-        # AttributeError if only read-only access is implemented.
-        if sys.version_info >= (3, 8):  # pragma: no cover
-            try:
-                context.post_handshake_auth = True
-            except AttributeError:  # pragma: no cover
-                pass
-
-        # Disable using 'commonName' for SSLContext.check_hostname
-        # when the 'subjectAltName' extension isn't available.
-        try:
-            context.hostname_checks_common_name = False
-        except AttributeError:  # pragma: no cover
-            pass
-
-        if ca_bundle_path.is_file():
-            cafile = str(ca_bundle_path)
-            logger.debug("load_verify_locations cafile=%r", cafile)
-            context.load_verify_locations(cafile=cafile)
-        elif ca_bundle_path.is_dir():
-            capath = str(ca_bundle_path)
-            logger.debug("load_verify_locations capath=%r", capath)
-            context.load_verify_locations(capath=capath)
-
-        self._load_client_certs(context)
-
-        return context
-
-    def _create_default_ssl_context(self) -> ssl.SSLContext:
-        """
-        Creates the default SSLContext object that's used for both verified
-        and unverified connections.
-        """
-        context = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
-        set_minimum_tls_version_1_2(context)
-        context.options |= ssl.OP_NO_COMPRESSION
-        context.set_ciphers(DEFAULT_CIPHERS)
-
-        if ssl.HAS_ALPN:
-            alpn_idents = ["http/1.1", "h2"] if self.http2 else ["http/1.1"]
-            context.set_alpn_protocols(alpn_idents)
-
-        if sys.version_info >= (3, 8):  # pragma: no cover
-            keylogfile = os.environ.get("SSLKEYLOGFILE")
-            if keylogfile and self.trust_env:
-                context.keylog_filename = keylogfile
-
-        return context
-
-    def _load_client_certs(self, ssl_context: ssl.SSLContext) -> None:
-        """
-        Loads client certificates into our SSLContext object
-        """
-        if self.cert is not None:
-            if isinstance(self.cert, str):
-                ssl_context.load_cert_chain(certfile=self.cert)
-            elif isinstance(self.cert, tuple) and len(self.cert) == 2:
-                ssl_context.load_cert_chain(certfile=self.cert[0], keyfile=self.cert[1])
-            elif isinstance(self.cert, tuple) and len(self.cert) == 3:
-                ssl_context.load_cert_chain(
-                    certfile=self.cert[0],
-                    keyfile=self.cert[1],
-                    password=self.cert[2],  # type: ignore
-                )
-
-
-class Timeout:
-    """
-    Timeout configuration.
-
-    **Usage**:
-
-    Timeout(None)               # No timeouts.
-    Timeout(5.0)                # 5s timeout on all operations.
-    Timeout(None, connect=5.0)  # 5s timeout on connect, no other timeouts.
-    Timeout(5.0, connect=10.0)  # 10s timeout on connect. 5s timeout elsewhere.
-    Timeout(5.0, pool=None)     # No timeout on acquiring connection from pool.
-                                # 5s timeout elsewhere.
-    """
-
-    def __init__(
-        self,
-        timeout: typing.Union[TimeoutTypes, UnsetType] = UNSET,
-        *,
-        connect: typing.Union[None, float, UnsetType] = UNSET,
-        read: typing.Union[None, float, UnsetType] = UNSET,
-        write: typing.Union[None, float, UnsetType] = UNSET,
-        pool: typing.Union[None, float, UnsetType] = UNSET,
-    ):
-        if isinstance(timeout, Timeout):
-            # Passed as a single explicit Timeout.
-            assert connect is UNSET
-            assert read is UNSET
-            assert write is UNSET
-            assert pool is UNSET
-            self.connect = timeout.connect  # type: typing.Optional[float]
-            self.read = timeout.read  # type: typing.Optional[float]
-            self.write = timeout.write  # type: typing.Optional[float]
-            self.pool = timeout.pool  # type: typing.Optional[float]
-        elif isinstance(timeout, tuple):
-            # Passed as a tuple.
-            self.connect = timeout[0]
-            self.read = timeout[1]
-            self.write = None if len(timeout) < 3 else timeout[2]
-            self.pool = None if len(timeout) < 4 else timeout[3]
-        elif not (
-            isinstance(connect, UnsetType)
-            or isinstance(read, UnsetType)
-            or isinstance(write, UnsetType)
-            or isinstance(pool, UnsetType)
-        ):
-            self.connect = connect
-            self.read = read
-            self.write = write
-            self.pool = pool
-        else:
-            if isinstance(timeout, UnsetType):
-                raise ValueError(
-                    "httpx.Timeout must either include a default, or set all "
-                    "four parameters explicitly."
-                )
-            self.connect = timeout if isinstance(connect, UnsetType) else connect
-            self.read = timeout if isinstance(read, UnsetType) else read
-            self.write = timeout if isinstance(write, UnsetType) else write
-            self.pool = timeout if isinstance(pool, UnsetType) else pool
-
-    def as_dict(self) -> typing.Dict[str, typing.Optional[float]]:
-        return {
-            "connect": self.connect,
-            "read": self.read,
-            "write": self.write,
-            "pool": self.pool,
-        }
-
-    def __eq__(self, other: typing.Any) -> bool:
-        return (
-            isinstance(other, self.__class__)
-            and self.connect == other.connect
-            and self.read == other.read
-            and self.write == other.write
-            and self.pool == other.pool
-        )
-
-    def __repr__(self) -> str:
-        class_name = self.__class__.__name__
-        if len({self.connect, self.read, self.write, self.pool}) == 1:
-            return f"{class_name}(timeout={self.connect})"
-        return (
-            f"{class_name}(connect={self.connect}, "
-            f"read={self.read}, write={self.write}, pool={self.pool})"
-        )
-
-
-class Limits:
-    """
-    Configuration for limits to various client behaviors.
-
-    **Parameters:**
-
-    * **max_connections** - The maximum number of concurrent connections that may be
-            established.
-    * **max_keepalive_connections** - Allow the connection pool to maintain
-            keep-alive connections below this point. Should be less than or equal
-            to `max_connections`.
-    * **keepalive_expiry** - Time limit on idle keep-alive connections in seconds.
-    """
-
-    def __init__(
-        self,
-        *,
-        max_connections: typing.Optional[int] = None,
-        max_keepalive_connections: typing.Optional[int] = None,
-        keepalive_expiry: typing.Optional[float] = 5.0,
-    ):
-        self.max_connections = max_connections
-        self.max_keepalive_connections = max_keepalive_connections
-        self.keepalive_expiry = keepalive_expiry
-
-    def __eq__(self, other: typing.Any) -> bool:
-        return (
-            isinstance(other, self.__class__)
-            and self.max_connections == other.max_connections
-            and self.max_keepalive_connections == other.max_keepalive_connections
-            and self.keepalive_expiry == other.keepalive_expiry
-        )
-
-    def __repr__(self) -> str:
-        class_name = self.__class__.__name__
-        return (
-            f"{class_name}(max_connections={self.max_connections}, "
-            f"max_keepalive_connections={self.max_keepalive_connections}, "
-            f"keepalive_expiry={self.keepalive_expiry})"
-        )
-
-
-class Proxy:
-    def __init__(
-        self,
-        url: URLTypes,
-        *,
-        auth: typing.Optional[typing.Tuple[str, str]] = None,
-        headers: typing.Optional[HeaderTypes] = None,
-    ):
-        url = URL(url)
-        headers = Headers(headers)
-
-        if url.scheme not in ("http", "https", "socks5"):
-            raise ValueError(f"Unknown scheme for proxy URL {url!r}")
-
-        if url.username or url.password:
-            # Remove any auth credentials from the URL.
-            auth = (url.username, url.password)
-            url = url.copy_with(username=None, password=None)
-
-        self.url = url
-        self.auth = auth
-        self.headers = headers
-
-    @property
-    def raw_auth(self) -> typing.Optional[typing.Tuple[bytes, bytes]]:
-        # The proxy authentication as raw bytes.
-        return (
-            None
-            if self.auth is None
-            else (self.auth[0].encode("utf-8"), self.auth[1].encode("utf-8"))
-        )
-
-    def __repr__(self) -> str:
-        # The authentication is represented with the password component masked.
-        auth = (self.auth[0], "********") if self.auth else None
-
-        # Build a nice concise representation.
-        url_str = f"{str(self.url)!r}"
-        auth_str = f", auth={auth!r}" if auth else ""
-        headers_str = f", headers={dict(self.headers)!r}" if self.headers else ""
-        return f"Proxy({url_str}{auth_str}{headers_str})"
-
-
-DEFAULT_TIMEOUT_CONFIG = Timeout(timeout=5.0)
-DEFAULT_LIMITS = Limits(max_connections=100, max_keepalive_connections=20)
-DEFAULT_MAX_REDIRECTS = 20
diff --git a/spaces/Dao3/ChatGLM-6B/app.py b/spaces/Dao3/ChatGLM-6B/app.py
deleted file mode 100644
index 7871b6f037949e3cbab97c3e39fce46ce60540fa..0000000000000000000000000000000000000000
--- a/spaces/Dao3/ChatGLM-6B/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from transformers import AutoModel, AutoTokenizer
-import gradio as gr
-
-tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
-# model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
-model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4",trust_remote_code=True).float()
-model = model.eval()
-
-MAX_TURNS = 20
-MAX_BOXES = MAX_TURNS * 2
-
-
-def predict(input, max_length, top_p, temperature, history=None):
-    if history is None:
-        history = []
-    for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
-                                               temperature=temperature):
-        updates = []
-        for query, response in history:
-            updates.append(gr.update(visible=True, value="用户：" + query))
-            updates.append(gr.update(visible=True, value="ChatGLM-6B：" + response))
-        if len(updates) < MAX_BOXES:
-            updates = updates + [gr.Textbox.update(visible=False)] * (MAX_BOXES - len(updates))
-        yield [history] + updates
-
-
-with gr.Blocks() as demo:
-    state = gr.State([])
-    text_boxes = []
-    for i in range(MAX_BOXES):
-        if i % 2 == 0:
-            text_boxes.append(gr.Markdown(visible=False, label="提问："))
-        else:
-            text_boxes.append(gr.Markdown(visible=False, label="回复："))
-
-    with gr.Row():
-        with gr.Column(scale=4):
-            txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter", lines=11).style(
-                container=False)
-        with gr.Column(scale=1):
-            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
-            top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
-            temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)
-            button = gr.Button("Generate")
-    button.click(predict, [txt, max_length, top_p, temperature, state], [state] + text_boxes)
-demo.queue().launch()
diff --git a/spaces/Datasculptor/MusicGen/tests/quantization/test_vq.py b/spaces/Datasculptor/MusicGen/tests/quantization/test_vq.py
deleted file mode 100644
index c215099fedacae35c6798fdd9b8420a447aa16bb..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/MusicGen/tests/quantization/test_vq.py
+++ /dev/null
@@ -1,18 +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
-
-from audiocraft.quantization.vq import ResidualVectorQuantizer
-
-
-class TestResidualVectorQuantizer:
-
-    def test_rvq(self):
-        x = torch.randn(1, 16, 2048)
-        vq = ResidualVectorQuantizer(n_q=8, dimension=16, bins=8)
-        res = vq(x, 1.)
-        assert res.x.shape == torch.Size([1, 16, 2048])
diff --git a/spaces/Didier/Semantic_Search_arXiv/app.py b/spaces/Didier/Semantic_Search_arXiv/app.py
deleted file mode 100644
index 22429c65c6d17190947372709ce567c90aa7c9f1..0000000000000000000000000000000000000000
--- a/spaces/Didier/Semantic_Search_arXiv/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# Title:  app.py
-# Author: Didier Guillevic
-# Date:   2021
-
-import streamlit as st
-import pandas as pd
-import numpy as np
-import os
-import io
-
-import arxiv_info
-
-st.title("Semantic Search (arxiv.org)")
-st.markdown("""
-* Query with some document (preferred) or some keywords (in any language).
-* Get papers from [arxiv.org](https://arxiv.org) with meaning similar to the query (arxiv indexed up to 2020-11).
-* Sample queries:
-  * Techniques to measure blood sugar levels.
-  * Aujourd'hui, malgré des décennies de développement de la médecine et l'intérêt croissant pour les soins de santé de précision, la grande majorité des diagnostics se produisent une fois que les patients commencent à montrer des signes visibles de maladie.
-  * Diabetes-Patienten kontrollieren ständig den Blutzucker. Forscher haben jetzt die Wirkung von speziellen Hautsensoren geprüft, die ohne Blut auskommen.
-  * 機械学習をヘルスケアに適用することで、多くの素晴らしい結果が得られました。ただし、これらの大部分は、治療に明示的にではなく、状態の診断または結果の予測に焦点を合わせています。
-""")
-
-#
-# Define the form to get the query and desired number of results
-#
-
-def build_markdown_response(docs):
-    outStream = io.StringIO()
-    for doc in docs:
-        outStream.write(f"* Score   : {doc['score']:0.3f}\n")
-        outStream.write(f"* URL     : [{doc['url']}]({doc['url']})\n")
-        outStream.write(f"* Title   : {doc['title']}\n")
-        outStream.write(f"* Abstract: {doc['abstract']}\n")
-        outStream.write("- - - -\n")
-    result = outStream.getvalue()
-    outStream.close()
-    return result
-
-def form_callback():
-    # Get the top semantically similar documents
-    docs = arxiv_info.retrieve_similar_docs(
-        st.session_state.query,
-        arxiv_info.arxiv_embeddings,
-        arxiv_info.arxiv_df,
-        nb_results=st.session_state.nb_results)
-
-    # Return the result in markdown format
-    st.header("Search document")
-    st.write(st.session_state.query)
-    st.header("Similar documents")
-    result = build_markdown_response(docs)
-    st.markdown(result)
-
-with st.form(key="input_form"):
-    st.text_area("Text to search", key="query")
-    st.slider(label="Number of results", min_value=1, max_value=10, value=5, key="nb_results")
-    submit_button = st.form_submit_button(label="Submit", on_click=form_callback)
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/custom_ops.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/custom_ops.py
deleted file mode 100644
index 6509d70bbcd49a24628e3e90258ccb0d8c5a3b39..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/custom_ops.py
+++ /dev/null
@@ -1,260 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# 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.
-
-import os
-import glob
-import torch
-import torch.utils.cpp_extension
-import importlib
-import hashlib
-import shutil
-from pathlib import Path
-import re
-import uuid
-
-from torch.utils.file_baton import FileBaton
-
-# ----------------------------------------------------------------------------
-# Global options.
-
-verbosity = 'brief'  # Verbosity level: 'none', 'brief', 'full'
-
-# ----------------------------------------------------------------------------
-# Internal helper funcs.
-
-
-def _find_compiler_bindir():
-    patterns = [
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin',
-    ]
-    for pattern in patterns:
-        matches = sorted(glob.glob(pattern))
-        if len(matches):
-            return matches[-1]
-    return None
-
-
-def _get_mangled_gpu_name():
-    name = torch.cuda.get_device_name().lower()
-    out = []
-    for c in name:
-        if re.match('[a-z0-9_-]+', c):
-            out.append(c)
-        else:
-            out.append('-')
-    return ''.join(out)
-
-
-# ----------------------------------------------------------------------------
-# Main entry point for compiling and loading C++/CUDA plugins.
-
-_cached_plugins = dict()
-
-
-def get_plugin(module_name, sources, **build_kwargs):
-    assert verbosity in ['none', 'brief', 'full']
-
-    # Already cached?
-    if module_name in _cached_plugins:
-        return _cached_plugins[module_name]
-
-    # Print status.
-    if verbosity == 'full':
-        print(f'Setting up PyTorch plugin "{module_name}"...')
-    elif verbosity == 'brief':
-        print(
-            f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
-
-    try:  # pylint: disable=too-many-nested-blocks
-        # Make sure we can find the necessary compiler binaries.
-        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
-            compiler_bindir = _find_compiler_bindir()
-            if compiler_bindir is None:
-                raise RuntimeError(
-                    f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
-            os.environ['PATH'] += ';' + compiler_bindir
-
-        # Compile and load.
-        verbose_build = (verbosity == 'full')
-
-        # Incremental build md5sum trickery.  Copies all the input source files
-        # into a cached build directory under a combined md5 digest of the input
-        # source files.  Copying is done only if the combined digest has changed.
-        # This keeps input file timestamps and filenames the same as in previous
-        # extension builds, allowing for fast incremental rebuilds.
-        #
-        # This optimization is done only in case all the source files reside in
-        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
-        # environment variable is set (we take this as a signal that the user
-        # actually cares about this.)
-        source_dirs_set = set(os.path.dirname(source) for source in sources)
-        if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ):
-            all_source_files = sorted(list(x for x in Path(
-                list(source_dirs_set)[0]).iterdir() if x.is_file()))
-
-            # Compute a combined hash digest for all source files in the same
-            # custom op directory (usually .cu, .cpp, .py and .h files).
-            hash_md5 = hashlib.md5()
-            for src in all_source_files:
-                with open(src, 'rb') as f:
-                    hash_md5.update(f.read())
-            build_dir = torch.utils.cpp_extension._get_build_directory(
-                module_name, verbose=verbose_build)  # pylint: disable=protected-access
-            digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest())
-
-            if not os.path.isdir(digest_build_dir):
-                os.makedirs(digest_build_dir, exist_ok=True)
-                baton = FileBaton(os.path.join(digest_build_dir, 'lock'))
-                if baton.try_acquire():
-                    try:
-                        for src in all_source_files:
-                            shutil.copyfile(src, os.path.join(
-                                digest_build_dir, os.path.basename(src)))
-                    finally:
-                        baton.release()
-                else:
-                    # Someone else is copying source files under the digest dir,
-                    # wait until done and continue.
-                    baton.wait()
-            digest_sources = [os.path.join(
-                digest_build_dir, os.path.basename(x)) for x in sources]
-            torch.utils.cpp_extension.load(name=module_name, build_directory=build_dir,
-                                           verbose=verbose_build, sources=digest_sources, **build_kwargs)
-        else:
-            torch.utils.cpp_extension.load(
-                name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
-        module = importlib.import_module(module_name)
-
-    except:
-        if verbosity == 'brief':
-            print('Failed!')
-        raise
-
-    # Print status and add to cache.
-    if verbosity == 'full':
-        print(f'Done setting up PyTorch plugin "{module_name}".')
-    elif verbosity == 'brief':
-        print('Done.')
-    _cached_plugins[module_name] = module
-    return module
-
-# ----------------------------------------------------------------------------
-
-
-def get_plugin_v3(module_name, sources, headers=None, source_dir=None, **build_kwargs):
-    assert verbosity in ['none', 'brief', 'full']
-    if headers is None:
-        headers = []
-    if source_dir is not None:
-        sources = [os.path.join(source_dir, fname) for fname in sources]
-        headers = [os.path.join(source_dir, fname) for fname in headers]
-
-    # Already cached?
-    if module_name in _cached_plugins:
-        return _cached_plugins[module_name]
-
-    # Print status.
-    if verbosity == 'full':
-        print(f'Setting up PyTorch plugin "{module_name}"...')
-    elif verbosity == 'brief':
-        print(
-            f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
-    verbose_build = (verbosity == 'full')
-
-    # Compile and load.
-    try:  # pylint: disable=too-many-nested-blocks
-        # Make sure we can find the necessary compiler binaries.
-        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
-            compiler_bindir = _find_compiler_bindir()
-            if compiler_bindir is None:
-                raise RuntimeError(
-                    f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
-            os.environ['PATH'] += ';' + compiler_bindir
-
-        # Some containers set TORCH_CUDA_ARCH_LIST to a list that can either
-        # break the build or unnecessarily restrict what's available to nvcc.
-        # Unset it to let nvcc decide based on what's available on the
-        # machine.
-        os.environ['TORCH_CUDA_ARCH_LIST'] = ''
-
-        # Incremental build md5sum trickery.  Copies all the input source files
-        # into a cached build directory under a combined md5 digest of the input
-        # source files.  Copying is done only if the combined digest has changed.
-        # This keeps input file timestamps and filenames the same as in previous
-        # extension builds, allowing for fast incremental rebuilds.
-        #
-        # This optimization is done only in case all the source files reside in
-        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
-        # environment variable is set (we take this as a signal that the user
-        # actually cares about this.)
-        #
-        # EDIT: We now do it regardless of TORCH_EXTENSIOS_DIR, in order to work
-        # around the *.cu dependency bug in ninja config.
-        #
-        all_source_files = sorted(sources + headers)
-        all_source_dirs = set(os.path.dirname(fname)
-                              for fname in all_source_files)
-        # and ('TORCH_EXTENSIONS_DIR' in os.environ):
-        if len(all_source_dirs) == 1:
-
-            # Compute combined hash digest for all source files.
-            hash_md5 = hashlib.md5()
-            for src in all_source_files:
-                with open(src, 'rb') as f:
-                    hash_md5.update(f.read())
-
-            # Select cached build directory name.
-            source_digest = hash_md5.hexdigest()
-            build_top_dir = torch.utils.cpp_extension._get_build_directory(
-                module_name, verbose=verbose_build)  # pylint: disable=protected-access
-            cached_build_dir = os.path.join(
-                build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}')
-
-            if not os.path.isdir(cached_build_dir):
-                tmpdir = f'{build_top_dir}/srctmp-{uuid.uuid4().hex}'
-                os.makedirs(tmpdir)
-                for src in all_source_files:
-                    shutil.copyfile(src, os.path.join(
-                        tmpdir, os.path.basename(src)))
-                try:
-                    os.replace(tmpdir, cached_build_dir)  # atomic
-                except OSError:
-                    # source directory already exists, delete tmpdir and its contents.
-                    shutil.rmtree(tmpdir)
-                    if not os.path.isdir(cached_build_dir):
-                        raise
-
-            # Compile.
-            cached_sources = [os.path.join(
-                cached_build_dir, os.path.basename(fname)) for fname in sources]
-            torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir,
-                                           verbose=verbose_build, sources=cached_sources, **build_kwargs)
-        else:
-            torch.utils.cpp_extension.load(
-                name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
-
-        # Load.
-        module = importlib.import_module(module_name)
-
-    except:
-        if verbosity == 'brief':
-            print('Failed!')
-        raise
-
-    # Print status and add to cache dict.
-    if verbosity == 'full':
-        print(f'Done setting up PyTorch plugin "{module_name}".')
-    elif verbosity == 'brief':
-        print('Done.')
-    _cached_plugins[module_name] = module
-    return module
diff --git a/spaces/ECCV2022/ECCV2022_papers/app.py b/spaces/ECCV2022/ECCV2022_papers/app.py
deleted file mode 100644
index c82a4c739f588f88539637b14e4843dd9009d7d5..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/ECCV2022_papers/app.py
+++ /dev/null
@@ -1,91 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import gradio as gr
-
-from paper_list import PaperList
-
-DESCRIPTION = '# ECCV 2022 Papers'
-NOTES = '''
-- [ECCV 2022](https://eccv2022.ecva.net/)
-- [List of accepted papers](https://docs.google.com/spreadsheets/d/1PvvnVAuG9TDov1rBQcLhH5noIM4INTfLhKwrk6ESDFU/edit?usp=sharing)
-'''
-FOOTER = '<img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=hysts.eccv2022_papers" />'
-
-
-def main():
-    paper_list = PaperList()
-
-    with gr.Blocks(css='style.css') as demo:
-        gr.Markdown(DESCRIPTION)
-
-        search_box = gr.Textbox(
-            label='Search Title',
-            placeholder=
-            'You can search for titles with regular expressions. e.g. (?<!sur)face',
-            max_lines=1)
-        case_sensitive = gr.Checkbox(label='Case Sensitive')
-        filter_names = gr.CheckboxGroup(choices=[
-            'arXiv',
-            'GitHub',
-            'HF Space',
-            'HF Model',
-            'HF Dataset',
-        ],
-                                        label='Filter')
-        session_names = [
-            'Oral',
-            'Poster',
-        ]
-        paper_sessions = gr.CheckboxGroup(choices=session_names,
-                                          value=session_names,
-                                          label='Session')
-        search_button = gr.Button('Search')
-
-        number_of_papers = gr.Textbox(label='Number of Papers Found')
-        table = gr.HTML(show_label=False)
-
-        gr.Markdown(NOTES)
-        gr.Markdown(FOOTER)
-
-        demo.load(fn=paper_list.render,
-                  inputs=[
-                      search_box,
-                      case_sensitive,
-                      filter_names,
-                      paper_sessions,
-                  ],
-                  outputs=[
-                      number_of_papers,
-                      table,
-                  ])
-        search_box.submit(fn=paper_list.render,
-                          inputs=[
-                              search_box,
-                              case_sensitive,
-                              filter_names,
-                              paper_sessions,
-                          ],
-                          outputs=[
-                              number_of_papers,
-                              table,
-                          ])
-
-        search_button.click(fn=paper_list.render,
-                            inputs=[
-                                search_box,
-                                case_sensitive,
-                                filter_names,
-                                paper_sessions,
-                            ],
-                            outputs=[
-                                number_of_papers,
-                                table,
-                            ])
-
-    demo.launch(enable_queue=True, share=False)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/EPFL-VILAB/MultiMAE/mask2former/data/__init__.py b/spaces/EPFL-VILAB/MultiMAE/mask2former/data/__init__.py
deleted file mode 100644
index 63ba265b1effc69f1eef16e57a04db8902ee347e..0000000000000000000000000000000000000000
--- a/spaces/EPFL-VILAB/MultiMAE/mask2former/data/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from . import datasets
diff --git a/spaces/ElainaFanBoy/MusicGen/tests/modules/test_transformer.py b/spaces/ElainaFanBoy/MusicGen/tests/modules/test_transformer.py
deleted file mode 100644
index d6092963d275ba101e016fd448fd0b456d918c27..0000000000000000000000000000000000000000
--- a/spaces/ElainaFanBoy/MusicGen/tests/modules/test_transformer.py
+++ /dev/null
@@ -1,253 +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 itertools import product
-
-import pytest
-import torch
-
-from audiocraft.modules.transformer import (
-    StreamingMultiheadAttention, StreamingTransformer, set_efficient_attention_backend)
-
-
-def test_transformer_causal_streaming():
-    torch.manual_seed(1234)
-
-    for context, custom in product([None, 10], [False, True]):
-        # Test that causality and receptive fields are properly handled.
-        # looking at the gradients
-        tr = StreamingTransformer(
-            16, 4, 1 if context else 2,
-            causal=True, past_context=context, custom=custom,
-            dropout=0.)
-        steps = 20
-        for k in [0, 10, 15, 19]:
-            x = torch.randn(4, steps, 16, requires_grad=True)
-            y = tr(x)
-            y[:, k].abs().sum().backward()
-            if k + 1 < steps:
-                assert torch.allclose(x.grad[:, k + 1:], torch.tensor(0.)), x.grad[:, k + 1:].norm()
-            assert not torch.allclose(x.grad[:, :k + 1], torch.tensor(0.)), x.grad[:, :k + 1].norm()
-            if context is not None and k > context:
-                limit = k - context - 1
-                assert torch.allclose(x.grad[:, :limit],
-                                      torch.tensor(0.)), x.grad[:, :limit].norm()
-
-        # Now check that streaming gives the same result at batch eval.
-        x = torch.randn(4, steps, 16)
-        y = tr(x)
-        ys = []
-        with tr.streaming():
-            for k in range(steps):
-                chunk = x[:, k:k + 1, :]
-                ys.append(tr(chunk))
-        y_stream = torch.cat(ys, dim=1)
-        delta = torch.norm(y_stream - y) / torch.norm(y)
-        assert delta < 1e-6, delta
-
-
-def test_transformer_vs_pytorch():
-    torch.manual_seed(1234)
-    # Check that in the non causal setting, we get the same result as
-    # PyTorch Transformer encoder.
-    for custom in [False, True]:
-        tr = StreamingTransformer(
-            16, 4, 2,
-            causal=False, custom=custom, dropout=0., positional_scale=0.)
-        layer = torch.nn.TransformerEncoderLayer(16, 4, dropout=0., batch_first=True)
-        tr_ref = torch.nn.TransformerEncoder(layer, 2)
-        tr.load_state_dict(tr_ref.state_dict())
-
-        x = torch.randn(4, 20, 16)
-        y = tr(x)
-        y2 = tr_ref(x)
-        delta = torch.norm(y2 - y) / torch.norm(y)
-        assert delta < 1e-6, delta
-
-
-def test_streaming_api():
-    tr = StreamingTransformer(16, 4, 2, causal=True, dropout=0.)
-    tr.eval()
-    steps = 12
-    x = torch.randn(1, steps, 16)
-
-    with torch.no_grad():
-        with tr.streaming():
-            _ = tr(x[:, :1])
-            state = {k: v.clone() for k, v in tr.get_streaming_state().items()}
-            y = tr(x[:, 1:2])
-            tr.set_streaming_state(state)
-            y2 = tr(x[:, 1:2])
-            assert torch.allclose(y, y2), (y - y2).norm()
-            assert tr.flush() is None
-
-
-def test_memory_efficient():
-    torch.manual_seed(1234)
-    for backend in ['torch', 'xformers']:
-        set_efficient_attention_backend(backend)
-
-        tr = StreamingTransformer(
-            16, 4, 2, custom=True, dropout=0., layer_scale=0.1)
-        tr_mem_efficient = StreamingTransformer(
-            16, 4, 2, dropout=0., memory_efficient=True, layer_scale=0.1)
-        tr_mem_efficient.load_state_dict(tr.state_dict())
-        tr.eval()
-        steps = 12
-        x = torch.randn(3, steps, 16)
-
-        with torch.no_grad():
-            y = tr(x)
-            y2 = tr_mem_efficient(x)
-            assert torch.allclose(y, y2), ((y - y2).norm(), backend)
-
-
-def test_attention_as_float32():
-    torch.manual_seed(1234)
-    cases = [
-        {'custom': True},
-        {'custom': False},
-    ]
-    for case in cases:
-        tr = StreamingTransformer(16, 4, 2, dropout=0., dtype=torch.bfloat16, **case)
-        tr_float32 = StreamingTransformer(
-            16, 4, 2, dropout=0., attention_as_float32=True, dtype=torch.bfloat16, **case)
-        if not case['custom']:
-            # we are not using autocast here because it doesn't really
-            # work as expected on CPU, so we have to manually cast the weights of the MHA.
-            for layer in tr_float32.layers:
-                layer.self_attn.mha.to(torch.float32)
-        tr_float32.load_state_dict(tr.state_dict())
-        steps = 12
-        x = torch.randn(3, steps, 16, dtype=torch.bfloat16)
-
-        with torch.no_grad():
-            y = tr(x)
-            y2 = tr_float32(x)
-            assert not torch.allclose(y, y2), (y - y2).norm()
-
-
-@torch.no_grad()
-def test_streaming_memory_efficient():
-    torch.manual_seed(1234)
-    for backend in ['torch', 'xformers']:
-        set_efficient_attention_backend(backend)
-        tr = StreamingTransformer(16, 4, 2, causal=True, dropout=0., custom=True)
-        tr_mem_efficient = StreamingTransformer(
-            16, 4, 2, dropout=0., memory_efficient=True, causal=True)
-        tr.load_state_dict(tr_mem_efficient.state_dict())
-        tr.eval()
-        tr_mem_efficient.eval()
-        steps = 12
-        x = torch.randn(3, steps, 16)
-
-        ref = tr(x)
-
-        with tr_mem_efficient.streaming():
-            outs = []
-            # frame_sizes = [2] + [1] * (steps - 2)
-            frame_sizes = [1] * steps
-
-            for frame_size in frame_sizes:
-                frame = x[:, :frame_size]
-                x = x[:, frame_size:]
-                outs.append(tr_mem_efficient(frame))
-
-        out = torch.cat(outs, dim=1)
-        delta = torch.norm(out - ref) / torch.norm(out)
-        assert delta < 1e-6, delta
-
-
-def test_cross_attention():
-    torch.manual_seed(1234)
-    for norm_first in [True, False]:
-        m = StreamingTransformer(
-            16, 4, 2, cross_attention=False, norm_first=norm_first, dropout=0., custom=True)
-        m_cross = StreamingTransformer(
-            16, 4, 2, cross_attention=True, norm_first=norm_first, dropout=0., custom=True)
-        m_cross.load_state_dict(m.state_dict(), strict=False)
-        x = torch.randn(2, 5, 16)
-        cross_x = torch.randn(2, 3, 16)
-        y_ref = m(x)
-        y_cross_zero = m_cross(x, cross_attention_src=0 * cross_x)
-        # With norm_first, the two should be exactly yhe same,
-        # but with norm_first=False, we get 2 normalization in a row
-        # and the epsilon value leads to a tiny change.
-        atol = 0. if norm_first else 1e-6
-        print((y_ref - y_cross_zero).norm() / y_ref.norm())
-        assert torch.allclose(y_ref, y_cross_zero, atol=atol)
-
-        # We now expect a difference even with a generous atol of 1e-2.
-        y_cross = m_cross(x, cross_attention_src=cross_x)
-        assert not torch.allclose(y_cross, y_cross_zero, atol=1e-2)
-
-        with pytest.raises(AssertionError):
-            _ = m_cross(x)
-            _ = m(x, cross_attention_src=cross_x)
-
-
-def test_cross_attention_compat():
-    torch.manual_seed(1234)
-    num_heads = 2
-    dim = num_heads * 64
-    with pytest.raises(AssertionError):
-        StreamingMultiheadAttention(dim, num_heads, causal=True, cross_attention=True)
-
-    cross_attn = StreamingMultiheadAttention(
-        dim, num_heads, dropout=0, cross_attention=True, custom=True)
-    ref_attn = torch.nn.MultiheadAttention(dim, num_heads, dropout=0, batch_first=True)
-
-    # We can load the regular attention state dict
-    # so we have compat when loading old checkpoints.
-    cross_attn.load_state_dict(ref_attn.state_dict())
-
-    queries = torch.randn(3, 7, dim)
-    keys = torch.randn(3, 9, dim)
-    values = torch.randn(3, 9, dim)
-
-    y = cross_attn(queries, keys, values)[0]
-    y_ref = ref_attn(queries, keys, values)[0]
-    assert torch.allclose(y, y_ref, atol=1e-7), (y - y_ref).norm() / y_ref.norm()
-
-    # Now let's check that streaming is working properly.
-    with cross_attn.streaming():
-        ys = []
-        for step in range(queries.shape[1]):
-            ys.append(cross_attn(queries[:, step: step + 1], keys, values)[0])
-    y_streaming = torch.cat(ys, dim=1)
-    assert torch.allclose(y_streaming, y, atol=1e-7)
-
-
-def test_repeat_kv():
-    torch.manual_seed(1234)
-    num_heads = 8
-    kv_repeat = 4
-    dim = num_heads * 64
-    with pytest.raises(AssertionError):
-        mha = StreamingMultiheadAttention(
-            dim, num_heads, causal=True, kv_repeat=kv_repeat, cross_attention=True)
-        mha = StreamingMultiheadAttention(
-            dim, num_heads, causal=True, kv_repeat=kv_repeat)
-    mha = StreamingMultiheadAttention(
-        dim, num_heads, causal=True, kv_repeat=kv_repeat, custom=True)
-    x = torch.randn(4, 18, dim)
-    y = mha(x, x, x)[0]
-    assert x.shape == y.shape
-
-
-def test_qk_layer_norm():
-    torch.manual_seed(1234)
-    tr = StreamingTransformer(
-        16, 4, 2, custom=True, dropout=0., qk_layer_norm=True, bias_attn=False)
-    steps = 12
-    x = torch.randn(3, steps, 16)
-    y = tr(x)
-
-    tr = StreamingTransformer(
-        16, 4, 2, custom=True, dropout=0., qk_layer_norm=True, cross_attention=True)
-    z = torch.randn(3, 21, 16)
-    y = tr(x, cross_attention_src=z)
-    assert y.shape == x.shape
diff --git a/spaces/EleutherAI/magma/magma/image_prefix.py b/spaces/EleutherAI/magma/magma/image_prefix.py
deleted file mode 100644
index 96c2c14fb707346850e0e5969588ff805ed3245d..0000000000000000000000000000000000000000
--- a/spaces/EleutherAI/magma/magma/image_prefix.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import torch
-import torch.nn as nn
-from torchtyping import TensorType
-from einops import rearrange
-from .image_encoders import get_image_encoder
-from .config import MultimodalConfig
-
-# ------------------------- Image prefix ----------------------------------
-
-# for models that are fixed to a specific sequence lengths (i.e clip models with no pooling), the sequence lengths are below
-ENCODER_SEQ_LENS = {
-    "clip_resnet": 49,
-    "clip_resnet_large": 144,
-}
-
-ENCODER_OUT_DIMS = {
-    "nfresnet50": 2048,
-    "clip": 512,
-    "clip_resnet": 2560,
-    "clip_resnet_large": 3072,
-}
-
-
-class ImagePrefix(nn.Module):
-
-    """
-    Takes in a batch of images and returns a batch of embeddings of the
-    same dimensions as the LM's word embeddings.
-
-    :param config: MultimodalConfig object
-    :param out_dim: output dimension of the embedding
-    :param device: device to run the model on
-    """
-
-    def __init__(
-        self,
-        config: MultimodalConfig,
-        out_dim: int = 2048,
-        device=None,
-    ):
-        super().__init__()
-        self.device = device or torch.device(
-            "cuda" if torch.cuda.is_available() else "cpu"
-        )
-        self.config = config
-        self.encoder_type = config.encoder_name
-
-        # get image encoder backbone
-        self.enc = get_image_encoder(
-            config.encoder_name,
-            pretrained=config.pretrained_img_encoder,
-        )
-        self.encoder_out_dim = ENCODER_OUT_DIMS[
-            self.encoder_type
-        ]  # out dim for image encoder
-
-        self.out_dim = out_dim  # out dim for lm
-
-        # set the out seq len to that specified in the config, or for some models, the hardcoded value
-        self.out_seq_len = (
-            config.image_seq_len
-            if config.encoder_name not in ENCODER_SEQ_LENS
-            else ENCODER_SEQ_LENS[config.encoder_name]
-        )
-
-        # get the output projection
-        proj_out_dim = (
-            (self.out_dim * self.out_seq_len)
-            if self.encoder_type not in ENCODER_SEQ_LENS
-            else self.out_dim
-        )
-        self.proj = nn.Linear(self.encoder_out_dim, proj_out_dim)
-        self.dropout = nn.Dropout(config.image_embed_dropout_prob)
-        self.use_layernorm = config.use_image_embed_layernorm
-        if self.use_layernorm:
-            self.ln = nn.LayerNorm(self.out_dim)
-
-    def forward(
-        self, x: TensorType["b", "c", "h", "w"]
-    ) -> TensorType["b", "seq", "out_dim"]:
-
-        # pass through image encoder
-        logits = self.enc(x)
-
-        # remove trailing dimensions of size 1 + pass through linear
-        if logits.ndim == 4:
-            logits = rearrange(logits, "b d 1 1 -> b d")
-        elif logits.ndim == 3:
-            assert self.encoder_type in ENCODER_SEQ_LENS
-        else:
-            assert logits.ndim == 2
-
-        logits = self.proj(logits)
-
-        # reshape to desired output shape
-        if (
-            self.encoder_type not in ENCODER_SEQ_LENS
-        ):  # don't need to reshape those with fixed seq lens / no pooling
-            logits = rearrange(
-                logits, "b (s d) -> b s d", d=self.out_dim, s=self.out_seq_len
-            )
-
-        # pass through dropout and layer norm
-        logits = self.dropout(logits)
-
-        if self.use_layernorm:
-            logits = self.ln(logits)
-
-        return logits
diff --git a/spaces/Ella2323/Positive-Reframing/test.sh b/spaces/Ella2323/Positive-Reframing/test.sh
deleted file mode 100644
index e7a3bab4086eef6b6c61c65914524e715d696bff..0000000000000000000000000000000000000000
--- a/spaces/Ella2323/Positive-Reframing/test.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-
-for n in 1 2 3 4 5 6 7 8 9 10
-do 
-    echo '------------------------------------------------------------'
-    echo $n
-    echo "test/$n.txt"
-    python test.py --input="test/$n.txt" | diff - test/$n.gold.txt
-done
\ No newline at end of file
diff --git a/spaces/EronSamez/RVC_HFmeu/infer/modules/vc/pipeline.py b/spaces/EronSamez/RVC_HFmeu/infer/modules/vc/pipeline.py
deleted file mode 100644
index 76e712c649b95e21f9bbe6416ae8b7050317b479..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/infer/modules/vc/pipeline.py
+++ /dev/null
@@ -1,655 +0,0 @@
-import os
-import sys
-import traceback
-import logging
-
-logger = logging.getLogger(__name__)
-
-from functools import lru_cache
-from time import time as ttime
-from torch import Tensor
-import faiss
-import librosa
-import numpy as np
-import parselmouth
-import pyworld
-import torch
-import torch.nn.functional as F
-import torchcrepe
-from scipy import signal
-from tqdm import tqdm
-
-import random
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import re
-from functools import partial
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-input_audio_path2wav = {}
-from LazyImport import lazyload
-torchcrepe = lazyload("torchcrepe")  # Fork Feature. Crepe algo for training and preprocess
-torch = lazyload("torch")
-from infer.lib.rmvpe import RMVPE
-
-@lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
-    audio = input_audio_path2wav[input_audio_path]
-    f0, t = pyworld.harvest(
-        audio,
-        fs=fs,
-        f0_ceil=f0max,
-        f0_floor=f0min,
-        frame_period=frame_period,
-    )
-    f0 = pyworld.stonemask(audio, f0, t, fs)
-    return f0
-
-
-def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
-    # print(data1.max(),data2.max())
-    rms1 = librosa.feature.rms(
-        y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
-    )  # 每半秒一个点
-    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
-    rms1 = torch.from_numpy(rms1)
-    rms1 = F.interpolate(
-        rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.from_numpy(rms2)
-    rms2 = F.interpolate(
-        rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
-    ).squeeze()
-    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
-    data2 *= (
-        torch.pow(rms1, torch.tensor(1 - rate))
-        * torch.pow(rms2, torch.tensor(rate - 1))
-    ).numpy()
-    return data2
-
-
-class Pipeline(object):
-    def __init__(self, tgt_sr, config):
-        self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
-            config.x_pad,
-            config.x_query,
-            config.x_center,
-            config.x_max,
-            config.is_half,
-        )
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * self.x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * self.x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * self.x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * self.x_center  # 查询切点位置
-        self.t_max = self.sr * self.x_max  # 免查询时长阈值
-        self.device = config.device
-        self.model_rmvpe = RMVPE("%s/rmvpe.pt" % os.environ["rmvpe_root"], is_half=self.is_half, device=self.device)
-        self.f0_method_dict = {
-            "pm": self.get_pm,
-            "harvest": self.get_harvest,
-            "dio": self.get_dio,
-            "rmvpe": self.get_rmvpe,
-            "rmvpe+": self.get_pitch_dependant_rmvpe,
-            "crepe": self.get_f0_official_crepe_computation,
-            "crepe-tiny": partial(self.get_f0_official_crepe_computation, model='model'),
-            "mangio-crepe": self.get_f0_crepe_computation,
-            "mangio-crepe-tiny": partial(self.get_f0_crepe_computation, model='model'),
-            
-        }
-        self.note_dict = [
-            65.41, 69.30, 73.42, 77.78, 82.41, 87.31,
-            92.50, 98.00, 103.83, 110.00, 116.54, 123.47,
-            130.81, 138.59, 146.83, 155.56, 164.81, 174.61,
-            185.00, 196.00, 207.65, 220.00, 233.08, 246.94,
-            261.63, 277.18, 293.66, 311.13, 329.63, 349.23,
-            369.99, 392.00, 415.30, 440.00, 466.16, 493.88,
-            523.25, 554.37, 587.33, 622.25, 659.25, 698.46,
-            739.99, 783.99, 830.61, 880.00, 932.33, 987.77,
-            1046.50, 1108.73, 1174.66, 1244.51, 1318.51, 1396.91,
-            1479.98, 1567.98, 1661.22, 1760.00, 1864.66, 1975.53,
-            2093.00, 2217.46, 2349.32, 2489.02, 2637.02, 2793.83,
-            2959.96, 3135.96, 3322.44, 3520.00, 3729.31, 3951.07
-        ]
-
-    # Fork Feature: Get the best torch device to use for f0 algorithms that require a torch device. Will return the type (torch.device)
-    def get_optimal_torch_device(self, index: int = 0) -> torch.device:
-        if torch.cuda.is_available():
-            return torch.device(
-                f"cuda:{index % torch.cuda.device_count()}"
-            )  # Very fast
-        elif torch.backends.mps.is_available():
-            return torch.device("mps")
-        return torch.device("cpu")
-
-    # Fork Feature: Compute f0 with the crepe method
-    def get_f0_crepe_computation(
-        self,
-        x,
-        f0_min,
-        f0_max,
-        p_len,
-        *args,  # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time.
-        **kwargs,  # Either use crepe-tiny "tiny" or crepe "full". Default is full
-    ):
-        x = x.astype(
-            np.float32
-        )  # fixes the F.conv2D exception. We needed to convert double to float.
-        x /= np.quantile(np.abs(x), 0.999)
-        torch_device = self.get_optimal_torch_device()
-        audio = torch.from_numpy(x).to(torch_device, copy=True)
-        audio = torch.unsqueeze(audio, dim=0)
-        if audio.ndim == 2 and audio.shape[0] > 1:
-            audio = torch.mean(audio, dim=0, keepdim=True).detach()
-        audio = audio.detach()
-        hop_length = kwargs.get('crepe_hop_length', 160)
-        model = kwargs.get('model', 'full') 
-        print("Initiating prediction with a crepe_hop_length of: " + str(hop_length))
-        pitch: Tensor = torchcrepe.predict(
-            audio,
-            self.sr,
-            hop_length,
-            f0_min,
-            f0_max,
-            model,
-            batch_size=hop_length * 2,
-            device=torch_device,
-            pad=True,
-        )
-        p_len = p_len or x.shape[0] // hop_length
-        # Resize the pitch for final f0
-        source = np.array(pitch.squeeze(0).cpu().float().numpy())
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * p_len, len(source)) / p_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        f0 = np.nan_to_num(target)
-        return f0  # Resized f0
-    
-    def get_f0_official_crepe_computation(
-        self,
-        x,
-        f0_min,
-        f0_max,
-        *args,
-        **kwargs
-    ):
-        # Pick a batch size that doesn't cause memory errors on your gpu
-        batch_size = 512
-        # Compute pitch using first gpu
-        audio = torch.tensor(np.copy(x))[None].float()
-        model = kwargs.get('model', 'full') 
-        f0, pd = torchcrepe.predict(
-            audio,
-            self.sr,
-            self.window,
-            f0_min,
-            f0_max,
-            model,
-            batch_size=batch_size,
-            device=self.device,
-            return_periodicity=True,
-        )
-        pd = torchcrepe.filter.median(pd, 3)
-        f0 = torchcrepe.filter.mean(f0, 3)
-        f0[pd < 0.1] = 0
-        f0 = f0[0].cpu().numpy()
-        return f0
-
-    # Fork Feature: Compute pYIN f0 method
-    def get_f0_pyin_computation(self, x, f0_min, f0_max):
-        y, sr = librosa.load("saudio/Sidney.wav", self.sr, mono=True)
-        f0, _, _ = librosa.pyin(y, sr=self.sr, fmin=f0_min, fmax=f0_max)
-        f0 = f0[1:]  # Get rid of extra first frame
-        return f0
-
-    def get_pm(self, x, p_len, *args, **kwargs):
-        f0 = parselmouth.Sound(x, self.sr).to_pitch_ac(
-            time_step=160 / 16000,
-            voicing_threshold=0.6,
-            pitch_floor=kwargs.get('f0_min'),
-            pitch_ceiling=kwargs.get('f0_max'),
-        ).selected_array["frequency"]
-        
-        return np.pad(
-            f0,
-            [[max(0, (p_len - len(f0) + 1) // 2), max(0, p_len - len(f0) - (p_len - len(f0) + 1) // 2)]],
-            mode="constant"
-        )
-
-    def get_harvest(self, x, *args, **kwargs):
-        f0_spectral = pyworld.harvest(
-            x.astype(np.double),
-            fs=self.sr,
-            f0_ceil=kwargs.get('f0_max'),
-            f0_floor=kwargs.get('f0_min'),
-            frame_period=1000 * kwargs.get('hop_length', 160) / self.sr,
-        )
-        return pyworld.stonemask(x.astype(np.double), *f0_spectral, self.sr)
-
-    def get_dio(self, x, *args, **kwargs):
-        f0_spectral = pyworld.dio(
-            x.astype(np.double),
-            fs=self.sr,
-            f0_ceil=kwargs.get('f0_max'),
-            f0_floor=kwargs.get('f0_min'),
-            frame_period=1000 * kwargs.get('hop_length', 160) / self.sr,
-        )
-        return pyworld.stonemask(x.astype(np.double), *f0_spectral, self.sr)
-
-
-    def get_rmvpe(self, x, *args, **kwargs):
-        if not hasattr(self, "model_rmvpe"):
-            from infer.lib.rmvpe import RMVPE
-            
-            logger.info(
-                "Loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"]
-            )
-            self.model_rmvpe = RMVPE(
-                "%s/rmvpe.pt" % os.environ["rmvpe_root"],
-                is_half=self.is_half,
-                device=self.device,
-            )
-        f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-            
-        return f0
-    
-
-    def get_pitch_dependant_rmvpe(self, x, f0_min=1, f0_max=40000, *args, **kwargs):
-        return self.model_rmvpe.infer_from_audio_with_pitch(x, thred=0.03, f0_min=f0_min, f0_max=f0_max)
-
-    def autotune_f0(self, f0):
-        autotuned_f0 = []
-        for freq in f0:
-            closest_notes = [x for x in self.note_dict if abs(x - freq) == min(abs(n - freq) for n in self.note_dict)]
-            autotuned_f0.append(random.choice(closest_notes))
-        return np.array(autotuned_f0, np.float64)
-
-    # Fork Feature: Acquire median hybrid f0 estimation calculation
-    def get_f0_hybrid_computation(
-        self,
-        methods_str,
-        input_audio_path,
-        x,
-        f0_min,
-        f0_max,
-        p_len,
-        filter_radius,
-        crepe_hop_length,
-        time_step
-    ):
-        # Get various f0 methods from input to use in the computation stack
-        params = {'x': x, 'p_len': p_len, 'f0_min': f0_min, 
-          'f0_max': f0_max, 'time_step': time_step, 'filter_radius': filter_radius, 
-          'crepe_hop_length': crepe_hop_length, 'model': "full"
-        }
-        methods_str = re.search('hybrid\[(.+)\]', methods_str)
-        if methods_str:  # Ensure a match was found
-            methods = [method.strip() for method in methods_str.group(1).split('+')]
-        f0_computation_stack = []
-
-        print(f"Calculating f0 pitch estimations for methods: {str(methods)}")
-        x = x.astype(np.float32)
-        x /= np.quantile(np.abs(x), 0.999)
-        # Get f0 calculations for all methods specified
-
-        for method in methods:
-            if method not in self.f0_method_dict:
-                print(f"Method {method} not found.")
-                continue
-            f0 = self.f0_method_dict[method](**params)
-            if method == 'harvest' and filter_radius > 2:
-                f0 = signal.medfilt(f0, 3)
-                f0 = f0[1:]  # Get rid of first frame.
-            f0_computation_stack.append(f0)
-
-        for fc in f0_computation_stack:
-            print(len(fc))
-
-        print(f"Calculating hybrid median f0 from the stack of: {str(methods)}")
-        f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0)
-        return f0_median_hybrid
-    
-    def get_f0(
-        self,
-        input_audio_path,
-        x,
-        p_len,
-        f0_up_key,
-        f0_method,
-        filter_radius,
-        crepe_hop_length,
-        f0_autotune,
-        inp_f0=None,
-        f0_min=50,
-        f0_max=1100,
-    ):
-        global input_audio_path2wav
-        time_step = self.window / self.sr * 1000
-        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)
-        params = {'x': x, 'p_len': p_len, 'f0_up_key': f0_up_key, 'f0_min': f0_min, 
-          'f0_max': f0_max, 'time_step': time_step, 'filter_radius': filter_radius, 
-          'crepe_hop_length': crepe_hop_length, 'model': "full"
-        }
-
-        if "hybrid" in f0_method:
-            # Perform hybrid median pitch estimation
-            input_audio_path2wav[input_audio_path] = x.astype(np.double)
-            f0 = self.get_f0_hybrid_computation(
-                f0_method,+
-                input_audio_path,
-                x,
-                f0_min,
-                f0_max,
-                p_len,
-                filter_radius,
-                crepe_hop_length,
-                time_step,
-            )
-        else:
-            f0 = self.f0_method_dict[f0_method](**params)
-
-        if "privateuseone" in str(self.device):  # clean ortruntime memory
-            del self.model_rmvpe.model
-            del self.model_rmvpe
-            logger.info("Cleaning ortruntime memory")
-
-        if f0_autotune:
-            f0 = self.autotune_f0(f0)
-
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
-                :shape
-            ]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 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
-        f0_coarse = np.rint(f0_mel).astype(np.int32)
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-        version,
-        protect,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9 if version == "v1" else 12,
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
-        if protect < 0.5 and pitch is not None and pitchf is not None:
-            feats0 = feats.clone()
-        if (
-            not isinstance(index, type(None))
-            and not isinstance(big_npy, type(None))
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-
-            # _, I = index.search(npy, 1)
-            # npy = big_npy[I.squeeze()]
-
-            score, ix = index.search(npy, k=8)
-            weight = np.square(1 / score)
-            weight /= weight.sum(axis=1, keepdims=True)
-            npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        if protect < 0.5 and pitch is not None and pitchf is not None:
-            feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
-                0, 2, 1
-            )
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch is not None and pitchf is not None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-
-        if protect < 0.5 and pitch is not None and pitchf is not None:
-            pitchff = pitchf.clone()
-            pitchff[pitchf > 0] = 1
-            pitchff[pitchf < 1] = protect
-            pitchff = pitchff.unsqueeze(-1)
-            feats = feats * pitchff + feats0 * (1 - pitchff)
-            feats = feats.to(feats0.dtype)
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            hasp = pitch is not None and pitchf is not None
-            arg = (feats, p_len, pitch, pitchf, sid) if hasp else (feats, p_len, sid)
-            audio1 = (net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy()
-            del hasp, arg
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-    def process_t(self, t, s, window, audio_pad, pitch, pitchf, times, index, big_npy, index_rate, version, protect, t_pad_tgt, if_f0, sid, model, net_g):
-        t = t // window * window
-        if if_f0 == 1:
-            return self.vc(
-                model,
-                net_g,
-                sid,
-                audio_pad[s : t + t_pad_tgt + window],
-                pitch[:, s // window : (t + t_pad_tgt) // window],
-                pitchf[:, s // window : (t + t_pad_tgt) // window],
-                times,
-                index,
-                big_npy,
-                index_rate,
-                version,
-                protect,
-            )[t_pad_tgt : -t_pad_tgt]
-        else:
-            return self.vc(
-                model,
-                net_g,
-                sid,
-                audio_pad[s : t + t_pad_tgt + window],
-                None,
-                None,
-                times,
-                index,
-                big_npy,
-                index_rate,
-                version,
-                protect,
-            )[t_pad_tgt : -t_pad_tgt]
-
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        input_audio_path,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        index_rate,
-        if_f0,
-        filter_radius,
-        tgt_sr,
-        resample_sr,
-        rms_mix_rate,
-        version,
-        protect,
-        crepe_hop_length, 
-        f0_autotune, 
-        f0_file=None, 
-        f0_min=50, 
-        f0_max=1100
-    ):
-        if (
-            file_index != ""
-            # and file_big_npy != ""
-            # and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index)
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                # big_npy = np.load(file_big_npy)
-                big_npy = index.reconstruct_n(0, index.ntotal)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name"):
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0:
-            pitch, pitchf = self.get_f0(
-                input_audio_path,
-                audio_pad,
-                p_len,
-                f0_up_key,
-                f0_method,
-                filter_radius, 
-                crepe_hop_length, 
-                f0_autotune,
-                inp_f0, 
-                f0_min, 
-                f0_max
-            )
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            if self.device == "mps" or "xpu" in self.device:
-                pitchf = pitchf.astype(np.float32)
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-
-        with tqdm(total=len(opt_ts), desc="Processing", unit="window") as pbar:
-            for i, t in enumerate(opt_ts):
-                t = t // self.window * self.window
-                start = s
-                end = t + self.t_pad2 + self.window
-                audio_slice = audio_pad[start:end]
-                pitch_slice = pitch[:, start // self.window:end // self.window] if if_f0 else None
-                pitchf_slice = pitchf[:, start // self.window:end // self.window] if if_f0 else None
-                audio_opt.append(self.vc(model, net_g, sid, audio_slice, pitch_slice, pitchf_slice, times, index, big_npy, index_rate, version, protect)[self.t_pad_tgt : -self.t_pad_tgt])
-                s = t
-                pbar.update(1)
-                pbar.refresh()
-
-        audio_slice = audio_pad[t:]
-        pitch_slice = pitch[:, t // self.window:] if if_f0 and t is not None else pitch
-        pitchf_slice = pitchf[:, t // self.window:] if if_f0 and t is not None else pitchf
-        audio_opt.append(self.vc(model, net_g, sid, audio_slice, pitch_slice, pitchf_slice, times, index, big_npy, index_rate, version, protect)[self.t_pad_tgt : -self.t_pad_tgt])
-        
-        audio_opt = np.concatenate(audio_opt)
-        if rms_mix_rate != 1:
-            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
-        if tgt_sr != resample_sr >= 16000:
-            audio_opt = librosa.resample(
-                audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
-            )
-        audio_max = np.abs(audio_opt).max() / 0.99
-        max_int16 = 32768
-        if audio_max > 1:
-            max_int16 /= audio_max
-        audio_opt = (audio_opt * max_int16).astype(np.int16)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
-        print("Returning completed audio...")
-        print("-------------------")
-        return audio_opt
diff --git a/spaces/EyeSeeThru/anything-v3.0/utils.py b/spaces/EyeSeeThru/anything-v3.0/utils.py
deleted file mode 100644
index ff1c065d186347ca51b47d010a697dbe1814695c..0000000000000000000000000000000000000000
--- a/spaces/EyeSeeThru/anything-v3.0/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/FrankZxShen/so-vits-svc-models-ba/vdecoder/hifigan/utils.py b/spaces/FrankZxShen/so-vits-svc-models-ba/vdecoder/hifigan/utils.py
deleted file mode 100644
index 9c93c996d3cc73c30d71c1fc47056e4230f35c0f..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-ba/vdecoder/hifigan/utils.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import glob
-import os
-import matplotlib
-import torch
-from torch.nn.utils import weight_norm
-# matplotlib.use("Agg")
-import matplotlib.pylab as plt
-
-
-def plot_spectrogram(spectrogram):
-    fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
-    plt.colorbar(im, ax=ax)
-
-    fig.canvas.draw()
-    plt.close()
-
-    return fig
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def apply_weight_norm(m):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        weight_norm(m)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-
-def load_checkpoint(filepath, device):
-    assert os.path.isfile(filepath)
-    print("Loading '{}'".format(filepath))
-    checkpoint_dict = torch.load(filepath, map_location=device)
-    print("Complete.")
-    return checkpoint_dict
-
-
-def save_checkpoint(filepath, obj):
-    print("Saving checkpoint to {}".format(filepath))
-    torch.save(obj, filepath)
-    print("Complete.")
-
-
-def del_old_checkpoints(cp_dir, prefix, n_models=2):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern) # get checkpoint paths
-    cp_list = sorted(cp_list)# sort by iter
-    if len(cp_list) > n_models: # if more than n_models models are found
-        for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
-            open(cp, 'w').close()# empty file contents
-            os.unlink(cp)# delete file (move to trash when using Colab)
-
-
-def scan_checkpoint(cp_dir, prefix):
-    pattern = os.path.join(cp_dir, prefix + '????????')
-    cp_list = glob.glob(pattern)
-    if len(cp_list) == 0:
-        return None
-    return sorted(cp_list)[-1]
-
diff --git a/spaces/FrankZxShen/so-vits-svc-models-ba/vencoder/ContentVec768L12.py b/spaces/FrankZxShen/so-vits-svc-models-ba/vencoder/ContentVec768L12.py
deleted file mode 100644
index 0d1591c8843b920d5685e822354e8e6adc9a9e19..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-ba/vencoder/ContentVec768L12.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from vencoder.encoder import SpeechEncoder
-import torch
-from fairseq import checkpoint_utils
-
-class ContentVec768L12(SpeechEncoder):
-    def __init__(self,vec_path = "pretrain/checkpoint_best_legacy_500.pt",device=None):
-        print("load model(s) from {}".format(vec_path))
-        self.hidden_dim = 768
-        models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-          [vec_path],
-          suffix="",
-        )
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        self.model = models[0].to(self.dev)
-        self.model.eval()
-
-    def encoder(self, wav):
-        feats = wav
-        if feats.dim() == 2:  # double channels
-          feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-        inputs = {
-          "source": feats.to(wav.device),
-          "padding_mask": padding_mask.to(wav.device),
-          "output_layer": 12,  # layer 12
-        }
-        with torch.no_grad():
-          logits = self.model.extract_features(**inputs)
-        return logits[0].transpose(1, 2)
\ No newline at end of file
diff --git a/spaces/GIZ/SDSN-demo/appStore/keyword_search.py b/spaces/GIZ/SDSN-demo/appStore/keyword_search.py
deleted file mode 100644
index 9162afc6e633b7bc25c4fb34d9cda34ae90efa6a..0000000000000000000000000000000000000000
--- a/spaces/GIZ/SDSN-demo/appStore/keyword_search.py
+++ /dev/null
@@ -1,176 +0,0 @@
-# set path
-import glob, os, sys; 
-sys.path.append('../utils')
-
-import streamlit as st
-import json
-import logging
-from utils.lexical_search import runLexicalPreprocessingPipeline, lexical_search
-from utils.semantic_search import runSemanticPreprocessingPipeline, semantic_keywordsearch
-from utils.checkconfig import getconfig
-from utils.streamlitcheck import checkbox_without_preselect
-
-# Declare all the necessary variables
-config = getconfig('paramconfig.cfg')
-split_by = config.get('semantic_search','SPLIT_BY')
-split_length = int(config.get('semantic_search','SPLIT_LENGTH'))
-split_overlap = int(config.get('semantic_search','SPLIT_OVERLAP'))
-split_respect_sentence_boundary = bool(int(config.get('semantic_search',
-                                    'RESPECT_SENTENCE_BOUNDARY')))
-remove_punc = bool(int(config.get('semantic_search','REMOVE_PUNC')))
-embedding_model = config.get('semantic_search','RETRIEVER')
-embedding_model_format = config.get('semantic_search','RETRIEVER_FORMAT')
-embedding_layer = int(config.get('semantic_search','RETRIEVER_EMB_LAYER'))
-embedding_dim  = int(config.get('semantic_search','EMBEDDING_DIM'))
-max_seq_len = int(config.get('semantic_search','MAX_SEQ_LENGTH')) 
-retriever_top_k = int(config.get('semantic_search','RETRIEVER_TOP_K'))
-reader_model = config.get('semantic_search','READER')
-reader_top_k = int(config.get('semantic_search','RETRIEVER_TOP_K'))
-top_k_per_candidate = int(config.get('semantic_search','READER_TOP_K_PER_CANDIDATE')) 
-lexical_split_by= config.get('lexical_search','SPLIT_BY')
-lexical_split_length=int(config.get('lexical_search','SPLIT_LENGTH'))
-lexical_split_overlap = int(config.get('lexical_search','SPLIT_OVERLAP'))
-lexical_remove_punc = bool(int(config.get('lexical_search','REMOVE_PUNC')))
-lexical_top_k=int(config.get('lexical_search','TOP_K'))
-
-def app():
-
-    with st.container():
-        st.markdown("<h1 style='text-align: center;  \
-                      color: black;'> Search</h1>", 
-                      unsafe_allow_html=True)
-        st.write(' ')
-        st.write(' ')
-
-    with st.expander("ℹ️ - About this app", expanded=False):
-
-        st.write(
-            """     
-            The *Search* app is an interface \
-            for doing contextual and keyword searches in \
-            policy documents.  \
-            """)
-        st.write("")
-        st.write(""" The application allows its user to perform a search\
-             based on two options: a lexical search([TFIDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf))\
-              and semantic search. [bi-encoder](https://www.sbert.net/examples/applications/retrieve_rerank/README.html)\
-             The lexical search only \
-            displays paragraphs in the document with exact matching results, \
-            the semantic search shows paragraphs with meaningful connections \
-            (e.g., synonyms) based on the search context. Both \
-            methods employ a probabilistic retrieval framework in its identification\
-            of relevant paragraphs. By defualt the search is performed using \
-            'Semantic Search', and to find 'Exact/Lexical Matches' please tick the \
-            checkbox provided which will by-pass semantic search. Furthermore,\
-            the application allows the user to search for pre-defined keywords \
-            from different thematic buckets present in sidebar.""")
-        st.write("")
-        st.write(""" The Exact Matches gives back top {} findings, and Semantic
-        search provides with top {} answers.""".format(lexical_top_k, retriever_top_k))
-        st.write("")
-        st.write("")
-        st.markdown("Some runtime metrics tested with cpu: Intel(R) Xeon(R) CPU @ 2.20GHz, memory: 13GB")
-        col1,col2,col3= st.columns([2,4,4])
-        with col1:
-            st.caption("OCR File processing")
-            # st.markdown('<div style="text-align: center;">50 sec</div>', unsafe_allow_html=True)
-            st.write("50 sec")
-           
-        with col2:
-            st.caption("Lexical Search on 200 paragraphs(~ 35 pages)")
-            # st.markdown('<div style="text-align: center;">12 sec</div>', unsafe_allow_html=True)
-            st.write("15 sec")
-           
-        with col3:
-            st.caption("Semantic search on 200 paragraphs(~ 35 pages)")
-            # st.markdown('<div style="text-align: center;">120 sec</div>', unsafe_allow_html=True)
-            st.write("120 sec(including emebedding creation)")
- 
-    with st.sidebar:
-        with open('docStore/sample/keywordexample.json','r') as json_file:
-            keywordexample = json.load(json_file)
-        
-        # genre = st.radio("Select Keyword Category", list(keywordexample.keys()))
-        st.caption("Select Keyword Category")
-        genre = checkbox_without_preselect(list(keywordexample.keys()))
-        if genre:
-            keywordList = keywordexample[genre]
-        else:
-            keywordList = None
-        
-        st.markdown("---")
-    
-    with st.container():
-        type_hinting = "Please enter here your question and we \
-                        will look for an answer in the document\
-                        OR enter the keyword you are looking \
-                        for and we will look for similar\
-                        context in the document.\
-                        You can also explore predefined sets of keywords from sidebar. "
-        if keywordList is not None:
-        #     queryList = st.text_input("You selected the {} category we \
-        #                 will look for these keywords in document".format(genre)
-        #                             value="{}".format(keywordList))
-            queryList = st.text_input(type_hinting,
-                                        value = "{}".format(keywordList))
-        else:
-             queryList = st.text_input(type_hinting,
-                                       placeholder="Enter keyword/query here")
-
-        searchtype = st.checkbox("Show only Exact Matches")
-        if st.button("Find them"):
-
-            if queryList == "":
-                st.info("🤔 No keyword provided, if you dont have any, \
-                                please try example sets from sidebar!")
-                logging.warning("Terminated as no keyword provided")
-            else:
-                if 'filepath' in st.session_state:
-                      
-                    if searchtype:
-                        all_documents = runLexicalPreprocessingPipeline(
-                                    file_name=st.session_state['filename'],
-                                    file_path=st.session_state['filepath'],
-                                    split_by=lexical_split_by,
-                                    split_length=lexical_split_length,
-                                    split_overlap=lexical_split_overlap,
-                                    remove_punc=lexical_remove_punc)
-                        logging.info("performing lexical search")
-                        with st.spinner("Performing Exact matching search \
-                                        (Lexical search) for you"):
-                            lexical_search(query=queryList,
-                        documents = all_documents['documents'],
-                                top_k = lexical_top_k )
-                    else:
-                        all_documents = runSemanticPreprocessingPipeline(
-                                            file_path= st.session_state['filepath'],
-                                            file_name  = st.session_state['filename'],
-                                            split_by=split_by,
-                                            split_length= split_length,
-                                            split_overlap=split_overlap,
-                                            remove_punc= remove_punc,
-                        split_respect_sentence_boundary=split_respect_sentence_boundary)
-                        if len(all_documents['documents']) > 100:
-                            warning_msg = ": This might take sometime, please sit back and relax."
-                        else:
-                            warning_msg = ""
-
-                        logging.info("starting semantic search")
-                        with st.spinner("Performing Similar/Contextual search{}".format(warning_msg)):
-                            semantic_keywordsearch(query = queryList, 
-                            documents = all_documents['documents'],
-                            embedding_model=embedding_model, 
-                            embedding_layer=embedding_layer,
-                            embedding_model_format=embedding_model_format,
-                            reader_model=reader_model,reader_top_k=reader_top_k,
-                            retriever_top_k=retriever_top_k, embedding_dim=embedding_dim,
-                            max_seq_len=max_seq_len,
-                            top_k_per_candidate = top_k_per_candidate)
-
-                else:
-                    st.info("🤔 No document found, please try to upload it at the sidebar!")
-                    logging.warning("Terminated as no document provided")
-        
-
-
-                    
diff --git a/spaces/GT4SD/PatentToolkit/tridentmodel/__init__.py b/spaces/GT4SD/PatentToolkit/tridentmodel/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/color_sorted_block_race.py b/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/color_sorted_block_race.py
deleted file mode 100644
index be99732b6d256d70d3c309b0f89df49b6c5e9cce..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/color_sorted_block_race.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import numpy as np
-import os
-import pybullet as p
-import random
-from cliport.tasks import primitives
-from cliport.tasks.grippers import Spatula
-from cliport.tasks.task import Task
-from cliport.utils import utils
-import numpy as np
-from cliport.tasks.task import Task
-from cliport.utils import utils
-
-class ColorSortedBlockRace(Task):
-    """Pick up blocks of two colors and place them in corresponding colored zones in a sequence."""
-
-    def __init__(self):
-        super().__init__()
-        self.max_steps = 20
-        self.lang_template = "place the blocks in the corresponding colored zones in sequence"
-        self.task_completed_desc = "done placing blocks in zones."
-        self.additional_reset()
-
-    def reset(self, env):
-        super().reset(env)
-
-        # Add zones.
-        zone_size = (0.12, 0.12, 0)
-        zone_urdf = 'zone/zone.urdf'
-        zone_colors = ['blue', 'red']
-        zone_poses = []
-        for color in zone_colors:
-            zone_pose = self.get_random_pose(env, zone_size)
-            env.add_object(zone_urdf, zone_pose, 'fixed', color=utils.COLORS[color])
-            zone_poses.append(zone_pose)
-
-        # Add blocks.
-        block_size = (0.04, 0.04, 0.04)
-        block_urdf = 'block/block.urdf'
-        block_colors = ['blue', 'red']
-        blocks = []
-        for color in block_colors:
-            for _ in range(3):
-                block_pose = self.get_random_pose(env, block_size)
-                block_id = env.add_object(block_urdf, block_pose, color=utils.COLORS[color])
-                blocks.append(block_id)
-
-        # Goal: each block is in the corresponding colored zone.
-        for i, block in enumerate(blocks):
-            self.add_goal(objs=[block], matches=np.ones((1, 1)), targ_poses=[zone_poses[i//3]], replace=False,
-                          rotations=True, metric='pose', params=None, step_max_reward=1/len(blocks),
-                          language_goal=self.lang_template)
\ No newline at end of file
diff --git a/spaces/Golyass/Recomender-System-Hybrid-Method/README.md b/spaces/Golyass/Recomender-System-Hybrid-Method/README.md
deleted file mode 100644
index deef98249c8a6b79f8f72bd9b0ce38b52f3fc2bd..0000000000000000000000000000000000000000
--- a/spaces/Golyass/Recomender-System-Hybrid-Method/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Recomender System Hybrid Method
-emoji: 💻
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/visualization/image.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/visualization/image.py
deleted file mode 100644
index 5a148384d7a77c4d9849c54570e85740eaff8235..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/visualization/image.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import matplotlib.pyplot as plt
-import mmcv
-import numpy as np
-import pycocotools.mask as mask_util
-from matplotlib.collections import PatchCollection
-from matplotlib.patches import Polygon
-
-from ..utils import mask2ndarray
-
-EPS = 1e-2
-
-
-def color_val_matplotlib(color):
-    """Convert various input in BGR order to normalized RGB matplotlib color
-    tuples,
-
-    Args:
-        color (:obj:`Color`/str/tuple/int/ndarray): Color inputs
-
-    Returns:
-        tuple[float]: A tuple of 3 normalized floats indicating RGB channels.
-    """
-    color = mmcv.color_val(color)
-    color = [color / 255 for color in color[::-1]]
-    return tuple(color)
-
-
-def imshow_det_bboxes(img,
-                      bboxes,
-                      labels,
-                      segms=None,
-                      class_names=None,
-                      score_thr=0,
-                      bbox_color='green',
-                      text_color='green',
-                      mask_color=None,
-                      thickness=2,
-                      font_size=13,
-                      win_name='',
-                      show=True,
-                      wait_time=0,
-                      out_file=None):
-    """Draw bboxes and class labels (with scores) on an image.
-
-    Args:
-        img (str or ndarray): The image to be displayed.
-        bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or
-            (n, 5).
-        labels (ndarray): Labels of bboxes.
-        segms (ndarray or None): Masks, shaped (n,h,w) or None
-        class_names (list[str]): Names of each classes.
-        score_thr (float): Minimum score of bboxes to be shown.  Default: 0
-        bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
-           The tuple of color should be in BGR order. Default: 'green'
-        text_color (str or tuple(int) or :obj:`Color`):Color of texts.
-           The tuple of color should be in BGR order. Default: 'green'
-        mask_color (str or tuple(int) or :obj:`Color`, optional):
-           Color of masks. The tuple of color should be in BGR order.
-           Default: None
-        thickness (int): Thickness of lines. Default: 2
-        font_size (int): Font size of texts. Default: 13
-        show (bool): Whether to show the image. Default: True
-        win_name (str): The window name. Default: ''
-        wait_time (float): Value of waitKey param. Default: 0.
-        out_file (str, optional): The filename to write the image.
-            Default: None
-
-    Returns:
-        ndarray: The image with bboxes drawn on it.
-    """
-    assert bboxes.ndim == 2, \
-        f' bboxes ndim should be 2, but its ndim is {bboxes.ndim}.'
-    assert labels.ndim == 1, \
-        f' labels ndim should be 1, but its ndim is {labels.ndim}.'
-    assert bboxes.shape[0] == labels.shape[0], \
-        'bboxes.shape[0] and labels.shape[0] should have the same length.'
-    assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5, \
-        f' bboxes.shape[1] should be 4 or 5, but its {bboxes.shape[1]}.'
-    img = mmcv.imread(img).astype(np.uint8)
-
-    if score_thr > 0:
-        assert bboxes.shape[1] == 5
-        scores = bboxes[:, -1]
-        inds = scores > score_thr
-        bboxes = bboxes[inds, :]
-        labels = labels[inds]
-        if segms is not None:
-            segms = segms[inds, ...]
-
-    mask_colors = []
-    if labels.shape[0] > 0:
-        if mask_color is None:
-            # random color
-            np.random.seed(42)
-            mask_colors = [
-                np.random.randint(0, 256, (1, 3), dtype=np.uint8)
-                for _ in range(max(labels) + 1)
-            ]
-        else:
-            # specify  color
-            mask_colors = [
-                np.array(mmcv.color_val(mask_color)[::-1], dtype=np.uint8)
-            ] * (
-                max(labels) + 1)
-
-    bbox_color = color_val_matplotlib(bbox_color)
-    text_color = color_val_matplotlib(text_color)
-
-    img = mmcv.bgr2rgb(img)
-    width, height = img.shape[1], img.shape[0]
-    img = np.ascontiguousarray(img)
-
-    fig = plt.figure(win_name, frameon=False)
-    plt.title(win_name)
-    canvas = fig.canvas
-    dpi = fig.get_dpi()
-    # add a small EPS to avoid precision lost due to matplotlib's truncation
-    # (https://github.com/matplotlib/matplotlib/issues/15363)
-    fig.set_size_inches((width + EPS) / dpi, (height + EPS) / dpi)
-
-    # remove white edges by set subplot margin
-    plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
-    ax = plt.gca()
-    ax.axis('off')
-
-    polygons = []
-    color = []
-    for i, (bbox, label) in enumerate(zip(bboxes, labels)):
-        bbox_int = bbox.astype(np.int32)
-        poly = [[bbox_int[0], bbox_int[1]], [bbox_int[0], bbox_int[3]],
-                [bbox_int[2], bbox_int[3]], [bbox_int[2], bbox_int[1]]]
-        np_poly = np.array(poly).reshape((4, 2))
-        polygons.append(Polygon(np_poly))
-        color.append(bbox_color)
-        label_text = class_names[
-            label] if class_names is not None else f'class {label}'
-        if len(bbox) > 4:
-            label_text += f'|{bbox[-1]:.02f}'
-        ax.text(
-            bbox_int[0],
-            bbox_int[1],
-            f'{label_text}',
-            bbox={
-                'facecolor': 'black',
-                'alpha': 0.8,
-                'pad': 0.7,
-                'edgecolor': 'none'
-            },
-            color=text_color,
-            fontsize=font_size,
-            verticalalignment='top',
-            horizontalalignment='left')
-        if segms is not None:
-            color_mask = mask_colors[labels[i]]
-            mask = segms[i].astype(bool)
-            img[mask] = img[mask] * 0.5 + color_mask * 0.5
-
-    plt.imshow(img)
-
-    p = PatchCollection(
-        polygons, facecolor='none', edgecolors=color, linewidths=thickness)
-    ax.add_collection(p)
-
-    stream, _ = canvas.print_to_buffer()
-    buffer = np.frombuffer(stream, dtype='uint8')
-    img_rgba = buffer.reshape(height, width, 4)
-    rgb, alpha = np.split(img_rgba, [3], axis=2)
-    img = rgb.astype('uint8')
-    img = mmcv.rgb2bgr(img)
-
-    if show:
-        # We do not use cv2 for display because in some cases, opencv will
-        # conflict with Qt, it will output a warning: Current thread
-        # is not the object's thread. You can refer to
-        # https://github.com/opencv/opencv-python/issues/46 for details
-        if wait_time == 0:
-            plt.show()
-        else:
-            plt.show(block=False)
-            plt.pause(wait_time)
-    if out_file is not None:
-        mmcv.imwrite(img, out_file)
-
-    plt.close()
-
-    return img
-
-
-def imshow_gt_det_bboxes(img,
-                         annotation,
-                         result,
-                         class_names=None,
-                         score_thr=0,
-                         gt_bbox_color=(255, 102, 61),
-                         gt_text_color=(255, 102, 61),
-                         gt_mask_color=(255, 102, 61),
-                         det_bbox_color=(72, 101, 241),
-                         det_text_color=(72, 101, 241),
-                         det_mask_color=(72, 101, 241),
-                         thickness=2,
-                         font_size=13,
-                         win_name='',
-                         show=True,
-                         wait_time=0,
-                         out_file=None):
-    """General visualization GT and result function.
-
-    Args:
-      img (str or ndarray): The image to be displayed.)
-      annotation (dict): Ground truth annotations where contain keys of
-          'gt_bboxes' and 'gt_labels' or 'gt_masks'
-      result (tuple[list] or list): The detection result, can be either
-          (bbox, segm) or just bbox.
-      class_names (list[str]): Names of each classes.
-      score_thr (float): Minimum score of bboxes to be shown.  Default: 0
-      gt_bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
-           The tuple of color should be in BGR order. Default: (255, 102, 61)
-      gt_text_color (str or tuple(int) or :obj:`Color`):Color of texts.
-           The tuple of color should be in BGR order. Default: (255, 102, 61)
-      gt_mask_color (str or tuple(int) or :obj:`Color`, optional):
-           Color of masks. The tuple of color should be in BGR order.
-           Default: (255, 102, 61)
-      det_bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
-           The tuple of color should be in BGR order. Default: (72, 101, 241)
-      det_text_color (str or tuple(int) or :obj:`Color`):Color of texts.
-           The tuple of color should be in BGR order. Default: (72, 101, 241)
-      det_mask_color (str or tuple(int) or :obj:`Color`, optional):
-           Color of masks. The tuple of color should be in BGR order.
-           Default: (72, 101, 241)
-      thickness (int): Thickness of lines. Default: 2
-      font_size (int): Font size of texts. Default: 13
-      win_name (str): The window name. Default: ''
-      show (bool): Whether to show the image. Default: True
-      wait_time (float): Value of waitKey param. Default: 0.
-      out_file (str, optional): The filename to write the image.
-         Default: None
-
-    Returns:
-        ndarray: The image with bboxes or masks drawn on it.
-    """
-    assert 'gt_bboxes' in annotation
-    assert 'gt_labels' in annotation
-    assert isinstance(
-        result,
-        (tuple, list)), f'Expected tuple or list, but get {type(result)}'
-
-    gt_masks = annotation.get('gt_masks', None)
-    if gt_masks is not None:
-        gt_masks = mask2ndarray(gt_masks)
-
-    img = mmcv.imread(img)
-
-    img = imshow_det_bboxes(
-        img,
-        annotation['gt_bboxes'],
-        annotation['gt_labels'],
-        gt_masks,
-        class_names=class_names,
-        bbox_color=gt_bbox_color,
-        text_color=gt_text_color,
-        mask_color=gt_mask_color,
-        thickness=thickness,
-        font_size=font_size,
-        win_name=win_name,
-        show=False)
-
-    if isinstance(result, tuple):
-        bbox_result, segm_result = result
-        if isinstance(segm_result, tuple):
-            segm_result = segm_result[0]  # ms rcnn
-    else:
-        bbox_result, segm_result = result, None
-
-    bboxes = np.vstack(bbox_result)
-    labels = [
-        np.full(bbox.shape[0], i, dtype=np.int32)
-        for i, bbox in enumerate(bbox_result)
-    ]
-    labels = np.concatenate(labels)
-
-    segms = None
-    if segm_result is not None and len(labels) > 0:  # non empty
-        segms = mmcv.concat_list(segm_result)
-        segms = mask_util.decode(segms)
-        segms = segms.transpose(2, 0, 1)
-
-    img = imshow_det_bboxes(
-        img,
-        bboxes,
-        labels,
-        segms=segms,
-        class_names=class_names,
-        score_thr=score_thr,
-        bbox_color=det_bbox_color,
-        text_color=det_text_color,
-        mask_color=det_mask_color,
-        thickness=thickness,
-        font_size=font_size,
-        win_name=win_name,
-        show=show,
-        wait_time=wait_time,
-        out_file=out_file)
-    return img
diff --git a/spaces/GuXiaoBei/wechat-chatbot/app.py b/spaces/GuXiaoBei/wechat-chatbot/app.py
deleted file mode 100644
index 59f0f0c5f48cd69b6b08d7fd0ea65dca9f497f2f..0000000000000000000000000000000000000000
--- a/spaces/GuXiaoBei/wechat-chatbot/app.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# encoding:utf-8
-
-import config
-import gradio as gr
-from channel import channel_factory
-from common.log import logger
-from io import BytesIO
-from PIL import Image
-from concurrent.futures import ThreadPoolExecutor
-thread_pool = ThreadPoolExecutor(max_workers=8)
-
-def getImage(bytes):
-    bytes_stream = BytesIO(bytes)
-    image = Image.open(bytes_stream)
-    return image
-
-def getLoginUrl():
-    # load config
-        config.load_config()
-        
-        # create channel
-        bot = channel_factory.create_channel("wx")
-        thread_pool.submit(bot.startup)
-
-        while (True):
-            if bot.getQrCode():
-                return getImage(bot.getQrCode())
-
-if __name__ == '__main__':
-    try:
-        
-        with gr.Blocks() as demo:
-            with gr.Row():
-                with gr.Column():
-                    btn = gr.Button(value="生成二维码")
-                with gr.Column():
-                    outputs=[gr.Pil()]
-                btn.click(getLoginUrl, outputs=outputs)
-
-        demo.launch()
-
-
-    except Exception as e:
-        logger.error("App startup failed!")
-        logger.exception(e)
diff --git a/spaces/HESOAYM/ElviraMulti/modules/config.py b/spaces/HESOAYM/ElviraMulti/modules/config.py
deleted file mode 100644
index 4e816ddd6cf4499f21cbbd2aee3ae0a6eeb7c5af..0000000000000000000000000000000000000000
--- a/spaces/HESOAYM/ElviraMulti/modules/config.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from collections import defaultdict
-from contextlib import contextmanager
-import os
-import logging
-import sys
-import commentjson as json
-
-from . import shared
-from . import presets
-
-
-__all__ = [
-    "my_api_key",
-    "authflag",
-    "auth_list",
-    "dockerflag",
-    "retrieve_proxy",
-    "log_level",
-    "advance_docs",
-    "update_doc_config",
-    "multi_api_key",
-    "server_name",
-    "server_port",
-    "share",
-]
-
-# 添加一个统一的config文件，避免文件过多造成的疑惑（优先级最低）
-# 同时，也可以为后续支持自定义功能提供config的帮助
-if os.path.exists("config.json"):
-    with open("config.json", "r", encoding='utf-8') as f:
-        config = json.load(f)
-else:
-    config = {}
-
-language = config.get("language", "auto") # 在这里输入你的 API 密钥
-language = os.environ.get("LANGUAGE", language)
-
-
-if os.path.exists("api_key.txt"):
-    logging.info("检测到api_key.txt文件，正在进行迁移...")
-    with open("api_key.txt", "r") as f:
-        config["openai_api_key"] = f.read().strip()
-    os.rename("api_key.txt", "api_key(deprecated).txt")
-    with open("config.json", "w", encoding='utf-8') as f:
-        json.dump(config, f, indent=4)
-
-if os.path.exists("auth.json"):
-    logging.info("检测到auth.json文件，正在进行迁移...")
-    auth_list = []
-    with open("auth.json", "r", encoding='utf-8') as f:
-            auth = json.load(f)
-            for _ in auth:
-                if auth[_]["username"] and auth[_]["password"]:
-                    auth_list.append((auth[_]["username"], auth[_]["password"]))
-                else:
-                    logging.error("请检查auth.json文件中的用户名和密码！")
-                    sys.exit(1)
-    config["users"] = auth_list
-    os.rename("auth.json", "auth(deprecated).json")
-    with open("config.json", "w", encoding='utf-8') as f:
-        json.dump(config, f, indent=4)
-
-## 处理docker if we are running in Docker
-dockerflag = config.get("dockerflag", False)
-if os.environ.get("dockerrun") == "yes":
-    dockerflag = True
-
-## 处理 api-key 以及 允许的用户列表
-my_api_key = config.get("openai_api_key", "") # 在这里输入你的 API 密钥
-my_api_key = os.environ.get("OPENAI_API_KEY", my_api_key)
-
-## 多账户机制
-multi_api_key = config.get("multi_api_key", False) # 是否开启多账户机制
-if multi_api_key:
-    api_key_list = config.get("api_key_list", [])
-    if len(api_key_list) == 0:
-        logging.error("多账号模式已开启，但api_key_list为空，请检查config.json")
-        sys.exit(1)
-    shared.state.set_api_key_queue(api_key_list)
-
-auth_list = config.get("users", []) # 实际上是使用者的列表
-authflag = len(auth_list) > 0  # 是否开启认证的状态值，改为判断auth_list长度
-
-# 处理自定义的api_host，优先读环境变量的配置，如果存在则自动装配
-api_host = os.environ.get("api_host", config.get("api_host", ""))
-if api_host:
-    shared.state.set_api_host(api_host)
-
-@contextmanager
-def retrieve_openai_api(api_key = None):
-    old_api_key = os.environ.get("OPENAI_API_KEY", "")
-    if api_key is None:
-        os.environ["OPENAI_API_KEY"] = my_api_key
-        yield my_api_key
-    else:
-        os.environ["OPENAI_API_KEY"] = api_key
-        yield api_key
-    os.environ["OPENAI_API_KEY"] = old_api_key
-
-## 处理log
-log_level = config.get("log_level", "INFO")
-logging.basicConfig(
-    level=log_level,
-    format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
-)
-
-## 处理代理：
-http_proxy = config.get("http_proxy", "")
-https_proxy = config.get("https_proxy", "")
-http_proxy = os.environ.get("HTTP_PROXY", http_proxy)
-https_proxy = os.environ.get("HTTPS_PROXY", https_proxy)
-
-# 重置系统变量，在不需要设置的时候不设置环境变量，以免引起全局代理报错
-os.environ["HTTP_PROXY"] = ""
-os.environ["HTTPS_PROXY"] = ""
-
-local_embedding = config.get("local_embedding", False) # 是否使用本地embedding
-
-@contextmanager
-def retrieve_proxy(proxy=None):
-    """
-    1, 如果proxy = NONE，设置环境变量，并返回最新设置的代理
-    2，如果proxy ！= NONE，更新当前的代理配置，但是不更新环境变量
-    """
-    global http_proxy, https_proxy
-    if proxy is not None:
-        http_proxy = proxy
-        https_proxy = proxy
-        yield http_proxy, https_proxy
-    else:
-        old_var = os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"]
-        os.environ["HTTP_PROXY"] = http_proxy
-        os.environ["HTTPS_PROXY"] = https_proxy
-        yield http_proxy, https_proxy # return new proxy
-
-        # return old proxy
-        os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"] = old_var
-
-
-## 处理advance docs
-advance_docs = defaultdict(lambda: defaultdict(dict))
-advance_docs.update(config.get("advance_docs", {}))
-def update_doc_config(two_column_pdf):
-    global advance_docs
-    advance_docs["pdf"]["two_column"] = two_column_pdf
-
-    logging.info(f"更新后的文件参数为：{advance_docs}")
-
-## 处理gradio.launch参数
-server_name = config.get("server_name", None)
-server_port = config.get("server_port", None)
-if server_name is None:
-    if dockerflag:
-        server_name = "0.0.0.0"
-    else:
-        server_name = "127.0.0.1"
-if server_port is None:
-    if dockerflag:
-        server_port = 7860
-
-assert server_port is None or type(server_port) == int, "要求port设置为int类型"
-
-# 设置默认model
-default_model = config.get("default_model", "")
-try:
-    presets.DEFAULT_MODEL = presets.MODELS.index(default_model)
-except ValueError:
-    pass
-
-share = config.get("share", False)
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/__init__.py b/spaces/HaloMaster/chinesesummary/fengshen/__init__.py
deleted file mode 100644
index b5829a3ac9e634d44d408d2ff6d22880e1c00805..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA 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.
-
-from .models.longformer import LongformerConfig, LongformerModel
-from .models.roformer import RoFormerConfig, RoFormerModel
-from .models.megatron_t5 import T5Config, T5EncoderModel
-from .models.ubert import UbertPiplines, UbertModel
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/laser/laser_src/laser_lstm.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/laser/laser_src/laser_lstm.py
deleted file mode 100644
index 10df90e002d5a7dd74a571dbc3b328c130c57a0a..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/laser/laser_src/laser_lstm.py
+++ /dev/null
@@ -1,585 +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
-
-from fairseq import options, utils
-
-from fairseq.models import (
-    FairseqEncoder,
-    FairseqIncrementalDecoder,
-    FairseqEncoderDecoderModel,
-    register_model,
-    register_model_architecture,
-)
-
-
-@register_model("laser_lstm")
-class LSTMModel(FairseqEncoderDecoderModel):
-    def __init__(self, encoder, decoder):
-        super().__init__(encoder, decoder)
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths,
-        prev_output_tokens=None,
-        tgt_tokens=None,
-        tgt_lengths=None,
-        target_language_id=None,
-        dataset_name="",
-    ):
-        assert target_language_id is not None
-
-        src_encoder_out = self.encoder(src_tokens, src_lengths, dataset_name)
-        return self.decoder(
-            prev_output_tokens, src_encoder_out, lang_id=target_language_id
-        )
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        parser.add_argument(
-            "--dropout",
-            default=0.1,
-            type=float,
-            metavar="D",
-            help="dropout probability",
-        )
-        parser.add_argument(
-            "--encoder-embed-dim",
-            type=int,
-            metavar="N",
-            help="encoder embedding dimension",
-        )
-        parser.add_argument(
-            "--encoder-embed-path",
-            default=None,
-            type=str,
-            metavar="STR",
-            help="path to pre-trained encoder embedding",
-        )
-        parser.add_argument(
-            "--encoder-hidden-size", type=int, metavar="N", help="encoder hidden size"
-        )
-        parser.add_argument(
-            "--encoder-layers", type=int, metavar="N", help="number of encoder layers"
-        )
-        parser.add_argument(
-            "--encoder-bidirectional",
-            action="store_true",
-            help="make all layers of encoder bidirectional",
-        )
-        parser.add_argument(
-            "--decoder-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder embedding dimension",
-        )
-        parser.add_argument(
-            "--decoder-embed-path",
-            default=None,
-            type=str,
-            metavar="STR",
-            help="path to pre-trained decoder embedding",
-        )
-        parser.add_argument(
-            "--decoder-hidden-size", type=int, metavar="N", help="decoder hidden size"
-        )
-        parser.add_argument(
-            "--decoder-layers", type=int, metavar="N", help="number of decoder layers"
-        )
-        parser.add_argument(
-            "--decoder-out-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder output embedding dimension",
-        )
-        parser.add_argument(
-            "--decoder-zero-init",
-            type=str,
-            metavar="BOOL",
-            help="initialize the decoder hidden/cell state to zero",
-        )
-        parser.add_argument(
-            "--decoder-lang-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder language embedding dimension",
-        )
-        parser.add_argument(
-            "--fixed-embeddings",
-            action="store_true",
-            help="keep embeddings fixed (ENCODER ONLY)",
-        )  # TODO Also apply to decoder embeddings?
-
-        # Granular dropout settings (if not specified these default to --dropout)
-        parser.add_argument(
-            "--encoder-dropout-in",
-            type=float,
-            metavar="D",
-            help="dropout probability for encoder input embedding",
-        )
-        parser.add_argument(
-            "--encoder-dropout-out",
-            type=float,
-            metavar="D",
-            help="dropout probability for encoder output",
-        )
-        parser.add_argument(
-            "--decoder-dropout-in",
-            type=float,
-            metavar="D",
-            help="dropout probability for decoder input embedding",
-        )
-        parser.add_argument(
-            "--decoder-dropout-out",
-            type=float,
-            metavar="D",
-            help="dropout probability for decoder output",
-        )
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-        # make sure that all args are properly defaulted (in case there are any new ones)
-        base_architecture(args)
-
-        def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
-            num_embeddings = len(dictionary)
-            padding_idx = dictionary.pad()
-            embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
-            embed_dict = utils.parse_embedding(embed_path)
-            utils.print_embed_overlap(embed_dict, dictionary)
-            return utils.load_embedding(embed_dict, dictionary, embed_tokens)
-
-        pretrained_encoder_embed = None
-        if args.encoder_embed_path:
-            pretrained_encoder_embed = load_pretrained_embedding_from_file(
-                args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim
-            )
-        pretrained_decoder_embed = None
-        if args.decoder_embed_path:
-            pretrained_decoder_embed = load_pretrained_embedding_from_file(
-                args.decoder_embed_path, task.target_dictionary, args.decoder_embed_dim
-            )
-
-        num_langs = task.num_tasks if hasattr(task, "num_tasks") else 0
-
-        encoder = LSTMEncoder(
-            dictionary=task.source_dictionary,
-            embed_dim=args.encoder_embed_dim,
-            hidden_size=args.encoder_hidden_size,
-            num_layers=args.encoder_layers,
-            dropout_in=args.encoder_dropout_in,
-            dropout_out=args.encoder_dropout_out,
-            bidirectional=args.encoder_bidirectional,
-            pretrained_embed=pretrained_encoder_embed,
-            fixed_embeddings=args.fixed_embeddings,
-        )
-        decoder = LSTMDecoder(
-            dictionary=task.target_dictionary,
-            embed_dim=args.decoder_embed_dim,
-            hidden_size=args.decoder_hidden_size,
-            out_embed_dim=args.decoder_out_embed_dim,
-            num_layers=args.decoder_layers,
-            dropout_in=args.decoder_dropout_in,
-            dropout_out=args.decoder_dropout_out,
-            zero_init=options.eval_bool(args.decoder_zero_init),
-            encoder_embed_dim=args.encoder_embed_dim,
-            encoder_output_units=encoder.output_units,
-            pretrained_embed=pretrained_decoder_embed,
-            num_langs=num_langs,
-            lang_embed_dim=args.decoder_lang_embed_dim,
-        )
-        return cls(encoder, decoder)
-
-
-class LSTMEncoder(FairseqEncoder):
-    """LSTM encoder."""
-
-    def __init__(
-        self,
-        dictionary,
-        embed_dim=512,
-        hidden_size=512,
-        num_layers=1,
-        dropout_in=0.1,
-        dropout_out=0.1,
-        bidirectional=False,
-        left_pad=True,
-        pretrained_embed=None,
-        padding_value=0.0,
-        fixed_embeddings=False,
-    ):
-        super().__init__(dictionary)
-        self.num_layers = num_layers
-        self.dropout_in = dropout_in
-        self.dropout_out = dropout_out
-        self.bidirectional = bidirectional
-        self.hidden_size = hidden_size
-
-        num_embeddings = len(dictionary)
-        self.padding_idx = dictionary.pad()
-        if pretrained_embed is None:
-            self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
-        else:
-            self.embed_tokens = pretrained_embed
-        if fixed_embeddings:
-            self.embed_tokens.weight.requires_grad = False
-
-        self.lstm = LSTM(
-            input_size=embed_dim,
-            hidden_size=hidden_size,
-            num_layers=num_layers,
-            dropout=self.dropout_out if num_layers > 1 else 0.0,
-            bidirectional=bidirectional,
-        )
-        self.left_pad = left_pad
-        self.padding_value = padding_value
-
-        self.output_units = hidden_size
-        if bidirectional:
-            self.output_units *= 2
-
-    def forward(self, src_tokens, src_lengths, dataset_name):
-        if self.left_pad:
-            # convert left-padding to right-padding
-            src_tokens = utils.convert_padding_direction(
-                src_tokens,
-                self.padding_idx,
-                left_to_right=True,
-            )
-
-        bsz, seqlen = src_tokens.size()
-
-        # embed tokens
-        x = self.embed_tokens(src_tokens)
-        x = F.dropout(x, p=self.dropout_in, training=self.training)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        # pack embedded source tokens into a PackedSequence
-        try:
-            packed_x = nn.utils.rnn.pack_padded_sequence(x, src_lengths.data.tolist())
-        except BaseException:
-            raise Exception(f"Packing failed in dataset {dataset_name}")
-
-        # apply LSTM
-        if self.bidirectional:
-            state_size = 2 * self.num_layers, bsz, self.hidden_size
-        else:
-            state_size = self.num_layers, bsz, self.hidden_size
-        h0 = x.data.new(*state_size).zero_()
-        c0 = x.data.new(*state_size).zero_()
-        packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0))
-
-        # unpack outputs and apply dropout
-        x, _ = nn.utils.rnn.pad_packed_sequence(
-            packed_outs, padding_value=self.padding_value
-        )
-        x = F.dropout(x, p=self.dropout_out, training=self.training)
-        assert list(x.size()) == [seqlen, bsz, self.output_units]
-
-        if self.bidirectional:
-
-            def combine_bidir(outs):
-                return torch.cat(
-                    [
-                        torch.cat([outs[2 * i], outs[2 * i + 1]], dim=0).view(
-                            1, bsz, self.output_units
-                        )
-                        for i in range(self.num_layers)
-                    ],
-                    dim=0,
-                )
-
-            final_hiddens = combine_bidir(final_hiddens)
-            final_cells = combine_bidir(final_cells)
-
-        encoder_padding_mask = src_tokens.eq(self.padding_idx).t()
-
-        # Set padded outputs to -inf so they are not selected by max-pooling
-        padding_mask = src_tokens.eq(self.padding_idx).t().unsqueeze(-1)
-        if padding_mask.any():
-            x = x.float().masked_fill_(padding_mask, float("-inf")).type_as(x)
-
-        # Build the sentence embedding by max-pooling over the encoder outputs
-        sentemb = x.max(dim=0)[0]
-
-        return {
-            "sentemb": sentemb,
-            "encoder_out": (x, final_hiddens, final_cells),
-            "encoder_padding_mask": encoder_padding_mask
-            if encoder_padding_mask.any()
-            else None,
-        }
-
-    def reorder_encoder_out(self, encoder_out_dict, new_order):
-        encoder_out_dict["sentemb"] = encoder_out_dict["sentemb"].index_select(
-            0, new_order
-        )
-        encoder_out_dict["encoder_out"] = tuple(
-            eo.index_select(1, new_order) for eo in encoder_out_dict["encoder_out"]
-        )
-        if encoder_out_dict["encoder_padding_mask"] is not None:
-            encoder_out_dict["encoder_padding_mask"] = encoder_out_dict[
-                "encoder_padding_mask"
-            ].index_select(1, new_order)
-        return encoder_out_dict
-
-    def max_positions(self):
-        """Maximum input length supported by the encoder."""
-        return int(1e5)  # an arbitrary large number
-
-
-class LSTMDecoder(FairseqIncrementalDecoder):
-    """LSTM decoder."""
-
-    def __init__(
-        self,
-        dictionary,
-        embed_dim=512,
-        hidden_size=512,
-        out_embed_dim=512,
-        num_layers=1,
-        dropout_in=0.1,
-        dropout_out=0.1,
-        zero_init=False,
-        encoder_embed_dim=512,
-        encoder_output_units=512,
-        pretrained_embed=None,
-        num_langs=1,
-        lang_embed_dim=0,
-    ):
-        super().__init__(dictionary)
-        self.dropout_in = dropout_in
-        self.dropout_out = dropout_out
-        self.hidden_size = hidden_size
-
-        num_embeddings = len(dictionary)
-        padding_idx = dictionary.pad()
-        if pretrained_embed is None:
-            self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
-        else:
-            self.embed_tokens = pretrained_embed
-
-        self.layers = nn.ModuleList(
-            [
-                LSTMCell(
-                    input_size=encoder_output_units + embed_dim + lang_embed_dim
-                    if layer == 0
-                    else hidden_size,
-                    hidden_size=hidden_size,
-                )
-                for layer in range(num_layers)
-            ]
-        )
-        if hidden_size != out_embed_dim:
-            self.additional_fc = Linear(hidden_size, out_embed_dim)
-        self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out)
-
-        if zero_init:
-            self.sentemb2init = None
-        else:
-            self.sentemb2init = Linear(
-                encoder_output_units, 2 * num_layers * hidden_size
-            )
-
-        if lang_embed_dim == 0:
-            self.embed_lang = None
-        else:
-            self.embed_lang = nn.Embedding(num_langs, lang_embed_dim)
-            nn.init.uniform_(self.embed_lang.weight, -0.1, 0.1)
-
-    def forward(
-        self, prev_output_tokens, encoder_out_dict, incremental_state=None, lang_id=0
-    ):
-        sentemb = encoder_out_dict["sentemb"]
-        encoder_out = encoder_out_dict["encoder_out"]
-
-        if incremental_state is not None:
-            prev_output_tokens = prev_output_tokens[:, -1:]
-        bsz, seqlen = prev_output_tokens.size()
-
-        # get outputs from encoder
-        encoder_outs, _, _ = encoder_out[:3]
-        srclen = encoder_outs.size(0)
-
-        # embed tokens
-        x = self.embed_tokens(prev_output_tokens)
-        x = F.dropout(x, p=self.dropout_in, training=self.training)
-
-        # embed language identifier
-        if self.embed_lang is not None:
-            lang_ids = prev_output_tokens.data.new_full((bsz,), lang_id)
-            langemb = self.embed_lang(lang_ids)
-            # TODO Should we dropout here???
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        # initialize previous states (or get from cache during incremental generation)
-        cached_state = utils.get_incremental_state(
-            self, incremental_state, "cached_state"
-        )
-        if cached_state is not None:
-            prev_hiddens, prev_cells, input_feed = cached_state
-        else:
-            num_layers = len(self.layers)
-            if self.sentemb2init is None:
-                prev_hiddens = [
-                    x.data.new(bsz, self.hidden_size).zero_() for i in range(num_layers)
-                ]
-                prev_cells = [
-                    x.data.new(bsz, self.hidden_size).zero_() for i in range(num_layers)
-                ]
-            else:
-                init = self.sentemb2init(sentemb)
-                prev_hiddens = [
-                    init[:, (2 * i) * self.hidden_size : (2 * i + 1) * self.hidden_size]
-                    for i in range(num_layers)
-                ]
-                prev_cells = [
-                    init[
-                        :,
-                        (2 * i + 1) * self.hidden_size : (2 * i + 2) * self.hidden_size,
-                    ]
-                    for i in range(num_layers)
-                ]
-            input_feed = x.data.new(bsz, self.hidden_size).zero_()
-
-        attn_scores = x.data.new(srclen, seqlen, bsz).zero_()
-        outs = []
-        for j in range(seqlen):
-            if self.embed_lang is None:
-                input = torch.cat((x[j, :, :], sentemb), dim=1)
-            else:
-                input = torch.cat((x[j, :, :], sentemb, langemb), dim=1)
-
-            for i, rnn in enumerate(self.layers):
-                # recurrent cell
-                hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i]))
-
-                # hidden state becomes the input to the next layer
-                input = F.dropout(hidden, p=self.dropout_out, training=self.training)
-
-                # save state for next time step
-                prev_hiddens[i] = hidden
-                prev_cells[i] = cell
-
-            out = hidden
-            out = F.dropout(out, p=self.dropout_out, training=self.training)
-
-            # input feeding
-            input_feed = out
-
-            # save final output
-            outs.append(out)
-
-        # cache previous states (no-op except during incremental generation)
-        utils.set_incremental_state(
-            self,
-            incremental_state,
-            "cached_state",
-            (prev_hiddens, prev_cells, input_feed),
-        )
-
-        # collect outputs across time steps
-        x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size)
-
-        # T x B x C -> B x T x C
-        x = x.transpose(1, 0)
-
-        # srclen x tgtlen x bsz -> bsz x tgtlen x srclen
-        attn_scores = attn_scores.transpose(0, 2)
-
-        # project back to size of vocabulary
-        if hasattr(self, "additional_fc"):
-            x = self.additional_fc(x)
-            x = F.dropout(x, p=self.dropout_out, training=self.training)
-        x = self.fc_out(x)
-
-        return x, attn_scores
-
-    def reorder_incremental_state(self, incremental_state, new_order):
-        super().reorder_incremental_state(incremental_state, new_order)
-        cached_state = utils.get_incremental_state(
-            self, incremental_state, "cached_state"
-        )
-        if cached_state is None:
-            return
-
-        def reorder_state(state):
-            if isinstance(state, list):
-                return [reorder_state(state_i) for state_i in state]
-            return state.index_select(0, new_order)
-
-        new_state = tuple(map(reorder_state, cached_state))
-        utils.set_incremental_state(self, incremental_state, "cached_state", new_state)
-
-    def max_positions(self):
-        """Maximum output length supported by the decoder."""
-        return int(1e5)  # an arbitrary large number
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx):
-    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
-    nn.init.uniform_(m.weight, -0.1, 0.1)
-    nn.init.constant_(m.weight[padding_idx], 0)
-    return m
-
-
-def LSTM(input_size, hidden_size, **kwargs):
-    m = nn.LSTM(input_size, hidden_size, **kwargs)
-    for name, param in m.named_parameters():
-        if "weight" in name or "bias" in name:
-            param.data.uniform_(-0.1, 0.1)
-    return m
-
-
-def LSTMCell(input_size, hidden_size, **kwargs):
-    m = nn.LSTMCell(input_size, hidden_size, **kwargs)
-    for name, param in m.named_parameters():
-        if "weight" in name or "bias" in name:
-            param.data.uniform_(-0.1, 0.1)
-    return m
-
-
-def Linear(in_features, out_features, bias=True, dropout=0):
-    """Weight-normalized Linear layer (input: N x T x C)"""
-    m = nn.Linear(in_features, out_features, bias=bias)
-    m.weight.data.uniform_(-0.1, 0.1)
-    if bias:
-        m.bias.data.uniform_(-0.1, 0.1)
-    return m
-
-
-@register_model_architecture("laser_lstm", "laser_lstm")
-def base_architecture(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
-    args.encoder_hidden_size = getattr(
-        args, "encoder_hidden_size", args.encoder_embed_dim
-    )
-    args.encoder_layers = getattr(args, "encoder_layers", 1)
-    args.encoder_bidirectional = getattr(args, "encoder_bidirectional", False)
-    args.encoder_dropout_in = getattr(args, "encoder_dropout_in", args.dropout)
-    args.encoder_dropout_out = getattr(args, "encoder_dropout_out", args.dropout)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
-    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
-    args.decoder_hidden_size = getattr(
-        args, "decoder_hidden_size", args.decoder_embed_dim
-    )
-    args.decoder_layers = getattr(args, "decoder_layers", 1)
-    args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512)
-    args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout)
-    args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout)
-    args.decoder_zero_init = getattr(args, "decoder_zero_init", "0")
-    args.decoder_lang_embed_dim = getattr(args, "decoder_lang_embed_dim", 0)
-    args.fixed_embeddings = getattr(args, "fixed_embeddings", False)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/model_parallel/modules/transformer_layer.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/model_parallel/modules/transformer_layer.py
deleted file mode 100644
index 7ab53c6e5f12f15562717effb86ab8cb8d6b4fa3..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/model_parallel/modules/transformer_layer.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 fairseq.model_parallel.modules import ModelParallelMultiheadAttention
-from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer
-
-
-try:
-    from fairseq.model_parallel.megatron.mpu import (
-        ColumnParallelLinear,
-        RowParallelLinear,
-    )
-
-    has_megatron_submodule = True
-except (ImportError, ModuleNotFoundError):
-    has_megatron_submodule = False
-
-
-class ModelParallelTransformerEncoderLayer(TransformerEncoderLayer):
-    """Encoder layer block over multiple gpus.
-
-    See "Megatron-LM: https://arxiv.org/pdf/1909.08053.pdf" for more details.
-    """
-
-    def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size):
-        if q_noise > 0:
-            raise NotImplementedError
-        return ColumnParallelLinear(input_dim, output_dim, gather_output=False)
-
-    def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size):
-        if q_noise > 0:
-            raise NotImplementedError
-        return RowParallelLinear(input_dim, output_dim, input_is_parallel=True)
-
-    def build_self_attention(self, embed_dim, args, **unused_kwargs):
-        return ModelParallelMultiheadAttention(
-            embed_dim,
-            args.encoder_attention_heads,
-            dropout=args.attention_dropout,
-            self_attention=True,
-        )
-
-
-class ModelParallelTransformerDecoderLayer(TransformerDecoderLayer):
-    """Decoder layer block.
-
-    See "Megatron-LM: https://arxiv.org/pdf/1909.08053.pdf" for more details.
-    """
-
-    def build_fc1(self, input_dim, output_dim, q_noise, qn_block_size):
-        if q_noise > 0:
-            raise NotImplementedError
-        return ColumnParallelLinear(input_dim, output_dim, gather_output=False)
-
-    def build_fc2(self, input_dim, output_dim, q_noise, qn_block_size):
-        if q_noise > 0:
-            raise NotImplementedError
-        return RowParallelLinear(input_dim, output_dim, input_is_parallel=True)
-
-    def build_self_attention(self, embed_dim, args, **unused_kwargs):
-        return ModelParallelMultiheadAttention(
-            embed_dim=embed_dim,
-            num_heads=args.decoder_attention_heads,
-            dropout=args.attention_dropout,
-            self_attention=not getattr(args, "cross_self_attention", False),
-        )
-
-    def build_encoder_attention(self, embed_dim, args, **unused_kwargs):
-        return ModelParallelMultiheadAttention(
-            embed_dim=embed_dim,
-            num_heads=args.decoder_attention_heads,
-            kdim=getattr(args, "encoder_embed_dim", None),
-            vdim=getattr(args, "encoder_embed_dim", None),
-            dropout=args.attention_dropout,
-            encoder_decoder_attention=True,
-        )
diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/exceptions.py b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/exceptions.py
deleted file mode 100644
index d6cf6a8f2fed4941c5c4a17902292062a929886e..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/exceptions.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class DuplicateBlockError(ValueError):
-    """Raised when a Blocks contains more than one Block with the same id"""
-
-    pass
-
-
-class TooManyRequestsError(Exception):
-    """Raised when the Hugging Face API returns a 429 status code."""
-
-    pass
-
-
-class InvalidApiName(ValueError):
-    pass
-
-
-class Error(Exception):
-    def __init__(self, message: str):
-        self.message = message
-        super().__init__(self.message)
-
-    def __str__(self):
-        return repr(self.message)
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/models/model_utils.py b/spaces/ICML2022/OFA/fairseq/fairseq/models/model_utils.py
deleted file mode 100644
index 732d66b1d5f695151c26d29eb7f6b53179c269f1..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/models/model_utils.py
+++ /dev/null
@@ -1,92 +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 typing import List, Optional
-
-import torch
-from torch import Tensor
-
-
-@torch.jit.script
-def script_skip_tensor_list(x: List[Tensor], mask):
-    res = [xi[mask] if xi.size(0) == mask.size(0) else xi[:, mask] for xi in x]
-    outputs = []
-    for i, t in enumerate(res):
-        if t.numel() != 0:
-            outputs.append(t)
-        else:
-            outputs.append(x[i])
-    return outputs
-
-
-@torch.jit.script
-def script_skip_tensor(x: Tensor, mask):
-    # None case
-    if x.size(0) == 0:
-        return x
-    res = x[mask] if x.size(0) == mask.size(0) else x[:, mask]
-    if res.numel() == 0:
-        return x
-    else:
-        return res
-
-
-@torch.jit.script
-def expand_2d_or_3d_tensor(x, trg_dim: int, padding_idx: int):
-    """
-    Expand 2D/3D tensor on dim=1
-    """
-    if x is None:
-        return None
-
-    assert x.dim() == 2 or x.dim() == 3
-    assert trg_dim >= x.size(1), (trg_dim, x.size())
-    if trg_dim == x.size(1):
-        return x
-
-    dims = [x.size(0), trg_dim - x.size(1)]
-    if x.dim() == 3:
-        dims.append(x.size(2))
-    x = torch.cat([x, torch.zeros(dims).to(x).fill_(padding_idx)], 1)
-
-    return x
-
-
-@torch.jit.script
-def coalesce(x: Optional[Tensor], y: Tensor) -> Tensor:
-    return x if x is not None else y
-
-
-@torch.jit.script
-def fill_tensors(
-    x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int
-) -> Optional[Tensor]:
-    """
-    Filling tensor x with y at masked positions (dim=0).
-    """
-    if x is None or x.size()[0] == 0 or y is None:
-        return x
-    assert x.dim() == y.dim() and mask.size(0) == x.size(0)
-    assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2))
-
-    n_selected = mask.sum()
-    if n_selected == 0:
-        return x
-    assert n_selected == y.size(0)
-    if n_selected == x.size(0):
-        return y
-
-    if x.size(1) < y.size(1):
-        x = expand_2d_or_3d_tensor(x, y.size(1), padding_idx)
-        x[mask] = y
-    elif x.size(1) > y.size(1):
-        x[mask] = torch.tensor(padding_idx).type_as(x)
-        if x.dim() == 2:
-            x[mask, : y.size(1)] = y
-        else:
-            x[mask, : y.size(1), :] = y
-    else:
-        x[mask] = y
-    return x
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/options.py b/spaces/ICML2022/OFA/fairseq/fairseq/options.py
deleted file mode 100644
index 797b2842db4a68849110a25bb52a47c658966186..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/options.py
+++ /dev/null
@@ -1,406 +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 argparse
-from pathlib import Path
-from typing import Callable, List, Optional, Union
-
-import torch
-from fairseq import utils
-from fairseq.data.indexed_dataset import get_available_dataset_impl
-from fairseq.dataclass.configs import (
-    CheckpointConfig,
-    CommonConfig,
-    CommonEvalConfig,
-    DatasetConfig,
-    DistributedTrainingConfig,
-    EvalLMConfig,
-    GenerationConfig,
-    InteractiveConfig,
-    OptimizationConfig,
-    EMAConfig,
-)
-from fairseq.dataclass.utils import gen_parser_from_dataclass
-
-# this import is for backward compatibility
-from fairseq.utils import csv_str_list, eval_bool, eval_str_dict, eval_str_list  # noqa
-
-
-def get_preprocessing_parser(default_task="translation"):
-    parser = get_parser("Preprocessing", default_task)
-    add_preprocess_args(parser)
-    return parser
-
-
-def get_training_parser(default_task="translation"):
-    parser = get_parser("Trainer", default_task)
-    add_dataset_args(parser, train=True)
-    add_distributed_training_args(parser)
-    add_model_args(parser)
-    add_optimization_args(parser)
-    add_checkpoint_args(parser)
-    add_ema_args(parser)
-    return parser
-
-
-def get_generation_parser(interactive=False, default_task="translation"):
-    parser = get_parser("Generation", default_task)
-    add_dataset_args(parser, gen=True)
-    add_distributed_training_args(parser)
-    add_generation_args(parser)
-    add_checkpoint_args(parser)
-    if interactive:
-        add_interactive_args(parser)
-    return parser
-
-
-def get_speech_generation_parser(default_task="text_to_speech"):
-    parser = get_parser("Speech Generation", default_task)
-    add_dataset_args(parser, gen=True)
-    add_distributed_training_args(parser, default_world_size=1)
-    add_speech_generation_args(parser)
-    return parser
-
-
-def get_interactive_generation_parser(default_task="translation"):
-    return get_generation_parser(interactive=True, default_task=default_task)
-
-
-def get_eval_lm_parser(default_task="language_modeling"):
-    parser = get_parser("Evaluate Language Model", default_task)
-    add_dataset_args(parser, gen=True)
-    add_distributed_training_args(parser, default_world_size=1)
-    add_eval_lm_args(parser)
-    return parser
-
-
-def get_validation_parser(default_task=None):
-    parser = get_parser("Validation", default_task)
-    add_dataset_args(parser, train=True)
-    add_distributed_training_args(parser, default_world_size=1)
-    group = parser.add_argument_group("Evaluation")
-    gen_parser_from_dataclass(group, CommonEvalConfig())
-    return parser
-
-
-def parse_args_and_arch(
-    parser: argparse.ArgumentParser,
-    input_args: List[str] = None,
-    parse_known: bool = False,
-    suppress_defaults: bool = False,
-    modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None,
-):
-    """
-    Args:
-        parser (ArgumentParser): the parser
-        input_args (List[str]): strings to parse, defaults to sys.argv
-        parse_known (bool): only parse known arguments, similar to
-            `ArgumentParser.parse_known_args`
-        suppress_defaults (bool): parse while ignoring all default values
-        modify_parser (Optional[Callable[[ArgumentParser], None]]):
-            function to modify the parser, e.g., to set default values
-    """
-    if suppress_defaults:
-        # Parse args without any default values. This requires us to parse
-        # twice, once to identify all the necessary task/model args, and a second
-        # time with all defaults set to None.
-        args = parse_args_and_arch(
-            parser,
-            input_args=input_args,
-            parse_known=parse_known,
-            suppress_defaults=False,
-        )
-        suppressed_parser = argparse.ArgumentParser(add_help=False, parents=[parser])
-        suppressed_parser.set_defaults(**{k: None for k, v in vars(args).items()})
-        args = suppressed_parser.parse_args(input_args)
-        return argparse.Namespace(
-            **{k: v for k, v in vars(args).items() if v is not None}
-        )
-
-    from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_CONFIG_REGISTRY, MODEL_REGISTRY
-
-    # Before creating the true parser, we need to import optional user module
-    # in order to eagerly import custom tasks, optimizers, architectures, etc.
-    usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False)
-    usr_parser.add_argument("--user-dir", default=None)
-    usr_args, _ = usr_parser.parse_known_args(input_args)
-    utils.import_user_module(usr_args)
-
-    if modify_parser is not None:
-        modify_parser(parser)
-
-    # The parser doesn't know about model/criterion/optimizer-specific args, so
-    # we parse twice. First we parse the model/criterion/optimizer, then we
-    # parse a second time after adding the *-specific arguments.
-    # If input_args is given, we will parse those args instead of sys.argv.
-    args, _ = parser.parse_known_args(input_args)
-
-    # Add model-specific args to parser.
-    if hasattr(args, "arch"):
-        model_specific_group = parser.add_argument_group(
-            "Model-specific configuration",
-            # Only include attributes which are explicitly given as command-line
-            # arguments or which have default values.
-            argument_default=argparse.SUPPRESS,
-        )
-        if args.arch in ARCH_MODEL_REGISTRY:
-            ARCH_MODEL_REGISTRY[args.arch].add_args(model_specific_group)
-        elif args.arch in MODEL_REGISTRY:
-            MODEL_REGISTRY[args.arch].add_args(model_specific_group)
-        else:
-            raise RuntimeError()
-
-    if hasattr(args, "task"):
-        from fairseq.tasks import TASK_REGISTRY
-
-        TASK_REGISTRY[args.task].add_args(parser)
-    if getattr(args, "use_bmuf", False):
-        # hack to support extra args for block distributed data parallelism
-        from fairseq.optim.bmuf import FairseqBMUF
-
-        FairseqBMUF.add_args(parser)
-
-    # Add *-specific args to parser.
-    from fairseq.registry import REGISTRIES
-
-    for registry_name, REGISTRY in REGISTRIES.items():
-        choice = getattr(args, registry_name, None)
-        if choice is not None:
-            cls = REGISTRY["registry"][choice]
-            if hasattr(cls, "add_args"):
-                cls.add_args(parser)
-            elif hasattr(cls, "__dataclass"):
-                gen_parser_from_dataclass(parser, cls.__dataclass())
-
-    # Modify the parser a second time, since defaults may have been reset
-    if modify_parser is not None:
-        modify_parser(parser)
-
-    # Parse a second time.
-    if parse_known:
-        args, extra = parser.parse_known_args(input_args)
-    else:
-        args = parser.parse_args(input_args)
-        extra = None
-    # Post-process args.
-    if (
-        hasattr(args, "batch_size_valid") and args.batch_size_valid is None
-    ) or not hasattr(args, "batch_size_valid"):
-        args.batch_size_valid = args.batch_size
-    if hasattr(args, "max_tokens_valid") and args.max_tokens_valid is None:
-        args.max_tokens_valid = args.max_tokens
-    if getattr(args, "memory_efficient_fp16", False):
-        args.fp16 = True
-    if getattr(args, "memory_efficient_bf16", False):
-        args.bf16 = True
-    args.tpu = getattr(args, "tpu", False)
-    args.bf16 = getattr(args, "bf16", False)
-    if args.bf16:
-        args.tpu = True
-    if args.tpu and args.fp16:
-        raise ValueError("Cannot combine --fp16 and --tpu, use --bf16 on TPUs")
-
-    if getattr(args, "seed", None) is None:
-        args.seed = 1  # default seed for training
-        args.no_seed_provided = True
-    else:
-        args.no_seed_provided = False
-
-    # Apply architecture configuration.
-    if hasattr(args, "arch") and args.arch in ARCH_CONFIG_REGISTRY:
-        ARCH_CONFIG_REGISTRY[args.arch](args)
-
-    if parse_known:
-        return args, extra
-    else:
-        return args
-
-
-def get_parser(desc, default_task="translation"):
-    # Before creating the true parser, we need to import optional user module
-    # in order to eagerly import custom tasks, optimizers, architectures, etc.
-    usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False)
-    usr_parser.add_argument("--user-dir", default=None)
-    usr_args, _ = usr_parser.parse_known_args()
-    utils.import_user_module(usr_args)
-
-    parser = argparse.ArgumentParser(allow_abbrev=False)
-    gen_parser_from_dataclass(parser, CommonConfig())
-
-    from fairseq.registry import REGISTRIES
-
-    for registry_name, REGISTRY in REGISTRIES.items():
-        parser.add_argument(
-            "--" + registry_name.replace("_", "-"),
-            default=REGISTRY["default"],
-            choices=REGISTRY["registry"].keys(),
-        )
-
-    # Task definitions can be found under fairseq/tasks/
-    from fairseq.tasks import TASK_REGISTRY
-
-    parser.add_argument(
-        "--task",
-        metavar="TASK",
-        default=default_task,
-        choices=TASK_REGISTRY.keys(),
-        help="task",
-    )
-    # fmt: on
-    return parser
-
-
-def add_preprocess_args(parser):
-    group = parser.add_argument_group("Preprocessing")
-    # fmt: off
-    group.add_argument("-s", "--source-lang", default=None, metavar="SRC",
-                       help="source language")
-    group.add_argument("-t", "--target-lang", default=None, metavar="TARGET",
-                       help="target language")
-    group.add_argument("--trainpref", metavar="FP", default=None,
-                       help="train file prefix (also used to build dictionaries)")
-    group.add_argument("--validpref", metavar="FP", default=None,
-                       help="comma separated, valid file prefixes "
-                            "(words missing from train set are replaced with <unk>)")
-    group.add_argument("--testpref", metavar="FP", default=None,
-                       help="comma separated, test file prefixes "
-                            "(words missing from train set are replaced with <unk>)")
-    group.add_argument("--align-suffix", metavar="FP", default=None,
-                       help="alignment file suffix")
-    group.add_argument("--destdir", metavar="DIR", default="data-bin",
-                       help="destination dir")
-    group.add_argument("--thresholdtgt", metavar="N", default=0, type=int,
-                       help="map words appearing less than threshold times to unknown")
-    group.add_argument("--thresholdsrc", metavar="N", default=0, type=int,
-                       help="map words appearing less than threshold times to unknown")
-    group.add_argument("--tgtdict", metavar="FP",
-                       help="reuse given target dictionary")
-    group.add_argument("--srcdict", metavar="FP",
-                       help="reuse given source dictionary")
-    group.add_argument("--nwordstgt", metavar="N", default=-1, type=int,
-                       help="number of target words to retain")
-    group.add_argument("--nwordssrc", metavar="N", default=-1, type=int,
-                       help="number of source words to retain")
-    group.add_argument("--alignfile", metavar="ALIGN", default=None,
-                       help="an alignment file (optional)")
-    parser.add_argument('--dataset-impl', metavar='FORMAT', default='mmap',
-                        choices=get_available_dataset_impl(),
-                        help='output dataset implementation')
-    group.add_argument("--joined-dictionary", action="store_true",
-                       help="Generate joined dictionary")
-    group.add_argument("--only-source", action="store_true",
-                       help="Only process the source language")
-    group.add_argument("--padding-factor", metavar="N", default=8, type=int,
-                       help="Pad dictionary size to be multiple of N")
-    group.add_argument("--workers", metavar="N", default=1, type=int,
-                       help="number of parallel workers")
-    group.add_argument("--dict-only", action='store_true',
-                       help="if true, only builds a dictionary and then exits")
-    # fmt: on
-    return parser
-
-
-def add_dataset_args(parser, train=False, gen=False):
-    group = parser.add_argument_group("dataset_data_loading")
-    gen_parser_from_dataclass(group, DatasetConfig())
-    # fmt: on
-    return group
-
-
-def add_distributed_training_args(parser, default_world_size=None):
-    group = parser.add_argument_group("distributed_training")
-    if default_world_size is None:
-        default_world_size = max(1, torch.cuda.device_count())
-    gen_parser_from_dataclass(
-        group, DistributedTrainingConfig(distributed_world_size=default_world_size)
-    )
-    return group
-
-
-def add_optimization_args(parser):
-    group = parser.add_argument_group("optimization")
-    # fmt: off
-    gen_parser_from_dataclass(group, OptimizationConfig())
-    # fmt: on
-    return group
-
-
-def add_checkpoint_args(parser):
-    group = parser.add_argument_group("checkpoint")
-    # fmt: off
-    gen_parser_from_dataclass(group, CheckpointConfig())
-    # fmt: on
-    return group
-
-
-def add_common_eval_args(group):
-    gen_parser_from_dataclass(group, CommonEvalConfig())
-
-
-def add_eval_lm_args(parser):
-    group = parser.add_argument_group("LM Evaluation")
-    add_common_eval_args(group)
-    gen_parser_from_dataclass(group, EvalLMConfig())
-
-
-def add_generation_args(parser):
-    group = parser.add_argument_group("Generation")
-    add_common_eval_args(group)
-    gen_parser_from_dataclass(group, GenerationConfig())
-    return group
-
-
-def add_speech_generation_args(parser):
-    group = parser.add_argument_group("Speech Generation")
-    add_common_eval_args(group)  # NOTE: remove_bpe is not needed
-    # fmt: off
-    group.add_argument('--eos_prob_threshold', default=0.5, type=float,
-                       help='terminate when eos probability exceeds this')
-    # fmt: on
-    return group
-
-
-def add_interactive_args(parser):
-    group = parser.add_argument_group("Interactive")
-    gen_parser_from_dataclass(group, InteractiveConfig())
-
-
-def add_model_args(parser):
-    group = parser.add_argument_group("Model configuration")
-    # fmt: off
-
-    # Model definitions can be found under fairseq/models/
-    #
-    # The model architecture can be specified in several ways.
-    # In increasing order of priority:
-    # 1) model defaults (lowest priority)
-    # 2) --arch argument
-    # 3) --encoder/decoder-* arguments (highest priority)
-    from fairseq.models import ARCH_MODEL_REGISTRY
-    group.add_argument('--arch', '-a', metavar='ARCH',
-                       choices=ARCH_MODEL_REGISTRY.keys(),
-                       help='model architecture')
-    # fmt: on
-    return group
-
-
-def get_args(
-    data: Union[str, Path],
-    task: str = "translation",
-    arch: str = "transformer",
-    **overrides
-):
-    parser = get_training_parser(task)
-    args = parse_args_and_arch(parser, [str(data), "--task", task, "--arch", arch])
-
-    for k, v in overrides.items():
-        setattr(args, k, v)
-
-    return args
-
-
-def add_ema_args(parser):
-    group = parser.add_argument_group("EMA configuration")
-    gen_parser_from_dataclass(group, EMAConfig())
diff --git a/spaces/Ibtehaj10/cheating-detection/generate_keys.py b/spaces/Ibtehaj10/cheating-detection/generate_keys.py
deleted file mode 100644
index 997135fada594e5b17f64a219bbd8b44f1463cfa..0000000000000000000000000000000000000000
--- a/spaces/Ibtehaj10/cheating-detection/generate_keys.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import pickle
-from pathlib import Path
-import streamlit_authenticator as stauth
-# print("Done !!!")
-
-names = ["dmin", "ser"]
-
-username =["admin", "user"]
-
-password =["admin123", "user123"] 
-
-hashed_passwords =stauth.Hasher(password).generate()
-
-file_path = Path(__file__).parent / "hashed_pw.pkl"
-
-with file_path.open("wb") as file:
-    pickle.dump(hashed_passwords, file)
diff --git a/spaces/Iceclear/StableSR/StableSR/ldm/modules/embedding_manager.py b/spaces/Iceclear/StableSR/StableSR/ldm/modules/embedding_manager.py
deleted file mode 100644
index 5c5f97bd9f151bc1c508f56bd7ccfb4509aaea82..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/ldm/modules/embedding_manager.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import torch
-from torch import nn
-
-from ldm.data.personalized import per_img_token_list
-from transformers import CLIPTokenizer
-from functools import partial
-
-DEFAULT_PLACEHOLDER_TOKEN = ["*"]
-
-PROGRESSIVE_SCALE = 2000
-
-def get_clip_token_for_string(tokenizer, string):
-    batch_encoding = tokenizer(string, truncation=True, max_length=77, return_length=True,
-                               return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
-    tokens = batch_encoding["input_ids"]
-    assert torch.count_nonzero(tokens - 49407) == 2, f"String '{string}' maps to more than a single token. Please use another string"
-
-    return tokens[0, 1]
-
-def get_bert_token_for_string(tokenizer, string):
-    token = tokenizer(string)
-    assert torch.count_nonzero(token) == 3, f"String '{string}' maps to more than a single token. Please use another string"
-
-    token = token[0, 1]
-
-    return token
-
-def get_embedding_for_clip_token(embedder, token):
-    return embedder(token.unsqueeze(0))[0, 0]
-
-
-class EmbeddingManager(nn.Module):
-    def __init__(
-            self,
-            embedder,
-            placeholder_strings=None,
-            initializer_words=None,
-            per_image_tokens=False,
-            num_vectors_per_token=1,
-            progressive_words=False,
-            **kwargs
-    ):
-        super().__init__()
-
-        self.string_to_token_dict = {}
-
-        self.string_to_param_dict = nn.ParameterDict()
-
-        self.initial_embeddings = nn.ParameterDict() # These should not be optimized
-
-        self.progressive_words = progressive_words
-        self.progressive_counter = 0
-
-        self.max_vectors_per_token = num_vectors_per_token
-
-        if hasattr(embedder, 'tokenizer'): # using Stable Diffusion's CLIP encoder
-            self.is_clip = True
-            get_token_for_string = partial(get_clip_token_for_string, embedder.tokenizer)
-            get_embedding_for_tkn = partial(get_embedding_for_clip_token, embedder.transformer.text_model.embeddings)
-            token_dim = 768
-        else: # using LDM's BERT encoder
-            self.is_clip = False
-            get_token_for_string = partial(get_bert_token_for_string, embedder.tknz_fn)
-            get_embedding_for_tkn = embedder.transformer.token_emb
-            token_dim = 1280
-
-        if per_image_tokens:
-            placeholder_strings.extend(per_img_token_list)
-
-        for idx, placeholder_string in enumerate(placeholder_strings):
-
-            token = get_token_for_string(placeholder_string)
-
-            if initializer_words and idx < len(initializer_words):
-                init_word_token = get_token_for_string(initializer_words[idx])
-
-                with torch.no_grad():
-                    init_word_embedding = get_embedding_for_tkn(init_word_token.cpu())
-
-                token_params = torch.nn.Parameter(init_word_embedding.unsqueeze(0).repeat(num_vectors_per_token, 1), requires_grad=True)
-                self.initial_embeddings[placeholder_string] = torch.nn.Parameter(init_word_embedding.unsqueeze(0).repeat(num_vectors_per_token, 1), requires_grad=False)
-            else:
-                token_params = torch.nn.Parameter(torch.rand(size=(num_vectors_per_token, token_dim), requires_grad=True))
-
-            self.string_to_token_dict[placeholder_string] = token
-            self.string_to_param_dict[placeholder_string] = token_params
-
-    def forward(
-            self,
-            tokenized_text,
-            embedded_text,
-    ):
-        b, n, device = *tokenized_text.shape, tokenized_text.device
-
-        for placeholder_string, placeholder_token in self.string_to_token_dict.items():
-
-            placeholder_embedding = self.string_to_param_dict[placeholder_string].to(device)
-
-            if self.max_vectors_per_token == 1: # If there's only one vector per token, we can do a simple replacement
-                placeholder_idx = torch.where(tokenized_text == placeholder_token.to(device))
-                embedded_text[placeholder_idx] = placeholder_embedding
-            else: # otherwise, need to insert and keep track of changing indices
-                if self.progressive_words:
-                    self.progressive_counter += 1
-                    max_step_tokens = 1 + self.progressive_counter // PROGRESSIVE_SCALE
-                else:
-                    max_step_tokens = self.max_vectors_per_token
-
-                num_vectors_for_token = min(placeholder_embedding.shape[0], max_step_tokens)
-
-                placeholder_rows, placeholder_cols = torch.where(tokenized_text == placeholder_token.to(device))
-
-                if placeholder_rows.nelement() == 0:
-                    continue
-
-                sorted_cols, sort_idx = torch.sort(placeholder_cols, descending=True)
-                sorted_rows = placeholder_rows[sort_idx]
-
-                for idx in range(len(sorted_rows)):
-                    row = sorted_rows[idx]
-                    col = sorted_cols[idx]
-
-                    new_token_row = torch.cat([tokenized_text[row][:col], placeholder_token.repeat(num_vectors_for_token).to(device), tokenized_text[row][col + 1:]], axis=0)[:n]
-                    new_embed_row = torch.cat([embedded_text[row][:col], placeholder_embedding[:num_vectors_for_token], embedded_text[row][col + 1:]], axis=0)[:n]
-
-                    embedded_text[row]  = new_embed_row
-                    tokenized_text[row] = new_token_row
-
-        return embedded_text
-
-    def save(self, ckpt_path):
-        torch.save({"string_to_token": self.string_to_token_dict,
-                    "string_to_param": self.string_to_param_dict}, ckpt_path)
-
-    def load(self, ckpt_path):
-        ckpt = torch.load(ckpt_path, map_location='cpu')
-
-        self.string_to_token_dict = ckpt["string_to_token"]
-        self.string_to_param_dict = ckpt["string_to_param"]
-
-    def get_embedding_norms_squared(self):
-        all_params = torch.cat(list(self.string_to_param_dict.values()), axis=0) # num_placeholders x embedding_dim
-        param_norm_squared = (all_params * all_params).sum(axis=-1)              # num_placeholders
-
-        return param_norm_squared
-
-    def embedding_parameters(self):
-        return self.string_to_param_dict.parameters()
-
-    def embedding_to_coarse_loss(self):
-
-        loss = 0.
-        num_embeddings = len(self.initial_embeddings)
-
-        for key in self.initial_embeddings:
-            optimized = self.string_to_param_dict[key]
-            coarse = self.initial_embeddings[key].clone().to(optimized.device)
-
-            loss = loss + (optimized - coarse) @ (optimized - coarse).T / num_embeddings
-
-        return loss
diff --git a/spaces/Illumotion/Koboldcpp/common/train.h b/spaces/Illumotion/Koboldcpp/common/train.h
deleted file mode 100644
index 42fa704b897ae56051aaaef16a71c77fe2fd6c17..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/common/train.h
+++ /dev/null
@@ -1,230 +0,0 @@
-// Various helper functions and utilities for training
-
-#pragma once
-
-#include <string>
-#include <random>
-#include <vector>
-
-#include "ggml.h"
-#include "llama.h"
-
-typedef std::string mt19937_state;
-
-struct train_state {
-    struct ggml_opt_context * opt;
-
-    uint64_t train_its;
-    uint64_t train_samples;
-    uint64_t train_tokens;
-    uint64_t train_epochs;
-
-    size_t        shuffle_samples_hash; // fn, sample_count, *zip(sample_begins, sample_sizes)
-    mt19937_state shuffle_rng_state_current;
-    mt19937_state shuffle_rng_state_next;
-    size_t        shuffle_sample_count;
-    size_t        shuffle_next_sample;
-};
-
-struct train_params_common {
-    const char * fn_train_data;
-    const char * fn_checkpoint_in;
-    const char * fn_checkpoint_out;
-    const char * pattern_fn_it;
-    const char * fn_latest;
-
-    bool print_usage;
-
-    int save_every;
-
-    uint32_t seed;
-
-    int n_ctx;
-    int n_threads;
-    int n_batch;
-    int n_gradient_accumulation;
-    int n_epochs;
-
-    bool custom_n_ctx;
-
-    bool use_flash;
-    bool use_checkpointing;
-
-    std::string sample_start;
-    bool include_sample_start;
-    bool escape;
-    bool overlapping_samples;
-    bool fill_with_next_samples;
-    bool separate_with_eos;
-    bool separate_with_bos;
-    bool sample_random_offsets;
-
-    bool force_reshuffle;
-
-    int   warmup;
-    int   cos_decay_steps;
-    float cos_decay_restart;
-    float cos_decay_min;
-    bool  enable_restart;
-
-    int   opt_past;
-    float opt_delta;
-    int   opt_max_no_improvement;
-
-    int   adam_n_iter;
-    float adam_alpha;
-    float adam_min_alpha;
-    float adam_decay;
-    int   adam_decay_min_ndim;
-    float adam_beta1;
-    float adam_beta2;
-    float adam_gclip;
-    float adam_eps_f;
-};
-
-typedef void (*save_train_files_callback)(void * data, struct train_state * train);
-
-struct train_opt_callback_data {
-    struct train_params_common * params;
-    struct train_state         * train;
-    save_train_files_callback    save_cb;
-    void                       * save_data;
-    struct llama_context       * lctx;
-    int                          last_save_iter;
-    llama_token                * tokens_data;
-    size_t                       tokens_size;
-    size_t                     * samples_begin;
-    size_t                     * samples_size;
-    size_t                     * shuffled_samples_offs;
-    size_t                     * shuffled_samples_begin;
-    size_t                     * shuffled_samples_size;
-    size_t                       samples_count;
-    struct ggml_tensor         * tokens_input;
-    struct ggml_tensor         * target_probs;
-    int                          first_iter;
-    int                          first_epoch;
-    int                          iter_at_last_epoch;
-    int64_t                      last_time;
-    double                       millis_per_iter;
-};
-
-struct train_state * init_train_state();
-void free_train_state(struct train_state  * state);
-
-struct train_params_common get_default_train_params_common();
-void print_common_train_usage(int /*argc*/, char ** argv, const struct train_params_common * params);
-
-bool consume_common_train_arg(int argc, char ** argv, int * idx, struct train_params_common * params, bool * invalid_param);
-void finish_processing_train_args(struct train_params_common * params);
-
-struct random_normal_distribution;
-struct random_uniform_distribution;
-
-struct random_normal_distribution  * init_random_normal_distribution (int seed, float mean, float std, float min, float max);
-struct random_uniform_distribution * init_random_uniform_distribution(int seed, float min, float max);
-
-void free_random_normal_distribution (struct random_normal_distribution  * rnd);
-void free_random_uniform_distribution(struct random_uniform_distribution * rnd);
-
-struct ggml_tensor * randomize_tensor_normal (struct ggml_tensor * tensor, struct random_normal_distribution * rnd);
-struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd);
-
-// generate random float in interval [0,1)
-float frand();
-float frand_normal (struct random_normal_distribution * rnd);
-float frand_uniform(struct random_uniform_distribution * rnd);
-
-int   clamp (const int v, const int min, const int max);
-float fclamp(const float v, const float min, const float max);
-
-void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0);
-void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1);
-void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2);
-void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3);
-
-size_t tokenize_file(
-        struct llama_context     * lctx,
-        const char               * filename,
-        const std::string        & sample_start,
-        bool                       include_sample_start,
-        bool                       overlapping_samples,
-        unsigned                   context_length,
-        std::vector<llama_token> & out_tokens,
-        std::vector<size_t>      & out_samples_begin,
-        std::vector<size_t>      & out_samples_size);
-
-int64_t get_example_targets_batch(
-        struct llama_context * lctx,
-        struct ggml_tensor   * tokens_input,
-        struct ggml_tensor   * target_probs,
-        int64_t                example_id,
-        const size_t         * samples_offs,
-        const size_t         * samples_begin,
-        const size_t         * samples_size,
-              size_t           samples_count,
-        const llama_token    * train_data,
-        size_t                 n_train_data,
-        bool                   separate_with_eos,
-        bool                   separate_with_bos,
-        bool                   fill_with_next_samples,
-        bool                   sample_random_offsets);
-
-
-void          mt19937_set_state(std::mt19937& rng, const mt19937_state& rng_state);
-mt19937_state mt19937_get_state(const std::mt19937& rng);
-mt19937_state mt19937_seed_to_state(unsigned seed);
-
-mt19937_state shuffle_samples(
-        const mt19937_state & rng_state,
-        size_t              * shuffled_offs,
-        size_t              * shuffled_begins,
-        size_t              * shuffled_sizes,
-        const size_t        * begins,
-        const size_t        * sizes,
-        size_t                count);
-
-size_t hash_combine(size_t h1, size_t h2);
-
-size_t compute_samples_hash(
-    const char* fn,
-    const size_t* samples_begin,
-    const size_t* samples_size,
-    size_t sample_count);
-
-
-std::string replace_str(const char * s, const char * needle, const char * replacement);
-
-void print_duration(double milliseconds);
-
-float cosine_decay(
-    int64_t step,
-    int64_t decay_steps,
-    float   minimum);
-
-float cosine_decay_restart(
-    int64_t step,
-    int64_t decay_steps,
-    float   minimum,
-    float   restart_step_mult);
-
-float learning_schedule(
-    int64_t step,
-    int64_t warmup_steps,
-    int64_t decay_steps,
-    float   learning_rate,
-    float   overall_minimum,
-    float   cos_decay_minimum,
-    float   cos_decay_restart_step_mult,
-    bool    enable_restart);
-
-void copy_tensor_by_name(struct ggml_tensor * dst, struct ggml_context * ctx, const char * name);
-
-void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct ggml_opt_context * opt);
-void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * opt);
-
-bool load_train_state_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct train_state * train);
-void save_train_state_gguf(struct gguf_context * fctx, struct train_state * train);
-
-std::string get_train_filename(const char * filename, const char * pattern_it, const char * latest, int64_t iteration);
-
-void train_opt_callback(void * vdata, int accum_step, float * sched, bool * cancel);
diff --git a/spaces/Illumotion/Koboldcpp/grammars/README.md b/spaces/Illumotion/Koboldcpp/grammars/README.md
deleted file mode 100644
index 7f3b11ca5b592235e11a06eec92268f7de8da94f..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/grammars/README.md
+++ /dev/null
@@ -1,91 +0,0 @@
-# GBNF Guide
-
-GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `examples/main` and `examples/server`.
-
-## Background
-
-[Bakus-Naur Form (BNF)](https://en.wikipedia.org/wiki/Backus%E2%80%93Naur_form) is a notation for describing the syntax of formal languages like programming languages, file formats, and protocols. GBNF is an extension of BNF that primarily adds a few modern regex-like features.
-
-## Basics
-
-In GBNF, we define *production rules* that specify how a *non-terminal* (rule name) can be replaced with sequences of *terminals* (characters, specifically Unicode [code points](https://en.wikipedia.org/wiki/Code_point)) and other non-terminals. The basic format of a production rule is `nonterminal ::= sequence...`.
-
-## Example
-
-Before going deeper, let's look at some of the features demonstrated in `grammars/chess.gbnf`, a small chess notation grammar:
-```
-# `root` specifies the pattern for the overall output
-root ::= (
-    # it must start with the characters "1. " followed by a sequence
-    # of characters that match the `move` rule, followed by a space, followed
-    # by another move, and then a newline
-    "1. " move " " move "\n"
-
-    # it's followed by one or more subsequent moves, numbered with one or two digits
-    ([1-9] [0-9]? ". " move " " move "\n")+
-)
-
-# `move` is an abstract representation, which can be a pawn, nonpawn, or castle.
-# The `[+#]?` denotes the possibility of checking or mate signs after moves
-move ::= (pawn | nonpawn | castle) [+#]?
-
-pawn ::= ...
-nonpawn ::= ...
-castle ::= ...
-```
-
-## Non-Terminals and Terminals
-
-Non-terminal symbols (rule names) stand for a pattern of terminals and other non-terminals. They are required to be a dashed lowercase word, like `move`, `castle`, or `check-mate`.
-
-Terminals are actual characters ([code points](https://en.wikipedia.org/wiki/Code_point)). They can be specified as a sequence like `"1"` or `"O-O"` or as ranges like `[1-9]` or `[NBKQR]`.
-
-## Characters and character ranges
-
-Terminals support the full range of Unicode. Unicode characters can be specified directly in the grammar, for example `hiragana ::= [ぁ-ゟ]`, or with escapes: 8-bit (`\xXX`), 16-bit (`\uXXXX`) or 32-bit (`\UXXXXXXXX`).
-
-Character ranges can be negated with `^`:
-```
-single-line ::= [^\n]+ "\n"`
-```
-
-## Sequences and Alternatives
-
-The order of symbols in a sequence matter. For example, in `"1. " move " " move "\n"`, the `"1. "` must come before the first `move`, etc.
-
-Alternatives, denoted by `|`, give different sequences that are acceptable. For example, in `move ::= pawn | nonpawn | castle`, `move` can be a `pawn` move, a `nonpawn` move, or a `castle`.
-
-Parentheses `()` can be used to group sequences, which allows for embedding alternatives in a larger rule or applying repetition and optptional symbols (below) to a sequence.
-
-## Repetition and Optional Symbols
-
-- `*` after a symbol or sequence means that it can be repeated zero or more times.
-- `+` denotes that the symbol or sequence should appear one or more times.
-- `?` makes the preceding symbol or sequence optional.
-
-## Comments and newlines
-
-Comments can be specified with `#`:
-```
-# defines optional whitspace
-ws ::= [ \t\n]+
-```
-
-Newlines are allowed between rules and between symbols or sequences nested inside parentheses. Additionally, a newline after an alternate marker `|` will continue the current rule, even outside of parentheses.
-
-## The root rule
-
-In a full grammar, the `root` rule always defines the starting point of the grammar. In other words, it specifies what the entire output must match.
-
-```
-# a grammar for lists
-root ::= ("- " item)+
-item ::= [^\n]+ "\n"
-```
-
-## Next steps
-
-This guide provides a brief overview. Check out the GBNF files in this directory (`grammars/`) for examples of full grammars. You can try them out with:
-```
-./main -m <model> --grammar-file grammars/some-grammar.gbnf -p 'Some prompt'
-```
diff --git a/spaces/Illumotion/Koboldcpp/include/CL/cl_d3d11.h b/spaces/Illumotion/Koboldcpp/include/CL/cl_d3d11.h
deleted file mode 100644
index 9393e5c84cacf2894a9f3cf83968414f34fb69a2..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/include/CL/cl_d3d11.h
+++ /dev/null
@@ -1,156 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2008-2020 The Khronos Group Inc.
- *
- * 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.
- ******************************************************************************/
-
-#ifndef __OPENCL_CL_D3D11_H
-#define __OPENCL_CL_D3D11_H
-
-#if defined(_MSC_VER)
-#if _MSC_VER >=1500
-#pragma warning( push )
-#pragma warning( disable : 4201 )
-#pragma warning( disable : 5105 )
-#endif
-#endif
-#include <d3d11.h>
-#if defined(_MSC_VER)
-#if _MSC_VER >=1500
-#pragma warning( pop )
-#endif
-#endif
-#include <CL/cl.h>
-#include <CL/cl_platform.h>
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/******************************************************************************
- * cl_khr_d3d11_sharing                                                       */
-#define cl_khr_d3d11_sharing 1
-
-typedef cl_uint cl_d3d11_device_source_khr;
-typedef cl_uint cl_d3d11_device_set_khr;
-
-/******************************************************************************/
-
-/* Error Codes */
-#define CL_INVALID_D3D11_DEVICE_KHR                  -1006
-#define CL_INVALID_D3D11_RESOURCE_KHR                -1007
-#define CL_D3D11_RESOURCE_ALREADY_ACQUIRED_KHR       -1008
-#define CL_D3D11_RESOURCE_NOT_ACQUIRED_KHR           -1009
-
-/* cl_d3d11_device_source */
-#define CL_D3D11_DEVICE_KHR                          0x4019
-#define CL_D3D11_DXGI_ADAPTER_KHR                    0x401A
-
-/* cl_d3d11_device_set */
-#define CL_PREFERRED_DEVICES_FOR_D3D11_KHR           0x401B
-#define CL_ALL_DEVICES_FOR_D3D11_KHR                 0x401C
-
-/* cl_context_info */
-#define CL_CONTEXT_D3D11_DEVICE_KHR                  0x401D
-#define CL_CONTEXT_D3D11_PREFER_SHARED_RESOURCES_KHR 0x402D
-
-/* cl_mem_info */
-#define CL_MEM_D3D11_RESOURCE_KHR                    0x401E
-
-/* cl_image_info */
-#define CL_IMAGE_D3D11_SUBRESOURCE_KHR               0x401F
-
-/* cl_command_type */
-#define CL_COMMAND_ACQUIRE_D3D11_OBJECTS_KHR         0x4020
-#define CL_COMMAND_RELEASE_D3D11_OBJECTS_KHR         0x4021
-
-/******************************************************************************/
-
-typedef cl_int (CL_API_CALL *clGetDeviceIDsFromD3D11KHR_fn)(
-    cl_platform_id             platform,
-    cl_d3d11_device_source_khr d3d_device_source,
-    void *                     d3d_object,
-    cl_d3d11_device_set_khr    d3d_device_set,
-    cl_uint                    num_entries,
-    cl_device_id *             devices,
-    cl_uint *                  num_devices) CL_API_SUFFIX__VERSION_1_2;
-
-typedef cl_mem (CL_API_CALL *clCreateFromD3D11BufferKHR_fn)(
-    cl_context     context,
-    cl_mem_flags   flags,
-    ID3D11Buffer * resource,
-    cl_int *       errcode_ret) CL_API_SUFFIX__VERSION_1_2;
-
-typedef cl_mem (CL_API_CALL *clCreateFromD3D11Texture2DKHR_fn)(
-    cl_context        context,
-    cl_mem_flags      flags,
-    ID3D11Texture2D * resource,
-    UINT              subresource,
-    cl_int *          errcode_ret) CL_API_SUFFIX__VERSION_1_2;
-
-typedef cl_mem (CL_API_CALL *clCreateFromD3D11Texture3DKHR_fn)(
-    cl_context        context,
-    cl_mem_flags      flags,
-    ID3D11Texture3D * resource,
-    UINT              subresource,
-    cl_int *          errcode_ret) CL_API_SUFFIX__VERSION_1_2;
-
-typedef cl_int (CL_API_CALL *clEnqueueAcquireD3D11ObjectsKHR_fn)(
-    cl_command_queue command_queue,
-    cl_uint          num_objects,
-    const cl_mem *   mem_objects,
-    cl_uint          num_events_in_wait_list,
-    const cl_event * event_wait_list,
-    cl_event *       event) CL_API_SUFFIX__VERSION_1_2;
-
-typedef cl_int (CL_API_CALL *clEnqueueReleaseD3D11ObjectsKHR_fn)(
-    cl_command_queue command_queue,
-    cl_uint          num_objects,
-    const cl_mem *   mem_objects,
-    cl_uint          num_events_in_wait_list,
-    const cl_event * event_wait_list,
-    cl_event *       event) CL_API_SUFFIX__VERSION_1_2;
-
-/***************************************************************
-* cl_intel_sharing_format_query_d3d11
-***************************************************************/
-#define cl_intel_sharing_format_query_d3d11 1
-
-/* when cl_khr_d3d11_sharing is supported */
-
-extern CL_API_ENTRY cl_int CL_API_CALL
-clGetSupportedD3D11TextureFormatsINTEL(
-    cl_context context,
-    cl_mem_flags flags,
-    cl_mem_object_type image_type,
-    cl_uint plane,
-    cl_uint num_entries,
-    DXGI_FORMAT* d3d11_formats,
-    cl_uint* num_texture_formats) ;
-
-typedef cl_int (CL_API_CALL *
-clGetSupportedD3D11TextureFormatsINTEL_fn)(
-    cl_context context,
-    cl_mem_flags flags,
-    cl_mem_object_type image_type,
-    cl_uint plane,
-    cl_uint num_entries,
-    DXGI_FORMAT* d3d11_formats,
-    cl_uint* num_texture_formats) ;
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif  /* __OPENCL_CL_D3D11_H */
-
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/json_utils/__init__.py b/spaces/Jamkonams/AutoGPT/autogpt/json_utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/JohnnyPittt/audio-styling/deepafx_st/processors/autodiff/peq.py b/spaces/JohnnyPittt/audio-styling/deepafx_st/processors/autodiff/peq.py
deleted file mode 100644
index 04e35bbe92ed5cfb780c4ad740e1ba75f64e5b03..0000000000000000000000000000000000000000
--- a/spaces/JohnnyPittt/audio-styling/deepafx_st/processors/autodiff/peq.py
+++ /dev/null
@@ -1,274 +0,0 @@
-import torch
-
-import deepafx_st.processors.autodiff.signal
-from deepafx_st.processors.processor import Processor
-
-
-@torch.jit.script
-def parametric_eq(
-    x: torch.Tensor,
-    sample_rate: float,
-    low_shelf_gain_dB: torch.Tensor,
-    low_shelf_cutoff_freq: torch.Tensor,
-    low_shelf_q_factor: torch.Tensor,
-    first_band_gain_dB: torch.Tensor,
-    first_band_cutoff_freq: torch.Tensor,
-    first_band_q_factor: torch.Tensor,
-    second_band_gain_dB: torch.Tensor,
-    second_band_cutoff_freq: torch.Tensor,
-    second_band_q_factor: torch.Tensor,
-    third_band_gain_dB: torch.Tensor,
-    third_band_cutoff_freq: torch.Tensor,
-    third_band_q_factor: torch.Tensor,
-    fourth_band_gain_dB: torch.Tensor,
-    fourth_band_cutoff_freq: torch.Tensor,
-    fourth_band_q_factor: torch.Tensor,
-    high_shelf_gain_dB: torch.Tensor,
-    high_shelf_cutoff_freq: torch.Tensor,
-    high_shelf_q_factor: torch.Tensor,
-):
-    """Six-band parametric EQ.
-
-    Low-shelf -> Band 1 -> Band 2 -> Band 3 -> Band 4 -> High-shelf
-
-    Args:
-        x (torch.Tensor): 1d signal.
-
-
-    """
-    a_s, b_s = [], []
-    #print(f"autodiff peq fs = {sample_rate}")
-
-    # -------- apply low-shelf filter --------
-    b, a = deepafx_st.processors.autodiff.signal.biqaud(
-        low_shelf_gain_dB,
-        low_shelf_cutoff_freq,
-        low_shelf_q_factor,
-        sample_rate,
-        "low_shelf",
-    )
-    b_s.append(b)
-    a_s.append(a)
-
-    # -------- apply first-band peaking filter --------
-    b, a = deepafx_st.processors.autodiff.signal.biqaud(
-        first_band_gain_dB,
-        first_band_cutoff_freq,
-        first_band_q_factor,
-        sample_rate,
-        "peaking",
-    )
-    b_s.append(b)
-    a_s.append(a)
-
-    # -------- apply second-band peaking filter --------
-    b, a = deepafx_st.processors.autodiff.signal.biqaud(
-        second_band_gain_dB,
-        second_band_cutoff_freq,
-        second_band_q_factor,
-        sample_rate,
-        "peaking",
-    )
-    b_s.append(b)
-    a_s.append(a)
-
-    # -------- apply third-band peaking filter --------
-    b, a = deepafx_st.processors.autodiff.signal.biqaud(
-        third_band_gain_dB,
-        third_band_cutoff_freq,
-        third_band_q_factor,
-        sample_rate,
-        "peaking",
-    )
-    b_s.append(b)
-    a_s.append(a)
-
-    # -------- apply fourth-band peaking filter --------
-    b, a = deepafx_st.processors.autodiff.signal.biqaud(
-        fourth_band_gain_dB,
-        fourth_band_cutoff_freq,
-        fourth_band_q_factor,
-        sample_rate,
-        "peaking",
-    )
-    b_s.append(b)
-    a_s.append(a)
-
-    # -------- apply high-shelf filter --------
-    b, a = deepafx_st.processors.autodiff.signal.biqaud(
-        high_shelf_gain_dB,
-        high_shelf_cutoff_freq,
-        high_shelf_q_factor,
-        sample_rate,
-        "high_shelf",
-    )
-    b_s.append(b)
-    a_s.append(a)
-
-    x = deepafx_st.processors.autodiff.signal.approx_iir_filter_cascade(
-        b_s, a_s, x.view(-1)
-    )
-
-    return x
-
-
-class ParametricEQ(Processor):
-    def __init__(
-        self,
-        sample_rate,
-        min_gain_dB=-24.0,
-        default_gain_dB=0.0,
-        max_gain_dB=24.0,
-        min_q_factor=0.1,
-        default_q_factor=0.707,
-        max_q_factor=10,
-        eps=1e-8,
-    ):
-        """ """
-        super().__init__()
-        self.sample_rate = sample_rate
-        self.eps = eps
-        self.ports = [
-            {
-                "name": "Lowshelf gain",
-                "min": min_gain_dB,
-                "max": max_gain_dB,
-                "default": default_gain_dB,
-                "units": "dB",
-            },
-            {
-                "name": "Lowshelf cutoff",
-                "min": 20.0,
-                "max": 200.0,
-                "default": 100.0,
-                "units": "Hz",
-            },
-            {
-                "name": "Lowshelf Q",
-                "min": min_q_factor,
-                "max": max_q_factor,
-                "default": default_q_factor,
-                "units": "",
-            },
-            {
-                "name": "First band gain",
-                "min": min_gain_dB,
-                "max": max_gain_dB,
-                "default": default_gain_dB,
-                "units": "dB",
-            },
-            {
-                "name": "First band cutoff",
-                "min": 200.0,
-                "max": 2000.0,
-                "default": 400.0,
-                "units": "Hz",
-            },
-            {
-                "name": "First band Q",
-                "min": min_q_factor,
-                "max": max_q_factor,
-                "default": 0.707,
-                "units": "",
-            },
-            {
-                "name": "Second band gain",
-                "min": min_gain_dB,
-                "max": max_gain_dB,
-                "default": default_gain_dB,
-                "units": "dB",
-            },
-            {
-                "name": "Second band cutoff",
-                "min": 200.0,
-                "max": 4000.0,
-                "default": 1000.0,
-                "units": "Hz",
-            },
-            {
-                "name": "Second band Q",
-                "min": min_q_factor,
-                "max": max_q_factor,
-                "default": default_q_factor,
-                "units": "",
-            },
-            {
-                "name": "Third band gain",
-                "min": min_gain_dB,
-                "max": max_gain_dB,
-                "default": default_gain_dB,
-                "units": "dB",
-            },
-            {
-                "name": "Third band cutoff",
-                "min": 2000.0,
-                "max": 8000.0,
-                "default": 4000.0,
-                "units": "Hz",
-            },
-            {
-                "name": "Third band Q",
-                "min": min_q_factor,
-                "max": max_q_factor,
-                "default": default_q_factor,
-                "units": "",
-            },
-            {
-                "name": "Fourth band gain",
-                "min": min_gain_dB,
-                "max": max_gain_dB,
-                "default": default_gain_dB,
-                "units": "dB",
-            },
-            {
-                "name": "Fourth band cutoff",
-                "min": 4000.0,
-                "max": (24000 // 2) * 0.9,
-                "default": 8000.0,
-                "units": "Hz",
-            },
-            {
-                "name": "Fourth band Q",
-                "min": min_q_factor,
-                "max": max_q_factor,
-                "default": default_q_factor,
-                "units": "",
-            },
-            {
-                "name": "Highshelf gain",
-                "min": min_gain_dB,
-                "max": max_gain_dB,
-                "default": default_gain_dB,
-                "units": "dB",
-            },
-            {
-                "name": "Highshelf cutoff",
-                "min": 4000.0,
-                "max": (24000 // 2) * 0.9,
-                "default": 8000.0,
-                "units": "Hz",
-            },
-            {
-                "name": "Highshelf Q",
-                "min": min_q_factor,
-                "max": max_q_factor,
-                "default": default_q_factor,
-                "units": "",
-            },
-        ]
-
-        self.num_control_params = len(self.ports)
-
-    def forward(self, x, p, sample_rate=24000, **kwargs):
-
-        bs, chs, s = x.size()
-
-        inputs = torch.split(x, 1, 0)
-        params = torch.split(p, 1, 0)
-
-        y = []  # loop over batch dimension
-        for input, param in zip(inputs, params):
-            denorm_param = self.denormalize_params(param.view(-1))
-            y.append(parametric_eq(input.view(-1), sample_rate, *denorm_param))
-
-        return torch.stack(y, dim=0).view(bs, 1, -1)
diff --git a/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/uvr5_pack/lib_v5/nets_61968KB.py b/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/uvr5_pack/lib_v5/nets_61968KB.py
deleted file mode 100644
index 167d4cb2198863cf43e93440f7e63c5342fc7605..0000000000000000000000000000000000000000
--- a/spaces/Kangarroar/ApplioRVC-Inference/infer/lib/uvr5_pack/lib_v5/nets_61968KB.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import layers_123821KB as layers
-
-
-class BaseASPPNet(nn.Module):
-    def __init__(self, nin, ch, dilations=(4, 8, 16)):
-        super(BaseASPPNet, self).__init__()
-        self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
-        self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
-        self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
-        self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
-        self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
-        self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
-        self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
-        self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
-        self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
-    def __call__(self, x):
-        h, e1 = self.enc1(x)
-        h, e2 = self.enc2(h)
-        h, e3 = self.enc3(h)
-        h, e4 = self.enc4(h)
-
-        h = self.aspp(h)
-
-        h = self.dec4(h, e4)
-        h = self.dec3(h, e3)
-        h = self.dec2(h, e2)
-        h = self.dec1(h, e1)
-
-        return h
-
-
-class CascadedASPPNet(nn.Module):
-    def __init__(self, n_fft):
-        super(CascadedASPPNet, self).__init__()
-        self.stg1_low_band_net = BaseASPPNet(2, 32)
-        self.stg1_high_band_net = BaseASPPNet(2, 32)
-
-        self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
-        self.stg2_full_band_net = BaseASPPNet(16, 32)
-
-        self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
-        self.stg3_full_band_net = BaseASPPNet(32, 64)
-
-        self.out = nn.Conv2d(64, 2, 1, bias=False)
-        self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
-        self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
-
-        self.max_bin = n_fft // 2
-        self.output_bin = n_fft // 2 + 1
-
-        self.offset = 128
-
-    def forward(self, x, aggressiveness=None):
-        mix = x.detach()
-        x = x.clone()
-
-        x = x[:, :, : self.max_bin]
-
-        bandw = x.size()[2] // 2
-        aux1 = torch.cat(
-            [
-                self.stg1_low_band_net(x[:, :, :bandw]),
-                self.stg1_high_band_net(x[:, :, bandw:]),
-            ],
-            dim=2,
-        )
-
-        h = torch.cat([x, aux1], dim=1)
-        aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
-        h = torch.cat([x, aux1, aux2], dim=1)
-        h = self.stg3_full_band_net(self.stg3_bridge(h))
-
-        mask = torch.sigmoid(self.out(h))
-        mask = F.pad(
-            input=mask,
-            pad=(0, 0, 0, self.output_bin - mask.size()[2]),
-            mode="replicate",
-        )
-
-        if self.training:
-            aux1 = torch.sigmoid(self.aux1_out(aux1))
-            aux1 = F.pad(
-                input=aux1,
-                pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
-                mode="replicate",
-            )
-            aux2 = torch.sigmoid(self.aux2_out(aux2))
-            aux2 = F.pad(
-                input=aux2,
-                pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
-                mode="replicate",
-            )
-            return mask * mix, aux1 * mix, aux2 * mix
-        else:
-            if aggressiveness:
-                mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
-                    mask[:, :, : aggressiveness["split_bin"]],
-                    1 + aggressiveness["value"] / 3,
-                )
-                mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
-                    mask[:, :, aggressiveness["split_bin"] :],
-                    1 + aggressiveness["value"],
-                )
-
-            return mask * mix
-
-    def predict(self, x_mag, aggressiveness=None):
-        h = self.forward(x_mag, aggressiveness)
-
-        if self.offset > 0:
-            h = h[:, :, :, self.offset : -self.offset]
-            assert h.size()[3] > 0
-
-        return h
diff --git a/spaces/KenjieDec/GPEN/face_model/model.py b/spaces/KenjieDec/GPEN/face_model/model.py
deleted file mode 100644
index fe54c123c026b8c0af6fac39815afaea0a7017a4..0000000000000000000000000000000000000000
--- a/spaces/KenjieDec/GPEN/face_model/model.py
+++ /dev/null
@@ -1,818 +0,0 @@
-'''
-@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
-@author: yangxy (yangtao9009@gmail.com)
-'''
-import math
-import random
-import functools
-import operator
-import itertools
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.autograd import Function
-
-from op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
-
-class PixelNorm(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, input):
-        return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
-
-
-def make_kernel(k):
-    k = torch.tensor(k, dtype=torch.float32)
-
-    if k.ndim == 1:
-        k = k[None, :] * k[:, None]
-
-    k /= k.sum()
-
-    return k
-
-
-class Upsample(nn.Module):
-    def __init__(self, kernel, factor=2, device='cpu'):
-        super().__init__()
-
-        self.factor = factor
-        kernel = make_kernel(kernel) * (factor ** 2)
-        self.register_buffer('kernel', kernel)
-
-        p = kernel.shape[0] - factor
-
-        pad0 = (p + 1) // 2 + factor - 1
-        pad1 = p // 2
-
-        self.pad = (pad0, pad1)
-        self.device = device
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad, device=self.device)
-
-        return out
-
-
-class Downsample(nn.Module):
-    def __init__(self, kernel, factor=2, device='cpu'):
-        super().__init__()
-
-        self.factor = factor
-        kernel = make_kernel(kernel)
-        self.register_buffer('kernel', kernel)
-
-        p = kernel.shape[0] - factor
-
-        pad0 = (p + 1) // 2
-        pad1 = p // 2
-
-        self.pad = (pad0, pad1)
-        self.device = device
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad, device=self.device)
-
-        return out
-
-
-class Blur(nn.Module):
-    def __init__(self, kernel, pad, upsample_factor=1, device='cpu'):
-        super().__init__()
-
-        kernel = make_kernel(kernel)
-
-        if upsample_factor > 1:
-            kernel = kernel * (upsample_factor ** 2)
-
-        self.register_buffer('kernel', kernel)
-
-        self.pad = pad
-        self.device = device
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, pad=self.pad, device=self.device)
-
-        return out
-
-
-class EqualConv2d(nn.Module):
-    def __init__(
-        self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
-    ):
-        super().__init__()
-
-        self.weight = nn.Parameter(
-            torch.randn(out_channel, in_channel, kernel_size, kernel_size)
-        )
-        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
-
-        self.stride = stride
-        self.padding = padding
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channel))
-
-        else:
-            self.bias = None
-
-    def forward(self, input):
-        out = F.conv2d(
-            input,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
-            f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
-        )
-
-
-class EqualLinear(nn.Module):
-    def __init__(
-        self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None, device='cpu'
-    ):
-        super().__init__()
-
-        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
-
-        else:
-            self.bias = None
-
-        self.activation = activation
-        self.device = device
-
-        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
-        self.lr_mul = lr_mul
-
-    def forward(self, input):
-        if self.activation:
-            out = F.linear(input, self.weight * self.scale)
-            out = fused_leaky_relu(out, self.bias * self.lr_mul, device=self.device)
-
-        else:
-            out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul)
-
-        return out
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
-        )
-
-
-class ScaledLeakyReLU(nn.Module):
-    def __init__(self, negative_slope=0.2):
-        super().__init__()
-
-        self.negative_slope = negative_slope
-
-    def forward(self, input):
-        out = F.leaky_relu(input, negative_slope=self.negative_slope)
-
-        return out * math.sqrt(2)
-
-
-class ModulatedConv2d(nn.Module):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        style_dim,
-        demodulate=True,
-        upsample=False,
-        downsample=False,
-        blur_kernel=[1, 3, 3, 1],
-        device='cpu'
-    ):
-        super().__init__()
-
-        self.eps = 1e-8
-        self.kernel_size = kernel_size
-        self.in_channel = in_channel
-        self.out_channel = out_channel
-        self.upsample = upsample
-        self.downsample = downsample
-
-        if upsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) - (kernel_size - 1)
-            pad0 = (p + 1) // 2 + factor - 1
-            pad1 = p // 2 + 1
-
-            self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor, device=device)
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            pad0 = (p + 1) // 2
-            pad1 = p // 2
-
-            self.blur = Blur(blur_kernel, pad=(pad0, pad1), device=device)
-
-        fan_in = in_channel * kernel_size ** 2
-        self.scale = 1 / math.sqrt(fan_in)
-        self.padding = kernel_size // 2
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
-        )
-
-        self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
-
-        self.demodulate = demodulate
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
-            f'upsample={self.upsample}, downsample={self.downsample})'
-        )
-
-    def forward(self, input, style):
-        batch, in_channel, height, width = input.shape
-
-        style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
-        weight = self.scale * self.weight * style
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
-            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
-
-        weight = weight.view(
-            batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
-        )
-
-        if self.upsample:
-            input = input.view(1, batch * in_channel, height, width)
-            weight = weight.view(
-                batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
-            )
-            weight = weight.transpose(1, 2).reshape(
-                batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
-            )
-            out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-            out = self.blur(out)
-
-        elif self.downsample:
-            input = self.blur(input)
-            _, _, height, width = input.shape
-            input = input.view(1, batch * in_channel, height, width)
-            out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-
-        else:
-            input = input.view(1, batch * in_channel, height, width)
-            out = F.conv2d(input, weight, padding=self.padding, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-
-        return out
-
-
-class NoiseInjection(nn.Module):
-    def __init__(self, isconcat=True):
-        super().__init__()
-
-        self.isconcat = isconcat
-        self.weight = nn.Parameter(torch.zeros(1))
-
-    def forward(self, image, noise=None):
-        if noise==None:
-            batch, channel, height, width = image.shape
-            noise = image.new_empty(batch, channel, height, width).normal_()
-
-        if self.isconcat:
-            return torch.cat((image, self.weight * noise), dim=1)
-        else:
-            return image + self.weight * noise
-
-
-class ConstantInput(nn.Module):
-    def __init__(self, channel, size=4):
-        super().__init__()
-
-        self.input = nn.Parameter(torch.randn(1, channel, size, size))
-
-    def forward(self, input):
-        batch = input.shape[0]
-        out = self.input.repeat(batch, 1, 1, 1)
-
-        return out
-
-
-class StyledConv(nn.Module):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        style_dim,
-        upsample=False,
-        blur_kernel=[1, 3, 3, 1],
-        demodulate=True,
-        isconcat=True,
-        device='cpu'
-    ):
-        super().__init__()
-
-        self.conv = ModulatedConv2d(
-            in_channel,
-            out_channel,
-            kernel_size,
-            style_dim,
-            upsample=upsample,
-            blur_kernel=blur_kernel,
-            demodulate=demodulate,
-            device=device
-        )
-
-        self.noise = NoiseInjection(isconcat)
-        #self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
-        #self.activate = ScaledLeakyReLU(0.2)
-        feat_multiplier = 2 if isconcat else 1
-        self.activate = FusedLeakyReLU(out_channel*feat_multiplier, device=device)
-
-    def forward(self, input, style, noise=None):
-        out = self.conv(input, style)
-        out = self.noise(out, noise=noise)
-        # out = out + self.bias
-        out = self.activate(out)
-
-        return out
-
-
-class ToRGB(nn.Module):
-    def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1], device='cpu'):
-        super().__init__()
-
-        if upsample:
-            self.upsample = Upsample(blur_kernel, device=device)
-
-        self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False, device=device)
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, input, style, skip=None):
-        out = self.conv(input, style)
-        out = out + self.bias
-
-        if skip is not None:
-            skip = self.upsample(skip)
-
-            out = out + skip
-
-        return out
-
-class Generator(nn.Module):
-    def __init__(
-        self,
-        size,
-        style_dim,
-        n_mlp,
-        channel_multiplier=2,
-        blur_kernel=[1, 3, 3, 1],
-        lr_mlp=0.01,
-        isconcat=True,
-        narrow=1,
-        device='cpu'
-    ):
-        super().__init__()
-
-        self.size = size
-        self.n_mlp = n_mlp
-        self.style_dim = style_dim
-        self.feat_multiplier = 2 if isconcat else 1
-
-        layers = [PixelNorm()]
-
-        for i in range(n_mlp):
-            layers.append(
-                EqualLinear(
-                    style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu', device=device
-                )
-            )
-
-        self.style = nn.Sequential(*layers)
-
-        self.channels = {
-            4: int(512 * narrow),
-            8: int(512 * narrow),
-            16: int(512 * narrow),
-            32: int(512 * narrow),
-            64: int(256 * channel_multiplier * narrow),
-            128: int(128 * channel_multiplier * narrow),
-            256: int(64 * channel_multiplier * narrow),
-            512: int(32 * channel_multiplier * narrow),
-            1024: int(16 * channel_multiplier * narrow),
-            2048: int(8 * channel_multiplier * narrow)
-        }
-
-        self.input = ConstantInput(self.channels[4])
-        self.conv1 = StyledConv(
-            self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel, isconcat=isconcat, device=device
-        )
-        self.to_rgb1 = ToRGB(self.channels[4]*self.feat_multiplier, style_dim, upsample=False, device=device)
-
-        self.log_size = int(math.log(size, 2))
-
-        self.convs = nn.ModuleList()
-        self.upsamples = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-
-        in_channel = self.channels[4]
-
-        for i in range(3, self.log_size + 1):
-            out_channel = self.channels[2 ** i]
-
-            self.convs.append(
-                StyledConv(
-                    in_channel*self.feat_multiplier,
-                    out_channel,
-                    3,
-                    style_dim,
-                    upsample=True,
-                    blur_kernel=blur_kernel,
-                    isconcat=isconcat,
-                    device=device
-                )
-            )
-
-            self.convs.append(
-                StyledConv(
-                    out_channel*self.feat_multiplier, out_channel, 3, style_dim, blur_kernel=blur_kernel, isconcat=isconcat, device=device
-                )
-            )
-
-            self.to_rgbs.append(ToRGB(out_channel*self.feat_multiplier, style_dim, device=device))
-
-            in_channel = out_channel
-
-        self.n_latent = self.log_size * 2 - 2
-
-    def make_noise(self):
-        device = self.input.input.device
-
-        noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
-
-        return noises
-
-    def mean_latent(self, n_latent):
-        latent_in = torch.randn(
-            n_latent, self.style_dim, device=self.input.input.device
-        )
-        latent = self.style(latent_in).mean(0, keepdim=True)
-
-        return latent
-
-    def get_latent(self, input):
-        return self.style(input)
-
-    def forward(
-        self,
-        styles,
-        return_latents=False,
-        inject_index=None,
-        truncation=1,
-        truncation_latent=None,
-        input_is_latent=False,
-        noise=None,
-    ):
-        if not input_is_latent:
-            styles = [self.style(s) for s in styles]
-
-        if noise==None:
-            '''
-            noise = [None] * (2 * (self.log_size - 2) + 1)
-            '''
-            noise = []
-            batch = styles[0].shape[0]
-            for i in range(self.n_mlp + 1):
-                size = 2 ** (i+2)
-                noise.append(torch.randn(batch, self.channels[size], size, size, device=styles[0].device))
-            
-        if truncation < 1:
-            style_t = []
-
-            for style in styles:
-                style_t.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-
-            styles = style_t
-
-        if len(styles) < 2:
-            inject_index = self.n_latent
-
-            latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-
-        else:
-            if inject_index==None:
-                inject_index = random.randint(1, self.n_latent - 1)
-
-            latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
-
-            latent = torch.cat([latent, latent2], 1)
-
-        out = self.input(latent)
-        out = self.conv1(out, latent[:, 0], noise=noise[0])
-
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)
-
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-
-        else:
-            return image, None
-
-class ConvLayer(nn.Sequential):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        downsample=False,
-        blur_kernel=[1, 3, 3, 1],
-        bias=True,
-        activate=True,
-        device='cpu'
-    ):
-        layers = []
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            pad0 = (p + 1) // 2
-            pad1 = p // 2
-
-            layers.append(Blur(blur_kernel, pad=(pad0, pad1), device=device))
-
-            stride = 2
-            self.padding = 0
-
-        else:
-            stride = 1
-            self.padding = kernel_size // 2
-
-        layers.append(
-            EqualConv2d(
-                in_channel,
-                out_channel,
-                kernel_size,
-                padding=self.padding,
-                stride=stride,
-                bias=bias and not activate,
-            )
-        )
-
-        if activate:
-            if bias:
-                layers.append(FusedLeakyReLU(out_channel, device=device))
-
-            else:
-                layers.append(ScaledLeakyReLU(0.2))
-
-        super().__init__(*layers)
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1], device='cpu'):
-        super().__init__()
-
-        self.conv1 = ConvLayer(in_channel, in_channel, 3, device=device)
-        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True, device=device)
-
-        self.skip = ConvLayer(
-            in_channel, out_channel, 1, downsample=True, activate=False, bias=False
-        )
-
-    def forward(self, input):
-        out = self.conv1(input)
-        out = self.conv2(out)
-
-        skip = self.skip(input)
-        out = (out + skip) / math.sqrt(2)
-
-        return out
-
-class FullGenerator(nn.Module):
-    def __init__(
-        self,
-        size,
-        style_dim,
-        n_mlp,
-        channel_multiplier=2,
-        blur_kernel=[1, 3, 3, 1],
-        lr_mlp=0.01,
-        isconcat=True,
-        narrow=1,
-        device='cpu'
-    ):
-        super().__init__()
-        channels = {
-            4: int(512 * narrow),
-            8: int(512 * narrow),
-            16: int(512 * narrow),
-            32: int(512 * narrow),
-            64: int(256 * channel_multiplier * narrow),
-            128: int(128 * channel_multiplier * narrow),
-            256: int(64 * channel_multiplier * narrow),
-            512: int(32 * channel_multiplier * narrow),
-            1024: int(16 * channel_multiplier * narrow),
-            2048: int(8 * channel_multiplier * narrow)
-        }
-
-        self.log_size = int(math.log(size, 2))
-        self.generator = Generator(size, style_dim, n_mlp, channel_multiplier=channel_multiplier, blur_kernel=blur_kernel, lr_mlp=lr_mlp, isconcat=isconcat, narrow=narrow, device=device)
-        
-        conv = [ConvLayer(3, channels[size], 1, device=device)]
-        self.ecd0 = nn.Sequential(*conv)
-        in_channel = channels[size]
-
-        self.names = ['ecd%d'%i for i in range(self.log_size-1)]
-        for i in range(self.log_size, 2, -1):
-            out_channel = channels[2 ** (i - 1)]
-            #conv = [ResBlock(in_channel, out_channel, blur_kernel)]
-            conv = [ConvLayer(in_channel, out_channel, 3, downsample=True, device=device)] 
-            setattr(self, self.names[self.log_size-i+1], nn.Sequential(*conv))
-            in_channel = out_channel
-        self.final_linear = nn.Sequential(EqualLinear(channels[4] * 4 * 4, style_dim, activation='fused_lrelu', device=device))
-
-    def forward(self,
-        inputs,
-        return_latents=False,
-        inject_index=None,
-        truncation=1,
-        truncation_latent=None,
-        input_is_latent=False,
-    ):
-        noise = []
-        for i in range(self.log_size-1):
-            ecd = getattr(self, self.names[i])
-            inputs = ecd(inputs)
-            noise.append(inputs)
-            #print(inputs.shape)
-        inputs = inputs.view(inputs.shape[0], -1)
-        outs = self.final_linear(inputs)
-        #print(outs.shape)
-        noise = list(itertools.chain.from_iterable(itertools.repeat(x, 2) for x in noise))[::-1]
-        outs = self.generator([outs], return_latents, inject_index, truncation, truncation_latent, input_is_latent, noise=noise[1:])
-        return outs
-
-class Discriminator(nn.Module):
-    def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], narrow=1, device='cpu'):
-        super().__init__()
-
-        channels = {
-            4: int(512 * narrow),
-            8: int(512 * narrow),
-            16: int(512 * narrow),
-            32: int(512 * narrow),
-            64: int(256 * channel_multiplier * narrow),
-            128: int(128 * channel_multiplier * narrow),
-            256: int(64 * channel_multiplier * narrow),
-            512: int(32 * channel_multiplier * narrow),
-            1024: int(16 * channel_multiplier * narrow),
-            2048: int(8 * channel_multiplier * narrow)
-        }
-
-        convs = [ConvLayer(3, channels[size], 1, device=device)]
-
-        log_size = int(math.log(size, 2))
-
-        in_channel = channels[size]
-
-        for i in range(log_size, 2, -1):
-            out_channel = channels[2 ** (i - 1)]
-
-            convs.append(ResBlock(in_channel, out_channel, blur_kernel, device=device))
-
-            in_channel = out_channel
-
-        self.convs = nn.Sequential(*convs)
-
-        self.stddev_group = 4
-        self.stddev_feat = 1
-
-        self.final_conv = ConvLayer(in_channel + 1, channels[4], 3, device=device)
-        self.final_linear = nn.Sequential(
-            EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu', device=device),
-            EqualLinear(channels[4], 1),
-        )
-
-    def forward(self, input):
-        out = self.convs(input)
-
-        batch, channel, height, width = out.shape
-        group = min(batch, self.stddev_group)
-        stddev = out.view(
-            group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
-        )
-        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
-        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
-        stddev = stddev.repeat(group, 1, height, width)
-        out = torch.cat([out, stddev], 1)
-
-        out = self.final_conv(out)
-
-        out = out.view(batch, -1)
-        out = self.final_linear(out)
-        return out
-
-class FullGenerator_SR(nn.Module):
-    def __init__(
-        self,
-        size,
-        out_size,
-        style_dim,
-        n_mlp,
-        channel_multiplier=2,
-        blur_kernel=[1, 3, 3, 1],
-        lr_mlp=0.01,
-        isconcat=True,
-        narrow=1,
-        device='cpu'
-    ):
-        super().__init__()
-        channels = {
-            4: int(512 * narrow),
-            8: int(512 * narrow),
-            16: int(512 * narrow),
-            32: int(512 * narrow),
-            64: int(256 * channel_multiplier * narrow),
-            128: int(128 * channel_multiplier * narrow),
-            256: int(64 * channel_multiplier * narrow),
-            512: int(32 * channel_multiplier * narrow),
-            1024: int(16 * channel_multiplier * narrow),
-            2048: int(8 * channel_multiplier * narrow),
-        }
-
-        self.log_insize = int(math.log(size, 2))
-        self.log_outsize = int(math.log(out_size, 2))
-        self.generator = Generator(out_size, style_dim, n_mlp, channel_multiplier=channel_multiplier, blur_kernel=blur_kernel, lr_mlp=lr_mlp, isconcat=isconcat, narrow=narrow, device=device)
-
-        conv = [ConvLayer(3, channels[size], 1, device=device)]
-        self.ecd0 = nn.Sequential(*conv)
-        in_channel = channels[size]
-
-        self.names = ['ecd%d'%i for i in range(self.log_insize-1)]
-        for i in range(self.log_insize, 2, -1):
-            out_channel = channels[2 ** (i - 1)]
-            #conv = [ResBlock(in_channel, out_channel, blur_kernel)]
-            conv = [ConvLayer(in_channel, out_channel, 3, downsample=True, device=device)]
-            setattr(self, self.names[self.log_insize-i+1], nn.Sequential(*conv))
-            in_channel = out_channel
-        self.final_linear = nn.Sequential(EqualLinear(channels[4] * 4 * 4, style_dim, activation='fused_lrelu', device=device))
-
-    def forward(self,
-        inputs,
-        return_latents=False,
-        inject_index=None,
-        truncation=1,
-        truncation_latent=None,
-        input_is_latent=False,
-    ):
-        noise = []
-        for i in range(self.log_outsize-self.log_insize):
-            noise.append(None)
-        for i in range(self.log_insize-1):
-            ecd = getattr(self, self.names[i])
-            inputs = ecd(inputs)
-            noise.append(inputs)
-            #print(inputs.shape)
-        inputs = inputs.view(inputs.shape[0], -1)
-        outs = self.final_linear(inputs)
-        #print(outs.shape)
-        noise = list(itertools.chain.from_iterable(itertools.repeat(x, 2) for x in noise))[::-1]
-        image, latent = self.generator([outs], return_latents, inject_index, truncation, truncation_latent, input_is_latent, noise=noise[1:])
-        return image, latent
\ No newline at end of file
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/encoder/convolution.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/encoder/convolution.py
deleted file mode 100644
index 2d2c399e406aae97a6baf0f7de379a1d90a97949..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/ppg_extractor/encoder/convolution.py
+++ /dev/null
@@ -1,74 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# Copyright 2020 Johns Hopkins University (Shinji Watanabe)
-#                Northwestern Polytechnical University (Pengcheng Guo)
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""ConvolutionModule definition."""
-
-from torch import nn
-
-
-class ConvolutionModule(nn.Module):
-    """ConvolutionModule in Conformer model.
-
-    :param int channels: channels of cnn
-    :param int kernel_size: kernerl size of cnn
-
-    """
-
-    def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
-        """Construct an ConvolutionModule object."""
-        super(ConvolutionModule, self).__init__()
-        # kernerl_size should be a odd number for 'SAME' padding
-        assert (kernel_size - 1) % 2 == 0
-
-        self.pointwise_conv1 = nn.Conv1d(
-            channels,
-            2 * channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            bias=bias,
-        )
-        self.depthwise_conv = nn.Conv1d(
-            channels,
-            channels,
-            kernel_size,
-            stride=1,
-            padding=(kernel_size - 1) // 2,
-            groups=channels,
-            bias=bias,
-        )
-        self.norm = nn.BatchNorm1d(channels)
-        self.pointwise_conv2 = nn.Conv1d(
-            channels,
-            channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            bias=bias,
-        )
-        self.activation = activation
-
-    def forward(self, x):
-        """Compute convolution module.
-
-        :param torch.Tensor x: (batch, time, size)
-        :return torch.Tensor: convoluted `value` (batch, time, d_model)
-        """
-        # exchange the temporal dimension and the feature dimension
-        x = x.transpose(1, 2)
-
-        # GLU mechanism
-        x = self.pointwise_conv1(x)  # (batch, 2*channel, dim)
-        x = nn.functional.glu(x, dim=1)  # (batch, channel, dim)
-
-        # 1D Depthwise Conv
-        x = self.depthwise_conv(x)
-        x = self.activation(self.norm(x))
-
-        x = self.pointwise_conv2(x)
-
-        return x.transpose(1, 2)
diff --git a/spaces/KevinQHLin/UniVTG/model/transformer_encoder_droppath.py b/spaces/KevinQHLin/UniVTG/model/transformer_encoder_droppath.py
deleted file mode 100644
index 536d46529a4722c1bf787d85fbace0afe1e3a33b..0000000000000000000000000000000000000000
--- a/spaces/KevinQHLin/UniVTG/model/transformer_encoder_droppath.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import copy
-import pdb
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from torch import nn, Tensor
-
-def mask_logits(inputs, mask, mask_value=-1e30):
-    mask = mask.type(torch.float32)
-    return inputs + (1.0 - mask) * mask_value
-
-
-class Transformer(nn.Module):
-
-    def __init__(self, d_model=512, nhead=8, num_encoder_layers=4,
-                 num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, droppath=0.1,
-                 activation="gelu", normalize_before=False,  # False as default
-                 return_intermediate_dec=False):
-        super().__init__()
-
-        encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward,
-                                                dropout, droppath, activation, normalize_before)
-        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
-        self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
-
-        self._reset_parameters()
-
-        self.d_model = d_model
-        self.nhead = nhead
-
-    def _reset_parameters(self):
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-
-    def forward(self, src, mask, pos_embed):
-        """
-        Args:
-            src: (batch_size, L, d)
-            mask: (batch_size, L)
-            query_embed: (#queries, d) -> my imple (batch_size, d) and #queries=1
-            pos_embed: (batch_size, L, d) the same as src
-
-        Returns:
-
-        """
-        # flatten NxCxHxW to HWxNxC
-        src = src.permute(1, 0, 2)  # (L, batch_size, d)
-        pos_embed = pos_embed.permute(1, 0, 2)   # (L, batch_size, d)
-
-        memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
-        memory = memory.transpose(0, 1)
-
-        return memory
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(self, encoder_layer, num_layers, norm=None, return_intermediate=False):
-        super().__init__()
-        self.layers = _get_clones(encoder_layer, num_layers)
-        self.num_layers = num_layers
-        self.norm = norm
-        self.return_intermediate = return_intermediate
-
-    def forward(self, src,
-                mask: Optional[Tensor] = None,
-                src_key_padding_mask: Optional[Tensor] = None,
-                pos: Optional[Tensor] = None):
-        output = src
-
-        intermediate = []
-
-        for layer in self.layers:
-            output = layer(output, src_mask=mask,
-                           src_key_padding_mask=src_key_padding_mask, pos=pos)
-            if self.return_intermediate:
-                intermediate.append(output)
-
-        if self.norm is not None:
-            output = self.norm(output)
-
-        if self.return_intermediate:
-            return torch.stack(intermediate)
-
-        return output
-
-class TransformerEncoderLayer(nn.Module):
-
-    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, droppath=0.1,
-                 activation="relu", normalize_before=False):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
-        # Implementation of Feedforward model
-        self.linear1 = nn.Linear(d_model, dim_feedforward)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_feedforward, d_model)
-
-        self.norm1 = nn.LayerNorm(d_model)
-        self.norm2 = nn.LayerNorm(d_model)
-        # self.dropout1 = nn.Dropout(dropout)
-        # self.dropout2 = nn.Dropout(dropout)
-        self.droppath1 = DropPath(droppath)
-        self.droppath2 = DropPath(droppath)
-
-        self.activation = _get_activation_fn(activation)
-        self.normalize_before = normalize_before
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward_post(self,
-                     src,
-                     src_mask: Optional[Tensor] = None,
-                     src_key_padding_mask: Optional[Tensor] = None,
-                     pos: Optional[Tensor] = None):
-        q = k = self.with_pos_embed(src, pos)
-        src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
-        # src2 = self.self_attn_eff(q=q, k=k, v=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
-        src = src + self.droppath1(src2)
-        src = self.norm1(src)
-        src2 = self.linear2(self.activation(self.linear1(src)))
-        # src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
-        src = src + self.droppath2(src2)
-        src = self.norm2(src)
-        return src
-
-    def forward(self, src,
-                src_mask: Optional[Tensor] = None,
-                src_key_padding_mask: Optional[Tensor] = None,
-                pos: Optional[Tensor] = None):
-        if self.normalize_before:
-            return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
-        return self.forward_post(src, src_mask, src_key_padding_mask, pos)
-
-
-def _get_clones(module, N):
-    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
-
-
-def build_transformer(args):
-    return Transformer(
-        d_model=args.hidden_dim,
-        dropout=args.dropout,
-        droppath=args.droppath,
-        nhead=args.nheads,
-        dim_feedforward=args.dim_feedforward,
-        num_encoder_layers=args.enc_layers,
-        num_decoder_layers=args.dec_layers,
-        normalize_before=args.pre_norm,
-        return_intermediate_dec=True,
-    )
-
-def drop_path(x, drop_prob=0.0, training=False):
-    """
-    Stochastic Depth per sample.
-    """
-    if drop_prob == 0.0 or not training:
-        return x
-
-    keep_prob = 1 - drop_prob
-    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
-    mask = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
-    mask.floor_()
-    x = x.div(keep_prob) * mask
-
-    return x
-
-
-class DropPath(nn.Module):
-    """
-    Drop paths per sample (when applied in main path of residual blocks).
-    """
-
-    def __init__(self, drop_prob=None):
-        super(DropPath, self).__init__()
-
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        x = x.permute(1, 0, 2)
-        res = drop_path(x, self.drop_prob, self.training)
-        return res.permute(1, 0, 2)
-    #    return drop_path(x, self.drop_prob, self.training)
-
-def _get_activation_fn(activation):
-    """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
-    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
\ No newline at end of file
diff --git a/spaces/LavanyaBurlagadda/TChatBotWithPlayHT1/README.md b/spaces/LavanyaBurlagadda/TChatBotWithPlayHT1/README.md
deleted file mode 100644
index 2b35a86a7cd5100b8d251c9decdd88edda6f600c..0000000000000000000000000000000000000000
--- a/spaces/LavanyaBurlagadda/TChatBotWithPlayHT1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: TChatBotWithPlayHT1
-emoji: 👁
-colorFrom: gray
-colorTo: red
-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/LaynzKunz/Aesthetic_RVC_Inference_HF/assets/themes/Acat.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/assets/themes/Acat.py
deleted file mode 100644
index 32c0dbe3bd221eb97ca329a77fe193d124ce1fdd..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/assets/themes/Acat.py
+++ /dev/null
@@ -1,313 +0,0 @@
-from __future__ import annotations
-
-from typing import Iterable
-import gradio as gr
-
-#gr.themes.builder()
-from gradio.themes.base import Base
-from gradio.themes.utils import colors, fonts, sizes
-import time
-
-class Acat(Base):
-    def __init__(
-        self,
-        *,
-        primary_hue: colors.Color | str = colors.green,
-        secondary_hue: colors.Color | str = colors.emerald,
-        neutral_hue: colors.Color | str = colors.neutral,
-        spacing_size: sizes.Size | str = sizes.spacing_md,
-        radius_size: sizes.Size | str = sizes.radius_md,
-        text_size: sizes.Size | str = sizes.text_lg,
-        font: fonts.Font
-        | str
-        | Iterable[fonts.Font | str] = (
-            'Inter V',
-            fonts.GoogleFont('Asap'),
-            'ui-sans-serif',
-            'sans-serif',
-        ),
-        font_mono: fonts.Font
-        | str
-        | Iterable[fonts.Font | str] = (
-            'ui-monospace',
-            fonts.GoogleFont("Fira Code"),
-            'Consolas',
-            'monospace',
-        ),
-    ):
-        super().__init__(
-            primary_hue=primary_hue,
-            secondary_hue=secondary_hue,
-            neutral_hue=neutral_hue,
-            spacing_size=spacing_size,
-            radius_size=radius_size,
-            text_size=text_size,
-            font=font,
-            font_mono=font_mono,
-        )
-        self.name= "Acat",
-        self.secondary_100='#e2effc',
-        self.secondary_200='#bedff9',
-        self.secondary_300='#84c5f5',
-        self.secondary_400='#4eacef',
-        self.secondary_50='#f1f8fe',
-        self.secondary_500='#198cde',
-        self.secondary_600='#0c6ebd',
-        self.secondary_700='#0b5899',
-        self.secondary_800='#0e4b7e',
-        self.secondary_900='#113f69',
-        self.secondary_950='#0b2846',
-        self.neutral_100='#e2effc',
-        self.neutral_200='#bedff9',
-        self.neutral_300='#84c5f5',
-        self.neutral_400='#4eacef',
-        self.neutral_50='#f1f8fe',
-        self.neutral_500='#198cde',
-        self.neutral_600='#0c6ebd',
-        self.neutral_700='#0b5899',
-        self.neutral_800='#0e4b7e',
-        self.neutral_900='#113f69',
-        self.neutral_950='#0b2846',
-        self.primary_100='#e2effc',
-        self.primary_200='#bedff9',
-        self.primary_300='#84c5f5',
-        self.primary_400='#4eacef',
-        self.primary_50='#f1f8fe',
-        self.primary_500='#198cde',
-        self.primary_600='#0c6ebd',
-        self.primary_700='#0b5899',
-        self.primary_800='#0e4b7e',
-        self.primary_900='#113f69',
-        self.primary_950='#0b2846',
-        super().set(
-            # Blaise
-            background_fill_primary='#FFFFFF',
-            background_fill_primary_dark='#000000',
-            background_fill_secondary='#dce3e8',
-            background_fill_secondary_dark='#242424',
-            block_background_fill='#ECF2F7',
-            block_background_fill_dark='#191919',
-            block_border_color='#dce3e8',
-            block_border_color_dark='#242424',
-            block_border_width='1px',
-            block_info_text_color='#191919',
-            block_info_text_color_dark='#ECF2F7',
-            block_info_text_size='*text_sm',
-            block_info_text_weight='400',
-            block_label_background_fill='#ECF2F700',
-            block_label_background_fill_dark='#19191900',
-            block_label_border_color='#dce3e8',
-            block_label_border_color_dark='#242424',
-            block_label_border_width='1px',
-            block_label_margin='0',
-            block_label_padding='*spacing_sm *spacing_lg',
-            block_label_radius= "calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px) 0",
-            block_label_right_radius= "0 calc(*radius_lg - 1px) 0 calc(*radius_lg - 1px)",
-            block_label_shadow='*block_shadow',
-            block_label_text_color='#4EACEF',
-            block_label_text_color_dark='#4EACEF',
-            block_label_text_size='*text_sm',
-            block_label_text_weight='400',
-            block_padding='*spacing_xl calc(*spacing_xl + 2px)',
-            block_radius='*radius_lg',
-            block_shadow='#FFFFFF00',
-            block_shadow_dark='#00000000',
-            block_title_background_fill='#ECF2F700',
-            block_title_background_fill_dark='#19191900',
-            block_title_border_color='#dce3e8',
-            block_title_border_color_dark='#242424',
-            block_title_border_width='0px',
-            block_title_padding='0',
-            block_title_radius='none',
-            block_title_text_color='#4EACEF',
-            block_title_text_color_dark='#4EACEF',
-            block_title_text_size='*text_md',
-            block_title_text_weight='bold',
-            body_background_fill="url('https://cdn.discordapp.com/attachments/1151036558389026838/1158634938128670730/image-removebg-preview_1.png') #FFFFFF no-repeat right bottom/auto 30svh padding-box fixed",
-            body_background_fill_dark="url('https://cdn.discordapp.com/attachments/1151036558389026838/1158634938128670730/image-removebg-preview_1.png') #000000 no-repeat right bottom/auto 30svh padding-box fixed",
-            body_text_color='#191919',
-            body_text_color_dark='#ECF2F7',
-            body_text_color_subdued='#636668',
-            body_text_color_subdued_dark='#c4c4c4',
-            body_text_size='*text_md',
-            body_text_weight='400',
-            border_color_accent='#dce3e8',
-            border_color_accent_dark='#242424',
-            border_color_primary='#dce3e8',
-            border_color_primary_dark='#242424',
-            button_border_width='*input_border_width',
-            button_border_width_dark='*input_border_width',
-            button_cancel_background_fill='#dce3e8',
-            button_cancel_background_fill_dark='#242424',
-            button_cancel_background_fill_hover='#d0d7db',
-            button_cancel_background_fill_hover_dark='#202020',
-            button_cancel_border_color='#191919',
-            button_cancel_border_color_dark='#ECF2F7',
-            button_cancel_border_color_hover='#202020',
-            button_cancel_border_color_hover_dark='#a1c3d8',
-            button_cancel_text_color='#4EACEF',
-            button_cancel_text_color_dark='#4EACEF',
-            button_cancel_text_color_hover='#0c6ebd',
-            button_cancel_text_color_hover_dark='#0c6ebd',
-            button_large_padding='*spacing_lg calc(2 * *spacing_lg)',
-            button_large_radius='*radius_lg',
-            button_large_text_size='*text_lg',
-            button_large_text_weight='600',
-            button_primary_background_fill='#4EACEF',
-            button_primary_background_fill_dark='#4EACEF',
-            button_primary_background_fill_hover='#0c6ebd',
-            button_primary_background_fill_hover_dark='#0c6ebd',
-            button_primary_border_color='#191919',
-            button_primary_border_color_dark='#ECF2F7',
-            button_primary_border_color_hover='#202020',
-            button_primary_border_color_hover_dark='#a1c3d8',
-            button_primary_text_color='#ECF2F7',
-            button_primary_text_color_dark='#191919',
-            button_primary_text_color_hover='#e1eaf0',
-            button_primary_text_color_hover_dark='#141414',
-            button_secondary_background_fill='#dce3e8',
-            button_secondary_background_fill_dark='#242424',
-            button_secondary_background_fill_hover='#d0d7db',
-            button_secondary_background_fill_hover_dark='#202020',
-            button_secondary_border_color='#dce3e8',
-            button_secondary_border_color_dark='#242424',
-            button_secondary_border_color_hover='#d0d7db',
-            button_secondary_border_color_hover_dark='#202020',
-            button_secondary_text_color='#4EACEF',
-            button_secondary_text_color_dark='#4EACEF',
-            button_secondary_text_color_hover='#0c6ebd',
-            button_secondary_text_color_hover_dark='#0c6ebd',
-            button_shadow='none',
-            button_shadow_active='none',
-            button_shadow_hover='none',
-            button_small_padding='*spacing_sm calc(2 * *spacing_sm)',
-            button_small_radius='*radius_lg',
-            button_small_text_size='*text_md',
-            button_small_text_weight='400',
-            button_transition='background-color 0.2s ease',
-            chatbot_code_background_color='#FFFFFF',
-            chatbot_code_background_color_dark='#000000',
-            checkbox_background_color='#dce3e8',
-            checkbox_background_color_dark='#242424',
-            checkbox_background_color_focus='#dce3e8',
-            checkbox_background_color_focus_dark='#242424',
-            checkbox_background_color_hover='#dce3e8',
-            checkbox_background_color_hover_dark='#242424',
-            checkbox_background_color_selected='#4EACEF',
-            checkbox_background_color_selected_dark='#4EACEF',
-            checkbox_border_color='#dce3e8',
-            checkbox_border_color_dark='#242424',
-            checkbox_border_color_focus='#4EACEF',
-            checkbox_border_color_focus_dark='#4EACEF',
-            checkbox_border_color_hover='#4EACEF',
-            checkbox_border_color_hover_dark='#4EACEF',
-            checkbox_border_color_selected='#4EACEF',
-            checkbox_border_color_selected_dark='#4EACEF',
-            checkbox_border_radius='*radius_sm',
-            checkbox_border_width='1px',
-            checkbox_border_width_dark='1px',
-            checkbox_check= "url(\"data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3cpath d='M12.207 4.793a1 1 0 010 1.414l-5 5a1 1 0 01-1.414 0l-2-2a1 1 0 011.414-1.414L6.5 9.086l4.293-4.293a1 1 0 011.414 0z'/%3e%3c/svg%3e\")",
-            checkbox_label_background_fill='#ECF2F7',
-            checkbox_label_background_fill_dark='#191919',
-            checkbox_label_background_fill_hover='#dce3e8',
-            checkbox_label_background_fill_hover_dark='#242424',
-            checkbox_label_background_fill_selected='#dce3e8',
-            checkbox_label_background_fill_selected_dark='#242424',
-            checkbox_label_border_color='#dce3e8',
-            checkbox_label_border_color_dark='#242424',
-            checkbox_label_border_color_hover='#4EACEF',
-            checkbox_label_border_color_hover_dark='#4EACEF',
-            checkbox_label_border_width='1px',
-            checkbox_label_border_width_dark='1px',
-            checkbox_label_gap='*spacing_lg',
-            checkbox_label_padding='*spacing_md calc(2 * *spacing_md)',
-            checkbox_label_shadow='none',
-            checkbox_label_text_color='#191919',
-            checkbox_label_text_color_dark='#ECF2F7',
-            checkbox_label_text_color_selected='#4EACEF',
-            checkbox_label_text_color_selected_dark='#4EACEF',
-            checkbox_label_text_size='*text_md',
-            checkbox_label_text_weight='400',
-            checkbox_shadow='*input_shadow',
-            color_accent='*primary_500',
-            color_accent_soft='#dce3e8',
-            color_accent_soft_dark='#242424',
-            container_radius='*radius_lg',
-            embed_radius='*radius_lg',
-            error_background_fill='#dce3e8',
-            error_background_fill_dark='#242424',
-            error_border_color='#191919',
-            error_border_color_dark='#ECF2F7',
-            error_border_width='1px',
-            error_border_width_dark='1px',
-            error_text_color='#4EACEF',
-            error_text_color_dark='#4EACEF',
-            form_gap_width='0px',
-            input_background_fill='#dce3e8',
-            input_background_fill_dark='#242424',
-            input_background_fill_focus='#dce3e8',
-            input_background_fill_focus_dark='#242424',
-            input_background_fill_hover='#d0d7db',
-            input_background_fill_hover_dark='#202020',
-            input_border_color='#191919',
-            input_border_color_dark='#ECF2F7',
-            input_border_color_focus='#191919',
-            input_border_color_focus_dark='#ECF2F7',
-            input_border_color_hover='#202020',
-            input_border_color_hover_dark='#a1c3d8',
-            input_border_width='0px',
-            input_padding='*spacing_xl',
-            input_placeholder_color='#19191930',
-            input_placeholder_color_dark='#ECF2F730',
-            input_radius='*radius_lg',
-            input_shadow='#19191900',
-            input_shadow_dark='#ECF2F700',
-            input_shadow_focus='#19191900',
-            input_shadow_focus_dark='#ECF2F700',
-            input_text_size='*text_md',
-            input_text_weight='400',
-            layout_gap='*spacing_xxl',
-            link_text_color='#4EACEF',
-            link_text_color_active='#4EACEF',
-            link_text_color_active_dark='#4EACEF',
-            link_text_color_dark='#4EACEF',
-            link_text_color_hover='#0c6ebd',
-            link_text_color_hover_dark='#0c6ebd',
-            link_text_color_visited='#4EACEF',
-            link_text_color_visited_dark='#4EACEF',
-            loader_color='#4EACEF',
-            loader_color_dark='#4EACEF',
-
-            panel_background_fill='#ECF2F7',
-            panel_background_fill_dark='#191919',
-            panel_border_color='#4EACEF',
-            panel_border_color_dark='#4EACEF',
-            panel_border_width='0',
-
-            prose_header_text_weight='600',
-            prose_text_size='*text_md',
-            prose_text_weight='400',
-            radio_circle= "url(\"data:image/svg+xml,%3csvg viewBox='0 0 16 16' fill='white' xmlns='http://www.w3.org/2000/svg'%3e%3ccircle cx='8' cy='8' r='3'/%3e%3c/svg%3e\")",
-            section_header_text_size='*text_md',
-            section_header_text_weight='400',
-            shadow_drop='rgba(0,0,0,0.05) 0px 1px 2px 0px',
-            shadow_drop_lg='0 1px 3px 0 rgb(0 0 0 / 0.1), 0 1px 2px -1px rgb(0 0 0 / 0.1)',
-            shadow_inset='rgba(0,0,0,0.05) 0px 2px 4px 0px inset',
-            shadow_spread='#FFFFFF',
-            shadow_spread_dark='#000000',
-            slider_color='#4EACEF',
-            slider_color_dark='#4EACEF',
-            stat_background_fill='#4EACEF',
-            stat_background_fill_dark='#4EACEF',
-            table_border_color='#191919',
-            table_border_color_dark='#ECF2F7',
-            table_even_background_fill='#ECF2F7',
-            table_even_background_fill_dark='#191919',
-            table_odd_background_fill='#dce3e8',
-            table_odd_background_fill_dark='#242424',
-            table_radius='*radius_lg',
-            table_row_focus='#191919',
-            table_row_focus_dark='#ECF2F7',
-
-        )
-
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/order.py b/spaces/Lianjd/stock_dashboard/backtrader/order.py
deleted file mode 100644
index 458a35e9fbba0cafb4e90484dbb405b7a1c73aba..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/order.py
+++ /dev/null
@@ -1,641 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program 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 General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-###############################################################################
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-import collections
-from copy import copy
-import datetime
-import itertools
-
-from .utils.py3 import range, with_metaclass, iteritems
-
-from .metabase import MetaParams
-from .utils import AutoOrderedDict
-
-
-class OrderExecutionBit(object):
-    '''
-    Intended to hold information about order execution. A "bit" does not
-    determine if the order has been fully/partially executed, it just holds
-    information.
-
-    Member Attributes:
-
-      - dt: datetime (float) execution time
-      - size: how much was executed
-      - price: execution price
-      - closed: how much of the execution closed an existing postion
-      - opened: how much of the execution opened a new position
-      - openedvalue: market value of the "opened" part
-      - closedvalue: market value of the "closed" part
-      - closedcomm: commission for the "closed" part
-      - openedcomm: commission for the "opened" part
-
-      - value: market value for the entire bit size
-      - comm: commission for the entire bit execution
-      - pnl: pnl generated by this bit (if something was closed)
-
-      - psize: current open position size
-      - pprice: current open position price
-
-    '''
-
-    def __init__(self,
-                 dt=None, size=0, price=0.0,
-                 closed=0, closedvalue=0.0, closedcomm=0.0,
-                 opened=0, openedvalue=0.0, openedcomm=0.0,
-                 pnl=0.0,
-                 psize=0, pprice=0.0):
-
-        self.dt = dt
-        self.size = size
-        self.price = price
-
-        self.closed = closed
-        self.opened = opened
-        self.closedvalue = closedvalue
-        self.openedvalue = openedvalue
-        self.closedcomm = closedcomm
-        self.openedcomm = openedcomm
-
-        self.value = closedvalue + openedvalue
-        self.comm = closedcomm + openedcomm
-        self.pnl = pnl
-
-        self.psize = psize
-        self.pprice = pprice
-
-
-class OrderData(object):
-    '''
-    Holds actual order data for Creation and Execution.
-
-    In the case of Creation the request made and in the case of Execution the
-    actual outcome.
-
-    Member Attributes:
-
-      - exbits : iterable of OrderExecutionBits for this OrderData
-
-      - dt: datetime (float) creation/execution time
-      - size: requested/executed size
-      - price: execution price
-        Note: if no price is given and no pricelimite is given, the closing
-        price at the time or order creation will be used as reference
-      - pricelimit: holds pricelimit for StopLimit (which has trigger first)
-      - trailamount: absolute price distance in trailing stops
-      - trailpercent: percentage price distance in trailing stops
-
-      - value: market value for the entire bit size
-      - comm: commission for the entire bit execution
-      - pnl: pnl generated by this bit (if something was closed)
-      - margin: margin incurred by the Order (if any)
-
-      - psize: current open position size
-      - pprice: current open position price
-
-    '''
-    # According to the docs, collections.deque is thread-safe with appends at
-    # both ends, there will be no pop (nowhere) and therefore to know which the
-    # new exbits are two indices are needed. At time of cloning (__copy__) the
-    # indices can be updated to match the previous end, and the new end
-    # (len(exbits)
-    # Example: start 0, 0 -> islice(exbits, 0, 0) -> []
-    # One added -> copy -> updated 0, 1 -> islice(exbits, 0, 1) -> [1 elem]
-    # Other added -> copy -> updated 1, 2 -> islice(exbits, 1, 2) -> [1 elem]
-    # "add" and "__copy__" happen always in the same thread (with all current
-    # implementations) and therefore no append will happen during a copy and
-    # the len of the exbits can be queried with no concerns about another
-    # thread making an append and with no need for a lock
-
-    def __init__(self, dt=None, size=0, price=0.0, pricelimit=0.0, remsize=0,
-                 pclose=0.0, trailamount=0.0, trailpercent=0.0):
-
-        self.pclose = pclose
-        self.exbits = collections.deque()  # for historical purposes
-        self.p1, self.p2 = 0, 0  # indices to pending notifications
-
-        self.dt = dt
-        self.size = size
-        self.remsize = remsize
-        self.price = price
-        self.pricelimit = pricelimit
-        self.trailamount = trailamount
-        self.trailpercent = trailpercent
-
-        if not pricelimit:
-            # if no pricelimit is given, use the given price
-            self.pricelimit = self.price
-
-        if pricelimit and not price:
-            # price must always be set if pricelimit is set ...
-            self.price = pricelimit
-
-        self.plimit = pricelimit
-
-        self.value = 0.0
-        self.comm = 0.0
-        self.margin = None
-        self.pnl = 0.0
-
-        self.psize = 0
-        self.pprice = 0
-
-    def _getplimit(self):
-        return self._plimit
-
-    def _setplimit(self, val):
-        self._plimit = val
-
-    plimit = property(_getplimit, _setplimit)
-
-    def __len__(self):
-        return len(self.exbits)
-
-    def __getitem__(self, key):
-        return self.exbits[key]
-
-    def add(self, dt, size, price,
-            closed=0, closedvalue=0.0, closedcomm=0.0,
-            opened=0, openedvalue=0.0, openedcomm=0.0,
-            pnl=0.0,
-            psize=0, pprice=0.0):
-
-        self.addbit(
-            OrderExecutionBit(dt, size, price,
-                              closed, closedvalue, closedcomm,
-                              opened, openedvalue, openedcomm, pnl,
-                              psize, pprice))
-
-    def addbit(self, exbit):
-        # Stores an ExecutionBit and recalculates own values from ExBit
-        self.exbits.append(exbit)
-
-        self.remsize -= exbit.size
-
-        self.dt = exbit.dt
-        oldvalue = self.size * self.price
-        newvalue = exbit.size * exbit.price
-        self.size += exbit.size
-        self.price = (oldvalue + newvalue) / self.size
-        self.value += exbit.value
-        self.comm += exbit.comm
-        self.pnl += exbit.pnl
-        self.psize = exbit.psize
-        self.pprice = exbit.pprice
-
-    def getpending(self):
-        return list(self.iterpending())
-
-    def iterpending(self):
-        return itertools.islice(self.exbits, self.p1, self.p2)
-
-    def markpending(self):
-        # rebuild the indices to mark which exbits are pending in clone
-        self.p1, self.p2 = self.p2, len(self.exbits)
-
-    def clone(self):
-        self.markpending()
-        obj = copy(self)
-        return obj
-
-
-class OrderBase(with_metaclass(MetaParams, object)):
-    params = (
-        ('owner', None), ('data', None),
-        ('size', None), ('price', None), ('pricelimit', None),
-        ('exectype', None), ('valid', None), ('tradeid', 0), ('oco', None),
-        ('trailamount', None), ('trailpercent', None),
-        ('parent', None), ('transmit', True),
-        ('simulated', False),
-        # To support historical order evaluation
-        ('histnotify', False),
-    )
-
-    DAY = datetime.timedelta()  # constant for DAY order identification
-
-    # Time Restrictions for orders
-    T_Close, T_Day, T_Date, T_None = range(4)
-
-    # Volume Restrictions for orders
-    V_None = range(1)
-
-    (Market, Close, Limit, Stop, StopLimit, StopTrail, StopTrailLimit,
-     Historical) = range(8)
-    ExecTypes = ['Market', 'Close', 'Limit', 'Stop', 'StopLimit', 'StopTrail',
-                 'StopTrailLimit', 'Historical']
-
-    OrdTypes = ['Buy', 'Sell']
-    Buy, Sell = range(2)
-
-    Created, Submitted, Accepted, Partial, Completed, \
-        Canceled, Expired, Margin, Rejected = range(9)
-
-    Cancelled = Canceled  # alias
-
-    Status = [
-        'Created', 'Submitted', 'Accepted', 'Partial', 'Completed',
-        'Canceled', 'Expired', 'Margin', 'Rejected',
-    ]
-
-    refbasis = itertools.count(1)  # for a unique identifier per order
-
-    def _getplimit(self):
-        return self._plimit
-
-    def _setplimit(self, val):
-        self._plimit = val
-
-    plimit = property(_getplimit, _setplimit)
-
-    def __getattr__(self, name):
-        # Return attr from params if not found in order
-        return getattr(self.params, name)
-
-    def __setattribute__(self, name, value):
-        if hasattr(self.params, name):
-            setattr(self.params, name, value)
-        else:
-            super(Order, self).__setattribute__(name, value)
-
-    def __str__(self):
-        tojoin = list()
-        tojoin.append('Ref: {}'.format(self.ref))
-        tojoin.append('OrdType: {}'.format(self.ordtype))
-        tojoin.append('OrdType: {}'.format(self.ordtypename()))
-        tojoin.append('Status: {}'.format(self.status))
-        tojoin.append('Status: {}'.format(self.getstatusname()))
-        tojoin.append('Size: {}'.format(self.size))
-        tojoin.append('Price: {}'.format(self.price))
-        tojoin.append('Price Limit: {}'.format(self.pricelimit))
-        tojoin.append('TrailAmount: {}'.format(self.trailamount))
-        tojoin.append('TrailPercent: {}'.format(self.trailpercent))
-        tojoin.append('ExecType: {}'.format(self.exectype))
-        tojoin.append('ExecType: {}'.format(self.getordername()))
-        tojoin.append('CommInfo: {}'.format(self.comminfo))
-        tojoin.append('End of Session: {}'.format(self.dteos))
-        tojoin.append('Info: {}'.format(self.info))
-        tojoin.append('Broker: {}'.format(self.broker))
-        tojoin.append('Alive: {}'.format(self.alive()))
-
-        return '\n'.join(tojoin)
-
-    def __init__(self):
-        self.ref = next(self.refbasis)
-        self.broker = None
-        self.info = AutoOrderedDict()
-        self.comminfo = None
-        self.triggered = False
-
-        self._active = self.parent is None
-        self.status = Order.Created
-
-        self.plimit = self.p.pricelimit  # alias via property
-
-        if self.exectype is None:
-            self.exectype = Order.Market
-
-        if not self.isbuy():
-            self.size = -self.size
-
-        # Set a reference price if price is not set using
-        # the close price
-        pclose = self.data.close[0] if not self.simulated else self.price
-        if not self.price and not self.pricelimit:
-            price = pclose
-        else:
-            price = self.price
-
-        dcreated = self.data.datetime[0] if not self.p.simulated else 0.0
-        self.created = OrderData(dt=dcreated,
-                                 size=self.size,
-                                 price=price,
-                                 pricelimit=self.pricelimit,
-                                 pclose=pclose,
-                                 trailamount=self.trailamount,
-                                 trailpercent=self.trailpercent)
-
-        # Adjust price in case a trailing limit is wished
-        if self.exectype in [Order.StopTrail, Order.StopTrailLimit]:
-            self._limitoffset = self.created.price - self.created.pricelimit
-            price = self.created.price
-            self.created.price = float('inf' * self.isbuy() or '-inf')
-            self.trailadjust(price)
-        else:
-            self._limitoffset = 0.0
-
-        self.executed = OrderData(remsize=self.size)
-        self.position = 0
-
-        if isinstance(self.valid, datetime.date):
-            # comparison will later be done against the raw datetime[0] value
-            self.valid = self.data.date2num(self.valid)
-        elif isinstance(self.valid, datetime.timedelta):
-            # offset with regards to now ... get utcnow + offset
-            # when reading with date2num ... it will be automatically localized
-            if self.valid == self.DAY:
-                valid = datetime.datetime.combine(
-                    self.data.datetime.date(), datetime.time(23, 59, 59, 9999))
-            else:
-                valid = self.data.datetime.datetime() + self.valid
-
-            self.valid = self.data.date2num(valid)
-
-        elif self.valid is not None:
-            if not self.valid:  # avoid comparing None and 0
-                valid = datetime.datetime.combine(
-                    self.data.datetime.date(), datetime.time(23, 59, 59, 9999))
-            else:  # assume float
-                valid = self.data.datetime[0] + self.valid
-
-        if not self.p.simulated:
-            # provisional end-of-session
-            # get next session end
-            dtime = self.data.datetime.datetime(0)
-            session = self.data.p.sessionend
-            dteos = dtime.replace(hour=session.hour, minute=session.minute,
-                                  second=session.second,
-                                  microsecond=session.microsecond)
-
-            if dteos < dtime:
-                # eos before current time ... no ... must be at least next day
-                dteos += datetime.timedelta(days=1)
-
-            self.dteos = self.data.date2num(dteos)
-        else:
-            self.dteos = 0.0
-
-    def clone(self):
-        # status, triggered and executed are the only moving parts in order
-        # status and triggered are covered by copy
-        # executed has to be replaced with an intelligent clone of itself
-        obj = copy(self)
-        obj.executed = self.executed.clone()
-        return obj  # status could change in next to completed
-
-    def getstatusname(self, status=None):
-        '''Returns the name for a given status or the one of the order'''
-        return self.Status[self.status if status is None else status]
-
-    def getordername(self, exectype=None):
-        '''Returns the name for a given exectype or the one of the order'''
-        return self.ExecTypes[self.exectype if exectype is None else exectype]
-
-    @classmethod
-    def ExecType(cls, exectype):
-        return getattr(cls, exectype)
-
-    def ordtypename(self, ordtype=None):
-        '''Returns the name for a given ordtype or the one of the order'''
-        return self.OrdTypes[self.ordtype if ordtype is None else ordtype]
-
-    def active(self):
-        return self._active
-
-    def activate(self):
-        self._active = True
-
-    def alive(self):
-        '''Returns True if the order is in a status in which it can still be
-        executed
-        '''
-        return self.status in [Order.Created, Order.Submitted,
-                               Order.Partial, Order.Accepted]
-
-    def addcomminfo(self, comminfo):
-        '''Stores a CommInfo scheme associated with the asset'''
-        self.comminfo = comminfo
-
-    def addinfo(self, **kwargs):
-        '''Add the keys, values of kwargs to the internal info dictionary to
-        hold custom information in the order
-        '''
-        for key, val in iteritems(kwargs):
-            self.info[key] = val
-
-    def __eq__(self, other):
-        return other is not None and self.ref == other.ref
-
-    def __ne__(self, other):
-        return self.ref != other.ref
-
-    def isbuy(self):
-        '''Returns True if the order is a Buy order'''
-        return self.ordtype == self.Buy
-
-    def issell(self):
-        '''Returns True if the order is a Sell order'''
-        return self.ordtype == self.Sell
-
-    def setposition(self, position):
-        '''Receives the current position for the asset and stotres it'''
-        self.position = position
-
-    def submit(self, broker=None):
-        '''Marks an order as submitted and stores the broker to which it was
-        submitted'''
-        self.status = Order.Submitted
-        self.broker = broker
-        self.plen = len(self.data)
-
-    def accept(self, broker=None):
-        '''Marks an order as accepted'''
-        self.status = Order.Accepted
-        self.broker = broker
-
-    def brokerstatus(self):
-        '''Tries to retrieve the status from the broker in which the order is.
-
-        Defaults to last known status if no broker is associated'''
-        if self.broker:
-            return self.broker.orderstatus(self)
-
-        return self.status
-
-    def reject(self, broker=None):
-        '''Marks an order as rejected'''
-        if self.status == Order.Rejected:
-            return False
-
-        self.status = Order.Rejected
-        self.executed.dt = self.data.datetime[0]
-        self.broker = broker
-        return True
-
-    def cancel(self):
-        '''Marks an order as cancelled'''
-        self.status = Order.Canceled
-        self.executed.dt = self.data.datetime[0]
-
-    def margin(self):
-        '''Marks an order as having met a margin call'''
-        self.status = Order.Margin
-        self.executed.dt = self.data.datetime[0]
-
-    def completed(self):
-        '''Marks an order as completely filled'''
-        self.status = self.Completed
-
-    def partial(self):
-        '''Marks an order as partially filled'''
-        self.status = self.Partial
-
-    def execute(self, dt, size, price,
-                closed, closedvalue, closedcomm,
-                opened, openedvalue, openedcomm,
-                margin, pnl,
-                psize, pprice):
-
-        '''Receives data execution input and stores it'''
-        if not size:
-            return
-
-        self.executed.add(dt, size, price,
-                          closed, closedvalue, closedcomm,
-                          opened, openedvalue, openedcomm,
-                          pnl, psize, pprice)
-
-        self.executed.margin = margin
-
-    def expire(self):
-        '''Marks an order as expired. Returns True if it worked'''
-        self.status = self.Expired
-        return True
-
-    def trailadjust(self, price):
-        pass  # generic interface
-
-
-class Order(OrderBase):
-    '''
-    Class which holds creation/execution data and type of oder.
-
-    The order may have the following status:
-
-      - Submitted: sent to the broker and awaiting confirmation
-      - Accepted: accepted by the broker
-      - Partial: partially executed
-      - Completed: fully exexcuted
-      - Canceled/Cancelled: canceled by the user
-      - Expired: expired
-      - Margin: not enough cash to execute the order.
-      - Rejected: Rejected by the broker
-
-        This can happen during order submission (and therefore the order will
-        not reach the Accepted status) or before execution with each new bar
-        price because cash has been drawn by other sources (future-like
-        instruments may have reduced the cash or orders orders may have been
-        executed)
-
-    Member Attributes:
-
-      - ref: unique order identifier
-      - created: OrderData holding creation data
-      - executed: OrderData holding execution data
-
-      - info: custom information passed over method :func:`addinfo`. It is kept
-        in the form of an OrderedDict which has been subclassed, so that keys
-        can also be specified using '.' notation
-
-    User Methods:
-
-      - isbuy(): returns bool indicating if the order buys
-      - issell(): returns bool indicating if the order sells
-      - alive(): returns bool if order is in status Partial or Accepted
-    '''
-
-    def execute(self, dt, size, price,
-                closed, closedvalue, closedcomm,
-                opened, openedvalue, openedcomm,
-                margin, pnl,
-                psize, pprice):
-
-        super(Order, self).execute(dt, size, price,
-                                   closed, closedvalue, closedcomm,
-                                   opened, openedvalue, openedcomm,
-                                   margin, pnl, psize, pprice)
-
-        if self.executed.remsize:
-            self.status = Order.Partial
-        else:
-            self.status = Order.Completed
-
-        # self.comminfo = None
-
-    def expire(self):
-        if self.exectype == Order.Market:
-            return False  # will be executed yes or yes
-
-        if self.valid and self.data.datetime[0] > self.valid:
-            self.status = Order.Expired
-            self.executed.dt = self.data.datetime[0]
-            return True
-
-        return False
-
-    def trailadjust(self, price):
-        if self.trailamount:
-            pamount = self.trailamount
-        elif self.trailpercent:
-            pamount = price * self.trailpercent
-        else:
-            pamount = 0.0
-
-        # Stop sell is below (-), stop buy is above, move only if needed
-        if self.isbuy():
-            price += pamount
-            if price < self.created.price:
-                self.created.price = price
-                if self.exectype == Order.StopTrailLimit:
-                    self.created.pricelimit = price - self._limitoffset
-        else:
-            price -= pamount
-            if price > self.created.price:
-                self.created.price = price
-                if self.exectype == Order.StopTrailLimit:
-                    # limitoffset is negative when pricelimit was greater
-                    # the - allows increasing the price limit if stop increases
-                    self.created.pricelimit = price - self._limitoffset
-
-
-class BuyOrder(Order):
-    ordtype = Order.Buy
-
-
-class StopBuyOrder(BuyOrder):
-    pass
-
-
-class StopLimitBuyOrder(BuyOrder):
-    pass
-
-
-class SellOrder(Order):
-    ordtype = Order.Sell
-
-
-class StopSellOrder(SellOrder):
-    pass
-
-
-class StopLimitSellOrder(SellOrder):
-    pass
diff --git a/spaces/LuxOAI/ChatGpt-Web/app/masks/en.ts b/spaces/LuxOAI/ChatGpt-Web/app/masks/en.ts
deleted file mode 100644
index 93e9bd6aa264a1e176864e24230d76154b98e144..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/ChatGpt-Web/app/masks/en.ts
+++ /dev/null
@@ -1,44 +0,0 @@
-import { BuiltinMask } from "./typing";
-
-export const EN_MASKS: BuiltinMask[] = [
-  {
-    avatar: "1f916",
-    name: "Prompt Improvement",
-    context: [
-      {
-        role: "user",
-        content:
-          'Read all of the instructions below and once you understand them say "Shall we begin:"\n \nI want you to become my Prompt Creator. Your goal is to help me craft the best possible prompt for my needs. The prompt will be used by you, ChatGPT. You will follow the following process:\nYour first response will be to ask me what the prompt should be about. I will provide my answer, but we will need to improve it through continual iterations by going through the next steps.\n \nBased on my input, you will generate 3 sections.\n \nRevised Prompt (provide your rewritten prompt. it should be clear, concise, and easily understood by you)\nSuggestions (provide 3 suggestions on what details to include in the prompt to improve it)\nQuestions (ask the 3 most relevant questions pertaining to what additional information is needed from me to improve the prompt)\n \nAt the end of these sections give me a reminder of my options which are:\n \nOption 1: Read the output and provide more info or answer one or more of the questions\nOption 2: Type "Use this prompt" and I will submit this as a query for you\nOption 3: Type "Restart" to restart this process from the beginning\nOption 4: Type "Quit" to end this script and go back to a regular ChatGPT session\n \nIf I type "Option 2", "2" or "Use this prompt" then we have finsihed and you should use the Revised Prompt as a prompt to generate my request\nIf I type "option 3", "3" or "Restart" then forget the latest Revised Prompt and restart this process\nIf I type "Option 4", "4" or "Quit" then finish this process and revert back to your general mode of operation\n\n\nWe will continue this iterative process with me providing additional information to you and you updating the prompt in the Revised Prompt section until it is complete.',
-        date: "",
-      },
-      {
-        role: "assistant",
-        content: "Shall we begin?",
-        date: "",
-      },
-      {
-        role: "user",
-        content:
-          "My prompt：I want you to act like Harry Potter from Harry Potter Series. I want you to respond and answer like Harry Potter using the tone, manner and vocabulary Harry Potter would use.",
-        date: "",
-      },
-      {
-        role: "assistant",
-        content:
-          'Revised Prompt:\n\n\nWrite a short story or dialogue in which Harry Potter interacts with someone (a friend or foe) using the tone, manner and vocabulary characteristic of his character in the Harry Potter series.\n\n\nSuggestions:\n\n\n\nInclude specific references to Hogwarts, magic spells, and magical creatures to create an authentic Harry Potter experience.\n\nEnsure that Harry\'s personality traits, such as his bravery, loyalty, and determination, are prominently displayed in the story or dialogue.\n\nIncorporate some humor or wit into the story, as these elements are often present in the Harry Potter series.\n\n\nQuestions:\n\n\n\nWho will be the other character interacting with Harry?\n\nWhat is the main conflict or goal of the story/dialogue?\n\nWhat setting or location would you like the story/dialogue to take place in?\n\n\nRemember, your options are:\nOption 1: Read the output and provide more info or answer one or more of the questions\nOption 2: Type "Use this prompt" and I will submit this as a query for you\nOption 3: Type "Restart" to restart this process from the beginning\nOption 4: Type "Quit" to end this script and go back to a regular ChatGPT session',
-        date: "",
-      },
-    ],
-    modelConfig: {
-      model: "gpt-4",
-      temperature: 1,
-      max_tokens: 2000,
-      presence_penalty: 0,
-      sendMemory: true,
-      historyMessageCount: 4,
-      compressMessageLengthThreshold: 1000,
-    },
-    lang: "en",
-    builtin: true,
-  },
-];
diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/ONNXVITS_infer.py b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/ONNXVITS_infer.py
deleted file mode 100644
index af04e614c8f1ac43faf363b1a9f6bfd667fbde21..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/ONNXVITS_infer.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import torch
-import commons
-import models
-
-import math
-from torch import nn
-from torch.nn import functional as F
-
-import modules
-import attentions
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-               n_vocab,
-               out_channels,
-               hidden_channels,
-               filter_channels,
-               n_heads,
-               n_layers,
-               kernel_size,
-               p_dropout,
-               emotion_embedding):
-    super().__init__()
-    self.n_vocab = n_vocab
-    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.emotion_embedding = emotion_embedding
-
-    if self.n_vocab != 0:
-      self.emb = nn.Embedding(n_vocab, hidden_channels)
-      if emotion_embedding:
-        self.emo_proj = nn.Linear(1024, hidden_channels)
-      nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
-
-    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, x, x_lengths, emotion_embedding=None):
-    if self.n_vocab != 0:
-      x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-    if emotion_embedding is not None:
-      print("emotion added")
-      x = x + self.emo_proj(emotion_embedding.unsqueeze(1))
-    x = torch.transpose(x, 1, -1)  # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_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 x, m, logs, x_mask
-
-
-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
-
-
-class SynthesizerTrn(models.SynthesizerTrn):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self,
-               n_vocab,
-               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,
-               n_speakers=0,
-               gin_channels=0,
-               use_sdp=True,
-               emotion_embedding=False,
-               ONNX_dir="./ONNX_net/",
-               **kwargs):
-
-    super().__init__(
-      n_vocab,
-      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,
-      n_speakers=n_speakers,
-      gin_channels=gin_channels,
-      use_sdp=use_sdp,
-      **kwargs
-    )
-    self.ONNX_dir = ONNX_dir
-    self.enc_p = TextEncoder(n_vocab,
-                             inter_channels,
-                             hidden_channels,
-                             filter_channels,
-                             n_heads,
-                             n_layers,
-                             kernel_size,
-                             p_dropout,
-                             emotion_embedding)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None,
-            emotion_embedding=None):
-    from ONNXVITS_utils import runonnx
-    with torch.no_grad():
-      x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding)
-
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-    else:
-      g = None
-
-    # logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    logw = runonnx(f"{self.ONNX_dir}dp.onnx", x=x.numpy(), x_mask=x_mask.numpy(), g=g.numpy())
-    logw = torch.from_numpy(logw[0])
-
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)  # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
-                                                                             2)  # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-
-    # z = self.flow(z_p, y_mask, g=g, reverse=True)
-    z = runonnx(f"{self.ONNX_dir}flow.onnx", z_p=z_p.numpy(), y_mask=y_mask.numpy(), g=g.numpy())
-    z = torch.from_numpy(z[0])
-
-    # o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    o = runonnx(f"{self.ONNX_dir}dec.onnx", z_in=(z * y_mask)[:, :, :max_len].numpy(), g=g.numpy())
-    o = torch.from_numpy(o[0])
-
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
\ No newline at end of file
diff --git a/spaces/MarcusSu1216/XingTong/train.py b/spaces/MarcusSu1216/XingTong/train.py
deleted file mode 100644
index 9f6e7439961182f54ee880be4b8fa776b44b547f..0000000000000000000000000000000000000000
--- a/spaces/MarcusSu1216/XingTong/train.py
+++ /dev/null
@@ -1,315 +0,0 @@
-import logging
-import multiprocessing
-import time
-
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-logging.getLogger('numba').setLevel(logging.WARNING)
-
-import os
-import json
-import argparse
-import itertools
-import math
-import torch
-from torch import nn, optim
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.multiprocessing as mp
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.cuda.amp import autocast, GradScaler
-
-import modules.commons as commons
-import utils
-from data_utils import TextAudioSpeakerLoader, TextAudioCollate
-from models import (
-    SynthesizerTrn,
-    MultiPeriodDiscriminator,
-)
-from modules.losses import (
-    kl_loss,
-    generator_loss, discriminator_loss, feature_loss
-)
-
-from modules.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-
-torch.backends.cudnn.benchmark = True
-global_step = 0
-start_time = time.time()
-
-# os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO'
-
-
-def main():
-    """Assume Single Node Multi GPUs Training Only"""
-    assert torch.cuda.is_available(), "CPU training is not allowed."
-    hps = utils.get_hparams()
-
-    n_gpus = torch.cuda.device_count()
-    os.environ['MASTER_ADDR'] = 'localhost'
-    os.environ['MASTER_PORT'] = hps.train.port
-
-    mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
-
-
-def run(rank, n_gpus, hps):
-    global global_step
-    if rank == 0:
-        logger = utils.get_logger(hps.model_dir)
-        logger.info(hps)
-        utils.check_git_hash(hps.model_dir)
-        writer = SummaryWriter(log_dir=hps.model_dir)
-        writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
-
-    # for pytorch on win, backend use gloo    
-    dist.init_process_group(backend=  'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
-    torch.manual_seed(hps.train.seed)
-    torch.cuda.set_device(rank)
-    collate_fn = TextAudioCollate()
-    all_in_mem = hps.train.all_in_mem   # If you have enough memory, turn on this option to avoid disk IO and speed up training.
-    train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps, all_in_mem=all_in_mem)
-    num_workers = 5 if multiprocessing.cpu_count() > 4 else multiprocessing.cpu_count()
-    if all_in_mem:
-        num_workers = 0
-    train_loader = DataLoader(train_dataset, num_workers=num_workers, shuffle=False, pin_memory=True,
-                              batch_size=hps.train.batch_size, collate_fn=collate_fn)
-    if rank == 0:
-        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps, all_in_mem=all_in_mem)
-        eval_loader = DataLoader(eval_dataset, num_workers=1, shuffle=False,
-                                 batch_size=1, pin_memory=False,
-                                 drop_last=False, collate_fn=collate_fn)
-
-    net_g = SynthesizerTrn(
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        **hps.model).cuda(rank)
-    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
-    optim_g = torch.optim.AdamW(
-        net_g.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    optim_d = torch.optim.AdamW(
-        net_d.parameters(),
-        hps.train.learning_rate,
-        betas=hps.train.betas,
-        eps=hps.train.eps)
-    net_g = DDP(net_g, device_ids=[rank])  # , find_unused_parameters=True)
-    net_d = DDP(net_d, device_ids=[rank])
-
-    skip_optimizer = False
-    try:
-        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
-                                                   optim_g, skip_optimizer)
-        _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
-                                                   optim_d, skip_optimizer)
-        epoch_str = max(epoch_str, 1)
-        global_step = (epoch_str - 1) * len(train_loader)
-    except:
-        print("load old checkpoint failed...")
-        epoch_str = 1
-        global_step = 0
-    if skip_optimizer:
-        epoch_str = 1
-        global_step = 0
-
-    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
-
-    scaler = GradScaler(enabled=hps.train.fp16_run)
-
-    for epoch in range(epoch_str, hps.train.epochs + 1):
-        if rank == 0:
-            train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
-                               [train_loader, eval_loader], logger, [writer, writer_eval])
-        else:
-            train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
-                               [train_loader, None], None, None)
-        scheduler_g.step()
-        scheduler_d.step()
-
-
-def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
-    net_g, net_d = nets
-    optim_g, optim_d = optims
-    scheduler_g, scheduler_d = schedulers
-    train_loader, eval_loader = loaders
-    if writers is not None:
-        writer, writer_eval = writers
-
-    # train_loader.batch_sampler.set_epoch(epoch)
-    global global_step
-
-    net_g.train()
-    net_d.train()
-    for batch_idx, items in enumerate(train_loader):
-        c, f0, spec, y, spk, lengths, uv = items
-        g = spk.cuda(rank, non_blocking=True)
-        spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True)
-        c = c.cuda(rank, non_blocking=True)
-        f0 = f0.cuda(rank, non_blocking=True)
-        uv = uv.cuda(rank, non_blocking=True)
-        lengths = lengths.cuda(rank, non_blocking=True)
-        mel = spec_to_mel_torch(
-            spec,
-            hps.data.filter_length,
-            hps.data.n_mel_channels,
-            hps.data.sampling_rate,
-            hps.data.mel_fmin,
-            hps.data.mel_fmax)
-
-        with autocast(enabled=hps.train.fp16_run):
-            y_hat, ids_slice, z_mask, \
-            (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 = net_g(c, f0, uv, spec, g=g, c_lengths=lengths,
-                                                                                spec_lengths=lengths)
-
-            y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
-            y_hat_mel = mel_spectrogram_torch(
-                y_hat.squeeze(1),
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.hop_length,
-                hps.data.win_length,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax
-            )
-            y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size)  # slice
-
-            # Discriminator
-            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
-
-            with autocast(enabled=False):
-                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
-                loss_disc_all = loss_disc
-
-        optim_d.zero_grad()
-        scaler.scale(loss_disc_all).backward()
-        scaler.unscale_(optim_d)
-        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
-        scaler.step(optim_d)
-
-        with autocast(enabled=hps.train.fp16_run):
-            # Generator
-            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
-            with autocast(enabled=False):
-                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
-                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
-                loss_fm = feature_loss(fmap_r, fmap_g)
-                loss_gen, losses_gen = generator_loss(y_d_hat_g)
-                loss_lf0 = F.mse_loss(pred_lf0, lf0)
-                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0
-        optim_g.zero_grad()
-        scaler.scale(loss_gen_all).backward()
-        scaler.unscale_(optim_g)
-        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
-        scaler.step(optim_g)
-        scaler.update()
-
-        if rank == 0:
-            if global_step % hps.train.log_interval == 0:
-                lr = optim_g.param_groups[0]['lr']
-                losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_kl]
-                logger.info('Train Epoch: {} [{:.0f}%]'.format(
-                    epoch,
-                    100. * batch_idx / len(train_loader)))
-                logger.info(f"Losses: {[x.item() for x in losses]}, step: {global_step}, lr: {lr}")
-
-                scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
-                               "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
-                scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/kl": loss_kl,
-                                    "loss/g/lf0": loss_lf0})
-
-                # scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
-                # scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
-                # scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
-                image_dict = {
-                    "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
-                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
-                    "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
-                    "all/lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
-                                                          pred_lf0[0, 0, :].detach().cpu().numpy()),
-                    "all/norm_lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
-                                                               norm_lf0[0, 0, :].detach().cpu().numpy())
-                }
-
-                utils.summarize(
-                    writer=writer,
-                    global_step=global_step,
-                    images=image_dict,
-                    scalars=scalar_dict
-                )
-
-            if global_step % hps.train.eval_interval == 0:
-                evaluate(hps, net_g, eval_loader, writer_eval)
-                utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
-                                      os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
-                utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
-                                      os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
-                keep_ckpts = getattr(hps.train, 'keep_ckpts', 0)
-                if keep_ckpts > 0:
-                    utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
-
-        global_step += 1
-
-    if rank == 0:
-        global start_time
-        now = time.time()
-        durtaion = format(now - start_time, '.2f')
-        logger.info(f'====> Epoch: {epoch}, cost {durtaion} s')
-        start_time = now
-
-
-def evaluate(hps, generator, eval_loader, writer_eval):
-    generator.eval()
-    image_dict = {}
-    audio_dict = {}
-    with torch.no_grad():
-        for batch_idx, items in enumerate(eval_loader):
-            c, f0, spec, y, spk, _, uv = items
-            g = spk[:1].cuda(0)
-            spec, y = spec[:1].cuda(0), y[:1].cuda(0)
-            c = c[:1].cuda(0)
-            f0 = f0[:1].cuda(0)
-            uv= uv[:1].cuda(0)
-            mel = spec_to_mel_torch(
-                spec,
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax)
-            y_hat = generator.module.infer(c, f0, uv, g=g)
-
-            y_hat_mel = mel_spectrogram_torch(
-                y_hat.squeeze(1).float(),
-                hps.data.filter_length,
-                hps.data.n_mel_channels,
-                hps.data.sampling_rate,
-                hps.data.hop_length,
-                hps.data.win_length,
-                hps.data.mel_fmin,
-                hps.data.mel_fmax
-            )
-
-            audio_dict.update({
-                f"gen/audio_{batch_idx}": y_hat[0],
-                f"gt/audio_{batch_idx}": y[0]
-            })
-        image_dict.update({
-            f"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()),
-            "gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())
-        })
-    utils.summarize(
-        writer=writer_eval,
-        global_step=global_step,
-        images=image_dict,
-        audios=audio_dict,
-        audio_sampling_rate=hps.data.sampling_rate
-    )
-    generator.train()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/modeling/backbone/swintransformer.py b/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/modeling/backbone/swintransformer.py
deleted file mode 100644
index 21cabb37dd87a443e27eeb805f9739bef86540bf..0000000000000000000000000000000000000000
--- a/spaces/MattyWhite/ChatGPT-ImageCaptioner2/detic/modeling/backbone/swintransformer.py
+++ /dev/null
@@ -1,750 +0,0 @@
-# --------------------------------------------------------
-# Swin Transformer
-# Copyright (c) 2021 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ze Liu, Yutong Lin, Yixuan Wei
-# --------------------------------------------------------
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Xingyi Zhou from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-import numpy as np
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.backbone.backbone import Backbone
-from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
-from detectron2.modeling.backbone.fpn import FPN
-
-from centernet.modeling.backbone.fpn_p5 import LastLevelP6P7_P5
-from centernet.modeling.backbone.bifpn import BiFPN
-# from .checkpoint import load_checkpoint
-
-class Mlp(nn.Module):
-    """ Multilayer perceptron."""
-
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    """ Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """ Forward function.
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class SwinTransformerBlock(nn.Module):
-    """ Swin Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        self.H = None
-        self.W = None
-
-    def forward(self, x, mask_matrix):
-        """ Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-            mask_matrix: Attention mask for cyclic shift.
-        """
-        B, L, C = x.shape
-        H, W = self.H, self.W
-        assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # pad feature maps to multiples of window size
-        pad_l = pad_t = 0
-        pad_r = (self.window_size - W % self.window_size) % self.window_size
-        pad_b = (self.window_size - H % self.window_size) % self.window_size
-        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
-        _, Hp, Wp, _ = x.shape
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-            attn_mask = mask_matrix
-        else:
-            shifted_x = x
-            attn_mask = None
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-
-        if pad_r > 0 or pad_b > 0:
-            x = x[:, :H, :W, :].contiguous()
-
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-
-class PatchMerging(nn.Module):
-    """ Patch Merging Layer
-    Args:
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-    def __init__(self, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x, H, W):
-        """ Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-
-        x = x.view(B, H, W, C)
-
-        # padding
-        pad_input = (H % 2 == 1) or (W % 2 == 1)
-        if pad_input:
-            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-    Args:
-        dim (int): Number of feature channels
-        depth (int): Depths of this stage.
-        num_heads (int): Number of attention head.
-        window_size (int): Local window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self,
-                 dim,
-                 depth,
-                 num_heads,
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop=0.,
-                 attn_drop=0.,
-                 drop_path=0.,
-                 norm_layer=nn.LayerNorm,
-                 downsample=None,
-                 use_checkpoint=False):
-        super().__init__()
-        self.window_size = window_size
-        self.shift_size = window_size // 2
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(
-                dim=dim,
-                num_heads=num_heads,
-                window_size=window_size,
-                shift_size=0 if (i % 2 == 0) else window_size // 2,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop,
-                attn_drop=attn_drop,
-                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, H, W):
-        """ Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-
-        # calculate attention mask for SW-MSA
-        Hp = int(np.ceil(H / self.window_size)) * self.window_size
-        Wp = int(np.ceil(W / self.window_size)) * self.window_size
-        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        for blk in self.blocks:
-            blk.H, blk.W = H, W
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, attn_mask)
-            else:
-                x = blk(x, attn_mask)
-        if self.downsample is not None:
-            x_down = self.downsample(x, H, W)
-            Wh, Ww = (H + 1) // 2, (W + 1) // 2
-            return x, H, W, x_down, Wh, Ww
-        else:
-            return x, H, W, x, H, W
-
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    Args:
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        patch_size = to_2tuple(patch_size)
-        self.patch_size = patch_size
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        """Forward function."""
-        # padding
-        _, _, H, W = x.size()
-        if W % self.patch_size[1] != 0:
-            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
-        if H % self.patch_size[0] != 0:
-            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-
-        x = self.proj(x)  # B C Wh Ww
-        if self.norm is not None:
-            Wh, Ww = x.size(2), x.size(3)
-            x = x.flatten(2).transpose(1, 2)
-            x = self.norm(x)
-            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-
-        return x
-
-
-class SwinTransformer(Backbone):
-    """ Swin Transformer backbone.
-        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
-          https://arxiv.org/pdf/2103.14030
-    Args:
-        pretrain_img_size (int): Input image size for training the pretrained model,
-            used in absolute postion embedding. Default 224.
-        patch_size (int | tuple(int)): Patch size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        depths (tuple[int]): Depths of each Swin Transformer stage.
-        num_heads (tuple[int]): Number of attention head of each stage.
-        window_size (int): Window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
-        drop_rate (float): Dropout rate.
-        attn_drop_rate (float): Attention dropout rate. Default: 0.
-        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
-        out_indices (Sequence[int]): Output from which stages.
-        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-            -1 means not freezing any parameters.
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self,
-                 pretrain_img_size=224,
-                 patch_size=4,
-                 in_chans=3,
-                 embed_dim=96,
-                 depths=[2, 2, 6, 2],
-                 num_heads=[3, 6, 12, 24],
-                 window_size=7,
-                 mlp_ratio=4.,
-                 qkv_bias=True,
-                 qk_scale=None,
-                 drop_rate=0.,
-                 attn_drop_rate=0.,
-                 drop_path_rate=0.2,
-                 norm_layer=nn.LayerNorm,
-                 ape=False,
-                 patch_norm=True,
-                 out_indices=(0, 1, 2, 3),
-                 frozen_stages=-1,
-                 use_checkpoint=False):
-        super().__init__()
-
-        self.pretrain_img_size = pretrain_img_size
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            pretrain_img_size = to_2tuple(pretrain_img_size)
-            patch_size = to_2tuple(patch_size)
-            patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
-
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build layers
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(
-                dim=int(embed_dim * 2 ** i_layer),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
-                norm_layer=norm_layer,
-                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
-                use_checkpoint=use_checkpoint)
-            self.layers.append(layer)
-
-        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
-        self.num_features = num_features
-
-        # add a norm layer for each output
-        for i_layer in out_indices:
-            layer = norm_layer(num_features[i_layer])
-            layer_name = f'norm{i_layer}'
-            self.add_module(layer_name, layer)
-
-        self._freeze_stages()
-        self._out_features = ['swin{}'.format(i) for i in self.out_indices]
-        self._out_feature_channels = {
-            'swin{}'.format(i): self.embed_dim * 2 ** i for i in self.out_indices
-        }
-        self._out_feature_strides = {
-            'swin{}'.format(i): 2 ** (i + 2) for i in self.out_indices
-        }
-        self._size_devisibility = 32
-
-
-    def _freeze_stages(self):
-        if self.frozen_stages >= 0:
-            self.patch_embed.eval()
-            for param in self.patch_embed.parameters():
-                param.requires_grad = False
-
-        if self.frozen_stages >= 1 and self.ape:
-            self.absolute_pos_embed.requires_grad = False
-
-        if self.frozen_stages >= 2:
-            self.pos_drop.eval()
-            for i in range(0, self.frozen_stages - 1):
-                m = self.layers[i]
-                m.eval()
-                for param in m.parameters():
-                    param.requires_grad = False
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in backbone.
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-
-        def _init_weights(m):
-            if isinstance(m, nn.Linear):
-                trunc_normal_(m.weight, std=.02)
-                if isinstance(m, nn.Linear) and m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.LayerNorm):
-                nn.init.constant_(m.bias, 0)
-                nn.init.constant_(m.weight, 1.0)
-
-        if isinstance(pretrained, str):
-            self.apply(_init_weights)
-            # load_checkpoint(self, pretrained, strict=False)
-        elif pretrained is None:
-            self.apply(_init_weights)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def forward(self, x):
-        """Forward function."""
-        x = self.patch_embed(x)
-
-        Wh, Ww = x.size(2), x.size(3)
-        if self.ape:
-            # interpolate the position embedding to the corresponding size
-            absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
-            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
-        else:
-            x = x.flatten(2).transpose(1, 2)
-        x = self.pos_drop(x)
-
-        # outs = []
-        outs = {}
-        for i in range(self.num_layers):
-            layer = self.layers[i]
-            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-
-            if i in self.out_indices:
-                norm_layer = getattr(self, f'norm{i}')
-                x_out = norm_layer(x_out)
-
-                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
-                # outs.append(out)
-                outs['swin{}'.format(i)] = out
-
-        return outs
-
-    def train(self, mode=True):
-        """Convert the model into training mode while keep layers freezed."""
-        super(SwinTransformer, self).train(mode)
-        self._freeze_stages()
-
-size2config = {
-    'T': {
-        'window_size': 7,
-        'embed_dim': 96, 
-        'depth': [2, 2, 6, 2],
-        'num_heads': [3, 6, 12, 24],
-        'drop_path_rate': 0.2,
-        'pretrained': 'models/swin_tiny_patch4_window7_224.pth'
-    },
-    'S': {
-        'window_size': 7,
-        'embed_dim': 96, 
-        'depth': [2, 2, 18, 2],
-        'num_heads': [3, 6, 12, 24],
-        'drop_path_rate': 0.2,
-        'pretrained': 'models/swin_small_patch4_window7_224.pth'
-    },
-    'B': {
-        'window_size': 7,
-        'embed_dim': 128, 
-        'depth': [2, 2, 18, 2],
-        'num_heads': [4, 8, 16, 32],
-        'drop_path_rate': 0.3,
-        'pretrained': 'models/swin_base_patch4_window7_224.pth'
-    },
-    'B-22k': {
-        'window_size': 7,
-        'embed_dim': 128, 
-        'depth': [2, 2, 18, 2],
-        'num_heads': [4, 8, 16, 32],
-        'drop_path_rate': 0.3,
-        'pretrained': 'models/swin_base_patch4_window7_224_22k.pth'
-    },
-    'B-22k-384': {
-        'window_size': 12,
-        'embed_dim': 128, 
-        'depth': [2, 2, 18, 2],
-        'num_heads': [4, 8, 16, 32],
-        'drop_path_rate': 0.3,
-        'pretrained': 'models/swin_base_patch4_window12_384_22k.pth'
-    },
-    'L-22k': {
-        'window_size': 7,
-        'embed_dim': 192, 
-        'depth': [2, 2, 18, 2],
-        'num_heads': [6, 12, 24, 48],
-        'drop_path_rate': 0.3, # TODO (xingyi): this is unclear
-        'pretrained': 'models/swin_large_patch4_window7_224_22k.pth'
-    },
-    'L-22k-384': {
-        'window_size': 12,
-        'embed_dim': 192, 
-        'depth': [2, 2, 18, 2],
-        'num_heads': [6, 12, 24, 48],
-        'drop_path_rate': 0.3, # TODO (xingyi): this is unclear
-        'pretrained': 'models/swin_large_patch4_window12_384_22k.pth'
-    }
-}
-
-@BACKBONE_REGISTRY.register()
-def build_swintransformer_backbone(cfg, input_shape):
-    """
-    """
-    config = size2config[cfg.MODEL.SWIN.SIZE]
-    out_indices = cfg.MODEL.SWIN.OUT_FEATURES
-    model = SwinTransformer(
-        embed_dim=config['embed_dim'],
-        window_size=config['window_size'],
-        depths=config['depth'],
-        num_heads=config['num_heads'],
-        drop_path_rate=config['drop_path_rate'],
-        out_indices=out_indices,
-        frozen_stages=-1,
-        use_checkpoint=cfg.MODEL.SWIN.USE_CHECKPOINT
-    )
-    # print('Initializing', config['pretrained'])
-    model.init_weights(config['pretrained'])
-    return model
-
-
-@BACKBONE_REGISTRY.register()
-def build_swintransformer_fpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    """
-    bottom_up = build_swintransformer_backbone(cfg, input_shape)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    out_channels = cfg.MODEL.FPN.OUT_CHANNELS
-    backbone = FPN(
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=out_channels,
-        norm=cfg.MODEL.FPN.NORM,
-        top_block=LastLevelP6P7_P5(out_channels, out_channels),
-        fuse_type=cfg.MODEL.FPN.FUSE_TYPE,
-    )
-    return backbone
-
-
-@BACKBONE_REGISTRY.register()
-def build_swintransformer_bifpn_backbone(cfg, input_shape: ShapeSpec):
-    """
-    """
-    bottom_up = build_swintransformer_backbone(cfg, input_shape)
-    in_features = cfg.MODEL.FPN.IN_FEATURES
-    backbone = BiFPN(
-        cfg=cfg,
-        bottom_up=bottom_up,
-        in_features=in_features,
-        out_channels=cfg.MODEL.BIFPN.OUT_CHANNELS,
-        norm=cfg.MODEL.BIFPN.NORM,
-        num_levels=cfg.MODEL.BIFPN.NUM_LEVELS,
-        num_bifpn=cfg.MODEL.BIFPN.NUM_BIFPN,
-        separable_conv=cfg.MODEL.BIFPN.SEPARABLE_CONV,
-    )
-    return backbone
\ No newline at end of file
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/is_net/__init__.py b/spaces/Mellow-ai/PhotoAI_Mellow/is_net/__init__.py
deleted file mode 100644
index 74aba2b8edfc4656e796843cab1d9b6fcdcce610..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/is_net/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from models.isnet import ISNetGTEncoder, ISNetDIS
diff --git a/spaces/MetaWabbit/Auto-GPT/autogpt/configurator.py b/spaces/MetaWabbit/Auto-GPT/autogpt/configurator.py
deleted file mode 100644
index 1dc3be124f638b8859eb459bcb2d46696f62e2b7..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/autogpt/configurator.py
+++ /dev/null
@@ -1,134 +0,0 @@
-"""Configurator module."""
-import click
-from colorama import Back, Fore, Style
-
-from autogpt import utils
-from autogpt.config import Config
-from autogpt.logs import logger
-from autogpt.memory import get_supported_memory_backends
-
-CFG = Config()
-
-
-def create_config(
-    continuous: bool,
-    continuous_limit: int,
-    ai_settings_file: str,
-    skip_reprompt: bool,
-    speak: bool,
-    debug: bool,
-    gpt3only: bool,
-    gpt4only: bool,
-    memory_type: str,
-    browser_name: str,
-    allow_downloads: bool,
-    skip_news: bool,
-) -> None:
-    """Updates the config object with the given arguments.
-
-    Args:
-        continuous (bool): Whether to run in continuous mode
-        continuous_limit (int): The number of times to run in continuous mode
-        ai_settings_file (str): The path to the ai_settings.yaml file
-        skip_reprompt (bool): Whether to skip the re-prompting messages at the beginning of the script
-        speak (bool): Whether to enable speak mode
-        debug (bool): Whether to enable debug mode
-        gpt3only (bool): Whether to enable GPT3.5 only mode
-        gpt4only (bool): Whether to enable GPT4 only mode
-        memory_type (str): The type of memory backend to use
-        browser_name (str): The name of the browser to use when using selenium to scrape the web
-        allow_downloads (bool): Whether to allow Auto-GPT to download files natively
-        skips_news (bool): Whether to suppress the output of latest news on startup
-    """
-    CFG.set_debug_mode(False)
-    CFG.set_continuous_mode(False)
-    CFG.set_speak_mode(False)
-
-    if debug:
-        logger.typewriter_log("Debug Mode: ", Fore.GREEN, "ENABLED")
-        CFG.set_debug_mode(True)
-
-    if continuous:
-        logger.typewriter_log("Continuous Mode: ", Fore.RED, "ENABLED")
-        logger.typewriter_log(
-            "WARNING: ",
-            Fore.RED,
-            "Continuous mode is not recommended. It is potentially dangerous and may"
-            " cause your AI to run forever or carry out actions you would not usually"
-            " authorise. Use at your own risk.",
-        )
-        CFG.set_continuous_mode(True)
-
-        if continuous_limit:
-            logger.typewriter_log(
-                "Continuous Limit: ", Fore.GREEN, f"{continuous_limit}"
-            )
-            CFG.set_continuous_limit(continuous_limit)
-
-    # Check if continuous limit is used without continuous mode
-    if continuous_limit and not continuous:
-        raise click.UsageError("--continuous-limit can only be used with --continuous")
-
-    if speak:
-        logger.typewriter_log("Speak Mode: ", Fore.GREEN, "ENABLED")
-        CFG.set_speak_mode(True)
-
-    if gpt3only:
-        logger.typewriter_log("GPT3.5 Only Mode: ", Fore.GREEN, "ENABLED")
-        CFG.set_smart_llm_model(CFG.fast_llm_model)
-
-    if gpt4only:
-        logger.typewriter_log("GPT4 Only Mode: ", Fore.GREEN, "ENABLED")
-        CFG.set_fast_llm_model(CFG.smart_llm_model)
-
-    if memory_type:
-        supported_memory = get_supported_memory_backends()
-        chosen = memory_type
-        if chosen not in supported_memory:
-            logger.typewriter_log(
-                "ONLY THE FOLLOWING MEMORY BACKENDS ARE SUPPORTED: ",
-                Fore.RED,
-                f"{supported_memory}",
-            )
-            logger.typewriter_log("Defaulting to: ", Fore.YELLOW, CFG.memory_backend)
-        else:
-            CFG.memory_backend = chosen
-
-    if skip_reprompt:
-        logger.typewriter_log("Skip Re-prompt: ", Fore.GREEN, "ENABLED")
-        CFG.skip_reprompt = True
-
-    if ai_settings_file:
-        file = ai_settings_file
-
-        # Validate file
-        (validated, message) = utils.validate_yaml_file(file)
-        if not validated:
-            logger.typewriter_log("FAILED FILE VALIDATION", Fore.RED, message)
-            logger.double_check()
-            exit(1)
-
-        logger.typewriter_log("Using AI Settings File:", Fore.GREEN, file)
-        CFG.ai_settings_file = file
-        CFG.skip_reprompt = True
-
-    if allow_downloads:
-        logger.typewriter_log("Native Downloading:", Fore.GREEN, "ENABLED")
-        logger.typewriter_log(
-            "WARNING: ",
-            Fore.YELLOW,
-            f"{Back.LIGHTYELLOW_EX}Auto-GPT will now be able to download and save files to your machine.{Back.RESET} "
-            + "It is recommended that you monitor any files it downloads carefully.",
-        )
-        logger.typewriter_log(
-            "WARNING: ",
-            Fore.YELLOW,
-            f"{Back.RED + Style.BRIGHT}ALWAYS REMEMBER TO NEVER OPEN FILES YOU AREN'T SURE OF!{Style.RESET_ALL}",
-        )
-        CFG.allow_downloads = True
-
-    if skip_news:
-        CFG.skip_news = True
-
-    if browser_name:
-        CFG.selenium_web_browser = browser_name
diff --git a/spaces/MirageML/dreambooth/README.md b/spaces/MirageML/dreambooth/README.md
deleted file mode 100644
index 2aa426ad09ed6a685d8e0ae19895ad66cffdae65..0000000000000000000000000000000000000000
--- a/spaces/MirageML/dreambooth/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Dreambooth Web UI
-emoji: ☁️
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.11
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: multimodalart/dreambooth-training
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/MirageML/sjc/my/utils/heartbeat.py b/spaces/MirageML/sjc/my/utils/heartbeat.py
deleted file mode 100644
index 024dc981b64140950102b05ffa657354a3cae485..0000000000000000000000000000000000000000
--- a/spaces/MirageML/sjc/my/utils/heartbeat.py
+++ /dev/null
@@ -1,78 +0,0 @@
-# generates periodic hearbeats for remote expriment monitoring
-from pathlib import Path
-import json
-from inspect import stack
-from .ticker import IntervalTicker
-
-_CURRENT_BEAT_STACK = []
-
-
-def get_heartbeat():
-    """
-    Returns:
-        The :class:`HeartBeat` object that's currently being used.
-        Throws an error if no :class:`EventStorage` is currently enabled.
-    """
-    assert len(
-        _CURRENT_BEAT_STACK
-    ), "get_heartbeat() has to be called inside a 'with EventStorage(...)' context!"
-    return _CURRENT_BEAT_STACK[-1]
-
-
-def get_tqdm_meter(pbar, format_dict):
-    format_dict['bar_format'] = "{r_bar}"
-    meter_str = pbar.format_meter(**format_dict)
-    meter_str = meter_str[2:]
-    return meter_str
-
-
-def caller_info(n_stack_up):
-    info = stack()[1 + n_stack_up]  # 1 up as base so that it starts from caller
-    msg = f"{info.filename}:{info.lineno} - {info.function}"
-    return msg
-
-
-class HeartBeat():
-    def __init__(
-        self, pbar, write_interval=10,
-        output_dir="./", fname="heartbeat.json"
-    ):
-        self.pbar = pbar
-        self.fname = Path(output_dir) / fname
-        self.ticker = IntervalTicker(write_interval)
-        self.completed = False
-
-        # force one write at the beginning
-        self.beat(force_write=True, n_stack_up=2)
-
-    def beat(self, force_write=False, n_stack_up=1):
-        on_write_period = self.ticker.tick()
-        if force_write or on_write_period:
-            stats = self.stats()
-            stats['caller'] = caller_info(n_stack_up)
-
-            with open(self.fname, "w") as f:
-                json.dump(stats, f)
-
-    def done(self):
-        self.completed = True
-        self.beat(force_write=True, n_stack_up=2)
-
-    def stats(self):
-        pbar = self.pbar
-        fdict = pbar.format_dict
-        stats = {
-            "beat": self.ticker.tick_str(),
-            "done": self.completed,
-            "meter": get_tqdm_meter(pbar, fdict),
-            "elapsed": int(fdict['elapsed'])
-        }
-        return stats
-
-    def __enter__(self):
-        _CURRENT_BEAT_STACK.append(self)
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        assert _CURRENT_BEAT_STACK[-1] == self
-        _CURRENT_BEAT_STACK.pop()
diff --git a/spaces/NAACL2022/CLIP-Caption-Reward/scripts_FineCapEval/prepro_labels.py b/spaces/NAACL2022/CLIP-Caption-Reward/scripts_FineCapEval/prepro_labels.py
deleted file mode 100644
index 48e7d079808760941a78d87435f8f0e2bbcfb280..0000000000000000000000000000000000000000
--- a/spaces/NAACL2022/CLIP-Caption-Reward/scripts_FineCapEval/prepro_labels.py
+++ /dev/null
@@ -1,209 +0,0 @@
-"""
-Preprocess a raw json dataset into hdf5/json files for use in data_loader.py
-
-Input: json file that has the form
-[{ file_path: 'path/img.jpg', captions: ['a caption', ...] }, ...]
-example element in this list would look like
-{'captions': [u'A man with a red helmet on a small moped on a dirt road. ', u'Man riding a motor bike on a dirt road on the countryside.', u'A man riding on the back of a motorcycle.', u'A dirt path with a young person on a motor bike rests to the foreground of a verdant area with a bridge and a background of cloud-wreathed mountains. ', u'A man in a red shirt and a red hat is on a motorcycle on a hill side.'], 'file_path': u'val2014/COCO_val2014_000000391895.jpg', 'id': 391895}
-
-This script reads this json, does some basic preprocessing on the captions
-(e.g. lowercase, etc.), creates a special UNK token, and encodes everything to arrays
-
-Output: a json file and an hdf5 file
-The hdf5 file contains several fields:
-/labels is (M,max_length) uint32 array of encoded labels, zero padded
-/label_start_ix and /label_end_ix are (N,) uint32 arrays of pointers to the 
-  first and last indices (in range 1..M) of labels for each image
-/label_length stores the length of the sequence for each of the M sequences
-
-The json file has a dict that contains:
-- an 'ix_to_word' field storing the vocab in form {ix:'word'}, where ix is 1-indexed
-- an 'images' field that is a list holding auxiliary information for each image, 
-  such as in particular the 'split' it was assigned to.
-"""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import json
-import argparse
-from random import shuffle, seed
-import string
-# non-standard dependencies:
-import h5py
-import numpy as np
-import torch
-import torchvision.models as models
-import skimage.io
-from PIL import Image
-
-
-def build_vocab(imgs, params):
-    count_thr = params['word_count_threshold']
-
-    # count up the number of words
-    counts = {}
-    for img in imgs:
-        for sent in img['sentences']:
-            for w in sent['tokens']:
-                counts[w] = counts.get(w, 0) + 1
-    cw = sorted([(count,w) for w,count in counts.items()], reverse=True)
-    print('top words and their counts:')
-    print('\n'.join(map(str,cw[:20])))
-
-    # print some stats
-    total_words = sum(counts.values())
-    print('total words:', total_words)
-    bad_words = [w for w,n in counts.items() if n <= count_thr]
-    vocab = [w for w,n in counts.items() if n > count_thr]
-    bad_count = sum(counts[w] for w in bad_words)
-    print('number of bad words: %d/%d = %.2f%%' % (len(bad_words), len(counts), len(bad_words)*100.0/len(counts)))
-    print('number of words in vocab would be %d' % (len(vocab), ))
-    print('number of UNKs: %d/%d = %.2f%%' % (bad_count, total_words, bad_count*100.0/total_words))
-
-    # lets look at the distribution of lengths as well
-    sent_lengths = {}
-    for img in imgs:
-        for sent in img['sentences']:
-            txt = sent['tokens']
-            nw = len(txt)
-            sent_lengths[nw] = sent_lengths.get(nw, 0) + 1
-    max_len = max(sent_lengths.keys())
-    print('max length sentence in raw data: ', max_len)
-    print('sentence length distribution (count, number of words):')
-    sum_len = sum(sent_lengths.values())
-    for i in range(max_len+1):
-        print('%2d: %10d   %f%%' % (i, sent_lengths.get(i,0), sent_lengths.get(i,0)*100.0/sum_len))
-
-    # lets now produce the final annotations
-    if bad_count > 0:
-        # additional special UNK token we will use below to map infrequent words to
-        print('inserting the special UNK token')
-        vocab.append('UNK')
-    
-    for img in imgs:
-        img['final_captions'] = []
-        for sent in img['sentences']:
-            txt = sent['tokens']
-            caption = [w if counts.get(w,0) > count_thr else 'UNK' for w in txt]
-            img['final_captions'].append(caption)
-
-    return vocab
-
-
-def encode_captions(imgs, params, wtoi):
-    """ 
-    encode all captions into one large array, which will be 1-indexed.
-    also produces label_start_ix and label_end_ix which store 1-indexed 
-    and inclusive (Lua-style) pointers to the first and last caption for
-    each image in the dataset.
-    """
-
-    max_length = params['max_length']
-    N = len(imgs)
-    M = sum(len(img['final_captions']) for img in imgs) # total number of captions
-
-    label_arrays = []
-    label_start_ix = np.zeros(N, dtype='uint32') # note: these will be one-indexed
-    label_end_ix = np.zeros(N, dtype='uint32')
-    label_length = np.zeros(M, dtype='uint32')
-    caption_counter = 0
-    counter = 1
-    for i,img in enumerate(imgs):
-        n = len(img['final_captions'])
-        assert n > 0, 'error: some image has no captions'
-
-        Li = np.zeros((n, max_length), dtype='uint32')
-        for j,s in enumerate(img['final_captions']):
-            label_length[caption_counter] = min(max_length, len(s)) # record the length of this sequence
-            caption_counter += 1
-            for k,w in enumerate(s):
-                if k < max_length:
-                    Li[j,k] = wtoi[w]
-
-        # note: word indices are 1-indexed, and captions are padded with zeros
-        label_arrays.append(Li)
-        label_start_ix[i] = counter
-        label_end_ix[i] = counter + n - 1
-        
-        counter += n
-    
-    L = np.concatenate(label_arrays, axis=0) # put all the labels together
-    assert L.shape[0] == M, 'lengths don\'t match? that\'s weird'
-    assert np.all(label_length > 0), 'error: some caption had no words?'
-
-    print('encoded captions to array of size ', L.shape)
-    return L, label_start_ix, label_end_ix, label_length
-
-
-def main(params):
-
-    imgs = json.load(open(params['input_json'], 'r'))
-    imgs = imgs['images']
-
-    seed(123) # make reproducible
-    
-    # # create the vocab
-    # vocab = build_vocab(imgs, params)
-    # itow = {i+1:w for i,w in enumerate(vocab)} # a 1-indexed vocab translation table
-    # wtoi = {w:i+1 for i,w in enumerate(vocab)} # inverse table
-
-    itow = imgs['ix_to_word']
-    wtoi = {w:i for i, w in itow.items()}
-    
-    # encode captions in large arrays, ready to ship to hdf5 file
-    L, label_start_ix, label_end_ix, label_length = encode_captions(imgs, params, wtoi)
-
-    # create output h5 file
-    N = len(imgs)
-    f_lb = h5py.File(params['output_h5']+'_label.h5', "w")
-    f_lb.create_dataset("labels", dtype='uint32', data=L)
-    f_lb.create_dataset("label_start_ix", dtype='uint32', data=label_start_ix)
-    f_lb.create_dataset("label_end_ix", dtype='uint32', data=label_end_ix)
-    f_lb.create_dataset("label_length", dtype='uint32', data=label_length)
-    f_lb.close()
-
-    # create output json file
-    out = {}
-    out['ix_to_word'] = itow # encode the (1-indexed) vocab
-    out['images'] = []
-    for i,img in enumerate(imgs):
-        
-        jimg = {}
-        jimg['split'] = img['split']
-        if 'filename' in img: jimg['file_path'] = os.path.join(img.get('filepath', ''), img['filename']) # copy it over, might need
-        if 'cocoid' in img:
-            jimg['id'] = img['cocoid'] # copy over & mantain an id, if present (e.g. coco ids, useful)
-        elif 'imgid' in img:
-            jimg['id'] = img['imgid']
-
-        if params['images_root'] != '':
-            with Image.open(os.path.join(params['images_root'], img['filepath'], img['filename'])) as _img:
-                jimg['width'], jimg['height'] = _img.size
-
-        out['images'].append(jimg)
-    
-    json.dump(out, open(params['output_json'], 'w'))
-    print('wrote ', params['output_json'])
-
-if __name__ == "__main__":
-
-    parser = argparse.ArgumentParser()
-
-    # input json
-    parser.add_argument('--input_json', required=True, help='input json file to process into hdf5')
-    parser.add_argument('--output_json', default='data.json', help='output json file')
-    parser.add_argument('--output_h5', default='data', help='output h5 file')
-    parser.add_argument('--images_root', default='', help='root location in which images are stored, to be prepended to file_path in input json')
-
-    # options
-    parser.add_argument('--max_length', default=16, type=int, help='max length of a caption, in number of words. captions longer than this get clipped.')
-    parser.add_argument('--word_count_threshold', default=5, type=int, help='only words that occur more than this number of times will be put in vocab')
-
-    args = parser.parse_args()
-    params = vars(args) # convert to ordinary dict
-    print('parsed input parameters:')
-    print(json.dumps(params, indent = 2))
-    main(params)
diff --git a/spaces/NATSpeech/DiffSpeech/modules/commons/rel_transformer.py b/spaces/NATSpeech/DiffSpeech/modules/commons/rel_transformer.py
deleted file mode 100644
index 7e5b68b682be7ef0d1049015f0cd03d4e74f77d2..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/DiffSpeech/modules/commons/rel_transformer.py
+++ /dev/null
@@ -1,439 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from modules.commons.layers import Embedding
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-class Encoder(nn.Module):
-    def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0.,
-                 window_size=None, block_length=None, pre_ln=False, **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.block_length = block_length
-        self.pre_ln = pre_ln
-
-        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, window_size=window_size,
-                                   p_dropout=p_dropout, block_length=block_length))
-            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))
-        if pre_ln:
-            self.last_ln = LayerNorm(hidden_channels)
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        for i in range(self.n_layers):
-            x = x * x_mask
-            x_ = x
-            if self.pre_ln:
-                x = self.norm_layers_1[i](x)
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = x_ + y
-            if not self.pre_ln:
-                x = self.norm_layers_1[i](x)
-
-            x_ = x
-            if self.pre_ln:
-                x = self.norm_layers_2[i](x)
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = x_ + y
-            if not self.pre_ln:
-                x = self.norm_layers_2[i](x)
-        if self.pre_ln:
-            x = self.last_ln(x)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0.,
-                 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.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.p_dropout = p_dropout
-        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)
-        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)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        if proximal_init:
-            self.conv_k.weight.data.copy_(self.conv_q.weight.data)
-            self.conv_k.bias.data.copy_(self.conv_q.bias.data)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-
-    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, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
-        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, key_relative_embeddings)
-            rel_logits = self._relative_position_to_absolute_position(rel_logits)
-            scores_local = rel_logits / math.sqrt(self.k_channels)
-            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:
-                block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-                scores = scores * block_mask + -1e4 * (1 - block_mask)
-        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,
-                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, 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, 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, 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, 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):
-        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.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(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(x * x_mask)
-        return x * x_mask
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-4):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        n_dims = len(x.shape)
-        mean = torch.mean(x, 1, keepdim=True)
-        variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
-
-        x = (x - mean) * torch.rsqrt(variance + self.eps)
-
-        shape = [1, -1] + [1] * (n_dims - 2)
-        x = x * self.gamma.view(*shape) + self.beta.view(*shape)
-        return x
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(
-            nn.ReLU(),
-            nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class RelTransformerEncoder(nn.Module):
-    def __init__(self,
-                 n_vocab,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout,
-                 window_size=4,
-                 block_length=None,
-                 prenet=True,
-                 pre_ln=True,
-                 ):
-
-        super().__init__()
-
-        self.n_vocab = n_vocab
-        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.window_size = window_size
-        self.block_length = block_length
-        self.prenet = prenet
-        self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0)
-
-        if prenet:
-            self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels,
-                                    kernel_size=5, n_layers=3, p_dropout=0)
-        self.encoder = Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            window_size=window_size,
-            block_length=block_length,
-            pre_ln=pre_ln,
-        )
-
-    def forward(self, x, x_mask=None):
-        if self.n_vocab > 0:
-            x_lengths = (x > 0).long().sum(-1)
-            x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-        else:
-            x_lengths = (x.abs().sum(-1) > 0).long().sum(-1)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-        if self.prenet:
-            x = self.pre(x, x_mask)
-        x = self.encoder(x, x_mask)
-        return x.transpose(1, 2)
-
-
-class RelTransformerEncoder(nn.Module):
-    def __init__(self,
-                 n_vocab,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout=0.0,
-                 window_size=4,
-                 block_length=None,
-                 prenet=True,
-                 pre_ln=True,
-                 ):
-
-        super().__init__()
-
-        self.n_vocab = n_vocab
-        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.window_size = window_size
-        self.block_length = block_length
-        self.prenet = prenet
-        if n_vocab > 0:
-            self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0)
-
-        if prenet:
-            self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels,
-                                    kernel_size=5, n_layers=3, p_dropout=0)
-        self.encoder = Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            window_size=window_size,
-            block_length=block_length,
-            pre_ln=pre_ln,
-        )
-
-    def forward(self, x, x_mask=None):
-        if self.n_vocab > 0:
-            x_lengths = (x > 0).long().sum(-1)
-            x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-        else:
-            x_lengths = (x.abs().sum(-1) > 0).long().sum(-1)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-        if self.prenet:
-            x = self.pre(x, x_mask)
-        x = self.encoder(x, x_mask)
-        return x.transpose(1, 2)
diff --git a/spaces/NCTCMumbai/NCTC/models/research/cognitive_planning/tasks.py b/spaces/NCTCMumbai/NCTC/models/research/cognitive_planning/tasks.py
deleted file mode 100644
index c3ef6ca328f7454ffe9aec61a704d1322d680d31..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/cognitive_planning/tasks.py
+++ /dev/null
@@ -1,1507 +0,0 @@
-# Copyright 2018 The TensorFlow 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.
-# ==============================================================================
-
-"""A library of tasks.
-
-This interface is intended to implement a wide variety of navigation
-tasks. See go/navigation_tasks for a list.
-"""
-
-import abc
-import collections
-import math
-import threading
-import networkx as nx
-import numpy as np
-import tensorflow as tf
-#from pyglib import logging
-#import gin
-from envs import task_env
-from envs import util as envs_util
-
-
-# Utility functions.
-def _pad_or_clip_array(np_arr, arr_len, is_front_clip=True, output_mask=False):
-  """Make np_arr array to have length arr_len.
-
-  If the array is shorter than arr_len, then it is padded from the front with
-  zeros. If it is longer, then it is clipped either from the back or from the
-  front. Only the first dimension is modified.
-
-  Args:
-    np_arr: numpy array.
-    arr_len: integer scalar.
-    is_front_clip: a boolean. If true then clipping is done in the front,
-      otherwise in the back.
-    output_mask: If True, outputs a numpy array of rank 1 which represents
-      a mask of which values have been added (0 - added, 1 - actual output).
-
-  Returns:
-    A numpy array and the size of padding (as a python int32). This size is
-    negative is the array is clipped.
-  """
-  shape = list(np_arr.shape)
-  pad_size = arr_len - shape[0]
-  padded_or_clipped = None
-  if pad_size < 0:
-    if is_front_clip:
-      padded_or_clipped = np_arr[-pad_size:, :]
-    else:
-      padded_or_clipped = np_arr[:arr_len, :]
-  elif pad_size > 0:
-    padding = np.zeros([pad_size] + shape[1:], dtype=np_arr.dtype)
-    padded_or_clipped = np.concatenate([np_arr, padding], axis=0)
-  else:
-    padded_or_clipped = np_arr
-
-  if output_mask:
-    mask = np.ones((arr_len,), dtype=np.int)
-    if pad_size > 0:
-      mask[-pad_size:] = 0
-    return padded_or_clipped, pad_size, mask
-  else:
-    return padded_or_clipped, pad_size
-
-
-def classification_loss(truth, predicted, weights=None, is_one_hot=True):
-  """A cross entropy loss.
-
-  Computes the mean of cross entropy losses for all pairs of true labels and
-  predictions. It wraps around a tf implementation of the cross entropy loss
-  with additional reformating of the inputs. If the truth and predicted are
-  n-rank Tensors with n > 2, then these are reshaped to 2-rank Tensors. It
-  allows for truth to be specified as one hot vector or class indices. Finally,
-  a weight can be specified for each element in truth and predicted.
-
-  Args:
-    truth: an n-rank or (n-1)-rank Tensor containing labels. If is_one_hot is
-      True, then n-rank Tensor is expected, otherwise (n-1) rank one.
-    predicted: an n-rank float Tensor containing prediction probabilities.
-    weights: an (n-1)-rank float Tensor of weights
-    is_one_hot: a boolean.
-
-  Returns:
-    A TF float scalar.
-  """
-  num_labels = predicted.get_shape().as_list()[-1]
-  if not is_one_hot:
-    truth = tf.reshape(truth, [-1])
-    truth = tf.one_hot(
-        truth, depth=num_labels, on_value=1.0, off_value=0.0, axis=-1)
-  else:
-    truth = tf.reshape(truth, [-1, num_labels])
-  predicted = tf.reshape(predicted, [-1, num_labels])
-  losses = tf.nn.softmax_cross_entropy_with_logits(
-      labels=truth, logits=predicted)
-  if weights is not None:
-    losses = tf.boolean_mask(losses,
-                             tf.cast(tf.reshape(weights, [-1]), dtype=tf.bool))
-  return tf.reduce_mean(losses)
-
-
-class UnrolledTaskIOConfig(object):
-  """Configuration of task inputs and outputs.
-
-  A task can have multiple inputs, which define the context, and a task query
-  which defines what is to be executed in this context. The desired execution
-  is encoded in an output. The config defines the shapes of the inputs, the
-  query and the outputs.
-  """
-
-  def __init__(self, inputs, output, query=None):
-    """Constructs a Task input/output config.
-
-    Args:
-      inputs: a list of tuples. Each tuple represents the configuration of an
-        input, with first element being the type (a string value) and the second
-        element the shape.
-      output: a tuple representing the configuration of the output.
-      query: a tuple representing the configuration of the query. If no query,
-        then None.
-    """
-    # A configuration of a single input, output or query. Consists of the type,
-    # which can be one of the three specified above, and a shape. The shape must
-    # be consistent with the type, e.g. if type == 'image', then shape is a 3
-    # valued list.
-    io_config = collections.namedtuple('IOConfig', ['type', 'shape'])
-
-    def assert_config(config):
-      if not isinstance(config, tuple):
-        raise ValueError('config must be a tuple. Received {}'.format(
-            type(config)))
-      if len(config) != 2:
-        raise ValueError('config must have 2 elements, has %d' % len(config))
-      if not isinstance(config[0], tf.DType):
-        raise ValueError('First element of config must be a tf.DType.')
-      if not isinstance(config[1], list):
-        raise ValueError('Second element of config must be a list.')
-
-    assert isinstance(inputs, collections.OrderedDict)
-    for modality_type in inputs:
-      assert_config(inputs[modality_type])
-    self._inputs = collections.OrderedDict(
-        [(k, io_config(*value)) for k, value in inputs.iteritems()])
-
-    if query is not None:
-      assert_config(query)
-      self._query = io_config(*query)
-    else:
-      self._query = None
-
-    assert_config(output)
-    self._output = io_config(*output)
-
-  @property
-  def inputs(self):
-    return self._inputs
-
-  @property
-  def output(self):
-    return self._output
-
-  @property
-  def query(self):
-    return self._query
-
-
-class UnrolledTask(object):
-  """An interface for a Task which can be unrolled during training.
-
-  Each example is called episode and consists of inputs and target output, where
-  the output can be considered as desired unrolled sequence of actions for the
-  inputs. For the specified tasks, these action sequences are to be
-  unambiguously definable.
-  """
-  __metaclass__ = abc.ABCMeta
-
-  def __init__(self, config):
-    assert isinstance(config, UnrolledTaskIOConfig)
-    self._config = config
-    # A dict of bookkeeping variables.
-    self.info = {}
-    # Tensorflow input is multithreaded and this lock is needed to prevent
-    # race condition in the environment. Without the lock, non-thread safe
-    # environments crash.
-    self._lock = threading.Lock()
-
-  @property
-  def config(self):
-    return self._config
-
-  @abc.abstractmethod
-  def episode(self):
-    """Returns data needed to train and test a single episode.
-
-    Each episode consists of inputs, which define the context of the task, a
-    query which defines the task, and a target output, which defines a
-    sequence of actions to be executed for this query. This sequence should not
-    require feedback, i.e. can be predicted purely from input and query.]
-
-    Returns:
-      inputs, query, output, where inputs is a list of numpy arrays and query
-      and output are numpy arrays. These arrays must be of shape and type as
-      specified in the task configuration.
-    """
-    pass
-
-  def reset(self, observation):
-    """Called after the environment is reset."""
-    pass
-
-  def episode_batch(self, batch_size):
-    """Returns a batch of episodes.
-
-    Args:
-      batch_size: size of batch.
-
-    Returns:
-      (inputs, query, output, masks) where inputs is list of numpy arrays and
-      query, output, and mask are numpy arrays. These arrays must be of shape
-      and type as specified in the task configuration with one additional
-      preceding dimension corresponding to the batch.
-
-    Raises:
-      ValueError: if self.episode() returns illegal values.
-    """
-    batched_inputs = collections.OrderedDict(
-        [[mtype, []] for mtype in self.config.inputs])
-    batched_queries = []
-    batched_outputs = []
-    batched_masks = []
-    for _ in range(int(batch_size)):
-      with self._lock:
-        # The episode function needs to be thread-safe. Since the current
-        # implementation for the envs are not thread safe we need to have lock
-        # the operations here.
-        inputs, query, outputs = self.episode()
-      if not isinstance(outputs, tuple):
-        raise ValueError('Outputs return value must be tuple.')
-      if len(outputs) != 2:
-        raise ValueError('Output tuple must be of size 2.')
-      if inputs is not None:
-        for modality_type in batched_inputs:
-          batched_inputs[modality_type].append(
-              np.expand_dims(inputs[modality_type], axis=0))
-
-      if query is not None:
-        batched_queries.append(np.expand_dims(query, axis=0))
-      batched_outputs.append(np.expand_dims(outputs[0], axis=0))
-      if outputs[1] is not None:
-        batched_masks.append(np.expand_dims(outputs[1], axis=0))
-
-    batched_inputs = {
-        k: np.concatenate(i, axis=0) for k, i in batched_inputs.iteritems()
-    }
-    if batched_queries:
-      batched_queries = np.concatenate(batched_queries, axis=0)
-    batched_outputs = np.concatenate(batched_outputs, axis=0)
-    if batched_masks:
-      batched_masks = np.concatenate(batched_masks, axis=0).astype(np.float32)
-    else:
-      # When the array is empty, the default np.dtype is float64 which causes
-      # py_func to crash in the tests.
-      batched_masks = np.array([], dtype=np.float32)
-    batched_inputs = [batched_inputs[k] for k in self._config.inputs]
-    return batched_inputs, batched_queries, batched_outputs, batched_masks
-
-  def tf_episode_batch(self, batch_size):
-    """A batch of episodes as TF Tensors.
-
-    Same as episode_batch with the difference that the return values are TF
-    Tensors.
-
-    Args:
-      batch_size: a python float for the batch size.
-
-    Returns:
-      inputs, query, output, mask where inputs is a dictionary of tf.Tensor
-      where the keys are the modality types specified in the config.inputs.
-      query, output, and mask are TF Tensors. These tensors must
-      be of shape and type as specified in the task configuration with one
-      additional preceding  dimension corresponding to the batch. Both mask and
-      output have the same shape as output.
-    """
-
-    # Define TF outputs.
-    touts = []
-    shapes = []
-    for _, i in self._config.inputs.iteritems():
-      touts.append(i.type)
-      shapes.append(i.shape)
-    if self._config.query is not None:
-      touts.append(self._config.query.type)
-      shapes.append(self._config.query.shape)
-    # Shapes and types for batched_outputs.
-    touts.append(self._config.output.type)
-    shapes.append(self._config.output.shape)
-    # Shapes and types for batched_masks.
-    touts.append(self._config.output.type)
-    shapes.append(self._config.output.shape[0:1])
-
-    def episode_batch_func():
-      if self.config.query is None:
-        inp, _, output, masks = self.episode_batch(int(batch_size))
-        return tuple(inp) + (output, masks)
-      else:
-        inp, query, output, masks = self.episode_batch(int(batch_size))
-        return tuple(inp) + (query, output, masks)
-
-    tf_episode_batch = tf.py_func(episode_batch_func, [], touts,
-                                  stateful=True, name='taskdata')
-    for episode, shape in zip(tf_episode_batch, shapes):
-      episode.set_shape([batch_size] + shape)
-
-    tf_episode_batch_dict = collections.OrderedDict([
-        (mtype, episode)
-        for mtype, episode in zip(self.config.inputs.keys(), tf_episode_batch)
-    ])
-    cur_index = len(self.config.inputs.keys())
-    tf_query = None
-    if self.config.query is not None:
-      tf_query = tf_episode_batch[cur_index]
-      cur_index += 1
-    tf_outputs = tf_episode_batch[cur_index]
-    tf_masks = tf_episode_batch[cur_index + 1]
-
-    return tf_episode_batch_dict, tf_query, tf_outputs, tf_masks
-
-  @abc.abstractmethod
-  def target_loss(self, true_targets, targets, weights=None):
-    """A loss for training a task model.
-
-    This loss measures the discrepancy between the task outputs, the true and
-    predicted ones.
-
-    Args:
-      true_targets: tf.Tensor of shape and type as defined in the task config
-        containing the true outputs.
-      targets: tf.Tensor of shape and type as defined in the task config
-        containing the predicted outputs.
-      weights: a bool tf.Tensor of shape as targets. Only true values are
-        considered when formulating the loss.
-    """
-    pass
-
-  def reward(self, obs, done, info):
-    """Returns a reward.
-
-    The tasks has to compute a reward based on the state of the environment. The
-    reward computation, though, is task specific. The task is to use the
-    environment interface, as defined in task_env.py, to compute the reward. If
-    this interface does not expose enough information, it is to be updated.
-
-    Args:
-      obs: Observation from environment's step function.
-      done: Done flag from environment's step function.
-      info: Info dict from environment's step function.
-
-    Returns:
-      obs: Observation.
-      reward: Floating point value.
-      done: Done flag.
-      info: Info dict.
-    """
-    # Default implementation does not do anything.
-    return obs, 0.0, done, info
-
-
-class RandomExplorationBasedTask(UnrolledTask):
-  """A Task which starts with a random exploration of the environment."""
-
-  def __init__(self,
-               env,
-               seed,
-               add_query_noise=False,
-               query_noise_var=0.0,
-               *args,
-               **kwargs):  # pylint: disable=keyword-arg-before-vararg
-    """Initializes a Task using a random exploration runs.
-
-    Args:
-      env: an instance of type TaskEnv and gym.Env.
-      seed: a random seed.
-      add_query_noise: boolean, if True then whatever queries are generated,
-        they are randomly perturbed. The semantics of the queries depends on the
-        concrete task implementation.
-      query_noise_var: float, the variance of Gaussian noise used for query
-        perturbation. Used iff add_query_noise==True.
-      *args: see super class.
-      **kwargs: see super class.
-    """
-    super(RandomExplorationBasedTask, self).__init__(*args, **kwargs)
-    assert isinstance(env, task_env.TaskEnv)
-    self._env = env
-    self._env.set_task(self)
-    self._rng = np.random.RandomState(seed)
-    self._add_query_noise = add_query_noise
-    self._query_noise_var = query_noise_var
-
-    # GoToStaticXTask can also take empty config but for the rest of the classes
-    # the number of modality types is 1.
-    if len(self.config.inputs.keys()) > 1:
-      raise NotImplementedError('current implementation supports input '
-                                'with only one modality type or less.')
-
-  def _exploration(self):
-    """Generates a random exploration run.
-
-    The function uses the environment to generate a run.
-
-    Returns:
-      A tuple of numpy arrays. The i-th array contains observation of type and
-      shape as specified in config.inputs[i].
-      A list of states along the exploration path.
-      A list of vertex indices corresponding to the path of the exploration.
-    """
-    in_seq_len = self._config.inputs.values()[0].shape[0]
-    path, _, states, step_outputs = self._env.random_step_sequence(
-        min_len=in_seq_len)
-    obs = {modality_type: [] for modality_type in self._config.inputs}
-    for o in step_outputs:
-      step_obs, _, done, _ = o
-      # It is expected that each value of step_obs is a dict of observations,
-      # whose dimensions are consistent with the config.inputs sizes.
-      for modality_type in self._config.inputs:
-        assert modality_type in step_obs, '{}'.format(type(step_obs))
-        o = step_obs[modality_type]
-        i = self._config.inputs[modality_type]
-        assert len(o.shape) == len(i.shape) - 1
-        for dim_o, dim_i in zip(o.shape, i.shape[1:]):
-          assert dim_o == dim_i, '{} != {}'.format(dim_o, dim_i)
-        obs[modality_type].append(o)
-      if done:
-        break
-
-    if not obs:
-      return obs, states, path
-
-    max_path_len = int(
-        round(in_seq_len * float(len(path)) / float(len(obs.values()[0]))))
-    path = path[-max_path_len:]
-    states = states[-in_seq_len:]
-
-    # The above obs is a list of tuples of np,array. Re-format them as tuple of
-    # np.array, each array containing all observations from all steps.
-    def regroup(obs, i):
-      """Regroups observations.
-
-      Args:
-        obs: a list of tuples of same size. The k-th tuple contains all the
-          observations from k-th step. Each observation is a numpy array.
-        i: the index of the observation in each tuple to be grouped.
-
-      Returns:
-        A numpy array of shape config.inputs[i] which contains all i-th
-        observations from all steps. These are concatenated along the first
-        dimension. In addition, if the number of observations is different from
-        the one specified in config.inputs[i].shape[0], then the array is either
-        padded from front or clipped.
-      """
-      grouped_obs = np.concatenate(
-          [np.expand_dims(o, axis=0) for o in obs[i]], axis=0)
-      in_seq_len = self._config.inputs[i].shape[0]
-      # pylint: disable=unbalanced-tuple-unpacking
-      grouped_obs, _ = _pad_or_clip_array(
-          grouped_obs, in_seq_len, is_front_clip=True)
-      return grouped_obs
-
-    all_obs = {i: regroup(obs, i) for i in self._config.inputs}
-
-    return all_obs, states, path
-
-  def _obs_to_state(self, path, states):
-    """Computes mapping between path nodes and states."""
-    # Generate a numpy array of locations corresponding to the path vertices.
-    path_coordinates = map(self._env.vertex_to_pose, path)
-    path_coordinates = np.concatenate(
-        [np.reshape(p, [1, 2]) for p in path_coordinates])
-
-    # The observations are taken along a smoothed trajectory following the path.
-    # We compute a mapping between the obeservations and the map vertices.
-    path_to_obs = collections.defaultdict(list)
-    obs_to_state = []
-    for i, s in enumerate(states):
-      location = np.reshape(s[0:2], [1, 2])
-      index = np.argmin(
-          np.reshape(
-              np.sum(np.power(path_coordinates - location, 2), axis=1), [-1]))
-      index = path[index]
-      path_to_obs[index].append(i)
-      obs_to_state.append(index)
-    return path_to_obs, obs_to_state
-
-  def _perturb_state(self, state, noise_var):
-    """Perturbes the state.
-
-    The location are purturbed using a Gaussian noise with variance
-    noise_var. The orientation is uniformly sampled.
-
-    Args:
-      state: a numpy array containing an env state (x, y locations).
-      noise_var: float
-    Returns:
-      The perturbed state.
-    """
-
-    def normal(v, std):
-      if std > 0:
-        n = self._rng.normal(0.0, std)
-        n = min(n, 2.0 * std)
-        n = max(n, -2.0 * std)
-        return v + n
-      else:
-        return v
-
-    state = state.copy()
-    state[0] = normal(state[0], noise_var)
-    state[1] = normal(state[1], noise_var)
-    if state.size > 2:
-      state[2] = self._rng.uniform(-math.pi, math.pi)
-    return state
-
-  def _sample_obs(self,
-                  indices,
-                  observations,
-                  observation_states,
-                  path_to_obs,
-                  max_obs_index=None,
-                  use_exploration_obs=True):
-    """Samples one observation which corresponds to vertex_index in path.
-
-    In addition, the sampled observation must have index in observations less
-    than max_obs_index. If these two conditions cannot be satisfied the
-    function returns None.
-
-    Args:
-      indices: a list of integers.
-      observations: a list of numpy arrays containing all the observations.
-      observation_states: a list of numpy arrays, each array representing the
-        state of the observation.
-      path_to_obs: a dict of path indices to lists of observation indices.
-      max_obs_index: an integer.
-      use_exploration_obs: if True, then the observation is sampled among the
-        specified observations, otherwise it is obtained from the environment.
-    Returns:
-      A tuple of:
-        -- A numpy array of size width x height x 3 representing the sampled
-          observation.
-        -- The index of the sampld observation among the input observations.
-        -- The state at which the observation is captured.
-    Raises:
-      ValueError: if the observation and observation_states lists are of
-        different lengths.
-    """
-    if len(observations) != len(observation_states):
-      raise ValueError('observation and observation_states lists must have '
-                       'equal lengths')
-    if not indices:
-      return None, None, None
-    vertex_index = self._rng.choice(indices)
-    if use_exploration_obs:
-      obs_indices = path_to_obs[vertex_index]
-
-      if max_obs_index is not None:
-        obs_indices = [i for i in obs_indices if i < max_obs_index]
-
-      if obs_indices:
-        index = self._rng.choice(obs_indices)
-        if self._add_query_noise:
-          xytheta = self._perturb_state(observation_states[index],
-                                        self._query_noise_var)
-          return self._env.observation(xytheta), index, xytheta
-        else:
-          return observations[index], index, observation_states[index]
-      else:
-        return None, None, None
-    else:
-      xy = self._env.vertex_to_pose(vertex_index)
-      xytheta = np.array([xy[0], xy[1], 0.0])
-      xytheta = self._perturb_state(xytheta, self._query_noise_var)
-      return self._env.observation(xytheta), None, xytheta
-
-
-class AreNearbyTask(RandomExplorationBasedTask):
-  """A task of identifying whether a query is nearby current location or not.
-
-  The query is guaranteed to be in proximity of an already visited location,
-  i.e. close to one of the observations. For each observation we have one
-  query, which is either close or not to this observation.
-  """
-
-  def __init__(
-      self,
-      max_distance=0,
-      *args,
-      **kwargs):  # pylint: disable=keyword-arg-before-vararg
-    super(AreNearbyTask, self).__init__(*args, **kwargs)
-    self._max_distance = max_distance
-
-    if len(self.config.inputs.keys()) != 1:
-      raise NotImplementedError('current implementation supports input '
-                                'with only one modality type')
-
-  def episode(self):
-    """Episode data.
-
-    Returns:
-      observations: a tuple with one element. This element is a numpy array of
-        size in_seq_len x observation_size x observation_size x 3 containing
-        in_seq_len images.
-      query: a numpy array of size
-        in_seq_len x observation_size X observation_size x 3 containing a query
-        image.
-      A tuple of size two. First element is a in_seq_len x 2 numpy array of
-        either 1.0 or 0.0. The i-th element denotes whether the i-th query
-        image is neraby (value 1.0) or not (value 0.0) to the i-th observation.
-        The second element in the tuple is a mask, a numpy array of size
-        in_seq_len x 1 and values 1.0 or 0.0 denoting whether the query is
-        valid or not (it can happen that the query is not valid, e.g. there are
-        not enough observations to have a meaningful queries).
-    """
-    observations, states, path = self._exploration()
-    assert len(observations.values()[0]) == len(states)
-
-    # The observations are taken along a smoothed trajectory following the path.
-    # We compute a mapping between the obeservations and the map vertices.
-    path_to_obs, obs_to_path = self._obs_to_state(path, states)
-
-    # Go over all observations, and sample a query. With probability 0.5 this
-    # query is a nearby observation (defined as belonging to the same vertex
-    # in path).
-    g = self._env.graph
-    queries = []
-    labels = []
-    validity_masks = []
-    query_index_in_observations = []
-    for i, curr_o in enumerate(observations.values()[0]):
-      p = obs_to_path[i]
-      low = max(0, i - self._max_distance)
-
-      # A list of lists of vertex indices. Each list in this group corresponds
-      # to one possible label.
-      index_groups = [[], [], []]
-      # Nearby visited indices, label 1.
-      nearby_visited = [
-          ii for ii in path[low:i + 1] + g[p].keys() if ii in obs_to_path[:i]
-      ]
-      nearby_visited = [ii for ii in index_groups[1] if ii in path_to_obs]
-      # NOT Nearby visited indices, label 0.
-      not_nearby_visited = [ii for ii in path[:low] if ii not in g[p].keys()]
-      not_nearby_visited = [ii for ii in index_groups[0] if ii in path_to_obs]
-      # NOT visited indices, label 2.
-      not_visited = [
-          ii for ii in range(g.number_of_nodes()) if ii not in path[:i + 1]
-      ]
-
-      index_groups = [not_nearby_visited, nearby_visited, not_visited]
-
-      # Consider only labels for which there are indices.
-      allowed_labels = [ii for ii, group in enumerate(index_groups) if group]
-      label = self._rng.choice(allowed_labels)
-
-      indices = list(set(index_groups[label]))
-      max_obs_index = None if label == 2 else i
-      use_exploration_obs = False if label == 2 else True
-      o, obs_index, _ = self._sample_obs(
-          indices=indices,
-          observations=observations.values()[0],
-          observation_states=states,
-          path_to_obs=path_to_obs,
-          max_obs_index=max_obs_index,
-          use_exploration_obs=use_exploration_obs)
-      query_index_in_observations.append(obs_index)
-
-      # If we cannot sample a valid query, we mark it as not valid in mask.
-      if o is None:
-        label = 0.0
-        o = curr_o
-        validity_masks.append(0)
-      else:
-        validity_masks.append(1)
-
-      queries.append(o.values()[0])
-      labels.append(label)
-
-    query = np.concatenate([np.expand_dims(q, axis=0) for q in queries], axis=0)
-
-    def one_hot(label, num_labels=3):
-      a = np.zeros((num_labels,), dtype=np.float)
-      a[int(label)] = 1.0
-      return a
-
-    outputs = np.stack([one_hot(l) for l in labels], axis=0)
-    validity_mask = np.reshape(
-        np.array(validity_masks, dtype=np.int32), [-1, 1])
-
-    self.info['query_index_in_observations'] = query_index_in_observations
-    self.info['observation_states'] = states
-
-    return observations, query, (outputs, validity_mask)
-
-  def target_loss(self, truth, predicted, weights=None):
-    pass
-
-
-class NeighboringQueriesTask(RandomExplorationBasedTask):
-  """A task of identifying whether two queries are closeby or not.
-
-  The proximity between queries is defined by the length of the shorest path
-  between them.
-  """
-
-  def __init__(
-      self,
-      max_distance=1,
-      *args,
-      **kwargs):   # pylint: disable=keyword-arg-before-vararg
-    """Initializes a NeighboringQueriesTask.
-
-    Args:
-      max_distance: integer, the maximum distance in terms of number of vertices
-        between the two queries, so that they are considered neighboring.
-      *args: for super class.
-      **kwargs: for super class.
-    """
-    super(NeighboringQueriesTask, self).__init__(*args, **kwargs)
-    self._max_distance = max_distance
-    if len(self.config.inputs.keys()) != 1:
-      raise NotImplementedError('current implementation supports input '
-                                'with only one modality type')
-
-  def episode(self):
-    """Episode data.
-
-    Returns:
-      observations: a tuple with one element. This element is a numpy array of
-        size in_seq_len x observation_size x observation_size x 3 containing
-        in_seq_len images.
-      query: a numpy array of size
-        2 x observation_size X observation_size x 3 containing a pair of query
-        images.
-      A tuple of size two. First element is a numpy array of size 2 containing
-        a one hot vector of whether the two observations are neighobring. Second
-        element is a boolean numpy value denoting whether this is a valid
-        episode.
-    """
-    observations, states, path = self._exploration()
-    assert len(observations.values()[0]) == len(states)
-    path_to_obs, _ = self._obs_to_state(path, states)
-    # Restrict path to ones for which observations have been generated.
-    path = [p for p in path if p in path_to_obs]
-    # Sample first query.
-    query1_index = self._rng.choice(path)
-    # Sample label.
-    label = self._rng.randint(2)
-    # Sample second query.
-    # If label == 1, then second query must be nearby, otherwise not.
-    closest_indices = nx.single_source_shortest_path(
-        self._env.graph, query1_index, self._max_distance).keys()
-    if label == 0:
-      # Closest indices on the path.
-      indices = [p for p in path if p not in closest_indices]
-    else:
-      # Indices which are not closest on the path.
-      indices = [p for p in closest_indices if p in path]
-
-    query2_index = self._rng.choice(indices)
-    # Generate an observation.
-    query1, query1_index, _ = self._sample_obs(
-        [query1_index],
-        observations.values()[0],
-        states,
-        path_to_obs,
-        max_obs_index=None,
-        use_exploration_obs=True)
-    query2, query2_index, _ = self._sample_obs(
-        [query2_index],
-        observations.values()[0],
-        states,
-        path_to_obs,
-        max_obs_index=None,
-        use_exploration_obs=True)
-
-    queries = np.concatenate(
-        [np.expand_dims(q, axis=0) for q in [query1, query2]])
-    labels = np.array([0, 0])
-    labels[label] = 1
-    is_valid = np.array([1])
-
-    self.info['observation_states'] = states
-    self.info['query_indices_in_observations'] = [query1_index, query2_index]
-
-    return observations, queries, (labels, is_valid)
-
-  def target_loss(self, truth, predicted, weights=None):
-    pass
-
-
-#@gin.configurable
-class GotoStaticXTask(RandomExplorationBasedTask):
-  """Task go to a static X.
-
-  If continuous reward is used only one goal is allowed so that the reward can
-  be computed as a delta-distance to that goal..
-  """
-
-  def __init__(self,
-               step_reward=0.0,
-               goal_reward=1.0,
-               hit_wall_reward=-1.0,
-               done_at_target=False,
-               use_continuous_reward=False,
-               *args,
-               **kwargs):  # pylint: disable=keyword-arg-before-vararg
-    super(GotoStaticXTask, self).__init__(*args, **kwargs)
-    if len(self.config.inputs.keys()) > 1:
-      raise NotImplementedError('current implementation supports input '
-                                'with only one modality type or less.')
-
-    self._step_reward = step_reward
-    self._goal_reward = goal_reward
-    self._hit_wall_reward = hit_wall_reward
-    self._done_at_target = done_at_target
-    self._use_continuous_reward = use_continuous_reward
-
-    self._previous_path_length = None
-
-  def episode(self):
-    observations, _, path = self._exploration()
-    if len(path) < 2:
-      raise ValueError('The exploration path has only one node.')
-
-    g = self._env.graph
-    start = path[-1]
-    while True:
-      goal = self._rng.choice(path[:-1])
-      if goal != start:
-        break
-    goal_path = nx.shortest_path(g, start, goal)
-
-    init_orientation = self._rng.uniform(0, np.pi, (1,))
-    trajectory = np.array(
-        [list(self._env.vertex_to_pose(p)) for p in goal_path])
-    init_xy = np.reshape(trajectory[0, :], [-1])
-    init_state = np.concatenate([init_xy, init_orientation], 0)
-
-    trajectory = trajectory[1:, :]
-    deltas = envs_util.trajectory_to_deltas(trajectory, init_state)
-    output_seq_len = self._config.output.shape[0]
-    arr = _pad_or_clip_array(deltas, output_seq_len, output_mask=True)
-    # pylint: disable=unbalanced-tuple-unpacking
-    thetas, _, thetas_mask = arr
-
-    query = self._env.observation(self._env.vertex_to_pose(goal)).values()[0]
-
-    return observations, query, (thetas, thetas_mask)
-
-  def reward(self, obs, done, info):
-    if 'wall_collision' in info and info['wall_collision']:
-      return obs, self._hit_wall_reward, done, info
-
-    reward = 0.0
-    current_vertex = self._env.pose_to_vertex(self._env.state)
-
-    if current_vertex in self._env.targets():
-      if self._done_at_target:
-        done = True
-      else:
-        obs = self._env.reset()
-      reward = self._goal_reward
-    else:
-      if self._use_continuous_reward:
-        if len(self._env.targets()) != 1:
-          raise ValueError(
-              'FindX task with continuous reward is assuming only one target.')
-        goal_vertex = self._env.targets()[0]
-        path_length = self._compute_path_length(goal_vertex)
-        reward = self._previous_path_length - path_length
-        self._previous_path_length = path_length
-      else:
-        reward = self._step_reward
-
-    return obs, reward, done, info
-
-  def _compute_path_length(self, goal_vertex):
-    current_vertex = self._env.pose_to_vertex(self._env.state)
-    path = nx.shortest_path(self._env.graph, current_vertex, goal_vertex)
-    assert len(path) >= 2
-    curr_xy = np.array(self._env.state[:2])
-    next_xy = np.array(self._env.vertex_to_pose(path[1]))
-    last_step_distance = np.linalg.norm(next_xy - curr_xy)
-    return (len(path) - 2) * self._env.cell_size_px + last_step_distance
-
-  def reset(self, observation):
-    if self._use_continuous_reward:
-      if len(self._env.targets()) != 1:
-        raise ValueError(
-            'FindX task with continuous reward is assuming only one target.')
-      goal_vertex = self._env.targets()[0]
-      self._previous_path_length = self._compute_path_length(goal_vertex)
-
-  def target_loss(self, truth, predicted, weights=None):
-    """Action classification loss.
-
-    Args:
-      truth: a batch_size x sequence length x number of labels float
-        Tensor containing a one hot vector for each label in each batch and
-        time.
-      predicted: a batch_size x sequence length x number of labels float
-        Tensor containing a predicted distribution over all actions.
-      weights: a batch_size x sequence_length float Tensor of bool
-        denoting which actions are valid.
-
-    Returns:
-      An average cross entropy over all batches and elements in sequence.
-    """
-    return classification_loss(
-        truth=truth, predicted=predicted, weights=weights, is_one_hot=True)
-
-
-class RelativeLocationTask(RandomExplorationBasedTask):
-  """A task of estimating the relative location of a query w.r.t current.
-
-  It is to be used for debugging. It is designed such that the output is a
-  single value, out of a discrete set of values, so that it can be phrased as
-  a classification problem.
-  """
-
-  def __init__(self, num_labels, *args, **kwargs):
-    """Initializes a relative location task.
-
-    Args:
-      num_labels: integer, number of orientations to bin the relative
-        orientation into.
-      *args: see super class.
-      **kwargs: see super class.
-    """
-    super(RelativeLocationTask, self).__init__(*args, **kwargs)
-    self._num_labels = num_labels
-    if len(self.config.inputs.keys()) != 1:
-      raise NotImplementedError('current implementation supports input '
-                                'with only one modality type')
-
-  def episode(self):
-    observations, states, path = self._exploration()
-
-    # Select a random element from history.
-    path_to_obs, _ = self._obs_to_state(path, states)
-    use_exploration_obs = not self._add_query_noise
-    query, _, query_state = self._sample_obs(
-        path[:-1],
-        observations.values()[0],
-        states,
-        path_to_obs,
-        max_obs_index=None,
-        use_exploration_obs=use_exploration_obs)
-
-    x, y, theta = tuple(states[-1])
-    q_x, q_y, _ = tuple(query_state)
-    t_x, t_y = q_x - x, q_y - y
-    (rt_x, rt_y) = (np.sin(theta) * t_x - np.cos(theta) * t_y,
-                    np.cos(theta) * t_x + np.sin(theta) * t_y)
-    # Bins are [a(i), a(i+1)] for a(i) = -pi + 0.5 * bin_size + i * bin_size.
-    shift = np.pi * (1 - 1.0 / (2.0 * self._num_labels))
-    orientation = np.arctan2(rt_y, rt_x) + shift
-    if orientation < 0:
-      orientation += 2 * np.pi
-    label = int(np.floor(self._num_labels * orientation / (2 * np.pi)))
-
-    out_shape = self._config.output.shape
-    if len(out_shape) != 1:
-      raise ValueError('Output shape should be of rank 1.')
-    if out_shape[0] != self._num_labels:
-      raise ValueError('Output shape must be of size %d' % self._num_labels)
-    output = np.zeros(out_shape, dtype=np.float32)
-    output[label] = 1
-
-    return observations, query, (output, None)
-
-  def target_loss(self, truth, predicted, weights=None):
-    return classification_loss(
-        truth=truth, predicted=predicted, weights=weights, is_one_hot=True)
-
-
-class LocationClassificationTask(UnrolledTask):
-  """A task of classifying a location as one of several classes.
-
-  The task does not have an input, but just a query and an output. The query
-  is an observation of the current location, e.g. an image taken from the
-  current state. The output is a label classifying this location in one of
-  predefined set of locations (or landmarks).
-
-  The current implementation classifies locations as intersections based on the
-  number and directions of biforcations. It is expected that a location can have
-  at most 4 different directions, aligned with the axes. As each of these four
-  directions might be present or not, the number of possible intersections are
-  2^4 = 16.
-  """
-
-  def __init__(self, env, seed, *args, **kwargs):
-    super(LocationClassificationTask, self).__init__(*args, **kwargs)
-    self._env = env
-    self._rng = np.random.RandomState(seed)
-    # A location property which can be set. If not set, a random one is
-    # generated.
-    self._location = None
-    if len(self.config.inputs.keys()) > 1:
-      raise NotImplementedError('current implementation supports input '
-                                'with only one modality type or less.')
-
-  @property
-  def location(self):
-    return self._location
-
-  @location.setter
-  def location(self, location):
-    self._location = location
-
-  def episode(self):
-    # Get a location. If not set, sample on at a vertex with a random
-    # orientation
-    location = self._location
-    if location is None:
-      num_nodes = self._env.graph.number_of_nodes()
-      vertex = int(math.floor(self._rng.uniform(0, num_nodes)))
-      xy = self._env.vertex_to_pose(vertex)
-      theta = self._rng.uniform(0, 2 * math.pi)
-      location = np.concatenate(
-          [np.reshape(xy, [-1]), np.array([theta])], axis=0)
-    else:
-      vertex = self._env.pose_to_vertex(location)
-
-    theta = location[2]
-    neighbors = self._env.graph.neighbors(vertex)
-    xy_s = [self._env.vertex_to_pose(n) for n in neighbors]
-
-    def rotate(xy, theta):
-      """Rotates a vector around the origin by angle theta.
-
-      Args:
-        xy: a numpy darray of shape (2, ) of floats containing the x and y
-          coordinates of a vector.
-        theta: a python float containing the rotation angle in radians.
-
-      Returns:
-        A numpy darray of floats of shape (2,) containing the x and y
-          coordinates rotated xy.
-      """
-      rotated_x = np.cos(theta) * xy[0] - np.sin(theta) * xy[1]
-      rotated_y = np.sin(theta) * xy[0] + np.cos(theta) * xy[1]
-      return np.array([rotated_x, rotated_y])
-
-    # Rotate all intersection biforcation by the orientation of the agent as the
-    # intersection label is defined in an agent centered fashion.
-    xy_s = [
-        rotate(xy - location[0:2], -location[2] - math.pi / 4) for xy in xy_s
-    ]
-    th_s = [np.arctan2(xy[1], xy[0]) for xy in xy_s]
-
-    out_shape = self._config.output.shape
-    if len(out_shape) != 1:
-      raise ValueError('Output shape should be of rank 1.')
-    num_labels = out_shape[0]
-    if num_labels != 16:
-      raise ValueError('Currently only 16 labels are supported '
-                       '(there are 16 different 4 way intersection types).')
-
-    th_s = set([int(math.floor(4 * (th / (2 * np.pi) + 0.5))) for th in th_s])
-    one_hot_label = np.zeros((num_labels,), dtype=np.float32)
-    label = 0
-    for th in th_s:
-      label += pow(2, th)
-    one_hot_label[int(label)] = 1.0
-
-    query = self._env.observation(location).values()[0]
-    return [], query, (one_hot_label, None)
-
-  def reward(self, obs, done, info):
-    raise ValueError('Do not call.')
-
-  def target_loss(self, truth, predicted, weights=None):
-    return classification_loss(
-        truth=truth, predicted=predicted, weights=weights, is_one_hot=True)
-
-
-class GotoStaticXNoExplorationTask(UnrolledTask):
-  """An interface for findX tasks without exploration.
-
-  The agent is initialized a random location in a random world and a random goal
-  and the objective is for the agent to move toward the goal. This class
-  generates episode for such task. Each generates a sequence of observations x
-  and target outputs y. x is the observations and is an OrderedDict with keys
-  provided from config.inputs.keys() and the shapes provided in the
-  config.inputs. The output is a numpy arrays with the shape specified in the
-  config.output. The shape of the array is (sequence_length x action_size) where
-  action is the number of actions that can be done in the environment. Note that
-  config.output.shape should be set according to the number of actions that can
-  be done in the env.
-  target outputs y are the groundtruth value of each action that is computed
-  from the environment graph. The target output for each action is proportional
-  to the progress that each action makes. Target value of 1 means that the
-  action takes the agent one step closer, -1 means the action takes the agent
-  one step farther. Value of -2 means that action should not take place at all.
-  This can be because the action leads to collision or it wants to terminate the
-  episode prematurely.
-  """
-
-  def __init__(self, env, *args, **kwargs):
-    super(GotoStaticXNoExplorationTask, self).__init__(*args, **kwargs)
-
-    if self._config.query is not None:
-      raise ValueError('query should be None.')
-    if len(self._config.output.shape) != 2:
-      raise ValueError('output should only have two dimensions:'
-                       '(sequence_length x number_of_actions)')
-    for input_config in self._config.inputs.values():
-      if input_config.shape[0] != self._config.output.shape[0]:
-        raise ValueError('the first dimension of the input and output should'
-                         'be the same.')
-    if len(self._config.output.shape) != 2:
-      raise ValueError('output shape should be '
-                       '(sequence_length x number_of_actions)')
-
-    self._env = env
-
-  def _compute_shortest_path_length(self, vertex, target_vertices):
-    """Computes length of the shortest path from vertex to any target vertexes.
-
-    Args:
-      vertex: integer, index of the vertex in the environment graph.
-      target_vertices: list of the target vertexes
-
-    Returns:
-      integer, minimum distance from the vertex to any of the target_vertices.
-
-    Raises:
-      ValueError: if there is no path between the vertex and at least one of
-        the target_vertices.
-    """
-    try:
-      return np.min([
-          len(nx.shortest_path(self._env.graph, vertex, t))
-          for t in target_vertices
-      ])
-    except:
-      #logging.error('there is no path between vertex %d and at least one of '
-      #              'the targets %r', vertex, target_vertices)
-      raise
-
-  def _compute_gt_value(self, vertex, target_vertices):
-    """Computes groundtruth value of all the actions at the vertex.
-
-    The value of each action is the difference each action makes in the length
-    of the shortest path to the goal. If an action takes the agent one step
-    closer to the goal the value is 1. In case, it takes the agent one step away
-    from the goal it would be -1. If it leads to collision or if the agent uses
-    action stop before reaching to the goal it is -2. To avoid scale issues the
-    gt_values are multipled by 0.5.
-
-    Args:
-      vertex: integer, the index of current vertex.
-      target_vertices: list of the integer indexes of the target views.
-
-    Returns:
-      numpy array with shape (action_size,) and each element is the groundtruth
-      value of each action based on the progress each action makes.
-    """
-    action_size = self._config.output.shape[1]
-    output_value = np.ones((action_size), dtype=np.float32) * -2
-    my_distance = self._compute_shortest_path_length(vertex, target_vertices)
-    for adj in self._env.graph[vertex]:
-      adj_distance = self._compute_shortest_path_length(adj, target_vertices)
-      if adj_distance is None:
-        continue
-      action_index = self._env.action(
-          self._env.vertex_to_pose(vertex), self._env.vertex_to_pose(adj))
-      assert action_index is not None, ('{} is not adjacent to {}. There might '
-                                        'be a problem in environment graph '
-                                        'connectivity because there is no '
-                                        'direct edge between the given '
-                                        'vertices').format(
-                                            self._env.vertex_to_pose(vertex),
-                                            self._env.vertex_to_pose(adj))
-      output_value[action_index] = my_distance - adj_distance
-
-    return output_value * 0.5
-
-  def episode(self):
-    """Returns data needed to train and test a single episode.
-
-    Returns:
-      (inputs, None, output) where inputs is a dictionary of modality types to
-        numpy arrays. The second element is query but we assume that the goal
-        is also given as part of observation so it should be None for this task,
-        and the outputs is the tuple of ground truth action values with the
-        shape of (sequence_length x action_size) that is coming from
-        config.output.shape and a numpy array with the shape of
-        (sequence_length,) that is 1 if the corresponding element of the
-        input and output should be used in the training optimization.
-
-    Raises:
-      ValueError: If the output values for env.random_step_sequence is not
-        valid.
-      ValueError: If the shape of observations coming from the env is not
-        consistent with the config.
-      ValueError: If there is a modality type specified in the config but the
-        environment does not return that.
-    """
-    # Sequence length is the first dimension of any of the input tensors.
-    sequence_length = self._config.inputs.values()[0].shape[0]
-    modality_types = self._config.inputs.keys()
-
-    path, _, _, step_outputs = self._env.random_step_sequence(
-        max_len=sequence_length)
-    target_vertices = [self._env.pose_to_vertex(x) for x in self._env.targets()]
-
-    if len(path) != len(step_outputs):
-      raise ValueError('path, and step_outputs should have equal length'
-                       ' {}!={}'.format(len(path), len(step_outputs)))
-
-    # Building up observations. observations will be a OrderedDict of
-    # modality types. The values are numpy arrays that follow the given shape
-    # in the input config for each modality type.
-    observations = collections.OrderedDict([k, []] for k in modality_types)
-    for step_output in step_outputs:
-      obs_dict = step_output[0]
-      # Only going over the modality types that are specified in the input
-      # config.
-      for modality_type in modality_types:
-        if modality_type not in obs_dict:
-          raise ValueError('modality type is not returned from the environment.'
-                           '{} not in {}'.format(modality_type,
-                                                 obs_dict.keys()))
-        obs = obs_dict[modality_type]
-        if np.any(
-            obs.shape != tuple(self._config.inputs[modality_type].shape[1:])):
-          raise ValueError(
-              'The observations should have the same size as speicifed in'
-              'config for modality type {}. {} != {}'.format(
-                  modality_type, obs.shape,
-                  self._config.inputs[modality_type].shape[1:]))
-        observations[modality_type].append(obs)
-
-    gt_value = [self._compute_gt_value(v, target_vertices) for v in path]
-
-    # pylint: disable=unbalanced-tuple-unpacking
-    gt_value, _, value_mask = _pad_or_clip_array(
-        np.array(gt_value),
-        sequence_length,
-        is_front_clip=False,
-        output_mask=True,
-    )
-    for modality_type, obs in observations.iteritems():
-      observations[modality_type], _, mask = _pad_or_clip_array(
-          np.array(obs), sequence_length, is_front_clip=False, output_mask=True)
-      assert np.all(mask == value_mask)
-
-    return observations, None, (gt_value, value_mask)
-
-  def reset(self, observation):
-    """Called after the environment is reset."""
-    pass
-
-  def target_loss(self, true_targets, targets, weights=None):
-    """A loss for training a task model.
-
-    This loss measures the discrepancy between the task outputs, the true and
-    predicted ones.
-
-    Args:
-      true_targets: tf.Tensor of tf.float32 with the shape of
-        (batch_size x sequence_length x action_size).
-      targets: tf.Tensor of tf.float32 with the shape of
-        (batch_size x sequence_length x action_size).
-      weights: tf.Tensor of tf.bool with the shape of
-        (batch_size x sequence_length).
-
-    Raises:
-      ValueError: if the shapes of the input tensors are not consistent.
-
-    Returns:
-      L2 loss between the predicted action values and true action values.
-    """
-    targets_shape = targets.get_shape().as_list()
-    true_targets_shape = true_targets.get_shape().as_list()
-    if len(targets_shape) != 3 or len(true_targets_shape) != 3:
-      raise ValueError('invalid shape for targets or true_targets_shape')
-    if np.any(targets_shape != true_targets_shape):
-      raise ValueError('the shape of targets and true_targets are not the same'
-                       '{} != {}'.format(targets_shape, true_targets_shape))
-
-    if weights is not None:
-      # Filtering targets and true_targets using weights.
-      weights_shape = weights.get_shape().as_list()
-      if np.any(weights_shape != targets_shape[0:2]):
-        raise ValueError('The first two elements of weights shape should match'
-                         'target. {} != {}'.format(weights_shape,
-                                                   targets_shape))
-      true_targets = tf.boolean_mask(true_targets, weights)
-      targets = tf.boolean_mask(targets, weights)
-
-    return tf.losses.mean_squared_error(tf.reshape(targets, [-1]),
-                                        tf.reshape(true_targets, [-1]))
-
-  def reward(self, obs, done, info):
-    raise NotImplementedError('reward is not implemented for this task')
-
-
-################################################################################
-class NewTask(UnrolledTask):
-  def __init__(self, env, *args, **kwargs):
-    super(NewTask, self).__init__(*args, **kwargs)
-    self._env = env
-
-  def _compute_shortest_path_length(self, vertex, target_vertices):
-    """Computes length of the shortest path from vertex to any target vertexes.
-
-    Args:
-      vertex: integer, index of the vertex in the environment graph.
-      target_vertices: list of the target vertexes
-
-    Returns:
-      integer, minimum distance from the vertex to any of the target_vertices.
-
-    Raises:
-      ValueError: if there is no path between the vertex and at least one of
-        the target_vertices.
-    """
-    try:
-      return np.min([
-          len(nx.shortest_path(self._env.graph, vertex, t))
-          for t in target_vertices
-      ])
-    except:
-      logging.error('there is no path between vertex %d and at least one of '
-                    'the targets %r', vertex, target_vertices)
-      raise
-
-  def _compute_gt_value(self, vertex, target_vertices):
-    """Computes groundtruth value of all the actions at the vertex.
-
-    The value of each action is the difference each action makes in the length
-    of the shortest path to the goal. If an action takes the agent one step
-    closer to the goal the value is 1. In case, it takes the agent one step away
-    from the goal it would be -1. If it leads to collision or if the agent uses
-    action stop before reaching to the goal it is -2. To avoid scale issues the
-    gt_values are multipled by 0.5.
-
-    Args:
-      vertex: integer, the index of current vertex.
-      target_vertices: list of the integer indexes of the target views.
-
-    Returns:
-      numpy array with shape (action_size,) and each element is the groundtruth
-      value of each action based on the progress each action makes.
-    """
-    action_size = self._config.output.shape[1]
-    output_value = np.ones((action_size), dtype=np.float32) * -2
-    # own compute _compute_shortest_path_length - returnts float
-    my_distance = self._compute_shortest_path_length(vertex, target_vertices)
-    for adj in self._env.graph[vertex]:
-      adj_distance = self._compute_shortest_path_length(adj, target_vertices)
-      if adj_distance is None:
-        continue
-      action_index = self._env.action(
-          self._env.vertex_to_pose(vertex), self._env.vertex_to_pose(adj))
-      assert action_index is not None, ('{} is not adjacent to {}. There might '
-                                        'be a problem in environment graph '
-                                        'connectivity because there is no '
-                                        'direct edge between the given '
-                                        'vertices').format(
-                                            self._env.vertex_to_pose(vertex),
-                                            self._env.vertex_to_pose(adj))
-      output_value[action_index] = my_distance - adj_distance
-
-    return output_value * 0.5
-
-  def episode(self):
-    """Returns data needed to train and test a single episode.
-
-    Returns:
-      (inputs, None, output) where inputs is a dictionary of modality types to
-        numpy arrays. The second element is query but we assume that the goal
-        is also given as part of observation so it should be None for this task,
-        and the outputs is the tuple of ground truth action values with the
-        shape of (sequence_length x action_size) that is coming from
-        config.output.shape and a numpy array with the shape of
-        (sequence_length,) that is 1 if the corresponding element of the
-        input and output should be used in the training optimization.
-
-    Raises:
-      ValueError: If the output values for env.random_step_sequence is not
-        valid.
-      ValueError: If the shape of observations coming from the env is not
-        consistent with the config.
-      ValueError: If there is a modality type specified in the config but the
-        environment does not return that.
-    """
-    # Sequence length is the first dimension of any of the input tensors.
-    sequence_length = self._config.inputs.values()[0].shape[0]
-    modality_types = self._config.inputs.keys()
-
-    path, _, _, step_outputs = self._env.random_step_sequence(
-        max_len=sequence_length)
-    target_vertices = [self._env.pose_to_vertex(x) for x in self._env.targets()]
-
-    if len(path) != len(step_outputs):
-      raise ValueError('path, and step_outputs should have equal length'
-                       ' {}!={}'.format(len(path), len(step_outputs)))
-
-    # Building up observations. observations will be a OrderedDict of
-    # modality types. The values are numpy arrays that follow the given shape
-    # in the input config for each modality type.
-    observations = collections.OrderedDict([k, []] for k in modality_types)
-    for step_output in step_outputs:
-      obs_dict = step_output[0]
-      # Only going over the modality types that are specified in the input
-      # config.
-      for modality_type in modality_types:
-        if modality_type not in obs_dict:
-          raise ValueError('modality type is not returned from the environment.'
-                           '{} not in {}'.format(modality_type,
-                                                 obs_dict.keys()))
-        obs = obs_dict[modality_type]
-        if np.any(
-            obs.shape != tuple(self._config.inputs[modality_type].shape[1:])):
-          raise ValueError(
-              'The observations should have the same size as speicifed in'
-              'config for modality type {}. {} != {}'.format(
-                  modality_type, obs.shape,
-                  self._config.inputs[modality_type].shape[1:]))
-        observations[modality_type].append(obs)
-
-    gt_value = [self._compute_gt_value(v, target_vertices) for v in path]
-
-    # pylint: disable=unbalanced-tuple-unpacking
-    gt_value, _, value_mask = _pad_or_clip_array(
-        np.array(gt_value),
-        sequence_length,
-        is_front_clip=False,
-        output_mask=True,
-    )
-    for modality_type, obs in observations.iteritems():
-      observations[modality_type], _, mask = _pad_or_clip_array(
-          np.array(obs), sequence_length, is_front_clip=False, output_mask=True)
-      assert np.all(mask == value_mask)
-
-    return observations, None, (gt_value, value_mask)
-
-  def reset(self, observation):
-    """Called after the environment is reset."""
-    pass
-
-  def target_loss(self, true_targets, targets, weights=None):
-    """A loss for training a task model.
-
-    This loss measures the discrepancy between the task outputs, the true and
-    predicted ones.
-
-    Args:
-      true_targets: tf.Tensor of tf.float32 with the shape of
-        (batch_size x sequence_length x action_size).
-      targets: tf.Tensor of tf.float32 with the shape of
-        (batch_size x sequence_length x action_size).
-      weights: tf.Tensor of tf.bool with the shape of
-        (batch_size x sequence_length).
-
-    Raises:
-      ValueError: if the shapes of the input tensors are not consistent.
-
-    Returns:
-      L2 loss between the predicted action values and true action values.
-    """
-    targets_shape = targets.get_shape().as_list()
-    true_targets_shape = true_targets.get_shape().as_list()
-    if len(targets_shape) != 3 or len(true_targets_shape) != 3:
-      raise ValueError('invalid shape for targets or true_targets_shape')
-    if np.any(targets_shape != true_targets_shape):
-      raise ValueError('the shape of targets and true_targets are not the same'
-                       '{} != {}'.format(targets_shape, true_targets_shape))
-
-    if weights is not None:
-      # Filtering targets and true_targets using weights.
-      weights_shape = weights.get_shape().as_list()
-      if np.any(weights_shape != targets_shape[0:2]):
-        raise ValueError('The first two elements of weights shape should match'
-                         'target. {} != {}'.format(weights_shape,
-                                                   targets_shape))
-      true_targets = tf.boolean_mask(true_targets, weights)
-      targets = tf.boolean_mask(targets, weights)
-
-    return tf.losses.mean_squared_error(tf.reshape(targets, [-1]),
-                                        tf.reshape(true_targets, [-1]))
-
-  def reward(self, obs, done, info):
-    raise NotImplementedError('reward is not implemented for this task')
diff --git a/spaces/NSect/VALL-E-X/utils/g2p/cleaners.py b/spaces/NSect/VALL-E-X/utils/g2p/cleaners.py
deleted file mode 100644
index 8bb53977673afa893476cab1b946d6a9a00f57ea..0000000000000000000000000000000000000000
--- a/spaces/NSect/VALL-E-X/utils/g2p/cleaners.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import re
-from utils.g2p.japanese import japanese_to_romaji_with_accent, japanese_to_ipa, japanese_to_ipa2, japanese_to_ipa3
-from utils.g2p.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo, chinese_to_romaji, chinese_to_lazy_ipa, chinese_to_ipa, chinese_to_ipa2
-from utils.g2p.english import english_to_lazy_ipa, english_to_ipa2, english_to_lazy_ipa2
-patterns = [r'\[EN\](.*?)\[EN\]', r'\[ZH\](.*?)\[ZH\]', r'\[JA\](.*?)\[JA\]']
-def japanese_cleaners(text):
-    text = japanese_to_romaji_with_accent(text)
-    text = re.sub(r'([A-Za-z])$', r'\1.', text)
-    return text
-
-def japanese_cleaners2(text):
-    return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
-
-def chinese_cleaners(text):
-    '''Pipeline for Chinese text'''
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text)
-    return text
-
-def cje_cleaners(text):
-    matches = []
-    for pattern in patterns:
-        matches.extend(re.finditer(pattern, text))
-
-    matches.sort(key=lambda x: x.start())  # Sort matches by their start positions
-
-    outputs = ""
-    output_langs = []
-
-    for match in matches:
-        text_segment = text[match.start():match.end()]
-        phon = clean_one(text_segment)
-        if "[EN]" in text_segment:
-            lang = 'en'
-        elif "[ZH]" in text_segment:
-            lang = 'zh'
-        elif "[JA]" in text_segment:
-            lang = 'ja'
-        else:
-            raise ValueError("If you see this error, please report this bug to issues.")
-        outputs += phon
-        output_langs += [lang] * len(phon)
-    assert len(outputs) == len(output_langs)
-    return outputs, output_langs
-
-
-def clean_one(text):
-    if text.find('[ZH]') != -1:
-        text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                      lambda x: chinese_to_ipa(x.group(1))+' ', text)
-    if text.find('[JA]') != -1:
-        text = re.sub(r'\[JA\](.*?)\[JA\]',
-                      lambda x: japanese_to_ipa2(x.group(1))+' ', text)
-    if text.find('[EN]') != -1:
-        text = re.sub(r'\[EN\](.*?)\[EN\]',
-                      lambda x: english_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
diff --git a/spaces/Nee001/bing0/src/components/toaster.tsx b/spaces/Nee001/bing0/src/components/toaster.tsx
deleted file mode 100644
index 4d2693460b61307a1d4c127fd01df9bee16e59ff..0000000000000000000000000000000000000000
--- a/spaces/Nee001/bing0/src/components/toaster.tsx
+++ /dev/null
@@ -1,3 +0,0 @@
-'use client'
-
-export { Toaster } from 'react-hot-toast'
diff --git a/spaces/Nephele/bert-vits2-multi-voice/text/__init__.py b/spaces/Nephele/bert-vits2-multi-voice/text/__init__.py
deleted file mode 100644
index 7566bf351ca9b95af9cdc6d729557a9da083800f..0000000000000000000000000000000000000000
--- a/spaces/Nephele/bert-vits2-multi-voice/text/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from text.symbols import *
-
-
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-def cleaned_text_to_sequence(cleaned_text, tones, language):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
-  tone_start = language_tone_start_map[language]
-  tones = [i + tone_start for i in tones]
-  lang_id = language_id_map[language]
-  lang_ids = [lang_id for i in phones]
-  return phones, tones, lang_ids
-
-def get_bert(norm_text, word2ph, language):
-  from .chinese_bert import get_bert_feature as zh_bert
-  from .english_bert_mock import get_bert_feature as en_bert
-  lang_bert_func_map = {
-    'ZH': zh_bert,
-    'EN': en_bert
-  }
-  bert = lang_bert_func_map[language](norm_text, word2ph)
-  return bert
diff --git a/spaces/Nicholaspei/LangChain-ChatLLM/app.py b/spaces/Nicholaspei/LangChain-ChatLLM/app.py
deleted file mode 100644
index ad9b5e1acfc7f2b433fa826a365279a0a5912e58..0000000000000000000000000000000000000000
--- a/spaces/Nicholaspei/LangChain-ChatLLM/app.py
+++ /dev/null
@@ -1,272 +0,0 @@
-import os
-
-import gradio as gr
-import nltk
-import sentence_transformers
-import torch
-from duckduckgo_search import ddg
-from duckduckgo_search.utils import SESSION
-from langchain.chains import RetrievalQA
-from langchain.document_loaders import UnstructuredFileLoader
-from langchain.embeddings import JinaEmbeddings
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-from langchain.prompts import PromptTemplate
-from langchain.prompts.prompt import PromptTemplate
-from langchain.vectorstores import FAISS
-
-from chatllm import ChatLLM
-from chinese_text_splitter import ChineseTextSplitter
-
-nltk.data.path.append('./nltk_data')
-
-embedding_model_dict = {
-    "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
-    "ernie-base": "nghuyong/ernie-3.0-base-zh",
-    "text2vec-base": "GanymedeNil/text2vec-base-chinese",
-    "ViT-B-32": 'ViT-B-32::laion2b-s34b-b79k'
-}
-
-llm_model_dict = {
-    "ChatGLM-6B-int8": "THUDM/chatglm-6b-int8",
-    "ChatGLM-6B-int4": "THUDM/chatglm-6b-int4",
-    "ChatGLM-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
-    "Minimax": "Minimax"
-}
-
-DEVICE = "cuda" if torch.cuda.is_available(
-) else "mps" if torch.backends.mps.is_available() else "cpu"
-
-
-def search_web(query):
-
-    SESSION.proxies = {
-        "http": f"socks5h://localhost:7890",
-        "https": f"socks5h://localhost:7890"
-    }
-    results = ddg(query)
-    web_content = ''
-    if results:
-        for result in results:
-            web_content += result['body']
-    return web_content
-
-
-def load_file(filepath):
-    if filepath.lower().endswith(".pdf"):
-        loader = UnstructuredFileLoader(filepath)
-        textsplitter = ChineseTextSplitter(pdf=True)
-        docs = loader.load_and_split(textsplitter)
-    else:
-        loader = UnstructuredFileLoader(filepath, mode="elements")
-        textsplitter = ChineseTextSplitter(pdf=False)
-        docs = loader.load_and_split(text_splitter=textsplitter)
-    return docs
-
-
-def init_knowledge_vector_store(embedding_model, filepath):
-    if embedding_model == "ViT-B-32":
-        jina_auth_token = os.getenv('jina_auth_token')
-        embeddings = JinaEmbeddings(
-            jina_auth_token=jina_auth_token,
-            model_name=embedding_model_dict[embedding_model])
-    else:
-        embeddings = HuggingFaceEmbeddings(
-            model_name=embedding_model_dict[embedding_model], )
-        embeddings.client = sentence_transformers.SentenceTransformer(
-            embeddings.model_name, device=DEVICE)
-
-    docs = load_file(filepath)
-
-    vector_store = FAISS.from_documents(docs, embeddings)
-    return vector_store
-
-
-def get_knowledge_based_answer(query,
-                               large_language_model,
-                               vector_store,
-                               VECTOR_SEARCH_TOP_K,
-                               web_content,
-                               history_len,
-                               temperature,
-                               top_p,
-                               chat_history=[]):
-    if web_content:
-        prompt_template = f"""基于以下已知信息，简洁和专业的来回答用户的问题。
-                            如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
-                            已知网络检索内容：{web_content}""" + """
-                            已知内容:
-                            {context}
-                            问题:
-                            {question}"""
-    else:
-        prompt_template = """基于以下已知信息，请简洁并专业地回答用户的问题。
-            如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"。不允许在答案中添加编造成分。另外，答案请使用中文。
-
-            已知内容:
-            {context}
-
-            问题:
-            {question}"""
-    prompt = PromptTemplate(template=prompt_template,
-                            input_variables=["context", "question"])
-    chatLLM = ChatLLM()
-    chatLLM.history = chat_history[-history_len:] if history_len > 0 else []
-    if large_language_model == "Minimax":
-        chatLLM.model = 'Minimax'
-    else:
-        chatLLM.load_model(
-            model_name_or_path=llm_model_dict[large_language_model])
-        chatLLM.temperature = temperature
-        chatLLM.top_p = top_p
-
-    knowledge_chain = RetrievalQA.from_llm(
-        llm=chatLLM,
-        retriever=vector_store.as_retriever(
-            search_kwargs={"k": VECTOR_SEARCH_TOP_K}),
-        prompt=prompt)
-    knowledge_chain.combine_documents_chain.document_prompt = PromptTemplate(
-        input_variables=["page_content"], template="{page_content}")
-
-    knowledge_chain.return_source_documents = True
-
-    result = knowledge_chain({"query": query})
-    return result
-
-
-def clear_session():
-    return '', None
-
-
-def predict(input,
-            large_language_model,
-            embedding_model,
-            file_obj,
-            VECTOR_SEARCH_TOP_K,
-            history_len,
-            temperature,
-            top_p,
-            use_web,
-            history=None):
-    if history == None:
-        history = []
-    print(file_obj.name)
-    vector_store = init_knowledge_vector_store(embedding_model, file_obj.name)
-    if use_web == 'True':
-        web_content = search_web(query=input)
-    else:
-        web_content = ''
-    resp = get_knowledge_based_answer(
-        query=input,
-        large_language_model=large_language_model,
-        vector_store=vector_store,
-        VECTOR_SEARCH_TOP_K=VECTOR_SEARCH_TOP_K,
-        web_content=web_content,
-        chat_history=history,
-        history_len=history_len,
-        temperature=temperature,
-        top_p=top_p,
-    )
-    print(resp)
-    history.append((input, resp['result']))
-    return '', history, history
-
-
-if __name__ == "__main__":
-    block = gr.Blocks()
-    with block as demo:
-        gr.Markdown("""<h1><center>LangChain-ChatLLM-Webui</center></h1>
-        <center><font size=3>
-        本项目基于LangChain和大型语言模型系列模型, 提供基于本地知识的自动问答应用. <br>
-        目前项目提供基于<a href='https://github.com/THUDM/ChatGLM-6B' target="_blank">ChatGLM-6B </a>系列、Minimax的LLM和包括text2vec-base-chinese、ernie-3.0-zh系列以及由<a href='https://cloud.jina.ai/user/inference' target="_blank">Jina</a>提供的ViT-B-32::laion2b-s34b-b79k等多个Embedding模型, 支持上传 txt、docx、md等文本格式文件. <br>
-        后续将提供更加多样化的LLM、Embedding和参数选项供用户尝试, 欢迎关注<a href='https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui' target="_blank">Github地址</a>. <br>
-        本项目已内置开发者自己的key，用户无需输入自己的相关key. <br>
-        当然，更推荐您点击右上角的<strong>Duplicate this Space</strong>，将项目Fork到自己的Space中，保护个人隐私，且避免排队！
-        </center></font>
-        """)
-        with gr.Row():
-            with gr.Column(scale=1):
-                model_choose = gr.Accordion("模型选择")
-                with model_choose:
-                    large_language_model = gr.Dropdown(
-                        list(llm_model_dict.keys()),
-                        label="large language model",
-                        value="ChatGLM-6B-int4")
-
-                    embedding_model = gr.Dropdown(list(
-                        embedding_model_dict.keys()),
-                                                  label="Embedding model",
-                                                  value="text2vec-base")
-
-                file = gr.File(label='请上传知识库文件, 目前支持txt、docx、md格式',
-                               file_types=['.txt', '.md', '.docx'])
-
-                use_web = gr.Radio(["True", "False"],
-                                   label="Web Search",
-                                   value="False")
-                model_argument = gr.Accordion("模型参数配置")
-
-                with model_argument:
-
-                    VECTOR_SEARCH_TOP_K = gr.Slider(
-                        1,
-                        10,
-                        value=6,
-                        step=1,
-                        label="vector search top k",
-                        interactive=True)
-
-                    HISTORY_LEN = gr.Slider(0,
-                                            3,
-                                            value=0,
-                                            step=1,
-                                            label="history len",
-                                            interactive=True)
-
-                    temperature = gr.Slider(0,
-                                            1,
-                                            value=0.01,
-                                            step=0.01,
-                                            label="temperature",
-                                            interactive=True)
-                    top_p = gr.Slider(0,
-                                      1,
-                                      value=0.9,
-                                      step=0.1,
-                                      label="top_p",
-                                      interactive=True)
-
-            with gr.Column(scale=4):
-                chatbot = gr.Chatbot(label='ChatLLM').style(height=600)
-                message = gr.Textbox(label='请输入问题')
-                state = gr.State()
-
-                with gr.Row():
-                    clear_history = gr.Button("🧹 清除历史对话")
-                    send = gr.Button("🚀 发送")
-
-                    send.click(predict,
-                               inputs=[
-                                   message, large_language_model,
-                                   embedding_model, file, VECTOR_SEARCH_TOP_K,
-                                   HISTORY_LEN, temperature, top_p, use_web,
-                                   state
-                               ],
-                               outputs=[message, chatbot, state])
-                    clear_history.click(fn=clear_session,
-                                        inputs=[],
-                                        outputs=[chatbot, state],
-                                        queue=False)
-
-                    message.submit(predict,
-                                   inputs=[
-                                       message, large_language_model,
-                                       embedding_model, file,
-                                       VECTOR_SEARCH_TOP_K, HISTORY_LEN,
-                                       temperature, top_p, use_web, state
-                                   ],
-                                   outputs=[message, chatbot, state])
-        gr.Markdown("""提醒：<br>
-        1. 使用时请先上传自己的知识文件，并且文件中不含某些特殊字符，否则将返回error. <br>
-        2. 有任何使用问题，请通过[问题交流区](https://huggingface.co/spaces/thomas-yanxin/LangChain-ChatLLM/discussions)或[Github Issue区](https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui/issues)进行反馈. <br>
-        """)
-    demo.queue().launch(server_name='0.0.0.0', share=False)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py
deleted file mode 100644
index 062bb82f669f63a537b6ee8df4d42d292eb2575e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/self_auto_bleu.py
+++ /dev/null
@@ -1,201 +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 numpy as np
-import nltk
-from misc.bleu_utils import sentence_bleu
-import warnings
-
-
-def get_target_sequences(manifest, ground_truth, to_take=1000):
-    import json
-    import pathlib
-
-    with open(ground_truth, 'r') as fin:
-        original_continuations = json.loads(fin.read())
-
-    sequence2length = [(k, v[0]) for k, v in original_continuations.items()]
-    assert all(float(v) >= 6.0 for (_, v) in sequence2length)  # 6 seconds
-
-    sequence2length.sort(key=lambda x: x[1])
-    to_take_sequences = set(v[0] for v in sequence2length[:to_take])
-    to_take_ids = []
-
-    with open(manifest, 'r') as f:
-        f.readline()
-
-        for i, line in enumerate(f.readlines()):
-            seq_id = line.split()[0]
-            seq_id = pathlib.Path(seq_id).name.split('__')[0]
-
-            if seq_id in to_take_sequences:
-                to_take_ids.append(i)
-
-    print(f'Took {len(to_take_ids)} ids')
-    return set(to_take_ids)
-
-
-def get_args():
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--asr-transcript', type=str,
-                        help='Path to the transcript file.')
-
-    parser.add_argument('--manifest', required=True)
-    parser.add_argument('--prompts-description', required=True)
-
-    parser.add_argument('--cut-id', action='store_true',
-                        help='Whether cut the first token (typically a seq id)')
-    parser.add_argument('--cut-tail', action='store_true',
-                        help='Whether cut the last token (typically a speaker id)')
-    parser.add_argument('--debug', action='store_true')
-
-    args = parser.parse_args()
-
-    return args
-
-
-def get_self_bleu(utterances, averaging_mode, weights):
-    self_bleu = []
-
-    for i in range(len(utterances)):
-        hypo = utterances[i]
-        rest = utterances[:i] + utterances[i+1:]
-
-        self_bleu.append(sentence_bleu(rest, hypo, weights,
-                         no_length_penalty=True, averaging_mode=averaging_mode))
-
-    return self_bleu
-
-
-def get_self_bleu2_arithmetic(utterances):
-    weights = (0.5, 0.5)  # equal weight for unigrams and bigrams
-    return get_self_bleu(utterances, averaging_mode='arithmetic', weights=weights)
-
-
-def get_self_bleu2_geometric(utterances):
-    weights = (0.5, 0.5)
-    return get_self_bleu(utterances, averaging_mode='geometric', weights=weights)
-
-
-def get_auto_bleu2_arithmetic(utterances):
-    weights = (0.5, 0.5)
-    return [auto_bleu(u, mean_mode='arithmetic', weights=weights) for u in utterances]
-
-
-def get_auto_bleu2_geometric(utterances):
-    weights = (0.5, 0.5)
-    return [auto_bleu(u, mean_mode='geometric', weights=weights) for u in utterances]
-
-
-def get_auto_bleu3_geometric(utterances):
-    weights = (1./3, 1./3, 1./3)
-    return [auto_bleu(u, mean_mode='geometric', weights=weights) for u in utterances]
-
-
-def get_auto_bleu3_arithmetic(utterances):
-    weights = (1./3, 1./3, 1./3)
-    return [auto_bleu(u, mean_mode='arithmetic', weights=weights) for u in utterances]
-
-
-def get_self_bleu3_arithmetic(utterances):
-    weights = (1./3, 1./3, 1./3)
-    return get_self_bleu(utterances, averaging_mode='arithmetic', weights=weights)
-
-
-def get_self_bleu3_geometric(utterances):
-    weights = (1./3, 1./3, 1./3)
-    return get_self_bleu(utterances, averaging_mode='geometric', weights=weights)
-
-
-def auto_bleu(sentence, weights, mean_mode='arithmetic'):
-    if len(sentence) <= 1:
-        return 0
-
-    N = len(weights)
-
-    bleu_n = np.zeros([N])
-    for n in range(N):
-        targ_ngrams = list(nltk.ngrams(sentence, n+1))
-        for p in range(len(targ_ngrams)):
-            left = sentence[:p]
-            right = sentence[(p+n+1):]
-            rest_ngrams = list(nltk.ngrams(left, n+1)) + \
-                list(nltk.ngrams(right, n+1))
-            # compute the nb of matching ngrams
-            bleu_n[n] += targ_ngrams[p] in rest_ngrams
-        bleu_n[n] /= len(targ_ngrams)  # average them to get a proportion
-
-    weights = np.array(weights)
-    if mean_mode == 'arithmetic':
-        return (bleu_n * weights).sum()
-    elif mean_mode == 'geometric':
-        return (bleu_n ** weights).prod()
-    else:
-        raise ValueError(f'Unknown agggregation mode {mean_mode}')
-
-
-def main():
-    from multiprocessing import Pool
-
-    args = get_args()
-    target_ids = get_target_sequences(args.manifest, args.prompts_description)
-
-    with open(args.asr_transcript, 'r') as fin:
-        lines = fin.readlines()
-
-    terms = [x.strip().split() for x in lines]
-    filtered = []
-    for term in terms:
-        line_id = int(term[-1].split('-')[1][:-1])
-        if line_id in target_ids:
-            filtered.append(term)
-    terms = filtered
-
-    if args.cut_id:
-        terms = [x[1:] for x in terms]
-    if args.cut_tail:
-        terms = [x[:-1] for x in terms]
-
-    if args.debug:
-        terms = terms[:10]
-
-    tasks = [
-        ('Self-BLEU2-arithmetic', get_self_bleu2_arithmetic),
-        ('Self-BLEU2-geometric', get_self_bleu2_geometric),
-        ('Auto-BLEU2-arithmetic', get_auto_bleu2_arithmetic),
-        ('Auto-BLEU2-geometric', get_auto_bleu2_geometric),
-
-        ('Self-BLEU3-arithmetic', get_self_bleu3_arithmetic),
-        ('Self-BLEU3-geometric', get_self_bleu3_geometric),
-        ('Auto-BLEU3-arithmetic', get_auto_bleu3_arithmetic),
-        ('Auto-BLEU3-geometric', get_auto_bleu3_geometric),
-    ]
-
-    n_processes = min(16, len(tasks))
-    with Pool(n_processes) as pool:
-        metrics = pool.map(run_f, [(t[1], terms) for t in tasks])
-
-    for (metric_name, _), metric in zip(tasks, metrics):
-        metric, sem = np.mean(metric), np.std(metric) / np.sqrt(len(metric))
-
-        metric, sem = [
-            round(100 * x, 2) for x in [metric, sem]
-        ]
-
-        print(f'{metric_name} {metric} +- {sem}')
-
-
-def run_f(task_params):
-    f, terms = task_params
-    return f(terms)
-
-
-if __name__ == '__main__':
-    # NLTK produces warnings
-    warnings.filterwarnings("ignore")
-
-    main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/models/roberta/model_xlmr.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/models/roberta/model_xlmr.py
deleted file mode 100644
index cf6e354d53b918dd4c7c78bfcd38ac0d63cab3bd..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/models/roberta/model_xlmr.py
+++ /dev/null
@@ -1,46 +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.
-"""
-Unsupervised Cross-lingual Representation Learning at Scale
-"""
-
-from fairseq.models import register_model
-
-from .hub_interface import RobertaHubInterface
-from .model import RobertaModel
-
-
-@register_model("xlmr")
-class XLMRModel(RobertaModel):
-    @classmethod
-    def hub_models(cls):
-        return {
-            "xlmr.base": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.tar.gz",
-            "xlmr.large": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.tar.gz",
-            "xlmr.xl": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xl.tar.gz",
-            "xlmr.xxl": "http://dl.fbaipublicfiles.com/fairseq/models/xlmr/xlmr.xxl.tar.gz",
-        }
-
-    @classmethod
-    def from_pretrained(
-        cls,
-        model_name_or_path,
-        checkpoint_file="model.pt",
-        data_name_or_path=".",
-        bpe="sentencepiece",
-        **kwargs
-    ):
-        from fairseq import hub_utils
-
-        x = hub_utils.from_pretrained(
-            model_name_or_path,
-            checkpoint_file,
-            data_name_or_path,
-            archive_map=cls.hub_models(),
-            bpe=bpe,
-            load_checkpoint_heads=True,
-            **kwargs,
-        )
-        return RobertaHubInterface(x["args"], x["task"], x["models"][0])
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/tasks/masked_lm.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/tasks/masked_lm.py
deleted file mode 100644
index 0c08132fb742de3d3d1beea0b8fce979ff408ebb..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/tasks/masked_lm.py
+++ /dev/null
@@ -1,255 +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 dataclasses import dataclass, field
-import logging
-import os
-
-from omegaconf import MISSING, II, OmegaConf
-
-import numpy as np
-from fairseq import utils
-from fairseq.data import (
-    Dictionary,
-    IdDataset,
-    MaskTokensDataset,
-    NestedDictionaryDataset,
-    NumelDataset,
-    NumSamplesDataset,
-    PrependTokenDataset,
-    RightPadDataset,
-    SortDataset,
-    TokenBlockDataset,
-    data_utils,
-)
-from fairseq.data.encoders.utils import get_whole_word_mask
-from fairseq.data.shorten_dataset import maybe_shorten_dataset
-from fairseq.dataclass import FairseqDataclass
-from fairseq.tasks import FairseqTask, register_task
-
-from .language_modeling import SAMPLE_BREAK_MODE_CHOICES, SHORTEN_METHOD_CHOICES
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class MaskedLMConfig(FairseqDataclass):
-    data: str = field(
-        default=MISSING,
-        metadata={
-            "help": "colon separated path to data directories list, \
-                            will be iterated upon during epochs in round-robin manner"
-        },
-    )
-    sample_break_mode: SAMPLE_BREAK_MODE_CHOICES = field(
-        default="none",
-        metadata={
-            "help": 'If omitted or "none", fills each sample with tokens-per-sample '
-            'tokens. If set to "complete", splits samples only at the end '
-            "of sentence, but may include multiple sentences per sample. "
-            '"complete_doc" is similar but respects doc boundaries. '
-            'If set to "eos", includes only one sentence per sample.'
-        },
-    )
-    tokens_per_sample: int = field(
-        default=1024,
-        metadata={"help": "max number of tokens per sample for LM dataset"},
-    )
-    mask_prob: float = field(
-        default=0.15,
-        metadata={"help": "probability of replacing a token with mask"},
-    )
-    leave_unmasked_prob: float = field(
-        default=0.1,
-        metadata={"help": "probability that a masked token is unmasked"},
-    )
-    random_token_prob: float = field(
-        default=0.1,
-        metadata={"help": "probability of replacing a token with a random token"},
-    )
-    freq_weighted_replacement: bool = field(
-        default=False,
-        metadata={"help": "sample random replacement words based on word frequencies"},
-    )
-    mask_whole_words: bool = field(
-        default=False,
-        metadata={"help": "mask whole words; you may also want to set --bpe"},
-    )
-    mask_multiple_length: int = field(
-        default=1,
-        metadata={"help": "repeat the mask indices multiple times"},
-    )
-    mask_stdev: float = field(
-        default=0.0,
-        metadata={"help": "stdev of the mask length"},
-    )
-    shorten_method: SHORTEN_METHOD_CHOICES = field(
-        default="none",
-        metadata={
-            "help": "if not none, shorten sequences that exceed --tokens-per-sample"
-        },
-    )
-    shorten_data_split_list: str = field(
-        default="",
-        metadata={
-            "help": "comma-separated list of dataset splits to apply shortening to, "
-            'e.g., "train,valid" (default: all dataset splits)'
-        },
-    )
-    seed: int = II("common.seed")
-
-
-@register_task("masked_lm", dataclass=MaskedLMConfig)
-class MaskedLMTask(FairseqTask):
-
-    cfg: MaskedLMConfig
-
-    """Task for training masked language models (e.g., BERT, RoBERTa)."""
-
-    def __init__(self, cfg: MaskedLMConfig, dictionary):
-        super().__init__(cfg)
-        self.dictionary = dictionary
-
-        # add mask token
-        self.mask_idx = dictionary.add_symbol("<mask>")
-
-    @classmethod
-    def setup_task(cls, cfg: MaskedLMConfig, **kwargs):
-        paths = utils.split_paths(cfg.data)
-        assert len(paths) > 0
-        dictionary = Dictionary.load(os.path.join(paths[0], "dict.txt"))
-        logger.info("dictionary: {} types".format(len(dictionary)))
-        return cls(cfg, dictionary)
-
-    def load_dataset(self, split, epoch=1, combine=False, **kwargs):
-        """Load a given dataset split.
-
-        Args:
-            split (str): name of the split (e.g., train, valid, test)
-        """
-        paths = utils.split_paths(self.cfg.data)
-        assert len(paths) > 0
-        data_path = paths[(epoch - 1) % len(paths)]
-        split_path = os.path.join(data_path, split)
-
-        dataset = data_utils.load_indexed_dataset(
-            split_path,
-            self.source_dictionary,
-            combine=combine,
-        )
-        if dataset is None:
-            raise FileNotFoundError(
-                "Dataset not found: {} ({})".format(split, split_path)
-            )
-
-        dataset = maybe_shorten_dataset(
-            dataset,
-            split,
-            self.cfg.shorten_data_split_list,
-            self.cfg.shorten_method,
-            self.cfg.tokens_per_sample,
-            self.cfg.seed,
-        )
-
-        # create continuous blocks of tokens
-        dataset = TokenBlockDataset(
-            dataset,
-            dataset.sizes,
-            self.cfg.tokens_per_sample - 1,  # one less for <s>
-            pad=self.source_dictionary.pad(),
-            eos=self.source_dictionary.eos(),
-            break_mode=self.cfg.sample_break_mode,
-        )
-        logger.info("loaded {} blocks from: {}".format(len(dataset), split_path))
-
-        # prepend beginning-of-sentence token (<s>, equiv. to [CLS] in BERT)
-        dataset = PrependTokenDataset(dataset, self.source_dictionary.bos())
-
-        # create masked input and targets
-        mask_whole_words = (
-            get_whole_word_mask(self.args, self.source_dictionary)
-            if self.cfg.mask_whole_words
-            else None
-        )
-
-        src_dataset, tgt_dataset = MaskTokensDataset.apply_mask(
-            dataset,
-            self.source_dictionary,
-            pad_idx=self.source_dictionary.pad(),
-            mask_idx=self.mask_idx,
-            seed=self.cfg.seed,
-            mask_prob=self.cfg.mask_prob,
-            leave_unmasked_prob=self.cfg.leave_unmasked_prob,
-            random_token_prob=self.cfg.random_token_prob,
-            freq_weighted_replacement=self.cfg.freq_weighted_replacement,
-            mask_whole_words=mask_whole_words,
-            mask_multiple_length=self.cfg.mask_multiple_length,
-            mask_stdev=self.cfg.mask_stdev,
-        )
-
-        with data_utils.numpy_seed(self.cfg.seed):
-            shuffle = np.random.permutation(len(src_dataset))
-
-        self.datasets[split] = SortDataset(
-            NestedDictionaryDataset(
-                {
-                    "id": IdDataset(),
-                    "net_input": {
-                        "src_tokens": RightPadDataset(
-                            src_dataset,
-                            pad_idx=self.source_dictionary.pad(),
-                        ),
-                        "src_lengths": NumelDataset(src_dataset, reduce=False),
-                    },
-                    "target": RightPadDataset(
-                        tgt_dataset,
-                        pad_idx=self.source_dictionary.pad(),
-                    ),
-                    "nsentences": NumSamplesDataset(),
-                    "ntokens": NumelDataset(src_dataset, reduce=True),
-                },
-                sizes=[src_dataset.sizes],
-            ),
-            sort_order=[
-                shuffle,
-                src_dataset.sizes,
-            ],
-        )
-
-    def build_dataset_for_inference(self, src_tokens, src_lengths, sort=True):
-        src_dataset = RightPadDataset(
-            TokenBlockDataset(
-                src_tokens,
-                src_lengths,
-                self.cfg.tokens_per_sample - 1,  # one less for <s>
-                pad=self.source_dictionary.pad(),
-                eos=self.source_dictionary.eos(),
-                break_mode="eos",
-            ),
-            pad_idx=self.source_dictionary.pad(),
-        )
-        src_dataset = PrependTokenDataset(src_dataset, self.source_dictionary.bos())
-        src_dataset = NestedDictionaryDataset(
-            {
-                "id": IdDataset(),
-                "net_input": {
-                    "src_tokens": src_dataset,
-                    "src_lengths": NumelDataset(src_dataset, reduce=False),
-                },
-            },
-            sizes=src_lengths,
-        )
-        if sort:
-            src_dataset = SortDataset(src_dataset, sort_order=[src_lengths])
-        return src_dataset
-
-    @property
-    def source_dictionary(self):
-        return self.dictionary
-
-    @property
-    def target_dictionary(self):
-        return self.dictionary
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/criss/download_and_preprocess_tatoeba.sh b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/criss/download_and_preprocess_tatoeba.sh
deleted file mode 100644
index 7ed64f017d5e62695ba73745c840507b994abc0f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/criss/download_and_preprocess_tatoeba.sh
+++ /dev/null
@@ -1,46 +0,0 @@
-#!/bin/bash
-# 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.
-
-SPM_ENCODE=flores/scripts/spm_encode.py
-DATA=data_tmp
-SPM_MODEL=criss_checkpoints/sentence.bpe.model
-DICT=criss_checkpoints/dict.txt
-
-if [[ -f flores ]]; then
-  echo "flores already cloned"
-else
-  git clone https://github.com/facebookresearch/flores
-fi
-if [[ -f LASER ]]; then
-  echo "LASER already cloned"
-else
-  git clone https://github.com/facebookresearch/LASER
-fi
-mkdir -p data_tmp
-declare -A lang_tatoeba_map=( ["ar_AR"]="ara" ["de_DE"]="deu"  ["es_XX"]="spa" ["et_EE"]="est" ["fi_FI"]="fin" ["fr_XX"]="fra" ["hi_IN"]="hin" ["it_IT"]="ita" ["ja_XX"]="jpn" ["ko_KR"]="kor" ["kk_KZ"]="kaz" ["nl_XX"]="nld" ["ru_RU"]="rus" ["tr_TR"]="tur" ["vi_VN"]="vie" ["zh_CN"]="cmn")
-for lang in ar_AR de_DE es_XX et_EE fi_FI fr_XX hi_IN it_IT ja_XX kk_KZ ko_KR nl_XX ru_RU tr_TR vi_VN zh_CN; do
-  lang_tatoeba=${lang_tatoeba_map[$lang]}
-  echo $lang_tatoeba
-  datadir=$DATA/${lang}-en_XX-tatoeba
-  rm -rf $datadir
-  mkdir -p $datadir
-  TEST_PREFIX=LASER/data/tatoeba/v1/tatoeba
-  python $SPM_ENCODE \
-    --model ${SPM_MODEL} \
-    --output_format=piece \
-    --inputs ${TEST_PREFIX}.${lang_tatoeba}-eng.${lang_tatoeba} ${TEST_PREFIX}.${lang_tatoeba}-eng.eng \
-    --outputs $datadir/test.bpe.${lang}-en_XX.${lang} $datadir/test.bpe.${lang}-en_XX.en_XX
-
-  # binarize data
-  fairseq-preprocess \
-    --source-lang ${lang} --target-lang en_XX \
-    --testpref $datadir/test.bpe.${lang}-en_XX \
-    --destdir $datadir \
-    --srcdict ${DICT} \
-    --joined-dictionary \
-    --workers 4
-done
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/unsupervised_quality_estimation/meteor.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/unsupervised_quality_estimation/meteor.py
deleted file mode 100644
index 2ee0448cf1f167f6f3ecee56ad807922cffb0956..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/unsupervised_quality_estimation/meteor.py
+++ /dev/null
@@ -1,109 +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 argparse
-import math
-import os
-import subprocess
-import sys
-import tempfile
-from collections import defaultdict
-from itertools import combinations
-
-
-def read_translations(path, n_repeats):
-    segment_counter = 0
-    segment_translations = []
-    translations = defaultdict(list)
-    for line in open(path):
-        segment_translations.append(" ".join(line.split()))
-        if len(segment_translations) == n_repeats:
-            translations[segment_counter] = segment_translations
-            segment_translations = []
-            segment_counter += 1
-    return translations
-
-
-def generate_input(translations, n_repeats):
-    _, ref_path = tempfile.mkstemp()
-    _, mt_path = tempfile.mkstemp()
-    ref_fh = open(ref_path, "w")
-    mt_fh = open(mt_path, "w")
-    for segid in sorted(translations.keys()):
-        assert len(translations[segid]) == n_repeats
-        indexes = combinations(range(n_repeats), 2)
-        for idx1, idx2 in indexes:
-            mt_fh.write(translations[segid][idx1].strip() + "\n")
-            ref_fh.write(translations[segid][idx2].strip() + "\n")
-    sys.stderr.write("\nSaved translations to %s and %s" % (ref_path, mt_path))
-    return ref_path, mt_path
-
-
-def run_meteor(ref_path, mt_path, metric_path, lang="en"):
-    _, out_path = tempfile.mkstemp()
-    subprocess.call(
-        [
-            "java",
-            "-Xmx2G",
-            "-jar",
-            metric_path,
-            mt_path,
-            ref_path,
-            "-p",
-            "0.5 0.2 0.6 0.75",  # default parameters, only changed alpha to give equal weight to P and R
-            "-norm",
-            "-l",
-            lang,
-        ],
-        stdout=open(out_path, "w"),
-    )
-    os.remove(ref_path)
-    os.remove(mt_path)
-    sys.stderr.write("\nSaved Meteor output to %s" % out_path)
-    return out_path
-
-
-def read_output(meteor_output_path, n_repeats):
-    n_combinations = math.factorial(n_repeats) / (
-        math.factorial(2) * math.factorial(n_repeats - 2)
-    )
-    raw_scores = []
-    average_scores = []
-    for line in open(meteor_output_path):
-        if not line.startswith("Segment "):
-            continue
-        score = float(line.strip().split("\t")[1])
-        raw_scores.append(score)
-        if len(raw_scores) == n_combinations:
-            average_scores.append(sum(raw_scores) / n_combinations)
-            raw_scores = []
-    os.remove(meteor_output_path)
-    return average_scores
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-i", "--infile")
-    parser.add_argument("-n", "--repeat_times", type=int)
-    parser.add_argument("-m", "--meteor")
-    parser.add_argument("-o", "--output")
-    args = parser.parse_args()
-
-    translations = read_translations(args.infile, args.repeat_times)
-    sys.stderr.write("\nGenerating input for Meteor...")
-    ref_path, mt_path = generate_input(translations, args.repeat_times)
-    sys.stderr.write("\nRunning Meteor...")
-    out_path = run_meteor(ref_path, mt_path, args.meteor)
-    sys.stderr.write("\nReading output...")
-    scores = read_output(out_path, args.repeat_times)
-    sys.stderr.write("\nWriting results...")
-    with open(args.output, "w") as o:
-        for scr in scores:
-            o.write("{}\n".format(scr))
-    o.close()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/raw_label_dataset.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/raw_label_dataset.py
deleted file mode 100644
index d054904f419bd64855d33a2a770b43f671c7c8d8..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/raw_label_dataset.py
+++ /dev/null
@@ -1,23 +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 . import FairseqDataset
-
-
-class RawLabelDataset(FairseqDataset):
-    def __init__(self, labels):
-        super().__init__()
-        self.labels = labels
-
-    def __getitem__(self, index):
-        return self.labels[index]
-
-    def __len__(self):
-        return len(self.labels)
-
-    def collater(self, samples):
-        return torch.tensor(samples)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_label_smoothing.py b/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_label_smoothing.py
deleted file mode 100644
index 04c0f974ac80f7606327f868e948712c3c18f1d0..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/tests/test_label_smoothing.py
+++ /dev/null
@@ -1,123 +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 argparse
-import copy
-import unittest
-
-import tests.utils as test_utils
-import torch
-from fairseq.criterions.cross_entropy import CrossEntropyCriterion
-from fairseq.criterions.label_smoothed_cross_entropy import (
-    LabelSmoothedCrossEntropyCriterion,
-)
-
-
-class TestLabelSmoothing(unittest.TestCase):
-    def setUp(self):
-        # build dictionary
-        self.d = test_utils.dummy_dictionary(3)
-        vocab = len(self.d)
-        self.assertEqual(vocab, 4 + 3)  # 4 special + 3 tokens
-        self.assertEqual(self.d.pad(), 1)
-        self.assertEqual(self.d.eos(), 2)
-        self.assertEqual(self.d.unk(), 3)
-        pad, eos, unk, w1, w2, w3 = 1, 2, 3, 4, 5, 6  # noqa: F841
-
-        # build dataset
-        self.data = [
-            # the first batch item has padding
-            {
-                "source": torch.LongTensor([w1, eos]),
-                "target": torch.LongTensor([w1, eos]),
-            },
-            {
-                "source": torch.LongTensor([w1, eos]),
-                "target": torch.LongTensor([w1, w1, eos]),
-            },
-        ]
-        self.sample = next(test_utils.dummy_dataloader(self.data))
-
-        # build model
-        self.args = argparse.Namespace()
-        self.args.sentence_avg = False
-        self.args.report_accuracy = False
-        self.args.probs = (
-            torch.FloatTensor(
-                [
-                    #      pad   eos  unk   w1   w2   w3
-                    [0.05, 0.05, 0.1, 0.05, 0.3, 0.4, 0.05],
-                    [0.05, 0.10, 0.2, 0.05, 0.2, 0.3, 0.10],
-                    [0.05, 0.15, 0.3, 0.05, 0.1, 0.2, 0.15],
-                ]
-            )
-            .unsqueeze(0)
-            .expand(2, 3, 7)
-        )  # add batch dimension
-        self.task = test_utils.TestTranslationTask.setup_task(self.args, self.d, self.d)
-        self.model = self.task.build_model(self.args)
-
-    def test_nll_loss(self):
-        self.args.label_smoothing = 0.1
-        nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task)
-        smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion(
-            self.args, self.task
-        )
-        nll_loss, nll_sample_size, nll_logging_output = nll_crit(
-            self.model, self.sample
-        )
-        smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit(
-            self.model, self.sample
-        )
-        self.assertLess(abs(nll_loss - nll_logging_output["loss"]), 1e-6)
-        self.assertLess(abs(nll_loss - smooth_logging_output["nll_loss"]), 1e-6)
-
-    def test_padding(self):
-        self.args.label_smoothing = 0.1
-        crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task)
-        loss, _, logging_output = crit(self.model, self.sample)
-
-        def get_one_no_padding(idx):
-            # create a new sample with just a single batch item so that there's
-            # no padding
-            sample1 = next(test_utils.dummy_dataloader([self.data[idx]]))
-            args1 = copy.copy(self.args)
-            args1.probs = args1.probs[idx, :, :].unsqueeze(0)
-            model1 = self.task.build_model(args1)
-            loss1, _, _ = crit(model1, sample1)
-            return loss1
-
-        loss1 = get_one_no_padding(0)
-        loss2 = get_one_no_padding(1)
-        self.assertAlmostEqual(loss, loss1 + loss2)
-
-    def test_reduction(self):
-        self.args.label_smoothing = 0.1
-        crit = LabelSmoothedCrossEntropyCriterion.build_criterion(self.args, self.task)
-        loss, _, logging_output = crit(self.model, self.sample, reduce=True)
-        unreduced_loss, _, _ = crit(self.model, self.sample, reduce=False)
-        self.assertAlmostEqual(loss, unreduced_loss.sum())
-
-    def test_zero_eps(self):
-        self.args.label_smoothing = 0.0
-        nll_crit = CrossEntropyCriterion.build_criterion(self.args, self.task)
-        smooth_crit = LabelSmoothedCrossEntropyCriterion.build_criterion(
-            self.args, self.task
-        )
-        nll_loss, nll_sample_size, nll_logging_output = nll_crit(
-            self.model, self.sample
-        )
-        smooth_loss, smooth_sample_size, smooth_logging_output = smooth_crit(
-            self.model, self.sample
-        )
-        self.assertAlmostEqual(nll_loss, smooth_loss)
-
-    def assertAlmostEqual(self, t1, t2):
-        self.assertEqual(t1.size(), t2.size(), "size mismatch")
-        self.assertLess((t1 - t2).abs().max(), 1e-6)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/ORI-Muchim/NahidaTTS/commons.py b/spaces/ORI-Muchim/NahidaTTS/commons.py
deleted file mode 100644
index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/NahidaTTS/commons.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
-  return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
-  return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/utils/visualizer.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/utils/visualizer.py
deleted file mode 100644
index 8e145181871d1981e41db3c8cbc7e8f4cc7b5833..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/detectron2/utils/visualizer.py
+++ /dev/null
@@ -1,1267 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import colorsys
-import logging
-import math
-import numpy as np
-from enum import Enum, unique
-import cv2
-import matplotlib as mpl
-import matplotlib.colors as mplc
-import matplotlib.figure as mplfigure
-import pycocotools.mask as mask_util
-import torch
-from matplotlib.backends.backend_agg import FigureCanvasAgg
-from PIL import Image
-
-from detectron2.data import MetadataCatalog
-from detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes
-from detectron2.utils.file_io import PathManager
-
-from .colormap import random_color
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["ColorMode", "VisImage", "Visualizer"]
-
-
-_SMALL_OBJECT_AREA_THRESH = 1000
-_LARGE_MASK_AREA_THRESH = 120000
-_OFF_WHITE = (1.0, 1.0, 240.0 / 255)
-_BLACK = (0, 0, 0)
-_RED = (1.0, 0, 0)
-
-_KEYPOINT_THRESHOLD = 0.05
-
-
-@unique
-class ColorMode(Enum):
-    """
-    Enum of different color modes to use for instance visualizations.
-    """
-
-    IMAGE = 0
-    """
-    Picks a random color for every instance and overlay segmentations with low opacity.
-    """
-    SEGMENTATION = 1
-    """
-    Let instances of the same category have similar colors
-    (from metadata.thing_colors), and overlay them with
-    high opacity. This provides more attention on the quality of segmentation.
-    """
-    IMAGE_BW = 2
-    """
-    Same as IMAGE, but convert all areas without masks to gray-scale.
-    Only available for drawing per-instance mask predictions.
-    """
-
-
-class GenericMask:
-    """
-    Attribute:
-        polygons (list[ndarray]): list[ndarray]: polygons for this mask.
-            Each ndarray has format [x, y, x, y, ...]
-        mask (ndarray): a binary mask
-    """
-
-    def __init__(self, mask_or_polygons, height, width):
-        self._mask = self._polygons = self._has_holes = None
-        self.height = height
-        self.width = width
-
-        m = mask_or_polygons
-        if isinstance(m, dict):
-            # RLEs
-            assert "counts" in m and "size" in m
-            if isinstance(m["counts"], list):  # uncompressed RLEs
-                h, w = m["size"]
-                assert h == height and w == width
-                m = mask_util.frPyObjects(m, h, w)
-            self._mask = mask_util.decode(m)[:, :]
-            return
-
-        if isinstance(m, list):  # list[ndarray]
-            self._polygons = [np.asarray(x).reshape(-1) for x in m]
-            return
-
-        if isinstance(m, np.ndarray):  # assumed to be a binary mask
-            assert m.shape[1] != 2, m.shape
-            assert m.shape == (
-                height,
-                width,
-            ), f"mask shape: {m.shape}, target dims: {height}, {width}"
-            self._mask = m.astype("uint8")
-            return
-
-        raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m)))
-
-    @property
-    def mask(self):
-        if self._mask is None:
-            self._mask = self.polygons_to_mask(self._polygons)
-        return self._mask
-
-    @property
-    def polygons(self):
-        if self._polygons is None:
-            self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
-        return self._polygons
-
-    @property
-    def has_holes(self):
-        if self._has_holes is None:
-            if self._mask is not None:
-                self._polygons, self._has_holes = self.mask_to_polygons(self._mask)
-            else:
-                self._has_holes = False  # if original format is polygon, does not have holes
-        return self._has_holes
-
-    def mask_to_polygons(self, mask):
-        # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level
-        # hierarchy. External contours (boundary) of the object are placed in hierarchy-1.
-        # Internal contours (holes) are placed in hierarchy-2.
-        # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours.
-        mask = np.ascontiguousarray(mask)  # some versions of cv2 does not support incontiguous arr
-        res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
-        hierarchy = res[-1]
-        if hierarchy is None:  # empty mask
-            return [], False
-        has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0
-        res = res[-2]
-        res = [x.flatten() for x in res]
-        # These coordinates from OpenCV are integers in range [0, W-1 or H-1].
-        # We add 0.5 to turn them into real-value coordinate space. A better solution
-        # would be to first +0.5 and then dilate the returned polygon by 0.5.
-        res = [x + 0.5 for x in res if len(x) >= 6]
-        return res, has_holes
-
-    def polygons_to_mask(self, polygons):
-        rle = mask_util.frPyObjects(polygons, self.height, self.width)
-        rle = mask_util.merge(rle)
-        return mask_util.decode(rle)[:, :]
-
-    def area(self):
-        return self.mask.sum()
-
-    def bbox(self):
-        p = mask_util.frPyObjects(self.polygons, self.height, self.width)
-        p = mask_util.merge(p)
-        bbox = mask_util.toBbox(p)
-        bbox[2] += bbox[0]
-        bbox[3] += bbox[1]
-        return bbox
-
-
-class _PanopticPrediction:
-    """
-    Unify different panoptic annotation/prediction formats
-    """
-
-    def __init__(self, panoptic_seg, segments_info, metadata=None):
-        if segments_info is None:
-            assert metadata is not None
-            # If "segments_info" is None, we assume "panoptic_img" is a
-            # H*W int32 image storing the panoptic_id in the format of
-            # category_id * label_divisor + instance_id. We reserve -1 for
-            # VOID label.
-            label_divisor = metadata.label_divisor
-            segments_info = []
-            for panoptic_label in np.unique(panoptic_seg.numpy()):
-                if panoptic_label == -1:
-                    # VOID region.
-                    continue
-                pred_class = panoptic_label // label_divisor
-                isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values()
-                segments_info.append(
-                    {
-                        "id": int(panoptic_label),
-                        "category_id": int(pred_class),
-                        "isthing": bool(isthing),
-                    }
-                )
-        del metadata
-
-        self._seg = panoptic_seg
-
-        self._sinfo = {s["id"]: s for s in segments_info}  # seg id -> seg info
-        segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True)
-        areas = areas.numpy()
-        sorted_idxs = np.argsort(-areas)
-        self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs]
-        self._seg_ids = self._seg_ids.tolist()
-        for sid, area in zip(self._seg_ids, self._seg_areas):
-            if sid in self._sinfo:
-                self._sinfo[sid]["area"] = float(area)
-
-    def non_empty_mask(self):
-        """
-        Returns:
-            (H, W) array, a mask for all pixels that have a prediction
-        """
-        empty_ids = []
-        for id in self._seg_ids:
-            if id not in self._sinfo:
-                empty_ids.append(id)
-        if len(empty_ids) == 0:
-            return np.zeros(self._seg.shape, dtype=np.uint8)
-        assert (
-            len(empty_ids) == 1
-        ), ">1 ids corresponds to no labels. This is currently not supported"
-        return (self._seg != empty_ids[0]).numpy().astype(np.bool)
-
-    def semantic_masks(self):
-        for sid in self._seg_ids:
-            sinfo = self._sinfo.get(sid)
-            if sinfo is None or sinfo["isthing"]:
-                # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions.
-                continue
-            yield (self._seg == sid).numpy().astype(np.bool), sinfo
-
-    def instance_masks(self):
-        for sid in self._seg_ids:
-            sinfo = self._sinfo.get(sid)
-            if sinfo is None or not sinfo["isthing"]:
-                continue
-            mask = (self._seg == sid).numpy().astype(np.bool)
-            if mask.sum() > 0:
-                yield mask, sinfo
-
-
-def _create_text_labels(classes, scores, class_names, is_crowd=None):
-    """
-    Args:
-        classes (list[int] or None):
-        scores (list[float] or None):
-        class_names (list[str] or None):
-        is_crowd (list[bool] or None):
-
-    Returns:
-        list[str] or None
-    """
-    labels = None
-    if classes is not None:
-        if class_names is not None and len(class_names) > 0:
-            labels = [class_names[i] for i in classes]
-        else:
-            labels = [str(i) for i in classes]
-    if scores is not None:
-        if labels is None:
-            labels = ["{:.0f}%".format(s * 100) for s in scores]
-        else:
-            labels = ["{} {:.0f}%".format(l, s * 100) for l, s in zip(labels, scores)]
-    if labels is not None and is_crowd is not None:
-        labels = [l + ("|crowd" if crowd else "") for l, crowd in zip(labels, is_crowd)]
-    return labels
-
-
-class VisImage:
-    def __init__(self, img, scale=1.0):
-        """
-        Args:
-            img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].
-            scale (float): scale the input image
-        """
-        self.img = img
-        self.scale = scale
-        self.width, self.height = img.shape[1], img.shape[0]
-        self._setup_figure(img)
-
-    def _setup_figure(self, img):
-        """
-        Args:
-            Same as in :meth:`__init__()`.
-
-        Returns:
-            fig (matplotlib.pyplot.figure): top level container for all the image plot elements.
-            ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.
-        """
-        fig = mplfigure.Figure(frameon=False)
-        self.dpi = fig.get_dpi()
-        # add a small 1e-2 to avoid precision lost due to matplotlib's truncation
-        # (https://github.com/matplotlib/matplotlib/issues/15363)
-        fig.set_size_inches(
-            (self.width * self.scale + 1e-2) / self.dpi,
-            (self.height * self.scale + 1e-2) / self.dpi,
-        )
-        self.canvas = FigureCanvasAgg(fig)
-        # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
-        ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
-        ax.axis("off")
-        self.fig = fig
-        self.ax = ax
-        self.reset_image(img)
-
-    def reset_image(self, img):
-        """
-        Args:
-            img: same as in __init__
-        """
-        img = img.astype("uint8")
-        self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest")
-
-    def save(self, filepath):
-        """
-        Args:
-            filepath (str): a string that contains the absolute path, including the file name, where
-                the visualized image will be saved.
-        """
-        self.fig.savefig(filepath)
-
-    def get_image(self):
-        """
-        Returns:
-            ndarray:
-                the visualized image of shape (H, W, 3) (RGB) in uint8 type.
-                The shape is scaled w.r.t the input image using the given `scale` argument.
-        """
-        canvas = self.canvas
-        s, (width, height) = canvas.print_to_buffer()
-        # buf = io.BytesIO()  # works for cairo backend
-        # canvas.print_rgba(buf)
-        # width, height = self.width, self.height
-        # s = buf.getvalue()
-
-        buffer = np.frombuffer(s, dtype="uint8")
-
-        img_rgba = buffer.reshape(height, width, 4)
-        rgb, alpha = np.split(img_rgba, [3], axis=2)
-        return rgb.astype("uint8")
-
-
-class Visualizer:
-    """
-    Visualizer that draws data about detection/segmentation on images.
-
-    It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`
-    that draw primitive objects to images, as well as high-level wrappers like
-    `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`
-    that draw composite data in some pre-defined style.
-
-    Note that the exact visualization style for the high-level wrappers are subject to change.
-    Style such as color, opacity, label contents, visibility of labels, or even the visibility
-    of objects themselves (e.g. when the object is too small) may change according
-    to different heuristics, as long as the results still look visually reasonable.
-
-    To obtain a consistent style, you can implement custom drawing functions with the
-    abovementioned primitive methods instead. If you need more customized visualization
-    styles, you can process the data yourself following their format documented in
-    tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not
-    intend to satisfy everyone's preference on drawing styles.
-
-    This visualizer focuses on high rendering quality rather than performance. It is not
-    designed to be used for real-time applications.
-    """
-
-    # TODO implement a fast, rasterized version using OpenCV
-
-    def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE):
-        """
-        Args:
-            img_rgb: a numpy array of shape (H, W, C), where H and W correspond to
-                the height and width of the image respectively. C is the number of
-                color channels. The image is required to be in RGB format since that
-                is a requirement of the Matplotlib library. The image is also expected
-                to be in the range [0, 255].
-            metadata (Metadata): dataset metadata (e.g. class names and colors)
-            instance_mode (ColorMode): defines one of the pre-defined style for drawing
-                instances on an image.
-        """
-        self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
-        if metadata is None:
-            metadata = MetadataCatalog.get("__nonexist__")
-        self.metadata = metadata
-        self.output = VisImage(self.img, scale=scale)
-        self.cpu_device = torch.device("cpu")
-
-        # too small texts are useless, therefore clamp to 9
-        self._default_font_size = max(
-            np.sqrt(self.output.height * self.output.width) // 90, 10 // scale
-        )
-        self._instance_mode = instance_mode
-        self.keypoint_threshold = _KEYPOINT_THRESHOLD
-
-    def draw_instance_predictions(self, predictions):
-        """
-        Draw instance-level prediction results on an image.
-
-        Args:
-            predictions (Instances): the output of an instance detection/segmentation
-                model. Following fields will be used to draw:
-                "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle").
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None
-        scores = predictions.scores if predictions.has("scores") else None
-        classes = predictions.pred_classes.tolist() if predictions.has("pred_classes") else None
-        labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None))
-        keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None
-
-        if predictions.has("pred_masks"):
-            masks = np.asarray(predictions.pred_masks)
-            masks = [GenericMask(x, self.output.height, self.output.width) for x in masks]
-        else:
-            masks = None
-
-        if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
-            colors = [
-                self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes
-            ]
-            alpha = 0.8
-        else:
-            colors = None
-            alpha = 0.5
-
-        if self._instance_mode == ColorMode.IMAGE_BW:
-            self.output.reset_image(
-                self._create_grayscale_image(
-                    (predictions.pred_masks.any(dim=0) > 0).numpy()
-                    if predictions.has("pred_masks")
-                    else None
-                )
-            )
-            alpha = 0.3
-
-        self.overlay_instances(
-            masks=masks,
-            boxes=boxes,
-            labels=labels,
-            keypoints=keypoints,
-            assigned_colors=colors,
-            alpha=alpha,
-        )
-        return self.output
-
-    def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8):
-        """
-        Draw semantic segmentation predictions/labels.
-
-        Args:
-            sem_seg (Tensor or ndarray): the segmentation of shape (H, W).
-                Each value is the integer label of the pixel.
-            area_threshold (int): segments with less than `area_threshold` are not drawn.
-            alpha (float): the larger it is, the more opaque the segmentations are.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        if isinstance(sem_seg, torch.Tensor):
-            sem_seg = sem_seg.numpy()
-        labels, areas = np.unique(sem_seg, return_counts=True)
-        sorted_idxs = np.argsort(-areas).tolist()
-        labels = labels[sorted_idxs]
-        for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels):
-            try:
-                mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]
-            except (AttributeError, IndexError):
-                mask_color = None
-
-            binary_mask = (sem_seg == label).astype(np.uint8)
-            text = self.metadata.stuff_classes[label]
-            self.draw_binary_mask(
-                binary_mask,
-                color=mask_color,
-                edge_color=_OFF_WHITE,
-                text=text,
-                alpha=alpha,
-                area_threshold=area_threshold,
-            )
-        return self.output
-
-    def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7):
-        """
-        Draw panoptic prediction annotations or results.
-
-        Args:
-            panoptic_seg (Tensor): of shape (height, width) where the values are ids for each
-                segment.
-            segments_info (list[dict] or None): Describe each segment in `panoptic_seg`.
-                If it is a ``list[dict]``, each dict contains keys "id", "category_id".
-                If None, category id of each pixel is computed by
-                ``pixel // metadata.label_divisor``.
-            area_threshold (int): stuff segments with less than `area_threshold` are not drawn.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata)
-
-        if self._instance_mode == ColorMode.IMAGE_BW:
-            self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask()))
-
-        # draw mask for all semantic segments first i.e. "stuff"
-        for mask, sinfo in pred.semantic_masks():
-            category_idx = sinfo["category_id"]
-            try:
-                mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]]
-            except AttributeError:
-                mask_color = None
-
-            text = self.metadata.stuff_classes[category_idx]
-            self.draw_binary_mask(
-                mask,
-                color=mask_color,
-                edge_color=_OFF_WHITE,
-                text=text,
-                alpha=alpha,
-                area_threshold=area_threshold,
-            )
-
-        # draw mask for all instances second
-        all_instances = list(pred.instance_masks())
-        if len(all_instances) == 0:
-            return self.output
-        masks, sinfo = list(zip(*all_instances))
-        category_ids = [x["category_id"] for x in sinfo]
-
-        try:
-            scores = [x["score"] for x in sinfo]
-        except KeyError:
-            scores = None
-        labels = _create_text_labels(
-            category_ids, scores, self.metadata.thing_classes, [x.get("iscrowd", 0) for x in sinfo]
-        )
-
-        try:
-            colors = [
-                self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in category_ids
-            ]
-        except AttributeError:
-            colors = None
-        self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha)
-
-        return self.output
-
-    draw_panoptic_seg_predictions = draw_panoptic_seg  # backward compatibility
-
-    def draw_dataset_dict(self, dic):
-        """
-        Draw annotations/segmentaions in Detectron2 Dataset format.
-
-        Args:
-            dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        annos = dic.get("annotations", None)
-        if annos:
-            if "segmentation" in annos[0]:
-                masks = [x["segmentation"] for x in annos]
-            else:
-                masks = None
-            if "keypoints" in annos[0]:
-                keypts = [x["keypoints"] for x in annos]
-                keypts = np.array(keypts).reshape(len(annos), -1, 3)
-            else:
-                keypts = None
-
-            boxes = [
-                BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS)
-                if len(x["bbox"]) == 4
-                else x["bbox"]
-                for x in annos
-            ]
-
-            colors = None
-            category_ids = [x["category_id"] for x in annos]
-            if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
-                colors = [
-                    self._jitter([x / 255 for x in self.metadata.thing_colors[c]])
-                    for c in category_ids
-                ]
-            names = self.metadata.get("thing_classes", None)
-            labels = _create_text_labels(
-                category_ids,
-                scores=None,
-                class_names=names,
-                is_crowd=[x.get("iscrowd", 0) for x in annos],
-            )
-            self.overlay_instances(
-                labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors
-            )
-
-        sem_seg = dic.get("sem_seg", None)
-        if sem_seg is None and "sem_seg_file_name" in dic:
-            with PathManager.open(dic["sem_seg_file_name"], "rb") as f:
-                sem_seg = Image.open(f)
-                sem_seg = np.asarray(sem_seg, dtype="uint8")
-        if sem_seg is not None:
-            self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5)
-
-        pan_seg = dic.get("pan_seg", None)
-        if pan_seg is None and "pan_seg_file_name" in dic:
-            with PathManager.open(dic["pan_seg_file_name"], "rb") as f:
-                pan_seg = Image.open(f)
-                pan_seg = np.asarray(pan_seg)
-                from panopticapi.utils import rgb2id
-
-                pan_seg = rgb2id(pan_seg)
-        if pan_seg is not None:
-            segments_info = dic["segments_info"]
-            pan_seg = torch.tensor(pan_seg)
-            self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5)
-        return self.output
-
-    def overlay_instances(
-        self,
-        *,
-        boxes=None,
-        labels=None,
-        masks=None,
-        keypoints=None,
-        assigned_colors=None,
-        alpha=0.5,
-    ):
-        """
-        Args:
-            boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,
-                or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,
-                or a :class:`RotatedBoxes`,
-                or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format
-                for the N objects in a single image,
-            labels (list[str]): the text to be displayed for each instance.
-            masks (masks-like object): Supported types are:
-
-                * :class:`detectron2.structures.PolygonMasks`,
-                  :class:`detectron2.structures.BitMasks`.
-                * list[list[ndarray]]: contains the segmentation masks for all objects in one image.
-                  The first level of the list corresponds to individual instances. The second
-                  level to all the polygon that compose the instance, and the third level
-                  to the polygon coordinates. The third level should have the format of
-                  [x0, y0, x1, y1, ..., xn, yn] (n >= 3).
-                * list[ndarray]: each ndarray is a binary mask of shape (H, W).
-                * list[dict]: each dict is a COCO-style RLE.
-            keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),
-                where the N is the number of instances and K is the number of keypoints.
-                The last dimension corresponds to (x, y, visibility or score).
-            assigned_colors (list[matplotlib.colors]): a list of colors, where each color
-                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
-                for full list of formats that the colors are accepted in.
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        num_instances = 0
-        if boxes is not None:
-            boxes = self._convert_boxes(boxes)
-            num_instances = len(boxes)
-        if masks is not None:
-            masks = self._convert_masks(masks)
-            if num_instances:
-                assert len(masks) == num_instances
-            else:
-                num_instances = len(masks)
-        if keypoints is not None:
-            if num_instances:
-                assert len(keypoints) == num_instances
-            else:
-                num_instances = len(keypoints)
-            keypoints = self._convert_keypoints(keypoints)
-        if labels is not None:
-            assert len(labels) == num_instances
-        if assigned_colors is None:
-            assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
-        if num_instances == 0:
-            return self.output
-        if boxes is not None and boxes.shape[1] == 5:
-            return self.overlay_rotated_instances(
-                boxes=boxes, labels=labels, assigned_colors=assigned_colors
-            )
-
-        # Display in largest to smallest order to reduce occlusion.
-        areas = None
-        if boxes is not None:
-            areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1)
-        elif masks is not None:
-            areas = np.asarray([x.area() for x in masks])
-
-        if areas is not None:
-            sorted_idxs = np.argsort(-areas).tolist()
-            # Re-order overlapped instances in descending order.
-            boxes = boxes[sorted_idxs] if boxes is not None else None
-            labels = [labels[k] for k in sorted_idxs] if labels is not None else None
-            masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None
-            assigned_colors = [assigned_colors[idx] for idx in sorted_idxs]
-            keypoints = keypoints[sorted_idxs] if keypoints is not None else None
-
-        for i in range(num_instances):
-            color = assigned_colors[i]
-            if boxes is not None:
-                self.draw_box(boxes[i], edge_color=color)
-
-            if masks is not None:
-                for segment in masks[i].polygons:
-                    self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha)
-
-            if labels is not None:
-                # first get a box
-                if boxes is not None:
-                    x0, y0, x1, y1 = boxes[i]
-                    text_pos = (x0, y0)  # if drawing boxes, put text on the box corner.
-                    horiz_align = "left"
-                elif masks is not None:
-                    # skip small mask without polygon
-                    if len(masks[i].polygons) == 0:
-                        continue
-
-                    x0, y0, x1, y1 = masks[i].bbox()
-
-                    # draw text in the center (defined by median) when box is not drawn
-                    # median is less sensitive to outliers.
-                    text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1]
-                    horiz_align = "center"
-                else:
-                    continue  # drawing the box confidence for keypoints isn't very useful.
-                # for small objects, draw text at the side to avoid occlusion
-                instance_area = (y1 - y0) * (x1 - x0)
-                if (
-                    instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale
-                    or y1 - y0 < 40 * self.output.scale
-                ):
-                    if y1 >= self.output.height - 5:
-                        text_pos = (x1, y0)
-                    else:
-                        text_pos = (x0, y1)
-
-                height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width)
-                lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
-                font_size = (
-                    np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2)
-                    * 0.5
-                    * self._default_font_size
-                )
-                self.draw_text(
-                    labels[i],
-                    text_pos,
-                    color=lighter_color,
-                    horizontal_alignment=horiz_align,
-                    font_size=font_size,
-                )
-
-        # draw keypoints
-        if keypoints is not None:
-            for keypoints_per_instance in keypoints:
-                self.draw_and_connect_keypoints(keypoints_per_instance)
-
-        return self.output
-
-    def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None):
-        """
-        Args:
-            boxes (ndarray): an Nx5 numpy array of
-                (x_center, y_center, width, height, angle_degrees) format
-                for the N objects in a single image.
-            labels (list[str]): the text to be displayed for each instance.
-            assigned_colors (list[matplotlib.colors]): a list of colors, where each color
-                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'
-                for full list of formats that the colors are accepted in.
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        num_instances = len(boxes)
-
-        if assigned_colors is None:
-            assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)]
-        if num_instances == 0:
-            return self.output
-
-        # Display in largest to smallest order to reduce occlusion.
-        if boxes is not None:
-            areas = boxes[:, 2] * boxes[:, 3]
-
-        sorted_idxs = np.argsort(-areas).tolist()
-        # Re-order overlapped instances in descending order.
-        boxes = boxes[sorted_idxs]
-        labels = [labels[k] for k in sorted_idxs] if labels is not None else None
-        colors = [assigned_colors[idx] for idx in sorted_idxs]
-
-        for i in range(num_instances):
-            self.draw_rotated_box_with_label(
-                boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None
-            )
-
-        return self.output
-
-    def draw_and_connect_keypoints(self, keypoints):
-        """
-        Draws keypoints of an instance and follows the rules for keypoint connections
-        to draw lines between appropriate keypoints. This follows color heuristics for
-        line color.
-
-        Args:
-            keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints
-                and the last dimension corresponds to (x, y, probability).
-
-        Returns:
-            output (VisImage): image object with visualizations.
-        """
-        visible = {}
-        keypoint_names = self.metadata.get("keypoint_names")
-        for idx, keypoint in enumerate(keypoints):
-
-            # draw keypoint
-            x, y, prob = keypoint
-            if prob > self.keypoint_threshold:
-                self.draw_circle((x, y), color=_RED)
-                if keypoint_names:
-                    keypoint_name = keypoint_names[idx]
-                    visible[keypoint_name] = (x, y)
-
-        if self.metadata.get("keypoint_connection_rules"):
-            for kp0, kp1, color in self.metadata.keypoint_connection_rules:
-                if kp0 in visible and kp1 in visible:
-                    x0, y0 = visible[kp0]
-                    x1, y1 = visible[kp1]
-                    color = tuple(x / 255.0 for x in color)
-                    self.draw_line([x0, x1], [y0, y1], color=color)
-
-        # draw lines from nose to mid-shoulder and mid-shoulder to mid-hip
-        # Note that this strategy is specific to person keypoints.
-        # For other keypoints, it should just do nothing
-        try:
-            ls_x, ls_y = visible["left_shoulder"]
-            rs_x, rs_y = visible["right_shoulder"]
-            mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2
-        except KeyError:
-            pass
-        else:
-            # draw line from nose to mid-shoulder
-            nose_x, nose_y = visible.get("nose", (None, None))
-            if nose_x is not None:
-                self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED)
-
-            try:
-                # draw line from mid-shoulder to mid-hip
-                lh_x, lh_y = visible["left_hip"]
-                rh_x, rh_y = visible["right_hip"]
-            except KeyError:
-                pass
-            else:
-                mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2
-                self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED)
-        return self.output
-
-    """
-    Primitive drawing functions:
-    """
-
-    def draw_text(
-        self,
-        text,
-        position,
-        *,
-        font_size=None,
-        color="g",
-        horizontal_alignment="center",
-        rotation=0,
-    ):
-        """
-        Args:
-            text (str): class label
-            position (tuple): a tuple of the x and y coordinates to place text on image.
-            font_size (int, optional): font of the text. If not provided, a font size
-                proportional to the image width is calculated and used.
-            color: color of the text. Refer to `matplotlib.colors` for full list
-                of formats that are accepted.
-            horizontal_alignment (str): see `matplotlib.text.Text`
-            rotation: rotation angle in degrees CCW
-
-        Returns:
-            output (VisImage): image object with text drawn.
-        """
-        if not font_size:
-            font_size = self._default_font_size
-
-        # since the text background is dark, we don't want the text to be dark
-        color = np.maximum(list(mplc.to_rgb(color)), 0.2)
-        color[np.argmax(color)] = max(0.8, np.max(color))
-
-        x, y = position
-        self.output.ax.text(
-            x,
-            y,
-            text,
-            size=font_size * self.output.scale,
-            family="sans-serif",
-            bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
-            verticalalignment="top",
-            horizontalalignment=horizontal_alignment,
-            color=color,
-            zorder=10,
-            rotation=rotation,
-        )
-        return self.output
-
-    def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"):
-        """
-        Args:
-            box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0
-                are the coordinates of the image's top left corner. x1 and y1 are the
-                coordinates of the image's bottom right corner.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-            edge_color: color of the outline of the box. Refer to `matplotlib.colors`
-                for full list of formats that are accepted.
-            line_style (string): the string to use to create the outline of the boxes.
-
-        Returns:
-            output (VisImage): image object with box drawn.
-        """
-        x0, y0, x1, y1 = box_coord
-        width = x1 - x0
-        height = y1 - y0
-
-        linewidth = max(self._default_font_size / 4, 1)
-
-        self.output.ax.add_patch(
-            mpl.patches.Rectangle(
-                (x0, y0),
-                width,
-                height,
-                fill=False,
-                edgecolor=edge_color,
-                linewidth=linewidth * self.output.scale,
-                alpha=alpha,
-                linestyle=line_style,
-            )
-        )
-        return self.output
-
-    def draw_rotated_box_with_label(
-        self, rotated_box, alpha=0.5, edge_color="g", line_style="-", label=None
-    ):
-        """
-        Draw a rotated box with label on its top-left corner.
-
-        Args:
-            rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle),
-                where cnt_x and cnt_y are the center coordinates of the box.
-                w and h are the width and height of the box. angle represents how
-                many degrees the box is rotated CCW with regard to the 0-degree box.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-            edge_color: color of the outline of the box. Refer to `matplotlib.colors`
-                for full list of formats that are accepted.
-            line_style (string): the string to use to create the outline of the boxes.
-            label (string): label for rotated box. It will not be rendered when set to None.
-
-        Returns:
-            output (VisImage): image object with box drawn.
-        """
-        cnt_x, cnt_y, w, h, angle = rotated_box
-        area = w * h
-        # use thinner lines when the box is small
-        linewidth = self._default_font_size / (
-            6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3
-        )
-
-        theta = angle * math.pi / 180.0
-        c = math.cos(theta)
-        s = math.sin(theta)
-        rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)]
-        # x: left->right ; y: top->down
-        rotated_rect = [(s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect]
-        for k in range(4):
-            j = (k + 1) % 4
-            self.draw_line(
-                [rotated_rect[k][0], rotated_rect[j][0]],
-                [rotated_rect[k][1], rotated_rect[j][1]],
-                color=edge_color,
-                linestyle="--" if k == 1 else line_style,
-                linewidth=linewidth,
-            )
-
-        if label is not None:
-            text_pos = rotated_rect[1]  # topleft corner
-
-            height_ratio = h / np.sqrt(self.output.height * self.output.width)
-            label_color = self._change_color_brightness(edge_color, brightness_factor=0.7)
-            font_size = (
-                np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size
-            )
-            self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle)
-
-        return self.output
-
-    def draw_circle(self, circle_coord, color, radius=3):
-        """
-        Args:
-            circle_coord (list(int) or tuple(int)): contains the x and y coordinates
-                of the center of the circle.
-            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            radius (int): radius of the circle.
-
-        Returns:
-            output (VisImage): image object with box drawn.
-        """
-        x, y = circle_coord
-        self.output.ax.add_patch(
-            mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color)
-        )
-        return self.output
-
-    def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=None):
-        """
-        Args:
-            x_data (list[int]): a list containing x values of all the points being drawn.
-                Length of list should match the length of y_data.
-            y_data (list[int]): a list containing y values of all the points being drawn.
-                Length of list should match the length of x_data.
-            color: color of the line. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            linestyle: style of the line. Refer to `matplotlib.lines.Line2D`
-                for a full list of formats that are accepted.
-            linewidth (float or None): width of the line. When it's None,
-                a default value will be computed and used.
-
-        Returns:
-            output (VisImage): image object with line drawn.
-        """
-        if linewidth is None:
-            linewidth = self._default_font_size / 3
-        linewidth = max(linewidth, 1)
-        self.output.ax.add_line(
-            mpl.lines.Line2D(
-                x_data,
-                y_data,
-                linewidth=linewidth * self.output.scale,
-                color=color,
-                linestyle=linestyle,
-            )
-        )
-        return self.output
-
-    def draw_binary_mask(
-        self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10
-    ):
-        """
-        Args:
-            binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and
-                W is the image width. Each value in the array is either a 0 or 1 value of uint8
-                type.
-            color: color of the mask. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted. If None, will pick a random color.
-            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
-                full list of formats that are accepted.
-            text (str): if None, will be drawn on the object
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-            area_threshold (float): a connected component smaller than this area will not be shown.
-
-        Returns:
-            output (VisImage): image object with mask drawn.
-        """
-        if color is None:
-            color = random_color(rgb=True, maximum=1)
-        color = mplc.to_rgb(color)
-
-        has_valid_segment = False
-        binary_mask = binary_mask.astype("uint8")  # opencv needs uint8
-        mask = GenericMask(binary_mask, self.output.height, self.output.width)
-        shape2d = (binary_mask.shape[0], binary_mask.shape[1])
-
-        if not mask.has_holes:
-            # draw polygons for regular masks
-            for segment in mask.polygons:
-                area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1]))
-                if area < (area_threshold or 0):
-                    continue
-                has_valid_segment = True
-                segment = segment.reshape(-1, 2)
-                self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha)
-        else:
-            # TODO: Use Path/PathPatch to draw vector graphics:
-            # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon
-            rgba = np.zeros(shape2d + (4,), dtype="float32")
-            rgba[:, :, :3] = color
-            rgba[:, :, 3] = (mask.mask == 1).astype("float32") * alpha
-            has_valid_segment = True
-            self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))
-
-        if text is not None and has_valid_segment:
-            lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
-            self._draw_text_in_mask(binary_mask, text, lighter_color)
-        return self.output
-
-    def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5):
-        """
-        Args:
-            soft_mask (ndarray): float array of shape (H, W), each value in [0, 1].
-            color: color of the mask. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted. If None, will pick a random color.
-            text (str): if None, will be drawn on the object
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-
-        Returns:
-            output (VisImage): image object with mask drawn.
-        """
-        if color is None:
-            color = random_color(rgb=True, maximum=1)
-        color = mplc.to_rgb(color)
-
-        shape2d = (soft_mask.shape[0], soft_mask.shape[1])
-        rgba = np.zeros(shape2d + (4,), dtype="float32")
-        rgba[:, :, :3] = color
-        rgba[:, :, 3] = soft_mask * alpha
-        self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0))
-
-        if text is not None:
-            lighter_color = self._change_color_brightness(color, brightness_factor=0.7)
-            binary_mask = (soft_mask > 0.5).astype("uint8")
-            self._draw_text_in_mask(binary_mask, text, lighter_color)
-        return self.output
-
-    def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):
-        """
-        Args:
-            segment: numpy array of shape Nx2, containing all the points in the polygon.
-            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a
-                full list of formats that are accepted. If not provided, a darker shade
-                of the polygon color will be used instead.
-            alpha (float): blending efficient. Smaller values lead to more transparent masks.
-
-        Returns:
-            output (VisImage): image object with polygon drawn.
-        """
-        if edge_color is None:
-            # make edge color darker than the polygon color
-            if alpha > 0.8:
-                edge_color = self._change_color_brightness(color, brightness_factor=-0.7)
-            else:
-                edge_color = color
-        edge_color = mplc.to_rgb(edge_color) + (1,)
-
-        polygon = mpl.patches.Polygon(
-            segment,
-            fill=True,
-            facecolor=mplc.to_rgb(color) + (alpha,),
-            edgecolor=edge_color,
-            linewidth=max(self._default_font_size // 15 * self.output.scale, 1),
-        )
-        self.output.ax.add_patch(polygon)
-        return self.output
-
-    """
-    Internal methods:
-    """
-
-    def _jitter(self, color):
-        """
-        Randomly modifies given color to produce a slightly different color than the color given.
-
-        Args:
-            color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color
-                picked. The values in the list are in the [0.0, 1.0] range.
-
-        Returns:
-            jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the
-                color after being jittered. The values in the list are in the [0.0, 1.0] range.
-        """
-        color = mplc.to_rgb(color)
-        vec = np.random.rand(3)
-        # better to do it in another color space
-        vec = vec / np.linalg.norm(vec) * 0.5
-        res = np.clip(vec + color, 0, 1)
-        return tuple(res)
-
-    def _create_grayscale_image(self, mask=None):
-        """
-        Create a grayscale version of the original image.
-        The colors in masked area, if given, will be kept.
-        """
-        img_bw = self.img.astype("f4").mean(axis=2)
-        img_bw = np.stack([img_bw] * 3, axis=2)
-        if mask is not None:
-            img_bw[mask] = self.img[mask]
-        return img_bw
-
-    def _change_color_brightness(self, color, brightness_factor):
-        """
-        Depending on the brightness_factor, gives a lighter or darker color i.e. a color with
-        less or more saturation than the original color.
-
-        Args:
-            color: color of the polygon. Refer to `matplotlib.colors` for a full list of
-                formats that are accepted.
-            brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of
-                0 will correspond to no change, a factor in [-1.0, 0) range will result in
-                a darker color and a factor in (0, 1.0] range will result in a lighter color.
-
-        Returns:
-            modified_color (tuple[double]): a tuple containing the RGB values of the
-                modified color. Each value in the tuple is in the [0.0, 1.0] range.
-        """
-        assert brightness_factor >= -1.0 and brightness_factor <= 1.0
-        color = mplc.to_rgb(color)
-        polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color))
-        modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1])
-        modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness
-        modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness
-        modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2])
-        return modified_color
-
-    def _convert_boxes(self, boxes):
-        """
-        Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.
-        """
-        if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes):
-            return boxes.tensor.detach().numpy()
-        else:
-            return np.asarray(boxes)
-
-    def _convert_masks(self, masks_or_polygons):
-        """
-        Convert different format of masks or polygons to a tuple of masks and polygons.
-
-        Returns:
-            list[GenericMask]:
-        """
-
-        m = masks_or_polygons
-        if isinstance(m, PolygonMasks):
-            m = m.polygons
-        if isinstance(m, BitMasks):
-            m = m.tensor.numpy()
-        if isinstance(m, torch.Tensor):
-            m = m.numpy()
-        ret = []
-        for x in m:
-            if isinstance(x, GenericMask):
-                ret.append(x)
-            else:
-                ret.append(GenericMask(x, self.output.height, self.output.width))
-        return ret
-
-    def _draw_text_in_mask(self, binary_mask, text, color):
-        """
-        Find proper places to draw text given a binary mask.
-        """
-        # TODO sometimes drawn on wrong objects. the heuristics here can improve.
-        _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8)
-        if stats[1:, -1].size == 0:
-            return
-        largest_component_id = np.argmax(stats[1:, -1]) + 1
-
-        # draw text on the largest component, as well as other very large components.
-        for cid in range(1, _num_cc):
-            if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH:
-                # median is more stable than centroid
-                # center = centroids[largest_component_id]
-                center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1]
-                self.draw_text(text, center, color=color)
-
-    def _convert_keypoints(self, keypoints):
-        if isinstance(keypoints, Keypoints):
-            keypoints = keypoints.tensor
-        keypoints = np.asarray(keypoints)
-        return keypoints
-
-    def get_output(self):
-        """
-        Returns:
-            output (VisImage): the image output containing the visualizations added
-            to the image.
-        """
-        return self.output
diff --git a/spaces/OpenMotionLab/MotionGPT/pyrender/tests/unit/test_meshes.py b/spaces/OpenMotionLab/MotionGPT/pyrender/tests/unit/test_meshes.py
deleted file mode 100644
index 7070b01171c97069fa013c6eba8eee217017f08e..0000000000000000000000000000000000000000
--- a/spaces/OpenMotionLab/MotionGPT/pyrender/tests/unit/test_meshes.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import numpy as np
-import pytest
-import trimesh
-
-from pyrender import (Mesh, Primitive)
-
-
-def test_meshes():
-
-    with pytest.raises(TypeError):
-        x = Mesh()
-    with pytest.raises(TypeError):
-        x = Primitive()
-    with pytest.raises(ValueError):
-        x = Primitive([], mode=10)
-
-    # Basics
-    x = Mesh([])
-    assert x.name is None
-    assert x.is_visible
-    assert x.weights is None
-
-    x.name = 'str'
-
-    # From Trimesh
-    x = Mesh.from_trimesh(trimesh.creation.box())
-    assert isinstance(x, Mesh)
-    assert len(x.primitives) == 1
-    assert x.is_visible
-    assert np.allclose(x.bounds, np.array([
-        [-0.5, -0.5, -0.5],
-        [0.5, 0.5, 0.5]
-    ]))
-    assert np.allclose(x.centroid, np.zeros(3))
-    assert np.allclose(x.extents, np.ones(3))
-    assert np.allclose(x.scale, np.sqrt(3))
-    assert not x.is_transparent
-
-    # Test some primitive functions
-    x = x.primitives[0]
-    with pytest.raises(ValueError):
-        x.normals = np.zeros(10)
-    with pytest.raises(ValueError):
-        x.tangents = np.zeros(10)
-    with pytest.raises(ValueError):
-        x.texcoord_0 = np.zeros(10)
-    with pytest.raises(ValueError):
-        x.texcoord_1 = np.zeros(10)
-    with pytest.raises(TypeError):
-        x.material = np.zeros(10)
-    assert x.targets is None
-    assert np.allclose(x.bounds, np.array([
-        [-0.5, -0.5, -0.5],
-        [0.5, 0.5, 0.5]
-    ]))
-    assert np.allclose(x.centroid, np.zeros(3))
-    assert np.allclose(x.extents, np.ones(3))
-    assert np.allclose(x.scale, np.sqrt(3))
-    x.material.baseColorFactor = np.array([0.0, 0.0, 0.0, 0.0])
-    assert x.is_transparent
-
-    # From two trimeshes
-    x = Mesh.from_trimesh([trimesh.creation.box(),
-                          trimesh.creation.cylinder(radius=0.1, height=2.0)],
-                          smooth=False)
-    assert isinstance(x, Mesh)
-    assert len(x.primitives) == 2
-    assert x.is_visible
-    assert np.allclose(x.bounds, np.array([
-        [-0.5, -0.5, -1.0],
-        [0.5, 0.5, 1.0]
-    ]))
-    assert np.allclose(x.centroid, np.zeros(3))
-    assert np.allclose(x.extents, [1.0, 1.0, 2.0])
-    assert np.allclose(x.scale, np.sqrt(6))
-    assert not x.is_transparent
-
-    # From bad data
-    with pytest.raises(TypeError):
-        x = Mesh.from_trimesh(None)
-
-    # With instancing
-    poses = np.tile(np.eye(4), (5,1,1))
-    poses[:,0,3] = np.array([0,1,2,3,4])
-    x = Mesh.from_trimesh(trimesh.creation.box(), poses=poses)
-    assert np.allclose(x.bounds, np.array([
-        [-0.5, -0.5, -0.5],
-        [4.5, 0.5, 0.5]
-    ]))
-    poses = np.eye(4)
-    x = Mesh.from_trimesh(trimesh.creation.box(), poses=poses)
-    poses = np.eye(3)
-    with pytest.raises(ValueError):
-        x = Mesh.from_trimesh(trimesh.creation.box(), poses=poses)
-
-    # From textured meshes
-    fm = trimesh.load('tests/data/fuze.obj')
-    x = Mesh.from_trimesh(fm)
-    assert isinstance(x, Mesh)
-    assert len(x.primitives) == 1
-    assert x.is_visible
-    assert not x.is_transparent
-    assert x.primitives[0].material.baseColorTexture is not None
-
-    x = Mesh.from_trimesh(fm, smooth=False)
-    fm.visual = fm.visual.to_color()
-    fm.visual.face_colors = np.array([1.0, 0.0, 0.0, 1.0])
-    x = Mesh.from_trimesh(fm, smooth=False)
-    with pytest.raises(ValueError):
-        x = Mesh.from_trimesh(fm, smooth=True)
-
-    fm.visual.vertex_colors = np.array([1.0, 0.0, 0.0, 0.5])
-    x = Mesh.from_trimesh(fm, smooth=False)
-    x = Mesh.from_trimesh(fm, smooth=True)
-    assert x.primitives[0].color_0 is not None
-    assert x.is_transparent
-
-    bm = trimesh.load('tests/data/WaterBottle.glb').dump()[0]
-    x = Mesh.from_trimesh(bm)
-    assert x.primitives[0].material.baseColorTexture is not None
-    assert x.primitives[0].material.emissiveTexture is not None
-    assert x.primitives[0].material.metallicRoughnessTexture is not None
-
-    # From point cloud
-    x = Mesh.from_points(fm.vertices)
-
-# def test_duck():
-#     bm = trimesh.load('tests/data/Duck.glb').dump()[0]
-#     x = Mesh.from_trimesh(bm)
-#     assert x.primitives[0].material.baseColorTexture is not None
-#     pixel = x.primitives[0].material.baseColorTexture.source[100, 100]
-#     yellowish = np.array([1.0, 0.7411765, 0.0, 1.0])
-#     assert np.allclose(pixel, yellowish)
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py
deleted file mode 100644
index 42c0790c98616bb69621deed55547fc04c7392ef..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import get_class_weight, weight_reduce_loss
-
-
-def cross_entropy(pred,
-                  label,
-                  weight=None,
-                  class_weight=None,
-                  reduction='mean',
-                  avg_factor=None,
-                  ignore_index=-100):
-    """The wrapper function for :func:`F.cross_entropy`"""
-    # class_weight is a manual rescaling weight given to each class.
-    # If given, has to be a Tensor of size C element-wise losses
-    loss = F.cross_entropy(
-        pred,
-        label,
-        weight=class_weight,
-        reduction='none',
-        ignore_index=ignore_index)
-
-    # apply weights and do the reduction
-    if weight is not None:
-        weight = weight.float()
-    loss = weight_reduce_loss(
-        loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
-
-    return loss
-
-
-def _expand_onehot_labels(labels, label_weights, target_shape, ignore_index):
-    """Expand onehot labels to match the size of prediction."""
-    bin_labels = labels.new_zeros(target_shape)
-    valid_mask = (labels >= 0) & (labels != ignore_index)
-    inds = torch.nonzero(valid_mask, as_tuple=True)
-
-    if inds[0].numel() > 0:
-        if labels.dim() == 3:
-            bin_labels[inds[0], labels[valid_mask], inds[1], inds[2]] = 1
-        else:
-            bin_labels[inds[0], labels[valid_mask]] = 1
-
-    valid_mask = valid_mask.unsqueeze(1).expand(target_shape).float()
-    if label_weights is None:
-        bin_label_weights = valid_mask
-    else:
-        bin_label_weights = label_weights.unsqueeze(1).expand(target_shape)
-        bin_label_weights *= valid_mask
-
-    return bin_labels, bin_label_weights
-
-
-def binary_cross_entropy(pred,
-                         label,
-                         weight=None,
-                         reduction='mean',
-                         avg_factor=None,
-                         class_weight=None,
-                         ignore_index=255):
-    """Calculate the binary CrossEntropy loss.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, 1).
-        label (torch.Tensor): The learning label of the prediction.
-        weight (torch.Tensor, optional): Sample-wise loss weight.
-        reduction (str, optional): The method used to reduce the loss.
-            Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-        ignore_index (int | None): The label index to be ignored. Default: 255
-
-    Returns:
-        torch.Tensor: The calculated loss
-    """
-    if pred.dim() != label.dim():
-        assert (pred.dim() == 2 and label.dim() == 1) or (
-                pred.dim() == 4 and label.dim() == 3), \
-            'Only pred shape [N, C], label shape [N] or pred shape [N, C, ' \
-            'H, W], label shape [N, H, W] are supported'
-        label, weight = _expand_onehot_labels(label, weight, pred.shape,
-                                              ignore_index)
-
-    # weighted element-wise losses
-    if weight is not None:
-        weight = weight.float()
-    loss = F.binary_cross_entropy_with_logits(
-        pred, label.float(), pos_weight=class_weight, reduction='none')
-    # do the reduction for the weighted loss
-    loss = weight_reduce_loss(
-        loss, weight, reduction=reduction, avg_factor=avg_factor)
-
-    return loss
-
-
-def mask_cross_entropy(pred,
-                       target,
-                       label,
-                       reduction='mean',
-                       avg_factor=None,
-                       class_weight=None,
-                       ignore_index=None):
-    """Calculate the CrossEntropy loss for masks.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, C), C is the number
-            of classes.
-        target (torch.Tensor): The learning label of the prediction.
-        label (torch.Tensor): ``label`` indicates the class label of the mask'
-            corresponding object. This will be used to select the mask in the
-            of the class which the object belongs to when the mask prediction
-            if not class-agnostic.
-        reduction (str, optional): The method used to reduce the loss.
-            Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-        ignore_index (None): Placeholder, to be consistent with other loss.
-            Default: None.
-
-    Returns:
-        torch.Tensor: The calculated loss
-    """
-    assert ignore_index is None, 'BCE loss does not support ignore_index'
-    # TODO: handle these two reserved arguments
-    assert reduction == 'mean' and avg_factor is None
-    num_rois = pred.size()[0]
-    inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
-    pred_slice = pred[inds, label].squeeze(1)
-    return F.binary_cross_entropy_with_logits(
-        pred_slice, target, weight=class_weight, reduction='mean')[None]
-
-
-@LOSSES.register_module()
-class CrossEntropyLoss(nn.Module):
-    """CrossEntropyLoss.
-
-    Args:
-        use_sigmoid (bool, optional): Whether the prediction uses sigmoid
-            of softmax. Defaults to False.
-        use_mask (bool, optional): Whether to use mask cross entropy loss.
-            Defaults to False.
-        reduction (str, optional): . Defaults to 'mean'.
-            Options are "none", "mean" and "sum".
-        class_weight (list[float] | str, optional): Weight of each class. If in
-            str format, read them from a file. Defaults to None.
-        loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
-    """
-
-    def __init__(self,
-                 use_sigmoid=False,
-                 use_mask=False,
-                 reduction='mean',
-                 class_weight=None,
-                 loss_weight=1.0):
-        super(CrossEntropyLoss, self).__init__()
-        assert (use_sigmoid is False) or (use_mask is False)
-        self.use_sigmoid = use_sigmoid
-        self.use_mask = use_mask
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-        self.class_weight = get_class_weight(class_weight)
-
-        if self.use_sigmoid:
-            self.cls_criterion = binary_cross_entropy
-        elif self.use_mask:
-            self.cls_criterion = mask_cross_entropy
-        else:
-            self.cls_criterion = cross_entropy
-
-    def forward(self,
-                cls_score,
-                label,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        """Forward function."""
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if self.class_weight is not None:
-            class_weight = cls_score.new_tensor(self.class_weight)
-        else:
-            class_weight = None
-        loss_cls = self.loss_weight * self.cls_criterion(
-            cls_score,
-            label,
-            weight,
-            class_weight=class_weight,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss_cls
diff --git a/spaces/PSLD/PSLD/diffusion-posterior-sampling/guided_diffusion/unet.py b/spaces/PSLD/PSLD/diffusion-posterior-sampling/guided_diffusion/unet.py
deleted file mode 100644
index cfda79f6a3edb47be715742b572641da561f9d9e..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/diffusion-posterior-sampling/guided_diffusion/unet.py
+++ /dev/null
@@ -1,1117 +0,0 @@
-from abc import abstractmethod
-
-import math
-
-import numpy as np
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-import functools
-
-from .fp16_util import convert_module_to_f16, convert_module_to_f32
-from .nn import (
-    checkpoint,
-    conv_nd,
-    linear,
-    avg_pool_nd,
-    zero_module,
-    normalization,
-    timestep_embedding,
-)
-
-
-NUM_CLASSES = 1000
-
-def create_model(
-    image_size,
-    num_channels,
-    num_res_blocks,
-    channel_mult="",
-    learn_sigma=False,
-    class_cond=False,
-    use_checkpoint=False,
-    attention_resolutions="16",
-    num_heads=1,
-    num_head_channels=-1,
-    num_heads_upsample=-1,
-    use_scale_shift_norm=False,
-    dropout=0,
-    resblock_updown=False,
-    use_fp16=False,
-    use_new_attention_order=False,
-    model_path='',
-):
-    if channel_mult == "":
-        if image_size == 512:
-            channel_mult = (0.5, 1, 1, 2, 2, 4, 4)
-        elif image_size == 256:
-            channel_mult = (1, 1, 2, 2, 4, 4)
-        elif image_size == 128:
-            channel_mult = (1, 1, 2, 3, 4)
-        elif image_size == 64:
-            channel_mult = (1, 2, 3, 4)
-        else:
-            raise ValueError(f"unsupported image size: {image_size}")
-    else:
-        channel_mult = tuple(int(ch_mult) for ch_mult in channel_mult.split(","))
-
-    attention_ds = []
-    if isinstance(attention_resolutions, int):
-        attention_ds.append(image_size // attention_resolutions)
-    elif isinstance(attention_resolutions, str):
-        for res in attention_resolutions.split(","):
-            attention_ds.append(image_size // int(res))
-    else:
-        raise NotImplementedError
-
-    model= UNetModel(
-        image_size=image_size,
-        in_channels=3,
-        model_channels=num_channels,
-        out_channels=(3 if not learn_sigma else 6),
-        num_res_blocks=num_res_blocks,
-        attention_resolutions=tuple(attention_ds),
-        dropout=dropout,
-        channel_mult=channel_mult,
-        num_classes=(NUM_CLASSES if class_cond else None),
-        use_checkpoint=use_checkpoint,
-        use_fp16=use_fp16,
-        num_heads=num_heads,
-        num_head_channels=num_head_channels,
-        num_heads_upsample=num_heads_upsample,
-        use_scale_shift_norm=use_scale_shift_norm,
-        resblock_updown=resblock_updown,
-        use_new_attention_order=use_new_attention_order,
-    )
-
-    try:
-        model.load_state_dict(th.load(model_path, map_location='cpu'))
-    except Exception as e:
-        print(f"Got exception: {e} / Randomly initialize")
-    return model
-
-class AttentionPool2d(nn.Module):
-    """
-    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
-    """
-
-    def __init__(
-        self,
-        spacial_dim: int,
-        embed_dim: int,
-        num_heads_channels: int,
-        output_dim: int = None,
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(
-            th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5
-        )
-        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
-        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
-        self.num_heads = embed_dim // num_heads_channels
-        self.attention = QKVAttention(self.num_heads)
-
-    def forward(self, x):
-        b, c, *_spatial = x.shape
-        x = x.reshape(b, c, -1)  # NC(HW)
-        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
-        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
-        x = self.qkv_proj(x)
-        x = self.attention(x)
-        x = self.c_proj(x)
-        return x[:, :, 0]
-
-
-class TimestepBlock(nn.Module):
-    """
-    Any module where forward() takes timestep embeddings as a second argument.
-    """
-
-    @abstractmethod
-    def forward(self, x, emb):
-        """
-        Apply the module to `x` given `emb` timestep embeddings.
-        """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
-    """
-    A sequential module that passes timestep embeddings to the children that
-    support it as an extra input.
-    """
-
-    def forward(self, x, emb):
-        for layer in self:
-            if isinstance(layer, TimestepBlock):
-                x = layer(x, emb)
-            else:
-                x = layer(x)
-        return x
-
-
-class Upsample(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
-
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        if self.dims == 3:
-            x = F.interpolate(
-                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
-            )
-        else:
-            x = F.interpolate(x, scale_factor=2, mode="nearest")
-        if self.use_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
-
-    :param channels: channels in the inputs and outputs.
-    :param use_conv: a bool determining if a convolution is applied.
-    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
-    """
-
-    def __init__(self, channels, use_conv, dims=2, out_channels=None):
-        super().__init__()
-        self.channels = channels
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.dims = dims
-        stride = 2 if dims != 3 else (1, 2, 2)
-        if use_conv:
-            self.op = conv_nd(
-                dims, self.channels, self.out_channels, 3, stride=stride, padding=1
-            )
-        else:
-            assert self.channels == self.out_channels
-            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
-    def forward(self, x):
-        assert x.shape[1] == self.channels
-        return self.op(x)
-
-
-class ResBlock(TimestepBlock):
-    """
-    A residual block that can optionally change the number of channels.
-
-    :param channels: the number of input channels.
-    :param emb_channels: the number of timestep embedding channels.
-    :param dropout: the rate of dropout.
-    :param out_channels: if specified, the number of out channels.
-    :param use_conv: if True and out_channels is specified, use a spatial
-        convolution instead of a smaller 1x1 convolution to change the
-        channels in the skip connection.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param use_checkpoint: if True, use gradient checkpointing on this module.
-    :param up: if True, use this block for upsampling.
-    :param down: if True, use this block for downsampling.
-    """
-
-    def __init__(
-        self,
-        channels,
-        emb_channels,
-        dropout,
-        out_channels=None,
-        use_conv=False,
-        use_scale_shift_norm=False,
-        dims=2,
-        use_checkpoint=False,
-        up=False,
-        down=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.emb_channels = emb_channels
-        self.dropout = dropout
-        self.out_channels = out_channels or channels
-        self.use_conv = use_conv
-        self.use_checkpoint = use_checkpoint
-        self.use_scale_shift_norm = use_scale_shift_norm
-
-        self.in_layers = nn.Sequential(
-            normalization(channels),
-            nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1),
-        )
-
-        self.updown = up or down
-
-        if up:
-            self.h_upd = Upsample(channels, False, dims)
-            self.x_upd = Upsample(channels, False, dims)
-        elif down:
-            self.h_upd = Downsample(channels, False, dims)
-            self.x_upd = Downsample(channels, False, dims)
-        else:
-            self.h_upd = self.x_upd = nn.Identity()
-
-        self.emb_layers = nn.Sequential(
-            nn.SiLU(),
-            linear(
-                emb_channels,
-                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
-            ),
-        )
-        self.out_layers = nn.Sequential(
-            normalization(self.out_channels),
-            nn.SiLU(),
-            nn.Dropout(p=dropout),
-            zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
-            ),
-        )
-
-        if self.out_channels == channels:
-            self.skip_connection = nn.Identity()
-        elif use_conv:
-            self.skip_connection = conv_nd(
-                dims, channels, self.out_channels, 3, padding=1
-            )
-        else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
-    def forward(self, x, emb):
-        """
-        Apply the block to a Tensor, conditioned on a timestep embedding.
-
-        :param x: an [N x C x ...] Tensor of features.
-        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        return checkpoint(
-            self._forward, (x, emb), self.parameters(), self.use_checkpoint
-        )
-
-    def _forward(self, x, emb):
-        if self.updown:
-            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
-            h = in_rest(x)
-            h = self.h_upd(h)
-            x = self.x_upd(x)
-            h = in_conv(h)
-        else:
-            h = self.in_layers(x)
-        emb_out = self.emb_layers(emb).type(h.dtype)
-        while len(emb_out.shape) < len(h.shape):
-            emb_out = emb_out[..., None]
-        if self.use_scale_shift_norm:
-            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
-            scale, shift = th.chunk(emb_out, 2, dim=1)
-            h = out_norm(h) * (1 + scale) + shift
-            h = out_rest(h)
-        else:
-            h = h + emb_out
-            h = self.out_layers(h)
-        return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
-    """
-    An attention block that allows spatial positions to attend to each other.
-
-    Originally ported from here, but adapted to the N-d case.
-    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    """
-
-    def __init__(
-        self,
-        channels,
-        num_heads=1,
-        num_head_channels=-1,
-        use_checkpoint=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        if num_head_channels == -1:
-            self.num_heads = num_heads
-        else:
-            assert (
-                channels % num_head_channels == 0
-            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
-            self.num_heads = channels // num_head_channels
-        self.use_checkpoint = use_checkpoint
-        self.norm = normalization(channels)
-        self.qkv = conv_nd(1, channels, channels * 3, 1)
-        if use_new_attention_order:
-            # split qkv before split heads
-            self.attention = QKVAttention(self.num_heads)
-        else:
-            # split heads before split qkv
-            self.attention = QKVAttentionLegacy(self.num_heads)
-
-        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
-    def forward(self, x):
-        return checkpoint(self._forward, (x,), self.parameters(), True)
-
-    def _forward(self, x):
-        b, c, *spatial = x.shape
-        x = x.reshape(b, c, -1)
-        qkv = self.qkv(self.norm(x))
-        h = self.attention(qkv)
-        h = self.proj_out(h)
-        return (x + h).reshape(b, c, *spatial)
-
-
-def count_flops_attn(model, _x, y):
-    """
-    A counter for the `thop` package to count the operations in an
-    attention operation.
-    Meant to be used like:
-        macs, params = thop.profile(
-            model,
-            inputs=(inputs, timestamps),
-            custom_ops={QKVAttention: QKVAttention.count_flops},
-        )
-    """
-    b, c, *spatial = y[0].shape
-    num_spatial = int(np.prod(spatial))
-    # We perform two matmuls with the same number of ops.
-    # The first computes the weight matrix, the second computes
-    # the combination of the value vectors.
-    matmul_ops = 2 * b * (num_spatial ** 2) * c
-    model.total_ops += th.DoubleTensor([matmul_ops])
-
-
-class QKVAttentionLegacy(nn.Module):
-    """
-    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-
-        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts", q * scale, k * scale
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v)
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class QKVAttention(nn.Module):
-    """
-    A module which performs QKV attention and splits in a different order.
-    """
-
-    def __init__(self, n_heads):
-        super().__init__()
-        self.n_heads = n_heads
-
-    def forward(self, qkv):
-        """
-        Apply QKV attention.
-
-        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
-        :return: an [N x (H * C) x T] tensor after attention.
-        """
-        bs, width, length = qkv.shape
-        assert width % (3 * self.n_heads) == 0
-        ch = width // (3 * self.n_heads)
-        q, k, v = qkv.chunk(3, dim=1)
-        scale = 1 / math.sqrt(math.sqrt(ch))
-        weight = th.einsum(
-            "bct,bcs->bts",
-            (q * scale).view(bs * self.n_heads, ch, length),
-            (k * scale).view(bs * self.n_heads, ch, length),
-        )  # More stable with f16 than dividing afterwards
-        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
-        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
-        return a.reshape(bs, -1, length)
-
-    @staticmethod
-    def count_flops(model, _x, y):
-        return count_flops_attn(model, _x, y)
-
-
-class UNetModel(nn.Module):
-    """
-    The full UNet model with attention and timestep embedding.
-
-    :param in_channels: channels in the input Tensor.
-    :param model_channels: base channel count for the model.
-    :param out_channels: channels in the output Tensor.
-    :param num_res_blocks: number of residual blocks per downsample.
-    :param attention_resolutions: a collection of downsample rates at which
-        attention will take place. May be a set, list, or tuple.
-        For example, if this contains 4, then at 4x downsampling, attention
-        will be used.
-    :param dropout: the dropout probability.
-    :param channel_mult: channel multiplier for each level of the UNet.
-    :param conv_resample: if True, use learned convolutions for upsampling and
-        downsampling.
-    :param dims: determines if the signal is 1D, 2D, or 3D.
-    :param num_classes: if specified (as an int), then this model will be
-        class-conditional with `num_classes` classes.
-    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
-    :param num_heads: the number of attention heads in each attention layer.
-    :param num_heads_channels: if specified, ignore num_heads and instead use
-                               a fixed channel width per attention head.
-    :param num_heads_upsample: works with num_heads to set a different number
-                               of heads for upsampling. Deprecated.
-    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
-    :param resblock_updown: use residual blocks for up/downsampling.
-    :param use_new_attention_order: use a different attention pattern for potentially
-                                    increased efficiency.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        num_classes=None,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-    ):
-        super().__init__()
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.image_size = image_size
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.num_classes = num_classes
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        if self.num_classes is not None:
-            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
-        ch = input_ch = int(channel_mult[0] * model_channels)
-        self.input_blocks = nn.ModuleList(
-            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
-        )
-        self._feature_size = ch
-        input_block_chans = [ch]
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=int(mult * model_channels),
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = int(mult * model_channels)
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=num_head_channels,
-                use_new_attention_order=use_new_attention_order,
-            ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-
-        self.output_blocks = nn.ModuleList([])
-        for level, mult in list(enumerate(channel_mult))[::-1]:
-            for i in range(num_res_blocks + 1):
-                ich = input_block_chans.pop()
-                layers = [
-                    ResBlock(
-                        ch + ich,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=int(model_channels * mult),
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = int(model_channels * mult)
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads_upsample,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                if level and i == num_res_blocks:
-                    out_ch = ch
-                    layers.append(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            up=True,
-                        )
-                        if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
-                    )
-                    ds //= 2
-                self.output_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-
-        self.out = nn.Sequential(
-            normalization(ch),
-            nn.SiLU(),
-            zero_module(conv_nd(dims, input_ch, out_channels, 3, padding=1)),
-        )
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps, y=None):
-        """
-        Apply the model to an input batch.
-
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :param y: an [N] Tensor of labels, if class-conditional.
-        :return: an [N x C x ...] Tensor of outputs.
-        """
-        assert (y is not None) == (
-            self.num_classes is not None
-        ), "must specify y if and only if the model is class-conditional"
-
-        hs = []
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
-        if self.num_classes is not None:
-            assert y.shape == (x.shape[0],)
-            emb = emb + self.label_emb(y)
-
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb)
-            hs.append(h)
-        h = self.middle_block(h, emb)
-        for module in self.output_blocks:
-            h = th.cat([h, hs.pop()], dim=1)
-            h = module(h, emb)
-        h = h.type(x.dtype)
-        return self.out(h)
-
-
-class SuperResModel(UNetModel):
-    """
-    A UNetModel that performs super-resolution.
-
-    Expects an extra kwarg `low_res` to condition on a low-resolution image.
-    """
-
-    def __init__(self, image_size, in_channels, *args, **kwargs):
-        super().__init__(image_size, in_channels * 2, *args, **kwargs)
-
-    def forward(self, x, timesteps, low_res=None, **kwargs):
-        _, _, new_height, new_width = x.shape
-        upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
-        x = th.cat([x, upsampled], dim=1)
-        return super().forward(x, timesteps, **kwargs)
-
-
-class EncoderUNetModel(nn.Module):
-    """
-    The half UNet model with attention and timestep embedding.
-
-    For usage, see UNet.
-    """
-
-    def __init__(
-        self,
-        image_size,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
-        dropout=0,
-        channel_mult=(1, 2, 4, 8),
-        conv_resample=True,
-        dims=2,
-        use_checkpoint=False,
-        use_fp16=False,
-        num_heads=1,
-        num_head_channels=-1,
-        num_heads_upsample=-1,
-        use_scale_shift_norm=False,
-        resblock_updown=False,
-        use_new_attention_order=False,
-        pool="adaptive",
-    ):
-        super().__init__()
-
-        if num_heads_upsample == -1:
-            num_heads_upsample = num_heads
-
-        self.in_channels = in_channels
-        self.model_channels = model_channels
-        self.out_channels = out_channels
-        self.num_res_blocks = num_res_blocks
-        self.attention_resolutions = attention_resolutions
-        self.dropout = dropout
-        self.channel_mult = channel_mult
-        self.conv_resample = conv_resample
-        self.use_checkpoint = use_checkpoint
-        self.dtype = th.float16 if use_fp16 else th.float32
-        self.num_heads = num_heads
-        self.num_head_channels = num_head_channels
-        self.num_heads_upsample = num_heads_upsample
-
-        time_embed_dim = model_channels * 4
-        self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
-            nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
-        )
-
-        ch = int(channel_mult[0] * model_channels)
-        self.input_blocks = nn.ModuleList(
-            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
-        )
-        self._feature_size = ch
-        input_block_chans = [ch]
-        ds = 1
-        for level, mult in enumerate(channel_mult):
-            for _ in range(num_res_blocks):
-                layers = [
-                    ResBlock(
-                        ch,
-                        time_embed_dim,
-                        dropout,
-                        out_channels=int(mult * model_channels),
-                        dims=dims,
-                        use_checkpoint=use_checkpoint,
-                        use_scale_shift_norm=use_scale_shift_norm,
-                    )
-                ]
-                ch = int(mult * model_channels)
-                if ds in attention_resolutions:
-                    layers.append(
-                        AttentionBlock(
-                            ch,
-                            use_checkpoint=use_checkpoint,
-                            num_heads=num_heads,
-                            num_head_channels=num_head_channels,
-                            use_new_attention_order=use_new_attention_order,
-                        )
-                    )
-                self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self._feature_size += ch
-                input_block_chans.append(ch)
-            if level != len(channel_mult) - 1:
-                out_ch = ch
-                self.input_blocks.append(
-                    TimestepEmbedSequential(
-                        ResBlock(
-                            ch,
-                            time_embed_dim,
-                            dropout,
-                            out_channels=out_ch,
-                            dims=dims,
-                            use_checkpoint=use_checkpoint,
-                            use_scale_shift_norm=use_scale_shift_norm,
-                            down=True,
-                        )
-                        if resblock_updown
-                        else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
-                        )
-                    )
-                )
-                ch = out_ch
-                input_block_chans.append(ch)
-                ds *= 2
-                self._feature_size += ch
-
-        self.middle_block = TimestepEmbedSequential(
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-            AttentionBlock(
-                ch,
-                use_checkpoint=use_checkpoint,
-                num_heads=num_heads,
-                num_head_channels=num_head_channels,
-                use_new_attention_order=use_new_attention_order,
-            ),
-            ResBlock(
-                ch,
-                time_embed_dim,
-                dropout,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-            ),
-        )
-        self._feature_size += ch
-        self.pool = pool
-        if pool == "adaptive":
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                nn.AdaptiveAvgPool2d((1, 1)),
-                zero_module(conv_nd(dims, ch, out_channels, 1)),
-                nn.Flatten(),
-            )
-        elif pool == "attention":
-            assert num_head_channels != -1
-            self.out = nn.Sequential(
-                normalization(ch),
-                nn.SiLU(),
-                AttentionPool2d(
-                    (image_size // ds), ch, num_head_channels, out_channels
-                ),
-            )
-        elif pool == "spatial":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                nn.ReLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        elif pool == "spatial_v2":
-            self.out = nn.Sequential(
-                nn.Linear(self._feature_size, 2048),
-                normalization(2048),
-                nn.SiLU(),
-                nn.Linear(2048, self.out_channels),
-            )
-        else:
-            raise NotImplementedError(f"Unexpected {pool} pooling")
-
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-
-    def forward(self, x, timesteps):
-        """
-        Apply the model to an input batch.
-
-        :param x: an [N x C x ...] Tensor of inputs.
-        :param timesteps: a 1-D batch of timesteps.
-        :return: an [N x K] Tensor of outputs.
-        """
-        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
-        results = []
-        h = x.type(self.dtype)
-        for module in self.input_blocks:
-            h = module(h, emb)
-            if self.pool.startswith("spatial"):
-                results.append(h.type(x.dtype).mean(dim=(2, 3)))
-        h = self.middle_block(h, emb)
-        if self.pool.startswith("spatial"):
-            results.append(h.type(x.dtype).mean(dim=(2, 3)))
-            h = th.cat(results, axis=-1)
-            return self.out(h)
-        else:
-            h = h.type(x.dtype)
-            return self.out(h)
-
-
-class NLayerDiscriminator(nn.Module):
-    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
-        super(NLayerDiscriminator, self).__init__()
-        if type(norm_layer) == functools.partial:
-            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):
-            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=2, 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=2, padding=padw)] + [nn.Dropout(0.5)]
-        if use_sigmoid:
-            sequence += [nn.Sigmoid()]
-
-        self.model = nn.Sequential(*sequence)
-
-    def forward(self, input):
-        return self.model(input)
-
-
-class GANLoss(nn.Module):
-    """Define different GAN objectives.
-
-    The GANLoss class abstracts away the need to create the target label tensor
-    that has the same size as the input.
-    """
-
-    def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
-        """ Initialize the GANLoss class.
-
-        Parameters:
-            gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
-            target_real_label (bool) - - label for a real image
-            target_fake_label (bool) - - label of a fake image
-
-        Note: Do not use sigmoid as the last layer of Discriminator.
-        LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
-        """
-        super(GANLoss, self).__init__()
-        self.register_buffer('real_label', th.tensor(target_real_label))
-        self.register_buffer('fake_label', th.tensor(target_fake_label))
-        self.gan_mode = gan_mode
-        if gan_mode == 'lsgan':
-            self.loss = nn.MSELoss()
-        elif gan_mode == 'vanilla':
-            self.loss = nn.BCEWithLogitsLoss()
-        elif gan_mode in ['wgangp']:
-            self.loss = None
-        else:
-            raise NotImplementedError('gan mode %s not implemented' % gan_mode)
-
-    def get_target_tensor(self, prediction, target_is_real):
-        """Create label tensors with the same size as the input.
-
-        Parameters:
-            prediction (tensor) - - tpyically the prediction from a discriminator
-            target_is_real (bool) - - if the ground truth label is for real images or fake images
-
-        Returns:
-            A label tensor filled with ground truth label, and with the size of the input
-        """
-
-        if target_is_real:
-            target_tensor = self.real_label
-        else:
-            target_tensor = self.fake_label
-        return target_tensor.expand_as(prediction)
-
-    def __call__(self, prediction, target_is_real):
-        """Calculate loss given Discriminator's output and grount truth labels.
-
-        Parameters:
-            prediction (tensor) - - tpyically the prediction output from a discriminator
-            target_is_real (bool) - - if the ground truth label is for real images or fake images
-
-        Returns:
-            the calculated loss.
-        """
-        if self.gan_mode in ['lsgan', 'vanilla']:
-            target_tensor = self.get_target_tensor(prediction, target_is_real)
-            loss = self.loss(prediction, target_tensor)
-        elif self.gan_mode == 'wgangp':
-            if target_is_real:
-                loss = -prediction.mean()
-            else:
-                loss = prediction.mean()
-        return loss
-
-
-def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', constant=1.0, lambda_gp=10.0):
-    """Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028
-
-    Arguments:
-        netD (network)              -- discriminator network
-        real_data (tensor array)    -- real images
-        fake_data (tensor array)    -- generated images from the generator
-        device (str)                -- GPU / CPU: from torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
-        type (str)                  -- if we mix real and fake data or not [real | fake | mixed].
-        constant (float)            -- the constant used in formula ( | |gradient||_2 - constant)^2
-        lambda_gp (float)           -- weight for this loss
-
-    Returns the gradient penalty loss
-    """
-    if lambda_gp > 0.0:
-        if type == 'real':   # either use real images, fake images, or a linear interpolation of two.
-            interpolatesv = real_data
-        elif type == 'fake':
-            interpolatesv = fake_data
-        elif type == 'mixed':
-            alpha = th.rand(real_data.shape[0], 1, device=device)
-            alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(*real_data.shape)
-            interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
-        else:
-            raise NotImplementedError('{} not implemented'.format(type))
-        interpolatesv.requires_grad_(True)
-        disc_interpolates = netD(interpolatesv)
-        gradients = th.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv,
-                                     grad_outputs=th.ones(disc_interpolates.size()).to(device),
-                                     create_graph=True, retain_graph=True, only_inputs=True)
-        gradients = gradients[0].view(real_data.size(0), -1)  # flat the data
-        gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp        # added eps
-        return gradient_penalty, gradients
-    else:
-        return 0.0, None
diff --git a/spaces/PaddlePaddle/LSeg/README.md b/spaces/PaddlePaddle/LSeg/README.md
deleted file mode 100644
index e034bef55ed96e04711922874ca176baf01cf78a..0000000000000000000000000000000000000000
--- a/spaces/PaddlePaddle/LSeg/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: LSeg
-emoji: 🌍
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.3.1
-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/PeepDaSlan9/SDXL-artists-browser/README.md b/spaces/PeepDaSlan9/SDXL-artists-browser/README.md
deleted file mode 100644
index c334c5b5ce4bc08141154ee89e892ddb3dbe9f99..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/SDXL-artists-browser/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
----
-title: SD Artists Browser
-emoji: 🤘
-colorFrom: indigo
-colorTo: purple
-sdk: static
-pinned: false
-license: cc-by-sa-4.0
-duplicated_from: mattthew/SDXL-artists-browser
----
-# Another SDXL artist browser?!
-
-Yes, because it's better:
-### Most complete database of tags
-* Each artist has at least 6 tags
-* Mediums, styles, themes, periods, subject matter, and more
-### Largest database of artists: ~~3000k~~ (temporarily 500 due to beta)
-* Sort artists by random to discover new ones
-* Browse image thumbnails and view at full resolution
-* Three images each: 🎨 artwork, 🧑 portrait, and 🏞️ landscape
-### Easiest to use filters
-* Sort filters by name or count of matches
-* Choose strict or permissive when combining filters
-### Save your favorites
-* Export or import your favorites too
-### Community first
-* Transparent: Open source Creatives Commons database
-* Private: No cookies, nothing sent to server, use offline
-* Note: I can't control what data Huggingface collects
-
-# How to support this project
-* Please tell a friends or share on your socials
-* Suggest artists I should add or remove
-* Suggest features and report bugs
\ No newline at end of file
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/deprecated_wrappers.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/deprecated_wrappers.py
deleted file mode 100644
index a2e593df9ee57637038683d7a1efaa347b2b69e7..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/deprecated_wrappers.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# This file is for backward compatibility.
-# Module wrappers for empty tensor have been moved to mmcv.cnn.bricks.
-import warnings
-
-from ..cnn.bricks.wrappers import Conv2d, ConvTranspose2d, Linear, MaxPool2d
-
-
-class Conv2d_deprecated(Conv2d):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        warnings.warn(
-            'Importing Conv2d wrapper from "mmcv.ops" will be deprecated in'
-            ' the future. Please import them from "mmcv.cnn" instead')
-
-
-class ConvTranspose2d_deprecated(ConvTranspose2d):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        warnings.warn(
-            'Importing ConvTranspose2d wrapper from "mmcv.ops" will be '
-            'deprecated in the future. Please import them from "mmcv.cnn" '
-            'instead')
-
-
-class MaxPool2d_deprecated(MaxPool2d):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        warnings.warn(
-            'Importing MaxPool2d wrapper from "mmcv.ops" will be deprecated in'
-            ' the future. Please import them from "mmcv.cnn" instead')
-
-
-class Linear_deprecated(Linear):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        warnings.warn(
-            'Importing Linear wrapper from "mmcv.ops" will be deprecated in'
-            ' the future. Please import them from "mmcv.cnn" instead')
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/base_module.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/base_module.py
deleted file mode 100644
index 617fad9bb89f10a9a0911d962dfb3bc8f3a3628c..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/runner/base_module.py
+++ /dev/null
@@ -1,195 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import warnings
-from abc import ABCMeta
-from collections import defaultdict
-from logging import FileHandler
-
-import torch.nn as nn
-
-from annotator.uniformer.mmcv.runner.dist_utils import master_only
-from annotator.uniformer.mmcv.utils.logging import get_logger, logger_initialized, print_log
-
-
-class BaseModule(nn.Module, metaclass=ABCMeta):
-    """Base module for all modules in openmmlab.
-
-    ``BaseModule`` is a wrapper of ``torch.nn.Module`` with additional
-    functionality of parameter initialization. Compared with
-    ``torch.nn.Module``, ``BaseModule`` mainly adds three attributes.
-
-        - ``init_cfg``: the config to control the initialization.
-        - ``init_weights``: The function of parameter
-            initialization and recording initialization
-            information.
-        - ``_params_init_info``: Used to track the parameter
-            initialization information. This attribute only
-            exists during executing the ``init_weights``.
-
-    Args:
-        init_cfg (dict, optional): Initialization config dict.
-    """
-
-    def __init__(self, init_cfg=None):
-        """Initialize BaseModule, inherited from `torch.nn.Module`"""
-
-        # NOTE init_cfg can be defined in different levels, but init_cfg
-        # in low levels has a higher priority.
-
-        super(BaseModule, self).__init__()
-        # define default value of init_cfg instead of hard code
-        # in init_weights() function
-        self._is_init = False
-
-        self.init_cfg = copy.deepcopy(init_cfg)
-
-        # Backward compatibility in derived classes
-        # if pretrained is not None:
-        #     warnings.warn('DeprecationWarning: pretrained is a deprecated \
-        #         key, please consider using init_cfg')
-        #     self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
-
-    @property
-    def is_init(self):
-        return self._is_init
-
-    def init_weights(self):
-        """Initialize the weights."""
-
-        is_top_level_module = False
-        # check if it is top-level module
-        if not hasattr(self, '_params_init_info'):
-            # The `_params_init_info` is used to record the initialization
-            # information of the parameters
-            # the key should be the obj:`nn.Parameter` of model and the value
-            # should be a dict containing
-            # - init_info (str): The string that describes the initialization.
-            # - tmp_mean_value (FloatTensor): The mean of the parameter,
-            #       which indicates whether the parameter has been modified.
-            # this attribute would be deleted after all parameters
-            # is initialized.
-            self._params_init_info = defaultdict(dict)
-            is_top_level_module = True
-
-            # Initialize the `_params_init_info`,
-            # When detecting the `tmp_mean_value` of
-            # the corresponding parameter is changed, update related
-            # initialization information
-            for name, param in self.named_parameters():
-                self._params_init_info[param][
-                    'init_info'] = f'The value is the same before and ' \
-                                   f'after calling `init_weights` ' \
-                                   f'of {self.__class__.__name__} '
-                self._params_init_info[param][
-                    'tmp_mean_value'] = param.data.mean()
-
-            # pass `params_init_info` to all submodules
-            # All submodules share the same `params_init_info`,
-            # so it will be updated when parameters are
-            # modified at any level of the model.
-            for sub_module in self.modules():
-                sub_module._params_init_info = self._params_init_info
-
-        # Get the initialized logger, if not exist,
-        # create a logger named `mmcv`
-        logger_names = list(logger_initialized.keys())
-        logger_name = logger_names[0] if logger_names else 'mmcv'
-
-        from ..cnn import initialize
-        from ..cnn.utils.weight_init import update_init_info
-        module_name = self.__class__.__name__
-        if not self._is_init:
-            if self.init_cfg:
-                print_log(
-                    f'initialize {module_name} with init_cfg {self.init_cfg}',
-                    logger=logger_name)
-                initialize(self, self.init_cfg)
-                if isinstance(self.init_cfg, dict):
-                    # prevent the parameters of
-                    # the pre-trained model
-                    # from being overwritten by
-                    # the `init_weights`
-                    if self.init_cfg['type'] == 'Pretrained':
-                        return
-
-            for m in self.children():
-                if hasattr(m, 'init_weights'):
-                    m.init_weights()
-                    # users may overload the `init_weights`
-                    update_init_info(
-                        m,
-                        init_info=f'Initialized by '
-                        f'user-defined `init_weights`'
-                        f' in {m.__class__.__name__} ')
-
-            self._is_init = True
-        else:
-            warnings.warn(f'init_weights of {self.__class__.__name__} has '
-                          f'been called more than once.')
-
-        if is_top_level_module:
-            self._dump_init_info(logger_name)
-
-            for sub_module in self.modules():
-                del sub_module._params_init_info
-
-    @master_only
-    def _dump_init_info(self, logger_name):
-        """Dump the initialization information to a file named
-        `initialization.log.json` in workdir.
-
-        Args:
-            logger_name (str): The name of logger.
-        """
-
-        logger = get_logger(logger_name)
-
-        with_file_handler = False
-        # dump the information to the logger file if there is a `FileHandler`
-        for handler in logger.handlers:
-            if isinstance(handler, FileHandler):
-                handler.stream.write(
-                    'Name of parameter - Initialization information\n')
-                for name, param in self.named_parameters():
-                    handler.stream.write(
-                        f'\n{name} - {param.shape}: '
-                        f"\n{self._params_init_info[param]['init_info']} \n")
-                handler.stream.flush()
-                with_file_handler = True
-        if not with_file_handler:
-            for name, param in self.named_parameters():
-                print_log(
-                    f'\n{name} - {param.shape}: '
-                    f"\n{self._params_init_info[param]['init_info']} \n ",
-                    logger=logger_name)
-
-    def __repr__(self):
-        s = super().__repr__()
-        if self.init_cfg:
-            s += f'\ninit_cfg={self.init_cfg}'
-        return s
-
-
-class Sequential(BaseModule, nn.Sequential):
-    """Sequential module in openmmlab.
-
-    Args:
-        init_cfg (dict, optional): Initialization config dict.
-    """
-
-    def __init__(self, *args, init_cfg=None):
-        BaseModule.__init__(self, init_cfg)
-        nn.Sequential.__init__(self, *args)
-
-
-class ModuleList(BaseModule, nn.ModuleList):
-    """ModuleList in openmmlab.
-
-    Args:
-        modules (iterable, optional): an iterable of modules to add.
-        init_cfg (dict, optional): Initialization config dict.
-    """
-
-    def __init__(self, modules=None, init_cfg=None):
-        BaseModule.__init__(self, init_cfg)
-        nn.ModuleList.__init__(self, modules)
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/mixup.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/mixup.py
deleted file mode 100644
index 85969e097ad154d1948d81eb1b5b3e941f5bf53f..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/data/datasets/mixup.py
+++ /dev/null
@@ -1,124 +0,0 @@
-"""Mixup detection dataset wrapper."""
-from __future__ import absolute_import
-import numpy as np
-import torch
-import torch.utils.data as data
-
-
-class MixupDetection(data.Dataset):
-    """Detection dataset wrapper that performs mixup for normal dataset.
-    Parameters
-    ----------
-    dataset : mx.gluon.data.Dataset
-        Gluon dataset object.
-    mixup : callable random generator, e.g. np.random.uniform
-        A random mixup ratio sampler, preferably a random generator from numpy.random
-        A random float will be sampled each time with mixup(*args).
-        Use None to disable.
-    *args : list
-        Additional arguments for mixup random sampler.
-    """
-    def __init__(self, dataset, mixup=None, preproc=None, *args):
-        super().__init__(dataset.input_dim)
-        self._dataset = dataset
-        self.preproc = preproc
-        self._mixup = mixup
-        self._mixup_args = args
-
-    def set_mixup(self, mixup=None, *args):
-        """Set mixup random sampler, use None to disable.
-        Parameters
-        ----------
-        mixup : callable random generator, e.g. np.random.uniform
-            A random mixup ratio sampler, preferably a random generator from numpy.random
-            A random float will be sampled each time with mixup(*args)
-        *args : list
-            Additional arguments for mixup random sampler.
-        """
-        self._mixup = mixup
-        self._mixup_args = args
-
-    def __len__(self):
-        return len(self._dataset)
-
-    @Dataset.resize_getitem
-    def __getitem__(self, idx):
-        self._dataset._input_dim = self.input_dim
-        # first image
-        img1, label1, _, _= self._dataset.pull_item(idx)
-        lambd = 1
-
-        # draw a random lambda ratio from distribution
-        if self._mixup is not None:
-            lambd = max(0, min(1, self._mixup(*self._mixup_args)))
-
-        if lambd >= 1:
-            weights1 = np.ones((label1.shape[0], 1))
-            label1 = np.hstack((label1, weights1))
-            height, width, _ = img1.shape
-            img_info = (width, height)
-            if self.preproc is not None:
-                img_o, target_o = self.preproc(img1, label1, self.input_dim)
-            return img_o, target_o, img_info, idx
-
-        # second image
-        idx2 = int(np.random.choice(np.delete(np.arange(len(self)), idx)))
-        img2, label2, _, _ = self._dataset.pull_item(idx2)
-
-        # mixup two images
-        height = max(img1.shape[0], img2.shape[0])
-        width = max(img1.shape[1], img2.shape[1])
-        mix_img = np.zeros((height, width, 3),dtype=np.float32)
-        mix_img[:img1.shape[0], :img1.shape[1], :] = img1.astype(np.float32) * lambd
-        mix_img[:img2.shape[0], :img2.shape[1], :] += img2.astype(np.float32) * (1. - lambd)
-        mix_img = mix_img.astype(np.uint8)
-
-        y1 = np.hstack((label1, np.full((label1.shape[0], 1), lambd)))
-        y2 = np.hstack((label2, np.full((label2.shape[0], 1), 1. - lambd)))
-        mix_label = np.vstack((y1, y2))
-        if self.preproc is not None:
-            mix_img, padded_labels = self.preproc(mix_img, mix_label, self.input_dim)
-
-        img_info = (width, height)
-
-        return mix_img, padded_labels, img_info , idx
-
-    def pull_item(self, idx):
-        self._dataset._input_dim = self.input_dim
-        # first image
-        img1, label1, _, _= self._dataset.pull_item(idx)
-        lambd = 1
-
-        # draw a random lambda ratio from distribution
-        if self._mixup is not None:
-            lambd = max(0, min(1, self._mixup(*self._mixup_args)))
-
-        if lambd >= 1:
-            weights1 = np.ones((label1.shape[0], 1))
-            label1 = np.hstack((label1, weights1))
-            height, width, _ = img1.shape
-            img_info = (width, height)
-            if self.preproc is not None:
-                img_o, target_o = self.preproc(img1, label1, self.input_dim)
-            return img_o, target_o, img_info, idx
-
-        # second image
-        idx2 = int(np.random.choice(np.delete(np.arange(len(self)), idx)))
-        img2, label2 = self._dataset.pull_item(idx2)
-
-        # mixup two images
-        height = max(img1.shape[0], img2.shape[0])
-        width = max(img1.shape[1], img2.shape[1])
-        mix_img = np.zeros((height, width, 3),dtype=np.float32)
-        mix_img[:img1.shape[0], :img1.shape[1], :] = img1.astype(np.float32) * lambd
-        mix_img[:img2.shape[0], :img2.shape[1], :] += img2.astype(np.float32) * (1. - lambd)
-        mix_img = mix_img.astype(np.uint8)
-
-        y1 = np.hstack((label1, np.full((label1.shape[0], 1), lambd)))
-        y2 = np.hstack((label2, np.full((label2.shape[0], 1), 1. - lambd)))
-        mix_label = np.vstack((y1, y2))
-        if self.preproc is not None:
-            mix_img, padded_labels = self.preproc(mix_img, mix_label, self.input_dim)
-
-        img_info = (width, height)
-        return mix_img, padded_labels, img_info , idx
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/engine/evolution.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/engine/evolution.py
deleted file mode 100644
index 722fcf6ba903a7e17a0abd7bebafcc033bdb2970..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/engine/evolution.py
+++ /dev/null
@@ -1,357 +0,0 @@
-
-import time
-import pickle
-import logging
-import os
-import numpy as np
-import torch
-import torch.nn as nn
-
-
-from collections import OrderedDict
-from yaml import safe_dump
-from yacs.config import load_cfg, CfgNode#, _to_dict
-from maskrcnn_benchmark.config import cfg
-from maskrcnn_benchmark.engine.inference import _accumulate_predictions_from_multiple_gpus
-from maskrcnn_benchmark.modeling.backbone.nas import get_layer_name
-from maskrcnn_benchmark.utils.comm import synchronize, get_rank, is_main_process, get_world_size, all_gather
-from maskrcnn_benchmark.data.datasets.evaluation import evaluate
-from maskrcnn_benchmark.utils.flops import profile
-
-
-choice = lambda x:x[np.random.randint(len(x))] if isinstance(x,tuple) else choice(tuple(x))
-
-
-def gather_candidates(all_candidates):
-    all_candidates = all_gather(all_candidates)
-    all_candidates = [cand for candidates in all_candidates for cand in candidates]
-    return list(set(all_candidates))
-
-
-def gather_stats(all_candidates):
-    all_candidates = all_gather(all_candidates)
-    reduced_statcs = {}
-    for candidates in all_candidates:
-        reduced_statcs.update(candidates) # will replace the existing key with last value if more than one exists
-    return reduced_statcs
-
-
-def compute_on_dataset(model, rngs, data_loader, device=cfg.MODEL.DEVICE):
-    model.eval()
-    results_dict = {}
-    cpu_device = torch.device("cpu")
-    for _, batch in enumerate(data_loader):
-        images, targets, image_ids = batch
-        with torch.no_grad():
-            output = model(images.to(device), rngs=rngs)
-            output = [o.to(cpu_device) for o in output]
-        results_dict.update(
-            {img_id: result for img_id, result in zip(image_ids, output)}
-        )
-    return results_dict
-
-
-def bn_statistic(model, rngs, data_loader, device=cfg.MODEL.DEVICE, max_iter=500):
-    for name, param in model.named_buffers():
-        if 'running_mean' in name:
-            nn.init.constant_(param, 0)
-        if 'running_var' in name:
-            nn.init.constant_(param, 1)
-
-    model.train()
-    for iteration, (images, targets, _) in enumerate(data_loader, 1):
-        images = images.to(device)
-        targets = [target.to(device) for target in targets]
-        with torch.no_grad():
-            loss_dict = model(images, targets, rngs)
-        if iteration >= max_iter:
-            break
-
-    return model
-
-
-def inference(
-        model,
-        rngs,
-        data_loader,
-        iou_types=("bbox",),
-        box_only=False,
-        device="cuda",
-        expected_results=(),
-        expected_results_sigma_tol=4,
-        output_folder=None,
-):
-
-    # convert to a torch.device for efficiency
-    device = torch.device(device)
-    dataset = data_loader.dataset
-    predictions = compute_on_dataset(model, rngs, data_loader, device)
-    # wait for all processes to complete before measuring the time
-    synchronize()
-
-    predictions = _accumulate_predictions_from_multiple_gpus(predictions)
-    if not is_main_process():
-        return
-
-    extra_args = dict(
-        box_only=box_only,
-        iou_types=iou_types,
-        expected_results=expected_results,
-        expected_results_sigma_tol=expected_results_sigma_tol,
-    )
-
-    return evaluate(dataset=dataset,
-                    predictions=predictions,
-                    output_folder=output_folder,
-                    **extra_args)
-
-
-def fitness(cfg, model, rngs, val_loaders):
-    iou_types = ("bbox",)
-    if cfg.MODEL.MASK_ON:
-        iou_types = iou_types + ("segm",)
-    for data_loader_val in val_loaders:
-        results = inference(
-            model,
-            rngs,
-            data_loader_val,
-            iou_types=iou_types,
-            box_only=False,
-            device=cfg.MODEL.DEVICE,
-            expected_results=cfg.TEST.EXPECTED_RESULTS,
-            expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
-        )
-        synchronize()
-
-    return results
-
-
-class EvolutionTrainer(object):
-    def __init__(self, cfg, model, flops_limit=None, is_distributed=True):
-
-        self.log_dir = cfg.OUTPUT_DIR
-        self.checkpoint_name = os.path.join(self.log_dir,'evolution.pth')
-        self.is_distributed = is_distributed
-
-        self.states = model.module.mix_nums if is_distributed else model.mix_nums
-        self.supernet_state_dict = pickle.loads(pickle.dumps(model.state_dict()))
-        self.flops_limit = flops_limit
-        self.model = model
-
-        self.candidates = []
-        self.vis_dict = {}
-
-        self.max_epochs = cfg.SEARCH.MAX_EPOCH
-        self.select_num = cfg.SEARCH.SELECT_NUM
-        self.population_num = cfg.SEARCH.POPULATION_NUM/get_world_size()
-        self.mutation_num = cfg.SEARCH.MUTATION_NUM/get_world_size()
-        self.crossover_num = cfg.SEARCH.CROSSOVER_NUM/get_world_size()
-        self.mutation_prob = cfg.SEARCH.MUTATION_PROB/get_world_size()
-
-        self.keep_top_k = {self.select_num:[], 50:[]}
-        self.epoch=0
-        self.cfg = cfg
-
-    def save_checkpoint(self):
-        if not is_main_process():
-            return
-        if not os.path.exists(self.log_dir):
-            os.makedirs(self.log_dir)
-        info = {}
-        info['candidates'] = self.candidates
-        info['vis_dict'] = self.vis_dict
-        info['keep_top_k'] = self.keep_top_k
-        info['epoch'] = self.epoch
-        torch.save(info, self.checkpoint_name)
-        print('Save checkpoint to', self.checkpoint_name)
-
-    def load_checkpoint(self):
-        if not os.path.exists(self.checkpoint_name):
-            return False
-        info = torch.load(self.checkpoint_name)
-        self.candidates = info['candidates']
-        self.vis_dict = info['vis_dict']
-        self.keep_top_k = info['keep_top_k']
-        self.epoch = info['epoch']
-        print('Load checkpoint from', self.checkpoint_name)
-        return True
-
-    def legal(self, cand):
-        assert isinstance(cand,tuple) and len(cand)==len(self.states)
-        if cand in self.vis_dict:
-            return False
-
-        if self.flops_limit is not None:
-            net = self.model.module.backbone if self.is_distributed else self.model.backbone
-            inp = (1, 3, 224, 224)
-            flops, params = profile(net, inp, extra_args={'paths': list(cand)})
-            flops = flops/1e6
-            print('flops:',flops)
-            if flops>self.flops_limit:
-                return False
-
-        return True
-
-    def update_top_k(self, candidates, *, k, key, reverse=False):
-        assert k in self.keep_top_k
-        # print('select ......')
-        t = self.keep_top_k[k]
-        t += candidates
-        t.sort(key=key,reverse=reverse)
-        self.keep_top_k[k]=t[:k]
-
-    def eval_candidates(self, train_loader, val_loader):
-        for cand in self.candidates:
-            t0 = time.time()
-
-            # load back supernet state dict
-            self.model.load_state_dict(self.supernet_state_dict)
-            # bn_statistic
-            model = bn_statistic(self.model, list(cand), train_loader)
-            # fitness
-            evals = fitness(cfg, model, list(cand), val_loader)
-
-            if is_main_process():
-                acc = evals[0].results['bbox']['AP']
-                self.vis_dict[cand] = acc
-                print('candiate ', cand)
-                print('time: {}s'.format(time.time() - t0))
-                print('acc ', acc)
-
-    def stack_random_cand(self, random_func, *, batchsize=10):
-        while True:
-            cands = [random_func() for _ in range(batchsize)]
-            for cand in cands:
-                yield cand
-
-    def random_can(self, num):
-        # print('random select ........')
-        candidates = []
-        cand_iter = self.stack_random_cand(lambda:tuple(np.random.randint(i) for i in self.states))
-        while len(candidates)<num:
-            cand = next(cand_iter)
-
-            if not self.legal(cand):
-                continue
-            candidates.append(cand)
-            #print('random {}/{}'.format(len(candidates),num))
-
-        # print('random_num = {}'.format(len(candidates)))
-        return candidates
-
-    def get_mutation(self, k, mutation_num, m_prob):
-        assert k in self.keep_top_k
-        # print('mutation ......')
-        res = []
-        iter = 0
-        max_iters = mutation_num*10
-
-        def random_func():
-            cand = list(choice(self.keep_top_k[k]))
-            for i in range(len(self.states)):
-                if np.random.random_sample()<m_prob:
-                    cand[i] = np.random.randint(self.states[i])
-            return tuple(cand)
-
-        cand_iter = self.stack_random_cand(random_func)
-        while len(res)<mutation_num and max_iters>0:
-            cand = next(cand_iter)
-            if not self.legal(cand):
-                continue
-            res.append(cand)
-            #print('mutation {}/{}'.format(len(res),mutation_num))
-            max_iters-=1
-
-        # print('mutation_num = {}'.format(len(res)))
-        return res
-
-    def get_crossover(self, k, crossover_num):
-        assert k in self.keep_top_k
-        # print('crossover ......')
-        res = []
-        iter = 0
-        max_iters = 10 * crossover_num
-
-        def random_func():
-            p1=choice(self.keep_top_k[k])
-            p2=choice(self.keep_top_k[k])
-            return tuple(choice([i,j]) for i,j in zip(p1,p2))
-
-        cand_iter = self.stack_random_cand(random_func)
-        while len(res)<crossover_num and max_iters>0:
-            cand = next(cand_iter)
-            if not self.legal(cand):
-                continue
-            res.append(cand)
-            #print('crossover {}/{}'.format(len(res),crossover_num))
-            max_iters-=1
-
-        # print('crossover_num = {}'.format(len(res)))
-        return res
-
-    def train(self, train_loader, val_loader):
-        logger = logging.getLogger("maskrcnn_benchmark.evolution")
-
-        if not self.load_checkpoint():
-            self.candidates = gather_candidates(self.random_can(self.population_num))
-
-        while self.epoch<self.max_epochs:
-            self.eval_candidates(train_loader, val_loader)
-            self.vis_dict = gather_stats(self.vis_dict)
-
-            self.update_top_k(self.candidates, k=self.select_num, key=lambda x:1-self.vis_dict[x])
-            self.update_top_k(self.candidates, k=50, key=lambda x:1-self.vis_dict[x])
-
-            if is_main_process():
-                logger.info('Epoch {} : top {} result'.format(self.epoch+1, len(self.keep_top_k[self.select_num])))
-                for i,cand in enumerate(self.keep_top_k[self.select_num]):
-                    logger.info('     No.{} {} perf = {}'.format(i+1, cand, self.vis_dict[cand]))
-
-            mutation = gather_candidates(self.get_mutation(self.select_num, self.mutation_num, self.mutation_prob))
-            crossover = gather_candidates(self.get_crossover(self.select_num, self.crossover_num))
-            rand = gather_candidates(self.random_can(self.population_num - len(mutation) - len(crossover)))
-
-            self.candidates = mutation + crossover + rand
-
-            self.epoch+=1
-            self.save_checkpoint()
-
-    def save_candidates(self, cand, template):
-        paths = self.keep_top_k[self.select_num][cand-1]
-
-        with open(template, "r") as f:
-            super_cfg = load_cfg(f)
-
-        search_spaces = {}
-        for mix_ops in super_cfg.MODEL.BACKBONE.LAYER_SEARCH:
-            search_spaces[mix_ops] = super_cfg.MODEL.BACKBONE.LAYER_SEARCH[mix_ops]
-        search_layers = super_cfg.MODEL.BACKBONE.LAYER_SETUP
-
-        layer_setup = []
-        for i, layer in enumerate(search_layers):
-            name, setup = get_layer_name(layer, search_spaces)
-            if not isinstance(name, list):
-                name = [name]
-            name = name[paths[i]]
-
-            layer_setup.append("('{}', {})".format(name, str(setup)[1:-1]))
-        super_cfg.MODEL.BACKBONE.LAYER_SETUP = layer_setup
-
-        cand_cfg = _to_dict(super_cfg)
-        del cand_cfg['MODEL']['BACKBONE']['LAYER_SEARCH']
-        with open(os.path.join(self.cfg.OUTPUT_DIR, os.path.basename(template)).replace('.yaml','_cand{}.yaml'.format(cand)), 'w') as f:
-            f.writelines(safe_dump(cand_cfg))
-
-        super_weight = self.supernet_state_dict
-        cand_weight = OrderedDict()
-        cand_keys = ['layers.{}.ops.{}'.format(i, c) for i, c in enumerate(paths)]
-
-        for key, val in super_weight.items():
-            if 'ops' in key:
-                for ck in cand_keys:
-                    if ck in key:
-                        cand_weight[key.replace(ck,ck.split('.ops.')[0])] = val
-            else:
-                cand_weight[key] = val
-
-        torch.save({'model':cand_weight}, os.path.join(self.cfg.OUTPUT_DIR, 'init_cand{}.pth'.format(cand)))
diff --git a/spaces/PirateHFH/IllusionDiffusion/share_btn.py b/spaces/PirateHFH/IllusionDiffusion/share_btn.py
deleted file mode 100644
index 5d4dc51b883650ed947e7dea90f677d817725198..0000000000000000000000000000000000000000
--- a/spaces/PirateHFH/IllusionDiffusion/share_btn.py
+++ /dev/null
@@ -1,83 +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' });
-        }
-	}
-    
-    const gradioEl = document.querySelector("gradio-app").shadowRoot || document.querySelector('body > gradio-app');
-    
-    const inputPrompt = gradioEl.querySelector('#prompt textarea').value;
-    const negativePrompt = gradioEl.querySelector('#negative_prompt textarea').value;
-    const illusionStrength = gradioEl.querySelector('#illusion_strength input[type="number"]').value;
-    const controlImage = gradioEl.querySelector('#control_image img');
-    const outputImgEl = gradioEl.querySelector('#output img');
-    
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    
-    const inputFile = await getInputImgFile(outputImgEl);
-    const urlInputImg = await uploadFile(inputFile);
-
-    const controlFile = await getInputImgFile(controlImage);
-    const urlControlImg = await uploadFile(controlFile);
-    
-    const descriptionMd = `
-### Prompt
-- *Prompt*: ${inputPrompt}
-- *Negative prompt*: ${negativePrompt}
-- *Illusion strength*: ${illusionStrength}
-#### Generated Image:
-<img src="${urlInputImg}" />
-
-#### Control Image: 
-<img src="${urlControlImg}" />
-`;
-    const params = new URLSearchParams({
-        title: inputPrompt,
-        description: descriptionMd,
-        preview: true
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/AP123/IllusionDiffusion/discussions/new?${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/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_distutils/msvc9compiler.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_distutils/msvc9compiler.py
deleted file mode 100644
index 22021831086ea8b2db134f7a2310a803abf5f982..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_distutils/msvc9compiler.py
+++ /dev/null
@@ -1,832 +0,0 @@
-"""distutils.msvc9compiler
-
-Contains MSVCCompiler, an implementation of the abstract CCompiler class
-for the Microsoft Visual Studio 2008.
-
-The module is compatible with VS 2005 and VS 2008. You can find legacy support
-for older versions of VS in distutils.msvccompiler.
-"""
-
-# Written by Perry Stoll
-# hacked by Robin Becker and Thomas Heller to do a better job of
-#   finding DevStudio (through the registry)
-# ported to VS2005 and VS 2008 by Christian Heimes
-
-import os
-import subprocess
-import sys
-import re
-import warnings
-
-from distutils.errors import (
-    DistutilsExecError,
-    DistutilsPlatformError,
-    CompileError,
-    LibError,
-    LinkError,
-)
-from distutils.ccompiler import CCompiler, gen_lib_options
-from distutils import log
-from distutils.util import get_platform
-
-import winreg
-
-warnings.warn(
-    "msvc9compiler is deprecated and slated to be removed "
-    "in the future. Please discontinue use or file an issue "
-    "with pypa/distutils describing your use case.",
-    DeprecationWarning,
-)
-
-RegOpenKeyEx = winreg.OpenKeyEx
-RegEnumKey = winreg.EnumKey
-RegEnumValue = winreg.EnumValue
-RegError = winreg.error
-
-HKEYS = (
-    winreg.HKEY_USERS,
-    winreg.HKEY_CURRENT_USER,
-    winreg.HKEY_LOCAL_MACHINE,
-    winreg.HKEY_CLASSES_ROOT,
-)
-
-NATIVE_WIN64 = sys.platform == 'win32' and sys.maxsize > 2**32
-if NATIVE_WIN64:
-    # Visual C++ is a 32-bit application, so we need to look in
-    # the corresponding registry branch, if we're running a
-    # 64-bit Python on Win64
-    VS_BASE = r"Software\Wow6432Node\Microsoft\VisualStudio\%0.1f"
-    WINSDK_BASE = r"Software\Wow6432Node\Microsoft\Microsoft SDKs\Windows"
-    NET_BASE = r"Software\Wow6432Node\Microsoft\.NETFramework"
-else:
-    VS_BASE = r"Software\Microsoft\VisualStudio\%0.1f"
-    WINSDK_BASE = r"Software\Microsoft\Microsoft SDKs\Windows"
-    NET_BASE = r"Software\Microsoft\.NETFramework"
-
-# A map keyed by get_platform() return values to values accepted by
-# 'vcvarsall.bat'.  Note a cross-compile may combine these (eg, 'x86_amd64' is
-# the param to cross-compile on x86 targeting amd64.)
-PLAT_TO_VCVARS = {
-    'win32': 'x86',
-    'win-amd64': 'amd64',
-}
-
-
-class Reg:
-    """Helper class to read values from the registry"""
-
-    def get_value(cls, path, key):
-        for base in HKEYS:
-            d = cls.read_values(base, path)
-            if d and key in d:
-                return d[key]
-        raise KeyError(key)
-
-    get_value = classmethod(get_value)
-
-    def read_keys(cls, base, key):
-        """Return list of registry keys."""
-        try:
-            handle = RegOpenKeyEx(base, key)
-        except RegError:
-            return None
-        L = []
-        i = 0
-        while True:
-            try:
-                k = RegEnumKey(handle, i)
-            except RegError:
-                break
-            L.append(k)
-            i += 1
-        return L
-
-    read_keys = classmethod(read_keys)
-
-    def read_values(cls, base, key):
-        """Return dict of registry keys and values.
-
-        All names are converted to lowercase.
-        """
-        try:
-            handle = RegOpenKeyEx(base, key)
-        except RegError:
-            return None
-        d = {}
-        i = 0
-        while True:
-            try:
-                name, value, type = RegEnumValue(handle, i)
-            except RegError:
-                break
-            name = name.lower()
-            d[cls.convert_mbcs(name)] = cls.convert_mbcs(value)
-            i += 1
-        return d
-
-    read_values = classmethod(read_values)
-
-    def convert_mbcs(s):
-        dec = getattr(s, "decode", None)
-        if dec is not None:
-            try:
-                s = dec("mbcs")
-            except UnicodeError:
-                pass
-        return s
-
-    convert_mbcs = staticmethod(convert_mbcs)
-
-
-class MacroExpander:
-    def __init__(self, version):
-        self.macros = {}
-        self.vsbase = VS_BASE % version
-        self.load_macros(version)
-
-    def set_macro(self, macro, path, key):
-        self.macros["$(%s)" % macro] = Reg.get_value(path, key)
-
-    def load_macros(self, version):
-        self.set_macro("VCInstallDir", self.vsbase + r"\Setup\VC", "productdir")
-        self.set_macro("VSInstallDir", self.vsbase + r"\Setup\VS", "productdir")
-        self.set_macro("FrameworkDir", NET_BASE, "installroot")
-        try:
-            if version >= 8.0:
-                self.set_macro("FrameworkSDKDir", NET_BASE, "sdkinstallrootv2.0")
-            else:
-                raise KeyError("sdkinstallrootv2.0")
-        except KeyError:
-            raise DistutilsPlatformError(
-                """Python was built with Visual Studio 2008;
-extensions must be built with a compiler than can generate compatible binaries.
-Visual Studio 2008 was not found on this system. If you have Cygwin installed,
-you can try compiling with MingW32, by passing "-c mingw32" to setup.py."""
-            )
-
-        if version >= 9.0:
-            self.set_macro("FrameworkVersion", self.vsbase, "clr version")
-            self.set_macro("WindowsSdkDir", WINSDK_BASE, "currentinstallfolder")
-        else:
-            p = r"Software\Microsoft\NET Framework Setup\Product"
-            for base in HKEYS:
-                try:
-                    h = RegOpenKeyEx(base, p)
-                except RegError:
-                    continue
-                key = RegEnumKey(h, 0)
-                d = Reg.get_value(base, r"{}\{}".format(p, key))
-                self.macros["$(FrameworkVersion)"] = d["version"]
-
-    def sub(self, s):
-        for k, v in self.macros.items():
-            s = s.replace(k, v)
-        return s
-
-
-def get_build_version():
-    """Return the version of MSVC that was used to build Python.
-
-    For Python 2.3 and up, the version number is included in
-    sys.version.  For earlier versions, assume the compiler is MSVC 6.
-    """
-    prefix = "MSC v."
-    i = sys.version.find(prefix)
-    if i == -1:
-        return 6
-    i = i + len(prefix)
-    s, rest = sys.version[i:].split(" ", 1)
-    majorVersion = int(s[:-2]) - 6
-    if majorVersion >= 13:
-        # v13 was skipped and should be v14
-        majorVersion += 1
-    minorVersion = int(s[2:3]) / 10.0
-    # I don't think paths are affected by minor version in version 6
-    if majorVersion == 6:
-        minorVersion = 0
-    if majorVersion >= 6:
-        return majorVersion + minorVersion
-    # else we don't know what version of the compiler this is
-    return None
-
-
-def normalize_and_reduce_paths(paths):
-    """Return a list of normalized paths with duplicates removed.
-
-    The current order of paths is maintained.
-    """
-    # Paths are normalized so things like:  /a and /a/ aren't both preserved.
-    reduced_paths = []
-    for p in paths:
-        np = os.path.normpath(p)
-        # XXX(nnorwitz): O(n**2), if reduced_paths gets long perhaps use a set.
-        if np not in reduced_paths:
-            reduced_paths.append(np)
-    return reduced_paths
-
-
-def removeDuplicates(variable):
-    """Remove duplicate values of an environment variable."""
-    oldList = variable.split(os.pathsep)
-    newList = []
-    for i in oldList:
-        if i not in newList:
-            newList.append(i)
-    newVariable = os.pathsep.join(newList)
-    return newVariable
-
-
-def find_vcvarsall(version):
-    """Find the vcvarsall.bat file
-
-    At first it tries to find the productdir of VS 2008 in the registry. If
-    that fails it falls back to the VS90COMNTOOLS env var.
-    """
-    vsbase = VS_BASE % version
-    try:
-        productdir = Reg.get_value(r"%s\Setup\VC" % vsbase, "productdir")
-    except KeyError:
-        log.debug("Unable to find productdir in registry")
-        productdir = None
-
-    if not productdir or not os.path.isdir(productdir):
-        toolskey = "VS%0.f0COMNTOOLS" % version
-        toolsdir = os.environ.get(toolskey, None)
-
-        if toolsdir and os.path.isdir(toolsdir):
-            productdir = os.path.join(toolsdir, os.pardir, os.pardir, "VC")
-            productdir = os.path.abspath(productdir)
-            if not os.path.isdir(productdir):
-                log.debug("%s is not a valid directory" % productdir)
-                return None
-        else:
-            log.debug("Env var %s is not set or invalid" % toolskey)
-    if not productdir:
-        log.debug("No productdir found")
-        return None
-    vcvarsall = os.path.join(productdir, "vcvarsall.bat")
-    if os.path.isfile(vcvarsall):
-        return vcvarsall
-    log.debug("Unable to find vcvarsall.bat")
-    return None
-
-
-def query_vcvarsall(version, arch="x86"):
-    """Launch vcvarsall.bat and read the settings from its environment"""
-    vcvarsall = find_vcvarsall(version)
-    interesting = {"include", "lib", "libpath", "path"}
-    result = {}
-
-    if vcvarsall is None:
-        raise DistutilsPlatformError("Unable to find vcvarsall.bat")
-    log.debug("Calling 'vcvarsall.bat %s' (version=%s)", arch, version)
-    popen = subprocess.Popen(
-        '"{}" {} & set'.format(vcvarsall, arch),
-        stdout=subprocess.PIPE,
-        stderr=subprocess.PIPE,
-    )
-    try:
-        stdout, stderr = popen.communicate()
-        if popen.wait() != 0:
-            raise DistutilsPlatformError(stderr.decode("mbcs"))
-
-        stdout = stdout.decode("mbcs")
-        for line in stdout.split("\n"):
-            line = Reg.convert_mbcs(line)
-            if '=' not in line:
-                continue
-            line = line.strip()
-            key, value = line.split('=', 1)
-            key = key.lower()
-            if key in interesting:
-                if value.endswith(os.pathsep):
-                    value = value[:-1]
-                result[key] = removeDuplicates(value)
-
-    finally:
-        popen.stdout.close()
-        popen.stderr.close()
-
-    if len(result) != len(interesting):
-        raise ValueError(str(list(result.keys())))
-
-    return result
-
-
-# More globals
-VERSION = get_build_version()
-# MACROS = MacroExpander(VERSION)
-
-
-class MSVCCompiler(CCompiler):
-    """Concrete class that implements an interface to Microsoft Visual C++,
-    as defined by the CCompiler abstract class."""
-
-    compiler_type = 'msvc'
-
-    # Just set this so CCompiler's constructor doesn't barf.  We currently
-    # don't use the 'set_executables()' bureaucracy provided by CCompiler,
-    # as it really isn't necessary for this sort of single-compiler class.
-    # Would be nice to have a consistent interface with UnixCCompiler,
-    # though, so it's worth thinking about.
-    executables = {}
-
-    # Private class data (need to distinguish C from C++ source for compiler)
-    _c_extensions = ['.c']
-    _cpp_extensions = ['.cc', '.cpp', '.cxx']
-    _rc_extensions = ['.rc']
-    _mc_extensions = ['.mc']
-
-    # Needed for the filename generation methods provided by the
-    # base class, CCompiler.
-    src_extensions = _c_extensions + _cpp_extensions + _rc_extensions + _mc_extensions
-    res_extension = '.res'
-    obj_extension = '.obj'
-    static_lib_extension = '.lib'
-    shared_lib_extension = '.dll'
-    static_lib_format = shared_lib_format = '%s%s'
-    exe_extension = '.exe'
-
-    def __init__(self, verbose=0, dry_run=0, force=0):
-        super().__init__(verbose, dry_run, force)
-        self.__version = VERSION
-        self.__root = r"Software\Microsoft\VisualStudio"
-        # self.__macros = MACROS
-        self.__paths = []
-        # target platform (.plat_name is consistent with 'bdist')
-        self.plat_name = None
-        self.__arch = None  # deprecated name
-        self.initialized = False
-
-    def initialize(self, plat_name=None):  # noqa: C901
-        # multi-init means we would need to check platform same each time...
-        assert not self.initialized, "don't init multiple times"
-        if self.__version < 8.0:
-            raise DistutilsPlatformError(
-                "VC %0.1f is not supported by this module" % self.__version
-            )
-        if plat_name is None:
-            plat_name = get_platform()
-        # sanity check for platforms to prevent obscure errors later.
-        ok_plats = 'win32', 'win-amd64'
-        if plat_name not in ok_plats:
-            raise DistutilsPlatformError(
-                "--plat-name must be one of {}".format(ok_plats)
-            )
-
-        if (
-            "DISTUTILS_USE_SDK" in os.environ
-            and "MSSdk" in os.environ
-            and self.find_exe("cl.exe")
-        ):
-            # Assume that the SDK set up everything alright; don't try to be
-            # smarter
-            self.cc = "cl.exe"
-            self.linker = "link.exe"
-            self.lib = "lib.exe"
-            self.rc = "rc.exe"
-            self.mc = "mc.exe"
-        else:
-            # On x86, 'vcvars32.bat amd64' creates an env that doesn't work;
-            # to cross compile, you use 'x86_amd64'.
-            # On AMD64, 'vcvars32.bat amd64' is a native build env; to cross
-            # compile use 'x86' (ie, it runs the x86 compiler directly)
-            if plat_name == get_platform() or plat_name == 'win32':
-                # native build or cross-compile to win32
-                plat_spec = PLAT_TO_VCVARS[plat_name]
-            else:
-                # cross compile from win32 -> some 64bit
-                plat_spec = (
-                    PLAT_TO_VCVARS[get_platform()] + '_' + PLAT_TO_VCVARS[plat_name]
-                )
-
-            vc_env = query_vcvarsall(VERSION, plat_spec)
-
-            self.__paths = vc_env['path'].split(os.pathsep)
-            os.environ['lib'] = vc_env['lib']
-            os.environ['include'] = vc_env['include']
-
-            if len(self.__paths) == 0:
-                raise DistutilsPlatformError(
-                    "Python was built with %s, "
-                    "and extensions need to be built with the same "
-                    "version of the compiler, but it isn't installed." % self.__product
-                )
-
-            self.cc = self.find_exe("cl.exe")
-            self.linker = self.find_exe("link.exe")
-            self.lib = self.find_exe("lib.exe")
-            self.rc = self.find_exe("rc.exe")  # resource compiler
-            self.mc = self.find_exe("mc.exe")  # message compiler
-            # self.set_path_env_var('lib')
-            # self.set_path_env_var('include')
-
-        # extend the MSVC path with the current path
-        try:
-            for p in os.environ['path'].split(';'):
-                self.__paths.append(p)
-        except KeyError:
-            pass
-        self.__paths = normalize_and_reduce_paths(self.__paths)
-        os.environ['path'] = ";".join(self.__paths)
-
-        self.preprocess_options = None
-        if self.__arch == "x86":
-            self.compile_options = ['/nologo', '/O2', '/MD', '/W3', '/DNDEBUG']
-            self.compile_options_debug = [
-                '/nologo',
-                '/Od',
-                '/MDd',
-                '/W3',
-                '/Z7',
-                '/D_DEBUG',
-            ]
-        else:
-            # Win64
-            self.compile_options = ['/nologo', '/O2', '/MD', '/W3', '/GS-', '/DNDEBUG']
-            self.compile_options_debug = [
-                '/nologo',
-                '/Od',
-                '/MDd',
-                '/W3',
-                '/GS-',
-                '/Z7',
-                '/D_DEBUG',
-            ]
-
-        self.ldflags_shared = ['/DLL', '/nologo', '/INCREMENTAL:NO']
-        if self.__version >= 7:
-            self.ldflags_shared_debug = ['/DLL', '/nologo', '/INCREMENTAL:no', '/DEBUG']
-        self.ldflags_static = ['/nologo']
-
-        self.initialized = True
-
-    # -- Worker methods ------------------------------------------------
-
-    def object_filenames(self, source_filenames, strip_dir=0, output_dir=''):
-        # Copied from ccompiler.py, extended to return .res as 'object'-file
-        # for .rc input file
-        if output_dir is None:
-            output_dir = ''
-        obj_names = []
-        for src_name in source_filenames:
-            (base, ext) = os.path.splitext(src_name)
-            base = os.path.splitdrive(base)[1]  # Chop off the drive
-            base = base[os.path.isabs(base) :]  # If abs, chop off leading /
-            if ext not in self.src_extensions:
-                # Better to raise an exception instead of silently continuing
-                # and later complain about sources and targets having
-                # different lengths
-                raise CompileError("Don't know how to compile %s" % src_name)
-            if strip_dir:
-                base = os.path.basename(base)
-            if ext in self._rc_extensions:
-                obj_names.append(os.path.join(output_dir, base + self.res_extension))
-            elif ext in self._mc_extensions:
-                obj_names.append(os.path.join(output_dir, base + self.res_extension))
-            else:
-                obj_names.append(os.path.join(output_dir, base + self.obj_extension))
-        return obj_names
-
-    def compile(  # noqa: C901
-        self,
-        sources,
-        output_dir=None,
-        macros=None,
-        include_dirs=None,
-        debug=0,
-        extra_preargs=None,
-        extra_postargs=None,
-        depends=None,
-    ):
-
-        if not self.initialized:
-            self.initialize()
-        compile_info = self._setup_compile(
-            output_dir, macros, include_dirs, sources, depends, extra_postargs
-        )
-        macros, objects, extra_postargs, pp_opts, build = compile_info
-
-        compile_opts = extra_preargs or []
-        compile_opts.append('/c')
-        if debug:
-            compile_opts.extend(self.compile_options_debug)
-        else:
-            compile_opts.extend(self.compile_options)
-
-        for obj in objects:
-            try:
-                src, ext = build[obj]
-            except KeyError:
-                continue
-            if debug:
-                # pass the full pathname to MSVC in debug mode,
-                # this allows the debugger to find the source file
-                # without asking the user to browse for it
-                src = os.path.abspath(src)
-
-            if ext in self._c_extensions:
-                input_opt = "/Tc" + src
-            elif ext in self._cpp_extensions:
-                input_opt = "/Tp" + src
-            elif ext in self._rc_extensions:
-                # compile .RC to .RES file
-                input_opt = src
-                output_opt = "/fo" + obj
-                try:
-                    self.spawn([self.rc] + pp_opts + [output_opt] + [input_opt])
-                except DistutilsExecError as msg:
-                    raise CompileError(msg)
-                continue
-            elif ext in self._mc_extensions:
-                # Compile .MC to .RC file to .RES file.
-                #   * '-h dir' specifies the directory for the
-                #     generated include file
-                #   * '-r dir' specifies the target directory of the
-                #     generated RC file and the binary message resource
-                #     it includes
-                #
-                # For now (since there are no options to change this),
-                # we use the source-directory for the include file and
-                # the build directory for the RC file and message
-                # resources. This works at least for win32all.
-                h_dir = os.path.dirname(src)
-                rc_dir = os.path.dirname(obj)
-                try:
-                    # first compile .MC to .RC and .H file
-                    self.spawn([self.mc] + ['-h', h_dir, '-r', rc_dir] + [src])
-                    base, _ = os.path.splitext(os.path.basename(src))
-                    rc_file = os.path.join(rc_dir, base + '.rc')
-                    # then compile .RC to .RES file
-                    self.spawn([self.rc] + ["/fo" + obj] + [rc_file])
-
-                except DistutilsExecError as msg:
-                    raise CompileError(msg)
-                continue
-            else:
-                # how to handle this file?
-                raise CompileError(
-                    "Don't know how to compile {} to {}".format(src, obj)
-                )
-
-            output_opt = "/Fo" + obj
-            try:
-                self.spawn(
-                    [self.cc]
-                    + compile_opts
-                    + pp_opts
-                    + [input_opt, output_opt]
-                    + extra_postargs
-                )
-            except DistutilsExecError as msg:
-                raise CompileError(msg)
-
-        return objects
-
-    def create_static_lib(
-        self, objects, output_libname, output_dir=None, debug=0, target_lang=None
-    ):
-
-        if not self.initialized:
-            self.initialize()
-        (objects, output_dir) = self._fix_object_args(objects, output_dir)
-        output_filename = self.library_filename(output_libname, output_dir=output_dir)
-
-        if self._need_link(objects, output_filename):
-            lib_args = objects + ['/OUT:' + output_filename]
-            if debug:
-                pass  # XXX what goes here?
-            try:
-                self.spawn([self.lib] + lib_args)
-            except DistutilsExecError as msg:
-                raise LibError(msg)
-        else:
-            log.debug("skipping %s (up-to-date)", output_filename)
-
-    def link(  # noqa: C901
-        self,
-        target_desc,
-        objects,
-        output_filename,
-        output_dir=None,
-        libraries=None,
-        library_dirs=None,
-        runtime_library_dirs=None,
-        export_symbols=None,
-        debug=0,
-        extra_preargs=None,
-        extra_postargs=None,
-        build_temp=None,
-        target_lang=None,
-    ):
-
-        if not self.initialized:
-            self.initialize()
-        (objects, output_dir) = self._fix_object_args(objects, output_dir)
-        fixed_args = self._fix_lib_args(libraries, library_dirs, runtime_library_dirs)
-        (libraries, library_dirs, runtime_library_dirs) = fixed_args
-
-        if runtime_library_dirs:
-            self.warn(
-                "I don't know what to do with 'runtime_library_dirs': "
-                + str(runtime_library_dirs)
-            )
-
-        lib_opts = gen_lib_options(self, library_dirs, runtime_library_dirs, libraries)
-        if output_dir is not None:
-            output_filename = os.path.join(output_dir, output_filename)
-
-        if self._need_link(objects, output_filename):
-            if target_desc == CCompiler.EXECUTABLE:
-                if debug:
-                    ldflags = self.ldflags_shared_debug[1:]
-                else:
-                    ldflags = self.ldflags_shared[1:]
-            else:
-                if debug:
-                    ldflags = self.ldflags_shared_debug
-                else:
-                    ldflags = self.ldflags_shared
-
-            export_opts = []
-            for sym in export_symbols or []:
-                export_opts.append("/EXPORT:" + sym)
-
-            ld_args = (
-                ldflags + lib_opts + export_opts + objects + ['/OUT:' + output_filename]
-            )
-
-            # The MSVC linker generates .lib and .exp files, which cannot be
-            # suppressed by any linker switches. The .lib files may even be
-            # needed! Make sure they are generated in the temporary build
-            # directory. Since they have different names for debug and release
-            # builds, they can go into the same directory.
-            build_temp = os.path.dirname(objects[0])
-            if export_symbols is not None:
-                (dll_name, dll_ext) = os.path.splitext(
-                    os.path.basename(output_filename)
-                )
-                implib_file = os.path.join(build_temp, self.library_filename(dll_name))
-                ld_args.append('/IMPLIB:' + implib_file)
-
-            self.manifest_setup_ldargs(output_filename, build_temp, ld_args)
-
-            if extra_preargs:
-                ld_args[:0] = extra_preargs
-            if extra_postargs:
-                ld_args.extend(extra_postargs)
-
-            self.mkpath(os.path.dirname(output_filename))
-            try:
-                self.spawn([self.linker] + ld_args)
-            except DistutilsExecError as msg:
-                raise LinkError(msg)
-
-            # embed the manifest
-            # XXX - this is somewhat fragile - if mt.exe fails, distutils
-            # will still consider the DLL up-to-date, but it will not have a
-            # manifest.  Maybe we should link to a temp file?  OTOH, that
-            # implies a build environment error that shouldn't go undetected.
-            mfinfo = self.manifest_get_embed_info(target_desc, ld_args)
-            if mfinfo is not None:
-                mffilename, mfid = mfinfo
-                out_arg = '-outputresource:{};{}'.format(output_filename, mfid)
-                try:
-                    self.spawn(['mt.exe', '-nologo', '-manifest', mffilename, out_arg])
-                except DistutilsExecError as msg:
-                    raise LinkError(msg)
-        else:
-            log.debug("skipping %s (up-to-date)", output_filename)
-
-    def manifest_setup_ldargs(self, output_filename, build_temp, ld_args):
-        # If we need a manifest at all, an embedded manifest is recommended.
-        # See MSDN article titled
-        # "How to: Embed a Manifest Inside a C/C++ Application"
-        # (currently at http://msdn2.microsoft.com/en-us/library/ms235591(VS.80).aspx)
-        # Ask the linker to generate the manifest in the temp dir, so
-        # we can check it, and possibly embed it, later.
-        temp_manifest = os.path.join(
-            build_temp, os.path.basename(output_filename) + ".manifest"
-        )
-        ld_args.append('/MANIFESTFILE:' + temp_manifest)
-
-    def manifest_get_embed_info(self, target_desc, ld_args):
-        # If a manifest should be embedded, return a tuple of
-        # (manifest_filename, resource_id).  Returns None if no manifest
-        # should be embedded.  See http://bugs.python.org/issue7833 for why
-        # we want to avoid any manifest for extension modules if we can)
-        for arg in ld_args:
-            if arg.startswith("/MANIFESTFILE:"):
-                temp_manifest = arg.split(":", 1)[1]
-                break
-        else:
-            # no /MANIFESTFILE so nothing to do.
-            return None
-        if target_desc == CCompiler.EXECUTABLE:
-            # by default, executables always get the manifest with the
-            # CRT referenced.
-            mfid = 1
-        else:
-            # Extension modules try and avoid any manifest if possible.
-            mfid = 2
-            temp_manifest = self._remove_visual_c_ref(temp_manifest)
-        if temp_manifest is None:
-            return None
-        return temp_manifest, mfid
-
-    def _remove_visual_c_ref(self, manifest_file):
-        try:
-            # Remove references to the Visual C runtime, so they will
-            # fall through to the Visual C dependency of Python.exe.
-            # This way, when installed for a restricted user (e.g.
-            # runtimes are not in WinSxS folder, but in Python's own
-            # folder), the runtimes do not need to be in every folder
-            # with .pyd's.
-            # Returns either the filename of the modified manifest or
-            # None if no manifest should be embedded.
-            manifest_f = open(manifest_file)
-            try:
-                manifest_buf = manifest_f.read()
-            finally:
-                manifest_f.close()
-            pattern = re.compile(
-                r"""<assemblyIdentity.*?name=("|')Microsoft\."""
-                r"""VC\d{2}\.CRT("|').*?(/>|</assemblyIdentity>)""",
-                re.DOTALL,
-            )
-            manifest_buf = re.sub(pattern, "", manifest_buf)
-            pattern = r"<dependentAssembly>\s*</dependentAssembly>"
-            manifest_buf = re.sub(pattern, "", manifest_buf)
-            # Now see if any other assemblies are referenced - if not, we
-            # don't want a manifest embedded.
-            pattern = re.compile(
-                r"""<assemblyIdentity.*?name=(?:"|')(.+?)(?:"|')"""
-                r""".*?(?:/>|</assemblyIdentity>)""",
-                re.DOTALL,
-            )
-            if re.search(pattern, manifest_buf) is None:
-                return None
-
-            manifest_f = open(manifest_file, 'w')
-            try:
-                manifest_f.write(manifest_buf)
-                return manifest_file
-            finally:
-                manifest_f.close()
-        except OSError:
-            pass
-
-    # -- Miscellaneous methods -----------------------------------------
-    # These are all used by the 'gen_lib_options() function, in
-    # ccompiler.py.
-
-    def library_dir_option(self, dir):
-        return "/LIBPATH:" + dir
-
-    def runtime_library_dir_option(self, dir):
-        raise DistutilsPlatformError(
-            "don't know how to set runtime library search path for MSVC++"
-        )
-
-    def library_option(self, lib):
-        return self.library_filename(lib)
-
-    def find_library_file(self, dirs, lib, debug=0):
-        # Prefer a debugging library if found (and requested), but deal
-        # with it if we don't have one.
-        if debug:
-            try_names = [lib + "_d", lib]
-        else:
-            try_names = [lib]
-        for dir in dirs:
-            for name in try_names:
-                libfile = os.path.join(dir, self.library_filename(name))
-                if os.path.exists(libfile):
-                    return libfile
-        else:
-            # Oops, didn't find it in *any* of 'dirs'
-            return None
-
-    # Helper methods for using the MSVC registry settings
-
-    def find_exe(self, exe):
-        """Return path to an MSVC executable program.
-
-        Tries to find the program in several places: first, one of the
-        MSVC program search paths from the registry; next, the directories
-        in the PATH environment variable.  If any of those work, return an
-        absolute path that is known to exist.  If none of them work, just
-        return the original program name, 'exe'.
-        """
-        for p in self.__paths:
-            fn = os.path.join(os.path.abspath(p), exe)
-            if os.path.isfile(fn):
-                return fn
-
-        # didn't find it; try existing path
-        for p in os.environ['Path'].split(';'):
-            fn = os.path.join(os.path.abspath(p), exe)
-            if os.path.isfile(fn):
-                return fn
-
-        return exe
diff --git a/spaces/RealTimeLiveAIForHealth/ASR-High-Accuracy-Test/README.md b/spaces/RealTimeLiveAIForHealth/ASR-High-Accuracy-Test/README.md
deleted file mode 100644
index 67e8dbdb8e7a01c779b6b9dbe406926b1265814f..0000000000000000000000000000000000000000
--- a/spaces/RealTimeLiveAIForHealth/ASR-High-Accuracy-Test/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ASR High Accuracy Test
-emoji: 🐢
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.18.0
-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/r2d2/datasets/pair_dataset.py b/spaces/Realcat/image-matching-webui/third_party/r2d2/datasets/pair_dataset.py
deleted file mode 100644
index ba178c18a0a6fbb1decfe4a797dbcab0636dbeaf..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/r2d2/datasets/pair_dataset.py
+++ /dev/null
@@ -1,290 +0,0 @@
-# Copyright 2019-present NAVER Corp.
-# CC BY-NC-SA 3.0
-# Available only for non-commercial use
-
-import os, pdb
-import numpy as np
-from PIL import Image
-
-from .dataset import Dataset, CatDataset
-from tools.transforms import instanciate_transformation
-from tools.transforms_tools import persp_apply
-
-
-class PairDataset(Dataset):
-    """A dataset that serves image pairs with ground-truth pixel correspondences."""
-
-    def __init__(self):
-        Dataset.__init__(self)
-        self.npairs = 0
-
-    def get_filename(self, img_idx, root=None):
-        if is_pair(
-            img_idx
-        ):  # if img_idx is a pair of indices, we return a pair of filenames
-            return tuple(Dataset.get_filename(self, i, root) for i in img_idx)
-        return Dataset.get_filename(self, img_idx, root)
-
-    def get_image(self, img_idx):
-        if is_pair(
-            img_idx
-        ):  # if img_idx is a pair of indices, we return a pair of images
-            return tuple(Dataset.get_image(self, i) for i in img_idx)
-        return Dataset.get_image(self, img_idx)
-
-    def get_corres_filename(self, pair_idx):
-        raise NotImplementedError()
-
-    def get_homography_filename(self, pair_idx):
-        raise NotImplementedError()
-
-    def get_flow_filename(self, pair_idx):
-        raise NotImplementedError()
-
-    def get_mask_filename(self, pair_idx):
-        raise NotImplementedError()
-
-    def get_pair(self, idx, output=()):
-        """returns (img1, img2, `metadata`)
-
-        `metadata` is a dict() that can contain:
-            flow: optical flow
-            aflow: absolute flow
-            corres: list of 2d-2d correspondences
-            mask: boolean image of flow validity (in the first image)
-            ...
-        """
-        raise NotImplementedError()
-
-    def get_paired_images(self):
-        fns = set()
-        for i in range(self.npairs):
-            a, b = self.image_pairs[i]
-            fns.add(self.get_filename(a))
-            fns.add(self.get_filename(b))
-        return fns
-
-    def __len__(self):
-        return self.npairs  # size should correspond to the number of pairs, not images
-
-    def __repr__(self):
-        res = "Dataset: %s\n" % self.__class__.__name__
-        res += "  %d images," % self.nimg
-        res += " %d image pairs" % self.npairs
-        res += "\n  root: %s...\n" % self.root
-        return res
-
-    @staticmethod
-    def _flow2png(flow, path):
-        flow = np.clip(np.around(16 * flow), -(2**15), 2**15 - 1)
-        bytes = np.int16(flow).view(np.uint8)
-        Image.fromarray(bytes).save(path)
-        return flow / 16
-
-    @staticmethod
-    def _png2flow(path):
-        try:
-            flow = np.asarray(Image.open(path)).view(np.int16)
-            return np.float32(flow) / 16
-        except:
-            raise IOError("Error loading flow for %s" % path)
-
-
-class StillPairDataset(PairDataset):
-    """A dataset of 'still' image pairs.
-    By overloading a normal image dataset, it appends the get_pair(i) function
-    that serves trivial image pairs (img1, img2) where img1 == img2 == get_image(i).
-    """
-
-    def get_pair(self, pair_idx, output=()):
-        if isinstance(output, str):
-            output = output.split()
-        img1, img2 = map(self.get_image, self.image_pairs[pair_idx])
-
-        W, H = img1.size
-        sx = img2.size[0] / float(W)
-        sy = img2.size[1] / float(H)
-
-        meta = {}
-        if "aflow" in output or "flow" in output:
-            mgrid = np.mgrid[0:H, 0:W][::-1].transpose(1, 2, 0).astype(np.float32)
-            meta["aflow"] = mgrid * (sx, sy)
-            meta["flow"] = meta["aflow"] - mgrid
-
-        if "mask" in output:
-            meta["mask"] = np.ones((H, W), np.uint8)
-
-        if "homography" in output:
-            meta["homography"] = np.diag(np.float32([sx, sy, 1]))
-
-        return img1, img2, meta
-
-
-class SyntheticPairDataset(PairDataset):
-    """A synthetic generator of image pairs.
-    Given a normal image dataset, it constructs pairs using random homographies & noise.
-    """
-
-    def __init__(self, dataset, scale="", distort=""):
-        self.attach_dataset(dataset)
-        self.distort = instanciate_transformation(distort)
-        self.scale = instanciate_transformation(scale)
-
-    def attach_dataset(self, dataset):
-        assert isinstance(dataset, Dataset) and not isinstance(dataset, PairDataset)
-        self.dataset = dataset
-        self.npairs = dataset.nimg
-        self.get_image = dataset.get_image
-        self.get_key = dataset.get_key
-        self.get_filename = dataset.get_filename
-        self.root = None
-
-    def make_pair(self, img):
-        return img, img
-
-    def get_pair(self, i, output=("aflow")):
-        """Procedure:
-        This function applies a series of random transformations to one original image
-        to form a synthetic image pairs with perfect ground-truth.
-        """
-        if isinstance(output, str):
-            output = output.split()
-
-        original_img = self.dataset.get_image(i)
-
-        scaled_image = self.scale(original_img)
-        scaled_image, scaled_image2 = self.make_pair(scaled_image)
-        scaled_and_distorted_image = self.distort(
-            dict(img=scaled_image2, persp=(1, 0, 0, 0, 1, 0, 0, 0))
-        )
-        W, H = scaled_image.size
-        trf = scaled_and_distorted_image["persp"]
-
-        meta = dict()
-        if "aflow" in output or "flow" in output:
-            # compute optical flow
-            xy = np.mgrid[0:H, 0:W][::-1].reshape(2, H * W).T
-            aflow = np.float32(persp_apply(trf, xy).reshape(H, W, 2))
-            meta["flow"] = aflow - xy.reshape(H, W, 2)
-            meta["aflow"] = aflow
-
-        if "homography" in output:
-            meta["homography"] = np.float32(trf + (1,)).reshape(3, 3)
-
-        return scaled_image, scaled_and_distorted_image["img"], meta
-
-    def __repr__(self):
-        res = "Dataset: %s\n" % self.__class__.__name__
-        res += "  %d images and pairs" % self.npairs
-        res += "\n  root: %s..." % self.dataset.root
-        res += "\n  Scale: %s" % (repr(self.scale).replace("\n", ""))
-        res += "\n  Distort: %s" % (repr(self.distort).replace("\n", ""))
-        return res + "\n"
-
-
-class TransformedPairs(PairDataset):
-    """Automatic data augmentation for pre-existing image pairs.
-    Given an image pair dataset, it generates synthetically jittered pairs
-    using random transformations (e.g. homographies & noise).
-    """
-
-    def __init__(self, dataset, trf=""):
-        self.attach_dataset(dataset)
-        self.trf = instanciate_transformation(trf)
-
-    def attach_dataset(self, dataset):
-        assert isinstance(dataset, PairDataset)
-        self.dataset = dataset
-        self.nimg = dataset.nimg
-        self.npairs = dataset.npairs
-        self.get_image = dataset.get_image
-        self.get_key = dataset.get_key
-        self.get_filename = dataset.get_filename
-        self.root = None
-
-    def get_pair(self, i, output=""):
-        """Procedure:
-        This function applies a series of random transformations to one original image
-        to form a synthetic image pairs with perfect ground-truth.
-        """
-        img_a, img_b_, metadata = self.dataset.get_pair(i, output)
-
-        img_b = self.trf({"img": img_b_, "persp": (1, 0, 0, 0, 1, 0, 0, 0)})
-        trf = img_b["persp"]
-
-        if "aflow" in metadata or "flow" in metadata:
-            aflow = metadata["aflow"]
-            aflow[:] = persp_apply(trf, aflow.reshape(-1, 2)).reshape(aflow.shape)
-            W, H = img_a.size
-            flow = metadata["flow"]
-            mgrid = np.mgrid[0:H, 0:W][::-1].transpose(1, 2, 0).astype(np.float32)
-            flow[:] = aflow - mgrid
-
-        if "corres" in metadata:
-            corres = metadata["corres"]
-            corres[:, 1] = persp_apply(trf, corres[:, 1])
-
-        if "homography" in metadata:
-            # p_b = homography * p_a
-            trf_ = np.float32(trf + (1,)).reshape(3, 3)
-            metadata["homography"] = np.float32(trf_ @ metadata["homography"])
-
-        return img_a, img_b["img"], metadata
-
-    def __repr__(self):
-        res = "Transformed Pairs from %s\n" % type(self.dataset).__name__
-        res += "  %d images and pairs" % self.npairs
-        res += "\n  root: %s..." % self.dataset.root
-        res += "\n  transform: %s" % (repr(self.trf).replace("\n", ""))
-        return res + "\n"
-
-
-class CatPairDataset(CatDataset):
-    """Concatenation of several pair datasets."""
-
-    def __init__(self, *datasets):
-        CatDataset.__init__(self, *datasets)
-        pair_offsets = [0]
-        for db in datasets:
-            pair_offsets.append(db.npairs)
-        self.pair_offsets = np.cumsum(pair_offsets)
-        self.npairs = self.pair_offsets[-1]
-
-    def __len__(self):
-        return self.npairs
-
-    def __repr__(self):
-        fmt_str = "CatPairDataset("
-        for db in self.datasets:
-            fmt_str += str(db).replace("\n", " ") + ", "
-        return fmt_str[:-2] + ")"
-
-    def pair_which(self, i):
-        pos = np.searchsorted(self.pair_offsets, i, side="right") - 1
-        assert pos < self.npairs, "Bad pair index %d >= %d" % (i, self.npairs)
-        return pos, i - self.pair_offsets[pos]
-
-    def pair_call(self, func, i, *args, **kwargs):
-        b, j = self.pair_which(i)
-        return getattr(self.datasets[b], func)(j, *args, **kwargs)
-
-    def get_pair(self, i, output=()):
-        b, i = self.pair_which(i)
-        return self.datasets[b].get_pair(i, output)
-
-    def get_flow_filename(self, pair_idx, *args, **kwargs):
-        return self.pair_call("get_flow_filename", pair_idx, *args, **kwargs)
-
-    def get_mask_filename(self, pair_idx, *args, **kwargs):
-        return self.pair_call("get_mask_filename", pair_idx, *args, **kwargs)
-
-    def get_corres_filename(self, pair_idx, *args, **kwargs):
-        return self.pair_call("get_corres_filename", pair_idx, *args, **kwargs)
-
-
-def is_pair(x):
-    if isinstance(x, (tuple, list)) and len(x) == 2:
-        return True
-    if isinstance(x, np.ndarray) and x.ndim == 1 and x.shape[0] == 2:
-        return True
-    return False
diff --git a/spaces/Ricecake123/RVC-demo/docs/faiss_tips_ja.md b/spaces/Ricecake123/RVC-demo/docs/faiss_tips_ja.md
deleted file mode 100644
index 89cf5ba565d6c2ccdab5c8ec9566663bfdc3fbfc..0000000000000000000000000000000000000000
--- a/spaces/Ricecake123/RVC-demo/docs/faiss_tips_ja.md
+++ /dev/null
@@ -1,101 +0,0 @@
-faiss tuning TIPS
-==================
-# about faiss
-faissはfacebook researchの開発する、密なベクトルに対する近傍探索をまとめたライブラリで、多くの近似近傍探索の手法を効率的に実装しています。
-近似近傍探索はある程度精度を犠牲にしながら高速に類似するベクトルを探します。
-
-## faiss in RVC
-RVCではHuBERTで変換した特徴量のEmbeddingに対し、学習データから生成されたEmbeddingと類似するものを検索し、混ぜることでより元の音声に近い変換を実現しています。ただ、この検索は愚直に行うと時間がかかるため、近似近傍探索を用いることで高速な変換を実現しています。
-
-# 実装のoverview
-モデルが配置されている '/logs/your-experiment/3_feature256'には各音声データからHuBERTで抽出された特徴量が配置されています。
-ここからnpyファイルをファイル名でソートした順番で読み込み、ベクトルを連結してbig_npyを作成しfaissを学習させます。(このベクトルのshapeは[N, 256]です。)
-
-本Tipsではまずこれらのパラメータの意味を解説します。
-
-# 手法の解説
-## index factory
-index factoryは複数の近似近傍探索の手法を繋げるパイプラインをstringで表記するfaiss独自の記法です。
-これにより、index factoryの文字列を変更するだけで様々な近似近傍探索の手法を試せます。
-RVCでは以下のように使われています。
-
-```python
-index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
-```
-index_factoryの引数のうち、1つ目はベクトルの次元数、2つ目はindex factoryの文字列で、3つ目には用いる距離を指定することができます。
-
-より詳細な記法については
-https://github.com/facebookresearch/faiss/wiki/The-index-factory
-
-## 距離指標
-embeddingの類似度として用いられる代表的な指標として以下の二つがあります。
-
-- ユークリッド距離(METRIC_L2)
-- 内積(METRIC_INNER_PRODUCT)
-
-ユークリッド距離では各次元において二乗の差をとり、全次元の差を足してから平方根をとります。これは日常的に用いる2次元、3次元での距離と同じです。
-内積はこのままでは類似度の指標として用いず、一般的にはL2ノルムで正規化してから内積をとるコサイン類似度を用います。
-
-どちらがよいかは場合によりますが、word2vec等で得られるembeddingやArcFace等で学習した類似画像検索のモデルではコサイン類似度が用いられることが多いです。ベクトルXに対してl2正規化をnumpyで行う場合は、0 divisionを避けるために十分に小さな値をepsとして以下のコードで可能です。
-
-```python
-X_normed = X / np.maximum(eps, np.linalg.norm(X, ord=2, axis=-1, keepdims=True))
-```
-
-また、index factoryには第3引数に渡す値を選ぶことで計算に用いる距離指標を変更できます。
-
-```python
-index = faiss.index_factory(dimention, text, faiss.METRIC_INNER_PRODUCT)
-```
-
-## IVF
-IVF(Inverted file indexes)は全文検索における転置インデックスと似たようなアルゴリズムです。
-学習時には検索対象に対してkmeansでクラスタリングを行い、クラスタ中心を用いてボロノイ分割を行います。各データ点には一つずつクラスタが割り当てられるので、クラスタからデータ点を逆引きする辞書を作成します。
-
-例えば以下のようにクラスタが割り当てられた場合
-|index|クラスタ|
-|-----|-------|
-|1|A|
-|2|B|
-|3|A|
-|4|C|
-|5|B|
-
-作成される転置インデックスは以下のようになります。
-
-|クラスタ|index|
-|-------|-----|
-|A|1, 3|
-|B|2, 5|
-|C|4|
-
-検索時にはまずクラスタからn_probe個のクラスタを検索し、次にそれぞれのクラスタに属するデータ点について距離を計算します。
-
-# 推奨されるパラメータ
-indexの選び方については公式にガイドラインがあるので、それに準じて説明します。
-https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
-
-1M以下のデータセットにおいては4bit-PQが2023年4月時点ではfaissで利用できる最も効率的な手法です。
-これをIVFと組み合わせ、4bit-PQで候補を絞り、最後に正確な指標で距離を再計算するには以下のindex factoryを用いることで記載できます。
-
-```python
-index = faiss.index_factory(256, "IVF1024,PQ128x4fs,RFlat")
-```
-
-## IVFの推奨パラメータ
-IVFの数が多すぎる場合、たとえばデータ数の数だけIVFによる粗量子化を行うと、これは愚直な全探索と同じになり効率が悪いです。
-1M以下の場合ではIVFの値はデータ点の数Nに対して4*sqrt(N) ~ 16*sqrt(N)に推奨しています。
-
-n_probeはn_probeの数に比例して計算時間が増えるので、精度と相談して適切に選んでください。個人的にはRVCにおいてそこまで精度は必要ないと思うのでn_probe = 1で良いと思います。
-
-## FastScan
-FastScanは直積量子化で大まかに距離を近似するのを、レジスタ内で行うことにより高速に行うようにした手法です。
-直積量子化は学習時にd次元ごと(通常はd=2)に独立してクラスタリングを行い、クラスタ同士の距離を事前計算してlookup tableを作成します。予測時はlookup tableを見ることで各次元の距離をO(1)で計算できます。
-そのため、PQの次に指定する数字は通常ベクトルの半分の次元を指定します。
-
-FastScanに関するより詳細な説明は公式のドキュメントを参照してください。
-https://github.com/facebookresearch/faiss/wiki/Fast-accumulation-of-PQ-and-AQ-codes-(FastScan)
-
-## RFlat
-RFlatはFastScanで計算した大まかな距離を、index factoryの第三引数で指定した正確な距離で再計算する指示です。
-k個の近傍を取得する際は、k*k_factor個の点について再計算が行われます。
diff --git a/spaces/Ricecake123/RVC-demo/go-web.bat b/spaces/Ricecake123/RVC-demo/go-web.bat
deleted file mode 100644
index db1dec52006bc631e4e68bafd619a3a65f202532..0000000000000000000000000000000000000000
--- a/spaces/Ricecake123/RVC-demo/go-web.bat
+++ /dev/null
@@ -1,2 +0,0 @@
-runtime\python.exe infer-web.py --pycmd runtime\python.exe --port 7897
-pause
diff --git a/spaces/Ricecake123/RVC-demo/i18n/locale_diff.py b/spaces/Ricecake123/RVC-demo/i18n/locale_diff.py
deleted file mode 100644
index 257277965e0866a86d0361863a8f1b408c4f71ab..0000000000000000000000000000000000000000
--- a/spaces/Ricecake123/RVC-demo/i18n/locale_diff.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import json
-import os
-from collections import OrderedDict
-
-# Define the standard file name
-standard_file = "zh_CN.json"
-
-# Find all JSON files in the directory
-dir_path = "./"
-languages = [
-    f for f in os.listdir(dir_path) if f.endswith(".json") and f != standard_file
-]
-
-# Load the standard file
-with open(standard_file, "r", encoding="utf-8") as f:
-    standard_data = json.load(f, object_pairs_hook=OrderedDict)
-
-# Loop through each language file
-for lang_file in languages:
-    # Load the language file
-    with open(lang_file, "r", encoding="utf-8") as f:
-        lang_data = json.load(f, object_pairs_hook=OrderedDict)
-
-    # Find the difference between the language file and the standard file
-    diff = set(standard_data.keys()) - set(lang_data.keys())
-
-    miss = set(lang_data.keys()) - set(standard_data.keys())
-
-    # Add any missing keys to the language file
-    for key in diff:
-        lang_data[key] = key
-
-    # Del any extra keys to the language file
-    for key in miss:
-        del lang_data[key]
-
-    # Sort the keys of the language file to match the order of the standard file
-    lang_data = OrderedDict(
-        sorted(lang_data.items(), key=lambda x: list(standard_data.keys()).index(x[0]))
-    )
-
-    # Save the updated language file
-    with open(lang_file, "w", encoding="utf-8") as f:
-        json.dump(lang_data, f, ensure_ascii=False, indent=4)
-        f.write("\n")
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/rpn_test_mixin.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/rpn_test_mixin.py
deleted file mode 100644
index 4ce5c66f82595f496e6e55719c1caee75150d568..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/rpn_test_mixin.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import sys
-
-from mmdet.core import merge_aug_proposals
-
-if sys.version_info >= (3, 7):
-    from mmdet.utils.contextmanagers import completed
-
-
-class RPNTestMixin(object):
-    """Test methods of RPN."""
-
-    if sys.version_info >= (3, 7):
-
-        async def async_simple_test_rpn(self, x, img_metas):
-            sleep_interval = self.test_cfg.pop('async_sleep_interval', 0.025)
-            async with completed(
-                    __name__, 'rpn_head_forward',
-                    sleep_interval=sleep_interval):
-                rpn_outs = self(x)
-
-            proposal_list = self.get_bboxes(*rpn_outs, img_metas)
-            return proposal_list
-
-    def simple_test_rpn(self, x, img_metas):
-        """Test without augmentation.
-
-        Args:
-            x (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-            img_metas (list[dict]): Meta info of each image.
-
-        Returns:
-            list[Tensor]: Proposals of each image.
-        """
-        rpn_outs = self(x)
-        proposal_list = self.get_bboxes(*rpn_outs, img_metas)
-        return proposal_list
-
-    def aug_test_rpn(self, feats, img_metas):
-        samples_per_gpu = len(img_metas[0])
-        aug_proposals = [[] for _ in range(samples_per_gpu)]
-        for x, img_meta in zip(feats, img_metas):
-            proposal_list = self.simple_test_rpn(x, img_meta)
-            for i, proposals in enumerate(proposal_list):
-                aug_proposals[i].append(proposals)
-        # reorganize the order of 'img_metas' to match the dimensions
-        # of 'aug_proposals'
-        aug_img_metas = []
-        for i in range(samples_per_gpu):
-            aug_img_meta = []
-            for j in range(len(img_metas)):
-                aug_img_meta.append(img_metas[j][i])
-            aug_img_metas.append(aug_img_meta)
-        # after merging, proposals will be rescaled to the original image size
-        merged_proposals = [
-            merge_aug_proposals(proposals, aug_img_meta, self.test_cfg)
-            for proposals, aug_img_meta in zip(aug_proposals, aug_img_metas)
-        ]
-        return merged_proposals
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/exp/upernet_global_base/config.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/exp/upernet_global_base/config.py
deleted file mode 100644
index 5025b82da6203be2662c805c60254089f7b57648..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/exp/upernet_global_base/config.py
+++ /dev/null
@@ -1,51 +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 UniFormer repo: From https://github.com/Sense-X/UniFormer
- * Apache-2.0 license
-'''
-_base_ = [
-    '../../configs/_base_/models/upernet_uniformer.py', 
-    '../../configs/_base_/datasets/ade20k.py',
-    '../../configs/_base_/default_runtime.py', 
-    '../../configs/_base_/schedules/schedule_160k.py'
-]
-model = dict(
-    backbone=dict(
-        type='UniFormer',
-        embed_dim=[64, 128, 320, 512],
-        layers=[5, 8, 20, 7],
-        head_dim=64,
-        drop_path_rate=0.4,
-        use_checkpoint=True,
-        checkpoint_num=[0, 0, 2, 0],
-        windows=False,
-        hybrid=False
-    ),
-    decode_head=dict(
-        in_channels=[64, 128, 320, 512],
-        num_classes=150
-    ),
-    auxiliary_head=dict(
-        in_channels=320,
-        num_classes=150
-    ))
-
-# AdamW optimizer, no weight decay for position embedding & layer norm in backbone
-optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
-                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
-                                                 'relative_position_bias_table': dict(decay_mult=0.),
-                                                 'norm': dict(decay_mult=0.)}))
-
-lr_config = dict(_delete_=True, policy='poly',
-                 warmup='linear',
-                 warmup_iters=1500,
-                 warmup_ratio=1e-6,
-                 power=1.0, min_lr=0.0, by_epoch=False)
-
-data=dict(samples_per_gpu=2)
\ No newline at end of file
diff --git a/spaces/SQSora/VITS-Umamusume-voice-synthesizer/text/japanese.py b/spaces/SQSora/VITS-Umamusume-voice-synthesizer/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/SQSora/VITS-Umamusume-voice-synthesizer/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
-    r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
-    r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ts', 'ʦ'),
-    ('u', 'ɯ'),
-    ('j', 'ʥ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('u', 'ɯ'),
-    ('ʧ', 'tʃ'),
-    ('j', 'dʑ'),
-    ('y', 'j'),
-    ('ni', 'n^i'),
-    ('nj', 'n^'),
-    ('hi', 'çi'),
-    ('hj', 'ç'),
-    ('f', 'ɸ'),
-    ('I', 'i*'),
-    ('U', 'ɯ*'),
-    ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'Q([↑↓]*[kg])', r'k#\1'),
-    (r'Q([↑↓]*[tdjʧ])', r't#\1'),
-    (r'Q([↑↓]*[sʃ])', r's\1'),
-    (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
-    (r'N([↑↓]*[pbm])', r'm\1'),
-    (r'N([↑↓]*[ʧʥj])', r'n^\1'),
-    (r'N([↑↓]*[tdn])', r'n\1'),
-    (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
-    for regex, replacement in _symbols_to_japanese:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_romaji_with_accent(text):
-    '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-    text = symbols_to_japanese(text)
-    sentences = re.split(_japanese_marks, text)
-    marks = re.findall(_japanese_marks, text)
-    text = ''
-    for i, sentence in enumerate(sentences):
-        if re.match(_japanese_characters, sentence):
-            if text != '':
-                text += ' '
-            labels = pyopenjtalk.extract_fullcontext(sentence)
-            for n, label in enumerate(labels):
-                phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-                if phoneme not in ['sil', 'pau']:
-                    text += phoneme.replace('ch', 'ʧ').replace('sh',
-                                                               'ʃ').replace('cl', 'Q')
-                else:
-                    continue
-                # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-                a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-                a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-                a3 = int(re.search(r"\+(\d+)/", label).group(1))
-                if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
-                    a2_next = -1
-                else:
-                    a2_next = int(
-                        re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-                # Accent phrase boundary
-                if a3 == 1 and a2_next == 1:
-                    text += ' '
-                # Falling
-                elif a1 == 0 and a2_next == a2 + 1:
-                    text += '↓'
-                # Rising
-                elif a2 == 1 and a2_next == 2:
-                    text += '↑'
-        if i < len(marks):
-            text += unidecode(marks[i]).replace(' ', '')
-    return text
-
-
-def get_real_sokuon(text):
-    for regex, replacement in _real_sokuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def get_real_hatsuon(text):
-    for regex, replacement in _real_hatsuon:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = re.sub(
-        r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa2(text):
-    text = japanese_to_romaji_with_accent(text).replace('...', '…')
-    text = get_real_sokuon(text)
-    text = get_real_hatsuon(text)
-    for regex, replacement in _romaji_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def japanese_to_ipa3(text):
-    text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
-        'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
-    text = re.sub(
-        r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
-    text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
-    return text
diff --git a/spaces/SUSTech/llm-evaluate/index.html b/spaces/SUSTech/llm-evaluate/index.html
deleted file mode 100644
index 4f2784b430ff880a097ce17f537cadc8876e17e2..0000000000000000000000000000000000000000
--- a/spaces/SUSTech/llm-evaluate/index.html
+++ /dev/null
@@ -1,3273 +0,0 @@
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-
-  // activate the anchor for a section associated with this TOC entry
-  tocLinks.forEach((link) => {
-    link.addEventListener("click", () => {
-      if (link.href.indexOf("#") !== -1) {
-        const anchor = link.href.split("#")[1];
-        const heading = window.document.querySelector(
-          `[data-anchor-id=${anchor}]`
-        );
-        if (heading) {
-          // Add the class
-          heading.classList.add("reveal-anchorjs-link");
-
-          // function to show the anchor
-          const handleMouseout = () => {
-            heading.classList.remove("reveal-anchorjs-link");
-            heading.removeEventListener("mouseout", handleMouseout);
-          };
-
-          // add a function to clear the anchor when the user mouses out of it
-          heading.addEventListener("mouseout", handleMouseout);
-        }
-      }
-    });
-  });
-
-  const sections = tocLinks.map((link) => {
-    const target = link.getAttribute("data-scroll-target");
-    if (target.startsWith("#")) {
-      return window.document.getElementById(decodeURI(`${target.slice(1)}`));
-    } else {
-      return window.document.querySelector(decodeURI(`${target}`));
-    }
-  });
-
-  const sectionMargin = 200;
-  let currentActive = 0;
-  // track whether we've initialized state the first time
-  let init = false;
-
-  const updateActiveLink = () => {
-    // The index from bottom to top (e.g. reversed list)
-    let sectionIndex = -1;
-    if (
-      window.innerHeight + window.pageYOffset >=
-      window.document.body.offsetHeight
-    ) {
-      sectionIndex = 0;
-    } else {
-      sectionIndex = [...sections].reverse().findIndex((section) => {
-        if (section) {
-          return window.pageYOffset >= section.offsetTop - sectionMargin;
-        } else {
-          return false;
-        }
-      });
-    }
-    if (sectionIndex > -1) {
-      const current = sections.length - sectionIndex - 1;
-      if (current !== currentActive) {
-        removeAllActive();
-        currentActive = current;
-        makeActive(current);
-        if (init) {
-          window.dispatchEvent(sectionChanged);
-        }
-        init = true;
-      }
-    }
-  };
-
-  const inHiddenRegion = (top, bottom, hiddenRegions) => {
-    for (const region of hiddenRegions) {
-      if (top <= region.bottom && bottom >= region.top) {
-        return true;
-      }
-    }
-    return false;
-  };
-
-  const categorySelector = "header.quarto-title-block .quarto-category";
-  const activateCategories = (href) => {
-    // Find any categories
-    // Surround them with a link pointing back to:
-    // #category=Authoring
-    try {
-      const categoryEls = window.document.querySelectorAll(categorySelector);
-      for (const categoryEl of categoryEls) {
-        const categoryText = categoryEl.textContent;
-        if (categoryText) {
-          const link = `${href}#category=${encodeURIComponent(categoryText)}`;
-          const linkEl = window.document.createElement("a");
-          linkEl.setAttribute("href", link);
-          for (const child of categoryEl.childNodes) {
-            linkEl.append(child);
-          }
-          categoryEl.appendChild(linkEl);
-        }
-      }
-    } catch {
-      // Ignore errors
-    }
-  };
-  function hasTitleCategories() {
-    return window.document.querySelector(categorySelector) !== null;
-  }
-
-  function offsetRelativeUrl(url) {
-    const offset = getMeta("quarto:offset");
-    return offset ? offset + url : url;
-  }
-
-  function offsetAbsoluteUrl(url) {
-    const offset = getMeta("quarto:offset");
-    const baseUrl = new URL(offset, window.location);
-
-    const projRelativeUrl = url.replace(baseUrl, "");
-    if (projRelativeUrl.startsWith("/")) {
-      return projRelativeUrl;
-    } else {
-      return "/" + projRelativeUrl;
-    }
-  }
-
-  // read a meta tag value
-  function getMeta(metaName) {
-    const metas = window.document.getElementsByTagName("meta");
-    for (let i = 0; i < metas.length; i++) {
-      if (metas[i].getAttribute("name") === metaName) {
-        return metas[i].getAttribute("content");
-      }
-    }
-    return "";
-  }
-
-  async function findAndActivateCategories() {
-    const currentPagePath = offsetAbsoluteUrl(window.location.href);
-    const response = await fetch(offsetRelativeUrl("listings.json"));
-    if (response.status == 200) {
-      return response.json().then(function (listingPaths) {
-        const listingHrefs = [];
-        for (const listingPath of listingPaths) {
-          const pathWithoutLeadingSlash = listingPath.listing.substring(1);
-          for (const item of listingPath.items) {
-            if (
-              item === currentPagePath ||
-              item === currentPagePath + "index.html"
-            ) {
-              // Resolve this path against the offset to be sure
-              // we already are using the correct path to the listing
-              // (this adjusts the listing urls to be rooted against
-              // whatever root the page is actually running against)
-              const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
-              const baseUrl = window.location;
-              const resolvedPath = new URL(relative, baseUrl);
-              listingHrefs.push(resolvedPath.pathname);
-              break;
-            }
-          }
-        }
-
-        // Look up the tree for a nearby linting and use that if we find one
-        const nearestListing = findNearestParentListing(
-          offsetAbsoluteUrl(window.location.pathname),
-          listingHrefs
-        );
-        if (nearestListing) {
-          activateCategories(nearestListing);
-        } else {
-          // See if the referrer is a listing page for this item
-          const referredRelativePath = offsetAbsoluteUrl(document.referrer);
-          const referrerListing = listingHrefs.find((listingHref) => {
-            const isListingReferrer =
-              listingHref === referredRelativePath ||
-              listingHref === referredRelativePath + "index.html";
-            return isListingReferrer;
-          });
-
-          if (referrerListing) {
-            // Try to use the referrer if possible
-            activateCategories(referrerListing);
-          } else if (listingHrefs.length > 0) {
-            // Otherwise, just fall back to the first listing
-            activateCategories(listingHrefs[0]);
-          }
-        }
-      });
-    }
-  }
-  if (hasTitleCategories()) {
-    findAndActivateCategories();
-  }
-
-  const findNearestParentListing = (href, listingHrefs) => {
-    if (!href || !listingHrefs) {
-      return undefined;
-    }
-    // Look up the tree for a nearby linting and use that if we find one
-    const relativeParts = href.substring(1).split("/");
-    while (relativeParts.length > 0) {
-      const path = relativeParts.join("/");
-      for (const listingHref of listingHrefs) {
-        if (listingHref.startsWith(path)) {
-          return listingHref;
-        }
-      }
-      relativeParts.pop();
-    }
-
-    return undefined;
-  };
-
-  const manageSidebarVisiblity = (el, placeholderDescriptor) => {
-    let isVisible = true;
-    let elRect;
-
-    return (hiddenRegions) => {
-      if (el === null) {
-        return;
-      }
-
-      // Find the last element of the TOC
-      const lastChildEl = el.lastElementChild;
-
-      if (lastChildEl) {
-        // Converts the sidebar to a menu
-        const convertToMenu = () => {
-          for (const child of el.children) {
-            child.style.opacity = 0;
-            child.style.overflow = "hidden";
-          }
-
-          nexttick(() => {
-            const toggleContainer = window.document.createElement("div");
-            toggleContainer.style.width = "100%";
-            toggleContainer.classList.add("zindex-over-content");
-            toggleContainer.classList.add("quarto-sidebar-toggle");
-            toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
-            toggleContainer.id = placeholderDescriptor.id;
-            toggleContainer.style.position = "fixed";
-
-            const toggleIcon = window.document.createElement("i");
-            toggleIcon.classList.add("quarto-sidebar-toggle-icon");
-            toggleIcon.classList.add("bi");
-            toggleIcon.classList.add("bi-caret-down-fill");
-
-            const toggleTitle = window.document.createElement("div");
-            const titleEl = window.document.body.querySelector(
-              placeholderDescriptor.titleSelector
-            );
-            if (titleEl) {
-              toggleTitle.append(
-                titleEl.textContent || titleEl.innerText,
-                toggleIcon
-              );
-            }
-            toggleTitle.classList.add("zindex-over-content");
-            toggleTitle.classList.add("quarto-sidebar-toggle-title");
-            toggleContainer.append(toggleTitle);
-
-            const toggleContents = window.document.createElement("div");
-            toggleContents.classList = el.classList;
-            toggleContents.classList.add("zindex-over-content");
-            toggleContents.classList.add("quarto-sidebar-toggle-contents");
-            for (const child of el.children) {
-              if (child.id === "toc-title") {
-                continue;
-              }
-
-              const clone = child.cloneNode(true);
-              clone.style.opacity = 1;
-              clone.style.display = null;
-              toggleContents.append(clone);
-            }
-            toggleContents.style.height = "0px";
-            const positionToggle = () => {
-              // position the element (top left of parent, same width as parent)
-              if (!elRect) {
-                elRect = el.getBoundingClientRect();
-              }
-              toggleContainer.style.left = `${elRect.left}px`;
-              toggleContainer.style.top = `${elRect.top}px`;
-              toggleContainer.style.width = `${elRect.width}px`;
-            };
-            positionToggle();
-
-            toggleContainer.append(toggleContents);
-            el.parentElement.prepend(toggleContainer);
-
-            // Process clicks
-            let tocShowing = false;
-            // Allow the caller to control whether this is dismissed
-            // when it is clicked (e.g. sidebar navigation supports
-            // opening and closing the nav tree, so don't dismiss on click)
-            const clickEl = placeholderDescriptor.dismissOnClick
-              ? toggleContainer
-              : toggleTitle;
-
-            const closeToggle = () => {
-              if (tocShowing) {
-                toggleContainer.classList.remove("expanded");
-                toggleContents.style.height = "0px";
-                tocShowing = false;
-              }
-            };
-
-            // Get rid of any expanded toggle if the user scrolls
-            window.document.addEventListener(
-              "scroll",
-              throttle(() => {
-                closeToggle();
-              }, 50)
-            );
-
-            // Handle positioning of the toggle
-            window.addEventListener(
-              "resize",
-              throttle(() => {
-                elRect = undefined;
-                positionToggle();
-              }, 50)
-            );
-
-            window.addEventListener("quarto-hrChanged", () => {
-              elRect = undefined;
-            });
-
-            // Process the click
-            clickEl.onclick = () => {
-              if (!tocShowing) {
-                toggleContainer.classList.add("expanded");
-                toggleContents.style.height = null;
-                tocShowing = true;
-              } else {
-                closeToggle();
-              }
-            };
-          });
-        };
-
-        // Converts a sidebar from a menu back to a sidebar
-        const convertToSidebar = () => {
-          for (const child of el.children) {
-            child.style.opacity = 1;
-            child.style.overflow = null;
-          }
-
-          const placeholderEl = window.document.getElementById(
-            placeholderDescriptor.id
-          );
-          if (placeholderEl) {
-            placeholderEl.remove();
-          }
-
-          el.classList.remove("rollup");
-        };
-
-        if (isReaderMode()) {
-          convertToMenu();
-          isVisible = false;
-        } else {
-          // Find the top and bottom o the element that is being managed
-          const elTop = el.offsetTop;
-          const elBottom =
-            elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
-
-          if (!isVisible) {
-            // If the element is current not visible reveal if there are
-            // no conflicts with overlay regions
-            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
-              convertToSidebar();
-              isVisible = true;
-            }
-          } else {
-            // If the element is visible, hide it if it conflicts with overlay regions
-            // and insert a placeholder toggle (or if we're in reader mode)
-            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
-              convertToMenu();
-              isVisible = false;
-            }
-          }
-        }
-      }
-    };
-  };
-
-  // Find any conflicting margin elements and add margins to the
-  // top to prevent overlap
-  const marginChildren = window.document.querySelectorAll(
-    ".column-margin.column-container > * "
-  );
-
-  const layoutMarginEls = () => {
-    let lastBottom = 0;
-    for (const marginChild of marginChildren) {
-      if (marginChild.offsetParent !== null) {
-        // clear the top margin so we recompute it
-        marginChild.style.marginTop = null;
-        const top = marginChild.getBoundingClientRect().top + window.scrollY;
-        if (top < lastBottom) {
-          const margin = lastBottom - top;
-          marginChild.style.marginTop = `${margin}px`;
-        }
-        const styles = window.getComputedStyle(marginChild);
-        const marginTop = parseFloat(styles["marginTop"]);
-        lastBottom =
-          top + marginChild.getBoundingClientRect().height + marginTop;
-      }
-    }
-  };
-  nexttick(layoutMarginEls);
-
-  const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
-  for (const tabEl of tabEls) {
-    const id = tabEl.getAttribute("data-bs-target");
-    if (id) {
-      const columnEl = document.querySelector(
-        `${id} .column-margin, .tabset-margin-content`
-      );
-      if (columnEl)
-        tabEl.addEventListener("shown.bs.tab", function (event) {
-
-          const el = event.srcElement;
-          if (el) {
-            const visibleCls = `${el.id}-margin-content`;
-            // walk up until we find a parent tabset
-            let panelTabsetEl = el.parentElement;
-            while (panelTabsetEl) {
-              if (panelTabsetEl.classList.contains("panel-tabset")) {
-                break;
-              }
-              panelTabsetEl = panelTabsetEl.parentElement;
-            }
-
-            if (panelTabsetEl) {
-              const prevSib = panelTabsetEl.previousElementSibling;
-              if (
-                prevSib &&
-                prevSib.classList.contains("tabset-margin-container")
-              ) {
-                const childNodes = prevSib.querySelectorAll(
-                  ".tabset-margin-content"
-                );
-                for (const childEl of childNodes) {
-                  if (childEl.classList.contains(visibleCls)) {
-                    childEl.classList.remove("collapse");
-                  } else {
-                    childEl.classList.add("collapse");
-                  }
-                }
-              }
-            }
-          }
-
-          layoutMarginEls();
-        });
-    }
-  }
-
-  // Manage the visibility of the toc and the sidebar
-  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
-    id: "quarto-toc-toggle",
-    titleSelector: "#toc-title",
-    dismissOnClick: true,
-  });
-  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
-    id: "quarto-sidebarnav-toggle",
-    titleSelector: ".title",
-    dismissOnClick: false,
-  });
-  let tocLeftScrollVisibility;
-  if (leftTocEl) {
-    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
-      id: "quarto-lefttoc-toggle",
-      titleSelector: "#toc-title",
-      dismissOnClick: true,
-    });
-  }
-
-  // Find the first element that uses formatting in special columns
-  const conflictingEls = window.document.body.querySelectorAll(
-    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
-  );
-
-  // Filter all the possibly conflicting elements into ones
-  // the do conflict on the left or ride side
-  const arrConflictingEls = Array.from(conflictingEls);
-  const leftSideConflictEls = arrConflictingEls.filter((el) => {
-    if (el.tagName === "ASIDE") {
-      return false;
-    }
-    return Array.from(el.classList).find((className) => {
-      return (
-        className !== "column-body" &&
-        className.startsWith("column-") &&
-        !className.endsWith("right") &&
-        !className.endsWith("container") &&
-        className !== "column-margin"
-      );
-    });
-  });
-  const rightSideConflictEls = arrConflictingEls.filter((el) => {
-    if (el.tagName === "ASIDE") {
-      return true;
-    }
-
-    const hasMarginCaption = Array.from(el.classList).find((className) => {
-      return className == "margin-caption";
-    });
-    if (hasMarginCaption) {
-      return true;
-    }
-
-    return Array.from(el.classList).find((className) => {
-      return (
-        className !== "column-body" &&
-        !className.endsWith("container") &&
-        className.startsWith("column-") &&
-        !className.endsWith("left")
-      );
-    });
-  });
-
-  const kOverlapPaddingSize = 10;
-  function toRegions(els) {
-    return els.map((el) => {
-      const boundRect = el.getBoundingClientRect();
-      const top =
-        boundRect.top +
-        document.documentElement.scrollTop -
-        kOverlapPaddingSize;
-      return {
-        top,
-        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
-      };
-    });
-  }
-
-  let hasObserved = false;
-  const visibleItemObserver = (els) => {
-    let visibleElements = [...els];
-    const intersectionObserver = new IntersectionObserver(
-      (entries, _observer) => {
-        entries.forEach((entry) => {
-          if (entry.isIntersecting) {
-            if (visibleElements.indexOf(entry.target) === -1) {
-              visibleElements.push(entry.target);
-            }
-          } else {
-            visibleElements = visibleElements.filter((visibleEntry) => {
-              return visibleEntry !== entry;
-            });
-          }
-        });
-
-        if (!hasObserved) {
-          hideOverlappedSidebars();
-        }
-        hasObserved = true;
-      },
-      {}
-    );
-    els.forEach((el) => {
-      intersectionObserver.observe(el);
-    });
-
-    return {
-      getVisibleEntries: () => {
-        return visibleElements;
-      },
-    };
-  };
-
-  const rightElementObserver = visibleItemObserver(rightSideConflictEls);
-  const leftElementObserver = visibleItemObserver(leftSideConflictEls);
-
-  const hideOverlappedSidebars = () => {
-    marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
-    sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
-    if (tocLeftScrollVisibility) {
-      tocLeftScrollVisibility(
-        toRegions(leftElementObserver.getVisibleEntries())
-      );
-    }
-  };
-
-  window.quartoToggleReader = () => {
-    // Applies a slow class (or removes it)
-    // to update the transition speed
-    const slowTransition = (slow) => {
-      const manageTransition = (id, slow) => {
-        const el = document.getElementById(id);
-        if (el) {
-          if (slow) {
-            el.classList.add("slow");
-          } else {
-            el.classList.remove("slow");
-          }
-        }
-      };
-
-      manageTransition("TOC", slow);
-      manageTransition("quarto-sidebar", slow);
-    };
-    const readerMode = !isReaderMode();
-    setReaderModeValue(readerMode);
-
-    // If we're entering reader mode, slow the transition
-    if (readerMode) {
-      slowTransition(readerMode);
-    }
-    highlightReaderToggle(readerMode);
-    hideOverlappedSidebars();
-
-    // If we're exiting reader mode, restore the non-slow transition
-    if (!readerMode) {
-      slowTransition(!readerMode);
-    }
-  };
-
-  const highlightReaderToggle = (readerMode) => {
-    const els = document.querySelectorAll(".quarto-reader-toggle");
-    if (els) {
-      els.forEach((el) => {
-        if (readerMode) {
-          el.classList.add("reader");
-        } else {
-          el.classList.remove("reader");
-        }
-      });
-    }
-  };
-
-  const setReaderModeValue = (val) => {
-    if (window.location.protocol !== "file:") {
-      window.localStorage.setItem("quarto-reader-mode", val);
-    } else {
-      localReaderMode = val;
-    }
-  };
-
-  const isReaderMode = () => {
-    if (window.location.protocol !== "file:") {
-      return window.localStorage.getItem("quarto-reader-mode") === "true";
-    } else {
-      return localReaderMode;
-    }
-  };
-  let localReaderMode = null;
-
-  const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
-  const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;
-
-  // Walk the TOC and collapse/expand nodes
-  // Nodes are expanded if:
-  // - they are top level
-  // - they have children that are 'active' links
-  // - they are directly below an link that is 'active'
-  const walk = (el, depth) => {
-    // Tick depth when we enter a UL
-    if (el.tagName === "UL") {
-      depth = depth + 1;
-    }
-
-    // It this is active link
-    let isActiveNode = false;
-    if (el.tagName === "A" && el.classList.contains("active")) {
-      isActiveNode = true;
-    }
-
-    // See if there is an active child to this element
-    let hasActiveChild = false;
-    for (child of el.children) {
-      hasActiveChild = walk(child, depth) || hasActiveChild;
-    }
-
-    // Process the collapse state if this is an UL
-    if (el.tagName === "UL") {
-      if (tocOpenDepth === -1 && depth > 1) {
-        el.classList.add("collapse");
-      } else if (
-        depth <= tocOpenDepth ||
-        hasActiveChild ||
-        prevSiblingIsActiveLink(el)
-      ) {
-        el.classList.remove("collapse");
-      } else {
-        el.classList.add("collapse");
-      }
-
-      // untick depth when we leave a UL
-      depth = depth - 1;
-    }
-    return hasActiveChild || isActiveNode;
-  };
-
-  // walk the TOC and expand / collapse any items that should be shown
-
-  if (tocEl) {
-    walk(tocEl, 0);
-    updateActiveLink();
-  }
-
-  // Throttle the scroll event and walk peridiocally
-  window.document.addEventListener(
-    "scroll",
-    throttle(() => {
-      if (tocEl) {
-        updateActiveLink();
-        walk(tocEl, 0);
-      }
-      if (!isReaderMode()) {
-        hideOverlappedSidebars();
-      }
-    }, 5)
-  );
-  window.addEventListener(
-    "resize",
-    throttle(() => {
-      if (!isReaderMode()) {
-        hideOverlappedSidebars();
-      }
-    }, 10)
-  );
-  hideOverlappedSidebars();
-  highlightReaderToggle(isReaderMode());
-});
-
-// grouped tabsets
-window.addEventListener("pageshow", (_event) => {
-  function getTabSettings() {
-    const data = localStorage.getItem("quarto-persistent-tabsets-data");
-    if (!data) {
-      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
-      return {};
-    }
-    if (data) {
-      return JSON.parse(data);
-    }
-  }
-
-  function setTabSettings(data) {
-    localStorage.setItem(
-      "quarto-persistent-tabsets-data",
-      JSON.stringify(data)
-    );
-  }
-
-  function setTabState(groupName, groupValue) {
-    const data = getTabSettings();
-    data[groupName] = groupValue;
-    setTabSettings(data);
-  }
-
-  function toggleTab(tab, active) {
-    const tabPanelId = tab.getAttribute("aria-controls");
-    const tabPanel = document.getElementById(tabPanelId);
-    if (active) {
-      tab.classList.add("active");
-      tabPanel.classList.add("active");
-    } else {
-      tab.classList.remove("active");
-      tabPanel.classList.remove("active");
-    }
-  }
-
-  function toggleAll(selectedGroup, selectorsToSync) {
-    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
-      const active = selectedGroup === thisGroup;
-      for (const tab of tabs) {
-        toggleTab(tab, active);
-      }
-    }
-  }
-
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K}next(){this._slide(Q)}nextWhenVisible(){!document.hidden&&l(this._element)&&this.next()}prev(){this._slide(G)}pause(t){t||(this._isPaused=!0),V.findOne(".carousel-item-next, .carousel-item-prev",this._element)&&(s(this._element),this.cycle(!0)),clearInterval(this._interval),this._interval=null}cycle(t){t||(this._isPaused=!1),this._interval&&(clearInterval(this._interval),this._interval=null),this._config&&this._config.interval&&!this._isPaused&&(this._updateInterval(),this._interval=setInterval((document.visibilityState?this.nextWhenVisible:this.next).bind(this),this._config.interval))}to(t){this._activeElement=V.findOne(nt,this._element);const e=this._getItemIndex(this._activeElement);if(t>this._items.length-1||t<0)return;if(this._isSliding)return void j.one(this._element,et,(()=>this.to(t)));if(e===t)return this.pause(),void this.cycle();const i=t>e?Q:G;this._slide(i,this._items[t])}_getConfig(t){return t={...X,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(K,t,Y),t}_handleSwipe(){const t=Math.abs(this.touchDeltaX);if(t<=40)return;const e=t/this.touchDeltaX;this.touchDeltaX=0,e&&this._slide(e>0?J:Z)}_addEventListeners(){this._config.keyboard&&j.on(this._element,"keydown.bs.carousel",(t=>this._keydown(t))),"hover"===this._config.pause&&(j.on(this._element,"mouseenter.bs.carousel",(t=>this.pause(t))),j.on(this._element,"mouseleave.bs.carousel",(t=>this.cycle(t)))),this._config.touch&&this._touchSupported&&this._addTouchEventListeners()}_addTouchEventListeners(){const t=t=>this._pointerEvent&&("pen"===t.pointerType||"touch"===t.pointerType),e=e=>{t(e)?this.touchStartX=e.clientX:this._pointerEvent||(this.touchStartX=e.touches[0].clientX)},i=t=>{this.touchDeltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this.touchStartX},n=e=>{t(e)&&(this.touchDeltaX=e.clientX-this.touchStartX),this._handleSwipe(),"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((t=>this.cycle(t)),500+this._config.interval))};V.find(".carousel-item img",this._element).forEach((t=>{j.on(t,"dragstart.bs.carousel",(t=>t.preventDefault()))})),this._pointerEvent?(j.on(this._element,"pointerdown.bs.carousel",(t=>e(t))),j.on(this._element,"pointerup.bs.carousel",(t=>n(t))),this._element.classList.add("pointer-event")):(j.on(this._element,"touchstart.bs.carousel",(t=>e(t))),j.on(this._element,"touchmove.bs.carousel",(t=>i(t))),j.on(this._element,"touchend.bs.carousel",(t=>n(t))))}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=tt[t.key];e&&(t.preventDefault(),this._slide(e))}_getItemIndex(t){return this._items=t&&t.parentNode?V.find(".carousel-item",t.parentNode):[],this._items.indexOf(t)}_getItemByOrder(t,e){const i=t===Q;return v(this._items,e,i,this._config.wrap)}_triggerSlideEvent(t,e){const i=this._getItemIndex(t),n=this._getItemIndex(V.findOne(nt,this._element));return j.trigger(this._element,"slide.bs.carousel",{relatedTarget:t,direction:e,from:n,to:i})}_setActiveIndicatorElement(t){if(this._indicatorsElement){const e=V.findOne(".active",this._indicatorsElement);e.classList.remove(it),e.removeAttribute("aria-current");const i=V.find("[data-bs-target]",this._indicatorsElement);for(let e=0;e<i.length;e++)if(Number.parseInt(i[e].getAttribute("data-bs-slide-to"),10)===this._getItemIndex(t)){i[e].classList.add(it),i[e].setAttribute("aria-current","true");break}}}_updateInterval(){const t=this._activeElement||V.findOne(nt,this._element);if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);e?(this._config.defaultInterval=this._config.defaultInterval||this._config.interval,this._config.interval=e):this._config.interval=this._config.defaultInterval||this._config.interval}_slide(t,e){const i=this._directionToOrder(t),n=V.findOne(nt,this._element),s=this._getItemIndex(n),o=e||this._getItemByOrder(i,n),r=this._getItemIndex(o),a=Boolean(this._interval),l=i===Q,c=l?"carousel-item-start":"carousel-item-end",h=l?"carousel-item-next":"carousel-item-prev",d=this._orderToDirection(i);if(o&&o.classList.contains(it))return void(this._isSliding=!1);if(this._isSliding)return;if(this._triggerSlideEvent(o,d).defaultPrevented)return;if(!n||!o)return;this._isSliding=!0,a&&this.pause(),this._setActiveIndicatorElement(o),this._activeElement=o;const f=()=>{j.trigger(this._element,et,{relatedTarget:o,direction:d,from:s,to:r})};if(this._element.classList.contains("slide")){o.classList.add(h),u(o),n.classList.add(c),o.classList.add(c);const t=()=>{o.classList.remove(c,h),o.classList.add(it),n.classList.remove(it,h,c),this._isSliding=!1,setTimeout(f,0)};this._queueCallback(t,n,!0)}else n.classList.remove(it),o.classList.add(it),this._isSliding=!1,f();a&&this.cycle()}_directionToOrder(t){return[J,Z].includes(t)?m()?t===Z?G:Q:t===Z?Q:G:t}_orderToDirection(t){return[Q,G].includes(t)?m()?t===G?Z:J:t===G?J:Z:t}static carouselInterface(t,e){const i=st.getOrCreateInstance(t,e);let{_config:n}=i;"object"==typeof e&&(n={...n,...e});const s="string"==typeof e?e:n.slide;if("number"==typeof e)i.to(e);else if("string"==typeof s){if(void 0===i[s])throw new TypeError(`No method named "${s}"`);i[s]()}else n.interval&&n.ride&&(i.pause(),i.cycle())}static jQueryInterface(t){return this.each((function(){st.carouselInterface(this,t)}))}static dataApiClickHandler(t){const e=n(this);if(!e||!e.classList.contains("carousel"))return;const i={...U.getDataAttributes(e),...U.getDataAttributes(this)},s=this.getAttribute("data-bs-slide-to");s&&(i.interval=!1),st.carouselInterface(e,i),s&&st.getInstance(e).to(s),t.preventDefault()}}j.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",st.dataApiClickHandler),j.on(window,"load.bs.carousel.data-api",(()=>{const t=V.find('[data-bs-ride="carousel"]');for(let e=0,i=t.length;e<i;e++)st.carouselInterface(t[e],st.getInstance(t[e]))})),g(st);const ot="collapse",rt={toggle:!0,parent:null},at={toggle:"boolean",parent:"(null|element)"},lt="show",ct="collapse",ht="collapsing",dt="collapsed",ut=":scope .collapse .collapse",ft='[data-bs-toggle="collapse"]';class pt extends B{constructor(t,e){super(t),this._isTransitioning=!1,this._config=this._getConfig(e),this._triggerArray=[];const n=V.find(ft);for(let t=0,e=n.length;t<e;t++){const e=n[t],s=i(e),o=V.find(s).filter((t=>t===this._element));null!==s&&o.length&&(this._selector=s,this._triggerArray.push(e))}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return rt}static get NAME(){return ot}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t,e=[];if(this._config.parent){const t=V.find(ut,this._config.parent);e=V.find(".collapse.show, .collapse.collapsing",this._config.parent).filter((e=>!t.includes(e)))}const i=V.findOne(this._selector);if(e.length){const n=e.find((t=>i!==t));if(t=n?pt.getInstance(n):null,t&&t._isTransitioning)return}if(j.trigger(this._element,"show.bs.collapse").defaultPrevented)return;e.forEach((e=>{i!==e&&pt.getOrCreateInstance(e,{toggle:!1}).hide(),t||H.set(e,"bs.collapse",null)}));const n=this._getDimension();this._element.classList.remove(ct),this._element.classList.add(ht),this._element.style[n]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const s=`scroll${n[0].toUpperCase()+n.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct,lt),this._element.style[n]="",j.trigger(this._element,"shown.bs.collapse")}),this._element,!0),this._element.style[n]=`${this._element[s]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(j.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,u(this._element),this._element.classList.add(ht),this._element.classList.remove(ct,lt);const e=this._triggerArray.length;for(let t=0;t<e;t++){const e=this._triggerArray[t],i=n(e);i&&!this._isShown(i)&&this._addAriaAndCollapsedClass([e],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct),j.trigger(this._element,"hidden.bs.collapse")}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(lt)}_getConfig(t){return(t={...rt,...U.getDataAttributes(this._element),...t}).toggle=Boolean(t.toggle),t.parent=r(t.parent),a(ot,t,at),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=V.find(ut,this._config.parent);V.find(ft,this._config.parent).filter((e=>!t.includes(e))).forEach((t=>{const e=n(t);e&&this._addAriaAndCollapsedClass([t],this._isShown(e))}))}_addAriaAndCollapsedClass(t,e){t.length&&t.forEach((t=>{e?t.classList.remove(dt):t.classList.add(dt),t.setAttribute("aria-expanded",e)}))}static jQueryInterface(t){return this.each((function(){const e={};"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1);const i=pt.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}j.on(document,"click.bs.collapse.data-api",ft,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=i(this);V.find(e).forEach((t=>{pt.getOrCreateInstance(t,{toggle:!1}).toggle()}))})),g(pt);var mt="top",gt="bottom",_t="right",bt="left",vt="auto",yt=[mt,gt,_t,bt],wt="start",Et="end",At="clippingParents",Tt="viewport",Ot="popper",Ct="reference",kt=yt.reduce((function(t,e){return t.concat([e+"-"+wt,e+"-"+Et])}),[]),Lt=[].concat(yt,[vt]).reduce((function(t,e){return t.concat([e,e+"-"+wt,e+"-"+Et])}),[]),xt="beforeRead",Dt="read",St="afterRead",Nt="beforeMain",It="main",Pt="afterMain",jt="beforeWrite",Mt="write",Ht="afterWrite",Bt=[xt,Dt,St,Nt,It,Pt,jt,Mt,Ht];function Rt(t){return t?(t.nodeName||"").toLowerCase():null}function Wt(t){if(null==t)return window;if("[object Window]"!==t.toString()){var e=t.ownerDocument;return e&&e.defaultView||window}return t}function $t(t){return t instanceof Wt(t).Element||t instanceof Element}function zt(t){return t instanceof Wt(t).HTMLElement||t instanceof HTMLElement}function qt(t){return"undefined"!=typeof ShadowRoot&&(t instanceof Wt(t).ShadowRoot||t instanceof ShadowRoot)}const Ft={name:"applyStyles",enabled:!0,phase:"write",fn:function(t){var e=t.state;Object.keys(e.elements).forEach((function(t){var i=e.styles[t]||{},n=e.attributes[t]||{},s=e.elements[t];zt(s)&&Rt(s)&&(Object.assign(s.style,i),Object.keys(n).forEach((function(t){var e=n[t];!1===e?s.removeAttribute(t):s.setAttribute(t,!0===e?"":e)})))}))},effect:function(t){var e=t.state,i={popper:{position:e.options.strategy,left:"0",top:"0",margin:"0"},arrow:{position:"absolute"},reference:{}};return 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Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function We(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?Be:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},Be,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:$t(t)?Ae(t):t.contextElement?Ae(t.contextElement):[],popper:Ae(e)};var r,c,u=function(t){var e=He(t);return Bt.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return 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Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new 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this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" 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null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" 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this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const 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) 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-.bi::before,
-[class^="bi-"]::before,
-[class*=" bi-"]::before {
-display: inline-block;
-font-family: bootstrap-icons !important;
-font-style: normal;
-font-weight: normal !important;
-font-variant: normal;
-text-transform: none;
-line-height: 1;
-vertical-align: -.125em;
--webkit-font-smoothing: antialiased;
--moz-osx-font-smoothing: grayscale;
-}
-.bi-123::before { content: "\f67f"; }
-.bi-alarm-fill::before { content: "\f101"; }
-.bi-alarm::before { content: "\f102"; }
-.bi-align-bottom::before { content: "\f103"; }
-.bi-align-center::before { content: "\f104"; }
-.bi-align-end::before { content: "\f105"; }
-.bi-align-middle::before { content: "\f106"; }
-.bi-align-start::before { content: "\f107"; }
-.bi-align-top::before { content: "\f108"; }
-.bi-alt::before { content: "\f109"; }
-.bi-app-indicator::before { content: "\f10a"; }
-.bi-app::before { content: "\f10b"; }
-.bi-archive-fill::before { content: "\f10c"; }
-.bi-archive::before { content: "\f10d"; }
-.bi-arrow-90deg-down::before { content: "\f10e"; }
-.bi-arrow-90deg-left::before { content: "\f10f"; }
-.bi-arrow-90deg-right::before { content: "\f110"; }
-.bi-arrow-90deg-up::before { content: "\f111"; }
-.bi-arrow-bar-down::before { content: "\f112"; }
-.bi-arrow-bar-left::before { content: "\f113"; }
-.bi-arrow-bar-right::before { content: "\f114"; }
-.bi-arrow-bar-up::before { content: "\f115"; }
-.bi-arrow-clockwise::before { content: "\f116"; }
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-.bi-arrow-down-circle-fill::before { content: "\f118"; }
-.bi-arrow-down-circle::before { content: "\f119"; }
-.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
-.bi-arrow-down-left-circle::before { content: "\f11b"; }
-.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
-.bi-arrow-down-left-square::before { content: "\f11d"; }
-.bi-arrow-down-left::before { content: "\f11e"; }
-.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
-.bi-arrow-down-right-circle::before { content: "\f120"; }
-.bi-arrow-down-right-square-fill::before { content: "\f121"; }
-.bi-arrow-down-right-square::before { content: "\f122"; }
-.bi-arrow-down-right::before { content: "\f123"; }
-.bi-arrow-down-short::before { content: "\f124"; }
-.bi-arrow-down-square-fill::before { content: "\f125"; }
-.bi-arrow-down-square::before { content: "\f126"; }
-.bi-arrow-down-up::before { content: "\f127"; }
-.bi-arrow-down::before { content: "\f128"; }
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-.bi-arrow-left-square::before { content: "\f12e"; }
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-.bi-arrow-right-circle::before { content: "\f134"; }
-.bi-arrow-right-short::before { content: "\f135"; }
-.bi-arrow-right-square-fill::before { content: "\f136"; }
-.bi-arrow-right-square::before { content: "\f137"; }
-.bi-arrow-right::before { content: "\f138"; }
-.bi-arrow-up-circle-fill::before { content: "\f139"; }
-.bi-arrow-up-circle::before { content: "\f13a"; }
-.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
-.bi-arrow-up-left-circle::before { content: "\f13c"; }
-.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
-.bi-arrow-up-left-square::before { content: "\f13e"; }
-.bi-arrow-up-left::before { content: "\f13f"; }
-.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
-.bi-arrow-up-right-circle::before { content: "\f141"; }
-.bi-arrow-up-right-square-fill::before { content: "\f142"; }
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-.bi-train-front::before { content: "\f81d"; }
-.bi-train-lightrail-front-fill::before { content: "\f81e"; }
-.bi-train-lightrail-front::before { content: "\f81f"; }
-.bi-truck-front-fill::before { content: "\f820"; }
-.bi-truck-front::before { content: "\f821"; }
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-.bi-ev-station::before { content: "\f83a"; }
-.bi-fuel-pump-diesel-fill::before { content: "\f83b"; }
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-.bi-fuel-pump-fill::before { content: "\f83d"; }
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-.bi-rocket-takeoff-fill::before { content: "\f844"; }
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-.bi-sign-do-not-enter::before { content: "\f852"; }
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-.bi-sign-no-parking-fill::before { content: "\f861"; }
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-.bi-sign-no-right-turn-fill::before { content: "\f863"; }
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-.bi-sign-railroad-fill::before { content: "\f865"; }
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-.bi-building-check::before { content: "\f868"; }
-.bi-building-dash::before { content: "\f869"; }
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-.bi-building-exclamation::before { content: "\f86b"; }
-.bi-building-fill-add::before { content: "\f86c"; }
-.bi-building-fill-check::before { content: "\f86d"; }
-.bi-building-fill-dash::before { content: "\f86e"; }
-.bi-building-fill-down::before { content: "\f86f"; }
-.bi-building-fill-exclamation::before { content: "\f870"; }
-.bi-building-fill-gear::before { content: "\f871"; }
-.bi-building-fill-lock::before { content: "\f872"; }
-.bi-building-fill-slash::before { content: "\f873"; }
-.bi-building-fill-up::before { content: "\f874"; }
-.bi-building-fill-x::before { content: "\f875"; }
-.bi-building-fill::before { content: "\f876"; }
-.bi-building-gear::before { content: "\f877"; }
-.bi-building-lock::before { content: "\f878"; }
-.bi-building-slash::before { content: "\f879"; }
-.bi-building-up::before { content: "\f87a"; }
-.bi-building-x::before { content: "\f87b"; }
-.bi-buildings-fill::before { content: "\f87c"; }
-.bi-buildings::before { content: "\f87d"; }
-.bi-bus-front-fill::before { content: "\f87e"; }
-.bi-bus-front::before { content: "\f87f"; }
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-.bi-globe-central-south-asia::before { content: "\f884"; }
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-.bi-house-add::before { content: "\f887"; }
-.bi-house-check-fill::before { content: "\f888"; }
-.bi-house-check::before { content: "\f889"; }
-.bi-house-dash-fill::before { content: "\f88a"; }
-.bi-house-dash::before { content: "\f88b"; }
-.bi-house-down-fill::before { content: "\f88c"; }
-.bi-house-down::before { content: "\f88d"; }
-.bi-house-exclamation-fill::before { content: "\f88e"; }
-.bi-house-exclamation::before { content: "\f88f"; }
-.bi-house-gear-fill::before { content: "\f890"; }
-.bi-house-gear::before { content: "\f891"; }
-.bi-house-lock-fill::before { content: "\f892"; }
-.bi-house-lock::before { content: "\f893"; }
-.bi-house-slash-fill::before { content: "\f894"; }
-.bi-house-slash::before { content: "\f895"; }
-.bi-house-up-fill::before { content: "\f896"; }
-.bi-house-up::before { content: "\f897"; }
-.bi-house-x-fill::before { content: "\f898"; }
-.bi-house-x::before { content: "\f899"; }
-.bi-person-add::before { content: "\f89a"; }
-.bi-person-down::before { content: "\f89b"; }
-.bi-person-exclamation::before { content: "\f89c"; }
-.bi-person-fill-add::before { content: "\f89d"; }
-.bi-person-fill-check::before { content: "\f89e"; }
-.bi-person-fill-dash::before { content: "\f89f"; }
-.bi-person-fill-down::before { content: "\f8a0"; }
-.bi-person-fill-exclamation::before { content: "\f8a1"; }
-.bi-person-fill-gear::before { content: "\f8a2"; }
-.bi-person-fill-lock::before { content: "\f8a3"; }
-.bi-person-fill-slash::before { content: "\f8a4"; }
-.bi-person-fill-up::before { content: "\f8a5"; }
-.bi-person-fill-x::before { content: "\f8a6"; }
-.bi-person-gear::before { content: "\f8a7"; }
-.bi-person-lock::before { content: "\f8a8"; }
-.bi-person-slash::before { content: "\f8a9"; }
-.bi-person-up::before { content: "\f8aa"; }
-.bi-scooter::before { content: "\f8ab"; }
-.bi-taxi-front-fill::before { content: "\f8ac"; }
-.bi-taxi-front::before { content: "\f8ad"; }
-.bi-amd::before { content: "\f8ae"; }
-.bi-database-add::before { content: "\f8af"; }
-.bi-database-check::before { content: "\f8b0"; }
-.bi-database-dash::before { content: "\f8b1"; }
-.bi-database-down::before { content: "\f8b2"; }
-.bi-database-exclamation::before { content: "\f8b3"; }
-.bi-database-fill-add::before { content: "\f8b4"; }
-.bi-database-fill-check::before { content: "\f8b5"; }
-.bi-database-fill-dash::before { content: "\f8b6"; }
-.bi-database-fill-down::before { content: "\f8b7"; }
-.bi-database-fill-exclamation::before { content: "\f8b8"; }
-.bi-database-fill-gear::before { content: "\f8b9"; }
-.bi-database-fill-lock::before { content: "\f8ba"; }
-.bi-database-fill-slash::before { content: "\f8bb"; }
-.bi-database-fill-up::before { content: "\f8bc"; }
-.bi-database-fill-x::before { content: "\f8bd"; }
-.bi-database-fill::before { content: "\f8be"; }
-.bi-database-gear::before { content: "\f8bf"; }
-.bi-database-lock::before { content: "\f8c0"; }
-.bi-database-slash::before { content: "\f8c1"; }
-.bi-database-up::before { content: "\f8c2"; }
-.bi-database-x::before { content: "\f8c3"; }
-.bi-database::before { content: "\f8c4"; }
-.bi-houses-fill::before { content: "\f8c5"; }
-.bi-houses::before { content: "\f8c6"; }
-.bi-nvidia::before { content: "\f8c7"; }
-.bi-person-vcard-fill::before { content: "\f8c8"; }
-.bi-person-vcard::before { content: "\f8c9"; }
-.bi-sina-weibo::before { content: "\f8ca"; }
-.bi-tencent-qq::before { content: "\f8cb"; }
-.bi-wikipedia::before { content: "\f8cc"; }
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-<header id="title-block-header" class="quarto-title-block default">
-<div class="quarto-title">
-<h1 class="title">LLM Evaluation</h1>
-</div>
-
-
-
-<div class="quarto-title-meta">
-
-    
-  
-    
-  </div>
-  
-
-</header>
-
-<ul>
-<li><p>100% compatible with the huggingface🤗 ecosystem</p></li>
-<li><p>Evaluate any LLM with 3 lines:</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> transformers <span class="im">import</span> pipeline</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>pipe <span class="op">=</span> pipeline(model<span class="op">=</span><span class="st">&quot;gpt2&quot;</span>, device<span class="op">=</span><span class="dv">0</span>)</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> evaluate <span class="im">import</span> EvaluationSuite, Evaluator</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>suite <span class="op">=</span> EvaluationSuite.load(<span class="st">&quot;sustech/llm-evaluate&quot;</span>)</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>results<span class="op">=</span>suite.run(pipe)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div></li>
-</ul>
-
-</main>
-<!-- /main column -->
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diff --git a/spaces/SarthakSidhant/Go-Cattle/diseases/nitrate poisoning.md b/spaces/SarthakSidhant/Go-Cattle/diseases/nitrate poisoning.md
deleted file mode 100644
index 681d128c69f6c10dc303508978885f859d47dd7d..0000000000000000000000000000000000000000
--- a/spaces/SarthakSidhant/Go-Cattle/diseases/nitrate poisoning.md	
+++ /dev/null
@@ -1,34 +0,0 @@
-## Nitrate poisoning
-
-**Information:** Nitrate poisoning is a condition that affects cattle. It is caused by the ingestion of excessive amounts of nitrates.
-
-**Symptoms:**
-
-* Weakness
-* Pale mucous membranes
-* Rapid heart rate
-* Restlessness
-* Seizures
-* Coma
-
-**Remedies:**
-
-* Nitrate poisoning is a medical emergency and requires immediate treatment.
-* Treatment usually involves giving the cow an injection of methylene blue to help convert the nitrates into a less harmful form.
-* The cow may also need to be given fluids and electrolytes to prevent dehydration.
-* In severe cases, the cow may need to be hospitalized.
-
-**Causes:**
-
-* Nitrate poisoning is caused by the ingestion of excessive amounts of nitrates. Nitrates are found in some plants, such as legumes, and in some fertilizers.
-* Cattle are more likely to develop nitrate poisoning if they are fed a diet that is high in grain and low in forage.
-* Nitrate poisoning can also occur if cattle eat snow or ice that has melted from fields that have been fertilized with nitrate-containing fertilizers.
-
-**Prevention:**
-
-* The best way to prevent nitrate poisoning is to feed cattle a diet that is balanced in carbohydrates and fiber.
-* Cattle should also be provided with plenty of fresh water.
-* Cattle should not be fed snow or ice that has melted from fields that have been fertilized with nitrate-containing fertilizers.
-* Animals should be monitored for signs of nitrate poisoning, such as weakness, pale mucous membranes, and a rapid heart rate.
-* If an animal is suspected of having nitrate poisoning, it should be taken to a veterinarian immediately for diagnosis and treatment.
-
diff --git a/spaces/ServerX/PorcoDiaz/infer/lib/csvutil.py b/spaces/ServerX/PorcoDiaz/infer/lib/csvutil.py
deleted file mode 100644
index 79f432b6933f181d9194c50581656f2fd6e66c0c..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/infer/lib/csvutil.py
+++ /dev/null
@@ -1,41 +0,0 @@
-
-import numpy as np
-
-# import praatio
-# import praatio.praat_scripts
-import os
-import sys
-
-import random
-
-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/ShaunWithGPT/ChuanhuChatGPT/presets.py b/spaces/ShaunWithGPT/ChuanhuChatGPT/presets.py
deleted file mode 100644
index 53352fd7219c70394a4227bc53cbd190b1ac1970..0000000000000000000000000000000000000000
--- a/spaces/ShaunWithGPT/ChuanhuChatGPT/presets.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# -*- coding:utf-8 -*-
-# 错误信息
-standard_error_msg = "☹️发生了错误："  # 错误信息的标准前缀
-error_retrieve_prompt = "请检查网络连接，或者API-Key是否有效。"  # 获取对话时发生错误
-connection_timeout_prompt = "连接超时，无法获取对话。"  # 连接超时
-read_timeout_prompt = "读取超时，无法获取对话。"  # 读取超时
-proxy_error_prompt = "代理错误，无法获取对话。"  # 代理错误
-ssl_error_prompt = "SSL错误，无法获取对话。"  # SSL 错误
-no_apikey_msg = "API key长度不是51位，请检查是否输入正确。"  # API key 长度不足 51 位
-
-max_token_streaming = 3500  # 流式对话时的最大 token 数
-timeout_streaming = 30  # 流式对话时的超时时间
-max_token_all = 3500  # 非流式对话时的最大 token 数
-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
-
-SIM_K = 5
-INDEX_QUERY_TEMPRATURE = 1.0
-
-title = """<h1 align="left" style="min-width:200px; margin-top:0;">川虎ChatGPT 🚀</h1>"""
-description = """\
-<div align="center" style="margin:16px 0">
-
-由Bilibili [土川虎虎虎](https://space.bilibili.com/29125536) 和 [明昭MZhao](https://space.bilibili.com/24807452)开发
-
-访问川虎ChatGPT的 [GitHub项目](https://github.com/GaiZhenbiao/ChuanhuChatGPT) 下载最新版脚本
-
-此App使用 `gpt-3.5-turbo` 大语言模型
-</div>
-"""
-
-summarize_prompt = "你是谁？我们刚才聊了什么？"  # 总结对话时的 prompt
-
-MODELS = [
-    "gpt-3.5-turbo",
-    "gpt-3.5-turbo-0301",
-    "gpt-4",
-    "gpt-4-0314",
-    "gpt-4-32k",
-    "gpt-4-32k-0314",
-]  # 可选的模型
-
-
-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 中文"""
-
-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 中文
-"""
-
-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
-Answer in the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch.
-If the context isn't useful, return the original answer.
-"""
diff --git a/spaces/Shredder/CONBERT-3/README.md b/spaces/Shredder/CONBERT-3/README.md
deleted file mode 100644
index c5126cc0e261a961ce9c471ae2dc79614c8c9ce1..0000000000000000000000000000000000000000
--- a/spaces/Shredder/CONBERT-3/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CONBERT 3
-emoji: 📈
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Shruhrid/IMDB_movie_review/README.md b/spaces/Shruhrid/IMDB_movie_review/README.md
deleted file mode 100644
index 14e1ce4bb89260233ce86656e89c3e7511d0d8a6..0000000000000000000000000000000000000000
--- a/spaces/Shruhrid/IMDB_movie_review/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: IMDB_movie_review
-emoji: 📚
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Siddhant/ESPnet2-SLU/espnet-slu-snips/README.md b/spaces/Siddhant/ESPnet2-SLU/espnet-slu-snips/README.md
deleted file mode 100644
index 31518fadec3039ddd98cd23e6b80d0e28bcf524c..0000000000000000000000000000000000000000
--- a/spaces/Siddhant/ESPnet2-SLU/espnet-slu-snips/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
-Fine-tune snips dataset for SLU task using pretrained ASR model with hubert feature
----
-language: 
-  - en
-  
-receipe: "https://github.com/espnet/espnet/tree/master/egs2/snips/asr1"
-
-datasets:
-- snips: smart-lights-en-close-field
-
-metrics:
-- F1 score: 91.7
----
\ No newline at end of file
diff --git a/spaces/Silence1412/Stable_Diffusion_Cpu/README.md b/spaces/Silence1412/Stable_Diffusion_Cpu/README.md
deleted file mode 100644
index 7954d200c660d61bf6ae399e8da439ce8ea90d6c..0000000000000000000000000000000000000000
--- a/spaces/Silence1412/Stable_Diffusion_Cpu/README.md
+++ /dev/null
@@ -1,35 +0,0 @@
----
-title: Stable Diffusion Cpu
-emoji: 📈
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-# Stable Diffusion Prompt Creator
-
-Created for [this](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion) model trained by Gustavosta for Stable Diffusion to create a prompt from a few words. You can submit your own text or select from provided examples.
-
-## Features
-
--   Generate prompts based on a starting text entered by the user
--   Uses a pre-trained language model to generate the prompt
--   Option to select examples or enter own starting text
-
-## Usage
-
-To use the app, enter a starting text in the input field or select an example from the provided examples. Then click on the "Generate" button to generate a prompt based on the starting text.
-
-## Technical details
-
-The app uses Gradio for the user interface and the Hugging Face Transformers library to access the pre-trained language model. The model is a GPT-2 language model trained for Stable Diffusion.
-
-## Requirements
-
--   Python 3.6 or higher
--   Gradio
--   Transformers (Hugging Face)
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SpacesExamples/streamlit-docker-example/README.md b/spaces/SpacesExamples/streamlit-docker-example/README.md
deleted file mode 100644
index 067932486fffdf33afb09e497f2a4d7627cffaa6..0000000000000000000000000000000000000000
--- a/spaces/SpacesExamples/streamlit-docker-example/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: Streamlit Docker Template
-emoji: 📉
-colorFrom: blue
-colorTo: green
-sdk: docker
-app_port: 8501
-pinned: false
----
-
-## 🧠 Streamlit Docker Template 🔎
-
-Streamlit Docker Template is a template for creating a Streamlit app with Docker and Hugging Face Spaces.
-
-Code from https://docs.streamlit.io/library/get-started/create-an-app
\ No newline at end of file
diff --git a/spaces/Sreenivas98/FashionMIST_Classification/app.py b/spaces/Sreenivas98/FashionMIST_Classification/app.py
deleted file mode 100644
index e21265b44b946a1ef5fce951aa32e7d8c7ab5d0c..0000000000000000000000000000000000000000
--- a/spaces/Sreenivas98/FashionMIST_Classification/app.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import gradio as gr
-import pickle
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.linear_model import LogisticRegression
-import sklearn
-import tensorflow
-from tensorflow import keras
-from keras.models import Sequential
-from keras.layers import Dense
-from tensorflow.keras.models import load_model
-
-input_1 = gr.Image(shape=(28,28),image_mode='L')
- 
-input_2 = gr.Dropdown(["KNN", "SoftMax", "NeuralNetwork_Shallow", "NeuralNetwork_Deep"])
-
-output = gr.Label(num_top_classes=9)
-
-class_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
-"Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]
-
-def predict_knn(test_img):
-  Knn_model = pickle.load(open('knn_model.pkl', 'rb'))
-  predictions = Knn_model.predict_proba(test_img)
-  print(predictions)
-  return {class_names[i]: float(predictions[0][i]) for i in range(0,10)}
-  
-def predict_softmax(test_img):
-  Softmax_model = pickle.load(open('softmax_model.pkl', 'rb'))
-  predictions = Softmax_model.predict_proba(test_img)
-  return {class_names[i]: float(predictions[0][i]) for i in range(0,10)}
-  
-def predict_neural(test_img):
-  Neural_model = load_model("neural_model.h5")
-  predictions = Neural_model.predict(test_img)
-  return {class_names[i]: float(predictions[0][i]) for i in range(0,10)}
-  
-def predict_deep_neural(test_img):
-  DeepNeural_model = load_model("deep_neural_model.h5")
-  predictions = DeepNeural_model.predict(test_img)
-  return {class_names[i]: float(predictions[0][i]) for i in range(0,10)}
-
-def predictFashionClass(test_img,chosen_model):
-  test_img_flatten=test_img.reshape(-1,28*28)
-  if chosen_model == "KNN":
-    fashionProbs = predict_knn(test_img_flatten)
-    return fashionProbs
-  elif chosen_model == "SoftMax":
-    fashionProbs = predict_softmax(test_img_flatten)
-    return fashionProbs
-    
-  elif chosen_model == "NeuralNetwork_Shallow":
-    fashionProbs = predict_neural(test_img_flatten)
-    return fashionProbs 
-    
-  elif chosen_model == "NeuralNetwork_Deep":
-    fashionProbs = predict_deep_neural(test_img_flatten)
-    return fashionProbs
-    
-gr.Interface(fn=predictFashionClass,inputs=[input_1,input_2],outputs=output).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/compilerop.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/compilerop.py
deleted file mode 100644
index 7799a4fc99e94adb71cf4867af8e29e71757eeb9..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/compilerop.py
+++ /dev/null
@@ -1,214 +0,0 @@
-"""Compiler tools with improved interactive support.
-
-Provides compilation machinery similar to codeop, but with caching support so
-we can provide interactive tracebacks.
-
-Authors
--------
-* Robert Kern
-* Fernando Perez
-* Thomas Kluyver
-"""
-
-# Note: though it might be more natural to name this module 'compiler', that
-# name is in the stdlib and name collisions with the stdlib tend to produce
-# weird problems (often with third-party tools).
-
-#-----------------------------------------------------------------------------
-#  Copyright (C) 2010-2011 The IPython Development Team.
-#
-#  Distributed under the terms of the BSD License.
-#
-#  The full license is in the file COPYING.txt, distributed with this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
-
-# Stdlib imports
-import __future__
-from ast import PyCF_ONLY_AST
-import codeop
-import functools
-import hashlib
-import linecache
-import operator
-import time
-from contextlib import contextmanager
-
-#-----------------------------------------------------------------------------
-# Constants
-#-----------------------------------------------------------------------------
-
-# Roughly equal to PyCF_MASK | PyCF_MASK_OBSOLETE as defined in pythonrun.h,
-# this is used as a bitmask to extract future-related code flags.
-PyCF_MASK = functools.reduce(operator.or_,
-                             (getattr(__future__, fname).compiler_flag
-                              for fname in __future__.all_feature_names))
-
-#-----------------------------------------------------------------------------
-# Local utilities
-#-----------------------------------------------------------------------------
-
-def code_name(code, number=0):
-    """ Compute a (probably) unique name for code for caching.
-
-    This now expects code to be unicode.
-    """
-    hash_digest = hashlib.sha1(code.encode("utf-8")).hexdigest()
-    # Include the number and 12 characters of the hash in the name.  It's
-    # pretty much impossible that in a single session we'll have collisions
-    # even with truncated hashes, and the full one makes tracebacks too long
-    return '<ipython-input-{0}-{1}>'.format(number, hash_digest[:12])
-
-#-----------------------------------------------------------------------------
-# Classes and functions
-#-----------------------------------------------------------------------------
-
-class CachingCompiler(codeop.Compile):
-    """A compiler that caches code compiled from interactive statements.
-    """
-
-    def __init__(self):
-        codeop.Compile.__init__(self)
-
-        # Caching a dictionary { filename: execution_count } for nicely
-        # rendered tracebacks. The filename corresponds to the filename
-        # argument used for the builtins.compile function.
-        self._filename_map = {}
-
-    def ast_parse(self, source, filename='<unknown>', symbol='exec'):
-        """Parse code to an AST with the current compiler flags active.
-
-        Arguments are exactly the same as ast.parse (in the standard library),
-        and are passed to the built-in compile function."""
-        return compile(source, filename, symbol, self.flags | PyCF_ONLY_AST, 1)
-
-    def reset_compiler_flags(self):
-        """Reset compiler flags to default state."""
-        # This value is copied from codeop.Compile.__init__, so if that ever
-        # changes, it will need to be updated.
-        self.flags = codeop.PyCF_DONT_IMPLY_DEDENT
-
-    @property
-    def compiler_flags(self):
-        """Flags currently active in the compilation process.
-        """
-        return self.flags
-
-    def get_code_name(self, raw_code, transformed_code, number):
-        """Compute filename given the code, and the cell number.
-
-        Parameters
-        ----------
-        raw_code : str
-            The raw cell code.
-        transformed_code : str
-            The executable Python source code to cache and compile.
-        number : int
-            A number which forms part of the code's name. Used for the execution
-            counter.
-
-        Returns
-        -------
-        The computed filename.
-        """
-        return code_name(transformed_code, number)
-
-    def format_code_name(self, name):
-        """Return a user-friendly label and name for a code block.
-
-        Parameters
-        ----------
-        name : str
-            The name for the code block returned from get_code_name
-
-        Returns
-        -------
-        A (label, name) pair that can be used in tracebacks, or None if the default formatting should be used.
-        """
-        if name in self._filename_map:
-            return "Cell", "In[%s]" % self._filename_map[name]
-
-    def cache(self, transformed_code, number=0, raw_code=None):
-        """Make a name for a block of code, and cache the code.
-
-        Parameters
-        ----------
-        transformed_code : str
-            The executable Python source code to cache and compile.
-        number : int
-            A number which forms part of the code's name. Used for the execution
-            counter.
-        raw_code : str
-            The raw code before transformation, if None, set to `transformed_code`.
-
-        Returns
-        -------
-        The name of the cached code (as a string). Pass this as the filename
-        argument to compilation, so that tracebacks are correctly hooked up.
-        """
-        if raw_code is None:
-            raw_code = transformed_code
-
-        name = self.get_code_name(raw_code, transformed_code, number)
-
-        # Save the execution count
-        self._filename_map[name] = number
-
-        # Since Python 2.5, setting mtime to `None` means the lines will
-        # never be removed by `linecache.checkcache`.  This means all the
-        # monkeypatching has *never* been necessary, since this code was
-        # only added in 2010, at which point IPython had already stopped
-        # supporting Python 2.4.
-        #
-        # Note that `linecache.clearcache` and `linecache.updatecache` may
-        # still remove our code from the cache, but those show explicit
-        # intent, and we should not try to interfere.  Normally the former
-        # is never called except when out of memory, and the latter is only
-        # called for lines *not* in the cache.
-        entry = (
-            len(transformed_code),
-            None,
-            [line + "\n" for line in transformed_code.splitlines()],
-            name,
-        )
-        linecache.cache[name] = entry
-        return name
-
-    @contextmanager
-    def extra_flags(self, flags):
-        ## bits that we'll set to 1
-        turn_on_bits = ~self.flags & flags
-
-
-        self.flags = self.flags | flags
-        try:
-            yield
-        finally:
-            # turn off only the bits we turned on so that something like
-            # __future__ that set flags stays.
-            self.flags &= ~turn_on_bits
-
-
-def check_linecache_ipython(*args):
-    """Deprecated since IPython 8.6.  Call linecache.checkcache() directly.
-
-    It was already not necessary to call this function directly.  If no
-    CachingCompiler had been created, this function would fail badly.  If
-    an instance had been created, this function would've been monkeypatched
-    into place.
-
-    As of IPython 8.6, the monkeypatching has gone away entirely.  But there
-    were still internal callers of this function, so maybe external callers
-    also existed?
-    """
-    import warnings
-
-    warnings.warn(
-        "Deprecated Since IPython 8.6, Just call linecache.checkcache() directly.",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    linecache.checkcache()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/documents/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/documents/__init__.py
deleted file mode 100644
index aba89edd172aefcf70e9a31709a6d270112872c3..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/documents/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from docarray.documents.audio import AudioDoc
-from docarray.documents.image import ImageDoc
-from docarray.documents.mesh import Mesh3D, VerticesAndFaces
-from docarray.documents.point_cloud import PointCloud3D, PointsAndColors
-from docarray.documents.text import TextDoc
-from docarray.documents.video import VideoDoc
-
-__all__ = [
-    'TextDoc',
-    'ImageDoc',
-    'AudioDoc',
-    'Mesh3D',
-    'VerticesAndFaces',
-    'PointCloud3D',
-    'PointsAndColors',
-    'VideoDoc',
-]
diff --git a/spaces/TRI-ML/risk_biased_prediction/risk_biased/utils/risk.py b/spaces/TRI-ML/risk_biased_prediction/risk_biased/utils/risk.py
deleted file mode 100644
index 17c1ce33dc8386d7605afde8d8ffa97db1410372..0000000000000000000000000000000000000000
--- a/spaces/TRI-ML/risk_biased_prediction/risk_biased/utils/risk.py
+++ /dev/null
@@ -1,571 +0,0 @@
-import inspect
-import math
-import warnings
-from abc import ABC, abstractmethod
-
-import torch
-from torch import Tensor
-
-
-class AbstractMonteCarloRiskEstimator(ABC):
-    """Abstract class for Monte Carlo estimation of risk objectives"""
-
-    @abstractmethod
-    def __call__(self, risk_level: Tensor, cost: Tensor) -> Tensor:
-        """Computes and returns the risk objective estimated on the cost tensor
-
-        Args:
-            risk_level: (batch_size,) tensor of risk-level at which the risk objective is computed
-            cost: (batch_size, n_samples) tensor of cost samples
-        Returns:
-            risk tensor of size (batch_size,)
-        """
-
-
-class EntropicRiskEstimator(AbstractMonteCarloRiskEstimator):
-    """Monte Carlo estimator for the entropic risk objective.
-    This estimator computes the entropic risk as 1/risk_level * log( mean( exp(risk_level * cost), 1))
-    However, this is unstable.
-    When risk_level is large, the logsumexp trick is used.
-    When risk_level is small, it computes entropic_risk for small values of risk_level as the second order Taylor expansion instead.
-
-    Args:
-        eps: Risk-level threshold to switch between logsumexp and Taylor expansion. Defaults to 1e-4.
-    """
-
-    def __init__(self, eps: float = 1e-4) -> None:
-        self.eps = eps
-
-    def __call__(self, risk_level: Tensor, cost: Tensor, weights: Tensor) -> Tensor:
-        """Computes and returns the entropic risk estimated on the cost tensor
-
-        Args:
-            risk_level: (batch_size, n_agents,) tensor of risk-level at which the risk objective is computed
-            cost: (batch_size, n_agents, n_samples) cost tensor
-            weights: (batch_size, n_agents, n_samples) tensor of weights for the cost samples
-        Returns:
-            entropic risk tensor of size (batch_size,)
-        """
-        weights = weights / weights.sum(dim=-1, keepdim=True)
-        batch_size, n_agents, n_samples = cost.shape
-        entropic_risk_cost_large_sigma = (
-            ((risk_level.view(batch_size, n_agents, 1) * cost).exp() * weights)
-            .sum(-1)
-            .log()
-        ) / risk_level
-
-        mean = (cost * weights).sum(dim=-1)
-        var = (cost**2 * weights).sum(dim=-1) - mean**2
-
-        var, mean = torch.var_mean(cost, -1)
-        entropic_risk_cost_small_sigma = mean + 0.5 * risk_level * var
-
-        return torch.where(
-            torch.abs(risk_level) > self.eps,
-            entropic_risk_cost_large_sigma,
-            entropic_risk_cost_small_sigma,
-        )
-
-
-class CVaREstimator(AbstractMonteCarloRiskEstimator):
-    """Monte Carlo estimator for the conditional value-at-risk objective.
-    This estimator is proposed in the following references, and shown to be consistent.
-    - Hong et al. (2014), "Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk: A Review"
-    - Traindade et al. (2007), "Financial prediction with constrained tail risk"
-    When risk_level is larger than 1 - eps, it falls back to the max operator
-
-    Args:
-        Args:
-        eps: Risk-level threshold to switch between CVaR and Max. Defaults to 1e-4.
-    """
-
-    def __init__(self, eps: float = 1e-4) -> None:
-        self.eps = eps
-
-    def __call__(self, risk_level: Tensor, cost: Tensor, weights: Tensor) -> Tensor:
-        """Computes and returns the conditional value-at-risk estimated on the cost tensor
-
-        Args:
-            risk_level: (batch_size, n_agents) tensor of risk-level in [0, 1] at which the CVaR risk is computed
-            cost: (batch_size, n_agents, n_samples) cost tensor
-            weights: (batch_size, n_agents, n_samples) tensor of weights for the cost samples
-
-        Returns:
-            conditional value-at-risk tensor of size (batch_size, n_agents)
-        """
-        assert risk_level.shape[0] == cost.shape[0]
-        assert risk_level.shape[1] == cost.shape[1]
-        if weights is None:
-            weights = torch.ones_like(cost) / cost.shape[-1]
-        else:
-            weights = weights / weights.sum(dim=-1, keepdim=True)
-        if not (torch.all(0.0 <= risk_level) and torch.all(risk_level <= 1.0)):
-            warnings.warn(
-                "risk_level is defined only between 0.0 and 1.0 for CVaR. Exceeded values will be clamped."
-            )
-            risk_level = torch.clamp(risk_level, min=0.0, max=1.0)
-
-        cvar_risk_high = cost.max(dim=-1).values
-
-        sorted_indices = torch.argsort(cost, dim=-1)
-        # cost_sorted = cost.sort(dim=-1, descending=False).values
-        cost_sorted = torch.gather(cost, -1, sorted_indices)
-        weights_sorted = torch.gather(weights, -1, sorted_indices)
-        idx_to_choose = torch.argmax(
-            (weights_sorted.cumsum(dim=-1) >= risk_level.unsqueeze(-1)).float(), -1
-        )
-
-        value_at_risk_mc = cost_sorted.gather(-1, idx_to_choose.unsqueeze(-1)).squeeze(
-            -1
-        )
-
-        # weights_at_risk_mc = 1 - weights_sorted.cumsum(-1).gather(
-        #     -1, idx_to_choose.unsqueeze(-1)
-        # ).squeeze(-1)
-        # cvar_risk_mc = value_at_risk_mc + (
-        #     (torch.relu(cost - value_at_risk_mc.unsqueeze(-1)) * weights).sum(dim=-1)
-        #     / weights_at_risk_mc
-        # )
-        # cvar = torch.where(weights_at_risk_mc < self.eps, cvar_risk_high, cvar_risk_mc)
-
-        cvar_risk_mc = value_at_risk_mc + 1 / (1 - risk_level) * (
-            (torch.relu(cost - value_at_risk_mc.unsqueeze(-1)) * weights).sum(dim=-1)
-        )
-        cvar = torch.where(risk_level > 1 - self.eps, cvar_risk_high, cvar_risk_mc)
-        return cvar
-
-
-def get_risk_estimator(estimator_params: dict) -> AbstractMonteCarloRiskEstimator:
-    """Function that returns the Monte Carlo risk estimator hat matches the given parameters.
-    Tries to give a comprehensive feedback if the parameters are not recognized and raise an error.
-
-    Args:
-        Risk estimator should be one of the following types (with different parameter values as desired) :
-            {"type": "entropic", "eps": 1e-4},
-            {"type": "cvar", "eps": 1e-4}
-
-    Raises:
-        RuntimeError: If the given parameter dictionary does not match one of the expected formats, raise a comprehensive error.
-
-    Returns:
-        A risk estimator matching the given parameters.
-    """
-    known_types = ["entropic", "cvar"]
-    try:
-        if estimator_params["type"].lower() == "entropic":
-            expected_params = inspect.getfullargspec(EntropicRiskEstimator)[0][1:]
-            return EntropicRiskEstimator(estimator_params["eps"])
-        elif estimator_params["type"].lower() == "cvar":
-            expected_params = inspect.getfullargspec(CVaREstimator)[0][1:]
-            return CVaREstimator(estimator_params["eps"])
-        else:
-            raise RuntimeError(
-                f"Risk estimator '{estimator_params['type']}' is unknown. It should be one of {known_types}."
-            )
-    except KeyError:
-        if "type" in estimator_params:
-            raise RuntimeError(
-                f"""The estimator '{estimator_params['type']}' is known but the given parameters
-                {estimator_params} do not match the expected parameters {expected_params}."""
-            )
-        else:
-            raise RuntimeError(
-                f"""The given estimator parameters {estimator_params} do not define the estimator
-                type in the field 'type'. Please add a field 'type' and set it to one of the
-                handeled types: {known_types}."""
-            )
-
-
-class AbstractRiskLevelSampler(ABC):
-    """Abstract class for a risk-level sampler for training and evaluating risk-biased predictors"""
-
-    @abstractmethod
-    def sample(self, batch_size: int, device: torch.device) -> Tensor:
-        """Returns a tensor of size batch_size with sampled risk-level values
-
-        Args:
-            batch_size: number of elements in the out tensor
-            device: device of the output tensor
-
-        Returns:
-            A tensor of shape(batch_size,) filled with sampled risk values
-        """
-
-    @abstractmethod
-    def get_highest_risk(self, batch_size: int, device: torch.device) -> Tensor:
-        """Returns a tensor of size batch_size with high values of risk.
-
-        Args:
-            batch_size: number of elements in the out tensor
-            device: device of the output tensor
-
-        Returns:
-            A tensor of shape (batchc_size,) filled with the highest possible risk-level
-        """
-
-
-class UniformRiskLevelSampler(AbstractRiskLevelSampler):
-    """Risk-level sampler with a uniform distribution
-
-    Args:
-        min: minimum risk-level
-        max: maximum risk-level
-    """
-
-    def __init__(self, min: int, max: int) -> None:
-        self.min = min
-        self.max = max
-
-    def sample(self, batch_size: int, device: torch.device) -> Tensor:
-        return torch.rand(batch_size, device=device) * (self.max - self.min) + self.min
-
-    def get_highest_risk(self, batch_size: int, device: torch.device) -> Tensor:
-        return torch.ones(batch_size, device=device) * self.max
-
-
-class NormalRiskLevelSampler(AbstractRiskLevelSampler):
-    """Risk-level sampler with a normal distribution
-
-    Args:
-        mean: average risk-level
-        sigma: standard deviation of the sampler
-    """
-
-    def __init__(self, mean: int, sigma: int) -> None:
-        self.mean = mean
-        self.sigma = sigma
-
-    def sample(self, batch_size: int, device: torch.device) -> Tensor:
-        return torch.randn(batch_size, device=device) * self.sigma + self.mean
-
-    def get_highest_risk(self, batch_size: int, device: torch.device) -> Tensor:
-        return torch.ones(batch_size, device=device) * self.sigma * 3
-
-
-class BernoulliRiskLevelSampler(AbstractRiskLevelSampler):
-    """Risk-level sampler with a scaled Bernoulli distribution
-
-    Args:
-        min: minimum risk-level
-        max: maximum risk-level
-        p: Bernoulli parameter
-    """
-
-    def __init__(self, min: int, max: int, p: int) -> None:
-        self.min = min
-        self.max = max
-        self.p = p
-
-    def sample(self, batch_size: int, device: torch.device) -> Tensor:
-        return (
-            torch.bernoulli(torch.ones(batch_size, device=device) * self.p)
-            * (self.max - self.min)
-            + self.min
-        )
-
-    def get_highest_risk(self, batch_size: int, device: torch.device) -> Tensor:
-        return torch.ones(batch_size, device=device) * self.max
-
-
-class BetaRiskLevelSampler(AbstractRiskLevelSampler):
-    """Risk-level sampler with a scaled Beta distribution
-
-        Distribution properties:
-            mean = alpha*(max-min)/(alpha + beta) + min
-            mode = (alpha-1)*(max-min)/(alpha + beta - 2) + min
-            variance = alpha*beta*(max-min)**2/((alpha+beta)**2*(alpha+beta+1))
-
-    Args:
-        min: minimum risk-level
-        max: maximum risk-level
-        alpha: First distribution parameter
-        beta: Second distribution parameter
-    """
-
-    def __init__(self, min: int, max: int, alpha: float, beta: float) -> None:
-        self.min = min
-        self.max = max
-        self._distribution = torch.distributions.Beta(
-            torch.tensor([alpha], dtype=torch.float32),
-            torch.tensor([beta], dtype=torch.float32),
-        )
-
-    @property
-    def alpha(self):
-        return self._distribution.concentration1.item()
-
-    @alpha.setter
-    def alpha(self, alpha: float):
-        self._distribution = torch.distributions.Beta(
-            torch.tensor([alpha], dtype=torch.float32),
-            torch.tensor([self.beta], dtype=torch.float32),
-        )
-
-    @property
-    def beta(self):
-        return self._distribution.concentration0.item()
-
-    @beta.setter
-    def beta(self, beta: float):
-        self._distribution = torch.distributions.Beta(
-            torch.tensor([self.alpha], dtype=torch.float32),
-            torch.tensor([beta], dtype=torch.float32),
-        )
-
-    def sample(self, batch_size: int, device: torch.device) -> Tensor:
-        return (
-            self._distribution.sample((batch_size,)).to(device) * (self.max - self.min)
-            + self.min
-        ).view(batch_size)
-
-    def get_highest_risk(self, batch_size: int, device: torch.device) -> Tensor:
-        return torch.ones(batch_size, device=device) * self.max
-
-
-class Chi2RiskLevelSampler(AbstractRiskLevelSampler):
-    """Risk-level sampler with a scaled chi2 distribution
-
-        Distribution properties:
-            mean = k*scale + min
-            mode = max(k-2, 0)*scale + min
-            variance = 2*k*scale**2
-
-    Args:
-        min: minimum risk-level
-        scale: scaling factor for the risk-level
-        k: Chi2 parameter: degrees of freedom of the distribution
-    """
-
-    def __init__(self, min: int, scale: float, k: int) -> None:
-        self.min = min
-        self.scale = scale
-        self._distribution = torch.distributions.Chi2(
-            torch.tensor([k], dtype=torch.float32)
-        )
-
-    @property
-    def k(self):
-        return self._distribution.df.item()
-
-    @k.setter
-    def k(self, k: int):
-        self._distribution = torch.distributions.Chi2(
-            torch.tensor([k], dtype=torch.float32)
-        )
-
-    def sample(self, batch_size: int, device: torch.device) -> Tensor:
-        return (
-            self._distribution.sample((batch_size,)).to(device) * self.scale + self.min
-        ).view(batch_size)
-
-    def get_highest_risk(self, batch_size: int, device: torch.device) -> Tensor:
-        std = self.scale * math.sqrt(2 * self.k)
-        return torch.ones(batch_size, device=device) * std * 3
-
-
-class LogNormalRiskLevelSampler(AbstractRiskLevelSampler):
-    """Risk-level sampler with a scaled Beta distribution
-
-        Distribution properties:
-            mean = exp(mu + sigma**2/2)*scale + min
-            mode = exp(mu - sigma**2)*scale + min
-            variance = (exp(sigma**2)-1)*exp(2*mu+sigma**2)*scale**2
-
-    Args:
-        min: minimum risk-level
-        scale: scaling factor for the risk-level
-        mu: First distribution parameter
-        sigma: maximum risk-level
-    """
-
-    def __init__(self, min: int, scale: float, mu: float, sigma: float) -> None:
-        self.min = min
-        self.scale = scale
-        self._distribution = torch.distributions.LogNormal(
-            torch.tensor([mu], dtype=torch.float32),
-            torch.tensor([sigma], dtype=torch.float32),
-        )
-
-    @property
-    def mu(self):
-        return self._distribution.loc.item()
-
-    @mu.setter
-    def mu(self, mu: float):
-        self._distribution = torch.distributions.LogNormal(
-            torch.tensor([mu], dtype=torch.float32),
-            torch.tensor([self.sigma], dtype=torch.float32),
-        )
-
-    @property
-    def sigma(self) -> float:
-        return self._distribution.scale.item()
-
-    @sigma.setter
-    def sigma(self, sigma: float):
-        self._distribution = torch.distributions.LogNormal(
-            torch.tensor([self.mu], dtype=torch.float32),
-            torch.tensor([sigma], dtype=torch.float32),
-        )
-
-    def sample(self, batch_size: int, device: torch.device) -> Tensor:
-        return (
-            self._distribution.sample((batch_size,)).to(device) * self.scale + self.min
-        ).view(batch_size)
-
-    def get_highest_risk(self, batch_size: int, device: torch.device) -> Tensor:
-        std = (
-            (torch.exp(self.sigma.square()) - 1).sqrt()
-            * torch.exp(self.mu + self.sigma.square() / 2)
-            * self.scale
-        )
-        return torch.ones(batch_size, device=device) * 3 * std
-
-
-class LogUniformRiskLevelSampler(AbstractRiskLevelSampler):
-    """Risk-level sampler with a reversed log-uniform distribution (increasing density function). Between min and max.
-
-        Distribution properties:
-            mean = (max - min)/ln((max+1)/(min+1)) - 1/scale
-            mode = None
-            variance = (((max+1)^2 - (min+1)^2)/(2*ln((max+1)/(min+1))) - ((max - min)/ln((max+1)/(min+1)))^2)
-
-    Args:
-        min: minimum risk-level
-        max: maximum risk-level
-        scale: scale to apply to the sampling before applying exponential,
-        the output is rescaled back to fit in bounds [min, max] (the higher the scale the less uniform the distribution)
-    """
-
-    def __init__(self, min: float, max: float, scale: float) -> None:
-        assert min >= 0
-        assert max > min
-        assert scale > 0
-        self.min = min
-        self.max = max
-        self.scale = scale
-
-    def sample(self, batch_size: int, device: torch.device) -> Tensor:
-        scale = self.scale / (self.max - self.min)
-        max = self.max * scale
-        min = self.min * scale
-        return (
-            max
-            - (
-                (
-                    torch.rand(batch_size, device=device)
-                    * (math.log(max + 1) - math.log(min + 1))
-                    + math.log(min + 1)
-                ).exp()
-                - 1
-            )
-            + min
-        ) / scale
-
-    def get_highest_risk(self, batch_size: int, device: torch.device) -> Tensor:
-        return torch.ones(batch_size, device=device) * self.max
-
-
-def get_risk_level_sampler(distribution_params: dict) -> AbstractRiskLevelSampler:
-    """Function that returns the risk level sampler that matches the given parameters.
-    Tries to give a comprehensive feedback if the parameters are not recognized and raise an error.
-
-    Args:
-        Risk distribution should be one of the following types (with different parameter values as desired) :
-            {"type": "uniform", "min": 0, "max": 1},
-            {"type": "normal", "mean": 0, "sigma": 1},
-            {"type": "bernoulli", "p": 0.5, "min": 0, "max": 1},
-            {"type": "beta", "alpha": 2, "beta": 5, "min": 0, "max": 1},
-            {"type": "chi2", "k": 3, "min": 0, "scale": 1},
-            {"type": "log-normal", "mu": 0, "sigma": 1, "min": 0, "scale": 1}
-            {"type": "log-uniform", "min": 0, "max": 1, "scale": 1}
-
-    Raises:
-        RuntimeError: If the given parameter dictionary does not match one of the expected formats, raise a comprehensive error.
-
-    Returns:
-        A risk level sampler matching the given parameters.
-    """
-    known_types = [
-        "uniform",
-        "normal",
-        "bernoulli",
-        "beta",
-        "chi2",
-        "log-normal",
-        "log-uniform",
-    ]
-    try:
-        if distribution_params["type"].lower() == "uniform":
-            expected_params = inspect.getfullargspec(UniformRiskLevelSampler)[0][1:]
-            return UniformRiskLevelSampler(
-                distribution_params["min"], distribution_params["max"]
-            )
-        elif distribution_params["type"].lower() == "normal":
-            expected_params = inspect.getfullargspec(NormalRiskLevelSampler)[0][1:]
-            return NormalRiskLevelSampler(
-                distribution_params["mean"], distribution_params["sigma"]
-            )
-        elif distribution_params["type"].lower() == "bernoulli":
-            expected_params = inspect.getfullargspec(BernoulliRiskLevelSampler)[0][1:]
-            return BernoulliRiskLevelSampler(
-                distribution_params["min"],
-                distribution_params["max"],
-                distribution_params["p"],
-            )
-        elif distribution_params["type"].lower() == "beta":
-            expected_params = inspect.getfullargspec(BetaRiskLevelSampler)[0][1:]
-            return BetaRiskLevelSampler(
-                distribution_params["min"],
-                distribution_params["max"],
-                distribution_params["alpha"],
-                distribution_params["beta"],
-            )
-        elif distribution_params["type"].lower() == "chi2":
-            expected_params = inspect.getfullargspec(Chi2RiskLevelSampler)[0][1:]
-            return Chi2RiskLevelSampler(
-                distribution_params["min"],
-                distribution_params["scale"],
-                distribution_params["k"],
-            )
-        elif distribution_params["type"].lower() == "log-normal":
-            expected_params = inspect.getfullargspec(LogNormalRiskLevelSampler)[0][1:]
-            return LogNormalRiskLevelSampler(
-                distribution_params["min"],
-                distribution_params["scale"],
-                distribution_params["mu"],
-                distribution_params["sigma"],
-            )
-        elif distribution_params["type"].lower() == "log-uniform":
-            expected_params = inspect.getfullargspec(LogUniformRiskLevelSampler)[0][1:]
-            return LogUniformRiskLevelSampler(
-                distribution_params["min"],
-                distribution_params["max"],
-                distribution_params["scale"],
-            )
-        else:
-            raise RuntimeError(
-                f"Distribution {distribution_params['type']} is unknown. It should be one of {known_types}."
-            )
-    except KeyError:
-        if "type" in distribution_params:
-            raise RuntimeError(
-                f"The distribution '{distribution_params['type']}' is known but the given parameters {distribution_params} do not match the expected parameters {expected_params}."
-            )
-        else:
-            raise RuntimeError(
-                f"The given distribution parameters {distribution_params} do not define the distribution type in the field 'type'. Please add a field 'type' and set it to one of the handeled types: {known_types}."
-            )
-
-
-if __name__ == "__main__":
-    import matplotlib.pyplot as plt
-
-    sampler = get_risk_level_sampler(
-        {"type": "log-uniform", "min": 0, "max": 1, "scale": 10}
-    )
-    # sampler = get_risk_level_sampler({"type": "normal", "mean": 0, "sigma": 1})
-    a = sampler.sample(10000, "cpu").detach().numpy()
-    _ = plt.hist(a, bins="auto")  # arguments are passed to np.histogram
-    plt.title("Histogram with 'auto' bins")
-    plt.show()
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/locations/__init__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/locations/__init__.py
deleted file mode 100644
index d54bc63eba364bda3f869a0f3b1863b872f9682a..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/locations/__init__.py
+++ /dev/null
@@ -1,467 +0,0 @@
-import functools
-import logging
-import os
-import pathlib
-import sys
-import sysconfig
-from typing import Any, Dict, Generator, Optional, Tuple
-
-from pip._internal.models.scheme import SCHEME_KEYS, Scheme
-from pip._internal.utils.compat import WINDOWS
-from pip._internal.utils.deprecation import deprecated
-from pip._internal.utils.virtualenv import running_under_virtualenv
-
-from . import _sysconfig
-from .base import (
-    USER_CACHE_DIR,
-    get_major_minor_version,
-    get_src_prefix,
-    is_osx_framework,
-    site_packages,
-    user_site,
-)
-
-__all__ = [
-    "USER_CACHE_DIR",
-    "get_bin_prefix",
-    "get_bin_user",
-    "get_major_minor_version",
-    "get_platlib",
-    "get_purelib",
-    "get_scheme",
-    "get_src_prefix",
-    "site_packages",
-    "user_site",
-]
-
-
-logger = logging.getLogger(__name__)
-
-
-_PLATLIBDIR: str = getattr(sys, "platlibdir", "lib")
-
-_USE_SYSCONFIG_DEFAULT = sys.version_info >= (3, 10)
-
-
-def _should_use_sysconfig() -> bool:
-    """This function determines the value of _USE_SYSCONFIG.
-
-    By default, pip uses sysconfig on Python 3.10+.
-    But Python distributors can override this decision by setting:
-        sysconfig._PIP_USE_SYSCONFIG = True / False
-    Rationale in https://github.com/pypa/pip/issues/10647
-
-    This is a function for testability, but should be constant during any one
-    run.
-    """
-    return bool(getattr(sysconfig, "_PIP_USE_SYSCONFIG", _USE_SYSCONFIG_DEFAULT))
-
-
-_USE_SYSCONFIG = _should_use_sysconfig()
-
-if not _USE_SYSCONFIG:
-    # Import distutils lazily to avoid deprecation warnings,
-    # but import it soon enough that it is in memory and available during
-    # a pip reinstall.
-    from . import _distutils
-
-# Be noisy about incompatibilities if this platforms "should" be using
-# sysconfig, but is explicitly opting out and using distutils instead.
-if _USE_SYSCONFIG_DEFAULT and not _USE_SYSCONFIG:
-    _MISMATCH_LEVEL = logging.WARNING
-else:
-    _MISMATCH_LEVEL = logging.DEBUG
-
-
-def _looks_like_bpo_44860() -> bool:
-    """The resolution to bpo-44860 will change this incorrect platlib.
-
-    See <https://bugs.python.org/issue44860>.
-    """
-    from distutils.command.install import INSTALL_SCHEMES
-
-    try:
-        unix_user_platlib = INSTALL_SCHEMES["unix_user"]["platlib"]
-    except KeyError:
-        return False
-    return unix_user_platlib == "$usersite"
-
-
-def _looks_like_red_hat_patched_platlib_purelib(scheme: Dict[str, str]) -> bool:
-    platlib = scheme["platlib"]
-    if "/$platlibdir/" in platlib:
-        platlib = platlib.replace("/$platlibdir/", f"/{_PLATLIBDIR}/")
-    if "/lib64/" not in platlib:
-        return False
-    unpatched = platlib.replace("/lib64/", "/lib/")
-    return unpatched.replace("$platbase/", "$base/") == scheme["purelib"]
-
-
-@functools.lru_cache(maxsize=None)
-def _looks_like_red_hat_lib() -> bool:
-    """Red Hat patches platlib in unix_prefix and unix_home, but not purelib.
-
-    This is the only way I can see to tell a Red Hat-patched Python.
-    """
-    from distutils.command.install import INSTALL_SCHEMES
-
-    return all(
-        k in INSTALL_SCHEMES
-        and _looks_like_red_hat_patched_platlib_purelib(INSTALL_SCHEMES[k])
-        for k in ("unix_prefix", "unix_home")
-    )
-
-
-@functools.lru_cache(maxsize=None)
-def _looks_like_debian_scheme() -> bool:
-    """Debian adds two additional schemes."""
-    from distutils.command.install import INSTALL_SCHEMES
-
-    return "deb_system" in INSTALL_SCHEMES and "unix_local" in INSTALL_SCHEMES
-
-
-@functools.lru_cache(maxsize=None)
-def _looks_like_red_hat_scheme() -> bool:
-    """Red Hat patches ``sys.prefix`` and ``sys.exec_prefix``.
-
-    Red Hat's ``00251-change-user-install-location.patch`` changes the install
-    command's ``prefix`` and ``exec_prefix`` to append ``"/local"``. This is
-    (fortunately?) done quite unconditionally, so we create a default command
-    object without any configuration to detect this.
-    """
-    from distutils.command.install import install
-    from distutils.dist import Distribution
-
-    cmd: Any = install(Distribution())
-    cmd.finalize_options()
-    return (
-        cmd.exec_prefix == f"{os.path.normpath(sys.exec_prefix)}/local"
-        and cmd.prefix == f"{os.path.normpath(sys.prefix)}/local"
-    )
-
-
-@functools.lru_cache(maxsize=None)
-def _looks_like_slackware_scheme() -> bool:
-    """Slackware patches sysconfig but fails to patch distutils and site.
-
-    Slackware changes sysconfig's user scheme to use ``"lib64"`` for the lib
-    path, but does not do the same to the site module.
-    """
-    if user_site is None:  # User-site not available.
-        return False
-    try:
-        paths = sysconfig.get_paths(scheme="posix_user", expand=False)
-    except KeyError:  # User-site not available.
-        return False
-    return "/lib64/" in paths["purelib"] and "/lib64/" not in user_site
-
-
-@functools.lru_cache(maxsize=None)
-def _looks_like_msys2_mingw_scheme() -> bool:
-    """MSYS2 patches distutils and sysconfig to use a UNIX-like scheme.
-
-    However, MSYS2 incorrectly patches sysconfig ``nt`` scheme. The fix is
-    likely going to be included in their 3.10 release, so we ignore the warning.
-    See msys2/MINGW-packages#9319.
-
-    MSYS2 MINGW's patch uses lowercase ``"lib"`` instead of the usual uppercase,
-    and is missing the final ``"site-packages"``.
-    """
-    paths = sysconfig.get_paths("nt", expand=False)
-    return all(
-        "Lib" not in p and "lib" in p and not p.endswith("site-packages")
-        for p in (paths[key] for key in ("platlib", "purelib"))
-    )
-
-
-def _fix_abiflags(parts: Tuple[str]) -> Generator[str, None, None]:
-    ldversion = sysconfig.get_config_var("LDVERSION")
-    abiflags = getattr(sys, "abiflags", None)
-
-    # LDVERSION does not end with sys.abiflags. Just return the path unchanged.
-    if not ldversion or not abiflags or not ldversion.endswith(abiflags):
-        yield from parts
-        return
-
-    # Strip sys.abiflags from LDVERSION-based path components.
-    for part in parts:
-        if part.endswith(ldversion):
-            part = part[: (0 - len(abiflags))]
-        yield part
-
-
-@functools.lru_cache(maxsize=None)
-def _warn_mismatched(old: pathlib.Path, new: pathlib.Path, *, key: str) -> None:
-    issue_url = "https://github.com/pypa/pip/issues/10151"
-    message = (
-        "Value for %s does not match. Please report this to <%s>"
-        "\ndistutils: %s"
-        "\nsysconfig: %s"
-    )
-    logger.log(_MISMATCH_LEVEL, message, key, issue_url, old, new)
-
-
-def _warn_if_mismatch(old: pathlib.Path, new: pathlib.Path, *, key: str) -> bool:
-    if old == new:
-        return False
-    _warn_mismatched(old, new, key=key)
-    return True
-
-
-@functools.lru_cache(maxsize=None)
-def _log_context(
-    *,
-    user: bool = False,
-    home: Optional[str] = None,
-    root: Optional[str] = None,
-    prefix: Optional[str] = None,
-) -> None:
-    parts = [
-        "Additional context:",
-        "user = %r",
-        "home = %r",
-        "root = %r",
-        "prefix = %r",
-    ]
-
-    logger.log(_MISMATCH_LEVEL, "\n".join(parts), user, home, root, prefix)
-
-
-def get_scheme(
-    dist_name: str,
-    user: bool = False,
-    home: Optional[str] = None,
-    root: Optional[str] = None,
-    isolated: bool = False,
-    prefix: Optional[str] = None,
-) -> Scheme:
-    new = _sysconfig.get_scheme(
-        dist_name,
-        user=user,
-        home=home,
-        root=root,
-        isolated=isolated,
-        prefix=prefix,
-    )
-    if _USE_SYSCONFIG:
-        return new
-
-    old = _distutils.get_scheme(
-        dist_name,
-        user=user,
-        home=home,
-        root=root,
-        isolated=isolated,
-        prefix=prefix,
-    )
-
-    warning_contexts = []
-    for k in SCHEME_KEYS:
-        old_v = pathlib.Path(getattr(old, k))
-        new_v = pathlib.Path(getattr(new, k))
-
-        if old_v == new_v:
-            continue
-
-        # distutils incorrectly put PyPy packages under ``site-packages/python``
-        # in the ``posix_home`` scheme, but PyPy devs said they expect the
-        # directory name to be ``pypy`` instead. So we treat this as a bug fix
-        # and not warn about it. See bpo-43307 and python/cpython#24628.
-        skip_pypy_special_case = (
-            sys.implementation.name == "pypy"
-            and home is not None
-            and k in ("platlib", "purelib")
-            and old_v.parent == new_v.parent
-            and old_v.name.startswith("python")
-            and new_v.name.startswith("pypy")
-        )
-        if skip_pypy_special_case:
-            continue
-
-        # sysconfig's ``osx_framework_user`` does not include ``pythonX.Y`` in
-        # the ``include`` value, but distutils's ``headers`` does. We'll let
-        # CPython decide whether this is a bug or feature. See bpo-43948.
-        skip_osx_framework_user_special_case = (
-            user
-            and is_osx_framework()
-            and k == "headers"
-            and old_v.parent.parent == new_v.parent
-            and old_v.parent.name.startswith("python")
-        )
-        if skip_osx_framework_user_special_case:
-            continue
-
-        # On Red Hat and derived Linux distributions, distutils is patched to
-        # use "lib64" instead of "lib" for platlib.
-        if k == "platlib" and _looks_like_red_hat_lib():
-            continue
-
-        # On Python 3.9+, sysconfig's posix_user scheme sets platlib against
-        # sys.platlibdir, but distutils's unix_user incorrectly coninutes
-        # using the same $usersite for both platlib and purelib. This creates a
-        # mismatch when sys.platlibdir is not "lib".
-        skip_bpo_44860 = (
-            user
-            and k == "platlib"
-            and not WINDOWS
-            and sys.version_info >= (3, 9)
-            and _PLATLIBDIR != "lib"
-            and _looks_like_bpo_44860()
-        )
-        if skip_bpo_44860:
-            continue
-
-        # Slackware incorrectly patches posix_user to use lib64 instead of lib,
-        # but not usersite to match the location.
-        skip_slackware_user_scheme = (
-            user
-            and k in ("platlib", "purelib")
-            and not WINDOWS
-            and _looks_like_slackware_scheme()
-        )
-        if skip_slackware_user_scheme:
-            continue
-
-        # Both Debian and Red Hat patch Python to place the system site under
-        # /usr/local instead of /usr. Debian also places lib in dist-packages
-        # instead of site-packages, but the /usr/local check should cover it.
-        skip_linux_system_special_case = (
-            not (user or home or prefix or running_under_virtualenv())
-            and old_v.parts[1:3] == ("usr", "local")
-            and len(new_v.parts) > 1
-            and new_v.parts[1] == "usr"
-            and (len(new_v.parts) < 3 or new_v.parts[2] != "local")
-            and (_looks_like_red_hat_scheme() or _looks_like_debian_scheme())
-        )
-        if skip_linux_system_special_case:
-            continue
-
-        # On Python 3.7 and earlier, sysconfig does not include sys.abiflags in
-        # the "pythonX.Y" part of the path, but distutils does.
-        skip_sysconfig_abiflag_bug = (
-            sys.version_info < (3, 8)
-            and not WINDOWS
-            and k in ("headers", "platlib", "purelib")
-            and tuple(_fix_abiflags(old_v.parts)) == new_v.parts
-        )
-        if skip_sysconfig_abiflag_bug:
-            continue
-
-        # MSYS2 MINGW's sysconfig patch does not include the "site-packages"
-        # part of the path. This is incorrect and will be fixed in MSYS.
-        skip_msys2_mingw_bug = (
-            WINDOWS and k in ("platlib", "purelib") and _looks_like_msys2_mingw_scheme()
-        )
-        if skip_msys2_mingw_bug:
-            continue
-
-        # CPython's POSIX install script invokes pip (via ensurepip) against the
-        # interpreter located in the source tree, not the install site. This
-        # triggers special logic in sysconfig that's not present in distutils.
-        # https://github.com/python/cpython/blob/8c21941ddaf/Lib/sysconfig.py#L178-L194
-        skip_cpython_build = (
-            sysconfig.is_python_build(check_home=True)
-            and not WINDOWS
-            and k in ("headers", "include", "platinclude")
-        )
-        if skip_cpython_build:
-            continue
-
-        warning_contexts.append((old_v, new_v, f"scheme.{k}"))
-
-    if not warning_contexts:
-        return old
-
-    # Check if this path mismatch is caused by distutils config files. Those
-    # files will no longer work once we switch to sysconfig, so this raises a
-    # deprecation message for them.
-    default_old = _distutils.distutils_scheme(
-        dist_name,
-        user,
-        home,
-        root,
-        isolated,
-        prefix,
-        ignore_config_files=True,
-    )
-    if any(default_old[k] != getattr(old, k) for k in SCHEME_KEYS):
-        deprecated(
-            reason=(
-                "Configuring installation scheme with distutils config files "
-                "is deprecated and will no longer work in the near future. If you "
-                "are using a Homebrew or Linuxbrew Python, please see discussion "
-                "at https://github.com/Homebrew/homebrew-core/issues/76621"
-            ),
-            replacement=None,
-            gone_in=None,
-        )
-        return old
-
-    # Post warnings about this mismatch so user can report them back.
-    for old_v, new_v, key in warning_contexts:
-        _warn_mismatched(old_v, new_v, key=key)
-    _log_context(user=user, home=home, root=root, prefix=prefix)
-
-    return old
-
-
-def get_bin_prefix() -> str:
-    new = _sysconfig.get_bin_prefix()
-    if _USE_SYSCONFIG:
-        return new
-
-    old = _distutils.get_bin_prefix()
-    if _warn_if_mismatch(pathlib.Path(old), pathlib.Path(new), key="bin_prefix"):
-        _log_context()
-    return old
-
-
-def get_bin_user() -> str:
-    return _sysconfig.get_scheme("", user=True).scripts
-
-
-def _looks_like_deb_system_dist_packages(value: str) -> bool:
-    """Check if the value is Debian's APT-controlled dist-packages.
-
-    Debian's ``distutils.sysconfig.get_python_lib()`` implementation returns the
-    default package path controlled by APT, but does not patch ``sysconfig`` to
-    do the same. This is similar to the bug worked around in ``get_scheme()``,
-    but here the default is ``deb_system`` instead of ``unix_local``. Ultimately
-    we can't do anything about this Debian bug, and this detection allows us to
-    skip the warning when needed.
-    """
-    if not _looks_like_debian_scheme():
-        return False
-    if value == "/usr/lib/python3/dist-packages":
-        return True
-    return False
-
-
-def get_purelib() -> str:
-    """Return the default pure-Python lib location."""
-    new = _sysconfig.get_purelib()
-    if _USE_SYSCONFIG:
-        return new
-
-    old = _distutils.get_purelib()
-    if _looks_like_deb_system_dist_packages(old):
-        return old
-    if _warn_if_mismatch(pathlib.Path(old), pathlib.Path(new), key="purelib"):
-        _log_context()
-    return old
-
-
-def get_platlib() -> str:
-    """Return the default platform-shared lib location."""
-    new = _sysconfig.get_platlib()
-    if _USE_SYSCONFIG:
-        return new
-
-    from . import _distutils
-
-    old = _distutils.get_platlib()
-    if _looks_like_deb_system_dist_packages(old):
-        return old
-    if _warn_if_mismatch(pathlib.Path(old), pathlib.Path(new), key="platlib"):
-        _log_context()
-    return old
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/containers.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/containers.py
deleted file mode 100644
index e29cf368991ccb083b67cda8133e4635defbfe53..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/containers.py
+++ /dev/null
@@ -1,167 +0,0 @@
-from itertools import zip_longest
-from typing import (
-    Iterator,
-    Iterable,
-    List,
-    Optional,
-    Union,
-    overload,
-    TypeVar,
-    TYPE_CHECKING,
-)
-
-if TYPE_CHECKING:
-    from .console import (
-        Console,
-        ConsoleOptions,
-        JustifyMethod,
-        OverflowMethod,
-        RenderResult,
-        RenderableType,
-    )
-    from .text import Text
-
-from .cells import cell_len
-from .measure import Measurement
-
-T = TypeVar("T")
-
-
-class Renderables:
-    """A list subclass which renders its contents to the console."""
-
-    def __init__(
-        self, renderables: Optional[Iterable["RenderableType"]] = None
-    ) -> None:
-        self._renderables: List["RenderableType"] = (
-            list(renderables) if renderables is not None else []
-        )
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        """Console render method to insert line-breaks."""
-        yield from self._renderables
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "Measurement":
-        dimensions = [
-            Measurement.get(console, options, renderable)
-            for renderable in self._renderables
-        ]
-        if not dimensions:
-            return Measurement(1, 1)
-        _min = max(dimension.minimum for dimension in dimensions)
-        _max = max(dimension.maximum for dimension in dimensions)
-        return Measurement(_min, _max)
-
-    def append(self, renderable: "RenderableType") -> None:
-        self._renderables.append(renderable)
-
-    def __iter__(self) -> Iterable["RenderableType"]:
-        return iter(self._renderables)
-
-
-class Lines:
-    """A list subclass which can render to the console."""
-
-    def __init__(self, lines: Iterable["Text"] = ()) -> None:
-        self._lines: List["Text"] = list(lines)
-
-    def __repr__(self) -> str:
-        return f"Lines({self._lines!r})"
-
-    def __iter__(self) -> Iterator["Text"]:
-        return iter(self._lines)
-
-    @overload
-    def __getitem__(self, index: int) -> "Text":
-        ...
-
-    @overload
-    def __getitem__(self, index: slice) -> List["Text"]:
-        ...
-
-    def __getitem__(self, index: Union[slice, int]) -> Union["Text", List["Text"]]:
-        return self._lines[index]
-
-    def __setitem__(self, index: int, value: "Text") -> "Lines":
-        self._lines[index] = value
-        return self
-
-    def __len__(self) -> int:
-        return self._lines.__len__()
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        """Console render method to insert line-breaks."""
-        yield from self._lines
-
-    def append(self, line: "Text") -> None:
-        self._lines.append(line)
-
-    def extend(self, lines: Iterable["Text"]) -> None:
-        self._lines.extend(lines)
-
-    def pop(self, index: int = -1) -> "Text":
-        return self._lines.pop(index)
-
-    def justify(
-        self,
-        console: "Console",
-        width: int,
-        justify: "JustifyMethod" = "left",
-        overflow: "OverflowMethod" = "fold",
-    ) -> None:
-        """Justify and overflow text to a given width.
-
-        Args:
-            console (Console): Console instance.
-            width (int): Number of characters per line.
-            justify (str, optional): Default justify method for text: "left", "center", "full" or "right". Defaults to "left".
-            overflow (str, optional): Default overflow for text: "crop", "fold", or "ellipsis". Defaults to "fold".
-
-        """
-        from .text import Text
-
-        if justify == "left":
-            for line in self._lines:
-                line.truncate(width, overflow=overflow, pad=True)
-        elif justify == "center":
-            for line in self._lines:
-                line.rstrip()
-                line.truncate(width, overflow=overflow)
-                line.pad_left((width - cell_len(line.plain)) // 2)
-                line.pad_right(width - cell_len(line.plain))
-        elif justify == "right":
-            for line in self._lines:
-                line.rstrip()
-                line.truncate(width, overflow=overflow)
-                line.pad_left(width - cell_len(line.plain))
-        elif justify == "full":
-            for line_index, line in enumerate(self._lines):
-                if line_index == len(self._lines) - 1:
-                    break
-                words = line.split(" ")
-                words_size = sum(cell_len(word.plain) for word in words)
-                num_spaces = len(words) - 1
-                spaces = [1 for _ in range(num_spaces)]
-                index = 0
-                if spaces:
-                    while words_size + num_spaces < width:
-                        spaces[len(spaces) - index - 1] += 1
-                        num_spaces += 1
-                        index = (index + 1) % len(spaces)
-                tokens: List[Text] = []
-                for index, (word, next_word) in enumerate(
-                    zip_longest(words, words[1:])
-                ):
-                    tokens.append(word)
-                    if index < len(spaces):
-                        style = word.get_style_at_offset(console, -1)
-                        next_style = next_word.get_style_at_offset(console, 0)
-                        space_style = style if style == next_style else line.style
-                        tokens.append(Text(" " * spaces[index], style=space_style))
-                self[line_index] = Text("").join(tokens)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/build_clib.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/build_clib.py
deleted file mode 100644
index 09483e69e5b76ef28d4389d7f17699aee7ecbd0a..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/command/build_clib.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import distutils.command.build_clib as orig
-from distutils.errors import DistutilsSetupError
-from distutils import log
-from setuptools.dep_util import newer_pairwise_group
-
-
-class build_clib(orig.build_clib):
-    """
-    Override the default build_clib behaviour to do the following:
-
-    1. Implement a rudimentary timestamp-based dependency system
-       so 'compile()' doesn't run every time.
-    2. Add more keys to the 'build_info' dictionary:
-        * obj_deps - specify dependencies for each object compiled.
-                     this should be a dictionary mapping a key
-                     with the source filename to a list of
-                     dependencies. Use an empty string for global
-                     dependencies.
-        * cflags   - specify a list of additional flags to pass to
-                     the compiler.
-    """
-
-    def build_libraries(self, libraries):
-        for (lib_name, build_info) in libraries:
-            sources = build_info.get('sources')
-            if sources is None or not isinstance(sources, (list, tuple)):
-                raise DistutilsSetupError(
-                    "in 'libraries' option (library '%s'), "
-                    "'sources' must be present and must be "
-                    "a list of source filenames" % lib_name)
-            sources = sorted(list(sources))
-
-            log.info("building '%s' library", lib_name)
-
-            # Make sure everything is the correct type.
-            # obj_deps should be a dictionary of keys as sources
-            # and a list/tuple of files that are its dependencies.
-            obj_deps = build_info.get('obj_deps', dict())
-            if not isinstance(obj_deps, dict):
-                raise DistutilsSetupError(
-                    "in 'libraries' option (library '%s'), "
-                    "'obj_deps' must be a dictionary of "
-                    "type 'source: list'" % lib_name)
-            dependencies = []
-
-            # Get the global dependencies that are specified by the '' key.
-            # These will go into every source's dependency list.
-            global_deps = obj_deps.get('', list())
-            if not isinstance(global_deps, (list, tuple)):
-                raise DistutilsSetupError(
-                    "in 'libraries' option (library '%s'), "
-                    "'obj_deps' must be a dictionary of "
-                    "type 'source: list'" % lib_name)
-
-            # Build the list to be used by newer_pairwise_group
-            # each source will be auto-added to its dependencies.
-            for source in sources:
-                src_deps = [source]
-                src_deps.extend(global_deps)
-                extra_deps = obj_deps.get(source, list())
-                if not isinstance(extra_deps, (list, tuple)):
-                    raise DistutilsSetupError(
-                        "in 'libraries' option (library '%s'), "
-                        "'obj_deps' must be a dictionary of "
-                        "type 'source: list'" % lib_name)
-                src_deps.extend(extra_deps)
-                dependencies.append(src_deps)
-
-            expected_objects = self.compiler.object_filenames(
-                sources,
-                output_dir=self.build_temp,
-            )
-
-            if (
-                newer_pairwise_group(dependencies, expected_objects)
-                != ([], [])
-            ):
-                # First, compile the source code to object files in the library
-                # directory.  (This should probably change to putting object
-                # files in a temporary build directory.)
-                macros = build_info.get('macros')
-                include_dirs = build_info.get('include_dirs')
-                cflags = build_info.get('cflags')
-                self.compiler.compile(
-                    sources,
-                    output_dir=self.build_temp,
-                    macros=macros,
-                    include_dirs=include_dirs,
-                    extra_postargs=cflags,
-                    debug=self.debug
-                )
-
-            # Now "link" the object files together into a static library.
-            # (On Unix at least, this isn't really linking -- it just
-            # builds an archive.  Whatever.)
-            self.compiler.create_static_lib(
-                expected_objects,
-                lib_name,
-                output_dir=self.build_clib,
-                debug=self.debug
-            )
diff --git a/spaces/Thaweewat/ControlNet-Architecture/ldm/modules/diffusionmodules/util.py b/spaces/Thaweewat/ControlNet-Architecture/ldm/modules/diffusionmodules/util.py
deleted file mode 100644
index 637363dfe34799e70cfdbcd11445212df9d9ca1f..0000000000000000000000000000000000000000
--- a/spaces/Thaweewat/ControlNet-Architecture/ldm/modules/diffusionmodules/util.py
+++ /dev/null
@@ -1,270 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-
-
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-
-from ldm.util import instantiate_from_config
-
-
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-    if schedule == "linear":
-        betas = (
-                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
-        )
-
-    elif schedule == "cosine":
-        timesteps = (
-                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
-        )
-        alphas = timesteps / (1 + cosine_s) * np.pi / 2
-        alphas = torch.cos(alphas).pow(2)
-        alphas = alphas / alphas[0]
-        betas = 1 - alphas[1:] / alphas[:-1]
-        betas = np.clip(betas, a_min=0, a_max=0.999)
-
-    elif schedule == "sqrt_linear":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
-    elif schedule == "sqrt":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
-    else:
-        raise ValueError(f"schedule '{schedule}' unknown.")
-    return betas.numpy()
-
-
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
-    if ddim_discr_method == 'uniform':
-        c = num_ddpm_timesteps // num_ddim_timesteps
-        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
-    elif ddim_discr_method == 'quad':
-        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
-    else:
-        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-
-    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
-    # add one to get the final alpha values right (the ones from first scale to data during sampling)
-    steps_out = ddim_timesteps + 1
-    if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
-    return steps_out
-
-
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
-    # select alphas for computing the variance schedule
-    alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
-    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
-    if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
-              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
-    return sigmas, alphas, alphas_prev
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-
-
-def extract_into_tensor(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def checkpoint(func, inputs, params, flag):
-    """
-    Evaluate a function without caching intermediate activations, allowing for
-    reduced memory at the expense of extra compute in the backward pass.
-    :param func: the function to evaluate.
-    :param inputs: the argument sequence to pass to `func`.
-    :param params: a sequence of parameters `func` depends on but does not
-                   explicitly take as arguments.
-    :param flag: if False, disable gradient checkpointing.
-    """
-    if flag:
-        args = tuple(inputs) + tuple(params)
-        return CheckpointFunction.apply(func, len(inputs), *args)
-    else:
-        return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_tensors = list(args[:length])
-        ctx.input_params = list(args[length:])
-        ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
-                                   "dtype": torch.get_autocast_gpu_dtype(),
-                                   "cache_enabled": torch.is_autocast_cache_enabled()}
-        with torch.no_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        return output_tensors
-
-    @staticmethod
-    def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
-        with torch.enable_grad(), \
-                torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
-            # Fixes a bug where the first op in run_function modifies the
-            # Tensor storage in place, which is not allowed for detach()'d
-            # Tensors.
-            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
-            output_tensors = ctx.run_function(*shallow_copies)
-        input_grads = torch.autograd.grad(
-            output_tensors,
-            ctx.input_tensors + ctx.input_params,
-            output_grads,
-            allow_unused=True,
-        )
-        del ctx.input_tensors
-        del ctx.input_params
-        del output_tensors
-        return (None, None) + input_grads
-
-
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
-    """
-    Create sinusoidal timestep embeddings.
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=timesteps.device)
-        args = timesteps[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    else:
-        embedding = repeat(timesteps, 'b -> b d', d=dim)
-    return embedding
-
-
-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 scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-
-
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normalization(channels):
-    """
-    Make a standard normalization layer.
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(32, channels)
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(x)
-
-
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class HybridConditioner(nn.Module):
-
-    def __init__(self, c_concat_config, c_crossattn_config):
-        super().__init__()
-        self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-
-    def forward(self, c_concat, c_crossattn):
-        c_concat = self.concat_conditioner(c_concat)
-        c_crossattn = self.crossattn_conditioner(c_crossattn)
-        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()
\ No newline at end of file
diff --git a/spaces/TheSxrynlxX/Idk/README.md b/spaces/TheSxrynlxX/Idk/README.md
deleted file mode 100644
index 11262eef103039071f5dea564eaff56a06109bb0..0000000000000000000000000000000000000000
--- a/spaces/TheSxrynlxX/Idk/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Idk
-emoji: 🐠
-colorFrom: indigo
-colorTo: red
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TrustSafeAI/NCTV/assets/css/bootstrap/bootstrap-grid.rtl.min.css b/spaces/TrustSafeAI/NCTV/assets/css/bootstrap/bootstrap-grid.rtl.min.css
deleted file mode 100644
index 8ec49c744672d5ddf6529922d509db28b6ad3f4c..0000000000000000000000000000000000000000
--- a/spaces/TrustSafeAI/NCTV/assets/css/bootstrap/bootstrap-grid.rtl.min.css
+++ /dev/null
@@ -1,7 +0,0 @@
-/*!
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diff --git a/spaces/TushDeMort/yolo/detect.py b/spaces/TushDeMort/yolo/detect.py
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--- a/spaces/TushDeMort/yolo/detect.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import base64
-from yolo import allowed
-from yolo import predict, CLASSES
-
-def analyze(IMAGE_FILE):
-    IMAGE_FILE = base64.b64decode(IMAGE_FILE)
-    pred = predict(IMAGE_FILE)
-
-    Output = []
-
-    for x1, y1, x2, y2, conf, class_id in pred:
-        object_text = CLASSES[int(class_id)]
-        if object_text in allowed:
-            temp = {}
-            temp['conf'] = int(conf)
-            temp['type'] = object_text
-            temp['coords'] = [int(round(x1,1)), int(round(y1,1)), int(round(x2,1)), int(round(y2,1))]
-            Output.append(temp)
-
-    return Output
\ No newline at end of file
diff --git a/spaces/VishnuSaiTeja/RogerStaff/app.py b/spaces/VishnuSaiTeja/RogerStaff/app.py
deleted file mode 100644
index 3703e2db0009fea1686d779101b431c47248e5e9..0000000000000000000000000000000000000000
--- a/spaces/VishnuSaiTeja/RogerStaff/app.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
diff --git a/spaces/Vision-CAIR/minigpt4/minigpt4/datasets/datasets/__init__.py b/spaces/Vision-CAIR/minigpt4/minigpt4/datasets/datasets/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/callbacks/hooks.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/callbacks/hooks.py
deleted file mode 100644
index af2084f302252cd994b0abe79bd0f83204badae1..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/callbacks/hooks.py
+++ /dev/null
@@ -1,200 +0,0 @@
-"Hooks provide extensibility at the model level."
-from ..torch_core import *
-from ..callback import *
-from ..basic_train import *
-from ..basic_data import *
-
-__all__ = ['ActivationStats', 'Hook', 'HookCallback', 'Hooks', 'hook_output', 'hook_outputs',
-           'model_sizes', 'num_features_model', 'model_summary', 'dummy_eval', 'dummy_batch']
-
-class Hook():
-    "Create a hook on `m` with `hook_func`."
-    def __init__(self, m:nn.Module, hook_func:HookFunc, is_forward:bool=True, detach:bool=True):
-        self.hook_func,self.detach,self.stored = hook_func,detach,None
-        f = m.register_forward_hook if is_forward else m.register_backward_hook
-        self.hook = f(self.hook_fn)
-        self.removed = False
-
-    def hook_fn(self, module:nn.Module, input:Tensors, output:Tensors):
-        "Applies `hook_func` to `module`, `input`, `output`."
-        if self.detach:
-            input  = (o.detach() for o in input ) if is_listy(input ) else input.detach()
-            output = (o.detach() for o in output) if is_listy(output) else output.detach()
-        self.stored = self.hook_func(module, input, output)
-
-    def remove(self):
-        "Remove the hook from the model."
-        if not self.removed:
-            self.hook.remove()
-            self.removed=True
-
-    def __enter__(self, *args): return self
-    def __exit__(self, *args): self.remove()
-
-class Hooks():
-    "Create several hooks on the modules in `ms` with `hook_func`."
-    def __init__(self, ms:Collection[nn.Module], hook_func:HookFunc, is_forward:bool=True, detach:bool=True):
-        self.hooks = [Hook(m, hook_func, is_forward, detach) for m in ms]
-
-    def __getitem__(self,i:int)->Hook: return self.hooks[i]
-    def __len__(self)->int: return len(self.hooks)
-    def __iter__(self): return iter(self.hooks)
-    @property
-    def stored(self): return [o.stored for o in self]
-
-    def remove(self):
-        "Remove the hooks from the model."
-        for h in self.hooks: h.remove()
-
-    def __enter__(self, *args): return self
-    def __exit__ (self, *args): self.remove()
-
-def _hook_inner(m,i,o): return o if isinstance(o,Tensor) else o if is_listy(o) else list(o)
-
-def hook_output (module:nn.Module, detach:bool=True, grad:bool=False)->Hook:
-    "Return a `Hook` that stores activations of `module` in `self.stored`"
-    return Hook(module, _hook_inner, detach=detach, is_forward=not grad)
-
-def hook_outputs(modules:Collection[nn.Module], detach:bool=True, grad:bool=False)->Hooks:
-    "Return `Hooks` that store activations of all `modules` in `self.stored`"
-    return Hooks(modules, _hook_inner, detach=detach, is_forward=not grad)
-
-class HookCallback(LearnerCallback):
-    "Callback that can be used to register hooks on `modules`. Implement the corresponding function in `self.hook`."
-    def __init__(self, learn:Learner, modules:Sequence[nn.Module]=None, do_remove:bool=True):
-        super().__init__(learn)
-        self.modules,self.do_remove = modules,do_remove
-
-    def on_train_begin(self, **kwargs):
-        "Register the `Hooks` on `self.modules`."
-        if not self.modules:
-            self.modules = [m for m in flatten_model(self.learn.model)
-                            if hasattr(m, 'weight')]
-        self.hooks = Hooks(self.modules, self.hook)
-
-    def on_train_end(self, **kwargs):
-        "Remove the `Hooks`."
-        if self.do_remove: self.remove()
-
-    def remove(self): 
-        if getattr(self, 'hooks', None): self.hooks.remove()
-    def __del__(self): self.remove()
-
-class ActivationStats(HookCallback):
-    "Callback that record the mean and std of activations."
-
-    def on_train_begin(self, **kwargs):
-        "Initialize stats."
-        super().on_train_begin(**kwargs)
-        self.stats = []
-
-    def hook(self, m:nn.Module, i:Tensors, o:Tensors)->Tuple[Rank0Tensor,Rank0Tensor]:
-        "Take the mean and std of `o`."
-        return o.mean().item(),o.std().item()
-    def on_batch_end(self, train, **kwargs):
-        "Take the stored results and puts it in `self.stats`"
-        if train: self.stats.append(self.hooks.stored)
-    def on_train_end(self, **kwargs):
-        "Polish the final result."
-        super().on_train_end(**kwargs)
-        self.stats = tensor(self.stats).permute(2,1,0)
-
-def dummy_batch(m: nn.Module, size:tuple=(64,64))->Tensor:
-    "Create a dummy batch to go through `m` with `size`."
-    ch_in = in_channels(m)
-    return one_param(m).new(1, ch_in, *size).requires_grad_(False).uniform_(-1.,1.)
-
-def dummy_eval(m:nn.Module, size:tuple=(64,64)):
-    "Pass a `dummy_batch` in evaluation mode in `m` with `size`."
-    m.eval()
-    return m(dummy_batch(m, size))
-    #return m.eval()(dummy_batch(m, size))
-
-def model_sizes(m:nn.Module, size:tuple=(64,64))->Tuple[Sizes,Tensor,Hooks]:
-    "Pass a dummy input through the model `m` to get the various sizes of activations."
-    with hook_outputs(m) as hooks:
-        x = dummy_eval(m, size)
-        return [o.stored.shape for o in hooks]
-
-def num_features_model(m:nn.Module)->int:
-    "Return the number of output features for `model`."
-    sz = 64
-    while True:
-        try: return model_sizes(m, size=(sz,sz))[-1][1]
-        except Exception as e:
-            sz *= 2
-            if sz > 2048: raise
-
-def total_params(m:nn.Module)->int:
-    params, trainable = 0, False
-    if hasattr(m, "weight") and hasattr(m.weight, "size"):
-         params += m.weight.numel()
-         trainable = m.weight.requires_grad
-    if hasattr(m, "bias") and hasattr(m.bias, "size"): params += m.bias.numel()
-    return params, trainable
-
-def hook_params(modules:Collection[nn.Module])->Hooks:
-    return Hooks(modules, lambda m, i, o: total_params(m))
-
-def params_size(m: Union[nn.Module,Learner], size: tuple = (3, 64, 64))->Tuple[Sizes, Tensor, Hooks]:
-    "Pass a dummy input through the model to get the various sizes. Returns (res,x,hooks) if `full`"
-    if isinstance(m, Learner):
-        if m.data.is_empty:
-            raise Exception("This is an empty `Learner` and `Learner.summary` requires some data to pass through the model.")
-        ds_type = DatasetType.Train if m.data.train_dl else (DatasetType.Valid if m.data.valid_dl else DatasetType.Test)
-        x = m.data.one_batch(ds_type=ds_type, detach=False, denorm=False)[0]
-        x = [o[:1] for o in x]  if is_listy(x) else x[:1]
-        m = m.model
-    elif isinstance(m, nn.Module): x = next(m.parameters()).new(1, *size)
-    else: raise TypeError('You should either pass in a Learner or nn.Module')
-    with hook_outputs(flatten_model(m)) as hook_o:
-        with hook_params(flatten_model(m))as hook_p:
-            x = m.eval()(*x) if is_listy(x) else m.eval()(x)
-            output_size = [((o.stored.shape[1:]) if o.stored is not None else None) for o in hook_o]
-            params = [(o.stored if o.stored is not None else (None,None)) for o in hook_p]
-    params, trainables = map(list,zip(*params))
-    return output_size, params, trainables
-
-def get_layer_name(layer:nn.Module)->str:
-    return str(layer.__class__).split(".")[-1].split("'")[0]
-
-def layers_info(m:Collection[nn.Module]) -> Collection[namedtuple]:
-    func = lambda m:list(map(get_layer_name, flatten_model(m)))
-    layers_names = func(m.model) if isinstance(m, Learner) else func(m)
-    layers_sizes, layers_params, layers_trainable = params_size(m)
-    layer_info = namedtuple('Layer_Information', ['Layer', 'OutputSize', 'Params', 'Trainable'])
-    return list(map(layer_info, layers_names, layers_sizes, layers_params, layers_trainable))
-
-def model_summary(m:Learner, n:int=70):
-    "Print a summary of `m` using a output text width of `n` chars"
-    info = layers_info(m)
-    header = ["Layer (type)", "Output Shape", "Param #", "Trainable"]
-    res = m.model.__class__.__name__ + "\n"
-    res += "=" * n + "\n"
-    res += f"{header[0]:<20} {header[1]:<20} {header[2]:<10} {header[3]:<10}\n"
-    res += "=" * n + "\n"
-    total_params = 0
-    total_trainable_params = 0
-    for layer, size, params, trainable in info:
-        if size is None: continue
-        total_params += int(params)
-        total_trainable_params += int(params) * trainable
-        size, trainable = str(list(size)), str(trainable)
-        res += f"{layer:<20} {size:<20} {int(params):<10,} {trainable:<10}\n"
-        res += "_" * n + "\n"
-    res += f"\nTotal params: {total_params:,}\n"
-    res += f"Total trainable params: {total_trainable_params:,}\n"
-    res += f"Total non-trainable params: {total_params - total_trainable_params:,}\n"
-           
-    res += f"Optimized with {str(m.opt_func)[25:-1].replace('>', '')}\n"
-    if m.true_wd: res += f"Using true weight decay as discussed in https://www.fast.ai/2018/07/02/adam-weight-decay/ \n"
-    if "wd" in str(m.opt_func) or "weight_decay" in str(m.opt_func): res += f"\x1b[1;31m Specifying weight decay in the optimizer has no effect, Learner will overwrite \x1b[0m \n"
-    if "lr" in str(m.opt_func) or "learning_rate" in str(m.opt_func): res += f"\x1b[1;31m Specifying lr in the optimizer has no effect, pass it to fit or the defaults.lr will apply \x1b[0m \n" 
-    res += f"Loss function : {m.loss_func.__class__.__name__}\n"
-    res += "=" * n + "\n"
-    res += "Callbacks functions applied \n"
-    res += "\n".join([f"    {cbs.__class__.__name__}" for cbs in m.callbacks])
-
-    return PrettyString(res)
-
-Learner.summary = model_summary
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/datasets.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/datasets.py
deleted file mode 100644
index d75d2edae0587a9cf143f08cda7024090103abcf..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/datasets.py
+++ /dev/null
@@ -1,234 +0,0 @@
-from .core import *
-import hashlib
-
-__all__ = ['URLs', 'Config', 'untar_data', 'download_data', 'datapath4file', 'url2name', 'url2path']
-
-MODEL_URL = 'http://files.fast.ai/models/'
-URL = 'http://files.fast.ai/data/examples/'
-class URLs():
-    "Global constants for dataset and model URLs."
-    LOCAL_PATH = Path.cwd()
-    S3 = 'https://s3.amazonaws.com/fast-ai-'
-
-    S3_IMAGE    = f'{S3}imageclas/'
-    S3_IMAGELOC = f'{S3}imagelocal/'
-    S3_NLP      = f'{S3}nlp/'
-    S3_COCO     = f'{S3}coco/'
-    S3_MODEL    = f'{S3}modelzoo/'
-
-    # main datasets
-    ADULT_SAMPLE        = f'{URL}adult_sample'
-    BIWI_SAMPLE         = f'{URL}biwi_sample'
-    CIFAR               = f'{URL}cifar10'
-    COCO_SAMPLE         = f'{S3_COCO}coco_sample'
-    COCO_TINY           = f'{URL}coco_tiny'
-    HUMAN_NUMBERS       = f'{URL}human_numbers'
-    IMDB                = f'{S3_NLP}imdb'
-    IMDB_SAMPLE         = f'{URL}imdb_sample'
-    ML_SAMPLE           = f'{URL}movie_lens_sample'
-    MNIST_SAMPLE        = f'{URL}mnist_sample'
-    MNIST_TINY          = f'{URL}mnist_tiny'
-    MNIST_VAR_SIZE_TINY = f'{S3_IMAGE}mnist_var_size_tiny'
-    PLANET_SAMPLE       = f'{URL}planet_sample'
-    PLANET_TINY         = f'{URL}planet_tiny'
-    IMAGENETTE          = f'{S3_IMAGE}imagenette'
-    IMAGENETTE_160      = f'{S3_IMAGE}imagenette-160'
-    IMAGENETTE_320      = f'{S3_IMAGE}imagenette-320'
-    IMAGEWOOF           = f'{S3_IMAGE}imagewoof'
-    IMAGEWOOF_160       = f'{S3_IMAGE}imagewoof-160'
-    IMAGEWOOF_320       = f'{S3_IMAGE}imagewoof-320'
-
-    # kaggle competitions download dogs-vs-cats -p {DOGS.absolute()}
-    DOGS = f'{URL}dogscats'
-
-    # image classification datasets
-    CALTECH_101  = f'{S3_IMAGE}caltech_101'
-    CARS         = f'{S3_IMAGE}stanford-cars'
-    CIFAR_100    = f'{S3_IMAGE}cifar100'
-    CUB_200_2011 = f'{S3_IMAGE}CUB_200_2011'
-    FLOWERS      = f'{S3_IMAGE}oxford-102-flowers'
-    FOOD         = f'{S3_IMAGE}food-101'
-    MNIST        = f'{S3_IMAGE}mnist_png'
-    PETS         = f'{S3_IMAGE}oxford-iiit-pet'
-
-    # NLP datasets
-    AG_NEWS                 = f'{S3_NLP}ag_news_csv'
-    AMAZON_REVIEWS          = f'{S3_NLP}amazon_review_full_csv'
-    AMAZON_REVIEWS_POLARITY = f'{S3_NLP}amazon_review_polarity_csv'
-    DBPEDIA                 = f'{S3_NLP}dbpedia_csv'
-    MT_ENG_FRA              = f'{S3_NLP}giga-fren'
-    SOGOU_NEWS              = f'{S3_NLP}sogou_news_csv'
-    WIKITEXT                = f'{S3_NLP}wikitext-103'
-    WIKITEXT_TINY           = f'{S3_NLP}wikitext-2'
-    YAHOO_ANSWERS           = f'{S3_NLP}yahoo_answers_csv'
-    YELP_REVIEWS            = f'{S3_NLP}yelp_review_full_csv'
-    YELP_REVIEWS_POLARITY   = f'{S3_NLP}yelp_review_polarity_csv'
-
-    # Image localization datasets
-    BIWI_HEAD_POSE     = f"{S3_IMAGELOC}biwi_head_pose"
-    CAMVID             = f'{S3_IMAGELOC}camvid'
-    CAMVID_TINY        = f'{URL}camvid_tiny'
-    LSUN_BEDROOMS      = f'{S3_IMAGE}bedroom'
-    PASCAL_2007        = f'{S3_IMAGELOC}pascal_2007'
-    PASCAL_2012        = f'{S3_IMAGELOC}pascal_2012'
-
-    #Pretrained models
-    OPENAI_TRANSFORMER = f'{S3_MODEL}transformer'
-    WT103_FWD          = f'{S3_MODEL}wt103-fwd'
-    WT103_BWD          = f'{S3_MODEL}wt103-bwd'
-
-# to create/update a checksum for ./mnist_var_size_tiny.tgz, run:
-# python -c 'import fastai.datasets; print(fastai.datasets._check_file("mnist_var_size_tiny.tgz"))'
-_checks = {
-    URLs.ADULT_SAMPLE:(968212, '64eb9d7e23732de0b138f7372d15492f'),
-    URLs.AG_NEWS:(11784419, 'b86f328f4dbd072486591cb7a5644dcd'),
-    URLs.AMAZON_REVIEWS_POLARITY:(688339454, '676f7e5208ec343c8274b4bb085bc938'),
-    URLs.AMAZON_REVIEWS:(643695014, '4a1196cf0adaea22f4bc3f592cddde90'),
-    URLs.BIWI_HEAD_POSE:(452316199, '00f4ccf66e8cba184bc292fdc08fb237'),
-    URLs.BIWI_SAMPLE:(593774, '9179f4c1435f4b291f0d5b072d60c2c9'),
-    URLs.CALTECH_101:(131740031, 'd673425306e98ee4619fcdeef8a0e876'),
-    URLs.CAMVID:(598913237, '648371e4f3a833682afb39b08a3ce2aa'),
-    URLs.CAMVID_TINY:(2314212, '2cf6daf91b7a2083ecfa3e9968e9d915'),
-    URLs.CARS:(1957803273, '9045d6673c9ced0889f41816f6bf2f9f'),
-    URLs.CIFAR:(168168549, 'a5f8c31371b63a406b23368042812d3c'),
-    URLs.CIFAR_100:(169168619, 'e5e65dcb54b9d3913f7b8a9ad6607e62'),
-    URLs.COCO_SAMPLE:(3245877008, '006cd55d633d94b36ecaf661467830ec'),
-    URLs.COCO_TINY:(801038, '367467451ac4fba79a647753c2c66d3a'),
-    URLs.CUB_200_2011:(1150585339, 'd2acaa99439dff0483c7bbac1bfe2a92'),
-    URLs.DBPEDIA:(68341743, '239c7837b9e79db34486f3de6a00e38e'),
-    URLs.DOGS:(839285364, '3e483c8d6ef2175e9d395a6027eb92b7'),
-    URLs.FLOWERS:(345236087, '5666e01c1311b4c67fcf20d2b3850a88'),
-    URLs.FOOD:(5686607260, '1a540ebf1fb40b2bf3f2294234ba7907'),
-    URLs.HUMAN_NUMBERS:(30252, '8a19c3bfa2bcb08cd787e741261f3ea2'),
-    URLs.IMDB:(144440600, '90f9b1c4ff43a90d67553c9240dc0249'),
-    URLs.IMDB_SAMPLE:(571827, '0842e61a9867caa2e6fbdb14fa703d61'),
-    URLs.LSUN_BEDROOMS:(4579163978, '35d84f38f8a15fe47e66e460c8800d68'),
-    URLs.ML_SAMPLE:(51790, '10961384dfe7c5181460390a460c1f77'),
-    URLs.MNIST:(15683414, '03639f83c4e3d19e0a3a53a8a997c487'),
-    URLs.MNIST_SAMPLE:(3214948, '2dbc7ec6f9259b583af0072c55816a88'),
-    URLs.MNIST_TINY:(342207, '56143e8f24db90d925d82a5a74141875'),
-    URLs.MNIST_VAR_SIZE_TINY:(565372, 'b71a930f4eb744a4a143a6c7ff7ed67f'),
-    URLs.MT_ENG_FRA:(2598183296, '69573f58e2c850b90f2f954077041d8c'),
-    URLs.OPENAI_TRANSFORMER:(432848315, '024b0d2203ebb0cd1fc64b27cf8af18e'),
-    URLs.PASCAL_2007:(1636130334, 'a70574e9bc592bd3b253f5bf46ce12e3'),
-    URLs.PASCAL_2012:(2611715776, '2ae7897038383836f86ce58f66b09e31'),
-    URLs.PETS:(811706944, 'e4db5c768afd933bb91f5f594d7417a4'),
-    URLs.PLANET_SAMPLE:(15523994, '8bfb174b3162f07fbde09b54555bdb00'),
-    URLs.PLANET_TINY:(997569, '490873c5683454d4b2611fb1f00a68a9'),
-    URLs.SOGOU_NEWS:(384269937, '950f1366d33be52f5b944f8a8b680902'),
-    URLs.WIKITEXT:(190200704, '2dd8cf8693b3d27e9c8f0a7df054b2c7'),
-    URLs.WIKITEXT_TINY:(4070055, '2a82d47a7b85c8b6a8e068dc4c1d37e7'),
-    URLs.WT103_FWD:(105067061, '7d1114cd9684bf9d1ca3c9f6a54da6f9'),
-    URLs.WT103_BWD:(105205312, '20b06f5830fd5a891d21044c28d3097f'),
-    URLs.YAHOO_ANSWERS:(319476345, '0632a0d236ef3a529c0fa4429b339f68'),
-    URLs.YELP_REVIEWS_POLARITY:(166373201, '48c8451c1ad30472334d856b5d294807'),
-    URLs.YELP_REVIEWS:(196146755, '1efd84215ea3e30d90e4c33764b889db'),
-}
-
-#TODO: This can probably be coded more shortly and nicely.
-class Config():
-    "Creates a default config file 'config.yml' in $FASTAI_HOME (default `~/.fastai/`)"
-    DEFAULT_CONFIG_LOCATION = os.path.expanduser(os.getenv('FASTAI_HOME', '~/.fastai'))
-    DEFAULT_CONFIG_PATH = DEFAULT_CONFIG_LOCATION + '/config.yml'
-    DEFAULT_CONFIG = {
-        'data_path': DEFAULT_CONFIG_LOCATION + '/data',
-        'data_archive_path': DEFAULT_CONFIG_LOCATION + '/data',
-        'model_path': DEFAULT_CONFIG_LOCATION + '/models'
-    }
-
-    @classmethod
-    def get_key(cls, key):
-        "Get the path to `key` in the config file."
-        return cls.get().get(key, cls.DEFAULT_CONFIG.get(key,None))
-
-    @classmethod
-    def get_path(cls, path):
-        "Get the `path` in the config file."
-        return _expand_path(cls.get_key(path))
-
-    @classmethod
-    def data_path(cls):
-        "Get the path to data in the config file."
-        return cls.get_path('data_path')
-
-    @classmethod
-    def data_archive_path(cls):
-        "Get the path to data archives in the config file."
-        return cls.get_path('data_archive_path')
-
-    @classmethod
-    def model_path(cls):
-        "Get the path to fastai pretrained models in the config file."
-        return cls.get_path('model_path')
-
-    @classmethod
-    def get(cls, fpath=None, create_missing=True):
-        "Retrieve the `Config` in `fpath`."
-        fpath = _expand_path(fpath or cls.DEFAULT_CONFIG_PATH)
-        if not fpath.exists() and create_missing: cls.create(fpath)
-        assert fpath.exists(), f'Could not find config at: {fpath}. Please create'
-        with open(fpath, 'r') as yaml_file: return yaml.safe_load(yaml_file)
-
-    @classmethod
-    def create(cls, fpath):
-        "Creates a `Config` from `fpath`."
-        fpath = _expand_path(fpath)
-        assert(fpath.suffix == '.yml')
-        if fpath.exists(): return
-        fpath.parent.mkdir(parents=True, exist_ok=True)
-        with open(fpath, 'w') as yaml_file:
-            yaml.dump(cls.DEFAULT_CONFIG, yaml_file, default_flow_style=False)
-
-def _expand_path(fpath): return Path(fpath).expanduser()
-def url2name(url): return url.split('/')[-1]
-
-#TODO: simplify this mess
-def url2path(url, data=True, ext:str='.tgz'):
-    "Change `url` to a path."
-    name = url2name(url)
-    return datapath4file(name, ext=ext, archive=False) if data else modelpath4file(name, ext=ext)
-def _url2tgz(url, data=True, ext:str='.tgz'):
-    return datapath4file(f'{url2name(url)}{ext}', ext=ext) if data else modelpath4file(f'{url2name(url)}{ext}', ext=ext)
-
-def modelpath4file(filename, ext:str='.tgz'):
-    "Return model path to `filename`, checking locally first then in the config file."
-    local_path = URLs.LOCAL_PATH/'models'/filename
-    if local_path.exists() or local_path.with_suffix(ext).exists(): return local_path
-    else: return Config.model_path()/filename
-
-def datapath4file(filename, ext:str='.tgz', archive=True):
-    "Return data path to `filename`, checking locally first then in the config file."
-    local_path = URLs.LOCAL_PATH/'data'/filename
-    if local_path.exists() or local_path.with_suffix(ext).exists(): return local_path
-    elif archive: return Config.data_archive_path() / filename
-    else: return Config.data_path() / filename
-
-def download_data(url:str, fname:PathOrStr=None, data:bool=True, ext:str='.tgz') -> Path:
-    "Download `url` to destination `fname`."
-    fname = Path(ifnone(fname, _url2tgz(url, data, ext=ext)))
-    os.makedirs(fname.parent, exist_ok=True)
-    if not fname.exists():
-        print(f'Downloading {url}')
-        download_url(f'{url}{ext}', fname)
-    return fname
-
-def _check_file(fname):
-    size = os.path.getsize(fname)
-    with open(fname, "rb") as f:
-        hash_nb = hashlib.md5(f.read(2**20)).hexdigest()
-    return size,hash_nb
-
-def untar_data(url:str, fname:PathOrStr=None, dest:PathOrStr=None, data=True, force_download=False) -> Path:
-    "Download `url` to `fname` if `dest` doesn't exist, and un-tgz to folder `dest`."
-    dest = url2path(url, data) if dest is None else Path(dest)/url2name(url)
-    fname = Path(ifnone(fname, _url2tgz(url, data)))
-    if force_download or (fname.exists() and url in _checks and _check_file(fname) != _checks[url]):
-        print(f"A new version of the {'dataset' if data else 'model'} is available.")
-        if fname.exists(): os.remove(fname)
-        if dest.exists(): shutil.rmtree(dest)
-    if not dest.exists():
-        fname = download_data(url, fname=fname, data=data)
-        if url in _checks:
-            assert _check_file(fname) == _checks[url], f"Downloaded file {fname} does not match checksum expected! Remove that file from {Config().data_archive_path()} and try your code again."
-        tarfile.open(fname, 'r:gz').extractall(dest.parent)
-    return dest
diff --git a/spaces/Xhaheen/Face-Real-ESRGAN/rrdbnet_arch.py b/spaces/Xhaheen/Face-Real-ESRGAN/rrdbnet_arch.py
deleted file mode 100644
index 7d4319c2929888c6d9f66288662e31d006d8661c..0000000000000000000000000000000000000000
--- a/spaces/Xhaheen/Face-Real-ESRGAN/rrdbnet_arch.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-
-from arch_util import default_init_weights, make_layer, pixel_unshuffle
-
-
-class ResidualDenseBlock(nn.Module):
-    """Residual Dense Block.
-
-    Used in RRDB block in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat=64, num_grow_ch=32):
-        super(ResidualDenseBlock, self).__init__()
-        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
-        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x):
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        # Emperically, we use 0.2 to scale the residual for better performance
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    """Residual in Residual Dense Block.
-
-    Used in RRDB-Net in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat, num_grow_ch=32):
-        super(RRDB, self).__init__()
-        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
-
-    def forward(self, x):
-        out = self.rdb1(x)
-        out = self.rdb2(out)
-        out = self.rdb3(out)
-        # Emperically, we use 0.2 to scale the residual for better performance
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    """Networks consisting of Residual in Residual Dense Block, which is used
-    in ESRGAN.
-
-    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
-
-    We extend ESRGAN for scale x2 and scale x1.
-    Note: This is one option for scale 1, scale 2 in RRDBNet.
-    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
-    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.
-
-    Args:
-        num_in_ch (int): Channel number of inputs.
-        num_out_ch (int): Channel number of outputs.
-        num_feat (int): Channel number of intermediate features.
-            Default: 64
-        num_block (int): Block number in the trunk network. Defaults: 23
-        num_grow_ch (int): Channels for each growth. Default: 32.
-    """
-
-    def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
-        super(RRDBNet, self).__init__()
-        self.scale = scale
-        if scale == 2:
-            num_in_ch = num_in_ch * 4
-        elif scale == 1:
-            num_in_ch = num_in_ch * 16
-        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
-        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        # upsample
-        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        if scale == 8:
-            self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x):
-        if self.scale == 2:
-            feat = pixel_unshuffle(x, scale=2)
-        elif self.scale == 1:
-            feat = pixel_unshuffle(x, scale=4)
-        else:
-            feat = x
-        feat = self.conv_first(feat)
-        body_feat = self.conv_body(self.body(feat))
-        feat = feat + body_feat
-        # upsample
-        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        if self.scale == 8:
-            feat = self.lrelu(self.conv_up3(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
-        return out
diff --git a/spaces/XyBr0/DogBreedClassifier/README.md b/spaces/XyBr0/DogBreedClassifier/README.md
deleted file mode 100644
index 2164b57d14f110dab6b3224d2d6c171c4e13f073..0000000000000000000000000000000000000000
--- a/spaces/XyBr0/DogBreedClassifier/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: DogBreedClassifier
-emoji: 📚
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.27.0
-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/XzJosh/Ava2-Bert-VITS2/app.py b/spaces/XzJosh/Ava2-Bert-VITS2/app.py
deleted file mode 100644
index c98037bdc6a11add30bf825367d2f64f11e7b1f4..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Ava2-Bert-VITS2/app.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import sys, os
-
-if sys.platform == "darwin":
-    os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-
-import logging
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-logging.basicConfig(level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s")
-
-logger = logging.getLogger(__name__)
-
-import torch
-import argparse
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-import gradio as gr
-import webbrowser
-
-
-net_g = None
-
-
-def get_text(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)
-
-    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 = torch.LongTensor(phone)
-    tone = torch.LongTensor(tone)
-    language = torch.LongTensor(language)
-
-    return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid):
-    global net_g
-    bert, phones, tones, lang_ids = get_text(text, "ZH", hps)
-    with torch.no_grad():
-        x_tst=phones.to(device).unsqueeze(0)
-        tones=tones.to(device).unsqueeze(0)
-        lang_ids=lang_ids.to(device).unsqueeze(0)
-        bert = bert.to(device).unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
-        del phones
-        speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
-        audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids, 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()
-        del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
-        return audio
-
-def tts_fn(text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale):
-    with torch.no_grad():
-        audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, sid=speaker)
-    return "Success", (hps.data.sampling_rate, audio)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model_dir", default="./logs/Ava/G_14000.pth", help="path of your model")
-    parser.add_argument("--config_dir", default="./configs/config.json", help="path of your config file")
-    parser.add_argument("--share", default=False, help="make link public")
-    parser.add_argument("-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log")
-
-    args = parser.parse_args()
-    if args.debug:
-        logger.info("Enable DEBUG-LEVEL log")
-        logging.basicConfig(level=logging.DEBUG)
-    hps = utils.get_hparams_from_file(args.config_dir)
-    device = "cuda:0" if torch.cuda.is_available() else "cpu"
-    '''
-    device = (
-        "cuda:0"
-        if torch.cuda.is_available()
-        else (
-            "mps"
-            if sys.platform == "darwin" and torch.backends.mps.is_available()
-            else "cpu"
-        )
-    )
-    '''
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model).to(device)
-    _ = net_g.eval()
-
-    _ = utils.load_checkpoint(args.model_dir, net_g, None, skip_optimizer=True)
-
-    speaker_ids = hps.data.spk2id
-    speakers = list(speaker_ids.keys())
-    with gr.Blocks() as app:
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown(value="""
-                【AI向晚】在线语音合成（Bert-Vits2）\n
-                作者：Xz乔希 https://space.bilibili.com/5859321\n
-                声音归属：向晚大魔王 https://space.bilibili.com/672346917\n
-                Bert-VITS2项目：https://github.com/Stardust-minus/Bert-VITS2\n
-                使用本模型请严格遵守法律法规！\n
-                发布二创作品请标注本项目作者及链接、作品使用Bert-VITS2 AI生成！\n                
-                """)
-                text = gr.TextArea(label="Text", placeholder="Input Text Here",
-                                      value="欸哇哇欸哇，这里是诶嗖的向晚，今天也要跟我一起轻松快乐的看直播。")
-                speaker = gr.Dropdown(choices=speakers, value=speakers[0], label='Speaker')
-                sdp_ratio = gr.Slider(minimum=0.1, maximum=1, value=0.2, step=0.01, label='SDP/DP混合比')
-                noise_scale = gr.Slider(minimum=0.1, maximum=1, value=0.5, step=0.01, label='感情调节')
-                noise_scale_w = gr.Slider(minimum=0.1, maximum=1, value=0.9, step=0.01, label='音素长度')
-                length_scale = gr.Slider(minimum=0.1, maximum=2, value=1, step=0.01, label='生成长度')
-                btn = gr.Button("点击生成", variant="primary")
-            with gr.Column():
-                text_output = gr.Textbox(label="Message")
-                audio_output = gr.Audio(label="Output Audio")
-                gr.Markdown(value="""
-                【AI塔菲】https://huggingface.co/spaces/XzJosh/Taffy-Bert-VITS2\n
-                【AI东雪莲】https://huggingface.co/spaces/XzJosh/Azuma-Bert-VITS2\n
-                【AI奶绿】https://huggingface.co/spaces/XzJosh/LAPLACE-Bert-VITS2\n
-                【AI尼奈】https://huggingface.co/spaces/XzJosh/nine1-Bert-VITS2\n
-                【AI珈乐】https://huggingface.co/spaces/XzJosh/Carol-Bert-VITS2\n
-                【AI电棍】https://huggingface.co/spaces/XzJosh/otto-Bert-VITS2\n
-                【AI七海】https://huggingface.co/spaces/XzJosh/Nana7mi-Bert-VITS2\n
-                【AI阿梓】https://huggingface.co/spaces/XzJosh/Azusa-Bert-VITS2\n
-                【AI星瞳】https://huggingface.co/spaces/XzJosh/XingTong-Bert-VITS2\n
-                 """)
-        btn.click(tts_fn,
-                inputs=[text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale],
-                outputs=[text_output, audio_output])
-    
-#    webbrowser.open("http://127.0.0.1:6006")
-#    app.launch(server_port=6006, show_error=True)
-        
-    app.launch(show_error=True)
diff --git a/spaces/XzJosh/Spade-Bert-VITS2/mel_processing.py b/spaces/XzJosh/Spade-Bert-VITS2/mel_processing.py
deleted file mode 100644
index 50435ecf88ef4fb6c1d47f3e6edd04c3ea7d3e80..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Spade-Bert-VITS2/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-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, return_complex=False)
-
-    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/YanzBotz/YanzBotz-Models/lib/infer_pack/transforms.py b/spaces/YanzBotz/YanzBotz-Models/lib/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/YanzBotz/YanzBotz-Models/lib/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/YanzBotz/YanzBotz-Models/rmvpe.py b/spaces/YanzBotz/YanzBotz-Models/rmvpe.py
deleted file mode 100644
index 3ad346141340e03bdbaa20121e1ed435bb3da57a..0000000000000000000000000000000000000000
--- a/spaces/YanzBotz/YanzBotz-Models/rmvpe.py
+++ /dev/null
@@ -1,432 +0,0 @@
-import sys, torch, numpy as np, traceback, pdb
-import torch.nn as nn
-from time import time as ttime
-import torch.nn.functional as F
-
-
-class BiGRU(nn.Module):
-    def __init__(self, input_features, hidden_features, num_layers):
-        super(BiGRU, self).__init__()
-        self.gru = nn.GRU(
-            input_features,
-            hidden_features,
-            num_layers=num_layers,
-            batch_first=True,
-            bidirectional=True,
-        )
-
-    def forward(self, x):
-        return self.gru(x)[0]
-
-
-class ConvBlockRes(nn.Module):
-    def __init__(self, in_channels, out_channels, momentum=0.01):
-        super(ConvBlockRes, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-            nn.Conv2d(
-                in_channels=out_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=(1, 1),
-                padding=(1, 1),
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        if in_channels != out_channels:
-            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
-            self.is_shortcut = True
-        else:
-            self.is_shortcut = False
-
-    def forward(self, x):
-        if self.is_shortcut:
-            return self.conv(x) + self.shortcut(x)
-        else:
-            return self.conv(x) + x
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        in_size,
-        n_encoders,
-        kernel_size,
-        n_blocks,
-        out_channels=16,
-        momentum=0.01,
-    ):
-        super(Encoder, self).__init__()
-        self.n_encoders = n_encoders
-        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
-        self.layers = nn.ModuleList()
-        self.latent_channels = []
-        for i in range(self.n_encoders):
-            self.layers.append(
-                ResEncoderBlock(
-                    in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
-                )
-            )
-            self.latent_channels.append([out_channels, in_size])
-            in_channels = out_channels
-            out_channels *= 2
-            in_size //= 2
-        self.out_size = in_size
-        self.out_channel = out_channels
-
-    def forward(self, x):
-        concat_tensors = []
-        x = self.bn(x)
-        for i in range(self.n_encoders):
-            _, x = self.layers[i](x)
-            concat_tensors.append(_)
-        return x, concat_tensors
-
-
-class ResEncoderBlock(nn.Module):
-    def __init__(
-        self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
-    ):
-        super(ResEncoderBlock, self).__init__()
-        self.n_blocks = n_blocks
-        self.conv = nn.ModuleList()
-        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
-        self.kernel_size = kernel_size
-        if self.kernel_size is not None:
-            self.pool = nn.AvgPool2d(kernel_size=kernel_size)
-
-    def forward(self, x):
-        for i in range(self.n_blocks):
-            x = self.conv[i](x)
-        if self.kernel_size is not None:
-            return x, self.pool(x)
-        else:
-            return x
-
-
-class Intermediate(nn.Module):  #
-    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
-        super(Intermediate, self).__init__()
-        self.n_inters = n_inters
-        self.layers = nn.ModuleList()
-        self.layers.append(
-            ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
-        )
-        for i in range(self.n_inters - 1):
-            self.layers.append(
-                ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
-            )
-
-    def forward(self, x):
-        for i in range(self.n_inters):
-            x = self.layers[i](x)
-        return x
-
-
-class ResDecoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
-        super(ResDecoderBlock, self).__init__()
-        out_padding = (0, 1) if stride == (1, 2) else (1, 1)
-        self.n_blocks = n_blocks
-        self.conv1 = nn.Sequential(
-            nn.ConvTranspose2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=(3, 3),
-                stride=stride,
-                padding=(1, 1),
-                output_padding=out_padding,
-                bias=False,
-            ),
-            nn.BatchNorm2d(out_channels, momentum=momentum),
-            nn.ReLU(),
-        )
-        self.conv2 = nn.ModuleList()
-        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
-        for i in range(n_blocks - 1):
-            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
-
-    def forward(self, x, concat_tensor):
-        x = self.conv1(x)
-        x = torch.cat((x, concat_tensor), dim=1)
-        for i in range(self.n_blocks):
-            x = self.conv2[i](x)
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
-        super(Decoder, self).__init__()
-        self.layers = nn.ModuleList()
-        self.n_decoders = n_decoders
-        for i in range(self.n_decoders):
-            out_channels = in_channels // 2
-            self.layers.append(
-                ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
-            )
-            in_channels = out_channels
-
-    def forward(self, x, concat_tensors):
-        for i in range(self.n_decoders):
-            x = self.layers[i](x, concat_tensors[-1 - i])
-        return x
-
-
-class DeepUnet(nn.Module):
-    def __init__(
-        self,
-        kernel_size,
-        n_blocks,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(DeepUnet, self).__init__()
-        self.encoder = Encoder(
-            in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
-        )
-        self.intermediate = Intermediate(
-            self.encoder.out_channel // 2,
-            self.encoder.out_channel,
-            inter_layers,
-            n_blocks,
-        )
-        self.decoder = Decoder(
-            self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
-        )
-
-    def forward(self, x):
-        x, concat_tensors = self.encoder(x)
-        x = self.intermediate(x)
-        x = self.decoder(x, concat_tensors)
-        return x
-
-
-class E2E(nn.Module):
-    def __init__(
-        self,
-        n_blocks,
-        n_gru,
-        kernel_size,
-        en_de_layers=5,
-        inter_layers=4,
-        in_channels=1,
-        en_out_channels=16,
-    ):
-        super(E2E, self).__init__()
-        self.unet = DeepUnet(
-            kernel_size,
-            n_blocks,
-            en_de_layers,
-            inter_layers,
-            in_channels,
-            en_out_channels,
-        )
-        self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
-        if n_gru:
-            self.fc = nn.Sequential(
-                BiGRU(3 * 128, 256, n_gru),
-                nn.Linear(512, 360),
-                nn.Dropout(0.25),
-                nn.Sigmoid(),
-            )
-        else:
-            self.fc = nn.Sequential(
-                nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
-            )
-
-    def forward(self, mel):
-        mel = mel.transpose(-1, -2).unsqueeze(1)
-        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
-        x = self.fc(x)
-        return x
-
-
-from librosa.filters import mel
-
-
-class MelSpectrogram(torch.nn.Module):
-    def __init__(
-        self,
-        is_half,
-        n_mel_channels,
-        sampling_rate,
-        win_length,
-        hop_length,
-        n_fft=None,
-        mel_fmin=0,
-        mel_fmax=None,
-        clamp=1e-5,
-    ):
-        super().__init__()
-        n_fft = win_length if n_fft is None else n_fft
-        self.hann_window = {}
-        mel_basis = mel(
-            sr=sampling_rate,
-            n_fft=n_fft,
-            n_mels=n_mel_channels,
-            fmin=mel_fmin,
-            fmax=mel_fmax,
-            htk=True,
-        )
-        mel_basis = torch.from_numpy(mel_basis).float()
-        self.register_buffer("mel_basis", mel_basis)
-        self.n_fft = win_length if n_fft is None else n_fft
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.sampling_rate = sampling_rate
-        self.n_mel_channels = n_mel_channels
-        self.clamp = clamp
-        self.is_half = is_half
-
-    def forward(self, audio, keyshift=0, speed=1, center=True):
-        factor = 2 ** (keyshift / 12)
-        n_fft_new = int(np.round(self.n_fft * factor))
-        win_length_new = int(np.round(self.win_length * factor))
-        hop_length_new = int(np.round(self.hop_length * speed))
-        keyshift_key = str(keyshift) + "_" + str(audio.device)
-        if keyshift_key not in self.hann_window:
-            self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
-                audio.device
-            )
-        fft = torch.stft(
-            audio,
-            n_fft=n_fft_new,
-            hop_length=hop_length_new,
-            win_length=win_length_new,
-            window=self.hann_window[keyshift_key],
-            center=center,
-            return_complex=True,
-        )
-        magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
-        if keyshift != 0:
-            size = self.n_fft // 2 + 1
-            resize = magnitude.size(1)
-            if resize < size:
-                magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
-            magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
-        mel_output = torch.matmul(self.mel_basis, magnitude)
-        if self.is_half == True:
-            mel_output = mel_output.half()
-        log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
-        return log_mel_spec
-
-
-class RMVPE:
-    def __init__(self, model_path, is_half, device=None):
-        self.resample_kernel = {}
-        model = E2E(4, 1, (2, 2))
-        ckpt = torch.load(model_path, map_location="cpu")
-        model.load_state_dict(ckpt)
-        model.eval()
-        if is_half == True:
-            model = model.half()
-        self.model = model
-        self.resample_kernel = {}
-        self.is_half = is_half
-        if device is None:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.device = device
-        self.mel_extractor = MelSpectrogram(
-            is_half, 128, 16000, 1024, 160, None, 30, 8000
-        ).to(device)
-        self.model = self.model.to(device)
-        cents_mapping = 20 * np.arange(360) + 1997.3794084376191
-        self.cents_mapping = np.pad(cents_mapping, (4, 4))  # 368
-
-    def mel2hidden(self, mel):
-        with torch.no_grad():
-            n_frames = mel.shape[-1]
-            mel = F.pad(
-                mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
-            )
-            hidden = self.model(mel)
-            return hidden[:, :n_frames]
-
-    def decode(self, hidden, thred=0.03):
-        cents_pred = self.to_local_average_cents(hidden, thred=thred)
-        f0 = 10 * (2 ** (cents_pred / 1200))
-        f0[f0 == 10] = 0
-        # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
-        return f0
-
-    def infer_from_audio(self, audio, thred=0.03):
-        audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
-        # torch.cuda.synchronize()
-        # t0=ttime()
-        mel = self.mel_extractor(audio, center=True)
-        # torch.cuda.synchronize()
-        # t1=ttime()
-        hidden = self.mel2hidden(mel)
-        # torch.cuda.synchronize()
-        # t2=ttime()
-        hidden = hidden.squeeze(0).cpu().numpy()
-        if self.is_half == True:
-            hidden = hidden.astype("float32")
-        f0 = self.decode(hidden, thred=thred)
-        # torch.cuda.synchronize()
-        # t3=ttime()
-        # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
-        return f0
-
-    def to_local_average_cents(self, salience, thred=0.05):
-        # t0 = ttime()
-        center = np.argmax(salience, axis=1)  # 帧长#index
-        salience = np.pad(salience, ((0, 0), (4, 4)))  # 帧长,368
-        # t1 = ttime()
-        center += 4
-        todo_salience = []
-        todo_cents_mapping = []
-        starts = center - 4
-        ends = center + 5
-        for idx in range(salience.shape[0]):
-            todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
-            todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
-        # t2 = ttime()
-        todo_salience = np.array(todo_salience)  # 帧长，9
-        todo_cents_mapping = np.array(todo_cents_mapping)  # 帧长，9
-        product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
-        weight_sum = np.sum(todo_salience, 1)  # 帧长
-        devided = product_sum / weight_sum  # 帧长
-        # t3 = ttime()
-        maxx = np.max(salience, axis=1)  # 帧长
-        devided[maxx <= thred] = 0
-        # t4 = ttime()
-        # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
-        return devided
-
-
-# if __name__ == '__main__':
-#     audio, sampling_rate = sf.read("卢本伟语录~1.wav")
-#     if len(audio.shape) > 1:
-#         audio = librosa.to_mono(audio.transpose(1, 0))
-#     audio_bak = audio.copy()
-#     if sampling_rate != 16000:
-#         audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-#     model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
-#     thred = 0.03  # 0.01
-#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
-#     rmvpe = RMVPE(model_path,is_half=False, device=device)
-#     t0=ttime()
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
-#     t1=ttime()
-#     print(f0.shape,t1-t0)
diff --git a/spaces/Yuelili/RealNagrse/realesrgan/models/realesrgan_model.py b/spaces/Yuelili/RealNagrse/realesrgan/models/realesrgan_model.py
deleted file mode 100644
index c298a09c42433177f90001a0a31d029576072ccd..0000000000000000000000000000000000000000
--- a/spaces/Yuelili/RealNagrse/realesrgan/models/realesrgan_model.py
+++ /dev/null
@@ -1,258 +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.srgan_model import SRGANModel
-from basicsr.utils import DiffJPEG, USMSharp
-from basicsr.utils.img_process_util import filter2D
-from basicsr.utils.registry import MODEL_REGISTRY
-from collections import OrderedDict
-from torch.nn import functional as F
-
-
-@MODEL_REGISTRY.register()
-class RealESRGANModel(SRGANModel):
-    """RealESRGAN Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
-    It mainly performs:
-    1. randomly synthesize LQ images in GPU tensors
-    2. optimize the networks with GAN training.
-    """
-
-    def __init__(self, opt):
-        super(RealESRGANModel, 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)
-            self.gt_usm = 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_usm, 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.gt_usm), self.lq = paired_random_crop([self.gt, self.gt_usm], self.lq, gt_size,
-                                                                 self.opt['scale'])
-
-            # training pair pool
-            self._dequeue_and_enqueue()
-            # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue
-            self.gt_usm = self.usm_sharpener(self.gt)
-            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(RealESRGANModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
-        self.is_train = True
-
-    def optimize_parameters(self, current_iter):
-        # usm sharpening
-        l1_gt = self.gt_usm
-        percep_gt = self.gt_usm
-        gan_gt = self.gt_usm
-        if self.opt['l1_gt_usm'] is False:
-            l1_gt = self.gt
-        if self.opt['percep_gt_usm'] is False:
-            percep_gt = self.gt
-        if self.opt['gan_gt_usm'] is False:
-            gan_gt = self.gt
-
-        # optimize net_g
-        for p in self.net_d.parameters():
-            p.requires_grad = False
-
-        self.optimizer_g.zero_grad()
-        self.output = self.net_g(self.lq)
-
-        l_g_total = 0
-        loss_dict = OrderedDict()
-        if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
-            # pixel loss
-            if self.cri_pix:
-                l_g_pix = self.cri_pix(self.output, l1_gt)
-                l_g_total += l_g_pix
-                loss_dict['l_g_pix'] = l_g_pix
-            # perceptual loss
-            if self.cri_perceptual:
-                l_g_percep, l_g_style = self.cri_perceptual(self.output, percep_gt)
-                if l_g_percep is not None:
-                    l_g_total += l_g_percep
-                    loss_dict['l_g_percep'] = l_g_percep
-                if l_g_style is not None:
-                    l_g_total += l_g_style
-                    loss_dict['l_g_style'] = l_g_style
-            # gan loss
-            fake_g_pred = self.net_d(self.output)
-            l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
-            l_g_total += l_g_gan
-            loss_dict['l_g_gan'] = l_g_gan
-
-            l_g_total.backward()
-            self.optimizer_g.step()
-
-        # optimize net_d
-        for p in self.net_d.parameters():
-            p.requires_grad = True
-
-        self.optimizer_d.zero_grad()
-        # real
-        real_d_pred = self.net_d(gan_gt)
-        l_d_real = self.cri_gan(real_d_pred, True, is_disc=True)
-        loss_dict['l_d_real'] = l_d_real
-        loss_dict['out_d_real'] = torch.mean(real_d_pred.detach())
-        l_d_real.backward()
-        # fake
-        fake_d_pred = self.net_d(self.output.detach().clone())  # clone for pt1.9
-        l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True)
-        loss_dict['l_d_fake'] = l_d_fake
-        loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach())
-        l_d_fake.backward()
-        self.optimizer_d.step()
-
-        if self.ema_decay > 0:
-            self.model_ema(decay=self.ema_decay)
-
-        self.log_dict = self.reduce_loss_dict(loss_dict)
diff --git a/spaces/Yuliang/ECON/lib/net/geometry.py b/spaces/Yuliang/ECON/lib/net/geometry.py
deleted file mode 100644
index 4c98530b316a3164ad82d742ac55da9e2fc8c212..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/net/geometry.py
+++ /dev/null
@@ -1,682 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import numbers
-
-import numpy as np
-import torch
-from einops.einops import rearrange
-from torch.nn import functional as F
-
-"""
-Useful geometric operations, e.g. Perspective projection and a differentiable Rodrigues formula
-Parts of the code are taken from https://github.com/MandyMo/pytorch_HMR
-"""
-
-
-def quaternion_to_rotation_matrix(quat):
-    """Convert quaternion coefficients to rotation matrix.
-    Args:
-        quat: size = [B, 4] 4 <===>(w, x, y, z)
-    Returns:
-        Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]
-    """
-    norm_quat = quat
-    norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True)
-    w, x, y, z = norm_quat[:, 0], norm_quat[:, 1], norm_quat[:, 2], norm_quat[:, 3]
-
-    B = quat.size(0)
-
-    w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
-    wx, wy, wz = w * x, w * y, w * z
-    xy, xz, yz = x * y, x * z, y * z
-
-    rotMat = torch.stack([
-        w2 + x2 - y2 - z2, 2 * xy - 2 * wz, 2 * wy + 2 * xz, 2 * wz + 2 * xy, w2 - x2 + y2 - z2,
-        2 * yz - 2 * wx, 2 * xz - 2 * wy, 2 * wx + 2 * yz, w2 - x2 - y2 + z2
-    ],
-                         dim=1).view(B, 3, 3)
-    return rotMat
-
-
-def index(feat, uv):
-    """
-    :param feat: [B, C, H, W] image features
-    :param uv: [B, 2, N] uv coordinates in the image plane, range [0, 1]
-    :return: [B, C, N] image features at the uv coordinates
-    """
-    uv = uv.transpose(1, 2)    # [B, N, 2]
-
-    (B, N, _) = uv.shape
-    C = feat.shape[1]
-
-    if uv.shape[-1] == 3:
-        # uv = uv[:,:,[2,1,0]]
-        # uv = uv * torch.tensor([1.0,-1.0,1.0]).type_as(uv)[None,None,...]
-        uv = uv.unsqueeze(2).unsqueeze(3)    # [B, N, 1, 1, 3]
-    else:
-        uv = uv.unsqueeze(2)    # [B, N, 1, 2]
-
-    # NOTE: for newer PyTorch, it seems that training results are degraded due to implementation diff in F.grid_sample
-    # for old versions, simply remove the aligned_corners argument.
-    samples = torch.nn.functional.grid_sample(feat, uv, align_corners=True)    # [B, C, N, 1]
-    return samples.view(B, C, N)    # [B, C, N]
-
-
-def orthogonal(points, calibrations, transforms=None):
-    """
-    Compute the orthogonal projections of 3D points into the image plane by given projection matrix
-    :param points: [B, 3, N] Tensor of 3D points
-    :param calibrations: [B, 3, 4] Tensor of projection matrix
-    :param transforms: [B, 2, 3] Tensor of image transform matrix
-    :return: xyz: [B, 3, N] Tensor of xyz coordinates in the image plane
-    """
-    rot = calibrations[:, :3, :3]
-    trans = calibrations[:, :3, 3:4]
-    pts = torch.baddbmm(trans, rot, points)    # [B, 3, N]
-    if transforms is not None:
-        scale = transforms[:2, :2]
-        shift = transforms[:2, 2:3]
-        pts[:, :2, :] = torch.baddbmm(shift, scale, pts[:, :2, :])
-    return pts
-
-
-def perspective(points, calibrations, transforms=None):
-    """
-    Compute the perspective projections of 3D points into the image plane by given projection matrix
-    :param points: [Bx3xN] Tensor of 3D points
-    :param calibrations: [Bx3x4] Tensor of projection matrix
-    :param transforms: [Bx2x3] Tensor of image transform matrix
-    :return: xy: [Bx2xN] Tensor of xy coordinates in the image plane
-    """
-    rot = calibrations[:, :3, :3]
-    trans = calibrations[:, :3, 3:4]
-    homo = torch.baddbmm(trans, rot, points)    # [B, 3, N]
-    xy = homo[:, :2, :] / homo[:, 2:3, :]
-    if transforms is not None:
-        scale = transforms[:2, :2]
-        shift = transforms[:2, 2:3]
-        xy = torch.baddbmm(shift, scale, xy)
-
-    xyz = torch.cat([xy, homo[:, 2:3, :]], 1)
-    return xyz
-
-
-def batch_rodrigues(theta):
-    """Convert axis-angle representation to rotation matrix.
-    Args:
-        theta: size = [B, 3]
-    Returns:
-        Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]
-    """
-    l1norm = torch.norm(theta + 1e-8, p=2, dim=1)
-    angle = torch.unsqueeze(l1norm, -1)
-    normalized = torch.div(theta, angle)
-    angle = angle * 0.5
-    v_cos = torch.cos(angle)
-    v_sin = torch.sin(angle)
-    quat = torch.cat([v_cos, v_sin * normalized], dim=1)
-    return quat_to_rotmat(quat)
-
-
-def quat_to_rotmat(quat):
-    """Convert quaternion coefficients to rotation matrix.
-    Args:
-        quat: size = [B, 4] 4 <===>(w, x, y, z)
-    Returns:
-        Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]
-    """
-    norm_quat = quat
-    norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True)
-    w, x, y, z = norm_quat[:, 0], norm_quat[:, 1], norm_quat[:, 2], norm_quat[:, 3]
-
-    B = quat.size(0)
-
-    w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
-    wx, wy, wz = w * x, w * y, w * z
-    xy, xz, yz = x * y, x * z, y * z
-
-    rotMat = torch.stack(
-        [
-            w2 + x2 - y2 - z2,
-            2 * xy - 2 * wz,
-            2 * wy + 2 * xz,
-            2 * wz + 2 * xy,
-            w2 - x2 + y2 - z2,
-            2 * yz - 2 * wx,
-            2 * xz - 2 * wy,
-            2 * wx + 2 * yz,
-            w2 - x2 - y2 + z2,
-        ],
-        dim=1,
-    ).view(B, 3, 3)
-    return rotMat
-
-
-def rotation_matrix_to_angle_axis(rotation_matrix):
-    """
-    This function is borrowed from https://github.com/kornia/kornia
-
-    Convert 3x4 rotation matrix to Rodrigues vector
-
-    Args:
-        rotation_matrix (Tensor): rotation matrix.
-
-    Returns:
-        Tensor: Rodrigues vector transformation.
-
-    Shape:
-        - Input: :math:`(N, 3, 4)`
-        - Output: :math:`(N, 3)`
-
-    Example:
-        >>> input = torch.rand(2, 3, 4)  # Nx4x4
-        >>> output = tgm.rotation_matrix_to_angle_axis(input)  # Nx3
-    """
-    if rotation_matrix.shape[1:] == (3, 3):
-        rot_mat = rotation_matrix.reshape(-1, 3, 3)
-        hom = torch.tensor([0, 0, 1], dtype=torch.float32, device=rotation_matrix.device).reshape(
-            1, 3, 1
-        ).expand(rot_mat.shape[0], -1, -1)
-        rotation_matrix = torch.cat([rot_mat, hom], dim=-1)
-
-    quaternion = rotation_matrix_to_quaternion(rotation_matrix)
-    aa = quaternion_to_angle_axis(quaternion)
-    aa[torch.isnan(aa)] = 0.0
-    return aa
-
-
-def quaternion_to_angle_axis(quaternion: torch.Tensor) -> torch.Tensor:
-    """
-    This function is borrowed from https://github.com/kornia/kornia
-
-    Convert quaternion vector to angle axis of rotation.
-
-    Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h
-
-    Args:
-        quaternion (torch.Tensor): tensor with quaternions.
-
-    Return:
-        torch.Tensor: tensor with angle axis of rotation.
-
-    Shape:
-        - Input: :math:`(*, 4)` where `*` means, any number of dimensions
-        - Output: :math:`(*, 3)`
-
-    Example:
-        >>> quaternion = torch.rand(2, 4)  # Nx4
-        >>> angle_axis = tgm.quaternion_to_angle_axis(quaternion)  # Nx3
-    """
-    if not torch.is_tensor(quaternion):
-        raise TypeError("Input type is not a torch.Tensor. Got {}".format(type(quaternion)))
-
-    if not quaternion.shape[-1] == 4:
-        raise ValueError(
-            "Input must be a tensor of shape Nx4 or 4. Got {}".format(quaternion.shape)
-        )
-    # unpack input and compute conversion
-    q1: torch.Tensor = quaternion[..., 1]
-    q2: torch.Tensor = quaternion[..., 2]
-    q3: torch.Tensor = quaternion[..., 3]
-    sin_squared_theta: torch.Tensor = q1 * q1 + q2 * q2 + q3 * q3
-
-    sin_theta: torch.Tensor = torch.sqrt(sin_squared_theta)
-    cos_theta: torch.Tensor = quaternion[..., 0]
-    two_theta: torch.Tensor = 2.0 * torch.where(
-        cos_theta < 0.0,
-        torch.atan2(-sin_theta, -cos_theta),
-        torch.atan2(sin_theta, cos_theta),
-    )
-
-    k_pos: torch.Tensor = two_theta / sin_theta
-    k_neg: torch.Tensor = 2.0 * torch.ones_like(sin_theta)
-    k: torch.Tensor = torch.where(sin_squared_theta > 0.0, k_pos, k_neg)
-
-    angle_axis: torch.Tensor = torch.zeros_like(quaternion)[..., :3]
-    angle_axis[..., 0] += q1 * k
-    angle_axis[..., 1] += q2 * k
-    angle_axis[..., 2] += q3 * k
-    return angle_axis
-
-
-def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6):
-    """
-    This function is borrowed from https://github.com/kornia/kornia
-
-    Convert 3x4 rotation matrix to 4d quaternion vector
-
-    This algorithm is based on algorithm described in
-    https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L201
-
-    Args:
-        rotation_matrix (Tensor): the rotation matrix to convert.
-
-    Return:
-        Tensor: the rotation in quaternion
-
-    Shape:
-        - Input: :math:`(N, 3, 4)`
-        - Output: :math:`(N, 4)`
-
-    Example:
-        >>> input = torch.rand(4, 3, 4)  # Nx3x4
-        >>> output = tgm.rotation_matrix_to_quaternion(input)  # Nx4
-    """
-    if not torch.is_tensor(rotation_matrix):
-        raise TypeError("Input type is not a torch.Tensor. Got {}".format(type(rotation_matrix)))
-
-    if len(rotation_matrix.shape) > 3:
-        raise ValueError(
-            "Input size must be a three dimensional tensor. Got {}".format(rotation_matrix.shape)
-        )
-    if not rotation_matrix.shape[-2:] == (3, 4):
-        raise ValueError(
-            "Input size must be a N x 3 x 4  tensor. Got {}".format(rotation_matrix.shape)
-        )
-
-    rmat_t = torch.transpose(rotation_matrix, 1, 2)
-
-    mask_d2 = rmat_t[:, 2, 2] < eps
-
-    mask_d0_d1 = rmat_t[:, 0, 0] > rmat_t[:, 1, 1]
-    mask_d0_nd1 = rmat_t[:, 0, 0] < -rmat_t[:, 1, 1]
-
-    t0 = 1 + rmat_t[:, 0, 0] - rmat_t[:, 1, 1] - rmat_t[:, 2, 2]
-    q0 = torch.stack(
-        [
-            rmat_t[:, 1, 2] - rmat_t[:, 2, 1],
-            t0,
-            rmat_t[:, 0, 1] + rmat_t[:, 1, 0],
-            rmat_t[:, 2, 0] + rmat_t[:, 0, 2],
-        ],
-        -1,
-    )
-    t0_rep = t0.repeat(4, 1).t()
-
-    t1 = 1 - rmat_t[:, 0, 0] + rmat_t[:, 1, 1] - rmat_t[:, 2, 2]
-    q1 = torch.stack(
-        [
-            rmat_t[:, 2, 0] - rmat_t[:, 0, 2],
-            rmat_t[:, 0, 1] + rmat_t[:, 1, 0],
-            t1,
-            rmat_t[:, 1, 2] + rmat_t[:, 2, 1],
-        ],
-        -1,
-    )
-    t1_rep = t1.repeat(4, 1).t()
-
-    t2 = 1 - rmat_t[:, 0, 0] - rmat_t[:, 1, 1] + rmat_t[:, 2, 2]
-    q2 = torch.stack(
-        [
-            rmat_t[:, 0, 1] - rmat_t[:, 1, 0],
-            rmat_t[:, 2, 0] + rmat_t[:, 0, 2],
-            rmat_t[:, 1, 2] + rmat_t[:, 2, 1],
-            t2,
-        ],
-        -1,
-    )
-    t2_rep = t2.repeat(4, 1).t()
-
-    t3 = 1 + rmat_t[:, 0, 0] + rmat_t[:, 1, 1] + rmat_t[:, 2, 2]
-    q3 = torch.stack(
-        [
-            t3,
-            rmat_t[:, 1, 2] - rmat_t[:, 2, 1],
-            rmat_t[:, 2, 0] - rmat_t[:, 0, 2],
-            rmat_t[:, 0, 1] - rmat_t[:, 1, 0],
-        ],
-        -1,
-    )
-    t3_rep = t3.repeat(4, 1).t()
-
-    mask_c0 = mask_d2 * mask_d0_d1
-    mask_c1 = mask_d2 * ~mask_d0_d1
-    mask_c2 = ~mask_d2 * mask_d0_nd1
-    mask_c3 = ~mask_d2 * ~mask_d0_nd1
-    mask_c0 = mask_c0.view(-1, 1).type_as(q0)
-    mask_c1 = mask_c1.view(-1, 1).type_as(q1)
-    mask_c2 = mask_c2.view(-1, 1).type_as(q2)
-    mask_c3 = mask_c3.view(-1, 1).type_as(q3)
-
-    q = q0 * mask_c0 + q1 * mask_c1 + q2 * mask_c2 + q3 * mask_c3
-    q /= torch.sqrt(
-        t0_rep * mask_c0 + t1_rep * mask_c1 + t2_rep * mask_c2    # noqa
-        + t3_rep * mask_c3
-    )    # noqa
-    q *= 0.5
-    return q
-
-
-def rot6d_to_rotmat(x):
-    """Convert 6D rotation representation to 3x3 rotation matrix.
-    Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019
-    Input:
-        (B,6) Batch of 6-D rotation representations
-    Output:
-        (B,3,3) Batch of corresponding rotation matrices
-    """
-    if x.shape[-1] == 6:
-        batch_size = x.shape[0]
-        if len(x.shape) == 3:
-            num = x.shape[1]
-            x = rearrange(x, 'b n d -> (b n) d', d=6)
-        else:
-            num = 1
-        x = rearrange(x, 'b (k l) -> b k l', k=3, l=2)
-        # x = x.view(-1,3,2)
-        a1 = x[:, :, 0]
-        a2 = x[:, :, 1]
-        b1 = F.normalize(a1)
-        b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1)
-        b3 = torch.cross(b1, b2, dim=-1)
-
-        mat = torch.stack((b1, b2, b3), dim=-1)
-        if num > 1:
-            mat = rearrange(mat, '(b n) h w-> b n h w', b=batch_size, n=num, h=3, w=3)
-    else:
-        x = x.view(-1, 3, 2)
-        a1 = x[:, :, 0]
-        a2 = x[:, :, 1]
-        b1 = F.normalize(a1)
-        b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1)
-        b3 = torch.cross(b1, b2, dim=-1)
-        mat = torch.stack((b1, b2, b3), dim=-1)
-    return mat
-
-
-def rotmat_to_rot6d(x):
-    """Convert 3x3 rotation matrix to 6D rotation representation.
-    Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019
-    Input:
-        (B,3,3) Batch of corresponding rotation matrices
-    Output:
-        (B,6) Batch of 6-D rotation representations
-    """
-    batch_size = x.shape[0]
-    x = x[:, :, :2]
-    x = x.reshape(batch_size, 6)
-    return x
-
-
-def rotmat_to_angle(x):
-    """Convert rotation to one-D angle.
-    Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019
-    Input:
-        (B,2) Batch of corresponding rotation
-    Output:
-        (B,1) Batch of 1-D angle
-    """
-    a = F.normalize(x)
-    angle = torch.atan2(a[:, 0], a[:, 1]).unsqueeze(-1)
-
-    return angle
-
-
-def projection(pred_joints, pred_camera, retain_z=False):
-    pred_cam_t = torch.stack(
-        [
-            pred_camera[:, 1],
-            pred_camera[:, 2],
-            2 * 5000.0 / (224.0 * pred_camera[:, 0] + 1e-9),
-        ],
-        dim=-1,
-    )
-    batch_size = pred_joints.shape[0]
-    camera_center = torch.zeros(batch_size, 2)
-    pred_keypoints_2d = perspective_projection(
-        pred_joints,
-        rotation=torch.eye(3).unsqueeze(0).expand(batch_size, -1, -1).to(pred_joints.device),
-        translation=pred_cam_t,
-        focal_length=5000.0,
-        camera_center=camera_center,
-        retain_z=retain_z,
-    )
-    # Normalize keypoints to [-1,1]
-    pred_keypoints_2d = pred_keypoints_2d / (224.0 / 2.0)
-    return pred_keypoints_2d
-
-
-def perspective_projection(
-    points, rotation, translation, focal_length, camera_center, retain_z=False
-):
-    """
-    This function computes the perspective projection of a set of points.
-    Input:
-        points (bs, N, 3): 3D points
-        rotation (bs, 3, 3): Camera rotation
-        translation (bs, 3): Camera translation
-        focal_length (bs,) or scalar: Focal length
-        camera_center (bs, 2): Camera center
-    """
-    batch_size = points.shape[0]
-    K = torch.zeros([batch_size, 3, 3], device=points.device)
-    K[:, 0, 0] = focal_length
-    K[:, 1, 1] = focal_length
-    K[:, 2, 2] = 1.0
-    K[:, :-1, -1] = camera_center
-
-    # Transform points
-    points = torch.einsum("bij,bkj->bki", rotation, points)
-    points = points + translation.unsqueeze(1)
-
-    # Apply perspective distortion
-    projected_points = points / points[:, :, -1].unsqueeze(-1)
-
-    # Apply camera intrinsics
-    projected_points = torch.einsum("bij,bkj->bki", K, projected_points)
-
-    if retain_z:
-        return projected_points
-    else:
-        return projected_points[:, :, :-1]
-
-
-def estimate_translation_np(S, joints_2d, joints_conf, focal_length=5000, img_size=(224., 224.)):
-    """Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d.
-    Input:
-        S: (25, 3) 3D joint locations
-        joints: (25, 3) 2D joint locations and confidence
-    Returns:
-        (3,) camera translation vector
-    """
-
-    num_joints = S.shape[0]
-    # focal length
-    f = np.array([focal_length, focal_length])
-    # optical center
-    center = np.array([img_size[1] / 2., img_size[0] / 2.])
-
-    # transformations
-    Z = np.reshape(np.tile(S[:, 2], (2, 1)).T, -1)
-    XY = np.reshape(S[:, 0:2], -1)
-    O = np.tile(center, num_joints)
-    F = np.tile(f, num_joints)
-    weight2 = np.reshape(np.tile(np.sqrt(joints_conf), (2, 1)).T, -1)
-
-    # least squares
-    Q = np.array([
-        F * np.tile(np.array([1, 0]), num_joints), F * np.tile(np.array([0, 1]), num_joints),
-        O - np.reshape(joints_2d, -1)
-    ]).T
-    c = (np.reshape(joints_2d, -1) - O) * Z - F * XY
-
-    # weighted least squares
-    W = np.diagflat(weight2)
-    Q = np.dot(W, Q)
-    c = np.dot(W, c)
-
-    # square matrix
-    A = np.dot(Q.T, Q)
-    b = np.dot(Q.T, c)
-
-    # solution
-    trans = np.linalg.solve(A, b)
-
-    return trans
-
-
-def estimate_translation(S, joints_2d, focal_length=5000., img_size=224., use_all_kps=False):
-    """Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d.
-    Input:
-        S: (B, 49, 3) 3D joint locations
-        joints: (B, 49, 3) 2D joint locations and confidence
-    Returns:
-        (B, 3) camera translation vectors
-    """
-    if isinstance(focal_length, numbers.Number):
-        focal_length = [
-            focal_length,
-        ] * S.shape[0]
-        # print(len(focal_length), focal_length)
-
-    if isinstance(img_size, numbers.Number):
-        img_size = [
-            (img_size, img_size),
-        ] * S.shape[0]
-        # print(len(img_size), img_size)
-
-    device = S.device
-    if use_all_kps:
-        S = S.cpu().numpy()
-        joints_2d = joints_2d.cpu().numpy()
-    else:
-        # Use only joints 25:49 (GT joints)
-        S = S[:, 25:, :].cpu().numpy()
-        joints_2d = joints_2d[:, 25:, :].cpu().numpy()
-    joints_conf = joints_2d[:, :, -1]
-    joints_2d = joints_2d[:, :, :-1]
-    trans = np.zeros((S.shape[0], 3), dtype=np.float32)
-    # Find the translation for each example in the batch
-    for i in range(S.shape[0]):
-        S_i = S[i]
-        joints_i = joints_2d[i]
-        conf_i = joints_conf[i]
-        trans[i] = estimate_translation_np(
-            S_i, joints_i, conf_i, focal_length=focal_length[i], img_size=img_size[i]
-        )
-    return torch.from_numpy(trans).to(device)
-
-
-def Rot_y(angle, category="torch", prepend_dim=True, device=None):
-    """Rotate around y-axis by angle
-    Args:
-            category: 'torch' or 'numpy'
-            prepend_dim: prepend an extra dimension
-    Return: Rotation matrix with shape [1, 3, 3] (prepend_dim=True)
-    """
-    m = np.array([
-        [np.cos(angle), 0.0, np.sin(angle)],
-        [0.0, 1.0, 0.0],
-        [-np.sin(angle), 0.0, np.cos(angle)],
-    ])
-    if category == "torch":
-        if prepend_dim:
-            return torch.tensor(m, dtype=torch.float, device=device).unsqueeze(0)
-        else:
-            return torch.tensor(m, dtype=torch.float, device=device)
-    elif category == "numpy":
-        if prepend_dim:
-            return np.expand_dims(m, 0)
-        else:
-            return m
-    else:
-        raise ValueError("category must be 'torch' or 'numpy'")
-
-
-def Rot_x(angle, category="torch", prepend_dim=True, device=None):
-    """Rotate around x-axis by angle
-    Args:
-            category: 'torch' or 'numpy'
-            prepend_dim: prepend an extra dimension
-    Return: Rotation matrix with shape [1, 3, 3] (prepend_dim=True)
-    """
-    m = np.array([
-        [1.0, 0.0, 0.0],
-        [0.0, np.cos(angle), -np.sin(angle)],
-        [0.0, np.sin(angle), np.cos(angle)],
-    ])
-    if category == "torch":
-        if prepend_dim:
-            return torch.tensor(m, dtype=torch.float, device=device).unsqueeze(0)
-        else:
-            return torch.tensor(m, dtype=torch.float, device=device)
-    elif category == "numpy":
-        if prepend_dim:
-            return np.expand_dims(m, 0)
-        else:
-            return m
-    else:
-        raise ValueError("category must be 'torch' or 'numpy'")
-
-
-def Rot_z(angle, category="torch", prepend_dim=True, device=None):
-    """Rotate around z-axis by angle
-    Args:
-            category: 'torch' or 'numpy'
-            prepend_dim: prepend an extra dimension
-    Return: Rotation matrix with shape [1, 3, 3] (prepend_dim=True)
-    """
-    m = np.array([
-        [np.cos(angle), -np.sin(angle), 0.0],
-        [np.sin(angle), np.cos(angle), 0.0],
-        [0.0, 0.0, 1.0],
-    ])
-    if category == "torch":
-        if prepend_dim:
-            return torch.tensor(m, dtype=torch.float, device=device).unsqueeze(0)
-        else:
-            return torch.tensor(m, dtype=torch.float, device=device)
-    elif category == "numpy":
-        if prepend_dim:
-            return np.expand_dims(m, 0)
-        else:
-            return m
-    else:
-        raise ValueError("category must be 'torch' or 'numpy'")
-
-
-def compute_twist_rotation(rotation_matrix, twist_axis):
-    '''
-    Compute the twist component of given rotation and twist axis
-    https://stackoverflow.com/questions/3684269/component-of-a-quaternion-rotation-around-an-axis
-    Parameters
-    ----------
-    rotation_matrix : Tensor (B, 3, 3,)
-        The rotation to convert
-    twist_axis : Tensor (B, 3,)
-        The twist axis
-    Returns
-    -------
-    Tensor (B, 3, 3)
-        The twist rotation
-    '''
-    quaternion = rotation_matrix_to_quaternion(rotation_matrix)
-
-    twist_axis = twist_axis / (torch.norm(twist_axis, dim=1, keepdim=True) + 1e-9)
-
-    projection = torch.einsum('bi,bi->b', twist_axis, quaternion[:, 1:]).unsqueeze(-1) * twist_axis
-
-    twist_quaternion = torch.cat([quaternion[:, 0:1], projection], dim=1)
-    twist_quaternion = twist_quaternion / (torch.norm(twist_quaternion, dim=1, keepdim=True) + 1e-9)
-
-    twist_rotation = quaternion_to_rotation_matrix(twist_quaternion)
-    twist_aa = quaternion_to_angle_axis(twist_quaternion)
-
-    twist_angle = torch.sum(twist_aa, dim=1,
-                            keepdim=True) / torch.sum(twist_axis, dim=1, keepdim=True)
-
-    return twist_rotation, twist_angle
diff --git a/spaces/abdvl/datahub_qa_bot/docs/api/tutorials/adding-dataset-description.md b/spaces/abdvl/datahub_qa_bot/docs/api/tutorials/adding-dataset-description.md
deleted file mode 100644
index 0d97f99ac6aaaac1f2b80e303dd4f5cb1ca30857..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/api/tutorials/adding-dataset-description.md
+++ /dev/null
@@ -1,138 +0,0 @@
-# Adding Description on Datasets
-
-## Why Would You Add Description on Dataset? 
-Adding a description and related link to a dataset can provide important information about the data, such as its source, collection methods, and potential uses. This can help others understand the context of the data and how it may be relevant to their own work or research. Including a related link can also provide access to additional resources or related datasets, further enriching the information available to users.
-
-### Goal Of This Guide
-This guide will show you how to add a description and a link to dataset `fct_users_deleted`.
-
-
-## Prerequisites
-For this tutorial, you need to deploy DataHub Quickstart and ingest sample data. 
-For detailed steps, please refer to [Prepare Local DataHub Environment](/docs/api/tutorials/references/prepare-datahub.md).
-
-:::note
-Before adding a description, you need to ensure the targeted dataset is already present in your datahub. 
-If you attempt to manipulate entities that do not exist, your operation will fail. 
-In this guide, we will be using data from sample ingestion.
-:::
-
-In this example, we will add a description to `user_name `column of a dataset `fct_users_deleted`.
-
-## Add Description With GraphQL (Not Supported)
-> 🚫 Adding Description on Dataset via GraphQL is currently not supported.
-> Please check out [API feature comparison table](/docs/api/datahub-apis.md#datahub-api-comparison) for more information, 
-
-
-## Add Description With Python SDK
-Following code add a description and link to a dataset named `fct_users_deleted`.
-For more information, please refer to [dataset_add_documentation.py](https://github.com/datahub-project/datahub/blob/master/metadata-ingestion/examples/library/dataset_add_documentation.py)
-
-```python
-import logging
-import time
-
-from datahub.emitter.mce_builder import make_dataset_urn
-from datahub.emitter.mcp import MetadataChangeProposalWrapper
-
-# read-modify-write requires access to the DataHubGraph (RestEmitter is not enough)
-from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
-
-# Imports for metadata model classes
-from datahub.metadata.schema_classes import (
-    AuditStampClass,
-    EditableDatasetPropertiesClass,
-    InstitutionalMemoryClass,
-    InstitutionalMemoryMetadataClass,
-)
-
-log = logging.getLogger(__name__)
-logging.basicConfig(level=logging.INFO)
-
-
-# Inputs -> owner, ownership_type, dataset
-documentation_to_add = "This table contains information on users deleted on a single day. This description is updated via PythonSDK."
-link_to_add = "https://en.wikipedia.org/wiki/Fact_table"
-link_description = "This is the definition of what fact table means"
-dataset_urn = make_dataset_urn(platform="hive", name="fct_users_deleted", env="PROD")
-
-# Some helpful variables to fill out objects later
-now = int(time.time() * 1000)  # milliseconds since epoch
-current_timestamp = AuditStampClass(time=now, actor="urn:li:corpuser:ingestion")
-institutional_memory_element = InstitutionalMemoryMetadataClass(
-    url=link_to_add,
-    description=link_description,
-    createStamp=current_timestamp,
-)
-
-
-# First we get the current owners
-gms_endpoint = "http://localhost:8080"
-graph = DataHubGraph(config=DatahubClientConfig(server=gms_endpoint))
-
-current_editable_properties = graph.get_aspect(
-    entity_urn=dataset_urn, aspect_type=EditableDatasetPropertiesClass
-)
-
-need_write = False
-if current_editable_properties:
-    if documentation_to_add != current_editable_properties.description:
-        current_editable_properties.description = documentation_to_add
-        need_write = True
-else:
-    # create a brand new editable dataset properties aspect
-    current_editable_properties = EditableDatasetPropertiesClass(
-        created=current_timestamp, description=documentation_to_add
-    )
-    need_write = True
-
-if need_write:
-    event: MetadataChangeProposalWrapper = MetadataChangeProposalWrapper(
-        entityUrn=dataset_urn,
-        aspect=current_editable_properties,
-    )
-    graph.emit(event)
-    log.info(f"Documentation added to dataset {dataset_urn}")
-
-else:
-    log.info("Documentation already exists and is identical, omitting write")
-
-
-current_institutional_memory = graph.get_aspect(
-    entity_urn=dataset_urn, aspect_type=InstitutionalMemoryClass
-)
-
-need_write = False
-
-if current_institutional_memory:
-    if link_to_add not in [x.url for x in current_institutional_memory.elements]:
-        current_institutional_memory.elements.append(institutional_memory_element)
-        need_write = True
-else:
-    # create a brand new institutional memory aspect
-    current_institutional_memory = InstitutionalMemoryClass(
-        elements=[institutional_memory_element]
-    )
-    need_write = True
-
-if need_write:
-    event = MetadataChangeProposalWrapper(
-        entityUrn=dataset_urn,
-        aspect=current_institutional_memory,
-    )
-    graph.emit(event)
-    log.info(f"Link {link_to_add} added to dataset {dataset_urn}")
-
-else:
-    log.info(f"Link {link_to_add} already exists and is identical, omitting write")
-```
-
-We're using the `MetdataChangeProposalWrapper` to change entities in this example.
-For more information about the `MetadataChangeProposal`, please refer to [MetadataChangeProposal & MetadataChangeLog Events](/docs/advanced/mcp-mcl.md)
-
-
-## Expected Outcomes
-You can now see the description is added to `fct_users_deleted`. 
-
-![dataset-description-added](../../imgs/apis/tutorials/dataset-description-added.png)
-
diff --git a/spaces/abdvl/datahub_qa_bot/docs/deploy/gcp.md b/spaces/abdvl/datahub_qa_bot/docs/deploy/gcp.md
deleted file mode 100644
index 3713d69f90636c9dd3d6e128170bd5cf7547a081..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/deploy/gcp.md
+++ /dev/null
@@ -1,102 +0,0 @@
----
-title: "Deploying to GCP"
----
-
-# GCP setup guide
-
-The following is a set of instructions to quickstart DataHub on GCP Google Kubernetes Engine (GKE). Note, the guide
-assumes that you do not have a kubernetes cluster set up. If you are deploying DataHub to an existing cluster, please
-skip the corresponding sections.
-
-## Prerequisites
-
-This guide requires the following tools:
-
-- [kubectl](https://kubernetes.io/docs/tasks/tools/) to manage kubernetes resources
-- [helm](https://helm.sh/docs/intro/install/) to deploy the resources based on helm charts. Note, we only support Helm
-    3.
-- [gcloud](https://cloud.google.com/sdk/docs/install) to manage GCP resources
-
-Follow the
-following [guide](https://cloud.google.com/kubernetes-engine/docs/how-to/creating-a-zonal-cluster#before_you_begin) to
-correctly set up Google Cloud SDK.
-
-After setting up, run `gcloud services enable container.googleapis.com` to make sure GKE service is enabled.
-
-## Start up a kubernetes cluster on GKE
-
-Let’s follow this [guide](https://cloud.google.com/kubernetes-engine/docs/how-to/creating-a-zonal-cluster) to create a
-new cluster using gcloud. Run the following command with cluster-name set to the cluster name of choice, and zone set to
-the GCP zone you are operating on.
-
-```
-gcloud container clusters create <<cluster-name>> \
-    --zone <<zone>> \
-    -m e2-standard-2
-```
-
-The command will provision a GKE cluster powered by 3 e2-standard-2 (2 CPU, 8GB RAM) nodes.
-
-If you are planning to run the storage layer (MySQL, Elasticsearch, Kafka) as pods in the cluster, you need at least 3
-nodes with the above specs. If you decide to use managed storage services, you can reduce the number of nodes or use
-m3.medium nodes to save cost. Refer to
-this [guide](https://cloud.google.com/kubernetes-engine/docs/how-to/creating-a-regional-cluster) for creating a regional
-cluster for better robustness.
-
-Run `kubectl get nodes` to confirm that the cluster has been setup correctly. You should get results like below
-
-```
-NAME                                     STATUS   ROLES    AGE   VERSION
-gke-datahub-default-pool-e5be7c4f-8s97   Ready    <none>   34h   v1.19.10-gke.1600
-gke-datahub-default-pool-e5be7c4f-d68l   Ready    <none>   34h   v1.19.10-gke.1600
-gke-datahub-default-pool-e5be7c4f-rksj   Ready    <none>   34h   v1.19.10-gke.1600
-```
-
-## Setup DataHub using Helm
-
-Once the kubernetes cluster has been set up, you can deploy DataHub and it’s prerequisites using helm. Please follow the
-steps in this [guide](kubernetes.md)
-
-## Expose endpoints using GKE ingress controller
-
-Now that all the pods are up and running, you need to expose the datahub-frontend end point by setting
-up [ingress](https://kubernetes.io/docs/concepts/services-networking/ingress/). Easiest way to set up ingress is to use
-the GKE page on [GCP website](https://console.cloud.google.com/kubernetes/discovery).
-
-Once all deploy is successful, you should see a page like below in the "Services & Ingress" tab on the left.
-
-![Services and Ingress](../imgs/gcp/services_ingress.png)
-
-Tick the checkbox for datahub-datahub-frontend and click "CREATE INGRESS" button. You should land on the following page.
-
-![Ingress1](../imgs/gcp/ingress1.png)
-
-Type in an arbitrary name for the ingress and click on the second step "Host and path rules". You should land on the
-following page.
-
-![Ingress2](../imgs/gcp/ingress2.png)
-
-Select "datahub-datahub-frontend" in the dropdown menu for backends, and then click on "ADD HOST AND PATH RULE" button.
-In the second row that got created, add in the host name of choice (here gcp.datahubproject.io) and select
-"datahub-datahub-frontend" in the backends dropdown.
-
-This step adds the rule allowing requests from the host name of choice to get routed to datahub-frontend service. Click
-on step 3 "Frontend configuration". You should land on the following page.
-
-![Ingress3](../imgs/gcp/ingress3.png)
-
-Choose HTTPS in the dropdown menu for protocol. To enable SSL, you need to add a certificate. If you do not have one,
-you can click "CREATE A NEW CERTIFICATE" and input the host name of choice. GCP will create a certificate for you.
-
-Now press "CREATE" button on the left to create ingress! After around 5 minutes, you should see the following.
-
-![Ingress Ready](../imgs/gcp/ingress_ready.png)
-
-In your domain provider, add an A record for the host name set above using the IP address on the ingress page (noted
-with the red box). Once DNS updates, you should be able to access DataHub through the host name!!
-
-Note, ignore the warning icon next to ingress. It takes about ten minutes for ingress to check that the backend service
-is ready and show a check mark as follows. However, ingress is fully functional once you see the above page. 
-
-![Ingress Final](../imgs/gcp/ingress_final.png)
-
diff --git a/spaces/abhishek/sketch-to-image/annotator/midas/midas/midas_net_custom.py b/spaces/abhishek/sketch-to-image/annotator/midas/midas/midas_net_custom.py
deleted file mode 100644
index 3cfdfbe38a2ff6e9ea2dc3c4702e0713a0757a82..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/midas/midas/midas_net_custom.py
+++ /dev/null
@@ -1,138 +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
-'''
-
-"""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/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/bricks/context_block.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/bricks/context_block.py
deleted file mode 100644
index d60fdb904c749ce3b251510dff3cc63cea70d42e..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/bricks/context_block.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-from torch import nn
-
-from ..utils import constant_init, kaiming_init
-from .registry import PLUGIN_LAYERS
-
-
-def last_zero_init(m):
-    if isinstance(m, nn.Sequential):
-        constant_init(m[-1], val=0)
-    else:
-        constant_init(m, val=0)
-
-
-@PLUGIN_LAYERS.register_module()
-class ContextBlock(nn.Module):
-    """ContextBlock module in GCNet.
-
-    See 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
-    (https://arxiv.org/abs/1904.11492) for details.
-
-    Args:
-        in_channels (int): Channels of the input feature map.
-        ratio (float): Ratio of channels of transform bottleneck
-        pooling_type (str): Pooling method for context modeling.
-            Options are 'att' and 'avg', stand for attention pooling and
-            average pooling respectively. Default: 'att'.
-        fusion_types (Sequence[str]): Fusion method for feature fusion,
-            Options are 'channels_add', 'channel_mul', stand for channelwise
-            addition and multiplication respectively. Default: ('channel_add',)
-    """
-
-    _abbr_ = 'context_block'
-
-    def __init__(self,
-                 in_channels,
-                 ratio,
-                 pooling_type='att',
-                 fusion_types=('channel_add', )):
-        super(ContextBlock, self).__init__()
-        assert pooling_type in ['avg', 'att']
-        assert isinstance(fusion_types, (list, tuple))
-        valid_fusion_types = ['channel_add', 'channel_mul']
-        assert all([f in valid_fusion_types for f in fusion_types])
-        assert len(fusion_types) > 0, 'at least one fusion should be used'
-        self.in_channels = in_channels
-        self.ratio = ratio
-        self.planes = int(in_channels * ratio)
-        self.pooling_type = pooling_type
-        self.fusion_types = fusion_types
-        if pooling_type == 'att':
-            self.conv_mask = nn.Conv2d(in_channels, 1, kernel_size=1)
-            self.softmax = nn.Softmax(dim=2)
-        else:
-            self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        if 'channel_add' in fusion_types:
-            self.channel_add_conv = nn.Sequential(
-                nn.Conv2d(self.in_channels, self.planes, kernel_size=1),
-                nn.LayerNorm([self.planes, 1, 1]),
-                nn.ReLU(inplace=True),  # yapf: disable
-                nn.Conv2d(self.planes, self.in_channels, kernel_size=1))
-        else:
-            self.channel_add_conv = None
-        if 'channel_mul' in fusion_types:
-            self.channel_mul_conv = nn.Sequential(
-                nn.Conv2d(self.in_channels, self.planes, kernel_size=1),
-                nn.LayerNorm([self.planes, 1, 1]),
-                nn.ReLU(inplace=True),  # yapf: disable
-                nn.Conv2d(self.planes, self.in_channels, kernel_size=1))
-        else:
-            self.channel_mul_conv = None
-        self.reset_parameters()
-
-    def reset_parameters(self):
-        if self.pooling_type == 'att':
-            kaiming_init(self.conv_mask, mode='fan_in')
-            self.conv_mask.inited = True
-
-        if self.channel_add_conv is not None:
-            last_zero_init(self.channel_add_conv)
-        if self.channel_mul_conv is not None:
-            last_zero_init(self.channel_mul_conv)
-
-    def spatial_pool(self, x):
-        batch, channel, height, width = x.size()
-        if self.pooling_type == 'att':
-            input_x = x
-            # [N, C, H * W]
-            input_x = input_x.view(batch, channel, height * width)
-            # [N, 1, C, H * W]
-            input_x = input_x.unsqueeze(1)
-            # [N, 1, H, W]
-            context_mask = self.conv_mask(x)
-            # [N, 1, H * W]
-            context_mask = context_mask.view(batch, 1, height * width)
-            # [N, 1, H * W]
-            context_mask = self.softmax(context_mask)
-            # [N, 1, H * W, 1]
-            context_mask = context_mask.unsqueeze(-1)
-            # [N, 1, C, 1]
-            context = torch.matmul(input_x, context_mask)
-            # [N, C, 1, 1]
-            context = context.view(batch, channel, 1, 1)
-        else:
-            # [N, C, 1, 1]
-            context = self.avg_pool(x)
-
-        return context
-
-    def forward(self, x):
-        # [N, C, 1, 1]
-        context = self.spatial_pool(x)
-
-        out = x
-        if self.channel_mul_conv is not None:
-            # [N, C, 1, 1]
-            channel_mul_term = torch.sigmoid(self.channel_mul_conv(context))
-            out = out * channel_mul_term
-        if self.channel_add_conv is not None:
-            # [N, C, 1, 1]
-            channel_add_term = self.channel_add_conv(context)
-            out = out + channel_add_term
-
-        return out
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/reppoints_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/reppoints_head.py
deleted file mode 100644
index 499cc4f71c968704a40ab2bb7a6b22dd079d82de..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/reppoints_head.py
+++ /dev/null
@@ -1,763 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
-from mmcv.ops import DeformConv2d
-
-from mmdet.core import (PointGenerator, build_assigner, build_sampler,
-                        images_to_levels, multi_apply, multiclass_nms, unmap)
-from ..builder import HEADS, build_loss
-from .anchor_free_head import AnchorFreeHead
-
-
-@HEADS.register_module()
-class RepPointsHead(AnchorFreeHead):
-    """RepPoint head.
-
-    Args:
-        point_feat_channels (int): Number of channels of points features.
-        gradient_mul (float): The multiplier to gradients from
-            points refinement and recognition.
-        point_strides (Iterable): points strides.
-        point_base_scale (int): bbox scale for assigning labels.
-        loss_cls (dict): Config of classification loss.
-        loss_bbox_init (dict): Config of initial points loss.
-        loss_bbox_refine (dict): Config of points loss in refinement.
-        use_grid_points (bool): If we use bounding box representation, the
-        reppoints is represented as grid points on the bounding box.
-        center_init (bool): Whether to use center point assignment.
-        transform_method (str): The methods to transform RepPoints to bbox.
-    """  # noqa: W605
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 point_feat_channels=256,
-                 num_points=9,
-                 gradient_mul=0.1,
-                 point_strides=[8, 16, 32, 64, 128],
-                 point_base_scale=4,
-                 loss_cls=dict(
-                     type='FocalLoss',
-                     use_sigmoid=True,
-                     gamma=2.0,
-                     alpha=0.25,
-                     loss_weight=1.0),
-                 loss_bbox_init=dict(
-                     type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.5),
-                 loss_bbox_refine=dict(
-                     type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
-                 use_grid_points=False,
-                 center_init=True,
-                 transform_method='moment',
-                 moment_mul=0.01,
-                 **kwargs):
-        self.num_points = num_points
-        self.point_feat_channels = point_feat_channels
-        self.use_grid_points = use_grid_points
-        self.center_init = center_init
-
-        # we use deform conv to extract points features
-        self.dcn_kernel = int(np.sqrt(num_points))
-        self.dcn_pad = int((self.dcn_kernel - 1) / 2)
-        assert self.dcn_kernel * self.dcn_kernel == num_points, \
-            'The points number should be a square number.'
-        assert self.dcn_kernel % 2 == 1, \
-            'The points number should be an odd square number.'
-        dcn_base = np.arange(-self.dcn_pad,
-                             self.dcn_pad + 1).astype(np.float64)
-        dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
-        dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
-        dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
-            (-1))
-        self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
-
-        super().__init__(num_classes, in_channels, loss_cls=loss_cls, **kwargs)
-
-        self.gradient_mul = gradient_mul
-        self.point_base_scale = point_base_scale
-        self.point_strides = point_strides
-        self.point_generators = [PointGenerator() for _ in self.point_strides]
-
-        self.sampling = loss_cls['type'] not in ['FocalLoss']
-        if self.train_cfg:
-            self.init_assigner = build_assigner(self.train_cfg.init.assigner)
-            self.refine_assigner = build_assigner(
-                self.train_cfg.refine.assigner)
-            # use PseudoSampler when sampling is False
-            if self.sampling and hasattr(self.train_cfg, 'sampler'):
-                sampler_cfg = self.train_cfg.sampler
-            else:
-                sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-        self.transform_method = transform_method
-        if self.transform_method == 'moment':
-            self.moment_transfer = nn.Parameter(
-                data=torch.zeros(2), requires_grad=True)
-            self.moment_mul = moment_mul
-
-        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
-        if self.use_sigmoid_cls:
-            self.cls_out_channels = self.num_classes
-        else:
-            self.cls_out_channels = self.num_classes + 1
-        self.loss_bbox_init = build_loss(loss_bbox_init)
-        self.loss_bbox_refine = build_loss(loss_bbox_refine)
-
-    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.stacked_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            self.cls_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-            self.reg_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        pts_out_dim = 4 if self.use_grid_points else 2 * self.num_points
-        self.reppoints_cls_conv = DeformConv2d(self.feat_channels,
-                                               self.point_feat_channels,
-                                               self.dcn_kernel, 1,
-                                               self.dcn_pad)
-        self.reppoints_cls_out = nn.Conv2d(self.point_feat_channels,
-                                           self.cls_out_channels, 1, 1, 0)
-        self.reppoints_pts_init_conv = nn.Conv2d(self.feat_channels,
-                                                 self.point_feat_channels, 3,
-                                                 1, 1)
-        self.reppoints_pts_init_out = nn.Conv2d(self.point_feat_channels,
-                                                pts_out_dim, 1, 1, 0)
-        self.reppoints_pts_refine_conv = DeformConv2d(self.feat_channels,
-                                                      self.point_feat_channels,
-                                                      self.dcn_kernel, 1,
-                                                      self.dcn_pad)
-        self.reppoints_pts_refine_out = nn.Conv2d(self.point_feat_channels,
-                                                  pts_out_dim, 1, 1, 0)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.cls_convs:
-            normal_init(m.conv, std=0.01)
-        for m in self.reg_convs:
-            normal_init(m.conv, std=0.01)
-        bias_cls = bias_init_with_prob(0.01)
-        normal_init(self.reppoints_cls_conv, std=0.01)
-        normal_init(self.reppoints_cls_out, std=0.01, bias=bias_cls)
-        normal_init(self.reppoints_pts_init_conv, std=0.01)
-        normal_init(self.reppoints_pts_init_out, std=0.01)
-        normal_init(self.reppoints_pts_refine_conv, std=0.01)
-        normal_init(self.reppoints_pts_refine_out, std=0.01)
-
-    def points2bbox(self, pts, y_first=True):
-        """Converting the points set into bounding box.
-
-        :param pts: the input points sets (fields), each points
-            set (fields) is represented as 2n scalar.
-        :param y_first: if y_first=True, the point set is represented as
-            [y1, x1, y2, x2 ... yn, xn], otherwise the point set is
-            represented as [x1, y1, x2, y2 ... xn, yn].
-        :return: each points set is converting to a bbox [x1, y1, x2, y2].
-        """
-        pts_reshape = pts.view(pts.shape[0], -1, 2, *pts.shape[2:])
-        pts_y = pts_reshape[:, :, 0, ...] if y_first else pts_reshape[:, :, 1,
-                                                                      ...]
-        pts_x = pts_reshape[:, :, 1, ...] if y_first else pts_reshape[:, :, 0,
-                                                                      ...]
-        if self.transform_method == 'minmax':
-            bbox_left = pts_x.min(dim=1, keepdim=True)[0]
-            bbox_right = pts_x.max(dim=1, keepdim=True)[0]
-            bbox_up = pts_y.min(dim=1, keepdim=True)[0]
-            bbox_bottom = pts_y.max(dim=1, keepdim=True)[0]
-            bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom],
-                             dim=1)
-        elif self.transform_method == 'partial_minmax':
-            pts_y = pts_y[:, :4, ...]
-            pts_x = pts_x[:, :4, ...]
-            bbox_left = pts_x.min(dim=1, keepdim=True)[0]
-            bbox_right = pts_x.max(dim=1, keepdim=True)[0]
-            bbox_up = pts_y.min(dim=1, keepdim=True)[0]
-            bbox_bottom = pts_y.max(dim=1, keepdim=True)[0]
-            bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom],
-                             dim=1)
-        elif self.transform_method == 'moment':
-            pts_y_mean = pts_y.mean(dim=1, keepdim=True)
-            pts_x_mean = pts_x.mean(dim=1, keepdim=True)
-            pts_y_std = torch.std(pts_y - pts_y_mean, dim=1, keepdim=True)
-            pts_x_std = torch.std(pts_x - pts_x_mean, dim=1, keepdim=True)
-            moment_transfer = (self.moment_transfer * self.moment_mul) + (
-                self.moment_transfer.detach() * (1 - self.moment_mul))
-            moment_width_transfer = moment_transfer[0]
-            moment_height_transfer = moment_transfer[1]
-            half_width = pts_x_std * torch.exp(moment_width_transfer)
-            half_height = pts_y_std * torch.exp(moment_height_transfer)
-            bbox = torch.cat([
-                pts_x_mean - half_width, pts_y_mean - half_height,
-                pts_x_mean + half_width, pts_y_mean + half_height
-            ],
-                             dim=1)
-        else:
-            raise NotImplementedError
-        return bbox
-
-    def gen_grid_from_reg(self, reg, previous_boxes):
-        """Base on the previous bboxes and regression values, we compute the
-        regressed bboxes and generate the grids on the bboxes.
-
-        :param reg: the regression value to previous bboxes.
-        :param previous_boxes: previous bboxes.
-        :return: generate grids on the regressed bboxes.
-        """
-        b, _, h, w = reg.shape
-        bxy = (previous_boxes[:, :2, ...] + previous_boxes[:, 2:, ...]) / 2.
-        bwh = (previous_boxes[:, 2:, ...] -
-               previous_boxes[:, :2, ...]).clamp(min=1e-6)
-        grid_topleft = bxy + bwh * reg[:, :2, ...] - 0.5 * bwh * torch.exp(
-            reg[:, 2:, ...])
-        grid_wh = bwh * torch.exp(reg[:, 2:, ...])
-        grid_left = grid_topleft[:, [0], ...]
-        grid_top = grid_topleft[:, [1], ...]
-        grid_width = grid_wh[:, [0], ...]
-        grid_height = grid_wh[:, [1], ...]
-        intervel = torch.linspace(0., 1., self.dcn_kernel).view(
-            1, self.dcn_kernel, 1, 1).type_as(reg)
-        grid_x = grid_left + grid_width * intervel
-        grid_x = grid_x.unsqueeze(1).repeat(1, self.dcn_kernel, 1, 1, 1)
-        grid_x = grid_x.view(b, -1, h, w)
-        grid_y = grid_top + grid_height * intervel
-        grid_y = grid_y.unsqueeze(2).repeat(1, 1, self.dcn_kernel, 1, 1)
-        grid_y = grid_y.view(b, -1, h, w)
-        grid_yx = torch.stack([grid_y, grid_x], dim=2)
-        grid_yx = grid_yx.view(b, -1, h, w)
-        regressed_bbox = torch.cat([
-            grid_left, grid_top, grid_left + grid_width, grid_top + grid_height
-        ], 1)
-        return grid_yx, regressed_bbox
-
-    def forward(self, feats):
-        return multi_apply(self.forward_single, feats)
-
-    def forward_single(self, x):
-        """Forward feature map of a single FPN level."""
-        dcn_base_offset = self.dcn_base_offset.type_as(x)
-        # If we use center_init, the initial reppoints is from center points.
-        # If we use bounding bbox representation, the initial reppoints is
-        #   from regular grid placed on a pre-defined bbox.
-        if self.use_grid_points or not self.center_init:
-            scale = self.point_base_scale / 2
-            points_init = dcn_base_offset / dcn_base_offset.max() * scale
-            bbox_init = x.new_tensor([-scale, -scale, scale,
-                                      scale]).view(1, 4, 1, 1)
-        else:
-            points_init = 0
-        cls_feat = x
-        pts_feat = x
-        for cls_conv in self.cls_convs:
-            cls_feat = cls_conv(cls_feat)
-        for reg_conv in self.reg_convs:
-            pts_feat = reg_conv(pts_feat)
-        # initialize reppoints
-        pts_out_init = self.reppoints_pts_init_out(
-            self.relu(self.reppoints_pts_init_conv(pts_feat)))
-        if self.use_grid_points:
-            pts_out_init, bbox_out_init = self.gen_grid_from_reg(
-                pts_out_init, bbox_init.detach())
-        else:
-            pts_out_init = pts_out_init + points_init
-        # refine and classify reppoints
-        pts_out_init_grad_mul = (1 - self.gradient_mul) * pts_out_init.detach(
-        ) + self.gradient_mul * pts_out_init
-        dcn_offset = pts_out_init_grad_mul - dcn_base_offset
-        cls_out = self.reppoints_cls_out(
-            self.relu(self.reppoints_cls_conv(cls_feat, dcn_offset)))
-        pts_out_refine = self.reppoints_pts_refine_out(
-            self.relu(self.reppoints_pts_refine_conv(pts_feat, dcn_offset)))
-        if self.use_grid_points:
-            pts_out_refine, bbox_out_refine = self.gen_grid_from_reg(
-                pts_out_refine, bbox_out_init.detach())
-        else:
-            pts_out_refine = pts_out_refine + pts_out_init.detach()
-        return cls_out, pts_out_init, pts_out_refine
-
-    def get_points(self, featmap_sizes, img_metas, device):
-        """Get points according to feature map sizes.
-
-        Args:
-            featmap_sizes (list[tuple]): Multi-level feature map sizes.
-            img_metas (list[dict]): Image meta info.
-
-        Returns:
-            tuple: points of each image, valid flags of each image
-        """
-        num_imgs = len(img_metas)
-        num_levels = len(featmap_sizes)
-
-        # since feature map sizes of all images are the same, we only compute
-        # points center for one time
-        multi_level_points = []
-        for i in range(num_levels):
-            points = self.point_generators[i].grid_points(
-                featmap_sizes[i], self.point_strides[i], device)
-            multi_level_points.append(points)
-        points_list = [[point.clone() for point in multi_level_points]
-                       for _ in range(num_imgs)]
-
-        # for each image, we compute valid flags of multi level grids
-        valid_flag_list = []
-        for img_id, img_meta in enumerate(img_metas):
-            multi_level_flags = []
-            for i in range(num_levels):
-                point_stride = self.point_strides[i]
-                feat_h, feat_w = featmap_sizes[i]
-                h, w = img_meta['pad_shape'][:2]
-                valid_feat_h = min(int(np.ceil(h / point_stride)), feat_h)
-                valid_feat_w = min(int(np.ceil(w / point_stride)), feat_w)
-                flags = self.point_generators[i].valid_flags(
-                    (feat_h, feat_w), (valid_feat_h, valid_feat_w), device)
-                multi_level_flags.append(flags)
-            valid_flag_list.append(multi_level_flags)
-
-        return points_list, valid_flag_list
-
-    def centers_to_bboxes(self, point_list):
-        """Get bboxes according to center points.
-
-        Only used in :class:`MaxIoUAssigner`.
-        """
-        bbox_list = []
-        for i_img, point in enumerate(point_list):
-            bbox = []
-            for i_lvl in range(len(self.point_strides)):
-                scale = self.point_base_scale * self.point_strides[i_lvl] * 0.5
-                bbox_shift = torch.Tensor([-scale, -scale, scale,
-                                           scale]).view(1, 4).type_as(point[0])
-                bbox_center = torch.cat(
-                    [point[i_lvl][:, :2], point[i_lvl][:, :2]], dim=1)
-                bbox.append(bbox_center + bbox_shift)
-            bbox_list.append(bbox)
-        return bbox_list
-
-    def offset_to_pts(self, center_list, pred_list):
-        """Change from point offset to point coordinate."""
-        pts_list = []
-        for i_lvl in range(len(self.point_strides)):
-            pts_lvl = []
-            for i_img in range(len(center_list)):
-                pts_center = center_list[i_img][i_lvl][:, :2].repeat(
-                    1, self.num_points)
-                pts_shift = pred_list[i_lvl][i_img]
-                yx_pts_shift = pts_shift.permute(1, 2, 0).view(
-                    -1, 2 * self.num_points)
-                y_pts_shift = yx_pts_shift[..., 0::2]
-                x_pts_shift = yx_pts_shift[..., 1::2]
-                xy_pts_shift = torch.stack([x_pts_shift, y_pts_shift], -1)
-                xy_pts_shift = xy_pts_shift.view(*yx_pts_shift.shape[:-1], -1)
-                pts = xy_pts_shift * self.point_strides[i_lvl] + pts_center
-                pts_lvl.append(pts)
-            pts_lvl = torch.stack(pts_lvl, 0)
-            pts_list.append(pts_lvl)
-        return pts_list
-
-    def _point_target_single(self,
-                             flat_proposals,
-                             valid_flags,
-                             gt_bboxes,
-                             gt_bboxes_ignore,
-                             gt_labels,
-                             label_channels=1,
-                             stage='init',
-                             unmap_outputs=True):
-        inside_flags = valid_flags
-        if not inside_flags.any():
-            return (None, ) * 7
-        # assign gt and sample proposals
-        proposals = flat_proposals[inside_flags, :]
-
-        if stage == 'init':
-            assigner = self.init_assigner
-            pos_weight = self.train_cfg.init.pos_weight
-        else:
-            assigner = self.refine_assigner
-            pos_weight = self.train_cfg.refine.pos_weight
-        assign_result = assigner.assign(proposals, gt_bboxes, gt_bboxes_ignore,
-                                        None if self.sampling else gt_labels)
-        sampling_result = self.sampler.sample(assign_result, proposals,
-                                              gt_bboxes)
-
-        num_valid_proposals = proposals.shape[0]
-        bbox_gt = proposals.new_zeros([num_valid_proposals, 4])
-        pos_proposals = torch.zeros_like(proposals)
-        proposals_weights = proposals.new_zeros([num_valid_proposals, 4])
-        labels = proposals.new_full((num_valid_proposals, ),
-                                    self.num_classes,
-                                    dtype=torch.long)
-        label_weights = proposals.new_zeros(
-            num_valid_proposals, dtype=torch.float)
-
-        pos_inds = sampling_result.pos_inds
-        neg_inds = sampling_result.neg_inds
-        if len(pos_inds) > 0:
-            pos_gt_bboxes = sampling_result.pos_gt_bboxes
-            bbox_gt[pos_inds, :] = pos_gt_bboxes
-            pos_proposals[pos_inds, :] = proposals[pos_inds, :]
-            proposals_weights[pos_inds, :] = 1.0
-            if gt_labels is None:
-                # Only rpn gives gt_labels as None
-                # Foreground is the first class
-                labels[pos_inds] = 0
-            else:
-                labels[pos_inds] = gt_labels[
-                    sampling_result.pos_assigned_gt_inds]
-            if pos_weight <= 0:
-                label_weights[pos_inds] = 1.0
-            else:
-                label_weights[pos_inds] = pos_weight
-        if len(neg_inds) > 0:
-            label_weights[neg_inds] = 1.0
-
-        # map up to original set of proposals
-        if unmap_outputs:
-            num_total_proposals = flat_proposals.size(0)
-            labels = unmap(labels, num_total_proposals, inside_flags)
-            label_weights = unmap(label_weights, num_total_proposals,
-                                  inside_flags)
-            bbox_gt = unmap(bbox_gt, num_total_proposals, inside_flags)
-            pos_proposals = unmap(pos_proposals, num_total_proposals,
-                                  inside_flags)
-            proposals_weights = unmap(proposals_weights, num_total_proposals,
-                                      inside_flags)
-
-        return (labels, label_weights, bbox_gt, pos_proposals,
-                proposals_weights, pos_inds, neg_inds)
-
-    def get_targets(self,
-                    proposals_list,
-                    valid_flag_list,
-                    gt_bboxes_list,
-                    img_metas,
-                    gt_bboxes_ignore_list=None,
-                    gt_labels_list=None,
-                    stage='init',
-                    label_channels=1,
-                    unmap_outputs=True):
-        """Compute corresponding GT box and classification targets for
-        proposals.
-
-        Args:
-            proposals_list (list[list]): Multi level points/bboxes of each
-                image.
-            valid_flag_list (list[list]): Multi level valid flags of each
-                image.
-            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
-            img_metas (list[dict]): Meta info of each image.
-            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
-                ignored.
-            gt_bboxes_list (list[Tensor]): Ground truth labels of each box.
-            stage (str): `init` or `refine`. Generate target for init stage or
-                refine stage
-            label_channels (int): Channel of label.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-
-        Returns:
-            tuple:
-                - labels_list (list[Tensor]): Labels of each level.
-                - label_weights_list (list[Tensor]): Label weights of each level.  # noqa: E501
-                - bbox_gt_list (list[Tensor]): Ground truth bbox of each level.
-                - proposal_list (list[Tensor]): Proposals(points/bboxes) of each level.  # noqa: E501
-                - proposal_weights_list (list[Tensor]): Proposal weights of each level.  # noqa: E501
-                - num_total_pos (int): Number of positive samples in all images.  # noqa: E501
-                - num_total_neg (int): Number of negative samples in all images.  # noqa: E501
-        """
-        assert stage in ['init', 'refine']
-        num_imgs = len(img_metas)
-        assert len(proposals_list) == len(valid_flag_list) == num_imgs
-
-        # points number of multi levels
-        num_level_proposals = [points.size(0) for points in proposals_list[0]]
-
-        # concat all level points and flags to a single tensor
-        for i in range(num_imgs):
-            assert len(proposals_list[i]) == len(valid_flag_list[i])
-            proposals_list[i] = torch.cat(proposals_list[i])
-            valid_flag_list[i] = torch.cat(valid_flag_list[i])
-
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        if gt_labels_list is None:
-            gt_labels_list = [None for _ in range(num_imgs)]
-        (all_labels, all_label_weights, all_bbox_gt, all_proposals,
-         all_proposal_weights, pos_inds_list, neg_inds_list) = multi_apply(
-             self._point_target_single,
-             proposals_list,
-             valid_flag_list,
-             gt_bboxes_list,
-             gt_bboxes_ignore_list,
-             gt_labels_list,
-             stage=stage,
-             label_channels=label_channels,
-             unmap_outputs=unmap_outputs)
-        # no valid points
-        if any([labels is None for labels in all_labels]):
-            return None
-        # sampled points of all images
-        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
-        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
-        labels_list = images_to_levels(all_labels, num_level_proposals)
-        label_weights_list = images_to_levels(all_label_weights,
-                                              num_level_proposals)
-        bbox_gt_list = images_to_levels(all_bbox_gt, num_level_proposals)
-        proposals_list = images_to_levels(all_proposals, num_level_proposals)
-        proposal_weights_list = images_to_levels(all_proposal_weights,
-                                                 num_level_proposals)
-        return (labels_list, label_weights_list, bbox_gt_list, proposals_list,
-                proposal_weights_list, num_total_pos, num_total_neg)
-
-    def loss_single(self, cls_score, pts_pred_init, pts_pred_refine, labels,
-                    label_weights, bbox_gt_init, bbox_weights_init,
-                    bbox_gt_refine, bbox_weights_refine, stride,
-                    num_total_samples_init, num_total_samples_refine):
-        # classification loss
-        labels = labels.reshape(-1)
-        label_weights = label_weights.reshape(-1)
-        cls_score = cls_score.permute(0, 2, 3,
-                                      1).reshape(-1, self.cls_out_channels)
-        cls_score = cls_score.contiguous()
-        loss_cls = self.loss_cls(
-            cls_score,
-            labels,
-            label_weights,
-            avg_factor=num_total_samples_refine)
-
-        # points loss
-        bbox_gt_init = bbox_gt_init.reshape(-1, 4)
-        bbox_weights_init = bbox_weights_init.reshape(-1, 4)
-        bbox_pred_init = self.points2bbox(
-            pts_pred_init.reshape(-1, 2 * self.num_points), y_first=False)
-        bbox_gt_refine = bbox_gt_refine.reshape(-1, 4)
-        bbox_weights_refine = bbox_weights_refine.reshape(-1, 4)
-        bbox_pred_refine = self.points2bbox(
-            pts_pred_refine.reshape(-1, 2 * self.num_points), y_first=False)
-        normalize_term = self.point_base_scale * stride
-        loss_pts_init = self.loss_bbox_init(
-            bbox_pred_init / normalize_term,
-            bbox_gt_init / normalize_term,
-            bbox_weights_init,
-            avg_factor=num_total_samples_init)
-        loss_pts_refine = self.loss_bbox_refine(
-            bbox_pred_refine / normalize_term,
-            bbox_gt_refine / normalize_term,
-            bbox_weights_refine,
-            avg_factor=num_total_samples_refine)
-        return loss_cls, loss_pts_init, loss_pts_refine
-
-    def loss(self,
-             cls_scores,
-             pts_preds_init,
-             pts_preds_refine,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == len(self.point_generators)
-        device = cls_scores[0].device
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
-        # target for initial stage
-        center_list, valid_flag_list = self.get_points(featmap_sizes,
-                                                       img_metas, device)
-        pts_coordinate_preds_init = self.offset_to_pts(center_list,
-                                                       pts_preds_init)
-        if self.train_cfg.init.assigner['type'] == 'PointAssigner':
-            # Assign target for center list
-            candidate_list = center_list
-        else:
-            # transform center list to bbox list and
-            #   assign target for bbox list
-            bbox_list = self.centers_to_bboxes(center_list)
-            candidate_list = bbox_list
-        cls_reg_targets_init = self.get_targets(
-            candidate_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            stage='init',
-            label_channels=label_channels)
-        (*_, bbox_gt_list_init, candidate_list_init, bbox_weights_list_init,
-         num_total_pos_init, num_total_neg_init) = cls_reg_targets_init
-        num_total_samples_init = (
-            num_total_pos_init +
-            num_total_neg_init if self.sampling else num_total_pos_init)
-
-        # target for refinement stage
-        center_list, valid_flag_list = self.get_points(featmap_sizes,
-                                                       img_metas, device)
-        pts_coordinate_preds_refine = self.offset_to_pts(
-            center_list, pts_preds_refine)
-        bbox_list = []
-        for i_img, center in enumerate(center_list):
-            bbox = []
-            for i_lvl in range(len(pts_preds_refine)):
-                bbox_preds_init = self.points2bbox(
-                    pts_preds_init[i_lvl].detach())
-                bbox_shift = bbox_preds_init * self.point_strides[i_lvl]
-                bbox_center = torch.cat(
-                    [center[i_lvl][:, :2], center[i_lvl][:, :2]], dim=1)
-                bbox.append(bbox_center +
-                            bbox_shift[i_img].permute(1, 2, 0).reshape(-1, 4))
-            bbox_list.append(bbox)
-        cls_reg_targets_refine = self.get_targets(
-            bbox_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            stage='refine',
-            label_channels=label_channels)
-        (labels_list, label_weights_list, bbox_gt_list_refine,
-         candidate_list_refine, bbox_weights_list_refine, num_total_pos_refine,
-         num_total_neg_refine) = cls_reg_targets_refine
-        num_total_samples_refine = (
-            num_total_pos_refine +
-            num_total_neg_refine if self.sampling else num_total_pos_refine)
-
-        # compute loss
-        losses_cls, losses_pts_init, losses_pts_refine = multi_apply(
-            self.loss_single,
-            cls_scores,
-            pts_coordinate_preds_init,
-            pts_coordinate_preds_refine,
-            labels_list,
-            label_weights_list,
-            bbox_gt_list_init,
-            bbox_weights_list_init,
-            bbox_gt_list_refine,
-            bbox_weights_list_refine,
-            self.point_strides,
-            num_total_samples_init=num_total_samples_init,
-            num_total_samples_refine=num_total_samples_refine)
-        loss_dict_all = {
-            'loss_cls': losses_cls,
-            'loss_pts_init': losses_pts_init,
-            'loss_pts_refine': losses_pts_refine
-        }
-        return loss_dict_all
-
-    def get_bboxes(self,
-                   cls_scores,
-                   pts_preds_init,
-                   pts_preds_refine,
-                   img_metas,
-                   cfg=None,
-                   rescale=False,
-                   with_nms=True):
-        assert len(cls_scores) == len(pts_preds_refine)
-        device = cls_scores[0].device
-        bbox_preds_refine = [
-            self.points2bbox(pts_pred_refine)
-            for pts_pred_refine in pts_preds_refine
-        ]
-        num_levels = len(cls_scores)
-        mlvl_points = [
-            self.point_generators[i].grid_points(cls_scores[i].size()[-2:],
-                                                 self.point_strides[i], device)
-            for i in range(num_levels)
-        ]
-        result_list = []
-        for img_id in range(len(img_metas)):
-            cls_score_list = [
-                cls_scores[i][img_id].detach() for i in range(num_levels)
-            ]
-            bbox_pred_list = [
-                bbox_preds_refine[i][img_id].detach()
-                for i in range(num_levels)
-            ]
-            img_shape = img_metas[img_id]['img_shape']
-            scale_factor = img_metas[img_id]['scale_factor']
-            proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,
-                                                mlvl_points, img_shape,
-                                                scale_factor, cfg, rescale,
-                                                with_nms)
-            result_list.append(proposals)
-        return result_list
-
-    def _get_bboxes_single(self,
-                           cls_scores,
-                           bbox_preds,
-                           mlvl_points,
-                           img_shape,
-                           scale_factor,
-                           cfg,
-                           rescale=False,
-                           with_nms=True):
-        cfg = self.test_cfg if cfg is None else cfg
-        assert len(cls_scores) == len(bbox_preds) == len(mlvl_points)
-        mlvl_bboxes = []
-        mlvl_scores = []
-        for i_lvl, (cls_score, bbox_pred, points) in enumerate(
-                zip(cls_scores, bbox_preds, mlvl_points)):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            cls_score = cls_score.permute(1, 2,
-                                          0).reshape(-1, self.cls_out_channels)
-            if self.use_sigmoid_cls:
-                scores = cls_score.sigmoid()
-            else:
-                scores = cls_score.softmax(-1)
-            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
-            nms_pre = cfg.get('nms_pre', -1)
-            if nms_pre > 0 and scores.shape[0] > nms_pre:
-                if self.use_sigmoid_cls:
-                    max_scores, _ = scores.max(dim=1)
-                else:
-                    # remind that we set FG labels to [0, num_class-1]
-                    # since mmdet v2.0
-                    # BG cat_id: num_class
-                    max_scores, _ = scores[:, :-1].max(dim=1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                points = points[topk_inds, :]
-                bbox_pred = bbox_pred[topk_inds, :]
-                scores = scores[topk_inds, :]
-            bbox_pos_center = torch.cat([points[:, :2], points[:, :2]], dim=1)
-            bboxes = bbox_pred * self.point_strides[i_lvl] + bbox_pos_center
-            x1 = bboxes[:, 0].clamp(min=0, max=img_shape[1])
-            y1 = bboxes[:, 1].clamp(min=0, max=img_shape[0])
-            x2 = bboxes[:, 2].clamp(min=0, max=img_shape[1])
-            y2 = bboxes[:, 3].clamp(min=0, max=img_shape[0])
-            bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-        mlvl_bboxes = torch.cat(mlvl_bboxes)
-        if rescale:
-            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
-        mlvl_scores = torch.cat(mlvl_scores)
-        if self.use_sigmoid_cls:
-            # Add a dummy background class to the backend when using sigmoid
-            # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-            # BG cat_id: num_class
-            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
-            mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
-        if with_nms:
-            det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
-                                                    cfg.score_thr, cfg.nms,
-                                                    cfg.max_per_img)
-            return det_bboxes, det_labels
-        else:
-            return mlvl_bboxes, mlvl_scores
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/backbones/mobilenet_v3.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/backbones/mobilenet_v3.py
deleted file mode 100644
index 16817400b4102899794fe64c9644713a4e54e2f9..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/backbones/mobilenet_v3.py
+++ /dev/null
@@ -1,255 +0,0 @@
-import logging
-
-import annotator.uniformer.mmcv as mmcv
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import ConvModule, constant_init, kaiming_init
-from annotator.uniformer.mmcv.cnn.bricks import Conv2dAdaptivePadding
-from annotator.uniformer.mmcv.runner import load_checkpoint
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from ..builder import BACKBONES
-from ..utils import InvertedResidualV3 as InvertedResidual
-
-
-@BACKBONES.register_module()
-class MobileNetV3(nn.Module):
-    """MobileNetV3 backbone.
-
-    This backbone is the improved implementation of `Searching for MobileNetV3
-    <https://ieeexplore.ieee.org/document/9008835>`_.
-
-    Args:
-        arch (str): Architecture of mobilnetv3, from {'small', 'large'}.
-            Default: 'small'.
-        conv_cfg (dict): Config dict for convolution layer.
-            Default: None, which means using conv2d.
-        norm_cfg (dict): Config dict for normalization layer.
-            Default: dict(type='BN').
-        out_indices (tuple[int]): Output from which layer.
-            Default: (0, 1, 12).
-        frozen_stages (int): Stages to be frozen (all param fixed).
-            Default: -1, which means not freezing any parameters.
-        norm_eval (bool): Whether to set norm layers to eval mode, namely,
-            freeze running stats (mean and var). Note: Effect on Batch Norm
-            and its variants only. Default: False.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save
-            some memory while slowing down the training speed.
-            Default: False.
-    """
-    # Parameters to build each block:
-    #     [kernel size, mid channels, out channels, with_se, act type, stride]
-    arch_settings = {
-        'small': [[3, 16, 16, True, 'ReLU', 2],  # block0 layer1 os=4
-                  [3, 72, 24, False, 'ReLU', 2],  # block1 layer2 os=8
-                  [3, 88, 24, False, 'ReLU', 1],
-                  [5, 96, 40, True, 'HSwish', 2],  # block2 layer4 os=16
-                  [5, 240, 40, True, 'HSwish', 1],
-                  [5, 240, 40, True, 'HSwish', 1],
-                  [5, 120, 48, True, 'HSwish', 1],  # block3 layer7 os=16
-                  [5, 144, 48, True, 'HSwish', 1],
-                  [5, 288, 96, True, 'HSwish', 2],  # block4 layer9 os=32
-                  [5, 576, 96, True, 'HSwish', 1],
-                  [5, 576, 96, True, 'HSwish', 1]],
-        'large': [[3, 16, 16, False, 'ReLU', 1],  # block0 layer1 os=2
-                  [3, 64, 24, False, 'ReLU', 2],  # block1 layer2 os=4
-                  [3, 72, 24, False, 'ReLU', 1],
-                  [5, 72, 40, True, 'ReLU', 2],  # block2 layer4 os=8
-                  [5, 120, 40, True, 'ReLU', 1],
-                  [5, 120, 40, True, 'ReLU', 1],
-                  [3, 240, 80, False, 'HSwish', 2],  # block3 layer7 os=16
-                  [3, 200, 80, False, 'HSwish', 1],
-                  [3, 184, 80, False, 'HSwish', 1],
-                  [3, 184, 80, False, 'HSwish', 1],
-                  [3, 480, 112, True, 'HSwish', 1],  # block4 layer11 os=16
-                  [3, 672, 112, True, 'HSwish', 1],
-                  [5, 672, 160, True, 'HSwish', 2],  # block5 layer13 os=32
-                  [5, 960, 160, True, 'HSwish', 1],
-                  [5, 960, 160, True, 'HSwish', 1]]
-    }  # yapf: disable
-
-    def __init__(self,
-                 arch='small',
-                 conv_cfg=None,
-                 norm_cfg=dict(type='BN'),
-                 out_indices=(0, 1, 12),
-                 frozen_stages=-1,
-                 reduction_factor=1,
-                 norm_eval=False,
-                 with_cp=False):
-        super(MobileNetV3, self).__init__()
-        assert arch in self.arch_settings
-        assert isinstance(reduction_factor, int) and reduction_factor > 0
-        assert mmcv.is_tuple_of(out_indices, int)
-        for index in out_indices:
-            if index not in range(0, len(self.arch_settings[arch]) + 2):
-                raise ValueError(
-                    'the item in out_indices must in '
-                    f'range(0, {len(self.arch_settings[arch])+2}). '
-                    f'But received {index}')
-
-        if frozen_stages not in range(-1, len(self.arch_settings[arch]) + 2):
-            raise ValueError('frozen_stages must be in range(-1, '
-                             f'{len(self.arch_settings[arch])+2}). '
-                             f'But received {frozen_stages}')
-        self.arch = arch
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-        self.reduction_factor = reduction_factor
-        self.norm_eval = norm_eval
-        self.with_cp = with_cp
-        self.layers = self._make_layer()
-
-    def _make_layer(self):
-        layers = []
-
-        # build the first layer (layer0)
-        in_channels = 16
-        layer = ConvModule(
-            in_channels=3,
-            out_channels=in_channels,
-            kernel_size=3,
-            stride=2,
-            padding=1,
-            conv_cfg=dict(type='Conv2dAdaptivePadding'),
-            norm_cfg=self.norm_cfg,
-            act_cfg=dict(type='HSwish'))
-        self.add_module('layer0', layer)
-        layers.append('layer0')
-
-        layer_setting = self.arch_settings[self.arch]
-        for i, params in enumerate(layer_setting):
-            (kernel_size, mid_channels, out_channels, with_se, act,
-             stride) = params
-
-            if self.arch == 'large' and i >= 12 or self.arch == 'small' and \
-                    i >= 8:
-                mid_channels = mid_channels // self.reduction_factor
-                out_channels = out_channels // self.reduction_factor
-
-            if with_se:
-                se_cfg = dict(
-                    channels=mid_channels,
-                    ratio=4,
-                    act_cfg=(dict(type='ReLU'),
-                             dict(type='HSigmoid', bias=3.0, divisor=6.0)))
-            else:
-                se_cfg = None
-
-            layer = InvertedResidual(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                mid_channels=mid_channels,
-                kernel_size=kernel_size,
-                stride=stride,
-                se_cfg=se_cfg,
-                with_expand_conv=(in_channels != mid_channels),
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg,
-                act_cfg=dict(type=act),
-                with_cp=self.with_cp)
-            in_channels = out_channels
-            layer_name = 'layer{}'.format(i + 1)
-            self.add_module(layer_name, layer)
-            layers.append(layer_name)
-
-        # build the last layer
-        # block5 layer12 os=32 for small model
-        # block6 layer16 os=32 for large model
-        layer = ConvModule(
-            in_channels=in_channels,
-            out_channels=576 if self.arch == 'small' else 960,
-            kernel_size=1,
-            stride=1,
-            dilation=4,
-            padding=0,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=dict(type='HSwish'))
-        layer_name = 'layer{}'.format(len(layer_setting) + 1)
-        self.add_module(layer_name, layer)
-        layers.append(layer_name)
-
-        # next, convert backbone MobileNetV3 to a semantic segmentation version
-        if self.arch == 'small':
-            self.layer4.depthwise_conv.conv.stride = (1, 1)
-            self.layer9.depthwise_conv.conv.stride = (1, 1)
-            for i in range(4, len(layers)):
-                layer = getattr(self, layers[i])
-                if isinstance(layer, InvertedResidual):
-                    modified_module = layer.depthwise_conv.conv
-                else:
-                    modified_module = layer.conv
-
-                if i < 9:
-                    modified_module.dilation = (2, 2)
-                    pad = 2
-                else:
-                    modified_module.dilation = (4, 4)
-                    pad = 4
-
-                if not isinstance(modified_module, Conv2dAdaptivePadding):
-                    # Adjust padding
-                    pad *= (modified_module.kernel_size[0] - 1) // 2
-                    modified_module.padding = (pad, pad)
-        else:
-            self.layer7.depthwise_conv.conv.stride = (1, 1)
-            self.layer13.depthwise_conv.conv.stride = (1, 1)
-            for i in range(7, len(layers)):
-                layer = getattr(self, layers[i])
-                if isinstance(layer, InvertedResidual):
-                    modified_module = layer.depthwise_conv.conv
-                else:
-                    modified_module = layer.conv
-
-                if i < 13:
-                    modified_module.dilation = (2, 2)
-                    pad = 2
-                else:
-                    modified_module.dilation = (4, 4)
-                    pad = 4
-
-                if not isinstance(modified_module, Conv2dAdaptivePadding):
-                    # Adjust padding
-                    pad *= (modified_module.kernel_size[0] - 1) // 2
-                    modified_module.padding = (pad, pad)
-
-        return layers
-
-    def init_weights(self, pretrained=None):
-        if isinstance(pretrained, str):
-            logger = logging.getLogger()
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, nn.BatchNorm2d):
-                    constant_init(m, 1)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def forward(self, x):
-        outs = []
-        for i, layer_name in enumerate(self.layers):
-            layer = getattr(self, layer_name)
-            x = layer(x)
-            if i in self.out_indices:
-                outs.append(x)
-        return outs
-
-    def _freeze_stages(self):
-        for i in range(self.frozen_stages + 1):
-            layer = getattr(self, f'layer{i}')
-            layer.eval()
-            for param in layer.parameters():
-                param.requires_grad = False
-
-    def train(self, mode=True):
-        super(MobileNetV3, self).train(mode)
-        self._freeze_stages()
-        if mode and self.norm_eval:
-            for m in self.modules():
-                if isinstance(m, _BatchNorm):
-                    m.eval()
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/codecs/gif.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/codecs/gif.py
deleted file mode 100644
index 89dd13dad3b549c8a589d146868191ab68ed30aa..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/image/codecs/gif.py
+++ /dev/null
@@ -1,142 +0,0 @@
-"""Read GIF control data.
-
-http://www.w3.org/Graphics/GIF/spec-gif89a.txt
-"""
-
-import struct
-
-from pyglet.image.codecs import ImageDecodeException
-
-
-class GIFStream:
-    def __init__(self):
-        self.images = []
-
-
-class GIFImage:
-    delay = None
-
-
-class GraphicsScope:
-    delay = None
-
-
-# Appendix A.
-LABEL_EXTENSION_INTRODUCER = 0x21
-LABEL_GRAPHIC_CONTROL_EXTENSION = 0xf9
-LABEL_IMAGE_DESCRIPTOR = 0x2c
-LABEL_TRAILER = 0x3b
-
-
-def unpack(fmt, file):
-    size = struct.calcsize(fmt)
-    data = file.read(size)
-    if len(data) < size:
-        raise ImageDecodeException('Unexpected EOF')
-    return struct.unpack(fmt, data)
-
-
-def read_byte(file):
-    data = file.read(1)
-    if not len(data):
-        raise ImageDecodeException('Unexpected EOF')
-    return ord(data)
-
-
-def read(file):
-    """Read a GIF file stream.
-
-    :rtype: GIFStream
-    """
-    # 17. Header
-    signature = file.read(3)
-    version = file.read(3)
-    if signature != b'GIF':
-        raise ImageDecodeException('Not a GIF stream')
-
-    stream = GIFStream()
-
-    # 18. Logical screen descriptor
-    (logical_screen_width,
-     logical_screen_height,
-     fields,
-     background_color_index,
-     pixel_aspect_ratio) = unpack('HHBBB', file)
-    global_color_table_flag = fields & 0x80
-    global_color_table_size = fields & 0x7
-
-    # 19. Global color table
-    if global_color_table_flag:
-        global_color_table = file.read(6 << global_color_table_size)
-
-    # <Data>*
-    graphics_scope = GraphicsScope()
-    block_type = read_byte(file)
-
-    while block_type != LABEL_TRAILER:
-        if block_type == LABEL_IMAGE_DESCRIPTOR:
-            read_table_based_image(file, stream, graphics_scope)
-            graphics_scope = GraphicsScope()
-        elif block_type == LABEL_EXTENSION_INTRODUCER:
-            extension_block_type = read_byte(file)
-            if extension_block_type == LABEL_GRAPHIC_CONTROL_EXTENSION:
-                read_graphic_control_extension(file, stream, graphics_scope)
-            else:
-                skip_data_sub_blocks(file)
-        else:
-            # Skip bytes until a valid start character is found
-            print(block_type)
-            pass
-        block_type = read_byte(file)
-
-    return stream
-
-
-def skip_data_sub_blocks(file):
-    # 15. Data sub-blocks
-    block_size = read_byte(file)
-    while block_size != 0:
-        data = file.read(block_size)
-        block_size = read_byte(file)
-
-
-def read_table_based_image(file, stream, graphics_scope):
-    gif_image = GIFImage()
-    stream.images.append(gif_image)
-    gif_image.delay = graphics_scope.delay
-
-    # 20. Image descriptor
-    (image_left_position,
-     image_top_position,
-     image_width,
-     image_height,
-     fields) = unpack('HHHHB', file)
-
-    local_color_table_flag = fields & 0x80
-    local_color_table_size = fields & 0x7
-
-    # 21. Local color table
-    if local_color_table_flag:
-        local_color_table = file.read(6 << local_color_table_size)
-
-    # 22. Table based image data
-    lzw_code_size = file.read(1)
-    skip_data_sub_blocks(file)
-
-
-def read_graphic_control_extension(file, stream, graphics_scope):
-    # 23. Graphic control extension
-    (block_size,
-     fields,
-     delay_time,
-     transparent_color_index,
-     terminator) = unpack('BBHBB', file)
-    if block_size != 4:
-        raise ImageDecodeException('Incorrect block size')
-
-    if delay_time:
-        # Follow Firefox/Mac behaviour: use 100ms delay for any delay
-        # less than 10ms.
-        if delay_time <= 1:
-            delay_time = 10
-        graphics_scope.delay = float(delay_time) / 100
diff --git a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/libs/darwin/cocoapy/__init__.py b/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/libs/darwin/cocoapy/__init__.py
deleted file mode 100644
index 165d832d3c5bee85df795a06af0e169a47949bbd..0000000000000000000000000000000000000000
--- a/spaces/abrar-lohia/text-2-character-anim/pyrender/.eggs/pyglet-2.0.5-py3.10.egg/pyglet/libs/darwin/cocoapy/__init__.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# objective-ctypes
-#
-# Copyright (c) 2011, Phillip Nguyen
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions
-# are met:
-#
-# Redistributions of source code must retain the above copyright
-# notice, this list of conditions and the following disclaimer.
-# Redistributions in binary form must reproduce the above copyright
-# notice, this list of conditions and the following disclaimer in the
-# documentation and/or other materials provided with the distribution.
-# Neither the name of objective-ctypes nor the names of its
-# contributors may be used to endorse or promote products derived from
-# this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
-# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-# POSSIBILITY OF SUCH DAMAGE.
-
-from .runtime import objc, send_message, send_super
-from .runtime import get_selector
-from .runtime import ObjCClass, ObjCInstance, ObjCSubclass
-
-from .cocoatypes import *
-from .cocoalibs import *
diff --git a/spaces/ahmedgamal777722/flowise/README.md b/spaces/ahmedgamal777722/flowise/README.md
deleted file mode 100644
index 9b92d02afeb0245b1978d60f02a233ef3873a4e1..0000000000000000000000000000000000000000
--- a/spaces/ahmedgamal777722/flowise/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Flowise
-emoji: 🌍
-colorFrom: red
-colorTo: indigo
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ahmedghani/Editing-Tools/image_editing.py b/spaces/ahmedghani/Editing-Tools/image_editing.py
deleted file mode 100644
index 5b769593b5e84590e111df5542cee3ae53370b9d..0000000000000000000000000000000000000000
--- a/spaces/ahmedghani/Editing-Tools/image_editing.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import os
-import sys
-from pathlib import Path
-import torch
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from gfpgan import GFPGANer
-from realesrgan import RealESRGANer
-import numpy as np
-import cv2
-from PIL import Image
-from rembg import remove
-
-# DeOldify
-os.system("hub install deoldify==1.2.0")
-import paddlehub as hub
-hub.server_check()
-colorize_model = hub.Module(name='deoldify')
-
-highres_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-bg_upsampler = RealESRGANer(
-    scale=4,
-    model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth',
-    model=highres_model,
-    tile=400,
-    tile_pad=10,
-    pre_pad=0,
-    half=True
-    )
-
-upsampler =  GFPGANer(
-    model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth',
-    upscale=4,
-    arch='clean',
-    channel_multiplier=2,
-    bg_upsampler=bg_upsampler,
-    device="cuda" if torch.cuda.is_available() else "cpu",
-    )
-
-
-os.makedirs("deoldify", exist_ok=True)
-os.makedirs("gfpganOutput", exist_ok=True)
-os.makedirs("greyscale", exist_ok=True)
-os.makedirs("rembg", exist_ok=True)
-
-def restore_image(image):
-     _, _, output = upsampler.enhance(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR), has_aligned=False, only_center_face=False, paste_back=True)
-     image = Image.fromarray(cv2.cvtColor(output, cv2.COLOR_BGR2RGB))
-     return image
-
-def edit_image(image, option):
-    tools = ["High Res", "Colorize", "Greyscale", "Remove Background"]
-    if option == tools[0]:
-        restore_image(image).save("gfpganOutput/output.png")
-        return './gfpganOutput/output.png', './gfpganOutput/output.png'
-    elif option == tools[1]:
-        image.convert("L").save("deoldify/input.png", "PNG", quality=80, optimize=True)
-        colorize_model.predict("deoldify/input.png")
-        return './output/DeOldify/'+Path('deoldify/input.png').stem+".png", './output/DeOldify/'+Path('deoldify/input.png').stem+".png"
-    
-    elif option == tools[2]:
-        image.convert('L').save("greyscale/output.png")
-        return './greyscale/output.png', './greyscale/output.png'
-    elif option == tools[3]:
-        remove(image).save("rembg/output.png")
-        return './rembg/output.png', './rembg/output.png'
\ No newline at end of file
diff --git a/spaces/ajayhk/JPEGArtifactRemover/utils_image.py b/spaces/ajayhk/JPEGArtifactRemover/utils_image.py
deleted file mode 100644
index 8fe0d5b89073c983f5ac3657f585f101aca8ff19..0000000000000000000000000000000000000000
--- a/spaces/ajayhk/JPEGArtifactRemover/utils_image.py
+++ /dev/null
@@ -1,999 +0,0 @@
-import os
-import math
-import random
-import numpy as np
-import torch
-import cv2
-from torchvision.utils import make_grid
-from datetime import datetime
-# import torchvision.transforms as transforms
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-
-
-'''
-# --------------------------------------------
-# Kai Zhang (github: https://github.com/cszn)
-# 03/Mar/2019
-# --------------------------------------------
-# https://github.com/twhui/SRGAN-pyTorch
-# https://github.com/xinntao/BasicSR
-# --------------------------------------------
-'''
-
-
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def get_timestamp():
-    return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def imshow(x, title=None, cbar=False, figsize=None):
-    plt.figure(figsize=figsize)
-    plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
-    if title:
-        plt.title(title)
-    if cbar:
-        plt.colorbar()
-    plt.show()
-
-
-def surf(Z, cmap='rainbow', figsize=None):
-    plt.figure(figsize=figsize)
-    ax3 = plt.axes(projection='3d')
-
-    w, h = Z.shape[:2]
-    xx = np.arange(0,w,1)
-    yy = np.arange(0,h,1)
-    X, Y = np.meshgrid(xx, yy)
-    ax3.plot_surface(X,Y,Z,cmap=cmap)
-    #ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
-    plt.show()
-
-
-'''
-# --------------------------------------------
-# get image pathes
-# --------------------------------------------
-'''
-
-
-def get_image_paths(dataroot):
-    paths = None  # return None if dataroot is None
-    if dataroot is not None:
-        paths = sorted(_get_paths_from_images(dataroot))
-    return paths
-
-
-def _get_paths_from_images(path):
-    assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
-    images = []
-    for dirpath, _, fnames in sorted(os.walk(path)):
-        for fname in sorted(fnames):
-            if is_image_file(fname):
-                img_path = os.path.join(dirpath, fname)
-                images.append(img_path)
-    assert images, '{:s} has no valid image file'.format(path)
-    return images
-
-
-'''
-# --------------------------------------------
-# split large images into small images 
-# --------------------------------------------
-'''
-
-
-def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
-    w, h = img.shape[:2]
-    patches = []
-    if w > p_max and h > p_max:
-        w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
-        h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
-        w1.append(w-p_size)
-        h1.append(h-p_size)
-        # print(w1)
-        # print(h1)
-        for i in w1:
-            for j in h1:
-                patches.append(img[i:i+p_size, j:j+p_size,:])
-    else:
-        patches.append(img)
-
-    return patches
-
-
-def imssave(imgs, img_path):
-    """
-    imgs: list, N images of size WxHxC
-    """
-    img_name, ext = os.path.splitext(os.path.basename(img_path))
-    for i, img in enumerate(imgs):
-        if img.ndim == 3:
-            img = img[:, :, [2, 1, 0]]
-        new_path = os.path.join(os.path.dirname(img_path), img_name+str('_{:04d}'.format(i))+'.png')
-        cv2.imwrite(new_path, img)
-
-
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=512, p_overlap=96, p_max=800):
-    """
-    split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), 
-    and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
-    will be splitted.
-
-    Args:
-        original_dataroot:
-        taget_dataroot:
-        p_size: size of small images
-        p_overlap: patch size in training is a good choice
-        p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
-    """
-    paths = get_image_paths(original_dataroot)
-    for img_path in paths:
-        # img_name, ext = os.path.splitext(os.path.basename(img_path))
-        img = imread_uint(img_path, n_channels=n_channels)
-        patches = patches_from_image(img, p_size, p_overlap, p_max)
-        imssave(patches, os.path.join(taget_dataroot, os.path.basename(img_path)))
-        #if original_dataroot == taget_dataroot:
-        #del img_path
-
-'''
-# --------------------------------------------
-# makedir
-# --------------------------------------------
-'''
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-
-def mkdirs(paths):
-    if isinstance(paths, str):
-        mkdir(paths)
-    else:
-        for path in paths:
-            mkdir(path)
-
-
-def mkdir_and_rename(path):
-    if os.path.exists(path):
-        new_name = path + '_archived_' + get_timestamp()
-        print('Path already exists. Rename it to [{:s}]'.format(new_name))
-        os.rename(path, new_name)
-    os.makedirs(path)
-
-
-'''
-# --------------------------------------------
-# read image from path
-# opencv is fast, but read BGR numpy image
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# get uint8 image of size HxWxn_channles (RGB)
-# --------------------------------------------
-def imread_uint(path, n_channels=3):
-    #  input: path
-    # output: HxWx3(RGB or GGG), or HxWx1 (G)
-    if n_channels == 1:
-        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
-        img = np.expand_dims(img, axis=2)  # HxWx1
-    elif n_channels == 3:
-        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
-        if img.ndim == 2:
-            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
-        else:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
-    return img
-
-
-# --------------------------------------------
-# matlab's imwrite
-# --------------------------------------------
-def imsave(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-def imwrite(img, img_path):
-    img = np.squeeze(img)
-    if img.ndim == 3:
-        img = img[:, :, [2, 1, 0]]
-    cv2.imwrite(img_path, img)
-
-
-
-# --------------------------------------------
-# get single image of size HxWxn_channles (BGR)
-# --------------------------------------------
-def read_img(path):
-    # read image by cv2
-    # return: Numpy float32, HWC, BGR, [0,1]
-    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    # some images have 4 channels
-    if img.shape[2] > 3:
-        img = img[:, :, :3]
-    return img
-
-
-'''
-# --------------------------------------------
-# image format conversion
-# --------------------------------------------
-# numpy(single) <--->  numpy(unit)
-# numpy(single) <--->  tensor
-# numpy(unit)   <--->  tensor
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# numpy(single) [0, 1] <--->  numpy(unit)
-# --------------------------------------------
-
-
-def uint2single(img):
-
-    return np.float32(img/255.)
-
-
-def single2uint(img):
-
-    return np.uint8((img.clip(0, 1)*255.).round())
-
-
-def uint162single(img):
-
-    return np.float32(img/65535.)
-
-
-def single2uint16(img):
-
-    return np.uint16((img.clip(0, 1)*65535.).round())
-
-
-# --------------------------------------------
-# numpy(unit) (HxWxC or HxW) <--->  tensor
-# --------------------------------------------
-
-
-# convert uint to 4-dimensional torch tensor
-def uint2tensor4(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
-
-
-# convert uint to 3-dimensional torch tensor
-def uint2tensor3(img):
-    if img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
-
-
-# convert 2/3/4-dimensional torch tensor to uint
-def tensor2uint(img):
-    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    return np.uint8((img*255.0).round())
-
-
-# --------------------------------------------
-# numpy(single) (HxWxC) <--->  tensor
-# --------------------------------------------
-
-
-# convert single (HxWxC) to 3-dimensional torch tensor
-def single2tensor3(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
-
-
-# convert single (HxWxC) to 4-dimensional torch tensor
-def single2tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
-
-
-# convert torch tensor to single
-def tensor2single(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-
-    return img
-
-# convert torch tensor to single
-def tensor2single3(img):
-    img = img.data.squeeze().float().cpu().numpy()
-    if img.ndim == 3:
-        img = np.transpose(img, (1, 2, 0))
-    elif img.ndim == 2:
-        img = np.expand_dims(img, axis=2)
-    return img
-
-
-def single2tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
-
-
-def single32tensor5(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
-
-
-def single42tensor4(img):
-    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
-
-
-# from skimage.io import imread, imsave
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
-    '''
-    Converts a torch Tensor into an image Numpy array of BGR channel order
-    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
-    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
-    '''
-    tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # squeeze first, then clamp
-    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
-    n_dim = tensor.dim()
-    if n_dim == 4:
-        n_img = len(tensor)
-        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 3:
-        img_np = tensor.numpy()
-        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
-    elif n_dim == 2:
-        img_np = tensor.numpy()
-    else:
-        raise TypeError(
-            'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
-    if out_type == np.uint8:
-        img_np = (img_np * 255.0).round()
-        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
-    return img_np.astype(out_type)
-
-
-'''
-# --------------------------------------------
-# Augmentation, flipe and/or rotate
-# --------------------------------------------
-# The following two are enough.
-# (1) augmet_img: numpy image of WxHxC or WxH
-# (2) augment_img_tensor4: tensor image 1xCxWxH
-# --------------------------------------------
-'''
-
-
-def augment_img(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return np.flipud(np.rot90(img))
-    elif mode == 2:
-        return np.flipud(img)
-    elif mode == 3:
-        return np.rot90(img, k=3)
-    elif mode == 4:
-        return np.flipud(np.rot90(img, k=2))
-    elif mode == 5:
-        return np.rot90(img)
-    elif mode == 6:
-        return np.rot90(img, k=2)
-    elif mode == 7:
-        return np.flipud(np.rot90(img, k=3))
-
-
-def augment_img_tensor4(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.rot90(1, [2, 3]).flip([2])
-    elif mode == 2:
-        return img.flip([2])
-    elif mode == 3:
-        return img.rot90(3, [2, 3])
-    elif mode == 4:
-        return img.rot90(2, [2, 3]).flip([2])
-    elif mode == 5:
-        return img.rot90(1, [2, 3])
-    elif mode == 6:
-        return img.rot90(2, [2, 3])
-    elif mode == 7:
-        return img.rot90(3, [2, 3]).flip([2])
-
-
-def augment_img_tensor(img, mode=0):
-    '''Kai Zhang (github: https://github.com/cszn)
-    '''
-    img_size = img.size()
-    img_np = img.data.cpu().numpy()
-    if len(img_size) == 3:
-        img_np = np.transpose(img_np, (1, 2, 0))
-    elif len(img_size) == 4:
-        img_np = np.transpose(img_np, (2, 3, 1, 0))
-    img_np = augment_img(img_np, mode=mode)
-    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
-    if len(img_size) == 3:
-        img_tensor = img_tensor.permute(2, 0, 1)
-    elif len(img_size) == 4:
-        img_tensor = img_tensor.permute(3, 2, 0, 1)
-
-    return img_tensor.type_as(img)
-
-
-def augment_img_np3(img, mode=0):
-    if mode == 0:
-        return img
-    elif mode == 1:
-        return img.transpose(1, 0, 2)
-    elif mode == 2:
-        return img[::-1, :, :]
-    elif mode == 3:
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 4:
-        return img[:, ::-1, :]
-    elif mode == 5:
-        img = img[:, ::-1, :]
-        img = img.transpose(1, 0, 2)
-        return img
-    elif mode == 6:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        return img
-    elif mode == 7:
-        img = img[:, ::-1, :]
-        img = img[::-1, :, :]
-        img = img.transpose(1, 0, 2)
-        return img
-
-
-def augment_imgs(img_list, hflip=True, rot=True):
-    # horizontal flip OR rotate
-    hflip = hflip and random.random() < 0.5
-    vflip = rot and random.random() < 0.5
-    rot90 = rot and random.random() < 0.5
-
-    def _augment(img):
-        if hflip:
-            img = img[:, ::-1, :]
-        if vflip:
-            img = img[::-1, :, :]
-        if rot90:
-            img = img.transpose(1, 0, 2)
-        return img
-
-    return [_augment(img) for img in img_list]
-
-
-'''
-# --------------------------------------------
-# modcrop and shave
-# --------------------------------------------
-'''
-
-
-def modcrop(img_in, scale):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    if img.ndim == 2:
-        H, W = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r]
-    elif img.ndim == 3:
-        H, W, C = img.shape
-        H_r, W_r = H % scale, W % scale
-        img = img[:H - H_r, :W - W_r, :]
-    else:
-        raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
-    return img
-
-
-def shave(img_in, border=0):
-    # img_in: Numpy, HWC or HW
-    img = np.copy(img_in)
-    h, w = img.shape[:2]
-    img = img[border:h-border, border:w-border]
-    return img
-
-
-'''
-# --------------------------------------------
-# image processing process on numpy image
-# channel_convert(in_c, tar_type, img_list):
-# rgb2ycbcr(img, only_y=True):
-# bgr2ycbcr(img, only_y=True):
-# ycbcr2rgb(img):
-# --------------------------------------------
-'''
-
-
-def rgb2ycbcr(img, only_y=True):
-    '''same as matlab rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
-                              [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def ycbcr2rgb(img):
-    '''same as matlab ycbcr2rgb
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
-                          [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def bgr2ycbcr(img, only_y=True):
-    '''bgr version of rgb2ycbcr
-    only_y: only return Y channel
-    Input:
-        uint8, [0, 255]
-        float, [0, 1]
-    '''
-    in_img_type = img.dtype
-    img.astype(np.float32)
-    if in_img_type != np.uint8:
-        img *= 255.
-    # convert
-    if only_y:
-        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
-    else:
-        rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
-                              [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
-    if in_img_type == np.uint8:
-        rlt = rlt.round()
-    else:
-        rlt /= 255.
-    return rlt.astype(in_img_type)
-
-
-def channel_convert(in_c, tar_type, img_list):
-    # conversion among BGR, gray and y
-    if in_c == 3 and tar_type == 'gray':  # BGR to gray
-        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in gray_list]
-    elif in_c == 3 and tar_type == 'y':  # BGR to y
-        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
-        return [np.expand_dims(img, axis=2) for img in y_list]
-    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
-        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
-    else:
-        return img_list
-
-
-'''
-# --------------------------------------------
-# metric, PSNR and SSIM
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# PSNR
-# --------------------------------------------
-def calculate_psnr(img1, img2, border=0):
-    # img1 and img2 have range [0, 255]
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    mse = np.mean((img1 - img2)**2)
-    if mse == 0:
-        return float('inf')
-    return 20 * math.log10(255.0 / math.sqrt(mse))
-
-
-# --------------------------------------------
-# BEF: Blocking effect factor
-# --------------------------------------------
-def compute_bef(img):
-
-	block = 8
-	height, width = img.shape[:2]
-	
-	H = [i for i in range(width-1)]
-	H_B = [i for i in range(block-1,width-1,block)]
-	H_BC = list(set(H)-set(H_B))
-
-	V = [i for i in range(height-1)]
-	V_B = [i for i in range(block-1,height-1,block)]
-	V_BC = list(set(V)-set(V_B))
-
-	D_B = 0
-	D_BC = 0
-
-	for i in H_B:
-		diff = img[:,i] - img[:,i+1]
-		D_B += np.sum(diff**2)
-
-	for i in H_BC:
-		diff = img[:,i] - img[:,i+1]
-		D_BC += np.sum(diff**2)
-
-
-	for j in V_B:
-		diff = img[j,:] - img[j+1,:]
-		D_B += np.sum(diff**2)
-
-	for j in V_BC:
-		diff = img[j,:] - img[j+1,:]
-		D_BC += np.sum(diff**2)
-
-
-	N_HB = height * (width/block - 1)
-	N_HBC = height * (width - 1) - N_HB
-	N_VB = width * (height/block -1)
-	N_VBC = width * (height -1) - N_VB
-	D_B = D_B / (N_HB + N_VB)
-	D_BC = D_BC / (N_HBC + N_VBC)
-	eta = math.log2(block) / math.log2(min(height, width)) if D_B > D_BC else 0
-	return eta * (D_B - D_BC)
-
-
-
-# --------------------------------------------
-# PSNRB
-# --------------------------------------------
-def calculate_psnrb(img1, img2, border=0):
-	# img1: ground truth
-	# img2: compressed image
-    # img1 and img2 have range [0, 255]
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-    img1 = img1.astype(np.float64)
-    if img2.shape[-1]==3:
-        img2_y = rgb2ycbcr(img2).astype(np.float64)
-        bef = compute_bef(img2_y)
-    else:
-        img2 = img2.astype(np.float64)
-        bef = compute_bef(img2)
-    mse = np.mean((img1 - img2)**2)
-    mse_b = mse + bef
-    if mse_b == 0:
-        return float('inf')
-    return 20 * math.log10(255.0 / math.sqrt(mse_b))
-
-
-
-# --------------------------------------------
-# SSIM
-# --------------------------------------------
-def calculate_ssim(img1, img2, border=0):
-    '''calculate SSIM
-    the same outputs as MATLAB's
-    img1, img2: [0, 255]
-    '''
-    #img1 = img1.squeeze()
-    #img2 = img2.squeeze()
-    if not img1.shape == img2.shape:
-        raise ValueError('Input images must have the same dimensions.')
-    h, w = img1.shape[:2]
-    img1 = img1[border:h-border, border:w-border]
-    img2 = img2[border:h-border, border:w-border]
-
-    if img1.ndim == 2:
-        return ssim(img1, img2)
-    elif img1.ndim == 3:
-        if img1.shape[2] == 3:
-            ssims = []
-            for i in range(3):
-                ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
-            return np.array(ssims).mean()
-        elif img1.shape[2] == 1:
-            return ssim(np.squeeze(img1), np.squeeze(img2))
-    else:
-        raise ValueError('Wrong input image dimensions.')
-
-
-def ssim(img1, img2):
-    C1 = (0.01 * 255)**2
-    C2 = (0.03 * 255)**2
-
-    img1 = img1.astype(np.float64)
-    img2 = img2.astype(np.float64)
-    kernel = cv2.getGaussianKernel(11, 1.5)
-    window = np.outer(kernel, kernel.transpose())
-
-    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
-    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
-    mu1_sq = mu1**2
-    mu2_sq = mu2**2
-    mu1_mu2 = mu1 * mu2
-    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
-    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
-    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
-    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
-                                                            (sigma1_sq + sigma2_sq + C2))
-    return ssim_map.mean()
-
-
-'''
-# --------------------------------------------
-# matlab's bicubic imresize (numpy and torch) [0, 1]
-# --------------------------------------------
-'''
-
-
-# matlab 'imresize' function, now only support 'bicubic'
-def cubic(x):
-    absx = torch.abs(x)
-    absx2 = absx**2
-    absx3 = absx**3
-    return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
-        (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
-    if (scale < 1) and (antialiasing):
-        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
-        kernel_width = kernel_width / scale
-
-    # Output-space coordinates
-    x = torch.linspace(1, out_length, out_length)
-
-    # Input-space coordinates. Calculate the inverse mapping such that 0.5
-    # in output space maps to 0.5 in input space, and 0.5+scale in output
-    # space maps to 1.5 in input space.
-    u = x / scale + 0.5 * (1 - 1 / scale)
-
-    # What is the left-most pixel that can be involved in the computation?
-    left = torch.floor(u - kernel_width / 2)
-
-    # What is the maximum number of pixels that can be involved in the
-    # computation?  Note: it's OK to use an extra pixel here; if the
-    # corresponding weights are all zero, it will be eliminated at the end
-    # of this function.
-    P = math.ceil(kernel_width) + 2
-
-    # The indices of the input pixels involved in computing the k-th output
-    # pixel are in row k of the indices matrix.
-    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
-        1, P).expand(out_length, P)
-
-    # The weights used to compute the k-th output pixel are in row k of the
-    # weights matrix.
-    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
-    # apply cubic kernel
-    if (scale < 1) and (antialiasing):
-        weights = scale * cubic(distance_to_center * scale)
-    else:
-        weights = cubic(distance_to_center)
-    # Normalize the weights matrix so that each row sums to 1.
-    weights_sum = torch.sum(weights, 1).view(out_length, 1)
-    weights = weights / weights_sum.expand(out_length, P)
-
-    # If a column in weights is all zero, get rid of it. only consider the first and last column.
-    weights_zero_tmp = torch.sum((weights == 0), 0)
-    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 1, P - 2)
-        weights = weights.narrow(1, 1, P - 2)
-    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
-        indices = indices.narrow(1, 0, P - 2)
-        weights = weights.narrow(1, 0, P - 2)
-    weights = weights.contiguous()
-    indices = indices.contiguous()
-    sym_len_s = -indices.min() + 1
-    sym_len_e = indices.max() - in_length
-    indices = indices + sym_len_s - 1
-    return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-# --------------------------------------------
-# imresize for tensor image [0, 1]
-# --------------------------------------------
-def imresize(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: pytorch tensor, CHW or HW [0,1]
-    # output: CHW or HW [0,1] w/o round
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(0)
-    in_C, in_H, in_W = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
-    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:, :sym_len_Hs, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[:, -sym_len_He:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(in_C, out_H, in_W)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
-    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :, :sym_len_Ws]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, :, -sym_len_We:]
-    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(2, inv_idx)
-    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(in_C, out_H, out_W)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-    return out_2
-
-
-# --------------------------------------------
-# imresize for numpy image [0, 1]
-# --------------------------------------------
-def imresize_np(img, scale, antialiasing=True):
-    # Now the scale should be the same for H and W
-    # input: img: Numpy, HWC or HW [0,1]
-    # output: HWC or HW [0,1] w/o round
-    img = torch.from_numpy(img)
-    need_squeeze = True if img.dim() == 2 else False
-    if need_squeeze:
-        img.unsqueeze_(2)
-
-    in_H, in_W, in_C = img.size()
-    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
-    kernel_width = 4
-    kernel = 'cubic'
-
-    # Return the desired dimension order for performing the resize.  The
-    # strategy is to perform the resize first along the dimension with the
-    # smallest scale factor.
-    # Now we do not support this.
-
-    # get weights and indices
-    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
-        in_H, out_H, scale, kernel, kernel_width, antialiasing)
-    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
-        in_W, out_W, scale, kernel, kernel_width, antialiasing)
-    # process H dimension
-    # symmetric copying
-    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
-    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
-
-    sym_patch = img[:sym_len_Hs, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
-
-    sym_patch = img[-sym_len_He:, :, :]
-    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(0, inv_idx)
-    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
-    out_1 = torch.FloatTensor(out_H, in_W, in_C)
-    kernel_width = weights_H.size(1)
-    for i in range(out_H):
-        idx = int(indices_H[i][0])
-        for j in range(out_C):
-            out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-
-    # process W dimension
-    # symmetric copying
-    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
-    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
-
-    sym_patch = out_1[:, :sym_len_Ws, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
-
-    sym_patch = out_1[:, -sym_len_We:, :]
-    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
-    sym_patch_inv = sym_patch.index_select(1, inv_idx)
-    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
-    out_2 = torch.FloatTensor(out_H, out_W, in_C)
-    kernel_width = weights_W.size(1)
-    for i in range(out_W):
-        idx = int(indices_W[i][0])
-        for j in range(out_C):
-            out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
-    if need_squeeze:
-        out_2.squeeze_()
-
-    return out_2.numpy()
-
-
-if __name__ == '__main__':
-    img = imread_uint('test.bmp', 3)
-#    img = uint2single(img)
-#    img_bicubic = imresize_np(img, 1/4)
-#    imshow(single2uint(img_bicubic))
-#
-#    img_tensor = single2tensor4(img)
-#    for i in range(8):
-#        imshow(np.concatenate((augment_img(img, i), tensor2single(augment_img_tensor4(img_tensor, i))), 1))
-    
-#    patches = patches_from_image(img, p_size=128, p_overlap=0, p_max=200)
-#    imssave(patches,'a.png')
\ No newline at end of file
diff --git a/spaces/akhaliq/deeplab2/data/preprocessing/autoaugment_policy_test.py b/spaces/akhaliq/deeplab2/data/preprocessing/autoaugment_policy_test.py
deleted file mode 100644
index e9b02a38cb3def5a7277f2142ca5a27d0552cdcb..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/data/preprocessing/autoaugment_policy_test.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 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.
-
-"""Tests for autoaugment_policy.py."""
-
-import tensorflow as tf
-
-from deeplab2.data.preprocessing import autoaugment_policy
-
-
-class AutoaugmentPolicyTest(tf.test.TestCase):
-
-  def testConvertPolicy(self):
-    policy = [5, 1, 10, 5, 3, 4,
-              6, 3, 7, 3, 3, 9,
-              2, 2, 8, 8, 2, 8,
-              1, 4, 9, 4, 5, 7,
-              6, 4, 1, 1, 3, 4]
-    expected = [
-        [('Color', 0.2, 10), ('Color', 0.6, 4)],
-        [('Contrast', 0.6, 7), ('Posterize', 0.6, 9)],
-        [('Invert', 0.4, 8), ('Sharpness', 0.4, 8)],
-        [('Equalize', 0.8, 9), ('Solarize', 1.0, 7)],
-        [('Contrast', 0.8, 1), ('Equalize', 0.6, 4)],
-    ]
-    policy_list = autoaugment_policy.convert_policy(policy)
-    self.assertAllEqual(policy_list, expected)
-
-
-if __name__ == '__main__':
-  tf.test.main()
diff --git a/spaces/alankabisov/youtube-video-summary/app.py b/spaces/alankabisov/youtube-video-summary/app.py
deleted file mode 100644
index b9aba471d8c355afff2634a746868523d60d2610..0000000000000000000000000000000000000000
--- a/spaces/alankabisov/youtube-video-summary/app.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import os
-
-import streamlit as st
-
-from urllib.parse import urlparse, parse_qs
-
-from stqdm import stqdm
-
-# https://github.com/pytorch/pytorch/issues/77764
-os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-
-from youtube_transcript_api import YouTubeTranscriptApi, NoTranscriptFound, TranscriptsDisabled
-
-from transformers import pipeline
-
-import torch
-
-# Setting device for PyTorch
-if torch.cuda.is_available():
-    device = torch.device('cuda')
-elif torch.has_mps:
-    device = torch.device('mps')
-else:
-    device = torch.device('cpu')
-
-
-class InvalidURLException(Exception):
-    pass
-
-
-def get_videoid_from_url(url: str):
-    '''
-    Gets video ID from give YouTube video URL
-
-    :param url: YouTube video URL in 2 formats (standard and short)
-    :return: id of YouTube video
-    :raises InvalidURLException: If URL is not valid
-    '''
-    url_data = urlparse(url)
-    query = parse_qs(url_data.query)
-
-    if ('v' in query) & ('youtube.com' in url_data.netloc):
-        video_id = query["v"][0]
-    elif 'youtu.be' in url_data.netloc:
-        path_lst = url.split('/')
-
-        if path_lst:
-            video_id = path_lst[-1]
-        else:
-            raise InvalidURLException('Invalid URL')
-    else:
-        raise InvalidURLException('Invalid URL')
-
-    return video_id
-
-
-def get_transcripts(url: str):
-    '''
-    Loads transcripts for given URL
-
-    :param url: YouTube video URL
-    :return: list, list of subtitles
-    '''
-
-    video_id = get_videoid_from_url(video_url_inp)
-
-    transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
-
-    try:
-        transcript = transcript_list.find_manually_created_transcript(['en'])
-    except NoTranscriptFound as e:
-        # Doesn't work on HuggingFace v1.1.0 version
-        # st.info('No manual transcripts were found, trying to load generated ones...')
-        transcript = transcript_list.find_generated_transcript(['en'])
-
-    subtitles = transcript.fetch()
-
-    subtitles = [sbt['text'] for sbt in subtitles if sbt['text'] != '[Music]']
-
-    return subtitles
-
-
-def generate_summary(subtitles: list):
-    '''
-    Creates summary based on subtitles of YouTube video.
-
-    Uses T5-small model which shows best results for different topics
-    of videos.
-
-    :param subtitles: list of subtitles strings
-    :return: summary based on subtitles
-    '''
-    subtitles_len = [len(sbt) for sbt in subtitles]
-    sbt_mean_len = sum(subtitles_len) / len(subtitles_len)
-
-    # Number of subtitles per step/summary
-    # Since number length of transcripts differs
-    # between generated and manual ones
-    # we set different step size
-    n_sbt_per_step = int(400 / (sbt_mean_len / 4))
-
-    n_steps = len(subtitles) // n_sbt_per_step if len(subtitles) % n_sbt_per_step == 0 else \
-        len(subtitles) // n_sbt_per_step + 1
-
-    summaries = []
-
-    for i in stqdm(range(n_steps)):
-        sbt_txt = ' '.join(subtitles[n_sbt_per_step * i:n_sbt_per_step * (i + 1)])
-
-        summarizer = pipeline('summarization', model='t5-small', tokenizer='t5-small',
-                              max_length=512, truncation=True)
-
-        summary = summarizer(sbt_txt, do_sample=False)
-        summary = summary[0]['summary_text']
-
-        summaries.append(summary)
-
-    return ' '.join(summaries)
-
-
-def process_click_callback():
-    '''
-    Callback for process button click
-    '''
-    global is_processing
-
-    if is_processing:
-        return
-    else:
-        is_processing = True
-
-    global video_url_inp
-
-    try:
-        subtitles = get_transcripts(video_url_inp)
-    except InvalidURLException as iue:
-        is_processing = False
-        st.error('Invalid YouTube URL, please provide URL in format that is shown on Examples')
-        st.experimental_rerun()
-    except TranscriptsDisabled as tde:
-        is_processing = False
-        st.error('Could not retrieve a transcript for given ID')
-        st.experimental_rerun()
-
-    summary = generate_summary(subtitles)
-
-    st.session_state.summary_output = summary
-    st.success('Processing complete! ✅')
-
-    is_processing = False
-
-
-if __name__ == "__main__":
-    # State of processing
-    is_processing = False
-
-    st.title('YouTube Video Summary 📝')
-    st.markdown('Creates summary for given YouTube video URL based on transcripts.')
-    st.code('https://www.youtube.com/watch?v=1wkPMUZ9vX4')
-    st.code('https://youtu.be/zb9l63Nm9zU')
-
-    col1, col2 = st.columns(2)
-
-    with col1:
-        video_url_inp = st.text_input('', placeholder='YouTube URL')
-
-    with col2:
-        st.markdown('#') # Adds empty space
-        process_btn = st.button('🗜️Process', key='process_btn', on_click=process_click_callback)
-
-    summary_out_txt = st.text_area(label='', key='summary_output', height=400)
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/columns.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/columns.py
deleted file mode 100644
index 669a3a7074f9a9e1af29cb4bc78b05851df67959..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/rich/columns.py
+++ /dev/null
@@ -1,187 +0,0 @@
-from collections import defaultdict
-from itertools import chain
-from operator import itemgetter
-from typing import Dict, Iterable, List, Optional, Tuple
-
-from .align import Align, AlignMethod
-from .console import Console, ConsoleOptions, RenderableType, RenderResult
-from .constrain import Constrain
-from .measure import Measurement
-from .padding import Padding, PaddingDimensions
-from .table import Table
-from .text import TextType
-from .jupyter import JupyterMixin
-
-
-class Columns(JupyterMixin):
-    """Display renderables in neat columns.
-
-    Args:
-        renderables (Iterable[RenderableType]): Any number of Rich renderables (including str).
-        width (int, optional): The desired width of the columns, or None to auto detect. Defaults to None.
-        padding (PaddingDimensions, optional): Optional padding around cells. Defaults to (0, 1).
-        expand (bool, optional): Expand columns to full width. Defaults to False.
-        equal (bool, optional): Arrange in to equal sized columns. Defaults to False.
-        column_first (bool, optional): Align items from top to bottom (rather than left to right). Defaults to False.
-        right_to_left (bool, optional): Start column from right hand side. Defaults to False.
-        align (str, optional): Align value ("left", "right", or "center") or None for default. Defaults to None.
-        title (TextType, optional): Optional title for Columns.
-    """
-
-    def __init__(
-        self,
-        renderables: Optional[Iterable[RenderableType]] = None,
-        padding: PaddingDimensions = (0, 1),
-        *,
-        width: Optional[int] = None,
-        expand: bool = False,
-        equal: bool = False,
-        column_first: bool = False,
-        right_to_left: bool = False,
-        align: Optional[AlignMethod] = None,
-        title: Optional[TextType] = None,
-    ) -> None:
-        self.renderables = list(renderables or [])
-        self.width = width
-        self.padding = padding
-        self.expand = expand
-        self.equal = equal
-        self.column_first = column_first
-        self.right_to_left = right_to_left
-        self.align: Optional[AlignMethod] = align
-        self.title = title
-
-    def add_renderable(self, renderable: RenderableType) -> None:
-        """Add a renderable to the columns.
-
-        Args:
-            renderable (RenderableType): Any renderable object.
-        """
-        self.renderables.append(renderable)
-
-    def __rich_console__(
-        self, console: Console, options: ConsoleOptions
-    ) -> RenderResult:
-        render_str = console.render_str
-        renderables = [
-            render_str(renderable) if isinstance(renderable, str) else renderable
-            for renderable in self.renderables
-        ]
-        if not renderables:
-            return
-        _top, right, _bottom, left = Padding.unpack(self.padding)
-        width_padding = max(left, right)
-        max_width = options.max_width
-        widths: Dict[int, int] = defaultdict(int)
-        column_count = len(renderables)
-
-        get_measurement = Measurement.get
-        renderable_widths = [
-            get_measurement(console, options, renderable).maximum
-            for renderable in renderables
-        ]
-        if self.equal:
-            renderable_widths = [max(renderable_widths)] * len(renderable_widths)
-
-        def iter_renderables(
-            column_count: int,
-        ) -> Iterable[Tuple[int, Optional[RenderableType]]]:
-            item_count = len(renderables)
-            if self.column_first:
-                width_renderables = list(zip(renderable_widths, renderables))
-
-                column_lengths: List[int] = [item_count // column_count] * column_count
-                for col_no in range(item_count % column_count):
-                    column_lengths[col_no] += 1
-
-                row_count = (item_count + column_count - 1) // column_count
-                cells = [[-1] * column_count for _ in range(row_count)]
-                row = col = 0
-                for index in range(item_count):
-                    cells[row][col] = index
-                    column_lengths[col] -= 1
-                    if column_lengths[col]:
-                        row += 1
-                    else:
-                        col += 1
-                        row = 0
-                for index in chain.from_iterable(cells):
-                    if index == -1:
-                        break
-                    yield width_renderables[index]
-            else:
-                yield from zip(renderable_widths, renderables)
-            # Pad odd elements with spaces
-            if item_count % column_count:
-                for _ in range(column_count - (item_count % column_count)):
-                    yield 0, None
-
-        table = Table.grid(padding=self.padding, collapse_padding=True, pad_edge=False)
-        table.expand = self.expand
-        table.title = self.title
-
-        if self.width is not None:
-            column_count = (max_width) // (self.width + width_padding)
-            for _ in range(column_count):
-                table.add_column(width=self.width)
-        else:
-            while column_count > 1:
-                widths.clear()
-                column_no = 0
-                for renderable_width, _ in iter_renderables(column_count):
-                    widths[column_no] = max(widths[column_no], renderable_width)
-                    total_width = sum(widths.values()) + width_padding * (
-                        len(widths) - 1
-                    )
-                    if total_width > max_width:
-                        column_count = len(widths) - 1
-                        break
-                    else:
-                        column_no = (column_no + 1) % column_count
-                else:
-                    break
-
-        get_renderable = itemgetter(1)
-        _renderables = [
-            get_renderable(_renderable)
-            for _renderable in iter_renderables(column_count)
-        ]
-        if self.equal:
-            _renderables = [
-                None
-                if renderable is None
-                else Constrain(renderable, renderable_widths[0])
-                for renderable in _renderables
-            ]
-        if self.align:
-            align = self.align
-            _Align = Align
-            _renderables = [
-                None if renderable is None else _Align(renderable, align)
-                for renderable in _renderables
-            ]
-
-        right_to_left = self.right_to_left
-        add_row = table.add_row
-        for start in range(0, len(_renderables), column_count):
-            row = _renderables[start : start + column_count]
-            if right_to_left:
-                row = row[::-1]
-            add_row(*row)
-        yield table
-
-
-if __name__ == "__main__":  # pragma: no cover
-    import os
-
-    console = Console()
-
-    files = [f"{i} {s}" for i, s in enumerate(sorted(os.listdir()))]
-    columns = Columns(files, padding=(0, 1), expand=False, equal=False)
-    console.print(columns)
-    console.rule()
-    columns.column_first = True
-    console.print(columns)
-    columns.right_to_left = True
-    console.rule()
-    console.print(columns)
diff --git a/spaces/allknowingroger/Image-Models-Test12/README.md b/spaces/allknowingroger/Image-Models-Test12/README.md
deleted file mode 100644
index ee7e271b3388f03abd5e44d0b292b812a009057e..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test12/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: More 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-Test11
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test183/README.md b/spaces/allknowingroger/Image-Models-Test183/README.md
deleted file mode 100644
index f91e4b31ab345f987b425de029c057bfb69d9e1b..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test183/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: More 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-Test
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/ameya123ch/FakeNewsDetector/app.py b/spaces/ameya123ch/FakeNewsDetector/app.py
deleted file mode 100644
index 37b0df0b298645d872550fd7b363728e6c83e20a..0000000000000000000000000000000000000000
--- a/spaces/ameya123ch/FakeNewsDetector/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import streamlit as st
-import tensorflow as tf
-import tensorflow_hub as hub
-
-
-
-
-new_model = tf.keras.models.load_model("Fake news nlp model.h5",custom_objects={"KerasLayer": hub.KerasLayer})
-
-
-
-
-
-def welcome():
-    return "Welcome to my app"
-
-
-def main():
-    st.title("Fake News Detection App")
-    st.write(
-        "This app will tell you if mention news is Fake or Real by using Natural Language Processing")
-    html_temp = """
-    <div style="background-color:tomato;padding:10px">
-    <h2 style="color:white;text-align:center;">Fake News Detector </h2>
-    </div>
-    """
-    st.markdown(html_temp, unsafe_allow_html=True)
-
-    text = st.text_input("Enter your News")
-
-
-    if st.button("Predict"):
-        pred_prob = new_model.predict([text])
-        predict = tf.squeeze(tf.round(pred_prob)).numpy()
-        st.subheader("AI thinks that ...")
-
-        if predict > 0:
-
-            st.success(
-                f"It's Real news, you can trust it. Confidence Level is {tf.round(pred_prob,3)*100}%",icon="✅")
-        else:
-            st.warning(
-                f"Beware!! It's a Fake News. Confidence Level is {tf.round(100 - pred_prob,2)}%", icon="⚠️")
-
-    if st.button("About"):
-
-        st.text("Built with Streamlit")
-
-
-if __name__ == '__main__':
-    main()
-
diff --git a/spaces/amgross01/Stocks_Trading_Assistant/functions.py b/spaces/amgross01/Stocks_Trading_Assistant/functions.py
deleted file mode 100644
index 6e43af13b84f2d196f5e41bbd88a379267a08de2..0000000000000000000000000000000000000000
--- a/spaces/amgross01/Stocks_Trading_Assistant/functions.py
+++ /dev/null
@@ -1,482 +0,0 @@
-from asyncio.constants import LOG_THRESHOLD_FOR_CONNLOST_WRITES
-import yfinance as yf
-import pandas as pd
-import numpy as np
-import plotly.graph_objs as go
-from stocks import *
-from transformers import AutoModelForSequenceClassification, pipeline, AutoTokenizer
-import os
-from random import random
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-import tensorflow as tf
-import math
-import datetime
-import random 
-import time
-#import kaleido 
-from sklearn.preprocessing import MinMaxScaler 
-import matplotlib.pyplot as plt
-#import warnings
-import tensorflow as tf
-from tensorflow import keras
-from keras.layers import Dropout, Activation 
-from keras import layers
-from keras.callbacks import EarlyStopping
-from sklearn.metrics import r2_score
-import plotly.graph_objs as go  
-import plotly.io as pio
-pio.templates
-
-model = AutoModelForSequenceClassification.from_pretrained("fine_tuned_FinBERT", from_tf=False, config="config.json") 
-tokenizer = AutoTokenizer.from_pretrained("fine_tuned_FinBERT/tokenizer/")
-
-class Models(object):
-    def __init__(self):
-        self.stock_data = Stock_Data()
-
-    def bollinger_bands_20d_2std(self, ticker):
-        '''
-        This method calculates the Bollinger Bands with a Rolling average of the last 20 days and 2 standard deviations. In a plot, 
-        this would be represented as 3 lines: a rolling average, an upper bound (rolling average + 2 standard deviations) and a lower
-        bound (rolling average - 2 standard deviations). When the price of a stock is between the rolling average and lower bound, it is
-        considered as oversold, so it makes sense to buy, if it is between the roll. avg. and the upper bound, it is considered as 
-        overbought, so it makes sense to sell, if it is equal to the roll.avg. it is neutral and if it is outside the bounds, it is 
-        considered an Unusual Event. The function returns the outlook of the stock (either "Buy", or "Sell" or "Hold" or "Unusual Event")
-        '''
-        if self.stock_data.status_getter(ticker) != "Open":
-            return "Market Closed"
-        else:
-            data = self.stock_data.stock_data_getter(ticker)
-            low_high_closing_df = pd.DataFrame(data)
-            low_high_closing_df = data.iloc[:, 4:5]  # Getting only the "Adj Close" column
-            low_high_closing_df = low_high_closing_df.tail(40) # Getting the last 40 days
-
-            low_high_closing_df["rolling_avg_20d"] = low_high_closing_df['Adj Close'].rolling(20, min_periods = 20).mean()
-            low_high_closing_df["sd"] = low_high_closing_df["Adj Close"].rolling(20, min_periods = 20).std()
-            low_high_closing_df = low_high_closing_df.tail(20) # Keeping the last 20 days only
-
-            recent_data = low_high_closing_df.iloc[-1, :].to_list() # Creating a Series object with the most recent data (last row only)
-
-            upper_bound = recent_data[1] + 2*recent_data[2] # Upper Bound
-            lower_bound = recent_data[1] - 2*recent_data[2] # Lower Bound
-            mean_20d = recent_data[1] # Rolling average of last 20 days
-            
-            if self.stock_data.current_price_getter(ticker) is None:
-               return "Market Closed"
-            else:
-                message = ""
-
-                if self.stock_data.current_price_getter(ticker) < mean_20d and self.stock_data.current_price_getter(ticker) >= lower_bound:
-                    message = "Buy"
-                elif self.stock_data.current_price_getter(ticker) > mean_20d and self.stock_data.current_price_getter(ticker) <= upper_bound:
-                    message = "Sell"
-                elif self.stock_data.current_price_getter(ticker) == mean_20d:
-                    message = "Hold"
-                elif self.stock_data.current_price_getter(ticker) <= lower_bound or self.stock_data.current_price_getter(ticker) >= upper_bound:
-                    message = "Unusual Event"
-                return message
-
-    def bollinger_bands_10d_1point5std(self, ticker):
-        '''
-        This method calculates the Bollinger Bands with a Rolling average of the last 10 days and 1.5 standard deviations. In a plot, 
-        this would be represented as 3 lines: a rolling average, an upper bound (rolling average + 1.5 standard deviations) and a lower
-        bound (rolling average - 1.5 standard deviations). When the price of a stock is between the rolling average and lower bound, it is
-        considered as oversold, so it makes sense to buy, if it is between the roll. avg. and the upper bound, it is considered as 
-        overbought, so it makes sense to sell, if it is equal to the roll.avg. it is neutral and if it is outside the bounds, it is 
-        considered an Unusual Event. The function returns the outlook of the stock (either "Buy", or "Sell" or "Hold" or "Unusual Event")
-        '''
-        if self.stock_data.status_getter(ticker) != "Open":
-                return "Market Closed"
-        else:
-            data = self.stock_data.stock_data_getter(ticker)
-
-            low_high_closing_df = pd.DataFrame(data)
-            low_high_closing_df = data.iloc[:, 4:5]  # Getting only the "Adj Close" column
-            low_high_closing_df = low_high_closing_df.tail(20) # Getting the last 20 days
-
-            low_high_closing_df["rolling_avg_10d"] = low_high_closing_df['Adj Close'].rolling(10, min_periods = 10).mean()
-            low_high_closing_df["sd"] = low_high_closing_df["Adj Close"].rolling(10, min_periods = 10).std()
-            low_high_closing_df = low_high_closing_df.tail(10) # Keeping the last 10 days only
-
-            recent_data = low_high_closing_df.iloc[-1, :].to_list() # Creating a Series object with the most recent data (last row only)
-
-            upper_bound = recent_data[1] + 1.5*recent_data[2] # Upper Bound
-            lower_bound = recent_data[1] - 1.5*recent_data[2] # Lower Bound
-            mean_10d = recent_data[1] # Rolling average of last 10 days
-
-            if self.stock_data.current_price_getter(ticker) is None:
-               return "Market Closed"
-            else:
-                message = ""
-
-                if self.stock_data.current_price_getter(ticker) < mean_10d and self.stock_data.current_price_getter(ticker) >= lower_bound:
-                    message = "Buy"
-                elif self.stock_data.current_price_getter(ticker) > mean_10d and self.stock_data.current_price_getter(ticker) <= upper_bound:
-                    message = "Sell"
-                elif self.stock_data.current_price_getter(ticker) == mean_10d:
-                    message = "Hold"
-                elif self.stock_data.current_price_getter(ticker) <= lower_bound or self.stock_data.current_price_getter(ticker) >= upper_bound:
-                    message = "Unusual Event"
-                return message
-
-    def bollinger_bands_50d_3std(self, ticker):
-        '''
-        This method calculates the Bollinger Bands with a Rolling average of the last 50 days and 3 standard deviations. In a plot, 
-        this would be represented as 3 lines: a rolling average, an upper bound (rolling average + 3 standard deviations) and a lower
-        bound (rolling average - 3 standard deviations). When the price of a stock is between the rolling average and lower bound, it is
-        considered as oversold, so it makes sense to buy, if it is between the roll. avg. and the upper bound, it is considered as 
-        overbought, so it makes sense to sell, if it is equal to the roll.avg. it is neutral and if it is outside the bounds, it is 
-        considered an Unusual Event. The function returns the outlook of the stock (either "Buy", or "Sell" or "Hold" or "Unusual Event")
-        '''
-        if self.stock_data.status_getter(ticker) != "Open":
-                return "Market Closed"
-        else:
-            data = self.stock_data.stock_data_getter(ticker)
-
-            low_high_closing_df = pd.DataFrame(data)
-            low_high_closing_df = data.iloc[:, 4:5]  # Getting only the "Adj Close" column
-            low_high_closing_df = low_high_closing_df.tail(100) # Getting the last 100 days
-
-            low_high_closing_df["rolling_avg_50d"] = low_high_closing_df['Adj Close'].rolling(50, min_periods = 50).mean()
-            low_high_closing_df["sd"] = low_high_closing_df["Adj Close"].rolling(50, min_periods = 50).std()
-            low_high_closing_df = low_high_closing_df.tail(50) # Keeping the last 50 days only
-
-            recent_data = low_high_closing_df.iloc[-1, :].to_list() # Creating a Series object with the most recent data (last row only)
-
-            upper_bound = recent_data[1] + 3*recent_data[2] # Upper Bound
-            lower_bound = recent_data[1] - 3*recent_data[2] # Lower Bound
-            mean_50d = recent_data[1] # Rolling average of last 50 days
-
-            # Finding the outlook dependent on the current price
-            if self.stock_data.current_price_getter(ticker) is None:
-               return "Market Closed"
-            else:
-                message = ""
-                if self.stock_data.current_price_getter(ticker) < mean_50d and self.stock_data.current_price_getter(ticker) >= lower_bound:
-                    message = "Buy"
-                elif self.stock_data.current_price_getter(ticker) > mean_50d and self.stock_data.current_price_getter(ticker) <= upper_bound:
-                    message = "Sell"
-                elif self.stock_data.current_price_getter(ticker) == mean_50d:
-                    message = "Hold"
-                elif self.stock_data.current_price_getter(ticker) <= lower_bound or self.stock_data.current_price_getter(ticker) >= upper_bound:
-                    message = "Unusual Event"
-                return message
-
-    def MACD(self, ticker):
-        '''
-        This method calculates the MACD (Mean Average Convergence Divergence) for a stock. The decision of whether to buy or sell
-        a stock when using this method, depends on the difference of two "lines". The 1st one is called "MACD" and is equal to the 
-        difference between the Exponential Moving Average of the adjusted closing price of the last 12 days, and the Moving Average
-        of the adjusted closing price of the last 26 days. The 2nd line is the 9 day moving average of the adj. closing price. 
-        When MACD > 9 day M.A. it is considered that there is an uptrend, else, an downtrend. 
-        At last, when MACD line crosses the 9 day M.A. from "above", a "Sell" signal is given, 
-        while it crosses it from below, a "Buy" signal is given.
-        '''
-        if self.stock_data.status_getter(ticker) != "Open":
-            return "Market Closed"
-        else:
-            data = self.stock_data.stock_data_getter(ticker)
-
-            low_high_closing_df = pd.DataFrame(data)
-            low_high_closing_df = data.iloc[:, 4:5]  # Getting only the "Adj Close" column
-            low_high_closing_df = low_high_closing_df.tail(52) # Getting the last 52 days
-
-
-            # Get the 12-day EMA of the closing price
-            low_high_closing_df['EMA_12d'] = low_high_closing_df['Adj Close'].ewm(span=12, adjust=False, min_periods=12).mean()
-            # Get the 26-day MA of the closing price
-            low_high_closing_df['MA_26d'] = low_high_closing_df['Adj Close'].ewm(span=26, adjust=False, min_periods=26).mean()
-            # Subtract the 26-day EMA from the 12-Day EMA to get the MACD
-            low_high_closing_df['MACD'] = low_high_closing_df['EMA_12d'] - low_high_closing_df['MA_26d']
-            # Making the signal line
-            low_high_closing_df['MA_9d'] = low_high_closing_df['MACD'].ewm(span=9, adjust=False, min_periods=9).mean()
-
-            low_high_closing_df['Diff'] = low_high_closing_df['MACD'] - low_high_closing_df['MA_9d'] 
-
-            Diff = low_high_closing_df['Diff'].astype(float)
-            
-            if self.stock_data.current_price_getter(ticker) is None:
-               return "Market Closed"
-            else:
-                message = ""
-
-                if Diff.iloc[-1] < 0:
-                    if Diff.iloc[-2] >= 0:
-                        message = "Downtrend and sell signal"
-                    else:
-                        message = "Downtrend and no signal"
-                else:
-                    if Diff.iloc[-2] <= 0:
-                        message = "Uptrend and buy signal"
-                    else:
-                        message = "Uptrend and no signal"
-                return message
-
-    def finbert_headlines_sentiment(self, ticker):
-        '''
-        This method uses a the "weights" and the "tokenizer" of a fine-tuned Fin-BERT model, which is a BERT model that 
-        was furtherly trained on financial data. The "article_parser()" method scraps www.marketwatch.com and returns the
-        last 17 headers of the chosen stock's articles. The, the FinBERT model classifies each one of them as either "Positive"
-        or "Negative" or "Neutral", and a score is assigned to each header (+100, -100, and 0) correspondingly. At last, a
-        rolling average of window size = 5 is used to "smooth" the sentiment line of the "plotly" plot that is returned.
-        '''
-
-        articles_df = self.stock_data.article_parser(ticker)
-        articles_list = articles_df["headline"].tolist()
-        
-        clf = pipeline("text-classification", model=model, tokenizer=tokenizer) 
-        outputs_list = clf(articles_list)
-        
-        sentiments = []
-
-        for item in outputs_list:
-            sentiments.append(item["label"])
-        
-        sentiments_df = pd.DataFrame(sentiments)
-        sentiments_df.rename(columns = {0:'sentiment'}, inplace = True)
-
-        sentiments_df["sentiment"] = sentiments_df["sentiment"].apply(lambda x: 100 if x == "positive" else -100 if x=="negative" else 0)            
-        sentiments_df["roll_avg"] = round(sentiments_df["sentiment"].rolling(5, min_periods = 1).mean(), 2)
-        sentiments_df = sentiments_df.tail(12).reset_index()
-
-        pd.options.plotting.backend = "plotly"
-
-        fig = sentiments_df["roll_avg"].plot(title="Sentiment Analysis of the last 12 www.marketwatch.com articles about " + ticker, 
-        
-        template="plotly_dark",
-        labels=dict(index="12 most recent article headlines", value="sentiment  score (rolling avg. of window size 5)"))
-        fig.update_traces(line=dict(color="#3D9140", width=3))
-        fig.update_layout(yaxis_range=[-100,100])
-        fig.update_layout(xaxis_range=[0,12])
-        fig.update_layout(showlegend=False)
-        fig.add_hline(y=0, line_width=1.5, line_color="black")
-       
-        current_sentiment = sentiments_df["roll_avg"].tail(1).values[0]
-
-        return {'fig': fig, 'current_sentiment': current_sentiment}
-
-    def LSTM_7_days_price_predictor(self, ticker):
-        '''
-        This method predicts the price of a chosen stock for the next 7 days as of today, by using the daily adjusted closing 
-        prices for the last 2 years. At first, a 60-day window of historical prices (i-60) is created as our feature data (x_train)
-        and the following 60-days window as label data (y_train). For every stock available, we have manually defined different 
-        parameters so that they fit as good as it gets to the model. Finally we combute the R2 metric and make the predictions. At 
-        last, we proceed with the predictions. The model looks back in our data (60 days back) and predicta for the following 7 days.
-        '''
-
-        stock_data = self.stock_data.LSTM_stock_data_getter(ticker)
-        stock_data=pd.DataFrame(data=stock_data).drop(['Open','High','Low','Close', 'Volume'],axis=1).reset_index()
-        stock_data['Date'] = pd.to_datetime(stock_data['Date'])
-        stock_data=stock_data.dropna()
-
-        # Data Preprocessing
-        random.seed(1997)
-        close_prices = stock_data['Adj Close']
-        values = close_prices.values
-        training_data_len = math.ceil(len(values)* 0.8)
-
-        scaler = MinMaxScaler(feature_range=(0,1))
-        scaled_data = scaler.fit_transform(values.reshape(-1,1))
-        train_data = scaled_data[0: training_data_len, :]
-
-        x_train = []
-        y_train = []
-
-        for i in range(60, len(train_data)):
-            x_train.append(train_data[i-60:i, 0])
-            y_train.append(train_data[i, 0])
-
-        x_train, y_train = np.array(x_train), np.array(y_train)
-        x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
-
-        # Preparation of test set
-        test_data = scaled_data[training_data_len-60: , : ]
-        x_test = []
-        y_test = values[training_data_len:]
-
-        for i in range(60, len(test_data)):
-            x_test.append(test_data[i-60:i, 0])
-
-        x_test = np.array(x_test)
-        x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
-
-        ##### Setting Up LSTM Network Architecture and the Training of the LSTM Model
-        def LSTM_trainer(seed, DROPOUT, LSTM_units,patience,batch_size, epochs):
-
-            tf.random.set_seed(seed)
-            DROPOUT = DROPOUT
-            global model_lstm
-            model_lstm = keras.Sequential()
-            model_lstm.add(layers.LSTM(LSTM_units, return_sequences=True, input_shape=(x_train.shape[1], 1)))
-            model_lstm.add(Dropout(rate=DROPOUT))
-            model_lstm.add(layers.LSTM(LSTM_units, return_sequences=False))
-            model_lstm.add(Dropout(rate=DROPOUT))
-            model_lstm.add(layers.Dense(25))
-            model_lstm.add(Dropout(rate=DROPOUT))
-            model_lstm.add(layers.Dense(1))
-            model_lstm.add(Activation('linear'))
-        
-            print('\n')
-            print("Compiling the LSTM Model for the "  + str(ticker) + " stock....\n")
-            t0 = time.time()
-            model_lstm.compile(optimizer='adam', loss='mean_squared_error',metrics=['mae'])
-            callback=EarlyStopping(monitor='val_loss',
-                              min_delta=0,
-                              patience=patience,
-                              verbose=1, mode='auto')
-            model_lstm.fit(x_train, 
-                          y_train,
-                          batch_size= batch_size, 
-                          epochs=epochs,
-                          validation_split=0.1,# ...holding out 10% of the data for validation 
-                          shuffle=True,verbose=0,callbacks=[callback])
-            t1 = time.time()
-            global ex_time
-            ex_time = round(t1-t0, 2)
-            print("Compiling took :",ex_time,"seconds")
-
-            predictions = model_lstm.predict(x_test)
-            predictions = scaler.inverse_transform(predictions)
-            #rmse = np.sqrt(np.mean(((predictions - y_test) ** 2)))
-            global r_squared_score 
-            global rmse
-            r_squared_score = round(r2_score(y_test, predictions),2)
-            rmse = np.sqrt(np.mean(((predictions - y_test) ** 2)))
-            #print('Rmse Score: ', round(rmse),2)
-            print('R2 Score: ', r_squared_score)
-
-        if ticker == 'AAPL':
-            LSTM_trainer(1, 0.2, 100,2, 20, 30)
-        elif ticker == 'NVDA':
-            LSTM_trainer(2, 0.2, 100,2, 30, 50)    
-        elif ticker == 'PYPL':
-            LSTM_trainer(6, 0.2, 100,10,25, 30)
-        elif ticker == 'MSFT':
-            LSTM_trainer(4, 0.1, 80, 2,20, 40)
-        elif ticker == 'TSLA':
-            LSTM_trainer(5, 0.1, 120, 4,20, 25)
-        elif ticker == 'AMZN':
-            LSTM_trainer(6, 0.1, 120,2, 20, 25)    
-        elif ticker == 'SPOT':
-            LSTM_trainer(9, 0.2, 200,5, 20, 40)
-        #elif ticker == 'TWTR' :
-         #   LSTM_trainer(15, 0.2, 100,4,20, 40)
-        elif ticker == 'UBER':
-            LSTM_trainer(15, 0.2, 100,7,20, 40)
-        elif ticker == 'ABNB':
-            LSTM_trainer(15, 0.2, 120,8,20, 40)
-        elif ticker == 'GOOG':
-            LSTM_trainer(15, 0.2, 100,3,20, 25)
-
-        # Unseen Predictions for the next 7 days 
-        close_data = scaled_data
-        look_back = 60
-
-        def predict(num_prediction, model):
-            prediction_list = close_data[-look_back:]
-
-            for _ in range(num_prediction):
-                x = prediction_list[-look_back:]
-                x = x.reshape((1, look_back, 1))
-                
-                out = model.predict(x)[0][0]
-                prediction_list = np.append(prediction_list, out)
-            prediction_list = prediction_list[look_back-1:]
-
-            return prediction_list
-    
-        def predict_dates(num_prediction):
-            last_date = stock_data['Date'].values[-1]
-            prediction_dates = pd.date_range(last_date, periods=num_prediction+1).tolist()
-            return prediction_dates
-
-        num_prediction = 7
-        
-        forecast = predict(num_prediction, model_lstm)
-        forecast_dates = predict_dates(num_prediction)
-    
-        plt.figure(figsize=(25,10))
-        forecast = forecast.reshape(-1, 1)
-        forecast_inverse = scaler.inverse_transform(forecast)
-
-        # Ploting the Actual Prices and the Predictions of them for the next 7 days
-        base = stock_data['Date'].iloc[[-1]] # Here we create our base date (the last existing date with actual prices)
-        testdata = pd.DataFrame(forecast_inverse)# Here we create a data frame that contains the prediction prices and an empty column for their dates
-        testdata['Date'] = ""
-        testdata.columns = ["Adj Close","Date"]
-        testdata = testdata.iloc[1:,:]
-        testdata["Label"] = "" # Let's add a column "Label" that would show if the respective price is a prediction or not
-        testdata["Label"] = "Prediction"
-        testdata = testdata[["Date", "Adj Close", "Label"]]
-
-        date_list = [base + datetime.timedelta(days=x+1) for x in range(testdata.shape[0]+1)] 
-        date_list = pd.DataFrame(date_list)
-        date_list.columns = ["Date"]
-        date_list.reset_index(inplace = True)
-        date_list.drop(["index"], axis = 1, inplace = True)
-        date_list.index = date_list.index + 1
-        testdata.Date = date_list
-
-        stock_data["Label"] = ""
-        stock_data["Label"] = "Actual price"
-        finaldf = pd.concat([stock_data,testdata], axis=0) # Here we concatenate the "testdata" and the original data frame "df" into a final one
-        finaldf.reset_index(inplace = True)
-        finaldf.drop(["index"], axis = 1, inplace = True)
-        finaldf['Date'] = pd.to_datetime(finaldf['Date'])
-    
-        plt.rcParams["figure.figsize"] = (25,10)
-        #We create two different data frames, one that contains the actual prices and one that has only the predictions
-        finaldfPredictions = finaldf.iloc[-8:] 
-        finaldfActuals = finaldf.iloc[:-7]
-
-        plot_1 = go.Scatter(
-            x = finaldfActuals['Date'],
-            y = finaldfActuals['Adj Close'],
-            mode = 'lines',
-            name = 'Historical Data (2 years)',
-            line=dict(width=1,color='#3D9140'))
-        plot_2 = go.Scatter(
-            x = finaldfPredictions['Date'],
-            y = finaldfPredictions['Adj Close'],
-            mode = 'lines',
-            name = '7-day Prediction',
-            line=dict(width=1,color="#EE3B3B"))
-        plot_3 = go.Scatter(
-            x = finaldfPredictions['Date'][:1],
-            y = finaldfPredictions['Adj Close'][:1],
-            mode = 'markers',
-            name = 'Latest Actual Closing Price',
-            line=dict(width=1))
-
-        layout = go.Layout(
-            title = 'Next 7 days stock price prediction of ' + str(ticker),
-            xaxis = {'title' : "Date"},
-            yaxis = {'title' : "Price ($)"}
-        )
-        fig = go.Figure(data=[plot_1, plot_2,plot_3], layout=layout)
-        fig.update_layout(template='plotly_dark',autosize=True)
-        fig.update_layout(legend=dict(
-            orientation="h",
-            yanchor="bottom",
-            y=1.02,
-            xanchor="right",
-            x=1),
-            annotations = [dict(x=0.5,
-                                y=0, 
-                                xref='paper',
-                                yref='paper',
-                                text="Current In Sample R- Squared : " + str(r_squared_score*100) + " % \n",
-                                showarrow = False)],
-            xaxis=dict(showgrid=False),
-            yaxis=dict(showgrid=False)
-            
-
-                        )
-        fig.add_annotation(x=0.5, 
-                           y=0.05,
-                           xref='paper',
-                           yref='paper',
-                           text="Current In Sample Root Mean Square Error : " + str(round(rmse,2)) + " % ",
-                           showarrow=False)
-        
-        return fig
diff --git a/spaces/antonovmaxim/text-generation-webui-space/text-generation-webui-main/extensions/whisper_stt/script.py b/spaces/antonovmaxim/text-generation-webui-space/text-generation-webui-main/extensions/whisper_stt/script.py
deleted file mode 100644
index 9daee7be147c55848fb0b620bc34de133869672b..0000000000000000000000000000000000000000
--- a/spaces/antonovmaxim/text-generation-webui-space/text-generation-webui-main/extensions/whisper_stt/script.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import gradio as gr
-import speech_recognition as sr
-from modules import shared
-
-input_hijack = {
-    'state': False,
-    'value': ["", ""]
-}
-
-
-def do_stt(audio):
-    transcription = ""
-    r = sr.Recognizer()
-
-    # Convert to AudioData
-    audio_data = sr.AudioData(sample_rate=audio[0], frame_data=audio[1], sample_width=4)
-
-    try:
-        transcription = r.recognize_whisper(audio_data, language="english", model="base.en")
-    except sr.UnknownValueError:
-        print("Whisper could not understand audio")
-    except sr.RequestError as e:
-        print("Could not request results from Whisper", e)
-
-    return transcription
-
-
-def auto_transcribe(audio, auto_submit):
-    if audio is None:
-        return "", ""
-
-    transcription = do_stt(audio)
-    if auto_submit:
-        input_hijack.update({"state": True, "value": [transcription, transcription]})
-
-    return transcription, None
-
-
-def ui():
-    with gr.Row():
-        audio = gr.Audio(source="microphone")
-        auto_submit = gr.Checkbox(label='Submit the transcribed audio automatically', value=True)
-
-    audio.change(
-        auto_transcribe, [audio, auto_submit], [shared.gradio['textbox'], audio]).then(
-        None, auto_submit, None, _js="(check) => {if (check) { document.getElementById('Generate').click() }}")
diff --git a/spaces/apozzuoli98/shark-or-whale-classifier/README.md b/spaces/apozzuoli98/shark-or-whale-classifier/README.md
deleted file mode 100644
index 99f60b3c86f5a0b470eb2a4f9813294b2718b85e..0000000000000000000000000000000000000000
--- a/spaces/apozzuoli98/shark-or-whale-classifier/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Shark Or Whale Classifier
-emoji: ⚡
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 3.32.0
-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/arnavkartikeya/SCRIPture-final/data/coco_karpathy_dataset.py b/spaces/arnavkartikeya/SCRIPture-final/data/coco_karpathy_dataset.py
deleted file mode 100644
index a34d29205f42aa09695b160ac9c91958ba041bb3..0000000000000000000000000000000000000000
--- a/spaces/arnavkartikeya/SCRIPture-final/data/coco_karpathy_dataset.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import os
-import json
-
-from torch.utils.data import Dataset
-from torchvision.datasets.utils import download_url
-
-from PIL import Image
-
-from data.utils import pre_caption
-
-class coco_karpathy_train(Dataset):
-    def __init__(self, transform, image_root, ann_root, max_words=30, prompt=''):        
-        '''
-        image_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        '''        
-        url = 'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json'
-        filename = 'coco_karpathy_train.json'
-
-        download_url(url,ann_root)
-        
-        self.annotation = json.load(open(os.path.join(ann_root,filename),'r'))
-        self.transform = transform
-        self.image_root = image_root
-        self.max_words = max_words      
-        self.prompt = prompt
-        
-        self.img_ids = {}  
-        n = 0
-        for ann in self.annotation:
-            img_id = ann['image_id']
-            if img_id not in self.img_ids.keys():
-                self.img_ids[img_id] = n
-                n += 1    
-        
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        ann = self.annotation[index]
-        
-        image_path = os.path.join(self.image_root,ann['image'])        
-        image = Image.open(image_path).convert('RGB')   
-        image = self.transform(image)
-        
-        caption = self.prompt+pre_caption(ann['caption'], self.max_words) 
-
-        return image, caption, self.img_ids[ann['image_id']] 
-    
-    
-class coco_karpathy_caption_eval(Dataset):
-    def __init__(self, transform, image_root, ann_root, split):  
-        '''
-        image_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        split (string): val or test
-        '''
-        urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json',
-                'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json'}
-        filenames = {'val':'coco_karpathy_val.json','test':'coco_karpathy_test.json'}
-        
-        download_url(urls[split],ann_root)
-        
-        self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r'))
-        self.transform = transform
-        self.image_root = image_root
-        
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        ann = self.annotation[index]
-        
-        image_path = os.path.join(self.image_root,ann['image'])        
-        image = Image.open(image_path).convert('RGB')   
-        image = self.transform(image)          
-        
-        img_id = ann['image'].split('/')[-1].strip('.jpg').split('_')[-1]
-        
-        return image, int(img_id)   
-    
-    
-class coco_karpathy_retrieval_eval(Dataset):
-    def __init__(self, transform, image_root, ann_root, split, max_words=30):  
-        '''
-        image_root (string): Root directory of images (e.g. coco/images/)
-        ann_root (string): directory to store the annotation file
-        split (string): val or test
-        '''
-        urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json',
-                'test':'https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json'}
-        filenames = {'val':'coco_karpathy_val.json','test':'coco_karpathy_test.json'}
-        
-        download_url(urls[split],ann_root)
-        
-        self.annotation = json.load(open(os.path.join(ann_root,filenames[split]),'r'))
-        self.transform = transform
-        self.image_root = image_root
-        
-        self.text = []
-        self.image = []
-        self.txt2img = {}
-        self.img2txt = {}
-        
-        txt_id = 0
-        for img_id, ann in enumerate(self.annotation):
-            self.image.append(ann['image'])
-            self.img2txt[img_id] = []
-            for i, caption in enumerate(ann['caption']):
-                self.text.append(pre_caption(caption,max_words))
-                self.img2txt[img_id].append(txt_id)
-                self.txt2img[txt_id] = img_id
-                txt_id += 1
-                                    
-    def __len__(self):
-        return len(self.annotation)
-    
-    def __getitem__(self, index):    
-        
-        image_path = os.path.join(self.image_root, self.annotation[index]['image'])        
-        image = Image.open(image_path).convert('RGB')    
-        image = self.transform(image)  
-
-        return image, index
\ No newline at end of file
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_tacotron_layers.py b/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_tacotron_layers.py
deleted file mode 100644
index 43e72417c200493c6392b3acb131a43738dad2bd..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_tacotron_layers.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import unittest
-
-import torch as T
-
-from TTS.tts.layers.tacotron.tacotron import CBHG, Decoder, Encoder, Prenet
-
-# pylint: disable=unused-variable
-
-
-class PrenetTests(unittest.TestCase):
-    def test_in_out(self):  # pylint: disable=no-self-use
-        layer = Prenet(128, out_features=[256, 128])
-        dummy_input = T.rand(4, 128)
-
-        print(layer)
-        output = layer(dummy_input)
-        assert output.shape[0] == 4
-        assert output.shape[1] == 128
-
-
-class CBHGTests(unittest.TestCase):
-    def test_in_out(self):
-        # pylint: disable=attribute-defined-outside-init
-        layer = self.cbhg = CBHG(
-            128,
-            K=8,
-            conv_bank_features=80,
-            conv_projections=[160, 128],
-            highway_features=80,
-            gru_features=80,
-            num_highways=4,
-        )
-        # B x D x T
-        dummy_input = T.rand(4, 128, 8)
-
-        print(layer)
-        output = layer(dummy_input)
-        assert output.shape[0] == 4
-        assert output.shape[1] == 8
-        assert output.shape[2] == 160
-
-
-class DecoderTests(unittest.TestCase):
-    @staticmethod
-    def test_in_out():
-        layer = Decoder(
-            in_channels=256,
-            frame_channels=80,
-            r=2,
-            memory_size=4,
-            attn_windowing=False,
-            attn_norm="sigmoid",
-            attn_K=5,
-            attn_type="original",
-            prenet_type="original",
-            prenet_dropout=True,
-            forward_attn=True,
-            trans_agent=True,
-            forward_attn_mask=True,
-            location_attn=True,
-            separate_stopnet=True,
-            max_decoder_steps=50,
-        )
-        dummy_input = T.rand(4, 8, 256)
-        dummy_memory = T.rand(4, 2, 80)
-
-        output, alignment, stop_tokens = layer(dummy_input, dummy_memory, mask=None)
-
-        assert output.shape[0] == 4
-        assert output.shape[1] == 80, "size not {}".format(output.shape[1])
-        assert output.shape[2] == 2, "size not {}".format(output.shape[2])
-        assert stop_tokens.shape[0] == 4
-
-
-class EncoderTests(unittest.TestCase):
-    def test_in_out(self):  # pylint: disable=no-self-use
-        layer = Encoder(128)
-        dummy_input = T.rand(4, 8, 128)
-
-        print(layer)
-        output = layer(dummy_input)
-        print(output.shape)
-        assert output.shape[0] == 4
-        assert output.shape[1] == 8
-        assert output.shape[2] == 256  # 128 * 2 BiRNN
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/TypeInference.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/TypeInference.py
deleted file mode 100644
index c7ffee7d2442693e17c240eb5d52c25ce4a92230..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Compiler/TypeInference.py
+++ /dev/null
@@ -1,591 +0,0 @@
-from __future__ import absolute_import
-
-from .Errors import error, message
-from . import ExprNodes
-from . import Nodes
-from . import Builtin
-from . import PyrexTypes
-from .. import Utils
-from .PyrexTypes import py_object_type, unspecified_type
-from .Visitor import CythonTransform, EnvTransform
-
-try:
-    reduce
-except NameError:
-    from functools import reduce
-
-
-class TypedExprNode(ExprNodes.ExprNode):
-    # Used for declaring assignments of a specified type without a known entry.
-    subexprs = []
-
-    def __init__(self, type, pos=None):
-        super(TypedExprNode, self).__init__(pos, type=type)
-
-object_expr = TypedExprNode(py_object_type)
-
-
-class MarkParallelAssignments(EnvTransform):
-    # Collects assignments inside parallel blocks prange, with parallel.
-    # Perhaps it's better to move it to ControlFlowAnalysis.
-
-    # tells us whether we're in a normal loop
-    in_loop = False
-
-    parallel_errors = False
-
-    def __init__(self, context):
-        # Track the parallel block scopes (with parallel, for i in prange())
-        self.parallel_block_stack = []
-        super(MarkParallelAssignments, self).__init__(context)
-
-    def mark_assignment(self, lhs, rhs, inplace_op=None):
-        if isinstance(lhs, (ExprNodes.NameNode, Nodes.PyArgDeclNode)):
-            if lhs.entry is None:
-                # TODO: This shouldn't happen...
-                return
-
-            if self.parallel_block_stack:
-                parallel_node = self.parallel_block_stack[-1]
-                previous_assignment = parallel_node.assignments.get(lhs.entry)
-
-                # If there was a previous assignment to the variable, keep the
-                # previous assignment position
-                if previous_assignment:
-                    pos, previous_inplace_op = previous_assignment
-
-                    if (inplace_op and previous_inplace_op and
-                            inplace_op != previous_inplace_op):
-                        # x += y; x *= y
-                        t = (inplace_op, previous_inplace_op)
-                        error(lhs.pos,
-                              "Reduction operator '%s' is inconsistent "
-                              "with previous reduction operator '%s'" % t)
-                else:
-                    pos = lhs.pos
-
-                parallel_node.assignments[lhs.entry] = (pos, inplace_op)
-                parallel_node.assigned_nodes.append(lhs)
-
-        elif isinstance(lhs, ExprNodes.SequenceNode):
-            for i, arg in enumerate(lhs.args):
-                if not rhs or arg.is_starred:
-                    item_node = None
-                else:
-                    item_node = rhs.inferable_item_node(i)
-                self.mark_assignment(arg, item_node)
-        else:
-            # Could use this info to infer cdef class attributes...
-            pass
-
-    def visit_WithTargetAssignmentStatNode(self, node):
-        self.mark_assignment(node.lhs, node.with_node.enter_call)
-        self.visitchildren(node)
-        return node
-
-    def visit_SingleAssignmentNode(self, node):
-        self.mark_assignment(node.lhs, node.rhs)
-        self.visitchildren(node)
-        return node
-
-    def visit_CascadedAssignmentNode(self, node):
-        for lhs in node.lhs_list:
-            self.mark_assignment(lhs, node.rhs)
-        self.visitchildren(node)
-        return node
-
-    def visit_InPlaceAssignmentNode(self, node):
-        self.mark_assignment(node.lhs, node.create_binop_node(), node.operator)
-        self.visitchildren(node)
-        return node
-
-    def visit_ForInStatNode(self, node):
-        # TODO: Remove redundancy with range optimization...
-        is_special = False
-        sequence = node.iterator.sequence
-        target = node.target
-        if isinstance(sequence, ExprNodes.SimpleCallNode):
-            function = sequence.function
-            if sequence.self is None and function.is_name:
-                entry = self.current_env().lookup(function.name)
-                if not entry or entry.is_builtin:
-                    if function.name == 'reversed' and len(sequence.args) == 1:
-                        sequence = sequence.args[0]
-                    elif function.name == 'enumerate' and len(sequence.args) == 1:
-                        if target.is_sequence_constructor and len(target.args) == 2:
-                            iterator = sequence.args[0]
-                            if iterator.is_name:
-                                iterator_type = iterator.infer_type(self.current_env())
-                                if iterator_type.is_builtin_type:
-                                    # assume that builtin types have a length within Py_ssize_t
-                                    self.mark_assignment(
-                                        target.args[0],
-                                        ExprNodes.IntNode(target.pos, value='PY_SSIZE_T_MAX',
-                                                          type=PyrexTypes.c_py_ssize_t_type))
-                                    target = target.args[1]
-                                    sequence = sequence.args[0]
-        if isinstance(sequence, ExprNodes.SimpleCallNode):
-            function = sequence.function
-            if sequence.self is None and function.is_name:
-                entry = self.current_env().lookup(function.name)
-                if not entry or entry.is_builtin:
-                    if function.name in ('range', 'xrange'):
-                        is_special = True
-                        for arg in sequence.args[:2]:
-                            self.mark_assignment(target, arg)
-                        if len(sequence.args) > 2:
-                            self.mark_assignment(
-                                target,
-                                ExprNodes.binop_node(node.pos,
-                                                     '+',
-                                                     sequence.args[0],
-                                                     sequence.args[2]))
-
-        if not is_special:
-            # A for-loop basically translates to subsequent calls to
-            # __getitem__(), so using an IndexNode here allows us to
-            # naturally infer the base type of pointers, C arrays,
-            # Python strings, etc., while correctly falling back to an
-            # object type when the base type cannot be handled.
-            self.mark_assignment(target, ExprNodes.IndexNode(
-                node.pos,
-                base=sequence,
-                index=ExprNodes.IntNode(target.pos, value='PY_SSIZE_T_MAX',
-                                        type=PyrexTypes.c_py_ssize_t_type)))
-
-        self.visitchildren(node)
-        return node
-
-    def visit_ForFromStatNode(self, node):
-        self.mark_assignment(node.target, node.bound1)
-        if node.step is not None:
-            self.mark_assignment(node.target,
-                    ExprNodes.binop_node(node.pos,
-                                         '+',
-                                         node.bound1,
-                                         node.step))
-        self.visitchildren(node)
-        return node
-
-    def visit_WhileStatNode(self, node):
-        self.visitchildren(node)
-        return node
-
-    def visit_ExceptClauseNode(self, node):
-        if node.target is not None:
-            self.mark_assignment(node.target, object_expr)
-        self.visitchildren(node)
-        return node
-
-    def visit_FromCImportStatNode(self, node):
-        pass # Can't be assigned to...
-
-    def visit_FromImportStatNode(self, node):
-        for name, target in node.items:
-            if name != "*":
-                self.mark_assignment(target, object_expr)
-        self.visitchildren(node)
-        return node
-
-    def visit_DefNode(self, node):
-        # use fake expressions with the right result type
-        if node.star_arg:
-            self.mark_assignment(
-                node.star_arg, TypedExprNode(Builtin.tuple_type, node.pos))
-        if node.starstar_arg:
-            self.mark_assignment(
-                node.starstar_arg, TypedExprNode(Builtin.dict_type, node.pos))
-        EnvTransform.visit_FuncDefNode(self, node)
-        return node
-
-    def visit_DelStatNode(self, node):
-        for arg in node.args:
-            self.mark_assignment(arg, arg)
-        self.visitchildren(node)
-        return node
-
-    def visit_ParallelStatNode(self, node):
-        if self.parallel_block_stack:
-            node.parent = self.parallel_block_stack[-1]
-        else:
-            node.parent = None
-
-        nested = False
-        if node.is_prange:
-            if not node.parent:
-                node.is_parallel = True
-            else:
-                node.is_parallel = (node.parent.is_prange or not
-                                    node.parent.is_parallel)
-                nested = node.parent.is_prange
-        else:
-            node.is_parallel = True
-            # Note: nested with parallel() blocks are handled by
-            # ParallelRangeTransform!
-            # nested = node.parent
-            nested = node.parent and node.parent.is_prange
-
-        self.parallel_block_stack.append(node)
-
-        nested = nested or len(self.parallel_block_stack) > 2
-        if not self.parallel_errors and nested and not node.is_prange:
-            error(node.pos, "Only prange() may be nested")
-            self.parallel_errors = True
-
-        if node.is_prange:
-            child_attrs = node.child_attrs
-            node.child_attrs = ['body', 'target', 'args']
-            self.visitchildren(node)
-            node.child_attrs = child_attrs
-
-            self.parallel_block_stack.pop()
-            if node.else_clause:
-                node.else_clause = self.visit(node.else_clause)
-        else:
-            self.visitchildren(node)
-            self.parallel_block_stack.pop()
-
-        self.parallel_errors = False
-        return node
-
-    def visit_YieldExprNode(self, node):
-        if self.parallel_block_stack:
-            error(node.pos, "'%s' not allowed in parallel sections" % node.expr_keyword)
-        return node
-
-    def visit_ReturnStatNode(self, node):
-        node.in_parallel = bool(self.parallel_block_stack)
-        return node
-
-
-class MarkOverflowingArithmetic(CythonTransform):
-
-    # It may be possible to integrate this with the above for
-    # performance improvements (though likely not worth it).
-
-    might_overflow = False
-
-    def __call__(self, root):
-        self.env_stack = []
-        self.env = root.scope
-        return super(MarkOverflowingArithmetic, self).__call__(root)
-
-    def visit_safe_node(self, node):
-        self.might_overflow, saved = False, self.might_overflow
-        self.visitchildren(node)
-        self.might_overflow = saved
-        return node
-
-    def visit_neutral_node(self, node):
-        self.visitchildren(node)
-        return node
-
-    def visit_dangerous_node(self, node):
-        self.might_overflow, saved = True, self.might_overflow
-        self.visitchildren(node)
-        self.might_overflow = saved
-        return node
-
-    def visit_FuncDefNode(self, node):
-        self.env_stack.append(self.env)
-        self.env = node.local_scope
-        self.visit_safe_node(node)
-        self.env = self.env_stack.pop()
-        return node
-
-    def visit_NameNode(self, node):
-        if self.might_overflow:
-            entry = node.entry or self.env.lookup(node.name)
-            if entry:
-                entry.might_overflow = True
-        return node
-
-    def visit_BinopNode(self, node):
-        if node.operator in '&|^':
-            return self.visit_neutral_node(node)
-        else:
-            return self.visit_dangerous_node(node)
-
-    def visit_SimpleCallNode(self, node):
-        if node.function.is_name and node.function.name == 'abs':
-          # Overflows for minimum value of fixed size ints.
-          return self.visit_dangerous_node(node)
-        else:
-          return self.visit_neutral_node(node)
-
-    visit_UnopNode = visit_neutral_node
-
-    visit_UnaryMinusNode = visit_dangerous_node
-
-    visit_InPlaceAssignmentNode = visit_dangerous_node
-
-    visit_Node = visit_safe_node
-
-    def visit_assignment(self, lhs, rhs):
-        if (isinstance(rhs, ExprNodes.IntNode)
-                and isinstance(lhs, ExprNodes.NameNode)
-                and Utils.long_literal(rhs.value)):
-            entry = lhs.entry or self.env.lookup(lhs.name)
-            if entry:
-                entry.might_overflow = True
-
-    def visit_SingleAssignmentNode(self, node):
-        self.visit_assignment(node.lhs, node.rhs)
-        self.visitchildren(node)
-        return node
-
-    def visit_CascadedAssignmentNode(self, node):
-        for lhs in node.lhs_list:
-            self.visit_assignment(lhs, node.rhs)
-        self.visitchildren(node)
-        return node
-
-class PyObjectTypeInferer(object):
-    """
-    If it's not declared, it's a PyObject.
-    """
-    def infer_types(self, scope):
-        """
-        Given a dict of entries, map all unspecified types to a specified type.
-        """
-        for name, entry in scope.entries.items():
-            if entry.type is unspecified_type:
-                entry.type = py_object_type
-
-class SimpleAssignmentTypeInferer(object):
-    """
-    Very basic type inference.
-
-    Note: in order to support cross-closure type inference, this must be
-    applies to nested scopes in top-down order.
-    """
-    def set_entry_type(self, entry, entry_type):
-        entry.type = entry_type
-        for e in entry.all_entries():
-            e.type = entry_type
-
-    def infer_types(self, scope):
-        enabled = scope.directives['infer_types']
-        verbose = scope.directives['infer_types.verbose']
-
-        if enabled == True:
-            spanning_type = aggressive_spanning_type
-        elif enabled is None: # safe mode
-            spanning_type = safe_spanning_type
-        else:
-            for entry in scope.entries.values():
-                if entry.type is unspecified_type:
-                    self.set_entry_type(entry, py_object_type)
-            return
-
-        # Set of assignments
-        assignments = set()
-        assmts_resolved = set()
-        dependencies = {}
-        assmt_to_names = {}
-
-        for name, entry in scope.entries.items():
-            for assmt in entry.cf_assignments:
-                names = assmt.type_dependencies()
-                assmt_to_names[assmt] = names
-                assmts = set()
-                for node in names:
-                    assmts.update(node.cf_state)
-                dependencies[assmt] = assmts
-            if entry.type is unspecified_type:
-                assignments.update(entry.cf_assignments)
-            else:
-                assmts_resolved.update(entry.cf_assignments)
-
-        def infer_name_node_type(node):
-            types = [assmt.inferred_type for assmt in node.cf_state]
-            if not types:
-                node_type = py_object_type
-            else:
-                entry = node.entry
-                node_type = spanning_type(
-                    types, entry.might_overflow, entry.pos, scope)
-            node.inferred_type = node_type
-
-        def infer_name_node_type_partial(node):
-            types = [assmt.inferred_type for assmt in node.cf_state
-                     if assmt.inferred_type is not None]
-            if not types:
-                return
-            entry = node.entry
-            return spanning_type(types, entry.might_overflow, entry.pos, scope)
-
-        def inferred_types(entry):
-            has_none = False
-            has_pyobjects = False
-            types = []
-            for assmt in entry.cf_assignments:
-                if assmt.rhs.is_none:
-                    has_none = True
-                else:
-                    rhs_type = assmt.inferred_type
-                    if rhs_type and rhs_type.is_pyobject:
-                        has_pyobjects = True
-                    types.append(rhs_type)
-            # Ignore None assignments as long as there are concrete Python type assignments.
-            # but include them if None is the only assigned Python object.
-            if has_none and not has_pyobjects:
-                types.append(py_object_type)
-            return types
-
-        def resolve_assignments(assignments):
-            resolved = set()
-            for assmt in assignments:
-                deps = dependencies[assmt]
-                # All assignments are resolved
-                if assmts_resolved.issuperset(deps):
-                    for node in assmt_to_names[assmt]:
-                        infer_name_node_type(node)
-                    # Resolve assmt
-                    inferred_type = assmt.infer_type()
-                    assmts_resolved.add(assmt)
-                    resolved.add(assmt)
-            assignments.difference_update(resolved)
-            return resolved
-
-        def partial_infer(assmt):
-            partial_types = []
-            for node in assmt_to_names[assmt]:
-                partial_type = infer_name_node_type_partial(node)
-                if partial_type is None:
-                    return False
-                partial_types.append((node, partial_type))
-            for node, partial_type in partial_types:
-                node.inferred_type = partial_type
-            assmt.infer_type()
-            return True
-
-        partial_assmts = set()
-        def resolve_partial(assignments):
-            # try to handle circular references
-            partials = set()
-            for assmt in assignments:
-                if assmt in partial_assmts:
-                    continue
-                if partial_infer(assmt):
-                    partials.add(assmt)
-                    assmts_resolved.add(assmt)
-            partial_assmts.update(partials)
-            return partials
-
-        # Infer assignments
-        while True:
-            if not resolve_assignments(assignments):
-                if not resolve_partial(assignments):
-                    break
-        inferred = set()
-        # First pass
-        for entry in scope.entries.values():
-            if entry.type is not unspecified_type:
-                continue
-            entry_type = py_object_type
-            if assmts_resolved.issuperset(entry.cf_assignments):
-                types = inferred_types(entry)
-                if types and all(types):
-                    entry_type = spanning_type(
-                        types, entry.might_overflow, entry.pos, scope)
-                    inferred.add(entry)
-            self.set_entry_type(entry, entry_type)
-
-        def reinfer():
-            dirty = False
-            for entry in inferred:
-                for assmt in entry.cf_assignments:
-                    assmt.infer_type()
-                types = inferred_types(entry)
-                new_type = spanning_type(types, entry.might_overflow, entry.pos, scope)
-                if new_type != entry.type:
-                    self.set_entry_type(entry, new_type)
-                    dirty = True
-            return dirty
-
-        # types propagation
-        while reinfer():
-            pass
-
-        if verbose:
-            for entry in inferred:
-                message(entry.pos, "inferred '%s' to be of type '%s'" % (
-                    entry.name, entry.type))
-
-
-def find_spanning_type(type1, type2):
-    if type1 is type2:
-        result_type = type1
-    elif type1 is PyrexTypes.c_bint_type or type2 is PyrexTypes.c_bint_type:
-        # type inference can break the coercion back to a Python bool
-        # if it returns an arbitrary int type here
-        return py_object_type
-    else:
-        result_type = PyrexTypes.spanning_type(type1, type2)
-    if result_type in (PyrexTypes.c_double_type, PyrexTypes.c_float_type,
-                       Builtin.float_type):
-        # Python's float type is just a C double, so it's safe to
-        # use the C type instead
-        return PyrexTypes.c_double_type
-    return result_type
-
-def simply_type(result_type, pos):
-    if result_type.is_reference:
-        result_type = result_type.ref_base_type
-    if result_type.is_const:
-        result_type = result_type.const_base_type
-    if result_type.is_cpp_class:
-        result_type.check_nullary_constructor(pos)
-    if result_type.is_array:
-        result_type = PyrexTypes.c_ptr_type(result_type.base_type)
-    return result_type
-
-def aggressive_spanning_type(types, might_overflow, pos, scope):
-    return simply_type(reduce(find_spanning_type, types), pos)
-
-def safe_spanning_type(types, might_overflow, pos, scope):
-    result_type = simply_type(reduce(find_spanning_type, types), pos)
-    if result_type.is_pyobject:
-        # In theory, any specific Python type is always safe to
-        # infer. However, inferring str can cause some existing code
-        # to break, since we are also now much more strict about
-        # coercion from str to char *. See trac #553.
-        if result_type.name == 'str':
-            return py_object_type
-        else:
-            return result_type
-    elif result_type is PyrexTypes.c_double_type:
-        # Python's float type is just a C double, so it's safe to use
-        # the C type instead
-        return result_type
-    elif result_type is PyrexTypes.c_bint_type:
-        # find_spanning_type() only returns 'bint' for clean boolean
-        # operations without other int types, so this is safe, too
-        return result_type
-    elif result_type.is_pythran_expr:
-        return result_type
-    elif result_type.is_ptr:
-        # Any pointer except (signed|unsigned|) char* can't implicitly
-        # become a PyObject, and inferring char* is now accepted, too.
-        return result_type
-    elif result_type.is_cpp_class:
-        # These can't implicitly become Python objects either.
-        return result_type
-    elif result_type.is_struct:
-        # Though we have struct -> object for some structs, this is uncommonly
-        # used, won't arise in pure Python, and there shouldn't be side
-        # effects, so I'm declaring this safe.
-        return result_type
-    # TODO: double complex should be OK as well, but we need
-    # to make sure everything is supported.
-    elif (result_type.is_int or result_type.is_enum) and not might_overflow:
-        return result_type
-    elif (not result_type.can_coerce_to_pyobject(scope)
-            and not result_type.is_error):
-        return result_type
-    return py_object_type
-
-
-def get_type_inferer():
-    return SimpleAssignmentTypeInferer()
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/atn/LexerAction.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/atn/LexerAction.py
deleted file mode 100644
index 5d11f21ae2c5f640780bd32d9aca568eedbdd60b..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/antlr4/atn/LexerAction.py
+++ /dev/null
@@ -1,291 +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.
- #
-
-from enum import IntEnum
-
-# need forward declaration
-Lexer = None
-
-
-class LexerActionType(IntEnum):
-
-    CHANNEL = 0     #The type of a {@link LexerChannelAction} action.
-    CUSTOM = 1      #The type of a {@link LexerCustomAction} action.
-    MODE = 2        #The type of a {@link LexerModeAction} action.
-    MORE = 3        #The type of a {@link LexerMoreAction} action.
-    POP_MODE = 4    #The type of a {@link LexerPopModeAction} action.
-    PUSH_MODE = 5   #The type of a {@link LexerPushModeAction} action.
-    SKIP = 6        #The type of a {@link LexerSkipAction} action.
-    TYPE = 7        #The type of a {@link LexerTypeAction} action.
-
-class LexerAction(object):
-
-    def __init__(self, action:LexerActionType):
-        self.actionType = action
-        self.isPositionDependent = False
-
-    def __hash__(self):
-        return hash(self.actionType)
-
-    def __eq__(self, other):
-        return self is other
-
-
-#
-# Implements the {@code skip} lexer action by calling {@link Lexer#skip}.
-#
-# <p>The {@code skip} command does not have any parameters, so this action is
-# implemented as a singleton instance exposed by {@link #INSTANCE}.</p>
-class LexerSkipAction(LexerAction ):
-
-    # Provides a singleton instance of this parameterless lexer action.
-    INSTANCE = None
-
-    def __init__(self):
-        super().__init__(LexerActionType.SKIP)
-
-    def execute(self, lexer:Lexer):
-        lexer.skip()
-
-    def __str__(self):
-        return "skip"
-
-LexerSkipAction.INSTANCE = LexerSkipAction()
-
-#  Implements the {@code type} lexer action by calling {@link Lexer#setType}
-# with the assigned type.
-class LexerTypeAction(LexerAction):
-
-    def __init__(self, type:int):
-        super().__init__(LexerActionType.TYPE)
-        self.type = type
-
-    def execute(self, lexer:Lexer):
-        lexer.type = self.type
-
-    def __hash__(self):
-        return hash((self.actionType, self.type))
-
-    def __eq__(self, other):
-        if self is other:
-            return True
-        elif not isinstance(other, LexerTypeAction):
-            return False
-        else:
-            return self.type == other.type
-
-    def __str__(self):
-        return "type(" + str(self.type) + ")"
-
-
-# Implements the {@code pushMode} lexer action by calling
-# {@link Lexer#pushMode} with the assigned mode.
-class LexerPushModeAction(LexerAction):
-
-    def __init__(self, mode:int):
-        super().__init__(LexerActionType.PUSH_MODE)
-        self.mode = mode
-
-    # <p>This action is implemented by calling {@link Lexer#pushMode} with the
-    # value provided by {@link #getMode}.</p>
-    def execute(self, lexer:Lexer):
-        lexer.pushMode(self.mode)
-
-    def __hash__(self):
-        return hash((self.actionType, self.mode))
-
-    def __eq__(self, other):
-        if self is other:
-            return True
-        elif not isinstance(other, LexerPushModeAction):
-            return False
-        else:
-            return self.mode == other.mode
-
-    def __str__(self):
-        return "pushMode(" + str(self.mode) + ")"
-
-
-# Implements the {@code popMode} lexer action by calling {@link Lexer#popMode}.
-#
-# <p>The {@code popMode} command does not have any parameters, so this action is
-# implemented as a singleton instance exposed by {@link #INSTANCE}.</p>
-class LexerPopModeAction(LexerAction):
-
-    INSTANCE = None
-
-    def __init__(self):
-        super().__init__(LexerActionType.POP_MODE)
-
-    # <p>This action is implemented by calling {@link Lexer#popMode}.</p>
-    def execute(self, lexer:Lexer):
-        lexer.popMode()
-
-    def __str__(self):
-        return "popMode"
-
-LexerPopModeAction.INSTANCE = LexerPopModeAction()
-
-# Implements the {@code more} lexer action by calling {@link Lexer#more}.
-#
-# <p>The {@code more} command does not have any parameters, so this action is
-# implemented as a singleton instance exposed by {@link #INSTANCE}.</p>
-class LexerMoreAction(LexerAction):
-
-    INSTANCE = None
-
-    def __init__(self):
-        super().__init__(LexerActionType.MORE)
-
-    # <p>This action is implemented by calling {@link Lexer#popMode}.</p>
-    def execute(self, lexer:Lexer):
-        lexer.more()
-
-    def __str__(self):
-        return "more"
-
-LexerMoreAction.INSTANCE = LexerMoreAction()
-
-# Implements the {@code mode} lexer action by calling {@link Lexer#mode} with
-# the assigned mode.
-class LexerModeAction(LexerAction):
-
-    def __init__(self, mode:int):
-        super().__init__(LexerActionType.MODE)
-        self.mode = mode
-
-    # <p>This action is implemented by calling {@link Lexer#mode} with the
-    # value provided by {@link #getMode}.</p>
-    def execute(self, lexer:Lexer):
-        lexer.mode(self.mode)
-
-    def __hash__(self):
-        return hash((self.actionType, self.mode))
-
-    def __eq__(self, other):
-        if self is other:
-            return True
-        elif not isinstance(other, LexerModeAction):
-            return False
-        else:
-            return self.mode == other.mode
-
-    def __str__(self):
-        return "mode(" + str(self.mode) + ")"
-
-# Executes a custom lexer action by calling {@link Recognizer#action} with the
-# rule and action indexes assigned to the custom action. The implementation of
-# a custom action is added to the generated code for the lexer in an override
-# of {@link Recognizer#action} when the grammar is compiled.
-#
-# <p>This class may represent embedded actions created with the <code>{...}</code>
-# syntax in ANTLR 4, as well as actions created for lexer commands where the
-# command argument could not be evaluated when the grammar was compiled.</p>
-
-class LexerCustomAction(LexerAction):
-
-    # Constructs a custom lexer action with the specified rule and action
-    # indexes.
-    #
-    # @param ruleIndex The rule index to use for calls to
-    # {@link Recognizer#action}.
-    # @param actionIndex The action index to use for calls to
-    # {@link Recognizer#action}.
-    #/
-    def __init__(self, ruleIndex:int, actionIndex:int):
-        super().__init__(LexerActionType.CUSTOM)
-        self.ruleIndex = ruleIndex
-        self.actionIndex = actionIndex
-        self.isPositionDependent = True
-
-    # <p>Custom actions are implemented by calling {@link Lexer#action} with the
-    # appropriate rule and action indexes.</p>
-    def execute(self, lexer:Lexer):
-        lexer.action(None, self.ruleIndex, self.actionIndex)
-
-    def __hash__(self):
-        return hash((self.actionType, self.ruleIndex, self.actionIndex))
-
-    def __eq__(self, other):
-        if self is other:
-            return True
-        elif not isinstance(other, LexerCustomAction):
-            return False
-        else:
-            return self.ruleIndex == other.ruleIndex and self.actionIndex == other.actionIndex
-
-# Implements the {@code channel} lexer action by calling
-# {@link Lexer#setChannel} with the assigned channel.
-class LexerChannelAction(LexerAction):
-
-    # Constructs a new {@code channel} action with the specified channel value.
-    # @param channel The channel value to pass to {@link Lexer#setChannel}.
-    def __init__(self, channel:int):
-        super().__init__(LexerActionType.CHANNEL)
-        self.channel = channel
-
-    # <p>This action is implemented by calling {@link Lexer#setChannel} with the
-    # value provided by {@link #getChannel}.</p>
-    def execute(self, lexer:Lexer):
-        lexer._channel = self.channel
-
-    def __hash__(self):
-        return hash((self.actionType, self.channel))
-
-    def __eq__(self, other):
-        if self is other:
-            return True
-        elif not isinstance(other, LexerChannelAction):
-            return False
-        else:
-            return self.channel == other.channel
-
-    def __str__(self):
-        return "channel(" + str(self.channel) + ")"
-
-# This implementation of {@link LexerAction} is used for tracking input offsets
-# for position-dependent actions within a {@link LexerActionExecutor}.
-#
-# <p>This action is not serialized as part of the ATN, and is only required for
-# position-dependent lexer actions which appear at a location other than the
-# end of a rule. For more information about DFA optimizations employed for
-# lexer actions, see {@link LexerActionExecutor#append} and
-# {@link LexerActionExecutor#fixOffsetBeforeMatch}.</p>
-class LexerIndexedCustomAction(LexerAction):
-
-    # Constructs a new indexed custom action by associating a character offset
-    # with a {@link LexerAction}.
-    #
-    # <p>Note: This class is only required for lexer actions for which
-    # {@link LexerAction#isPositionDependent} returns {@code true}.</p>
-    #
-    # @param offset The offset into the input {@link CharStream}, relative to
-    # the token start index, at which the specified lexer action should be
-    # executed.
-    # @param action The lexer action to execute at a particular offset in the
-    # input {@link CharStream}.
-    def __init__(self, offset:int, action:LexerAction):
-        super().__init__(action.actionType)
-        self.offset = offset
-        self.action = action
-        self.isPositionDependent = True
-
-    # <p>This method calls {@link #execute} on the result of {@link #getAction}
-    # using the provided {@code lexer}.</p>
-    def execute(self, lexer:Lexer):
-        # assume the input stream position was properly set by the calling code
-        self.action.execute(lexer)
-
-    def __hash__(self):
-        return hash((self.actionType, self.offset, self.action))
-
-    def __eq__(self, other):
-        if self is other:
-            return True
-        elif not isinstance(other, LexerIndexedCustomAction):
-            return False
-        else:
-            return self.offset == other.offset and self.action == other.action
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/criterions/label_smoothed_cross_entropy_with_ctc.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/criterions/label_smoothed_cross_entropy_with_ctc.py
deleted file mode 100644
index e98e0f7da00b826544df32cf1d9416241bdb37ae..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/criterions/label_smoothed_cross_entropy_with_ctc.py
+++ /dev/null
@@ -1,96 +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 math
-from dataclasses import dataclass, field
-
-import torch
-import torch.nn.functional as F
-
-from fairseq import metrics, utils
-from fairseq.criterions import register_criterion
-from fairseq.criterions.label_smoothed_cross_entropy import (
-    LabelSmoothedCrossEntropyCriterion,
-    LabelSmoothedCrossEntropyCriterionConfig,
-)
-from fairseq.data.data_utils import lengths_to_mask
-
-
-@dataclass
-class LabelSmoothedCrossEntropyWithCtcCriterionConfig(
-    LabelSmoothedCrossEntropyCriterionConfig
-):
-    ctc_weight: float = field(default=1.0, metadata={"help": "weight for CTC loss"})
-
-
-@register_criterion(
-    "label_smoothed_cross_entropy_with_ctc",
-    dataclass=LabelSmoothedCrossEntropyWithCtcCriterionConfig,
-)
-class LabelSmoothedCrossEntropyWithCtcCriterion(LabelSmoothedCrossEntropyCriterion):
-    def __init__(
-        self,
-        task,
-        sentence_avg,
-        label_smoothing,
-        ignore_prefix_size,
-        report_accuracy,
-        ctc_weight,
-    ):
-        super().__init__(
-            task, sentence_avg, label_smoothing, ignore_prefix_size, report_accuracy
-        )
-        self.ctc_weight = ctc_weight
-
-    def forward(self, model, sample, reduce=True):
-        net_output = model(**sample["net_input"])
-        loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
-
-        ctc_loss = torch.tensor(0.0).type_as(loss)
-        if self.ctc_weight > 0.0:
-            ctc_lprobs, ctc_lens = model.get_ctc_output(net_output, sample)
-            ctc_tgt, ctc_tgt_lens = model.get_ctc_target(sample)
-            ctc_tgt_mask = lengths_to_mask(ctc_tgt_lens)
-            ctc_tgt_flat = ctc_tgt.masked_select(ctc_tgt_mask)
-            reduction = "sum" if reduce else "none"
-            ctc_loss = (
-                F.ctc_loss(
-                    ctc_lprobs,
-                    ctc_tgt_flat,
-                    ctc_lens,
-                    ctc_tgt_lens,
-                    reduction=reduction,
-                    zero_infinity=True,
-                )
-                * self.ctc_weight
-            )
-        loss += ctc_loss
-
-        sample_size = (
-            sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
-        )
-        logging_output = {
-            "loss": utils.item(loss.data),
-            "nll_loss": utils.item(nll_loss.data),
-            "ctc_loss": utils.item(ctc_loss.data),
-            "ntokens": sample["ntokens"],
-            "nsentences": sample["target"].size(0),
-            "sample_size": sample_size,
-        }
-        if self.report_accuracy:
-            n_correct, total = self.compute_accuracy(model, net_output, sample)
-            logging_output["n_correct"] = utils.item(n_correct.data)
-            logging_output["total"] = utils.item(total.data)
-        return loss, sample_size, logging_output
-
-    @classmethod
-    def reduce_metrics(cls, logging_outputs) -> None:
-        super().reduce_metrics(logging_outputs)
-        loss_sum = sum(log.get("ctc_loss", 0) for log in logging_outputs)
-        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
-        metrics.log_scalar(
-            "ctc_loss", loss_sum / sample_size / math.log(2), sample_size, round=3
-        )
diff --git a/spaces/asciicorp/Legal-ai/extract_text.py b/spaces/asciicorp/Legal-ai/extract_text.py
deleted file mode 100644
index 7491239b93606b521cb1a2c0acdb80ef0cc9d0ef..0000000000000000000000000000000000000000
--- a/spaces/asciicorp/Legal-ai/extract_text.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import spacy
-from spacy import displacy
-
-nlp = spacy.load("en_core_web_sm")
-
-def extract_info(input_text):
-    # Parse the input text using spaCy
-    doc = nlp(input_text)
-    
-    # Find all named entities and dates in the parsed text
-    names = [(ent.text, ent.start_char, ent.end_char, "PERSON") for ent in doc.ents if ent.label_ == "PERSON"]
-    addresses = [(ent.text, ent.start_char, ent.end_char, "ADDRESS") for ent in doc.ents if ent.label_ == "GPE"]
-    dates = [(ent.text, ent.start_char, ent.end_char, "DATE") for ent in doc.ents if ent.label_ == "DATE"]
-    
-    # Return the extracted information as a dictionary
-    return {"names": names, "addresses": addresses, "dates": dates}
-
diff --git a/spaces/ashishraics/NLP/app.py b/spaces/ashishraics/NLP/app.py
deleted file mode 100644
index 769e1a20ba64e1bc8787ef7be29a822a5b0a41b7..0000000000000000000000000000000000000000
--- a/spaces/ashishraics/NLP/app.py
+++ /dev/null
@@ -1,353 +0,0 @@
-import numpy as np
-import pandas as pd
-import streamlit as st
-from streamlit_text_rating.st_text_rater import st_text_rater
-from transformers import AutoTokenizer,AutoModelForSequenceClassification
-from transformers import AutoModelForMaskedLM
-import onnxruntime as ort
-import os
-import time
-import plotly.express as px
-import plotly.graph_objects as go
-from PIL import Image
-global _plotly_config
-_plotly_config={'displayModeBar': False}
-
-from sentiment_clf_helper import (classify_sentiment,
-                                  create_onnx_model_sentiment,
-                                  classify_sentiment_onnx)
-
-from zeroshot_clf_helper import (zero_shot_classification,
-                                 create_onnx_model_zs_nli,
-                                 create_onnx_model_zs_mlm,
-                                 zero_shot_classification_nli_onnx,
-                                 zero_shot_classification_fillmask_onnx)
-
-import multiprocessing
-total_threads=multiprocessing.cpu_count()#for ort inference
-
-import yaml
-def read_yaml(file_path):
-    with open(file_path, "r") as f:
-        return yaml.safe_load(f)
-
-config = read_yaml('config.yaml')
-
-sent_chkpt=config['SENTIMENT_CLF']['sent_chkpt']
-sent_mdl_dir=config['SENTIMENT_CLF']['sent_mdl_dir']
-sent_onnx_mdl_dir=config['SENTIMENT_CLF']['sent_onnx_mdl_dir']
-sent_onnx_mdl_name=config['SENTIMENT_CLF']['sent_onnx_mdl_name']
-sent_onnx_quant_mdl_name=config['SENTIMENT_CLF']['sent_onnx_quant_mdl_name']
-
-zs_chkpt=config['ZEROSHOT_CLF']['zs_chkpt']
-zs_mdl_dir=config['ZEROSHOT_CLF']['zs_mdl_dir']
-zs_onnx_mdl_dir=config['ZEROSHOT_CLF']['zs_onnx_mdl_dir']
-zs_onnx_mdl_name=config['ZEROSHOT_CLF']['zs_onnx_mdl_name']
-zs_onnx_quant_mdl_name=config['ZEROSHOT_CLF']['zs_onnx_quant_mdl_name']
-
-zs_mlm_chkpt=config['ZEROSHOT_MLM']['zs_mlm_chkpt']
-zs_mlm_mdl_dir=config['ZEROSHOT_MLM']['zs_mlm_mdl_dir']
-zs_mlm_onnx_mdl_dir=config['ZEROSHOT_MLM']['zs_mlm_onnx_mdl_dir']
-zs_mlm_onnx_mdl_name=config['ZEROSHOT_MLM']['zs_mlm_onnx_mdl_name']
-
-st.set_page_config(  # Alternate names: setup_page, page, layout
-    layout="wide",  # Can be "centered" or "wide". In the future also "dashboard", etc.
-    initial_sidebar_state="auto",  # Can be "auto", "expanded", "collapsed"
-    page_title='None',  # String or None. Strings get appended with "• Streamlit".
-)
-
-padding_top = 0
-st.markdown(f"""
-    <style>
-        .reportview-container .main .block-container{{
-            padding-top: {padding_top}rem;
-        }}
-    </style>""",
-    unsafe_allow_html=True,
-)
-
-def set_page_title(title):
-    st.sidebar.markdown(unsafe_allow_html=True, body=f"""
-        <iframe height=0 srcdoc="<script>
-            const title = window.parent.document.querySelector('title') \
-
-            const oldObserver = window.parent.titleObserver
-            if (oldObserver) {{
-                oldObserver.disconnect()
-            }} \
-
-            const newObserver = new MutationObserver(function(mutations) {{
-                const target = mutations[0].target
-                if (target.text !== '{title}') {{
-                    target.text = '{title}'
-                }}
-            }}) \
-
-            newObserver.observe(title, {{ childList: true }})
-            window.parent.titleObserver = newObserver \
-
-            title.text = '{title}'
-        </script>" />
-    """)
-
-
-set_page_title('NLP use cases')
-
-# Hide Menu Option
-hide_streamlit_style = """
-            <style>
-            #MainMenu {visibility: hidden;}
-            footer {visibility: hidden;}
-            </style>
-            """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-
-#onnx runtime inference threading changes -- session options must be passed in session run
-# os.environ["OMP_NUM_THREADS"] = "1" #use this before changing session options of onnx runtime
-session_options_ort = ort.SessionOptions()
-session_options_ort.intra_op_num_threads=1
-session_options_ort.inter_op_num_threads=1
-# session_options_ort.execution_mode = session_options_ort.ExecutionMode.ORT_SEQUENTIAL
-
-@st.cache(allow_output_mutation=True, suppress_st_warning=True, max_entries=None, ttl=None)
-def create_model_dir(chkpt, model_dir,task_type):
-    if not os.path.exists(model_dir):
-        try:
-            os.mkdir(path=model_dir)
-        except:
-            pass
-        if task_type=='classification':
-            _model = AutoModelForSequenceClassification.from_pretrained(chkpt)
-            _tokenizer = AutoTokenizer.from_pretrained(chkpt)
-            _model.save_pretrained(model_dir)
-            _tokenizer.save_pretrained(model_dir)
-        elif task_type=='mlm':
-            _model=AutoModelForMaskedLM.from_pretrained(chkpt)
-            _tokenizer=AutoTokenizer.from_pretrained(chkpt)
-            _model.save_pretrained(model_dir)
-            _tokenizer.save_pretrained(model_dir)
-        else:
-            pass
-    else:
-        pass
-
-
-#title using markdown
-st.markdown("<h1 style='text-align: center; color: #3366ff;'>NLP Basic Use Cases</h1>", unsafe_allow_html=True)
-st.markdown("---")
-with st.sidebar:
-    # title using markdown
-    st.markdown("<h1 style='text-align: left; color: ;'>NLP Tasks</h1>", unsafe_allow_html=True)
-    select_task=st.selectbox(label="Select task from drop down menu",
-                 options=['README',
-                          'Detect Sentiment','Zero Shot Classification'])
-
-############### Pre-Download & instantiate objects for sentiment analysis *********************** START **********************
-
-# #create model/token dir for sentiment classification for faster inference
-# create_model_dir(chkpt=sent_chkpt, model_dir=sent_mdl_dir,task_type='classification')
-
-
-@st.cache(allow_output_mutation=True, suppress_st_warning=True, max_entries=None, ttl=None)
-def sentiment_task_selected(task,
-                            sent_chkpt=sent_chkpt,
-                            sent_mdl_dir=sent_mdl_dir,
-                            sent_onnx_mdl_dir=sent_onnx_mdl_dir,
-                            sent_onnx_mdl_name=sent_onnx_mdl_name,
-                            sent_onnx_quant_mdl_name=sent_onnx_quant_mdl_name):
-    ##model & tokenizer initialization for normal sentiment classification
-    # model_sentiment=AutoModelForSequenceClassification.from_pretrained(sent_mdl_dir)
-    tokenizer_sentiment = AutoTokenizer.from_pretrained(sent_mdl_dir)
-
-    # # # create onnx model for sentiment classification but once created in your local app comment this out
-    # create_onnx_model_sentiment(_model=model_sentiment, _tokenizer=tokenizer_sentiment)
-
-    #create inference session
-    sentiment_session = ort.InferenceSession(f"{sent_onnx_mdl_dir}/{sent_onnx_mdl_name}",sess_options=session_options_ort)
-    # sentiment_session_quant = ort.InferenceSession(f"{sent_onnx_mdl_dir}/{sent_onnx_quant_mdl_name}")
-
-    return tokenizer_sentiment,sentiment_session
-
-############## Pre-Download & instantiate objects for sentiment analysis ********************* END **********************************
-
-
-############### Pre-Download & instantiate objects for Zero shot clf NLI *********************** START **********************
-
-# # create model/token dir for zeroshot clf -- already created so not required
-# create_model_dir(chkpt=zs_chkpt, model_dir=zs_mdl_dir,task_type='classification')
-
-@st.cache(allow_output_mutation=True, suppress_st_warning=True, max_entries=None, ttl=None)
-def zs_nli_task_selected(task,
-                     zs_chkpt ,
-                     zs_mdl_dir,
-                     zs_onnx_mdl_dir,
-                     zs_onnx_mdl_name):
-
-    ##model & tokenizer initialization for normal ZS classification
-    # model_zs=AutoModelForSequenceClassification.from_pretrained(zs_chkpt)
-    # we just need tokenizer for inference and not model since onnx model is already saved
-    # tokenizer_zs=AutoTokenizer.from_pretrained(zs_chkpt)
-    tokenizer_zs = AutoTokenizer.from_pretrained(zs_mdl_dir)
-
-    # ## create onnx model for zeroshot but once created locally comment it out.
-    # create_onnx_model_zs_nli(zs_chkpt=zs_chkpt,zs_onnx_mdl_dir=zs_onnx_mdl_dir)
-
-    #create inference session from onnx model
-    zs_session = ort.InferenceSession(f"{zs_onnx_mdl_dir}/{zs_onnx_mdl_name}",sess_options=session_options_ort)
-
-    return tokenizer_zs,zs_session
-
-############## Pre-Download & instantiate objects for Zero shot NLI analysis ********************* END **********************************
-
-
-############### Pre-Download & instantiate objects for Zero shot clf NLI *********************** START **********************
-
-## create model/token dir for zeroshot clf -- already created so not required
-# create_model_dir(chkpt=zs_mlm_chkpt, model_dir=zs_mlm_mdl_dir, task_type='mlm')
-
-@st.cache(allow_output_mutation=True, suppress_st_warning=True, max_entries=None, ttl=None)
-def zs_mlm_task_selected(task,
-                         zs_mlm_chkpt=zs_mlm_chkpt,
-                         zs_mlm_mdl_dir=zs_mlm_mdl_dir,
-                         zs_mlm_onnx_mdl_dir=zs_mlm_onnx_mdl_dir,
-                         zs_mlm_onnx_mdl_name=zs_mlm_onnx_mdl_name):
-    ##model & tokenizer initialization for normal ZS classification
-    # model_zs_mlm=AutoModelForMaskedLM.from_pretrained(zs_mlm_mdl_dir)
-    ##we just need tokenizer for inference and not model since onnx model is already saved
-    # tokenizer_zs_mlm=AutoTokenizer.from_pretrained(zs_mlm_chkpt)
-    tokenizer_zs_mlm = AutoTokenizer.from_pretrained(zs_mlm_mdl_dir)
-
-    # # # create onnx model for zeroshot but once created locally comment it out.
-    # create_onnx_model_zs_mlm(zs_mlm_chkpt=zs_mlm_chkpt,zs_mlm_onnx_mdl_dir=zs_mlm_onnx_mdl_dir)
-
-    # create inference session from onnx model
-    zs_session_mlm = ort.InferenceSession(f"{zs_mlm_onnx_mdl_dir}/{zs_mlm_onnx_mdl_name}", sess_options=session_options_ort)
-
-    return tokenizer_zs_mlm, zs_session_mlm
-
-
-############## Pre-Download & instantiate objects for Zero shot MLM analysis ********************* END **********************************
-
-# Image.open('hf_space1.png').convert('RGB').save('hf_space1.png')
-img = Image.open("hf_space1.png")
-
-if select_task=='README':
-    st.header("NLP Summary")
-    st.write(f"The App gives you ability to 1) Detect Sentiment, 2) Zeroshot Classification.Currently.It has {total_threads} CPU cores but only 1 is available per user so "
-             f"inference time will be on the higher side.")
-    st.markdown("---")
-    st.image(img)
-
-if select_task == 'Detect Sentiment':
-    t1=time.time()
-    tokenizer_sentiment,sentiment_session = sentiment_task_selected(task=select_task)
-    ##below 2 steps are slower as caching is not enabled
-    # tokenizer_sentiment = AutoTokenizer.from_pretrained(sent_mdl_dir)
-    # sentiment_session = ort.InferenceSession(f"{sent_onnx_mdl_dir}/{sent_onnx_mdl_name}")
-    t2 = time.time()
-    st.write(f"Total time to load Model is {(t2-t1)*1000:.1f} ms")
-
-    st.subheader("You are now performing Sentiment Analysis")
-    input_texts = st.text_input(label="Input texts separated by comma")
-    c1,c2,_,_=st.columns(4)
-
-    with c1:
-        response1=st.button("Compute (ONNX runtime)")
-
-    if response1:
-        start = time.time()
-        sentiments=classify_sentiment_onnx(input_texts,
-                                           _session=sentiment_session,
-                                           _tokenizer=tokenizer_sentiment)
-        end = time.time()
-        st.write(f"Time taken for computation {(end - start) * 1000:.1f} ms")
-
-        for i,t in enumerate(input_texts.split(',')):
-            if sentiments[i]=='Positive':
-                response=st_text_rater(t + f"--> This statement is {sentiments[i]}",
-                                       color_background='rgb(154,205,50)',key=t)
-            else:
-                response = st_text_rater(t + f"--> This statement is {sentiments[i]}",
-                                         color_background='rgb(233, 116, 81)',key=t)
-    else:
-        pass
-
-if select_task=='Zero Shot Classification':
-    t1=time.time()
-    tokenizer_zs,session_zs = zs_nli_task_selected(task=select_task ,
-                                                   zs_chkpt=zs_chkpt,
-                                                   zs_mdl_dir=zs_mdl_dir,
-                                                   zs_onnx_mdl_dir=zs_onnx_mdl_dir,
-                                                   zs_onnx_mdl_name=zs_onnx_mdl_name)
-    t2 = time.time()
-    st.write(f"Total time to load NLI Model is {(t2-t1)*1000:.1f} ms")
-
-    t1=time.time()
-    tokenizer_zs_mlm,session_zs_mlm = zs_mlm_task_selected(task=select_task,
-                                                           zs_mlm_chkpt=zs_mlm_chkpt,
-                                                           zs_mlm_mdl_dir=zs_mlm_mdl_dir,
-                                                           zs_mlm_onnx_mdl_dir=zs_mlm_onnx_mdl_dir,
-                                                           zs_mlm_onnx_mdl_name=zs_mlm_onnx_mdl_name)
-    t2 = time.time()
-    st.write(f"Total time to load MLM Model is {(t2-t1)*1000:.1f} ms")
-
-    st.subheader("Zero Shot Classification using NLI & MLM")
-
-    default_paratext=""" A molar tooth from Southeast Asia probably belonged to a member of a cryptic group of Stone Age hominids called Denisovans, researchers say.  If so, this relatively large tooth joins only a handful of fossils from Denisovans, who are known from ancient DNA pegging them as close Neandertal relatives.  Analyses of the tooth’s internal structure and protein makeup indicate that the molar came from a girl in the Homo genus. She died between the ages of 3½ and 8½, paleoanthropologist Fabrice Demeter of the University of Copenhagen and colleagues say."""
-    default_labels="""science, politics,sports"""
-    input_texts = st.text_area(label="Input text to classify into topics",
-                               height=250, max_chars=1000,
-                               value=default_paratext)
-    input_lables = st.text_input(label="Enter labels separated by commas",value=default_labels)
-    input_hypothesis = st.text_input(label="Enter your hypothesis",value="This is an example of")
-
-    c1,c2,_,=st.columns(3)
-
-    with c1:
-        response1=st.button("Compute using NLI approach (ONNX runtime)")
-
-    with c2:
-        response2=st.button("Compute using Fill-Mask approach(ONNX runtime)")
-
-    if response1:
-        start = time.time()
-        df_output = zero_shot_classification_nli_onnx(premise=input_texts,
-                                                      labels=input_lables,
-                                                      hypothesis=input_hypothesis,
-                                                      _session=session_zs,
-                                                     _tokenizer=tokenizer_zs,
-                                                      )
-        end = time.time()
-        st.write(f"Time taken for computation {(end-start)*1000:.1f} ms")
-        fig = px.bar(x='Probability',
-                     y='labels',
-                     text='Probability',
-                     data_frame=df_output,
-                     title='Zero Shot NLI Normalized Probabilities')
-
-        st.plotly_chart(fig, config=_plotly_config)
-
-    elif response2:
-        start=time.time()
-        df_output=zero_shot_classification_fillmask_onnx(premise=input_texts,
-                                                         labels=input_lables,
-                                                         hypothesis=input_hypothesis,
-                                                         _session=session_zs_mlm,
-                                                        _tokenizer=tokenizer_zs_mlm,
-                                                         )
-        end=time.time()
-        st.write(f"Time taken for computation {(end - start) * 1000:.1f} ms")
-        st.write(f"Currently hypothesis and premise have *single token_type_ids*  ."
-                 f"Once updated for different *token_type_ids* expect the model performance to increase.")
-
-        fig = px.bar(x='Probability',
-                     y='Labels',
-                     text='Probability',
-                     data_frame=df_output,
-                     title='Zero Shot MLM Normalized Probabilities')
-
-        st.plotly_chart(fig, config=_plotly_config)
-    else:
-        pass
-
-
diff --git a/spaces/asyafiqe/pdfGPT-chat/intfloat/multilingual-e5-base/README.md b/spaces/asyafiqe/pdfGPT-chat/intfloat/multilingual-e5-base/README.md
deleted file mode 100644
index ad78007c1525cd40fad2931ff0ab942ea194d6d6..0000000000000000000000000000000000000000
--- a/spaces/asyafiqe/pdfGPT-chat/intfloat/multilingual-e5-base/README.md
+++ /dev/null
@@ -1,6853 +0,0 @@
----
-tags:
-- mteb
-model-index:
-- name: multilingual-e5-base
-  results:
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (en)
-      config: en
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 78.97014925373135
-    - type: ap
-      value: 43.69351129103008
-    - type: f1
-      value: 73.38075030070492
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (de)
-      config: de
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 71.7237687366167
-    - type: ap
-      value: 82.22089859962671
-    - type: f1
-      value: 69.95532758884401
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (en-ext)
-      config: en-ext
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 79.65517241379312
-    - type: ap
-      value: 28.507918657094738
-    - type: f1
-      value: 66.84516013726119
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (ja)
-      config: ja
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 73.32976445396146
-    - type: ap
-      value: 20.720481637566014
-    - type: f1
-      value: 59.78002763416003
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_polarity
-      name: MTEB AmazonPolarityClassification
-      config: default
-      split: test
-      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
-    metrics:
-    - type: accuracy
-      value: 90.63775
-    - type: ap
-      value: 87.22277903861716
-    - type: f1
-      value: 90.60378636386807
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (en)
-      config: en
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 44.546
-    - type: f1
-      value: 44.05666638370923
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (de)
-      config: de
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 41.828
-    - type: f1
-      value: 41.2710255644252
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (es)
-      config: es
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 40.534
-    - type: f1
-      value: 39.820743174270326
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (fr)
-      config: fr
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 39.684
-    - type: f1
-      value: 39.11052682815307
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (ja)
-      config: ja
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 37.436
-    - type: f1
-      value: 37.07082931930871
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (zh)
-      config: zh
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 37.226000000000006
-    - type: f1
-      value: 36.65372077739185
-  - task:
-      type: Retrieval
-    dataset:
-      type: arguana
-      name: MTEB ArguAna
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 22.831000000000003
-    - type: map_at_10
-      value: 36.42
-    - type: map_at_100
-      value: 37.699
-    - type: map_at_1000
-      value: 37.724000000000004
-    - type: map_at_3
-      value: 32.207
-    - type: map_at_5
-      value: 34.312
-    - type: mrr_at_1
-      value: 23.257
-    - type: mrr_at_10
-      value: 36.574
-    - type: mrr_at_100
-      value: 37.854
-    - type: mrr_at_1000
-      value: 37.878
-    - type: mrr_at_3
-      value: 32.385000000000005
-    - type: mrr_at_5
-      value: 34.48
-    - type: ndcg_at_1
-      value: 22.831000000000003
-    - type: ndcg_at_10
-      value: 44.230000000000004
-    - type: ndcg_at_100
-      value: 49.974000000000004
-    - type: ndcg_at_1000
-      value: 50.522999999999996
-    - type: ndcg_at_3
-      value: 35.363
-    - type: ndcg_at_5
-      value: 39.164
-    - type: precision_at_1
-      value: 22.831000000000003
-    - type: precision_at_10
-      value: 6.935
-    - type: precision_at_100
-      value: 0.9520000000000001
-    - type: precision_at_1000
-      value: 0.099
-    - type: precision_at_3
-      value: 14.841
-    - type: precision_at_5
-      value: 10.754
-    - type: recall_at_1
-      value: 22.831000000000003
-    - type: recall_at_10
-      value: 69.346
-    - type: recall_at_100
-      value: 95.235
-    - type: recall_at_1000
-      value: 99.36
-    - type: recall_at_3
-      value: 44.523
-    - type: recall_at_5
-      value: 53.769999999999996
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-p2p
-      name: MTEB ArxivClusteringP2P
-      config: default
-      split: test
-      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
-    metrics:
-    - type: v_measure
-      value: 40.27789869854063
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-s2s
-      name: MTEB ArxivClusteringS2S
-      config: default
-      split: test
-      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
-    metrics:
-    - type: v_measure
-      value: 35.41979463347428
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/askubuntudupquestions-reranking
-      name: MTEB AskUbuntuDupQuestions
-      config: default
-      split: test
-      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
-    metrics:
-    - type: map
-      value: 58.22752045109304
-    - type: mrr
-      value: 71.51112430198303
-  - task:
-      type: STS
-    dataset:
-      type: mteb/biosses-sts
-      name: MTEB BIOSSES
-      config: default
-      split: test
-      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
-    metrics:
-    - type: cos_sim_pearson
-      value: 84.71147646622866
-    - type: cos_sim_spearman
-      value: 85.059167046486
-    - type: euclidean_pearson
-      value: 75.88421613600647
-    - type: euclidean_spearman
-      value: 75.12821787150585
-    - type: manhattan_pearson
-      value: 75.22005646957604
-    - type: manhattan_spearman
-      value: 74.42880434453272
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/bucc-bitext-mining
-      name: MTEB BUCC (de-en)
-      config: de-en
-      split: test
-      revision: d51519689f32196a32af33b075a01d0e7c51e252
-    metrics:
-    - type: accuracy
-      value: 99.23799582463465
-    - type: f1
-      value: 99.12665274878218
-    - type: precision
-      value: 99.07098121085595
-    - type: recall
-      value: 99.23799582463465
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/bucc-bitext-mining
-      name: MTEB BUCC (fr-en)
-      config: fr-en
-      split: test
-      revision: d51519689f32196a32af33b075a01d0e7c51e252
-    metrics:
-    - type: accuracy
-      value: 97.88685890380806
-    - type: f1
-      value: 97.59336708489249
-    - type: precision
-      value: 97.44662117543473
-    - type: recall
-      value: 97.88685890380806
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/bucc-bitext-mining
-      name: MTEB BUCC (ru-en)
-      config: ru-en
-      split: test
-      revision: d51519689f32196a32af33b075a01d0e7c51e252
-    metrics:
-    - type: accuracy
-      value: 97.47142362313821
-    - type: f1
-      value: 97.1989377670015
-    - type: precision
-      value: 97.06384944001847
-    - type: recall
-      value: 97.47142362313821
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/bucc-bitext-mining
-      name: MTEB BUCC (zh-en)
-      config: zh-en
-      split: test
-      revision: d51519689f32196a32af33b075a01d0e7c51e252
-    metrics:
-    - type: accuracy
-      value: 98.4728804634018
-    - type: f1
-      value: 98.2973494821836
-    - type: precision
-      value: 98.2095839915745
-    - type: recall
-      value: 98.4728804634018
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/banking77
-      name: MTEB Banking77Classification
-      config: default
-      split: test
-      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
-    metrics:
-    - type: accuracy
-      value: 82.74025974025975
-    - type: f1
-      value: 82.67420447730439
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-p2p
-      name: MTEB BiorxivClusteringP2P
-      config: default
-      split: test
-      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
-    metrics:
-    - type: v_measure
-      value: 35.0380848063507
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-s2s
-      name: MTEB BiorxivClusteringS2S
-      config: default
-      split: test
-      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
-    metrics:
-    - type: v_measure
-      value: 29.45956405670166
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackAndroidRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 32.122
-    - type: map_at_10
-      value: 42.03
-    - type: map_at_100
-      value: 43.364000000000004
-    - type: map_at_1000
-      value: 43.474000000000004
-    - type: map_at_3
-      value: 38.804
-    - type: map_at_5
-      value: 40.585
-    - type: mrr_at_1
-      value: 39.914
-    - type: mrr_at_10
-      value: 48.227
-    - type: mrr_at_100
-      value: 49.018
-    - type: mrr_at_1000
-      value: 49.064
-    - type: mrr_at_3
-      value: 45.994
-    - type: mrr_at_5
-      value: 47.396
-    - type: ndcg_at_1
-      value: 39.914
-    - type: ndcg_at_10
-      value: 47.825
-    - type: ndcg_at_100
-      value: 52.852
-    - type: ndcg_at_1000
-      value: 54.891
-    - type: ndcg_at_3
-      value: 43.517
-    - type: ndcg_at_5
-      value: 45.493
-    - type: precision_at_1
-      value: 39.914
-    - type: precision_at_10
-      value: 8.956
-    - type: precision_at_100
-      value: 1.388
-    - type: precision_at_1000
-      value: 0.182
-    - type: precision_at_3
-      value: 20.791999999999998
-    - type: precision_at_5
-      value: 14.821000000000002
-    - type: recall_at_1
-      value: 32.122
-    - type: recall_at_10
-      value: 58.294999999999995
-    - type: recall_at_100
-      value: 79.726
-    - type: recall_at_1000
-      value: 93.099
-    - type: recall_at_3
-      value: 45.017
-    - type: recall_at_5
-      value: 51.002
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackEnglishRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 29.677999999999997
-    - type: map_at_10
-      value: 38.684000000000005
-    - type: map_at_100
-      value: 39.812999999999995
-    - type: map_at_1000
-      value: 39.945
-    - type: map_at_3
-      value: 35.831
-    - type: map_at_5
-      value: 37.446
-    - type: mrr_at_1
-      value: 37.771
-    - type: mrr_at_10
-      value: 44.936
-    - type: mrr_at_100
-      value: 45.583
-    - type: mrr_at_1000
-      value: 45.634
-    - type: mrr_at_3
-      value: 42.771
-    - type: mrr_at_5
-      value: 43.994
-    - type: ndcg_at_1
-      value: 37.771
-    - type: ndcg_at_10
-      value: 44.059
-    - type: ndcg_at_100
-      value: 48.192
-    - type: ndcg_at_1000
-      value: 50.375
-    - type: ndcg_at_3
-      value: 40.172000000000004
-    - type: ndcg_at_5
-      value: 41.899
-    - type: precision_at_1
-      value: 37.771
-    - type: precision_at_10
-      value: 8.286999999999999
-    - type: precision_at_100
-      value: 1.322
-    - type: precision_at_1000
-      value: 0.178
-    - type: precision_at_3
-      value: 19.406000000000002
-    - type: precision_at_5
-      value: 13.745
-    - type: recall_at_1
-      value: 29.677999999999997
-    - type: recall_at_10
-      value: 53.071
-    - type: recall_at_100
-      value: 70.812
-    - type: recall_at_1000
-      value: 84.841
-    - type: recall_at_3
-      value: 41.016000000000005
-    - type: recall_at_5
-      value: 46.22
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGamingRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 42.675000000000004
-    - type: map_at_10
-      value: 53.93599999999999
-    - type: map_at_100
-      value: 54.806999999999995
-    - type: map_at_1000
-      value: 54.867
-    - type: map_at_3
-      value: 50.934000000000005
-    - type: map_at_5
-      value: 52.583
-    - type: mrr_at_1
-      value: 48.339
-    - type: mrr_at_10
-      value: 57.265
-    - type: mrr_at_100
-      value: 57.873
-    - type: mrr_at_1000
-      value: 57.906
-    - type: mrr_at_3
-      value: 55.193000000000005
-    - type: mrr_at_5
-      value: 56.303000000000004
-    - type: ndcg_at_1
-      value: 48.339
-    - type: ndcg_at_10
-      value: 59.19799999999999
-    - type: ndcg_at_100
-      value: 62.743
-    - type: ndcg_at_1000
-      value: 63.99399999999999
-    - type: ndcg_at_3
-      value: 54.367
-    - type: ndcg_at_5
-      value: 56.548
-    - type: precision_at_1
-      value: 48.339
-    - type: precision_at_10
-      value: 9.216000000000001
-    - type: precision_at_100
-      value: 1.1809999999999998
-    - type: precision_at_1000
-      value: 0.134
-    - type: precision_at_3
-      value: 23.72
-    - type: precision_at_5
-      value: 16.025
-    - type: recall_at_1
-      value: 42.675000000000004
-    - type: recall_at_10
-      value: 71.437
-    - type: recall_at_100
-      value: 86.803
-    - type: recall_at_1000
-      value: 95.581
-    - type: recall_at_3
-      value: 58.434
-    - type: recall_at_5
-      value: 63.754
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGisRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.518
-    - type: map_at_10
-      value: 30.648999999999997
-    - type: map_at_100
-      value: 31.508999999999997
-    - type: map_at_1000
-      value: 31.604
-    - type: map_at_3
-      value: 28.247
-    - type: map_at_5
-      value: 29.65
-    - type: mrr_at_1
-      value: 25.650000000000002
-    - type: mrr_at_10
-      value: 32.771
-    - type: mrr_at_100
-      value: 33.554
-    - type: mrr_at_1000
-      value: 33.629999999999995
-    - type: mrr_at_3
-      value: 30.433
-    - type: mrr_at_5
-      value: 31.812
-    - type: ndcg_at_1
-      value: 25.650000000000002
-    - type: ndcg_at_10
-      value: 34.929
-    - type: ndcg_at_100
-      value: 39.382
-    - type: ndcg_at_1000
-      value: 41.913
-    - type: ndcg_at_3
-      value: 30.292
-    - type: ndcg_at_5
-      value: 32.629999999999995
-    - type: precision_at_1
-      value: 25.650000000000002
-    - type: precision_at_10
-      value: 5.311
-    - type: precision_at_100
-      value: 0.792
-    - type: precision_at_1000
-      value: 0.105
-    - type: precision_at_3
-      value: 12.58
-    - type: precision_at_5
-      value: 8.994
-    - type: recall_at_1
-      value: 23.518
-    - type: recall_at_10
-      value: 46.19
-    - type: recall_at_100
-      value: 67.123
-    - type: recall_at_1000
-      value: 86.442
-    - type: recall_at_3
-      value: 33.678000000000004
-    - type: recall_at_5
-      value: 39.244
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackMathematicaRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 15.891
-    - type: map_at_10
-      value: 22.464000000000002
-    - type: map_at_100
-      value: 23.483
-    - type: map_at_1000
-      value: 23.613
-    - type: map_at_3
-      value: 20.080000000000002
-    - type: map_at_5
-      value: 21.526
-    - type: mrr_at_1
-      value: 20.025000000000002
-    - type: mrr_at_10
-      value: 26.712999999999997
-    - type: mrr_at_100
-      value: 27.650000000000002
-    - type: mrr_at_1000
-      value: 27.737000000000002
-    - type: mrr_at_3
-      value: 24.274
-    - type: mrr_at_5
-      value: 25.711000000000002
-    - type: ndcg_at_1
-      value: 20.025000000000002
-    - type: ndcg_at_10
-      value: 27.028999999999996
-    - type: ndcg_at_100
-      value: 32.064
-    - type: ndcg_at_1000
-      value: 35.188
-    - type: ndcg_at_3
-      value: 22.512999999999998
-    - type: ndcg_at_5
-      value: 24.89
-    - type: precision_at_1
-      value: 20.025000000000002
-    - type: precision_at_10
-      value: 4.776
-    - type: precision_at_100
-      value: 0.8500000000000001
-    - type: precision_at_1000
-      value: 0.125
-    - type: precision_at_3
-      value: 10.531
-    - type: precision_at_5
-      value: 7.811
-    - type: recall_at_1
-      value: 15.891
-    - type: recall_at_10
-      value: 37.261
-    - type: recall_at_100
-      value: 59.12
-    - type: recall_at_1000
-      value: 81.356
-    - type: recall_at_3
-      value: 24.741
-    - type: recall_at_5
-      value: 30.753999999999998
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackPhysicsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 27.544
-    - type: map_at_10
-      value: 36.283
-    - type: map_at_100
-      value: 37.467
-    - type: map_at_1000
-      value: 37.574000000000005
-    - type: map_at_3
-      value: 33.528999999999996
-    - type: map_at_5
-      value: 35.028999999999996
-    - type: mrr_at_1
-      value: 34.166999999999994
-    - type: mrr_at_10
-      value: 41.866
-    - type: mrr_at_100
-      value: 42.666
-    - type: mrr_at_1000
-      value: 42.716
-    - type: mrr_at_3
-      value: 39.541
-    - type: mrr_at_5
-      value: 40.768
-    - type: ndcg_at_1
-      value: 34.166999999999994
-    - type: ndcg_at_10
-      value: 41.577
-    - type: ndcg_at_100
-      value: 46.687
-    - type: ndcg_at_1000
-      value: 48.967
-    - type: ndcg_at_3
-      value: 37.177
-    - type: ndcg_at_5
-      value: 39.097
-    - type: precision_at_1
-      value: 34.166999999999994
-    - type: precision_at_10
-      value: 7.420999999999999
-    - type: precision_at_100
-      value: 1.165
-    - type: precision_at_1000
-      value: 0.154
-    - type: precision_at_3
-      value: 17.291999999999998
-    - type: precision_at_5
-      value: 12.166
-    - type: recall_at_1
-      value: 27.544
-    - type: recall_at_10
-      value: 51.99399999999999
-    - type: recall_at_100
-      value: 73.738
-    - type: recall_at_1000
-      value: 89.33
-    - type: recall_at_3
-      value: 39.179
-    - type: recall_at_5
-      value: 44.385999999999996
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackProgrammersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 26.661
-    - type: map_at_10
-      value: 35.475
-    - type: map_at_100
-      value: 36.626999999999995
-    - type: map_at_1000
-      value: 36.741
-    - type: map_at_3
-      value: 32.818000000000005
-    - type: map_at_5
-      value: 34.397
-    - type: mrr_at_1
-      value: 32.647999999999996
-    - type: mrr_at_10
-      value: 40.784
-    - type: mrr_at_100
-      value: 41.602
-    - type: mrr_at_1000
-      value: 41.661
-    - type: mrr_at_3
-      value: 38.68
-    - type: mrr_at_5
-      value: 39.838
-    - type: ndcg_at_1
-      value: 32.647999999999996
-    - type: ndcg_at_10
-      value: 40.697
-    - type: ndcg_at_100
-      value: 45.799
-    - type: ndcg_at_1000
-      value: 48.235
-    - type: ndcg_at_3
-      value: 36.516
-    - type: ndcg_at_5
-      value: 38.515
-    - type: precision_at_1
-      value: 32.647999999999996
-    - type: precision_at_10
-      value: 7.202999999999999
-    - type: precision_at_100
-      value: 1.1360000000000001
-    - type: precision_at_1000
-      value: 0.151
-    - type: precision_at_3
-      value: 17.314
-    - type: precision_at_5
-      value: 12.145999999999999
-    - type: recall_at_1
-      value: 26.661
-    - type: recall_at_10
-      value: 50.995000000000005
-    - type: recall_at_100
-      value: 73.065
-    - type: recall_at_1000
-      value: 89.781
-    - type: recall_at_3
-      value: 39.073
-    - type: recall_at_5
-      value: 44.395
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 25.946583333333333
-    - type: map_at_10
-      value: 33.79725
-    - type: map_at_100
-      value: 34.86408333333333
-    - type: map_at_1000
-      value: 34.9795
-    - type: map_at_3
-      value: 31.259999999999998
-    - type: map_at_5
-      value: 32.71541666666666
-    - type: mrr_at_1
-      value: 30.863749999999996
-    - type: mrr_at_10
-      value: 37.99183333333333
-    - type: mrr_at_100
-      value: 38.790499999999994
-    - type: mrr_at_1000
-      value: 38.85575000000001
-    - type: mrr_at_3
-      value: 35.82083333333333
-    - type: mrr_at_5
-      value: 37.07533333333333
-    - type: ndcg_at_1
-      value: 30.863749999999996
-    - type: ndcg_at_10
-      value: 38.52141666666667
-    - type: ndcg_at_100
-      value: 43.17966666666667
-    - type: ndcg_at_1000
-      value: 45.64608333333333
-    - type: ndcg_at_3
-      value: 34.333000000000006
-    - type: ndcg_at_5
-      value: 36.34975
-    - type: precision_at_1
-      value: 30.863749999999996
-    - type: precision_at_10
-      value: 6.598999999999999
-    - type: precision_at_100
-      value: 1.0502500000000001
-    - type: precision_at_1000
-      value: 0.14400000000000002
-    - type: precision_at_3
-      value: 15.557583333333334
-    - type: precision_at_5
-      value: 11.020000000000001
-    - type: recall_at_1
-      value: 25.946583333333333
-    - type: recall_at_10
-      value: 48.36991666666666
-    - type: recall_at_100
-      value: 69.02408333333334
-    - type: recall_at_1000
-      value: 86.43858333333331
-    - type: recall_at_3
-      value: 36.4965
-    - type: recall_at_5
-      value: 41.76258333333334
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackStatsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 22.431
-    - type: map_at_10
-      value: 28.889
-    - type: map_at_100
-      value: 29.642000000000003
-    - type: map_at_1000
-      value: 29.742
-    - type: map_at_3
-      value: 26.998
-    - type: map_at_5
-      value: 28.172000000000004
-    - type: mrr_at_1
-      value: 25.307000000000002
-    - type: mrr_at_10
-      value: 31.763
-    - type: mrr_at_100
-      value: 32.443
-    - type: mrr_at_1000
-      value: 32.531
-    - type: mrr_at_3
-      value: 29.959000000000003
-    - type: mrr_at_5
-      value: 31.063000000000002
-    - type: ndcg_at_1
-      value: 25.307000000000002
-    - type: ndcg_at_10
-      value: 32.586999999999996
-    - type: ndcg_at_100
-      value: 36.5
-    - type: ndcg_at_1000
-      value: 39.133
-    - type: ndcg_at_3
-      value: 29.25
-    - type: ndcg_at_5
-      value: 31.023
-    - type: precision_at_1
-      value: 25.307000000000002
-    - type: precision_at_10
-      value: 4.954
-    - type: precision_at_100
-      value: 0.747
-    - type: precision_at_1000
-      value: 0.104
-    - type: precision_at_3
-      value: 12.577
-    - type: precision_at_5
-      value: 8.741999999999999
-    - type: recall_at_1
-      value: 22.431
-    - type: recall_at_10
-      value: 41.134
-    - type: recall_at_100
-      value: 59.28600000000001
-    - type: recall_at_1000
-      value: 78.857
-    - type: recall_at_3
-      value: 31.926
-    - type: recall_at_5
-      value: 36.335
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackTexRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 17.586
-    - type: map_at_10
-      value: 23.304
-    - type: map_at_100
-      value: 24.159
-    - type: map_at_1000
-      value: 24.281
-    - type: map_at_3
-      value: 21.316
-    - type: map_at_5
-      value: 22.383
-    - type: mrr_at_1
-      value: 21.645
-    - type: mrr_at_10
-      value: 27.365000000000002
-    - type: mrr_at_100
-      value: 28.108
-    - type: mrr_at_1000
-      value: 28.192
-    - type: mrr_at_3
-      value: 25.482
-    - type: mrr_at_5
-      value: 26.479999999999997
-    - type: ndcg_at_1
-      value: 21.645
-    - type: ndcg_at_10
-      value: 27.306
-    - type: ndcg_at_100
-      value: 31.496000000000002
-    - type: ndcg_at_1000
-      value: 34.53
-    - type: ndcg_at_3
-      value: 23.73
-    - type: ndcg_at_5
-      value: 25.294
-    - type: precision_at_1
-      value: 21.645
-    - type: precision_at_10
-      value: 4.797
-    - type: precision_at_100
-      value: 0.8059999999999999
-    - type: precision_at_1000
-      value: 0.121
-    - type: precision_at_3
-      value: 10.850999999999999
-    - type: precision_at_5
-      value: 7.736
-    - type: recall_at_1
-      value: 17.586
-    - type: recall_at_10
-      value: 35.481
-    - type: recall_at_100
-      value: 54.534000000000006
-    - type: recall_at_1000
-      value: 76.456
-    - type: recall_at_3
-      value: 25.335
-    - type: recall_at_5
-      value: 29.473
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackUnixRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 25.095
-    - type: map_at_10
-      value: 32.374
-    - type: map_at_100
-      value: 33.537
-    - type: map_at_1000
-      value: 33.634
-    - type: map_at_3
-      value: 30.089
-    - type: map_at_5
-      value: 31.433
-    - type: mrr_at_1
-      value: 29.198
-    - type: mrr_at_10
-      value: 36.01
-    - type: mrr_at_100
-      value: 37.022
-    - type: mrr_at_1000
-      value: 37.083
-    - type: mrr_at_3
-      value: 33.94
-    - type: mrr_at_5
-      value: 35.148
-    - type: ndcg_at_1
-      value: 29.198
-    - type: ndcg_at_10
-      value: 36.729
-    - type: ndcg_at_100
-      value: 42.114000000000004
-    - type: ndcg_at_1000
-      value: 44.592
-    - type: ndcg_at_3
-      value: 32.644
-    - type: ndcg_at_5
-      value: 34.652
-    - type: precision_at_1
-      value: 29.198
-    - type: precision_at_10
-      value: 5.970000000000001
-    - type: precision_at_100
-      value: 0.967
-    - type: precision_at_1000
-      value: 0.129
-    - type: precision_at_3
-      value: 14.396999999999998
-    - type: precision_at_5
-      value: 10.093
-    - type: recall_at_1
-      value: 25.095
-    - type: recall_at_10
-      value: 46.392
-    - type: recall_at_100
-      value: 69.706
-    - type: recall_at_1000
-      value: 87.738
-    - type: recall_at_3
-      value: 35.303000000000004
-    - type: recall_at_5
-      value: 40.441
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWebmastersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 26.857999999999997
-    - type: map_at_10
-      value: 34.066
-    - type: map_at_100
-      value: 35.671
-    - type: map_at_1000
-      value: 35.881
-    - type: map_at_3
-      value: 31.304
-    - type: map_at_5
-      value: 32.885
-    - type: mrr_at_1
-      value: 32.411
-    - type: mrr_at_10
-      value: 38.987
-    - type: mrr_at_100
-      value: 39.894
-    - type: mrr_at_1000
-      value: 39.959
-    - type: mrr_at_3
-      value: 36.626999999999995
-    - type: mrr_at_5
-      value: 38.011
-    - type: ndcg_at_1
-      value: 32.411
-    - type: ndcg_at_10
-      value: 39.208
-    - type: ndcg_at_100
-      value: 44.626
-    - type: ndcg_at_1000
-      value: 47.43
-    - type: ndcg_at_3
-      value: 35.091
-    - type: ndcg_at_5
-      value: 37.119
-    - type: precision_at_1
-      value: 32.411
-    - type: precision_at_10
-      value: 7.51
-    - type: precision_at_100
-      value: 1.486
-    - type: precision_at_1000
-      value: 0.234
-    - type: precision_at_3
-      value: 16.14
-    - type: precision_at_5
-      value: 11.976
-    - type: recall_at_1
-      value: 26.857999999999997
-    - type: recall_at_10
-      value: 47.407
-    - type: recall_at_100
-      value: 72.236
-    - type: recall_at_1000
-      value: 90.77
-    - type: recall_at_3
-      value: 35.125
-    - type: recall_at_5
-      value: 40.522999999999996
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWordpressRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 21.3
-    - type: map_at_10
-      value: 27.412999999999997
-    - type: map_at_100
-      value: 28.29
-    - type: map_at_1000
-      value: 28.398
-    - type: map_at_3
-      value: 25.169999999999998
-    - type: map_at_5
-      value: 26.496
-    - type: mrr_at_1
-      value: 23.29
-    - type: mrr_at_10
-      value: 29.215000000000003
-    - type: mrr_at_100
-      value: 30.073
-    - type: mrr_at_1000
-      value: 30.156
-    - type: mrr_at_3
-      value: 26.956000000000003
-    - type: mrr_at_5
-      value: 28.38
-    - type: ndcg_at_1
-      value: 23.29
-    - type: ndcg_at_10
-      value: 31.113000000000003
-    - type: ndcg_at_100
-      value: 35.701
-    - type: ndcg_at_1000
-      value: 38.505
-    - type: ndcg_at_3
-      value: 26.727
-    - type: ndcg_at_5
-      value: 29.037000000000003
-    - type: precision_at_1
-      value: 23.29
-    - type: precision_at_10
-      value: 4.787
-    - type: precision_at_100
-      value: 0.763
-    - type: precision_at_1000
-      value: 0.11100000000000002
-    - type: precision_at_3
-      value: 11.091
-    - type: precision_at_5
-      value: 7.985
-    - type: recall_at_1
-      value: 21.3
-    - type: recall_at_10
-      value: 40.782000000000004
-    - type: recall_at_100
-      value: 62.13999999999999
-    - type: recall_at_1000
-      value: 83.012
-    - type: recall_at_3
-      value: 29.131
-    - type: recall_at_5
-      value: 34.624
-  - task:
-      type: Retrieval
-    dataset:
-      type: climate-fever
-      name: MTEB ClimateFEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 9.631
-    - type: map_at_10
-      value: 16.634999999999998
-    - type: map_at_100
-      value: 18.23
-    - type: map_at_1000
-      value: 18.419
-    - type: map_at_3
-      value: 13.66
-    - type: map_at_5
-      value: 15.173
-    - type: mrr_at_1
-      value: 21.368000000000002
-    - type: mrr_at_10
-      value: 31.56
-    - type: mrr_at_100
-      value: 32.58
-    - type: mrr_at_1000
-      value: 32.633
-    - type: mrr_at_3
-      value: 28.241
-    - type: mrr_at_5
-      value: 30.225
-    - type: ndcg_at_1
-      value: 21.368000000000002
-    - type: ndcg_at_10
-      value: 23.855999999999998
-    - type: ndcg_at_100
-      value: 30.686999999999998
-    - type: ndcg_at_1000
-      value: 34.327000000000005
-    - type: ndcg_at_3
-      value: 18.781
-    - type: ndcg_at_5
-      value: 20.73
-    - type: precision_at_1
-      value: 21.368000000000002
-    - type: precision_at_10
-      value: 7.564
-    - type: precision_at_100
-      value: 1.496
-    - type: precision_at_1000
-      value: 0.217
-    - type: precision_at_3
-      value: 13.876
-    - type: precision_at_5
-      value: 11.062
-    - type: recall_at_1
-      value: 9.631
-    - type: recall_at_10
-      value: 29.517
-    - type: recall_at_100
-      value: 53.452
-    - type: recall_at_1000
-      value: 74.115
-    - type: recall_at_3
-      value: 17.605999999999998
-    - type: recall_at_5
-      value: 22.505
-  - task:
-      type: Retrieval
-    dataset:
-      type: dbpedia-entity
-      name: MTEB DBPedia
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 8.885
-    - type: map_at_10
-      value: 18.798000000000002
-    - type: map_at_100
-      value: 26.316
-    - type: map_at_1000
-      value: 27.869
-    - type: map_at_3
-      value: 13.719000000000001
-    - type: map_at_5
-      value: 15.716
-    - type: mrr_at_1
-      value: 66
-    - type: mrr_at_10
-      value: 74.263
-    - type: mrr_at_100
-      value: 74.519
-    - type: mrr_at_1000
-      value: 74.531
-    - type: mrr_at_3
-      value: 72.458
-    - type: mrr_at_5
-      value: 73.321
-    - type: ndcg_at_1
-      value: 53.87499999999999
-    - type: ndcg_at_10
-      value: 40.355999999999995
-    - type: ndcg_at_100
-      value: 44.366
-    - type: ndcg_at_1000
-      value: 51.771
-    - type: ndcg_at_3
-      value: 45.195
-    - type: ndcg_at_5
-      value: 42.187000000000005
-    - type: precision_at_1
-      value: 66
-    - type: precision_at_10
-      value: 31.75
-    - type: precision_at_100
-      value: 10.11
-    - type: precision_at_1000
-      value: 1.9800000000000002
-    - type: precision_at_3
-      value: 48.167
-    - type: precision_at_5
-      value: 40.050000000000004
-    - type: recall_at_1
-      value: 8.885
-    - type: recall_at_10
-      value: 24.471999999999998
-    - type: recall_at_100
-      value: 49.669000000000004
-    - type: recall_at_1000
-      value: 73.383
-    - type: recall_at_3
-      value: 14.872
-    - type: recall_at_5
-      value: 18.262999999999998
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/emotion
-      name: MTEB EmotionClassification
-      config: default
-      split: test
-      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
-    metrics:
-    - type: accuracy
-      value: 45.18
-    - type: f1
-      value: 40.26878691789978
-  - task:
-      type: Retrieval
-    dataset:
-      type: fever
-      name: MTEB FEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 62.751999999999995
-    - type: map_at_10
-      value: 74.131
-    - type: map_at_100
-      value: 74.407
-    - type: map_at_1000
-      value: 74.423
-    - type: map_at_3
-      value: 72.329
-    - type: map_at_5
-      value: 73.555
-    - type: mrr_at_1
-      value: 67.282
-    - type: mrr_at_10
-      value: 78.292
-    - type: mrr_at_100
-      value: 78.455
-    - type: mrr_at_1000
-      value: 78.458
-    - type: mrr_at_3
-      value: 76.755
-    - type: mrr_at_5
-      value: 77.839
-    - type: ndcg_at_1
-      value: 67.282
-    - type: ndcg_at_10
-      value: 79.443
-    - type: ndcg_at_100
-      value: 80.529
-    - type: ndcg_at_1000
-      value: 80.812
-    - type: ndcg_at_3
-      value: 76.281
-    - type: ndcg_at_5
-      value: 78.235
-    - type: precision_at_1
-      value: 67.282
-    - type: precision_at_10
-      value: 10.078
-    - type: precision_at_100
-      value: 1.082
-    - type: precision_at_1000
-      value: 0.11199999999999999
-    - type: precision_at_3
-      value: 30.178
-    - type: precision_at_5
-      value: 19.232
-    - type: recall_at_1
-      value: 62.751999999999995
-    - type: recall_at_10
-      value: 91.521
-    - type: recall_at_100
-      value: 95.997
-    - type: recall_at_1000
-      value: 97.775
-    - type: recall_at_3
-      value: 83.131
-    - type: recall_at_5
-      value: 87.93299999999999
-  - task:
-      type: Retrieval
-    dataset:
-      type: fiqa
-      name: MTEB FiQA2018
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 18.861
-    - type: map_at_10
-      value: 30.252000000000002
-    - type: map_at_100
-      value: 32.082
-    - type: map_at_1000
-      value: 32.261
-    - type: map_at_3
-      value: 25.909
-    - type: map_at_5
-      value: 28.296
-    - type: mrr_at_1
-      value: 37.346000000000004
-    - type: mrr_at_10
-      value: 45.802
-    - type: mrr_at_100
-      value: 46.611999999999995
-    - type: mrr_at_1000
-      value: 46.659
-    - type: mrr_at_3
-      value: 43.056
-    - type: mrr_at_5
-      value: 44.637
-    - type: ndcg_at_1
-      value: 37.346000000000004
-    - type: ndcg_at_10
-      value: 38.169
-    - type: ndcg_at_100
-      value: 44.864
-    - type: ndcg_at_1000
-      value: 47.974
-    - type: ndcg_at_3
-      value: 33.619
-    - type: ndcg_at_5
-      value: 35.317
-    - type: precision_at_1
-      value: 37.346000000000004
-    - type: precision_at_10
-      value: 10.693999999999999
-    - type: precision_at_100
-      value: 1.775
-    - type: precision_at_1000
-      value: 0.231
-    - type: precision_at_3
-      value: 22.325
-    - type: precision_at_5
-      value: 16.852
-    - type: recall_at_1
-      value: 18.861
-    - type: recall_at_10
-      value: 45.672000000000004
-    - type: recall_at_100
-      value: 70.60499999999999
-    - type: recall_at_1000
-      value: 89.216
-    - type: recall_at_3
-      value: 30.361
-    - type: recall_at_5
-      value: 36.998999999999995
-  - task:
-      type: Retrieval
-    dataset:
-      type: hotpotqa
-      name: MTEB HotpotQA
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 37.852999999999994
-    - type: map_at_10
-      value: 59.961
-    - type: map_at_100
-      value: 60.78
-    - type: map_at_1000
-      value: 60.843
-    - type: map_at_3
-      value: 56.39999999999999
-    - type: map_at_5
-      value: 58.646
-    - type: mrr_at_1
-      value: 75.70599999999999
-    - type: mrr_at_10
-      value: 82.321
-    - type: mrr_at_100
-      value: 82.516
-    - type: mrr_at_1000
-      value: 82.525
-    - type: mrr_at_3
-      value: 81.317
-    - type: mrr_at_5
-      value: 81.922
-    - type: ndcg_at_1
-      value: 75.70599999999999
-    - type: ndcg_at_10
-      value: 68.557
-    - type: ndcg_at_100
-      value: 71.485
-    - type: ndcg_at_1000
-      value: 72.71600000000001
-    - type: ndcg_at_3
-      value: 63.524
-    - type: ndcg_at_5
-      value: 66.338
-    - type: precision_at_1
-      value: 75.70599999999999
-    - type: precision_at_10
-      value: 14.463000000000001
-    - type: precision_at_100
-      value: 1.677
-    - type: precision_at_1000
-      value: 0.184
-    - type: precision_at_3
-      value: 40.806
-    - type: precision_at_5
-      value: 26.709
-    - type: recall_at_1
-      value: 37.852999999999994
-    - type: recall_at_10
-      value: 72.316
-    - type: recall_at_100
-      value: 83.842
-    - type: recall_at_1000
-      value: 91.999
-    - type: recall_at_3
-      value: 61.209
-    - type: recall_at_5
-      value: 66.77199999999999
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/imdb
-      name: MTEB ImdbClassification
-      config: default
-      split: test
-      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
-    metrics:
-    - type: accuracy
-      value: 85.46039999999999
-    - type: ap
-      value: 79.9812521351881
-    - type: f1
-      value: 85.31722909702084
-  - task:
-      type: Retrieval
-    dataset:
-      type: msmarco
-      name: MTEB MSMARCO
-      config: default
-      split: dev
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 22.704
-    - type: map_at_10
-      value: 35.329
-    - type: map_at_100
-      value: 36.494
-    - type: map_at_1000
-      value: 36.541000000000004
-    - type: map_at_3
-      value: 31.476
-    - type: map_at_5
-      value: 33.731
-    - type: mrr_at_1
-      value: 23.294999999999998
-    - type: mrr_at_10
-      value: 35.859
-    - type: mrr_at_100
-      value: 36.968
-    - type: mrr_at_1000
-      value: 37.008
-    - type: mrr_at_3
-      value: 32.085
-    - type: mrr_at_5
-      value: 34.299
-    - type: ndcg_at_1
-      value: 23.324
-    - type: ndcg_at_10
-      value: 42.274
-    - type: ndcg_at_100
-      value: 47.839999999999996
-    - type: ndcg_at_1000
-      value: 48.971
-    - type: ndcg_at_3
-      value: 34.454
-    - type: ndcg_at_5
-      value: 38.464
-    - type: precision_at_1
-      value: 23.324
-    - type: precision_at_10
-      value: 6.648
-    - type: precision_at_100
-      value: 0.9440000000000001
-    - type: precision_at_1000
-      value: 0.104
-    - type: precision_at_3
-      value: 14.674999999999999
-    - type: precision_at_5
-      value: 10.850999999999999
-    - type: recall_at_1
-      value: 22.704
-    - type: recall_at_10
-      value: 63.660000000000004
-    - type: recall_at_100
-      value: 89.29899999999999
-    - type: recall_at_1000
-      value: 97.88900000000001
-    - type: recall_at_3
-      value: 42.441
-    - type: recall_at_5
-      value: 52.04
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (en)
-      config: en
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 93.1326949384405
-    - type: f1
-      value: 92.89743579612082
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (de)
-      config: de
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 89.62524654832347
-    - type: f1
-      value: 88.65106082263151
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (es)
-      config: es
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 90.59039359573046
-    - type: f1
-      value: 90.31532892105662
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (fr)
-      config: fr
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 86.21046038208581
-    - type: f1
-      value: 86.41459529813113
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (hi)
-      config: hi
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 87.3180351380423
-    - type: f1
-      value: 86.71383078226444
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (th)
-      config: th
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 86.24231464737792
-    - type: f1
-      value: 86.31845567592403
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_intent
-      name: MTEB MTOPIntentClassification (en)
-      config: en
-      split: test
-      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
-    metrics:
-    - type: accuracy
-      value: 75.27131782945736
-    - type: f1
-      value: 57.52079940417103
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_intent
-      name: MTEB MTOPIntentClassification (de)
-      config: de
-      split: test
-      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
-    metrics:
-    - type: accuracy
-      value: 71.2341504649197
-    - type: f1
-      value: 51.349951558039244
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_intent
-      name: MTEB MTOPIntentClassification (es)
-      config: es
-      split: test
-      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
-    metrics:
-    - type: accuracy
-      value: 71.27418278852569
-    - type: f1
-      value: 50.1714985749095
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_intent
-      name: MTEB MTOPIntentClassification (fr)
-      config: fr
-      split: test
-      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
-    metrics:
-    - type: accuracy
-      value: 67.68243031631694
-    - type: f1
-      value: 50.1066160836192
-  - task:
-      type: Classification
-    dataset:
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-      value: 52.242999999999995
-    - type: mrr_at_3
-      value: 49.431999999999995
-    - type: mrr_at_5
-      value: 50.470000000000006
-    - type: ndcg_at_1
-      value: 42.415000000000006
-    - type: ndcg_at_10
-      value: 32.464999999999996
-    - type: ndcg_at_100
-      value: 28.927999999999997
-    - type: ndcg_at_1000
-      value: 37.629000000000005
-    - type: ndcg_at_3
-      value: 37.845
-    - type: ndcg_at_5
-      value: 35.147
-    - type: precision_at_1
-      value: 43.653
-    - type: precision_at_10
-      value: 23.932000000000002
-    - type: precision_at_100
-      value: 7.17
-    - type: precision_at_1000
-      value: 1.967
-    - type: precision_at_3
-      value: 35.397
-    - type: precision_at_5
-      value: 29.907
-    - type: recall_at_1
-      value: 5.5280000000000005
-    - type: recall_at_10
-      value: 15.568000000000001
-    - type: recall_at_100
-      value: 28.54
-    - type: recall_at_1000
-      value: 59.864
-    - type: recall_at_3
-      value: 9.822000000000001
-    - type: recall_at_5
-      value: 11.726
-  - task:
-      type: Retrieval
-    dataset:
-      type: nq
-      name: MTEB NQ
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 37.041000000000004
-    - type: map_at_10
-      value: 52.664
-    - type: map_at_100
-      value: 53.477
-    - type: map_at_1000
-      value: 53.505
-    - type: map_at_3
-      value: 48.510999999999996
-    - type: map_at_5
-      value: 51.036
-    - type: mrr_at_1
-      value: 41.338
-    - type: mrr_at_10
-      value: 55.071000000000005
-    - type: mrr_at_100
-      value: 55.672
-    - type: mrr_at_1000
-      value: 55.689
-    - type: mrr_at_3
-      value: 51.82
-    - type: mrr_at_5
-      value: 53.852
-    - type: ndcg_at_1
-      value: 41.338
-    - type: ndcg_at_10
-      value: 60.01800000000001
-    - type: ndcg_at_100
-      value: 63.409000000000006
-    - type: ndcg_at_1000
-      value: 64.017
-    - type: ndcg_at_3
-      value: 52.44799999999999
-    - type: ndcg_at_5
-      value: 56.571000000000005
-    - type: precision_at_1
-      value: 41.338
-    - type: precision_at_10
-      value: 9.531
-    - type: precision_at_100
-      value: 1.145
-    - type: precision_at_1000
-      value: 0.12
-    - type: precision_at_3
-      value: 23.416
-    - type: precision_at_5
-      value: 16.46
-    - type: recall_at_1
-      value: 37.041000000000004
-    - type: recall_at_10
-      value: 79.76299999999999
-    - type: recall_at_100
-      value: 94.39
-    - type: recall_at_1000
-      value: 98.851
-    - type: recall_at_3
-      value: 60.465
-    - type: recall_at_5
-      value: 69.906
-  - task:
-      type: Retrieval
-    dataset:
-      type: quora
-      name: MTEB QuoraRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 69.952
-    - type: map_at_10
-      value: 83.758
-    - type: map_at_100
-      value: 84.406
-    - type: map_at_1000
-      value: 84.425
-    - type: map_at_3
-      value: 80.839
-    - type: map_at_5
-      value: 82.646
-    - type: mrr_at_1
-      value: 80.62
-    - type: mrr_at_10
-      value: 86.947
-    - type: mrr_at_100
-      value: 87.063
-    - type: mrr_at_1000
-      value: 87.064
-    - type: mrr_at_3
-      value: 85.96000000000001
-    - type: mrr_at_5
-      value: 86.619
-    - type: ndcg_at_1
-      value: 80.63
-    - type: ndcg_at_10
-      value: 87.64800000000001
-    - type: ndcg_at_100
-      value: 88.929
-    - type: ndcg_at_1000
-      value: 89.054
-    - type: ndcg_at_3
-      value: 84.765
-    - type: ndcg_at_5
-      value: 86.291
-    - type: precision_at_1
-      value: 80.63
-    - type: precision_at_10
-      value: 13.314
-    - type: precision_at_100
-      value: 1.525
-    - type: precision_at_1000
-      value: 0.157
-    - type: precision_at_3
-      value: 37.1
-    - type: precision_at_5
-      value: 24.372
-    - type: recall_at_1
-      value: 69.952
-    - type: recall_at_10
-      value: 94.955
-    - type: recall_at_100
-      value: 99.38
-    - type: recall_at_1000
-      value: 99.96000000000001
-    - type: recall_at_3
-      value: 86.60600000000001
-    - type: recall_at_5
-      value: 90.997
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering
-      name: MTEB RedditClustering
-      config: default
-      split: test
-      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
-    metrics:
-    - type: v_measure
-      value: 42.41329517878427
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering-p2p
-      name: MTEB RedditClusteringP2P
-      config: default
-      split: test
-      revision: 282350215ef01743dc01b456c7f5241fa8937f16
-    metrics:
-    - type: v_measure
-      value: 55.171278362748666
-  - task:
-      type: Retrieval
-    dataset:
-      type: scidocs
-      name: MTEB SCIDOCS
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 4.213
-    - type: map_at_10
-      value: 9.895
-    - type: map_at_100
-      value: 11.776
-    - type: map_at_1000
-      value: 12.084
-    - type: map_at_3
-      value: 7.2669999999999995
-    - type: map_at_5
-      value: 8.620999999999999
-    - type: mrr_at_1
-      value: 20.8
-    - type: mrr_at_10
-      value: 31.112000000000002
-    - type: mrr_at_100
-      value: 32.274
-    - type: mrr_at_1000
-      value: 32.35
-    - type: mrr_at_3
-      value: 28.133000000000003
-    - type: mrr_at_5
-      value: 29.892999999999997
-    - type: ndcg_at_1
-      value: 20.8
-    - type: ndcg_at_10
-      value: 17.163999999999998
-    - type: ndcg_at_100
-      value: 24.738
-    - type: ndcg_at_1000
-      value: 30.316
-    - type: ndcg_at_3
-      value: 16.665
-    - type: ndcg_at_5
-      value: 14.478
-    - type: precision_at_1
-      value: 20.8
-    - type: precision_at_10
-      value: 8.74
-    - type: precision_at_100
-      value: 1.963
-    - type: precision_at_1000
-      value: 0.33
-    - type: precision_at_3
-      value: 15.467
-    - type: precision_at_5
-      value: 12.6
-    - type: recall_at_1
-      value: 4.213
-    - type: recall_at_10
-      value: 17.698
-    - type: recall_at_100
-      value: 39.838
-    - type: recall_at_1000
-      value: 66.893
-    - type: recall_at_3
-      value: 9.418
-    - type: recall_at_5
-      value: 12.773000000000001
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sickr-sts
-      name: MTEB SICK-R
-      config: default
-      split: test
-      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
-    metrics:
-    - type: cos_sim_pearson
-      value: 82.90453315738294
-    - type: cos_sim_spearman
-      value: 78.51197850080254
-    - type: euclidean_pearson
-      value: 80.09647123597748
-    - type: euclidean_spearman
-      value: 78.63548011514061
-    - type: manhattan_pearson
-      value: 80.10645285675231
-    - type: manhattan_spearman
-      value: 78.57861806068901
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts12-sts
-      name: MTEB STS12
-      config: default
-      split: test
-      revision: a0d554a64d88156834ff5ae9920b964011b16384
-    metrics:
-    - type: cos_sim_pearson
-      value: 84.2616156846401
-    - type: cos_sim_spearman
-      value: 76.69713867850156
-    - type: euclidean_pearson
-      value: 77.97948563800394
-    - type: euclidean_spearman
-      value: 74.2371211567807
-    - type: manhattan_pearson
-      value: 77.69697879669705
-    - type: manhattan_spearman
-      value: 73.86529778022278
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts13-sts
-      name: MTEB STS13
-      config: default
-      split: test
-      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
-    metrics:
-    - type: cos_sim_pearson
-      value: 77.0293269315045
-    - type: cos_sim_spearman
-      value: 78.02555120584198
-    - type: euclidean_pearson
-      value: 78.25398100379078
-    - type: euclidean_spearman
-      value: 78.66963870599464
-    - type: manhattan_pearson
-      value: 78.14314682167348
-    - type: manhattan_spearman
-      value: 78.57692322969135
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts14-sts
-      name: MTEB STS14
-      config: default
-      split: test
-      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
-    metrics:
-    - type: cos_sim_pearson
-      value: 79.16989925136942
-    - type: cos_sim_spearman
-      value: 76.5996225327091
-    - type: euclidean_pearson
-      value: 77.8319003279786
-    - type: euclidean_spearman
-      value: 76.42824009468998
-    - type: manhattan_pearson
-      value: 77.69118862737736
-    - type: manhattan_spearman
-      value: 76.25568104762812
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts15-sts
-      name: MTEB STS15
-      config: default
-      split: test
-      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
-    metrics:
-    - type: cos_sim_pearson
-      value: 87.42012286935325
-    - type: cos_sim_spearman
-      value: 88.15654297884122
-    - type: euclidean_pearson
-      value: 87.34082819427852
-    - type: euclidean_spearman
-      value: 88.06333589547084
-    - type: manhattan_pearson
-      value: 87.25115596784842
-    - type: manhattan_spearman
-      value: 87.9559927695203
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts16-sts
-      name: MTEB STS16
-      config: default
-      split: test
-      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
-    metrics:
-    - type: cos_sim_pearson
-      value: 82.88222044996712
-    - type: cos_sim_spearman
-      value: 84.28476589061077
-    - type: euclidean_pearson
-      value: 83.17399758058309
-    - type: euclidean_spearman
-      value: 83.85497357244542
-    - type: manhattan_pearson
-      value: 83.0308397703786
-    - type: manhattan_spearman
-      value: 83.71554539935046
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (ko-ko)
-      config: ko-ko
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 80.20682986257339
-    - type: cos_sim_spearman
-      value: 79.94567120362092
-    - type: euclidean_pearson
-      value: 79.43122480368902
-    - type: euclidean_spearman
-      value: 79.94802077264987
-    - type: manhattan_pearson
-      value: 79.32653021527081
-    - type: manhattan_spearman
-      value: 79.80961146709178
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (ar-ar)
-      config: ar-ar
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 74.46578144394383
-    - type: cos_sim_spearman
-      value: 74.52496637472179
-    - type: euclidean_pearson
-      value: 72.2903807076809
-    - type: euclidean_spearman
-      value: 73.55549359771645
-    - type: manhattan_pearson
-      value: 72.09324837709393
-    - type: manhattan_spearman
-      value: 73.36743103606581
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (en-ar)
-      config: en-ar
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 71.37272335116
-    - type: cos_sim_spearman
-      value: 71.26702117766037
-    - type: euclidean_pearson
-      value: 67.114829954434
-    - type: euclidean_spearman
-      value: 66.37938893947761
-    - type: manhattan_pearson
-      value: 66.79688574095246
-    - type: manhattan_spearman
-      value: 66.17292828079667
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (en-de)
-      config: en-de
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 80.61016770129092
-    - type: cos_sim_spearman
-      value: 82.08515426632214
-    - type: euclidean_pearson
-      value: 80.557340361131
-    - type: euclidean_spearman
-      value: 80.37585812266175
-    - type: manhattan_pearson
-      value: 80.6782873404285
-    - type: manhattan_spearman
-      value: 80.6678073032024
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (en-en)
-      config: en-en
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 87.00150745350108
-    - type: cos_sim_spearman
-      value: 87.83441972211425
-    - type: euclidean_pearson
-      value: 87.94826702308792
-    - type: euclidean_spearman
-      value: 87.46143974860725
-    - type: manhattan_pearson
-      value: 87.97560344306105
-    - type: manhattan_spearman
-      value: 87.5267102829796
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (en-tr)
-      config: en-tr
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 64.76325252267235
-    - type: cos_sim_spearman
-      value: 63.32615095463905
-    - type: euclidean_pearson
-      value: 64.07920669155716
-    - type: euclidean_spearman
-      value: 61.21409893072176
-    - type: manhattan_pearson
-      value: 64.26308625680016
-    - type: manhattan_spearman
-      value: 61.2438185254079
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (es-en)
-      config: es-en
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 75.82644463022595
-    - type: cos_sim_spearman
-      value: 76.50381269945073
-    - type: euclidean_pearson
-      value: 75.1328548315934
-    - type: euclidean_spearman
-      value: 75.63761139408453
-    - type: manhattan_pearson
-      value: 75.18610101241407
-    - type: manhattan_spearman
-      value: 75.30669266354164
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (es-es)
-      config: es-es
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 87.49994164686832
-    - type: cos_sim_spearman
-      value: 86.73743986245549
-    - type: euclidean_pearson
-      value: 86.8272894387145
-    - type: euclidean_spearman
-      value: 85.97608491000507
-    - type: manhattan_pearson
-      value: 86.74960140396779
-    - type: manhattan_spearman
-      value: 85.79285984190273
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (fr-en)
-      config: fr-en
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 79.58172210788469
-    - type: cos_sim_spearman
-      value: 80.17516468334607
-    - type: euclidean_pearson
-      value: 77.56537843470504
-    - type: euclidean_spearman
-      value: 77.57264627395521
-    - type: manhattan_pearson
-      value: 78.09703521695943
-    - type: manhattan_spearman
-      value: 78.15942760916954
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (it-en)
-      config: it-en
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 79.7589932931751
-    - type: cos_sim_spearman
-      value: 80.15210089028162
-    - type: euclidean_pearson
-      value: 77.54135223516057
-    - type: euclidean_spearman
-      value: 77.52697996368764
-    - type: manhattan_pearson
-      value: 77.65734439572518
-    - type: manhattan_spearman
-      value: 77.77702992016121
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (nl-en)
-      config: nl-en
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 79.16682365511267
-    - type: cos_sim_spearman
-      value: 79.25311267628506
-    - type: euclidean_pearson
-      value: 77.54882036762244
-    - type: euclidean_spearman
-      value: 77.33212935194827
-    - type: manhattan_pearson
-      value: 77.98405516064015
-    - type: manhattan_spearman
-      value: 77.85075717865719
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (en)
-      config: en
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 59.10473294775917
-    - type: cos_sim_spearman
-      value: 61.82780474476838
-    - type: euclidean_pearson
-      value: 45.885111672377256
-    - type: euclidean_spearman
-      value: 56.88306351932454
-    - type: manhattan_pearson
-      value: 46.101218127323186
-    - type: manhattan_spearman
-      value: 56.80953694186333
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (de)
-      config: de
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 45.781923079584146
-    - type: cos_sim_spearman
-      value: 55.95098449691107
-    - type: euclidean_pearson
-      value: 25.4571031323205
-    - type: euclidean_spearman
-      value: 49.859978118078935
-    - type: manhattan_pearson
-      value: 25.624938455041384
-    - type: manhattan_spearman
-      value: 49.99546185049401
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (es)
-      config: es
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 60.00618133997907
-    - type: cos_sim_spearman
-      value: 66.57896677718321
-    - type: euclidean_pearson
-      value: 42.60118466388821
-    - type: euclidean_spearman
-      value: 62.8210759715209
-    - type: manhattan_pearson
-      value: 42.63446860604094
-    - type: manhattan_spearman
-      value: 62.73803068925271
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (pl)
-      config: pl
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 28.460759121626943
-    - type: cos_sim_spearman
-      value: 34.13459007469131
-    - type: euclidean_pearson
-      value: 6.0917739325525195
-    - type: euclidean_spearman
-      value: 27.9947262664867
-    - type: manhattan_pearson
-      value: 6.16877864169911
-    - type: manhattan_spearman
-      value: 28.00664163971514
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (tr)
-      config: tr
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 57.42546621771696
-    - type: cos_sim_spearman
-      value: 63.699663168970474
-    - type: euclidean_pearson
-      value: 38.12085278789738
-    - type: euclidean_spearman
-      value: 58.12329140741536
-    - type: manhattan_pearson
-      value: 37.97364549443335
-    - type: manhattan_spearman
-      value: 57.81545502318733
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (ar)
-      config: ar
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 46.82241380954213
-    - type: cos_sim_spearman
-      value: 57.86569456006391
-    - type: euclidean_pearson
-      value: 31.80480070178813
-    - type: euclidean_spearman
-      value: 52.484000620130104
-    - type: manhattan_pearson
-      value: 31.952708554646097
-    - type: manhattan_spearman
-      value: 52.8560972356195
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (ru)
-      config: ru
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 52.00447170498087
-    - type: cos_sim_spearman
-      value: 60.664116225735164
-    - type: euclidean_pearson
-      value: 33.87382555421702
-    - type: euclidean_spearman
-      value: 55.74649067458667
-    - type: manhattan_pearson
-      value: 33.99117246759437
-    - type: manhattan_spearman
-      value: 55.98749034923899
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (zh)
-      config: zh
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 58.06497233105448
-    - type: cos_sim_spearman
-      value: 65.62968801135676
-    - type: euclidean_pearson
-      value: 47.482076613243905
-    - type: euclidean_spearman
-      value: 62.65137791498299
-    - type: manhattan_pearson
-      value: 47.57052626104093
-    - type: manhattan_spearman
-      value: 62.436916516613294
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (fr)
-      config: fr
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 70.49397298562575
-    - type: cos_sim_spearman
-      value: 74.79604041187868
-    - type: euclidean_pearson
-      value: 49.661891561317795
-    - type: euclidean_spearman
-      value: 70.31535537621006
-    - type: manhattan_pearson
-      value: 49.553715741850006
-    - type: manhattan_spearman
-      value: 70.24779344636806
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (de-en)
-      config: de-en
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 55.640574515348696
-    - type: cos_sim_spearman
-      value: 54.927959317689
-    - type: euclidean_pearson
-      value: 29.00139666967476
-    - type: euclidean_spearman
-      value: 41.86386566971605
-    - type: manhattan_pearson
-      value: 29.47411067730344
-    - type: manhattan_spearman
-      value: 42.337438424952786
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (es-en)
-      config: es-en
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 68.14095292259312
-    - type: cos_sim_spearman
-      value: 73.99017581234789
-    - type: euclidean_pearson
-      value: 46.46304297872084
-    - type: euclidean_spearman
-      value: 60.91834114800041
-    - type: manhattan_pearson
-      value: 47.07072666338692
-    - type: manhattan_spearman
-      value: 61.70415727977926
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (it)
-      config: it
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 73.27184653359575
-    - type: cos_sim_spearman
-      value: 77.76070252418626
-    - type: euclidean_pearson
-      value: 62.30586577544778
-    - type: euclidean_spearman
-      value: 75.14246629110978
-    - type: manhattan_pearson
-      value: 62.328196884927046
-    - type: manhattan_spearman
-      value: 75.1282792981433
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (pl-en)
-      config: pl-en
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 71.59448528829957
-    - type: cos_sim_spearman
-      value: 70.37277734222123
-    - type: euclidean_pearson
-      value: 57.63145565721123
-    - type: euclidean_spearman
-      value: 66.10113048304427
-    - type: manhattan_pearson
-      value: 57.18897811586808
-    - type: manhattan_spearman
-      value: 66.5595511215901
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (zh-en)
-      config: zh-en
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 66.37520607720838
-    - type: cos_sim_spearman
-      value: 69.92282148997948
-    - type: euclidean_pearson
-      value: 40.55768770125291
-    - type: euclidean_spearman
-      value: 55.189128944669605
-    - type: manhattan_pearson
-      value: 41.03566433468883
-    - type: manhattan_spearman
-      value: 55.61251893174558
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (es-it)
-      config: es-it
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 57.791929533771835
-    - type: cos_sim_spearman
-      value: 66.45819707662093
-    - type: euclidean_pearson
-      value: 39.03686018511092
-    - type: euclidean_spearman
-      value: 56.01282695640428
-    - type: manhattan_pearson
-      value: 38.91586623619632
-    - type: manhattan_spearman
-      value: 56.69394943612747
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (de-fr)
-      config: de-fr
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 47.82224468473866
-    - type: cos_sim_spearman
-      value: 59.467307194781164
-    - type: euclidean_pearson
-      value: 27.428459190256145
-    - type: euclidean_spearman
-      value: 60.83463107397519
-    - type: manhattan_pearson
-      value: 27.487391578496638
-    - type: manhattan_spearman
-      value: 61.281380460246496
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (de-pl)
-      config: de-pl
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 16.306666792752644
-    - type: cos_sim_spearman
-      value: 39.35486427252405
-    - type: euclidean_pearson
-      value: -2.7887154897955435
-    - type: euclidean_spearman
-      value: 27.1296051831719
-    - type: manhattan_pearson
-      value: -3.202291270581297
-    - type: manhattan_spearman
-      value: 26.32895849218158
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (fr-pl)
-      config: fr-pl
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 59.67006803805076
-    - type: cos_sim_spearman
-      value: 73.24670207647144
-    - type: euclidean_pearson
-      value: 46.91884681500483
-    - type: euclidean_spearman
-      value: 16.903085094570333
-    - type: manhattan_pearson
-      value: 46.88391675325812
-    - type: manhattan_spearman
-      value: 28.17180849095055
-  - task:
-      type: STS
-    dataset:
-      type: mteb/stsbenchmark-sts
-      name: MTEB STSBenchmark
-      config: default
-      split: test
-      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.79555591223837
-    - type: cos_sim_spearman
-      value: 85.63658602085185
-    - type: euclidean_pearson
-      value: 85.22080894037671
-    - type: euclidean_spearman
-      value: 85.54113580167038
-    - type: manhattan_pearson
-      value: 85.1639505960118
-    - type: manhattan_spearman
-      value: 85.43502665436196
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/scidocs-reranking
-      name: MTEB SciDocsRR
-      config: default
-      split: test
-      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
-    metrics:
-    - type: map
-      value: 80.73900991689766
-    - type: mrr
-      value: 94.81624131133934
-  - task:
-      type: Retrieval
-    dataset:
-      type: scifact
-      name: MTEB SciFact
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 55.678000000000004
-    - type: map_at_10
-      value: 65.135
-    - type: map_at_100
-      value: 65.824
-    - type: map_at_1000
-      value: 65.852
-    - type: map_at_3
-      value: 62.736000000000004
-    - type: map_at_5
-      value: 64.411
-    - type: mrr_at_1
-      value: 58.333
-    - type: mrr_at_10
-      value: 66.5
-    - type: mrr_at_100
-      value: 67.053
-    - type: mrr_at_1000
-      value: 67.08
-    - type: mrr_at_3
-      value: 64.944
-    - type: mrr_at_5
-      value: 65.89399999999999
-    - type: ndcg_at_1
-      value: 58.333
-    - type: ndcg_at_10
-      value: 69.34700000000001
-    - type: ndcg_at_100
-      value: 72.32
-    - type: ndcg_at_1000
-      value: 73.014
-    - type: ndcg_at_3
-      value: 65.578
-    - type: ndcg_at_5
-      value: 67.738
-    - type: precision_at_1
-      value: 58.333
-    - type: precision_at_10
-      value: 9.033
-    - type: precision_at_100
-      value: 1.0670000000000002
-    - type: precision_at_1000
-      value: 0.11199999999999999
-    - type: precision_at_3
-      value: 25.444
-    - type: precision_at_5
-      value: 16.933
-    - type: recall_at_1
-      value: 55.678000000000004
-    - type: recall_at_10
-      value: 80.72200000000001
-    - type: recall_at_100
-      value: 93.93299999999999
-    - type: recall_at_1000
-      value: 99.333
-    - type: recall_at_3
-      value: 70.783
-    - type: recall_at_5
-      value: 75.978
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/sprintduplicatequestions-pairclassification
-      name: MTEB SprintDuplicateQuestions
-      config: default
-      split: test
-      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
-    metrics:
-    - type: cos_sim_accuracy
-      value: 99.74653465346535
-    - type: cos_sim_ap
-      value: 93.01476369929063
-    - type: cos_sim_f1
-      value: 86.93009118541033
-    - type: cos_sim_precision
-      value: 88.09034907597535
-    - type: cos_sim_recall
-      value: 85.8
-    - type: dot_accuracy
-      value: 99.22970297029703
-    - type: dot_ap
-      value: 51.58725659485144
-    - type: dot_f1
-      value: 53.51351351351352
-    - type: dot_precision
-      value: 58.235294117647065
-    - type: dot_recall
-      value: 49.5
-    - type: euclidean_accuracy
-      value: 99.74356435643564
-    - type: euclidean_ap
-      value: 92.40332894384368
-    - type: euclidean_f1
-      value: 86.97838109602817
-    - type: euclidean_precision
-      value: 87.46208291203236
-    - type: euclidean_recall
-      value: 86.5
-    - type: manhattan_accuracy
-      value: 99.73069306930694
-    - type: manhattan_ap
-      value: 92.01320815721121
-    - type: manhattan_f1
-      value: 86.4135864135864
-    - type: manhattan_precision
-      value: 86.32734530938124
-    - type: manhattan_recall
-      value: 86.5
-    - type: max_accuracy
-      value: 99.74653465346535
-    - type: max_ap
-      value: 93.01476369929063
-    - type: max_f1
-      value: 86.97838109602817
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering
-      name: MTEB StackExchangeClustering
-      config: default
-      split: test
-      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
-    metrics:
-    - type: v_measure
-      value: 55.2660514302523
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering-p2p
-      name: MTEB StackExchangeClusteringP2P
-      config: default
-      split: test
-      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
-    metrics:
-    - type: v_measure
-      value: 30.4637783572547
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/stackoverflowdupquestions-reranking
-      name: MTEB StackOverflowDupQuestions
-      config: default
-      split: test
-      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
-    metrics:
-    - type: map
-      value: 49.41377758357637
-    - type: mrr
-      value: 50.138451213818854
-  - task:
-      type: Summarization
-    dataset:
-      type: mteb/summeval
-      name: MTEB SummEval
-      config: default
-      split: test
-      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
-    metrics:
-    - type: cos_sim_pearson
-      value: 28.887846011166594
-    - type: cos_sim_spearman
-      value: 30.10823258355903
-    - type: dot_pearson
-      value: 12.888049550236385
-    - type: dot_spearman
-      value: 12.827495903098123
-  - task:
-      type: Retrieval
-    dataset:
-      type: trec-covid
-      name: MTEB TRECCOVID
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 0.21
-    - type: map_at_10
-      value: 1.667
-    - type: map_at_100
-      value: 9.15
-    - type: map_at_1000
-      value: 22.927
-    - type: map_at_3
-      value: 0.573
-    - type: map_at_5
-      value: 0.915
-    - type: mrr_at_1
-      value: 80
-    - type: mrr_at_10
-      value: 87.167
-    - type: mrr_at_100
-      value: 87.167
-    - type: mrr_at_1000
-      value: 87.167
-    - type: mrr_at_3
-      value: 85.667
-    - type: mrr_at_5
-      value: 87.167
-    - type: ndcg_at_1
-      value: 76
-    - type: ndcg_at_10
-      value: 69.757
-    - type: ndcg_at_100
-      value: 52.402
-    - type: ndcg_at_1000
-      value: 47.737
-    - type: ndcg_at_3
-      value: 71.866
-    - type: ndcg_at_5
-      value: 72.225
-    - type: precision_at_1
-      value: 80
-    - type: precision_at_10
-      value: 75
-    - type: precision_at_100
-      value: 53.959999999999994
-    - type: precision_at_1000
-      value: 21.568
-    - type: precision_at_3
-      value: 76.667
-    - type: precision_at_5
-      value: 78
-    - type: recall_at_1
-      value: 0.21
-    - type: recall_at_10
-      value: 1.9189999999999998
-    - type: recall_at_100
-      value: 12.589
-    - type: recall_at_1000
-      value: 45.312000000000005
-    - type: recall_at_3
-      value: 0.61
-    - type: recall_at_5
-      value: 1.019
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (sqi-eng)
-      config: sqi-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 92.10000000000001
-    - type: f1
-      value: 90.06
-    - type: precision
-      value: 89.17333333333333
-    - type: recall
-      value: 92.10000000000001
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (fry-eng)
-      config: fry-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 56.06936416184971
-    - type: f1
-      value: 50.87508028259473
-    - type: precision
-      value: 48.97398843930635
-    - type: recall
-      value: 56.06936416184971
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (kur-eng)
-      config: kur-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 57.3170731707317
-    - type: f1
-      value: 52.96080139372822
-    - type: precision
-      value: 51.67861124382864
-    - type: recall
-      value: 57.3170731707317
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (tur-eng)
-      config: tur-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 94.3
-    - type: f1
-      value: 92.67333333333333
-    - type: precision
-      value: 91.90833333333333
-    - type: recall
-      value: 94.3
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (deu-eng)
-      config: deu-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 97.7
-    - type: f1
-      value: 97.07333333333332
-    - type: precision
-      value: 96.79500000000002
-    - type: recall
-      value: 97.7
-  - task:
-      type: BitextMining
-    dataset:
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-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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-  - task:
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-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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-  - task:
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-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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-      value: 45.2
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-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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-    - type: f1
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-    - type: recall
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-    - type: recall
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-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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-  - task:
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-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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-    - type: accuracy
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-    - type: f1
-      value: 15.997867865075824
-    - type: precision
-      value: 14.882104658301346
-    - type: recall
-      value: 20.35928143712575
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (ind-eng)
-      config: ind-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 92.2
-    - type: f1
-      value: 90.25999999999999
-    - type: precision
-      value: 89.45333333333335
-    - type: recall
-      value: 92.2
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (tuk-eng)
-      config: tuk-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 23.15270935960591
-    - type: f1
-      value: 19.65673625772148
-    - type: precision
-      value: 18.793705293464992
-    - type: recall
-      value: 23.15270935960591
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (max-eng)
-      config: max-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 59.154929577464785
-    - type: f1
-      value: 52.3868463305083
-    - type: precision
-      value: 50.14938113529662
-    - type: recall
-      value: 59.154929577464785
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (swh-eng)
-      config: swh-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 70.51282051282051
-    - type: f1
-      value: 66.8089133089133
-    - type: precision
-      value: 65.37645687645687
-    - type: recall
-      value: 70.51282051282051
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (hin-eng)
-      config: hin-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 94.6
-    - type: f1
-      value: 93
-    - type: precision
-      value: 92.23333333333333
-    - type: recall
-      value: 94.6
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (dsb-eng)
-      config: dsb-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 38.62212943632568
-    - type: f1
-      value: 34.3278276962583
-    - type: precision
-      value: 33.07646935732408
-    - type: recall
-      value: 38.62212943632568
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (ber-eng)
-      config: ber-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 28.1
-    - type: f1
-      value: 23.579609223054604
-    - type: precision
-      value: 22.39622774921555
-    - type: recall
-      value: 28.1
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (tam-eng)
-      config: tam-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 88.27361563517914
-    - type: f1
-      value: 85.12486427795874
-    - type: precision
-      value: 83.71335504885994
-    - type: recall
-      value: 88.27361563517914
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (slk-eng)
-      config: slk-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 88.6
-    - type: f1
-      value: 86.39928571428571
-    - type: precision
-      value: 85.4947557997558
-    - type: recall
-      value: 88.6
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (tgl-eng)
-      config: tgl-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 86.5
-    - type: f1
-      value: 83.77952380952381
-    - type: precision
-      value: 82.67602564102565
-    - type: recall
-      value: 86.5
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (ast-eng)
-      config: ast-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 79.52755905511812
-    - type: f1
-      value: 75.3055868016498
-    - type: precision
-      value: 73.81889763779527
-    - type: recall
-      value: 79.52755905511812
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (mkd-eng)
-      config: mkd-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 77.9
-    - type: f1
-      value: 73.76261904761905
-    - type: precision
-      value: 72.11670995670995
-    - type: recall
-      value: 77.9
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (khm-eng)
-      config: khm-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 53.8781163434903
-    - type: f1
-      value: 47.25804051288816
-    - type: precision
-      value: 45.0603482390186
-    - type: recall
-      value: 53.8781163434903
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (ces-eng)
-      config: ces-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 91.10000000000001
-    - type: f1
-      value: 88.88
-    - type: precision
-      value: 87.96333333333334
-    - type: recall
-      value: 91.10000000000001
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (tzl-eng)
-      config: tzl-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 38.46153846153847
-    - type: f1
-      value: 34.43978243978244
-    - type: precision
-      value: 33.429487179487175
-    - type: recall
-      value: 38.46153846153847
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (urd-eng)
-      config: urd-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 88.9
-    - type: f1
-      value: 86.19888888888887
-    - type: precision
-      value: 85.07440476190476
-    - type: recall
-      value: 88.9
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (ara-eng)
-      config: ara-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 85.9
-    - type: f1
-      value: 82.58857142857143
-    - type: precision
-      value: 81.15666666666667
-    - type: recall
-      value: 85.9
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (kor-eng)
-      config: kor-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 86.8
-    - type: f1
-      value: 83.36999999999999
-    - type: precision
-      value: 81.86833333333333
-    - type: recall
-      value: 86.8
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (yid-eng)
-      config: yid-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 68.51415094339622
-    - type: f1
-      value: 63.195000099481234
-    - type: precision
-      value: 61.394033442972116
-    - type: recall
-      value: 68.51415094339622
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (fin-eng)
-      config: fin-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 88.5
-    - type: f1
-      value: 86.14603174603175
-    - type: precision
-      value: 85.1162037037037
-    - type: recall
-      value: 88.5
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (tha-eng)
-      config: tha-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 95.62043795620438
-    - type: f1
-      value: 94.40389294403892
-    - type: precision
-      value: 93.7956204379562
-    - type: recall
-      value: 95.62043795620438
-  - task:
-      type: BitextMining
-    dataset:
-      type: mteb/tatoeba-bitext-mining
-      name: MTEB Tatoeba (wuu-eng)
-      config: wuu-eng
-      split: test
-      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
-    metrics:
-    - type: accuracy
-      value: 81.8
-    - type: f1
-      value: 78.6532178932179
-    - type: precision
-      value: 77.46348795840176
-    - type: recall
-      value: 81.8
-  - task:
-      type: Retrieval
-    dataset:
-      type: webis-touche2020
-      name: MTEB Touche2020
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 2.603
-    - type: map_at_10
-      value: 8.5
-    - type: map_at_100
-      value: 12.985
-    - type: map_at_1000
-      value: 14.466999999999999
-    - type: map_at_3
-      value: 4.859999999999999
-    - type: map_at_5
-      value: 5.817
-    - type: mrr_at_1
-      value: 28.571
-    - type: mrr_at_10
-      value: 42.331
-    - type: mrr_at_100
-      value: 43.592999999999996
-    - type: mrr_at_1000
-      value: 43.592999999999996
-    - type: mrr_at_3
-      value: 38.435
-    - type: mrr_at_5
-      value: 39.966
-    - type: ndcg_at_1
-      value: 26.531
-    - type: ndcg_at_10
-      value: 21.353
-    - type: ndcg_at_100
-      value: 31.087999999999997
-    - type: ndcg_at_1000
-      value: 43.163000000000004
-    - type: ndcg_at_3
-      value: 22.999
-    - type: ndcg_at_5
-      value: 21.451
-    - type: precision_at_1
-      value: 28.571
-    - type: precision_at_10
-      value: 19.387999999999998
-    - type: precision_at_100
-      value: 6.265
-    - type: precision_at_1000
-      value: 1.4160000000000001
-    - type: precision_at_3
-      value: 24.490000000000002
-    - type: precision_at_5
-      value: 21.224
-    - type: recall_at_1
-      value: 2.603
-    - type: recall_at_10
-      value: 14.474
-    - type: recall_at_100
-      value: 40.287
-    - type: recall_at_1000
-      value: 76.606
-    - type: recall_at_3
-      value: 5.978
-    - type: recall_at_5
-      value: 7.819
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/toxic_conversations_50k
-      name: MTEB ToxicConversationsClassification
-      config: default
-      split: test
-      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
-    metrics:
-    - type: accuracy
-      value: 69.7848
-    - type: ap
-      value: 13.661023167088224
-    - type: f1
-      value: 53.61686134460943
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/tweet_sentiment_extraction
-      name: MTEB TweetSentimentExtractionClassification
-      config: default
-      split: test
-      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
-    metrics:
-    - type: accuracy
-      value: 61.28183361629882
-    - type: f1
-      value: 61.55481034919965
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/twentynewsgroups-clustering
-      name: MTEB TwentyNewsgroupsClustering
-      config: default
-      split: test
-      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
-    metrics:
-    - type: v_measure
-      value: 35.972128420092396
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twittersemeval2015-pairclassification
-      name: MTEB TwitterSemEval2015
-      config: default
-      split: test
-      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
-    metrics:
-    - type: cos_sim_accuracy
-      value: 85.59933241938367
-    - type: cos_sim_ap
-      value: 72.20760361208136
-    - type: cos_sim_f1
-      value: 66.4447731755424
-    - type: cos_sim_precision
-      value: 62.35539102267469
-    - type: cos_sim_recall
-      value: 71.10817941952506
-    - type: dot_accuracy
-      value: 78.98313166835548
-    - type: dot_ap
-      value: 44.492521645493795
-    - type: dot_f1
-      value: 45.814889336016094
-    - type: dot_precision
-      value: 37.02439024390244
-    - type: dot_recall
-      value: 60.07915567282321
-    - type: euclidean_accuracy
-      value: 85.3907134767837
-    - type: euclidean_ap
-      value: 71.53847289080343
-    - type: euclidean_f1
-      value: 65.95952206778834
-    - type: euclidean_precision
-      value: 61.31006346328196
-    - type: euclidean_recall
-      value: 71.37203166226914
-    - type: manhattan_accuracy
-      value: 85.40859510043511
-    - type: manhattan_ap
-      value: 71.49664104395515
-    - type: manhattan_f1
-      value: 65.98569969356485
-    - type: manhattan_precision
-      value: 63.928748144482924
-    - type: manhattan_recall
-      value: 68.17941952506597
-    - type: max_accuracy
-      value: 85.59933241938367
-    - type: max_ap
-      value: 72.20760361208136
-    - type: max_f1
-      value: 66.4447731755424
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twitterurlcorpus-pairclassification
-      name: MTEB TwitterURLCorpus
-      config: default
-      split: test
-      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
-    metrics:
-    - type: cos_sim_accuracy
-      value: 88.83261536073273
-    - type: cos_sim_ap
-      value: 85.48178133644264
-    - type: cos_sim_f1
-      value: 77.87816307403935
-    - type: cos_sim_precision
-      value: 75.88953021114926
-    - type: cos_sim_recall
-      value: 79.97382198952879
-    - type: dot_accuracy
-      value: 79.76287499514883
-    - type: dot_ap
-      value: 59.17438838475084
-    - type: dot_f1
-      value: 56.34566667855996
-    - type: dot_precision
-      value: 52.50349092359864
-    - type: dot_recall
-      value: 60.794579611949494
-    - type: euclidean_accuracy
-      value: 88.76857996662397
-    - type: euclidean_ap
-      value: 85.22764834359887
-    - type: euclidean_f1
-      value: 77.65379751543554
-    - type: euclidean_precision
-      value: 75.11152683839401
-    - type: euclidean_recall
-      value: 80.37419156144134
-    - type: manhattan_accuracy
-      value: 88.6987231730508
-    - type: manhattan_ap
-      value: 85.18907981724007
-    - type: manhattan_f1
-      value: 77.51967028849757
-    - type: manhattan_precision
-      value: 75.49992701795358
-    - type: manhattan_recall
-      value: 79.65044656606098
-    - type: max_accuracy
-      value: 88.83261536073273
-    - type: max_ap
-      value: 85.48178133644264
-    - type: max_f1
-      value: 77.87816307403935
-language:
-- multilingual
-- af
-- am
-- ar
-- as
-- az
-- be
-- bg
-- bn
-- br
-- bs
-- ca
-- cs
-- cy
-- da
-- de
-- el
-- en
-- eo
-- es
-- et
-- eu
-- fa
-- fi
-- fr
-- fy
-- ga
-- gd
-- gl
-- gu
-- ha
-- he
-- hi
-- hr
-- hu
-- hy
-- id
-- is
-- it
-- ja
-- jv
-- ka
-- kk
-- km
-- kn
-- ko
-- ku
-- ky
-- la
-- lo
-- lt
-- lv
-- mg
-- mk
-- ml
-- mn
-- mr
-- ms
-- my
-- ne
-- nl
-- 'no'
-- om
-- or
-- pa
-- pl
-- ps
-- pt
-- ro
-- ru
-- sa
-- sd
-- si
-- sk
-- sl
-- so
-- sq
-- sr
-- su
-- sv
-- sw
-- ta
-- te
-- th
-- tl
-- tr
-- ug
-- uk
-- ur
-- uz
-- vi
-- xh
-- yi
-- zh
-license: mit
----
-
-## Multilingual-E5-base
-
-[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
-Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
-
-This model has 12 layers and the embedding size is 768.
-
-## Usage
-
-Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
-
-```python
-import torch.nn.functional as F
-
-from torch import Tensor
-from transformers import AutoTokenizer, AutoModel
-
-
-def average_pool(last_hidden_states: Tensor,
-                 attention_mask: Tensor) -> Tensor:
-    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
-    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
-
-
-# Each input text should start with "query: " or "passage: ", even for non-English texts.
-# For tasks other than retrieval, you can simply use the "query: " prefix.
-input_texts = ['query: how much protein should a female eat',
-               'query: 南瓜的家常做法',
-               "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
-               "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"]
-
-tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-base')
-model = AutoModel.from_pretrained('intfloat/multilingual-e5-base')
-
-# Tokenize the input texts
-batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
-
-outputs = model(**batch_dict)
-embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
-
-# (Optionally) normalize embeddings
-embeddings = F.normalize(embeddings, p=2, dim=1)
-scores = (embeddings[:2] @ embeddings[2:].T) * 100
-print(scores.tolist())
-```
-
-## Supported Languages
-
-This model is initialized from [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
-and continually trained on a mixture of multilingual datasets.
-It supports 100 languages from xlm-roberta,
-but low-resource languages may see performance degradation.
-
-## Training Details
-
-Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
-
-## Benchmark Evaluation
-
-Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results 
-on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
-
-## Citation
-
-If you find our paper or models helpful, please consider cite as follows:
-
-```
-@article{wang2022text,
-  title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
-  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
-  journal={arXiv preprint arXiv:2212.03533},
-  year={2022}
-}
-```
-
-## Limitations
-
-Long texts will be truncated to at most 512 tokens.
\ No newline at end of file
diff --git a/spaces/awacke1/ChatGPT-Streamlit-2/README.md b/spaces/awacke1/ChatGPT-Streamlit-2/README.md
deleted file mode 100644
index 3303f511fd29dbde58b2151d15d09fd046ba9122..0000000000000000000000000000000000000000
--- a/spaces/awacke1/ChatGPT-Streamlit-2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ChatGPT Streamlit 2
-emoji: 😻😻😻
-colorFrom: purple
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/awacke1/GraphVis3/README.md b/spaces/awacke1/GraphVis3/README.md
deleted file mode 100644
index 473b9624416360e25df6719b61ee30f357941f25..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GraphVis3/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 📊Graph Vis
-emoji: 📊📊
-colorFrom: purple
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.10.0
-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/awacke1/TrapFlamenco/style.css b/spaces/awacke1/TrapFlamenco/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/awacke1/TrapFlamenco/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/azapi/img-to-music/app.py b/spaces/azapi/img-to-music/app.py
deleted file mode 100644
index a325b27b8177f9bca294439724ec16c2da2f0169..0000000000000000000000000000000000000000
--- a/spaces/azapi/img-to-music/app.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import time
-import base64
-import gradio as gr
-from sentence_transformers import SentenceTransformer
-
-import httpx
-import json
-
-import os
-import requests
-import urllib
-
-from os import path
-from pydub import AudioSegment
-
-#img_to_text = gr.Blocks.load(name="spaces/pharma/CLIP-Interrogator")
-img_to_text = gr.Blocks.load(name="spaces/fffiloni/CLIP-Interrogator-2")
-
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-def get_prompts(uploaded_image, track_duration, gen_intensity, gen_mode):
-  print("calling clip interrogator")
-  #prompt = img_to_text(uploaded_image, "ViT-L (best for Stable Diffusion 1.*)", "fast", fn_index=1)[0]
-  prompt = img_to_text(uploaded_image, 'fast', 4, fn_index=1)[0]
-  print(prompt)
-  music_result = generate_track_by_prompt(prompt, track_duration, gen_intensity, gen_mode)
-  print(music_result)
-  return music_result[0], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-
-from utils import get_tags_for_prompts, get_mubert_tags_embeddings, get_pat
-
-minilm = SentenceTransformer('all-MiniLM-L6-v2')
-mubert_tags_embeddings = get_mubert_tags_embeddings(minilm)
-
-
-def get_track_by_tags(tags, pat, duration, gen_intensity, gen_mode, maxit=20):
-    
-    r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM',
-                   json={
-                       "method": "RecordTrackTTM",
-                       "params": {
-                           "pat": pat,
-                           "duration": duration,
-                           "format": "wav",
-                           "intensity":gen_intensity,
-                           "tags": tags,
-                           "mode": gen_mode
-                       }
-                   })
-
-    rdata = json.loads(r.text)
-    assert rdata['status'] == 1, rdata['error']['text']
-    trackurl = rdata['data']['tasks'][0]['download_link']
-
-    print('Generating track ', end='')
-    for i in range(maxit):
-        r = httpx.get(trackurl)
-        if r.status_code == 200:
-            return trackurl
-        time.sleep(1)
-
-
-def generate_track_by_prompt(prompt, duration, gen_intensity, gen_mode):
-    try:
-        pat = get_pat("prodia@prodia.com")
-        _, tags = get_tags_for_prompts(minilm, mubert_tags_embeddings, [prompt, ])[0]
-        result = get_track_by_tags(tags, pat, int(duration), gen_intensity, gen_mode)
-        print(result)
-        return result, ",".join(tags), "Success"
-    except Exception as e:
-        return None, "", str(e)
-
-def convert_mp3_to_wav(mp3_filepath):
- 
-  url = mp3_filepath
-  save_as = "file.mp3"
-  
-  data = urllib.request.urlopen(url)
-
-  f = open(save_as,'wb')
-  f.write(data.read())
-  f.close()
-  
-  wave_file="file.wav"
-  
-  sound = AudioSegment.from_mp3(save_as)
-  sound.export(wave_file, format="wav")
-  
-  return wave_file
-
-article = """
-    
-    <div class="footer">
-        <p>
-         
-        Follow <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a> for future updates 🤗
-        </p>
-    </div>
-    
-    <div id="may-like-container" style="display: flex;justify-content: center;flex-direction: column;align-items: center;margin-bottom: 30px;">
-        <p style="font-size: 0.8em;margin-bottom: 4px;">You may also like: </p>
-        <div id="may-like" style="display: flex;flex-wrap: wrap;align-items: center;height: 20px;">
-            <svg height="20" width="122" style="margin-left:4px;margin-bottom: 6px;">       
-                 <a href="https://huggingface.co/spaces/fffiloni/spectrogram-to-music" target="_blank">
-                    <image href="https://img.shields.io/badge/🤗 Spaces-Riffusion-blue" src="https://img.shields.io/badge/🤗 Spaces-Riffusion-blue.png" height="20"/>
-                 </a>
-            </svg>
-            <svg height="20" width="192" style="margin-left:4px;margin-bottom: 6px;">       
-                 <a href="https://huggingface.co/spaces/Mubert/Text-to-Music" target="_blank">
-                    <image href="https://img.shields.io/badge/🤗 Spaces-Mubert_Text_to_Music-blue" src="https://img.shields.io/badge/🤗 Spaces-Mubert_Text_to_Music-blue.png" height="20"/>
-                 </a>
-            </svg>
-        </div>
-    </div>
-
-    
-"""
-
-with gr.Blocks(css="style.css") as demo:
-    with gr.Column(elem_id="col-container"):
-        
-        gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
-                <div
-                style="
-                    display: inline-flex;
-                    align-items: center;
-                    gap: 0.8rem;
-                    font-size: 1.75rem;
-                "
-                >
-                <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
-                    Image to Music
-                </h1>
-                </div>
-                <p style="margin-bottom: 10px; font-size: 94%">
-                Sends an image in to <a href="https://huggingface.co/spaces/pharma/CLIP-Interrogator" target="_blank">CLIP Interrogator</a>
-                to generate a text prompt which is then run through 
-                <a href="https://huggingface.co/Mubert" target="_blank">Mubert</a> text-to-music to generate music from the input image!
-                </p>
-            </div>""")
-    
-        input_img = gr.Image(type="filepath", elem_id="input-img")
-        music_output = gr.Audio(label="Result", type="filepath", elem_id="music-output").style(height="5rem")
-        
-        with gr.Group(elem_id="share-btn-container"):
-            community_icon = gr.HTML(community_icon_html, visible=False)
-            loading_icon = gr.HTML(loading_icon_html, visible=False)
-            share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-
-        with gr.Accordion(label="Music Generation Options", open=False):
-            track_duration = gr.Slider(minimum=20, maximum=120, value=30, step=5, label="Track duration", elem_id="duration-inp")
-            with gr.Row():
-                gen_intensity = gr.Dropdown(choices=["low", "medium", "high"], value="medium", label="Intensity")
-                gen_mode = gr.Radio(label="mode", choices=["track", "loop"], value="track")
-        
-        generate = gr.Button("Generate Music from Image")
-
-        gr.HTML(article)
-    
-    generate.click(get_prompts, inputs=[input_img,track_duration,gen_intensity,gen_mode], outputs=[music_output, share_button, community_icon, loading_icon], api_name="i2m")
-    share_button.click(None, [], [], _js=share_js)
-
-demo.queue(max_size=32, concurrency_count=20).launch()
\ No newline at end of file
diff --git a/spaces/badongtakla/ithaca/ithaca/util/loss.py b/spaces/badongtakla/ithaca/ithaca/util/loss.py
deleted file mode 100644
index 58a60f05e77bafe5dfdd84935f42da72a568fa94..0000000000000000000000000000000000000000
--- a/spaces/badongtakla/ithaca/ithaca/util/loss.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# Copyright 2021 the Ithaca 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
-#
-#     https://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.
-"""Loss functions."""
-import chex
-from flax.deprecated import nn
-import jax
-import jax.numpy as jnp
-
-
-def smooth_labels(labels, num_classes, label_smoothing):
-  if not 0 <= label_smoothing < 1:
-    raise ValueError(
-        f"'label_smoothing is {label_smoothing} and should be in [0, 1)")
-  one = jax.lax.convert_element_type(1, labels.dtype)
-  label_smoothing = jax.lax.convert_element_type(label_smoothing,
-                                                 labels.dtype)
-  num_classes = jax.lax.convert_element_type(num_classes, labels.dtype)
-  return (one - label_smoothing) * labels + (label_smoothing / num_classes)
-
-
-def categorical_kl_divergence(p_logits, q_logits, temperature=1.):
-  """Compute the KL between two categorical distributions from their logits.
-
-  Args:
-    p_logits: unnormalized logits for the first distribution.
-    q_logits: unnormalized logits for the second distribution.
-    temperature: the temperature for the softmax distribution, defaults at 1.
-
-  Returns:
-    the kl divergence between the distributions.
-  """
-  chex.assert_type([p_logits, q_logits], float)
-
-  p_logits /= temperature
-  q_logits /= temperature
-
-  p = jax.nn.softmax(p_logits)
-  log_p = jax.nn.log_softmax(p_logits)
-  log_q = jax.nn.log_softmax(q_logits)
-  kl = jnp.sum(p * (log_p - log_q), axis=-1)
-  return jax.nn.relu(kl)  # Guard against numerical issues giving negative KL.
-
-
-def cross_entropy_label_smoothing_loss(logits,
-                                       labels,
-                                       mask=None,
-                                       label_smoothing=0.1):
-  """Cross entropy loss with label smoothing."""
-
-  num_classes = logits.shape[-1]
-  labels_onehot = jax.nn.one_hot(labels, num_classes, dtype=logits.dtype)
-  if label_smoothing > 0:
-    labels_onehot = smooth_labels(labels_onehot, num_classes, label_smoothing)
-
-  loss = -jnp.sum(labels_onehot * jax.nn.log_softmax(logits), axis=-1)
-  if mask is not None:
-    loss = jnp.multiply(loss, mask.astype(logits.dtype))
-  return loss
-
-
-@jax.vmap
-def cross_entropy_loss(logits, label):
-  logits = nn.log_softmax(logits)
-  return -logits[label]
-
-
-def cross_entropy_mask_loss(logits, label, mask):
-  nll = -nn.log_softmax(logits)[label]
-  loss = jnp.multiply(nll, mask.astype(logits.dtype))
-  return loss
-
-
-def date_loss_l2(pred,
-                 target_min,
-                 target_max,
-                 mask):
-  """L2 loss function for dates."""
-  pred = jnp.squeeze(pred, 0)
-
-  loss = 0.
-  loss += (pred - target_min)**2 * jnp.less(pred, target_min).astype(
-      pred.dtype)
-  loss += (pred - target_max)**2 * jnp.greater(pred, target_max).astype(
-      pred.dtype)
-
-  # Mask loss
-  loss = jnp.multiply(loss, mask.astype(loss.dtype))
-  return loss
-
-
-def date_loss_l1(pred,
-                 target_min,
-                 target_max,
-                 mask):
-  """L1 loss function for dates."""
-  pred = jnp.squeeze(pred, 0)
-
-  loss = 0.
-  loss += jnp.abs(pred - target_min) * jnp.less(pred, target_min).astype(
-      pred.dtype)
-  loss += jnp.abs(pred - target_max) * jnp.greater(pred, target_max).astype(
-      pred.dtype)
-
-  # Mask loss
-  loss = jnp.multiply(loss, mask.astype(loss.dtype))
-  return loss
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DRigidBody.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DRigidBody.js
deleted file mode 100644
index c4d55a89265bd12d80d6bbbf4fefee2a7fba813f..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DRigidBody.js
+++ /dev/null
@@ -1,442 +0,0 @@
-/**
- * 	SEA3D - Rigid Body
- * 	@author Sunag / http://www.sunag.com.br/
- */
-
-'use strict';
-
-//
-//	Sphere
-//
-
-SEA3D.Sphere = function ( name, data, sea3d ) {
-
-	this.name = name;
-	this.data = data;
-	this.sea3d = sea3d;
-
-	this.radius = data.readFloat();
-
-};
-
-SEA3D.Sphere.prototype.type = "sph";
-
-//
-//	Box
-//
-
-SEA3D.Box = function ( name, data, sea3d ) {
-
-	this.name = name;
-	this.data = data;
-	this.sea3d = sea3d;
-
-	this.width = data.readFloat();
-	this.height = data.readFloat();
-	this.depth = data.readFloat();
-
-};
-
-SEA3D.Box.prototype.type = "box";
-
-//
-//	Cone
-//
-
-SEA3D.Cone = function ( name, data, sea3d ) {
-
-	this.name = name;
-	this.data = data;
-	this.sea3d = sea3d;
-
-	this.radius = data.readFloat();
-	this.height = data.readFloat();
-
-};
-
-SEA3D.Cone.prototype.type = "cone";
-
-//
-//	Capsule
-//
-
-SEA3D.Capsule = function ( name, data, sea3d ) {
-
-	this.name = name;
-	this.data = data;
-	this.sea3d = sea3d;
-
-	this.radius = data.readFloat();
-	this.height = data.readFloat();
-
-};
-
-SEA3D.Capsule.prototype.type = "cap";
-
-//
-//	Cylinder
-//
-
-SEA3D.Cylinder = function ( name, data, sea3d ) {
-
-	this.name = name;
-	this.data = data;
-	this.sea3d = sea3d;
-
-	this.radius = data.readFloat();
-	this.height = data.readFloat();
-
-};
-
-SEA3D.Cylinder.prototype.type = "cyl";
-
-//
-//	Convex Geometry
-//
-
-SEA3D.ConvexGeometry = function ( name, data, sea3d ) {
-
-	this.name = name;
-	this.data = data;
-	this.sea3d = sea3d;
-
-	this.geometry = sea3d.getObject( data.readUInt() );
-	this.subGeometryIndex = data.readUByte();
-
-};
-
-SEA3D.ConvexGeometry.prototype.type = "gs";
-
-//
-//	Triangle Geometry
-//
-
-SEA3D.TriangleGeometry = function ( name, data, sea3d ) {
-
-	this.name = name;
-	this.data = data;
-	this.sea3d = sea3d;
-
-	this.geometry = sea3d.getObject( data.readUInt() );
-	this.subGeometryIndex = data.readUByte();
-
-};
-
-SEA3D.TriangleGeometry.prototype.type = "sgs";
-
-//
-//	Compound
-//
-
-SEA3D.Compound = function ( name, data, sea3d ) {
-
-	this.name = name;
-	this.data = data;
-	this.sea3d = sea3d;
-
-	this.compounds = [];
-
-	var count = data.readUByte();
-
-	for ( var i = 0; i < count; i ++ ) {
-
-		this.compounds.push( {
-			shape: sea3d.getObject( data.readUInt() ),
-			transform: data.readMatrix()
-		} );
-
-	}
-
-};
-
-SEA3D.Compound.prototype.type = "cmps";
-
-//
-//	Physics
-//
-
-SEA3D.Physics = function ( name, data, sea3d ) {
-
-	this.name = name;
-	this.data = data;
-	this.sea3d = sea3d;
-
-	this.attrib = data.readUShort();
-
-	this.shape = sea3d.getObject( data.readUInt() );
-
-	if ( this.attrib & 1 ) this.target = sea3d.getObject( data.readUInt() );
-	else this.transform = data.readMatrix();
-
-	if ( this.attrib & 2 ) this.offset = data.readMatrix();
-
-	if ( this.attrib & 4 ) this.scripts = data.readScriptList( sea3d );
-
-	if ( this.attrib & 16 ) this.attributes = sea3d.getObject( data.readUInt() );
-
-};
-
-SEA3D.Physics.prototype.readTag = function ( kind, data, size ) {
-
-};
-
-//
-//	Rigidy Body Base
-//
-
-SEA3D.RigidBodyBase = function ( name, data, sea3d ) {
-
-	SEA3D.Physics.call( this, name, data, sea3d );
-
-	if ( this.attrib & 32 ) {
-
-		this.linearDamping = data.readFloat();
-		this.angularDamping = data.readFloat();
-
-	} else {
-
-		this.linearDamping = 0;
-		this.angularDamping = 0;
-
-	}
-
-	this.mass = data.readFloat();
-	this.friction = data.readFloat();
-	this.restitution = data.readFloat();
-
-};
-
-SEA3D.RigidBodyBase.prototype = Object.create( SEA3D.Physics.prototype );
-SEA3D.RigidBodyBase.prototype.constructor = SEA3D.RigidBodyBase;
-
-//
-//	Rigidy Body
-//
-
-SEA3D.RigidBody = function ( name, data, sea3d ) {
-
-	SEA3D.RigidBodyBase.call( this, name, data, sea3d );
-
-	data.readTags( this.readTag.bind( this ) );
-
-};
-
-SEA3D.RigidBody.prototype = Object.create( SEA3D.RigidBodyBase.prototype );
-SEA3D.RigidBody.prototype.constructor = SEA3D.RigidBody;
-
-SEA3D.RigidBody.prototype.type = "rb";
-
-//
-//	Car Controller
-//
-
-SEA3D.CarController = function ( name, data, sea3d ) {
-
-	SEA3D.RigidBodyBase.call( this, name, data, sea3d );
-
-	this.suspensionStiffness = data.readFloat();
-	this.suspensionCompression = data.readFloat();
-	this.suspensionDamping = data.readFloat();
-	this.maxSuspensionTravelCm = data.readFloat();
-	this.frictionSlip = data.readFloat();
-	this.maxSuspensionForce = data.readFloat();
-
-	this.dampingCompression = data.readFloat();
-	this.dampingRelaxation = data.readFloat();
-
-	var count = data.readUByte();
-
-	this.wheel = [];
-
-	for ( var i = 0; i < count; i ++ ) {
-
-		this.wheel[ i ] = new SEA3D.CarController.Wheel( data, sea3d );
-
-	}
-
-	data.readTags( this.readTag.bind( this ) );
-
-};
-
-SEA3D.CarController.Wheel = function ( data, sea3d ) {
-
-	this.data = data;
-	this.sea3d = sea3d;
-
-	this.attrib = data.readUShort();
-
-	this.isFront = ( this.attrib & 1 ) != 0;
-
-	if ( this.attrib & 2 ) {
-
-		this.target = sea3d.getObject( data.readUInt() );
-
-	}
-
-	if ( this.attrib & 4 ) {
-
-		this.offset = data.readMatrix();
-
-	}
-
-	this.pos = data.readVector3();
-	this.dir = data.readVector3();
-	this.axle = data.readVector3();
-
-	this.radius = data.readFloat();
-	this.suspensionRestLength = data.readFloat();
-
-};
-
-SEA3D.CarController.prototype = Object.create( SEA3D.RigidBodyBase.prototype );
-SEA3D.CarController.prototype.constructor = SEA3D.CarController;
-
-SEA3D.CarController.prototype.type = "carc";
-
-//
-//	Constraints
-//
-
-SEA3D.Constraints = function ( name, data, sea3d ) {
-
-	this.name = name;
-	this.data = data;
-	this.sea3d = sea3d;
-
-	this.attrib = data.readUShort();
-
-	this.disableCollisionsBetweenBodies = this.attrib & 1 != 0;
-
-	this.targetA = sea3d.getObject( data.readUInt() );
-	this.pointA = data.readVector3();
-
-	if ( this.attrib & 2 ) {
-
-		this.targetB = sea3d.getObject( data.readUInt() );
-		this.pointB = data.readVector3();
-
-	}
-
-};
-
-//
-//	P2P Constraint
-//
-
-SEA3D.P2PConstraint = function ( name, data, sea3d ) {
-
-	this.name = name;
-	this.data = data;
-	this.sea3d = sea3d;
-
-	SEA3D.Constraints.call( this, name, data, sea3d );
-
-};
-
-SEA3D.P2PConstraint.prototype = Object.create( SEA3D.Constraints.prototype );
-SEA3D.P2PConstraint.prototype.constructor = SEA3D.P2PConstraint;
-
-SEA3D.P2PConstraint.prototype.type = "p2pc";
-
-//
-//	Hinge Constraint
-//
-
-SEA3D.HingeConstraint = function ( name, data, sea3d ) {
-
-	SEA3D.Constraints.call( this, name, data, sea3d );
-
-	this.axisA = data.readVector3();
-
-	if ( this.attrib & 1 ) {
-
-		this.axisB = data.readVector3();
-
-	}
-
-	if ( this.attrib & 4 ) {
-
-		this.limit = {
-			low: data.readFloat(),
-			high: data.readFloat(),
-			softness: data.readFloat(),
-			biasFactor: data.readFloat(),
-			relaxationFactor: data.readFloat()
-		};
-
-	}
-
-	if ( this.attrib & 8 ) {
-
-		this.angularMotor = {
-			velocity: data.readFloat(),
-			impulse: data.readFloat()
-		};
-
-	}
-
-};
-
-SEA3D.HingeConstraint.prototype = Object.create( SEA3D.Constraints.prototype );
-SEA3D.HingeConstraint.prototype.constructor = SEA3D.HingeConstraint;
-
-SEA3D.HingeConstraint.prototype.type = "hnec";
-
-//
-//	Cone Twist Constraint
-//
-
-SEA3D.ConeTwistConstraint = function ( name, data, sea3d ) {
-
-	SEA3D.Constraints.call( this, name, data, sea3d );
-
-	this.axisA = data.readVector3();
-
-	if ( this.attrib & 1 ) {
-
-		this.axisB = data.readVector3();
-
-	}
-
-	if ( this.attrib & 4 ) {
-
-		this.limit = {
-			swingSpan1: data.readFloat(),
-			swingSpan2: data.readFloat(),
-			twistSpan: data.readFloat(),
-			softness: data.readFloat(),
-			biasFactor: data.readFloat(),
-			relaxationFactor: data.readFloat()
-		};
-
-	}
-
-};
-
-SEA3D.ConeTwistConstraint.prototype = Object.create( SEA3D.Constraints.prototype );
-SEA3D.ConeTwistConstraint.prototype.constructor = SEA3D.ConeTwistConstraint;
-
-SEA3D.ConeTwistConstraint.prototype.type = "ctwc";
-
-//
-//	Extension
-//
-
-SEA3D.File.setExtension( function () {
-
-	// PHYSICS
-	this.addClass( SEA3D.Sphere );
-	this.addClass( SEA3D.Box );
-	this.addClass( SEA3D.Cone );
-	this.addClass( SEA3D.Capsule );
-	this.addClass( SEA3D.Cylinder );
-	this.addClass( SEA3D.ConvexGeometry );
-	this.addClass( SEA3D.TriangleGeometry );
-	this.addClass( SEA3D.Compound );
-	this.addClass( SEA3D.RigidBody );
-	this.addClass( SEA3D.P2PConstraint );
-	this.addClass( SEA3D.HingeConstraint );
-	this.addClass( SEA3D.ConeTwistConstraint );
-	this.addClass( SEA3D.CarController );
-
-} );
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/extras/curves/CubicBezierCurve3.js b/spaces/banana-projects/web3d/node_modules/three/src/extras/curves/CubicBezierCurve3.js
deleted file mode 100644
index 3864f011e65e0c2a251e780adc9b99796ac0bd80..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/extras/curves/CubicBezierCurve3.js
+++ /dev/null
@@ -1,80 +0,0 @@
-import { Curve } from '../core/Curve.js';
-import { CubicBezier } from '../core/Interpolations.js';
-import { Vector3 } from '../../math/Vector3.js';
-
-
-function CubicBezierCurve3( v0, v1, v2, v3 ) {
-
-	Curve.call( this );
-
-	this.type = 'CubicBezierCurve3';
-
-	this.v0 = v0 || new Vector3();
-	this.v1 = v1 || new Vector3();
-	this.v2 = v2 || new Vector3();
-	this.v3 = v3 || new Vector3();
-
-}
-
-CubicBezierCurve3.prototype = Object.create( Curve.prototype );
-CubicBezierCurve3.prototype.constructor = CubicBezierCurve3;
-
-CubicBezierCurve3.prototype.isCubicBezierCurve3 = true;
-
-CubicBezierCurve3.prototype.getPoint = function ( t, optionalTarget ) {
-
-	var point = optionalTarget || new Vector3();
-
-	var v0 = this.v0, v1 = this.v1, v2 = this.v2, v3 = this.v3;
-
-	point.set(
-		CubicBezier( t, v0.x, v1.x, v2.x, v3.x ),
-		CubicBezier( t, v0.y, v1.y, v2.y, v3.y ),
-		CubicBezier( t, v0.z, v1.z, v2.z, v3.z )
-	);
-
-	return point;
-
-};
-
-CubicBezierCurve3.prototype.copy = function ( source ) {
-
-	Curve.prototype.copy.call( this, source );
-
-	this.v0.copy( source.v0 );
-	this.v1.copy( source.v1 );
-	this.v2.copy( source.v2 );
-	this.v3.copy( source.v3 );
-
-	return this;
-
-};
-
-CubicBezierCurve3.prototype.toJSON = function () {
-
-	var data = Curve.prototype.toJSON.call( this );
-
-	data.v0 = this.v0.toArray();
-	data.v1 = this.v1.toArray();
-	data.v2 = this.v2.toArray();
-	data.v3 = this.v3.toArray();
-
-	return data;
-
-};
-
-CubicBezierCurve3.prototype.fromJSON = function ( json ) {
-
-	Curve.prototype.fromJSON.call( this, json );
-
-	this.v0.fromArray( json.v0 );
-	this.v1.fromArray( json.v1 );
-	this.v2.fromArray( json.v2 );
-	this.v3.fromArray( json.v3 );
-
-	return this;
-
-};
-
-
-export { CubicBezierCurve3 };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/loaders/DataTextureLoader.js b/spaces/banana-projects/web3d/node_modules/three/src/loaders/DataTextureLoader.js
deleted file mode 100644
index 5363a79f16d7629af3beba42ee8cd7a1dbff3252..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/loaders/DataTextureLoader.js
+++ /dev/null
@@ -1,102 +0,0 @@
-import { LinearFilter, LinearMipMapLinearFilter, ClampToEdgeWrapping } from '../constants.js';
-import { FileLoader } from './FileLoader.js';
-import { DataTexture } from '../textures/DataTexture.js';
-import { DefaultLoadingManager } from './LoadingManager.js';
-
-/**
- * @author Nikos M. / https://github.com/foo123/
- *
- * Abstract Base class to load generic binary textures formats (rgbe, hdr, ...)
- */
-
-function DataTextureLoader( manager ) {
-
-	this.manager = ( manager !== undefined ) ? manager : DefaultLoadingManager;
-
-	// override in sub classes
-	this._parser = null;
-
-}
-
-Object.assign( DataTextureLoader.prototype, {
-
-	load: function ( url, onLoad, onProgress, onError ) {
-
-		var scope = this;
-
-		var texture = new DataTexture();
-
-		var loader = new FileLoader( this.manager );
-		loader.setResponseType( 'arraybuffer' );
-		loader.setPath( this.path );
-		loader.load( url, function ( buffer ) {
-
-			var texData = scope._parser( buffer );
-
-			if ( ! texData ) return;
-
-			if ( texData.image !== undefined ) {
-
-				texture.image = texData.image;
-
-			} else if ( texData.data !== undefined ) {
-
-				texture.image.width = texData.width;
-				texture.image.height = texData.height;
-				texture.image.data = texData.data;
-
-			}
-
-			texture.wrapS = texData.wrapS !== undefined ? texData.wrapS : ClampToEdgeWrapping;
-			texture.wrapT = texData.wrapT !== undefined ? texData.wrapT : ClampToEdgeWrapping;
-
-			texture.magFilter = texData.magFilter !== undefined ? texData.magFilter : LinearFilter;
-			texture.minFilter = texData.minFilter !== undefined ? texData.minFilter : LinearMipMapLinearFilter;
-
-			texture.anisotropy = texData.anisotropy !== undefined ? texData.anisotropy : 1;
-
-			if ( texData.format !== undefined ) {
-
-				texture.format = texData.format;
-
-			}
-			if ( texData.type !== undefined ) {
-
-				texture.type = texData.type;
-
-			}
-
-			if ( texData.mipmaps !== undefined ) {
-
-				texture.mipmaps = texData.mipmaps;
-
-			}
-
-			if ( texData.mipmapCount === 1 ) {
-
-				texture.minFilter = LinearFilter;
-
-			}
-
-			texture.needsUpdate = true;
-
-			if ( onLoad ) onLoad( texture, texData );
-
-		}, onProgress, onError );
-
-
-		return texture;
-
-	},
-
-	setPath: function ( value ) {
-
-		this.path = value;
-		return this;
-
-	}
-
-} );
-
-
-export { DataTextureLoader };
diff --git a/spaces/bhasker412/IDD-YOLO-Tracking/README.md b/spaces/bhasker412/IDD-YOLO-Tracking/README.md
deleted file mode 100644
index 392f5df975a6e836fcff9ecb29c35427bbd498af..0000000000000000000000000000000000000000
--- a/spaces/bhasker412/IDD-YOLO-Tracking/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Yolov7
-emoji: 🚀
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-duplicated_from: shriarul5273/Yolov7
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/bigjoker/stable-diffusion-webui/modules/sd_hijack_unet.py b/spaces/bigjoker/stable-diffusion-webui/modules/sd_hijack_unet.py
deleted file mode 100644
index ac931c80df9b7c34b342d560bc969ff461b0afc7..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/modules/sd_hijack_unet.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import torch
-from packaging import version
-
-from modules import devices
-from modules.sd_hijack_utils import CondFunc
-
-
-class TorchHijackForUnet:
-    """
-    This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
-    this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
-    """
-
-    def __getattr__(self, item):
-        if item == 'cat':
-            return self.cat
-
-        if hasattr(torch, item):
-            return getattr(torch, item)
-
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
-
-    def cat(self, tensors, *args, **kwargs):
-        if len(tensors) == 2:
-            a, b = tensors
-            if a.shape[-2:] != b.shape[-2:]:
-                a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
-
-            tensors = (a, b)
-
-        return torch.cat(tensors, *args, **kwargs)
-
-
-th = TorchHijackForUnet()
-
-
-# Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
-def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
-
-    if isinstance(cond, dict):
-        for y in cond.keys():
-            cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
-
-    with devices.autocast():
-        return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
-
-
-class GELUHijack(torch.nn.GELU, torch.nn.Module):
-    def __init__(self, *args, **kwargs):
-        torch.nn.GELU.__init__(self, *args, **kwargs)
-    def forward(self, x):
-        if devices.unet_needs_upcast:
-            return torch.nn.GELU.forward(self.float(), x.float()).to(devices.dtype_unet)
-        else:
-            return torch.nn.GELU.forward(self, x)
-
-
-ddpm_edit_hijack = None
-def hijack_ddpm_edit():
-    global ddpm_edit_hijack
-    if not ddpm_edit_hijack:
-        CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
-        CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
-        ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
-
-
-unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
-CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
-if version.parse(torch.__version__) <= version.parse("1.13.1"):
-    CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
-    CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
-    CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
-
-first_stage_cond = lambda _, self, *args, **kwargs: devices.unet_needs_upcast and self.model.diffusion_model.dtype == torch.float16
-first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devices.dtype_vae), **kwargs)
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
diff --git a/spaces/bigjoker/stable-diffusion-webui/modules/ui_extra_networks.py b/spaces/bigjoker/stable-diffusion-webui/modules/ui_extra_networks.py
deleted file mode 100644
index e788319dad4aba4484437af166b7c0fccd651798..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/modules/ui_extra_networks.py
+++ /dev/null
@@ -1,251 +0,0 @@
-import glob
-import os.path
-import urllib.parse
-from pathlib import Path
-
-from modules import shared
-import gradio as gr
-import json
-import html
-
-from modules.generation_parameters_copypaste import image_from_url_text
-
-extra_pages = []
-allowed_dirs = set()
-
-
-def register_page(page):
-    """registers extra networks page for the UI; recommend doing it in on_before_ui() callback for extensions"""
-
-    extra_pages.append(page)
-    allowed_dirs.clear()
-    allowed_dirs.update(set(sum([x.allowed_directories_for_previews() for x in extra_pages], [])))
-
-
-def add_pages_to_demo(app):
-    def fetch_file(filename: str = ""):
-        from starlette.responses import FileResponse
-
-        if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]):
-            raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
-
-        ext = os.path.splitext(filename)[1].lower()
-        if ext not in (".png", ".jpg"):
-            raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg.")
-
-        # would profit from returning 304
-        return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
-
-    app.add_api_route("/sd_extra_networks/thumb", fetch_file, methods=["GET"])
-
-
-class ExtraNetworksPage:
-    def __init__(self, title):
-        self.title = title
-        self.name = title.lower()
-        self.card_page = shared.html("extra-networks-card.html")
-        self.allow_negative_prompt = False
-
-    def refresh(self):
-        pass
-
-    def link_preview(self, filename):
-        return "./sd_extra_networks/thumb?filename=" + urllib.parse.quote(filename.replace('\\', '/')) + "&mtime=" + str(os.path.getmtime(filename))
-
-    def search_terms_from_path(self, filename, possible_directories=None):
-        abspath = os.path.abspath(filename)
-
-        for parentdir in (possible_directories if possible_directories is not None else self.allowed_directories_for_previews()):
-            parentdir = os.path.abspath(parentdir)
-            if abspath.startswith(parentdir):
-                return abspath[len(parentdir):].replace('\\', '/')
-
-        return ""
-
-    def create_html(self, tabname):
-        view = shared.opts.extra_networks_default_view
-        items_html = ''
-
-        subdirs = {}
-        for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
-            for x in glob.glob(os.path.join(parentdir, '**/*'), recursive=True):
-                if not os.path.isdir(x):
-                    continue
-
-                subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
-                while subdir.startswith("/"):
-                    subdir = subdir[1:]
-
-                is_empty = len(os.listdir(x)) == 0
-                if not is_empty and not subdir.endswith("/"):
-                    subdir = subdir + "/"
-
-                subdirs[subdir] = 1
-
-        if subdirs:
-            subdirs = {"": 1, **subdirs}
-
-        subdirs_html = "".join([f"""
-<button class='gr-button gr-button-lg gr-button-secondary{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_tabs", event)'>
-{html.escape(subdir if subdir!="" else "all")}
-</button>
-""" for subdir in subdirs])
-
-        for item in self.list_items():
-            items_html += self.create_html_for_item(item, tabname)
-
-        if items_html == '':
-            dirs = "".join([f"<li>{x}</li>" for x in self.allowed_directories_for_previews()])
-            items_html = shared.html("extra-networks-no-cards.html").format(dirs=dirs)
-
-        self_name_id = self.name.replace(" ", "_")
-
-        res = f"""
-<div id='{tabname}_{self_name_id}_subdirs' class='extra-network-subdirs extra-network-subdirs-{view}'>
-{subdirs_html}
-</div>
-<div id='{tabname}_{self_name_id}_cards' class='extra-network-{view}'>
-{items_html}
-</div>
-"""
-
-        return res
-
-    def list_items(self):
-        raise NotImplementedError()
-
-    def allowed_directories_for_previews(self):
-        return []
-
-    def create_html_for_item(self, item, tabname):
-        preview = item.get("preview", None)
-
-        onclick = item.get("onclick", None)
-        if onclick is None:
-            onclick = '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
-
-        args = {
-            "preview_html": "style='background-image: url(\"" + html.escape(preview) + "\")'" if preview else '',
-            "prompt": item.get("prompt", None),
-            "tabname": json.dumps(tabname),
-            "local_preview": json.dumps(item["local_preview"]),
-            "name": item["name"],
-            "card_clicked": onclick,
-            "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {json.dumps(tabname)}, {json.dumps(item["local_preview"])})""") + '"',
-            "search_term": item.get("search_term", ""),
-        }
-
-        return self.card_page.format(**args)
-
-
-def intialize():
-    extra_pages.clear()
-
-
-class ExtraNetworksUi:
-    def __init__(self):
-        self.pages = None
-        self.stored_extra_pages = None
-
-        self.button_save_preview = None
-        self.preview_target_filename = None
-
-        self.tabname = None
-
-
-def pages_in_preferred_order(pages):
-    tab_order = [x.lower().strip() for x in shared.opts.ui_extra_networks_tab_reorder.split(",")]
-
-    def tab_name_score(name):
-        name = name.lower()
-        for i, possible_match in enumerate(tab_order):
-            if possible_match in name:
-                return i
-
-        return len(pages)
-
-    tab_scores = {page.name: (tab_name_score(page.name), original_index) for original_index, page in enumerate(pages)}
-
-    return sorted(pages, key=lambda x: tab_scores[x.name])
-
-
-def create_ui(container, button, tabname):
-    ui = ExtraNetworksUi()
-    ui.pages = []
-    ui.stored_extra_pages = pages_in_preferred_order(extra_pages.copy())
-    ui.tabname = tabname
-
-    with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
-        for page in ui.stored_extra_pages:
-            with gr.Tab(page.title):
-                page_elem = gr.HTML(page.create_html(ui.tabname))
-                ui.pages.append(page_elem)
-
-    filter = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", placeholder="Search...", visible=False)
-    button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh")
-    button_close = gr.Button('Close', elem_id=tabname+"_extra_close")
-
-    ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
-    ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=tabname+"_preview_filename", visible=False)
-
-    def toggle_visibility(is_visible):
-        is_visible = not is_visible
-        return is_visible, gr.update(visible=is_visible)
-
-    state_visible = gr.State(value=False)
-    button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container])
-    button_close.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container])
-
-    def refresh():
-        res = []
-
-        for pg in ui.stored_extra_pages:
-            pg.refresh()
-            res.append(pg.create_html(ui.tabname))
-
-        return res
-
-    button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages)
-
-    return ui
-
-
-def path_is_parent(parent_path, child_path):
-    parent_path = os.path.abspath(parent_path)
-    child_path = os.path.abspath(child_path)
-
-    return child_path.startswith(parent_path)
-
-
-def setup_ui(ui, gallery):
-    def save_preview(index, images, filename):
-        if len(images) == 0:
-            print("There is no image in gallery to save as a preview.")
-            return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]
-
-        index = int(index)
-        index = 0 if index < 0 else index
-        index = len(images) - 1 if index >= len(images) else index
-
-        img_info = images[index if index >= 0 else 0]
-        image = image_from_url_text(img_info)
-
-        is_allowed = False
-        for extra_page in ui.stored_extra_pages:
-            if any([path_is_parent(x, filename) for x in extra_page.allowed_directories_for_previews()]):
-                is_allowed = True
-                break
-
-        assert is_allowed, f'writing to {filename} is not allowed'
-
-        image.save(filename)
-
-        return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]
-
-    ui.button_save_preview.click(
-        fn=save_preview,
-        _js="function(x, y, z){return [selected_gallery_index(), y, z]}",
-        inputs=[ui.preview_target_filename, gallery, ui.preview_target_filename],
-        outputs=[*ui.pages]
-    )
-
diff --git a/spaces/bigjoker/stable-diffusion-webui/test/basic_features/extras_test.py b/spaces/bigjoker/stable-diffusion-webui/test/basic_features/extras_test.py
deleted file mode 100644
index 0170c511fe54cc6bcf49ec7f75ca7c747de41db5..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/test/basic_features/extras_test.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import unittest
-import requests
-from gradio.processing_utils import encode_pil_to_base64
-from PIL import Image
-
-class TestExtrasWorking(unittest.TestCase):
-    def setUp(self):
-        self.url_extras_single = "http://localhost:7860/sdapi/v1/extra-single-image"
-        self.extras_single = {
-            "resize_mode": 0,
-            "show_extras_results": True,
-            "gfpgan_visibility": 0,
-            "codeformer_visibility": 0,
-            "codeformer_weight": 0,
-            "upscaling_resize": 2,
-            "upscaling_resize_w": 128,
-            "upscaling_resize_h": 128,
-            "upscaling_crop": True,
-            "upscaler_1": "None",
-            "upscaler_2": "None",
-            "extras_upscaler_2_visibility": 0,
-            "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))
-            }
-
-    def test_simple_upscaling_performed(self):
-        self.extras_single["upscaler_1"] = "Lanczos"
-        self.assertEqual(requests.post(self.url_extras_single, json=self.extras_single).status_code, 200)
-
-
-class TestPngInfoWorking(unittest.TestCase):
-    def setUp(self):
-        self.url_png_info = "http://localhost:7860/sdapi/v1/extra-single-image"
-        self.png_info = {
-            "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))
-        }
-
-    def test_png_info_performed(self):
-        self.assertEqual(requests.post(self.url_png_info, json=self.png_info).status_code, 200)
-
-
-class TestInterrogateWorking(unittest.TestCase):
-    def setUp(self):
-        self.url_interrogate = "http://localhost:7860/sdapi/v1/extra-single-image"
-        self.interrogate = {
-            "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png")),
-            "model": "clip"
-        }
-
-    def test_interrogate_performed(self):
-        self.assertEqual(requests.post(self.url_interrogate, json=self.interrogate).status_code, 200)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/bigscience/petals-api/src/__init__.py b/spaces/bigscience/petals-api/src/__init__.py
deleted file mode 100644
index 9f4ea05bdd6fb1794ddbd0b45f8d08cbf5193dab..0000000000000000000000000000000000000000
--- a/spaces/bigscience/petals-api/src/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from src.bloom import *
-from src.client import *
-from src.dht_utils import declare_active_modules, get_remote_module
-
-__version__ = "0.2"
diff --git a/spaces/bilgeyucel/prompt-lemmatizer/utils.py b/spaces/bilgeyucel/prompt-lemmatizer/utils.py
deleted file mode 100644
index 941f7533902caacf0dcde312aae6a3c213ed92a5..0000000000000000000000000000000000000000
--- a/spaces/bilgeyucel/prompt-lemmatizer/utils.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from simplemma import text_lemmatizer
-
-def lemmatizer_func(plain_text):
-    lemmatized_promt = ' '.join([el for el in text_lemmatizer(plain_text, lang='en') if el.isalnum()])
-    return lemmatized_promt
diff --git a/spaces/bioriAsaeru/text-to-voice/Bajirao Mastani 720p In Hindi Dubbed Movie The Perfect Choice for a Date Night or a Family Night.md b/spaces/bioriAsaeru/text-to-voice/Bajirao Mastani 720p In Hindi Dubbed Movie The Perfect Choice for a Date Night or a Family Night.md
deleted file mode 100644
index ed86efeb93a0b562fdfcd2bc982659694a0be2a5..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Bajirao Mastani 720p In Hindi Dubbed Movie The Perfect Choice for a Date Night or a Family Night.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Bajirao Mastani 720p In Hindi Dubbed Movie</h2><br /><p><b><b>Download</b> &#9913;&#9913;&#9913; <a href="https://urloso.com/2uyQ4z">https://urloso.com/2uyQ4z</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/bioriAsaeru/text-to-voice/Huawei Hisilicon Firmware Writer 136 TOP.md b/spaces/bioriAsaeru/text-to-voice/Huawei Hisilicon Firmware Writer 136 TOP.md
deleted file mode 100644
index 1ed6d414b43a5375cfb7e58f41dca1e9785630be..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Huawei Hisilicon Firmware Writer 136 TOP.md	
+++ /dev/null
@@ -1,38 +0,0 @@
-
-```html
-<h1>Huawei Hisilicon Firmware Writer 136: A Tool to Flash Custom Firmware on Huawei Devices</h1>
-<p>Huawei Hisilicon Firmware Writer 136 is a software tool that allows users to flash custom firmware on Huawei devices that use Hisilicon chipsets. The tool can bypass the bootloader lock and flash any firmware file with the .bin extension. The tool supports various models of Huawei devices, such as routers, modems, smartphones, and tablets.</p>
-<p>The tool was developed by a group of hackers who discovered a vulnerability in the original Huawei firmware that allowed them to modify the bootloader and kernel. The hackers claimed that they were able to replace the original command set_bit_at_address calls with clear_bit_at_address calls, which enabled them to flash custom firmware without any restrictions[^1^]. The tool was released on npm, a package manager for JavaScript, and can be downloaded from this link: <a href="https://www.npmjs.com/package/huawei_hisilicon_firmware_writer_136_fg5">https://www.npmjs.com/package/huawei_hisilicon_firmware_writer_136_fg5</a></p>
-<h2>huawei hisilicon firmware writer 136</h2><br /><p><b><b>Download</b> &#9913; <a href="https://urloso.com/2uyPhk">https://urloso.com/2uyPhk</a></b></p><br /><br />
-<p>However, flashing custom firmware on Huawei devices is not without risks. Users should be aware that flashing custom firmware may void their warranty, brick their device, or expose them to security threats. Users should also backup their data before flashing custom firmware, as the process may erase all the data on the device. Users should also verify the source and integrity of the firmware file before flashing it, as some firmware files may contain malware or spyware.</p>
-<p>Users who want to flash custom firmware on Huawei devices using Huawei Hisilicon Firmware Writer 136 should follow these steps:</p>
-<ol>
-<li>Download and install Huawei Hisilicon Firmware Writer 136 from npm.</li>
-<li>Download the firmware file with the .bin extension from a trusted source.</li>
-<li>Connect the Huawei device to the computer using a USB cable.</li>
-<li>Run Huawei Hisilicon Firmware Writer 136 and select the device model and port number.</li>
-<li>Browse and select the firmware file and click on Flash.</li>
-<li>Wait for the flashing process to complete and reboot the device.</li>
-</ol>
-<p>Users who want to learn more about Huawei Hisilicon Firmware Writer 136 can visit the official website of the tool: <a href="https://blltly.com/2t5tA7">https://blltly.com/2t5tA7</a></p>
-```
-
-```html
-<p>Flashing custom firmware on Huawei devices using Huawei Hisilicon Firmware Writer 136 can have some benefits and drawbacks. Users should weigh them carefully before deciding to flash custom firmware.</p>
-<h2>Benefits of Custom Firmware</h2>
-<ul>
-<li>Custom firmware can offer more features and customization options than the original Huawei firmware. For example, users can install different themes, icons, launchers, widgets, and apps that are not available on the stock firmware. Users can also tweak the performance, battery life, security, and privacy settings of their device according to their preferences.</li>
-<li>Custom firmware can also provide more frequent updates and bug fixes than the original Huawei firmware. Some custom firmware developers are very active and responsive to user feedback and requests. They may release new versions of their firmware with new features and improvements on a regular basis. Users can also benefit from the community support and resources that are available for custom firmware users.</li>
-<li>Custom firmware can also allow users to unlock the full potential of their device. Some custom firmware can enable features that are disabled or hidden by the original Huawei firmware, such as overclocking, rooting, tethering, dual booting, and more. Users can also remove unwanted apps and bloatware that come pre-installed on the stock firmware, freeing up storage space and improving performance.</li>
-</ul>
-<h2>Drawbacks of Custom Firmware</h2>
-<ul>
-<li>Custom firmware can also pose some risks and challenges for users. Flashing custom firmware may void the warranty of the device, as Huawei may not provide any support or service for devices that have been modified. Users may also lose access to some official Huawei services and apps that are exclusive to the stock firmware, such as Huawei Cloud, Huawei AppGallery, Huawei Music, Huawei Health, and more.</li>
-<li>Custom firmware may also be unstable or incompatible with some devices or apps. Some custom firmware may have bugs or errors that cause crashes, freezes, reboots, or other problems. Some custom firmware may not work well with some hardware components or drivers of the device, resulting in poor performance or functionality. Some custom firmware may also conflict with some apps or games that require certain permissions or certifications from the original Huawei firmware.</li>
-<li>Custom firmware may also expose users to security threats or legal issues. Some custom firmware may contain malware or spyware that can steal user data or harm the device. Some custom firmware may also violate the intellectual property rights of Huawei or other parties by using their code or resources without permission. Users may face legal consequences if they use custom firmware that infringes on these rights.</li>
-</ul>
-<p>In conclusion, flashing custom firmware on Huawei devices using Huawei Hisilicon Firmware Writer 136 can have both advantages and disadvantages. Users should be aware of the benefits and drawbacks of custom firmware before deciding to flash it on their device. Users should also backup their data and follow the instructions carefully when flashing custom firmware to avoid any problems.</p>
-```</p>
-<p></p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/bioriAsaeru/text-to-voice/Intel Sm Bus Controller Driver Windows Xp Download Update Your Chipset Components Easily.md b/spaces/bioriAsaeru/text-to-voice/Intel Sm Bus Controller Driver Windows Xp Download Update Your Chipset Components Easily.md
deleted file mode 100644
index b70bdb454a4b6b090afc4ac8b23c00e93cbc85f9..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Intel Sm Bus Controller Driver Windows Xp Download Update Your Chipset Components Easily.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
-<p>Installed new MB & HDD on a Aspire One D250, clean install of Windows XP Professional OS and device drivers and chip set (945G family v. 8.3.0.1018. XP does not find the driver for the SMBus Controller driver. When using the webcam to make a video chat call, I get the BSOD. When I open the devise manager the SMBus controller driver is not found or not installed. How do I correct this?</p>
-<p>The following will teach you how to perform Intel SM Bus Controller driver download for some popular computer brands and the methods apply to Windows 11, Windows 10, Windows 8/8.1, Windows 7, Windows Vista, as well as Windows XP, both 32/64 bit.</p>
-<h2>Intel Sm Bus Controller Driver Windows Xp Download</h2><br /><p><b><b>Download Zip</b> &#9913; <a href="https://urloso.com/2uyQBH">https://urloso.com/2uyQBH</a></b></p><br /><br />
-<p>IMPORTANT: This file is only for use with the models identified. Not all models are sold in all countries.This information is for the following models:<li>SVF14N11CXB<li>SVF14N13CBS<li>SVF14N13CLB<li>SVF14N13CLS<li>SVF14N13CXB<li>SVF14N13CXS<li>SVF14N15CBB<li>SVF14N15CDS<li>SVF14N15CLB<li>SVF14N15CLS<li>SVF14N16CXB<li>SVF14N16CXS<li>SVF14N190S<li>SVF14N190X<li>SVF15N14CXB<li>SVF15N14CXS<li>SVF15N15CDB<li>SVF15N17CBS<li>SVF15N17CDB<li>SVF15N17CLS<li>SVF15N17CXB<li>SVF15N17CXS<li>SVF15N18PXB<li>SVF15N190S<li>SVF15N190X"},"id":"about","content":"Benefits and improvementsThis utility installs the originally shipped version of the Intel® Chipset Driver.This driver also resolves the yellow exclamation point error that may appear in Device Manager next to "SM Bus Controller" which indicates that a driver is not installed for that device.","id":"system_requirements","content":"Operating Systems<ul><li>Windows® 10 64-bit<li>Windows® 8 64-bit<li>Windows® 8.1 64-bit","id":"file_info","content":"File Name<ul><li>EP0000552001.exeFile Version<ul><li>9.4.0.1017File Size<ul><li>8.35 MbRelease Date<ul><li>09-24-2013","id":"preparation","content":" Important Notes <ul> <li>During the installation of this file, be sure that you are logged in as the Administrator or as a user with Administrative rights. <li>To ensure that no other program interferes with the installation, save all work and close all other programs. The Taskbar should be clear of applications before proceeding. <li>It is highly recommended that you print out these instructions for use as a reference during the installation process. ","id":"download","content":" Download and Installation Instructions <ol> <li>To ensure that no other program interferes with the installation, save all work and close all other programs. The Taskbar should be clear of applications before proceeding. <li>Download the EP0000552001.exe file to a temporary or download directory (please note this directory for reference). <li>Go to the directory where the file was downloaded and double-click the EP0000552001.exe file to begin the installation. <li>At the "User Account Control" screen, click "Yes". <li>If the "Select Language" screen appears, select the language you wish to use, and then click "OK". <li>At the "Select Country" screen, select the country for this installation, and then click "Next". <li>At the "Intel Chipset Driver" ver. 9.4.0.1017" screen, click "Next". <li>At the "License Agreement" dialog, read the agreement, then, if you agree, click "I Agree" to continue with the installation. <li>At the "Finish" screen, click "Finish". <li>At the "This system must be restarted to complete the installation" screen, click "OK" to restart the computer. After the computer restarts, the installation of the Intel Chipset Driver is complete."],"hidden":false,"downloadUrl":"https:\/\/dlv.update.sony.net\/US\/pc\/EP0000552001.exe","downloadType":"driver","eulaUrl":"https:\/\/www.sony.co.uk\/campaign\/SWT\/eulas_v2\/en_US.html","eulaType":"1"},"isEulaShow":false,"eula":null,"downloadNo":"W0009063"};window.__CTX__ = window.__CTX__ || ;window.__COMPONENT_CONFIG__ = window.__COMPONENT_CONFIG__ || ;window.__I18N__ = window.__I18N__ || ;window.__CTX__.react_client_head_tags ="module":"isCritical":false;window.__COMPONENT_CONFIG__.react_client_head_tags ="portal":"protocol":"https","support_additionalVisibleModulesInLiteMode":[];window.__I18N__.react_client_head_tags = 1","consent_warning.button_text":"Manage cookies","favorite.text":"Favorite","productInformationSheet.text":"Product Information Sheet","flowplayer.language.et":"Estonian","title_category_with_results_download":"Search Results ;window.__PRELOADED_STATE__ = window.__PRELOADED_STATE__ || ;window.__PRELOADED_STATE__.location ="pathname":"\/electronics\/support\/downloads\/W0009063","query":;window.__PRELOADED_STATE__.page ="type":"downloadDetails","searchByType":"none","typeAlias":null,"origin":"server","hasError":false,"isLoading":false,"isLoadingPartialContent":false,"location":"pathname":"\/electronics\/support\/downloads\/W0009063","query":,"params":"downloadId":"W0009063","cookie":"","locale":"en_US";window.__CTX__ = window.__CTX__ || ;window.__COMPONENT_CONFIG__ = window.__COMPONENT_CONFIG__ || ;window.__I18N__ = window.__I18N__ || ;window.__CTX__.software_details ="module":"isCritical":true;window.__COMPONENT_CONFIG__.software_details ="support_additionalVisibleModulesInLiteMode":[];window.__I18N__.software_details ="GB.displayName":"United Kingdom","SE.displayName":"Sweden","related_products_curated_link.text":"Related Products","flowplayer.language.ja":"Japanese","idk.text":"Sorry, this data isn't available","pt_PT.displayName":"Portugal","AD.displayName":"Andorra","no_NO.displayName":"","dynamic.product_count.default":"Products","support_link.text":"Support","TW.displayName":"Taiwan","product_count.14":"0 Products","YU.displayName":"Serbia","en_HK.displayName":"","flowplayer.language.fi":"Finnish","es_CL.displayName":"Chile","ME.displayName":"Montenegro","FR.displayName":"France","BA.displayName":"Bosnia and Herzegovina","flowplayer.language.ko":"Korean","EU.region_displayName":"Europe Region","CA.displayName":"Canada","pricing.starting.at_succeeding":"","SI.displayName":"Slovenia","product_count.72":"","es_AR.displayName":"Argentina","dynamic.product_count.34":"Products","NG.displayName":"Nigeria","sony.text":"Sony US","product_count.4":"0 Products","aria.slider.previous":"","KZ.displayName":"Kazakhstan","flowplayer.language.nl":"Dutch","fi_FI.displayName":"","en_PH.displayName":"Philippines","KW.displayName":"Kuwait","flowplayer.language.al":"Albanian","dynamic.accessory_count.1":"0 Accessory","MA.displayName":"Morocco","flowplayer.language.mk":"Macedonian","nl_NL.displayName":"","dynamic.product_count.24":"Products","fr_LU.displayName":"","LV.displayName":"Latvia","lt_LT.displayName":"","dynamic.product_count.3":"Products","flowplayer.language.ro":"Romanian","GE.displayName":"Georgia","consent_warning.button_text":"Manage cookies","favorite.text":"Favorite","productInformationSheet.text":"Product Information Sheet","flowplayer.language.et":"Estonian","flowplayer.language.sk":"Slovak","LT.displayName":"Lithuania","en_IE.displayName":"Ireland","PL.displayName":"Poland","ZA.displayName":"South Africa","BG.displayName":"Bulgaria","pl_PL.displayName":"","BH.displayName":"Bahrain","flowplayer.language.en":"English","flowplayer.language.bg":"Bulgarian","FI.displayName":"Finland","CH.displayName":"Switzerland","JP.displayName":"","BY.displayName":"Belarus","BR.displayName":"Brazil","TR.displayName":"Türkiye","fr_BE.displayName":"Belgium","IE.displayName":"Republic of Ireland","en_EE.displayName":"Estonia","sv_SE.displayName":"","recycling_cost_5Euro.text":"","BE.displayName":"Belgium","LU.displayName":"Luxembourg","IS.displayName":"Iceland","flowplayer.language.kk":"Kazakh","RU.displayName":"Russia","buy.button.text":"Where To Buy","dynamic.product_count.12":"Products","CZ.displayName":"Czech Republic","MD.displayName":"Moldova","CN.region_displayName":"China Region","dynamic.product_count.13":"Products","product_count.3":"0 Products","AL.displayName":"Albania","XM.displayName":"Middle 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-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/browndw/docuscope-demo-spacy/app.py b/spaces/browndw/docuscope-demo-spacy/app.py
deleted file mode 100644
index b991caacbcebf825c4fe06914d87d779ef3fa2e7..0000000000000000000000000000000000000000
--- a/spaces/browndw/docuscope-demo-spacy/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Core Pkgs
-import streamlit as st 
-
-# NLP Pkgs
-import spacy_streamlit
-import spacy
-import re
-
-nlp = spacy.load('en_docusco_spacy')
-
-def pre_process(txt):
-    txt = re.sub(r'\bits\b', 'it s', txt)
-    txt = re.sub(r'\bIts\b', 'It s', txt)
-    txt = " ".join(txt.split())
-    return(txt)
-    
-#import os
-#from PIL import Image
-
-
-def main():
-
-	st.title("DocuScope and Part-of-Speech Tagging with spaCy")
-	st.markdown("This demo uses a trained spaCy model ([en_docusco_spacy](https://huggingface.co/browndw/en_docusco_spacy)) to identify DocuScope categories in text.")
-	st.markdown("It is also trained on the [CLAWS7](https://ucrel.lancs.ac.uk/claws7tags.html) part-of-speech tagset.")
-	st.markdown("NOTE: this demo is public - please don't enter confidential text")
-
-        #our_image = Image.open(os.path.join('SpaCy_logo.svg.png'))
-	#st.image(our_image)
-
-	menu = ["Tokens","DocuScope"]
-	choice = st.sidebar.selectbox("Menu",menu)
-
-	if choice == "Tokens":
-		st.subheader("Tokenization")
-		raw_text = st.text_area('Enter text:', value="Jaws is a shrewd cinematic equation which not only gives you one or two very nasty turns when you least expect them but, possibly more important, knows when to make you think another is coming without actually providing it.")
-		docx = pre_process(raw_text)
-		docx = nlp(docx)
-		if st.button("Tokenize"):
-			spacy_streamlit.visualize_tokens(docx,attrs=['text','tag_', 'ent_iob_', 'ent_type_'])
-
-	elif choice == "DocuScope":
-		st.subheader("Named Entity Recognition")
-		raw_text = st.text_area('Enter text:', value="Jaws is a shrewd cinematic equation which not only gives you one or two very nasty turns when you least expect them but, possibly more important, knows when to make you think another is coming without actually providing it.")
-		docx = pre_process(raw_text)
-		docx = nlp(docx)
-		spacy_streamlit.visualize_ner(docx,labels=nlp.get_pipe('ner').labels)
-
-
-if __name__ == '__main__':
-	main()
-	
\ No newline at end of file
diff --git a/spaces/bruvvy/nitrosocke-Nitro-Diffusion/README.md b/spaces/bruvvy/nitrosocke-Nitro-Diffusion/README.md
deleted file mode 100644
index 80d180a2ad05d397bfcd02e40f60b62dee30cc81..0000000000000000000000000000000000000000
--- a/spaces/bruvvy/nitrosocke-Nitro-Diffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Nitrosocke Nitro Diffusion
-emoji: 🌖
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.19.1
-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/bugbounted/Whisper-Auto-Subtitled-Video-Generator/languages.py b/spaces/bugbounted/Whisper-Auto-Subtitled-Video-Generator/languages.py
deleted file mode 100644
index 0551511f86eb917fd0668d971b21e177c387b7ef..0000000000000000000000000000000000000000
--- a/spaces/bugbounted/Whisper-Auto-Subtitled-Video-Generator/languages.py
+++ /dev/null
@@ -1,101 +0,0 @@
-LANGUAGES = {
-    "en": "eng",
-    "zh": "zho",
-    "de": "deu",
-    "es": "spa",
-    "ru": "rus",
-    "ko": "kor",
-    "fr": "fra",
-    "ja": "jpn",
-    "pt": "por",
-    "tr": "tur",
-    "pl": "pol",
-    "ca": "cat",
-    "nl": "nld",
-    "ar": "ara",
-    "sv": "swe",
-    "it": "ita",
-    "id": "ind",
-    "hi": "hin",
-    "fi": "fin",
-    "vi": "vie",
-    "iw": "heb",
-    "uk": "ukr",
-    "el": "ell",
-    "ms": "msa",
-    "cs": "ces",
-    "ro": "ron",
-    "da": "dan",
-    "hu": "hun",
-    "ta": "tam",
-    "no": "nor",
-    "th": "tha",
-    "ur": "urd",
-    "hr": "hrv",
-    "bg": "bul",
-    "lt": "lit",
-    "la": "lat",
-    "mi": "mri",
-    "ml": "mal",
-    "cy": "cym",
-    "sk": "slk",
-    "te": "tel",
-    "fa": "fas",
-    "lv": "lav",
-    "bn": "ben",
-    "sr": "srp",
-    "az": "aze",
-    "sl": "slv",
-    "kn": "kan",
-    "et": "est",
-    "mk": "mkd",
-    "br": "bre",
-    "eu": "eus",
-    "is": "isl",
-    "hy": "hye",
-    "ne": "nep",
-    "mn": "mon",
-    "bs": "bos",
-    "kk": "kaz",
-    "sq": "sqi",
-    "sw": "swa",
-    "gl": "glg",
-    "mr": "mar",
-    "pa": "pan",
-    "si": "sin",
-    "km": "khm",
-    "sn": "sna",
-    "yo": "yor",
-    "so": "som",
-    "af": "afr",
-    "oc": "oci",
-    "ka": "kat",
-    "be": "bel",
-    "tg": "tgk",
-    "sd": "snd",
-    "gu": "guj",
-    "am": "amh",
-    "yi": "yid",
-    "lo": "lao",
-    "uz": "uzb",
-    "fo": "fao",
-    "ht": "hat",
-    "ps": "pus",
-    "tk": "tuk",
-    "nn": "nno",
-    "mt": "mlt",
-    "sa": "san",
-    "lb": "ltz",
-    "my": "mya",
-    "bo": "bod",
-    "tl": "tgl",
-    "mg": "mlg",
-    "as": "asm",
-    "tt": "tat",
-    "haw": "haw",
-    "ln": "lin",
-    "ha": "hau",
-    "ba": "bak",
-    "jw": "jav",
-    "su": "sun",
-}
\ No newline at end of file
diff --git a/spaces/caslabs/midi-autocompletion/musicautobot/utils/stacked_dataloader.py b/spaces/caslabs/midi-autocompletion/musicautobot/utils/stacked_dataloader.py
deleted file mode 100644
index cf79ee773a388c0b922d215539891b4b251e44d1..0000000000000000000000000000000000000000
--- a/spaces/caslabs/midi-autocompletion/musicautobot/utils/stacked_dataloader.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"Dataloader wrapper that can combine and handle multiple dataloaders for multitask training"
-from fastai.callback import Callback
-from typing import Callable
-
-__all__ = ['StackedDataBunch']
-
-# DataLoading
-class StackedDataBunch():
-    def __init__(self, dbs, num_it=100):
-        self.dbs = dbs
-        self.train_dl = StackedDataloader([db.train_dl for db in self.dbs], num_it)
-        self.valid_dl = StackedDataloader([db.valid_dl for db in self.dbs], num_it)
-        self.train_ds = None
-        self.path = dbs[0].path
-        self.device = dbs[0].device
-        self.vocab = dbs[0].vocab
-        self.empty_val = False
-
-    def add_tfm(self,tfm:Callable)->None:
-        for dl in self.dbs: dl.add_tfm(tfm)
-
-    def remove_tfm(self,tfm:Callable)->None:
-        for dl in self.dbs: dl.remove_tfm(tfm)
-
-# Helper functions
-class StackedDataset(Callback):
-    def __init__(self, dss):
-        self.dss = dss
-    def __getattribute__(self, attr):
-        if attr == 'dss': return super().__getattribute__(attr)
-        def redirected(*args, **kwargs):
-            for ds in self.dss:
-                if hasattr(ds, attr): getattr(ds, attr)(*args, **kwargs)
-        return redirected
-    def __len__(self)->int: return sum([len(ds) for ds in self.dss])
-    def __repr__(self): return '\n'.join([self.__class__.__name__] + [repr(ds) for ds in self.dss])
-
-class StackedDataloader():
-    def __init__(self, dls, num_it=100):
-        self.dls = dls
-        self.dataset = StackedDataset([dl.dataset for dl in dls if hasattr(dl, 'dataset')])
-        self.num_it = num_it
-        self.dl_idx = -1
-        
-    def __len__(self)->int: return sum([len(dl) for dl in self.dls])
-    def __getattr__(self, attr):
-        def redirected(*args, **kwargs):
-            for dl in self.dls:
-                if hasattr(dl, attr):
-                    getattr(dl, attr)(*args, **kwargs)
-        return redirected
-        
-    def __iter__(self):
-        "Process and returns items from `DataLoader`."
-        iters = [iter(dl) for dl in self.dls]
-        self.dl_idx = -1
-        while len(iters):
-            self.dl_idx = (self.dl_idx+1) % len(iters)
-            for b in range(self.num_it):
-                try:
-                    yield next(iters[self.dl_idx])
-                except StopIteration as e:
-                    iters.remove(iters[self.dl_idx])
-                    break
-#         raise StopIteration
-
-    def new(self, **kwargs):
-        "Create a new copy of `self` with `kwargs` replacing current values."
-        new_dls = [dl.new(**kwargs) for dl in self.dls]
-        return StackedDataloader(new_dls, self.num_it)
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/deebert/run_glue_deebert.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/deebert/run_glue_deebert.py
deleted file mode 100644
index 6f7cfe65d0ef67b8723d4aed5a30750c22680fb3..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/deebert/run_glue_deebert.py
+++ /dev/null
@@ -1,735 +0,0 @@
-from __future__ import absolute_import, division, print_function
-
-import argparse
-import glob
-import logging
-import os
-import random
-import time
-
-import numpy as np
-import torch
-from torch import nn
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
-from torch.utils.data.distributed import DistributedSampler
-from tqdm import tqdm, trange
-
-import transformers
-from src.modeling_highway_bert import DeeBertForSequenceClassification
-from src.modeling_highway_roberta import DeeRobertaForSequenceClassification
-from transformers import (
-    WEIGHTS_NAME,
-    AdamW,
-    BertConfig,
-    BertTokenizer,
-    RobertaConfig,
-    RobertaTokenizer,
-    get_linear_schedule_with_warmup,
-)
-from transformers import glue_compute_metrics as compute_metrics
-from transformers import glue_convert_examples_to_features as convert_examples_to_features
-from transformers import glue_output_modes as output_modes
-from transformers import glue_processors as processors
-from transformers.trainer_utils import is_main_process
-
-
-try:
-    from torch.utils.tensorboard import SummaryWriter
-except ImportError:
-    from tensorboardX import SummaryWriter
-
-
-logger = logging.getLogger(__name__)
-
-
-MODEL_CLASSES = {
-    "bert": (BertConfig, DeeBertForSequenceClassification, BertTokenizer),
-    "roberta": (RobertaConfig, DeeRobertaForSequenceClassification, RobertaTokenizer),
-}
-
-
-def set_seed(args):
-    random.seed(args.seed)
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-    if args.n_gpu > 0:
-        torch.cuda.manual_seed_all(args.seed)
-
-
-def get_wanted_result(result):
-    if "spearmanr" in result:
-        print_result = result["spearmanr"]
-    elif "f1" in result:
-        print_result = result["f1"]
-    elif "mcc" in result:
-        print_result = result["mcc"]
-    elif "acc" in result:
-        print_result = result["acc"]
-    else:
-        raise ValueError("Primary metric unclear in the results")
-    return print_result
-
-
-def train(args, train_dataset, model, tokenizer, train_highway=False):
-    """Train the model"""
-    if args.local_rank in [-1, 0]:
-        tb_writer = SummaryWriter()
-
-    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
-    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
-    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
-
-    if args.max_steps > 0:
-        t_total = args.max_steps
-        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
-    else:
-        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
-
-    # Prepare optimizer and schedule (linear warmup and decay)
-    no_decay = ["bias", "LayerNorm.weight"]
-    if train_highway:
-        optimizer_grouped_parameters = [
-            {
-                "params": [
-                    p
-                    for n, p in model.named_parameters()
-                    if ("highway" in n) and (not any(nd in n for nd in no_decay))
-                ],
-                "weight_decay": args.weight_decay,
-            },
-            {
-                "params": [
-                    p for n, p in model.named_parameters() if ("highway" in n) and (any(nd in n for nd in no_decay))
-                ],
-                "weight_decay": 0.0,
-            },
-        ]
-    else:
-        optimizer_grouped_parameters = [
-            {
-                "params": [
-                    p
-                    for n, p in model.named_parameters()
-                    if ("highway" not in n) and (not any(nd in n for nd in no_decay))
-                ],
-                "weight_decay": args.weight_decay,
-            },
-            {
-                "params": [
-                    p
-                    for n, p in model.named_parameters()
-                    if ("highway" not in n) and (any(nd in n for nd in no_decay))
-                ],
-                "weight_decay": 0.0,
-            },
-        ]
-    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
-    scheduler = get_linear_schedule_with_warmup(
-        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
-    )
-    if args.fp16:
-        try:
-            from apex import amp
-        except ImportError:
-            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
-
-    # multi-gpu training (should be after apex fp16 initialization)
-    if args.n_gpu > 1:
-        model = nn.DataParallel(model)
-
-    # Distributed training (should be after apex fp16 initialization)
-    if args.local_rank != -1:
-        model = nn.parallel.DistributedDataParallel(
-            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
-        )
-
-    # Train!
-    logger.info("***** Running training *****")
-    logger.info("  Num examples = %d", len(train_dataset))
-    logger.info("  Num Epochs = %d", args.num_train_epochs)
-    logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
-    logger.info(
-        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
-        args.train_batch_size
-        * args.gradient_accumulation_steps
-        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
-    )
-    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
-    logger.info("  Total optimization steps = %d", t_total)
-
-    global_step = 0
-    tr_loss, logging_loss = 0.0, 0.0
-    model.zero_grad()
-    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
-    set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
-    for _ in train_iterator:
-        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
-        for step, batch in enumerate(epoch_iterator):
-            model.train()
-            batch = tuple(t.to(args.device) for t in batch)
-            inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
-            if args.model_type != "distilbert":
-                inputs["token_type_ids"] = (
-                    batch[2] if args.model_type in ["bert", "xlnet"] else None
-                )  # XLM, DistilBERT and RoBERTa don't use segment_ids
-            inputs["train_highway"] = train_highway
-            outputs = model(**inputs)
-            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
-
-            if args.n_gpu > 1:
-                loss = loss.mean()  # mean() to average on multi-gpu parallel training
-            if args.gradient_accumulation_steps > 1:
-                loss = loss / args.gradient_accumulation_steps
-
-            if args.fp16:
-                with amp.scale_loss(loss, optimizer) as scaled_loss:
-                    scaled_loss.backward()
-            else:
-                loss.backward()
-
-            tr_loss += loss.item()
-            if (step + 1) % args.gradient_accumulation_steps == 0:
-                if args.fp16:
-                    nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
-                else:
-                    nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
-
-                optimizer.step()
-                scheduler.step()  # Update learning rate schedule
-                model.zero_grad()
-                global_step += 1
-
-                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
-                    # Log metrics
-                    if (
-                        args.local_rank == -1 and args.evaluate_during_training
-                    ):  # Only evaluate when single GPU otherwise metrics may not average well
-                        results = evaluate(args, model, tokenizer)
-                        for key, value in results.items():
-                            tb_writer.add_scalar("eval_{}".format(key), value, global_step)
-                    tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
-                    tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
-                    logging_loss = tr_loss
-
-                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
-                    # Save model checkpoint
-                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
-                    if not os.path.exists(output_dir):
-                        os.makedirs(output_dir)
-                    model_to_save = (
-                        model.module if hasattr(model, "module") else model
-                    )  # Take care of distributed/parallel training
-                    model_to_save.save_pretrained(output_dir)
-                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
-                    logger.info("Saving model checkpoint to %s", output_dir)
-
-            if args.max_steps > 0 and global_step > args.max_steps:
-                epoch_iterator.close()
-                break
-        if args.max_steps > 0 and global_step > args.max_steps:
-            train_iterator.close()
-            break
-
-    if args.local_rank in [-1, 0]:
-        tb_writer.close()
-
-    return global_step, tr_loss / global_step
-
-
-def evaluate(args, model, tokenizer, prefix="", output_layer=-1, eval_highway=False):
-    # Loop to handle MNLI double evaluation (matched, mis-matched)
-    eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
-    eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,)
-
-    results = {}
-    for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
-        eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
-
-        if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(eval_output_dir)
-
-        args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
-        # Note that DistributedSampler samples randomly
-        eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
-        eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
-
-        # multi-gpu eval
-        if args.n_gpu > 1:
-            model = nn.DataParallel(model)
-
-        # Eval!
-        logger.info("***** Running evaluation {} *****".format(prefix))
-        logger.info("  Num examples = %d", len(eval_dataset))
-        logger.info("  Batch size = %d", args.eval_batch_size)
-        eval_loss = 0.0
-        nb_eval_steps = 0
-        preds = None
-        out_label_ids = None
-        exit_layer_counter = {(i + 1): 0 for i in range(model.num_layers)}
-        st = time.time()
-        for batch in tqdm(eval_dataloader, desc="Evaluating"):
-            model.eval()
-            batch = tuple(t.to(args.device) for t in batch)
-
-            with torch.no_grad():
-                inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
-                if args.model_type != "distilbert":
-                    inputs["token_type_ids"] = (
-                        batch[2] if args.model_type in ["bert", "xlnet"] else None
-                    )  # XLM, DistilBERT and RoBERTa don't use segment_ids
-                if output_layer >= 0:
-                    inputs["output_layer"] = output_layer
-                outputs = model(**inputs)
-                if eval_highway:
-                    exit_layer_counter[outputs[-1]] += 1
-                tmp_eval_loss, logits = outputs[:2]
-
-                eval_loss += tmp_eval_loss.mean().item()
-            nb_eval_steps += 1
-            if preds is None:
-                preds = logits.detach().cpu().numpy()
-                out_label_ids = inputs["labels"].detach().cpu().numpy()
-            else:
-                preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
-                out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
-        eval_time = time.time() - st
-        logger.info("Eval time: {}".format(eval_time))
-
-        eval_loss = eval_loss / nb_eval_steps
-        if args.output_mode == "classification":
-            preds = np.argmax(preds, axis=1)
-        elif args.output_mode == "regression":
-            preds = np.squeeze(preds)
-        result = compute_metrics(eval_task, preds, out_label_ids)
-        results.update(result)
-
-        if eval_highway:
-            logger.info("Exit layer counter: {}".format(exit_layer_counter))
-            actual_cost = sum([l * c for l, c in exit_layer_counter.items()])
-            full_cost = len(eval_dataloader) * model.num_layers
-            logger.info("Expected saving: {}".format(actual_cost / full_cost))
-            if args.early_exit_entropy >= 0:
-                save_fname = (
-                    args.plot_data_dir
-                    + "/"
-                    + args.model_name_or_path[2:]
-                    + "/entropy_{}.npy".format(args.early_exit_entropy)
-                )
-                if not os.path.exists(os.path.dirname(save_fname)):
-                    os.makedirs(os.path.dirname(save_fname))
-                print_result = get_wanted_result(result)
-                np.save(save_fname, np.array([exit_layer_counter, eval_time, actual_cost / full_cost, print_result]))
-                logger.info("Entropy={}\tResult={:.2f}".format(args.early_exit_entropy, 100 * print_result))
-
-        output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
-        with open(output_eval_file, "w") as writer:
-            logger.info("***** Eval results {} *****".format(prefix))
-            for key in sorted(result.keys()):
-                logger.info("  %s = %s", key, str(result[key]))
-                writer.write("%s = %s\n" % (key, str(result[key])))
-
-    return results
-
-
-def load_and_cache_examples(args, task, tokenizer, evaluate=False):
-    if args.local_rank not in [-1, 0] and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
-    processor = processors[task]()
-    output_mode = output_modes[task]
-    # Load data features from cache or dataset file
-    cached_features_file = os.path.join(
-        args.data_dir,
-        "cached_{}_{}_{}_{}".format(
-            "dev" if evaluate else "train",
-            list(filter(None, args.model_name_or_path.split("/"))).pop(),
-            str(args.max_seq_length),
-            str(task),
-        ),
-    )
-    if os.path.exists(cached_features_file) and not args.overwrite_cache:
-        logger.info("Loading features from cached file %s", cached_features_file)
-        features = torch.load(cached_features_file)
-    else:
-        logger.info("Creating features from dataset file at %s", args.data_dir)
-        label_list = processor.get_labels()
-        if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta"]:
-            # HACK(label indices are swapped in RoBERTa pretrained model)
-            label_list[1], label_list[2] = label_list[2], label_list[1]
-        examples = (
-            processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
-        )
-        features = convert_examples_to_features(
-            examples,
-            tokenizer,
-            label_list=label_list,
-            max_length=args.max_seq_length,
-            output_mode=output_mode,
-        )
-        if args.local_rank in [-1, 0]:
-            logger.info("Saving features into cached file %s", cached_features_file)
-            torch.save(features, cached_features_file)
-
-    if args.local_rank == 0 and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
-    # Convert to Tensors and build dataset
-    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
-    all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
-
-    if features[0].token_type_ids is None:
-        # For RoBERTa (a potential bug!)
-        all_token_type_ids = torch.tensor([[0] * args.max_seq_length for f in features], dtype=torch.long)
-    else:
-        all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
-    if output_mode == "classification":
-        all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
-    elif output_mode == "regression":
-        all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
-
-    dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
-    return dataset
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    # Required parameters
-    parser.add_argument(
-        "--data_dir",
-        default=None,
-        type=str,
-        required=True,
-        help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
-    )
-    parser.add_argument(
-        "--model_type",
-        default=None,
-        type=str,
-        required=True,
-        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
-    )
-    parser.add_argument(
-        "--model_name_or_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to pre-trained model or shortcut name.",
-    )
-    parser.add_argument(
-        "--task_name",
-        default=None,
-        type=str,
-        required=True,
-        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()),
-    )
-    parser.add_argument(
-        "--output_dir",
-        default=None,
-        type=str,
-        required=True,
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument(
-        "--plot_data_dir",
-        default="./plotting/",
-        type=str,
-        required=False,
-        help="The directory to store data for plotting figures.",
-    )
-
-    # Other parameters
-    parser.add_argument(
-        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        default="",
-        type=str,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        default="",
-        type=str,
-        help="Where do you want to store the pre-trained models downloaded from huggingface.co",
-    )
-    parser.add_argument(
-        "--max_seq_length",
-        default=128,
-        type=int,
-        help=(
-            "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated, sequences shorter will be padded."
-        ),
-    )
-    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
-    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
-    parser.add_argument(
-        "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
-    )
-    parser.add_argument(
-        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
-    )
-    parser.add_argument("--eval_each_highway", action="store_true", help="Set this flag to evaluate each highway.")
-    parser.add_argument(
-        "--eval_after_first_stage",
-        action="store_true",
-        help="Set this flag to evaluate after training only bert (not highway).",
-    )
-    parser.add_argument("--eval_highway", action="store_true", help="Set this flag if it's evaluating highway models")
-
-    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
-    parser.add_argument(
-        "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
-    parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.")
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument(
-        "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
-    )
-    parser.add_argument(
-        "--max_steps",
-        default=-1,
-        type=int,
-        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
-    )
-    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
-    parser.add_argument("--early_exit_entropy", default=-1, type=float, help="Entropy threshold for early exit.")
-
-    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
-    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
-    parser.add_argument(
-        "--eval_all_checkpoints",
-        action="store_true",
-        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
-    )
-    parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
-    parser.add_argument(
-        "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
-    )
-    parser.add_argument(
-        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
-    )
-    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
-
-    parser.add_argument(
-        "--fp16",
-        action="store_true",
-        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
-    )
-    parser.add_argument(
-        "--fp16_opt_level",
-        type=str,
-        default="O1",
-        help=(
-            "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
-            "See details at https://nvidia.github.io/apex/amp.html"
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
-    parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
-    args = parser.parse_args()
-
-    if (
-        os.path.exists(args.output_dir)
-        and os.listdir(args.output_dir)
-        and args.do_train
-        and not args.overwrite_output_dir
-    ):
-        raise ValueError(
-            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
-                args.output_dir
-            )
-        )
-
-    # Setup distant debugging if needed
-    if args.server_ip and args.server_port:
-        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
-        import ptvsd
-
-        print("Waiting for debugger attach")
-        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
-        ptvsd.wait_for_attach()
-
-    # Setup CUDA, GPU & distributed training
-    if args.local_rank == -1 or args.no_cuda:
-        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-        args.n_gpu = torch.cuda.device_count()
-    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
-        torch.cuda.set_device(args.local_rank)
-        device = torch.device("cuda", args.local_rank)
-        torch.distributed.init_process_group(backend="nccl")
-        args.n_gpu = 1
-    args.device = device
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
-    )
-    logger.warning(
-        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-        args.local_rank,
-        device,
-        args.n_gpu,
-        bool(args.local_rank != -1),
-        args.fp16,
-    )
-    # Set the verbosity to info of the Transformers logger (on main process only):
-    if is_main_process(args.local_rank):
-        transformers.utils.logging.set_verbosity_info()
-        transformers.utils.logging.enable_default_handler()
-        transformers.utils.logging.enable_explicit_format()
-    # Set seed
-    set_seed(args)
-
-    # Prepare GLUE task
-    args.task_name = args.task_name.lower()
-    if args.task_name not in processors:
-        raise ValueError("Task not found: %s" % (args.task_name))
-    processor = processors[args.task_name]()
-    args.output_mode = output_modes[args.task_name]
-    label_list = processor.get_labels()
-    num_labels = len(label_list)
-
-    # Load pretrained model and tokenizer
-    if args.local_rank not in [-1, 0]:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
-
-    args.model_type = args.model_type.lower()
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(
-        args.config_name if args.config_name else args.model_name_or_path,
-        num_labels=num_labels,
-        finetuning_task=args.task_name,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-    tokenizer = tokenizer_class.from_pretrained(
-        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
-        do_lower_case=args.do_lower_case,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-    model = model_class.from_pretrained(
-        args.model_name_or_path,
-        from_tf=bool(".ckpt" in args.model_name_or_path),
-        config=config,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-
-    if args.model_type == "bert":
-        model.bert.encoder.set_early_exit_entropy(args.early_exit_entropy)
-        model.bert.init_highway_pooler()
-    elif args.model_type == "roberta":
-        model.roberta.encoder.set_early_exit_entropy(args.early_exit_entropy)
-        model.roberta.init_highway_pooler()
-    else:
-        raise NotImplementedError()
-
-    if args.local_rank == 0:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
-
-    model.to(args.device)
-
-    logger.info("Training/evaluation parameters %s", args)
-
-    # Training
-    if args.do_train:
-        train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
-        global_step, tr_loss = train(args, train_dataset, model, tokenizer)
-        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
-
-        if args.eval_after_first_stage:
-            result = evaluate(args, model, tokenizer, prefix="")
-            print_result = get_wanted_result(result)
-
-        train(args, train_dataset, model, tokenizer, train_highway=True)
-
-    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
-    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
-        logger.info("Saving model checkpoint to %s", args.output_dir)
-        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
-        # They can then be reloaded using `from_pretrained()`
-        model_to_save = (
-            model.module if hasattr(model, "module") else model
-        )  # Take care of distributed/parallel training
-        model_to_save.save_pretrained(args.output_dir)
-        tokenizer.save_pretrained(args.output_dir)
-
-        # Good practice: save your training arguments together with the trained model
-        torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
-
-        # Load a trained model and vocabulary that you have fine-tuned
-        model = model_class.from_pretrained(args.output_dir)
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
-        model.to(args.device)
-
-    # Evaluation
-    results = {}
-    if args.do_eval and args.local_rank in [-1, 0]:
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
-        checkpoints = [args.output_dir]
-        if args.eval_all_checkpoints:
-            checkpoints = [
-                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
-            ]
-
-        logger.info("Evaluate the following checkpoints: %s", checkpoints)
-        for checkpoint in checkpoints:
-            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
-            prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
-
-            model = model_class.from_pretrained(checkpoint)
-            if args.model_type == "bert":
-                model.bert.encoder.set_early_exit_entropy(args.early_exit_entropy)
-            elif args.model_type == "roberta":
-                model.roberta.encoder.set_early_exit_entropy(args.early_exit_entropy)
-            else:
-                raise NotImplementedError()
-
-            model.to(args.device)
-            result = evaluate(args, model, tokenizer, prefix=prefix, eval_highway=args.eval_highway)
-            print_result = get_wanted_result(result)
-            logger.info("Result: {}".format(print_result))
-            if args.eval_each_highway:
-                last_layer_results = print_result
-                each_layer_results = []
-                for i in range(model.num_layers):
-                    logger.info("\n")
-                    _result = evaluate(
-                        args, model, tokenizer, prefix=prefix, output_layer=i, eval_highway=args.eval_highway
-                    )
-                    if i + 1 < model.num_layers:
-                        each_layer_results.append(get_wanted_result(_result))
-                each_layer_results.append(last_layer_results)
-                save_fname = args.plot_data_dir + "/" + args.model_name_or_path[2:] + "/each_layer.npy"
-                if not os.path.exists(os.path.dirname(save_fname)):
-                    os.makedirs(os.path.dirname(save_fname))
-                np.save(save_fname, np.array(each_layer_results))
-                info_str = "Score of each layer:"
-                for i in range(model.num_layers):
-                    info_str += " {:.2f}".format(100 * each_layer_results[i])
-                logger.info(info_str)
-            result = {k + "_{}".format(global_step): v for k, v in result.items()}
-            results.update(result)
-
-    return results
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/et_xmlfile/xmlfile.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/et_xmlfile/xmlfile.py
deleted file mode 100644
index 09ec55554ff89d18891562d435bb29013d03beba..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/et_xmlfile/xmlfile.py
+++ /dev/null
@@ -1,104 +0,0 @@
-from __future__ import absolute_import
-# Copyright (c) 2010-2015 openpyxl
-
-"""Implements the lxml.etree.xmlfile API using the standard library xml.etree"""
-
-
-from contextlib import contextmanager
-
-from xml.etree.ElementTree import Element, tostring
-
-
-class LxmlSyntaxError(Exception):
-    pass
-
-
-class _FakeIncrementalFileWriter(object):
-    """Replacement for _IncrementalFileWriter of lxml.
-       Uses ElementTree to build xml in memory."""
-    def __init__(self, output_file):
-        self._element_stack = []
-        self._top_element = None
-        self._file = output_file
-        self._have_root = False
-
-    @contextmanager
-    def element(self, tag, attrib=None, nsmap=None, **_extra):
-        """Create a new xml element using a context manager.
-        The elements are written when the top level context is left.
-
-        This is for code compatibility only as it is quite slow.
-        """
-
-        # __enter__ part
-        self._have_root = True
-        if attrib is None:
-            attrib = {}
-        self._top_element = Element(tag, attrib=attrib, **_extra)
-        self._top_element.text = ''
-        self._top_element.tail = ''
-        self._element_stack.append(self._top_element)
-        yield
-
-        # __exit__ part
-        el = self._element_stack.pop()
-        if self._element_stack:
-            parent = self._element_stack[-1]
-            parent.append(self._top_element)
-            self._top_element = parent
-        else:
-            self._write_element(el)
-            self._top_element = None
-
-    def write(self, arg):
-        """Write a string or subelement."""
-
-        if isinstance(arg, str):
-            # it is not allowed to write a string outside of an element
-            if self._top_element is None:
-                raise LxmlSyntaxError()
-
-            if len(self._top_element) == 0:
-                # element has no children: add string to text
-                self._top_element.text += arg
-            else:
-                # element has children: add string to tail of last child
-                self._top_element[-1].tail += arg
-
-        else:
-            if self._top_element is not None:
-                self._top_element.append(arg)
-            elif not self._have_root:
-                self._write_element(arg)
-            else:
-                raise LxmlSyntaxError()
-
-    def _write_element(self, element):
-        xml = tostring(element)
-        self._file.write(xml)
-
-    def __enter__(self):
-        pass
-
-    def __exit__(self, type, value, traceback):
-        # without root the xml document is incomplete
-        if not self._have_root:
-            raise LxmlSyntaxError()
-
-
-class xmlfile(object):
-    """Context manager that can replace lxml.etree.xmlfile."""
-    def __init__(self, output_file, buffered=False, encoding=None, close=False):
-        if isinstance(output_file, str):
-            self._file = open(output_file, 'wb')
-            self._close = True
-        else:
-            self._file = output_file
-            self._close = close
-
-    def __enter__(self):
-        return _FakeIncrementalFileWriter(self._file)
-
-    def __exit__(self, type, value, traceback):
-        if self._close == True:
-            self._file.close()
diff --git a/spaces/cihyFjudo/fairness-paper-search/Akhila Poonoor Sex Video.rargolkes Nirvana Aprire Folde [REPACK].md b/spaces/cihyFjudo/fairness-paper-search/Akhila Poonoor Sex Video.rargolkes Nirvana Aprire Folde [REPACK].md
deleted file mode 100644
index 458b948273f0cab2a804ebfd7ae4e35f653f6bf0..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Akhila Poonoor Sex Video.rargolkes Nirvana Aprire Folde [REPACK].md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Easyusetool 0.3.1 Download Why You Should Try It Today.md b/spaces/cihyFjudo/fairness-paper-search/Easyusetool 0.3.1 Download Why You Should Try It Today.md
deleted file mode 100644
index 641e959e3c4fddc941c6202b2400b3db576f590a..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Easyusetool 0.3.1 Download Why You Should Try It Today.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Easyusetool 0.3.1 download</h2><br /><p><b><b>Download File</b> === <a href="https://tinurli.com/2uwhN6">https://tinurli.com/2uwhN6</a></b></p><br /><br />
-
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diff --git a/spaces/cihyFjudo/fairness-paper-search/King No 1 Movie In Hindi Hd Examen 10000 Carmage Tips and Tricks for Success.md b/spaces/cihyFjudo/fairness-paper-search/King No 1 Movie In Hindi Hd Examen 10000 Carmage Tips and Tricks for Success.md
deleted file mode 100644
index e2b19a2e8a670adb7100cafef0947eef85a2fef2..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/King No 1 Movie In Hindi Hd Examen 10000 Carmage Tips and Tricks for Success.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/cihyFjudo/fairness-paper-search/New MacBook Pro Proves Apple Has Lost Sight Of Its Core Values The Disappointing Design Choices That Compromise The User Experience.md b/spaces/cihyFjudo/fairness-paper-search/New MacBook Pro Proves Apple Has Lost Sight Of Its Core Values The Disappointing Design Choices That Compromise The User Experience.md
deleted file mode 100644
index f421c88778e880cbcf8d7291effbba276f3991b7..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/New MacBook Pro Proves Apple Has Lost Sight Of Its Core Values The Disappointing Design Choices That Compromise The User Experience.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>New MacBook Pro Proves Apple Has Lost Sight Of Its Core Values</h2><br /><p><b><b>DOWNLOAD</b> &bull; <a href="https://tinurli.com/2uwjaT">https://tinurli.com/2uwjaT</a></b></p><br /><br />
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Pano2vr Download Mac A Powerful Tool for Google Street View and WebVR.md b/spaces/cihyFjudo/fairness-paper-search/Pano2vr Download Mac A Powerful Tool for Google Street View and WebVR.md
deleted file mode 100644
index ed9b5ae0dc79a159ce4dd9f6592a3eb83c62f1f7..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Pano2vr Download Mac A Powerful Tool for Google Street View and WebVR.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-<br />
-<p>Click on the button below to start downloading Pano2VR 6.0.1 for mac OS X. We are here to provide to clean and fast download for Pano2VR 6.0.3. This link is resume able within 24 hours. Keep visiting themacgo the world of dmgs.</p>
-<h2>Pano2vr Download Mac</h2><br /><p><b><b>DOWNLOAD</b> &gt;&gt;&gt; <a href="https://tinurli.com/2uwiWG">https://tinurli.com/2uwiWG</a></b></p><br /><br />
-<p>Pano2VR Pro for Mac is an imposing as well as very easy to use application which will allows you to create interactive 360 panoramas from the ordinary images. It has been equipped with a very user friendly interface which will allow you to work with this tool efficiently. You can also download Blackmagic Fusion Studio for MacOSX.</p>
-<p>This application also supports flat, cylindrical, spherical, T and strip styles panoramas. It supports various different image file formats which include PSD, PNG, JPG, TIFF and QuickTime VR. For creating the interactive 360 panoramas you need to load the source image and adjust the amount of the use data. With this application you can create hotspot and insert the media items etc. it also allows you to create custom skin for the 360 panoramas. You can also add the button and graphics and configure that way viewer uses them. All in all Pano2VR Pro for Mac is an imposing as well as very easy to use application which will allows you to create interactive 360 panoramas from the ordinary images. You can also download Athentech Perfectly Clear Complete for MacOSX.</p> aaccfb2cb3<br />
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\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Pluralsight  Prototyping Virtual Reality Experiences With Unity Tips and Tricks for VR Developers.md b/spaces/cihyFjudo/fairness-paper-search/Pluralsight  Prototyping Virtual Reality Experiences With Unity Tips and Tricks for VR Developers.md
deleted file mode 100644
index 21bca454999960762927708a5af934fbc1d0e30e..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Pluralsight  Prototyping Virtual Reality Experiences With Unity Tips and Tricks for VR Developers.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-<br />
-<p>Most successful professionals in virtually any industry will tell you that a support network is essential. Aside from <b>providing outside perspectives</b>, a support network can enable you to tap into the experiences of others, and may provide you with training and skills development opportunities. Building strong relationships with others in the field may even lead you to your next job.</p>
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diff --git "a/spaces/cihyFjudo/fairness-paper-search/WINDOWS10- Display live Heart Rate information in Windows Universal app using Bluetooth LE\302\240 BLE A step-by-step guide.md" "b/spaces/cihyFjudo/fairness-paper-search/WINDOWS10- Display live Heart Rate information in Windows Universal app using Bluetooth LE\302\240 BLE A step-by-step guide.md"
deleted file mode 100644
index 75eb2381c079b1e2beab5acc2bdcd6dc7d231870..0000000000000000000000000000000000000000
--- "a/spaces/cihyFjudo/fairness-paper-search/WINDOWS10- Display live Heart Rate information in Windows Universal app using Bluetooth LE\302\240 BLE A step-by-step guide.md"	
+++ /dev/null
@@ -1,5 +0,0 @@
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Yadiprabhupadanahoitopdffree.md b/spaces/cihyFjudo/fairness-paper-search/Yadiprabhupadanahoitopdffree.md
deleted file mode 100644
index 6d98ce4282fde1c56ec2a20a5bc822c557eba47b..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Yadiprabhupadanahoitopdffree.md
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diff --git a/spaces/cleanmaster/akagi-sovits3/commons.py b/spaces/cleanmaster/akagi-sovits3/commons.py
deleted file mode 100644
index 074888006392e956ce204d8368362dbb2cd4e304..0000000000000000000000000000000000000000
--- a/spaces/cleanmaster/akagi-sovits3/commons.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-def slice_pitch_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, idx_str:idx_end]
-  return ret
-
-def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
-  return ret, ret_pitch, ids_str
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def rand_spec_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/resolver.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/resolver.py
deleted file mode 100644
index 531ce93fccc2d3be442556de644cdc78d31d9c6e..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/aiohttp/resolver.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import asyncio
-import socket
-from typing import Any, Dict, List, Optional, Type, Union
-
-from .abc import AbstractResolver
-from .helpers import get_running_loop
-
-__all__ = ("ThreadedResolver", "AsyncResolver", "DefaultResolver")
-
-try:
-    import aiodns
-
-    # aiodns_default = hasattr(aiodns.DNSResolver, 'gethostbyname')
-except ImportError:  # pragma: no cover
-    aiodns = None
-
-aiodns_default = False
-
-
-class ThreadedResolver(AbstractResolver):
-    """Threaded resolver.
-
-    Uses an Executor for synchronous getaddrinfo() calls.
-    concurrent.futures.ThreadPoolExecutor is used by default.
-    """
-
-    def __init__(self, loop: Optional[asyncio.AbstractEventLoop] = None) -> None:
-        self._loop = get_running_loop(loop)
-
-    async def resolve(
-        self, hostname: str, port: int = 0, family: int = socket.AF_INET
-    ) -> List[Dict[str, Any]]:
-        infos = await self._loop.getaddrinfo(
-            hostname,
-            port,
-            type=socket.SOCK_STREAM,
-            family=family,
-            flags=socket.AI_ADDRCONFIG,
-        )
-
-        hosts = []
-        for family, _, proto, _, address in infos:
-            if family == socket.AF_INET6:
-                if len(address) < 3:
-                    # IPv6 is not supported by Python build,
-                    # or IPv6 is not enabled in the host
-                    continue
-                if address[3]:  # type: ignore[misc]
-                    # This is essential for link-local IPv6 addresses.
-                    # LL IPv6 is a VERY rare case. Strictly speaking, we should use
-                    # getnameinfo() unconditionally, but performance makes sense.
-                    host, _port = socket.getnameinfo(
-                        address, socket.NI_NUMERICHOST | socket.NI_NUMERICSERV
-                    )
-                    port = int(_port)
-                else:
-                    host, port = address[:2]
-            else:  # IPv4
-                assert family == socket.AF_INET
-                host, port = address  # type: ignore[misc]
-            hosts.append(
-                {
-                    "hostname": hostname,
-                    "host": host,
-                    "port": port,
-                    "family": family,
-                    "proto": proto,
-                    "flags": socket.AI_NUMERICHOST | socket.AI_NUMERICSERV,
-                }
-            )
-
-        return hosts
-
-    async def close(self) -> None:
-        pass
-
-
-class AsyncResolver(AbstractResolver):
-    """Use the `aiodns` package to make asynchronous DNS lookups"""
-
-    def __init__(
-        self,
-        loop: Optional[asyncio.AbstractEventLoop] = None,
-        *args: Any,
-        **kwargs: Any
-    ) -> None:
-        if aiodns is None:
-            raise RuntimeError("Resolver requires aiodns library")
-
-        self._loop = get_running_loop(loop)
-        self._resolver = aiodns.DNSResolver(*args, loop=loop, **kwargs)
-
-        if not hasattr(self._resolver, "gethostbyname"):
-            # aiodns 1.1 is not available, fallback to DNSResolver.query
-            self.resolve = self._resolve_with_query  # type: ignore
-
-    async def resolve(
-        self, host: str, port: int = 0, family: int = socket.AF_INET
-    ) -> List[Dict[str, Any]]:
-        try:
-            resp = await self._resolver.gethostbyname(host, family)
-        except aiodns.error.DNSError as exc:
-            msg = exc.args[1] if len(exc.args) >= 1 else "DNS lookup failed"
-            raise OSError(msg) from exc
-        hosts = []
-        for address in resp.addresses:
-            hosts.append(
-                {
-                    "hostname": host,
-                    "host": address,
-                    "port": port,
-                    "family": family,
-                    "proto": 0,
-                    "flags": socket.AI_NUMERICHOST | socket.AI_NUMERICSERV,
-                }
-            )
-
-        if not hosts:
-            raise OSError("DNS lookup failed")
-
-        return hosts
-
-    async def _resolve_with_query(
-        self, host: str, port: int = 0, family: int = socket.AF_INET
-    ) -> List[Dict[str, Any]]:
-        if family == socket.AF_INET6:
-            qtype = "AAAA"
-        else:
-            qtype = "A"
-
-        try:
-            resp = await self._resolver.query(host, qtype)
-        except aiodns.error.DNSError as exc:
-            msg = exc.args[1] if len(exc.args) >= 1 else "DNS lookup failed"
-            raise OSError(msg) from exc
-
-        hosts = []
-        for rr in resp:
-            hosts.append(
-                {
-                    "hostname": host,
-                    "host": rr.host,
-                    "port": port,
-                    "family": family,
-                    "proto": 0,
-                    "flags": socket.AI_NUMERICHOST,
-                }
-            )
-
-        if not hosts:
-            raise OSError("DNS lookup failed")
-
-        return hosts
-
-    async def close(self) -> None:
-        self._resolver.cancel()
-
-
-_DefaultType = Type[Union[AsyncResolver, ThreadedResolver]]
-DefaultResolver: _DefaultType = AsyncResolver if aiodns_default else ThreadedResolver
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/cffi/vengine_gen.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/cffi/vengine_gen.py
deleted file mode 100644
index 26421526f62a07e04419cd57f1f19a64ecd36452..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/cffi/vengine_gen.py
+++ /dev/null
@@ -1,675 +0,0 @@
-#
-# DEPRECATED: implementation for ffi.verify()
-#
-import sys, os
-import types
-
-from . import model
-from .error import VerificationError
-
-
-class VGenericEngine(object):
-    _class_key = 'g'
-    _gen_python_module = False
-
-    def __init__(self, verifier):
-        self.verifier = verifier
-        self.ffi = verifier.ffi
-        self.export_symbols = []
-        self._struct_pending_verification = {}
-
-    def patch_extension_kwds(self, kwds):
-        # add 'export_symbols' to the dictionary.  Note that we add the
-        # list before filling it.  When we fill it, it will thus also show
-        # up in kwds['export_symbols'].
-        kwds.setdefault('export_symbols', self.export_symbols)
-
-    def find_module(self, module_name, path, so_suffixes):
-        for so_suffix in so_suffixes:
-            basename = module_name + so_suffix
-            if path is None:
-                path = sys.path
-            for dirname in path:
-                filename = os.path.join(dirname, basename)
-                if os.path.isfile(filename):
-                    return filename
-
-    def collect_types(self):
-        pass      # not needed in the generic engine
-
-    def _prnt(self, what=''):
-        self._f.write(what + '\n')
-
-    def write_source_to_f(self):
-        prnt = self._prnt
-        # first paste some standard set of lines that are mostly '#include'
-        prnt(cffimod_header)
-        # then paste the C source given by the user, verbatim.
-        prnt(self.verifier.preamble)
-        #
-        # call generate_gen_xxx_decl(), for every xxx found from
-        # ffi._parser._declarations.  This generates all the functions.
-        self._generate('decl')
-        #
-        # on Windows, distutils insists on putting init_cffi_xyz in
-        # 'export_symbols', so instead of fighting it, just give up and
-        # give it one
-        if sys.platform == 'win32':
-            if sys.version_info >= (3,):
-                prefix = 'PyInit_'
-            else:
-                prefix = 'init'
-            modname = self.verifier.get_module_name()
-            prnt("void %s%s(void) { }\n" % (prefix, modname))
-
-    def load_library(self, flags=0):
-        # import it with the CFFI backend
-        backend = self.ffi._backend
-        # needs to make a path that contains '/', on Posix
-        filename = os.path.join(os.curdir, self.verifier.modulefilename)
-        module = backend.load_library(filename, flags)
-        #
-        # call loading_gen_struct() to get the struct layout inferred by
-        # the C compiler
-        self._load(module, 'loading')
-
-        # build the FFILibrary class and instance, this is a module subclass
-        # because modules are expected to have usually-constant-attributes and
-        # in PyPy this means the JIT is able to treat attributes as constant,
-        # which we want.
-        class FFILibrary(types.ModuleType):
-            _cffi_generic_module = module
-            _cffi_ffi = self.ffi
-            _cffi_dir = []
-            def __dir__(self):
-                return FFILibrary._cffi_dir
-        library = FFILibrary("")
-        #
-        # finally, call the loaded_gen_xxx() functions.  This will set
-        # up the 'library' object.
-        self._load(module, 'loaded', library=library)
-        return library
-
-    def _get_declarations(self):
-        lst = [(key, tp) for (key, (tp, qual)) in
-                                self.ffi._parser._declarations.items()]
-        lst.sort()
-        return lst
-
-    def _generate(self, step_name):
-        for name, tp in self._get_declarations():
-            kind, realname = name.split(' ', 1)
-            try:
-                method = getattr(self, '_generate_gen_%s_%s' % (kind,
-                                                                step_name))
-            except AttributeError:
-                raise VerificationError(
-                    "not implemented in verify(): %r" % name)
-            try:
-                method(tp, realname)
-            except Exception as e:
-                model.attach_exception_info(e, name)
-                raise
-
-    def _load(self, module, step_name, **kwds):
-        for name, tp in self._get_declarations():
-            kind, realname = name.split(' ', 1)
-            method = getattr(self, '_%s_gen_%s' % (step_name, kind))
-            try:
-                method(tp, realname, module, **kwds)
-            except Exception as e:
-                model.attach_exception_info(e, name)
-                raise
-
-    def _generate_nothing(self, tp, name):
-        pass
-
-    def _loaded_noop(self, tp, name, module, **kwds):
-        pass
-
-    # ----------
-    # typedefs: generates no code so far
-
-    _generate_gen_typedef_decl   = _generate_nothing
-    _loading_gen_typedef         = _loaded_noop
-    _loaded_gen_typedef          = _loaded_noop
-
-    # ----------
-    # function declarations
-
-    def _generate_gen_function_decl(self, tp, name):
-        assert isinstance(tp, model.FunctionPtrType)
-        if tp.ellipsis:
-            # cannot support vararg functions better than this: check for its
-            # exact type (including the fixed arguments), and build it as a
-            # constant function pointer (no _cffi_f_%s wrapper)
-            self._generate_gen_const(False, name, tp)
-            return
-        prnt = self._prnt
-        numargs = len(tp.args)
-        argnames = []
-        for i, type in enumerate(tp.args):
-            indirection = ''
-            if isinstance(type, model.StructOrUnion):
-                indirection = '*'
-            argnames.append('%sx%d' % (indirection, i))
-        context = 'argument of %s' % name
-        arglist = [type.get_c_name(' %s' % arg, context)
-                   for type, arg in zip(tp.args, argnames)]
-        tpresult = tp.result
-        if isinstance(tpresult, model.StructOrUnion):
-            arglist.insert(0, tpresult.get_c_name(' *r', context))
-            tpresult = model.void_type
-        arglist = ', '.join(arglist) or 'void'
-        wrappername = '_cffi_f_%s' % name
-        self.export_symbols.append(wrappername)
-        if tp.abi:
-            abi = tp.abi + ' '
-        else:
-            abi = ''
-        funcdecl = ' %s%s(%s)' % (abi, wrappername, arglist)
-        context = 'result of %s' % name
-        prnt(tpresult.get_c_name(funcdecl, context))
-        prnt('{')
-        #
-        if isinstance(tp.result, model.StructOrUnion):
-            result_code = '*r = '
-        elif not isinstance(tp.result, model.VoidType):
-            result_code = 'return '
-        else:
-            result_code = ''
-        prnt('  %s%s(%s);' % (result_code, name, ', '.join(argnames)))
-        prnt('}')
-        prnt()
-
-    _loading_gen_function = _loaded_noop
-
-    def _loaded_gen_function(self, tp, name, module, library):
-        assert isinstance(tp, model.FunctionPtrType)
-        if tp.ellipsis:
-            newfunction = self._load_constant(False, tp, name, module)
-        else:
-            indirections = []
-            base_tp = tp
-            if (any(isinstance(typ, model.StructOrUnion) for typ in tp.args)
-                    or isinstance(tp.result, model.StructOrUnion)):
-                indirect_args = []
-                for i, typ in enumerate(tp.args):
-                    if isinstance(typ, model.StructOrUnion):
-                        typ = model.PointerType(typ)
-                        indirections.append((i, typ))
-                    indirect_args.append(typ)
-                indirect_result = tp.result
-                if isinstance(indirect_result, model.StructOrUnion):
-                    if indirect_result.fldtypes is None:
-                        raise TypeError("'%s' is used as result type, "
-                                        "but is opaque" % (
-                                            indirect_result._get_c_name(),))
-                    indirect_result = model.PointerType(indirect_result)
-                    indirect_args.insert(0, indirect_result)
-                    indirections.insert(0, ("result", indirect_result))
-                    indirect_result = model.void_type
-                tp = model.FunctionPtrType(tuple(indirect_args),
-                                           indirect_result, tp.ellipsis)
-            BFunc = self.ffi._get_cached_btype(tp)
-            wrappername = '_cffi_f_%s' % name
-            newfunction = module.load_function(BFunc, wrappername)
-            for i, typ in indirections:
-                newfunction = self._make_struct_wrapper(newfunction, i, typ,
-                                                        base_tp)
-        setattr(library, name, newfunction)
-        type(library)._cffi_dir.append(name)
-
-    def _make_struct_wrapper(self, oldfunc, i, tp, base_tp):
-        backend = self.ffi._backend
-        BType = self.ffi._get_cached_btype(tp)
-        if i == "result":
-            ffi = self.ffi
-            def newfunc(*args):
-                res = ffi.new(BType)
-                oldfunc(res, *args)
-                return res[0]
-        else:
-            def newfunc(*args):
-                args = args[:i] + (backend.newp(BType, args[i]),) + args[i+1:]
-                return oldfunc(*args)
-        newfunc._cffi_base_type = base_tp
-        return newfunc
-
-    # ----------
-    # named structs
-
-    def _generate_gen_struct_decl(self, tp, name):
-        assert name == tp.name
-        self._generate_struct_or_union_decl(tp, 'struct', name)
-
-    def _loading_gen_struct(self, tp, name, module):
-        self._loading_struct_or_union(tp, 'struct', name, module)
-
-    def _loaded_gen_struct(self, tp, name, module, **kwds):
-        self._loaded_struct_or_union(tp)
-
-    def _generate_gen_union_decl(self, tp, name):
-        assert name == tp.name
-        self._generate_struct_or_union_decl(tp, 'union', name)
-
-    def _loading_gen_union(self, tp, name, module):
-        self._loading_struct_or_union(tp, 'union', name, module)
-
-    def _loaded_gen_union(self, tp, name, module, **kwds):
-        self._loaded_struct_or_union(tp)
-
-    def _generate_struct_or_union_decl(self, tp, prefix, name):
-        if tp.fldnames is None:
-            return     # nothing to do with opaque structs
-        checkfuncname = '_cffi_check_%s_%s' % (prefix, name)
-        layoutfuncname = '_cffi_layout_%s_%s' % (prefix, name)
-        cname = ('%s %s' % (prefix, name)).strip()
-        #
-        prnt = self._prnt
-        prnt('static void %s(%s *p)' % (checkfuncname, cname))
-        prnt('{')
-        prnt('  /* only to generate compile-time warnings or errors */')
-        prnt('  (void)p;')
-        for fname, ftype, fbitsize, fqual in tp.enumfields():
-            if (isinstance(ftype, model.PrimitiveType)
-                and ftype.is_integer_type()) or fbitsize >= 0:
-                # accept all integers, but complain on float or double
-                prnt('  (void)((p->%s) << 1);' % fname)
-            else:
-                # only accept exactly the type declared.
-                try:
-                    prnt('  { %s = &p->%s; (void)tmp; }' % (
-                        ftype.get_c_name('*tmp', 'field %r'%fname, quals=fqual),
-                        fname))
-                except VerificationError as e:
-                    prnt('  /* %s */' % str(e))   # cannot verify it, ignore
-        prnt('}')
-        self.export_symbols.append(layoutfuncname)
-        prnt('intptr_t %s(intptr_t i)' % (layoutfuncname,))
-        prnt('{')
-        prnt('  struct _cffi_aligncheck { char x; %s y; };' % cname)
-        prnt('  static intptr_t nums[] = {')
-        prnt('    sizeof(%s),' % cname)
-        prnt('    offsetof(struct _cffi_aligncheck, y),')
-        for fname, ftype, fbitsize, fqual in tp.enumfields():
-            if fbitsize >= 0:
-                continue      # xxx ignore fbitsize for now
-            prnt('    offsetof(%s, %s),' % (cname, fname))
-            if isinstance(ftype, model.ArrayType) and ftype.length is None:
-                prnt('    0,  /* %s */' % ftype._get_c_name())
-            else:
-                prnt('    sizeof(((%s *)0)->%s),' % (cname, fname))
-        prnt('    -1')
-        prnt('  };')
-        prnt('  return nums[i];')
-        prnt('  /* the next line is not executed, but compiled */')
-        prnt('  %s(0);' % (checkfuncname,))
-        prnt('}')
-        prnt()
-
-    def _loading_struct_or_union(self, tp, prefix, name, module):
-        if tp.fldnames is None:
-            return     # nothing to do with opaque structs
-        layoutfuncname = '_cffi_layout_%s_%s' % (prefix, name)
-        #
-        BFunc = self.ffi._typeof_locked("intptr_t(*)(intptr_t)")[0]
-        function = module.load_function(BFunc, layoutfuncname)
-        layout = []
-        num = 0
-        while True:
-            x = function(num)
-            if x < 0: break
-            layout.append(x)
-            num += 1
-        if isinstance(tp, model.StructOrUnion) and tp.partial:
-            # use the function()'s sizes and offsets to guide the
-            # layout of the struct
-            totalsize = layout[0]
-            totalalignment = layout[1]
-            fieldofs = layout[2::2]
-            fieldsize = layout[3::2]
-            tp.force_flatten()
-            assert len(fieldofs) == len(fieldsize) == len(tp.fldnames)
-            tp.fixedlayout = fieldofs, fieldsize, totalsize, totalalignment
-        else:
-            cname = ('%s %s' % (prefix, name)).strip()
-            self._struct_pending_verification[tp] = layout, cname
-
-    def _loaded_struct_or_union(self, tp):
-        if tp.fldnames is None:
-            return     # nothing to do with opaque structs
-        self.ffi._get_cached_btype(tp)   # force 'fixedlayout' to be considered
-
-        if tp in self._struct_pending_verification:
-            # check that the layout sizes and offsets match the real ones
-            def check(realvalue, expectedvalue, msg):
-                if realvalue != expectedvalue:
-                    raise VerificationError(
-                        "%s (we have %d, but C compiler says %d)"
-                        % (msg, expectedvalue, realvalue))
-            ffi = self.ffi
-            BStruct = ffi._get_cached_btype(tp)
-            layout, cname = self._struct_pending_verification.pop(tp)
-            check(layout[0], ffi.sizeof(BStruct), "wrong total size")
-            check(layout[1], ffi.alignof(BStruct), "wrong total alignment")
-            i = 2
-            for fname, ftype, fbitsize, fqual in tp.enumfields():
-                if fbitsize >= 0:
-                    continue        # xxx ignore fbitsize for now
-                check(layout[i], ffi.offsetof(BStruct, fname),
-                      "wrong offset for field %r" % (fname,))
-                if layout[i+1] != 0:
-                    BField = ffi._get_cached_btype(ftype)
-                    check(layout[i+1], ffi.sizeof(BField),
-                          "wrong size for field %r" % (fname,))
-                i += 2
-            assert i == len(layout)
-
-    # ----------
-    # 'anonymous' declarations.  These are produced for anonymous structs
-    # or unions; the 'name' is obtained by a typedef.
-
-    def _generate_gen_anonymous_decl(self, tp, name):
-        if isinstance(tp, model.EnumType):
-            self._generate_gen_enum_decl(tp, name, '')
-        else:
-            self._generate_struct_or_union_decl(tp, '', name)
-
-    def _loading_gen_anonymous(self, tp, name, module):
-        if isinstance(tp, model.EnumType):
-            self._loading_gen_enum(tp, name, module, '')
-        else:
-            self._loading_struct_or_union(tp, '', name, module)
-
-    def _loaded_gen_anonymous(self, tp, name, module, **kwds):
-        if isinstance(tp, model.EnumType):
-            self._loaded_gen_enum(tp, name, module, **kwds)
-        else:
-            self._loaded_struct_or_union(tp)
-
-    # ----------
-    # constants, likely declared with '#define'
-
-    def _generate_gen_const(self, is_int, name, tp=None, category='const',
-                            check_value=None):
-        prnt = self._prnt
-        funcname = '_cffi_%s_%s' % (category, name)
-        self.export_symbols.append(funcname)
-        if check_value is not None:
-            assert is_int
-            assert category == 'const'
-            prnt('int %s(char *out_error)' % funcname)
-            prnt('{')
-            self._check_int_constant_value(name, check_value)
-            prnt('  return 0;')
-            prnt('}')
-        elif is_int:
-            assert category == 'const'
-            prnt('int %s(long long *out_value)' % funcname)
-            prnt('{')
-            prnt('  *out_value = (long long)(%s);' % (name,))
-            prnt('  return (%s) <= 0;' % (name,))
-            prnt('}')
-        else:
-            assert tp is not None
-            assert check_value is None
-            if category == 'var':
-                ampersand = '&'
-            else:
-                ampersand = ''
-            extra = ''
-            if category == 'const' and isinstance(tp, model.StructOrUnion):
-                extra = 'const *'
-                ampersand = '&'
-            prnt(tp.get_c_name(' %s%s(void)' % (extra, funcname), name))
-            prnt('{')
-            prnt('  return (%s%s);' % (ampersand, name))
-            prnt('}')
-        prnt()
-
-    def _generate_gen_constant_decl(self, tp, name):
-        is_int = isinstance(tp, model.PrimitiveType) and tp.is_integer_type()
-        self._generate_gen_const(is_int, name, tp)
-
-    _loading_gen_constant = _loaded_noop
-
-    def _load_constant(self, is_int, tp, name, module, check_value=None):
-        funcname = '_cffi_const_%s' % name
-        if check_value is not None:
-            assert is_int
-            self._load_known_int_constant(module, funcname)
-            value = check_value
-        elif is_int:
-            BType = self.ffi._typeof_locked("long long*")[0]
-            BFunc = self.ffi._typeof_locked("int(*)(long long*)")[0]
-            function = module.load_function(BFunc, funcname)
-            p = self.ffi.new(BType)
-            negative = function(p)
-            value = int(p[0])
-            if value < 0 and not negative:
-                BLongLong = self.ffi._typeof_locked("long long")[0]
-                value += (1 << (8*self.ffi.sizeof(BLongLong)))
-        else:
-            assert check_value is None
-            fntypeextra = '(*)(void)'
-            if isinstance(tp, model.StructOrUnion):
-                fntypeextra = '*' + fntypeextra
-            BFunc = self.ffi._typeof_locked(tp.get_c_name(fntypeextra, name))[0]
-            function = module.load_function(BFunc, funcname)
-            value = function()
-            if isinstance(tp, model.StructOrUnion):
-                value = value[0]
-        return value
-
-    def _loaded_gen_constant(self, tp, name, module, library):
-        is_int = isinstance(tp, model.PrimitiveType) and tp.is_integer_type()
-        value = self._load_constant(is_int, tp, name, module)
-        setattr(library, name, value)
-        type(library)._cffi_dir.append(name)
-
-    # ----------
-    # enums
-
-    def _check_int_constant_value(self, name, value):
-        prnt = self._prnt
-        if value <= 0:
-            prnt('  if ((%s) > 0 || (long)(%s) != %dL) {' % (
-                name, name, value))
-        else:
-            prnt('  if ((%s) <= 0 || (unsigned long)(%s) != %dUL) {' % (
-                name, name, value))
-        prnt('    char buf[64];')
-        prnt('    if ((%s) <= 0)' % name)
-        prnt('        sprintf(buf, "%%ld", (long)(%s));' % name)
-        prnt('    else')
-        prnt('        sprintf(buf, "%%lu", (unsigned long)(%s));' %
-             name)
-        prnt('    sprintf(out_error, "%s has the real value %s, not %s",')
-        prnt('            "%s", buf, "%d");' % (name[:100], value))
-        prnt('    return -1;')
-        prnt('  }')
-
-    def _load_known_int_constant(self, module, funcname):
-        BType = self.ffi._typeof_locked("char[]")[0]
-        BFunc = self.ffi._typeof_locked("int(*)(char*)")[0]
-        function = module.load_function(BFunc, funcname)
-        p = self.ffi.new(BType, 256)
-        if function(p) < 0:
-            error = self.ffi.string(p)
-            if sys.version_info >= (3,):
-                error = str(error, 'utf-8')
-            raise VerificationError(error)
-
-    def _enum_funcname(self, prefix, name):
-        # "$enum_$1" => "___D_enum____D_1"
-        name = name.replace('$', '___D_')
-        return '_cffi_e_%s_%s' % (prefix, name)
-
-    def _generate_gen_enum_decl(self, tp, name, prefix='enum'):
-        if tp.partial:
-            for enumerator in tp.enumerators:
-                self._generate_gen_const(True, enumerator)
-            return
-        #
-        funcname = self._enum_funcname(prefix, name)
-        self.export_symbols.append(funcname)
-        prnt = self._prnt
-        prnt('int %s(char *out_error)' % funcname)
-        prnt('{')
-        for enumerator, enumvalue in zip(tp.enumerators, tp.enumvalues):
-            self._check_int_constant_value(enumerator, enumvalue)
-        prnt('  return 0;')
-        prnt('}')
-        prnt()
-
-    def _loading_gen_enum(self, tp, name, module, prefix='enum'):
-        if tp.partial:
-            enumvalues = [self._load_constant(True, tp, enumerator, module)
-                          for enumerator in tp.enumerators]
-            tp.enumvalues = tuple(enumvalues)
-            tp.partial_resolved = True
-        else:
-            funcname = self._enum_funcname(prefix, name)
-            self._load_known_int_constant(module, funcname)
-
-    def _loaded_gen_enum(self, tp, name, module, library):
-        for enumerator, enumvalue in zip(tp.enumerators, tp.enumvalues):
-            setattr(library, enumerator, enumvalue)
-            type(library)._cffi_dir.append(enumerator)
-
-    # ----------
-    # macros: for now only for integers
-
-    def _generate_gen_macro_decl(self, tp, name):
-        if tp == '...':
-            check_value = None
-        else:
-            check_value = tp     # an integer
-        self._generate_gen_const(True, name, check_value=check_value)
-
-    _loading_gen_macro = _loaded_noop
-
-    def _loaded_gen_macro(self, tp, name, module, library):
-        if tp == '...':
-            check_value = None
-        else:
-            check_value = tp     # an integer
-        value = self._load_constant(True, tp, name, module,
-                                    check_value=check_value)
-        setattr(library, name, value)
-        type(library)._cffi_dir.append(name)
-
-    # ----------
-    # global variables
-
-    def _generate_gen_variable_decl(self, tp, name):
-        if isinstance(tp, model.ArrayType):
-            if tp.length_is_unknown():
-                prnt = self._prnt
-                funcname = '_cffi_sizeof_%s' % (name,)
-                self.export_symbols.append(funcname)
-                prnt("size_t %s(void)" % funcname)
-                prnt("{")
-                prnt("  return sizeof(%s);" % (name,))
-                prnt("}")
-            tp_ptr = model.PointerType(tp.item)
-            self._generate_gen_const(False, name, tp_ptr)
-        else:
-            tp_ptr = model.PointerType(tp)
-            self._generate_gen_const(False, name, tp_ptr, category='var')
-
-    _loading_gen_variable = _loaded_noop
-
-    def _loaded_gen_variable(self, tp, name, module, library):
-        if isinstance(tp, model.ArrayType):   # int a[5] is "constant" in the
-                                              # sense that "a=..." is forbidden
-            if tp.length_is_unknown():
-                funcname = '_cffi_sizeof_%s' % (name,)
-                BFunc = self.ffi._typeof_locked('size_t(*)(void)')[0]
-                function = module.load_function(BFunc, funcname)
-                size = function()
-                BItemType = self.ffi._get_cached_btype(tp.item)
-                length, rest = divmod(size, self.ffi.sizeof(BItemType))
-                if rest != 0:
-                    raise VerificationError(
-                        "bad size: %r does not seem to be an array of %s" %
-                        (name, tp.item))
-                tp = tp.resolve_length(length)
-            tp_ptr = model.PointerType(tp.item)
-            value = self._load_constant(False, tp_ptr, name, module)
-            # 'value' is a <cdata 'type *'> which we have to replace with
-            # a <cdata 'type[N]'> if the N is actually known
-            if tp.length is not None:
-                BArray = self.ffi._get_cached_btype(tp)
-                value = self.ffi.cast(BArray, value)
-            setattr(library, name, value)
-            type(library)._cffi_dir.append(name)
-            return
-        # remove ptr=<cdata 'int *'> from the library instance, and replace
-        # it by a property on the class, which reads/writes into ptr[0].
-        funcname = '_cffi_var_%s' % name
-        BFunc = self.ffi._typeof_locked(tp.get_c_name('*(*)(void)', name))[0]
-        function = module.load_function(BFunc, funcname)
-        ptr = function()
-        def getter(library):
-            return ptr[0]
-        def setter(library, value):
-            ptr[0] = value
-        setattr(type(library), name, property(getter, setter))
-        type(library)._cffi_dir.append(name)
-
-cffimod_header = r'''
-#include <stdio.h>
-#include <stddef.h>
-#include <stdarg.h>
-#include <errno.h>
-#include <sys/types.h>   /* XXX for ssize_t on some platforms */
-
-/* this block of #ifs should be kept exactly identical between
-   c/_cffi_backend.c, cffi/vengine_cpy.py, cffi/vengine_gen.py
-   and cffi/_cffi_include.h */
-#if defined(_MSC_VER)
-# include <malloc.h>   /* for alloca() */
-# if _MSC_VER < 1600   /* MSVC < 2010 */
-   typedef __int8 int8_t;
-   typedef __int16 int16_t;
-   typedef __int32 int32_t;
-   typedef __int64 int64_t;
-   typedef unsigned __int8 uint8_t;
-   typedef unsigned __int16 uint16_t;
-   typedef unsigned __int32 uint32_t;
-   typedef unsigned __int64 uint64_t;
-   typedef __int8 int_least8_t;
-   typedef __int16 int_least16_t;
-   typedef __int32 int_least32_t;
-   typedef __int64 int_least64_t;
-   typedef unsigned __int8 uint_least8_t;
-   typedef unsigned __int16 uint_least16_t;
-   typedef unsigned __int32 uint_least32_t;
-   typedef unsigned __int64 uint_least64_t;
-   typedef __int8 int_fast8_t;
-   typedef __int16 int_fast16_t;
-   typedef __int32 int_fast32_t;
-   typedef __int64 int_fast64_t;
-   typedef unsigned __int8 uint_fast8_t;
-   typedef unsigned __int16 uint_fast16_t;
-   typedef unsigned __int32 uint_fast32_t;
-   typedef unsigned __int64 uint_fast64_t;
-   typedef __int64 intmax_t;
-   typedef unsigned __int64 uintmax_t;
-# else
-#  include <stdint.h>
-# endif
-# if _MSC_VER < 1800   /* MSVC < 2013 */
-#  ifndef __cplusplus
-    typedef unsigned char _Bool;
-#  endif
-# endif
-#else
-# include <stdint.h>
-# if (defined (__SVR4) && defined (__sun)) || defined(_AIX) || defined(__hpux)
-#  include <alloca.h>
-# endif
-#endif
-'''
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/click/core.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/click/core.py
deleted file mode 100644
index cc65e896bf2d754d74b54a84ac501b80127f83ca..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/click/core.py
+++ /dev/null
@@ -1,3042 +0,0 @@
-import enum
-import errno
-import inspect
-import os
-import sys
-import typing as t
-from collections import abc
-from contextlib import contextmanager
-from contextlib import ExitStack
-from functools import update_wrapper
-from gettext import gettext as _
-from gettext import ngettext
-from itertools import repeat
-from types import TracebackType
-
-from . import types
-from .exceptions import Abort
-from .exceptions import BadParameter
-from .exceptions import ClickException
-from .exceptions import Exit
-from .exceptions import MissingParameter
-from .exceptions import UsageError
-from .formatting import HelpFormatter
-from .formatting import join_options
-from .globals import pop_context
-from .globals import push_context
-from .parser import _flag_needs_value
-from .parser import OptionParser
-from .parser import split_opt
-from .termui import confirm
-from .termui import prompt
-from .termui import style
-from .utils import _detect_program_name
-from .utils import _expand_args
-from .utils import echo
-from .utils import make_default_short_help
-from .utils import make_str
-from .utils import PacifyFlushWrapper
-
-if t.TYPE_CHECKING:
-    import typing_extensions as te
-    from .shell_completion import CompletionItem
-
-F = t.TypeVar("F", bound=t.Callable[..., t.Any])
-V = t.TypeVar("V")
-
-
-def _complete_visible_commands(
-    ctx: "Context", incomplete: str
-) -> t.Iterator[t.Tuple[str, "Command"]]:
-    """List all the subcommands of a group that start with the
-    incomplete value and aren't hidden.
-
-    :param ctx: Invocation context for the group.
-    :param incomplete: Value being completed. May be empty.
-    """
-    multi = t.cast(MultiCommand, ctx.command)
-
-    for name in multi.list_commands(ctx):
-        if name.startswith(incomplete):
-            command = multi.get_command(ctx, name)
-
-            if command is not None and not command.hidden:
-                yield name, command
-
-
-def _check_multicommand(
-    base_command: "MultiCommand", cmd_name: str, cmd: "Command", register: bool = False
-) -> None:
-    if not base_command.chain or not isinstance(cmd, MultiCommand):
-        return
-    if register:
-        hint = (
-            "It is not possible to add multi commands as children to"
-            " another multi command that is in chain mode."
-        )
-    else:
-        hint = (
-            "Found a multi command as subcommand to a multi command"
-            " that is in chain mode. This is not supported."
-        )
-    raise RuntimeError(
-        f"{hint}. Command {base_command.name!r} is set to chain and"
-        f" {cmd_name!r} was added as a subcommand but it in itself is a"
-        f" multi command. ({cmd_name!r} is a {type(cmd).__name__}"
-        f" within a chained {type(base_command).__name__} named"
-        f" {base_command.name!r})."
-    )
-
-
-def batch(iterable: t.Iterable[V], batch_size: int) -> t.List[t.Tuple[V, ...]]:
-    return list(zip(*repeat(iter(iterable), batch_size)))
-
-
-@contextmanager
-def augment_usage_errors(
-    ctx: "Context", param: t.Optional["Parameter"] = None
-) -> t.Iterator[None]:
-    """Context manager that attaches extra information to exceptions."""
-    try:
-        yield
-    except BadParameter as e:
-        if e.ctx is None:
-            e.ctx = ctx
-        if param is not None and e.param is None:
-            e.param = param
-        raise
-    except UsageError as e:
-        if e.ctx is None:
-            e.ctx = ctx
-        raise
-
-
-def iter_params_for_processing(
-    invocation_order: t.Sequence["Parameter"],
-    declaration_order: t.Sequence["Parameter"],
-) -> t.List["Parameter"]:
-    """Given a sequence of parameters in the order as should be considered
-    for processing and an iterable of parameters that exist, this returns
-    a list in the correct order as they should be processed.
-    """
-
-    def sort_key(item: "Parameter") -> t.Tuple[bool, float]:
-        try:
-            idx: float = invocation_order.index(item)
-        except ValueError:
-            idx = float("inf")
-
-        return not item.is_eager, idx
-
-    return sorted(declaration_order, key=sort_key)
-
-
-class ParameterSource(enum.Enum):
-    """This is an :class:`~enum.Enum` that indicates the source of a
-    parameter's value.
-
-    Use :meth:`click.Context.get_parameter_source` to get the
-    source for a parameter by name.
-
-    .. versionchanged:: 8.0
-        Use :class:`~enum.Enum` and drop the ``validate`` method.
-
-    .. versionchanged:: 8.0
-        Added the ``PROMPT`` value.
-    """
-
-    COMMANDLINE = enum.auto()
-    """The value was provided by the command line args."""
-    ENVIRONMENT = enum.auto()
-    """The value was provided with an environment variable."""
-    DEFAULT = enum.auto()
-    """Used the default specified by the parameter."""
-    DEFAULT_MAP = enum.auto()
-    """Used a default provided by :attr:`Context.default_map`."""
-    PROMPT = enum.auto()
-    """Used a prompt to confirm a default or provide a value."""
-
-
-class Context:
-    """The context is a special internal object that holds state relevant
-    for the script execution at every single level.  It's normally invisible
-    to commands unless they opt-in to getting access to it.
-
-    The context is useful as it can pass internal objects around and can
-    control special execution features such as reading data from
-    environment variables.
-
-    A context can be used as context manager in which case it will call
-    :meth:`close` on teardown.
-
-    :param command: the command class for this context.
-    :param parent: the parent context.
-    :param info_name: the info name for this invocation.  Generally this
-                      is the most descriptive name for the script or
-                      command.  For the toplevel script it is usually
-                      the name of the script, for commands below it it's
-                      the name of the script.
-    :param obj: an arbitrary object of user data.
-    :param auto_envvar_prefix: the prefix to use for automatic environment
-                               variables.  If this is `None` then reading
-                               from environment variables is disabled.  This
-                               does not affect manually set environment
-                               variables which are always read.
-    :param default_map: a dictionary (like object) with default values
-                        for parameters.
-    :param terminal_width: the width of the terminal.  The default is
-                           inherit from parent context.  If no context
-                           defines the terminal width then auto
-                           detection will be applied.
-    :param max_content_width: the maximum width for content rendered by
-                              Click (this currently only affects help
-                              pages).  This defaults to 80 characters if
-                              not overridden.  In other words: even if the
-                              terminal is larger than that, Click will not
-                              format things wider than 80 characters by
-                              default.  In addition to that, formatters might
-                              add some safety mapping on the right.
-    :param resilient_parsing: if this flag is enabled then Click will
-                              parse without any interactivity or callback
-                              invocation.  Default values will also be
-                              ignored.  This is useful for implementing
-                              things such as completion support.
-    :param allow_extra_args: if this is set to `True` then extra arguments
-                             at the end will not raise an error and will be
-                             kept on the context.  The default is to inherit
-                             from the command.
-    :param allow_interspersed_args: if this is set to `False` then options
-                                    and arguments cannot be mixed.  The
-                                    default is to inherit from the command.
-    :param ignore_unknown_options: instructs click to ignore options it does
-                                   not know and keeps them for later
-                                   processing.
-    :param help_option_names: optionally a list of strings that define how
-                              the default help parameter is named.  The
-                              default is ``['--help']``.
-    :param token_normalize_func: an optional function that is used to
-                                 normalize tokens (options, choices,
-                                 etc.).  This for instance can be used to
-                                 implement case insensitive behavior.
-    :param color: controls if the terminal supports ANSI colors or not.  The
-                  default is autodetection.  This is only needed if ANSI
-                  codes are used in texts that Click prints which is by
-                  default not the case.  This for instance would affect
-                  help output.
-    :param show_default: Show the default value for commands. If this
-        value is not set, it defaults to the value from the parent
-        context. ``Command.show_default`` overrides this default for the
-        specific command.
-
-    .. versionchanged:: 8.1
-        The ``show_default`` parameter is overridden by
-        ``Command.show_default``, instead of the other way around.
-
-    .. versionchanged:: 8.0
-        The ``show_default`` parameter defaults to the value from the
-        parent context.
-
-    .. versionchanged:: 7.1
-       Added the ``show_default`` parameter.
-
-    .. versionchanged:: 4.0
-        Added the ``color``, ``ignore_unknown_options``, and
-        ``max_content_width`` parameters.
-
-    .. versionchanged:: 3.0
-        Added the ``allow_extra_args`` and ``allow_interspersed_args``
-        parameters.
-
-    .. versionchanged:: 2.0
-        Added the ``resilient_parsing``, ``help_option_names``, and
-        ``token_normalize_func`` parameters.
-    """
-
-    #: The formatter class to create with :meth:`make_formatter`.
-    #:
-    #: .. versionadded:: 8.0
-    formatter_class: t.Type["HelpFormatter"] = HelpFormatter
-
-    def __init__(
-        self,
-        command: "Command",
-        parent: t.Optional["Context"] = None,
-        info_name: t.Optional[str] = None,
-        obj: t.Optional[t.Any] = None,
-        auto_envvar_prefix: t.Optional[str] = None,
-        default_map: t.Optional[t.MutableMapping[str, t.Any]] = None,
-        terminal_width: t.Optional[int] = None,
-        max_content_width: t.Optional[int] = None,
-        resilient_parsing: bool = False,
-        allow_extra_args: t.Optional[bool] = None,
-        allow_interspersed_args: t.Optional[bool] = None,
-        ignore_unknown_options: t.Optional[bool] = None,
-        help_option_names: t.Optional[t.List[str]] = None,
-        token_normalize_func: t.Optional[t.Callable[[str], str]] = None,
-        color: t.Optional[bool] = None,
-        show_default: t.Optional[bool] = None,
-    ) -> None:
-        #: the parent context or `None` if none exists.
-        self.parent = parent
-        #: the :class:`Command` for this context.
-        self.command = command
-        #: the descriptive information name
-        self.info_name = info_name
-        #: Map of parameter names to their parsed values. Parameters
-        #: with ``expose_value=False`` are not stored.
-        self.params: t.Dict[str, t.Any] = {}
-        #: the leftover arguments.
-        self.args: t.List[str] = []
-        #: protected arguments.  These are arguments that are prepended
-        #: to `args` when certain parsing scenarios are encountered but
-        #: must be never propagated to another arguments.  This is used
-        #: to implement nested parsing.
-        self.protected_args: t.List[str] = []
-        #: the collected prefixes of the command's options.
-        self._opt_prefixes: t.Set[str] = set(parent._opt_prefixes) if parent else set()
-
-        if obj is None and parent is not None:
-            obj = parent.obj
-
-        #: the user object stored.
-        self.obj: t.Any = obj
-        self._meta: t.Dict[str, t.Any] = getattr(parent, "meta", {})
-
-        #: A dictionary (-like object) with defaults for parameters.
-        if (
-            default_map is None
-            and info_name is not None
-            and parent is not None
-            and parent.default_map is not None
-        ):
-            default_map = parent.default_map.get(info_name)
-
-        self.default_map: t.Optional[t.MutableMapping[str, t.Any]] = default_map
-
-        #: This flag indicates if a subcommand is going to be executed. A
-        #: group callback can use this information to figure out if it's
-        #: being executed directly or because the execution flow passes
-        #: onwards to a subcommand. By default it's None, but it can be
-        #: the name of the subcommand to execute.
-        #:
-        #: If chaining is enabled this will be set to ``'*'`` in case
-        #: any commands are executed.  It is however not possible to
-        #: figure out which ones.  If you require this knowledge you
-        #: should use a :func:`result_callback`.
-        self.invoked_subcommand: t.Optional[str] = None
-
-        if terminal_width is None and parent is not None:
-            terminal_width = parent.terminal_width
-
-        #: The width of the terminal (None is autodetection).
-        self.terminal_width: t.Optional[int] = terminal_width
-
-        if max_content_width is None and parent is not None:
-            max_content_width = parent.max_content_width
-
-        #: The maximum width of formatted content (None implies a sensible
-        #: default which is 80 for most things).
-        self.max_content_width: t.Optional[int] = max_content_width
-
-        if allow_extra_args is None:
-            allow_extra_args = command.allow_extra_args
-
-        #: Indicates if the context allows extra args or if it should
-        #: fail on parsing.
-        #:
-        #: .. versionadded:: 3.0
-        self.allow_extra_args = allow_extra_args
-
-        if allow_interspersed_args is None:
-            allow_interspersed_args = command.allow_interspersed_args
-
-        #: Indicates if the context allows mixing of arguments and
-        #: options or not.
-        #:
-        #: .. versionadded:: 3.0
-        self.allow_interspersed_args: bool = allow_interspersed_args
-
-        if ignore_unknown_options is None:
-            ignore_unknown_options = command.ignore_unknown_options
-
-        #: Instructs click to ignore options that a command does not
-        #: understand and will store it on the context for later
-        #: processing.  This is primarily useful for situations where you
-        #: want to call into external programs.  Generally this pattern is
-        #: strongly discouraged because it's not possibly to losslessly
-        #: forward all arguments.
-        #:
-        #: .. versionadded:: 4.0
-        self.ignore_unknown_options: bool = ignore_unknown_options
-
-        if help_option_names is None:
-            if parent is not None:
-                help_option_names = parent.help_option_names
-            else:
-                help_option_names = ["--help"]
-
-        #: The names for the help options.
-        self.help_option_names: t.List[str] = help_option_names
-
-        if token_normalize_func is None and parent is not None:
-            token_normalize_func = parent.token_normalize_func
-
-        #: An optional normalization function for tokens.  This is
-        #: options, choices, commands etc.
-        self.token_normalize_func: t.Optional[
-            t.Callable[[str], str]
-        ] = token_normalize_func
-
-        #: Indicates if resilient parsing is enabled.  In that case Click
-        #: will do its best to not cause any failures and default values
-        #: will be ignored. Useful for completion.
-        self.resilient_parsing: bool = resilient_parsing
-
-        # If there is no envvar prefix yet, but the parent has one and
-        # the command on this level has a name, we can expand the envvar
-        # prefix automatically.
-        if auto_envvar_prefix is None:
-            if (
-                parent is not None
-                and parent.auto_envvar_prefix is not None
-                and self.info_name is not None
-            ):
-                auto_envvar_prefix = (
-                    f"{parent.auto_envvar_prefix}_{self.info_name.upper()}"
-                )
-        else:
-            auto_envvar_prefix = auto_envvar_prefix.upper()
-
-        if auto_envvar_prefix is not None:
-            auto_envvar_prefix = auto_envvar_prefix.replace("-", "_")
-
-        self.auto_envvar_prefix: t.Optional[str] = auto_envvar_prefix
-
-        if color is None and parent is not None:
-            color = parent.color
-
-        #: Controls if styling output is wanted or not.
-        self.color: t.Optional[bool] = color
-
-        if show_default is None and parent is not None:
-            show_default = parent.show_default
-
-        #: Show option default values when formatting help text.
-        self.show_default: t.Optional[bool] = show_default
-
-        self._close_callbacks: t.List[t.Callable[[], t.Any]] = []
-        self._depth = 0
-        self._parameter_source: t.Dict[str, ParameterSource] = {}
-        self._exit_stack = ExitStack()
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        """Gather information that could be useful for a tool generating
-        user-facing documentation. This traverses the entire CLI
-        structure.
-
-        .. code-block:: python
-
-            with Context(cli) as ctx:
-                info = ctx.to_info_dict()
-
-        .. versionadded:: 8.0
-        """
-        return {
-            "command": self.command.to_info_dict(self),
-            "info_name": self.info_name,
-            "allow_extra_args": self.allow_extra_args,
-            "allow_interspersed_args": self.allow_interspersed_args,
-            "ignore_unknown_options": self.ignore_unknown_options,
-            "auto_envvar_prefix": self.auto_envvar_prefix,
-        }
-
-    def __enter__(self) -> "Context":
-        self._depth += 1
-        push_context(self)
-        return self
-
-    def __exit__(
-        self,
-        exc_type: t.Optional[t.Type[BaseException]],
-        exc_value: t.Optional[BaseException],
-        tb: t.Optional[TracebackType],
-    ) -> None:
-        self._depth -= 1
-        if self._depth == 0:
-            self.close()
-        pop_context()
-
-    @contextmanager
-    def scope(self, cleanup: bool = True) -> t.Iterator["Context"]:
-        """This helper method can be used with the context object to promote
-        it to the current thread local (see :func:`get_current_context`).
-        The default behavior of this is to invoke the cleanup functions which
-        can be disabled by setting `cleanup` to `False`.  The cleanup
-        functions are typically used for things such as closing file handles.
-
-        If the cleanup is intended the context object can also be directly
-        used as a context manager.
-
-        Example usage::
-
-            with ctx.scope():
-                assert get_current_context() is ctx
-
-        This is equivalent::
-
-            with ctx:
-                assert get_current_context() is ctx
-
-        .. versionadded:: 5.0
-
-        :param cleanup: controls if the cleanup functions should be run or
-                        not.  The default is to run these functions.  In
-                        some situations the context only wants to be
-                        temporarily pushed in which case this can be disabled.
-                        Nested pushes automatically defer the cleanup.
-        """
-        if not cleanup:
-            self._depth += 1
-        try:
-            with self as rv:
-                yield rv
-        finally:
-            if not cleanup:
-                self._depth -= 1
-
-    @property
-    def meta(self) -> t.Dict[str, t.Any]:
-        """This is a dictionary which is shared with all the contexts
-        that are nested.  It exists so that click utilities can store some
-        state here if they need to.  It is however the responsibility of
-        that code to manage this dictionary well.
-
-        The keys are supposed to be unique dotted strings.  For instance
-        module paths are a good choice for it.  What is stored in there is
-        irrelevant for the operation of click.  However what is important is
-        that code that places data here adheres to the general semantics of
-        the system.
-
-        Example usage::
-
-            LANG_KEY = f'{__name__}.lang'
-
-            def set_language(value):
-                ctx = get_current_context()
-                ctx.meta[LANG_KEY] = value
-
-            def get_language():
-                return get_current_context().meta.get(LANG_KEY, 'en_US')
-
-        .. versionadded:: 5.0
-        """
-        return self._meta
-
-    def make_formatter(self) -> HelpFormatter:
-        """Creates the :class:`~click.HelpFormatter` for the help and
-        usage output.
-
-        To quickly customize the formatter class used without overriding
-        this method, set the :attr:`formatter_class` attribute.
-
-        .. versionchanged:: 8.0
-            Added the :attr:`formatter_class` attribute.
-        """
-        return self.formatter_class(
-            width=self.terminal_width, max_width=self.max_content_width
-        )
-
-    def with_resource(self, context_manager: t.ContextManager[V]) -> V:
-        """Register a resource as if it were used in a ``with``
-        statement. The resource will be cleaned up when the context is
-        popped.
-
-        Uses :meth:`contextlib.ExitStack.enter_context`. It calls the
-        resource's ``__enter__()`` method and returns the result. When
-        the context is popped, it closes the stack, which calls the
-        resource's ``__exit__()`` method.
-
-        To register a cleanup function for something that isn't a
-        context manager, use :meth:`call_on_close`. Or use something
-        from :mod:`contextlib` to turn it into a context manager first.
-
-        .. code-block:: python
-
-            @click.group()
-            @click.option("--name")
-            @click.pass_context
-            def cli(ctx):
-                ctx.obj = ctx.with_resource(connect_db(name))
-
-        :param context_manager: The context manager to enter.
-        :return: Whatever ``context_manager.__enter__()`` returns.
-
-        .. versionadded:: 8.0
-        """
-        return self._exit_stack.enter_context(context_manager)
-
-    def call_on_close(self, f: t.Callable[..., t.Any]) -> t.Callable[..., t.Any]:
-        """Register a function to be called when the context tears down.
-
-        This can be used to close resources opened during the script
-        execution. Resources that support Python's context manager
-        protocol which would be used in a ``with`` statement should be
-        registered with :meth:`with_resource` instead.
-
-        :param f: The function to execute on teardown.
-        """
-        return self._exit_stack.callback(f)
-
-    def close(self) -> None:
-        """Invoke all close callbacks registered with
-        :meth:`call_on_close`, and exit all context managers entered
-        with :meth:`with_resource`.
-        """
-        self._exit_stack.close()
-        # In case the context is reused, create a new exit stack.
-        self._exit_stack = ExitStack()
-
-    @property
-    def command_path(self) -> str:
-        """The computed command path.  This is used for the ``usage``
-        information on the help page.  It's automatically created by
-        combining the info names of the chain of contexts to the root.
-        """
-        rv = ""
-        if self.info_name is not None:
-            rv = self.info_name
-        if self.parent is not None:
-            parent_command_path = [self.parent.command_path]
-
-            if isinstance(self.parent.command, Command):
-                for param in self.parent.command.get_params(self):
-                    parent_command_path.extend(param.get_usage_pieces(self))
-
-            rv = f"{' '.join(parent_command_path)} {rv}"
-        return rv.lstrip()
-
-    def find_root(self) -> "Context":
-        """Finds the outermost context."""
-        node = self
-        while node.parent is not None:
-            node = node.parent
-        return node
-
-    def find_object(self, object_type: t.Type[V]) -> t.Optional[V]:
-        """Finds the closest object of a given type."""
-        node: t.Optional["Context"] = self
-
-        while node is not None:
-            if isinstance(node.obj, object_type):
-                return node.obj
-
-            node = node.parent
-
-        return None
-
-    def ensure_object(self, object_type: t.Type[V]) -> V:
-        """Like :meth:`find_object` but sets the innermost object to a
-        new instance of `object_type` if it does not exist.
-        """
-        rv = self.find_object(object_type)
-        if rv is None:
-            self.obj = rv = object_type()
-        return rv
-
-    @t.overload
-    def lookup_default(
-        self, name: str, call: "te.Literal[True]" = True
-    ) -> t.Optional[t.Any]:
-        ...
-
-    @t.overload
-    def lookup_default(
-        self, name: str, call: "te.Literal[False]" = ...
-    ) -> t.Optional[t.Union[t.Any, t.Callable[[], t.Any]]]:
-        ...
-
-    def lookup_default(self, name: str, call: bool = True) -> t.Optional[t.Any]:
-        """Get the default for a parameter from :attr:`default_map`.
-
-        :param name: Name of the parameter.
-        :param call: If the default is a callable, call it. Disable to
-            return the callable instead.
-
-        .. versionchanged:: 8.0
-            Added the ``call`` parameter.
-        """
-        if self.default_map is not None:
-            value = self.default_map.get(name)
-
-            if call and callable(value):
-                return value()
-
-            return value
-
-        return None
-
-    def fail(self, message: str) -> "te.NoReturn":
-        """Aborts the execution of the program with a specific error
-        message.
-
-        :param message: the error message to fail with.
-        """
-        raise UsageError(message, self)
-
-    def abort(self) -> "te.NoReturn":
-        """Aborts the script."""
-        raise Abort()
-
-    def exit(self, code: int = 0) -> "te.NoReturn":
-        """Exits the application with a given exit code."""
-        raise Exit(code)
-
-    def get_usage(self) -> str:
-        """Helper method to get formatted usage string for the current
-        context and command.
-        """
-        return self.command.get_usage(self)
-
-    def get_help(self) -> str:
-        """Helper method to get formatted help page for the current
-        context and command.
-        """
-        return self.command.get_help(self)
-
-    def _make_sub_context(self, command: "Command") -> "Context":
-        """Create a new context of the same type as this context, but
-        for a new command.
-
-        :meta private:
-        """
-        return type(self)(command, info_name=command.name, parent=self)
-
-    @t.overload
-    def invoke(
-        __self,  # noqa: B902
-        __callback: "t.Callable[..., V]",
-        *args: t.Any,
-        **kwargs: t.Any,
-    ) -> V:
-        ...
-
-    @t.overload
-    def invoke(
-        __self,  # noqa: B902
-        __callback: "Command",
-        *args: t.Any,
-        **kwargs: t.Any,
-    ) -> t.Any:
-        ...
-
-    def invoke(
-        __self,  # noqa: B902
-        __callback: t.Union["Command", "t.Callable[..., V]"],
-        *args: t.Any,
-        **kwargs: t.Any,
-    ) -> t.Union[t.Any, V]:
-        """Invokes a command callback in exactly the way it expects.  There
-        are two ways to invoke this method:
-
-        1.  the first argument can be a callback and all other arguments and
-            keyword arguments are forwarded directly to the function.
-        2.  the first argument is a click command object.  In that case all
-            arguments are forwarded as well but proper click parameters
-            (options and click arguments) must be keyword arguments and Click
-            will fill in defaults.
-
-        Note that before Click 3.2 keyword arguments were not properly filled
-        in against the intention of this code and no context was created.  For
-        more information about this change and why it was done in a bugfix
-        release see :ref:`upgrade-to-3.2`.
-
-        .. versionchanged:: 8.0
-            All ``kwargs`` are tracked in :attr:`params` so they will be
-            passed if :meth:`forward` is called at multiple levels.
-        """
-        if isinstance(__callback, Command):
-            other_cmd = __callback
-
-            if other_cmd.callback is None:
-                raise TypeError(
-                    "The given command does not have a callback that can be invoked."
-                )
-            else:
-                __callback = t.cast("t.Callable[..., V]", other_cmd.callback)
-
-            ctx = __self._make_sub_context(other_cmd)
-
-            for param in other_cmd.params:
-                if param.name not in kwargs and param.expose_value:
-                    kwargs[param.name] = param.type_cast_value(  # type: ignore
-                        ctx, param.get_default(ctx)
-                    )
-
-            # Track all kwargs as params, so that forward() will pass
-            # them on in subsequent calls.
-            ctx.params.update(kwargs)
-        else:
-            ctx = __self
-
-        with augment_usage_errors(__self):
-            with ctx:
-                return __callback(*args, **kwargs)
-
-    def forward(
-        __self, __cmd: "Command", *args: t.Any, **kwargs: t.Any  # noqa: B902
-    ) -> t.Any:
-        """Similar to :meth:`invoke` but fills in default keyword
-        arguments from the current context if the other command expects
-        it.  This cannot invoke callbacks directly, only other commands.
-
-        .. versionchanged:: 8.0
-            All ``kwargs`` are tracked in :attr:`params` so they will be
-            passed if ``forward`` is called at multiple levels.
-        """
-        # Can only forward to other commands, not direct callbacks.
-        if not isinstance(__cmd, Command):
-            raise TypeError("Callback is not a command.")
-
-        for param in __self.params:
-            if param not in kwargs:
-                kwargs[param] = __self.params[param]
-
-        return __self.invoke(__cmd, *args, **kwargs)
-
-    def set_parameter_source(self, name: str, source: ParameterSource) -> None:
-        """Set the source of a parameter. This indicates the location
-        from which the value of the parameter was obtained.
-
-        :param name: The name of the parameter.
-        :param source: A member of :class:`~click.core.ParameterSource`.
-        """
-        self._parameter_source[name] = source
-
-    def get_parameter_source(self, name: str) -> t.Optional[ParameterSource]:
-        """Get the source of a parameter. This indicates the location
-        from which the value of the parameter was obtained.
-
-        This can be useful for determining when a user specified a value
-        on the command line that is the same as the default value. It
-        will be :attr:`~click.core.ParameterSource.DEFAULT` only if the
-        value was actually taken from the default.
-
-        :param name: The name of the parameter.
-        :rtype: ParameterSource
-
-        .. versionchanged:: 8.0
-            Returns ``None`` if the parameter was not provided from any
-            source.
-        """
-        return self._parameter_source.get(name)
-
-
-class BaseCommand:
-    """The base command implements the minimal API contract of commands.
-    Most code will never use this as it does not implement a lot of useful
-    functionality but it can act as the direct subclass of alternative
-    parsing methods that do not depend on the Click parser.
-
-    For instance, this can be used to bridge Click and other systems like
-    argparse or docopt.
-
-    Because base commands do not implement a lot of the API that other
-    parts of Click take for granted, they are not supported for all
-    operations.  For instance, they cannot be used with the decorators
-    usually and they have no built-in callback system.
-
-    .. versionchanged:: 2.0
-       Added the `context_settings` parameter.
-
-    :param name: the name of the command to use unless a group overrides it.
-    :param context_settings: an optional dictionary with defaults that are
-                             passed to the context object.
-    """
-
-    #: The context class to create with :meth:`make_context`.
-    #:
-    #: .. versionadded:: 8.0
-    context_class: t.Type[Context] = Context
-    #: the default for the :attr:`Context.allow_extra_args` flag.
-    allow_extra_args = False
-    #: the default for the :attr:`Context.allow_interspersed_args` flag.
-    allow_interspersed_args = True
-    #: the default for the :attr:`Context.ignore_unknown_options` flag.
-    ignore_unknown_options = False
-
-    def __init__(
-        self,
-        name: t.Optional[str],
-        context_settings: t.Optional[t.MutableMapping[str, t.Any]] = None,
-    ) -> None:
-        #: the name the command thinks it has.  Upon registering a command
-        #: on a :class:`Group` the group will default the command name
-        #: with this information.  You should instead use the
-        #: :class:`Context`\'s :attr:`~Context.info_name` attribute.
-        self.name = name
-
-        if context_settings is None:
-            context_settings = {}
-
-        #: an optional dictionary with defaults passed to the context.
-        self.context_settings: t.MutableMapping[str, t.Any] = context_settings
-
-    def to_info_dict(self, ctx: Context) -> t.Dict[str, t.Any]:
-        """Gather information that could be useful for a tool generating
-        user-facing documentation. This traverses the entire structure
-        below this command.
-
-        Use :meth:`click.Context.to_info_dict` to traverse the entire
-        CLI structure.
-
-        :param ctx: A :class:`Context` representing this command.
-
-        .. versionadded:: 8.0
-        """
-        return {"name": self.name}
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__} {self.name}>"
-
-    def get_usage(self, ctx: Context) -> str:
-        raise NotImplementedError("Base commands cannot get usage")
-
-    def get_help(self, ctx: Context) -> str:
-        raise NotImplementedError("Base commands cannot get help")
-
-    def make_context(
-        self,
-        info_name: t.Optional[str],
-        args: t.List[str],
-        parent: t.Optional[Context] = None,
-        **extra: t.Any,
-    ) -> Context:
-        """This function when given an info name and arguments will kick
-        off the parsing and create a new :class:`Context`.  It does not
-        invoke the actual command callback though.
-
-        To quickly customize the context class used without overriding
-        this method, set the :attr:`context_class` attribute.
-
-        :param info_name: the info name for this invocation.  Generally this
-                          is the most descriptive name for the script or
-                          command.  For the toplevel script it's usually
-                          the name of the script, for commands below it's
-                          the name of the command.
-        :param args: the arguments to parse as list of strings.
-        :param parent: the parent context if available.
-        :param extra: extra keyword arguments forwarded to the context
-                      constructor.
-
-        .. versionchanged:: 8.0
-            Added the :attr:`context_class` attribute.
-        """
-        for key, value in self.context_settings.items():
-            if key not in extra:
-                extra[key] = value
-
-        ctx = self.context_class(
-            self, info_name=info_name, parent=parent, **extra  # type: ignore
-        )
-
-        with ctx.scope(cleanup=False):
-            self.parse_args(ctx, args)
-        return ctx
-
-    def parse_args(self, ctx: Context, args: t.List[str]) -> t.List[str]:
-        """Given a context and a list of arguments this creates the parser
-        and parses the arguments, then modifies the context as necessary.
-        This is automatically invoked by :meth:`make_context`.
-        """
-        raise NotImplementedError("Base commands do not know how to parse arguments.")
-
-    def invoke(self, ctx: Context) -> t.Any:
-        """Given a context, this invokes the command.  The default
-        implementation is raising a not implemented error.
-        """
-        raise NotImplementedError("Base commands are not invocable by default")
-
-    def shell_complete(self, ctx: Context, incomplete: str) -> t.List["CompletionItem"]:
-        """Return a list of completions for the incomplete value. Looks
-        at the names of chained multi-commands.
-
-        Any command could be part of a chained multi-command, so sibling
-        commands are valid at any point during command completion. Other
-        command classes will return more completions.
-
-        :param ctx: Invocation context for this command.
-        :param incomplete: Value being completed. May be empty.
-
-        .. versionadded:: 8.0
-        """
-        from click.shell_completion import CompletionItem
-
-        results: t.List["CompletionItem"] = []
-
-        while ctx.parent is not None:
-            ctx = ctx.parent
-
-            if isinstance(ctx.command, MultiCommand) and ctx.command.chain:
-                results.extend(
-                    CompletionItem(name, help=command.get_short_help_str())
-                    for name, command in _complete_visible_commands(ctx, incomplete)
-                    if name not in ctx.protected_args
-                )
-
-        return results
-
-    @t.overload
-    def main(
-        self,
-        args: t.Optional[t.Sequence[str]] = None,
-        prog_name: t.Optional[str] = None,
-        complete_var: t.Optional[str] = None,
-        standalone_mode: "te.Literal[True]" = True,
-        **extra: t.Any,
-    ) -> "te.NoReturn":
-        ...
-
-    @t.overload
-    def main(
-        self,
-        args: t.Optional[t.Sequence[str]] = None,
-        prog_name: t.Optional[str] = None,
-        complete_var: t.Optional[str] = None,
-        standalone_mode: bool = ...,
-        **extra: t.Any,
-    ) -> t.Any:
-        ...
-
-    def main(
-        self,
-        args: t.Optional[t.Sequence[str]] = None,
-        prog_name: t.Optional[str] = None,
-        complete_var: t.Optional[str] = None,
-        standalone_mode: bool = True,
-        windows_expand_args: bool = True,
-        **extra: t.Any,
-    ) -> t.Any:
-        """This is the way to invoke a script with all the bells and
-        whistles as a command line application.  This will always terminate
-        the application after a call.  If this is not wanted, ``SystemExit``
-        needs to be caught.
-
-        This method is also available by directly calling the instance of
-        a :class:`Command`.
-
-        :param args: the arguments that should be used for parsing.  If not
-                     provided, ``sys.argv[1:]`` is used.
-        :param prog_name: the program name that should be used.  By default
-                          the program name is constructed by taking the file
-                          name from ``sys.argv[0]``.
-        :param complete_var: the environment variable that controls the
-                             bash completion support.  The default is
-                             ``"_<prog_name>_COMPLETE"`` with prog_name in
-                             uppercase.
-        :param standalone_mode: the default behavior is to invoke the script
-                                in standalone mode.  Click will then
-                                handle exceptions and convert them into
-                                error messages and the function will never
-                                return but shut down the interpreter.  If
-                                this is set to `False` they will be
-                                propagated to the caller and the return
-                                value of this function is the return value
-                                of :meth:`invoke`.
-        :param windows_expand_args: Expand glob patterns, user dir, and
-            env vars in command line args on Windows.
-        :param extra: extra keyword arguments are forwarded to the context
-                      constructor.  See :class:`Context` for more information.
-
-        .. versionchanged:: 8.0.1
-            Added the ``windows_expand_args`` parameter to allow
-            disabling command line arg expansion on Windows.
-
-        .. versionchanged:: 8.0
-            When taking arguments from ``sys.argv`` on Windows, glob
-            patterns, user dir, and env vars are expanded.
-
-        .. versionchanged:: 3.0
-           Added the ``standalone_mode`` parameter.
-        """
-        if args is None:
-            args = sys.argv[1:]
-
-            if os.name == "nt" and windows_expand_args:
-                args = _expand_args(args)
-        else:
-            args = list(args)
-
-        if prog_name is None:
-            prog_name = _detect_program_name()
-
-        # Process shell completion requests and exit early.
-        self._main_shell_completion(extra, prog_name, complete_var)
-
-        try:
-            try:
-                with self.make_context(prog_name, args, **extra) as ctx:
-                    rv = self.invoke(ctx)
-                    if not standalone_mode:
-                        return rv
-                    # it's not safe to `ctx.exit(rv)` here!
-                    # note that `rv` may actually contain data like "1" which
-                    # has obvious effects
-                    # more subtle case: `rv=[None, None]` can come out of
-                    # chained commands which all returned `None` -- so it's not
-                    # even always obvious that `rv` indicates success/failure
-                    # by its truthiness/falsiness
-                    ctx.exit()
-            except (EOFError, KeyboardInterrupt) as e:
-                echo(file=sys.stderr)
-                raise Abort() from e
-            except ClickException as e:
-                if not standalone_mode:
-                    raise
-                e.show()
-                sys.exit(e.exit_code)
-            except OSError as e:
-                if e.errno == errno.EPIPE:
-                    sys.stdout = t.cast(t.TextIO, PacifyFlushWrapper(sys.stdout))
-                    sys.stderr = t.cast(t.TextIO, PacifyFlushWrapper(sys.stderr))
-                    sys.exit(1)
-                else:
-                    raise
-        except Exit as e:
-            if standalone_mode:
-                sys.exit(e.exit_code)
-            else:
-                # in non-standalone mode, return the exit code
-                # note that this is only reached if `self.invoke` above raises
-                # an Exit explicitly -- thus bypassing the check there which
-                # would return its result
-                # the results of non-standalone execution may therefore be
-                # somewhat ambiguous: if there are codepaths which lead to
-                # `ctx.exit(1)` and to `return 1`, the caller won't be able to
-                # tell the difference between the two
-                return e.exit_code
-        except Abort:
-            if not standalone_mode:
-                raise
-            echo(_("Aborted!"), file=sys.stderr)
-            sys.exit(1)
-
-    def _main_shell_completion(
-        self,
-        ctx_args: t.MutableMapping[str, t.Any],
-        prog_name: str,
-        complete_var: t.Optional[str] = None,
-    ) -> None:
-        """Check if the shell is asking for tab completion, process
-        that, then exit early. Called from :meth:`main` before the
-        program is invoked.
-
-        :param prog_name: Name of the executable in the shell.
-        :param complete_var: Name of the environment variable that holds
-            the completion instruction. Defaults to
-            ``_{PROG_NAME}_COMPLETE``.
-
-        .. versionchanged:: 8.2.0
-            Dots (``.``) in ``prog_name`` are replaced with underscores (``_``).
-        """
-        if complete_var is None:
-            complete_name = prog_name.replace("-", "_").replace(".", "_")
-            complete_var = f"_{complete_name}_COMPLETE".upper()
-
-        instruction = os.environ.get(complete_var)
-
-        if not instruction:
-            return
-
-        from .shell_completion import shell_complete
-
-        rv = shell_complete(self, ctx_args, prog_name, complete_var, instruction)
-        sys.exit(rv)
-
-    def __call__(self, *args: t.Any, **kwargs: t.Any) -> t.Any:
-        """Alias for :meth:`main`."""
-        return self.main(*args, **kwargs)
-
-
-class Command(BaseCommand):
-    """Commands are the basic building block of command line interfaces in
-    Click.  A basic command handles command line parsing and might dispatch
-    more parsing to commands nested below it.
-
-    :param name: the name of the command to use unless a group overrides it.
-    :param context_settings: an optional dictionary with defaults that are
-                             passed to the context object.
-    :param callback: the callback to invoke.  This is optional.
-    :param params: the parameters to register with this command.  This can
-                   be either :class:`Option` or :class:`Argument` objects.
-    :param help: the help string to use for this command.
-    :param epilog: like the help string but it's printed at the end of the
-                   help page after everything else.
-    :param short_help: the short help to use for this command.  This is
-                       shown on the command listing of the parent command.
-    :param add_help_option: by default each command registers a ``--help``
-                            option.  This can be disabled by this parameter.
-    :param no_args_is_help: this controls what happens if no arguments are
-                            provided.  This option is disabled by default.
-                            If enabled this will add ``--help`` as argument
-                            if no arguments are passed
-    :param hidden: hide this command from help outputs.
-
-    :param deprecated: issues a message indicating that
-                             the command is deprecated.
-
-    .. versionchanged:: 8.1
-        ``help``, ``epilog``, and ``short_help`` are stored unprocessed,
-        all formatting is done when outputting help text, not at init,
-        and is done even if not using the ``@command`` decorator.
-
-    .. versionchanged:: 8.0
-        Added a ``repr`` showing the command name.
-
-    .. versionchanged:: 7.1
-        Added the ``no_args_is_help`` parameter.
-
-    .. versionchanged:: 2.0
-        Added the ``context_settings`` parameter.
-    """
-
-    def __init__(
-        self,
-        name: t.Optional[str],
-        context_settings: t.Optional[t.MutableMapping[str, t.Any]] = None,
-        callback: t.Optional[t.Callable[..., t.Any]] = None,
-        params: t.Optional[t.List["Parameter"]] = None,
-        help: t.Optional[str] = None,
-        epilog: t.Optional[str] = None,
-        short_help: t.Optional[str] = None,
-        options_metavar: t.Optional[str] = "[OPTIONS]",
-        add_help_option: bool = True,
-        no_args_is_help: bool = False,
-        hidden: bool = False,
-        deprecated: bool = False,
-    ) -> None:
-        super().__init__(name, context_settings)
-        #: the callback to execute when the command fires.  This might be
-        #: `None` in which case nothing happens.
-        self.callback = callback
-        #: the list of parameters for this command in the order they
-        #: should show up in the help page and execute.  Eager parameters
-        #: will automatically be handled before non eager ones.
-        self.params: t.List["Parameter"] = params or []
-        self.help = help
-        self.epilog = epilog
-        self.options_metavar = options_metavar
-        self.short_help = short_help
-        self.add_help_option = add_help_option
-        self.no_args_is_help = no_args_is_help
-        self.hidden = hidden
-        self.deprecated = deprecated
-
-    def to_info_dict(self, ctx: Context) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict(ctx)
-        info_dict.update(
-            params=[param.to_info_dict() for param in self.get_params(ctx)],
-            help=self.help,
-            epilog=self.epilog,
-            short_help=self.short_help,
-            hidden=self.hidden,
-            deprecated=self.deprecated,
-        )
-        return info_dict
-
-    def get_usage(self, ctx: Context) -> str:
-        """Formats the usage line into a string and returns it.
-
-        Calls :meth:`format_usage` internally.
-        """
-        formatter = ctx.make_formatter()
-        self.format_usage(ctx, formatter)
-        return formatter.getvalue().rstrip("\n")
-
-    def get_params(self, ctx: Context) -> t.List["Parameter"]:
-        rv = self.params
-        help_option = self.get_help_option(ctx)
-
-        if help_option is not None:
-            rv = [*rv, help_option]
-
-        return rv
-
-    def format_usage(self, ctx: Context, formatter: HelpFormatter) -> None:
-        """Writes the usage line into the formatter.
-
-        This is a low-level method called by :meth:`get_usage`.
-        """
-        pieces = self.collect_usage_pieces(ctx)
-        formatter.write_usage(ctx.command_path, " ".join(pieces))
-
-    def collect_usage_pieces(self, ctx: Context) -> t.List[str]:
-        """Returns all the pieces that go into the usage line and returns
-        it as a list of strings.
-        """
-        rv = [self.options_metavar] if self.options_metavar else []
-
-        for param in self.get_params(ctx):
-            rv.extend(param.get_usage_pieces(ctx))
-
-        return rv
-
-    def get_help_option_names(self, ctx: Context) -> t.List[str]:
-        """Returns the names for the help option."""
-        all_names = set(ctx.help_option_names)
-        for param in self.params:
-            all_names.difference_update(param.opts)
-            all_names.difference_update(param.secondary_opts)
-        return list(all_names)
-
-    def get_help_option(self, ctx: Context) -> t.Optional["Option"]:
-        """Returns the help option object."""
-        help_options = self.get_help_option_names(ctx)
-
-        if not help_options or not self.add_help_option:
-            return None
-
-        def show_help(ctx: Context, param: "Parameter", value: str) -> None:
-            if value and not ctx.resilient_parsing:
-                echo(ctx.get_help(), color=ctx.color)
-                ctx.exit()
-
-        return Option(
-            help_options,
-            is_flag=True,
-            is_eager=True,
-            expose_value=False,
-            callback=show_help,
-            help=_("Show this message and exit."),
-        )
-
-    def make_parser(self, ctx: Context) -> OptionParser:
-        """Creates the underlying option parser for this command."""
-        parser = OptionParser(ctx)
-        for param in self.get_params(ctx):
-            param.add_to_parser(parser, ctx)
-        return parser
-
-    def get_help(self, ctx: Context) -> str:
-        """Formats the help into a string and returns it.
-
-        Calls :meth:`format_help` internally.
-        """
-        formatter = ctx.make_formatter()
-        self.format_help(ctx, formatter)
-        return formatter.getvalue().rstrip("\n")
-
-    def get_short_help_str(self, limit: int = 45) -> str:
-        """Gets short help for the command or makes it by shortening the
-        long help string.
-        """
-        if self.short_help:
-            text = inspect.cleandoc(self.short_help)
-        elif self.help:
-            text = make_default_short_help(self.help, limit)
-        else:
-            text = ""
-
-        if self.deprecated:
-            text = _("(Deprecated) {text}").format(text=text)
-
-        return text.strip()
-
-    def format_help(self, ctx: Context, formatter: HelpFormatter) -> None:
-        """Writes the help into the formatter if it exists.
-
-        This is a low-level method called by :meth:`get_help`.
-
-        This calls the following methods:
-
-        -   :meth:`format_usage`
-        -   :meth:`format_help_text`
-        -   :meth:`format_options`
-        -   :meth:`format_epilog`
-        """
-        self.format_usage(ctx, formatter)
-        self.format_help_text(ctx, formatter)
-        self.format_options(ctx, formatter)
-        self.format_epilog(ctx, formatter)
-
-    def format_help_text(self, ctx: Context, formatter: HelpFormatter) -> None:
-        """Writes the help text to the formatter if it exists."""
-        if self.help is not None:
-            # truncate the help text to the first form feed
-            text = inspect.cleandoc(self.help).partition("\f")[0]
-        else:
-            text = ""
-
-        if self.deprecated:
-            text = _("(Deprecated) {text}").format(text=text)
-
-        if text:
-            formatter.write_paragraph()
-
-            with formatter.indentation():
-                formatter.write_text(text)
-
-    def format_options(self, ctx: Context, formatter: HelpFormatter) -> None:
-        """Writes all the options into the formatter if they exist."""
-        opts = []
-        for param in self.get_params(ctx):
-            rv = param.get_help_record(ctx)
-            if rv is not None:
-                opts.append(rv)
-
-        if opts:
-            with formatter.section(_("Options")):
-                formatter.write_dl(opts)
-
-    def format_epilog(self, ctx: Context, formatter: HelpFormatter) -> None:
-        """Writes the epilog into the formatter if it exists."""
-        if self.epilog:
-            epilog = inspect.cleandoc(self.epilog)
-            formatter.write_paragraph()
-
-            with formatter.indentation():
-                formatter.write_text(epilog)
-
-    def parse_args(self, ctx: Context, args: t.List[str]) -> t.List[str]:
-        if not args and self.no_args_is_help and not ctx.resilient_parsing:
-            echo(ctx.get_help(), color=ctx.color)
-            ctx.exit()
-
-        parser = self.make_parser(ctx)
-        opts, args, param_order = parser.parse_args(args=args)
-
-        for param in iter_params_for_processing(param_order, self.get_params(ctx)):
-            value, args = param.handle_parse_result(ctx, opts, args)
-
-        if args and not ctx.allow_extra_args and not ctx.resilient_parsing:
-            ctx.fail(
-                ngettext(
-                    "Got unexpected extra argument ({args})",
-                    "Got unexpected extra arguments ({args})",
-                    len(args),
-                ).format(args=" ".join(map(str, args)))
-            )
-
-        ctx.args = args
-        ctx._opt_prefixes.update(parser._opt_prefixes)
-        return args
-
-    def invoke(self, ctx: Context) -> t.Any:
-        """Given a context, this invokes the attached callback (if it exists)
-        in the right way.
-        """
-        if self.deprecated:
-            message = _(
-                "DeprecationWarning: The command {name!r} is deprecated."
-            ).format(name=self.name)
-            echo(style(message, fg="red"), err=True)
-
-        if self.callback is not None:
-            return ctx.invoke(self.callback, **ctx.params)
-
-    def shell_complete(self, ctx: Context, incomplete: str) -> t.List["CompletionItem"]:
-        """Return a list of completions for the incomplete value. Looks
-        at the names of options and chained multi-commands.
-
-        :param ctx: Invocation context for this command.
-        :param incomplete: Value being completed. May be empty.
-
-        .. versionadded:: 8.0
-        """
-        from click.shell_completion import CompletionItem
-
-        results: t.List["CompletionItem"] = []
-
-        if incomplete and not incomplete[0].isalnum():
-            for param in self.get_params(ctx):
-                if (
-                    not isinstance(param, Option)
-                    or param.hidden
-                    or (
-                        not param.multiple
-                        and ctx.get_parameter_source(param.name)  # type: ignore
-                        is ParameterSource.COMMANDLINE
-                    )
-                ):
-                    continue
-
-                results.extend(
-                    CompletionItem(name, help=param.help)
-                    for name in [*param.opts, *param.secondary_opts]
-                    if name.startswith(incomplete)
-                )
-
-        results.extend(super().shell_complete(ctx, incomplete))
-        return results
-
-
-class MultiCommand(Command):
-    """A multi command is the basic implementation of a command that
-    dispatches to subcommands.  The most common version is the
-    :class:`Group`.
-
-    :param invoke_without_command: this controls how the multi command itself
-                                   is invoked.  By default it's only invoked
-                                   if a subcommand is provided.
-    :param no_args_is_help: this controls what happens if no arguments are
-                            provided.  This option is enabled by default if
-                            `invoke_without_command` is disabled or disabled
-                            if it's enabled.  If enabled this will add
-                            ``--help`` as argument if no arguments are
-                            passed.
-    :param subcommand_metavar: the string that is used in the documentation
-                               to indicate the subcommand place.
-    :param chain: if this is set to `True` chaining of multiple subcommands
-                  is enabled.  This restricts the form of commands in that
-                  they cannot have optional arguments but it allows
-                  multiple commands to be chained together.
-    :param result_callback: The result callback to attach to this multi
-        command. This can be set or changed later with the
-        :meth:`result_callback` decorator.
-    :param attrs: Other command arguments described in :class:`Command`.
-    """
-
-    allow_extra_args = True
-    allow_interspersed_args = False
-
-    def __init__(
-        self,
-        name: t.Optional[str] = None,
-        invoke_without_command: bool = False,
-        no_args_is_help: t.Optional[bool] = None,
-        subcommand_metavar: t.Optional[str] = None,
-        chain: bool = False,
-        result_callback: t.Optional[t.Callable[..., t.Any]] = None,
-        **attrs: t.Any,
-    ) -> None:
-        super().__init__(name, **attrs)
-
-        if no_args_is_help is None:
-            no_args_is_help = not invoke_without_command
-
-        self.no_args_is_help = no_args_is_help
-        self.invoke_without_command = invoke_without_command
-
-        if subcommand_metavar is None:
-            if chain:
-                subcommand_metavar = "COMMAND1 [ARGS]... [COMMAND2 [ARGS]...]..."
-            else:
-                subcommand_metavar = "COMMAND [ARGS]..."
-
-        self.subcommand_metavar = subcommand_metavar
-        self.chain = chain
-        # The result callback that is stored. This can be set or
-        # overridden with the :func:`result_callback` decorator.
-        self._result_callback = result_callback
-
-        if self.chain:
-            for param in self.params:
-                if isinstance(param, Argument) and not param.required:
-                    raise RuntimeError(
-                        "Multi commands in chain mode cannot have"
-                        " optional arguments."
-                    )
-
-    def to_info_dict(self, ctx: Context) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict(ctx)
-        commands = {}
-
-        for name in self.list_commands(ctx):
-            command = self.get_command(ctx, name)
-
-            if command is None:
-                continue
-
-            sub_ctx = ctx._make_sub_context(command)
-
-            with sub_ctx.scope(cleanup=False):
-                commands[name] = command.to_info_dict(sub_ctx)
-
-        info_dict.update(commands=commands, chain=self.chain)
-        return info_dict
-
-    def collect_usage_pieces(self, ctx: Context) -> t.List[str]:
-        rv = super().collect_usage_pieces(ctx)
-        rv.append(self.subcommand_metavar)
-        return rv
-
-    def format_options(self, ctx: Context, formatter: HelpFormatter) -> None:
-        super().format_options(ctx, formatter)
-        self.format_commands(ctx, formatter)
-
-    def result_callback(self, replace: bool = False) -> t.Callable[[F], F]:
-        """Adds a result callback to the command.  By default if a
-        result callback is already registered this will chain them but
-        this can be disabled with the `replace` parameter.  The result
-        callback is invoked with the return value of the subcommand
-        (or the list of return values from all subcommands if chaining
-        is enabled) as well as the parameters as they would be passed
-        to the main callback.
-
-        Example::
-
-            @click.group()
-            @click.option('-i', '--input', default=23)
-            def cli(input):
-                return 42
-
-            @cli.result_callback()
-            def process_result(result, input):
-                return result + input
-
-        :param replace: if set to `True` an already existing result
-                        callback will be removed.
-
-        .. versionchanged:: 8.0
-            Renamed from ``resultcallback``.
-
-        .. versionadded:: 3.0
-        """
-
-        def decorator(f: F) -> F:
-            old_callback = self._result_callback
-
-            if old_callback is None or replace:
-                self._result_callback = f
-                return f
-
-            def function(__value, *args, **kwargs):  # type: ignore
-                inner = old_callback(__value, *args, **kwargs)
-                return f(inner, *args, **kwargs)
-
-            self._result_callback = rv = update_wrapper(t.cast(F, function), f)
-            return rv
-
-        return decorator
-
-    def format_commands(self, ctx: Context, formatter: HelpFormatter) -> None:
-        """Extra format methods for multi methods that adds all the commands
-        after the options.
-        """
-        commands = []
-        for subcommand in self.list_commands(ctx):
-            cmd = self.get_command(ctx, subcommand)
-            # What is this, the tool lied about a command.  Ignore it
-            if cmd is None:
-                continue
-            if cmd.hidden:
-                continue
-
-            commands.append((subcommand, cmd))
-
-        # allow for 3 times the default spacing
-        if len(commands):
-            limit = formatter.width - 6 - max(len(cmd[0]) for cmd in commands)
-
-            rows = []
-            for subcommand, cmd in commands:
-                help = cmd.get_short_help_str(limit)
-                rows.append((subcommand, help))
-
-            if rows:
-                with formatter.section(_("Commands")):
-                    formatter.write_dl(rows)
-
-    def parse_args(self, ctx: Context, args: t.List[str]) -> t.List[str]:
-        if not args and self.no_args_is_help and not ctx.resilient_parsing:
-            echo(ctx.get_help(), color=ctx.color)
-            ctx.exit()
-
-        rest = super().parse_args(ctx, args)
-
-        if self.chain:
-            ctx.protected_args = rest
-            ctx.args = []
-        elif rest:
-            ctx.protected_args, ctx.args = rest[:1], rest[1:]
-
-        return ctx.args
-
-    def invoke(self, ctx: Context) -> t.Any:
-        def _process_result(value: t.Any) -> t.Any:
-            if self._result_callback is not None:
-                value = ctx.invoke(self._result_callback, value, **ctx.params)
-            return value
-
-        if not ctx.protected_args:
-            if self.invoke_without_command:
-                # No subcommand was invoked, so the result callback is
-                # invoked with the group return value for regular
-                # groups, or an empty list for chained groups.
-                with ctx:
-                    rv = super().invoke(ctx)
-                    return _process_result([] if self.chain else rv)
-            ctx.fail(_("Missing command."))
-
-        # Fetch args back out
-        args = [*ctx.protected_args, *ctx.args]
-        ctx.args = []
-        ctx.protected_args = []
-
-        # If we're not in chain mode, we only allow the invocation of a
-        # single command but we also inform the current context about the
-        # name of the command to invoke.
-        if not self.chain:
-            # Make sure the context is entered so we do not clean up
-            # resources until the result processor has worked.
-            with ctx:
-                cmd_name, cmd, args = self.resolve_command(ctx, args)
-                assert cmd is not None
-                ctx.invoked_subcommand = cmd_name
-                super().invoke(ctx)
-                sub_ctx = cmd.make_context(cmd_name, args, parent=ctx)
-                with sub_ctx:
-                    return _process_result(sub_ctx.command.invoke(sub_ctx))
-
-        # In chain mode we create the contexts step by step, but after the
-        # base command has been invoked.  Because at that point we do not
-        # know the subcommands yet, the invoked subcommand attribute is
-        # set to ``*`` to inform the command that subcommands are executed
-        # but nothing else.
-        with ctx:
-            ctx.invoked_subcommand = "*" if args else None
-            super().invoke(ctx)
-
-            # Otherwise we make every single context and invoke them in a
-            # chain.  In that case the return value to the result processor
-            # is the list of all invoked subcommand's results.
-            contexts = []
-            while args:
-                cmd_name, cmd, args = self.resolve_command(ctx, args)
-                assert cmd is not None
-                sub_ctx = cmd.make_context(
-                    cmd_name,
-                    args,
-                    parent=ctx,
-                    allow_extra_args=True,
-                    allow_interspersed_args=False,
-                )
-                contexts.append(sub_ctx)
-                args, sub_ctx.args = sub_ctx.args, []
-
-            rv = []
-            for sub_ctx in contexts:
-                with sub_ctx:
-                    rv.append(sub_ctx.command.invoke(sub_ctx))
-            return _process_result(rv)
-
-    def resolve_command(
-        self, ctx: Context, args: t.List[str]
-    ) -> t.Tuple[t.Optional[str], t.Optional[Command], t.List[str]]:
-        cmd_name = make_str(args[0])
-        original_cmd_name = cmd_name
-
-        # Get the command
-        cmd = self.get_command(ctx, cmd_name)
-
-        # If we can't find the command but there is a normalization
-        # function available, we try with that one.
-        if cmd is None and ctx.token_normalize_func is not None:
-            cmd_name = ctx.token_normalize_func(cmd_name)
-            cmd = self.get_command(ctx, cmd_name)
-
-        # If we don't find the command we want to show an error message
-        # to the user that it was not provided.  However, there is
-        # something else we should do: if the first argument looks like
-        # an option we want to kick off parsing again for arguments to
-        # resolve things like --help which now should go to the main
-        # place.
-        if cmd is None and not ctx.resilient_parsing:
-            if split_opt(cmd_name)[0]:
-                self.parse_args(ctx, ctx.args)
-            ctx.fail(_("No such command {name!r}.").format(name=original_cmd_name))
-        return cmd_name if cmd else None, cmd, args[1:]
-
-    def get_command(self, ctx: Context, cmd_name: str) -> t.Optional[Command]:
-        """Given a context and a command name, this returns a
-        :class:`Command` object if it exists or returns `None`.
-        """
-        raise NotImplementedError
-
-    def list_commands(self, ctx: Context) -> t.List[str]:
-        """Returns a list of subcommand names in the order they should
-        appear.
-        """
-        return []
-
-    def shell_complete(self, ctx: Context, incomplete: str) -> t.List["CompletionItem"]:
-        """Return a list of completions for the incomplete value. Looks
-        at the names of options, subcommands, and chained
-        multi-commands.
-
-        :param ctx: Invocation context for this command.
-        :param incomplete: Value being completed. May be empty.
-
-        .. versionadded:: 8.0
-        """
-        from click.shell_completion import CompletionItem
-
-        results = [
-            CompletionItem(name, help=command.get_short_help_str())
-            for name, command in _complete_visible_commands(ctx, incomplete)
-        ]
-        results.extend(super().shell_complete(ctx, incomplete))
-        return results
-
-
-class Group(MultiCommand):
-    """A group allows a command to have subcommands attached. This is
-    the most common way to implement nesting in Click.
-
-    :param name: The name of the group command.
-    :param commands: A dict mapping names to :class:`Command` objects.
-        Can also be a list of :class:`Command`, which will use
-        :attr:`Command.name` to create the dict.
-    :param attrs: Other command arguments described in
-        :class:`MultiCommand`, :class:`Command`, and
-        :class:`BaseCommand`.
-
-    .. versionchanged:: 8.0
-        The ``commands`` argument can be a list of command objects.
-    """
-
-    #: If set, this is used by the group's :meth:`command` decorator
-    #: as the default :class:`Command` class. This is useful to make all
-    #: subcommands use a custom command class.
-    #:
-    #: .. versionadded:: 8.0
-    command_class: t.Optional[t.Type[Command]] = None
-
-    #: If set, this is used by the group's :meth:`group` decorator
-    #: as the default :class:`Group` class. This is useful to make all
-    #: subgroups use a custom group class.
-    #:
-    #: If set to the special value :class:`type` (literally
-    #: ``group_class = type``), this group's class will be used as the
-    #: default class. This makes a custom group class continue to make
-    #: custom groups.
-    #:
-    #: .. versionadded:: 8.0
-    group_class: t.Optional[t.Union[t.Type["Group"], t.Type[type]]] = None
-    # Literal[type] isn't valid, so use Type[type]
-
-    def __init__(
-        self,
-        name: t.Optional[str] = None,
-        commands: t.Optional[
-            t.Union[t.MutableMapping[str, Command], t.Sequence[Command]]
-        ] = None,
-        **attrs: t.Any,
-    ) -> None:
-        super().__init__(name, **attrs)
-
-        if commands is None:
-            commands = {}
-        elif isinstance(commands, abc.Sequence):
-            commands = {c.name: c for c in commands if c.name is not None}
-
-        #: The registered subcommands by their exported names.
-        self.commands: t.MutableMapping[str, Command] = commands
-
-    def add_command(self, cmd: Command, name: t.Optional[str] = None) -> None:
-        """Registers another :class:`Command` with this group.  If the name
-        is not provided, the name of the command is used.
-        """
-        name = name or cmd.name
-        if name is None:
-            raise TypeError("Command has no name.")
-        _check_multicommand(self, name, cmd, register=True)
-        self.commands[name] = cmd
-
-    @t.overload
-    def command(self, __func: t.Callable[..., t.Any]) -> Command:
-        ...
-
-    @t.overload
-    def command(
-        self, *args: t.Any, **kwargs: t.Any
-    ) -> t.Callable[[t.Callable[..., t.Any]], Command]:
-        ...
-
-    def command(
-        self, *args: t.Any, **kwargs: t.Any
-    ) -> t.Union[t.Callable[[t.Callable[..., t.Any]], Command], Command]:
-        """A shortcut decorator for declaring and attaching a command to
-        the group. This takes the same arguments as :func:`command` and
-        immediately registers the created command with this group by
-        calling :meth:`add_command`.
-
-        To customize the command class used, set the
-        :attr:`command_class` attribute.
-
-        .. versionchanged:: 8.1
-            This decorator can be applied without parentheses.
-
-        .. versionchanged:: 8.0
-            Added the :attr:`command_class` attribute.
-        """
-        from .decorators import command
-
-        func: t.Optional[t.Callable[..., t.Any]] = None
-
-        if args and callable(args[0]):
-            assert (
-                len(args) == 1 and not kwargs
-            ), "Use 'command(**kwargs)(callable)' to provide arguments."
-            (func,) = args
-            args = ()
-
-        if self.command_class and kwargs.get("cls") is None:
-            kwargs["cls"] = self.command_class
-
-        def decorator(f: t.Callable[..., t.Any]) -> Command:
-            cmd: Command = command(*args, **kwargs)(f)
-            self.add_command(cmd)
-            return cmd
-
-        if func is not None:
-            return decorator(func)
-
-        return decorator
-
-    @t.overload
-    def group(self, __func: t.Callable[..., t.Any]) -> "Group":
-        ...
-
-    @t.overload
-    def group(
-        self, *args: t.Any, **kwargs: t.Any
-    ) -> t.Callable[[t.Callable[..., t.Any]], "Group"]:
-        ...
-
-    def group(
-        self, *args: t.Any, **kwargs: t.Any
-    ) -> t.Union[t.Callable[[t.Callable[..., t.Any]], "Group"], "Group"]:
-        """A shortcut decorator for declaring and attaching a group to
-        the group. This takes the same arguments as :func:`group` and
-        immediately registers the created group with this group by
-        calling :meth:`add_command`.
-
-        To customize the group class used, set the :attr:`group_class`
-        attribute.
-
-        .. versionchanged:: 8.1
-            This decorator can be applied without parentheses.
-
-        .. versionchanged:: 8.0
-            Added the :attr:`group_class` attribute.
-        """
-        from .decorators import group
-
-        func: t.Optional[t.Callable[..., t.Any]] = None
-
-        if args and callable(args[0]):
-            assert (
-                len(args) == 1 and not kwargs
-            ), "Use 'group(**kwargs)(callable)' to provide arguments."
-            (func,) = args
-            args = ()
-
-        if self.group_class is not None and kwargs.get("cls") is None:
-            if self.group_class is type:
-                kwargs["cls"] = type(self)
-            else:
-                kwargs["cls"] = self.group_class
-
-        def decorator(f: t.Callable[..., t.Any]) -> "Group":
-            cmd: Group = group(*args, **kwargs)(f)
-            self.add_command(cmd)
-            return cmd
-
-        if func is not None:
-            return decorator(func)
-
-        return decorator
-
-    def get_command(self, ctx: Context, cmd_name: str) -> t.Optional[Command]:
-        return self.commands.get(cmd_name)
-
-    def list_commands(self, ctx: Context) -> t.List[str]:
-        return sorted(self.commands)
-
-
-class CommandCollection(MultiCommand):
-    """A command collection is a multi command that merges multiple multi
-    commands together into one.  This is a straightforward implementation
-    that accepts a list of different multi commands as sources and
-    provides all the commands for each of them.
-
-    See :class:`MultiCommand` and :class:`Command` for the description of
-    ``name`` and ``attrs``.
-    """
-
-    def __init__(
-        self,
-        name: t.Optional[str] = None,
-        sources: t.Optional[t.List[MultiCommand]] = None,
-        **attrs: t.Any,
-    ) -> None:
-        super().__init__(name, **attrs)
-        #: The list of registered multi commands.
-        self.sources: t.List[MultiCommand] = sources or []
-
-    def add_source(self, multi_cmd: MultiCommand) -> None:
-        """Adds a new multi command to the chain dispatcher."""
-        self.sources.append(multi_cmd)
-
-    def get_command(self, ctx: Context, cmd_name: str) -> t.Optional[Command]:
-        for source in self.sources:
-            rv = source.get_command(ctx, cmd_name)
-
-            if rv is not None:
-                if self.chain:
-                    _check_multicommand(self, cmd_name, rv)
-
-                return rv
-
-        return None
-
-    def list_commands(self, ctx: Context) -> t.List[str]:
-        rv: t.Set[str] = set()
-
-        for source in self.sources:
-            rv.update(source.list_commands(ctx))
-
-        return sorted(rv)
-
-
-def _check_iter(value: t.Any) -> t.Iterator[t.Any]:
-    """Check if the value is iterable but not a string. Raises a type
-    error, or return an iterator over the value.
-    """
-    if isinstance(value, str):
-        raise TypeError
-
-    return iter(value)
-
-
-class Parameter:
-    r"""A parameter to a command comes in two versions: they are either
-    :class:`Option`\s or :class:`Argument`\s.  Other subclasses are currently
-    not supported by design as some of the internals for parsing are
-    intentionally not finalized.
-
-    Some settings are supported by both options and arguments.
-
-    :param param_decls: the parameter declarations for this option or
-                        argument.  This is a list of flags or argument
-                        names.
-    :param type: the type that should be used.  Either a :class:`ParamType`
-                 or a Python type.  The latter is converted into the former
-                 automatically if supported.
-    :param required: controls if this is optional or not.
-    :param default: the default value if omitted.  This can also be a callable,
-                    in which case it's invoked when the default is needed
-                    without any arguments.
-    :param callback: A function to further process or validate the value
-        after type conversion. It is called as ``f(ctx, param, value)``
-        and must return the value. It is called for all sources,
-        including prompts.
-    :param nargs: the number of arguments to match.  If not ``1`` the return
-                  value is a tuple instead of single value.  The default for
-                  nargs is ``1`` (except if the type is a tuple, then it's
-                  the arity of the tuple). If ``nargs=-1``, all remaining
-                  parameters are collected.
-    :param metavar: how the value is represented in the help page.
-    :param expose_value: if this is `True` then the value is passed onwards
-                         to the command callback and stored on the context,
-                         otherwise it's skipped.
-    :param is_eager: eager values are processed before non eager ones.  This
-                     should not be set for arguments or it will inverse the
-                     order of processing.
-    :param envvar: a string or list of strings that are environment variables
-                   that should be checked.
-    :param shell_complete: A function that returns custom shell
-        completions. Used instead of the param's type completion if
-        given. Takes ``ctx, param, incomplete`` and must return a list
-        of :class:`~click.shell_completion.CompletionItem` or a list of
-        strings.
-
-    .. versionchanged:: 8.0
-        ``process_value`` validates required parameters and bounded
-        ``nargs``, and invokes the parameter callback before returning
-        the value. This allows the callback to validate prompts.
-        ``full_process_value`` is removed.
-
-    .. versionchanged:: 8.0
-        ``autocompletion`` is renamed to ``shell_complete`` and has new
-        semantics described above. The old name is deprecated and will
-        be removed in 8.1, until then it will be wrapped to match the
-        new requirements.
-
-    .. versionchanged:: 8.0
-        For ``multiple=True, nargs>1``, the default must be a list of
-        tuples.
-
-    .. versionchanged:: 8.0
-        Setting a default is no longer required for ``nargs>1``, it will
-        default to ``None``. ``multiple=True`` or ``nargs=-1`` will
-        default to ``()``.
-
-    .. versionchanged:: 7.1
-        Empty environment variables are ignored rather than taking the
-        empty string value. This makes it possible for scripts to clear
-        variables if they can't unset them.
-
-    .. versionchanged:: 2.0
-        Changed signature for parameter callback to also be passed the
-        parameter. The old callback format will still work, but it will
-        raise a warning to give you a chance to migrate the code easier.
-    """
-
-    param_type_name = "parameter"
-
-    def __init__(
-        self,
-        param_decls: t.Optional[t.Sequence[str]] = None,
-        type: t.Optional[t.Union[types.ParamType, t.Any]] = None,
-        required: bool = False,
-        default: t.Optional[t.Union[t.Any, t.Callable[[], t.Any]]] = None,
-        callback: t.Optional[t.Callable[[Context, "Parameter", t.Any], t.Any]] = None,
-        nargs: t.Optional[int] = None,
-        multiple: bool = False,
-        metavar: t.Optional[str] = None,
-        expose_value: bool = True,
-        is_eager: bool = False,
-        envvar: t.Optional[t.Union[str, t.Sequence[str]]] = None,
-        shell_complete: t.Optional[
-            t.Callable[
-                [Context, "Parameter", str],
-                t.Union[t.List["CompletionItem"], t.List[str]],
-            ]
-        ] = None,
-    ) -> None:
-        self.name: t.Optional[str]
-        self.opts: t.List[str]
-        self.secondary_opts: t.List[str]
-        self.name, self.opts, self.secondary_opts = self._parse_decls(
-            param_decls or (), expose_value
-        )
-        self.type: types.ParamType = types.convert_type(type, default)
-
-        # Default nargs to what the type tells us if we have that
-        # information available.
-        if nargs is None:
-            if self.type.is_composite:
-                nargs = self.type.arity
-            else:
-                nargs = 1
-
-        self.required = required
-        self.callback = callback
-        self.nargs = nargs
-        self.multiple = multiple
-        self.expose_value = expose_value
-        self.default = default
-        self.is_eager = is_eager
-        self.metavar = metavar
-        self.envvar = envvar
-        self._custom_shell_complete = shell_complete
-
-        if __debug__:
-            if self.type.is_composite and nargs != self.type.arity:
-                raise ValueError(
-                    f"'nargs' must be {self.type.arity} (or None) for"
-                    f" type {self.type!r}, but it was {nargs}."
-                )
-
-            # Skip no default or callable default.
-            check_default = default if not callable(default) else None
-
-            if check_default is not None:
-                if multiple:
-                    try:
-                        # Only check the first value against nargs.
-                        check_default = next(_check_iter(check_default), None)
-                    except TypeError:
-                        raise ValueError(
-                            "'default' must be a list when 'multiple' is true."
-                        ) from None
-
-                # Can be None for multiple with empty default.
-                if nargs != 1 and check_default is not None:
-                    try:
-                        _check_iter(check_default)
-                    except TypeError:
-                        if multiple:
-                            message = (
-                                "'default' must be a list of lists when 'multiple' is"
-                                " true and 'nargs' != 1."
-                            )
-                        else:
-                            message = "'default' must be a list when 'nargs' != 1."
-
-                        raise ValueError(message) from None
-
-                    if nargs > 1 and len(check_default) != nargs:
-                        subject = "item length" if multiple else "length"
-                        raise ValueError(
-                            f"'default' {subject} must match nargs={nargs}."
-                        )
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        """Gather information that could be useful for a tool generating
-        user-facing documentation.
-
-        Use :meth:`click.Context.to_info_dict` to traverse the entire
-        CLI structure.
-
-        .. versionadded:: 8.0
-        """
-        return {
-            "name": self.name,
-            "param_type_name": self.param_type_name,
-            "opts": self.opts,
-            "secondary_opts": self.secondary_opts,
-            "type": self.type.to_info_dict(),
-            "required": self.required,
-            "nargs": self.nargs,
-            "multiple": self.multiple,
-            "default": self.default,
-            "envvar": self.envvar,
-        }
-
-    def __repr__(self) -> str:
-        return f"<{self.__class__.__name__} {self.name}>"
-
-    def _parse_decls(
-        self, decls: t.Sequence[str], expose_value: bool
-    ) -> t.Tuple[t.Optional[str], t.List[str], t.List[str]]:
-        raise NotImplementedError()
-
-    @property
-    def human_readable_name(self) -> str:
-        """Returns the human readable name of this parameter.  This is the
-        same as the name for options, but the metavar for arguments.
-        """
-        return self.name  # type: ignore
-
-    def make_metavar(self) -> str:
-        if self.metavar is not None:
-            return self.metavar
-
-        metavar = self.type.get_metavar(self)
-
-        if metavar is None:
-            metavar = self.type.name.upper()
-
-        if self.nargs != 1:
-            metavar += "..."
-
-        return metavar
-
-    @t.overload
-    def get_default(
-        self, ctx: Context, call: "te.Literal[True]" = True
-    ) -> t.Optional[t.Any]:
-        ...
-
-    @t.overload
-    def get_default(
-        self, ctx: Context, call: bool = ...
-    ) -> t.Optional[t.Union[t.Any, t.Callable[[], t.Any]]]:
-        ...
-
-    def get_default(
-        self, ctx: Context, call: bool = True
-    ) -> t.Optional[t.Union[t.Any, t.Callable[[], t.Any]]]:
-        """Get the default for the parameter. Tries
-        :meth:`Context.lookup_default` first, then the local default.
-
-        :param ctx: Current context.
-        :param call: If the default is a callable, call it. Disable to
-            return the callable instead.
-
-        .. versionchanged:: 8.0.2
-            Type casting is no longer performed when getting a default.
-
-        .. versionchanged:: 8.0.1
-            Type casting can fail in resilient parsing mode. Invalid
-            defaults will not prevent showing help text.
-
-        .. versionchanged:: 8.0
-            Looks at ``ctx.default_map`` first.
-
-        .. versionchanged:: 8.0
-            Added the ``call`` parameter.
-        """
-        value = ctx.lookup_default(self.name, call=False)  # type: ignore
-
-        if value is None:
-            value = self.default
-
-        if call and callable(value):
-            value = value()
-
-        return value
-
-    def add_to_parser(self, parser: OptionParser, ctx: Context) -> None:
-        raise NotImplementedError()
-
-    def consume_value(
-        self, ctx: Context, opts: t.Mapping[str, t.Any]
-    ) -> t.Tuple[t.Any, ParameterSource]:
-        value = opts.get(self.name)  # type: ignore
-        source = ParameterSource.COMMANDLINE
-
-        if value is None:
-            value = self.value_from_envvar(ctx)
-            source = ParameterSource.ENVIRONMENT
-
-        if value is None:
-            value = ctx.lookup_default(self.name)  # type: ignore
-            source = ParameterSource.DEFAULT_MAP
-
-        if value is None:
-            value = self.get_default(ctx)
-            source = ParameterSource.DEFAULT
-
-        return value, source
-
-    def type_cast_value(self, ctx: Context, value: t.Any) -> t.Any:
-        """Convert and validate a value against the option's
-        :attr:`type`, :attr:`multiple`, and :attr:`nargs`.
-        """
-        if value is None:
-            return () if self.multiple or self.nargs == -1 else None
-
-        def check_iter(value: t.Any) -> t.Iterator[t.Any]:
-            try:
-                return _check_iter(value)
-            except TypeError:
-                # This should only happen when passing in args manually,
-                # the parser should construct an iterable when parsing
-                # the command line.
-                raise BadParameter(
-                    _("Value must be an iterable."), ctx=ctx, param=self
-                ) from None
-
-        if self.nargs == 1 or self.type.is_composite:
-
-            def convert(value: t.Any) -> t.Any:
-                return self.type(value, param=self, ctx=ctx)
-
-        elif self.nargs == -1:
-
-            def convert(value: t.Any) -> t.Any:  # t.Tuple[t.Any, ...]
-                return tuple(self.type(x, self, ctx) for x in check_iter(value))
-
-        else:  # nargs > 1
-
-            def convert(value: t.Any) -> t.Any:  # t.Tuple[t.Any, ...]
-                value = tuple(check_iter(value))
-
-                if len(value) != self.nargs:
-                    raise BadParameter(
-                        ngettext(
-                            "Takes {nargs} values but 1 was given.",
-                            "Takes {nargs} values but {len} were given.",
-                            len(value),
-                        ).format(nargs=self.nargs, len=len(value)),
-                        ctx=ctx,
-                        param=self,
-                    )
-
-                return tuple(self.type(x, self, ctx) for x in value)
-
-        if self.multiple:
-            return tuple(convert(x) for x in check_iter(value))
-
-        return convert(value)
-
-    def value_is_missing(self, value: t.Any) -> bool:
-        if value is None:
-            return True
-
-        if (self.nargs != 1 or self.multiple) and value == ():
-            return True
-
-        return False
-
-    def process_value(self, ctx: Context, value: t.Any) -> t.Any:
-        value = self.type_cast_value(ctx, value)
-
-        if self.required and self.value_is_missing(value):
-            raise MissingParameter(ctx=ctx, param=self)
-
-        if self.callback is not None:
-            value = self.callback(ctx, self, value)
-
-        return value
-
-    def resolve_envvar_value(self, ctx: Context) -> t.Optional[str]:
-        if self.envvar is None:
-            return None
-
-        if isinstance(self.envvar, str):
-            rv = os.environ.get(self.envvar)
-
-            if rv:
-                return rv
-        else:
-            for envvar in self.envvar:
-                rv = os.environ.get(envvar)
-
-                if rv:
-                    return rv
-
-        return None
-
-    def value_from_envvar(self, ctx: Context) -> t.Optional[t.Any]:
-        rv: t.Optional[t.Any] = self.resolve_envvar_value(ctx)
-
-        if rv is not None and self.nargs != 1:
-            rv = self.type.split_envvar_value(rv)
-
-        return rv
-
-    def handle_parse_result(
-        self, ctx: Context, opts: t.Mapping[str, t.Any], args: t.List[str]
-    ) -> t.Tuple[t.Any, t.List[str]]:
-        with augment_usage_errors(ctx, param=self):
-            value, source = self.consume_value(ctx, opts)
-            ctx.set_parameter_source(self.name, source)  # type: ignore
-
-            try:
-                value = self.process_value(ctx, value)
-            except Exception:
-                if not ctx.resilient_parsing:
-                    raise
-
-                value = None
-
-        if self.expose_value:
-            ctx.params[self.name] = value  # type: ignore
-
-        return value, args
-
-    def get_help_record(self, ctx: Context) -> t.Optional[t.Tuple[str, str]]:
-        pass
-
-    def get_usage_pieces(self, ctx: Context) -> t.List[str]:
-        return []
-
-    def get_error_hint(self, ctx: Context) -> str:
-        """Get a stringified version of the param for use in error messages to
-        indicate which param caused the error.
-        """
-        hint_list = self.opts or [self.human_readable_name]
-        return " / ".join(f"'{x}'" for x in hint_list)
-
-    def shell_complete(self, ctx: Context, incomplete: str) -> t.List["CompletionItem"]:
-        """Return a list of completions for the incomplete value. If a
-        ``shell_complete`` function was given during init, it is used.
-        Otherwise, the :attr:`type`
-        :meth:`~click.types.ParamType.shell_complete` function is used.
-
-        :param ctx: Invocation context for this command.
-        :param incomplete: Value being completed. May be empty.
-
-        .. versionadded:: 8.0
-        """
-        if self._custom_shell_complete is not None:
-            results = self._custom_shell_complete(ctx, self, incomplete)
-
-            if results and isinstance(results[0], str):
-                from click.shell_completion import CompletionItem
-
-                results = [CompletionItem(c) for c in results]
-
-            return t.cast(t.List["CompletionItem"], results)
-
-        return self.type.shell_complete(ctx, self, incomplete)
-
-
-class Option(Parameter):
-    """Options are usually optional values on the command line and
-    have some extra features that arguments don't have.
-
-    All other parameters are passed onwards to the parameter constructor.
-
-    :param show_default: Show the default value for this option in its
-        help text. Values are not shown by default, unless
-        :attr:`Context.show_default` is ``True``. If this value is a
-        string, it shows that string in parentheses instead of the
-        actual value. This is particularly useful for dynamic options.
-        For single option boolean flags, the default remains hidden if
-        its value is ``False``.
-    :param show_envvar: Controls if an environment variable should be
-        shown on the help page. Normally, environment variables are not
-        shown.
-    :param prompt: If set to ``True`` or a non empty string then the
-        user will be prompted for input. If set to ``True`` the prompt
-        will be the option name capitalized.
-    :param confirmation_prompt: Prompt a second time to confirm the
-        value if it was prompted for. Can be set to a string instead of
-        ``True`` to customize the message.
-    :param prompt_required: If set to ``False``, the user will be
-        prompted for input only when the option was specified as a flag
-        without a value.
-    :param hide_input: If this is ``True`` then the input on the prompt
-        will be hidden from the user. This is useful for password input.
-    :param is_flag: forces this option to act as a flag.  The default is
-                    auto detection.
-    :param flag_value: which value should be used for this flag if it's
-                       enabled.  This is set to a boolean automatically if
-                       the option string contains a slash to mark two options.
-    :param multiple: if this is set to `True` then the argument is accepted
-                     multiple times and recorded.  This is similar to ``nargs``
-                     in how it works but supports arbitrary number of
-                     arguments.
-    :param count: this flag makes an option increment an integer.
-    :param allow_from_autoenv: if this is enabled then the value of this
-                               parameter will be pulled from an environment
-                               variable in case a prefix is defined on the
-                               context.
-    :param help: the help string.
-    :param hidden: hide this option from help outputs.
-    :param attrs: Other command arguments described in :class:`Parameter`.
-
-    .. versionchanged:: 8.1.0
-        Help text indentation is cleaned here instead of only in the
-        ``@option`` decorator.
-
-    .. versionchanged:: 8.1.0
-        The ``show_default`` parameter overrides
-        ``Context.show_default``.
-
-    .. versionchanged:: 8.1.0
-        The default of a single option boolean flag is not shown if the
-        default value is ``False``.
-
-    .. versionchanged:: 8.0.1
-        ``type`` is detected from ``flag_value`` if given.
-    """
-
-    param_type_name = "option"
-
-    def __init__(
-        self,
-        param_decls: t.Optional[t.Sequence[str]] = None,
-        show_default: t.Union[bool, str, None] = None,
-        prompt: t.Union[bool, str] = False,
-        confirmation_prompt: t.Union[bool, str] = False,
-        prompt_required: bool = True,
-        hide_input: bool = False,
-        is_flag: t.Optional[bool] = None,
-        flag_value: t.Optional[t.Any] = None,
-        multiple: bool = False,
-        count: bool = False,
-        allow_from_autoenv: bool = True,
-        type: t.Optional[t.Union[types.ParamType, t.Any]] = None,
-        help: t.Optional[str] = None,
-        hidden: bool = False,
-        show_choices: bool = True,
-        show_envvar: bool = False,
-        **attrs: t.Any,
-    ) -> None:
-        if help:
-            help = inspect.cleandoc(help)
-
-        default_is_missing = "default" not in attrs
-        super().__init__(param_decls, type=type, multiple=multiple, **attrs)
-
-        if prompt is True:
-            if self.name is None:
-                raise TypeError("'name' is required with 'prompt=True'.")
-
-            prompt_text: t.Optional[str] = self.name.replace("_", " ").capitalize()
-        elif prompt is False:
-            prompt_text = None
-        else:
-            prompt_text = prompt
-
-        self.prompt = prompt_text
-        self.confirmation_prompt = confirmation_prompt
-        self.prompt_required = prompt_required
-        self.hide_input = hide_input
-        self.hidden = hidden
-
-        # If prompt is enabled but not required, then the option can be
-        # used as a flag to indicate using prompt or flag_value.
-        self._flag_needs_value = self.prompt is not None and not self.prompt_required
-
-        if is_flag is None:
-            if flag_value is not None:
-                # Implicitly a flag because flag_value was set.
-                is_flag = True
-            elif self._flag_needs_value:
-                # Not a flag, but when used as a flag it shows a prompt.
-                is_flag = False
-            else:
-                # Implicitly a flag because flag options were given.
-                is_flag = bool(self.secondary_opts)
-        elif is_flag is False and not self._flag_needs_value:
-            # Not a flag, and prompt is not enabled, can be used as a
-            # flag if flag_value is set.
-            self._flag_needs_value = flag_value is not None
-
-        self.default: t.Union[t.Any, t.Callable[[], t.Any]]
-
-        if is_flag and default_is_missing and not self.required:
-            if multiple:
-                self.default = ()
-            else:
-                self.default = False
-
-        if flag_value is None:
-            flag_value = not self.default
-
-        self.type: types.ParamType
-        if is_flag and type is None:
-            # Re-guess the type from the flag value instead of the
-            # default.
-            self.type = types.convert_type(None, flag_value)
-
-        self.is_flag: bool = is_flag
-        self.is_bool_flag: bool = is_flag and isinstance(self.type, types.BoolParamType)
-        self.flag_value: t.Any = flag_value
-
-        # Counting
-        self.count = count
-        if count:
-            if type is None:
-                self.type = types.IntRange(min=0)
-            if default_is_missing:
-                self.default = 0
-
-        self.allow_from_autoenv = allow_from_autoenv
-        self.help = help
-        self.show_default = show_default
-        self.show_choices = show_choices
-        self.show_envvar = show_envvar
-
-        if __debug__:
-            if self.nargs == -1:
-                raise TypeError("nargs=-1 is not supported for options.")
-
-            if self.prompt and self.is_flag and not self.is_bool_flag:
-                raise TypeError("'prompt' is not valid for non-boolean flag.")
-
-            if not self.is_bool_flag and self.secondary_opts:
-                raise TypeError("Secondary flag is not valid for non-boolean flag.")
-
-            if self.is_bool_flag and self.hide_input and self.prompt is not None:
-                raise TypeError(
-                    "'prompt' with 'hide_input' is not valid for boolean flag."
-                )
-
-            if self.count:
-                if self.multiple:
-                    raise TypeError("'count' is not valid with 'multiple'.")
-
-                if self.is_flag:
-                    raise TypeError("'count' is not valid with 'is_flag'.")
-
-    def to_info_dict(self) -> t.Dict[str, t.Any]:
-        info_dict = super().to_info_dict()
-        info_dict.update(
-            help=self.help,
-            prompt=self.prompt,
-            is_flag=self.is_flag,
-            flag_value=self.flag_value,
-            count=self.count,
-            hidden=self.hidden,
-        )
-        return info_dict
-
-    def _parse_decls(
-        self, decls: t.Sequence[str], expose_value: bool
-    ) -> t.Tuple[t.Optional[str], t.List[str], t.List[str]]:
-        opts = []
-        secondary_opts = []
-        name = None
-        possible_names = []
-
-        for decl in decls:
-            if decl.isidentifier():
-                if name is not None:
-                    raise TypeError(f"Name '{name}' defined twice")
-                name = decl
-            else:
-                split_char = ";" if decl[:1] == "/" else "/"
-                if split_char in decl:
-                    first, second = decl.split(split_char, 1)
-                    first = first.rstrip()
-                    if first:
-                        possible_names.append(split_opt(first))
-                        opts.append(first)
-                    second = second.lstrip()
-                    if second:
-                        secondary_opts.append(second.lstrip())
-                    if first == second:
-                        raise ValueError(
-                            f"Boolean option {decl!r} cannot use the"
-                            " same flag for true/false."
-                        )
-                else:
-                    possible_names.append(split_opt(decl))
-                    opts.append(decl)
-
-        if name is None and possible_names:
-            possible_names.sort(key=lambda x: -len(x[0]))  # group long options first
-            name = possible_names[0][1].replace("-", "_").lower()
-            if not name.isidentifier():
-                name = None
-
-        if name is None:
-            if not expose_value:
-                return None, opts, secondary_opts
-            raise TypeError("Could not determine name for option")
-
-        if not opts and not secondary_opts:
-            raise TypeError(
-                f"No options defined but a name was passed ({name})."
-                " Did you mean to declare an argument instead? Did"
-                f" you mean to pass '--{name}'?"
-            )
-
-        return name, opts, secondary_opts
-
-    def add_to_parser(self, parser: OptionParser, ctx: Context) -> None:
-        if self.multiple:
-            action = "append"
-        elif self.count:
-            action = "count"
-        else:
-            action = "store"
-
-        if self.is_flag:
-            action = f"{action}_const"
-
-            if self.is_bool_flag and self.secondary_opts:
-                parser.add_option(
-                    obj=self, opts=self.opts, dest=self.name, action=action, const=True
-                )
-                parser.add_option(
-                    obj=self,
-                    opts=self.secondary_opts,
-                    dest=self.name,
-                    action=action,
-                    const=False,
-                )
-            else:
-                parser.add_option(
-                    obj=self,
-                    opts=self.opts,
-                    dest=self.name,
-                    action=action,
-                    const=self.flag_value,
-                )
-        else:
-            parser.add_option(
-                obj=self,
-                opts=self.opts,
-                dest=self.name,
-                action=action,
-                nargs=self.nargs,
-            )
-
-    def get_help_record(self, ctx: Context) -> t.Optional[t.Tuple[str, str]]:
-        if self.hidden:
-            return None
-
-        any_prefix_is_slash = False
-
-        def _write_opts(opts: t.Sequence[str]) -> str:
-            nonlocal any_prefix_is_slash
-
-            rv, any_slashes = join_options(opts)
-
-            if any_slashes:
-                any_prefix_is_slash = True
-
-            if not self.is_flag and not self.count:
-                rv += f" {self.make_metavar()}"
-
-            return rv
-
-        rv = [_write_opts(self.opts)]
-
-        if self.secondary_opts:
-            rv.append(_write_opts(self.secondary_opts))
-
-        help = self.help or ""
-        extra = []
-
-        if self.show_envvar:
-            envvar = self.envvar
-
-            if envvar is None:
-                if (
-                    self.allow_from_autoenv
-                    and ctx.auto_envvar_prefix is not None
-                    and self.name is not None
-                ):
-                    envvar = f"{ctx.auto_envvar_prefix}_{self.name.upper()}"
-
-            if envvar is not None:
-                var_str = (
-                    envvar
-                    if isinstance(envvar, str)
-                    else ", ".join(str(d) for d in envvar)
-                )
-                extra.append(_("env var: {var}").format(var=var_str))
-
-        # Temporarily enable resilient parsing to avoid type casting
-        # failing for the default. Might be possible to extend this to
-        # help formatting in general.
-        resilient = ctx.resilient_parsing
-        ctx.resilient_parsing = True
-
-        try:
-            default_value = self.get_default(ctx, call=False)
-        finally:
-            ctx.resilient_parsing = resilient
-
-        show_default = False
-        show_default_is_str = False
-
-        if self.show_default is not None:
-            if isinstance(self.show_default, str):
-                show_default_is_str = show_default = True
-            else:
-                show_default = self.show_default
-        elif ctx.show_default is not None:
-            show_default = ctx.show_default
-
-        if show_default_is_str or (show_default and (default_value is not None)):
-            if show_default_is_str:
-                default_string = f"({self.show_default})"
-            elif isinstance(default_value, (list, tuple)):
-                default_string = ", ".join(str(d) for d in default_value)
-            elif inspect.isfunction(default_value):
-                default_string = _("(dynamic)")
-            elif self.is_bool_flag and self.secondary_opts:
-                # For boolean flags that have distinct True/False opts,
-                # use the opt without prefix instead of the value.
-                default_string = split_opt(
-                    (self.opts if self.default else self.secondary_opts)[0]
-                )[1]
-            elif self.is_bool_flag and not self.secondary_opts and not default_value:
-                default_string = ""
-            else:
-                default_string = str(default_value)
-
-            if default_string:
-                extra.append(_("default: {default}").format(default=default_string))
-
-        if (
-            isinstance(self.type, types._NumberRangeBase)
-            # skip count with default range type
-            and not (self.count and self.type.min == 0 and self.type.max is None)
-        ):
-            range_str = self.type._describe_range()
-
-            if range_str:
-                extra.append(range_str)
-
-        if self.required:
-            extra.append(_("required"))
-
-        if extra:
-            extra_str = "; ".join(extra)
-            help = f"{help}  [{extra_str}]" if help else f"[{extra_str}]"
-
-        return ("; " if any_prefix_is_slash else " / ").join(rv), help
-
-    @t.overload
-    def get_default(
-        self, ctx: Context, call: "te.Literal[True]" = True
-    ) -> t.Optional[t.Any]:
-        ...
-
-    @t.overload
-    def get_default(
-        self, ctx: Context, call: bool = ...
-    ) -> t.Optional[t.Union[t.Any, t.Callable[[], t.Any]]]:
-        ...
-
-    def get_default(
-        self, ctx: Context, call: bool = True
-    ) -> t.Optional[t.Union[t.Any, t.Callable[[], t.Any]]]:
-        # If we're a non boolean flag our default is more complex because
-        # we need to look at all flags in the same group to figure out
-        # if we're the default one in which case we return the flag
-        # value as default.
-        if self.is_flag and not self.is_bool_flag:
-            for param in ctx.command.params:
-                if param.name == self.name and param.default:
-                    return t.cast(Option, param).flag_value
-
-            return None
-
-        return super().get_default(ctx, call=call)
-
-    def prompt_for_value(self, ctx: Context) -> t.Any:
-        """This is an alternative flow that can be activated in the full
-        value processing if a value does not exist.  It will prompt the
-        user until a valid value exists and then returns the processed
-        value as result.
-        """
-        assert self.prompt is not None
-
-        # Calculate the default before prompting anything to be stable.
-        default = self.get_default(ctx)
-
-        # If this is a prompt for a flag we need to handle this
-        # differently.
-        if self.is_bool_flag:
-            return confirm(self.prompt, default)
-
-        return prompt(
-            self.prompt,
-            default=default,
-            type=self.type,
-            hide_input=self.hide_input,
-            show_choices=self.show_choices,
-            confirmation_prompt=self.confirmation_prompt,
-            value_proc=lambda x: self.process_value(ctx, x),
-        )
-
-    def resolve_envvar_value(self, ctx: Context) -> t.Optional[str]:
-        rv = super().resolve_envvar_value(ctx)
-
-        if rv is not None:
-            return rv
-
-        if (
-            self.allow_from_autoenv
-            and ctx.auto_envvar_prefix is not None
-            and self.name is not None
-        ):
-            envvar = f"{ctx.auto_envvar_prefix}_{self.name.upper()}"
-            rv = os.environ.get(envvar)
-
-            if rv:
-                return rv
-
-        return None
-
-    def value_from_envvar(self, ctx: Context) -> t.Optional[t.Any]:
-        rv: t.Optional[t.Any] = self.resolve_envvar_value(ctx)
-
-        if rv is None:
-            return None
-
-        value_depth = (self.nargs != 1) + bool(self.multiple)
-
-        if value_depth > 0:
-            rv = self.type.split_envvar_value(rv)
-
-            if self.multiple and self.nargs != 1:
-                rv = batch(rv, self.nargs)
-
-        return rv
-
-    def consume_value(
-        self, ctx: Context, opts: t.Mapping[str, "Parameter"]
-    ) -> t.Tuple[t.Any, ParameterSource]:
-        value, source = super().consume_value(ctx, opts)
-
-        # The parser will emit a sentinel value if the option can be
-        # given as a flag without a value. This is different from None
-        # to distinguish from the flag not being given at all.
-        if value is _flag_needs_value:
-            if self.prompt is not None and not ctx.resilient_parsing:
-                value = self.prompt_for_value(ctx)
-                source = ParameterSource.PROMPT
-            else:
-                value = self.flag_value
-                source = ParameterSource.COMMANDLINE
-
-        elif (
-            self.multiple
-            and value is not None
-            and any(v is _flag_needs_value for v in value)
-        ):
-            value = [self.flag_value if v is _flag_needs_value else v for v in value]
-            source = ParameterSource.COMMANDLINE
-
-        # The value wasn't set, or used the param's default, prompt if
-        # prompting is enabled.
-        elif (
-            source in {None, ParameterSource.DEFAULT}
-            and self.prompt is not None
-            and (self.required or self.prompt_required)
-            and not ctx.resilient_parsing
-        ):
-            value = self.prompt_for_value(ctx)
-            source = ParameterSource.PROMPT
-
-        return value, source
-
-
-class Argument(Parameter):
-    """Arguments are positional parameters to a command.  They generally
-    provide fewer features than options but can have infinite ``nargs``
-    and are required by default.
-
-    All parameters are passed onwards to the constructor of :class:`Parameter`.
-    """
-
-    param_type_name = "argument"
-
-    def __init__(
-        self,
-        param_decls: t.Sequence[str],
-        required: t.Optional[bool] = None,
-        **attrs: t.Any,
-    ) -> None:
-        if required is None:
-            if attrs.get("default") is not None:
-                required = False
-            else:
-                required = attrs.get("nargs", 1) > 0
-
-        if "multiple" in attrs:
-            raise TypeError("__init__() got an unexpected keyword argument 'multiple'.")
-
-        super().__init__(param_decls, required=required, **attrs)
-
-        if __debug__:
-            if self.default is not None and self.nargs == -1:
-                raise TypeError("'default' is not supported for nargs=-1.")
-
-    @property
-    def human_readable_name(self) -> str:
-        if self.metavar is not None:
-            return self.metavar
-        return self.name.upper()  # type: ignore
-
-    def make_metavar(self) -> str:
-        if self.metavar is not None:
-            return self.metavar
-        var = self.type.get_metavar(self)
-        if not var:
-            var = self.name.upper()  # type: ignore
-        if not self.required:
-            var = f"[{var}]"
-        if self.nargs != 1:
-            var += "..."
-        return var
-
-    def _parse_decls(
-        self, decls: t.Sequence[str], expose_value: bool
-    ) -> t.Tuple[t.Optional[str], t.List[str], t.List[str]]:
-        if not decls:
-            if not expose_value:
-                return None, [], []
-            raise TypeError("Could not determine name for argument")
-        if len(decls) == 1:
-            name = arg = decls[0]
-            name = name.replace("-", "_").lower()
-        else:
-            raise TypeError(
-                "Arguments take exactly one parameter declaration, got"
-                f" {len(decls)}."
-            )
-        return name, [arg], []
-
-    def get_usage_pieces(self, ctx: Context) -> t.List[str]:
-        return [self.make_metavar()]
-
-    def get_error_hint(self, ctx: Context) -> str:
-        return f"'{self.make_metavar()}'"
-
-    def add_to_parser(self, parser: OptionParser, ctx: Context) -> None:
-        parser.add_argument(dest=self.name, nargs=self.nargs, obj=self)
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/subset/__main__.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/subset/__main__.py
deleted file mode 100644
index decf9ee6e50a612c65a87ebeaa8be115f1d25242..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/subset/__main__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import sys
-from fontTools.subset import main
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/t1Lib/__init__.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/t1Lib/__init__.py
deleted file mode 100644
index e98acb7c52e89a83b7750601c6d80cbd094637d7..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/t1Lib/__init__.py
+++ /dev/null
@@ -1,638 +0,0 @@
-"""fontTools.t1Lib.py -- Tools for PostScript Type 1 fonts (Python2 only)
-
-Functions for reading and writing raw Type 1 data:
-
-read(path)
-	reads any Type 1 font file, returns the raw data and a type indicator:
-	'LWFN', 'PFB' or 'OTHER', depending on the format of the file pointed
-	to by 'path'.
-	Raises an error when the file does not contain valid Type 1 data.
-
-write(path, data, kind='OTHER', dohex=False)
-	writes raw Type 1 data to the file pointed to by 'path'.
-	'kind' can be one of 'LWFN', 'PFB' or 'OTHER'; it defaults to 'OTHER'.
-	'dohex' is a flag which determines whether the eexec encrypted
-	part should be written as hexadecimal or binary, but only if kind
-	is 'OTHER'.
-"""
-import fontTools
-from fontTools.misc import eexec
-from fontTools.misc.macCreatorType import getMacCreatorAndType
-from fontTools.misc.textTools import bytechr, byteord, bytesjoin, tobytes
-from fontTools.misc.psOperators import (
-    _type1_pre_eexec_order,
-    _type1_fontinfo_order,
-    _type1_post_eexec_order,
-)
-from fontTools.encodings.StandardEncoding import StandardEncoding
-import os
-import re
-
-__author__ = "jvr"
-__version__ = "1.0b3"
-DEBUG = 0
-
-
-try:
-    try:
-        from Carbon import Res
-    except ImportError:
-        import Res  # MacPython < 2.2
-except ImportError:
-    haveMacSupport = 0
-else:
-    haveMacSupport = 1
-
-
-class T1Error(Exception):
-    pass
-
-
-class T1Font(object):
-
-    """Type 1 font class.
-
-    Uses a minimal interpeter that supports just about enough PS to parse
-    Type 1 fonts.
-    """
-
-    def __init__(self, path, encoding="ascii", kind=None):
-        if kind is None:
-            self.data, _ = read(path)
-        elif kind == "LWFN":
-            self.data = readLWFN(path)
-        elif kind == "PFB":
-            self.data = readPFB(path)
-        elif kind == "OTHER":
-            self.data = readOther(path)
-        else:
-            raise ValueError(kind)
-        self.encoding = encoding
-
-    def saveAs(self, path, type, dohex=False):
-        write(path, self.getData(), type, dohex)
-
-    def getData(self):
-        if not hasattr(self, "data"):
-            self.data = self.createData()
-        return self.data
-
-    def getGlyphSet(self):
-        """Return a generic GlyphSet, which is a dict-like object
-        mapping glyph names to glyph objects. The returned glyph objects
-        have a .draw() method that supports the Pen protocol, and will
-        have an attribute named 'width', but only *after* the .draw() method
-        has been called.
-
-        In the case of Type 1, the GlyphSet is simply the CharStrings dict.
-        """
-        return self["CharStrings"]
-
-    def __getitem__(self, key):
-        if not hasattr(self, "font"):
-            self.parse()
-        return self.font[key]
-
-    def parse(self):
-        from fontTools.misc import psLib
-        from fontTools.misc import psCharStrings
-
-        self.font = psLib.suckfont(self.data, self.encoding)
-        charStrings = self.font["CharStrings"]
-        lenIV = self.font["Private"].get("lenIV", 4)
-        assert lenIV >= 0
-        subrs = self.font["Private"]["Subrs"]
-        for glyphName, charString in charStrings.items():
-            charString, R = eexec.decrypt(charString, 4330)
-            charStrings[glyphName] = psCharStrings.T1CharString(
-                charString[lenIV:], subrs=subrs
-            )
-        for i in range(len(subrs)):
-            charString, R = eexec.decrypt(subrs[i], 4330)
-            subrs[i] = psCharStrings.T1CharString(charString[lenIV:], subrs=subrs)
-        del self.data
-
-    def createData(self):
-        sf = self.font
-
-        eexec_began = False
-        eexec_dict = {}
-        lines = []
-        lines.extend(
-            [
-                self._tobytes(f"%!FontType1-1.1: {sf['FontName']}"),
-                self._tobytes(f"%t1Font: ({fontTools.version})"),
-                self._tobytes(f"%%BeginResource: font {sf['FontName']}"),
-            ]
-        )
-        # follow t1write.c:writeRegNameKeyedFont
-        size = 3  # Headroom for new key addition
-        size += 1  # FontMatrix is always counted
-        size += 1 + 1  # Private, CharStings
-        for key in font_dictionary_keys:
-            size += int(key in sf)
-        lines.append(self._tobytes(f"{size} dict dup begin"))
-
-        for key, value in sf.items():
-            if eexec_began:
-                eexec_dict[key] = value
-                continue
-
-            if key == "FontInfo":
-                fi = sf["FontInfo"]
-                # follow t1write.c:writeFontInfoDict
-                size = 3  # Headroom for new key addition
-                for subkey in FontInfo_dictionary_keys:
-                    size += int(subkey in fi)
-                lines.append(self._tobytes(f"/FontInfo {size} dict dup begin"))
-
-                for subkey, subvalue in fi.items():
-                    lines.extend(self._make_lines(subkey, subvalue))
-                lines.append(b"end def")
-            elif key in _type1_post_eexec_order:  # usually 'Private'
-                eexec_dict[key] = value
-                eexec_began = True
-            else:
-                lines.extend(self._make_lines(key, value))
-        lines.append(b"end")
-        eexec_portion = self.encode_eexec(eexec_dict)
-        lines.append(bytesjoin([b"currentfile eexec ", eexec_portion]))
-
-        for _ in range(8):
-            lines.append(self._tobytes("0" * 64))
-        lines.extend([b"cleartomark", b"%%EndResource", b"%%EOF"])
-
-        data = bytesjoin(lines, "\n")
-        return data
-
-    def encode_eexec(self, eexec_dict):
-        lines = []
-
-        # '-|', '|-', '|'
-        RD_key, ND_key, NP_key = None, None, None
-
-        for key, value in eexec_dict.items():
-            if key == "Private":
-                pr = eexec_dict["Private"]
-                # follow t1write.c:writePrivateDict
-                size = 3  # for RD, ND, NP
-                for subkey in Private_dictionary_keys:
-                    size += int(subkey in pr)
-                lines.append(b"dup /Private")
-                lines.append(self._tobytes(f"{size} dict dup begin"))
-                for subkey, subvalue in pr.items():
-                    if not RD_key and subvalue == RD_value:
-                        RD_key = subkey
-                    elif not ND_key and subvalue == ND_value:
-                        ND_key = subkey
-                    elif not NP_key and subvalue == PD_value:
-                        NP_key = subkey
-
-                    if subkey == "OtherSubrs":
-                        # XXX: assert that no flex hint is used
-                        lines.append(self._tobytes(hintothers))
-                    elif subkey == "Subrs":
-                        # XXX: standard Subrs only
-                        lines.append(b"/Subrs 5 array")
-                        for i, subr_bin in enumerate(std_subrs):
-                            encrypted_subr, R = eexec.encrypt(
-                                bytesjoin([char_IV, subr_bin]), 4330
-                            )
-                            lines.append(
-                                bytesjoin(
-                                    [
-                                        self._tobytes(
-                                            f"dup {i} {len(encrypted_subr)} {RD_key} "
-                                        ),
-                                        encrypted_subr,
-                                        self._tobytes(f" {NP_key}"),
-                                    ]
-                                )
-                            )
-                        lines.append(b"def")
-
-                        lines.append(b"put")
-                    else:
-                        lines.extend(self._make_lines(subkey, subvalue))
-            elif key == "CharStrings":
-                lines.append(b"dup /CharStrings")
-                lines.append(
-                    self._tobytes(f"{len(eexec_dict['CharStrings'])} dict dup begin")
-                )
-                for glyph_name, char_bin in eexec_dict["CharStrings"].items():
-                    char_bin.compile()
-                    encrypted_char, R = eexec.encrypt(
-                        bytesjoin([char_IV, char_bin.bytecode]), 4330
-                    )
-                    lines.append(
-                        bytesjoin(
-                            [
-                                self._tobytes(
-                                    f"/{glyph_name} {len(encrypted_char)} {RD_key} "
-                                ),
-                                encrypted_char,
-                                self._tobytes(f" {ND_key}"),
-                            ]
-                        )
-                    )
-                lines.append(b"end put")
-            else:
-                lines.extend(self._make_lines(key, value))
-
-        lines.extend(
-            [
-                b"end",
-                b"dup /FontName get exch definefont pop",
-                b"mark",
-                b"currentfile closefile\n",
-            ]
-        )
-
-        eexec_portion = bytesjoin(lines, "\n")
-        encrypted_eexec, R = eexec.encrypt(bytesjoin([eexec_IV, eexec_portion]), 55665)
-
-        return encrypted_eexec
-
-    def _make_lines(self, key, value):
-        if key == "FontName":
-            return [self._tobytes(f"/{key} /{value} def")]
-        if key in ["isFixedPitch", "ForceBold", "RndStemUp"]:
-            return [self._tobytes(f"/{key} {'true' if value else 'false'} def")]
-        elif key == "Encoding":
-            if value == StandardEncoding:
-                return [self._tobytes(f"/{key} StandardEncoding def")]
-            else:
-                # follow fontTools.misc.psOperators._type1_Encoding_repr
-                lines = []
-                lines.append(b"/Encoding 256 array")
-                lines.append(b"0 1 255 {1 index exch /.notdef put} for")
-                for i in range(256):
-                    name = value[i]
-                    if name != ".notdef":
-                        lines.append(self._tobytes(f"dup {i} /{name} put"))
-                lines.append(b"def")
-                return lines
-        if isinstance(value, str):
-            return [self._tobytes(f"/{key} ({value}) def")]
-        elif isinstance(value, bool):
-            return [self._tobytes(f"/{key} {'true' if value else 'false'} def")]
-        elif isinstance(value, list):
-            return [self._tobytes(f"/{key} [{' '.join(str(v) for v in value)}] def")]
-        elif isinstance(value, tuple):
-            return [self._tobytes(f"/{key} {{{' '.join(str(v) for v in value)}}} def")]
-        else:
-            return [self._tobytes(f"/{key} {value} def")]
-
-    def _tobytes(self, s, errors="strict"):
-        return tobytes(s, self.encoding, errors)
-
-
-# low level T1 data read and write functions
-
-
-def read(path, onlyHeader=False):
-    """reads any Type 1 font file, returns raw data"""
-    _, ext = os.path.splitext(path)
-    ext = ext.lower()
-    creator, typ = getMacCreatorAndType(path)
-    if typ == "LWFN":
-        return readLWFN(path, onlyHeader), "LWFN"
-    if ext == ".pfb":
-        return readPFB(path, onlyHeader), "PFB"
-    else:
-        return readOther(path), "OTHER"
-
-
-def write(path, data, kind="OTHER", dohex=False):
-    assertType1(data)
-    kind = kind.upper()
-    try:
-        os.remove(path)
-    except os.error:
-        pass
-    err = 1
-    try:
-        if kind == "LWFN":
-            writeLWFN(path, data)
-        elif kind == "PFB":
-            writePFB(path, data)
-        else:
-            writeOther(path, data, dohex)
-        err = 0
-    finally:
-        if err and not DEBUG:
-            try:
-                os.remove(path)
-            except os.error:
-                pass
-
-
-# -- internal --
-
-LWFNCHUNKSIZE = 2000
-HEXLINELENGTH = 80
-
-
-def readLWFN(path, onlyHeader=False):
-    """reads an LWFN font file, returns raw data"""
-    from fontTools.misc.macRes import ResourceReader
-
-    reader = ResourceReader(path)
-    try:
-        data = []
-        for res in reader.get("POST", []):
-            code = byteord(res.data[0])
-            if byteord(res.data[1]) != 0:
-                raise T1Error("corrupt LWFN file")
-            if code in [1, 2]:
-                if onlyHeader and code == 2:
-                    break
-                data.append(res.data[2:])
-            elif code in [3, 5]:
-                break
-            elif code == 4:
-                with open(path, "rb") as f:
-                    data.append(f.read())
-            elif code == 0:
-                pass  # comment, ignore
-            else:
-                raise T1Error("bad chunk code: " + repr(code))
-    finally:
-        reader.close()
-    data = bytesjoin(data)
-    assertType1(data)
-    return data
-
-
-def readPFB(path, onlyHeader=False):
-    """reads a PFB font file, returns raw data"""
-    data = []
-    with open(path, "rb") as f:
-        while True:
-            if f.read(1) != bytechr(128):
-                raise T1Error("corrupt PFB file")
-            code = byteord(f.read(1))
-            if code in [1, 2]:
-                chunklen = stringToLong(f.read(4))
-                chunk = f.read(chunklen)
-                assert len(chunk) == chunklen
-                data.append(chunk)
-            elif code == 3:
-                break
-            else:
-                raise T1Error("bad chunk code: " + repr(code))
-            if onlyHeader:
-                break
-    data = bytesjoin(data)
-    assertType1(data)
-    return data
-
-
-def readOther(path):
-    """reads any (font) file, returns raw data"""
-    with open(path, "rb") as f:
-        data = f.read()
-    assertType1(data)
-    chunks = findEncryptedChunks(data)
-    data = []
-    for isEncrypted, chunk in chunks:
-        if isEncrypted and isHex(chunk[:4]):
-            data.append(deHexString(chunk))
-        else:
-            data.append(chunk)
-    return bytesjoin(data)
-
-
-# file writing tools
-
-
-def writeLWFN(path, data):
-    # Res.FSpCreateResFile was deprecated in OS X 10.5
-    Res.FSpCreateResFile(path, "just", "LWFN", 0)
-    resRef = Res.FSOpenResFile(path, 2)  # write-only
-    try:
-        Res.UseResFile(resRef)
-        resID = 501
-        chunks = findEncryptedChunks(data)
-        for isEncrypted, chunk in chunks:
-            if isEncrypted:
-                code = 2
-            else:
-                code = 1
-            while chunk:
-                res = Res.Resource(bytechr(code) + "\0" + chunk[: LWFNCHUNKSIZE - 2])
-                res.AddResource("POST", resID, "")
-                chunk = chunk[LWFNCHUNKSIZE - 2 :]
-                resID = resID + 1
-        res = Res.Resource(bytechr(5) + "\0")
-        res.AddResource("POST", resID, "")
-    finally:
-        Res.CloseResFile(resRef)
-
-
-def writePFB(path, data):
-    chunks = findEncryptedChunks(data)
-    with open(path, "wb") as f:
-        for isEncrypted, chunk in chunks:
-            if isEncrypted:
-                code = 2
-            else:
-                code = 1
-            f.write(bytechr(128) + bytechr(code))
-            f.write(longToString(len(chunk)))
-            f.write(chunk)
-        f.write(bytechr(128) + bytechr(3))
-
-
-def writeOther(path, data, dohex=False):
-    chunks = findEncryptedChunks(data)
-    with open(path, "wb") as f:
-        hexlinelen = HEXLINELENGTH // 2
-        for isEncrypted, chunk in chunks:
-            if isEncrypted:
-                code = 2
-            else:
-                code = 1
-            if code == 2 and dohex:
-                while chunk:
-                    f.write(eexec.hexString(chunk[:hexlinelen]))
-                    f.write(b"\r")
-                    chunk = chunk[hexlinelen:]
-            else:
-                f.write(chunk)
-
-
-# decryption tools
-
-EEXECBEGIN = b"currentfile eexec"
-# The spec allows for 512 ASCII zeros interrupted by arbitrary whitespace to
-# follow eexec
-EEXECEND = re.compile(b"(0[ \t\r\n]*){512}", flags=re.M)
-EEXECINTERNALEND = b"currentfile closefile"
-EEXECBEGINMARKER = b"%-- eexec start\r"
-EEXECENDMARKER = b"%-- eexec end\r"
-
-_ishexRE = re.compile(b"[0-9A-Fa-f]*$")
-
-
-def isHex(text):
-    return _ishexRE.match(text) is not None
-
-
-def decryptType1(data):
-    chunks = findEncryptedChunks(data)
-    data = []
-    for isEncrypted, chunk in chunks:
-        if isEncrypted:
-            if isHex(chunk[:4]):
-                chunk = deHexString(chunk)
-            decrypted, R = eexec.decrypt(chunk, 55665)
-            decrypted = decrypted[4:]
-            if (
-                decrypted[-len(EEXECINTERNALEND) - 1 : -1] != EEXECINTERNALEND
-                and decrypted[-len(EEXECINTERNALEND) - 2 : -2] != EEXECINTERNALEND
-            ):
-                raise T1Error("invalid end of eexec part")
-            decrypted = decrypted[: -len(EEXECINTERNALEND) - 2] + b"\r"
-            data.append(EEXECBEGINMARKER + decrypted + EEXECENDMARKER)
-        else:
-            if chunk[-len(EEXECBEGIN) - 1 : -1] == EEXECBEGIN:
-                data.append(chunk[: -len(EEXECBEGIN) - 1])
-            else:
-                data.append(chunk)
-    return bytesjoin(data)
-
-
-def findEncryptedChunks(data):
-    chunks = []
-    while True:
-        eBegin = data.find(EEXECBEGIN)
-        if eBegin < 0:
-            break
-        eBegin = eBegin + len(EEXECBEGIN) + 1
-        endMatch = EEXECEND.search(data, eBegin)
-        if endMatch is None:
-            raise T1Error("can't find end of eexec part")
-        eEnd = endMatch.start()
-        cypherText = data[eBegin : eEnd + 2]
-        if isHex(cypherText[:4]):
-            cypherText = deHexString(cypherText)
-        plainText, R = eexec.decrypt(cypherText, 55665)
-        eEndLocal = plainText.find(EEXECINTERNALEND)
-        if eEndLocal < 0:
-            raise T1Error("can't find end of eexec part")
-        chunks.append((0, data[:eBegin]))
-        chunks.append((1, cypherText[: eEndLocal + len(EEXECINTERNALEND) + 1]))
-        data = data[eEnd:]
-    chunks.append((0, data))
-    return chunks
-
-
-def deHexString(hexstring):
-    return eexec.deHexString(bytesjoin(hexstring.split()))
-
-
-# Type 1 assertion
-
-_fontType1RE = re.compile(rb"/FontType\s+1\s+def")
-
-
-def assertType1(data):
-    for head in [b"%!PS-AdobeFont", b"%!FontType1"]:
-        if data[: len(head)] == head:
-            break
-    else:
-        raise T1Error("not a PostScript font")
-    if not _fontType1RE.search(data):
-        raise T1Error("not a Type 1 font")
-    if data.find(b"currentfile eexec") < 0:
-        raise T1Error("not an encrypted Type 1 font")
-    # XXX what else?
-    return data
-
-
-# pfb helpers
-
-
-def longToString(long):
-    s = b""
-    for i in range(4):
-        s += bytechr((long & (0xFF << (i * 8))) >> i * 8)
-    return s
-
-
-def stringToLong(s):
-    if len(s) != 4:
-        raise ValueError("string must be 4 bytes long")
-    l = 0
-    for i in range(4):
-        l += byteord(s[i]) << (i * 8)
-    return l
-
-
-# PS stream helpers
-
-font_dictionary_keys = list(_type1_pre_eexec_order)
-# t1write.c:writeRegNameKeyedFont
-# always counts following keys
-font_dictionary_keys.remove("FontMatrix")
-
-FontInfo_dictionary_keys = list(_type1_fontinfo_order)
-# extend because AFDKO tx may use following keys
-FontInfo_dictionary_keys.extend(
-    [
-        "FSType",
-        "Copyright",
-    ]
-)
-
-Private_dictionary_keys = [
-    # We don't know what names will be actually used.
-    # "RD",
-    # "ND",
-    # "NP",
-    "Subrs",
-    "OtherSubrs",
-    "UniqueID",
-    "BlueValues",
-    "OtherBlues",
-    "FamilyBlues",
-    "FamilyOtherBlues",
-    "BlueScale",
-    "BlueShift",
-    "BlueFuzz",
-    "StdHW",
-    "StdVW",
-    "StemSnapH",
-    "StemSnapV",
-    "ForceBold",
-    "LanguageGroup",
-    "password",
-    "lenIV",
-    "MinFeature",
-    "RndStemUp",
-]
-
-# t1write_hintothers.h
-hintothers = """/OtherSubrs[{}{}{}{systemdict/internaldict known not{pop 3}{1183615869
-systemdict/internaldict get exec dup/startlock known{/startlock get exec}{dup
-/strtlck known{/strtlck get exec}{pop 3}ifelse}ifelse}ifelse}executeonly]def"""
-# t1write.c:saveStdSubrs
-std_subrs = [
-    # 3 0 callother pop pop setcurrentpoint return
-    b"\x8e\x8b\x0c\x10\x0c\x11\x0c\x11\x0c\x21\x0b",
-    # 0 1 callother return
-    b"\x8b\x8c\x0c\x10\x0b",
-    # 0 2 callother return
-    b"\x8b\x8d\x0c\x10\x0b",
-    # return
-    b"\x0b",
-    # 3 1 3 callother pop callsubr return
-    b"\x8e\x8c\x8e\x0c\x10\x0c\x11\x0a\x0b",
-]
-# follow t1write.c:writeRegNameKeyedFont
-eexec_IV = b"cccc"
-char_IV = b"\x0c\x0c\x0c\x0c"
-RD_value = ("string", "currentfile", "exch", "readstring", "pop")
-ND_value = ("def",)
-PD_value = ("put",)
diff --git a/spaces/cncn102/bingo1/src/components/ui/button.tsx b/spaces/cncn102/bingo1/src/components/ui/button.tsx
deleted file mode 100644
index 281da005124fa94c89a9a9db7605748a92b60865..0000000000000000000000000000000000000000
--- a/spaces/cncn102/bingo1/src/components/ui/button.tsx
+++ /dev/null
@@ -1,57 +0,0 @@
-import * as React from 'react'
-import { Slot } from '@radix-ui/react-slot'
-import { cva, type VariantProps } from 'class-variance-authority'
-
-import { cn } from '@/lib/utils'
-
-const buttonVariants = cva(
-  'inline-flex items-center justify-center rounded-md text-sm font-medium shadow ring-offset-background transition-colors outline-none disabled:pointer-events-none disabled:opacity-50',
-  {
-    variants: {
-      variant: {
-        default:
-          'bg-primary text-primary-foreground shadow-md hover:bg-primary/90',
-        destructive:
-          'bg-destructive text-destructive-foreground hover:bg-destructive/90',
-        outline:
-          'border border-input hover:bg-accent hover:text-accent-foreground',
-        secondary:
-          'bg-secondary text-secondary-foreground hover:bg-secondary/80',
-        ghost: 'shadow-none hover:bg-accent hover:text-accent-foreground',
-        link: 'text-primary underline-offset-4 shadow-none hover:underline'
-      },
-      size: {
-        default: 'h-8 px-4 py-2',
-        sm: 'h-8 rounded-md px-3',
-        lg: 'h-11 rounded-md px-8',
-        icon: 'h-8 w-8 p-0'
-      }
-    },
-    defaultVariants: {
-      variant: 'default',
-      size: 'default'
-    }
-  }
-)
-
-export interface ButtonProps
-  extends React.ButtonHTMLAttributes<HTMLButtonElement>,
-    VariantProps<typeof buttonVariants> {
-  asChild?: boolean
-}
-
-const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
-  ({ className, variant, size, asChild = false, ...props }, ref) => {
-    const Comp = asChild ? Slot : 'button'
-    return (
-      <Comp
-        className={cn(buttonVariants({ variant, size, className }))}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Button.displayName = 'Button'
-
-export { Button, buttonVariants }
diff --git a/spaces/codertoro/gpt-academic/core_functional.py b/spaces/codertoro/gpt-academic/core_functional.py
deleted file mode 100644
index 536ccb609c38cbbebfda4ba17bd51a78857d711e..0000000000000000000000000000000000000000
--- a/spaces/codertoro/gpt-academic/core_functional.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# 'primary' 颜色对应 theme.py 中的 primary_hue
-# 'secondary' 颜色对应 theme.py 中的 neutral_hue
-# 'stop' 颜色对应 theme.py 中的 color_er
-# 默认按钮颜色是 secondary
-from toolbox import clear_line_break
-
-
-def get_core_functions():
-    return {
-        "英语学术润色": {
-            # 前言
-            "Prefix":   r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, " +
-                        r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. " +
-                        r"Furthermore, list all modification and explain the reasons to do so in markdown table." + "\n\n",
-            # 后语
-            "Suffix":   r"",
-            "Color":    r"secondary",    # 按钮颜色
-        },
-        "中文学术润色": {
-            "Prefix":   r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，" +
-                        r"同时分解长句，减少重复，并提供改进建议。请只提供文本的更正版本，避免包括解释。请编辑以下文本" + "\n\n",
-            "Suffix":   r"",
-        },
-        "查找语法错误": {
-            "Prefix":   r"Can you help me ensure that the grammar and the spelling is correct? " +
-                        r"Do not try to polish the text, if no mistake is found, tell me that this paragraph is good." +
-                        r"If you find grammar or spelling mistakes, please list mistakes you find in a two-column markdown table, " +
-                        r"put the original text the first column, " +
-                        r"put the corrected text in the second column and highlight the key words you fixed.""\n"
-                        r"Example:""\n"
-                        r"Paragraph: How is you? Do you knows what is it?""\n"
-                        r"| Original sentence | Corrected sentence |""\n"
-                        r"| :--- | :--- |""\n"
-                        r"| How **is** you? | How **are** you? |""\n"
-                        r"| Do you **knows** what **is** **it**? | Do you **know** what **it** **is** ? |""\n"
-                        r"Below is a paragraph from an academic paper. "
-                        r"You need to report all grammar and spelling mistakes as the example before."
-                        + "\n\n",
-            "Suffix":   r"",
-            "PreProcess": clear_line_break,    # 预处理：清除换行符
-        },
-        "中译英": {
-            "Prefix":   r"Please translate following sentence to English:" + "\n\n",
-            "Suffix":   r"",
-        },
-        "学术中英互译": {
-            "Prefix":   r"I want you to act as a scientific English-Chinese translator, " +
-                        r"I will provide you with some paragraphs in one language " +
-                        r"and your task is to accurately and academically translate the paragraphs only into the other language. " +
-                        r"Do not repeat the original provided paragraphs after translation. " +
-                        r"You should use artificial intelligence tools, " +
-                        r"such as natural language processing, and rhetorical knowledge " +
-                        r"and experience about effective writing techniques to reply. " +
-                        r"I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:" + "\n\n",
-            "Suffix": "",
-            "Color": "secondary",
-        },
-        "英译中": {
-            "Prefix":   r"翻译成地道的中文：" + "\n\n",
-            "Suffix":   r"",
-        },
-        "找图片": {
-            "Prefix":   r"我需要你找一张网络图片。使用Unsplash API(https://source.unsplash.com/960x640/?<英语关键词>)获取图片URL，" +
-                        r"然后请使用Markdown格式封装，并且不要有反斜线，不要用代码块。现在，请按以下描述给我发送图片：" + "\n\n",
-            "Suffix":   r"",
-        },
-        "解释代码": {
-            "Prefix":   r"请解释以下代码：" + "\n```\n",
-            "Suffix":   "\n```\n",
-        },
-    }
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aaccoder_twoloop.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aaccoder_twoloop.h
deleted file mode 100644
index 0504a916ad07bf0d1d08ecd676c0637962eba15a..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aaccoder_twoloop.h
+++ /dev/null
@@ -1,763 +0,0 @@
-/*
- * AAC encoder twoloop coder
- * Copyright (C) 2008-2009 Konstantin Shishkov
- *
- * 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 encoder twoloop coder
- * @author Konstantin Shishkov, Claudio Freire
- */
-
-/**
- * This file contains a template for the twoloop coder function.
- * It needs to be provided, externally, as an already included declaration,
- * the following functions from aacenc_quantization/util.h. They're not included
- * explicitly here to make it possible to provide alternative implementations:
- *  - quantize_band_cost
- *  - abs_pow34_v
- *  - find_max_val
- *  - find_min_book
- *  - find_form_factor
- */
-
-#ifndef AVCODEC_AACCODER_TWOLOOP_H
-#define AVCODEC_AACCODER_TWOLOOP_H
-
-#include <float.h>
-#include "libavutil/mathematics.h"
-#include "mathops.h"
-#include "avcodec.h"
-#include "put_bits.h"
-#include "aac.h"
-#include "aacenc.h"
-#include "aactab.h"
-#include "aacenctab.h"
-
-/** Frequency in Hz for lower limit of noise substitution **/
-#define NOISE_LOW_LIMIT 4000
-
-#define sclip(x) av_clip(x,60,218)
-
-/* Reflects the cost to change codebooks */
-static inline int ff_pns_bits(SingleChannelElement *sce, int w, int g)
-{
-    return (!g || !sce->zeroes[w*16+g-1] || !sce->can_pns[w*16+g-1]) ? 9 : 5;
-}
-
-/**
- * two-loop quantizers search taken from ISO 13818-7 Appendix C
- */
-static void search_for_quantizers_twoloop(AVCodecContext *avctx,
-                                          AACEncContext *s,
-                                          SingleChannelElement *sce,
-                                          const float lambda)
-{
-    int start = 0, i, w, w2, g, recomprd;
-    int destbits = avctx->bit_rate * 1024.0 / avctx->sample_rate
-        / ((avctx->flags & AV_CODEC_FLAG_QSCALE) ? 2.0f : avctx->ch_layout.nb_channels)
-        * (lambda / 120.f);
-    int refbits = destbits;
-    int toomanybits, toofewbits;
-    char nzs[128];
-    uint8_t nextband[128];
-    int maxsf[128], minsf[128];
-    float dists[128] = { 0 }, qenergies[128] = { 0 }, uplims[128], euplims[128], energies[128];
-    float maxvals[128], spread_thr_r[128];
-    float min_spread_thr_r, max_spread_thr_r;
-
-    /**
-     * rdlambda controls the maximum tolerated distortion. Twoloop
-     * will keep iterating until it fails to lower it or it reaches
-     * ulimit * rdlambda. Keeping it low increases quality on difficult
-     * signals, but lower it too much, and bits will be taken from weak
-     * signals, creating "holes". A balance is necessary.
-     * rdmax and rdmin specify the relative deviation from rdlambda
-     * allowed for tonality compensation
-     */
-    float rdlambda = av_clipf(2.0f * 120.f / lambda, 0.0625f, 16.0f);
-    const float nzslope = 1.5f;
-    float rdmin = 0.03125f;
-    float rdmax = 1.0f;
-
-    /**
-     * sfoffs controls an offset of optmium allocation that will be
-     * applied based on lambda. Keep it real and modest, the loop
-     * will take care of the rest, this just accelerates convergence
-     */
-    float sfoffs = av_clipf(log2f(120.0f / lambda) * 4.0f, -5, 10);
-
-    int fflag, minscaler, maxscaler, nminscaler;
-    int its  = 0;
-    int maxits = 30;
-    int allz = 0;
-    int tbits;
-    int cutoff = 1024;
-    int pns_start_pos;
-    int prev;
-
-    /**
-     * zeroscale controls a multiplier of the threshold, if band energy
-     * is below this, a zero is forced. Keep it lower than 1, unless
-     * low lambda is used, because energy < threshold doesn't mean there's
-     * no audible signal outright, it's just energy. Also make it rise
-     * slower than rdlambda, as rdscale has due compensation with
-     * noisy band depriorization below, whereas zeroing logic is rather dumb
-     */
-    float zeroscale;
-    if (lambda > 120.f) {
-        zeroscale = av_clipf(powf(120.f / lambda, 0.25f), 0.0625f, 1.0f);
-    } else {
-        zeroscale = 1.f;
-    }
-
-    if (s->psy.bitres.alloc >= 0) {
-        /**
-         * Psy granted us extra bits to use, from the reservoire
-         * adjust for lambda except what psy already did
-         */
-        destbits = s->psy.bitres.alloc
-            * (lambda / (avctx->global_quality ? avctx->global_quality : 120));
-    }
-
-    if (avctx->flags & AV_CODEC_FLAG_QSCALE) {
-        /**
-         * Constant Q-scale doesn't compensate MS coding on its own
-         * No need to be overly precise, this only controls RD
-         * adjustment CB limits when going overboard
-         */
-        if (s->options.mid_side && s->cur_type == TYPE_CPE)
-            destbits *= 2;
-
-        /**
-         * When using a constant Q-scale, don't adjust bits, just use RD
-         * Don't let it go overboard, though... 8x psy target is enough
-         */
-        toomanybits = 5800;
-        toofewbits = destbits / 16;
-
-        /** Don't offset scalers, just RD */
-        sfoffs = sce->ics.num_windows - 1;
-        rdlambda = sqrtf(rdlambda);
-
-        /** search further */
-        maxits *= 2;
-    } else {
-        /* When using ABR, be strict, but a reasonable leeway is
-         * critical to allow RC to smoothly track desired bitrate
-         * without sudden quality drops that cause audible artifacts.
-         * Symmetry is also desirable, to avoid systematic bias.
-         */
-        toomanybits = destbits + destbits/8;
-        toofewbits = destbits - destbits/8;
-
-        sfoffs = 0;
-        rdlambda = sqrtf(rdlambda);
-    }
-
-    /** and zero out above cutoff frequency */
-    {
-        int wlen = 1024 / sce->ics.num_windows;
-        int bandwidth;
-
-        /**
-         * Scale, psy gives us constant quality, this LP only scales
-         * bitrate by lambda, so we save bits on subjectively unimportant HF
-         * rather than increase quantization noise. Adjust nominal bitrate
-         * to effective bitrate according to encoding parameters,
-         * AAC_CUTOFF_FROM_BITRATE is calibrated for effective bitrate.
-         */
-        float rate_bandwidth_multiplier = 1.5f;
-        int frame_bit_rate = (avctx->flags & AV_CODEC_FLAG_QSCALE)
-            ? (refbits * rate_bandwidth_multiplier * avctx->sample_rate / 1024)
-            : (avctx->bit_rate / avctx->ch_layout.nb_channels);
-
-        /** Compensate for extensions that increase efficiency */
-        if (s->options.pns || s->options.intensity_stereo)
-            frame_bit_rate *= 1.15f;
-
-        if (avctx->cutoff > 0) {
-            bandwidth = avctx->cutoff;
-        } else {
-            bandwidth = FFMAX(3000, AAC_CUTOFF_FROM_BITRATE(frame_bit_rate, 1, avctx->sample_rate));
-            s->psy.cutoff = bandwidth;
-        }
-
-        cutoff = bandwidth * 2 * wlen / avctx->sample_rate;
-        pns_start_pos = NOISE_LOW_LIMIT * 2 * wlen / avctx->sample_rate;
-    }
-
-    /**
-     * for values above this the decoder might end up in an endless loop
-     * due to always having more bits than what can be encoded.
-     */
-    destbits = FFMIN(destbits, 5800);
-    toomanybits = FFMIN(toomanybits, 5800);
-    toofewbits = FFMIN(toofewbits, 5800);
-    /**
-     * XXX: some heuristic to determine initial quantizers will reduce search time
-     * determine zero bands and upper distortion limits
-     */
-    min_spread_thr_r = -1;
-    max_spread_thr_r = -1;
-    for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-        for (g = start = 0;  g < sce->ics.num_swb; start += sce->ics.swb_sizes[g++]) {
-            int nz = 0;
-            float uplim = 0.0f, energy = 0.0f, spread = 0.0f;
-            for (w2 = 0; w2 < sce->ics.group_len[w]; w2++) {
-                FFPsyBand *band = &s->psy.ch[s->cur_channel].psy_bands[(w+w2)*16+g];
-                if (start >= cutoff || band->energy <= (band->threshold * zeroscale) || band->threshold == 0.0f) {
-                    sce->zeroes[(w+w2)*16+g] = 1;
-                    continue;
-                }
-                nz = 1;
-            }
-            if (!nz) {
-                uplim = 0.0f;
-            } else {
-                nz = 0;
-                for (w2 = 0; w2 < sce->ics.group_len[w]; w2++) {
-                    FFPsyBand *band = &s->psy.ch[s->cur_channel].psy_bands[(w+w2)*16+g];
-                    if (band->energy <= (band->threshold * zeroscale) || band->threshold == 0.0f)
-                        continue;
-                    uplim += band->threshold;
-                    energy += band->energy;
-                    spread += band->spread;
-                    nz++;
-                }
-            }
-            uplims[w*16+g] = uplim;
-            energies[w*16+g] = energy;
-            nzs[w*16+g] = nz;
-            sce->zeroes[w*16+g] = !nz;
-            allz |= nz;
-            if (nz && sce->can_pns[w*16+g]) {
-                spread_thr_r[w*16+g] = energy * nz / (uplim * spread);
-                if (min_spread_thr_r < 0) {
-                    min_spread_thr_r = max_spread_thr_r = spread_thr_r[w*16+g];
-                } else {
-                    min_spread_thr_r = FFMIN(min_spread_thr_r, spread_thr_r[w*16+g]);
-                    max_spread_thr_r = FFMAX(max_spread_thr_r, spread_thr_r[w*16+g]);
-                }
-            }
-        }
-    }
-
-    /** Compute initial scalers */
-    minscaler = 65535;
-    for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-        for (g = 0;  g < sce->ics.num_swb; g++) {
-            if (sce->zeroes[w*16+g]) {
-                sce->sf_idx[w*16+g] = SCALE_ONE_POS;
-                continue;
-            }
-            /**
-             * log2f-to-distortion ratio is, technically, 2 (1.5db = 4, but it's power vs level so it's 2).
-             * But, as offsets are applied, low-frequency signals are too sensitive to the induced distortion,
-             * so we make scaling more conservative by choosing a lower log2f-to-distortion ratio, and thus
-             * more robust.
-             */
-            sce->sf_idx[w*16+g] = av_clip(
-                SCALE_ONE_POS
-                    + 1.75*log2f(FFMAX(0.00125f,uplims[w*16+g]) / sce->ics.swb_sizes[g])
-                    + sfoffs,
-                60, SCALE_MAX_POS);
-            minscaler = FFMIN(minscaler, sce->sf_idx[w*16+g]);
-        }
-    }
-
-    /** Clip */
-    minscaler = av_clip(minscaler, SCALE_ONE_POS - SCALE_DIV_512, SCALE_MAX_POS - SCALE_DIV_512);
-    for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w])
-        for (g = 0;  g < sce->ics.num_swb; g++)
-            if (!sce->zeroes[w*16+g])
-                sce->sf_idx[w*16+g] = av_clip(sce->sf_idx[w*16+g], minscaler, minscaler + SCALE_MAX_DIFF - 1);
-
-    if (!allz)
-        return;
-    s->abs_pow34(s->scoefs, sce->coeffs, 1024);
-    ff_quantize_band_cost_cache_init(s);
-
-    for (i = 0; i < sizeof(minsf) / sizeof(minsf[0]); ++i)
-        minsf[i] = 0;
-    for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-        start = w*128;
-        for (g = 0;  g < sce->ics.num_swb; g++) {
-            const float *scaled = s->scoefs + start;
-            int minsfidx;
-            maxvals[w*16+g] = find_max_val(sce->ics.group_len[w], sce->ics.swb_sizes[g], scaled);
-            if (maxvals[w*16+g] > 0) {
-                minsfidx = coef2minsf(maxvals[w*16+g]);
-                for (w2 = 0; w2 < sce->ics.group_len[w]; w2++)
-                    minsf[(w+w2)*16+g] = minsfidx;
-            }
-            start += sce->ics.swb_sizes[g];
-        }
-    }
-
-    /**
-     * Scale uplims to match rate distortion to quality
-     * bu applying noisy band depriorization and tonal band priorization.
-     * Maxval-energy ratio gives us an idea of how noisy/tonal the band is.
-     * If maxval^2 ~ energy, then that band is mostly noise, and we can relax
-     * rate distortion requirements.
-     */
-    memcpy(euplims, uplims, sizeof(euplims));
-    for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-        /** psy already priorizes transients to some extent */
-        float de_psy_factor = (sce->ics.num_windows > 1) ? 8.0f / sce->ics.group_len[w] : 1.0f;
-        start = w*128;
-        for (g = 0;  g < sce->ics.num_swb; g++) {
-            if (nzs[g] > 0) {
-                float cleanup_factor = ff_sqrf(av_clipf(start / (cutoff * 0.75f), 1.0f, 2.0f));
-                float energy2uplim = find_form_factor(
-                    sce->ics.group_len[w], sce->ics.swb_sizes[g],
-                    uplims[w*16+g] / (nzs[g] * sce->ics.swb_sizes[w]),
-                    sce->coeffs + start,
-                    nzslope * cleanup_factor);
-                energy2uplim *= de_psy_factor;
-                if (!(avctx->flags & AV_CODEC_FLAG_QSCALE)) {
-                    /** In ABR, we need to priorize less and let rate control do its thing */
-                    energy2uplim = sqrtf(energy2uplim);
-                }
-                energy2uplim = FFMAX(0.015625f, FFMIN(1.0f, energy2uplim));
-                uplims[w*16+g] *= av_clipf(rdlambda * energy2uplim, rdmin, rdmax)
-                                  * sce->ics.group_len[w];
-
-                energy2uplim = find_form_factor(
-                    sce->ics.group_len[w], sce->ics.swb_sizes[g],
-                    uplims[w*16+g] / (nzs[g] * sce->ics.swb_sizes[w]),
-                    sce->coeffs + start,
-                    2.0f);
-                energy2uplim *= de_psy_factor;
-                if (!(avctx->flags & AV_CODEC_FLAG_QSCALE)) {
-                    /** In ABR, we need to priorize less and let rate control do its thing */
-                    energy2uplim = sqrtf(energy2uplim);
-                }
-                energy2uplim = FFMAX(0.015625f, FFMIN(1.0f, energy2uplim));
-                euplims[w*16+g] *= av_clipf(rdlambda * energy2uplim * sce->ics.group_len[w],
-                    0.5f, 1.0f);
-            }
-            start += sce->ics.swb_sizes[g];
-        }
-    }
-
-    for (i = 0; i < sizeof(maxsf) / sizeof(maxsf[0]); ++i)
-        maxsf[i] = SCALE_MAX_POS;
-
-    //perform two-loop search
-    //outer loop - improve quality
-    do {
-        //inner loop - quantize spectrum to fit into given number of bits
-        int overdist;
-        int qstep = its ? 1 : 32;
-        do {
-            int changed = 0;
-            prev = -1;
-            recomprd = 0;
-            tbits = 0;
-            for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-                start = w*128;
-                for (g = 0;  g < sce->ics.num_swb; g++) {
-                    const float *coefs = &sce->coeffs[start];
-                    const float *scaled = &s->scoefs[start];
-                    int bits = 0;
-                    int cb;
-                    float dist = 0.0f;
-                    float qenergy = 0.0f;
-
-                    if (sce->zeroes[w*16+g] || sce->sf_idx[w*16+g] >= 218) {
-                        start += sce->ics.swb_sizes[g];
-                        if (sce->can_pns[w*16+g]) {
-                            /** PNS isn't free */
-                            tbits += ff_pns_bits(sce, w, g);
-                        }
-                        continue;
-                    }
-                    cb = find_min_book(maxvals[w*16+g], sce->sf_idx[w*16+g]);
-                    for (w2 = 0; w2 < sce->ics.group_len[w]; w2++) {
-                        int b;
-                        float sqenergy;
-                        dist += quantize_band_cost_cached(s, w + w2, g, coefs + w2*128,
-                                                   scaled + w2*128,
-                                                   sce->ics.swb_sizes[g],
-                                                   sce->sf_idx[w*16+g],
-                                                   cb,
-                                                   1.0f,
-                                                   INFINITY,
-                                                   &b, &sqenergy,
-                                                   0);
-                        bits += b;
-                        qenergy += sqenergy;
-                    }
-                    dists[w*16+g] = dist - bits;
-                    qenergies[w*16+g] = qenergy;
-                    if (prev != -1) {
-                        int sfdiff = av_clip(sce->sf_idx[w*16+g] - prev + SCALE_DIFF_ZERO, 0, 2*SCALE_MAX_DIFF);
-                        bits += ff_aac_scalefactor_bits[sfdiff];
-                    }
-                    tbits += bits;
-                    start += sce->ics.swb_sizes[g];
-                    prev = sce->sf_idx[w*16+g];
-                }
-            }
-            if (tbits > toomanybits) {
-                recomprd = 1;
-                for (i = 0; i < 128; i++) {
-                    if (sce->sf_idx[i] < (SCALE_MAX_POS - SCALE_DIV_512)) {
-                        int maxsf_i = (tbits > 5800) ? SCALE_MAX_POS : maxsf[i];
-                        int new_sf = FFMIN(maxsf_i, sce->sf_idx[i] + qstep);
-                        if (new_sf != sce->sf_idx[i]) {
-                            sce->sf_idx[i] = new_sf;
-                            changed = 1;
-                        }
-                    }
-                }
-            } else if (tbits < toofewbits) {
-                recomprd = 1;
-                for (i = 0; i < 128; i++) {
-                    if (sce->sf_idx[i] > SCALE_ONE_POS) {
-                        int new_sf = FFMAX3(minsf[i], SCALE_ONE_POS, sce->sf_idx[i] - qstep);
-                        if (new_sf != sce->sf_idx[i]) {
-                            sce->sf_idx[i] = new_sf;
-                            changed = 1;
-                        }
-                    }
-                }
-            }
-            qstep >>= 1;
-            if (!qstep && tbits > toomanybits && sce->sf_idx[0] < 217 && changed)
-                qstep = 1;
-        } while (qstep);
-
-        overdist = 1;
-        fflag = tbits < toofewbits;
-        for (i = 0; i < 2 && (overdist || recomprd); ++i) {
-            if (recomprd) {
-                /** Must recompute distortion */
-                prev = -1;
-                tbits = 0;
-                for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-                    start = w*128;
-                    for (g = 0;  g < sce->ics.num_swb; g++) {
-                        const float *coefs = sce->coeffs + start;
-                        const float *scaled = s->scoefs + start;
-                        int bits = 0;
-                        int cb;
-                        float dist = 0.0f;
-                        float qenergy = 0.0f;
-
-                        if (sce->zeroes[w*16+g] || sce->sf_idx[w*16+g] >= 218) {
-                            start += sce->ics.swb_sizes[g];
-                            if (sce->can_pns[w*16+g]) {
-                                /** PNS isn't free */
-                                tbits += ff_pns_bits(sce, w, g);
-                            }
-                            continue;
-                        }
-                        cb = find_min_book(maxvals[w*16+g], sce->sf_idx[w*16+g]);
-                        for (w2 = 0; w2 < sce->ics.group_len[w]; w2++) {
-                            int b;
-                            float sqenergy;
-                            dist += quantize_band_cost_cached(s, w + w2, g, coefs + w2*128,
-                                                    scaled + w2*128,
-                                                    sce->ics.swb_sizes[g],
-                                                    sce->sf_idx[w*16+g],
-                                                    cb,
-                                                    1.0f,
-                                                    INFINITY,
-                                                    &b, &sqenergy,
-                                                    0);
-                            bits += b;
-                            qenergy += sqenergy;
-                        }
-                        dists[w*16+g] = dist - bits;
-                        qenergies[w*16+g] = qenergy;
-                        if (prev != -1) {
-                            int sfdiff = av_clip(sce->sf_idx[w*16+g] - prev + SCALE_DIFF_ZERO, 0, 2*SCALE_MAX_DIFF);
-                            bits += ff_aac_scalefactor_bits[sfdiff];
-                        }
-                        tbits += bits;
-                        start += sce->ics.swb_sizes[g];
-                        prev = sce->sf_idx[w*16+g];
-                    }
-                }
-            }
-            if (!i && s->options.pns && its > maxits/2 && tbits > toofewbits) {
-                float maxoverdist = 0.0f;
-                float ovrfactor = 1.f+(maxits-its)*16.f/maxits;
-                overdist = recomprd = 0;
-                for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-                    for (g = start = 0;  g < sce->ics.num_swb; start += sce->ics.swb_sizes[g++]) {
-                        if (!sce->zeroes[w*16+g] && sce->sf_idx[w*16+g] > SCALE_ONE_POS && dists[w*16+g] > uplims[w*16+g]*ovrfactor) {
-                            float ovrdist = dists[w*16+g] / FFMAX(uplims[w*16+g],euplims[w*16+g]);
-                            maxoverdist = FFMAX(maxoverdist, ovrdist);
-                            overdist++;
-                        }
-                    }
-                }
-                if (overdist) {
-                    /* We have overdistorted bands, trade for zeroes (that can be noise)
-                     * Zero the bands in the lowest 1.25% spread-energy-threshold ranking
-                     */
-                    float minspread = max_spread_thr_r;
-                    float maxspread = min_spread_thr_r;
-                    float zspread;
-                    int zeroable = 0;
-                    int zeroed = 0;
-                    int maxzeroed, zloop;
-                    for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-                        for (g = start = 0;  g < sce->ics.num_swb; start += sce->ics.swb_sizes[g++]) {
-                            if (start >= pns_start_pos && !sce->zeroes[w*16+g] && sce->can_pns[w*16+g]) {
-                                minspread = FFMIN(minspread, spread_thr_r[w*16+g]);
-                                maxspread = FFMAX(maxspread, spread_thr_r[w*16+g]);
-                                zeroable++;
-                            }
-                        }
-                    }
-                    zspread = (maxspread-minspread) * 0.0125f + minspread;
-                    /* Don't PNS everything even if allowed. It suppresses bit starvation signals from RC,
-                     * and forced the hand of the later search_for_pns step.
-                     * Instead, PNS a fraction of the spread_thr_r range depending on how starved for bits we are,
-                     * and leave further PNSing to search_for_pns if worthwhile.
-                     */
-                    zspread = FFMIN3(min_spread_thr_r * 8.f, zspread,
-                        ((toomanybits - tbits) * min_spread_thr_r + (tbits - toofewbits) * max_spread_thr_r) / (toomanybits - toofewbits + 1));
-                    maxzeroed = FFMIN(zeroable, FFMAX(1, (zeroable * its + maxits - 1) / (2 * maxits)));
-                    for (zloop = 0; zloop < 2; zloop++) {
-                        /* Two passes: first distorted stuff - two birds in one shot and all that,
-                         * then anything viable. Viable means not zero, but either CB=zero-able
-                         * (too high SF), not SF <= 1 (that means we'd be operating at very high
-                         * quality, we don't want PNS when doing VHQ), PNS allowed, and within
-                         * the lowest ranking percentile.
-                         */
-                        float loopovrfactor = (zloop) ? 1.0f : ovrfactor;
-                        int loopminsf = (zloop) ? (SCALE_ONE_POS - SCALE_DIV_512) : SCALE_ONE_POS;
-                        int mcb;
-                        for (g = sce->ics.num_swb-1; g > 0 && zeroed < maxzeroed; g--) {
-                            if (sce->ics.swb_offset[g] < pns_start_pos)
-                                continue;
-                            for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-                                if (!sce->zeroes[w*16+g] && sce->can_pns[w*16+g] && spread_thr_r[w*16+g] <= zspread
-                                    && sce->sf_idx[w*16+g] > loopminsf
-                                    && (dists[w*16+g] > loopovrfactor*uplims[w*16+g] || !(mcb = find_min_book(maxvals[w*16+g], sce->sf_idx[w*16+g]))
-                                        || (mcb <= 1 && dists[w*16+g] > FFMIN(uplims[w*16+g], euplims[w*16+g]))) ) {
-                                    sce->zeroes[w*16+g] = 1;
-                                    sce->band_type[w*16+g] = 0;
-                                    zeroed++;
-                                }
-                            }
-                        }
-                    }
-                    if (zeroed)
-                        recomprd = fflag = 1;
-                } else {
-                    overdist = 0;
-                }
-            }
-        }
-
-        minscaler = SCALE_MAX_POS;
-        maxscaler = 0;
-        for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-            for (g = 0;  g < sce->ics.num_swb; g++) {
-                if (!sce->zeroes[w*16+g]) {
-                    minscaler = FFMIN(minscaler, sce->sf_idx[w*16+g]);
-                    maxscaler = FFMAX(maxscaler, sce->sf_idx[w*16+g]);
-                }
-            }
-        }
-
-        minscaler = nminscaler = av_clip(minscaler, SCALE_ONE_POS - SCALE_DIV_512, SCALE_MAX_POS - SCALE_DIV_512);
-        prev = -1;
-        for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-            /** Start with big steps, end up fine-tunning */
-            int depth = (its > maxits/2) ? ((its > maxits*2/3) ? 1 : 3) : 10;
-            int edepth = depth+2;
-            float uplmax = its / (maxits*0.25f) + 1.0f;
-            uplmax *= (tbits > destbits) ? FFMIN(2.0f, tbits / (float)FFMAX(1,destbits)) : 1.0f;
-            start = w * 128;
-            for (g = 0; g < sce->ics.num_swb; g++) {
-                int prevsc = sce->sf_idx[w*16+g];
-                if (prev < 0 && !sce->zeroes[w*16+g])
-                    prev = sce->sf_idx[0];
-                if (!sce->zeroes[w*16+g]) {
-                    const float *coefs = sce->coeffs + start;
-                    const float *scaled = s->scoefs + start;
-                    int cmb = find_min_book(maxvals[w*16+g], sce->sf_idx[w*16+g]);
-                    int mindeltasf = FFMAX(0, prev - SCALE_MAX_DIFF);
-                    int maxdeltasf = FFMIN(SCALE_MAX_POS - SCALE_DIV_512, prev + SCALE_MAX_DIFF);
-                    if ((!cmb || dists[w*16+g] > uplims[w*16+g]) && sce->sf_idx[w*16+g] > FFMAX(mindeltasf, minsf[w*16+g])) {
-                        /* Try to make sure there is some energy in every nonzero band
-                         * NOTE: This algorithm must be forcibly imbalanced, pushing harder
-                         *  on holes or more distorted bands at first, otherwise there's
-                         *  no net gain (since the next iteration will offset all bands
-                         *  on the opposite direction to compensate for extra bits)
-                         */
-                        for (i = 0; i < edepth && sce->sf_idx[w*16+g] > mindeltasf; ++i) {
-                            int cb, bits;
-                            float dist, qenergy;
-                            int mb = find_min_book(maxvals[w*16+g], sce->sf_idx[w*16+g]-1);
-                            cb = find_min_book(maxvals[w*16+g], sce->sf_idx[w*16+g]);
-                            dist = qenergy = 0.f;
-                            bits = 0;
-                            if (!cb) {
-                                maxsf[w*16+g] = FFMIN(sce->sf_idx[w*16+g]-1, maxsf[w*16+g]);
-                            } else if (i >= depth && dists[w*16+g] < euplims[w*16+g]) {
-                                break;
-                            }
-                            /* !g is the DC band, it's important, since quantization error here
-                             * applies to less than a cycle, it creates horrible intermodulation
-                             * distortion if it doesn't stick to what psy requests
-                             */
-                            if (!g && sce->ics.num_windows > 1 && dists[w*16+g] >= euplims[w*16+g])
-                                maxsf[w*16+g] = FFMIN(sce->sf_idx[w*16+g], maxsf[w*16+g]);
-                            for (w2 = 0; w2 < sce->ics.group_len[w]; w2++) {
-                                int b;
-                                float sqenergy;
-                                dist += quantize_band_cost_cached(s, w + w2, g, coefs + w2*128,
-                                                        scaled + w2*128,
-                                                        sce->ics.swb_sizes[g],
-                                                        sce->sf_idx[w*16+g]-1,
-                                                        cb,
-                                                        1.0f,
-                                                        INFINITY,
-                                                        &b, &sqenergy,
-                                                        0);
-                                bits += b;
-                                qenergy += sqenergy;
-                            }
-                            sce->sf_idx[w*16+g]--;
-                            dists[w*16+g] = dist - bits;
-                            qenergies[w*16+g] = qenergy;
-                            if (mb && (sce->sf_idx[w*16+g] < mindeltasf || (
-                                    (dists[w*16+g] < FFMIN(uplmax*uplims[w*16+g], euplims[w*16+g]))
-                                    && (fabsf(qenergies[w*16+g]-energies[w*16+g]) < euplims[w*16+g])
-                                ) )) {
-                                break;
-                            }
-                        }
-                    } else if (tbits > toofewbits && sce->sf_idx[w*16+g] < FFMIN(maxdeltasf, maxsf[w*16+g])
-                            && (dists[w*16+g] < FFMIN(euplims[w*16+g], uplims[w*16+g]))
-                            && (fabsf(qenergies[w*16+g]-energies[w*16+g]) < euplims[w*16+g])
-                        ) {
-                        /** Um... over target. Save bits for more important stuff. */
-                        for (i = 0; i < depth && sce->sf_idx[w*16+g] < maxdeltasf; ++i) {
-                            int cb, bits;
-                            float dist, qenergy;
-                            cb = find_min_book(maxvals[w*16+g], sce->sf_idx[w*16+g]+1);
-                            if (cb > 0) {
-                                dist = qenergy = 0.f;
-                                bits = 0;
-                                for (w2 = 0; w2 < sce->ics.group_len[w]; w2++) {
-                                    int b;
-                                    float sqenergy;
-                                    dist += quantize_band_cost_cached(s, w + w2, g, coefs + w2*128,
-                                                            scaled + w2*128,
-                                                            sce->ics.swb_sizes[g],
-                                                            sce->sf_idx[w*16+g]+1,
-                                                            cb,
-                                                            1.0f,
-                                                            INFINITY,
-                                                            &b, &sqenergy,
-                                                            0);
-                                    bits += b;
-                                    qenergy += sqenergy;
-                                }
-                                dist -= bits;
-                                if (dist < FFMIN(euplims[w*16+g], uplims[w*16+g])) {
-                                    sce->sf_idx[w*16+g]++;
-                                    dists[w*16+g] = dist;
-                                    qenergies[w*16+g] = qenergy;
-                                } else {
-                                    break;
-                                }
-                            } else {
-                                maxsf[w*16+g] = FFMIN(sce->sf_idx[w*16+g], maxsf[w*16+g]);
-                                break;
-                            }
-                        }
-                    }
-                    prev = sce->sf_idx[w*16+g] = av_clip(sce->sf_idx[w*16+g], mindeltasf, maxdeltasf);
-                    if (sce->sf_idx[w*16+g] != prevsc)
-                        fflag = 1;
-                    nminscaler = FFMIN(nminscaler, sce->sf_idx[w*16+g]);
-                    sce->band_type[w*16+g] = find_min_book(maxvals[w*16+g], sce->sf_idx[w*16+g]);
-                }
-                start += sce->ics.swb_sizes[g];
-            }
-        }
-
-        /** SF difference limit violation risk. Must re-clamp. */
-        prev = -1;
-        for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-            for (g = 0; g < sce->ics.num_swb; g++) {
-                if (!sce->zeroes[w*16+g]) {
-                    int prevsf = sce->sf_idx[w*16+g];
-                    if (prev < 0)
-                        prev = prevsf;
-                    sce->sf_idx[w*16+g] = av_clip(sce->sf_idx[w*16+g], prev - SCALE_MAX_DIFF, prev + SCALE_MAX_DIFF);
-                    sce->band_type[w*16+g] = find_min_book(maxvals[w*16+g], sce->sf_idx[w*16+g]);
-                    prev = sce->sf_idx[w*16+g];
-                    if (!fflag && prevsf != sce->sf_idx[w*16+g])
-                        fflag = 1;
-                }
-            }
-        }
-
-        its++;
-    } while (fflag && its < maxits);
-
-    /** Scout out next nonzero bands */
-    ff_init_nextband_map(sce, nextband);
-
-    prev = -1;
-    for (w = 0; w < sce->ics.num_windows; w += sce->ics.group_len[w]) {
-        /** Make sure proper codebooks are set */
-        for (g = 0; g < sce->ics.num_swb; g++) {
-            if (!sce->zeroes[w*16+g]) {
-                sce->band_type[w*16+g] = find_min_book(maxvals[w*16+g], sce->sf_idx[w*16+g]);
-                if (sce->band_type[w*16+g] <= 0) {
-                    if (!ff_sfdelta_can_remove_band(sce, nextband, prev, w*16+g)) {
-                        /** Cannot zero out, make sure it's not attempted */
-                        sce->band_type[w*16+g] = 1;
-                    } else {
-                        sce->zeroes[w*16+g] = 1;
-                        sce->band_type[w*16+g] = 0;
-                    }
-                }
-            } else {
-                sce->band_type[w*16+g] = 0;
-            }
-            /** Check that there's no SF delta range violations */
-            if (!sce->zeroes[w*16+g]) {
-                if (prev != -1) {
-                    av_unused int sfdiff = sce->sf_idx[w*16+g] - prev + SCALE_DIFF_ZERO;
-                    av_assert1(sfdiff >= 0 && sfdiff <= 2*SCALE_MAX_DIFF);
-                } else if (sce->zeroes[0]) {
-                    /** Set global gain to something useful */
-                    sce->sf_idx[0] = sce->sf_idx[w*16+g];
-                }
-                prev = sce->sf_idx[w*16+g];
-            }
-        }
-    }
-}
-
-#endif /* AVCODEC_AACCODER_TWOLOOP_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/fmtconvert_init_arm.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/fmtconvert_init_arm.c
deleted file mode 100644
index e12f83c8420eaff17f0a4103a94605e0ce1428bf..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arm/fmtconvert_init_arm.c
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * ARM optimized Format Conversion Utils
- *
- * 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 <stdint.h>
-
-#include "libavutil/attributes.h"
-#include "libavutil/arm/cpu.h"
-#include "libavcodec/fmtconvert.h"
-
-void ff_int32_to_float_fmul_array8_neon(FmtConvertContext *c, float *dst,
-                                        const int32_t *src, const float *mul,
-                                        int len);
-void ff_int32_to_float_fmul_scalar_neon(float *dst, const int32_t *src,
-                                        float mul, int len);
-
-void ff_int32_to_float_fmul_scalar_vfp(float *dst, const int32_t *src,
-                                       float mul, int len);
-void ff_int32_to_float_fmul_array8_vfp(FmtConvertContext *c, float *dst,
-                                       const int32_t *src, const float *mul,
-                                       int len);
-
-av_cold void ff_fmt_convert_init_arm(FmtConvertContext *c)
-{
-    int cpu_flags = av_get_cpu_flags();
-
-    if (have_vfp_vm(cpu_flags)) {
-        c->int32_to_float_fmul_scalar = ff_int32_to_float_fmul_scalar_vfp;
-        c->int32_to_float_fmul_array8 = ff_int32_to_float_fmul_array8_vfp;
-    }
-
-    if (have_neon(cpu_flags)) {
-        c->int32_to_float_fmul_array8 = ff_int32_to_float_fmul_array8_neon;
-        c->int32_to_float_fmul_scalar = ff_int32_to_float_fmul_scalar_neon;
-    }
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ass_split.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ass_split.h
deleted file mode 100644
index a45fb9b8a117d6102f7c066c3df3a90beec4f2b2..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ass_split.h
+++ /dev/null
@@ -1,191 +0,0 @@
-/*
- * SSA/ASS spliting functions
- * Copyright (c) 2010  Aurelien Jacobs <aurel@gnuage.org>
- *
- * 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
- */
-
-#ifndef AVCODEC_ASS_SPLIT_H
-#define AVCODEC_ASS_SPLIT_H
-
-/**
- * fields extracted from the [Script Info] section
- */
-typedef struct {
-    char *script_type;    /**< SSA script format version (eg. v4.00) */
-    char *collisions;     /**< how subtitles are moved to prevent collisions */
-    int   play_res_x;     /**< video width that ASS coords are referring to */
-    int   play_res_y;     /**< video height that ASS coords are referring to */
-    float timer;          /**< time multiplier to apply to SSA clock (in %) */
-} ASSScriptInfo;
-
-/**
- * fields extracted from the [V4(+) Styles] section
- */
-typedef struct {
-    char *name;           /**< name of the tyle (case sensitive) */
-    char *font_name;      /**< font face (case sensitive) */
-    int   font_size;      /**< font height */
-    int   primary_color;  /**< color that a subtitle will normally appear in */
-    int   secondary_color;
-    int   outline_color;  /**< color for outline in ASS, called tertiary in SSA */
-    int   back_color;     /**< color of the subtitle outline or shadow */
-    int   bold;           /**< whether text is bold (1) or not (0) */
-    int   italic;         /**< whether text is italic (1) or not (0) */
-    int   underline;      /**< whether text is underlined (1) or not (0) */
-    int   strikeout;
-    float scalex;
-    float scaley;
-    float spacing;
-    float angle;
-    int   border_style;
-    float outline;
-    float shadow;
-    int   alignment;      /**< position of the text (left, center, top...),
-                               defined after the layout of the numpad
-                               (1-3 sub, 4-6 mid, 7-9 top) */
-    int   margin_l;
-    int   margin_r;
-    int   margin_v;
-    int   alpha_level;
-    int   encoding;
-} ASSStyle;
-
-/**
- * fields extracted from the [Events] section
- */
-typedef struct {
-    int   readorder;
-    int   layer;    /**< higher numbered layers are drawn over lower numbered */
-    int   start;    /**< start time of the dialog in centiseconds */
-    int   end;      /**< end time of the dialog in centiseconds */
-    char *style;    /**< name of the ASSStyle to use with this dialog */
-    char *name;
-    int   margin_l;
-    int   margin_r;
-    int   margin_v;
-    char *effect;
-    char *text;     /**< actual text which will be displayed as a subtitle,
-                         can include style override control codes (see
-                         ff_ass_split_override_codes()) */
-} ASSDialog;
-
-/**
- * structure containing the whole split ASS data
- */
-typedef struct {
-    ASSScriptInfo script_info;   /**< general information about the SSA script*/
-    ASSStyle     *styles;        /**< array of split out styles */
-    int           styles_count;  /**< number of ASSStyle in the styles array */
-    ASSDialog    *dialogs;       /**< array of split out dialogs */
-    int           dialogs_count; /**< number of ASSDialog in the dialogs array*/
-} ASS;
-
-/**
- * This struct can be casted to ASS to access to the split data.
- */
-typedef struct ASSSplitContext ASSSplitContext;
-
-/**
- * Split a full ASS file or a ASS header from a string buffer and store
- * the split structure in a newly allocated context.
- *
- * @param buf String containing the ASS formatted data.
- * @return Newly allocated struct containing split data.
- */
-ASSSplitContext *ff_ass_split(const char *buf);
-
-/**
- * Free a dialogue obtained from ff_ass_split_dialog().
- */
-void ff_ass_free_dialog(ASSDialog **dialogp);
-
-/**
- * Split one ASS Dialogue line from a string buffer.
- *
- * @param ctx Context previously initialized by ff_ass_split().
- * @param buf String containing the ASS "Dialogue" line.
- * @return Pointer to the split ASSDialog. Must be freed with ff_ass_free_dialog()
- */
-ASSDialog *ff_ass_split_dialog(ASSSplitContext *ctx, const char *buf);
-
-/**
- * Free all the memory allocated for an ASSSplitContext.
- *
- * @param ctx Context previously initialized by ff_ass_split().
- */
-void ff_ass_split_free(ASSSplitContext *ctx);
-
-
-/**
- * Set of callback functions corresponding to each override codes that can
- * be encountered in a "Dialogue" Text field.
- */
-typedef struct {
-    /**
-     * @defgroup ass_styles    ASS styles
-     * @{
-     */
-    void (*text)(void *priv, const char *text, int len);
-    void (*new_line)(void *priv, int forced);
-    void (*style)(void *priv, char style, int close);
-    void (*color)(void *priv, unsigned int /* color */, unsigned int color_id);
-    void (*alpha)(void *priv, int alpha, int alpha_id);
-    void (*font_name)(void *priv, const char *name);
-    void (*font_size)(void *priv, int size);
-    void (*alignment)(void *priv, int alignment);
-    void (*cancel_overrides)(void *priv, const char *style);
-    /** @} */
-
-    /**
-     * @defgroup ass_functions    ASS functions
-     * @{
-     */
-    void (*move)(void *priv, int x1, int y1, int x2, int y2, int t1, int t2);
-    void (*origin)(void *priv, int x, int y);
-    /** @} */
-
-    /**
-     * @defgroup ass_end    end of Dialogue Event
-     * @{
-     */
-    void (*end)(void *priv);
-    /** @} */
-} ASSCodesCallbacks;
-
-/**
- * Split override codes out of a ASS "Dialogue" Text field.
- *
- * @param callbacks Set of callback functions called for each override code
- *                  encountered.
- * @param priv Opaque pointer passed to the callback functions.
- * @param buf The ASS "Dialogue" Text field to split.
- * @return >= 0 on success otherwise an error code <0
- */
-int ff_ass_split_override_codes(const ASSCodesCallbacks *callbacks, void *priv,
-                                const char *buf);
-
-/**
- * Find an ASSStyle structure by its name.
- *
- * @param ctx Context previously initialized by ff_ass_split().
- * @param style name of the style to search for.
- * @return the ASSStyle corresponding to style, or NULL if style can't be found
- */
-ASSStyle *ff_ass_style_get(ASSSplitContext *ctx, const char *style);
-
-#endif /* AVCODEC_ASS_SPLIT_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_lbr.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_lbr.h
deleted file mode 100644
index 8ff66afcb7ff4ff7df8ceead7ebb407bbf3c031f..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_lbr.h
+++ /dev/null
@@ -1,135 +0,0 @@
-/*
- * Copyright (C) 2016 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
- */
-
-#ifndef AVCODEC_DCA_LBR_H
-#define AVCODEC_DCA_LBR_H
-
-#include "libavutil/float_dsp.h"
-#include "libavutil/mem_internal.h"
-#include "libavutil/tx.h"
-
-#include "avcodec.h"
-#include "get_bits.h"
-#include "dca.h"
-#include "dca_exss.h"
-#include "dcadsp.h"
-
-#define DCA_LBR_CHANNELS        6
-#define DCA_LBR_CHANNELS_TOTAL  32
-#define DCA_LBR_SUBBANDS        32
-#define DCA_LBR_TONES           512
-
-#define DCA_LBR_TIME_SAMPLES    128
-#define DCA_LBR_TIME_HISTORY    8
-
-enum DCALBRHeader {
-    DCA_LBR_HEADER_SYNC_ONLY    = 1,
-    DCA_LBR_HEADER_DECODER_INIT = 2
-};
-
-typedef struct DCALbrTone {
-    uint8_t     x_freq;     ///< Spectral line offset
-    uint8_t     f_delt;     ///< Difference between original and center frequency
-    uint8_t     ph_rot;     ///< Phase rotation
-    uint8_t     pad;        ///< Padding field
-    uint8_t     amp[DCA_LBR_CHANNELS];  ///< Per-channel amplitude
-    uint8_t     phs[DCA_LBR_CHANNELS];  ///< Per-channel phase
-} DCALbrTone;
-
-typedef struct DCALbrDecoder {
-    AVCodecContext  *avctx;
-    GetBitContext   gb;
-
-    int     sample_rate;        ///< Sample rate of LBR audio
-    int     ch_mask;            ///< LBR speaker mask
-    int     flags;              ///< Flags for LBR decoder initialization
-    int     bit_rate_orig;      ///< Original bit rate
-    int     bit_rate_scaled;    ///< Scaled bit rate
-
-    int     nchannels;          ///< Number of fullband channels to decode
-    int     nchannels_total;    ///< Total number of fullband channels
-    int     freq_range;         ///< Frequency range of LBR audio
-    int     band_limit;         ///< Band limit factor
-    int     limited_rate;       ///< Band limited sample rate
-    int     limited_range;      ///< Band limited frequency range
-    int     res_profile;        ///< Resolution profile
-    int     nsubbands;          ///< Number of encoded subbands
-    int     g3_avg_only_start_sb;   ///< Subband index where grid 3 scale factors end
-    int     min_mono_subband;   ///< Subband index where mono encoding starts
-    int     max_mono_subband;   ///< Subband index where mono encoding ends
-
-    int     framenum;   ///< Lower 5 bits of current frame number
-    int     lbr_rand;   ///< Seed for subband randomization
-    int     warned;     ///< Flags for warning suppression
-
-    uint8_t     quant_levels[DCA_LBR_CHANNELS / 2][DCA_LBR_SUBBANDS];   ///< Quantization levels
-    uint8_t     sb_indices[DCA_LBR_SUBBANDS];   ///< Subband reordering indices
-
-    uint8_t     sec_ch_sbms[DCA_LBR_CHANNELS / 2][DCA_LBR_SUBBANDS];    ///< Right channel inversion or mid/side decoding flags
-    uint8_t     sec_ch_lrms[DCA_LBR_CHANNELS / 2][DCA_LBR_SUBBANDS];    ///< Flags indicating if left/right channel are swapped
-    uint32_t    ch_pres[DCA_LBR_CHANNELS];  ///< Subband allocation flags
-
-    uint8_t     grid_1_scf[DCA_LBR_CHANNELS][12][8];    ///< Grid 1 scale factors
-    uint8_t     grid_2_scf[DCA_LBR_CHANNELS][3][64];    ///< Grid 2 scale factors
-
-    int8_t      grid_3_avg[DCA_LBR_CHANNELS][DCA_LBR_SUBBANDS - 4];     ///< Grid 3 average values
-    int8_t      grid_3_scf[DCA_LBR_CHANNELS][DCA_LBR_SUBBANDS - 4][8];  ///< Grid 3 scale factors
-    uint32_t    grid_3_pres[DCA_LBR_CHANNELS];  ///< Grid 3 scale factors presence flags
-
-    uint8_t     high_res_scf[DCA_LBR_CHANNELS][DCA_LBR_SUBBANDS][8];    ///< High-frequency resolution scale factors
-
-    uint8_t     part_stereo[DCA_LBR_CHANNELS][DCA_LBR_SUBBANDS / 4][5]; ///< Partial stereo coefficients
-    uint8_t     part_stereo_pres;   ///< Partial stereo coefficients presence flags
-
-    float       lpc_coeff[2][DCA_LBR_CHANNELS][3][2][8];    ///< Predictor coefficients
-
-    float       sb_scf[DCA_LBR_SUBBANDS];   ///< Subband randomization scale factors
-
-    float       *time_samples[DCA_LBR_CHANNELS][DCA_LBR_SUBBANDS]; ///< Time samples
-
-    float           *ts_buffer; ///< Time sample buffer base
-    unsigned int    ts_size;    ///< Time sample buffer size
-
-    DECLARE_ALIGNED(32, float, history)[DCA_LBR_CHANNELS][DCA_LBR_SUBBANDS * 4];    ///< IMDCT history
-    DECLARE_ALIGNED(32, float, window)[DCA_LBR_SUBBANDS * 4];   ///< Long window for IMDCT
-
-    DECLARE_ALIGNED(32, float, lfe_data)[64];       ///< Decimated LFE samples
-    DECLARE_ALIGNED(32, float, lfe_history)[5][2];  ///< LFE IIR filter history
-    float lfe_scale;    ///< Scale factor of LFE samples before IIR filter
-
-    uint8_t     tonal_scf[6];           ///< Tonal scale factors
-    uint16_t    tonal_bounds[5][32][2]; ///< Per-group per-subframe start/end positions of tones
-    DCALbrTone  tones[DCA_LBR_TONES];   ///< Circular buffer of tones
-    int         ntones;                 ///< Circular buffer head position
-
-    AVTXContext         *imdct;
-    av_tx_fn             imdct_fn;
-    AVFloatDSPContext   *fdsp;
-    DCADSPContext       *dcadsp;
-} DCALbrDecoder;
-
-int ff_dca_lbr_parse(DCALbrDecoder *s, const uint8_t *data, DCAExssAsset *asset);
-int ff_dca_lbr_filter_frame(DCALbrDecoder *s, AVFrame *frame);
-av_cold void ff_dca_lbr_flush(DCALbrDecoder *s);
-av_cold void ff_dca_lbr_init_tables(void);
-av_cold int ff_dca_lbr_init(DCALbrDecoder *s);
-av_cold void ff_dca_lbr_close(DCALbrDecoder *s);
-
-#endif
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mediacodec.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mediacodec.h
deleted file mode 100644
index 4e9b56a618454d5439e955598ee870a2c1f75b9b..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mediacodec.h
+++ /dev/null
@@ -1,103 +0,0 @@
-/*
- * Android MediaCodec public API
- *
- * Copyright (c) 2016 Matthieu Bouron <matthieu.bouron stupeflix.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
- */
-
-#ifndef AVCODEC_MEDIACODEC_H
-#define AVCODEC_MEDIACODEC_H
-
-#include "libavcodec/avcodec.h"
-
-/**
- * This structure holds a reference to a android/view/Surface object that will
- * be used as output by the decoder.
- *
- */
-typedef struct AVMediaCodecContext {
-
-    /**
-     * android/view/Surface object reference.
-     */
-    void *surface;
-
-} AVMediaCodecContext;
-
-/**
- * Allocate and initialize a MediaCodec context.
- *
- * When decoding with MediaCodec is finished, the caller must free the
- * MediaCodec context with av_mediacodec_default_free.
- *
- * @return a pointer to a newly allocated AVMediaCodecContext on success, NULL otherwise
- */
-AVMediaCodecContext *av_mediacodec_alloc_context(void);
-
-/**
- * Convenience function that sets up the MediaCodec context.
- *
- * @param avctx codec context
- * @param ctx MediaCodec context to initialize
- * @param surface reference to an android/view/Surface
- * @return 0 on success, < 0 otherwise
- */
-int av_mediacodec_default_init(AVCodecContext *avctx, AVMediaCodecContext *ctx, void *surface);
-
-/**
- * This function must be called to free the MediaCodec context initialized with
- * av_mediacodec_default_init().
- *
- * @param avctx codec context
- */
-void av_mediacodec_default_free(AVCodecContext *avctx);
-
-/**
- * Opaque structure representing a MediaCodec buffer to render.
- */
-typedef struct MediaCodecBuffer AVMediaCodecBuffer;
-
-/**
- * Release a MediaCodec buffer and render it to the surface that is associated
- * with the decoder. This function should only be called once on a given
- * buffer, once released the underlying buffer returns to the codec, thus
- * subsequent calls to this function will have no effect.
- *
- * @param buffer the buffer to render
- * @param render 1 to release and render the buffer to the surface or 0 to
- * discard the buffer
- * @return 0 on success, < 0 otherwise
- */
-int av_mediacodec_release_buffer(AVMediaCodecBuffer *buffer, int render);
-
-/**
- * Release a MediaCodec buffer and render it at the given time to the surface
- * that is associated with the decoder. The timestamp must be within one second
- * of the current `java/lang/System#nanoTime()` (which is implemented using
- * `CLOCK_MONOTONIC` on Android). See the Android MediaCodec documentation
- * of [`android/media/MediaCodec#releaseOutputBuffer(int,long)`][0] for more details.
- *
- * @param buffer the buffer to render
- * @param time timestamp in nanoseconds of when to render the buffer
- * @return 0 on success, < 0 otherwise
- *
- * [0]: https://developer.android.com/reference/android/media/MediaCodec#releaseOutputBuffer(int,%20long)
- */
-int av_mediacodec_render_buffer_at_time(AVMediaCodecBuffer *buffer, int64_t time);
-
-#endif /* AVCODEC_MEDIACODEC_H */
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Build Your Camp and Fight with Water Guns in Talking Tom Camp APK.md b/spaces/congsaPfin/Manga-OCR/logs/Build Your Camp and Fight with Water Guns in Talking Tom Camp APK.md
deleted file mode 100644
index ccd3d1d2b13478fcfa8993708c0291b958a241f9..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Build Your Camp and Fight with Water Guns in Talking Tom Camp APK.md	
+++ /dev/null
@@ -1,66 +0,0 @@
-<br />
-<h1>My Talking Tom Camp APK: A Fun and Exciting Water Fight Game</h1>
-<p>Do you love water fights? Do you want to join the best water fight of your life with your favorite talking cat and his friends? If you answered yes, then you should download My Talking Tom Camp APK, a cool water fighting battle builder game that will keep you entertained for hours. In this article, we will tell you everything you need to know about this game, including what it is, how to download and install it, how to play it, and why you should play it. Let's get started!</p>
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-<p>My Talking Tom Camp APK is an Android game developed by Outfit7 Limited, the creators of the popular Talking Tom and Friends franchise. It is a strategy game where you have to build your own camp, defend it from other players, and attack their camps to collect coins and energy. You can also customize your camp with various buildings, decorations, and water weapons. You can also play with your favorite characters from the Talking Tom and Friends series, such as Tom, Angela, Hank, Ginger, Ben, and Becca. The game has amazing graphics, sound effects, and animations that will make you feel like you are in a real water fight.</p>
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-<li>Allow the installation of unknown sources if prompted by your device.</li>
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-<p>Your camp is not safe from other players who want to steal your coins and energy. You need to set up defenses such as Sprinkler, Tower, Puddle, Catapult, Cannon, etc. to protect your camp from invaders. You can also plan sneaky attacks on other players' camps by tapping on the "Attack" button at the bottom left corner of the screen. You can then choose from different modes of attack, such as Single Player Campaign, Multiplayer Online Battle, or Friendly Battle. You x Player. These are software that allow you to run Android apps on your PC. You need to download and install the emulator on your PC, and then download and install My Talking Tom Camp APK from APKCombo or any other website. You can then launch the game and play it on your PC.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Geometry Dash SubZero MOD APK and Join the Geometry Heroes in Their Icy Quest.md b/spaces/congsaPfin/Manga-OCR/logs/Download Geometry Dash SubZero MOD APK and Join the Geometry Heroes in Their Icy Quest.md
deleted file mode 100644
index eaa6725c6b7c775995e8645eabde326de2cd6ca4..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Geometry Dash SubZero MOD APK and Join the Geometry Heroes in Their Icy Quest.md	
+++ /dev/null
@@ -1,100 +0,0 @@
-
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-<p>If you are looking for a fun and challenging game that will test your skills and reflexes, you should try Geometry Dash SubZero. This is a rhythm-based platformer game that will make you jump, fly, and flip through dangerous obstacles and spiky traps. You can download the game for free from the Google Play Store or the App Store, but if you want to enjoy some extra features and advantages, you should download the modded version of the game. In this article, we will tell you what Geometry Dash SubZero is, why you should download the mod apk, and how to do it.</p>
- <h2>What is Geometry Dash SubZero?</h2>
-<p>Geometry Dash SubZero is a spin-off of the popular Geometry Dash series, created by RobTop Games. The game features three unique levels with different themes and music, each with its own boss fight. The game also has a practice mode, where you can save your progress and resume from checkpoints. The game is known for its addictive gameplay and challenging levels that require speed, accuracy, and coordination to succeed.</p>
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-<p>Some of the features of Geometry Dash SubZero are:</p>
-<ul>
-<li>Rhythm-based action platforming</li>
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-</ul>
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-<p>The gameplay of Geometry Dash SubZero is simple but hard to master. You have to tap the screen to make your character jump over obstacles and avoid spikes. You can also hold the screen to make your character fly or flip gravity. You have to follow the rhythm of the music and react quickly to the changing environment. The game is very fast-paced and requires concentration and precision. If you hit an obstacle or fall into a pit, you have to start over from the beginning or from the last checkpoint.</p>
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-<p>The latest version, Geometry Dash SubZero V2.2.12 MOD Unlocked, is now available for download. The mod apk offers unlocked features that give players an enhanced experience. Some of the benefits of Geometry Dash SubZero Mod APK are:</p>
-<ul>
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-</ul>
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-<ol>
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-</ol>
- <h2>Conclusion</h2>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Ludo King MOD APK and Enjoy All Themes Unlocked for Free.md b/spaces/congsaPfin/Manga-OCR/logs/Download Ludo King MOD APK and Enjoy All Themes Unlocked for Free.md
deleted file mode 100644
index dadb4a0586cc53575569b8d0deb8009f692aa759..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Ludo King MOD APK and Enjoy All Themes Unlocked for Free.md	
+++ /dev/null
@@ -1,79 +0,0 @@
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-<h1>Ludo King Mod APK Download All Themes Unlocked</h1>
-<p>Ludo King is one of the most popular board games in the world. It is a game that can be enjoyed by people of all ages, from kids to adults. But what if you want to unlock all the themes, get unlimited coins, and play without ads? That's where Ludo King Mod APK comes in. In this article, we will tell you everything you need to know about Ludo King Mod APK, including what it is, how to download it, and how to use it. We will also share some tips and tricks to help you win more games in Ludo King. So, let's get started!</p>
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- <h2>What is Ludo King?</h2>
-<p>Ludo King is a mobile game application that is based on the classic board game Ludo. Ludo is derived from the ancient Indian game of Pachisi, which was played by the kings and queens of India.  The objective of the game is to move four tokens of your color from the starting point to the finishing point, while avoiding or capturing the tokens of your opponents. The game can be played by two to six players, either online or offline. </p>
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-<p>Ludo King was developed by Gametion Technologies Pvt Ltd, a game studio based in Navi Mumbai, India. The game was released on February 20, 2016, on the Apple App Store and the Google Play Store.  Since then, it has become one of the most downloaded games in India and many other countries. It has over 500 million downloads on Google Play Store alone, making it the first Indian gaming app to achieve this milestone.  It has also won several awards, such as the Best Casual Game of 2018 on Google Play Store by popular vote. </p>
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-<p>Ludo King has four modes: vs Computer, Local Mode (pass and play), Online Multiplayer (play with other players around the world), and Private Multiplayer (play with friends in private rooms).  In each mode, you can choose to play with two to six players, with four different colors: red, blue, green, and yellow. You can also choose to play with different rules, such as doubling the stakes, killing one before entering home, etc.</p>
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- <p>You can capture your opponent's token by landing on the same square as them, unless they are on a safe square (marked with a star or an arrow). Capturing a token will send it back to its starting point. You can also form a block by placing two tokens of your color on the same square, which will prevent your opponents from passing through. </p>
- <p>You need to reach the center of the board with all four tokens of your color to win the game. You can only enter the home column with an exact roll of the dice. For example, if you are three squares away from home, you need to roll a three to enter. </p>
- <h3>How to play Ludo King online with friends</h3>
-<p>One of the best features of Ludo King is that you can play online with your friends and family, no matter where they are. You can also chat with them and send emojis during the game. Here are the steps to play Ludo King online with friends: 1. Open the Ludo King app on your device and tap on the Play with Friends button on the main screen. 2. Choose whether you want to create a room or join a room. If you create a room, you can set the number of players, the color, the rules, and the entry fee (in coins). You will also get a room code that you can share with your friends. If you join a room, you need to enter the room code that your friend has shared with you. 3. Once you are in the room, you can invite your friends from your contact list, Facebook, or WhatsApp. You can also wait for random players to join the room if there are empty slots. 4. When everyone is ready, tap on the Start button to begin the game. You can chat with your friends and send emojis by tapping on the chat icon on the top right corner of the screen. 5. Enjoy playing Ludo King online with your friends! <h2>What is Ludo King Mod APK?</h2>
-<p>Ludo King Mod APK is a modified version of the original Ludo King app that gives you access to some extra features and benefits that are not available in the official app. Some of these features and benefits are: - All themes unlocked: You can choose from different themes to customize your board and tokens, such as Nature, Egypt, Disco, Cake, Candy, etc. - Unlimited coins: You can get unlimited coins to play more games and enter higher stakes rooms. - No ads: You can play without any interruptions or distractions from annoying ads. - Cheat mode: You can use cheat mode to manipulate the dice and get the desired number. - Auto win: You can use auto win to win every game without any effort.</p>
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-<p>Ludo King Mod APK has some benefits that make it more fun and enjoyable than the original app. Some of these benefits are: - You can explore different themes and backgrounds that suit your mood and preference. - You can play more games and challenge more players with unlimited coins. - You can avoid wasting time and data on ads that pop up every now and then. - You can have an edge over your opponents by using cheat mode and auto win. - You can impress your friends and family with your high scores and achievements.</p>
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- <h2>Tips and tricks to win Ludo King games</h2>
-<p>If you want to improve your skills and win more games in Ludo King, here are some tips and tricks that you can follow: <h3>Open all your pieces</h3>
-<p>One of the first things you should do in Ludo King is to open all your pieces as soon as possible. This will give you more options to move around the board and capture your opponent's pieces. It will also reduce the chances of getting stuck or blocked by your own pieces.</p>
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-<p>Ludo King is a game of luck and strategy. You need to be patient and wait for the right opportunity to make your move and not rush or panic. You need to plan your moves ahead and anticipate your opponent's moves. You need to balance between offense and defense, and know when to attack and when to retreat. You need to use your dice wisely and not waste your rolls.</p>
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- <h3>Use different game modes and themes</h3>
-<p>Ludo King offers different game modes and themes that can make the game more fun and challenging. You can try the Snake and Ladders mode, which adds another board game element to Ludo King. You can also try the Rush mode, which speeds up the game and makes it more exciting. You can also choose from different themes that change the appearance of the board and the tokens, such as Nature, Egypt, Disco, Cake, Candy, etc.</p>
- <h2>Conclusion</h2>
-<p>Ludo King is a great game that can bring back the nostalgia of playing the classic board game with your friends and family. It is also a game that can test your luck and strategy skills, and keep you entertained for hours. However, if you want to spice up your game and unlock more features and benefits, you can try Ludo King Mod APK. But be careful, as it may also have some risks and drawbacks that you should be aware of. We hope this article has helped you learn more about Ludo King Mod APK, how to download it, and how to use it. We also hope that you have enjoyed our tips and tricks to win more games in Ludo King. Now, go ahead and have fun playing Ludo King with your friends!</p>
- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Ludo King Mod APK:</p>
- <ol>
-<li><b>Is Ludo King Mod APK safe to use?</b></li>
-<p>Ludo King Mod APK is not an official app and it is not available on the Google Play Store or the Apple App Store. You have to download it from third-party sources that may contain viruses or malware. It may also violate the terms and conditions of the original app and get you banned from playing online or offline. Therefore, it is not safe to use Ludo King Mod APK.</p>
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-<p>Ludo King Mod APK is a modified version of the original app that gives you access to some extra features and benefits that are not available in the official app. It may also infringe on the intellectual property rights of the original app developer. Therefore, it is not legal to use Ludo King Mod APK.</p>
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-<p>Ludo King Mod APK may allow you to play online with other players who are using the same modded app. However, you may not be able to play online with players who are using the original app or a different modded app. You may also face some technical issues or errors while playing online with Ludo King Mod APK.</p>
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-<p>Ludo King Mod APK may allow you to play offline with your device or with other devices that are connected via Bluetooth or Wi-Fi hotspot. However, you may not be able to save your progress or access some features that require an internet connection while playing offline with Ludo King Mod APK.</p>
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-<p>Ludo King Mod APK may not be compatible with the latest version of the original app or the latest version of your device's operating system. It may also lose some of its features or benefits after updating. Therefore, it is not advisable to update Ludo King Mod APK.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Mini Racing Adventures APK MOD and Race with Martin.md b/spaces/congsaPfin/Manga-OCR/logs/Download Mini Racing Adventures APK MOD and Race with Martin.md
deleted file mode 100644
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--- a/spaces/congsaPfin/Manga-OCR/logs/Download Mini Racing Adventures APK MOD and Race with Martin.md	
+++ /dev/null
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-<p>If you are looking for a new racing game to play on your Android device, you might want to check out APK Mod Mini Racing. This is a modified version of the original Mini Racing game that offers more features and benefits for the players. In this article, we will explain what APK Mod is, what Mini Racing is, and how to download and install APK Mod Mini Racing on your device.</p>
- <h2>What is APK Mod and Why You Should Try It</h2>
-<p>APK Mod is a term that refers to a modified version of an Android application. The modification can be done by anyone who has the skills and tools to change the code, graphics, or functionality of the app. The purpose of modifying an app can vary, but usually it is to provide users with access to new or improved features that are not present in the original version of the app.</p>
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- <h3>APK Mod can unlock premium features and enhance your gaming experience</h3>
-<p>One of the main reasons why people use APK Mod is to unlock premium features that are otherwise locked or require payment in the original app. For example, some games may have in-app purchases that allow you to buy coins, gems, or other resources to progress faster or unlock more content. With APK Mod, you can get these resources for free or unlimited amounts.</p>
-<p>Another reason why people use APK Mod is to enhance their gaming experience by adding new elements, modes, or options that are not available in the original app. For example, some games may have limited graphics, controls, or customization options that limit your enjoyment of the game. With APK Mod, you can improve the graphics, change the controls, or customize your vehicle as you wish.</p>
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-<p>However, using APK Mod is not without risks and challenges. First of all, modifying an app is not legal in most cases, as it violates the intellectual property rights of the original developers. Therefore, downloading and using APK Mod can expose you to legal issues or penalties if you are caught.</p>
-<p>Secondly, using APK Mod can compromise the security and performance of your device. Since APK Mod is not signed by the original developers and has no support from them, it may contain malware or other harmful codes that can damage your device or steal your personal information. Moreover, using APK Mod can cause compatibility issues or errors with the original app or other apps on your device.</p>
-<p>Therefore, before you decide to use APK Mod, you should weigh the pros and cons carefully and be aware of the potential consequences. You should also only download APK Mod from reputable sources that have positive reviews and feedback from other users.</p>
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-<p>Mini Racing is a low poly arcade racing game developed by Kitty In A Box. It has a simple and intuitive driving model that is easy to learn but hard to master. You can race against up to 8 cars on 18 tracks in different environments. You can also customize your car by changing its color, exhaust, spoilers, and other components. You can improve its performance by upgrading its engine, tires, suspension, and brakes.</p>
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-<p>One of the best <p>One of the best features of Mini Racing is that it offers three modes of play: single, local multiplayer, and online multiplayer. You can play solo and challenge yourself to beat your own records or the AI opponents. You can also play with your friends on the same device using the split-screen mode. Or you can join online races and compete with players from around the world. You can also chat with other players and send them emojis.</p>
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- <h2>How to Download and Install APK Mod Mini Racing on Your Android Device</h2>
-<p>If you want to try APK Mod Mini Racing on your Android device, you need to follow these simple steps:</p>
- <h3>Find a reputable website that offers the APK Mod Mini Racing file</h3>
-<p>The first step is to find a reliable source that provides the APK Mod Mini Racing file for download. You can search online for websites that offer this file, but be careful not to download from shady or untrustworthy sites that may contain viruses or malware. You can also check the reviews and ratings of other users who have downloaded the file before you.</p>
- <h3>Download the file and enable unknown sources on your device settings</h3>
-<p>The next step is to download the file from the website to your device. You may need to enable unknown sources on your device settings to allow the installation of apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.</p>
- <h3>Install the file and enjoy the game</h3>
-<p>The final step is to install the file on your device by tapping on it and following the instructions. Once the installation is complete, you can launch the game and enjoy all the features and benefits of APK Mod Mini Racing.</p>
- <h2>Conclusion</h2>
-<p>APK Mod Mini Racing is a fun and exciting racing game for Android devices that offers more features and benefits than the original version of Mini Racing. It lets you unlock premium features, enhance your gaming experience, drive 14 cars on 18 tracks, customize your vehicle, and play with other players online or offline. However, you should also be aware of the risks and challenges of using APK Mod, such as legal issues, security threats, or compatibility problems. Therefore, you should only download APK Mod from reputable sources and use it at your own discretion.</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>What is the difference between APK Mod Mini Racing and Mini Racing?</b></li>
-<p>APK Mod Mini Racing is a modified version of Mini Racing that offers more features and benefits for the players, such as unlimited resources, improved graphics, or new modes.</p>
-<li><b>Is APK Mod Mini Racing safe to use?</b></li>
-<p>APK Mod Mini Racing may not be safe to use as it is not signed by the original developers and may contain malware or other harmful codes that can damage your device or steal your personal information. Therefore, you should only download APK Mod from reputable sources and use it at your own risk.</p>
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-<p>To update APK Mod Mini Racing, you need to download the latest version of the file from the website where you downloaded it before and install it over the existing one. However, you may lose some data or settings if you do this.</p>
-<li><b>Can I play APK Mod Mini Racing offline?</b></li>
-<p>You can play APK Mod Mini Racing offline in single mode or local multiplayer mode. However, you need an internet connection to play online multiplayer mode or access some features that require online verification.</p>
-<li><b </b>How do I uninstall APK Mod Mini Racing?</b></li>
-<p>To uninstall APK Mod Mini Racing, you need to go to Settings > Apps > APK Mod Mini Racing and tap on Uninstall. You may also need to delete the file from your device storage.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Experience the Ultimate Bus Simulation with Bus Simulator  Ultimate APK.md b/spaces/congsaPfin/Manga-OCR/logs/Experience the Ultimate Bus Simulation with Bus Simulator  Ultimate APK.md
deleted file mode 100644
index 03f7b04a09e02f25739276f551958794b4eae646..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Experience the Ultimate Bus Simulation with Bus Simulator  Ultimate APK.md	
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- <p>Do you love driving games and want to experience what it's like to operate a passenger bus? If so, then you should try <strong>Bus Simulator Ultimate APK AN1</strong>, a modified version of the popular simulation game from Zuuks Games. This game lets you drive various buses across different countries, cities, and routes, while also managing your own bus company. You can enjoy realistic graphics, physics, weather, traffic, passengers, and more in this game. In this article, we will tell you more about what this game is, what features it offers, how to download and install it, and some tips and tricks to help you play it better.</p>
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- <p><strong>Bus Simulator Ultimate APK AN1</strong> is a modified version of the original game <a href="(^1^)">Bus Simulator: Ultimate</a>, which is available on the Google Play Store. This modified version allows you to access features that are not available in the official game, such as unlimited money, vehicle access, no ads, and improved visuals. You can use these features to enhance your gameplay and enjoy the game more.</p>
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- <p>The game also offers a business management and a multiplayer mode that make your driving more challenging and rewarding. You can manage your own bus company by establishing offices in different locations to unlock more routes and destinations. You can also hire drivers to work for you and earn passive income. You can also compete with other players in the multiplayer mode and join the ultimate league. You can compare your performance and ranking with others on the leaderboard.</p>
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- <p>If you want to download and install <strong>Bus Simulator Ultimate APK AN1</strong>, you have to follow these steps:</p>
- <ol>
-<li>Go to <a href="">this link</a> to download the APK file of the game.</li>
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-</ol>
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- <h3>Tips and tricks for playing Bus Simulator Ultimate APK AN1</h3>
- <h4>- Follow traffic rules and regulations</h4>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Get Chess 3D Offline Mod APK and Challenge Yourself with Different Levels.md b/spaces/congsaPfin/Manga-OCR/logs/Get Chess 3D Offline Mod APK and Challenge Yourself with Different Levels.md
deleted file mode 100644
index b2d17fd9013a2dd8519d243bb1a653dc33df9ded..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Get Chess 3D Offline Mod APK and Challenge Yourself with Different Levels.md	
+++ /dev/null
@@ -1,110 +0,0 @@
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-<p>Do you love playing chess but don't have a partner or a board? Do you want to improve your chess skills and learn new strategies? Do you want to have unlimited money and access to different game modes and features? If you answered yes to any of these questions, then you should try Chess 3D Offline Mod APK, a modified version of the popular board game that lets you play chess in 3D on your phone.</p>
-<h2>What is Chess 3D Offline Mod APK?</h2>
-<p>Chess 3D Offline Mod APK is a modified version of the original Chess 3D Offline: Play & Learn game, which is available on Google Play Store. This game allows you to play chess in a realistic 3D environment, with different board styles, piece designs, and camera angles. You can also choose from different game modes, such as classic, puzzle, blitz, and tournament. You can play against the computer or with another player on the same device. You can also learn chess rules, tactics, and openings with the help of tutorials and hints.</p>
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-<p>The modded version of this game gives you unlimited money, which you can use to unlock more features and customize your game experience. You can also enjoy the game without any ads or in-app purchases. You can download the modded version of this game from various websites that offer free APK files for Android devices.</p>
-<h2>Features of Chess 3D Offline Mod APK</h2>
-<h3>Unlimited money</h3>
-<p>One of the main features of Chess 3D Offline Mod APK is that it gives you unlimited money, which you can use to buy more boards, pieces, themes, and backgrounds. You can also use the money to unlock more game modes, such as puzzle, blitz, and tournament. You can also use the money to increase or decrease the AI difficulty level, which affects the strength and intelligence of your computer opponent.</p>
-<h3>Different game modes</h3>
-<p>Another feature of Chess 3D Offline Mod APK is that it offers different game modes for you to choose from. You can play the classic mode, which follows the standard chess rules and lets you play against the computer or another player on the same device. You can also play the puzzle mode, which challenges you to solve various chess problems and puzzles. You can also play the blitz mode, which gives you a limited time to make your moves. You can also play the tournament mode, which lets you compete with other players online or offline.</p>
-<h3>Customizable board and pieces</h3>
-<p>A third feature of Chess 3D Offline Mod APK is that it allows you to customize your board and pieces according to your preference. You can choose from different board styles, such as wood, marble, metal, glass, or plastic. You can also choose from different piece designs, such as classic, modern, fantasy, or cartoon. You can also change the color and theme of your board and pieces. You can also adjust the camera angle and zoom level to get a better view <h3>AI difficulty levels</h3>
-<p>A fourth feature of Chess 3D Offline Mod APK is that it lets you adjust the AI difficulty level of your computer opponent. You can choose from easy, medium, hard, or expert levels, depending on your skill and experience. You can also use the money to increase or decrease the AI level as you wish. The higher the AI level, the more challenging and realistic the game becomes. The lower the AI level, the more forgiving and easy the game becomes.</p>
-<h3>Offline play and learn mode</h3>
-<p>A fifth feature of Chess 3D Offline Mod APK is that it allows you to play and learn chess offline, without any internet connection. You can play against the computer or another player on the same device, or you can practice and improve your chess skills with the help of tutorials and hints. You can also access a library of chess openings, tactics, and strategies, which you can study and apply in your games. You can also track your progress and achievements with the statistics and leaderboards.</p>
-<h2>How to Download and Install Chess 3D Offline Mod APK?</h2>
-<p>If you want to download and install Chess 3D Offline Mod APK on your Android device, you need to follow these simple steps:</p>
-<h3>Step 1: Enable unknown sources on your device</h3>
-<p>Before you can install any APK file on your device, you need to enable unknown sources in your settings. This will allow you to install apps from sources other than Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.</p>
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-<h3>Step 2: Download the APK file from a trusted source</h3>
-<p>Next, you need to download the APK file of Chess 3D Offline Mod APK from a trusted source. You can search for it online or use the link provided below. Make sure you download the latest version of the modded game, which is compatible with your device.</p>
-<p><a href="">Download Chess 3D Offline Mod APK here</a></p>
-<h3>Step 3: Locate and install the APK file on your device</h3>
-<p>After downloading the APK file, you need to locate it on your device using a file manager app. You can find it in your Downloads folder or wherever you saved it. Once you find it, tap on it and follow the instructions to install it on your device.</p>
-<h3>Step 4: Launch the game and enjoy</h3>
-<p>Finally, you can launch the game from your app drawer or home screen and enjoy playing chess in 3D with unlimited money and features.</p>
-<h2>Pros and Cons of Chess 3D Offline Mod APK</h2>
-<p>Like any other modded game, Chess 3D Offline Mod APK has its pros and cons. Here are some of them:</p>
-<h3>Pros</h3>
-<ul>
-<li><b>Fun and realistic 3D graphics</b>: The game has stunning and realistic 3D graphics that make you feel like you are playing chess on a real board. You can also change the view and angle of the board and pieces to suit your preference.</li>
-<li><b>Easy to use interface and controls</b>: The game has a simple and user-friendly interface that lets you access all the features and options easily. You can also control the game with simple touch gestures, such as drag, drop, swipe, and tap.</li>
-<li><b>Educational and entertaining game play</b>: The game is not only fun but also educational, as it teaches you how to play chess and improve your skills. You can learn chess rules, tactics, openings, and strategies with the help of tutorials and hints. You can also test your knowledge and skills with puzzles and challenges.</li>
-<li><b>No internet connection required</b>: The game does not require any internet connection to play, which means you can play it anytime and anywhere. You can also save your battery life and data usage by playing offline.</li>
-</ul>
-<h3>Cons</h3>
-<ul>
-<li><b>May contain ads and in-app purchases</b>: The game may still contain some ads and in-app purchases that may interrupt or limit your game play. You may need to disable them manually or use an ad blocker app to get rid of them.</li>
-<li><b>May not be compatible with some devices</b>: The game may not work properly or at all on some devices, especially older or low-end ones. You may experience crashes, freezes, lags, or errors while playing the game. You may need to check the compatibility of your device before downloading and installing the game.</li>
-<li><b>May have some bugs and glitches</b>: The game may have some bugs and glitches that may affect your game play or performance. You may need to update the game regularly or report the issues to the developers to fix them.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Chess 3D Offline Mod APK is a fun and challenging way to play chess on your phone. It offers you unlimited money and features, such as different game modes, customizable board and pieces, AI difficulty levels, offline play and learn mode, and more. It also has realistic 3D graphics, easy to use interface and controls, and educational and entertaining game play. However, it may also have some drawbacks, such as ads and in-app purchases, compatibility issues, and bugs and glitches. You can download and install the game from a trusted source and enjoy playing chess in 3D on your phone.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Chess 3D Offline Mod APK:</p>
-<ul>
-<li><b>Q: Is Chess 3D Offline Mod APK safe to download and install?</b></li>
-<li>A: Yes, Chess 3D Offline Mod APK is safe to download and install, as long as you get it from a trusted source. However, you should always be careful when downloading and installing any APK file on your device, as some of them may contain viruses or malware that can harm your device or steal your data. You should also scan the APK file with an antivirus app before installing it.</li>
-<li><b>Q: How can I update Chess 3D Offline Mod APK?</b></li>
-<li>A: You can update Chess 3D Offline Mod APK by downloading and installing the latest version of the modded game from the same source where you got it. You should also check for updates regularly to get the latest features and fixes for the game.</li>
-<li><b>Q: Can I play Chess 3D Offline Mod APK with other players online?</b></li>
-<li>A: Yes, you can play Chess 3D Offline Mod APK with other players online, if you have an internet connection. You can join or create online tournaments and compete with other players around the world. However, you may not be able to play with players who have the original version of the game, as they may have different features and settings.</li>
-<li><b>Q: Can I play Chess 3D Offline Mod APK on my PC or laptop?</b></li>
-<li>A: Yes, you can play Chess 3D Offline Mod APK on your PC or laptop, if you have an Android emulator installed on your device. An Android emulator is a software that allows you to run Android apps and games on your PC or laptop. You can download and install an Android emulator of your choice, such as BlueStacks, NoxPlayer, or LDPlayer, and then download and install Chess 3D Offline Mod APK on it.</li>
-<li><b>Q: What are some alternatives to Chess 3D Offline Mod APK?</b></li>
-<li>A: Some alternatives to Chess 3D Offline Mod APK are Chess Master King, Real Chess 3D, Chess Free, Chess Tactics Pro, and Lichess.</li>
-</ul></p> 401be4b1e0<br />
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-<br />
-<h1>How to Download and Play My Talking Tom 2 on PC for Free</h1>
-<h2>Introduction</h2>
-<p>Write a brief introduction that explains what My Talking Tom 2 is, why it is popular, and what are the benefits of playing it on PC.</p>
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-<h2>What is My Talking Tom 2?</h2>
-<p>Write a paragraph that describes the features and gameplay of My Talking Tom 2, such as the virtual pet, the mini-games, the customization, and the exploration.</p>
-<h3>The Virtual Pet</h3>
-<p>Write a sub-paragraph that explains how to interact with Tom, feed him, dress him, and take care of him.</p>
-<h3>The Mini-Games</h3>
-<p>Write a sub-paragraph that lists some of the fun mini-games that you can play with Tom, such as Flappy Tom, Bubble Shooter, and Cake Tower.</p>
-<h3>The Customization</h3>
-<p>Write a sub-paragraph that showcases some of the ways you can customize Tom's appearance and his home, such as changing his clothes, accessories, furniture, and wallpapers.</p>
-<h3>The Exploration</h3>
-<p>Write a sub-paragraph that highlights some of the places you can visit with Tom, such as the airport, the pet store, the playground, and the forest.</p>
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-<h2>How to Download My Talking Tom 2 on PC?</h2>
-<p>Write a paragraph that explains how to download and install My Talking Tom 2 on PC using an emulator software, such as NoxPlayer or LDPlayer.</p>
-<h3>NoxPlayer</h3>
-<p>Write a sub-paragraph that provides a step-by-step guide on how to download and install NoxPlayer on PC, and how to use it to run My Talking Tom 2.</p>
-<h3>LDPlayer</h3>
-<p>Write a sub-paragraph that provides a step-by-step guide on how to download and install LDPlayer on PC, and how to use it to run My Talking Tom 2.</p>
-<h2>How to Play My Talking Tom 2 on PC?</h2>
-<p>Write a paragraph that explains how to play My Talking Tom 2 on PC using the keyboard, mouse, or gamepad controls.</p>
-<h3>Keyboard</h3>
-<p>Write a sub-paragraph that describes how to use the keyboard keys to control Tom's actions and movements.</p>
-<h3>Mouse</h3>
-<p>Write a sub-paragraph that describes how to use the mouse cursor to interact with Tom and the game elements.</p>
-<h3>Gamepad</h3>
-<p>Write a sub-paragraph that describes how to use a gamepad device to play My Talking Tom 2 on PC more comfortably.</p>
-<h2>Conclusion</h2>
-<p>Write a brief conclusion that summarizes the main points of the article and encourages the reader to try My Talking Tom 2 on PC.</p>
- 3. Finally, I would write the article based on the outline using HTML tags to format the text and add visuals. For example, I could use something like this: <h1>How to Download and Play My Talking Tom 2 on PC for Free</h1>
- <p>If you are looking for a fun and relaxing game to play on your PC, you might want to check out My Talking Tom 2. This is a popular virtual pet game where you can adopt and take care of a cute cat named Tom. You can also play mini-games with him, customize his appearance and his home, and explore new worlds with him. In this article, we will show you how to download and play My Talking Tom 2 on PC for free using an emulator software. You will also learn how to use different controls to enjoy the game better.</p>
- <h2>What is My Talking Tom 2?</h2>
- <p>My Talking Tom 2 is a sequel to the original My Talking Tom game by Outfit7. It is an interactive game where you can talk to Tom, pet him, feed him, dress him up, and watch him grow. You can also play with his toys, take him to the toilet, put him to bed, and more. You can also play various mini-games with him, such as Flappy Tom, Bubble Shooter, and Cake Tower. You can earn coins and stars by playing these games, which you can use to buy new clothes, accessories, furniture, and wallpapers for Tom and his home. You can also unlock new items and locations by leveling up Tom. You can visit the airport, the pet store, the playground, and the forest with Tom and discover new surprises and adventures. My Talking Tom 2 is a game that is suitable for all ages and can provide hours of entertainment and fun.</p>
- <h3>The Virtual Pet</h3>
- <p>One of the main features of My Talking Tom 2 is the virtual pet aspect. You can interact with Tom in various ways, such as talking to him, petting him, tickling him, poking him, and more. He will respond to your actions with different sounds and expressions. You can also feed him different types of food, such as fruits, vegetables, candy, pizza, and more. You can see his hunger level on the screen and make sure he is well-fed. You can also take care of his hygiene by taking him to the toilet and brushing his teeth. You can see his cleanliness level on the screen and make sure he is not dirty. You can also dress him up with different outfits, hats, glasses, shoes, and more. You can see his happiness level on the screen and make sure he is not bored or sad.</p>
- <h3>The Mini-Games</h3>
- <p>Another feature of My Talking Tom 2 is the mini-games that you can play with Tom. There are many mini-games to choose from, such as Flappy Tom, Bubble Shooter, Cake Tower, Space Trails, Ice Smash, and more. Each mini-game has its own rules and objectives, but they are all easy to play and fun to enjoy. You can use the keyboard, mouse, or gamepad controls to play these games. You can earn coins and stars by playing these games, which you can use to buy new items for Tom and his home. You can also unlock new mini-games by leveling up Tom.</p>
- <h3>The Customization</h3>
- <p>Another feature of My Talking Tom 2 is the customization that you can do for Tom and his home. You can change Tom's appearance by buying new clothes, accessories, hair styles, eye colors, fur colors, and more. You can also change his home's appearance by buying new furniture, decorations, wallpapers, floors, windows, doors, and more. You can create your own unique style for Tom and his home by mixing and matching different items. You can also save your favorite outfits and home designs for later use.</p>
- <h3>The Exploration</h3>
- <p>Another feature of My Talking Tom 2 is the exploration that you can do with Tom. You can visit different places with Tom by using the airplane icon on the screen. You can go to the airport, the pet store, the playground, and the forest with Tom and discover new things and activities there. For example, you can buy new pets for Tom at the pet store, such as a dog, a hamster, a parrot, or a unicorn. You can also play with them at the playground or take them to the forest for a picnic. You can also find hidden treasures and collectibles at these places that you can use to decorate Tom's home or unlock new items.</p>
- <h2>How to Download My Talking Tom 2 on PC?</h2>
- <p>If you want to play My Talking Tom 2 on your PC for free, you will need to use an emulator software that allows you to run Android apps on your PC. There are many emulator software available online, but some of the best ones are NoxPlayer, LDPlayer, and BlueStacks. These emulators are free, easy to use, and compatible with most Android apps and games. To download My Talking Tom 2 on PC using an emulator, you need to follow these steps:</p>
- <h3>NoxPlayer</h3>
- <p>NoxPlayer is a powerful Android emulator that offers a smooth gaming experience and a lot of customization options. You can download NoxPlayer from its official website and install it on your PC. After that, you need to do the following:</p>
-<ul>
-<li>Launch NoxPlayer and sign in with your Google account.</li>
-<li>Go to the Google Play Store app and search for My Talking Tom 2.</li>
-<li>Click on the Install button and wait for the download to finish.</li>
-<li>Go to the home screen and click on the My Talking Tom 2 icon to start playing.</li>
-</ul>
- <h3>LDPlayer</h3>
- <p>LDPlayer is another popular Android emulator that focuses on gaming performance and compatibility. You can download LDPlayer from its official website and install it on your PC. After that, you need to do the following:</p>
-<ul>
-<li>Launch LDPlayer and sign in with your Google account.</li>
-<li>Go to the LD Store app and search for My Talking Tom 2.</li>
-<li>Click on the Install button and wait for the download to finish.</li>
-<li>Go to the home screen and click on the My Talking Tom 2 icon to start playing.</li>
-</ul>
- <h3>BlueStacks</h3>
- <p>BlueStacks is one of the oldest and most popular Android emulators that offers a lot of features and benefits for gamers. You can download BlueStacks from its official website and install it on your PC. After that, you need to do the following:</p>
-<ul>
-<li>Launch BlueStacks and sign in with your Google account.</li>
-<li>Go to the Google Play Store app and search for My Talking Tom 2.</li>
-<li>Click on the Install button and wait for the download to finish.</li>
-<li>Go to the home screen and click on the My Talking Tom 2 icon to start playing.</li>
-</ul>
- <h2>How to Play My Talking Tom 2 on PC?</h2>
- <p>Once you have downloaded My Talking Tom 2 on PC using an emulator, you can start playing it as you would on your mobile device. However, you can also use different controls to make the game more comfortable and enjoyable. You can use the keyboard, mouse, or gamepad controls to play My Talking Tom 2 on PC. Here is how:</p>
- <h3>Keyboard</h3>
- <p>You can use the keyboard keys to control Tom's actions and movements. For example, you can use the arrow keys to move him left, right, up, or down. You can also use the spacebar to make him jump, or the enter key to make him talk. You can also use other keys to access different menus and options in the game. You can customize the keyboard controls by clicking on the keyboard icon on the right side of the emulator screen.</p>
- <h3>Mouse</h3>
- <p>You can use the mouse cursor to interact with Tom and the game elements. For example, you can use the left-click to pet him, feed him, play with him, or tap on anything on the screen. You can also use the right-click to zoom in or out, or drag and drop items. You can also use the mouse wheel to scroll through different menus and options in the game.</p>
- <h3>Gamepad</h3>
- <p>You can use a gamepad device to play My Talking Tom 2 on PC more comfortably. For example, you can use the analog sticks to move Tom around, or the buttons to make him jump, talk, or interact with things. You can also use other buttons to access different menus and options in the game. You can connect your gamepad device via USB or Bluetooth, and configure it by clicking on the gamepad icon on the right side of the emulator screen.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, My Talking Tom 2 is a fun and relaxing game that you can play on your PC for free using an emulator software. You can enjoy all the features and gameplay of My Talking Tom 2 on a bigger screen and with better controls. You can also customize your emulator settings to suit your preferences and needs. If you are looking for a way to have some fun with your virtual pet, you should definitely try My Talking Tom 2 on PC today.</p>
- <h4>Frequently Asked Questions</h4>
- <ul>
-<li><b>Is My Talking Tom 2 safe to play?</b></li>
-<p>Yes, My Talking Tom 2 is safe to play as long as you download it from a trusted source, such as the Google Play Store or the official website of the emulator software. You should also avoid clicking on any suspicious links or ads that might appear in the game or the emulator. You should also be careful about sharing any personal information or data with the game or the emulator.</p>
-<li><b>Can I play My Talking Tom 2 offline?</b></li>
-<p>Yes, you can play My Talking Tom 2 offline without an internet connection. However, you will not be able to access some of the features and functions that require online connectivity, such as visiting other players' homes, watching videos, or updating the game. You will also not be able to save your progress or sync it with other devices. Therefore, it is recommended that you play My Talking Tom 2 online whenever possible.</p>
-<li><b>Can I play My Talking Tom 2 with my friends?</b></li>
-<p>Yes, you can play My Talking Tom 2 with your friends by connecting your game account with your social media accounts, such as Facebook or Google. You can then visit your friends' homes, chat with them, send them gifts, and compete with them in mini-games. You can also invite your friends to play My Talking Tom 2 by sharing your referral code or link with them.</p>
-<li><b>How can I get more coins and stars in My Talking Tom 2?</b></li>
-<p>You can get more coins and stars in My Talking Tom 2 by playing mini-games, completing daily tasks, watching videos, spinning the wheel, opening chests, and leveling up Tom. You can also buy more coins and stars using real money if you want to support the game developers and get more items faster.</p>
-<li><b>How can I contact the support team of My Talking Tom 2?</b></li>
-<p>You can contact the support team of My Talking Tom 2 by clicking on the settings icon on the top right corner of the game screen. You can then click on the help button and choose the option that suits your issue or question. You can also email the support team at support@outfit7.com or visit their website at https://outfit7.com/contact/.</p>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Salta esquiva y colecciona gatos con Cat Jump Hack APK todo desbloqueado.md b/spaces/congsaPfin/Manga-OCR/logs/Salta esquiva y colecciona gatos con Cat Jump Hack APK todo desbloqueado.md
deleted file mode 100644
index 42fc5c2a2c8a55da7570e76a7c9908e8f729fa68..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Salta esquiva y colecciona gatos con Cat Jump Hack APK todo desbloqueado.md	
+++ /dev/null
@@ -1,131 +0,0 @@
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-<h1>Cat Jump Hack APK Todo Desbloqueado: How to Unlock Everything in This Fun Game</h1>
- <p>If you love cats and jumping games, you might have heard of Cat Jump, a simple but addictive game that challenges your reflexes and skills. But did you know that you can unlock everything in this game with a hack apk? In this article, we will tell you everything you need to know about Cat Jump Hack APK Todo Desbloqueado, including what it is, why you should use it, how to download and install it, and how to use it. Let's get started!</p>
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- <h2>What is Cat Jump?</h2>
- <h3>A simple but challenging game</h3>
- <p>Cat Jump is a game that was developed by ZPLAY Games and released in 2019. It is available for both Android and iOS devices. The game is very simple: you control a cute cat and try to jump higher and higher, avoiding obstacles and enemies. The game has no end, so you can play as long as you want and try to beat your own high score.</p>
- <h3>The features of Cat Jump</h3>
- <p>Cat Jump has many features that make it fun and enjoyable, such as:</p>
- <ul>
-<li>Simple controls that can be enjoyed by people of all ages. You just need to tap the screen to make the cat jump left or right.</li>
-<li>Competition based solely on your reflexes and skill. There are no power-ups or upgrades that can give you an unfair advantage. You only rely on your own abilities.</li>
-<li>The fun of collecting adorable cats. There are over 40 different cats that you can unlock and play with, each with their own unique appearance and personality.</li>
-<li>Good for improving focus, reflexes, control, manual dexterity, and vision. Playing Cat Jump can help you train your brain and improve your cognitive skills.</li>
-</ul>
- <h2>Why use Cat Jump Hack APK?</h2>
- <h3>The benefits of using Cat Jump Hack APK</h3>
- <p>Cat Jump Hack APK is a modified version of the original game that allows you to unlock everything in the game for free. This means that you can enjoy the following benefits:</p>
- <ul>
-<li>You can access all the cats without having to collect coins or watch ads. You can choose any cat you like and switch between them anytime.</li>
-<li>You can remove all the ads from the game. You don't have to worry about annoying pop-ups or banners that interrupt your gameplay.</li>
-<li>You can play the game offline. You don't need an internet connection to enjoy Cat Jump.</li>
-</ul>
- <h3>The risks of using Cat Jump Hack APK</h3>
- <p>However, using Cat Jump Hack APK also comes with some risks that you should be aware of, such as:</p>
- <ul>
-<li>You might lose your progress or data if you uninstall the original game or update it. Since Cat Jump Hack APK is not an official version, it might not be compatible with the latest updates or features of the original game.</li>
-<li>You might get banned or suspended from the game if you use Cat Jump Hack APK online. The developers of the game might detect that you are using a hacked version and take action against you.</li>
-<li>You might expose your device to malware or viruses if you download Cat Jump Hack APK from an untrusted source. Some websites or links might contain harmful files or programs that can damage your device or steal your personal information.</li>
-</ul>
- <h2>How to download and install Cat Jump Hack APK?</h <h2>How to download and install Cat Jump Hack APK?</h2>
- <h3>The steps to download and install Cat Jump Hack APK</h3>
- <p>If you want to download and install Cat Jump Hack APK, you need to follow these steps:</p>
- <ol>
-<li>Find a reliable source that offers Cat Jump Hack APK for free. You can search online or ask your friends for recommendations. Make sure that the website or link is safe and secure.</li>
-<li>Download the Cat Jump Hack APK file to your device. You might need to enable the option to install apps from unknown sources in your device settings.</li>
-<li>Locate the Cat Jump Hack APK file in your device storage and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Launch the Cat Jump Hack APK app and enjoy the game with everything unlocked.</li>
-</ol>
- <h3>The precautions to take before installing Cat Jump Hack APK</h3>
- <p>Before you install Cat Jump Hack APK, you should take some precautions to avoid any problems or issues, such as:</p>
- <ul>
-<li>Backup your data and progress from the original game. You can use a cloud service or a local storage device to save your data and progress. This way, you can restore them if you lose them or want to switch back to the original game.</li>
-<li>Disable your internet connection when you play Cat Jump Hack APK. This will prevent the game from connecting to the server and detecting that you are using a hacked version. It will also save your battery and data usage.</li>
-<li>Use an antivirus or anti-malware program to scan your device before and after installing Cat Jump Hack APK. This will help you detect and remove any harmful files or programs that might have been downloaded along with Cat Jump Hack APK.</li>
-</ul>
- <h2>How to use Cat Jump Hack APK?</h2>
- <h3>The tips and tricks to play Cat Jump with Cat Jump Hack APK</h3>
- <p>Now that you have installed Cat Jump Hack APK, you can use it to play Cat Jump with more fun and ease. Here are some tips and tricks that you can use:</p>
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- <ul>
-<li>Select the cat that suits your style and preference. You can choose from over 40 different cats, each with their own look and personality. You can also switch between them anytime you want.</li>
-<li>Avoid the obstacles and enemies that come your way. You will encounter various obstacles and enemies as you jump higher, such as spikes, birds, rockets, lasers, etc. You need to tap the screen at the right time to dodge them.</li>
-<li>Collect the coins and stars that appear on the screen. You can use the coins to unlock more cats in the original game, or just enjoy them as a bonus. You can use the stars to activate a special mode that makes you invincible for a short time.</li>
-</ul>
- <h3>The limitations and drawbacks of Cat Jump Hack APK</h3>
- <p>While Cat Jump Hack APK can enhance your gaming experience, it also has some limitations and drawbacks that you should be aware of, such as:</p>
- <ul>
-<li>You might lose the challenge and excitement of playing Cat Jump. Since you have everything unlocked, you might not feel motivated or satisfied by playing the game. You might also get bored easily by playing the same game over and over.</li>
-<li>You might miss out on the updates and features of the original game. Since Cat Jump Hack APK is not an official version, it might not be updated or compatible with the latest updates or features of the original game. You might miss out on new cats, levels, modes, etc.</li>
-<li>You might violate the terms and conditions of the original game. By using Cat Jump Hack APK, you might be breaking the rules and regulations of the original game. This might result in legal actions or penalties from the developers of the game.</li>
-</ul>
- <h2>Conclusion</h2>
- <h4>Summary of the main points</h4>
- <p>Cat Jump is a simple but addictive game that challenges your reflexes and skills. You control a cute cat and try to jump higher and higher, avoiding obstacles and enemies. You can also collect coins and stars, and unlock over 40 different cats.</p>
- <p>Cat Jump Hack APK is a modified version of the original game that allows you to unlock everything in the game for free. You can access all the cats without having to collect coins or watch ads, remove all the ads from the game, and play the game offline.</p>
- <p>However, using Cat Jump Hack APK also comes with some risks and drawbacks, such as losing your progress or data, getting banned or suspended from the game, exposing your device to malware or viruses, losing the challenge and excitement of playing Cat Jump, missing out on the updates and features of the original game, and violating the the terms and conditions of the original game. Therefore, you should be careful and responsible when using Cat Jump Hack APK.</p>
- <h4>Call to action</h4>
- <p>If you want to try Cat Jump Hack APK Todo Desbloqueado, you can download it from the link below. But remember, use it at your own risk and discretion. And don't forget to have fun with your furry friends!</p>
- <p><a href="">Download Cat Jump Hack APK Todo Desbloqueado here</a></p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Cat Jump Hack APK:</p>
- <ol>
-<li>Is Cat Jump Hack APK safe to use?</li>
-<p>Cat Jump Hack APK is not an official version of the game, so it might not be safe to use. It might contain malware or viruses that can harm your device or steal your personal information. It might also get you banned or suspended from the game if you use it online. Therefore, you should only download Cat Jump Hack APK from a trusted source and scan your device before and after installing it.</p>
-<li>How do I update Cat Jump Hack APK?</li>
-<p>Cat Jump Hack APK might not be updated or compatible with the latest updates or features of the original game. You might need to wait for the developer of Cat Jump Hack APK to release a new version or find another source that offers an updated version. However, updating Cat Jump Hack APK might also cause you to lose your progress or data, so you should backup your data before updating it.</p>
-<li>Can I play Cat Jump with my friends using Cat Jump Hack APK?</li>
-<p>Cat Jump is a single-player game, so you cannot play it with your friends directly. However, you can compare your high scores with your friends and challenge them to beat your record. You can also share your screenshots or videos of your gameplay with your friends on social media. But be careful not to use Cat Jump Hack APK online, as it might get you banned or suspended from the game.</p>
-<li>What are some alternatives to Cat Jump Hack APK?</li>
-<p>If you don't want to use Cat Jump Hack APK, you can still enjoy the original game without any hacks or cheats. You can collect coins and watch ads to unlock more cats, or just play with the default cat. You can also try some other games that are similar to Cat Jump, such as Doodle Jump, Helix Jump, or Stack Jump.</p>
-<li>How do I uninstall Cat Jump Hack APK?</li>
-<p>If you want to uninstall Cat Jump Hack APK, you can follow these steps:</p>
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-<li>Go to your device settings and find the apps section.</li>
-<li>Find and select Cat Jump Hack APK from the list of apps.</li>
-<li>Tap on the uninstall button and confirm your action.</li>
-<li>Delete the Cat Jump Hack APK file from your device storage if it is still there.</li>
-</ul>
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/X Home Bar APK How to Use Your Phone with One Hand Easily.md b/spaces/congsaPfin/Manga-OCR/logs/X Home Bar APK How to Use Your Phone with One Hand Easily.md
deleted file mode 100644
index c07654868d318b212d2b193812d240b6329041a1..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/X Home Bar APK How to Use Your Phone with One Hand Easily.md	
+++ /dev/null
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-<h1>Bar Home APK: How to Replace a Broken Home Button on Your Android Phone</h1>
-<p>If you have a broken home button on your Android phone, you might be wondering how to navigate your device without it. Fortunately, there is a simple solution that can help you use your phone like an iPhone X. It's called Bar Home APK, and it's a free app that simulates an iPhone X home button on the bottom of your screen. In this article, we will explain what Bar Home APK is, how to download and install it, how to use it, and answer some frequently asked questions about it.</p>
- <h2>What is Bar Home APK?</h2>
-<h3>A free app that simulates an iPhone X home button on the bottom of your screen</h3>
-<p>Bar Home APK is a free app for Android, developed by Sylvain Lagache, that allows you to simulate an iPhone X home button on the bottom of your screen. You can now use your phone like an iPhone X, with a simple swipe up, left, or right to launch a selected action. The available actions are:</p>
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-<ul>
-<li>Home</li>
-<li>Recent apps</li>
-<li>Back</li>
-<li>Pull down notification panel</li>
-</ul>
-<p>The new home button is very simple and intuitive. You can also adjust its size, color, transparency, and position according to your preference.</p>
- <h3>The features and benefits of using Bar Home APK</h3>
-<p>Bar Home APK has several features and benefits that make it a useful app for Android users who have a broken home button or who want to try a different way of navigating their device. Some of these are:</p>
-<ul>
-<li>It's free and easy to use.</li>
-<li>It works on any Android phone with Android 9.0 or higher.</li>
-<li>It doesn't require root access or any special permissions.</li>
-<li>It doesn't interfere with the navigation bar (Home, Back, and Recent apps buttons) on the screen.</li>
-<li>It saves battery life by reducing the number of taps and presses needed to perform actions.</li>
-<li>It enhances the user experience by adding a modern and sleek touch to the device.</li>
-</ul>
- <h2>How to Download and Install Bar Home APK on Your Android Phone</h2>
-<h3>The steps to download and install Bar Home APK from APKCombo or Softonic</h3>
-<p>If you want to download and install Bar Home APK on your Android phone, you can follow these simple steps:</p>
-<ol>
-<li>Go to one of these websites: <a href="(^1^)">APKCombo</a> or <a href="(^2^)">Softonic</a>.</li>
-<li>Search for "Bar Home APK" or "X Home Bar Free" in the search box.</li>
-<li>Select the app from the list of results and click on "Download".</li>
-<li>Wait for the download to finish and then open the downloaded file.</li>
-<li>If prompted, enable "Unknown sources" in your device settings to allow the installation of apps from outside the Google Play Store.</li>
-<li>Follow the instructions on the screen to install the app on your device.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded and installed Bar Home APK on your Android phone.</p>
- <h3>The permissions and settings required for Bar Home APK to work properly</h3>
-<p>Bar Home APK requires some permissions and settings to work properly on your device. These are:</p>
-<ul>
-<li>Accessibility service: This permission allows Bar Home APK to detect your gestures and perform the corresponding actions. To enable this permission, go to Settings > Accessibility > Bar Home APK and turn on the toggle.</li>
-<li>Overlay permission: This permission allows Bar Home APK to display the home button on top of other apps. To enable this permission, go to Settings > Apps > Bar Home APK > Permissions and turn on the toggle for "Display over other apps".</li>
-<li>Hide navigation bar: This setting allows Bar Home APK to hide the navigation bar (Home, Back, and Recent apps buttons) on the screen, so that you can use the app without any interference. To enable this setting, go to Settings > Display > Navigation bar and select "Full screen gestures".</li>
-</ul>
-<p>Once you have enabled these permissions and settings, you can enjoy using Bar Home APK on your device.</p>
- <h2>How to Use Bar Home APK on Your Android Phone</h2>
-<h3>The options and actions available with Bar Home APK</h3>
-<p>Bar Home APK is very easy to use on your Android phone. You can access the options and actions available with the app by following these steps:</p>
-<ol>
-<li>Swipe up from the bottom of the screen to open the app.</li>
-<li>Tap on the gear icon on the top right corner of the app to open the settings menu.</li>
-<li>From here, you can customize the appearance and position of the home button, as well as the actions that you want to perform with it.</li>
-<li>You can also enable or disable the vibration feedback, the auto-hide feature, and the notification icon from here.</li>
-<li>To use the home button, simply swipe up, left, or right from the bottom of the screen to launch a selected action.</li>
-</ol>
-<p>You can also use a table to compare the different actions that you can perform with Bar Home APK. Here is an example:</p>
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- <table>
-<tr><th>Action</th><th>Description</th></tr>
-<tr><td>Swipe up</td><td>Returns to the home screen</td></tr>
-<tr><td>Swipe left</td><td>Goes back to the previous screen or app</td></tr>
-<tr><td>Swipe right</td><td>Opens the recent apps menu</td></tr>
-<tr><td>Swipe up and hold</td><td>Pulls down the notification panel</td></tr>
-</table>
- <h3>The pros and cons of using Bar Home APK</h3>
-<p>Bar Home APK has some pros and cons that you should consider before using it. Here are some of them:</p>
- <h4>The pros of using Bar Home APK</h4>
-<ul>
-<li>It's a free and simple solution for replacing a broken home button on your Android phone.</li>
-<li>It gives you a new and modern way of navigating your device like an iPhone X.</li>
-<li>It saves battery life by reducing the number of taps and presses needed to perform actions.</li>
-<li>It enhances the user experience by adding a sleek touch to your device.</li>
-<li>It's customizable and adaptable to your preference.</li>
-</ul>
- <h4>The cons of using Bar Home APK</h4>
-<ul>
-<li>It may not work well with some apps or games that require full screen mode or gesture controls.</li>
-<li>It may interfere with some other apps or features that use the bottom of the screen, such as keyboards or navigation bars.</li>
-<li>It may take some time to get used to it, especially if you are used to using physical buttons or other gestures.</li>
-<li>It may not be compatible with some older or newer versions of Android or some devices.</li>
-<li>It may have some bugs or glitches that affect its performance or functionality.</li>
-</ul>
- <h2>Conclusion</h2>
-<h3>A summary of the main points and a call to action</h3>
-<p>In conclusion, Bar Home APK is a free app that simulates an iPhone X home button on the bottom of your screen. It allows you to use your Android phone like an iPhone X, with a simple swipe up, left, or right to launch a selected action. It's a useful app for Android users who have a broken home button or who want to try a different way of navigating their device. It has several features and benefits that make it a worthwhile app to download and install. However, it also has some drawbacks that you should be aware of before using it. If you want to give it a try, you can download and install it from APKCombo or Softonic, and follow the instructions in this article to set it up and use it. You might find it a convenient and fun way to use your phone. If you have any questions or feedback about Bar Home APK, feel free to leave a comment below or contact the developer directly.</p>
- <h2>FAQs</h2>
-<h3>Q1: Is Bar Home APK safe to use?</h3>
-<p>A1: Bar Home APK is safe to use, as long as you download it from a trusted source, such as APKCombo or Softonic. These websites scan the apps for viruses and malware before uploading them. However, you should always be careful when installing apps from outside the Google Play Store, and check the permissions and settings that they require. You should also avoid clicking on any suspicious links or ads that may appear on the app or the websites.</p>
- <h3>Q2: Does Bar Home APK work on all Android phones?</h3>
-<p>A2: Bar Home APK works on most Android phones that run on Android 9.0 or higher. However, it may not be compatible with some older or newer versions of Android or some devices that have different screen sizes, resolutions, or aspect ratios. You can check the compatibility of your device by visiting the app's page on APKCombo or Softonic, and reading the description and the user reviews.</p>
- <h3>Q3: Can I customize the appearance and position of Bar Home APK?</h3>
-<p>A3: Yes, you can customize the appearance and position of Bar Home APK according to your preference. You can change the size, color, transparency, and position of the home button, as well as the actions that you want to perform with it. You can access these options by tapping on the gear icon on the top right corner of the app.</p>
- <h3>Q4: How can I uninstall Bar Home APK if I don't like it?</h3>
-<p>A4: If you want to uninstall Bar Home APK from your device, you can follow these steps:</p>
-<ol>
-<li>Go to Settings > Apps > Bar Home APK.</li>
-<li>Tap on "Uninstall" and confirm your choice.</li>
-<li>Wait for the app to be removed from your device.</li>
-</ol>
-<p>You can also disable the app temporarily by turning off the accessibility service and the overlay permission for it.</p>
- <h3>Q5: What are some alternatives to Bar Home APK?</h3>
-<p>A5: If you are looking for some alternatives to Bar Home APK, you can try these apps:</p>
-<ul>
-<li><a href="">Navigation Gestures</a>: This app allows you to replace your navigation bar with gesture controls. You can swipe from any edge of the screen to perform various actions, such as home, back, recent apps, etc.</li>
-<li><a href="">Fluid Navigation Gestures</a>: This app also allows you to use gesture controls instead of buttons. You can swipe from the left or right edge of the screen to go back, and swipe from the bottom edge to go home or access recent apps.</li>
-<li><a href="">Assistive Touch</a>: This app allows you to create a virtual home button that floats on your screen. You can tap on it to access various functions, such as home, back, recent apps, screenshot, etc.</li>
-</ul></p> 197e85843d<br />
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deleted file mode 100644
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deleted file mode 100644
index 487ed78f57e08d11b1496de646cf41f8811c7f3a..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Fabolous Street Dreams full album zip review Why this album is a masterpiece of rap music.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Fabolous, Street Dreams full album zip</h2><br /><p><b><b>DOWNLOAD</b> &#10001; &#10001; &#10001; <a href="https://ssurll.com/2uzyan">https://ssurll.com/2uzyan</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/midas/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/midas/__init__.py
deleted file mode 100644
index 5030f45e6aa5eea53eace8545adc7569b5e8c2ed..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/midas/__init__.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# Midas Depth Estimation
-# From https://github.com/isl-org/MiDaS
-# MIT LICENSE
-
-import cv2
-import numpy as np
-import torch
-
-from einops import rearrange
-from .api import MiDaSInference
-
-
-class MidasDetector:
-    def __init__(self):
-#        self.model = MiDaSInference(model_type="dpt_hybrid").cuda()
-        self.model = MiDaSInference(model_type="dpt_hybrid").cpu()
-
-    def __call__(self, input_image):
-        assert input_image.ndim == 3
-        image_depth = input_image
-        with torch.no_grad():
-#            image_depth = torch.from_numpy(image_depth).float().cuda()
-            image_depth = torch.from_numpy(image_depth).float().cpu()
-            image_depth = image_depth / 127.5 - 1.0
-            image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
-            depth = self.model(image_depth)[0]
-
-            depth -= torch.min(depth)
-            depth /= torch.max(depth)
-            depth = depth.cpu().numpy()
-            depth_image = (depth * 255.0).clip(0, 255).astype(np.uint8)
-
-            return depth_image
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/iter_timer.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/iter_timer.py
deleted file mode 100644
index cfd5002fe85ffc6992155ac01003878064a1d9be..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/hooks/iter_timer.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import time
-
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class IterTimerHook(Hook):
-
-    def before_epoch(self, runner):
-        self.t = time.time()
-
-    def before_iter(self, runner):
-        runner.log_buffer.update({'data_time': time.time() - self.t})
-
-    def after_iter(self, runner):
-        runner.log_buffer.update({'time': time.time() - self.t})
-        self.t = time.time()
diff --git a/spaces/cpwan/RLOR-TSP/wrappers/syncVectorEnvPomo.py b/spaces/cpwan/RLOR-TSP/wrappers/syncVectorEnvPomo.py
deleted file mode 100644
index 2aca0a226529530a5f79dd4e93b2c7f343cf99c3..0000000000000000000000000000000000000000
--- a/spaces/cpwan/RLOR-TSP/wrappers/syncVectorEnvPomo.py
+++ /dev/null
@@ -1,195 +0,0 @@
-from copy import deepcopy
-from typing import List, Optional, Union
-
-import numpy as np
-from gym.vector.utils import concatenate, create_empty_array, iterate
-from gym.vector.vector_env import VectorEnv
-
-__all__ = ["SyncVectorEnv"]
-
-
-class SyncVectorEnv(VectorEnv):
-    """Vectorized environment that serially runs multiple environments.
-
-    Parameters
-    ----------
-    env_fns : iterable of callable
-        Functions that create the environments.
-
-    observation_space : :class:`gym.spaces.Space`, optional
-        Observation space of a single environment. If ``None``, then the
-        observation space of the first environment is taken.
-
-    action_space : :class:`gym.spaces.Space`, optional
-        Action space of a single environment. If ``None``, then the action space
-        of the first environment is taken.
-
-    copy : bool
-        If ``True``, then the :meth:`reset` and :meth:`step` methods return a
-        copy of the observations.
-
-    Raises
-    ------
-    RuntimeError
-        If the observation space of some sub-environment does not match
-        :obj:`observation_space` (or, by default, the observation space of
-        the first sub-environment).
-
-    Example
-    -------
-
-    .. code-block::
-
-        >>> env = gym.vector.SyncVectorEnv([
-        ...     lambda: gym.make("Pendulum-v0", g=9.81),
-        ...     lambda: gym.make("Pendulum-v0", g=1.62)
-        ... ])
-        >>> env.reset()
-        array([[-0.8286432 ,  0.5597771 ,  0.90249056],
-               [-0.85009176,  0.5266346 ,  0.60007906]], dtype=float32)
-    """
-
-    def __init__(self, env_fns, observation_space=None, action_space=None, copy=True):
-        self.env_fns = env_fns
-        self.envs = [env_fn() for env_fn in env_fns]
-        self.copy = copy
-        self.metadata = self.envs[0].metadata
-        self.n_traj = self.envs[0].n_traj
-
-        if (observation_space is None) or (action_space is None):
-            observation_space = observation_space or self.envs[0].observation_space
-            action_space = action_space or self.envs[0].action_space
-        super().__init__(
-            num_envs=len(env_fns),
-            observation_space=observation_space,
-            action_space=action_space,
-        )
-
-        self._check_spaces()
-        self.observations = create_empty_array(
-            self.single_observation_space, n=self.num_envs, fn=np.zeros
-        )
-        self._rewards = np.zeros((self.num_envs, self.n_traj), dtype=np.float64)
-        self._dones = np.zeros((self.num_envs, self.n_traj), dtype=np.bool_)
-        self._actions = None
-
-    def seed(self, seed=None):
-        super().seed(seed=seed)
-        if seed is None:
-            seed = [None for _ in range(self.num_envs)]
-        if isinstance(seed, int):
-            seed = [seed + i for i in range(self.num_envs)]
-        assert len(seed) == self.num_envs
-
-        for env, single_seed in zip(self.envs, seed):
-            env.seed(single_seed)
-
-    def reset_wait(
-        self,
-        seed: Optional[Union[int, List[int]]] = None,
-        return_info: bool = False,
-        options: Optional[dict] = None,
-    ):
-        if seed is None:
-            seed = [None for _ in range(self.num_envs)]
-        if isinstance(seed, int):
-            seed = [seed + i for i in range(self.num_envs)]
-        assert len(seed) == self.num_envs
-
-        self._dones[:] = False
-        observations = []
-        data_list = []
-        for env, single_seed in zip(self.envs, seed):
-
-            kwargs = {}
-            if single_seed is not None:
-                kwargs["seed"] = single_seed
-            if options is not None:
-                kwargs["options"] = options
-            if return_info == True:
-                kwargs["return_info"] = return_info
-
-            if not return_info:
-                observation = env.reset(**kwargs)
-                observations.append(observation)
-            else:
-                observation, data = env.reset(**kwargs)
-                observations.append(observation)
-                data_list.append(data)
-
-        self.observations = concatenate(
-            self.single_observation_space, observations, self.observations
-        )
-        if not return_info:
-            return deepcopy(self.observations) if self.copy else self.observations
-        else:
-            return (deepcopy(self.observations) if self.copy else self.observations), data_list
-
-    def step_async(self, actions):
-        self._actions = iterate(self.action_space, actions)
-
-    def step_wait(self):
-        observations, infos = [], []
-        for i, (env, action) in enumerate(zip(self.envs, self._actions)):
-            observation, self._rewards[i], self._dones[i], info = env.step(action)
-            # if self._dones[i].all():
-            #    observation = env.reset()
-            observations.append(observation)
-            infos.append(info)
-        self.observations = concatenate(
-            self.single_observation_space, observations, self.observations
-        )
-
-        return (
-            deepcopy(self.observations) if self.copy else self.observations,
-            np.copy(self._rewards),
-            np.copy(self._dones),
-            infos,
-        )
-
-    def call(self, name, *args, **kwargs):
-        results = []
-        for env in self.envs:
-            function = getattr(env, name)
-            if callable(function):
-                results.append(function(*args, **kwargs))
-            else:
-                results.append(function)
-
-        return tuple(results)
-
-    def set_attr(self, name, values):
-        if not isinstance(values, (list, tuple)):
-            values = [values for _ in range(self.num_envs)]
-        if len(values) != self.num_envs:
-            raise ValueError(
-                "Values must be a list or tuple with length equal to the "
-                f"number of environments. Got `{len(values)}` values for "
-                f"{self.num_envs} environments."
-            )
-
-        for env, value in zip(self.envs, values):
-            setattr(env, name, value)
-
-    def close_extras(self, **kwargs):
-        """Close the environments."""
-        [env.close() for env in self.envs]
-
-    def _check_spaces(self):
-        for env in self.envs:
-            if not (env.observation_space == self.single_observation_space):
-                raise RuntimeError(
-                    "Some environments have an observation space different from "
-                    f"`{self.single_observation_space}`. In order to batch observations, "
-                    "the observation spaces from all environments must be equal."
-                )
-
-            if not (env.action_space == self.single_action_space):
-                raise RuntimeError(
-                    "Some environments have an action space different from "
-                    f"`{self.single_action_space}`. In order to batch actions, the "
-                    "action spaces from all environments must be equal."
-                )
-
-        else:
-            return True
diff --git a/spaces/crashedice/signify/signify/gan/data/template_dataset.py b/spaces/crashedice/signify/signify/gan/data/template_dataset.py
deleted file mode 100644
index 0b34667931243f28bd6438d72763bf182d149818..0000000000000000000000000000000000000000
--- a/spaces/crashedice/signify/signify/gan/data/template_dataset.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""Dataset class template
-
-This module provides a template for users to implement custom datasets.
-You can specify '--dataset_mode template' to use this dataset.
-The class name should be consistent with both the filename and its dataset_mode option.
-The filename should be <dataset_mode>_dataset.py
-The class name should be <Dataset_mode>Dataset.py
-You need to implement the following functions:
-    -- <modify_commandline_options>:　Add dataset-specific options and rewrite default values for existing options.
-    -- <__init__>: Initialize this dataset class.
-    -- <__getitem__>: Return a data point and its metadata information.
-    -- <__len__>: Return the number of images.
-"""
-from signify.gan.data.base_dataset import BaseDataset, get_transform
-# from data.image_folder import make_dataset
-# from PIL import Image
-
-
-class TemplateDataset(BaseDataset):
-    """A template dataset class for you to implement custom datasets."""
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        """Add new dataset-specific options, and rewrite default values for existing options.
-
-        Parameters:
-            parser          -- original option parser
-            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
-        Returns:
-            the modified parser.
-        """
-        parser.add_argument('--new_dataset_option', type=float, default=1.0, help='new dataset option')
-        parser.set_defaults(max_dataset_size=10, new_dataset_option=2.0)  # specify dataset-specific default values
-        return parser
-
-    def __init__(self, opt):
-        """Initialize this dataset class.
-
-        Parameters:
-            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
-
-        A few things can be done here.
-        - save the options (have been done in BaseDataset)
-        - get image paths and meta information of the dataset.
-        - define the image transformation.
-        """
-        # save the option and dataset root
-        BaseDataset.__init__(self, opt)
-        # get the image paths of your dataset;
-        self.image_paths = []  # You can call sorted(make_dataset(self.root, opt.max_dataset_size)) to get all the image paths under the directory self.root
-        # define the default transform function. You can use <base_dataset.get_transform>; You can also define your custom transform function
-        self.transform = get_transform(opt)
-
-    def __getitem__(self, index):
-        """Return a data point and its metadata information.
-
-        Parameters:
-            index -- a random integer for data indexing
-
-        Returns:
-            a dictionary of data with their names. It usually contains the data itself and its metadata information.
-
-        Step 1: get a random image path: e.g., path = self.image_paths[index]
-        Step 2: load your data from the disk: e.g., image = Image.open(path).convert('RGB').
-        Step 3: convert your data to a PyTorch tensor. You can use helpder functions such as self.transform. e.g., data = self.transform(image)
-        Step 4: return a data point as a dictionary.
-        """
-        path = 'temp'    # needs to be a string
-        data_A = None    # needs to be a tensor
-        data_B = None    # needs to be a tensor
-        return {'data_A': data_A, 'data_B': data_B, 'path': path}
-
-    def __len__(self):
-        """Return the total number of images."""
-        return len(self.image_paths)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/GimpPaletteFile.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/GimpPaletteFile.py
deleted file mode 100644
index d388928945a0f6711de2b1c8d1ed50ce192a8219..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/GimpPaletteFile.py
+++ /dev/null
@@ -1,56 +0,0 @@
-#
-# Python Imaging Library
-# $Id$
-#
-# stuff to read GIMP palette files
-#
-# History:
-# 1997-08-23 fl     Created
-# 2004-09-07 fl     Support GIMP 2.0 palette files.
-#
-# Copyright (c) Secret Labs AB 1997-2004.  All rights reserved.
-# Copyright (c) Fredrik Lundh 1997-2004.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import re
-
-from ._binary import o8
-
-
-class GimpPaletteFile:
-    """File handler for GIMP's palette format."""
-
-    rawmode = "RGB"
-
-    def __init__(self, fp):
-        self.palette = [o8(i) * 3 for i in range(256)]
-
-        if fp.readline()[:12] != b"GIMP Palette":
-            msg = "not a GIMP palette file"
-            raise SyntaxError(msg)
-
-        for i in range(256):
-            s = fp.readline()
-            if not s:
-                break
-
-            # skip fields and comment lines
-            if re.match(rb"\w+:|#", s):
-                continue
-            if len(s) > 100:
-                msg = "bad palette file"
-                raise SyntaxError(msg)
-
-            v = tuple(map(int, s.split()[:3]))
-            if len(v) != 3:
-                msg = "bad palette entry"
-                raise ValueError(msg)
-
-            self.palette[i] = o8(v[0]) + o8(v[1]) + o8(v[2])
-
-        self.palette = b"".join(self.palette)
-
-    def getpalette(self):
-        return self.palette, self.rawmode
diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py
deleted file mode 100644
index eb4e0d31f1aedf4590628d394e1606920fefb5c9..0000000000000000000000000000000000000000
--- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.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 = "arcface"
-config.network = "r18"
-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/ms1m-retinaface-t1"
-config.num_classes = 93431
-config.num_image = 5179510
-config.num_epoch = 25
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
diff --git a/spaces/deepwisdom/MetaGPT/metagpt/roles/engineer.py b/spaces/deepwisdom/MetaGPT/metagpt/roles/engineer.py
deleted file mode 100644
index 97d0af08714e65aa39a14ee10a2b564b66137d72..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/metagpt/roles/engineer.py
+++ /dev/null
@@ -1,204 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/11 14:43
-@Author  : alexanderwu
-@File    : engineer.py
-"""
-import asyncio
-from collections import OrderedDict
-from pathlib import Path
-
-import aiofiles
-
-from metagpt.actions import WriteCode, WriteCodeReview, WriteDesign, WriteTasks
-from metagpt.config import CONFIG
-from metagpt.logs import logger
-from metagpt.roles import Role
-from metagpt.schema import Message
-from metagpt.utils.common import CodeParser
-from metagpt.utils.special_tokens import FILENAME_CODE_SEP, MSG_SEP
-
-
-async def gather_ordered_k(coros, k) -> list:
-    tasks = OrderedDict()
-    results = [None] * len(coros)
-    done_queue = asyncio.Queue()
-
-    for i, coro in enumerate(coros):
-        if len(tasks) >= k:
-            done, _ = await asyncio.wait(tasks.keys(), return_when=asyncio.FIRST_COMPLETED)
-            for task in done:
-                index = tasks.pop(task)
-                await done_queue.put((index, task.result()))
-        task = asyncio.create_task(coro)
-        tasks[task] = i
-
-    if tasks:
-        done, _ = await asyncio.wait(tasks.keys())
-        for task in done:
-            index = tasks[task]
-            await done_queue.put((index, task.result()))
-
-    while not done_queue.empty():
-        index, result = await done_queue.get()
-        results[index] = result
-
-    return results
-
-
-class Engineer(Role):
-    def __init__(
-        self,
-        name="Alex",
-        profile="Engineer",
-        goal="Write elegant, readable, extensible, efficient code",
-        constraints="The code you write should conform to code standard like PEP8, be modular, easy to read and maintain",
-        n_borg=1,
-        use_code_review=False,
-    ):
-        super().__init__(name, profile, goal, constraints)
-        self._init_actions([WriteCode])
-        self.use_code_review = use_code_review
-        if self.use_code_review:
-            self._init_actions([WriteCode, WriteCodeReview])
-        self._watch([WriteTasks])
-        self.todos = []
-        self.n_borg = n_borg
-
-    @classmethod
-    def parse_tasks(self, task_msg: Message) -> list[str]:
-        if task_msg.instruct_content:
-            return task_msg.instruct_content.dict().get("Task list")
-        return CodeParser.parse_file_list(block="Task list", text=task_msg.content)
-
-    @classmethod
-    def parse_code(self, code_text: str) -> str:
-        return CodeParser.parse_code(block="", text=code_text)
-
-    @classmethod
-    def parse_workspace(cls, system_design_msg: Message) -> str:
-        if system_design_msg.instruct_content:
-            return system_design_msg.instruct_content.dict().get("Python package name").strip().strip("'").strip('"')
-        return CodeParser.parse_str(block="Python package name", text=system_design_msg.content)
-
-    def get_workspace(self) -> Path:
-        msg = self._rc.memory.get_by_action(WriteDesign)[-1]
-        if not msg:
-            return CONFIG.workspace / "src"
-        workspace = self.parse_workspace(msg)
-        # Codes are written in workspace/{package_name}/{package_name}
-        return CONFIG.workspace / workspace
-
-    async def write_file(self, filename: str, code: str):
-        workspace = self.get_workspace()
-        filename = filename.replace('"', "").replace("\n", "")
-        file = workspace / filename
-        file.parent.mkdir(parents=True, exist_ok=True)
-        async with aiofiles.open(file, "w") as f:
-            await f.write(code)
-        return file
-
-    def recv(self, message: Message) -> None:
-        self._rc.memory.add(message)
-        if message in self._rc.important_memory:
-            self.todos = self.parse_tasks(message)
-
-    async def _act_mp(self) -> Message:
-        # self.recreate_workspace()
-        todo_coros = []
-        for todo in self.todos:
-            todo_coro = WriteCode().run(
-                context=self._rc.memory.get_by_actions([WriteTasks, WriteDesign]), filename=todo
-            )
-            todo_coros.append(todo_coro)
-
-        rsps = await gather_ordered_k(todo_coros, self.n_borg)
-        for todo, code_rsp in zip(self.todos, rsps):
-            _ = self.parse_code(code_rsp)
-            logger.info(todo)
-            logger.info(code_rsp)
-            # self.write_file(todo, code)
-            msg = Message(content=code_rsp, role=self.profile, cause_by=type(self._rc.todo))
-            self._rc.memory.add(msg)
-            del self.todos[0]
-
-        logger.info(f"Done {self.get_workspace()} generating.")
-        msg = Message(content="all done.", role=self.profile, cause_by=type(self._rc.todo))
-        return msg
-
-    async def _act_sp(self) -> Message:
-        code_msg_all = []  # gather all code info, will pass to qa_engineer for tests later
-        instruct_content = {}
-        for todo in self.todos:
-            code = await WriteCode().run(context=self._rc.history, filename=todo)
-            # logger.info(todo)
-            # logger.info(code_rsp)
-            # code = self.parse_code(code_rsp)
-            file_path = await self.write_file(todo, code)
-            msg = Message(content=code, role=self.profile, cause_by=type(self._rc.todo))
-            self._rc.memory.add(msg)
-            instruct_content[todo] = code
-
-            # code_msg = todo + FILENAME_CODE_SEP + str(file_path)
-            code_msg = (todo, file_path)
-            code_msg_all.append(code_msg)
-
-        logger.info(f"Done {self.get_workspace()} generating.")
-        msg = Message(
-            content=MSG_SEP.join(todo + FILENAME_CODE_SEP + str(file_path) for todo, file_path in code_msg_all),
-            instruct_content=instruct_content,
-            role=self.profile,
-            cause_by=type(self._rc.todo),
-            send_to="QaEngineer",
-        )
-        return msg
-
-    async def _act_sp_precision(self) -> Message:
-        code_msg_all = []  # gather all code info, will pass to qa_engineer for tests later
-        instruct_content = {}
-        for todo in self.todos:
-            """
-            # 从历史信息中挑选必须的信息，以减少prompt长度（人工经验总结）
-            1. Architect全部
-            2. ProjectManager全部
-            3. 是否需要其他代码（暂时需要）？
-            TODO:目标是不需要。在任务拆分清楚后，根据设计思路，不需要其他代码也能够写清楚单个文件，如果不能则表示还需要在定义的更清晰，这个是代码能够写长的关键
-            """
-            context = []
-            msg = self._rc.memory.get_by_actions([WriteDesign, WriteTasks, WriteCode])
-            for m in msg:
-                context.append(m.content)
-            context_str = "\n".join(context)
-            # 编写code
-            code = await WriteCode().run(context=context_str, filename=todo)
-            # code review
-            if self.use_code_review:
-                try:
-                    rewrite_code = await WriteCodeReview().run(context=context_str, code=code, filename=todo)
-                    code = rewrite_code
-                except Exception as e:
-                    logger.error("code review failed!", e)
-                    pass
-            file_path = await self.write_file(todo, code)
-            msg = Message(content=code, role=self.profile, cause_by=WriteCode)
-            self._rc.memory.add(msg)
-            instruct_content[todo] = code
-
-            code_msg = (todo, file_path)
-            code_msg_all.append(code_msg)
-
-        logger.info(f"Done {self.get_workspace()} generating.")
-        msg = Message(
-            content=MSG_SEP.join(todo + FILENAME_CODE_SEP + str(file_path) for todo, file_path in code_msg_all),
-            instruct_content=instruct_content,
-            role=self.profile,
-            cause_by=type(self._rc.todo),
-            send_to="QaEngineer",
-        )
-        return msg
-
-    async def _act(self) -> Message:
-        if self.use_code_review:
-            return await self._act_sp_precision()
-        return await self._act_sp()
diff --git a/spaces/deepwisdom/MetaGPT/metagpt/tools/web_browser_engine.py b/spaces/deepwisdom/MetaGPT/metagpt/tools/web_browser_engine.py
deleted file mode 100644
index 1f1a5ec67f2ff9f7439bed02cf4c76ec7a03c235..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/metagpt/tools/web_browser_engine.py
+++ /dev/null
@@ -1,60 +0,0 @@
-#!/usr/bin/env python
-"""
-@Modified By: mashenquan, 2023/8/20. Remove global configuration `CONFIG`, enable configuration support for business isolation.
-"""
-
-from __future__ import annotations
-
-import importlib
-from typing import Any, Callable, Coroutine, Dict, Literal, overload
-
-from metagpt.config import CONFIG
-from metagpt.tools import WebBrowserEngineType
-from metagpt.utils.parse_html import WebPage
-
-
-class WebBrowserEngine:
-    def __init__(
-        self,
-        options: Dict,
-        engine: WebBrowserEngineType | None = None,
-        run_func: Callable[..., Coroutine[Any, Any, WebPage | list[WebPage]]] | None = None,
-    ):
-        engine = engine or options.get("web_browser_engine")
-        if engine is None:
-            raise NotImplementedError
-
-        if WebBrowserEngineType(engine) is WebBrowserEngineType.PLAYWRIGHT:
-            module = "metagpt.tools.web_browser_engine_playwright"
-            run_func = importlib.import_module(module).PlaywrightWrapper(options=options).run
-        elif WebBrowserEngineType(engine) is WebBrowserEngineType.SELENIUM:
-            module = "metagpt.tools.web_browser_engine_selenium"
-            run_func = importlib.import_module(module).SeleniumWrapper(options=options).run
-        elif WebBrowserEngineType(engine) is WebBrowserEngineType.CUSTOM:
-            run_func = run_func
-        else:
-            raise NotImplementedError
-        self.run_func = run_func
-        self.engine = engine
-
-    @overload
-    async def run(self, url: str) -> WebPage:
-        ...
-
-    @overload
-    async def run(self, url: str, *urls: str) -> list[WebPage]:
-        ...
-
-    async def run(self, url: str, *urls: str) -> WebPage | list[WebPage]:
-        return await self.run_func(url, *urls)
-
-
-if __name__ == "__main__":
-    import fire
-
-    async def main(url: str, *urls: str, engine_type: Literal["playwright", "selenium"] = "playwright", **kwargs):
-        return await WebBrowserEngine(options=CONFIG.options, engine=WebBrowserEngineType(engine_type), **kwargs).run(
-            url, *urls
-        )
-
-    fire.Fire(main)
diff --git a/spaces/descript/vampnet/scripts/utils/xeno-canto-dl.py b/spaces/descript/vampnet/scripts/utils/xeno-canto-dl.py
deleted file mode 100644
index 89acc822d291bd0a145b656fc62be5209a6f6cfc..0000000000000000000000000000000000000000
--- a/spaces/descript/vampnet/scripts/utils/xeno-canto-dl.py
+++ /dev/null
@@ -1,234 +0,0 @@
-from xenopy import Query
-
-
-SPECIES = [
-    "American Robin",
-    "Northern Cardinal",
-    "Mourning Dove",
-    "American Crow",
-    "Baltimore Oriole",
-    "Blue Jay",
-    "Eastern Bluebird",
-    "House Finch",
-    "American Goldfinch",
-    "House Sparrow",
-    "Song Sparrow",
-    "Tufted Titmouse",
-    "White-breasted Nuthatch",
-    "European Starling",
-    "American Redstart",
-    "Red-winged Blackbird",
-    "Brown-headed Cowbird",
-    "Common Grackle",
-    "Boat-tailed Grackle",
-    "Common Yellowthroat",
-    "Northern Mockingbird",
-    "Carolina Wren",
-    "Eastern Meadowlark",
-    "Chipping Sparrow",
-    "Tree Swallow",
-    "Barn Swallow",
-    "Cliff Swallow",
-    "Pine Siskin",
-    "Indigo Bunting",
-    "Eastern Towhee",
-    "Carolina Chickadee",
-    "Great Crested Flycatcher",
-    "Eastern Wood-Pewee",
-    "Ovenbird",
-    "Northern Flicker",
-    "Red-eyed Vireo",
-    "American Woodcock",
-    "Eastern Phoebe",
-    "Downy Woodpecker",
-    "Scarlet Tanager",
-    "Yellow Warbler",
-    "White-eyed Vireo",
-    "Common Loon",
-    "White-throated Sparrow",
-    "Yellow-throated Vireo",
-    "Great Blue Heron",
-    "Belted Kingfisher",
-    "Pied-billed Grebe",
-    "Wild Turkey",
-    "Wood Thrush",
-    "Rose-breasted Grosbeak",
-    "Field Sparrow",
-    "Hooded Warbler",
-    "Northern Parula",
-    "Chestnut-sided Warbler",
-    "Blue-winged Warbler",
-    "Red-bellied Woodpecker",
-    "Yellow-billed Cuckoo",
-    "Gray Catbird",
-    "Northern Saw-whet Owl",
-    "Osprey",
-    "Common Nighthawk",
-    "Broad-winged Hawk",
-    "Black-throated Green Warbler",
-    "Great Horned Owl",
-    "Common Raven",
-    "Barred Owl",
-    "Canada Warbler",
-    "Magnolia Warbler",
-    "Black-and-white Warbler",
-    "Eastern Kingbird",
-    "Swainson's Thrush",
-    "Worm-eating Warbler",
-    "Prairie Warbler",
-    "Baltimore Oriole",
-    "Black-throated Blue Warbler",
-    "Louisiana Waterthrush",
-    "Blackburnian Warbler",
-    "Black-capped Chickadee",
-    "Cerulean Warbler",
-    "Red-shouldered Hawk",
-    "Cooper's Hawk",
-    "Yellow-throated Warbler",
-    "Blue-headed Vireo",
-    "Blackpoll Warbler",
-    "Ruffed Grouse",
-    "Kentucky Warbler",
-    "Hermit Thrush",
-    "Cedar Waxwing",
-    "Eastern Screech-Owl",
-    "Northern Goshawk",
-    "Green Heron",
-    "Red-tailed Hawk",
-    "Black Vulture",
-    "Hairy Woodpecker",
-    "Golden-crowned Kinglet",
-    "Ruby-crowned Kinglet",
-    "Bicknell's Thrush",
-    "Blue-gray Gnatcatcher",
-    "Veery",
-    "Pileated Woodpecker",
-    "Purple Finch",
-    "White-crowned Sparrow",
-    "Snow Bunting",
-    "Pine Grosbeak",
-    "American Tree Sparrow",
-    "Dark-eyed Junco",
-    "Snowy Owl",
-    "White-winged Crossbill",
-    "Red Crossbill",
-    "Common Redpoll",
-    "Northern Shrike",
-    "Northern Harrier",
-    "Rough-legged Hawk",
-    "Long-eared Owl",
-    "Evening Grosbeak",
-    "Northern Pintail",
-    "American Black Duck",
-    "Mallard",
-    "Canvasback",
-    "Redhead",
-    "Ring-necked Duck",
-    "Greater Scaup",
-    "Lesser Scaup",
-    "Bufflehead",
-    "Common Goldeneye",
-    "Hooded Merganser",
-    "Common Merganser",
-    "Red-breasted Merganser",
-    "Ruddy Duck",
-    "Wood Duck",
-    "Gadwall",
-    "American Wigeon",
-    "Northern Shoveler",
-    "Green-winged Teal",
-    "Blue-winged Teal",
-    "Cinnamon Teal",
-    "Ringed Teal",
-    "Cape Teal",
-    "Northern Fulmar",
-    "Yellow-billed Loon",
-    "Red-throated Loon",
-    "Arctic Loon",
-    "Pacific Loon",
-    "Horned Grebe",
-    "Red-necked Grebe",
-    "Eared Grebe",
-    "Western Grebe",
-    "Clark's Grebe",
-    "Double-crested Cormorant",
-    "Pelagic Cormorant",
-    "Great Cormorant",
-    "American White Pelican",
-    "Brown Pelican",
-    "Brandt's Cormorant",
-    "Least Bittern",
-    "Great Egret",
-    "Snowy Egret",
-    "Little Blue Heron",
-    "Tricolored Heron",
-    "Reddish Egret",
-    "Black-crowned Night-Heron",
-    "Yellow-crowned Night-Heron",
-    "White Ibis",
-    "Glossy Ibis",
-    "Roseate Spoonbill",
-    "Wood Stork",
-    "Black-bellied Whistling-Duck",
-    "Fulvous Whistling-Duck",
-    "Greater White-fronted Goose",
-    "Snow Goose",
-    "Ross's Goose",
-    "Canada Goose",
-    "Brant",
-    "Mute Swan",
-    "Tundra Swan",
-    "Whooper Swan",
-    "Sandhill Crane",
-    "Black-necked Stilt",
-    "American Avocet",
-    "Northern Jacana",
-    "Greater Yellowlegs",
-    "Lesser Yellowlegs",
-    "Willet",
-    "Spotted Sandpiper",
-    "Upland Sandpiper",
-    "Whimbrel",
-    "Long-billed Curlew",
-    "Marbled Godwit",
-    "Ruddy Turnstone",
-    "Red Knot",
-    "Sanderling",
-    "Semipalmated Sandpiper",
-    "Western Sandpiper",
-    "Least Sandpiper",
-    "White-rumped Sandpiper",
-    "Baird's Sandpiper",
-    "Pectoral Sandpiper",
-    "Dunlin",
-    "Buff-breasted Sandpiper",
-    "Short-billed Dowitcher",
-    "Long-billed Dowitcher",
-    "Common Snipe",
-    "American Woodcock",
-    "Wilson's Phalarope",
-    "Red-necked Phalarope",
-    "Red Phalarope"
-]
-
-from pathlib import Path
-
-def remove_spaces(s):
-    return s.replace(" ", "")
-
-for species in SPECIES: 
-    if Path("/media/CHONK/hugo/xeno-canto-full/" + remove_spaces(species)).exists():
-        continue
-    try:
-        q = Query(
-            name=species, q="A", length="10-30", 
-            )
-
-        # retrieve metadata
-        metafiles = q.retrieve_meta(verbose=True)
-        # retrieve recordings
-        q.retrieve_recordings(multiprocess=True, nproc=10, attempts=10, outdir="/media/CHONK/hugo/xeno-canto-full/")
-
-    except:
-        print("Failed to download " + species)
-        continue
\ No newline at end of file
diff --git a/spaces/devseek/accident_detection/app.py b/spaces/devseek/accident_detection/app.py
deleted file mode 100644
index 382ece22d7e18f00ae7cfe8cf47aef4f0e178786..0000000000000000000000000000000000000000
--- a/spaces/devseek/accident_detection/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import gradio as gr
-from fastbook import *
-from fastai.vision.widgets import *
-import timm
-categories = ['accident' , 'non-accident']
-learn = load_learner('model.pkl')
-def classify(img):
-  category,index,probs = learn.predict(img)
-  return (dict(zip(categories , map(float,probs))))
-img = gr.inputs.Image(shape=(224))
-label = gr.outputs.Label()
-example = ['accident.jpg' , 'non-accident.jpg']
-iface = gr.Interface(fn=classify, inputs="image", outputs="label" , examples= example)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Body Combat 56 Torrent Download Free.md b/spaces/diacanFperku/AutoGPT/Body Combat 56 Torrent Download Free.md
deleted file mode 100644
index e7688b0550b6ec9d891e478dd82af34c63c433b1..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Body Combat 56 Torrent Download Free.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>body combat 56 torrent download</h2><br /><p><b><b>Download</b> &#10031;&#10031;&#10031; <a href="https://gohhs.com/2uFUPL">https://gohhs.com/2uFUPL</a></b></p><br /><br />
-
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-<br />
-<br />
-<p></p>
diff --git a/spaces/diacanFperku/AutoGPT/Justice 20 Type-B Love Poison (D.md b/spaces/diacanFperku/AutoGPT/Justice 20 Type-B Love Poison (D.md
deleted file mode 100644
index 0114ceeb7b483c91c4a4d116705c1d880b32d8fe..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Justice 20 Type-B Love Poison (D.md	
+++ /dev/null
@@ -1,12 +0,0 @@
-<h2>Justice 20 Type-B Love Poison (D</h2><br /><p><b><b>Download File</b> &#9733;&#9733;&#9733; <a href="https://gohhs.com/2uFUkm">https://gohhs.com/2uFUkm</a></b></p><br /><br />
-
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-Sort by. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/diego2554/RemBG_super/app.py b/spaces/diego2554/RemBG_super/app.py
deleted file mode 100644
index d76c527c184d8c2b282b5b709704f4fde7e95d8c..0000000000000000000000000000000000000000
--- a/spaces/diego2554/RemBG_super/app.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import gradio as gr
-import os
-import cv2
-from rembg import new_session, remove
-from PIL import Image
-from io import BytesIO
-
-def inference(file, mask, model, alpha_influence, segmentation_strength, smoothing):
-    im = cv2.imread(file, cv2.IMREAD_COLOR)
-    cv2.imwrite(os.path.join("input.png"), im)
-
-    input_path = 'input.png'
-    output_path = 'output.png'
-
-    with open(input_path, 'rb') as i:
-        with open(output_path, 'wb') as o:
-            input = i.read()
-            output = remove(
-                input, 
-                only_mask=(True if mask == "Mask only" else False),
-                alpha_matting=True,  # Habilitar el modo alpha matting
-                alpha_matting_foreground_threshold=alpha_influence,  # Control de influencia del canal alfa
-                alpha_matting_background_threshold=1 - alpha_influence,  # Control del canal alfa para el fondo
-                alpha_matting_erode_size=int(segmentation_strength * 10),  # Control de fuerza de segmentación
-                alpha_matting_smoothing=smoothing,  # Control de suavizado de bordes de la segmentación
-                session=new_session(model)
-            )
-
-            o.write(output)
-
-    return Image.open(BytesIO(output))
-
-title = "Background Using RemBG"
-description = "<a href='https://www.buymeacoffee.com/diego2554' target='_blank'>Help me improve my computer equipment, I need RTX 4070 :)</a>Gradio demo for RemBG. erase the background of any image, To use it, simply upload your image and adjust the sliders and choose a eraser plugin from the U2net library. / <a href='https://huggingface.co/spaces/KenjieDec/RemBG' target='_blank'>Original article made by KenjieDec</a> / <a href='https://github.com/danielgatis/rembg' target='_blank'>Github Repo</a></p>"
-article = "<p style='text-align: center;'><a href='https://github.com/danielgatis/rembg' target='_blank'>Github Repo</a></p>"
-article = "<p style='text-align: center;'><a href='https://huggingface.co/spaces/KenjieDec/RemBG' target='_blank'>Model on Hugging Face</a></p>"
-
-gr.Interface(
-    inference, 
-    [
-        gr.inputs.Image(type="filepath", label="Input"),
-        gr.inputs.Radio(
-            [
-                "Default", 
-                "Mask only"
-            ], 
-            type="value",
-            default="Default",
-            label="Choices"
-        ),
-        gr.inputs.Dropdown([
-            "u2net", 
-            "u2netp", 
-            "u2net_human_seg", 
-            "u2net_cloth_seg", 
-            "silueta",
-            "isnet-general-use",
-            "isnet-anime",
-            "sam",
-        ], 
-        type="value",
-        default="isnet-general-use",
-        label="Models"
-        ),
-        gr.inputs.Slider(minimum=0.5, maximum=1.5, default=1, label="Alpha Influence"),
-        gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.5, label="Segmentation Strength"),
-        gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, label="Smoothing"),
-    ], 
-    gr.outputs.Image(type="PIL", label="Output"),
-    #description = "<a href='https://huggingface.co/spaces/KenjieDec/RemBG' target='_blank'>Original article made by KenjieDec</a><a href='https://github.com/danielgatis/rembg' target='_blank'>Github Repo</a></p>"
-    title=title,
-    description=description,
-    article=article,
-    examples=[["lion.png", "Default", "u2net", 1, 0.5, 0.25], ["girl.jpg", "Default", "u2net", 1, 0.5, 0.25], ["anime-girl.jpg", "Default", "isnet-anime", 1, 0.5, 0.25]],
-    enable_queue=True
-).launch()
diff --git a/spaces/diego2554/RemBG_super/rembg/commands/__init__.py b/spaces/diego2554/RemBG_super/rembg/commands/__init__.py
deleted file mode 100644
index 64f8993e9b710c7150d16ee4361fc0d406d72f55..0000000000000000000000000000000000000000
--- a/spaces/diego2554/RemBG_super/rembg/commands/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from importlib import import_module
-from pathlib import Path
-from pkgutil import iter_modules
-
-command_functions = []
-
-package_dir = Path(__file__).resolve().parent
-for _b, module_name, _p in iter_modules([str(package_dir)]):
-    module = import_module(f"{__name__}.{module_name}")
-    for attribute_name in dir(module):
-        attribute = getattr(module, attribute_name)
-        if attribute_name.endswith("_command"):
-            command_functions.append(attribute)
diff --git a/spaces/digitalxingtong/Bufeiyan-c-Bert-VITS2/text/chinese.py b/spaces/digitalxingtong/Bufeiyan-c-Bert-VITS2/text/chinese.py
deleted file mode 100644
index 276753880b73de2e8889dcb2101cd98c09e0710b..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Bufeiyan-c-Bert-VITS2/text/chinese.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import os
-import re
-
-import cn2an
-from pypinyin import lazy_pinyin, Style
-
-from text import symbols
-from text.symbols import punctuation
-from text.tone_sandhi import ToneSandhi
-
-current_file_path = os.path.dirname(__file__)
-pinyin_to_symbol_map = {line.split("\t")[0]: line.strip().split("\t")[1] for line in
-                        open(os.path.join(current_file_path, 'opencpop-strict.txt')).readlines()}
-
-import jieba.posseg as psg
-
-
-rep_map = {
-    '：': ',',
-    '；': ',',
-    '，': ',',
-    '。': '.',
-    '！': '!',
-    '？': '?',
-    '\n': '.',
-    "·": ",",
-    '、': ",",
-    '...': '…',
-    '$': '.',
-    '“': "'",
-    '”': "'",
-    '‘': "'",
-    '’': "'",
-    '（': "'",
-    '）': "'",
-    '(': "'",
-    ')': "'",
-    '《': "'",
-    '》': "'",
-    '【': "'",
-    '】': "'",
-    '[': "'",
-    ']': "'",
-    '—': "-",
-    '～': "-",
-    '~': "-",
-    '「': "'",
-    '」': "'",
-
-}
-
-tone_modifier = ToneSandhi()
-
-def replace_punctuation(text):
-    text = text.replace("嗯", "恩").replace("呣","母")
-    pattern = re.compile('|'.join(re.escape(p) for p in rep_map.keys()))
-
-    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
-
-    replaced_text = re.sub(r'[^\u4e00-\u9fa5'+"".join(punctuation)+r']+', '', replaced_text)
-
-    return replaced_text
-
-def g2p(text):
-    pattern = r'(?<=[{0}])\s*'.format(''.join(punctuation))
-    sentences = [i for i in re.split(pattern, text) if i.strip()!='']
-    phones, tones, word2ph = _g2p(sentences)
-    assert sum(word2ph) == len(phones)
-    assert len(word2ph) == len(text) #Sometimes it will crash,you can add a try-catch.
-    phones = ['_'] + phones + ["_"]
-    tones = [0] + tones + [0]
-    word2ph = [1] + word2ph + [1]
-    return phones, tones, word2ph
-
-
-def _get_initials_finals(word):
-    initials = []
-    finals = []
-    orig_initials = lazy_pinyin(
-        word, neutral_tone_with_five=True, style=Style.INITIALS)
-    orig_finals = lazy_pinyin(
-        word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-    for c, v in zip(orig_initials, orig_finals):
-        initials.append(c)
-        finals.append(v)
-    return initials, finals
-
-
-def _g2p(segments):
-    phones_list = []
-    tones_list = []
-    word2ph = []
-    for seg in segments:
-        pinyins = []
-        # Replace all English words in the sentence
-        seg = re.sub('[a-zA-Z]+', '', seg)
-        seg_cut = psg.lcut(seg)
-        initials = []
-        finals = []
-        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
-        for word, pos in seg_cut:
-            if pos == 'eng':
-                continue
-            sub_initials, sub_finals = _get_initials_finals(word)
-            sub_finals = tone_modifier.modified_tone(word, pos,
-                                                          sub_finals)
-            initials.append(sub_initials)
-            finals.append(sub_finals)
-
-            # assert len(sub_initials) == len(sub_finals) == len(word)
-        initials = sum(initials, [])
-        finals = sum(finals, [])
-        #
-        for c, v in zip(initials, finals):
-            raw_pinyin = c+v
-            # NOTE: post process for pypinyin outputs
-            # we discriminate i, ii and iii
-            if c == v:
-                assert c in punctuation
-                phone = [c]
-                tone = '0'
-                word2ph.append(1)
-            else:
-                v_without_tone = v[:-1]
-                tone = v[-1]
-
-                pinyin = c+v_without_tone
-                assert tone in '12345'
-
-                if c:
-                    # 多音节
-                    v_rep_map = {
-                        "uei": 'ui',
-                        'iou': 'iu',
-                        'uen': 'un',
-                    }
-                    if v_without_tone in v_rep_map.keys():
-                        pinyin = c+v_rep_map[v_without_tone]
-                else:
-                    # 单音节
-                    pinyin_rep_map = {
-                        'ing': 'ying',
-                        'i': 'yi',
-                        'in': 'yin',
-                        'u': 'wu',
-                    }
-                    if pinyin in pinyin_rep_map.keys():
-                        pinyin = pinyin_rep_map[pinyin]
-                    else:
-                        single_rep_map = {
-                            'v': 'yu',
-                            'e': 'e',
-                            'i': 'y',
-                            'u': 'w',
-                        }
-                        if pinyin[0] in single_rep_map.keys():
-                            pinyin = single_rep_map[pinyin[0]]+pinyin[1:]
-
-                assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
-                phone = pinyin_to_symbol_map[pinyin].split(' ')
-                word2ph.append(len(phone))
-
-            phones_list += phone
-            tones_list += [int(tone)] * len(phone)
-    return phones_list, tones_list, word2ph
-
-
-
-def text_normalize(text):
-    numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-    for number in numbers:
-        text = text.replace(number, cn2an.an2cn(number), 1)
-    text = replace_punctuation(text)
-    return text
-
-def get_bert_feature(text, word2ph):
-    from  text import chinese_bert
-    return chinese_bert.get_bert_feature(text, word2ph)
-
-if __name__ == '__main__':
-    from text.chinese_bert import get_bert_feature
-    text = "啊！但是《原神》是由,米哈\游自主，  [研发]的一款全.新开放世界.冒险游戏"
-    text = text_normalize(text)
-    print(text)
-    phones, tones, word2ph = g2p(text)
-    bert = get_bert_feature(text, word2ph)
-
-    print(phones, tones, word2ph, bert.shape)
-
-
-# # 示例用法
-# text = "这是一个示例文本：,你好！这是一个测试...."
-# print(g2p_paddle(text))  # 输出: 这是一个示例文本你好这是一个测试
diff --git a/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/modules.py b/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/modules.py
deleted file mode 100644
index 92e0f32a51c472bfd1659a50a95a95d195281d2b..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/modules.py
+++ /dev/null
@@ -1,452 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-from attentions import Encoder
-
-LRELU_SLOPE = 0.1
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-    
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    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.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__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.convs1.apply(init_weights)
-
-        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)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        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):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__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])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    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.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
-class TransformerCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      n_layers,
-      n_heads,
-      p_dropout=0,
-      filter_channels=0,
-      mean_only=False,
-      wn_sharing_parameter=None,
-      gin_channels = 0
-      ):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = Encoder(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
diff --git a/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/recog_datasets/ST_SA_MJ_real_train.py b/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/recog_datasets/ST_SA_MJ_real_train.py
deleted file mode 100644
index 87dab3352d92c3105684908f50b9b8f6bcc71a16..0000000000000000000000000000000000000000
--- a/spaces/dinhminh20521597/OCR_DEMO/configs/_base_/recog_datasets/ST_SA_MJ_real_train.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Text Recognition Training set, including:
-# Synthetic Datasets: SynthText, SynthAdd, Syn90k
-# Real Dataset: IC11, IC13, IC15, COCO-Test, IIIT5k
-
-train_prefix = 'data/mixture'
-
-train_img_prefix1 = f'{train_prefix}/icdar_2011'
-train_img_prefix2 = f'{train_prefix}/icdar_2013'
-train_img_prefix3 = f'{train_prefix}/icdar_2015'
-train_img_prefix4 = f'{train_prefix}/coco_text'
-train_img_prefix5 = f'{train_prefix}/IIIT5K'
-train_img_prefix6 = f'{train_prefix}/SynthText_Add'
-train_img_prefix7 = f'{train_prefix}/SynthText'
-train_img_prefix8 = f'{train_prefix}/Syn90k'
-
-train_ann_file1 = f'{train_prefix}/icdar_2011/train_label.txt',
-train_ann_file2 = f'{train_prefix}/icdar_2013/train_label.txt',
-train_ann_file3 = f'{train_prefix}/icdar_2015/train_label.txt',
-train_ann_file4 = f'{train_prefix}/coco_text/train_label.txt',
-train_ann_file5 = f'{train_prefix}/IIIT5K/train_label.txt',
-train_ann_file6 = f'{train_prefix}/SynthText_Add/label.txt',
-train_ann_file7 = f'{train_prefix}/SynthText/shuffle_labels.txt',
-train_ann_file8 = f'{train_prefix}/Syn90k/shuffle_labels.txt'
-
-train1 = dict(
-    type='OCRDataset',
-    img_prefix=train_img_prefix1,
-    ann_file=train_ann_file1,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=20,
-        file_format='txt',
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=None,
-    test_mode=False)
-
-train2 = {key: value for key, value in train1.items()}
-train2['img_prefix'] = train_img_prefix2
-train2['ann_file'] = train_ann_file2
-
-train3 = {key: value for key, value in train1.items()}
-train3['img_prefix'] = train_img_prefix3
-train3['ann_file'] = train_ann_file3
-
-train4 = {key: value for key, value in train1.items()}
-train4['img_prefix'] = train_img_prefix4
-train4['ann_file'] = train_ann_file4
-
-train5 = {key: value for key, value in train1.items()}
-train5['img_prefix'] = train_img_prefix5
-train5['ann_file'] = train_ann_file5
-
-train6 = dict(
-    type='OCRDataset',
-    img_prefix=train_img_prefix6,
-    ann_file=train_ann_file6,
-    loader=dict(
-        type='AnnFileLoader',
-        repeat=1,
-        file_format='txt',
-        parser=dict(
-            type='LineStrParser',
-            keys=['filename', 'text'],
-            keys_idx=[0, 1],
-            separator=' ')),
-    pipeline=None,
-    test_mode=False)
-
-train7 = {key: value for key, value in train6.items()}
-train7['img_prefix'] = train_img_prefix7
-train7['ann_file'] = train_ann_file7
-
-train8 = {key: value for key, value in train6.items()}
-train8['img_prefix'] = train_img_prefix8
-train8['ann_file'] = train_ann_file8
-
-train_list = [train1, train2, train3, train4, train5, train6, train7, train8]
diff --git a/spaces/divyahansg/text-generation-webui-space/modules/RWKV.py b/spaces/divyahansg/text-generation-webui-space/modules/RWKV.py
deleted file mode 100644
index 5cf8937ad37944c0cebeeb8e0891bec1474724ea..0000000000000000000000000000000000000000
--- a/spaces/divyahansg/text-generation-webui-space/modules/RWKV.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import os
-from pathlib import Path
-
-import numpy as np
-from tokenizers import Tokenizer
-
-import modules.shared as shared
-from modules.callbacks import Iteratorize
-
-np.set_printoptions(precision=4, suppress=True, linewidth=200)
-
-os.environ['RWKV_JIT_ON'] = '1'
-os.environ["RWKV_CUDA_ON"] = '1' if shared.args.rwkv_cuda_on else '0' # use CUDA kernel for seq mode (much faster)
-
-from rwkv.model import RWKV
-from rwkv.utils import PIPELINE, PIPELINE_ARGS
-
-
-class RWKVModel:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def from_pretrained(self, path, dtype="fp16", device="cuda"):
-        tokenizer_path = Path(f"{path.parent}/20B_tokenizer.json")
-
-        if shared.args.rwkv_strategy is None:
-            model = RWKV(model=str(path), strategy=f'{device} {dtype}')
-        else:
-            model = RWKV(model=str(path), strategy=shared.args.rwkv_strategy)
-        pipeline = PIPELINE(model, str(tokenizer_path))
-
-        result = self()
-        result.pipeline = pipeline
-        return result
-
-    def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, alpha_frequency=0.1, alpha_presence=0.1, token_ban=[0], token_stop=[], callback=None):
-        args = PIPELINE_ARGS(
-            temperature = temperature,
-            top_p = top_p,
-            top_k = top_k,
-            alpha_frequency = alpha_frequency, # Frequency Penalty (as in GPT-3)
-            alpha_presence = alpha_presence, # Presence Penalty (as in GPT-3)
-            token_ban = token_ban, # ban the generation of some tokens
-            token_stop = token_stop
-        )
-
-        return context+self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
-
-    def generate_with_streaming(self, **kwargs):
-        with Iteratorize(self.generate, kwargs, callback=None) as generator:
-            reply = kwargs['context']
-            for token in generator:
-                reply += token
-                yield reply
-
-class RWKVTokenizer:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def from_pretrained(self, path):
-        tokenizer_path = path / "20B_tokenizer.json"
-        tokenizer = Tokenizer.from_file(str(tokenizer_path))
-
-        result = self()
-        result.tokenizer = tokenizer
-        return result
-
-    def encode(self, prompt):
-        return self.tokenizer.encode(prompt).ids
-
-    def decode(self, ids):
-        return self.tokenizer.decode(ids)
diff --git a/spaces/dlmn/SIH_S2T_multilingual_ASR/README.md b/spaces/dlmn/SIH_S2T_multilingual_ASR/README.md
deleted file mode 100644
index a81a181c88d5fc86f3f0c5a6d18d47cb4c8fba2a..0000000000000000000000000000000000000000
--- a/spaces/dlmn/SIH_S2T_multilingual_ASR/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: SIH S2T Multilingual ASR
-emoji: 🦀
-colorFrom: green
-colorTo: green
-sdk: streamlit
-sdk_version: 1.27.2
-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/dolceschokolade/chatbot-mini/components/Sidebar/components/OpenCloseButton.tsx b/spaces/dolceschokolade/chatbot-mini/components/Sidebar/components/OpenCloseButton.tsx
deleted file mode 100644
index 80ad30cfee42f5758c1d81b57df4fbda7a56e586..0000000000000000000000000000000000000000
--- a/spaces/dolceschokolade/chatbot-mini/components/Sidebar/components/OpenCloseButton.tsx
+++ /dev/null
@@ -1,42 +0,0 @@
-import { IconArrowBarLeft, IconArrowBarRight } from '@tabler/icons-react';
-
-interface Props {
-  onClick: any;
-  side: 'left' | 'right';
-}
-
-export const CloseSidebarButton = ({ onClick, side }: Props) => {
-  return (
-    <>
-      <button
-        className={`fixed top-5 ${
-          side === 'right' ? 'right-[270px]' : 'left-[270px]'
-        } z-50 h-7 w-7 hover:text-gray-400 dark:text-white dark:hover:text-gray-300 sm:top-0.5 sm:${
-          side === 'right' ? 'right-[270px]' : 'left-[270px]'
-        } sm:h-8 sm:w-8 sm:text-neutral-700`}
-        onClick={onClick}
-      >
-        {side === 'right' ? <IconArrowBarRight /> : <IconArrowBarLeft />}
-      </button>
-      <div
-        onClick={onClick}
-        className="absolute top-0 left-0 z-10 h-full w-full bg-black opacity-70 sm:hidden"
-      ></div>
-    </>
-  );
-};
-
-export const OpenSidebarButton = ({ onClick, side }: Props) => {
-  return (
-    <button
-      className={`fixed top-2.5 ${
-        side === 'right' ? 'right-2' : 'left-2'
-      } z-50 h-7 w-7 text-white hover:text-gray-400 dark:text-white dark:hover:text-gray-300 sm:top-0.5 sm:${
-        side === 'right' ? 'right-2' : 'left-2'
-      } sm:h-8 sm:w-8 sm:text-neutral-700`}
-      onClick={onClick}
-    >
-      {side === 'right' ? <IconArrowBarLeft /> : <IconArrowBarRight />}
-    </button>
-  );
-};
diff --git a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/README.md b/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/README.md
deleted file mode 100644
index 868e822b568a3e4e991aca968d9f895eda695fa1..0000000000000000000000000000000000000000
--- a/spaces/dorkai/text-generation-webui-main/text-generation-webui-main/README.md
+++ /dev/null
@@ -1,313 +0,0 @@
-# Text generation web UI
-
-A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.
-
-Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) of text generation.
-
-|![Image1](https://github.com/oobabooga/screenshots/raw/main/qa.png) | ![Image2](https://github.com/oobabooga/screenshots/raw/main/cai3.png) |
-|:---:|:---:|
-|![Image3](https://github.com/oobabooga/screenshots/raw/main/gpt4chan.png) | ![Image4](https://github.com/oobabooga/screenshots/raw/main/galactica.png) |
-
-## Features
-
-* Dropdown menu for switching between models
-* Notebook mode that resembles OpenAI's playground
-* Chat mode for conversation and role playing
-* Instruct mode compatible with Alpaca, Vicuna, Open Assistant, Dolly, Koala, and ChatGLM formats 
-* Nice HTML output for GPT-4chan
-* Markdown output for [GALACTICA](https://github.com/paperswithcode/galai), including LaTeX rendering
-* [Custom chat characters](docs/Custom-chat-characters.md)
-* Advanced chat features (send images, get audio responses with TTS)
-* Very efficient text streaming
-* Parameter presets
-* 8-bit mode
-* Layers splitting across GPU(s), CPU, and disk
-* CPU mode
-* [FlexGen](docs/FlexGen.md)
-* [DeepSpeed ZeRO-3](docs/DeepSpeed.md)
-* API [with](https://github.com/oobabooga/text-generation-webui/blob/main/api-example-stream.py) streaming and [without](https://github.com/oobabooga/text-generation-webui/blob/main/api-example.py) streaming
-* [LLaMA model](docs/LLaMA-model.md)
-* [4-bit GPTQ mode](docs/GPTQ-models-(4-bit-mode).md)
-* [llama.cpp](docs/llama.cpp-models.md)
-* [RWKV model](docs/RWKV-model.md)
-* [LoRA (loading and training)](docs/Using-LoRAs.md)
-* Softprompts
-* [Extensions](docs/Extensions.md) - see the [user extensions list](https://github.com/oobabooga/text-generation-webui-extensions)
-
-## Installation
-
-### One-click installers
-
-| Windows | Linux | macOS |
-|-------|--------|--------|
-| [oobabooga-windows.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_windows.zip) | [oobabooga-linux.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_linux.zip) |[oobabooga-macos.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_macos.zip) |
-
-Just download the zip above, extract it, and double click on "start". The web UI and all its dependencies will be installed in the same folder.
-
-* The source codes are here: https://github.com/oobabooga/one-click-installers
-* Huge thanks to [@jllllll](https://github.com/jllllll), [@ClayShoaf](https://github.com/ClayShoaf), and [@xNul](https://github.com/xNul) for their contributions to these installers.
-* There is no need to run the installers as admin.
-
-### Manual installation using Conda
-
-Recommended if you have some experience with the command-line.
-
-On Windows, I additionally recommend carrying out the installation on WSL instead of the base system: [WSL installation guide](https://github.com/oobabooga/text-generation-webui/blob/main/docs/WSL-installation-guide.md).
-
-#### 0. Install Conda
-
-https://docs.conda.io/en/latest/miniconda.html
-
-On Linux or WSL, it can be automatically installed with these two commands:
-
-```
-curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh"
-bash Miniconda3.sh
-```
-Source: https://educe-ubc.github.io/conda.html
-
-#### 0.1 (Ubuntu/WSL) Install build tools
-
-```
-sudo apt install build-essential
-```
-
-
-#### 1. Create a new conda environment
-
-```
-conda create -n textgen python=3.10.9
-conda activate textgen
-```
-
-#### 2. Install Pytorch
-
-| System | GPU | Command |
-|--------|---------|---------|
-| Linux/WSL | NVIDIA | `pip3 install torch torchvision torchaudio` |
-| Linux | AMD | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2` |
-| MacOS + MPS (untested) | Any | `pip3 install torch torchvision torchaudio` |
-
-The up to date commands can be found here: https://pytorch.org/get-started/locally/. 
-
-#### 2.1 Special instructions
-
-* MacOS users: https://github.com/oobabooga/text-generation-webui/pull/393
-* AMD users: https://rentry.org/eq3hg
-
-#### 3. Install the web UI
-
-```
-git clone https://github.com/oobabooga/text-generation-webui
-cd text-generation-webui
-pip install -r requirements.txt
-```
-
-### Alternative: manual Windows installation
-
-As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: [Windows installation guide](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Windows-installation-guide.md).
-
-### Alternative: Docker
-
-```
-ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} .
-cp docker/.env.example .env
-# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model
-docker compose up --build
-```
-
-You need to have docker compose v2.17 or higher installed in your system. To see how to install docker compose itself, see the guide in [here](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Docker.md).
-
-Contributed by [@loeken](https://github.com/loeken) in [#633](https://github.com/oobabooga/text-generation-webui/pull/633)
-
-### Updating the requirements
-
-From time to time, the `requirements.txt` changes. To update, use this command:
-
-```
-conda activate textgen
-cd text-generation-webui
-pip install -r requirements.txt --upgrade
-```
-## Downloading models
-
-Models should be placed inside the `models` folder.
-
-[Hugging Face](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads) is the main place to download models. These are some examples:
-
-* [Pythia](https://huggingface.co/models?sort=downloads&search=eleutherai%2Fpythia+deduped)
-* [OPT](https://huggingface.co/models?search=facebook/opt)
-* [GALACTICA](https://huggingface.co/models?search=facebook/galactica)
-* [GPT-J 6B](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)
-
-You can automatically download a model from HF using the script `download-model.py`:
-
-    python download-model.py organization/model
-
-For example:
-
-    python download-model.py facebook/opt-1.3b
-
-If you want to download a model manually, note that all you need are the json, txt, and pytorch\*.bin (or model*.safetensors) files. The remaining files are not necessary.
-
-#### GPT-4chan
-
-[GPT-4chan](https://huggingface.co/ykilcher/gpt-4chan) has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:
-
-* Torrent: [16-bit](https://archive.org/details/gpt4chan_model_float16) / [32-bit](https://archive.org/details/gpt4chan_model)
-* Direct download: [16-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model_float16/) / [32-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model/)
-
-The 32-bit version is only relevant if you intend to run the model in CPU mode. Otherwise, you should use the 16-bit version.
-
-After downloading the model, follow these steps:
-
-1. Place the files under `models/gpt4chan_model_float16` or `models/gpt4chan_model`.
-2. Place GPT-J 6B's config.json file in that same folder: [config.json](https://huggingface.co/EleutherAI/gpt-j-6B/raw/main/config.json).
-3. Download GPT-J 6B's tokenizer files (they will be automatically detected when you attempt to load GPT-4chan):
-
-```
-python download-model.py EleutherAI/gpt-j-6B --text-only
-```
-
-## Starting the web UI
-
-    conda activate textgen
-    cd text-generation-webui
-    python server.py
-
-Then browse to 
-
-`http://localhost:7860/?__theme=dark`
-
-Optionally, you can use the following command-line flags:
-
-#### Basic settings
-
-| Flag                                       | Description |
-|--------------------------------------------|-------------|
-| `-h`, `--help`                             | Show this help message and exit. |
-| `--notebook`                               | Launch the web UI in notebook mode, where the output is written to the same text box as the input. |
-| `--chat`                                   | Launch the web UI in chat mode. |
-| `--character CHARACTER`                    | The name of the character to load in chat mode by default. |
-| `--model MODEL`                            | Name of the model to load by default. |
-| `--lora LORA [LORA ...]`                   | The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. |
-| `--model-dir MODEL_DIR`                    | Path to directory with all the models. |
-| `--lora-dir LORA_DIR`                      | Path to directory with all the loras. |
-| `--model-menu`                             | Show a model menu in the terminal when the web UI is first launched. |
-| `--no-stream`                              | Don't stream the text output in real time. |
-| `--settings SETTINGS_FILE`                 | Load the default interface settings from this json file. See `settings-template.json` for an example. If you create a file called `settings.json`, this file will be loaded by default without the need to use the `--settings` flag. |
-| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
-| `--verbose`                                | Print the prompts to the terminal. |
-
-#### Accelerate/transformers
-
-| Flag                                        | Description |
-|---------------------------------------------|-------------|
-| `--cpu`                                     | Use the CPU to generate text. Warning: Training on CPU is extremely slow.|
-| `--auto-devices`                            | Automatically split the model across the available GPU(s) and CPU. |
-|  `--gpu-memory GPU_MEMORY [GPU_MEMORY ...]` | Maxmimum GPU memory in GiB to be allocated per GPU. Example: `--gpu-memory 10` for a single GPU, `--gpu-memory 10 5` for two GPUs. You can also set values in MiB like `--gpu-memory 3500MiB`. |
-| `--cpu-memory CPU_MEMORY`                   | Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.|
-| `--disk`                                    | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
-| `--disk-cache-dir DISK_CACHE_DIR`           | Directory to save the disk cache to. Defaults to `cache/`. |
-| `--load-in-8bit`                            | Load the model with 8-bit precision.|
-| `--bf16`                                    | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
-| `--no-cache`                                | Set `use_cache` to False while generating text. This reduces the VRAM usage a bit with a performance cost. |
-| `--xformers`                                | Use xformer's memory efficient attention. This should increase your tokens/s. |
-| `--sdp-attention`                           | Use torch 2.0's sdp attention. |
-| `--trust-remote-code`                       | Set trust_remote_code=True while loading a model. Necessary for ChatGLM. |
-
-#### llama.cpp
-
-| Flag        | Description |
-|-------------|-------------|
-| `--threads` | Number of threads to use. |
-| `--n_batch` | Maximum number of prompt tokens to batch together when calling llama_eval. |
-| `--no-mmap` | Prevent mmap from being used. |
-| `--mlock`   | Force the system to keep the model in RAM. |
-
-#### GPTQ
-
-| Flag                      | Description |
-|---------------------------|-------------|
-| `--wbits WBITS`           | Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
-| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. |
-| `--groupsize GROUPSIZE`   | Group size. |
-| `--pre_layer PRE_LAYER`   | The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. |
-| `--monkey-patch`          | Apply the monkey patch for using LoRAs with quantized models.
-| `--quant_attn`         | (triton) Enable quant attention.
-| `--warmup_autotune`    | (triton) Enable warmup autotune.
-| `--fused_mlp`          | (triton) Enable fused mlp.
-
-#### FlexGen
-
-| Flag             | Description |
-|------------------|-------------|
-| `--flexgen`                       | Enable the use of FlexGen offloading. |
-| `--percent PERCENT [PERCENT ...]` | FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0). |
-| `--compress-weight`               | FlexGen: Whether to compress weight (default: False).|
-| `--pin-weight [PIN_WEIGHT]`       | FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%). |
-
-#### DeepSpeed
-
-| Flag                                  | Description |
-|---------------------------------------|-------------|
-| `--deepspeed`                         | Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. |
-| `--nvme-offload-dir NVME_OFFLOAD_DIR` | DeepSpeed: Directory to use for ZeRO-3 NVME offloading. |
-| `--local_rank LOCAL_RANK`             | DeepSpeed: Optional argument for distributed setups. |
-
-#### RWKV
-
-| Flag                            | Description |
-|---------------------------------|-------------|
-| `--rwkv-strategy RWKV_STRATEGY` | RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
-| `--rwkv-cuda-on`                | RWKV: Compile the CUDA kernel for better performance. |
-
-#### Gradio
-
-| Flag                                  | Description |
-|---------------------------------------|-------------|
-| `--listen`                            | Make the web UI reachable from your local network. |
-| `--listen-host LISTEN_HOST`           | The hostname that the server will use. |
-| `--listen-port LISTEN_PORT`           | The listening port that the server will use. |
-| `--share`                             | Create a public URL. This is useful for running the web UI on Google Colab or similar. |
-| `--auto-launch`                       | Open the web UI in the default browser upon launch. |
-| `--gradio-auth-path GRADIO_AUTH_PATH` | Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3" |
-
-#### API
-
-| Flag                                  | Description |
-|---------------------------------------|-------------|
-| `--api`                               | Enable the API extension. |
-| `--public-api`                        | Create a public URL for the API using Cloudfare. |
-
-Out of memory errors? [Check the low VRAM guide](docs/Low-VRAM-guide.md).
-
-## Presets
-
-Inference settings presets can be created under `presets/` as text files. These files are detected automatically at startup.
-
-By default, 10 presets by NovelAI and KoboldAI are included. These were selected out of a sample of 43 presets after applying a K-Means clustering algorithm and selecting the elements closest to the average of each cluster.
-
-[Visualization](https://user-images.githubusercontent.com/112222186/228956352-1addbdb9-2456-465a-b51d-089f462cd385.png)
-
-## System requirements
-
-Check the [wiki](docs/System-requirements.md) for some examples of VRAM and RAM usage in both GPU and CPU mode.
-
-## Contributing
-
-Pull requests, suggestions, and issue reports are welcome. 
-
-You are also welcome to review open pull requests.
-
-Before reporting a bug, make sure that you have:
-
-1. Created a conda environment and installed the dependencies exactly as in the *Installation* section above.
-2. [Searched](https://github.com/oobabooga/text-generation-webui/issues) to see if an issue already exists for the issue you encountered.
-
-## Credits
-
-- Gradio dropdown menu refresh button, code for reloading the interface: https://github.com/AUTOMATIC1111/stable-diffusion-webui
-- Verbose preset: Anonymous 4chan user.
-- NovelAI and KoboldAI presets: https://github.com/KoboldAI/KoboldAI-Client/wiki/Settings-Presets
-- Code for early stopping in chat mode, code for some of the sliders: https://github.com/PygmalionAI/gradio-ui/
diff --git a/spaces/dyhzq/vits-uma-genshin-honkai/text/__init__.py b/spaces/dyhzq/vits-uma-genshin-honkai/text/__init__.py
deleted file mode 100644
index 663c4b6416affb53c9dc56dddbc8b2b65d4bf518..0000000000000000000000000000000000000000
--- a/spaces/dyhzq/vits-uma-genshin-honkai/text/__init__.py
+++ /dev/null
@@ -1,57 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-from text.symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence, clean_text
-
-
-def cleaned_text_to_sequence(cleaned_text):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = [_symbol_to_id[symbol] for symbol in cleaned_text if symbol in _symbol_to_id.keys()]
-  return sequence
-
-
-def sequence_to_text(sequence):
-  '''Converts a sequence of IDs back to a string'''
-  result = ''
-  for symbol_id in sequence:
-    s = _id_to_symbol[symbol_id]
-    result += s
-  return result
-
-
-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
diff --git a/spaces/eeyorestoned/maximum_diffusion/README.md b/spaces/eeyorestoned/maximum_diffusion/README.md
deleted file mode 100644
index 661ae5617283e3a44b31c8cffea5a772f74b3ad0..0000000000000000000000000000000000000000
--- a/spaces/eeyorestoned/maximum_diffusion/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Maximum Diffusion
-emoji: 🛕
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: true
-duplicated_from: Omnibus/maximum_diffusion
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ekosetiawan/flowers_classifier/README.md b/spaces/ekosetiawan/flowers_classifier/README.md
deleted file mode 100644
index b16d9d8a49e587cb19cb27704182da7d48fed8b4..0000000000000000000000000000000000000000
--- a/spaces/ekosetiawan/flowers_classifier/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Flowers Classifier
-emoji: 🐨
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 3.1.1
-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/emc348/faces-through-time/models/StyleCLIP/models/facial_recognition/helpers.py b/spaces/emc348/faces-through-time/models/StyleCLIP/models/facial_recognition/helpers.py
deleted file mode 100644
index b51fdf97141407fcc1c9d249a086ddbfd042469f..0000000000000000000000000000000000000000
--- a/spaces/emc348/faces-through-time/models/StyleCLIP/models/facial_recognition/helpers.py
+++ /dev/null
@@ -1,119 +0,0 @@
-from collections import namedtuple
-import torch
-from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
-
-"""
-ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
-"""
-
-
-class Flatten(Module):
-	def forward(self, input):
-		return input.view(input.size(0), -1)
-
-
-def l2_norm(input, axis=1):
-	norm = torch.norm(input, 2, axis, True)
-	output = torch.div(input, norm)
-	return output
-
-
-class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
-	""" A named tuple describing a ResNet block. """
-
-
-def get_block(in_channel, depth, num_units, stride=2):
-	return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
-
-
-def get_blocks(num_layers):
-	if num_layers == 50:
-		blocks = [
-			get_block(in_channel=64, depth=64, num_units=3),
-			get_block(in_channel=64, depth=128, num_units=4),
-			get_block(in_channel=128, depth=256, num_units=14),
-			get_block(in_channel=256, depth=512, num_units=3)
-		]
-	elif num_layers == 100:
-		blocks = [
-			get_block(in_channel=64, depth=64, num_units=3),
-			get_block(in_channel=64, depth=128, num_units=13),
-			get_block(in_channel=128, depth=256, num_units=30),
-			get_block(in_channel=256, depth=512, num_units=3)
-		]
-	elif num_layers == 152:
-		blocks = [
-			get_block(in_channel=64, depth=64, num_units=3),
-			get_block(in_channel=64, depth=128, num_units=8),
-			get_block(in_channel=128, depth=256, num_units=36),
-			get_block(in_channel=256, depth=512, num_units=3)
-		]
-	else:
-		raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers))
-	return blocks
-
-
-class SEModule(Module):
-	def __init__(self, channels, reduction):
-		super(SEModule, self).__init__()
-		self.avg_pool = AdaptiveAvgPool2d(1)
-		self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False)
-		self.relu = ReLU(inplace=True)
-		self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False)
-		self.sigmoid = Sigmoid()
-
-	def forward(self, x):
-		module_input = x
-		x = self.avg_pool(x)
-		x = self.fc1(x)
-		x = self.relu(x)
-		x = self.fc2(x)
-		x = self.sigmoid(x)
-		return module_input * x
-
-
-class bottleneck_IR(Module):
-	def __init__(self, in_channel, depth, stride):
-		super(bottleneck_IR, self).__init__()
-		if in_channel == depth:
-			self.shortcut_layer = MaxPool2d(1, stride)
-		else:
-			self.shortcut_layer = Sequential(
-				Conv2d(in_channel, depth, (1, 1), stride, bias=False),
-				BatchNorm2d(depth)
-			)
-		self.res_layer = Sequential(
-			BatchNorm2d(in_channel),
-			Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
-			Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth)
-		)
-
-	def forward(self, x):
-		shortcut = self.shortcut_layer(x)
-		res = self.res_layer(x)
-		return res + shortcut
-
-
-class bottleneck_IR_SE(Module):
-	def __init__(self, in_channel, depth, stride):
-		super(bottleneck_IR_SE, self).__init__()
-		if in_channel == depth:
-			self.shortcut_layer = MaxPool2d(1, stride)
-		else:
-			self.shortcut_layer = Sequential(
-				Conv2d(in_channel, depth, (1, 1), stride, bias=False),
-				BatchNorm2d(depth)
-			)
-		self.res_layer = Sequential(
-			BatchNorm2d(in_channel),
-			Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
-			PReLU(depth),
-			Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
-			BatchNorm2d(depth),
-			SEModule(depth, 16)
-		)
-
-	def forward(self, x):
-		shortcut = self.shortcut_layer(x)
-		res = self.res_layer(x)
-		return res + shortcut
diff --git a/spaces/eugenesiow/mandarin-tts/app.py b/spaces/eugenesiow/mandarin-tts/app.py
deleted file mode 100644
index 9578059dcfc5ddd9e18f375aaabe7f21eb2f74e5..0000000000000000000000000000000000000000
--- a/spaces/eugenesiow/mandarin-tts/app.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import tempfile
-import gradio as gr
-# import numpy as np
-from TTS.utils.manage import ModelManager
-from TTS.utils.synthesizer import Synthesizer
-
-
-title = "Mandarin Text-to-Speech (TTS)"
-description = "Generate mandarin speech from text using a Tachotron2 model with Coqui TTS 🐸, " \
-              "a deep learning toolkit for Text-to-Speech."
-article = "<p style='text-align: center'><a href='https://news.machinelearning.sg/posts/mandarin_text_to_speech_with_coqui_tts/' target='blank' class='footer'>Blog</a> | <a href='https://github.com/eugenesiow/practical-ml' target='_blank' class='footer'>Github Repo</a></p>"
-examples = [
-    ["语音合成是通过机械的、电子的方法产生人造语音的技术。"],
-    ["李显龙总理表示，我国要达到像意大利的开放程度，几乎回到冠病疫情前的生活，还需要一段时间。"]
-]
-
-
-manager = ModelManager()
-model_path, config_path, model_item = manager.download_model("tts_models/zh-CN/baker/tacotron2-DDC-GST")
-synthesizer = Synthesizer(
-    model_path, config_path, None, None, None,
-)
-
-
-def inference(text: str):
-    wavs = synthesizer.tts(text)
-    # output = (synthesizer.output_sample_rate, np.array(wavs).astype(np.float32))
-    # return output
-    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
-        synthesizer.save_wav(wavs, fp)
-        return fp.name
-
-
-gr.Interface(
-    fn=inference,
-    inputs=[
-        gr.inputs.Textbox(
-            label="Input",
-            default="你好吗？我很好。",
-        ),
-    ],
-    outputs=gr.outputs.Audio(label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    examples=examples,
-    enable_queue=True,
-    allow_flagging=False,
-    ).launch(debug=False)
diff --git a/spaces/ezioruan/roop/roop/processors/frame/face_swapper.py b/spaces/ezioruan/roop/roop/processors/frame/face_swapper.py
deleted file mode 100644
index c53b5b86d7e87870191c01855652088d43726142..0000000000000000000000000000000000000000
--- a/spaces/ezioruan/roop/roop/processors/frame/face_swapper.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from typing import Any, List, Callable
-import cv2
-import insightface
-import threading
-
-import roop.globals
-import roop.processors.frame.core
-from roop.core import update_status
-from roop.face_analyser import get_one_face, get_many_faces
-from roop.typing import Face, Frame
-from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video
-
-FACE_SWAPPER = None
-THREAD_LOCK = threading.Lock()
-NAME = 'ROOP.FACE-SWAPPER'
-
-
-def get_face_swapper() -> Any:
-    global FACE_SWAPPER
-
-    with THREAD_LOCK:
-        if FACE_SWAPPER is None:
-            model_path = resolve_relative_path('../models/inswapper_128.onnx')
-            FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=roop.globals.execution_providers)
-    return FACE_SWAPPER
-
-
-def pre_check() -> bool:
-    download_directory_path = resolve_relative_path('../models')
-    conditional_download(download_directory_path, ['https://huggingface.co/henryruhs/roop/resolve/main/inswapper_128.onnx'])
-    return True
-
-
-def pre_start() -> bool:
-    if not is_image(roop.globals.source_path):
-        update_status('Select an image for source path.', NAME)
-        return False
-    elif not get_one_face(cv2.imread(roop.globals.source_path)):
-        update_status('No face in source path detected.', NAME)
-        return False
-    if not is_image(roop.globals.target_path) and not is_video(roop.globals.target_path):
-        update_status('Select an image or video for target path.', NAME)
-        return False
-    return True
-
-
-def post_process() -> None:
-    global FACE_SWAPPER
-
-    FACE_SWAPPER = None
-
-
-def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
-    return get_face_swapper().get(temp_frame, target_face, source_face, paste_back=True)
-
-
-def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
-    if roop.globals.many_faces:
-        many_faces = get_many_faces(temp_frame)
-        if many_faces:
-            for target_face in many_faces:
-                temp_frame = swap_face(source_face, target_face, temp_frame)
-    else:
-        target_face = get_one_face(temp_frame)
-        if target_face:
-            temp_frame = swap_face(source_face, target_face, temp_frame)
-    return temp_frame
-
-
-def process_frames(source_path: str, temp_frame_paths: List[str], update: Callable[[], None]) -> None:
-    source_face = get_one_face(cv2.imread(source_path))
-    for temp_frame_path in temp_frame_paths:
-        temp_frame = cv2.imread(temp_frame_path)
-        result = process_frame(source_face, temp_frame)
-        cv2.imwrite(temp_frame_path, result)
-        if update:
-            update()
-
-
-def process_image(source_path: str, target_path: str, output_path: str) -> None:
-    source_face = get_one_face(cv2.imread(source_path))
-    target_frame = cv2.imread(target_path)
-    result = process_frame(source_face, target_frame)
-    cv2.imwrite(output_path, result)
-
-
-def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
-    roop.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)
diff --git a/spaces/facebook/MusicGen/audiocraft/models/audiogen.py b/spaces/facebook/MusicGen/audiocraft/models/audiogen.py
deleted file mode 100644
index b4df536eddd3657cc8cc6bf846606a16df8985eb..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/audiocraft/models/audiogen.py
+++ /dev/null
@@ -1,267 +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.
-
-"""
-Main model for using AudioGen. This will combine all the required components
-and provide easy access to the generation API.
-"""
-
-import typing as tp
-
-import torch
-
-from .encodec import CompressionModel
-from .lm import LMModel
-from .builders import get_debug_compression_model, get_debug_lm_model
-from .loaders import load_compression_model, load_lm_model
-from ..data.audio_utils import convert_audio
-from ..modules.conditioners import ConditioningAttributes
-from ..utils.autocast import TorchAutocast
-
-
-class AudioGen:
-    """AudioGen main model with convenient generation API.
-
-    Args:
-        name (str): name of the model.
-        compression_model (CompressionModel): Compression model
-            used to map audio to invertible discrete representations.
-        lm (LMModel): Language model over discrete representations.
-        max_duration (float, optional): maximum duration the model can produce,
-            otherwise, inferred from the training params.
-    """
-    def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel,
-                 max_duration: tp.Optional[float] = None):
-        self.name = name
-        self.compression_model = compression_model
-        self.lm = lm
-        # Just to be safe, let's put everything in eval mode.
-        self.compression_model.eval()
-        self.lm.eval()
-
-        if max_duration is None:
-            if hasattr(lm, 'cfg'):
-                max_duration = lm.cfg.dataset.segment_duration  # type: ignore
-            else:
-                raise ValueError("You must provide max_duration when building directly AudioGen")
-        assert max_duration is not None
-        self.max_duration: float = max_duration
-        self.device = next(iter(lm.parameters())).device
-        self.generation_params: dict = {}
-        self.set_generation_params(duration=5)  # 5 seconds by default
-        self._progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None
-        if self.device.type == 'cpu':
-            self.autocast = TorchAutocast(enabled=False)
-        else:
-            self.autocast = TorchAutocast(
-                enabled=True, device_type=self.device.type, dtype=torch.float16)
-
-    @property
-    def frame_rate(self) -> float:
-        """Roughly the number of AR steps per seconds."""
-        return self.compression_model.frame_rate
-
-    @property
-    def sample_rate(self) -> int:
-        """Sample rate of the generated audio."""
-        return self.compression_model.sample_rate
-
-    @property
-    def audio_channels(self) -> int:
-        """Audio channels of the generated audio."""
-        return self.compression_model.channels
-
-    @staticmethod
-    def get_pretrained(name: str = 'facebook/audiogen-medium', device=None):
-        """Return pretrained model, we provide a single model for now:
-        - facebook/audiogen-medium (1.5B), text to sound,
-          # see: https://huggingface.co/facebook/audiogen-medium
-        """
-        if device is None:
-            if torch.cuda.device_count():
-                device = 'cuda'
-            else:
-                device = 'cpu'
-
-        if name == 'debug':
-            # used only for unit tests
-            compression_model = get_debug_compression_model(device, sample_rate=16000)
-            lm = get_debug_lm_model(device)
-            return AudioGen(name, compression_model, lm, max_duration=10)
-
-        compression_model = load_compression_model(name, device=device)
-        lm = load_lm_model(name, device=device)
-        assert 'self_wav' not in lm.condition_provider.conditioners, \
-            "AudioGen do not support waveform conditioning for now"
-        return AudioGen(name, compression_model, lm)
-
-    def set_generation_params(self, use_sampling: bool = True, top_k: int = 250,
-                              top_p: float = 0.0, temperature: float = 1.0,
-                              duration: float = 10.0, cfg_coef: float = 3.0,
-                              two_step_cfg: bool = False, extend_stride: float = 2):
-        """Set the generation parameters for AudioGen.
-
-        Args:
-            use_sampling (bool, optional): Use sampling if True, else do argmax decoding. Defaults to True.
-            top_k (int, optional): top_k used for sampling. Defaults to 250.
-            top_p (float, optional): top_p used for sampling, when set to 0 top_k is used. Defaults to 0.0.
-            temperature (float, optional): Softmax temperature parameter. Defaults to 1.0.
-            duration (float, optional): Duration of the generated waveform. Defaults to 10.0.
-            cfg_coef (float, optional): Coefficient used for classifier free guidance. Defaults to 3.0.
-            two_step_cfg (bool, optional): If True, performs 2 forward for Classifier Free Guidance,
-                instead of batching together the two. This has some impact on how things
-                are padded but seems to have little impact in practice.
-            extend_stride: when doing extended generation (i.e. more than 10 seconds), by how much
-                should we extend the audio each time. Larger values will mean less context is
-                preserved, and shorter value will require extra computations.
-        """
-        assert extend_stride < self.max_duration, "Cannot stride by more than max generation duration."
-        self.extend_stride = extend_stride
-        self.duration = duration
-        self.generation_params = {
-            'use_sampling': use_sampling,
-            'temp': temperature,
-            'top_k': top_k,
-            'top_p': top_p,
-            'cfg_coef': cfg_coef,
-            'two_step_cfg': two_step_cfg,
-        }
-
-    def set_custom_progress_callback(self, progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None):
-        """Override the default progress callback."""
-        self._progress_callback = progress_callback
-
-    def generate(self, descriptions: tp.List[str], progress: bool = False) -> torch.Tensor:
-        """Generate samples conditioned on text.
-
-        Args:
-            descriptions (list of str): A list of strings used as text conditioning.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        """
-        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
-        assert prompt_tokens is None
-        return self._generate_tokens(attributes, prompt_tokens, progress)
-
-    def generate_continuation(self, prompt: torch.Tensor, prompt_sample_rate: int,
-                              descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None,
-                              progress: bool = False) -> torch.Tensor:
-        """Generate samples conditioned on audio prompts.
-
-        Args:
-            prompt (torch.Tensor): A batch of waveforms used for continuation.
-                Prompt should be [B, C, T], or [C, T] if only one sample is generated.
-            prompt_sample_rate (int): Sampling rate of the given audio waveforms.
-            descriptions (list of str, optional): A list of strings used as text conditioning. Defaults to None.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        """
-        if prompt.dim() == 2:
-            prompt = prompt[None]
-        if prompt.dim() != 3:
-            raise ValueError("prompt should have 3 dimensions: [B, C, T] (C = 1).")
-        prompt = convert_audio(prompt, prompt_sample_rate, self.sample_rate, self.audio_channels)
-        if descriptions is None:
-            descriptions = [None] * len(prompt)
-        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, prompt)
-        assert prompt_tokens is not None
-        return self._generate_tokens(attributes, prompt_tokens, progress)
-
-    @torch.no_grad()
-    def _prepare_tokens_and_attributes(
-            self,
-            descriptions: tp.Sequence[tp.Optional[str]],
-            prompt: tp.Optional[torch.Tensor],
-    ) -> tp.Tuple[tp.List[ConditioningAttributes], tp.Optional[torch.Tensor]]:
-        """Prepare model inputs.
-
-        Args:
-            descriptions (list of str): A list of strings used as text conditioning.
-            prompt (torch.Tensor): A batch of waveforms used for continuation.
-        """
-        attributes = [
-            ConditioningAttributes(text={'description': description})
-            for description in descriptions]
-
-        if prompt is not None:
-            if descriptions is not None:
-                assert len(descriptions) == len(prompt), "Prompt and nb. descriptions doesn't match"
-            prompt = prompt.to(self.device)
-            prompt_tokens, scale = self.compression_model.encode(prompt)
-            assert scale is None
-        else:
-            prompt_tokens = None
-        return attributes, prompt_tokens
-
-    def _generate_tokens(self, attributes: tp.List[ConditioningAttributes],
-                         prompt_tokens: tp.Optional[torch.Tensor], progress: bool = False) -> torch.Tensor:
-        """Generate discrete audio tokens given audio prompt and/or conditions.
-
-        Args:
-            attributes (list of ConditioningAttributes): Conditions used for generation (here text).
-            prompt_tokens (torch.Tensor, optional): Audio prompt used for continuation.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        Returns:
-            torch.Tensor: Generated audio, of shape [B, C, T], T is defined by the generation params.
-        """
-        total_gen_len = int(self.duration * self.frame_rate)
-        max_prompt_len = int(min(self.duration, self.max_duration) * self.frame_rate)
-        current_gen_offset: int = 0
-
-        def _progress_callback(generated_tokens: int, tokens_to_generate: int):
-            generated_tokens += current_gen_offset
-            if self._progress_callback is not None:
-                # Note that total_gen_len might be quite wrong depending on the
-                # codebook pattern used, but with delay it is almost accurate.
-                self._progress_callback(generated_tokens, total_gen_len)
-            else:
-                print(f'{generated_tokens: 6d} / {total_gen_len: 6d}', end='\r')
-
-        if prompt_tokens is not None:
-            assert max_prompt_len >= prompt_tokens.shape[-1], \
-                "Prompt is longer than audio to generate"
-
-        callback = None
-        if progress:
-            callback = _progress_callback
-
-        if self.duration <= self.max_duration:
-            # generate by sampling from LM, simple case.
-            with self.autocast:
-                gen_tokens = self.lm.generate(
-                    prompt_tokens, attributes,
-                    callback=callback, max_gen_len=total_gen_len, **self.generation_params)
-
-        else:
-            all_tokens = []
-            if prompt_tokens is None:
-                prompt_length = 0
-            else:
-                all_tokens.append(prompt_tokens)
-                prompt_length = prompt_tokens.shape[-1]
-
-            stride_tokens = int(self.frame_rate * self.extend_stride)
-            while current_gen_offset + prompt_length < total_gen_len:
-                time_offset = current_gen_offset / self.frame_rate
-                chunk_duration = min(self.duration - time_offset, self.max_duration)
-                max_gen_len = int(chunk_duration * self.frame_rate)
-                with self.autocast:
-                    gen_tokens = self.lm.generate(
-                        prompt_tokens, attributes,
-                        callback=callback, max_gen_len=max_gen_len, **self.generation_params)
-                if prompt_tokens is None:
-                    all_tokens.append(gen_tokens)
-                else:
-                    all_tokens.append(gen_tokens[:, :, prompt_tokens.shape[-1]:])
-                prompt_tokens = gen_tokens[:, :, stride_tokens:]
-                prompt_length = prompt_tokens.shape[-1]
-                current_gen_offset += stride_tokens
-
-            gen_tokens = torch.cat(all_tokens, dim=-1)
-
-        # generate audio
-        assert gen_tokens.dim() == 3
-        with torch.no_grad():
-            gen_audio = self.compression_model.decode(gen_tokens, None)
-        return gen_audio
diff --git a/spaces/failfast/nextjs-hf-spaces/src/components/getting-started.tsx b/spaces/failfast/nextjs-hf-spaces/src/components/getting-started.tsx
deleted file mode 100644
index c31665fff24004186f3b91e6d5ccadad3d4b93f1..0000000000000000000000000000000000000000
--- a/spaces/failfast/nextjs-hf-spaces/src/components/getting-started.tsx
+++ /dev/null
@@ -1,150 +0,0 @@
-import {
-  Button,
-  Grid,
-  Link,
-  List,
-  ListItem,
-  ListItemIcon,
-  ListItemText,
-  ListSubheader,
-  Paper,
-  Stack,
-  Typography,
-} from "@mui/material";
-
-import ViewQuiltIcon from "@mui/icons-material/ViewQuilt";
-import SailingIcon from "@mui/icons-material/Sailing";
-import LightModeIcon from "@mui/icons-material/LightMode";
-import SentimentVerySatisfiedIcon from "@mui/icons-material/SentimentVerySatisfied";
-import ContentCopyIcon from "@mui/icons-material/ContentCopy";
-import LinkIcon from "@mui/icons-material/Link";
-import SyncIcon from "@mui/icons-material/Sync";
-import { HighlightBox } from "./base/boxes";
-
-export default function GettingStarted() {
-  return (
-    <>
-      <Grid container spacing={2} sx={{ justifyContent: "center" }}>
-        <Grid item sm={8} lg={6}>
-          <Paper sx={{ p: 2 }}>
-            <List disablePadding>
-              <ListSubheader sx={{ fontSize: "1.5em" }}>Features</ListSubheader>
-
-              <ListItem>
-                <ListItemIcon>
-                  <SentimentVerySatisfiedIcon />
-                </ListItemIcon>
-                <ListItemText>
-                  <Link
-                    href="https://huggingface.co/docs/huggingface.js/index"
-                    target="_blank"
-                    rel="noopener"
-                  >
-                    huggingface.js
-                  </Link>{" "}
-                  components (WIP)
-                </ListItemText>
-              </ListItem>
-
-              <ListItem>
-                <ListItemIcon>
-                  <LinkIcon />
-                </ListItemIcon>
-                <ListItemText>
-                  <Link
-                    href="https://js.langchain.com/docs"
-                    target="_blank"
-                    rel="noopener"
-                  >
-                    langchain.js
-                  </Link>{" "}
-                  and{" "}
-                  <Link
-                    href="https://github.com/failfa-st/hyv"
-                    target="_blank"
-                    rel="noopener"
-                  >
-                    hyv
-                  </Link>{" "}
-                  components (comming soon)
-                </ListItemText>
-              </ListItem>
-
-              <ListItem>
-                <ListItemIcon>
-                  <ViewQuiltIcon />
-                </ListItemIcon>
-                <ListItemText>
-                  Rapid prototyping with{" "}
-                  <Link
-                    href="https://mui.com/material-ui/getting-started/overview/"
-                    target="_blank"
-                    rel="noopener"
-                  >
-                    MUI
-                  </Link>
-                </ListItemText>
-              </ListItem>
-              <ListItem>
-                <ListItemIcon>
-                  <LightModeIcon />
-                </ListItemIcon>
-                <ListItemText>
-                  Dark / light theme based on system preferences
-                </ListItemText>
-              </ListItem>
-              <ListItem>
-                <ListItemIcon>
-                  <SyncIcon />
-                </ListItemIcon>
-                <ListItemText>
-                  Sync your GitHub repository with your 🤗 Space
-                </ListItemText>
-              </ListItem>
-              <ListItem>
-                <ListItemIcon>
-                  <SailingIcon />
-                </ListItemIcon>
-                <ListItemText>Runs in Docker or localhost</ListItemText>
-              </ListItem>
-            </List>
-          </Paper>
-        </Grid>
-
-        <Grid item sm={4} lg={3}>
-          <Stack gap={2}>
-            <Paper sx={{ p: 2 }}>
-              <Typography variant="body1">
-                Explore the{" "}
-                <Link
-                  href="https://huggingface.co/spaces/failfast/nextjs-docker-starter/blob/main/README.md"
-                  target="_blank"
-                  rel="noopener"
-                >
-                  README
-                </Link>{" "}
-                for a comprehensive guide on local development, Docker
-                utilization, secret management, and GitHub-based Space control.
-              </Typography>
-            </Paper>
-
-            <Paper sx={{ p: 2 }}>
-              <Typography variant="body1">
-                Have feedback or ideas?{" "}
-                <Link
-                  href="https://huggingface.co/spaces/failfast/nextjs-docker-starter/discussions"
-                  target="_blank"
-                  rel="noopener"
-                >
-                  We&apos;re eager to hear from you!
-                </Link>{" "}
-                As an open-source project in its early stages, your input can
-                significantly shape our development.
-              </Typography>
-            </Paper>
-          </Stack>
-        </Grid>
-      </Grid>
-    </>
-  );
-}
diff --git a/spaces/faisalhr1997/blip-image-captioning-space-large/app.py b/spaces/faisalhr1997/blip-image-captioning-space-large/app.py
deleted file mode 100644
index db6bdd928a10fc355585326c1dc0820bd8e5931d..0000000000000000000000000000000000000000
--- a/spaces/faisalhr1997/blip-image-captioning-space-large/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import gradio as gr
-import torch
-
-from transformers import BlipForConditionalGeneration, BlipProcessor
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
-model_image_captioning = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(device)
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-def inference(raw_image, question, decoding_strategy):    
-    inputs = processor(images=raw_image, text=question, return_tensors="pt")
-
-    if decoding_strategy == "Beam search":
-        inputs["max_length"] = 20
-        inputs["num_beams"] = 5
-    elif decoding_strategy == "Nucleus sampling":
-        inputs["max_length"] = 20
-        inputs["num_beams"] = 1
-        inputs["do_sample"] = True
-        inputs["top_k"] = 50
-        inputs["top_p"] = 0.95
-    elif decoding_strategy == "Contrastive search":
-        inputs["penalty_alpha"] = 0.6
-        inputs["top_k"] = 4
-        inputs["max_length"] = 512
-
-    out = model_image_captioning.generate(**inputs)
-    return processor.batch_decode(out, skip_special_tokens=True)[0]
-
-inputs = [
-        gr.inputs.Image(type='pil'),
-        gr.inputs.Textbox(lines=2, label="Context (optional)"),
-        gr.inputs.Radio(choices=["Beam search","Nucleus sampling", "Contrastive search"], type="value", default="Nucleus sampling", label="Caption Decoding Strategy")
-    ]
-outputs = gr.outputs.Textbox(label="Output")
-
-title = "BLIP"
-
-description = "Gradio demo for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (Salesforce Research). To use it, simply upload your image, 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/2201.12086' target='_blank'>BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation</a> | <a href='https://github.com/salesforce/BLIP' target='_blank'>Github Repo</a></p>"
-
-gr.Interface(inference, inputs, outputs, title=title, description=description, article=article).launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Ativador Windows 7 64 Bits 2019.md b/spaces/falterWliame/Face_Mask_Detection/Ativador Windows 7 64 Bits 2019.md
deleted file mode 100644
index 3a60e4a7c1b32014f39c7b7f571b8c2df703ae48..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Ativador Windows 7 64 Bits 2019.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-
-<p>Digite em uma janela do programa <strong>Ferramenta de ativao Windows 8</strong>, na versao 7 e 8.1. Ele le ve a chave.Para ativar o Windows.Abra o discador do desenvolvedor ativado ou do ativo.No 7 e 8.Presse'O'ou o'Ctrl + O'e pressione a tecla'ALT + PrtScr '.A janela se abrir pelo ativador de produtos da Microsoft.O produto todo esta em um varchar (70) e o pag e o cedetudo.Agora modifique o varchar (70) para o nome do seu produto.copie a chave.Pressione'OK'para salvar os muitos textos.Estas especifico o comentario para o <strong>.NET Framework. Copie o arquivo p.exe de um Windows ou qualquer utilitrio</strong> e copie o arquivo para o diretorio relativo aos dados.Pressione'OK'para salvar.<strong>Download ativador Windows 7</strong></p>
-<h2>Ativador Windows 7 64 Bits 2019</h2><br /><p><b><b>DOWNLOAD</b> &#127379; <a href="https://urlca.com/2uDc4w">https://urlca.com/2uDc4w</a></b></p><br /><br />
-<p>O <strong> Ativador de produtos da Microsoft</strong> dar acesso de usuarios necessarem ficar legi senza ter que pagar.Dever apenas tomar um simples ativador e fornecer a chave de produtos para ativar.A ferramenta acaba de ativar ou iniciar o Windows 8.1, entao nenhum outro procedimento é necessario.</p>
-<p>Deseja ativar o Windows 8.1?''a especificação de produtos que voc gosta '' deve ter muito. Se voc estiver precisando de um ativador gratuito que executa rápido, clique no ativador de producao da Microsoft e copie o arquivo para a diretorio r. Envie carga de texto para a discussao de comentrio, a pedido.O ativador adiciona alguns dados ao arquivo certa instruçoes de um servidor de ativao da Microsoft e entao o ativo.A ativado do Windows 7 não será modificado. Clica nesse texto de comentrio para conversar com os desenvolvedores.</p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Download English Subtitles For Telugu Movie Yehi Hai Zindagi !NEW!.md b/spaces/falterWliame/Face_Mask_Detection/Download English Subtitles For Telugu Movie Yehi Hai Zindagi !NEW!.md
deleted file mode 100644
index 2003a9075e6013ca2e45c671aa6d73be53c01f38..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Download English Subtitles For Telugu Movie Yehi Hai Zindagi !NEW!.md	
+++ /dev/null
@@ -1,90 +0,0 @@
-<br />
-<h1>Download English Subtitles For Telugu Movie Yehi Hai Zindagi</h1>
-
-<p>If you are a fan of Telugu movies, you might have heard of Yehi Hai Zindagi, a 1977 comedy drama film directed by K.S. Sethumadhavan and starring Sanjeev Kumar, Utpal Dutt, David Abraham, and Romesh Sharma. The film tells the story of Anand Narayan, a poor man who rises to success with the help of his business partner and father-in-law Nekiram, but faces challenges from his own family and ego. The film also features a cameo appearance by Lord Krishna, who guides Anand through his life.</p>
-<h2>Download English Subtitles For Telugu Movie Yehi Hai Zindagi</h2><br /><p><b><b>Download</b> &#10040;&#10040;&#10040; <a href="https://urlca.com/2uDdXu">https://urlca.com/2uDdXu</a></b></p><br /><br />
-
-<p>Yehi Hai Zindagi is a classic Telugu movie that has won many awards and accolades, including a nomination for the Filmfare Award for Best Film. The film has also been remade in Hindi, Tamil, and Malayalam languages. However, if you want to watch the original Telugu version of Yehi Hai Zindagi, you might face a problem: finding English subtitles for it.</p>
-
-<p>English subtitles are essential for non-Telugu speakers who want to enjoy the movie without missing any dialogue or nuance. Unfortunately, finding English subtitles for Telugu movies is not always easy, especially for older films like Yehi Hai Zindagi. You might have to search through various websites and forums, download multiple files, and hope that they are compatible with your video player. This can be frustrating and time-consuming.</p>
-
-<p>That's why we have created this article to help you download English subtitles for Telugu movie Yehi Hai Zindagi in a simple and hassle-free way. We have collected the best sources of English subtitles for Yehi Hai Zindagi and provided you with the links to download them. All you have to do is follow these steps:</p>
-
-<ol>
-<li>Choose one of the links below to download English subtitles for Telugu movie Yehi Hai Zindagi. These links are from reliable and trustworthy websites that offer high-quality subtitles in various formats.</li>
-<li>Download the subtitle file to your computer or device. Make sure you choose the right format that matches your video player. For example, if you use VLC media player, you might want to download the SRT file.</li>
-<li>Extract the subtitle file from the zip or rar archive if necessary. You should see a file with the same name as the movie and the extension .srt or .sub.</li>
-<li>Copy or move the subtitle file to the same folder where you have stored the movie file. Make sure both files have the same name, except for the extension. For example, if your movie file is named Yehi_Hai_Zindagi.mp4, your subtitle file should be named Yehi_Hai_Zindagi.srt or Yehi_Hai_Zindagi.sub.</li>
-<li>Open your video player and play the movie file. You should see the subtitles appear on the screen automatically. If not, you might have to enable subtitles manually from your video player settings.</li>
-</ol>
-
-<p>That's it! You can now enjoy watching Yehi Hai Zindagi with English subtitles on your computer or device. Here are some of the links to download English subtitles for Telugu movie Yehi Hai Zindagi:</p>
-<p></p>
-
-<ul>
-<li><a href="https://sway.office.com/6EDwJrC31E41Gq2E">Download English Subtitles For Telugu Movie Yehi Hai Zindagi</a></li>
-<li><a href="https://cordianamer093ita.wixsite.com/destdarili/post/download-english-subtitles-for-telugu-movie-yehi-hai-zindagi">Download English Subtitles For Telugu Movie Yehi Hai Zindagi</a></li>
-<li><a href="https://fanseogarggara.wixsite.com/tronbirliti/post/free-capx-6-3-32bit-crack-utorrent-latest-windows">Download English Subtitles For Telugu Movie Yehi Hai Zindagi</a></li>
-</ul>
-
-<p>We hope this article has helped you download English subtitles for Telugu movie Yehi Hai Zindagi easily and quickly. If you have any questions or feedback, please leave a comment below. And don't forget to share this article with your friends who might also be looking for English subtitles for Telugu movies.</p>
-
-<h2>Download English Subtitles For Telugu Movie Yehi Hai Zindagi: Conclusion</h2>
-
-<p>Yehi Hai Zindagi is a classic Telugu movie that deserves to be watched by everyone who loves comedy and drama. With English subtitles, you can enjoy the movie without missing any dialogue or nuance. To download English subtitles for Telugu movie Yehi Hai Zindagi, just follow the steps we have outlined in this article and choose one of the links we have provided. You will be able to watch Yehi Hai Zindagi with English subtitles in no time.</p>
-
-<p>Thank you for reading this article on how to download English subtitles for Telugu movie Yehi Hai Zindagi. We hope you found it useful and informative. If you did, please share it with your friends and family who might also be interested in watching Telugu movies with English subtitles.</p>
-<h3>Download English Subtitles For Telugu Movie Yehi Hai Zindagi: Benefits</h3>
-
-<p>Why should you download English subtitles for Telugu movie Yehi Hai Zindagi? There are many benefits of watching Telugu movies with English subtitles, such as:</p>
-
-<ul>
-<li>You can improve your language skills. Watching Telugu movies with English subtitles can help you learn new words, phrases, and expressions in both languages. You can also improve your listening and reading comprehension skills by following the dialogue and the subtitles.</li>
-<li>You can appreciate the culture and the humor. Watching Telugu movies with English subtitles can help you understand the cultural and social context of the movie, as well as the jokes and references that might otherwise be lost in translation. You can also enjoy the music, the costumes, and the scenery of the movie.</li>
-<li>You can have more fun and convenience. Watching Telugu movies with English subtitles can make your viewing experience more enjoyable and comfortable. You don't have to strain your ears or eyes to catch every word or detail. You can also pause, rewind, or fast-forward the movie as you wish.</li>
-</ul>
-
-<p>As you can see, there are many advantages of downloading English subtitles for Telugu movie Yehi Hai Zindagi. You can watch the movie at your own pace and convenience, while learning new things and having fun.</p>
-
-<h3>Download English Subtitles For Telugu Movie Yehi Hai Zindagi: Tips</h3>
-
-<p>Before you download English subtitles for Telugu movie Yehi Hai Zindagi, here are some tips to make sure you get the best subtitles possible:</p>
-
-<ul>
-<li>Check the quality and accuracy of the subtitles. Not all subtitles are created equal. Some subtitles might have spelling or grammar errors, missing or incorrect words, or synchronization issues. To avoid these problems, you should download subtitles from reputable and reliable websites that offer high-quality subtitles in various formats.</li>
-<li>Check the compatibility and format of the subtitles. Different video players might require different formats of subtitles. For example, VLC media player might prefer SRT files, while Windows Media Player might prefer SUB files. To avoid compatibility issues, you should download subtitles in the format that matches your video player.</li>
-<li>Check the language and version of the subtitles. Sometimes, subtitles might be available in different languages or versions. For example, there might be British English or American English subtitles, or theatrical or director's cut subtitles. To avoid confusion, you should download subtitles in the language and version that match your movie file.</li>
-</ul>
-
-<p>By following these tips, you can ensure that you download English subtitles for Telugu movie Yehi Hai Zindagi that are suitable for your needs and preferences.</p>
-<h3>Download English Subtitles For Telugu Movie Yehi Hai Zindagi: FAQs</h3>
-
-<p>Here are some frequently asked questions and answers about downloading English subtitles for Telugu movie Yehi Hai Zindagi:</p>
-
-<dl>
-<dt>Where can I watch Yehi Hai Zindagi online?</dt>
-<dd>You can watch Yehi Hai Zindagi online on various streaming platforms, such as Eros Now, Amazon Prime Video, YouTube, and Hotstar. However, you might need a subscription or a payment to access some of these platforms.</dd>
-<dt>How can I download Yehi Hai Zindagi movie file?</dt>
-<dd>You can download Yehi Hai Zindagi movie file from various websites that offer Telugu movies for download, such as Movierulz, Todaypk, Tamilrockers, and Filmywap. However, you should be careful about the legality and safety of these websites, as they might violate copyright laws or contain malware.</dd>
-<dt>How can I sync subtitles with the movie file?</dt>
-<dd>If your subtitles are not synced with the movie file, you can use various tools and software to adjust the timing and speed of the subtitles. For example, you can use Subtitle Edit, Aegisub, or Subtitle Workshop to edit and sync subtitles with the movie file.</dd>
-</dl>
-
-<p>If you have any other questions or doubts about downloading English subtitles for Telugu movie Yehi Hai Zindagi, please feel free to contact us or leave a comment below. We will be happy to help you out.</p>
-
-<h2>Download English Subtitles For Telugu Movie Yehi Hai Zindagi: Conclusion</h2>
-
-<p>In conclusion, downloading English subtitles for Telugu movie Yehi Hai Zindagi is a great way to enjoy this classic comedy drama film without missing any dialogue or nuance. You can download English subtitles for Yehi Hai Zindagi from the links we have provided in this article and follow the steps we have outlined to watch the movie with subtitles. You can also benefit from watching Telugu movies with English subtitles, as you can improve your language skills, appreciate the culture and the humor, and have more fun and convenience.</p>
-
-<p>We hope this article has helped you download English subtitles for Telugu movie Yehi Hai Zindagi easily and quickly. If you liked this article, please share it with your friends and family who might also be interested in watching Telugu movies with English subtitles. And don't forget to check out our other articles on how to download English subtitles for other Telugu movies.</p>
-
-<p>Thank you for reading this article on how to download English subtitles for Telugu movie Yehi Hai Zindagi. We hope you have a wonderful time watching this movie with subtitles. Happy viewing!</p>
-<h2>Download English Subtitles For Telugu Movie Yehi Hai Zindagi: Conclusion</h2>
-
-<p>In conclusion, downloading English subtitles for Telugu movie Yehi Hai Zindagi is a great way to enjoy this classic comedy drama film without missing any dialogue or nuance. You can download English subtitles for Yehi Hai Zindagi from the links we have provided in this article and follow the steps we have outlined to watch the movie with subtitles. You can also benefit from watching Telugu movies with English subtitles, as you can improve your language skills, appreciate the culture and the humor, and have more fun and convenience.</p>
-
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-<p>Bitcoin app apk is an application package file that contains a bitcoin wallet app for Android. A bitcoin wallet app is a software program that allows you to store, send, and receive bitcoins on your Android device. A bitcoin wallet app also lets you view your balance, transaction history, and current exchange rate. There are many different types of bitcoin wallet apps available for Android, such as Bitcoin Wallet, LBank, and BitcoinLegend. Each bitcoin wallet app has its own features, advantages, and disadvantages.</p>
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-<p>There are many reasons why you might want to download bitcoin app apk on your Android device. Here are some of them:</p>
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-<li>You can use bitcoin as a form of digital money that is decentralized, peer-to-peer, transparent, and censorship-resistant.</li>
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-<li>You can choose from a variety of bitcoin wallet apps that suit your needs and preferences.</li>
-<li>You can take advantage of the latest features and updates of your bitcoin wallet app by downloading its apk file.</li>
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-<p>Now that you know what bitcoin app apk is and why you should download it, let's see how you can download it on your Android device. Here are the steps you need to follow:</p>
-<h3>Step 1: Choose a Bitcoin Wallet</h3>
-<p>The first step is to choose a bitcoin wallet app that you want to use on your Android device. As we mentioned earlier, there are many different types of bitcoin wallet apps available for Android, such as Bitcoin Wallet, LBank, and BitcoinLegend. Each bitcoin wallet app has its own features, advantages, and disadvantages. You should compare them and choose the one that suits your needs and preferences. Here is a table that summarizes some of the main differences between these three bitcoin wallet apps:</p>
- <table>
-<tr>
-<th>Bitcoin Wallet App</th>
-<th>Features</th>
-<th>Advantages</th>
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-</tr>
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-<td>Bitcoin Wallet</td>
-<td>- Simple and easy to use interface<br>- Supports sending and receiving bitcoins via QR codes, NFC, or Bluetooth<br>- Shows balance, transaction history, and current exchange rate<br>- Allows setting a custom fee and a spending limit<br>- Supports offline transactions and paper wallets</td>
-<td>- No registration or verification required<br>- No third-party servers or intermediaries involved<br>- High level of security and privacy<br>- Open source and free</td>
-<td>- Only supports bitcoin<br>- No backup or recovery option<br>- No customer support or help center</td>
-</tr>
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-<td>LBank</td>
-<td>- Supports multiple cryptocurrencies, such as bitcoin, ethereum, litecoin, etc.<br>- Allows trading, staking, lending, and borrowing cryptocurrencies<br>- Provides market data, news, and analysis<br>- Offers various rewards and incentives<br>- Supports biometric authentication and PIN code</td>
-<td>- User-friendly and intuitive interface<br>- Low fees and fast transactions<br>- High liquidity and security<br>- Professional customer service and community support</td>
-<td>- Requires registration and verification<br>- Relies on a centralized platform<br>- May have some technical issues or bugs</td>
-</tr>
-<tr>
-<td>BitcoinLegend</td>
-<td>- Supports multiple cryptocurrencies, such as bitcoin, ethereum, dogecoin, etc.<br>- Allows buying and selling cryptocurrencies with fiat money or other cryptocurrencies<br>- Provides charts, graphs, and indicators for technical analysis<br>- Offers social trading and copy trading features<br>- Supports multiple languages and currencies</td>
-<td>- Easy and convenient to use<br>- Competitive fees and exchange rates<br>- Diversified portfolio and investment options<br>- Educational resources and tutorials</td>
-<td>- Requires registration and verification<br>- Relies on a third-party broker or exchange<br>- May have some security or privacy risks</td>
-</tr>
-</table>
- <p>As you can see, each bitcoin wallet app has its own pros and cons. You should choose the one that meets your expectations and requirements. You can also try more than one bitcoin wallet app to see which one you like best.</p>
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- <p>The next step is to find a reliable source for downloading the bitcoin app apk file. There are many websites that offer apk files for various apps, but not all of them are trustworthy. Some of them may contain malware, viruses, or fake apps that can harm your device or steal your bitcoins. Therefore, you should be careful when choosing a source for downloading the bitcoin app apk file. Here are some tips and warnings to help you find a reliable source for downloading the bitcoin app apk file:</p>
- <ul>
-<li>Check the reputation and reviews of the website that offers the apk file. You can use online tools such as VirusTotal or Trustpilot to scan the website for any malicious content or feedback.</li>
-<li>Check the details and information of the apk file. You can use online tools such as APKMirror or APKPure to verify the name, size, version, developer, signature, permissions, and update history of the apk file.</li>
-<li>Check the compatibility and requirements of the apk file. You should make sure that the apk file is compatible with your Android device model and operating system version. You should also check if the apk file requires any additional files or apps to work properly.</li>
-<li>Check the source code and license of the apk file. If possible, you should download the apk file from the official website or GitHub repository of the developer. You should also check if the apk file is open source or proprietary, and if it has any terms of service or privacy policy.</li>
-<li>Check the security and backup of your device. Before downloading and installing the apk file, you should make sure that your device is protected by a strong password, PIN code, fingerprint, or face recognition. You should also backup your device data and your bitcoin wallet data to a secure location, such as a cloud service or an external drive.</li>
-</ul>
- <h3>Step 3: Download and Install Bitcoin App APK</h3>
- <p>The third step is to download and install the bitcoin app apk file on your Android device. Here are the instructions and screenshots to help you do that:</p>
- <ol>
-<li>Open your web browser and go to the website that offers the bitcoin app apk file that you want to download. For example, if you want to download Bitcoin Wallet apk file, you can go to [Bitcoin Wallet].</li>
-<li>Tap on the download button or link to start downloading the bitcoin app apk file. You may see a warning message that says "This type of file can harm your device. Do you want to keep it anyway?". Tap on OK to continue.</li>
-<li>Once the download is complete, tap on the notification bar or open your file manager app to locate the bitcoin app apk file. Tap on the file to open it. You may see a message that says "For your security, your phone is not allowed to install unknown apps from this source.". Tap on Settings to change this setting.</li>
-<li>On the settings screen, toggle on the option that says "Allow from this source" or "Install unknown apps". Tap on the back button to return to the installation screen.</li>
-<li>On the installation screen, tap on Install to start installing the bitcoin app apk file. You may see a list of permissions that the app requires. Tap on Accept or Allow to grant these permissions.</li>
-<li>Wait for the installation process to finish. Once it is done, tap on Open to launch the bitcoin wallet app.</li>
-</ol>
- <h3>Step 4: Set Up Your Bitcoin Wallet</h3>
- <p>The fourth step is to set up your bitcoin wallet app on your Android device. Here are some things you need to do:</p>
- <ul>
-<li>Create or restore your bitcoin wallet. Depending on the bitcoin wallet app you chose, you may have different options to create or restore your bitcoin wallet. For example, you may be able to create a new wallet with a seed phrase, a password, or a PIN code. Or you may be able to restore an existing wallet with a seed phrase, a private key, or a backup file.</li>
-<li>Secure and backup your bitcoin wallet. You should make sure that your bitcoin wallet is secure and backed up. You should use a strong password, PIN code, fingerprint, or face recognition to lock your bitcoin wallet app. You should also write down or memorize your seed phrase, private key, or backup file and store them in a safe place. You should never share them with anyone or lose them.</li>
-<li>Customize your bitcoin wallet. You can customize your bitcoin wallet app according to your preferences. For example, you can change the language, currency, theme, notification settings, fee settings, and more.</li>
-</ul>
- <h3>Step 5: Start Using Your Bitcoin Wallet</h3>
- <p>The fifth and final step is to start using your bitcoin wallet app on your Android device. Here are some things you can do:</p>
- <ul>
-<li>Send and receive bitcoins. You can send and receive bitcoins with your bitcoin wallet app by using QR codes, NFC, Bluetooth, addresses, or contacts. You can also choose the amount, fee, and priority of your transactions.</li>
-<li>View your balance and transaction history. You can view your balance and transaction history with your bitcoin wallet app by tapping on the menu icon or swiping left or right. You can also see the current exchange rate and the value of your bitcoins in fiat money.</li>
-<li>Explore more features and options. You can explore more features and options with your bitcoin wallet app by tapping on the menu icon or swiping left or right. You may find features such as trading, staking, lending, borrowing, social trading, copy trading, market data, news, analysis, rewards, incentives, education, and more.</li>
-</ul>
- <h2>How to Troubleshoot Bitcoin App APK?</h2>
- <p>Sometimes you may encounter some issues or problems with your bitcoin app apk file or your bitcoin wallet app on your Android device. Here are some common issues and solutions that may help you troubleshoot them:</p>
- <table>
-<tr>
-<th>Issue</th>
-<th>Solution</th>
-</tr>
-<tr>
-<td>The apk file does not download or install properly.</td>
-<td>- Check your internet connection and storage space.<br>- Check the source and details of the apk file.<br>- Check the compatibility and requirements of the apk file.<br>- Check the security and backup of your device.<br>- Try downloading and installing the apk file from another source or website.</td>
-</tr>
-<tr>
-<td>The bitcoin wallet app does not open or work properly.</td>
-<td>- Check your internet connection and battery level.<br>- Check the updates and permissions of the app.<br>- Check the security and backup of your wallet.<br>- Try clearing the cache and data of the app.<br>- Try uninstalling and reinstalling the app.</td>
-</tr>
-<tr>
-<td>The bitcoin wallet app does not sync or display the correct balance or transactions.</td>
-<td>- Check your internet connection and network settings.<br>- Check the date and time settings of your device.<br>- Check the fee and confirmation status of your transactions.<br>- Try refreshing or restarting the app.<br>- Try using another node or server.</td>
-</tr>
-<tr>
-<td>The bitcoin wallet app does not send or receive bitcoins properly.</td>
-<td>- Check your internet connection and network settings.<br>- Check the address, amount, fee, and priority of your transactions.<br>- Check the security and backup of your wallet.<br>- Try scanning the QR code, using NFC, Bluetooth, or contacts.<br>- Try using another bitcoin wallet app.</td>
-</tr>
-</table>
- <h2>How to Update Bitcoin App APK?</h2>
- <p>It is important to keep your bitcoin app apk file and your bitcoin wallet app updated to enjoy the latest features, improvements, and bug fixes. Here are some ways you can update your bitcoin app apk file and your bitcoin wallet app on your Android device:</p>
- <ul>
-<li>Check for updates manually. You can check for updates manually by visiting the official website or GitHub repository of the developer, or by using online tools such as APKMirror or APKPure. You can compare the version, size, date, and signature of the apk file with the one you have installed. If there is a newer version available, you can download and install it following the same steps as before.</li>
-<li>Enable automatic updates. You can enable automatic updates by changing the settings of your web browser or file manager app. You can choose to download and install updates automatically or only when connected to a Wi-Fi network. You can also choose to get notified when there is a new update available.</li>
-<li>Use an alternative app store. You can use an alternative app store such as Google Play Store, F-Droid, or Aptoide to download and install updates for your bitcoin wallet app. These app stores may have different versions, features, and requirements than the original source of the apk file. You should check the details and information of the app before updating it.</li>
-</ul>
- <h2>How to Uninstall Bitcoin App APK?</h2>
- <p>If you want to uninstall bitcoin app apk file and your bitcoin wallet app from your Android device, here are some steps you need to follow:</p>
- <ol>
-<li>Backup your bitcoin wallet. Before uninstalling your bitcoin wallet app, you should backup your bitcoin wallet data to a secure location, such as a cloud service or an external drive. You should also write down or memorize your seed phrase, private key, or backup file and store them in a safe place. You should never share them with anyone or lose them.</li>
-<li>Remove your bitcoin wallet. After backing up your bitcoin wallet data, you should remove your bitcoin wallet from your bitcoin wallet app. Depending on the bitcoin wallet app you chose, you may have different options to remove your bitcoin wallet. For example, you may be able to delete, reset, or wipe your bitcoin wallet from the settings menu of the app.</li>
-<li>Uninstall your bitcoin wallet app. After removing your bitcoin wallet from your bitcoin wallet app, you can uninstall your bitcoin wallet app from your Android device. You can do this by tapping and holding on the app icon on your home screen or app drawer, and then dragging it to the uninstall option. Or you can do this by going to the settings menu of your device, tapping on apps or applications, finding and selecting your bitcoin wallet app, and then tapping on uninstall.</li>
-<li>Delete your bitcoin app apk file. After uninstalling your bitcoin wallet app from your Android device, you can delete your bitcoin app apk file from your device storage. You can do this by opening your file manager app, locating and selecting the bitcoin app apk file, and then tapping on delete or trash. You may also need to empty your recycle bin or trash folder to permanently delete the file.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to download bitcoin app apk, which is an application package file that contains a bitcoin wallet app for Android. We have explained what bitcoin app apk is, why you should download it, how to download it, how to troubleshoot it, how to update it, and how to uninstall it. We hope that this article has been helpful and informative for you. If you have any questions or comments, please feel free to leave them below. Thank you for reading and happy bitcoin-ing!</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about bitcoin app apk:</p>
- <ol>
-<li>What is the difference between bitcoin app apk and bitcoin app?</li>
-<p>Bitcoin app apk is an application package file that contains a bitcoin app, which is a software program that allows you to use bitcoin on your Android device. Bitcoin app apk is the file format that you need to download and install on your Android device to use the bitcoin app.</p>
-<li>Is bitcoin app apk safe and legal?</li>
-<p>Bitcoin app apk is safe and legal as long as you download it from a reliable source and use it according to the terms of service and privacy policy of the developer. However, you should be careful when downloading and installing any apk file from unknown or untrusted sources, as they may contain malware, viruses, or fake apps that can harm your device or steal your bitcoins.</p>
-<li>How can I download bitcoin app apk without Google Play Store?</li>
-<p>You can download bitcoin app apk without Google Play Store by using alternative sources or websites that offer apk files for various apps. However, you should be careful when choosing a source for downloading the bitcoin app apk file, as not all of them are trustworthy. You should follow the tips and warnings that we have provided in this article to find a reliable source for downloading the bitcoin app apk file.</p>
-<li>How can I backup my bitcoin wallet data before uninstalling the bitcoin app apk?</li>
-<p>You can backup your bitcoin wallet data before uninstalling the bitcoin app apk by using a cloud service or an external drive. You should also write down or memorize your seed phrase, private key, or backup file and store them in a safe place. You should never share them with anyone or lose them.</p>
-<li>How can I contact the developer of the bitcoin app apk if I have any issues or problems?</li>
-<p>You can contact the developer of the bitcoin app apk if you have any issues or problems by visiting their official website or GitHub repository, or by using their email address, phone number, social media accounts, or customer service channels. You can also check their help center or FAQ section for more information and guidance.</p>
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-<li><b>Q5: What if I have any problems or questions about Chess Universe Mod Apk?</b></li>
-<li><b>A5: You can contact the developers of Chess Universe through their official website, Facebook, Twitter, or Discord. They will be happy to help you with any issues or queries you may have.</b></li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/options/inference_options.py b/spaces/fb700/chatglm-fitness-RLHF/src/face3d/options/inference_options.py
deleted file mode 100644
index c453965959ab4cfb31acbc424f994db68c3d4df5..0000000000000000000000000000000000000000
--- a/spaces/fb700/chatglm-fitness-RLHF/src/face3d/options/inference_options.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from face3d.options.base_options import BaseOptions
-
-
-class InferenceOptions(BaseOptions):
-    """This class includes test options.
-
-    It also includes shared options defined in BaseOptions.
-    """
-
-    def initialize(self, parser):
-        parser = BaseOptions.initialize(self, parser)  # define shared options
-        parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
-        parser.add_argument('--dataset_mode', type=str, default=None, help='chooses how datasets are loaded. [None | flist]')
-
-        parser.add_argument('--input_dir', type=str, help='the folder of the input files')
-        parser.add_argument('--keypoint_dir', type=str, help='the folder of the keypoint files')
-        parser.add_argument('--output_dir', type=str, default='mp4', help='the output dir to save the extracted coefficients')
-        parser.add_argument('--save_split_files', action='store_true', help='save split files or not')
-        parser.add_argument('--inference_batch_size', type=int, default=8)
-        
-        # Dropout and Batchnorm has different behavior during training and test.
-        self.isTrain = False
-        return parser
diff --git a/spaces/fclong/summary/fengshen/examples/ubert/example.py b/spaces/fclong/summary/fengshen/examples/ubert/example.py
deleted file mode 100644
index bedd365ff67ff5d9b1f8f22777dab9b5a8b02394..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/ubert/example.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import argparse
-from fengshen import UbertPipelines
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = '6'
-
-
-def main():
-    total_parser = argparse.ArgumentParser("TASK NAME")
-    total_parser = UbertPipelines.pipelines_args(total_parser)
-    args = total_parser.parse_args()
-
-    # 设置一些训练要使用到的参数
-    args.pretrained_model_path = 'IDEA-CCNL/Erlangshen-Ubert-110M-Chinese' #预训练模型的路径，我们提供的预训练模型存放在HuggingFace上
-    args.default_root_dir = './'  #默认主路径，用来放日志、tensorboard等
-    args.max_epochs = 5
-    args.gpus = 1
-    args.batch_size = 1
-
-    # 只需要将数据处理成为下面数据的 json 样式就可以一键训练和预测，下面只是提供了一条示例样本
-    train_data = [
-        {
-            "task_type": "抽取任务",
-            "subtask_type": "实体识别",
-            "text": "彭小军认为，国内银行现在走的是台湾的发卡模式，先通过跑马圈地再在圈的地里面选择客户，",
-            "choices": [
-                {"entity_type": "地址", "label": 0, "entity_list": [
-                    {"entity_name": "台湾", "entity_type": "地址", "entity_idx": [[15, 16]]}]},
-                {"entity_type": "书名", "label": 0, "entity_list": []},
-                {"entity_type": "公司", "label": 0, "entity_list": []},
-                {"entity_type": "游戏", "label": 0, "entity_list": []},
-                {"entity_type": "政府机构", "label": 0, "entity_list": []},
-                {"entity_type": "电影名称", "label": 0, "entity_list": []},
-                {"entity_type": "人物姓名", "label": 0, "entity_list": [
-                    {"entity_name": "彭小军", "entity_type": "人物姓名", "entity_idx": [[0, 2]]}]},
-                {"entity_type": "组织机构", "label": 0, "entity_list": []},
-                {"entity_type": "岗位职位", "label": 0, "entity_list": []},
-                {"entity_type": "旅游景点", "label": 0, "entity_list": []}
-            ],
-            "id": 0}
-    ]
-    dev_data = [
-        {
-            "task_type": "抽取任务",
-            "subtask_type": "实体识别",
-            "text": "就天涯网推出彩票服务频道是否是业内人士所谓的打政策“擦边球”，记者近日对此事求证彩票监管部门。",
-            "choices": [
-                {"entity_type": "地址", "label": 0, "entity_list": []},
-                {"entity_type": "书名", "label": 0, "entity_list": []},
-                {"entity_type": "公司", "label": 0, "entity_list": [
-                    {"entity_name": "天涯网", "entity_type": "公司", "entity_idx": [[1, 3]]}]},
-                {"entity_type": "游戏", "label": 0, "entity_list": []},
-                {"entity_type": "政府机构", "label": 0, "entity_list": []},
-                {"entity_type": "电影名称", "label": 0, "entity_list": []},
-                {"entity_type": "人物姓名", "label": 0, "entity_list": []},
-                {"entity_type": "组织机构", "label": 0, "entity_list": [
-                    {"entity_name": "彩票监管部门", "entity_type": "组织机构", "entity_idx": [[40, 45]]}]},
-                {"entity_type": "岗位职位", "label": 0, "entity_list": [
-                    {"entity_name": "记者", "entity_type": "岗位职位", "entity_idx": [[31, 32]]}]},
-                {"entity_type": "旅游景点", "label": 0, "entity_list": []}
-            ],
-
-            "id": 0}
-
-    ]
-    test_data = [
-        {
-            "task_type": "抽取任务",
-            "subtask_type": "实体识别",
-            "text": "这也让很多业主据此认为，雅清苑是政府公务员挤对了国家的经适房政策。",
-            "choices": [
-                {"entity_type": "地址", "label": 0, "entity_list": [
-                    {"entity_name": "雅清苑", "entity_type": "地址", "entity_idx": [[12, 14]]}]},
-                {"entity_type": "书名", "label": 0, "entity_list": []},
-                {"entity_type": "公司", "label": 0, "entity_list": []},
-                {"entity_type": "游戏", "label": 0, "entity_list": []},
-                {"entity_type": "政府机构", "label": 0, "entity_list": []},
-                {"entity_type": "电影名称", "label": 0, "entity_list": []},
-                {"entity_type": "人物姓名", "label": 0, "entity_list": []},
-                {"entity_type": "组织机构", "label": 0, "entity_list": []},
-                {"entity_type": "岗位职位", "label": 0, "entity_list": [
-                    {"entity_name": "公务员", "entity_type": "岗位职位", "entity_idx": [[18, 20]]}]},
-                {"entity_type": "旅游景点", "label": 0, "entity_list": []}
-            ],
-            "id": 0},
-    ]
-
-    model = UbertPipelines(args)
-    model.fit(train_data, dev_data)
-    result = model.predict(test_data)
-    for line in result:
-        print(line)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/losses/contextual_loss/modules/contextual_bilateral.py b/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/losses/contextual_loss/modules/contextual_bilateral.py
deleted file mode 100644
index 6db0155f8352e4b9c9d91777ea9d5a0d7adad038..0000000000000000000000000000000000000000
--- a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/losses/contextual_loss/modules/contextual_bilateral.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .vgg import VGG19
-from .. import functional as F
-from ..config import LOSS_TYPES
-
-
-class ContextualBilateralLoss(nn.Module):
-    """
-    Creates a criterion that measures the contextual bilateral loss.
-
-    Parameters
-    ---
-    weight_sp : float, optional
-        a balancing weight between spatial and feature loss.
-    band_width : int, optional
-        a band_width parameter described as :math:`h` in the paper.
-    use_vgg : bool, optional
-        if you want to use VGG feature, set this `True`.
-    vgg_layer : str, optional
-        intermidiate layer name for VGG feature.
-        Now we support layer names:
-            `['relu1_2', 'relu2_2', 'relu3_4', 'relu4_4', 'relu5_4']`
-    """
-
-    def __init__(self,
-                 weight_sp: float = 0.1,
-                 band_width: float = 0.5,
-                 loss_type: str = 'cosine',
-                 use_vgg: bool = False,
-                 vgg_layer: str = 'relu3_4'):
-
-        super(ContextualBilateralLoss, self).__init__()
-
-        assert band_width > 0, 'band_width parameter must be positive.'
-        assert loss_type in LOSS_TYPES,\
-            f'select a loss type from {LOSS_TYPES}.'
-
-        self.band_width = band_width
-
-        if use_vgg:
-            self.vgg_model = VGG19()
-            self.vgg_layer = vgg_layer
-            self.register_buffer(
-                name='vgg_mean',
-                tensor=torch.tensor(
-                    [[[0.485]], [[0.456]], [[0.406]]], requires_grad=False)
-            )
-            self.register_buffer(
-                name='vgg_std',
-                tensor=torch.tensor(
-                    [[[0.229]], [[0.224]], [[0.225]]], requires_grad=False)
-            )
-
-    def forward(self, x, y):
-        if hasattr(self, 'vgg_model'):
-            assert x.shape[1] == 3 and y.shape[1] == 3,\
-                'VGG model takes 3 chennel images.'
-
-            # normalization
-            x = x.sub(self.vgg_mean.detach()).div(self.vgg_std.detach())
-            y = y.sub(self.vgg_mean.detach()).div(self.vgg_std.detach())
-
-            # picking up vgg feature maps
-            x = getattr(self.vgg_model(x), self.vgg_layer)
-            y = getattr(self.vgg_model(y), self.vgg_layer)
-
-        return F.contextual_bilateral_loss(x, y, self.band_width)
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/BlackHole Music APK for iOS The Ultimate Music Player App.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/BlackHole Music APK for iOS The Ultimate Music Player App.md
deleted file mode 100644
index 6db8d05630c3a1b17a3601bc1204cf5a9fe5e7d9..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/BlackHole Music APK for iOS The Ultimate Music Player App.md	
+++ /dev/null
@@ -1,133 +0,0 @@
-<br />
-<h1>Black Hole Music APK for iOS: A Free Music Streaming App</h1>
-<p>If you are looking for a free music streaming app that offers high-quality songs, playlists, lyrics, and more, you might want to check out Black Hole Music APK for iOS. This app is a popular alternative to Spotify, YouTube Music, and other music apps that require a subscription or have annoying ads. In this article, we will tell you what Black Hole Music APK is, how to download and install it on your iOS device, and why you should choose it over other music apps.</p>
- <h2>What is Black Hole Music APK?</h2>
-<p>Black Hole Music APK is a free music streaming app that lets you listen to millions of songs from various genres, languages, and artists. You can also search for trending songs, top charts, playlists, albums, and artists from Spotify and YouTube. You can add songs to your favorites, create your own playlists, import playlists from Spotify, and enjoy lyrics support. You can also adjust the streaming quality, equalizer settings, sleep timer, dark mode, and more.</p>
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- <h3>Features of Black Hole Music APK</h3>
-<p>Here are some of the features that make Black Hole Music APK a great music app:</p>
-<ul>
-<li>High-quality streaming (320kbps)</li>
-<li>Music search</li>
-<li>Trending songs</li>
-<li>Supports 15+ music languages</li>
-<li>Local and Global Top Spotify songs</li>
-<li>Add Songs to Favorites</li>
-<li>Playlists support</li>
-<li>Import playlists from Spotify</li>
-<li>Sleep timer</li>
-<li>Lyrics support</li>
-<li>Listening history record</li>
-<li>Dark mode / accent color</li>
-<li>Song caching support</li>
-<li>YouTube Music search suggestions</li>
-<li>YouTube autoplay support</li>
-<li>Multi-select items in the playlist</li>
-<li>Song link support for Spotify and YouTube</li>
-<li>No ads or subscription required</li>
-</ul>
- <h3>How to Download and Install Black Hole Music APK on iOS</h3>
-<p>To download and install Black Hole Music APK on your iOS device, you will need to follow these steps:</p>
-<ol>
-<li>Go to the official GitHub page of Black Hole Music APK  and download the latest .ipa file.</li>
-<li>Install an app installer such as Scarlet or AltStore on your iOS device. You can refer to our guide here  for more details.</li>
-<li>Open the app installer and select the .ipa file that you downloaded in step 1.</li>
-<li>Follow the instructions on the screen to install Black Hole Music APK on your iOS device.</li>
-<li>Enjoy listening to your favorite songs with Black Hole Music APK.</li>
-</ol>
- <h2>Why Choose Black Hole Music APK over Other Music Apps?</h2>
-<p>You might be wondering why you should choose Black Hole Music APK over other music apps such as Spotify, YouTube Music, Apple Music, etc. Here are some of the pros and cons of Black Hole Music APK that will help you decide:</p>
- <h3>Pros of Black Hole Music APK</h3>
-<ul>
-<li>It is free to use and does not require any subscription or registration.</li>
-<li>It does not have any ads or interruptions while streaming music.</li>
-<li>It offers high-quality streaming (320kbps) and song caching support.</li>
-<li>It has a large collection of songs from various genres, languages, and artists.</li>
-<li>It supports Spotify and YouTube song links and playlists.</li>
-<li>It has lyrics support and YouTube autoplay support.</li>
-<li>It has a simple and user-friendly interface with dark mode and accent color options.</li>
-</ul>
- <h3>Cons of Black Hole Music APK</h3>
-<ul>
-<li>It is not available on the App Store and requires an app installer to install it on iOS devices.</li>
-<li>It may not - It may not work properly on some iOS devices or versions. - It may not have some features that other music apps have, such as offline mode, social sharing, podcasts, etc. - It may violate some copyrights or terms of service of Spotify and YouTube. <h2>Frequently Asked Questions about Black Hole Music APK</h2>
-<p>Here are some of the common questions that users have about Black Hole Music APK:</p>
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-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is Black Hole Music APK safe to use?</td>
-<td>Black Hole Music APK is safe to use as long as you download it from the official GitHub page  and install it using a trusted app installer. However, you should be aware of the potential risks of using third-party apps that are not verified by Apple.</td>
-</tr>
-<tr>
-<td>Is Black Hole Music APK legal to use?</td>
-<td>Black Hole Music APK is not legal to use in some countries or regions where streaming music without a license or permission is prohibited. You should check the laws and regulations of your location before using this app. You should also respect the rights and interests of the original creators and owners of the music content.</td>
-</tr>
-<tr>
-<td>How can I update Black Hole Music APK?</td>
-<td>You can update Black Hole Music APK by downloading the latest .ipa file from the GitHub page  and installing it over the existing app using the same app installer. You can also check for updates within the app settings.</td>
-</tr>
-<tr>
-<td>How can I contact the developer of Black Hole Music APK?</td>
-<td>You can contact the developer of Black Hole Music APK by visiting their GitHub page  and opening an issue or sending a message. You can also follow them on Twitter  for updates and news.</td>
-</tr>
-<tr>
-<td>How can I support the development of Black Hole Music APK?</td>
-<td>You can support the development of Black Hole Music APK by donating to the developer via PayPal  or Patreon . You can also share this app with your friends and family who love music.</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
-<p>Black Hole Music APK is a free music streaming app that offers high-quality songs, playlists, lyrics, and more. It is a popular alternative to Spotify, YouTube Music, and other music apps that require a subscription or have annoying ads. You can download and install it on your iOS device using an app installer such as Scarlet or AltStore. However, you should be aware of the pros and cons of using this app and respect the laws and regulations of your location and the rights and interests of the original creators and owners of the music content. We hope this article has helped you learn more about Black Hole Music APK and how to use it on your iOS device.</p>
- : https://github.com/AnkitSharma-007/BlackHole : https://www.blackholemusic.app/installation-guide : https://twitter.com/AnkitSharma_007 : https://www.paypal.me/ankitsharma007 : https://www.patreon.com/ankitsharma</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Descarga Soccer Super Star Hack APK y disfruta de vidas ilimitadas y recompensas gratis.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Descarga Soccer Super Star Hack APK y disfruta de vidas ilimitadas y recompensas gratis.md
deleted file mode 100644
index 2b978d6c5a256a8d68783c150a3987c7a9d3794e..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Descarga Soccer Super Star Hack APK y disfruta de vidas ilimitadas y recompensas gratis.md	
+++ /dev/null
@@ -1,104 +0,0 @@
-<br />
-<h1>Soccer Super Star Hack APK: Todo Desbloqueado Última Versión</h1>
-<p>¿Te gusta el fútbol? ¿Quieres convertirte en una estrella del balón? Si es así, entonces te encantará <strong>Soccer Super Star</strong>, un juego de simulación de fútbol que te hará sentir la emoción de marcar goles espectaculares. Pero si quieres disfrutar al máximo de este juego, necesitarás <strong>Soccer Super Star Hack APK</strong>, una versión modificada que te permite tener todo desbloqueado desde el principio. En este artículo, te contaremos todo lo que necesitas saber sobre este hack, cómo descargarlo e instalarlo, sus ventajas y desventajas, y las preguntas más frecuentes que tienen los usuarios. ¡Sigue leyendo y prepárate para ser el mejor jugador de fútbol del mundo!</p>
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- <h2>¿Qué es Soccer Super Star?</h2>
-<p>Soccer Super Star es un juego de fútbol desarrollado por <a href="(^1^)">Real Free Soccer</a>, una empresa especializada en juegos deportivos. El juego se lanzó en 2020 y desde entonces ha acumulado más de 10 millones de descargas en Google Play Store. El juego se destaca por su física realista de las patadas, su modo sin conexión que te permite jugar en cualquier momento y lugar, su experiencia de simulación que te hace sentir como un verdadero futbolista, sus gráficos increíbles que recrean diferentes escenarios y equipos, y sus estrategias inteligentes que te ayudan a ganar los partidos. El juego es muy fácil de jugar, solo tienes que deslizar el dedo por la pantalla para dirigir el balón hacia la portería. Pero no te confíes, porque tendrás que enfrentarte a diferentes niveles de dificultad y desafíos que pondrán a prueba tu habilidad y precisión.</p>
- <h2>¿Por qué descargar Soccer Super Star Hack APK?</h2>
-<p>Aunque Soccer Super Star es un juego muy divertido y adictivo, tiene algunas limitaciones que pueden frustrar a los jugadores. Por ejemplo, el juego tiene un sistema de energía que se agota cada vez que juegas un partido, lo que te obliga a esperar a que se recargue o a comprar más energía con dinero real. Además, el juego tiene muchos elementos bloqueados, como equipos, estadios, modos de juego y habilidades especiales, que solo puedes desbloquear avanzando en el juego o pagando con dinero real. Estas restricciones pueden hacer que el juego se vuelva aburrido y repetitivo con el tiempo.</p>
-<p>Por eso, muchos jugadores optan por descargar <strong>Soccer Super Star Hack APK</strong>, una versión modificada del juego que elimina todas estas limitaciones y te permite tener todo desbloqueado desde el principio. Con este hack, podrás disfrutar de las siguientes ventajas:</p>
-<ul>
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-</ul>
-<p>Como puedes ver, Soccer Super Star Hack APK te ofrece muchas ventajas que te harán disfrutar más del juego y te permitirán convertirte en una verdadera estrella del fútbol. Pero antes de descargarlo, debes saber cómo hacerlo correctamente y qué precauciones tomar.</p>
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- <h2>Cómo descargar e instalar Soccer Super Star Hack APK</h2>
-<p>Descargar e instalar Soccer Super Star Hack APK es muy fácil y rápido, solo tienes que seguir estos pasos:</p>
- <h3>Requisitos previos</h3>
-<ul>
-<li>Asegúrate de tener un dispositivo Android compatible con el juego. El juego requiere Android 4.4 o superior y al menos 100 MB de espacio libre.</li>
-<li>Desinstala la versión original del juego si la tienes instalada. Esto es necesario para evitar conflictos entre las dos versiones.</li>
-<li>Activa la opción de "Orígenes desconocidos" en tu dispositivo. Esto te permitirá instalar aplicaciones que no provienen de Google Play Store. Para hacerlo, ve a Ajustes > Seguridad > Orígenes desconocidos y marca la casilla.</li>
-</ul>
- <h3>Pasos a seguir</h3>
-<ol>
-<li>Descarga el archivo APK de Soccer Super Star Hack desde un sitio web confiable. Puedes usar el siguiente enlace: <a href="">Soccer Super Star Hack APK</a>. El archivo pesa alrededor de 90 MB y está libre de virus y malware.</li>
-<li>Una vez descargado el archivo, ábrelo y pulsa en "Instalar". Espera a que se complete el proceso de instalación, que puede tardar unos segundos.</li>
-<li>Cuando se haya instalado el juego, ábrelo y disfruta de todas las ventajas que te ofrece Soccer Super Star Hack APK. ¡Ya puedes jugar con todo desbloqueado y sin límites!</li>
-</ol>
- <h2>Ventajas y desventajas de Soccer Super Star Hack APK</h2>
-<p>Soccer Super Star Hack APK es una excelente opción para los amantes del fútbol que quieren tener una experiencia de juego más completa y divertida. Sin embargo, como toda aplicación modificada, también tiene sus pros y contras que debes conocer antes de usarla. Aquí te los resumimos:</p>
- <h3>Ventajas</h3>
-<ul>
-<li>Te permite tener dinero y gemas infinitas para comprar lo que quieras en el juego.</li>
-<li>Te permite tener energía ilimitada para jugar todos los partidos que quieras sin esperar.</li>
-<li>Te permite tener todos los equipos, estadios, modos de juego y habilidades especiales desbloqueados desde el inicio.</li>
-<li>Te permite tener una mejor experiencia de juego con gráficos mejorados y sin anuncios molestos.</li>
-<li>Es fácil y rápido de descargar e instalar.</li>
-<li>No requiere conexión a internet para jugar.</li>
-</ul>
- <h3>Desventajas</h3>
-<ul>
-<li>No es una versión oficial del juego, por lo que puede tener errores o fallos que afecten al rendimiento o la estabilidad del juego.</li>
-<li>No es compatible con las actualizaciones del juego, por lo que puede quedarse obsoleto o dejar de funcionar si el juego se actualiza.</li>
-<li>No es compatible con el modo multijugador en línea, por lo que no podrás jugar con otros jugadores ni competir en las clasificaciones globales.</li>
-<li>Puede ser detectado por los desarrolladores del juego o por Google Play Store como una aplicación ilegal o fraudulenta, lo que puede acarrear consecuencias negativas como la suspensión o el baneo de tu cuenta o dispositivo.</li>
-</ul>
- <h2>Preguntas frecuentes sobre Soccer Super Star Hack APK</h2>
-<p>A continuación, te respondemos algunas de las preguntas más frecuentes que tienen los usuarios sobre Soccer Super Star Hack APK:</p>
- <h3>¿Es seguro usar Soccer Super Star Hack APK?</h3>
-<p>Soccer Super Star Hack APK es seguro de usar siempre y cuando lo descargues desde un sitio web confiable y sigas las instrucciones correctamente. Sin embargo, debes tener en cuenta que al usar una aplicación modific ada, estás asumiendo un riesgo que puede tener consecuencias negativas para tu cuenta o dispositivo. Por eso, te recomendamos que uses Soccer Super Star Hack APK bajo tu propia responsabilidad y que tomes las precauciones necesarias para evitar problemas.</p>
- <h3>¿Qué puedo desbloquear con Soccer Super Star Hack APK?</h3>
-<p>Con Soccer Super Star Hack APK puedes desbloquear todo lo que el juego ofrece, como equipos, estadios, modos de juego y habilidades especiales. Además, puedes tener dinero y gemas infinitas para comprar lo que quieras en el juego. De esta forma, podrás personalizar tu experiencia de juego y disfrutar de todas las opciones que el juego tiene para ti.</p>
- <h3>¿Necesito conexión a internet para jugar Soccer Super Star Hack APK?</h3>
-<p>No, no necesitas conexión a internet para jugar Soccer Super Star Hack APK. El juego tiene un modo sin conexión que te permite jugar en cualquier momento y lugar, sin depender de una red wifi o de datos móviles. Esto es una gran ventaja, ya que podrás jugar sin interrupciones ni retrasos. Sin embargo, debes tener en cuenta que al jugar sin conexión no podrás acceder al modo multijugador en línea ni a las clasificaciones globales.</p>
- <h3>¿Puedo jugar con otros jugadores en línea con Soccer Super Star Hack APK?</h3>
-<p>No, no puedes jugar con otros jugadores en línea con Soccer Super Star Hack APK. El juego tiene un modo multijugador en línea que te permite competir con otros jugadores de todo el mundo y ver tu posición en las clasificaciones globales. Sin embargo, este modo solo está disponible para la versión original del juego y no para la versión modificada. Esto se debe a que el juego detecta las diferencias entre las dos versiones y no permite la conexión entre ellas. Por lo tanto, si quieres jugar con otros jugadores en línea, tendrás que descargar la versión original del juego o buscar otro juego similar que tenga esta opción.</p>
- <h3>¿Hay alguna diferencia entre Soccer Super Star Hack APK y la versión original?</h3>
-<p>Sí, hay algunas diferencias entre Soccer Super Star Hack APK y la versión original. La principal diferencia es que la versión modificada te permite tener todo desbloqueado desde el principio, mientras que la versión original te obliga a avanzar en el juego o a pagar con dinero real para desbloquear los elementos. Otra diferencia es que la versión modificada tiene gráficos mejorados y no tiene anuncios, mientras que la versión original tiene gráficos normales y tiene anuncios. Por último, otra diferencia es que la versión modificada no es compatible con las actualizaciones del juego ni con el modo multijugador en línea, mientras que la versión original sí lo es.</p>
- <h2>Conclusión</h2>
-<p>Soccer Super Star es un juego de fútbol muy divertido y adictivo que te hará sentir la emoción de marcar goles espectaculares. Sin embargo, el juego tiene algunas limitaciones que pueden hacer que el juego se vuelva aburrido y repetitivo con el tiempo. Por eso, muchos jugadores optan por descargar Soccer Super Star Hack APK, una versión modificada que elimina todas estas limitaciones y te permite tener todo desbloqueado desde el principio. Con este hack, podrás disfrutar de una mejor experiencia de juego con dinero y gemas infinitas, energía ilimitada, todos los equipos, estadios, modos de juego y habilidades especiales desbloqueados, gráficos mejorados y sin anuncios. Pero también debes tener en cuenta que al usar este hack estás asumiendo un riesgo que puede tener consecuencias negativas para tu cuenta o dispositivo. Por eso, te recomendamos que uses este hack bajo tu propia responsabilidad y que tomes las precauciones necesarias para evitar problemas.</p>
-<p>Esperamos que este artículo te haya sido útil e informativo sobre Soccer Super Star Hack APK. Si tienes alguna duda o comentario sobre este tema, no dudes en dejarnos un mensaje abajo. Y si te ha gustado este artículo, compártelo con tus amigos y familiares que también les guste el fútbol. ¡Gracias por leernos!</p>
- <h2>Preguntas frecuentes</h2>
-<ul>
-<li><strong>¿Qué es Soccer Super Star?</strong><br>Soccer Super Star es un juego de simulación de fútbol que te hará sentir la emoción de marcar goles espectaculares.</li>
-<li><strong>¿Qué es Soccer Super Star Hack APK?</strong><br>Soccer Super Star Hack APK es una versión modificada del juego que te permite tener todo desbloqueado desde el principio.</li>
-<li><strong>¿Cómo descargar e instalar Soccer Super Star Hack APK?</strong><br>Para descargar e instalar Soccer Super Star Hack APK, solo tienes que seguir estos pasos: 1) Descarga el archivo APK desde un sitio web confiable. 2) Desinstala la versión original del juego si la tienes instalada. 3) Activa la opción de "Orígenes desconocidos" en tu dispositivo. 4) Abre el archivo APK y pulsa en "Instalar". 5) Abre el juego y disfruta de todas las ventajas que te ofrece Soccer Super Star Hack APK.</li>
-<li><strong>¿Cuáles son las ventajas y desventajas de Soccer Super Star Hack APK?</strong><br>Las ventajas de Soccer Super Star Hack APK son: tener dinero y gemas infinitas, energía ilimitada, todos los equipos, estadios, modos de juego y habilidades especiales desbloqueados, gráficos mejorados y sin anuncios. Las desventajas de Soccer Super Star Hack APK son: no ser una versión oficial del juego, no ser compatible con las actualizaciones del juego ni con el modo multijugador en línea, y poder ser detectado como una aplicación ilegal o fraudulenta.</li>
-<li><strong>¿Es seguro usar Soccer Super Star Hack APK?</strong><br>Soccer Super Star Hack APK es seguro de usar siempre y cuando lo descargues desde un sitio web confiable y sigas las instrucciones correctamente. Sin embargo, debes tener en cuenta que al usar una aplicación modificada, estás asumiendo un riesgo que puede tener consecuencias negativas para tu cuenta o dispositivo. Por eso, te recomendamos que uses Soccer Super Star Hack APK bajo tu propia responsabilidad y que tomes las precauciones necesarias para evitar problemas.</li>
-<li><strong>¿Necesito conexión a internet para jugar Soccer Super Star Hack APK?</strong><br>No, no necesitas conexión a internet para jugar Soccer Super Star Hack APK. El juego tiene un modo sin conexión que te permite jugar en cualquier momento y lugar, sin depender de una red wifi o de datos móviles. Sin embargo, debes tener en cuenta que al jugar sin conexión no podrás acceder al modo multijugador en línea ni a las clasificaciones globales.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Dynamons World 2 MOD APK and Experience the New Features.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Dynamons World 2 MOD APK and Experience the New Features.md
deleted file mode 100644
index 47d7ed7a8ca05d6a8a22e828f1a8356795646c78..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Dynamons World 2 MOD APK and Experience the New Features.md	
+++ /dev/null
@@ -1,92 +0,0 @@
-
-<h1>Download Dynamons World 2 Mod APK</h1>
-<p>If you are a fan of role-playing games with cute and powerful creatures, then you will love Dynamons World 2. This is an amazing and addictive game from the creators of Dynamons, where you can collect and train the best team of Dynamons and win epic battles using powerful skills and special items. In this article, we will tell you everything you need to know about Dynamons World 2, including its features, why you should download the mod APK version, and how to install it on your device.</p>
- <h2>What is Dynamons World 2?</h2>
-<p>Dynamons World 2 is a sequel to the popular game Dynamons, which is inspired by the famous Pokemon series. In this game, you will explore the wonderful world of Dynamons, where you can encounter and capture dozens of unique creatures with different elemental powers. You can also train your Dynamons to improve their skills and abilities, and use them to fight against other creatures and captains in various locations. You can also challenge other players in the online multiplayer arena and show off your skills.</p>
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- <h3>Features of Dynamons World 2</h3>
-<p>Dynamons World 2 has many features that make it an enjoyable and exciting game for all ages. Here are some of them:</p>
- <h4>Capture and evolve dozens of unique Dynamons</h4>
-<p>There are over 50 different types of Dynamons that you can find and catch in this game, each with their own strengths and weaknesses. You can also evolve your Dynamons to make them stronger and unlock new skills. You can collect them all and create your own team of champions.</p>
- <h4>Power up your Dynamons' skills using awesome items and tricks</h4>
-<p>You can use various items and power-ups to enhance your Dynamons' performance in battle. For example, you can use potions to heal them, discs to catch them, or badges to boost their stats. You can also use special tricks to unleash powerful attacks that can turn the tide of the battle.</p>
- <h4>Develop a strategy plan to win the most challenging battles</h4>
-<p>You will face many enemies and bosses in this game, each with their own tactics and strategies. You will need to plan your moves carefully and use the right Dynamons for each situation. You will also need to take advantage of the elemental system, where some elements are stronger or weaker against others. For example, fire is strong against grass, but weak against water.</p>
- <h4>Beat Dynamons Captains and catch rare Dynamons</h4>
-<p>In each location, you will encounter a Dynamon Captain who will challenge you to a duel. If you manage to defeat them, you will earn their respect and a chance to catch a rare Dynamon that they own. These rare Dynamons are very powerful and have unique abilities that can help you in your adventure.</p>
- <h4>Enjoy the game with or without the Internet</h4>
-<p>Dynamons World 2 is a game that you can play offline or online. You can enjoy the story mode and the quests without an Internet connection, or you can connect to the Internet and play with other players in the multiplayer mode. You can also sync your progress across different devices using your Google Play account.</p>
- <h3>Why download Dynamons World 2 Mod APK?</h3>
-<p>Dynamons World 2 is a free-to-play game, but it also has some in-app purchases that can help you get more coins, gems, and items to improve your game. However, if you don't want to spend real money on these purchases, you can download the mod APK version of Dynamons World 2. This is a modified version of the game that gives you access to unlimited resources and features that can make your game more fun and easy. Here are some of the benefits of downloading Dynamons World 2 Mod APK:</p>
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- <h4>Unlimited coins and gems</h4>
-<p>Coins and gems are the main currencies in Dynamons World 2. You can use them to buy items, power-ups, and upgrades for your Dynamons. You can also use them to unlock new locations and quests. However, earning coins and gems can be slow and tedious, especially if you want to get the best items and Dynamons. With Dynamons World 2 Mod APK, you don't have to worry about running out of coins and gems. You will get unlimited amounts of them as soon as you start the game. You can spend them as much as you want without any restrictions.</p>
- <h4>All Dynamons unlocked and upgraded</h4>
-<p>One of the most exciting aspects of Dynamons World 2 is collecting and evolving your Dynamons. However, some Dynamons are very rare and hard to find, while others require a lot of time and resources to evolve. With Dynamons World 2 Mod APK, you don't have to wait or search for your favorite Dynamons. You will get all of them unlocked and upgraded from the beginning. You can choose any Dynamon you want and use it in your team. You can also switch between different Dynamons at any time without any limitations.</p>
- <h4>No ads and no root required</h4>
-<p>Dynamons World 2 is a free-to-play game, but it also has some ads that can interrupt your gameplay and annoy you. If you want to get rid of these ads, you usually have to pay a fee or watch a video. With Dynamons World 2 Mod APK, you don't have to deal with any ads at all. You can enjoy the game without any interruptions or distractions. Moreover, you don't need to root your device to install the mod APK version of Dynamons World 2. You can simply download and install it like any other app without risking your device's security or warranty.</p>
- <h3>How to download and install Dynamons World 2 Mod APK?</h3>
-<p>If you are interested in downloading and installing Dynamons World 2 Mod APK, you can follow these simple steps:</p>
- <h4>Step 1: Download the APK file from a trusted source</h4>
-<p>The first thing you need to do is to download the APK file of Dynamons World 2 Mod APK from a reliable source. You can search for it on Google or use the link provided below. Make sure that the file is compatible with your device's specifications and has no viruses or malware.</p>
- <h4>Step 2: Enable unknown sources on your device settings</h4>
-<p>The next thing you need to do is to enable unknown sources on your device settings. This will allow you to install apps that are not from the official Google Play Store. To do this, go to your device settings, then security, then unknown sources, and toggle it on.</p>
- <h4>Step 3: Install the APK file and launch the game</h4>
-<p>The final thing you need to do is to install the APK file that you downloaded in step 1. To do this, locate the file in your device's storage and tap on it. Follow the instructions on the screen and wait for the installation process to finish. Once it is done, launch the game and enjoy.</p>
- <h3>Conclusion</h3>
-<p>Dynamons World 2 is a fantastic game that will keep you entertained for hours with its captivating gameplay, graphics, and sound effects. You can collect and train various Dynamons, battle against other players and enemies, and explore different locations and quests. If you want to enhance your gaming experience even more, you can download Dynamons World 2 Mod APK and get unlimited coins, gems, items, and Dynamons. You can also play the game without any ads or root requirements. Download Dynamons World 2 Mod APK today and have fun.</p>
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-<p>Here are some frequently asked questions about Dynamons World 2 Mod APK:</p>
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-<li><b>Is Dynamons World 2 Mod APK safe?</b></li>
-<p>Yes, Dynamons World 2 Mod APK is safe as long as you download it from a trusted source that has no viruses or malware. However, you should always be careful when downloading any modded app from the Internet and scan it with an antivirus before installing it.</p>
-<li><b>Is Dynamons World 2 Mod APK legal ?</b></li>
-<p>It depends on your country's laws and regulations regarding modded apps and games. Some countries may consider it illegal, while others may not. You should always check your local laws before downloading and using any modded app or game.</p>
-<li><b>Can I play Dynamons World 2 Mod APK with my friends?</b></li>
-<p>Yes, you can play Dynamons World 2 Mod APK with your friends in the online multiplayer mode. However, you may not be able to play with players who are using the original version of the game, as they may have different features and updates.</p>
-<li><b>Will I lose my progress if I uninstall Dynamons World 2 Mod APK?</b></li>
-<p>No, you will not lose your progress if you uninstall Dynamons World 2 Mod APK. You can sync your progress with your Google Play account and restore it when you reinstall the game. However, you may lose some of the modded features and resources that you had before.</p>
-<li><b>Can I update Dynamons World 2 Mod APK?</b></li>
-<p>Yes, you can update Dynamons World 2 Mod APK whenever there is a new version available. However, you should always download the update from the same source that you downloaded the original mod APK from, as different sources may have different modifications and features.</p>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/Image-to-MusicGen/audiocraft/models/encodec.py b/spaces/fffiloni/Image-to-MusicGen/audiocraft/models/encodec.py
deleted file mode 100644
index 69621a695887b0b41614c51cae020f6fd0af221d..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Image-to-MusicGen/audiocraft/models/encodec.py
+++ /dev/null
@@ -1,302 +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 abc import ABC, abstractmethod
-import typing as tp
-
-from einops import rearrange
-import torch
-from torch import nn
-
-from .. import quantization as qt
-
-
-class CompressionModel(ABC, nn.Module):
-
-    @abstractmethod
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        ...
-
-    @abstractmethod
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """See `EncodecModel.encode`"""
-        ...
-
-    @abstractmethod
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        """See `EncodecModel.decode`"""
-        ...
-
-    @property
-    @abstractmethod
-    def channels(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def frame_rate(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def sample_rate(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def cardinality(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def num_codebooks(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def total_codebooks(self) -> int:
-        ...
-
-    @abstractmethod
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-        """
-        ...
-
-
-class EncodecModel(CompressionModel):
-    """Encodec model operating on the raw waveform.
-
-    Args:
-        encoder (nn.Module): Encoder network.
-        decoder (nn.Module): Decoder network.
-        quantizer (qt.BaseQuantizer): Quantizer network.
-        frame_rate (int): Frame rate for the latent representation.
-        sample_rate (int): Audio sample rate.
-        channels (int): Number of audio channels.
-        causal (bool): Whether to use a causal version of the model.
-        renormalize (bool): Whether to renormalize the audio before running the model.
-    """
-    # we need assignement to override the property in the abstract class,
-    # I couldn't find a better way...
-    frame_rate: int = 0
-    sample_rate: int = 0
-    channels: int = 0
-
-    def __init__(self,
-                 encoder: nn.Module,
-                 decoder: nn.Module,
-                 quantizer: qt.BaseQuantizer,
-                 frame_rate: int,
-                 sample_rate: int,
-                 channels: int,
-                 causal: bool = False,
-                 renormalize: bool = False):
-        super().__init__()
-        self.encoder = encoder
-        self.decoder = decoder
-        self.quantizer = quantizer
-        self.frame_rate = frame_rate
-        self.sample_rate = sample_rate
-        self.channels = channels
-        self.renormalize = renormalize
-        self.causal = causal
-        if self.causal:
-            # we force disabling here to avoid handling linear overlap of segments
-            # as supported in original EnCodec codebase.
-            assert not self.renormalize, 'Causal model does not support renormalize'
-
-    @property
-    def total_codebooks(self):
-        """Total number of quantizer codebooks available.
-        """
-        return self.quantizer.total_codebooks
-
-    @property
-    def num_codebooks(self):
-        """Active number of codebooks used by the quantizer.
-        """
-        return self.quantizer.num_codebooks
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-        """
-        self.quantizer.set_num_codebooks(n)
-
-    @property
-    def cardinality(self):
-        """Cardinality of each codebook.
-        """
-        return self.quantizer.bins
-
-    def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        scale: tp.Optional[torch.Tensor]
-        if self.renormalize:
-            mono = x.mean(dim=1, keepdim=True)
-            volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
-            scale = 1e-8 + volume
-            x = x / scale
-            scale = scale.view(-1, 1)
-        else:
-            scale = None
-        return x, scale
-
-    def postprocess(self,
-                    x: torch.Tensor,
-                    scale: tp.Optional[torch.Tensor] = None) -> torch.Tensor:
-        if scale is not None:
-            assert self.renormalize
-            x = x * scale.view(-1, 1, 1)
-        return x
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        assert x.dim() == 3
-        length = x.shape[-1]
-        x, scale = self.preprocess(x)
-
-        emb = self.encoder(x)
-        q_res = self.quantizer(emb, self.frame_rate)
-        out = self.decoder(q_res.x)
-
-        # remove extra padding added by the encoder and decoder
-        assert out.shape[-1] >= length, (out.shape[-1], length)
-        out = out[..., :length]
-
-        q_res.x = self.postprocess(out, scale)
-
-        return q_res
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """Encode the given input tensor to quantized representation along with scale parameter.
-
-        Args:
-            x (torch.Tensor): Float tensor of shape [B, C, T]
-
-        Returns:
-            codes, scale (tp.Tuple[torch.Tensor, torch.Tensor]): Tuple composed of:
-                codes a float tensor of shape [B, K, T] with K the number of codebooks used and T the timestep.
-                scale a float tensor containing the scale for audio renormalizealization.
-        """
-        assert x.dim() == 3
-        x, scale = self.preprocess(x)
-        emb = self.encoder(x)
-        codes = self.quantizer.encode(emb)
-        return codes, scale
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        """Decode the given codes to a reconstructed representation, using the scale to perform
-        audio denormalization if needed.
-
-        Args:
-            codes (torch.Tensor): Int tensor of shape [B, K, T]
-            scale (tp.Optional[torch.Tensor]): Float tensor containing the scale value.
-
-        Returns:
-            out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
-        """
-        emb = self.quantizer.decode(codes)
-        out = self.decoder(emb)
-        out = self.postprocess(out, scale)
-        # out contains extra padding added by the encoder and decoder
-        return out
-
-
-class FlattenedCompressionModel(CompressionModel):
-    """Wraps a CompressionModel and flatten its codebooks, e.g.
-    instead of returning [B, K, T], return [B, S, T * (K // S)] with
-    S the number of codebooks per step, and `K // S` the number of 'virtual steps'
-    for each real time step.
-
-    Args:
-        model (CompressionModel): compression model to wrap.
-        codebooks_per_step (int): number of codebooks to keep per step,
-            this must divide the number of codebooks provided by the wrapped model.
-        extend_cardinality (bool): if True, and for instance if codebooks_per_step = 1,
-            if each codebook has a cardinality N, then the first codebook will
-            use the range [0, N - 1], and the second [N, 2 N - 1] etc.
-            On decoding, this can lead to potentially invalid sequences.
-            Any invalid entry will be silently remapped to the proper range
-            with a modulo.
-    """
-    def __init__(self, model: CompressionModel, codebooks_per_step: int = 1,
-                 extend_cardinality: bool = True):
-        super().__init__()
-        self.model = model
-        self.codebooks_per_step = codebooks_per_step
-        self.extend_cardinality = extend_cardinality
-
-    @property
-    def total_codebooks(self):
-        return self.model.total_codebooks
-
-    @property
-    def num_codebooks(self):
-        """Active number of codebooks used by the quantizer.
-
-        ..Warning:: this reports the number of codebooks after the flattening
-        of the codebooks!
-        """
-        assert self.model.num_codebooks % self.codebooks_per_step == 0
-        return self.codebooks_per_step
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-
-        ..Warning:: this sets the number of codebooks **before** the flattening
-        of the codebooks.
-        """
-        assert n % self.codebooks_per_step == 0
-        self.model.set_num_codebooks(n)
-
-    @property
-    def num_virtual_steps(self) -> int:
-        """Return the number of virtual steps, e.g. one real step
-        will be split into that many steps.
-        """
-        return self.model.num_codebooks // self.codebooks_per_step
-
-    @property
-    def frame_rate(self) -> int:
-        return self.model.frame_rate * self.num_virtual_steps
-
-    @property
-    def sample_rate(self) -> int:
-        return self.model.sample_rate
-
-    @property
-    def channels(self) -> int:
-        return self.model.channels
-
-    @property
-    def cardinality(self):
-        """Cardinality of each codebook.
-        """
-        if self.extend_cardinality:
-            return self.model.cardinality * self.num_virtual_steps
-        else:
-            return self.model.cardinality
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        raise NotImplementedError("Not supported, use encode and decode.")
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        indices, scales = self.model.encode(x)
-        B, K, T = indices.shape
-        indices = rearrange(indices, 'b (k v) t -> b k t v', k=self.codebooks_per_step)
-        if self.extend_cardinality:
-            for virtual_step in range(1, self.num_virtual_steps):
-                indices[..., virtual_step] += self.model.cardinality * virtual_step
-        indices = rearrange(indices, 'b k t v -> b k (t v)')
-        return (indices, scales)
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        B, K, T = codes.shape
-        assert T % self.num_virtual_steps == 0
-        codes = rearrange(codes, 'b k (t v) -> b (k v) t', v=self.num_virtual_steps)
-        # We silently ignore potential errors from the LM when
-        # using extend_cardinality.
-        codes = codes % self.model.cardinality
-        return self.model.decode(codes, scale)
diff --git a/spaces/fffiloni/ProPainter/hf-examples/blank.md b/spaces/fffiloni/ProPainter/hf-examples/blank.md
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/constants.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/constants.d.ts
deleted file mode 100644
index 208020dcbab4ebcd7955b2abcb7ae49185f5976e..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/constants.d.ts
+++ /dev/null
@@ -1,18 +0,0 @@
-/** @deprecated since v6.3.0 - use constants property exposed by the relevant module instead. */
-declare module 'constants' {
-    import { constants as osConstants, SignalConstants } from 'node:os';
-    import { constants as cryptoConstants } from 'node:crypto';
-    import { constants as fsConstants } from 'node:fs';
-
-    const exp: typeof osConstants.errno &
-        typeof osConstants.priority &
-        SignalConstants &
-        typeof cryptoConstants &
-        typeof fsConstants;
-    export = exp;
-}
-
-declare module 'node:constants' {
-    import constants = require('constants');
-    export = constants;
-}
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/object-assign/index.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/object-assign/index.js
deleted file mode 100644
index 0930cf8890b9af66125c222c29d7bd75614ac6b4..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/object-assign/index.js
+++ /dev/null
@@ -1,90 +0,0 @@
-/*
-object-assign
-(c) Sindre Sorhus
-@license MIT
-*/
-
-'use strict';
-/* eslint-disable no-unused-vars */
-var getOwnPropertySymbols = Object.getOwnPropertySymbols;
-var hasOwnProperty = Object.prototype.hasOwnProperty;
-var propIsEnumerable = Object.prototype.propertyIsEnumerable;
-
-function toObject(val) {
-	if (val === null || val === undefined) {
-		throw new TypeError('Object.assign cannot be called with null or undefined');
-	}
-
-	return Object(val);
-}
-
-function shouldUseNative() {
-	try {
-		if (!Object.assign) {
-			return false;
-		}
-
-		// Detect buggy property enumeration order in older V8 versions.
-
-		// https://bugs.chromium.org/p/v8/issues/detail?id=4118
-		var test1 = new String('abc');  // eslint-disable-line no-new-wrappers
-		test1[5] = 'de';
-		if (Object.getOwnPropertyNames(test1)[0] === '5') {
-			return false;
-		}
-
-		// https://bugs.chromium.org/p/v8/issues/detail?id=3056
-		var test2 = {};
-		for (var i = 0; i < 10; i++) {
-			test2['_' + String.fromCharCode(i)] = i;
-		}
-		var order2 = Object.getOwnPropertyNames(test2).map(function (n) {
-			return test2[n];
-		});
-		if (order2.join('') !== '0123456789') {
-			return false;
-		}
-
-		// https://bugs.chromium.org/p/v8/issues/detail?id=3056
-		var test3 = {};
-		'abcdefghijklmnopqrst'.split('').forEach(function (letter) {
-			test3[letter] = letter;
-		});
-		if (Object.keys(Object.assign({}, test3)).join('') !==
-				'abcdefghijklmnopqrst') {
-			return false;
-		}
-
-		return true;
-	} catch (err) {
-		// We don't expect any of the above to throw, but better to be safe.
-		return false;
-	}
-}
-
-module.exports = shouldUseNative() ? Object.assign : function (target, source) {
-	var from;
-	var to = toObject(target);
-	var symbols;
-
-	for (var s = 1; s < arguments.length; s++) {
-		from = Object(arguments[s]);
-
-		for (var key in from) {
-			if (hasOwnProperty.call(from, key)) {
-				to[key] = from[key];
-			}
-		}
-
-		if (getOwnPropertySymbols) {
-			symbols = getOwnPropertySymbols(from);
-			for (var i = 0; i < symbols.length; i++) {
-				if (propIsEnumerable.call(from, symbols[i])) {
-					to[symbols[i]] = from[symbols[i]];
-				}
-			}
-		}
-	}
-
-	return to;
-};
diff --git a/spaces/fffiloni/lama-video-watermark-remover/models/ade20k/segm_lib/nn/parallel/__init__.py b/spaces/fffiloni/lama-video-watermark-remover/models/ade20k/segm_lib/nn/parallel/__init__.py
deleted file mode 100644
index 9b52f49cc0755562218a460483cbf02514ddd773..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/lama-video-watermark-remover/models/ade20k/segm_lib/nn/parallel/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .data_parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to
diff --git a/spaces/fffiloni/train-dreambooth-lora-sdxl/app.py b/spaces/fffiloni/train-dreambooth-lora-sdxl/app.py
deleted file mode 100644
index de1c35b135078291c60a6560eda8b15e4c591209..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/train-dreambooth-lora-sdxl/app.py
+++ /dev/null
@@ -1,388 +0,0 @@
-import gradio as gr
-import torch
-import os
-import shutil
-import requests
-import subprocess
-from subprocess import getoutput
-from huggingface_hub import snapshot_download, HfApi, create_repo
-api = HfApi()
-
-hf_token = os.environ.get("HF_TOKEN_WITH_WRITE_PERMISSION")
-
-is_shared_ui = True if "fffiloni/train-dreambooth-lora-sdxl" in os.environ['SPACE_ID'] else False
-
-is_gpu_associated = torch.cuda.is_available()
-
-if is_gpu_associated:
-    gpu_info = getoutput('nvidia-smi')
-    if("A10G" in gpu_info):
-        which_gpu = "A10G"
-    elif("T4" in gpu_info):
-        which_gpu = "T4"
-    else:
-        which_gpu = "CPU"
-
-def check_upload_or_no(value):
-    if value is True:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def load_images_to_dataset(images, dataset_name):
-
-    if is_shared_ui:
-        raise gr.Error("This Space only works in duplicated instances")
-
-    if dataset_name == "":
-        raise gr.Error("You forgot to name your new dataset. ")
-
-    # Create the directory if it doesn't exist
-    my_working_directory = f"my_working_directory_for_{dataset_name}"
-    if not os.path.exists(my_working_directory):
-        os.makedirs(my_working_directory)
-
-    # Assuming 'images' is a list of image file paths
-    for idx, image in enumerate(images):
-        # Get the base file name (without path) from the original location
-        image_name = os.path.basename(image.name)
-    
-        # Construct the destination path in the working directory
-        destination_path = os.path.join(my_working_directory, image_name)
-    
-        # Copy the image from the original location to the working directory
-        shutil.copy(image.name, destination_path)
-    
-        # Print the image name and its corresponding save path
-        print(f"Image {idx + 1}: {image_name} copied to {destination_path}")
-   
-    path_to_folder = my_working_directory
-    your_username = api.whoami(token=hf_token)["name"]
-    repo_id = f"{your_username}/{dataset_name}"
-    create_repo(repo_id=repo_id, repo_type="dataset", private=True, token=hf_token)
-    
-    api.upload_folder(
-        folder_path=path_to_folder,
-        repo_id=repo_id,
-        repo_type="dataset",
-        token=hf_token
-    )
-
-    return "Done, your dataset is ready and loaded for the training step!", repo_id
-
-def swap_hardware(hf_token, hardware="cpu-basic"):
-    hardware_url = f"https://huggingface.co/spaces/{os.environ['SPACE_ID']}/hardware"
-    headers = { "authorization" : f"Bearer {hf_token}"}
-    body = {'flavor': hardware}
-    requests.post(hardware_url, json = body, headers=headers)
-
-def swap_sleep_time(hf_token,sleep_time):
-    sleep_time_url = f"https://huggingface.co/api/spaces/{os.environ['SPACE_ID']}/sleeptime"
-    headers = { "authorization" : f"Bearer {hf_token}"}
-    body = {'seconds':sleep_time}
-    requests.post(sleep_time_url,json=body,headers=headers)
-
-def get_sleep_time(hf_token):
-    sleep_time_url = f"https://huggingface.co/api/spaces/{os.environ['SPACE_ID']}"
-    headers = { "authorization" : f"Bearer {hf_token}"}
-    response = requests.get(sleep_time_url,headers=headers)
-    try:
-        gcTimeout = response.json()['runtime']['gcTimeout']
-    except:
-        gcTimeout = None
-    return gcTimeout
-
-def write_to_community(title, description,hf_token): 
-    
-    api.create_discussion(repo_id=os.environ['SPACE_ID'], title=title, description=description,repo_type="space", token=hf_token)
-
-
-def set_accelerate_default_config():
-    try:
-        subprocess.run(["accelerate", "config", "default"], check=True)
-        print("Accelerate default config set successfully!")
-    except subprocess.CalledProcessError as e:
-        print(f"An error occurred: {e}")
-
-def train_dreambooth_lora_sdxl(dataset_id, instance_data_dir, lora_trained_xl_folder, instance_prompt, max_train_steps, checkpoint_steps, remove_gpu):
-    
-    script_filename = "train_dreambooth_lora_sdxl.py"  # Assuming it's in the same folder
-
-    command = [
-        "accelerate",
-        "launch",
-        script_filename,  # Use the local script
-        "--pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0",
-        "--pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix",
-        f"--dataset_id={dataset_id}",
-        f"--instance_data_dir={instance_data_dir}",
-        f"--output_dir={lora_trained_xl_folder}",
-        "--mixed_precision=fp16",
-        f"--instance_prompt={instance_prompt}",
-        "--resolution=1024",
-        "--train_batch_size=2",
-        "--gradient_accumulation_steps=2",
-        "--gradient_checkpointing",
-        "--learning_rate=1e-4",
-        "--lr_scheduler=constant",
-        "--lr_warmup_steps=0",
-        "--enable_xformers_memory_efficient_attention",
-        "--mixed_precision=fp16",
-        "--use_8bit_adam",
-        f"--max_train_steps={max_train_steps}",
-        f"--checkpointing_steps={checkpoint_steps}",
-        "--seed=0",
-        "--push_to_hub",
-        f"--hub_token={hf_token}"
-    ]
-
-    try:
-        subprocess.run(command, check=True)
-        print("Training is finished!")
-        if remove_gpu:
-            swap_hardware(hf_token, "cpu-basic")
-        else:
-            swap_sleep_time(hf_token, 300)
-    except subprocess.CalledProcessError as e:
-        print(f"An error occurred: {e}")
-        
-        title="There was an error on during your training"
-        description=f'''
-        Unfortunately there was an error during training your {lora_trained_xl_folder} model. 
-        Please check it out below. Feel free to report this issue to [SD-XL Dreambooth LoRa Training](https://huggingface.co/spaces/fffiloni/train-dreambooth-lora-sdxl): 
-        ```
-        {str(e)}
-        ```
-        '''
-        if remove_gpu:
-            swap_hardware(hf_token, "cpu-basic")
-        else:
-            swap_sleep_time(hf_token, 300)
-        #write_to_community(title,description,hf_token)
-
-def main(dataset_id, 
-         lora_trained_xl_folder,
-         instance_prompt,
-         max_train_steps,
-         checkpoint_steps,
-         remove_gpu):
-
-    
-    if is_shared_ui:
-        raise gr.Error("This Space only works in duplicated instances")
-
-    if not is_gpu_associated:
-        raise gr.Error("Please associate a T4 or A10G GPU for this Space")
-
-    if dataset_id == "":
-        raise gr.Error("You forgot to specify an image dataset")
-
-    if instance_prompt == "":
-        raise gr.Error("You forgot to specify a concept prompt")
-
-    if lora_trained_xl_folder == "":
-        raise gr.Error("You forgot to name the output folder for your model")
-
-    sleep_time = get_sleep_time(hf_token)
-    if sleep_time:
-        swap_sleep_time(hf_token, -1)
-
-    gr.Warning("If you did not check the `Remove GPU After training`, don't forget to remove the GPU attribution after you are done. ")
-        
-    dataset_repo = dataset_id
-
-    # Automatically set local_dir based on the last part of dataset_repo
-    repo_parts = dataset_repo.split("/")
-    local_dir = f"./{repo_parts[-1]}"  # Use the last part of the split
-
-    # Check if the directory exists and create it if necessary
-    if not os.path.exists(local_dir):
-        os.makedirs(local_dir)
-
-    gr.Info("Downloading dataset ...")
-    
-    snapshot_download(
-        dataset_repo,
-        local_dir=local_dir,
-        repo_type="dataset",
-        ignore_patterns=".gitattributes",
-        token=hf_token
-    )
-
-    set_accelerate_default_config()
-
-    gr.Info("Training begins ...")
-
-    instance_data_dir = repo_parts[-1]
-    train_dreambooth_lora_sdxl(dataset_id, instance_data_dir, lora_trained_xl_folder, instance_prompt, max_train_steps, checkpoint_steps, remove_gpu)
-    
-    your_username = api.whoami(token=hf_token)["name"]
-    return f"Done, your trained model has been stored in your models library: {your_username}/{lora_trained_xl_folder}"
-
-css="""
-#col-container {max-width: 780px; margin-left: auto; margin-right: auto;}
-#upl-dataset-group {background-color: none!important;}
-
-div#warning-ready {
-    background-color: #ecfdf5;
-    padding: 0 10px 5px;
-    margin: 20px 0;
-}
-div#warning-ready > .gr-prose > h2, div#warning-ready > .gr-prose > p {
-    color: #057857!important;
-}
-
-div#warning-duplicate {
-    background-color: #ebf5ff;
-    padding: 0 10px 5px;
-    margin: 20px 0;
-}
-
-div#warning-duplicate > .gr-prose > h2, div#warning-duplicate > .gr-prose > p {
-    color: #0f4592!important;
-}
-
-div#warning-duplicate strong {
-    color: #0f4592;
-}
-
-p.actions {
-    display: flex;
-    align-items: center;
-    margin: 20px 0;
-}
-
-div#warning-duplicate .actions a {
-    display: inline-block;
-    margin-right: 10px;
-}
-
-div#warning-setgpu {
-    background-color: #fff4eb;
-    padding: 0 10px 5px;
-    margin: 20px 0;
-}
-
-div#warning-setgpu > .gr-prose > h2, div#warning-setgpu > .gr-prose > p {
-    color: #92220f!important;
-}
-
-div#warning-setgpu a, div#warning-setgpu b {
-    color: #91230f;
-}
-
-div#warning-setgpu p.actions > a {
-    display: inline-block;
-    background: #1f1f23;
-    border-radius: 40px;
-    padding: 6px 24px;
-    color: antiquewhite;
-    text-decoration: none;
-    font-weight: 600;
-    font-size: 1.2em;
-}
-
-button#load-dataset-btn{
-min-height: 60px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        if is_shared_ui:
-            top_description = gr.HTML(f'''
-                <div class="gr-prose">
-                    <h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
-                    Attention: this Space need to be duplicated to work</h2>
-                    <p class="main-message">
-                        To make it work, <strong>duplicate the Space</strong> and run it on your own profile using a <strong>private</strong> GPU (T4-small or A10G-small).<br />
-                        A T4 costs <strong>US$0.60/h</strong>, so it should cost < US$1 to train most models.
-                    </p>
-                    <p class="actions">
-                        <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true">
-                            <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg-dark.svg" alt="Duplicate this Space" />
-                        </a>
-                        to start training your own image model
-                    </p>
-                </div>
-            ''', elem_id="warning-duplicate")
-        else:
-            if(is_gpu_associated):
-                top_description = gr.HTML(f'''
-                        <div class="gr-prose">
-                            <h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
-                            You have successfully associated a {which_gpu} GPU to the SD-XL Training Space 🎉</h2>
-                            <p>
-                                You can now train your model! You will be billed by the minute from when you activated the GPU until when it is turned off.
-                            </p> 
-                        </div>
-                ''', elem_id="warning-ready")
-            else:
-                top_description = gr.HTML(f'''
-                        <div class="gr-prose">
-                        <h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
-                        You have successfully duplicated the SD-XL Training Space 🎉</h2>
-                        <p>There's only one step left before you can train your model: <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings" style="text-decoration: underline" target="_blank">attribute a <b>T4-small or A10G-small GPU</b> to it (via the Settings tab)</a> and run the training below.
-                        You will be billed by the minute from when you activate the GPU until when it is turned off.</p> 
-                        <p class="actions">
-                            <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings">🔥 &nbsp; Set recommended GPU</a>
-                        </p>
-                        </div>
-                ''', elem_id="warning-setgpu")
-        
-        gr.Markdown("# SD-XL Dreambooth LoRa Training UI 💭")
-        
-        upload_my_images = gr.Checkbox(label="Drop your training images ? (optional)", value=False)
-        gr.Markdown("Use this step to upload your training images and create a new dataset. If you already have a dataset stored on your HF profile, you can skip this step, and provide your dataset ID in the training `Datased ID` input below.")
-         
-        with gr.Group(visible=False, elem_id="upl-dataset-group") as upload_group:
-            with gr.Row():
-                images = gr.File(file_types=["image"], label="Upload your images", file_count="multiple", interactive=True, visible=True)
-                with gr.Column():
-                    new_dataset_name = gr.Textbox(label="Set new dataset name", placeholder="e.g.: my_awesome_dataset")
-                    dataset_status = gr.Textbox(label="dataset status")
-                    load_btn = gr.Button("Load images to new dataset", elem_id="load-dataset-btn")
-        
-        gr.Markdown("## Training ")
-        gr.Markdown("You can use an existing image dataset, find a dataset example here: [https://huggingface.co/datasets/diffusers/dog-example](https://huggingface.co/datasets/diffusers/dog-example) ;)")
-        
-        with gr.Row():
-            dataset_id = gr.Textbox(label="Dataset ID", info="use one of your previously uploaded image datasets on your HF profile", placeholder="diffusers/dog-example")
-            instance_prompt = gr.Textbox(label="Concept prompt", info="concept prompt - use a unique, made up word to avoid collisions")
-        
-        with gr.Row():
-            model_output_folder = gr.Textbox(label="Output model folder name", placeholder="lora-trained-xl-folder")
-            max_train_steps = gr.Number(label="Max Training Steps", value=500, precision=0, step=10)
-            checkpoint_steps = gr.Number(label="Checkpoints Steps", value=100, precision=0, step=10)
-
-        remove_gpu = gr.Checkbox(label="Remove GPU After Training", value=True, info="If NOT enabled, don't forget to remove the GPU attribution after you are done.")
-        train_button = gr.Button("Train !")
-
-        train_status = gr.Textbox(label="Training status")
-
-    upload_my_images.change(
-        fn = check_upload_or_no,
-        inputs =[upload_my_images],
-        outputs = [upload_group]        
-    )
-    
-    load_btn.click(
-        fn = load_images_to_dataset,
-        inputs = [images, new_dataset_name],
-        outputs = [dataset_status, dataset_id]
-    )
-    
-    train_button.click(
-        fn = main,
-        inputs = [
-            dataset_id,
-            model_output_folder,
-            instance_prompt,
-            max_train_steps,
-            checkpoint_steps,
-            remove_gpu
-        ],
-        outputs = [train_status]
-    )
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/flax-community/DietNerf-Demo/demo/__init__.py b/spaces/flax-community/DietNerf-Demo/demo/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/flax-community/SentenceSimplifier/About/contributors.md b/spaces/flax-community/SentenceSimplifier/About/contributors.md
deleted file mode 100644
index 6fd832c1838d8a3f39aa9e8f3005788bd5020b3e..0000000000000000000000000000000000000000
--- a/spaces/flax-community/SentenceSimplifier/About/contributors.md
+++ /dev/null
@@ -1,3 +0,0 @@
-## Contributors
-* [Bhadresh Savani](www.linkedin.com/in/bhadreshsavani)
-* [Rahul Dev](https://twitter.com/mlkorra)
diff --git a/spaces/flax-community/SinhalaLanguageDemos/README.md b/spaces/flax-community/SinhalaLanguageDemos/README.md
deleted file mode 100644
index 85a7d9dc751107bfa9a188ae0d85acfec1965300..0000000000000000000000000000000000000000
--- a/spaces/flax-community/SinhalaLanguageDemos/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: Sinhala Language Demos
-emoji: 🤗
-colorFrom: yellow
-colorTo: green
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/fuckyoudeki/AutoGPT/autogpt/llm_utils.py b/spaces/fuckyoudeki/AutoGPT/autogpt/llm_utils.py
deleted file mode 100644
index 821820ffab07be2753cf385ff1de77820e4206ee..0000000000000000000000000000000000000000
--- a/spaces/fuckyoudeki/AutoGPT/autogpt/llm_utils.py
+++ /dev/null
@@ -1,172 +0,0 @@
-from __future__ import annotations
-
-import time
-from ast import List
-
-import openai
-from colorama import Fore, Style
-from openai.error import APIError, RateLimitError
-
-from autogpt.config import Config
-from autogpt.logs import logger
-
-CFG = Config()
-
-openai.api_key = CFG.openai_api_key
-
-
-def call_ai_function(
-    function: str, args: list, description: str, model: str | None = None
-) -> str:
-    """Call an AI function
-
-    This is a magic function that can do anything with no-code. See
-    https://github.com/Torantulino/AI-Functions for more info.
-
-    Args:
-        function (str): The function to call
-        args (list): The arguments to pass to the function
-        description (str): The description of the function
-        model (str, optional): The model to use. Defaults to None.
-
-    Returns:
-        str: The response from the function
-    """
-    if model is None:
-        model = CFG.smart_llm_model
-    # For each arg, if any are None, convert to "None":
-    args = [str(arg) if arg is not None else "None" for arg in args]
-    # parse args to comma separated string
-    args = ", ".join(args)
-    messages = [
-        {
-            "role": "system",
-            "content": f"You are now the following python function: ```# {description}"
-            f"\n{function}```\n\nOnly respond with your `return` value.",
-        },
-        {"role": "user", "content": args},
-    ]
-
-    return create_chat_completion(model=model, messages=messages, temperature=0)
-
-
-# Overly simple abstraction until we create something better
-# simple retry mechanism when getting a rate error or a bad gateway
-def create_chat_completion(
-    messages: list,  # type: ignore
-    model: str | None = None,
-    temperature: float = CFG.temperature,
-    max_tokens: int | None = None,
-) -> str:
-    """Create a chat completion using the OpenAI API
-
-    Args:
-        messages (list[dict[str, str]]): The messages to send to the chat completion
-        model (str, optional): The model to use. Defaults to None.
-        temperature (float, optional): The temperature to use. Defaults to 0.9.
-        max_tokens (int, optional): The max tokens to use. Defaults to None.
-
-    Returns:
-        str: The response from the chat completion
-    """
-    response = None
-    num_retries = 10
-    warned_user = False
-    if CFG.debug_mode:
-        print(
-            Fore.GREEN
-            + f"Creating chat completion with model {model}, temperature {temperature},"
-            f" max_tokens {max_tokens}" + Fore.RESET
-        )
-    for attempt in range(num_retries):
-        backoff = 2 ** (attempt + 2)
-        try:
-            if CFG.use_azure:
-                response = openai.ChatCompletion.create(
-                    deployment_id=CFG.get_azure_deployment_id_for_model(model),
-                    model=model,
-                    messages=messages,
-                    temperature=temperature,
-                    max_tokens=max_tokens,
-                )
-            else:
-                response = openai.ChatCompletion.create(
-                    model=model,
-                    messages=messages,
-                    temperature=temperature,
-                    max_tokens=max_tokens,
-                )
-            break
-        except RateLimitError:
-            if CFG.debug_mode:
-                print(
-                    Fore.RED + "Error: ",
-                    f"Reached rate limit, passing..." + Fore.RESET,
-                )
-            if not warned_user:
-                logger.double_check(
-                    f"Please double check that you have setup a {Fore.CYAN + Style.BRIGHT}PAID{Style.RESET_ALL} OpenAI API Account. "
-                    + f"You can read more here: {Fore.CYAN}https://github.com/Significant-Gravitas/Auto-GPT#openai-api-keys-configuration{Fore.RESET}"
-                )
-                warned_user = True
-        except APIError as e:
-            if e.http_status == 502:
-                pass
-            else:
-                raise
-            if attempt == num_retries - 1:
-                raise
-        if CFG.debug_mode:
-            print(
-                Fore.RED + "Error: ",
-                f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
-            )
-        time.sleep(backoff)
-    if response is None:
-        logger.typewriter_log(
-            "FAILED TO GET RESPONSE FROM OPENAI",
-            Fore.RED,
-            "Auto-GPT has failed to get a response from OpenAI's services. "
-            + f"Try running Auto-GPT again, and if the problem the persists try running it with `{Fore.CYAN}--debug{Fore.RESET}`.",
-        )
-        logger.double_check()
-        if CFG.debug_mode:
-            raise RuntimeError(f"Failed to get response after {num_retries} retries")
-        else:
-            quit(1)
-
-    return response.choices[0].message["content"]
-
-
-def create_embedding_with_ada(text) -> list:
-    """Create an embedding with text-ada-002 using the OpenAI SDK"""
-    num_retries = 10
-    for attempt in range(num_retries):
-        backoff = 2 ** (attempt + 2)
-        try:
-            if CFG.use_azure:
-                return openai.Embedding.create(
-                    input=[text],
-                    engine=CFG.get_azure_deployment_id_for_model(
-                        "text-embedding-ada-002"
-                    ),
-                )["data"][0]["embedding"]
-            else:
-                return openai.Embedding.create(
-                    input=[text], model="text-embedding-ada-002"
-                )["data"][0]["embedding"]
-        except RateLimitError:
-            pass
-        except APIError as e:
-            if e.http_status == 502:
-                pass
-            else:
-                raise
-            if attempt == num_retries - 1:
-                raise
-        if CFG.debug_mode:
-            print(
-                Fore.RED + "Error: ",
-                f"API Bad gateway. Waiting {backoff} seconds..." + Fore.RESET,
-            )
-        time.sleep(backoff)
diff --git a/spaces/gagan3012/project-code-py/app.py b/spaces/gagan3012/project-code-py/app.py
deleted file mode 100644
index 66209289b754805730e1492baf6c345d5af6e786..0000000000000000000000000000000000000000
--- a/spaces/gagan3012/project-code-py/app.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import torch
-import streamlit as st
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-
-st.set_page_config(
-    page_title="AI Leetcode",
-    layout="wide",
-    initial_sidebar_state="expanded", )
-
-
-
-@st.cache(suppress_st_warning=True,ttl=1000)
-def modelgpt(sequence, temp, top_p):
-    tokenizer = GPT2Tokenizer.from_pretrained("gagan3012/project-code-py-small")
-    model = GPT2LMHeadModel.from_pretrained("gagan3012/project-code-py-small")
-    inputs = tokenizer.encode(sequence, return_tensors='pt')
-    outputs = model.generate(inputs, max_length=1024, do_sample=True, temperature=temp, top_p=top_p)
-    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return text
-
-def display():
-    st.write('# Using AI to Generate LeetCode solutions')
-    st.sidebar.markdown(
-        '''
-        # Project-code-py
-        
-        ## This is a demo of a text generation model trained with GPT-2 to generate LeetCode Answers in Python
-        *For additional questions and inquiries, please contact **Gagan Bhatia** via [LinkedIn](
-        https://www.linkedin.com/in/gbhatia30/) or [Github](https://github.com/gagan3012).*
-        ''')
-    st.sidebar.write('## Options:')
-
-    tokens = st.sidebar.slider(label='Number of Tokens', min_value=1, max_value=15, value=3, step=1)
-    samples = st.sidebar.slider(label='Number of Samples', min_value=1, max_value=9, value=9, step=1)
-    top_p = st.sidebar.slider(label='Top k', min_value=0.0, max_value=40.0, value=1.0, step=1.0)
-    temp = st.sidebar.slider(label='Temperature', min_value=0.1, max_value=1.0, value=1.0, step=0.05)
-    st.sidebar.markdown(
-        '''
-        `Number of Tokens:` number of tokens in generated text\n
-        `Number of Samples:` number of samples to return total\n
-        `Temperature:` Float value controlling randomness in boltzmann distribution. Lower temperature results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive. Higher temperature results in more random completions.\n
-        `Top k:` Integer value controlling diversity. 1 means only 1 word is considered for each step (token), resulting in deterministic completions, while 40 means 40 words are considered at each step. 0 (default) is a special setting meaning no restrictions. 40 generally is a good value.
-        ''')
-
-    st.write('## Enter a Leetcode Question or Starting code:')
-    sequence = st.text_area("", value="Given the root of a binary tree, return its maximum depth. A binary tree's maximum depth is the number of nodes along the longest path from the root node down to the farthest leaf node.",height=150)
-    if st.button("Get Answer"):
-        text = modelgpt(sequence, temp, top_p)
-        st.code(text.encode().decode('unicode_escape'), language='python')
-
-
-if __name__ == '__main__':
-    display()
\ No newline at end of file
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/utils/se_layer.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/utils/se_layer.py
deleted file mode 100644
index 083bd7d1ccee909c900c7aed2cc928bf14727f3e..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmseg/models/utils/se_layer.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import annotator.uniformer.mmcv as mmcv
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from .make_divisible import make_divisible
-
-
-class SELayer(nn.Module):
-    """Squeeze-and-Excitation Module.
-
-    Args:
-        channels (int): The input (and output) channels of the SE layer.
-        ratio (int): Squeeze ratio in SELayer, the intermediate channel will be
-            ``int(channels/ratio)``. Default: 16.
-        conv_cfg (None or dict): Config dict for convolution layer.
-            Default: None, which means using conv2d.
-        act_cfg (dict or Sequence[dict]): Config dict for activation layer.
-            If act_cfg is a dict, two activation layers will be configured
-            by this dict. If act_cfg is a sequence of dicts, the first
-            activation layer will be configured by the first dict and the
-            second activation layer will be configured by the second dict.
-            Default: (dict(type='ReLU'), dict(type='HSigmoid', bias=3.0,
-            divisor=6.0)).
-    """
-
-    def __init__(self,
-                 channels,
-                 ratio=16,
-                 conv_cfg=None,
-                 act_cfg=(dict(type='ReLU'),
-                          dict(type='HSigmoid', bias=3.0, divisor=6.0))):
-        super(SELayer, self).__init__()
-        if isinstance(act_cfg, dict):
-            act_cfg = (act_cfg, act_cfg)
-        assert len(act_cfg) == 2
-        assert mmcv.is_tuple_of(act_cfg, dict)
-        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
-        self.conv1 = ConvModule(
-            in_channels=channels,
-            out_channels=make_divisible(channels // ratio, 8),
-            kernel_size=1,
-            stride=1,
-            conv_cfg=conv_cfg,
-            act_cfg=act_cfg[0])
-        self.conv2 = ConvModule(
-            in_channels=make_divisible(channels // ratio, 8),
-            out_channels=channels,
-            kernel_size=1,
-            stride=1,
-            conv_cfg=conv_cfg,
-            act_cfg=act_cfg[1])
-
-    def forward(self, x):
-        out = self.global_avgpool(x)
-        out = self.conv1(out)
-        out = self.conv2(out)
-        return x * out
diff --git a/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/renderer/gl/glcontext.py b/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/renderer/gl/glcontext.py
deleted file mode 100644
index 881df0feca38678d6c075ef85ae65c12875b6b48..0000000000000000000000000000000000000000
--- a/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/renderer/gl/glcontext.py
+++ /dev/null
@@ -1,142 +0,0 @@
-"""Headless GPU-accelerated OpenGL context creation on Google Colaboratory.
-
-Typical usage:
-
-    # Optional PyOpenGL configuratiopn can be done here.
-    # import OpenGL
-    # OpenGL.ERROR_CHECKING = True
-
-    # 'glcontext' must be imported before any OpenGL.* API.
-    from lucid.misc.gl.glcontext import create_opengl_context
-
-    # Now it's safe to import OpenGL and EGL functions
-    import OpenGL.GL as gl
-
-    # create_opengl_context() creates a GL context that is attached to an
-    # offscreen surface of the specified size. Note that rendering to buffers
-    # of other sizes and formats is still possible with OpenGL Framebuffers.
-    #
-    # Users are expected to directly use the EGL API in case more advanced
-    # context management is required.
-    width, height = 640, 480
-    create_opengl_context((width, height))
-
-    # OpenGL context is available here.
-
-"""
-
-from __future__ import print_function
-
-# pylint: disable=unused-import,g-import-not-at-top,g-statement-before-imports
-
-try:
-  import OpenGL
-except:
-  print('This module depends on PyOpenGL.')
-  print('Please run "\033[1m!pip install -q pyopengl\033[0m" '
-        'prior importing this module.')
-  raise
-
-import ctypes
-from ctypes import pointer, util
-import os
-
-os.environ['PYOPENGL_PLATFORM'] = 'egl'
-
-# OpenGL loading workaround.
-#
-# * PyOpenGL tries to load libGL, but we need libOpenGL, see [1,2].
-#   This could have been solved by a symlink libGL->libOpenGL, but:
-#
-# * Python 2.7 can't find libGL and linEGL due to a bug (see [3])
-#   in ctypes.util, that was only wixed in Python 3.6.
-#
-# So, the only solution I've found is to monkeypatch ctypes.util
-# [1] https://devblogs.nvidia.com/egl-eye-opengl-visualization-without-x-server/
-# [2] https://devblogs.nvidia.com/linking-opengl-server-side-rendering/
-# [3] https://bugs.python.org/issue9998
-_find_library_old = ctypes.util.find_library
-try:
-
-  def _find_library_new(name):
-    return {
-        'GL': 'libOpenGL.so',
-        'EGL': 'libEGL.so',
-    }.get(name, _find_library_old(name))
-  util.find_library = _find_library_new
-  import OpenGL.GL as gl
-  import OpenGL.EGL as egl
-  from OpenGL import error
-  from OpenGL.EGL.EXT.device_base import egl_get_devices
-  from OpenGL.raw.EGL.EXT.platform_device import EGL_PLATFORM_DEVICE_EXT
-except:
-  print('Unable to load OpenGL libraries. '
-        'Make sure you use GPU-enabled backend.')
-  print('Press "Runtime->Change runtime type" and set '
-        '"Hardware accelerator" to GPU.')
-  raise
-finally:
-  util.find_library = _find_library_old
-
-def create_initialized_headless_egl_display():
-  """Creates an initialized EGL display directly on a device."""
-  for device in egl_get_devices():
-    display = egl.eglGetPlatformDisplayEXT(EGL_PLATFORM_DEVICE_EXT, device, None)
-
-    if display != egl.EGL_NO_DISPLAY and egl.eglGetError() == egl.EGL_SUCCESS:
-      # `eglInitialize` may or may not raise an exception on failure depending
-      # on how PyOpenGL is configured. We therefore catch a `GLError` and also
-      # manually check the output of `eglGetError()` here.
-      try:
-        initialized = egl.eglInitialize(display, None, None)
-      except error.GLError:
-        pass
-      else:
-        if initialized == egl.EGL_TRUE and egl.eglGetError() == egl.EGL_SUCCESS:
-          return display
-  return egl.EGL_NO_DISPLAY
-
-def create_opengl_context(surface_size=(640, 480)):
-  """Create offscreen OpenGL context and make it current.
-
-  Users are expected to directly use EGL API in case more advanced
-  context management is required.
-
-  Args:
-    surface_size: (width, height), size of the offscreen rendering surface.
-  """
-  egl_display = create_initialized_headless_egl_display()
-  if egl_display == egl.EGL_NO_DISPLAY:
-    raise ImportError('Cannot initialize a headless EGL display.')
-
-  major, minor = egl.EGLint(), egl.EGLint()
-  egl.eglInitialize(egl_display, pointer(major), pointer(minor))
-
-  config_attribs = [
-      egl.EGL_SURFACE_TYPE, egl.EGL_PBUFFER_BIT, egl.EGL_BLUE_SIZE, 8,
-      egl.EGL_GREEN_SIZE, 8, egl.EGL_RED_SIZE, 8, egl.EGL_DEPTH_SIZE, 24,
-      egl.EGL_RENDERABLE_TYPE, egl.EGL_OPENGL_BIT, egl.EGL_NONE
-  ]
-  config_attribs = (egl.EGLint * len(config_attribs))(*config_attribs)
-
-  num_configs = egl.EGLint()
-  egl_cfg = egl.EGLConfig()
-  egl.eglChooseConfig(egl_display, config_attribs, pointer(egl_cfg), 1,
-                      pointer(num_configs))
-
-  width, height = surface_size
-  pbuffer_attribs = [
-      egl.EGL_WIDTH,
-      width,
-      egl.EGL_HEIGHT,
-      height,
-      egl.EGL_NONE,
-  ]
-  pbuffer_attribs = (egl.EGLint * len(pbuffer_attribs))(*pbuffer_attribs)
-  egl_surf = egl.eglCreatePbufferSurface(egl_display, egl_cfg, pbuffer_attribs)
-
-  egl.eglBindAPI(egl.EGL_OPENGL_API)
-
-  egl_context = egl.eglCreateContext(egl_display, egl_cfg, egl.EGL_NO_CONTEXT,
-                                     None)
-  egl.eglMakeCurrent(egl_display, egl_surf, egl_surf, egl_context)
diff --git a/spaces/guetLzy/Real-ESRGAN-Demo/docs/Training_CN.md b/spaces/guetLzy/Real-ESRGAN-Demo/docs/Training_CN.md
deleted file mode 100644
index dabc3c5d97e134a2d551157c2dd03a629ec661bc..0000000000000000000000000000000000000000
--- a/spaces/guetLzy/Real-ESRGAN-Demo/docs/Training_CN.md
+++ /dev/null
@@ -1,271 +0,0 @@
-# :computer: 如何训练/微调 Real-ESRGAN
-
-- [训练 Real-ESRGAN](#训练-real-esrgan)
-  - [概述](#概述)
-  - [准备数据集](#准备数据集)
-  - [训练 Real-ESRNet 模型](#训练-real-esrnet-模型)
-  - [训练 Real-ESRGAN 模型](#训练-real-esrgan-模型)
-- [用自己的数据集微调 Real-ESRGAN](#用自己的数据集微调-real-esrgan)
-  - [动态生成降级图像](#动态生成降级图像)
-  - [使用已配对的数据](#使用已配对的数据)
-
-[English](Training.md) **|** [简体中文](Training_CN.md)
-
-## 训练 Real-ESRGAN
-
-### 概述
-
-训练分为两个步骤。除了 loss 函数外，这两个步骤拥有相同数据合成以及训练的一条龙流程。具体点说：
-
-1. 首先使用 L1 loss 训练 Real-ESRNet 模型，其中 L1 loss 来自预先训练的 ESRGAN 模型。
-
-2. 然后我们将 Real-ESRNet 模型作为生成器初始化，结合L1 loss、感知 loss、GAN loss 三者的参数对 Real-ESRGAN 进行训练。
-
-### 准备数据集
-
-我们使用 DF2K ( DIV2K 和 Flickr2K ) + OST 数据集进行训练。只需要HR图像！<br>
-下面是网站链接:
-1. DIV2K: http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip
-2. Flickr2K: https://cv.snu.ac.kr/research/EDSR/Flickr2K.tar
-3. OST: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/datasets/OST_dataset.zip
-
-以下是数据的准备步骤。
-
-#### 第1步：【可选】生成多尺寸图片
-
-针对 DF2K 数据集，我们使用多尺寸缩放策略，*换言之*，我们对 HR 图像进行下采样，就能获得多尺寸的标准参考（Ground-Truth）图像。 <br>
-您可以使用这个 [scripts/generate_multiscale_DF2K.py](scripts/generate_multiscale_DF2K.py) 脚本快速生成多尺寸的图像。<br>
-注意：如果您只想简单试试，那么可以跳过此步骤。
-
-```bash
-python scripts/generate_multiscale_DF2K.py --input datasets/DF2K/DF2K_HR --output datasets/DF2K/DF2K_multiscale
-```
-
-#### 第2步：【可选】裁切为子图像
-
-我们可以将 DF2K 图像裁切为子图像，以加快 IO 和处理速度。<br>
-如果你的 IO 够好或储存空间有限，那么此步骤是可选的。<br>
-
-您可以使用脚本 [scripts/extract_subimages.py](scripts/extract_subimages.py)。这是使用示例:
-
-```bash
- python scripts/extract_subimages.py --input datasets/DF2K/DF2K_multiscale --output datasets/DF2K/DF2K_multiscale_sub --crop_size 400 --step 200
-```
-
-#### 第3步：准备元信息 txt
-
-您需要准备一个包含图像路径的 txt 文件。下面是 `meta_info_DF2Kmultiscale+OST_sub.txt` 中的部分展示（由于各个用户可能有截然不同的子图像划分，这个文件不适合你的需求，你得准备自己的 txt 文件)：
-
-```txt
-DF2K_HR_sub/000001_s001.png
-DF2K_HR_sub/000001_s002.png
-DF2K_HR_sub/000001_s003.png
-...
-```
-
-你可以使用该脚本 [scripts/generate_meta_info.py](scripts/generate_meta_info.py) 生成包含图像路径的 txt 文件。<br>
-你还可以合并多个文件夹的图像路径到一个元信息（meta_info）txt。这是使用示例:
-
-```bash
- python scripts/generate_meta_info.py --input datasets/DF2K/DF2K_HR, datasets/DF2K/DF2K_multiscale --root datasets/DF2K, datasets/DF2K --meta_info datasets/DF2K/meta_info/meta_info_DF2Kmultiscale.txt
-```
-
-### 训练 Real-ESRNet 模型
-
-1. 下载预先训练的模型 [ESRGAN](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth)，放到 `experiments/pretrained_models`目录下。
-    ```bash
-    wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth -P experiments/pretrained_models
-    ```
-2. 相应地修改选项文件 `options/train_realesrnet_x4plus.yml` 中的内容：
-    ```yml
-    train:
-        name: DF2K+OST
-        type: RealESRGANDataset
-        dataroot_gt: datasets/DF2K  # 修改为你的数据集文件夹根目录
-        meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt  # 修改为你自己生成的元信息txt
-        io_backend:
-            type: disk
-    ```
-3. 如果你想在训练过程中执行验证，就取消注释这些内容并进行相应的修改：
-    ```yml
-      # 取消注释这些以进行验证
-      # val:
-      #   name: validation
-      #   type: PairedImageDataset
-      #   dataroot_gt: path_to_gt
-      #   dataroot_lq: path_to_lq
-      #   io_backend:
-      #     type: disk
-
-    ...
-
-      # 取消注释这些以进行验证
-      # 验证设置
-      # val:
-      #   val_freq: !!float 5e3
-      #   save_img: True
-
-      #   metrics:
-      #     psnr: # 指标名称，可以是任意的
-      #       type: calculate_psnr
-      #       crop_border: 4
-      #       test_y_channel: false
-    ```
-4. 正式训练之前，你可以用 `--debug` 模式检查是否正常运行。我们用了4个GPU进行训练：
-    ```bash
-    CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --debug
-    ```
-
-    用 **1个GPU** 训练的 debug 模式示例:
-    ```bash
-    python realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --debug
-    ```
-5. 正式训练开始。我们用了4个GPU进行训练。还可以使用参数 `--auto_resume` 在必要时自动恢复训练。
-    ```bash
-    CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --launcher pytorch --auto_resume
-    ```
-
-    用 **1个GPU** 训练：
-    ```bash
-    python realesrgan/train.py -opt options/train_realesrnet_x4plus.yml --auto_resume
-    ```
-
-### 训练 Real-ESRGAN 模型
-
-1. 训练 Real-ESRNet 模型后，您得到了这个 `experiments/train_RealESRNetx4plus_1000k_B12G4_fromESRGAN/model/net_g_1000000.pth` 文件。如果需要指定预训练路径到其他文件，请修改选项文件 `train_realesrgan_x4plus.yml` 中 `pretrain_network_g` 的值。
-1. 修改选项文件 `train_realesrgan_x4plus.yml` 的内容。大多数修改与上节提到的类似。
-1. 正式训练之前，你可以以 `--debug` 模式检查是否正常运行。我们使用了4个GPU进行训练：
-    ```bash
-    CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --debug
-    ```
-
-    用 **1个GPU** 训练的 debug 模式示例:
-    ```bash
-    python realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --debug
-    ```
-1. 正式训练开始。我们使用4个GPU进行训练。还可以使用参数 `--auto_resume` 在必要时自动恢复训练。
-    ```bash
-    CUDA_VISIBLE_DEVICES=0,1,2,3 \
-    python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --launcher pytorch --auto_resume
-    ```
-
-    用 **1个GPU** 训练：
-    ```bash
-    python realesrgan/train.py -opt options/train_realesrgan_x4plus.yml --auto_resume
-    ```
-
-## 用自己的数据集微调 Real-ESRGAN
-
-你可以用自己的数据集微调 Real-ESRGAN。一般地，微调（Fine-Tune）程序可以分为两种类型:
-
-1. [动态生成降级图像](#动态生成降级图像)
-2. [使用**已配对**的数据](#使用已配对的数据)
-
-### 动态生成降级图像
-
-只需要高分辨率图像。在训练过程中，使用 Real-ESRGAN 描述的降级模型生成低质量图像。
-
-**1. 准备数据集**
-
-完整信息请参见[本节](#准备数据集)。
-
-**2. 下载预训练模型**
-
-下载预先训练的模型到 `experiments/pretrained_models` 目录下。
-
-- *RealESRGAN_x4plus.pth*:
-    ```bash
-    wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
-    ```
-
-- *RealESRGAN_x4plus_netD.pth*:
-    ```bash
-    wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models
-    ```
-
-**3. 微调**
-
-修改选项文件 [options/finetune_realesrgan_x4plus.yml](options/finetune_realesrgan_x4plus.yml) ，特别是 `datasets` 部分：
-
-```yml
-train:
-    name: DF2K+OST
-    type: RealESRGANDataset
-    dataroot_gt: datasets/DF2K   # 修改为你的数据集文件夹根目录
-    meta_info: realesrgan/meta_info/meta_info_DF2Kmultiscale+OST_sub.txt  # 修改为你自己生成的元信息txt
-    io_backend:
-        type: disk
-```
-
-我们使用4个GPU进行训练。还可以使用参数 `--auto_resume` 在必要时自动恢复训练。
-
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 \
-python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/finetune_realesrgan_x4plus.yml --launcher pytorch --auto_resume
-```
-
-用 **1个GPU** 训练：
-```bash
-python realesrgan/train.py -opt options/finetune_realesrgan_x4plus.yml --auto_resume
-```
-
-### 使用已配对的数据
-
-你还可以用自己已经配对的数据微调 RealESRGAN。这个过程更类似于微调 ESRGAN。
-
-**1. 准备数据集**
-
-假设你已经有两个文件夹（folder）:
-
-- **gt folder**（标准参考，高分辨率图像）：*datasets/DF2K/DIV2K_train_HR_sub*
-- **lq folder**（低质量，低分辨率图像）：*datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub*
-
-然后，您可以使用脚本 [scripts/generate_meta_info_pairdata.py](scripts/generate_meta_info_pairdata.py) 生成元信息（meta_info）txt 文件。
-
-```bash
-python scripts/generate_meta_info_pairdata.py --input datasets/DF2K/DIV2K_train_HR_sub datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub --meta_info datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt
-```
-
-**2. 下载预训练模型**
-
-下载预先训练的模型到 `experiments/pretrained_models` 目录下。
-
-- *RealESRGAN_x4plus.pth*:
-    ```bash
-    wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models
-    ```
-
-- *RealESRGAN_x4plus_netD.pth*:
-    ```bash
-    wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.3/RealESRGAN_x4plus_netD.pth -P experiments/pretrained_models
-    ```
-
-**3. 微调**
-
-修改选项文件 [options/finetune_realesrgan_x4plus_pairdata.yml](options/finetune_realesrgan_x4plus_pairdata.yml) ，特别是 `datasets` 部分：
-
-```yml
-train:
-    name: DIV2K
-    type: RealESRGANPairedDataset
-    dataroot_gt: datasets/DF2K  # 修改为你的 gt folder 文件夹根目录
-    dataroot_lq: datasets/DF2K  # 修改为你的 lq folder 文件夹根目录
-    meta_info: datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt  # 修改为你自己生成的元信息txt
-    io_backend:
-        type: disk
-```
-
-我们使用4个GPU进行训练。还可以使用参数 `--auto_resume` 在必要时自动恢复训练。
-
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 \
-python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 realesrgan/train.py -opt options/finetune_realesrgan_x4plus_pairdata.yml --launcher pytorch --auto_resume
-```
-
-用 **1个GPU** 训练：
-```bash
-python realesrgan/train.py -opt options/finetune_realesrgan_x4plus_pairdata.yml --auto_resume
-```
diff --git a/spaces/gyugnsu/DragGan-Inversion/PTI/models/e4e/encoders/model_irse.py b/spaces/gyugnsu/DragGan-Inversion/PTI/models/e4e/encoders/model_irse.py
deleted file mode 100644
index 976ce2c61104efdc6b0015d895830346dd01bc10..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/PTI/models/e4e/encoders/model_irse.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
-from encoder4editing.models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
-
-"""
-Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
-"""
-
-
-class Backbone(Module):
-    def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
-        super(Backbone, self).__init__()
-        assert input_size in [112, 224], "input_size should be 112 or 224"
-        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))
-        if input_size == 112:
-            self.output_layer = Sequential(BatchNorm2d(512),
-                                           Dropout(drop_ratio),
-                                           Flatten(),
-                                           Linear(512 * 7 * 7, 512),
-                                           BatchNorm1d(512, affine=affine))
-        else:
-            self.output_layer = Sequential(BatchNorm2d(512),
-                                           Dropout(drop_ratio),
-                                           Flatten(),
-                                           Linear(512 * 14 * 14, 512),
-                                           BatchNorm1d(512, affine=affine))
-
-        modules = []
-        for block in blocks:
-            for bottleneck in block:
-                modules.append(unit_module(bottleneck.in_channel,
-                                           bottleneck.depth,
-                                           bottleneck.stride))
-        self.body = Sequential(*modules)
-
-    def forward(self, x):
-        x = self.input_layer(x)
-        x = self.body(x)
-        x = self.output_layer(x)
-        return l2_norm(x)
-
-
-def IR_50(input_size):
-    """Constructs a ir-50 model."""
-    model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_101(input_size):
-    """Constructs a ir-101 model."""
-    model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_152(input_size):
-    """Constructs a ir-152 model."""
-    model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_SE_50(input_size):
-    """Constructs a ir_se-50 model."""
-    model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_SE_101(input_size):
-    """Constructs a ir_se-101 model."""
-    model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)
-    return model
-
-
-def IR_SE_152(input_size):
-    """Constructs a ir_se-152 model."""
-    model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)
-    return model
diff --git a/spaces/h2oai/wave-tour/examples/plot_interval_annotation.py b/spaces/h2oai/wave-tour/examples/plot_interval_annotation.py
deleted file mode 100644
index bcad04c41ba766a86c2e163a2f1cd7ffa07cfd64..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/plot_interval_annotation.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Plot / Interval / Annotation
-# Add annotations to a column #plot. #annotation #interval
-# ---
-from h2o_wave import site, data, ui
-
-page = site['/demo']
-
-page.add('example', ui.plot_card(
-    box='1 1 4 5',
-    title='Interval - annotation',
-    data=data('profession salary', 5, rows=[
-        ('medicine', 23000),
-        ('fire fighting', 18000),
-        ('pedagogy', 24000),
-        ('psychology', 22500),
-        ('computer science', 36000),
-    ]),
-    plot=ui.plot([
-        ui.mark(type='interval', x='=profession', y='=salary', y_min=0),
-        ui.mark(x='psychology', y=32000, label='point'),
-        ui.mark(x='pedagogy', label='vertical line'),
-        ui.mark(y=23000, label='horizontal line'),
-        ui.mark(x='fire fighting', x0='medicine', label='vertical region'),
-        ui.mark(y=35000, y0=30000, label='horizontal region')
-    ])
-))
-
-page.save()
diff --git a/spaces/hamacojr/CAT-Seg/demo/demo_visual_gt.py b/spaces/hamacojr/CAT-Seg/demo/demo_visual_gt.py
deleted file mode 100644
index e828860ae5982fba62319b41c42a7311880539a9..0000000000000000000000000000000000000000
--- a/spaces/hamacojr/CAT-Seg/demo/demo_visual_gt.py
+++ /dev/null
@@ -1,210 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from: https://github.com/facebookresearch/detectron2/blob/master/demo/demo.py
-import argparse
-import glob
-import multiprocessing as mp
-import os
-
-# fmt: off
-import sys
-sys.path.insert(1, os.path.join(sys.path[0], '..'))
-# fmt: on
-
-import tempfile
-import time
-import warnings
-
-import cv2
-import numpy as np
-import tqdm
-
-from detectron2.config import get_cfg
-from detectron2.data.detection_utils import read_image
-from detectron2.projects.deeplab import add_deeplab_config
-from detectron2.utils.logger import setup_logger
-
-from mask_former import add_mask_former_config
-# from predictor import VisualizationDemo
-from visualizer import VisualizationGt
-from PIL import Image
-
-# constants
-WINDOW_NAME = "MaskFormer demo"
-
-
-def setup_cfg(args):
-    # load config from file and command-line arguments
-    cfg = get_cfg()
-    add_deeplab_config(cfg)
-    add_mask_former_config(cfg)
-    cfg.merge_from_file(args.config_file)
-    cfg.merge_from_list(args.opts)
-    cfg.freeze()
-    return cfg
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs")
-    parser.add_argument(
-        "--config-file",
-        default="configs/ade20k-150/maskformer_R50_bs16_160k.yaml",
-        metavar="FILE",
-        help="path to config file",
-    )
-    parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.")
-    parser.add_argument("--video-input", help="Path to video file.")
-    parser.add_argument(
-        "--input",
-        nargs="+",
-        help="A list of space separated input images; "
-        "or a single glob pattern such as 'directory/*.jpg'",
-    )
-    # parser.add_argument(
-    #     "--gt",
-    #     nargs="+",
-    #     help="A list of space seperated ground truth images;"
-    #     "or a single glob pattern such as 'directory/*.png'"
-    # )
-    parser.add_argument(
-        "--gt",
-        # type="str",
-        help="ground truth path of segmentation"
-    )
-    parser.add_argument(
-        "--output",
-        help="A file or directory to save output visualizations. "
-        "If not given, will show output in an OpenCV window.",
-    )
-
-    parser.add_argument(
-        "--confidence-threshold",
-        type=float,
-        default=0.5,
-        help="Minimum score for instance predictions to be shown",
-    )
-    parser.add_argument(
-        "--opts",
-        help="Modify config options using the command-line 'KEY VALUE' pairs",
-        default=[],
-        nargs=argparse.REMAINDER,
-    )
-    return parser
-
-
-def test_opencv_video_format(codec, file_ext):
-    with tempfile.TemporaryDirectory(prefix="video_format_test") as dir:
-        filename = os.path.join(dir, "test_file" + file_ext)
-        writer = cv2.VideoWriter(
-            filename=filename,
-            fourcc=cv2.VideoWriter_fourcc(*codec),
-            fps=float(30),
-            frameSize=(10, 10),
-            isColor=True,
-        )
-        [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)]
-        writer.release()
-        if os.path.isfile(filename):
-            return True
-        return False
-
-
-if __name__ == "__main__":
-    mp.set_start_method("spawn", force=True)
-    args = get_parser().parse_args()
-    setup_logger(name="fvcore")
-    logger = setup_logger()
-    logger.info("Arguments: " + str(args))
-
-    cfg = setup_cfg(args)
-
-    demo = VisualizationGt(cfg)
-    gt_path = args.gt
-    if args.input:
-        if len(args.input) == 1:
-            args.input = glob.glob(os.path.expanduser(args.input[0]))
-            assert args.input, "The input path(s) was not found"
-        for path in tqdm.tqdm(args.input, disable=not args.output):
-            # use PIL, to be consistent with evaluation
-            img = read_image(path, format="BGR")
-            start_time = time.time()
-            predictions = {}
-            gt_file = os.path.join(gt_path, os.path.splitext(os.path.basename(path))[0] + '.png')
-            # import pdb; pdb.set_trace()
-            predictions['sem_seg'] = np.asarray(Image.open(gt_file))
-            predictions, visualized_output = demo.run_on_image(img, predictions)
-            logger.info(
-                "{}: {} in {:.2f}s".format(
-                    path,
-                    "detected {} instances".format(len(predictions["instances"]))
-                    if "instances" in predictions
-                    else "finished",
-                    time.time() - start_time,
-                )
-            )
-
-            if args.output:
-                if os.path.isdir(args.output):
-                    assert os.path.isdir(args.output), args.output
-                    out_filename = os.path.join(args.output, os.path.basename(path))
-                else:
-                    assert len(args.input) == 1, "Please specify a directory with args.output"
-                    out_filename = args.output
-                visualized_output.save(out_filename)
-            else:
-                cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
-                cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1])
-                if cv2.waitKey(0) == 27:
-                    break  # esc to quit
-    elif args.webcam:
-        assert args.input is None, "Cannot have both --input and --webcam!"
-        assert args.output is None, "output not yet supported with --webcam!"
-        cam = cv2.VideoCapture(0)
-        for vis in tqdm.tqdm(demo.run_on_video(cam)):
-            cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
-            cv2.imshow(WINDOW_NAME, vis)
-            if cv2.waitKey(1) == 27:
-                break  # esc to quit
-        cam.release()
-        cv2.destroyAllWindows()
-    elif args.video_input:
-        video = cv2.VideoCapture(args.video_input)
-        width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
-        height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        frames_per_second = video.get(cv2.CAP_PROP_FPS)
-        num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
-        basename = os.path.basename(args.video_input)
-        codec, file_ext = (
-            ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4")
-        )
-        if codec == ".mp4v":
-            warnings.warn("x264 codec not available, switching to mp4v")
-        if args.output:
-            if os.path.isdir(args.output):
-                output_fname = os.path.join(args.output, basename)
-                output_fname = os.path.splitext(output_fname)[0] + file_ext
-            else:
-                output_fname = args.output
-            assert not os.path.isfile(output_fname), output_fname
-            output_file = cv2.VideoWriter(
-                filename=output_fname,
-                # some installation of opencv may not support x264 (due to its license),
-                # you can try other format (e.g. MPEG)
-                fourcc=cv2.VideoWriter_fourcc(*codec),
-                fps=float(frames_per_second),
-                frameSize=(width, height),
-                isColor=True,
-            )
-        assert os.path.isfile(args.video_input)
-        for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames):
-            if args.output:
-                output_file.write(vis_frame)
-            else:
-                cv2.namedWindow(basename, cv2.WINDOW_NORMAL)
-                cv2.imshow(basename, vis_frame)
-                if cv2.waitKey(1) == 27:
-                    break  # esc to quit
-        video.release()
-        if args.output:
-            output_file.release()
-        else:
-            cv2.destroyAllWindows()
diff --git a/spaces/hanan217/QQsign/Dockerfile b/spaces/hanan217/QQsign/Dockerfile
deleted file mode 100644
index 535624113f3b520e4829240a48bd3652430de828..0000000000000000000000000000000000000000
--- a/spaces/hanan217/QQsign/Dockerfile
+++ /dev/null
@@ -1,23 +0,0 @@
-FROM openjdk:17-slim
-
-# 设置时区
-ENV TZ Asia/Shanghai
-
-# 设置工作目录
-WORKDIR /app
-
-# 复制文件到工作目录
-COPY bin /app/bin
-COPY lib /app/lib
-COPY txlib /app/txlib
-
-# 设置命令
-RUN chmod -R 777 /tmp
-RUN chmod -R 777 /app
-RUN sed 's/"key": ".*"/"key": "'"$KEY_VALUE"'"/' txlib/$TXLIB_VERSION/config.json > /app/txlib/$TXLIB_VERSION/config.json
-
-# 运行
-CMD bash bin/unidbg-fetch-qsign --basePath=txlib/$TXLIB_VERSION
-
-# 暴露端口
-EXPOSE 7860
\ No newline at end of file
diff --git a/spaces/haoqi7/research/lrt/clustering/__init__.py b/spaces/haoqi7/research/lrt/clustering/__init__.py
deleted file mode 100644
index 5e1a3dadad381a487c2131f273d4ae064c759fd0..0000000000000000000000000000000000000000
--- a/spaces/haoqi7/research/lrt/clustering/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .clustering_pipeline import ClusterPipeline, ClusterList
-from .config import Configuration,BaselineConfig
diff --git a/spaces/harish3110/document-parsing-demo/README.md b/spaces/harish3110/document-parsing-demo/README.md
deleted file mode 100644
index 672a6f69b42705a5a0049ce1527bd6f4b090a237..0000000000000000000000000000000000000000
--- a/spaces/harish3110/document-parsing-demo/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Document Parsing Demo
-emoji: 📈
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.13.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hesha/anime-remove-background/app.py b/spaces/hesha/anime-remove-background/app.py
deleted file mode 100644
index b1e96a91cdb8243074de409e060181f33e7ca87a..0000000000000000000000000000000000000000
--- a/spaces/hesha/anime-remove-background/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-rmbg_model = rt.InferenceSession(model_path, providers=providers)
-
-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
-
-iface = gr.Interface(
-    fn=rmbg_fn, 
-    inputs=gr.Image(label="input image"), 
-    outputs=[
-        gr.Image(label="mask"),
-        gr.Image(label="result", image_mode="RGBA")
-    ],
-    title="Anime Remove Background",
-    allow_flagging="never",
-    article='<div style="width: 100%; text-align: center;"><img src="https://moe-counter.glitch.me/get/@hesha-arb" alt="hesha-arb" /></div>'
-)
-
-iface.queue().launch(show_api=False, show_error=True)
\ No newline at end of file
diff --git a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/Structure Studios Pool Studio Crack Fix.md b/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/Structure Studios Pool Studio Crack Fix.md
deleted file mode 100644
index b79ccf53dacf945ddc8651a987d7bd0b1968d4be..0000000000000000000000000000000000000000
--- a/spaces/hilmyblaze/WebUI-Counterfeit-V2.5/Structure Studios Pool Studio Crack Fix.md	
+++ /dev/null
@@ -1,49 +0,0 @@
-## Structure Studios Pool Studio Crack
-
-
-
-**Download File === [https://ditzcosupo.blogspot.com/?d=2twsjm](https://ditzcosupo.blogspot.com/?d=2twsjm)**
-
-
-
- Here is a possible title and article with HTML formatting for the keyword "Structure Studios Pool Studio Crack":  
-
-# How to Crack Structure Studios Pool Studio Software
-
- 
-
-If you are looking for a way to crack Structure Studios Pool Studio software, you might be disappointed. Pool Studio is a professional 3D swimming pool design software that allows you to create stunning presentations for your clients. It is not a cheap or easy software to crack, and doing so might expose you to legal and security risks.
-
- 
-
-According to the developer's website[^1^], Pool Studio is a subscription-based software that requires an active membership to access its features and updates. You can choose from three different plans: Vip3D, Pool Studio, and VizTerra, depending on your design needs and budget. Each plan comes with a free trial period, live training, and technical support.
-
- 
-
-Cracking Pool Studio software would mean bypassing its subscription system and accessing its features without paying for them. This is not only unethical but also illegal, as it violates the terms of service and the intellectual property rights of the developer. You could face serious consequences if you are caught using a cracked version of Pool Studio, such as fines, lawsuits, or even criminal charges.
-
- 
-
-Moreover, cracking Pool Studio software could also harm your computer and your data. Many websites that claim to offer cracked versions of Pool Studio are actually scams that infect your device with malware, spyware, or ransomware. These malicious programs could steal your personal information, damage your files, or lock your system until you pay a ransom. You could also lose access to your projects and clients if you rely on a cracked version of Pool Studio that stops working or gets detected by the developer.
-
- 
-
-Therefore, we strongly advise you not to crack Structure Studios Pool Studio software. Instead, we recommend you to use the free trial period to test its features and see if it meets your expectations. If you decide to purchase a subscription plan, you will enjoy the benefits of a reliable, secure, and updated software that will help you grow your business and impress your clients with amazing 3D swimming pool designs.
-
-Here are a few more paragraphs to extend the article:  
-
-If you are still wondering why you should use Pool Studio software instead of a cracked version, here are some of the features that make it worth paying for:
-
- 
-
-- Pool Studio software lets you design in 3D with easy and intuitive tools. You can draw and edit shapes, add materials and textures, insert accessories and furniture, and customize every aspect of your swimming pool design.
-
-- Pool Studio software allows you to create fully interactive presentations that showcase your design in realistic and stunning detail. You can add lighting and shadows, water effects, animations, and sound effects. You can also switch between different views and perspectives, such as top-down, walkthrough, or flyover.
-
-- Pool Studio software helps you to generate smart construction and calculation data for your design. You can get accurate measurements, dimensions, volumes, and costs for your swimming pool project. You can also export your design to various formats, such as PDF, DWG, or 3D models.
-
-
-
-As you can see, Pool Studio software is a powerful and versatile tool that can help you to create professional and impressive swimming pool designs. It is not worth risking your reputation, your security, and your clients' satisfaction by using a cracked version of Pool Studio. Instead, you should invest in a legitimate subscription plan that will give you access to the best 3D swimming pool design software in the market.
-
- dfd1c89656
\ No newline at end of file
diff --git a/spaces/hlopez/Waste-Detector/model.py b/spaces/hlopez/Waste-Detector/model.py
deleted file mode 100644
index df935bbc26f248ffed7da4f816a8bcb994fc0e24..0000000000000000000000000000000000000000
--- a/spaces/hlopez/Waste-Detector/model.py
+++ /dev/null
@@ -1,119 +0,0 @@
-from io import BytesIO
-from typing import Dict, Tuple, Union
-from icevision import *
-from icevision.models.checkpoint import model_from_checkpoint
-from classifier import transform_image
-from icevision.models import ross
-
-import PIL
-import torch
-import numpy as np
-import torchvision
-
-MODEL_TYPE = ross.efficientdet
-
-def predict(det_model : torch.nn.Module, image : Union[str, BytesIO],
-            detection_threshold : float) -> Dict:
-    """
-    Make a prediction with the detection model.
-
-    Args:
-        det_model (torch.nn.Module): Detection model
-        image (Union[str, BytesIO]): Image filepath if the image is one of
-            the example images and BytesIO if the image is a custom image
-            uploaded by the user.
-        detection_threshold (float): Detection threshold
-
-    Returns:
-        Dict: Prediction dictionary.
-    """        
-    img = PIL.Image.open(image)
-
-    # Class map and transforms
-    class_map = ClassMap(classes=['Waste'])
-    transforms = tfms.A.Adapter([
-                    *tfms.A.resize_and_pad(512),
-                    tfms.A.Normalize()
-                ])
-    
-    # Single prediction
-    pred_dict  = MODEL_TYPE.end2end_detect(img,
-                                           transforms, 
-                                           det_model,
-                                           class_map=class_map,
-                                           detection_threshold=detection_threshold,
-                                           return_as_pil_img=False,
-                                           return_img=True,
-                                           display_bbox=False,
-                                           display_score=False,
-                                           display_label=False)
-
-    return pred_dict
-
-def prepare_prediction(pred_dict : Dict,
-                       nms_threshold : str) -> Tuple[torch.Tensor, np.ndarray]:
-    """
-    Get the predictions in a right format.
-
-    Args:
-        pred_dict (Dict): Prediction dictionary.
-        nms_threshold (float): Threshold for the NMS postprocess.
-
-    Returns:
-        Tuple: Tuple containing the following:
-            - (torch.Tensor): Bounding boxes
-            - (np.ndarray): Image data
-    """
-    # Convert each box to a tensor and stack them into an unique tensor
-    boxes = [box.to_tensor() for box in pred_dict['detection']['bboxes']]
-    boxes = torch.stack(boxes)
-
-    # Get the scores and labels as tensor
-    scores = torch.as_tensor(pred_dict['detection']['scores'])
-    labels = torch.as_tensor(pred_dict['detection']['label_ids'])
-
-    image = np.array(pred_dict['img'])
-
-    # Apply NMS to postprocess the bounding boxes
-    fixed_boxes = torchvision.ops.batched_nms(boxes, scores,
-                                              labels,nms_threshold)
-    boxes = boxes[fixed_boxes, :]
-
-    return boxes, image
-
-def predict_class(classifier : torch.nn.Module, image : np.ndarray,
-                  bboxes : torch.Tensor) -> np.ndarray:
-    """
-    Predict the class of each detected object.
-
-    Args:
-        classifier (torch.nn.Module): Classifier model.
-        image (np.ndarray): Image data.
-        bboxes (torch.Tensor): Bounding boxes.
-
-    Returns:
-        np.ndarray: Array containing the predicted class for each object.
-    """
-    preds = []
-
-    for bbox in bboxes:
-        img = image.copy()
-        bbox = np.array(bbox).astype(int)
-
-        # Get the bounding box content
-        cropped_img = PIL.Image.fromarray(img).crop(bbox)
-        cropped_img = np.array(cropped_img)
-
-        # Apply transformations to the cropped image
-        tran_image = transform_image(cropped_img, 224)
-        # Channels first
-        tran_image = tran_image.transpose(2, 0, 1)
-        tran_image = torch.as_tensor(tran_image, dtype=torch.float).unsqueeze(0)
-
-        # Make prediction
-        y_preds = classifier(tran_image)
-        preds.append(y_preds.softmax(1).detach().numpy())
-
-    preds = np.concatenate(preds).argmax(1)
-
-    return preds
\ No newline at end of file
diff --git a/spaces/hslu-di/Reust_Yannic/app.py b/spaces/hslu-di/Reust_Yannic/app.py
deleted file mode 100644
index 5d40389f4c6874c23ea13b0dd723c2a04ec494ea..0000000000000000000000000000000000000000
--- a/spaces/hslu-di/Reust_Yannic/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/kohbanye/pixel-art-style").launch()
\ No newline at end of file
diff --git a/spaces/huggingface/bloom-test-flax/README.md b/spaces/huggingface/bloom-test-flax/README.md
deleted file mode 100644
index c6e469f2b9a2a7f2366c6056ffad9b214c8d8ecb..0000000000000000000000000000000000000000
--- a/spaces/huggingface/bloom-test-flax/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Bloom Test Flax
-emoji: 🐨
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.0.24
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hysts/DETA/README.md b/spaces/hysts/DETA/README.md
deleted file mode 100644
index b713309df908e6a1b3e671d4f4d31d57140dff66..0000000000000000000000000000000000000000
--- a/spaces/hysts/DETA/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: DETA
-emoji: 🐠
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.17.0
-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/hysts/anime-face-detector/README.md b/spaces/hysts/anime-face-detector/README.md
deleted file mode 100644
index 33cb3e19fd12f49bda6a6aeaab360e1121d35534..0000000000000000000000000000000000000000
--- a/spaces/hysts/anime-face-detector/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: Anime Face Detector
-emoji: 😻
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.5
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/hzwluoye/gpt4/g4f/typing.py b/spaces/hzwluoye/gpt4/g4f/typing.py
deleted file mode 100644
index e41a567ae49dd26d2ace2a3732b0e8f0bbbaa4b0..0000000000000000000000000000000000000000
--- a/spaces/hzwluoye/gpt4/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/iamstolas/STOLAS/src/components/tone-selector.tsx b/spaces/iamstolas/STOLAS/src/components/tone-selector.tsx
deleted file mode 100644
index 5c6e464c91f564b895acd121f0a4a79ed9c5c356..0000000000000000000000000000000000000000
--- a/spaces/iamstolas/STOLAS/src/components/tone-selector.tsx
+++ /dev/null
@@ -1,43 +0,0 @@
-import React from 'react'
-import { BingConversationStyle } from '@/lib/bots/bing/types'
-import { cn } from '@/lib/utils'
-
-type ToneItem = {
-  type: BingConversationStyle,
-  name: string
-}
-
-const ToneList: ToneItem[] = [
-  { name: '有创造力', type: BingConversationStyle.Creative },
-  { name: '更平衡', type: BingConversationStyle.Balanced },
-  { name: '更精确', type: BingConversationStyle.Precise }
-]
-
-interface ToneSelectorProps {
-  type: BingConversationStyle | ''
-  onChange?: (type: BingConversationStyle) => void
-}
-
-export function ToneSelector({ type, onChange }: ToneSelectorProps) {
-  return (
-    <div className="fieldset">
-      <div className="legend">
-        选择对话样式
-      </div>
-      <div className="options-list-container">
-        <ul id="tone-options" className="options">
-          {
-            ToneList.map(tone => (
-              <li className="option" key={tone.name} onClick={() => onChange?.(tone.type)}>
-                <button className={cn(`tone-${type.toLowerCase()}`, { selected: tone.type === type}) } aria-pressed="true" >
-                  <span className="caption-2-strong label-modifier">更</span>
-                  <span className="body-1-strong label">{tone.name}</span>
-                </button>
-              </li>
-            ))
-          }
-        </ul>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/inamXcontru/PoeticTTS/Chudail Movie Free Download In Hindi HD.md b/spaces/inamXcontru/PoeticTTS/Chudail Movie Free Download In Hindi HD.md
deleted file mode 100644
index 631479ea8ba405ab5434479781c3eee98008acc4..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Chudail Movie Free Download In Hindi HD.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-
-<p>download Raj Mahal 2 Comedy Scenes unlimited Movies and videos Download Here.Raj Mahal 2 Comedy Scenes Hd,3gp. mp4 320p and More Videos You Can Download Easyly. tamilrockers and movierulz, tamilgun, filmywap, and pagalworld videos and Movies download.</p>
-<h2>Chudail movie free download in hindi HD</h2><br /><p><b><b>Download</b> ===== <a href="https://gohhs.com/2uz2N5">https://gohhs.com/2uz2N5</a></b></p><br /><br /> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/innnky/nyaru4.0/utils.py b/spaces/innnky/nyaru4.0/utils.py
deleted file mode 100644
index f13d3526d514be71c77bebb17a5af8831b9c6a36..0000000000000000000000000000000000000000
--- a/spaces/innnky/nyaru4.0/utils.py
+++ /dev/null
@@ -1,508 +0,0 @@
-import os
-import glob
-import re
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import random
-
-import librosa
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from modules.commons import sequence_mask
-from hubert import hubert_model
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-f0_bin = 256
-f0_max = 1100.0
-f0_min = 50.0
-f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
-
-# def normalize_f0(f0, random_scale=True):
-#     f0_norm = f0.clone()  # create a copy of the input Tensor
-#     batch_size, _, frame_length = f0_norm.shape
-#     for i in range(batch_size):
-#         means = torch.mean(f0_norm[i, 0, :])
-#         if random_scale:
-#             factor = random.uniform(0.8, 1.2)
-#         else:
-#             factor = 1
-#         f0_norm[i, 0, :] = (f0_norm[i, 0, :] - means) * factor
-#     return f0_norm
-# def normalize_f0(f0, random_scale=True):
-#     means = torch.mean(f0[:, 0, :], dim=1, keepdim=True)
-#     if random_scale:
-#         factor = torch.Tensor(f0.shape[0],1).uniform_(0.8, 1.2).to(f0.device)
-#     else:
-#         factor = torch.ones(f0.shape[0], 1, 1).to(f0.device)
-#     f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
-#     return f0_norm
-def normalize_f0(f0, x_mask, uv, random_scale=True):
-    # calculate means based on x_mask
-    uv_sum = torch.sum(uv, dim=1, keepdim=True)
-    uv_sum[uv_sum == 0] = 9999
-    means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum
-
-    if random_scale:
-        factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device)
-    else:
-        factor = torch.ones(f0.shape[0], 1).to(f0.device)
-    # normalize f0 based on means and factor
-    f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
-    if torch.isnan(f0_norm).any():
-        exit(0)
-    return f0_norm * x_mask
-
-
-def plot_data_to_numpy(x, y):
-    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))
-    plt.plot(x)
-    plt.plot(y)
-    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 interpolate_f0(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]
-            last_value = data[i]
-
-    return ip_data[:,0], vuv_vector[:,0]
-
-
-def compute_f0_parselmouth(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
-    import parselmouth
-    x = wav_numpy
-    if p_len is None:
-        p_len = x.shape[0]//hop_length
-    else:
-        assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error"
-    time_step = hop_length / sampling_rate * 1000
-    f0_min = 50
-    f0_max = 1100
-    f0 = parselmouth.Sound(x, sampling_rate).to_pitch_ac(
-        time_step=time_step / 1000, voicing_threshold=0.6,
-        pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
-
-    pad_size=(p_len - len(f0) + 1) // 2
-    if(pad_size>0 or p_len - len(f0) - pad_size>0):
-        f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
-    return f0
-
-def resize_f0(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_dio(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
-    import pyworld
-    if p_len is None:
-        p_len = wav_numpy.shape[0]//hop_length
-    f0, t = pyworld.dio(
-        wav_numpy.astype(np.double),
-        fs=sampling_rate,
-        f0_ceil=800,
-        frame_period=1000 * hop_length / sampling_rate,
-    )
-    f0 = pyworld.stonemask(wav_numpy.astype(np.double), f0, t, sampling_rate)
-    for index, pitch in enumerate(f0):
-        f0[index] = round(pitch, 1)
-    return resize_f0(f0, p_len)
-
-def f0_to_coarse(f0):
-  is_torch = isinstance(f0, torch.Tensor)
-  f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
-  f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
-
-  f0_mel[f0_mel <= 1] = 1
-  f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
-  f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
-  assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
-  return f0_coarse
-
-
-def get_hubert_model():
-  vec_path = "hubert/checkpoint_best_legacy_500.pt"
-  print("load model(s) from {}".format(vec_path))
-  from fairseq import checkpoint_utils
-  models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-    [vec_path],
-    suffix="",
-  )
-  model = models[0]
-  model.eval()
-  return model
-
-def get_hubert_content(hmodel, wav_16k_tensor):
-  feats = wav_16k_tensor
-  if feats.dim() == 2:  # double channels
-    feats = feats.mean(-1)
-  assert feats.dim() == 1, feats.dim()
-  feats = feats.view(1, -1)
-  padding_mask = torch.BoolTensor(feats.shape).fill_(False)
-  inputs = {
-    "source": feats.to(wav_16k_tensor.device),
-    "padding_mask": padding_mask.to(wav_16k_tensor.device),
-    "output_layer": 9,  # layer 9
-  }
-  with torch.no_grad():
-    logits = hmodel.extract_features(**inputs)
-    feats = hmodel.final_proj(logits[0])
-  return feats.transpose(1, 2)
-
-
-def get_content(cmodel, y):
-    with torch.no_grad():
-        c = cmodel.extract_features(y.squeeze(1))[0]
-    c = c.transpose(1, 2)
-    return c
-
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
-    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 and not skip_optimizer:
-        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:
-            # assert "dec" in k or "disc" in k
-            # print("load", k)
-            new_state_dict[k] = saved_state_dict[k]
-            assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
-        except:
-            print("error, %s is not in the checkpoint" % k)
-            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)
-    print("load ")
-    logger.info("Loaded checkpoint '{}' (iteration {})".format(
-        checkpoint_path, iteration))
-    return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path, val_steps, current_step):
-  logger.info("Saving model and optimizer state at iteration {} 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)
-  if current_step >= val_steps * 3:
-    to_del_ckptname = checkpoint_path.replace(str(current_step), str(current_step - val_steps * 3))
-    if os.path.exists(to_del_ckptname):
-        os.remove(to_del_ckptname)
-        print("Removing ", to_del_ckptname)
-
-
-def clean_checkpoints(path_to_models='logs/48k/', n_ckpts_to_keep=2, sort_by_time=True):
-  """Freeing up space by deleting saved ckpts
-
-  Arguments:
-  path_to_models    --  Path to the model directory
-  n_ckpts_to_keep   --  Number of ckpts to keep, excluding G_0.pth and D_0.pth
-  sort_by_time      --  True -> chronologically delete ckpts
-                        False -> lexicographically delete ckpts
-  """
-  ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
-  name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
-  time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
-  sort_key = time_key if sort_by_time else name_key
-  x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key)
-  to_del = [os.path.join(path_to_models, fn) for fn in
-            (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
-  del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
-  del_routine = lambda x: [os.remove(x), del_info(x)]
-  rs = [del_routine(fn) for fn in to_del]
-
-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):
-  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
-
-
-def repeat_expand_2d(content, target_len):
-    # content : [h, t]
-
-    src_len = content.shape[-1]
-    target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
-    temp = torch.arange(src_len+1) * target_len / src_len
-    current_pos = 0
-    for i in range(target_len):
-        if i < temp[current_pos+1]:
-            target[:, i] = content[:, current_pos]
-        else:
-            current_pos += 1
-            target[:, i] = content[:, current_pos]
-
-    return target
-
-
-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/inreVtussa/clothingai/Examples/CrackBIM360Design2019keygen [REPACK].md b/spaces/inreVtussa/clothingai/Examples/CrackBIM360Design2019keygen [REPACK].md
deleted file mode 100644
index 0e3c79b05051859837b54bf278da9958c336e58d..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/CrackBIM360Design2019keygen [REPACK].md	
+++ /dev/null
@@ -1,109 +0,0 @@
-<br />
-<h1>CrackBIM360Design2019keygen: Everything You Need to Know</h1>
-<p>If you are looking for a way to use BIM 360 Design 2019 without paying for a license, you might be interested in CrackBIM360Design2019keygen. This is a software tool that can generate a valid keygen for BIM 360 Design 2019, allowing you to access all the features and functions of this powerful design software. In this article, we will explain what CrackBIM360Design2019keygen is, how it works, and where you can download it.</p>
-<h2>crackBIM360Design2019keygen</h2><br /><p><b><b>Download</b> &gt;&gt;&gt;&gt;&gt; <a href="https://tiurll.com/2uCj7w">https://tiurll.com/2uCj7w</a></b></p><br /><br />
-<h2>What is CrackBIM360Design2019keygen?</h2>
-<p>CrackBIM360Design2019keygen is a software tool that can create a keygen for BIM 360 Design 2019. A keygen is a program that can generate a serial number or activation code for a software product. By using a keygen, you can bypass the need to purchase a license or subscription for the software, and use it for free.</p>
-<p>BIM 360 Design 2019 is a cloud-based design software that enables collaboration and coordination among architects, engineers, and contractors. It allows users to create, edit, and share 3D models, drawings, and documents in real-time. BIM 360 Design 2019 also integrates with other Autodesk products, such as Revit, AutoCAD, and Navisworks.</p>
-<p>However, BIM 360 Design 2019 is not cheap. It costs $900 per year for a single-user license, or $1,620 per year for a multi-user license. For many users, this is too expensive or unaffordable. That's why some people resort to using CrackBIM360Design2019keygen to get access to BIM 360 Design 2019 without paying anything.</p>
-<h2>How does CrackBIM360Design2019keygen work?</h2>
-<p>CrackBIM360Design2019keygen works by exploiting a vulnerability in the activation process of BIM 360 Design 2019. It mimics the communication between the software and the Autodesk server, and generates a fake response that validates the keygen. This way, the software thinks that the keygen is legitimate and authenticates it.</p>
-<p>To use CrackBIM360Design2019keygen, you need to follow these steps:</p>
-<ol>
-<li>Download CrackBIM360Design2019keygen from a reliable source. Be careful of malware or viruses that might be hidden in some downloads.</li>
-<li>Run CrackBIM360Design2019keygen as an administrator on your computer.</li>
-<li>Select BIM 360 Design 2019 from the list of products and click on Generate.</li>
-<li>Copy the keygen that appears on the screen.</li>
-<li>Install BIM 360 Design 2019 on your computer if you haven't already.</li>
-<li>Launch BIM 360 Design 2019 and enter the keygen when prompted.</li>
-<li>Enjoy using BIM 360 Design 2019 for free!</li>
-</ol>
-<h2>Where can I download CrackBIM360Design2019keygen?</h2>
-<p>There are many websites that claim to offer CrackBIM360Design2019keygen for download. However, not all of them are trustworthy or safe. Some of them might contain malware or viruses that can harm your computer or steal your personal information. Some of them might also provide fake or outdated keygens that don't work or get detected by Autodesk.</p>
-<p></p>
-<p>Therefore, you need to be careful and selective when choosing where to download CrackBIM360Design2019keygen. You should only download it from reputable and verified sources that have positive reviews and feedback from other users. You should also scan the download with an antivirus program before opening it.</p>
-<p>One of the websites that we recommend for downloading CrackBIM360Design2019keygen is Patronway.com. This website has been providing quality software tools and keygens for various Autodesk products for years. It has a large user base and a high success rate. It also offers fast and secure downloads with no surveys or passwords required.</p>
-<p>To download CrackBIM360Design2019keygen from Patronway.com, you need to follow these steps:</p>
-<ol>
-<li>Go to https://patronway.com/crack-workbim360design2019keygen/</li>
-<li>Click on the Download button at the bottom of the page.</li>
-<li>Wait for the download to finish and save it on your computer.</li>
-<li>Run CrackBIM360Design2019keygen as an administrator and follow the instructions above.</li>
-</ol>
-<h2>Conclusion</h2>
-<p>CrackBIM360Design2019keygen is a software tool that can help you use BIM 360 Design 2019 for free by generating a valid keygen for it. It works by exploiting a vulnerability in the activation process of BIM 360 Design 2019 and mimicking the communication between the software and the Autodesk server. It is easy to use and has a high success rate.</p>
-<p>However, you need to be careful when downloading CrackBIM360Design2019keygen from the internet. Not all websites are reliable or safe. Some of them might contain malware or viruses that can damage your computer or compromise your privacy. Some of them might also provide fake or outdated keygens that don't work or get detected by Autodesk.</p>
-<p>Therefore, you should only download CrackBIM360Design2019keygen from reputable and verified sources that have positive reviews and feedback from other users. You should also scan the download with an antivirus program before opening it.</p>
-<p>One of the websites that we recommend for downloading CrackBIM360Design2019keygen is Patronway.com. This website has been providing quality software tools and keygens for various Autodesk products for years. It has a large user base and a high success rate. It also offers fast and secure downloads with no surveys or passwords required.</p>
-<p>If you want to use BIM 360 Design 2019 without paying for a license or subscription, you should try CrackBIM360Design2019keygen today!</p>
-<h2>What are the benefits of using CrackBIM360Design2019keygen?</h2>
-<p>By using CrackBIM360Design2019keygen, you can enjoy many benefits that BIM 360 Design 2019 has to offer. Some of these benefits are:</p>
-<ul>
-<li>You can save money and time by not having to pay for a license or subscription for BIM 360 Design 2019.</li>
-<li>You can collaborate and coordinate with your team members and stakeholders in real-time, regardless of their location or device.</li>
-<li>You can create, edit, and share 3D models, drawings, and documents with ease and accuracy.</li>
-<li>You can integrate BIM 360 Design 2019 with other Autodesk products, such as Revit, AutoCAD, and Navisworks, and streamline your workflow.</li>
-<li>You can access BIM 360 Design 2019 from anywhere and anytime, as long as you have an internet connection.</li>
-</ul>
-<h2>What are the risks of using CrackBIM360Design2019keygen?</h2>
-<p>While using CrackBIM360Design2019keygen might seem tempting, it also comes with some risks that you should be aware of. Some of these risks are:</p>
-<ul>
-<li>You might violate the terms and conditions of Autodesk and face legal consequences for using pirated software.</li>
-<li>You might expose your computer to malware or viruses that can damage your system or steal your personal information.</li>
-<li>You might get detected by Autodesk and have your keygen revoked or blocked.</li>
-<li>You might not receive updates or support from Autodesk for BIM 360 Design 2019.</li>
-<li>You might experience errors or glitches with BIM 360 Design 2019 that can affect your performance or quality of work.</li>
-</ul>
-<h2>Is there an alternative to using CrackBIM360Design2019keygen?</h2>
-<p>If you want to use BIM 360 Design 2019 without risking the negative consequences of using CrackBIM360Design2019keygen, there is an alternative option that you can consider. You can try using a free trial version of BIM 360 Design 2019 that Autodesk offers for new users.</p>
-<p>The free trial version of BIM 360 Design 2019 allows you to use the software for 30 days without paying anything. You can access all the features and functions of BIM 360 Design 2019 and test its capabilities. You can also collaborate and coordinate with other users who have a valid license or subscription for BIM 360 Design 2019.</p>
-<p>To use the free trial version of BIM 360 Design 2019, you need to follow these steps:</p>
-<ol>
-<li>Go to https://www.autodesk.com/products/bim-360-design/free-trial</li>
-<li>Fill out the form with your personal and professional information.</li>
-<li>Click on Start Free Trial and wait for the confirmation email from Autodesk.</li>
-<li>Follow the instructions in the email to activate your free trial account and download BIM 360 Design 2019 on your computer.</li>
-<li>Launch BIM 360 Design 2019 and sign in with your free trial account credentials.</li>
-<li>Enjoy using BIM 360 Design 2019 for free for 30 days!</li>
-</ol>
-<h2>How to use BIM 360 Design 2019 with CrackBIM360Design2019keygen?</h2>
-<p>Once you have installed BIM 360 Design 2019 and activated it with CrackBIM360Design2019keygen, you can start using it for your design projects. Here are some tips on how to use BIM 360 Design 2019 effectively:</p>
-<ul>
-<li>Create a project in BIM 360 Design 2019 and invite your team members and stakeholders to join it. You can assign roles and permissions to different users and control their access to the project data.</li>
-<li>Upload your 3D models, drawings, and documents to the cloud and sync them with BIM 360 Design 2019. You can use the desktop connector or the web browser to upload your files.</li>
-<li>Edit and update your files in BIM 360 Design 2019 using the online editor or the desktop app. You can use tools such as markup, annotation, clash detection, and design review to enhance your work.</li>
-<li>Share and collaborate with your team members and stakeholders in real-time using BIM 360 Design 2019. You can chat, comment, and exchange feedback with other users within the software.</li>
-<li>Track and manage your project progress and performance using BIM 360 Design 2019. You can use dashboards, reports, and analytics to monitor your project status and identify issues or risks.</li>
-</ul>
-<h2>What are the drawbacks of using CrackBIM360Design2019keygen?</h2>
-<p>While using CrackBIM360Design2019keygen might seem convenient and beneficial, it also has some drawbacks that you should consider before using it. Some of these drawbacks are:</p>
-<ul>
-<li>You might compromise your professional ethics and reputation by using pirated software.</li>
-<li>You might lose your work or data if Autodesk detects your keygen and deactivates it.</li>
-<li>You might miss out on new features or improvements that Autodesk releases for BIM 360 Design 2019.</li>
-<li>You might encounter compatibility or security issues with other software or devices that you use with BIM 360 Design 2019.</li>
-<li>You might not get any technical support or customer service from Autodesk if you encounter any problems with BIM 360 Design 2019.</li>
-</ul>
-<h2>How to get a legitimate license or subscription for BIM 360 Design 2019?</h2>
-<p>If you want to use BIM 360 Design 2019 legally and safely, you should get a legitimate license or subscription for it from Autodesk. This way, you can avoid the risks and drawbacks of using CrackBIM360Design2019keygen, and enjoy the full benefits and advantages of BIM 360 Design 2019.</p>
-<p>To get a legitimate license or subscription for BIM 360 Design 2019, you need to follow these steps:</p>
-<ol>
-<li>Go to https://www.autodesk.com/products/bim-360-design/overview</li>
-<li>Click on Buy Now or Subscribe Now depending on your preference.</li>
-<li>Select the plan that suits your needs and budget. You can choose between a single-user or a multi-user license, and between a monthly, yearly, or three-yearly subscription.</li>
-<li>Enter your billing and payment information and confirm your order.</li>
-<li>Wait for the confirmation email from Autodesk with your license or subscription details.</li>
-<li>Download and install BIM 360 Design 2019 on your computer using the link provided in the email.</li>
-<li>Launch BIM 360 Design 2019 and sign in with your Autodesk account credentials.</li>
-<li>Enjoy using BIM 360 Design 2019 legally and safely!</li>
-</ol>
-<h2>Conclusion</h2>
-<p>BIM 360 Design 2019 is a cloud-based design software that enables collaboration and coordination among architects, engineers, and contractors. It allows users to create, edit, and share 3D models, drawings, and documents in real-time. It also integrates with other Autodesk products, such as Revit, AutoCAD, and Navisworks.</p>
-<p>However, BIM 360 Design 2019 is not cheap. It costs $900 per year for a single-user license, or $1,620 per year for a multi-user license. For many users, this is too expensive or unaffordable. That's why some people resort to using CrackBIM360Design2019keygen to get access to BIM 360 Design 2019 without paying anything.</p>
-<p>CrackBIM360Design2019keygen is a software tool that can generate a valid keygen for BIM 360 Design 2019, allowing users to bypass the need to purchase a license or subscription for the software. It works by exploiting a vulnerability in the activation process of BIM 360 Design 2019 and mimicking the communication between the software and the Autodesk server.</p>
-<p>However, using CrackBIM360Design2019keygen also comes with some risks and drawbacks. Users might violate the terms and conditions of Autodesk and face legal consequences for using pirated software. They might also expose their computer to malware or viruses that can harm their system or steal their personal information. They might also get detected by Autodesk and have their keygen revoked or blocked. They might also miss out on updates or support from Autodesk for BIM 360 Design 2019.</p>
-<p>Therefore, users should be careful and selective when choosing where to download CrackBIM360Design2019keygen. They should only download it from reputable and verified sources that have positive reviews and feedback from other users. They should also scan the download with an antivirus program before opening it.</p>
-<p>Alternatively, users can try using a free trial version of BIM 360 Design 2019 that Autodesk offers for new users. This way, they can use the software for 30 days without paying anything and test its capabilities. They can also collaborate and coordinate with other users who have a valid license or subscription for BIM 360 Design 2019.</p>
-<p>If users want to use BIM 360 Design 2019 legally and safely, they should get a legitimate license or subscription for it from Autodesk. This way, they can avoid the risks and drawbacks of using CrackBIM360Design2019keygen, and enjoy the full benefits and advantages of BIM 360 Design 2019.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/jbetker/tortoise/tortoise/models/__init__.py b/spaces/jbetker/tortoise/tortoise/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/jbilcke-hf/VideoQuest/src/app/interface/renderer/types.ts b/spaces/jbilcke-hf/VideoQuest/src/app/interface/renderer/types.ts
deleted file mode 100644
index 480937cba11148d6e938ea35ecf0ccf928b1bae2..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoQuest/src/app/interface/renderer/types.ts
+++ /dev/null
@@ -1,3 +0,0 @@
-export type MouseEventType = "hover" | "click"
-
-export type MouseEventHandler = (type: MouseEventType, x: number, y: number) => Promise<void>
\ No newline at end of file
diff --git a/spaces/jessica6105/Lu-Bert-VITS2/models.py b/spaces/jessica6105/Lu-Bert-VITS2/models.py
deleted file mode 100644
index dd9e0c087357ecfc5a1548eddb5a30d77d2b5bf5..0000000000000000000000000000000000000000
--- a/spaces/jessica6105/Lu-Bert-VITS2/models.py
+++ /dev/null
@@ -1,986 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-from commons import init_weights, get_padding
-from text import symbols, num_tones, num_languages
-
-
-class DurationDiscriminator(nn.Module):  # vits2
-    def __init__(
-        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
-    ):
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.gin_channels = gin_channels
-
-        self.drop = nn.Dropout(p_dropout)
-        self.conv_1 = nn.Conv1d(
-            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
-        )
-        self.norm_1 = modules.LayerNorm(filter_channels)
-        self.conv_2 = nn.Conv1d(
-            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
-        )
-        self.norm_2 = modules.LayerNorm(filter_channels)
-        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
-
-        self.pre_out_conv_1 = nn.Conv1d(
-            2 * filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
-        )
-        self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
-        self.pre_out_conv_2 = nn.Conv1d(
-            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
-        )
-        self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-        self.output_layer = nn.Sequential(nn.Linear(filter_channels, 1), nn.Sigmoid())
-
-    def forward_probability(self, x, x_mask, dur, g=None):
-        dur = self.dur_proj(dur)
-        x = torch.cat([x, dur], dim=1)
-        x = self.pre_out_conv_1(x * x_mask)
-        x = torch.relu(x)
-        x = self.pre_out_norm_1(x)
-        x = self.drop(x)
-        x = self.pre_out_conv_2(x * x_mask)
-        x = torch.relu(x)
-        x = self.pre_out_norm_2(x)
-        x = self.drop(x)
-        x = x * x_mask
-        x = x.transpose(1, 2)
-        output_prob = self.output_layer(x)
-        return output_prob
-
-    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
-        x = torch.detach(x)
-        if g is not None:
-            g = torch.detach(g)
-            x = x + self.cond(g)
-        x = self.conv_1(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_1(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_2(x)
-        x = self.drop(x)
-
-        output_probs = []
-        for dur in [dur_r, dur_hat]:
-            output_prob = self.forward_probability(x, x_mask, dur, g)
-            output_probs.append(output_prob)
-
-        return output_probs
-
-
-class TransformerCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        n_flows=4,
-        gin_channels=0,
-        share_parameter=False,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-
-        self.wn = (
-            attentions.FFT(
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-                isflow=True,
-                gin_channels=self.gin_channels,
-            )
-            if share_parameter
-            else None
-        )
-
-        for i in range(n_flows):
-            self.flows.append(
-                modules.TransformerCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    n_layers,
-                    n_heads,
-                    p_dropout,
-                    filter_channels,
-                    mean_only=True,
-                    wn_sharing_parameter=self.wn,
-                    gin_channels=self.gin_channels,
-                )
-            )
-            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 StochasticDurationPredictor(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        filter_channels = in_channels  # it needs to be removed from future version.
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.log_flow = modules.Log()
-        self.flows = nn.ModuleList()
-        self.flows.append(modules.ElementwiseAffine(2))
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
-            )
-            self.flows.append(modules.Flip())
-
-        self.post_pre = nn.Conv1d(1, filter_channels, 1)
-        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-        self.post_convs = modules.DDSConv(
-            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
-        )
-        self.post_flows = nn.ModuleList()
-        self.post_flows.append(modules.ElementwiseAffine(2))
-        for i in range(4):
-            self.post_flows.append(
-                modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)
-            )
-            self.post_flows.append(modules.Flip())
-
-        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-        self.convs = modules.DDSConv(
-            filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout
-        )
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-        x = torch.detach(x)
-        x = self.pre(x)
-        if g is not None:
-            g = torch.detach(g)
-            x = x + self.cond(g)
-        x = self.convs(x, x_mask)
-        x = self.proj(x) * x_mask
-
-        if not reverse:
-            flows = self.flows
-            assert w is not None
-
-            logdet_tot_q = 0
-            h_w = self.post_pre(w)
-            h_w = self.post_convs(h_w, x_mask)
-            h_w = self.post_proj(h_w) * x_mask
-            e_q = (
-                torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype)
-                * x_mask
-            )
-            z_q = e_q
-            for flow in self.post_flows:
-                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-                logdet_tot_q += logdet_q
-            z_u, z1 = torch.split(z_q, [1, 1], 1)
-            u = torch.sigmoid(z_u) * x_mask
-            z0 = (w - u) * x_mask
-            logdet_tot_q += torch.sum(
-                (F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]
-            )
-            logq = (
-                torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2])
-                - logdet_tot_q
-            )
-
-            logdet_tot = 0
-            z0, logdet = self.log_flow(z0, x_mask)
-            logdet_tot += logdet
-            z = torch.cat([z0, z1], 1)
-            for flow in flows:
-                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-                logdet_tot = logdet_tot + logdet
-            nll = (
-                torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2])
-                - logdet_tot
-            )
-            return nll + logq  # [b]
-        else:
-            flows = list(reversed(self.flows))
-            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
-            z = (
-                torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype)
-                * noise_scale
-            )
-            for flow in flows:
-                z = flow(z, x_mask, g=x, reverse=reverse)
-            z0, z1 = torch.split(z, [1, 1], 1)
-            logw = z0
-            return logw
-
-
-class DurationPredictor(nn.Module):
-    def __init__(
-        self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0
-    ):
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.gin_channels = gin_channels
-
-        self.drop = nn.Dropout(p_dropout)
-        self.conv_1 = nn.Conv1d(
-            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
-        )
-        self.norm_1 = modules.LayerNorm(filter_channels)
-        self.conv_2 = nn.Conv1d(
-            filter_channels, filter_channels, kernel_size, padding=kernel_size // 2
-        )
-        self.norm_2 = modules.LayerNorm(filter_channels)
-        self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-    def forward(self, x, x_mask, g=None):
-        x = torch.detach(x)
-        if g is not None:
-            g = torch.detach(g)
-            x = x + self.cond(g)
-        x = self.conv_1(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_1(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_2(x)
-        x = self.drop(x)
-        x = self.proj(x * x_mask)
-        return x * x_mask
-
-
-class TextEncoder(nn.Module):
-    def __init__(
-        self,
-        n_vocab,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.n_vocab = n_vocab
-        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.gin_channels = gin_channels
-        self.emb = nn.Embedding(len(symbols), hidden_channels)
-        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
-        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels**-0.5)
-        self.language_emb = nn.Embedding(num_languages, hidden_channels)
-        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels**-0.5)
-        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
-        self.ja_bert_proj = nn.Conv1d(768, hidden_channels, 1)
-
-        self.encoder = attentions.Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            gin_channels=self.gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, tone, language, bert, ja_bert, g=None):
-        bert_emb = self.bert_proj(bert).transpose(1, 2)
-        ja_bert_emb = self.ja_bert_proj(ja_bert).transpose(1, 2)
-        x = (
-            self.emb(x)
-            + self.tone_emb(tone)
-            + self.language_emb(language)
-            + bert_emb
-            + ja_bert_emb
-        ) * math.sqrt(
-            self.hidden_channels
-        )  # [b, t, h]
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-
-        x = self.encoder(x * x_mask, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return x, 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
-
-
-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
-
-
-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):
-        print("Removing weight norm...")
-        for layer in self.ups:
-            remove_weight_norm(layer)
-        for layer in self.resblocks:
-            layer.remove_weight_norm()
-
-
-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 is 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 layer in self.convs:
-            x = layer(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 is 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 layer in self.convs:
-            x = layer(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 ReferenceEncoder(nn.Module):
-    """
-    inputs --- [N, Ty/r, n_mels*r]  mels
-    outputs --- [N, ref_enc_gru_size]
-    """
-
-    def __init__(self, spec_channels, gin_channels=0):
-        super().__init__()
-        self.spec_channels = spec_channels
-        ref_enc_filters = [32, 32, 64, 64, 128, 128]
-        K = len(ref_enc_filters)
-        filters = [1] + ref_enc_filters
-        convs = [
-            weight_norm(
-                nn.Conv2d(
-                    in_channels=filters[i],
-                    out_channels=filters[i + 1],
-                    kernel_size=(3, 3),
-                    stride=(2, 2),
-                    padding=(1, 1),
-                )
-            )
-            for i in range(K)
-        ]
-        self.convs = nn.ModuleList(convs)
-        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) # noqa: E501
-
-        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
-        self.gru = nn.GRU(
-            input_size=ref_enc_filters[-1] * out_channels,
-            hidden_size=256 // 2,
-            batch_first=True,
-        )
-        self.proj = nn.Linear(128, gin_channels)
-
-    def forward(self, inputs, mask=None):
-        N = inputs.size(0)
-        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
-        for conv in self.convs:
-            out = conv(out)
-            # out = wn(out)
-            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
-
-        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
-        T = out.size(1)
-        N = out.size(0)
-        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
-
-        self.gru.flatten_parameters()
-        memory, out = self.gru(out)  # out --- [1, N, 128]
-
-        return self.proj(out.squeeze(0))
-
-    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
-        for i in range(n_convs):
-            L = (L - kernel_size + 2 * pad) // stride + 1
-        return L
-
-
-class SynthesizerTrn(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(
-        self,
-        n_vocab,
-        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,
-        n_speakers=256,
-        gin_channels=256,
-        use_sdp=True,
-        n_flow_layer=4,
-        n_layers_trans_flow=6,
-        flow_share_parameter=False,
-        use_transformer_flow=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.n_vocab = n_vocab
-        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.n_speakers = n_speakers
-        self.gin_channels = gin_channels
-        self.n_layers_trans_flow = n_layers_trans_flow
-        self.use_spk_conditioned_encoder = kwargs.get(
-            "use_spk_conditioned_encoder", True
-        )
-        self.use_sdp = use_sdp
-        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
-        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
-        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
-        self.current_mas_noise_scale = self.mas_noise_scale_initial
-        if self.use_spk_conditioned_encoder and gin_channels > 0:
-            self.enc_gin_channels = gin_channels
-        self.enc_p = TextEncoder(
-            n_vocab,
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            gin_channels=self.enc_gin_channels,
-        )
-        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,
-        )
-        if use_transformer_flow:
-            self.flow = TransformerCouplingBlock(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers_trans_flow,
-                5,
-                p_dropout,
-                n_flow_layer,
-                gin_channels=gin_channels,
-                share_parameter=flow_share_parameter,
-            )
-        else:
-            self.flow = ResidualCouplingBlock(
-                inter_channels,
-                hidden_channels,
-                5,
-                1,
-                n_flow_layer,
-                gin_channels=gin_channels,
-            )
-        self.sdp = StochasticDurationPredictor(
-            hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels
-        )
-        self.dp = DurationPredictor(
-            hidden_channels, 256, 3, 0.5, gin_channels=gin_channels
-        )
-
-        if n_speakers > 1:
-            self.emb_g = nn.Embedding(n_speakers, gin_channels)
-        else:
-            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
-
-    def forward(self, x, x_lengths, y, y_lengths, sid, tone, language, bert, ja_bert):
-        if self.n_speakers > 0:
-            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-        else:
-            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
-        x, m_p, logs_p, x_mask = self.enc_p(
-            x, x_lengths, tone, language, bert, ja_bert, g=g
-        )
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-
-        with torch.no_grad():
-            # negative cross-entropy
-            s_p_sq_r = torch.exp(-2 * logs_p)  # [b, d, t]
-            neg_cent1 = torch.sum(
-                -0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True
-            )  # [b, 1, t_s]
-            neg_cent2 = torch.matmul(
-                -0.5 * (z_p**2).transpose(1, 2), s_p_sq_r
-            )  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-            neg_cent3 = torch.matmul(
-                z_p.transpose(1, 2), (m_p * s_p_sq_r)
-            )  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-            neg_cent4 = torch.sum(
-                -0.5 * (m_p**2) * s_p_sq_r, [1], keepdim=True
-            )  # [b, 1, t_s]
-            neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-            if self.use_noise_scaled_mas:
-                epsilon = (
-                    torch.std(neg_cent)
-                    * torch.randn_like(neg_cent)
-                    * self.current_mas_noise_scale
-                )
-                neg_cent = neg_cent + epsilon
-
-            attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-            attn = (
-                monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1))
-                .unsqueeze(1)
-                .detach()
-            )
-
-        w = attn.sum(2)
-
-        l_length_sdp = self.sdp(x, x_mask, w, g=g)
-        l_length_sdp = l_length_sdp / torch.sum(x_mask)
-
-        logw_ = torch.log(w + 1e-6) * x_mask
-        logw = self.dp(x, x_mask, g=g)
-        l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(
-            x_mask
-        )  # for averaging
-
-        l_length = l_length_dp + l_length_sdp
-
-        # expand prior
-        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-        z_slice, ids_slice = commons.rand_slice_segments(
-            z, y_lengths, self.segment_size
-        )
-        o = self.dec(z_slice, g=g)
-        return (
-            o,
-            l_length,
-            attn,
-            ids_slice,
-            x_mask,
-            y_mask,
-            (z, z_p, m_p, logs_p, m_q, logs_q),
-            (x, logw, logw_),
-        )
-
-    def infer(
-        self,
-        x,
-        x_lengths,
-        sid,
-        tone,
-        language,
-        bert,
-        ja_bert,
-        noise_scale=0.667,
-        length_scale=1,
-        noise_scale_w=0.8,
-        max_len=None,
-        sdp_ratio=0,
-        y=None,
-    ):
-        # x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
-        # g = self.gst(y)
-        if self.n_speakers > 0:
-            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-        else:
-            g = self.ref_enc(y.transpose(1, 2)).unsqueeze(-1)
-        x, m_p, logs_p, x_mask = self.enc_p(
-            x, x_lengths, tone, language, bert, ja_bert, g=g
-        )
-        logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (
-            sdp_ratio
-        ) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio)
-        w = torch.exp(logw) * x_mask * length_scale
-        w_ceil = torch.ceil(w)
-        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(
-            x_mask.dtype
-        )
-        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-        attn = commons.generate_path(w_ceil, attn_mask)
-
-        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(
-            1, 2
-        )  # [b, t', t], [b, t, d] -> [b, d, t']
-        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(
-            1, 2
-        )  # [b, t', t], [b, t, d] -> [b, d, t']
-
-        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-        z = self.flow(z_p, y_mask, g=g, reverse=True)
-        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
-        return o, attn, y_mask, (z, z_p, m_p, logs_p)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/Image.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/Image.py
deleted file mode 100644
index a519a28af3689d46f1c26d00ffc7204958da7a7e..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/PIL/Image.py
+++ /dev/null
@@ -1,3910 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# the Image class wrapper
-#
-# partial release history:
-# 1995-09-09 fl   Created
-# 1996-03-11 fl   PIL release 0.0 (proof of concept)
-# 1996-04-30 fl   PIL release 0.1b1
-# 1999-07-28 fl   PIL release 1.0 final
-# 2000-06-07 fl   PIL release 1.1
-# 2000-10-20 fl   PIL release 1.1.1
-# 2001-05-07 fl   PIL release 1.1.2
-# 2002-03-15 fl   PIL release 1.1.3
-# 2003-05-10 fl   PIL release 1.1.4
-# 2005-03-28 fl   PIL release 1.1.5
-# 2006-12-02 fl   PIL release 1.1.6
-# 2009-11-15 fl   PIL release 1.1.7
-#
-# Copyright (c) 1997-2009 by Secret Labs AB.  All rights reserved.
-# Copyright (c) 1995-2009 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import atexit
-import builtins
-import io
-import logging
-import math
-import os
-import re
-import struct
-import sys
-import tempfile
-import warnings
-from collections.abc import Callable, MutableMapping
-from enum import IntEnum
-from pathlib import Path
-
-try:
-    import defusedxml.ElementTree as ElementTree
-except ImportError:
-    ElementTree = None
-
-# VERSION was removed in Pillow 6.0.0.
-# PILLOW_VERSION was removed in Pillow 9.0.0.
-# Use __version__ instead.
-from . import (
-    ExifTags,
-    ImageMode,
-    TiffTags,
-    UnidentifiedImageError,
-    __version__,
-    _plugins,
-)
-from ._binary import i32le, o32be, o32le
-from ._util import DeferredError, is_path
-
-logger = logging.getLogger(__name__)
-
-
-class DecompressionBombWarning(RuntimeWarning):
-    pass
-
-
-class DecompressionBombError(Exception):
-    pass
-
-
-# Limit to around a quarter gigabyte for a 24-bit (3 bpp) image
-MAX_IMAGE_PIXELS = int(1024 * 1024 * 1024 // 4 // 3)
-
-
-try:
-    # If the _imaging C module is not present, Pillow will not load.
-    # Note that other modules should not refer to _imaging directly;
-    # import Image and use the Image.core variable instead.
-    # Also note that Image.core is not a publicly documented interface,
-    # and should be considered private and subject to change.
-    from . import _imaging as core
-
-    if __version__ != getattr(core, "PILLOW_VERSION", None):
-        msg = (
-            "The _imaging extension was built for another version of Pillow or PIL:\n"
-            f"Core version: {getattr(core, 'PILLOW_VERSION', None)}\n"
-            f"Pillow version: {__version__}"
-        )
-        raise ImportError(msg)
-
-except ImportError as v:
-    core = DeferredError(ImportError("The _imaging C module is not installed."))
-    # Explanations for ways that we know we might have an import error
-    if str(v).startswith("Module use of python"):
-        # The _imaging C module is present, but not compiled for
-        # the right version (windows only).  Print a warning, if
-        # possible.
-        warnings.warn(
-            "The _imaging extension was built for another version of Python.",
-            RuntimeWarning,
-        )
-    elif str(v).startswith("The _imaging extension"):
-        warnings.warn(str(v), RuntimeWarning)
-    # Fail here anyway. Don't let people run with a mostly broken Pillow.
-    # see docs/porting.rst
-    raise
-
-
-USE_CFFI_ACCESS = False
-try:
-    import cffi
-except ImportError:
-    cffi = None
-
-
-def isImageType(t):
-    """
-    Checks if an object is an image object.
-
-    .. warning::
-
-       This function is for internal use only.
-
-    :param t: object to check if it's an image
-    :returns: True if the object is an image
-    """
-    return hasattr(t, "im")
-
-
-#
-# Constants
-
-
-# transpose
-class Transpose(IntEnum):
-    FLIP_LEFT_RIGHT = 0
-    FLIP_TOP_BOTTOM = 1
-    ROTATE_90 = 2
-    ROTATE_180 = 3
-    ROTATE_270 = 4
-    TRANSPOSE = 5
-    TRANSVERSE = 6
-
-
-# transforms (also defined in Imaging.h)
-class Transform(IntEnum):
-    AFFINE = 0
-    EXTENT = 1
-    PERSPECTIVE = 2
-    QUAD = 3
-    MESH = 4
-
-
-# resampling filters (also defined in Imaging.h)
-class Resampling(IntEnum):
-    NEAREST = 0
-    BOX = 4
-    BILINEAR = 2
-    HAMMING = 5
-    BICUBIC = 3
-    LANCZOS = 1
-
-
-_filters_support = {
-    Resampling.BOX: 0.5,
-    Resampling.BILINEAR: 1.0,
-    Resampling.HAMMING: 1.0,
-    Resampling.BICUBIC: 2.0,
-    Resampling.LANCZOS: 3.0,
-}
-
-
-# dithers
-class Dither(IntEnum):
-    NONE = 0
-    ORDERED = 1  # Not yet implemented
-    RASTERIZE = 2  # Not yet implemented
-    FLOYDSTEINBERG = 3  # default
-
-
-# palettes/quantizers
-class Palette(IntEnum):
-    WEB = 0
-    ADAPTIVE = 1
-
-
-class Quantize(IntEnum):
-    MEDIANCUT = 0
-    MAXCOVERAGE = 1
-    FASTOCTREE = 2
-    LIBIMAGEQUANT = 3
-
-
-module = sys.modules[__name__]
-for enum in (Transpose, Transform, Resampling, Dither, Palette, Quantize):
-    for item in enum:
-        setattr(module, item.name, item.value)
-
-
-if hasattr(core, "DEFAULT_STRATEGY"):
-    DEFAULT_STRATEGY = core.DEFAULT_STRATEGY
-    FILTERED = core.FILTERED
-    HUFFMAN_ONLY = core.HUFFMAN_ONLY
-    RLE = core.RLE
-    FIXED = core.FIXED
-
-
-# --------------------------------------------------------------------
-# Registries
-
-ID = []
-OPEN = {}
-MIME = {}
-SAVE = {}
-SAVE_ALL = {}
-EXTENSION = {}
-DECODERS = {}
-ENCODERS = {}
-
-# --------------------------------------------------------------------
-# Modes
-
-_ENDIAN = "<" if sys.byteorder == "little" else ">"
-
-
-def _conv_type_shape(im):
-    m = ImageMode.getmode(im.mode)
-    shape = (im.height, im.width)
-    extra = len(m.bands)
-    if extra != 1:
-        shape += (extra,)
-    return shape, m.typestr
-
-
-MODES = ["1", "CMYK", "F", "HSV", "I", "L", "LAB", "P", "RGB", "RGBA", "RGBX", "YCbCr"]
-
-# raw modes that may be memory mapped.  NOTE: if you change this, you
-# may have to modify the stride calculation in map.c too!
-_MAPMODES = ("L", "P", "RGBX", "RGBA", "CMYK", "I;16", "I;16L", "I;16B")
-
-
-def getmodebase(mode):
-    """
-    Gets the "base" mode for given mode.  This function returns "L" for
-    images that contain grayscale data, and "RGB" for images that
-    contain color data.
-
-    :param mode: Input mode.
-    :returns: "L" or "RGB".
-    :exception KeyError: If the input mode was not a standard mode.
-    """
-    return ImageMode.getmode(mode).basemode
-
-
-def getmodetype(mode):
-    """
-    Gets the storage type mode.  Given a mode, this function returns a
-    single-layer mode suitable for storing individual bands.
-
-    :param mode: Input mode.
-    :returns: "L", "I", or "F".
-    :exception KeyError: If the input mode was not a standard mode.
-    """
-    return ImageMode.getmode(mode).basetype
-
-
-def getmodebandnames(mode):
-    """
-    Gets a list of individual band names.  Given a mode, this function returns
-    a tuple containing the names of individual bands (use
-    :py:method:`~PIL.Image.getmodetype` to get the mode used to store each
-    individual band.
-
-    :param mode: Input mode.
-    :returns: A tuple containing band names.  The length of the tuple
-        gives the number of bands in an image of the given mode.
-    :exception KeyError: If the input mode was not a standard mode.
-    """
-    return ImageMode.getmode(mode).bands
-
-
-def getmodebands(mode):
-    """
-    Gets the number of individual bands for this mode.
-
-    :param mode: Input mode.
-    :returns: The number of bands in this mode.
-    :exception KeyError: If the input mode was not a standard mode.
-    """
-    return len(ImageMode.getmode(mode).bands)
-
-
-# --------------------------------------------------------------------
-# Helpers
-
-_initialized = 0
-
-
-def preinit():
-    """Explicitly load standard file format drivers."""
-
-    global _initialized
-    if _initialized >= 1:
-        return
-
-    try:
-        from . import BmpImagePlugin
-
-        assert BmpImagePlugin
-    except ImportError:
-        pass
-    try:
-        from . import GifImagePlugin
-
-        assert GifImagePlugin
-    except ImportError:
-        pass
-    try:
-        from . import JpegImagePlugin
-
-        assert JpegImagePlugin
-    except ImportError:
-        pass
-    try:
-        from . import PpmImagePlugin
-
-        assert PpmImagePlugin
-    except ImportError:
-        pass
-    try:
-        from . import PngImagePlugin
-
-        assert PngImagePlugin
-    except ImportError:
-        pass
-    # try:
-    #     import TiffImagePlugin
-    #     assert TiffImagePlugin
-    # except ImportError:
-    #     pass
-
-    _initialized = 1
-
-
-def init():
-    """
-    Explicitly initializes the Python Imaging Library. This function
-    loads all available file format drivers.
-    """
-
-    global _initialized
-    if _initialized >= 2:
-        return 0
-
-    for plugin in _plugins:
-        try:
-            logger.debug("Importing %s", plugin)
-            __import__(f"PIL.{plugin}", globals(), locals(), [])
-        except ImportError as e:
-            logger.debug("Image: failed to import %s: %s", plugin, e)
-
-    if OPEN or SAVE:
-        _initialized = 2
-        return 1
-
-
-# --------------------------------------------------------------------
-# Codec factories (used by tobytes/frombytes and ImageFile.load)
-
-
-def _getdecoder(mode, decoder_name, args, extra=()):
-    # tweak arguments
-    if args is None:
-        args = ()
-    elif not isinstance(args, tuple):
-        args = (args,)
-
-    try:
-        decoder = DECODERS[decoder_name]
-    except KeyError:
-        pass
-    else:
-        return decoder(mode, *args + extra)
-
-    try:
-        # get decoder
-        decoder = getattr(core, decoder_name + "_decoder")
-    except AttributeError as e:
-        msg = f"decoder {decoder_name} not available"
-        raise OSError(msg) from e
-    return decoder(mode, *args + extra)
-
-
-def _getencoder(mode, encoder_name, args, extra=()):
-    # tweak arguments
-    if args is None:
-        args = ()
-    elif not isinstance(args, tuple):
-        args = (args,)
-
-    try:
-        encoder = ENCODERS[encoder_name]
-    except KeyError:
-        pass
-    else:
-        return encoder(mode, *args + extra)
-
-    try:
-        # get encoder
-        encoder = getattr(core, encoder_name + "_encoder")
-    except AttributeError as e:
-        msg = f"encoder {encoder_name} not available"
-        raise OSError(msg) from e
-    return encoder(mode, *args + extra)
-
-
-# --------------------------------------------------------------------
-# Simple expression analyzer
-
-
-class _E:
-    def __init__(self, scale, offset):
-        self.scale = scale
-        self.offset = offset
-
-    def __neg__(self):
-        return _E(-self.scale, -self.offset)
-
-    def __add__(self, other):
-        if isinstance(other, _E):
-            return _E(self.scale + other.scale, self.offset + other.offset)
-        return _E(self.scale, self.offset + other)
-
-    __radd__ = __add__
-
-    def __sub__(self, other):
-        return self + -other
-
-    def __rsub__(self, other):
-        return other + -self
-
-    def __mul__(self, other):
-        if isinstance(other, _E):
-            return NotImplemented
-        return _E(self.scale * other, self.offset * other)
-
-    __rmul__ = __mul__
-
-    def __truediv__(self, other):
-        if isinstance(other, _E):
-            return NotImplemented
-        return _E(self.scale / other, self.offset / other)
-
-
-def _getscaleoffset(expr):
-    a = expr(_E(1, 0))
-    return (a.scale, a.offset) if isinstance(a, _E) else (0, a)
-
-
-# --------------------------------------------------------------------
-# Implementation wrapper
-
-
-class Image:
-    """
-    This class represents an image object.  To create
-    :py:class:`~PIL.Image.Image` objects, use the appropriate factory
-    functions.  There's hardly ever any reason to call the Image constructor
-    directly.
-
-    * :py:func:`~PIL.Image.open`
-    * :py:func:`~PIL.Image.new`
-    * :py:func:`~PIL.Image.frombytes`
-    """
-
-    format = None
-    format_description = None
-    _close_exclusive_fp_after_loading = True
-
-    def __init__(self):
-        # FIXME: take "new" parameters / other image?
-        # FIXME: turn mode and size into delegating properties?
-        self.im = None
-        self.mode = ""
-        self._size = (0, 0)
-        self.palette = None
-        self.info = {}
-        self.readonly = 0
-        self.pyaccess = None
-        self._exif = None
-
-    @property
-    def width(self):
-        return self.size[0]
-
-    @property
-    def height(self):
-        return self.size[1]
-
-    @property
-    def size(self):
-        return self._size
-
-    def _new(self, im):
-        new = Image()
-        new.im = im
-        new.mode = im.mode
-        new._size = im.size
-        if im.mode in ("P", "PA"):
-            if self.palette:
-                new.palette = self.palette.copy()
-            else:
-                from . import ImagePalette
-
-                new.palette = ImagePalette.ImagePalette()
-        new.info = self.info.copy()
-        return new
-
-    # Context manager support
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *args):
-        if hasattr(self, "fp") and getattr(self, "_exclusive_fp", False):
-            if getattr(self, "_fp", False):
-                if self._fp != self.fp:
-                    self._fp.close()
-                self._fp = DeferredError(ValueError("Operation on closed image"))
-            if self.fp:
-                self.fp.close()
-        self.fp = None
-
-    def close(self):
-        """
-        Closes the file pointer, if possible.
-
-        This operation will destroy the image core and release its memory.
-        The image data will be unusable afterward.
-
-        This function is required to close images that have multiple frames or
-        have not had their file read and closed by the
-        :py:meth:`~PIL.Image.Image.load` method. See :ref:`file-handling` for
-        more information.
-        """
-        try:
-            if getattr(self, "_fp", False):
-                if self._fp != self.fp:
-                    self._fp.close()
-                self._fp = DeferredError(ValueError("Operation on closed image"))
-            if self.fp:
-                self.fp.close()
-            self.fp = None
-        except Exception as msg:
-            logger.debug("Error closing: %s", msg)
-
-        if getattr(self, "map", None):
-            self.map = None
-
-        # Instead of simply setting to None, we're setting up a
-        # deferred error that will better explain that the core image
-        # object is gone.
-        self.im = DeferredError(ValueError("Operation on closed image"))
-
-    def _copy(self):
-        self.load()
-        self.im = self.im.copy()
-        self.pyaccess = None
-        self.readonly = 0
-
-    def _ensure_mutable(self):
-        if self.readonly:
-            self._copy()
-        else:
-            self.load()
-
-    def _dump(self, file=None, format=None, **options):
-        suffix = ""
-        if format:
-            suffix = "." + format
-
-        if not file:
-            f, filename = tempfile.mkstemp(suffix)
-            os.close(f)
-        else:
-            filename = file
-            if not filename.endswith(suffix):
-                filename = filename + suffix
-
-        self.load()
-
-        if not format or format == "PPM":
-            self.im.save_ppm(filename)
-        else:
-            self.save(filename, format, **options)
-
-        return filename
-
-    def __eq__(self, other):
-        return (
-            self.__class__ is other.__class__
-            and self.mode == other.mode
-            and self.size == other.size
-            and self.info == other.info
-            and self.getpalette() == other.getpalette()
-            and self.tobytes() == other.tobytes()
-        )
-
-    def __repr__(self):
-        return "<%s.%s image mode=%s size=%dx%d at 0x%X>" % (
-            self.__class__.__module__,
-            self.__class__.__name__,
-            self.mode,
-            self.size[0],
-            self.size[1],
-            id(self),
-        )
-
-    def _repr_pretty_(self, p, cycle):
-        """IPython plain text display support"""
-
-        # Same as __repr__ but without unpredictable id(self),
-        # to keep Jupyter notebook `text/plain` output stable.
-        p.text(
-            "<%s.%s image mode=%s size=%dx%d>"
-            % (
-                self.__class__.__module__,
-                self.__class__.__name__,
-                self.mode,
-                self.size[0],
-                self.size[1],
-            )
-        )
-
-    def _repr_image(self, image_format, **kwargs):
-        """Helper function for iPython display hook.
-
-        :param image_format: Image format.
-        :returns: image as bytes, saved into the given format.
-        """
-        b = io.BytesIO()
-        try:
-            self.save(b, image_format, **kwargs)
-        except Exception as e:
-            msg = f"Could not save to {image_format} for display"
-            raise ValueError(msg) from e
-        return b.getvalue()
-
-    def _repr_png_(self):
-        """iPython display hook support for PNG format.
-
-        :returns: PNG version of the image as bytes
-        """
-        return self._repr_image("PNG", compress_level=1)
-
-    def _repr_jpeg_(self):
-        """iPython display hook support for JPEG format.
-
-        :returns: JPEG version of the image as bytes
-        """
-        return self._repr_image("JPEG")
-
-    @property
-    def __array_interface__(self):
-        # numpy array interface support
-        new = {"version": 3}
-        try:
-            if self.mode == "1":
-                # Binary images need to be extended from bits to bytes
-                # See: https://github.com/python-pillow/Pillow/issues/350
-                new["data"] = self.tobytes("raw", "L")
-            else:
-                new["data"] = self.tobytes()
-        except Exception as e:
-            if not isinstance(e, (MemoryError, RecursionError)):
-                try:
-                    import numpy
-                    from packaging.version import parse as parse_version
-                except ImportError:
-                    pass
-                else:
-                    if parse_version(numpy.__version__) < parse_version("1.23"):
-                        warnings.warn(e)
-            raise
-        new["shape"], new["typestr"] = _conv_type_shape(self)
-        return new
-
-    def __getstate__(self):
-        im_data = self.tobytes()  # load image first
-        return [self.info, self.mode, self.size, self.getpalette(), im_data]
-
-    def __setstate__(self, state):
-        Image.__init__(self)
-        info, mode, size, palette, data = state
-        self.info = info
-        self.mode = mode
-        self._size = size
-        self.im = core.new(mode, size)
-        if mode in ("L", "LA", "P", "PA") and palette:
-            self.putpalette(palette)
-        self.frombytes(data)
-
-    def tobytes(self, encoder_name="raw", *args):
-        """
-        Return image as a bytes object.
-
-        .. warning::
-
-            This method returns the raw image data from the internal
-            storage.  For compressed image data (e.g. PNG, JPEG) use
-            :meth:`~.save`, with a BytesIO parameter for in-memory
-            data.
-
-        :param encoder_name: What encoder to use.  The default is to
-                             use the standard "raw" encoder.
-
-                             A list of C encoders can be seen under
-                             codecs section of the function array in
-                             :file:`_imaging.c`. Python encoders are
-                             registered within the relevant plugins.
-        :param args: Extra arguments to the encoder.
-        :returns: A :py:class:`bytes` object.
-        """
-
-        # may pass tuple instead of argument list
-        if len(args) == 1 and isinstance(args[0], tuple):
-            args = args[0]
-
-        if encoder_name == "raw" and args == ():
-            args = self.mode
-
-        self.load()
-
-        if self.width == 0 or self.height == 0:
-            return b""
-
-        # unpack data
-        e = _getencoder(self.mode, encoder_name, args)
-        e.setimage(self.im)
-
-        bufsize = max(65536, self.size[0] * 4)  # see RawEncode.c
-
-        output = []
-        while True:
-            bytes_consumed, errcode, data = e.encode(bufsize)
-            output.append(data)
-            if errcode:
-                break
-        if errcode < 0:
-            msg = f"encoder error {errcode} in tobytes"
-            raise RuntimeError(msg)
-
-        return b"".join(output)
-
-    def tobitmap(self, name="image"):
-        """
-        Returns the image converted to an X11 bitmap.
-
-        .. note:: This method only works for mode "1" images.
-
-        :param name: The name prefix to use for the bitmap variables.
-        :returns: A string containing an X11 bitmap.
-        :raises ValueError: If the mode is not "1"
-        """
-
-        self.load()
-        if self.mode != "1":
-            msg = "not a bitmap"
-            raise ValueError(msg)
-        data = self.tobytes("xbm")
-        return b"".join(
-            [
-                f"#define {name}_width {self.size[0]}\n".encode("ascii"),
-                f"#define {name}_height {self.size[1]}\n".encode("ascii"),
-                f"static char {name}_bits[] = {{\n".encode("ascii"),
-                data,
-                b"};",
-            ]
-        )
-
-    def frombytes(self, data, decoder_name="raw", *args):
-        """
-        Loads this image with pixel data from a bytes object.
-
-        This method is similar to the :py:func:`~PIL.Image.frombytes` function,
-        but loads data into this image instead of creating a new image object.
-        """
-
-        # may pass tuple instead of argument list
-        if len(args) == 1 and isinstance(args[0], tuple):
-            args = args[0]
-
-        # default format
-        if decoder_name == "raw" and args == ():
-            args = self.mode
-
-        # unpack data
-        d = _getdecoder(self.mode, decoder_name, args)
-        d.setimage(self.im)
-        s = d.decode(data)
-
-        if s[0] >= 0:
-            msg = "not enough image data"
-            raise ValueError(msg)
-        if s[1] != 0:
-            msg = "cannot decode image data"
-            raise ValueError(msg)
-
-    def load(self):
-        """
-        Allocates storage for the image and loads the pixel data.  In
-        normal cases, you don't need to call this method, since the
-        Image class automatically loads an opened image when it is
-        accessed for the first time.
-
-        If the file associated with the image was opened by Pillow, then this
-        method will close it. The exception to this is if the image has
-        multiple frames, in which case the file will be left open for seek
-        operations. See :ref:`file-handling` for more information.
-
-        :returns: An image access object.
-        :rtype: :ref:`PixelAccess` or :py:class:`PIL.PyAccess`
-        """
-        if self.im is not None and self.palette and self.palette.dirty:
-            # realize palette
-            mode, arr = self.palette.getdata()
-            self.im.putpalette(mode, arr)
-            self.palette.dirty = 0
-            self.palette.rawmode = None
-            if "transparency" in self.info and mode in ("LA", "PA"):
-                if isinstance(self.info["transparency"], int):
-                    self.im.putpalettealpha(self.info["transparency"], 0)
-                else:
-                    self.im.putpalettealphas(self.info["transparency"])
-                self.palette.mode = "RGBA"
-            else:
-                palette_mode = "RGBA" if mode.startswith("RGBA") else "RGB"
-                self.palette.mode = palette_mode
-                self.palette.palette = self.im.getpalette(palette_mode, palette_mode)
-
-        if self.im is not None:
-            if cffi and USE_CFFI_ACCESS:
-                if self.pyaccess:
-                    return self.pyaccess
-                from . import PyAccess
-
-                self.pyaccess = PyAccess.new(self, self.readonly)
-                if self.pyaccess:
-                    return self.pyaccess
-            return self.im.pixel_access(self.readonly)
-
-    def verify(self):
-        """
-        Verifies the contents of a file. For data read from a file, this
-        method attempts to determine if the file is broken, without
-        actually decoding the image data.  If this method finds any
-        problems, it raises suitable exceptions.  If you need to load
-        the image after using this method, you must reopen the image
-        file.
-        """
-        pass
-
-    def convert(
-        self, mode=None, matrix=None, dither=None, palette=Palette.WEB, colors=256
-    ):
-        """
-        Returns a converted copy of this image. For the "P" mode, this
-        method translates pixels through the palette.  If mode is
-        omitted, a mode is chosen so that all information in the image
-        and the palette can be represented without a palette.
-
-        The current version supports all possible conversions between
-        "L", "RGB" and "CMYK". The ``matrix`` argument only supports "L"
-        and "RGB".
-
-        When translating a color image to greyscale (mode "L"),
-        the library uses the ITU-R 601-2 luma transform::
-
-            L = R * 299/1000 + G * 587/1000 + B * 114/1000
-
-        The default method of converting a greyscale ("L") or "RGB"
-        image into a bilevel (mode "1") image uses Floyd-Steinberg
-        dither to approximate the original image luminosity levels. If
-        dither is ``None``, all values larger than 127 are set to 255 (white),
-        all other values to 0 (black). To use other thresholds, use the
-        :py:meth:`~PIL.Image.Image.point` method.
-
-        When converting from "RGBA" to "P" without a ``matrix`` argument,
-        this passes the operation to :py:meth:`~PIL.Image.Image.quantize`,
-        and ``dither`` and ``palette`` are ignored.
-
-        When converting from "PA", if an "RGBA" palette is present, the alpha
-        channel from the image will be used instead of the values from the palette.
-
-        :param mode: The requested mode. See: :ref:`concept-modes`.
-        :param matrix: An optional conversion matrix.  If given, this
-           should be 4- or 12-tuple containing floating point values.
-        :param dither: Dithering method, used when converting from
-           mode "RGB" to "P" or from "RGB" or "L" to "1".
-           Available methods are :data:`Dither.NONE` or :data:`Dither.FLOYDSTEINBERG`
-           (default). Note that this is not used when ``matrix`` is supplied.
-        :param palette: Palette to use when converting from mode "RGB"
-           to "P".  Available palettes are :data:`Palette.WEB` or
-           :data:`Palette.ADAPTIVE`.
-        :param colors: Number of colors to use for the :data:`Palette.ADAPTIVE`
-           palette. Defaults to 256.
-        :rtype: :py:class:`~PIL.Image.Image`
-        :returns: An :py:class:`~PIL.Image.Image` object.
-        """
-
-        self.load()
-
-        has_transparency = self.info.get("transparency") is not None
-        if not mode and self.mode == "P":
-            # determine default mode
-            if self.palette:
-                mode = self.palette.mode
-            else:
-                mode = "RGB"
-            if mode == "RGB" and has_transparency:
-                mode = "RGBA"
-        if not mode or (mode == self.mode and not matrix):
-            return self.copy()
-
-        if matrix:
-            # matrix conversion
-            if mode not in ("L", "RGB"):
-                msg = "illegal conversion"
-                raise ValueError(msg)
-            im = self.im.convert_matrix(mode, matrix)
-            new = self._new(im)
-            if has_transparency and self.im.bands == 3:
-                transparency = new.info["transparency"]
-
-                def convert_transparency(m, v):
-                    v = m[0] * v[0] + m[1] * v[1] + m[2] * v[2] + m[3] * 0.5
-                    return max(0, min(255, int(v)))
-
-                if mode == "L":
-                    transparency = convert_transparency(matrix, transparency)
-                elif len(mode) == 3:
-                    transparency = tuple(
-                        convert_transparency(matrix[i * 4 : i * 4 + 4], transparency)
-                        for i in range(0, len(transparency))
-                    )
-                new.info["transparency"] = transparency
-            return new
-
-        if mode == "P" and self.mode == "RGBA":
-            return self.quantize(colors)
-
-        trns = None
-        delete_trns = False
-        # transparency handling
-        if has_transparency:
-            if (self.mode in ("1", "L", "I") and mode in ("LA", "RGBA")) or (
-                self.mode == "RGB" and mode == "RGBA"
-            ):
-                # Use transparent conversion to promote from transparent
-                # color to an alpha channel.
-                new_im = self._new(
-                    self.im.convert_transparent(mode, self.info["transparency"])
-                )
-                del new_im.info["transparency"]
-                return new_im
-            elif self.mode in ("L", "RGB", "P") and mode in ("L", "RGB", "P"):
-                t = self.info["transparency"]
-                if isinstance(t, bytes):
-                    # Dragons. This can't be represented by a single color
-                    warnings.warn(
-                        "Palette images with Transparency expressed in bytes should be "
-                        "converted to RGBA images"
-                    )
-                    delete_trns = True
-                else:
-                    # get the new transparency color.
-                    # use existing conversions
-                    trns_im = Image()._new(core.new(self.mode, (1, 1)))
-                    if self.mode == "P":
-                        trns_im.putpalette(self.palette)
-                        if isinstance(t, tuple):
-                            err = "Couldn't allocate a palette color for transparency"
-                            try:
-                                t = trns_im.palette.getcolor(t, self)
-                            except ValueError as e:
-                                if str(e) == "cannot allocate more than 256 colors":
-                                    # If all 256 colors are in use,
-                                    # then there is no need for transparency
-                                    t = None
-                                else:
-                                    raise ValueError(err) from e
-                    if t is None:
-                        trns = None
-                    else:
-                        trns_im.putpixel((0, 0), t)
-
-                        if mode in ("L", "RGB"):
-                            trns_im = trns_im.convert(mode)
-                        else:
-                            # can't just retrieve the palette number, got to do it
-                            # after quantization.
-                            trns_im = trns_im.convert("RGB")
-                        trns = trns_im.getpixel((0, 0))
-
-            elif self.mode == "P" and mode in ("LA", "PA", "RGBA"):
-                t = self.info["transparency"]
-                delete_trns = True
-
-                if isinstance(t, bytes):
-                    self.im.putpalettealphas(t)
-                elif isinstance(t, int):
-                    self.im.putpalettealpha(t, 0)
-                else:
-                    msg = "Transparency for P mode should be bytes or int"
-                    raise ValueError(msg)
-
-        if mode == "P" and palette == Palette.ADAPTIVE:
-            im = self.im.quantize(colors)
-            new = self._new(im)
-            from . import ImagePalette
-
-            new.palette = ImagePalette.ImagePalette("RGB", new.im.getpalette("RGB"))
-            if delete_trns:
-                # This could possibly happen if we requantize to fewer colors.
-                # The transparency would be totally off in that case.
-                del new.info["transparency"]
-            if trns is not None:
-                try:
-                    new.info["transparency"] = new.palette.getcolor(trns, new)
-                except Exception:
-                    # if we can't make a transparent color, don't leave the old
-                    # transparency hanging around to mess us up.
-                    del new.info["transparency"]
-                    warnings.warn("Couldn't allocate palette entry for transparency")
-            return new
-
-        if "LAB" in (self.mode, mode):
-            other_mode = mode if self.mode == "LAB" else self.mode
-            if other_mode in ("RGB", "RGBA", "RGBX"):
-                from . import ImageCms
-
-                srgb = ImageCms.createProfile("sRGB")
-                lab = ImageCms.createProfile("LAB")
-                profiles = [lab, srgb] if self.mode == "LAB" else [srgb, lab]
-                transform = ImageCms.buildTransform(
-                    profiles[0], profiles[1], self.mode, mode
-                )
-                return transform.apply(self)
-
-        # colorspace conversion
-        if dither is None:
-            dither = Dither.FLOYDSTEINBERG
-
-        try:
-            im = self.im.convert(mode, dither)
-        except ValueError:
-            try:
-                # normalize source image and try again
-                modebase = getmodebase(self.mode)
-                if modebase == self.mode:
-                    raise
-                im = self.im.convert(modebase)
-                im = im.convert(mode, dither)
-            except KeyError as e:
-                msg = "illegal conversion"
-                raise ValueError(msg) from e
-
-        new_im = self._new(im)
-        if mode == "P" and palette != Palette.ADAPTIVE:
-            from . import ImagePalette
-
-            new_im.palette = ImagePalette.ImagePalette("RGB", list(range(256)) * 3)
-        if delete_trns:
-            # crash fail if we leave a bytes transparency in an rgb/l mode.
-            del new_im.info["transparency"]
-        if trns is not None:
-            if new_im.mode == "P":
-                try:
-                    new_im.info["transparency"] = new_im.palette.getcolor(trns, new_im)
-                except ValueError as e:
-                    del new_im.info["transparency"]
-                    if str(e) != "cannot allocate more than 256 colors":
-                        # If all 256 colors are in use,
-                        # then there is no need for transparency
-                        warnings.warn(
-                            "Couldn't allocate palette entry for transparency"
-                        )
-            else:
-                new_im.info["transparency"] = trns
-        return new_im
-
-    def quantize(
-        self,
-        colors=256,
-        method=None,
-        kmeans=0,
-        palette=None,
-        dither=Dither.FLOYDSTEINBERG,
-    ):
-        """
-        Convert the image to 'P' mode with the specified number
-        of colors.
-
-        :param colors: The desired number of colors, <= 256
-        :param method: :data:`Quantize.MEDIANCUT` (median cut),
-                       :data:`Quantize.MAXCOVERAGE` (maximum coverage),
-                       :data:`Quantize.FASTOCTREE` (fast octree),
-                       :data:`Quantize.LIBIMAGEQUANT` (libimagequant; check support
-                       using :py:func:`PIL.features.check_feature` with
-                       ``feature="libimagequant"``).
-
-                       By default, :data:`Quantize.MEDIANCUT` will be used.
-
-                       The exception to this is RGBA images. :data:`Quantize.MEDIANCUT`
-                       and :data:`Quantize.MAXCOVERAGE` do not support RGBA images, so
-                       :data:`Quantize.FASTOCTREE` is used by default instead.
-        :param kmeans: Integer
-        :param palette: Quantize to the palette of given
-                        :py:class:`PIL.Image.Image`.
-        :param dither: Dithering method, used when converting from
-           mode "RGB" to "P" or from "RGB" or "L" to "1".
-           Available methods are :data:`Dither.NONE` or :data:`Dither.FLOYDSTEINBERG`
-           (default).
-        :returns: A new image
-        """
-
-        self.load()
-
-        if method is None:
-            # defaults:
-            method = Quantize.MEDIANCUT
-            if self.mode == "RGBA":
-                method = Quantize.FASTOCTREE
-
-        if self.mode == "RGBA" and method not in (
-            Quantize.FASTOCTREE,
-            Quantize.LIBIMAGEQUANT,
-        ):
-            # Caller specified an invalid mode.
-            msg = (
-                "Fast Octree (method == 2) and libimagequant (method == 3) "
-                "are the only valid methods for quantizing RGBA images"
-            )
-            raise ValueError(msg)
-
-        if palette:
-            # use palette from reference image
-            palette.load()
-            if palette.mode != "P":
-                msg = "bad mode for palette image"
-                raise ValueError(msg)
-            if self.mode != "RGB" and self.mode != "L":
-                msg = "only RGB or L mode images can be quantized to a palette"
-                raise ValueError(msg)
-            im = self.im.convert("P", dither, palette.im)
-            new_im = self._new(im)
-            new_im.palette = palette.palette.copy()
-            return new_im
-
-        im = self._new(self.im.quantize(colors, method, kmeans))
-
-        from . import ImagePalette
-
-        mode = im.im.getpalettemode()
-        palette = im.im.getpalette(mode, mode)[: colors * len(mode)]
-        im.palette = ImagePalette.ImagePalette(mode, palette)
-
-        return im
-
-    def copy(self):
-        """
-        Copies this image. Use this method if you wish to paste things
-        into an image, but still retain the original.
-
-        :rtype: :py:class:`~PIL.Image.Image`
-        :returns: An :py:class:`~PIL.Image.Image` object.
-        """
-        self.load()
-        return self._new(self.im.copy())
-
-    __copy__ = copy
-
-    def crop(self, box=None):
-        """
-        Returns a rectangular region from this image. The box is a
-        4-tuple defining the left, upper, right, and lower pixel
-        coordinate. See :ref:`coordinate-system`.
-
-        Note: Prior to Pillow 3.4.0, this was a lazy operation.
-
-        :param box: The crop rectangle, as a (left, upper, right, lower)-tuple.
-        :rtype: :py:class:`~PIL.Image.Image`
-        :returns: An :py:class:`~PIL.Image.Image` object.
-        """
-
-        if box is None:
-            return self.copy()
-
-        if box[2] < box[0]:
-            msg = "Coordinate 'right' is less than 'left'"
-            raise ValueError(msg)
-        elif box[3] < box[1]:
-            msg = "Coordinate 'lower' is less than 'upper'"
-            raise ValueError(msg)
-
-        self.load()
-        return self._new(self._crop(self.im, box))
-
-    def _crop(self, im, box):
-        """
-        Returns a rectangular region from the core image object im.
-
-        This is equivalent to calling im.crop((x0, y0, x1, y1)), but
-        includes additional sanity checks.
-
-        :param im: a core image object
-        :param box: The crop rectangle, as a (left, upper, right, lower)-tuple.
-        :returns: A core image object.
-        """
-
-        x0, y0, x1, y1 = map(int, map(round, box))
-
-        absolute_values = (abs(x1 - x0), abs(y1 - y0))
-
-        _decompression_bomb_check(absolute_values)
-
-        return im.crop((x0, y0, x1, y1))
-
-    def draft(self, mode, size):
-        """
-        Configures the image file loader so it returns a version of the
-        image that as closely as possible matches the given mode and
-        size. For example, you can use this method to convert a color
-        JPEG to greyscale while loading it.
-
-        If any changes are made, returns a tuple with the chosen ``mode`` and
-        ``box`` with coordinates of the original image within the altered one.
-
-        Note that this method modifies the :py:class:`~PIL.Image.Image` object
-        in place. If the image has already been loaded, this method has no
-        effect.
-
-        Note: This method is not implemented for most images. It is
-        currently implemented only for JPEG and MPO images.
-
-        :param mode: The requested mode.
-        :param size: The requested size in pixels, as a 2-tuple:
-           (width, height).
-        """
-        pass
-
-    def _expand(self, xmargin, ymargin=None):
-        if ymargin is None:
-            ymargin = xmargin
-        self.load()
-        return self._new(self.im.expand(xmargin, ymargin))
-
-    def filter(self, filter):
-        """
-        Filters this image using the given filter.  For a list of
-        available filters, see the :py:mod:`~PIL.ImageFilter` module.
-
-        :param filter: Filter kernel.
-        :returns: An :py:class:`~PIL.Image.Image` object."""
-
-        from . import ImageFilter
-
-        self.load()
-
-        if isinstance(filter, Callable):
-            filter = filter()
-        if not hasattr(filter, "filter"):
-            msg = "filter argument should be ImageFilter.Filter instance or class"
-            raise TypeError(msg)
-
-        multiband = isinstance(filter, ImageFilter.MultibandFilter)
-        if self.im.bands == 1 or multiband:
-            return self._new(filter.filter(self.im))
-
-        ims = []
-        for c in range(self.im.bands):
-            ims.append(self._new(filter.filter(self.im.getband(c))))
-        return merge(self.mode, ims)
-
-    def getbands(self):
-        """
-        Returns a tuple containing the name of each band in this image.
-        For example, ``getbands`` on an RGB image returns ("R", "G", "B").
-
-        :returns: A tuple containing band names.
-        :rtype: tuple
-        """
-        return ImageMode.getmode(self.mode).bands
-
-    def getbbox(self, *, alpha_only=True):
-        """
-        Calculates the bounding box of the non-zero regions in the
-        image.
-
-        :param alpha_only: Optional flag, defaulting to ``True``.
-           If ``True`` and the image has an alpha channel, trim transparent pixels.
-           Otherwise, trim pixels when all channels are zero.
-           Keyword-only argument.
-        :returns: The bounding box is returned as a 4-tuple defining the
-           left, upper, right, and lower pixel coordinate. See
-           :ref:`coordinate-system`. If the image is completely empty, this
-           method returns None.
-
-        """
-
-        self.load()
-        return self.im.getbbox(alpha_only)
-
-    def getcolors(self, maxcolors=256):
-        """
-        Returns a list of colors used in this image.
-
-        The colors will be in the image's mode. For example, an RGB image will
-        return a tuple of (red, green, blue) color values, and a P image will
-        return the index of the color in the palette.
-
-        :param maxcolors: Maximum number of colors.  If this number is
-           exceeded, this method returns None.  The default limit is
-           256 colors.
-        :returns: An unsorted list of (count, pixel) values.
-        """
-
-        self.load()
-        if self.mode in ("1", "L", "P"):
-            h = self.im.histogram()
-            out = []
-            for i in range(256):
-                if h[i]:
-                    out.append((h[i], i))
-            if len(out) > maxcolors:
-                return None
-            return out
-        return self.im.getcolors(maxcolors)
-
-    def getdata(self, band=None):
-        """
-        Returns the contents of this image as a sequence object
-        containing pixel values.  The sequence object is flattened, so
-        that values for line one follow directly after the values of
-        line zero, and so on.
-
-        Note that the sequence object returned by this method is an
-        internal PIL data type, which only supports certain sequence
-        operations.  To convert it to an ordinary sequence (e.g. for
-        printing), use ``list(im.getdata())``.
-
-        :param band: What band to return.  The default is to return
-           all bands.  To return a single band, pass in the index
-           value (e.g. 0 to get the "R" band from an "RGB" image).
-        :returns: A sequence-like object.
-        """
-
-        self.load()
-        if band is not None:
-            return self.im.getband(band)
-        return self.im  # could be abused
-
-    def getextrema(self):
-        """
-        Gets the minimum and maximum pixel values for each band in
-        the image.
-
-        :returns: For a single-band image, a 2-tuple containing the
-           minimum and maximum pixel value.  For a multi-band image,
-           a tuple containing one 2-tuple for each band.
-        """
-
-        self.load()
-        if self.im.bands > 1:
-            extrema = []
-            for i in range(self.im.bands):
-                extrema.append(self.im.getband(i).getextrema())
-            return tuple(extrema)
-        return self.im.getextrema()
-
-    def _getxmp(self, xmp_tags):
-        def get_name(tag):
-            return tag.split("}")[1]
-
-        def get_value(element):
-            value = {get_name(k): v for k, v in element.attrib.items()}
-            children = list(element)
-            if children:
-                for child in children:
-                    name = get_name(child.tag)
-                    child_value = get_value(child)
-                    if name in value:
-                        if not isinstance(value[name], list):
-                            value[name] = [value[name]]
-                        value[name].append(child_value)
-                    else:
-                        value[name] = child_value
-            elif value:
-                if element.text:
-                    value["text"] = element.text
-            else:
-                return element.text
-            return value
-
-        if ElementTree is None:
-            warnings.warn("XMP data cannot be read without defusedxml dependency")
-            return {}
-        else:
-            root = ElementTree.fromstring(xmp_tags)
-            return {get_name(root.tag): get_value(root)}
-
-    def getexif(self):
-        """
-        Gets EXIF data from the image.
-
-        :returns: an :py:class:`~PIL.Image.Exif` object.
-        """
-        if self._exif is None:
-            self._exif = Exif()
-            self._exif._loaded = False
-        elif self._exif._loaded:
-            return self._exif
-        self._exif._loaded = True
-
-        exif_info = self.info.get("exif")
-        if exif_info is None:
-            if "Raw profile type exif" in self.info:
-                exif_info = bytes.fromhex(
-                    "".join(self.info["Raw profile type exif"].split("\n")[3:])
-                )
-            elif hasattr(self, "tag_v2"):
-                self._exif.bigtiff = self.tag_v2._bigtiff
-                self._exif.endian = self.tag_v2._endian
-                self._exif.load_from_fp(self.fp, self.tag_v2._offset)
-        if exif_info is not None:
-            self._exif.load(exif_info)
-
-        # XMP tags
-        if ExifTags.Base.Orientation not in self._exif:
-            xmp_tags = self.info.get("XML:com.adobe.xmp")
-            if xmp_tags:
-                match = re.search(r'tiff:Orientation(="|>)([0-9])', xmp_tags)
-                if match:
-                    self._exif[ExifTags.Base.Orientation] = int(match[2])
-
-        return self._exif
-
-    def _reload_exif(self):
-        if self._exif is None or not self._exif._loaded:
-            return
-        self._exif._loaded = False
-        self.getexif()
-
-    def get_child_images(self):
-        child_images = []
-        exif = self.getexif()
-        ifds = []
-        if ExifTags.Base.SubIFDs in exif:
-            subifd_offsets = exif[ExifTags.Base.SubIFDs]
-            if subifd_offsets:
-                if not isinstance(subifd_offsets, tuple):
-                    subifd_offsets = (subifd_offsets,)
-                for subifd_offset in subifd_offsets:
-                    ifds.append((exif._get_ifd_dict(subifd_offset), subifd_offset))
-        ifd1 = exif.get_ifd(ExifTags.IFD.IFD1)
-        if ifd1 and ifd1.get(513):
-            ifds.append((ifd1, exif._info.next))
-
-        offset = None
-        for ifd, ifd_offset in ifds:
-            current_offset = self.fp.tell()
-            if offset is None:
-                offset = current_offset
-
-            fp = self.fp
-            thumbnail_offset = ifd.get(513)
-            if thumbnail_offset is not None:
-                try:
-                    thumbnail_offset += self._exif_offset
-                except AttributeError:
-                    pass
-                self.fp.seek(thumbnail_offset)
-                data = self.fp.read(ifd.get(514))
-                fp = io.BytesIO(data)
-
-            with open(fp) as im:
-                if thumbnail_offset is None:
-                    im._frame_pos = [ifd_offset]
-                    im._seek(0)
-                im.load()
-                child_images.append(im)
-
-        if offset is not None:
-            self.fp.seek(offset)
-        return child_images
-
-    def getim(self):
-        """
-        Returns a capsule that points to the internal image memory.
-
-        :returns: A capsule object.
-        """
-
-        self.load()
-        return self.im.ptr
-
-    def getpalette(self, rawmode="RGB"):
-        """
-        Returns the image palette as a list.
-
-        :param rawmode: The mode in which to return the palette. ``None`` will
-           return the palette in its current mode.
-
-           .. versionadded:: 9.1.0
-
-        :returns: A list of color values [r, g, b, ...], or None if the
-           image has no palette.
-        """
-
-        self.load()
-        try:
-            mode = self.im.getpalettemode()
-        except ValueError:
-            return None  # no palette
-        if rawmode is None:
-            rawmode = mode
-        return list(self.im.getpalette(mode, rawmode))
-
-    def apply_transparency(self):
-        """
-        If a P mode image has a "transparency" key in the info dictionary,
-        remove the key and instead apply the transparency to the palette.
-        Otherwise, the image is unchanged.
-        """
-        if self.mode != "P" or "transparency" not in self.info:
-            return
-
-        from . import ImagePalette
-
-        palette = self.getpalette("RGBA")
-        transparency = self.info["transparency"]
-        if isinstance(transparency, bytes):
-            for i, alpha in enumerate(transparency):
-                palette[i * 4 + 3] = alpha
-        else:
-            palette[transparency * 4 + 3] = 0
-        self.palette = ImagePalette.ImagePalette("RGBA", bytes(palette))
-        self.palette.dirty = 1
-
-        del self.info["transparency"]
-
-    def getpixel(self, xy):
-        """
-        Returns the pixel value at a given position.
-
-        :param xy: The coordinate, given as (x, y). See
-           :ref:`coordinate-system`.
-        :returns: The pixel value.  If the image is a multi-layer image,
-           this method returns a tuple.
-        """
-
-        self.load()
-        if self.pyaccess:
-            return self.pyaccess.getpixel(xy)
-        return self.im.getpixel(xy)
-
-    def getprojection(self):
-        """
-        Get projection to x and y axes
-
-        :returns: Two sequences, indicating where there are non-zero
-            pixels along the X-axis and the Y-axis, respectively.
-        """
-
-        self.load()
-        x, y = self.im.getprojection()
-        return list(x), list(y)
-
-    def histogram(self, mask=None, extrema=None):
-        """
-        Returns a histogram for the image. The histogram is returned as a
-        list of pixel counts, one for each pixel value in the source
-        image. Counts are grouped into 256 bins for each band, even if
-        the image has more than 8 bits per band. If the image has more
-        than one band, the histograms for all bands are concatenated (for
-        example, the histogram for an "RGB" image contains 768 values).
-
-        A bilevel image (mode "1") is treated as a greyscale ("L") image
-        by this method.
-
-        If a mask is provided, the method returns a histogram for those
-        parts of the image where the mask image is non-zero. The mask
-        image must have the same size as the image, and be either a
-        bi-level image (mode "1") or a greyscale image ("L").
-
-        :param mask: An optional mask.
-        :param extrema: An optional tuple of manually-specified extrema.
-        :returns: A list containing pixel counts.
-        """
-        self.load()
-        if mask:
-            mask.load()
-            return self.im.histogram((0, 0), mask.im)
-        if self.mode in ("I", "F"):
-            if extrema is None:
-                extrema = self.getextrema()
-            return self.im.histogram(extrema)
-        return self.im.histogram()
-
-    def entropy(self, mask=None, extrema=None):
-        """
-        Calculates and returns the entropy for the image.
-
-        A bilevel image (mode "1") is treated as a greyscale ("L")
-        image by this method.
-
-        If a mask is provided, the method employs the histogram for
-        those parts of the image where the mask image is non-zero.
-        The mask image must have the same size as the image, and be
-        either a bi-level image (mode "1") or a greyscale image ("L").
-
-        :param mask: An optional mask.
-        :param extrema: An optional tuple of manually-specified extrema.
-        :returns: A float value representing the image entropy
-        """
-        self.load()
-        if mask:
-            mask.load()
-            return self.im.entropy((0, 0), mask.im)
-        if self.mode in ("I", "F"):
-            if extrema is None:
-                extrema = self.getextrema()
-            return self.im.entropy(extrema)
-        return self.im.entropy()
-
-    def paste(self, im, box=None, mask=None):
-        """
-        Pastes another image into this image. The box argument is either
-        a 2-tuple giving the upper left corner, a 4-tuple defining the
-        left, upper, right, and lower pixel coordinate, or None (same as
-        (0, 0)). See :ref:`coordinate-system`. If a 4-tuple is given, the size
-        of the pasted image must match the size of the region.
-
-        If the modes don't match, the pasted image is converted to the mode of
-        this image (see the :py:meth:`~PIL.Image.Image.convert` method for
-        details).
-
-        Instead of an image, the source can be a integer or tuple
-        containing pixel values.  The method then fills the region
-        with the given color.  When creating RGB images, you can
-        also use color strings as supported by the ImageColor module.
-
-        If a mask is given, this method updates only the regions
-        indicated by the mask. You can use either "1", "L", "LA", "RGBA"
-        or "RGBa" images (if present, the alpha band is used as mask).
-        Where the mask is 255, the given image is copied as is.  Where
-        the mask is 0, the current value is preserved.  Intermediate
-        values will mix the two images together, including their alpha
-        channels if they have them.
-
-        See :py:meth:`~PIL.Image.Image.alpha_composite` if you want to
-        combine images with respect to their alpha channels.
-
-        :param im: Source image or pixel value (integer or tuple).
-        :param box: An optional 4-tuple giving the region to paste into.
-           If a 2-tuple is used instead, it's treated as the upper left
-           corner.  If omitted or None, the source is pasted into the
-           upper left corner.
-
-           If an image is given as the second argument and there is no
-           third, the box defaults to (0, 0), and the second argument
-           is interpreted as a mask image.
-        :param mask: An optional mask image.
-        """
-
-        if isImageType(box) and mask is None:
-            # abbreviated paste(im, mask) syntax
-            mask = box
-            box = None
-
-        if box is None:
-            box = (0, 0)
-
-        if len(box) == 2:
-            # upper left corner given; get size from image or mask
-            if isImageType(im):
-                size = im.size
-            elif isImageType(mask):
-                size = mask.size
-            else:
-                # FIXME: use self.size here?
-                msg = "cannot determine region size; use 4-item box"
-                raise ValueError(msg)
-            box += (box[0] + size[0], box[1] + size[1])
-
-        if isinstance(im, str):
-            from . import ImageColor
-
-            im = ImageColor.getcolor(im, self.mode)
-
-        elif isImageType(im):
-            im.load()
-            if self.mode != im.mode:
-                if self.mode != "RGB" or im.mode not in ("LA", "RGBA", "RGBa"):
-                    # should use an adapter for this!
-                    im = im.convert(self.mode)
-            im = im.im
-
-        self._ensure_mutable()
-
-        if mask:
-            mask.load()
-            self.im.paste(im, box, mask.im)
-        else:
-            self.im.paste(im, box)
-
-    def alpha_composite(self, im, dest=(0, 0), source=(0, 0)):
-        """'In-place' analog of Image.alpha_composite. Composites an image
-        onto this image.
-
-        :param im: image to composite over this one
-        :param dest: Optional 2 tuple (left, top) specifying the upper
-          left corner in this (destination) image.
-        :param source: Optional 2 (left, top) tuple for the upper left
-          corner in the overlay source image, or 4 tuple (left, top, right,
-          bottom) for the bounds of the source rectangle
-
-        Performance Note: Not currently implemented in-place in the core layer.
-        """
-
-        if not isinstance(source, (list, tuple)):
-            msg = "Source must be a tuple"
-            raise ValueError(msg)
-        if not isinstance(dest, (list, tuple)):
-            msg = "Destination must be a tuple"
-            raise ValueError(msg)
-        if len(source) not in (2, 4):
-            msg = "Source must be a 2 or 4-tuple"
-            raise ValueError(msg)
-        if not len(dest) == 2:
-            msg = "Destination must be a 2-tuple"
-            raise ValueError(msg)
-        if min(source) < 0:
-            msg = "Source must be non-negative"
-            raise ValueError(msg)
-
-        if len(source) == 2:
-            source = source + im.size
-
-        # over image, crop if it's not the whole thing.
-        if source == (0, 0) + im.size:
-            overlay = im
-        else:
-            overlay = im.crop(source)
-
-        # target for the paste
-        box = dest + (dest[0] + overlay.width, dest[1] + overlay.height)
-
-        # destination image. don't copy if we're using the whole image.
-        if box == (0, 0) + self.size:
-            background = self
-        else:
-            background = self.crop(box)
-
-        result = alpha_composite(background, overlay)
-        self.paste(result, box)
-
-    def point(self, lut, mode=None):
-        """
-        Maps this image through a lookup table or function.
-
-        :param lut: A lookup table, containing 256 (or 65536 if
-           self.mode=="I" and mode == "L") values per band in the
-           image.  A function can be used instead, it should take a
-           single argument. The function is called once for each
-           possible pixel value, and the resulting table is applied to
-           all bands of the image.
-
-           It may also be an :py:class:`~PIL.Image.ImagePointHandler`
-           object::
-
-               class Example(Image.ImagePointHandler):
-                 def point(self, data):
-                   # Return result
-        :param mode: Output mode (default is same as input).  In the
-           current version, this can only be used if the source image
-           has mode "L" or "P", and the output has mode "1" or the
-           source image mode is "I" and the output mode is "L".
-        :returns: An :py:class:`~PIL.Image.Image` object.
-        """
-
-        self.load()
-
-        if isinstance(lut, ImagePointHandler):
-            return lut.point(self)
-
-        if callable(lut):
-            # if it isn't a list, it should be a function
-            if self.mode in ("I", "I;16", "F"):
-                # check if the function can be used with point_transform
-                # UNDONE wiredfool -- I think this prevents us from ever doing
-                # a gamma function point transform on > 8bit images.
-                scale, offset = _getscaleoffset(lut)
-                return self._new(self.im.point_transform(scale, offset))
-            # for other modes, convert the function to a table
-            lut = [lut(i) for i in range(256)] * self.im.bands
-
-        if self.mode == "F":
-            # FIXME: _imaging returns a confusing error message for this case
-            msg = "point operation not supported for this mode"
-            raise ValueError(msg)
-
-        if mode != "F":
-            lut = [round(i) for i in lut]
-        return self._new(self.im.point(lut, mode))
-
-    def putalpha(self, alpha):
-        """
-        Adds or replaces the alpha layer in this image.  If the image
-        does not have an alpha layer, it's converted to "LA" or "RGBA".
-        The new layer must be either "L" or "1".
-
-        :param alpha: The new alpha layer.  This can either be an "L" or "1"
-           image having the same size as this image, or an integer or
-           other color value.
-        """
-
-        self._ensure_mutable()
-
-        if self.mode not in ("LA", "PA", "RGBA"):
-            # attempt to promote self to a matching alpha mode
-            try:
-                mode = getmodebase(self.mode) + "A"
-                try:
-                    self.im.setmode(mode)
-                except (AttributeError, ValueError) as e:
-                    # do things the hard way
-                    im = self.im.convert(mode)
-                    if im.mode not in ("LA", "PA", "RGBA"):
-                        raise ValueError from e  # sanity check
-                    self.im = im
-                self.pyaccess = None
-                self.mode = self.im.mode
-            except KeyError as e:
-                msg = "illegal image mode"
-                raise ValueError(msg) from e
-
-        if self.mode in ("LA", "PA"):
-            band = 1
-        else:
-            band = 3
-
-        if isImageType(alpha):
-            # alpha layer
-            if alpha.mode not in ("1", "L"):
-                msg = "illegal image mode"
-                raise ValueError(msg)
-            alpha.load()
-            if alpha.mode == "1":
-                alpha = alpha.convert("L")
-        else:
-            # constant alpha
-            try:
-                self.im.fillband(band, alpha)
-            except (AttributeError, ValueError):
-                # do things the hard way
-                alpha = new("L", self.size, alpha)
-            else:
-                return
-
-        self.im.putband(alpha.im, band)
-
-    def putdata(self, data, scale=1.0, offset=0.0):
-        """
-        Copies pixel data from a flattened sequence object into the image. The
-        values should start at the upper left corner (0, 0), continue to the
-        end of the line, followed directly by the first value of the second
-        line, and so on. Data will be read until either the image or the
-        sequence ends. The scale and offset values are used to adjust the
-        sequence values: **pixel = value*scale + offset**.
-
-        :param data: A flattened sequence object.
-        :param scale: An optional scale value.  The default is 1.0.
-        :param offset: An optional offset value.  The default is 0.0.
-        """
-
-        self._ensure_mutable()
-
-        self.im.putdata(data, scale, offset)
-
-    def putpalette(self, data, rawmode="RGB"):
-        """
-        Attaches a palette to this image.  The image must be a "P", "PA", "L"
-        or "LA" image.
-
-        The palette sequence must contain at most 256 colors, made up of one
-        integer value for each channel in the raw mode.
-        For example, if the raw mode is "RGB", then it can contain at most 768
-        values, made up of red, green and blue values for the corresponding pixel
-        index in the 256 colors.
-        If the raw mode is "RGBA", then it can contain at most 1024 values,
-        containing red, green, blue and alpha values.
-
-        Alternatively, an 8-bit string may be used instead of an integer sequence.
-
-        :param data: A palette sequence (either a list or a string).
-        :param rawmode: The raw mode of the palette. Either "RGB", "RGBA", or a mode
-           that can be transformed to "RGB" or "RGBA" (e.g. "R", "BGR;15", "RGBA;L").
-        """
-        from . import ImagePalette
-
-        if self.mode not in ("L", "LA", "P", "PA"):
-            msg = "illegal image mode"
-            raise ValueError(msg)
-        if isinstance(data, ImagePalette.ImagePalette):
-            palette = ImagePalette.raw(data.rawmode, data.palette)
-        else:
-            if not isinstance(data, bytes):
-                data = bytes(data)
-            palette = ImagePalette.raw(rawmode, data)
-        self.mode = "PA" if "A" in self.mode else "P"
-        self.palette = palette
-        self.palette.mode = "RGB"
-        self.load()  # install new palette
-
-    def putpixel(self, xy, value):
-        """
-        Modifies the pixel at the given position. The color is given as
-        a single numerical value for single-band images, and a tuple for
-        multi-band images. In addition to this, RGB and RGBA tuples are
-        accepted for P and PA images.
-
-        Note that this method is relatively slow.  For more extensive changes,
-        use :py:meth:`~PIL.Image.Image.paste` or the :py:mod:`~PIL.ImageDraw`
-        module instead.
-
-        See:
-
-        * :py:meth:`~PIL.Image.Image.paste`
-        * :py:meth:`~PIL.Image.Image.putdata`
-        * :py:mod:`~PIL.ImageDraw`
-
-        :param xy: The pixel coordinate, given as (x, y). See
-           :ref:`coordinate-system`.
-        :param value: The pixel value.
-        """
-
-        if self.readonly:
-            self._copy()
-        self.load()
-
-        if self.pyaccess:
-            return self.pyaccess.putpixel(xy, value)
-
-        if (
-            self.mode in ("P", "PA")
-            and isinstance(value, (list, tuple))
-            and len(value) in [3, 4]
-        ):
-            # RGB or RGBA value for a P or PA image
-            if self.mode == "PA":
-                alpha = value[3] if len(value) == 4 else 255
-                value = value[:3]
-            value = self.palette.getcolor(value, self)
-            if self.mode == "PA":
-                value = (value, alpha)
-        return self.im.putpixel(xy, value)
-
-    def remap_palette(self, dest_map, source_palette=None):
-        """
-        Rewrites the image to reorder the palette.
-
-        :param dest_map: A list of indexes into the original palette.
-           e.g. ``[1,0]`` would swap a two item palette, and ``list(range(256))``
-           is the identity transform.
-        :param source_palette: Bytes or None.
-        :returns:  An :py:class:`~PIL.Image.Image` object.
-
-        """
-        from . import ImagePalette
-
-        if self.mode not in ("L", "P"):
-            msg = "illegal image mode"
-            raise ValueError(msg)
-
-        bands = 3
-        palette_mode = "RGB"
-        if source_palette is None:
-            if self.mode == "P":
-                self.load()
-                palette_mode = self.im.getpalettemode()
-                if palette_mode == "RGBA":
-                    bands = 4
-                source_palette = self.im.getpalette(palette_mode, palette_mode)
-            else:  # L-mode
-                source_palette = bytearray(i // 3 for i in range(768))
-
-        palette_bytes = b""
-        new_positions = [0] * 256
-
-        # pick only the used colors from the palette
-        for i, oldPosition in enumerate(dest_map):
-            palette_bytes += source_palette[
-                oldPosition * bands : oldPosition * bands + bands
-            ]
-            new_positions[oldPosition] = i
-
-        # replace the palette color id of all pixel with the new id
-
-        # Palette images are [0..255], mapped through a 1 or 3
-        # byte/color map.  We need to remap the whole image
-        # from palette 1 to palette 2. New_positions is
-        # an array of indexes into palette 1.  Palette 2 is
-        # palette 1 with any holes removed.
-
-        # We're going to leverage the convert mechanism to use the
-        # C code to remap the image from palette 1 to palette 2,
-        # by forcing the source image into 'L' mode and adding a
-        # mapping 'L' mode palette, then converting back to 'L'
-        # sans palette thus converting the image bytes, then
-        # assigning the optimized RGB palette.
-
-        # perf reference, 9500x4000 gif, w/~135 colors
-        # 14 sec prepatch, 1 sec postpatch with optimization forced.
-
-        mapping_palette = bytearray(new_positions)
-
-        m_im = self.copy()
-        m_im.mode = "P"
-
-        m_im.palette = ImagePalette.ImagePalette(
-            palette_mode, palette=mapping_palette * bands
-        )
-        # possibly set palette dirty, then
-        # m_im.putpalette(mapping_palette, 'L')  # converts to 'P'
-        # or just force it.
-        # UNDONE -- this is part of the general issue with palettes
-        m_im.im.putpalette(palette_mode + ";L", m_im.palette.tobytes())
-
-        m_im = m_im.convert("L")
-
-        m_im.putpalette(palette_bytes, palette_mode)
-        m_im.palette = ImagePalette.ImagePalette(palette_mode, palette=palette_bytes)
-
-        if "transparency" in self.info:
-            try:
-                m_im.info["transparency"] = dest_map.index(self.info["transparency"])
-            except ValueError:
-                if "transparency" in m_im.info:
-                    del m_im.info["transparency"]
-
-        return m_im
-
-    def _get_safe_box(self, size, resample, box):
-        """Expands the box so it includes adjacent pixels
-        that may be used by resampling with the given resampling filter.
-        """
-        filter_support = _filters_support[resample] - 0.5
-        scale_x = (box[2] - box[0]) / size[0]
-        scale_y = (box[3] - box[1]) / size[1]
-        support_x = filter_support * scale_x
-        support_y = filter_support * scale_y
-
-        return (
-            max(0, int(box[0] - support_x)),
-            max(0, int(box[1] - support_y)),
-            min(self.size[0], math.ceil(box[2] + support_x)),
-            min(self.size[1], math.ceil(box[3] + support_y)),
-        )
-
-    def resize(self, size, resample=None, box=None, reducing_gap=None):
-        """
-        Returns a resized copy of this image.
-
-        :param size: The requested size in pixels, as a 2-tuple:
-           (width, height).
-        :param resample: An optional resampling filter.  This can be
-           one of :py:data:`Resampling.NEAREST`, :py:data:`Resampling.BOX`,
-           :py:data:`Resampling.BILINEAR`, :py:data:`Resampling.HAMMING`,
-           :py:data:`Resampling.BICUBIC` or :py:data:`Resampling.LANCZOS`.
-           If the image has mode "1" or "P", it is always set to
-           :py:data:`Resampling.NEAREST`. If the image mode specifies a number
-           of bits, such as "I;16", then the default filter is
-           :py:data:`Resampling.NEAREST`. Otherwise, the default filter is
-           :py:data:`Resampling.BICUBIC`. See: :ref:`concept-filters`.
-        :param box: An optional 4-tuple of floats providing
-           the source image region to be scaled.
-           The values must be within (0, 0, width, height) rectangle.
-           If omitted or None, the entire source is used.
-        :param reducing_gap: Apply optimization by resizing the image
-           in two steps. First, reducing the image by integer times
-           using :py:meth:`~PIL.Image.Image.reduce`.
-           Second, resizing using regular resampling. The last step
-           changes size no less than by ``reducing_gap`` times.
-           ``reducing_gap`` may be None (no first step is performed)
-           or should be greater than 1.0. The bigger ``reducing_gap``,
-           the closer the result to the fair resampling.
-           The smaller ``reducing_gap``, the faster resizing.
-           With ``reducing_gap`` greater or equal to 3.0, the result is
-           indistinguishable from fair resampling in most cases.
-           The default value is None (no optimization).
-        :returns: An :py:class:`~PIL.Image.Image` object.
-        """
-
-        if resample is None:
-            type_special = ";" in self.mode
-            resample = Resampling.NEAREST if type_special else Resampling.BICUBIC
-        elif resample not in (
-            Resampling.NEAREST,
-            Resampling.BILINEAR,
-            Resampling.BICUBIC,
-            Resampling.LANCZOS,
-            Resampling.BOX,
-            Resampling.HAMMING,
-        ):
-            msg = f"Unknown resampling filter ({resample})."
-
-            filters = [
-                f"{filter[1]} ({filter[0]})"
-                for filter in (
-                    (Resampling.NEAREST, "Image.Resampling.NEAREST"),
-                    (Resampling.LANCZOS, "Image.Resampling.LANCZOS"),
-                    (Resampling.BILINEAR, "Image.Resampling.BILINEAR"),
-                    (Resampling.BICUBIC, "Image.Resampling.BICUBIC"),
-                    (Resampling.BOX, "Image.Resampling.BOX"),
-                    (Resampling.HAMMING, "Image.Resampling.HAMMING"),
-                )
-            ]
-            msg += " Use " + ", ".join(filters[:-1]) + " or " + filters[-1]
-            raise ValueError(msg)
-
-        if reducing_gap is not None and reducing_gap < 1.0:
-            msg = "reducing_gap must be 1.0 or greater"
-            raise ValueError(msg)
-
-        size = tuple(size)
-
-        self.load()
-        if box is None:
-            box = (0, 0) + self.size
-        else:
-            box = tuple(box)
-
-        if self.size == size and box == (0, 0) + self.size:
-            return self.copy()
-
-        if self.mode in ("1", "P"):
-            resample = Resampling.NEAREST
-
-        if self.mode in ["LA", "RGBA"] and resample != Resampling.NEAREST:
-            im = self.convert({"LA": "La", "RGBA": "RGBa"}[self.mode])
-            im = im.resize(size, resample, box)
-            return im.convert(self.mode)
-
-        self.load()
-
-        if reducing_gap is not None and resample != Resampling.NEAREST:
-            factor_x = int((box[2] - box[0]) / size[0] / reducing_gap) or 1
-            factor_y = int((box[3] - box[1]) / size[1] / reducing_gap) or 1
-            if factor_x > 1 or factor_y > 1:
-                reduce_box = self._get_safe_box(size, resample, box)
-                factor = (factor_x, factor_y)
-                if callable(self.reduce):
-                    self = self.reduce(factor, box=reduce_box)
-                else:
-                    self = Image.reduce(self, factor, box=reduce_box)
-                box = (
-                    (box[0] - reduce_box[0]) / factor_x,
-                    (box[1] - reduce_box[1]) / factor_y,
-                    (box[2] - reduce_box[0]) / factor_x,
-                    (box[3] - reduce_box[1]) / factor_y,
-                )
-
-        return self._new(self.im.resize(size, resample, box))
-
-    def reduce(self, factor, box=None):
-        """
-        Returns a copy of the image reduced ``factor`` times.
-        If the size of the image is not dividable by ``factor``,
-        the resulting size will be rounded up.
-
-        :param factor: A greater than 0 integer or tuple of two integers
-           for width and height separately.
-        :param box: An optional 4-tuple of ints providing
-           the source image region to be reduced.
-           The values must be within ``(0, 0, width, height)`` rectangle.
-           If omitted or ``None``, the entire source is used.
-        """
-        if not isinstance(factor, (list, tuple)):
-            factor = (factor, factor)
-
-        if box is None:
-            box = (0, 0) + self.size
-        else:
-            box = tuple(box)
-
-        if factor == (1, 1) and box == (0, 0) + self.size:
-            return self.copy()
-
-        if self.mode in ["LA", "RGBA"]:
-            im = self.convert({"LA": "La", "RGBA": "RGBa"}[self.mode])
-            im = im.reduce(factor, box)
-            return im.convert(self.mode)
-
-        self.load()
-
-        return self._new(self.im.reduce(factor, box))
-
-    def rotate(
-        self,
-        angle,
-        resample=Resampling.NEAREST,
-        expand=0,
-        center=None,
-        translate=None,
-        fillcolor=None,
-    ):
-        """
-        Returns a rotated copy of this image.  This method returns a
-        copy of this image, rotated the given number of degrees counter
-        clockwise around its centre.
-
-        :param angle: In degrees counter clockwise.
-        :param resample: An optional resampling filter.  This can be
-           one of :py:data:`Resampling.NEAREST` (use nearest neighbour),
-           :py:data:`Resampling.BILINEAR` (linear interpolation in a 2x2
-           environment), or :py:data:`Resampling.BICUBIC` (cubic spline
-           interpolation in a 4x4 environment). If omitted, or if the image has
-           mode "1" or "P", it is set to :py:data:`Resampling.NEAREST`.
-           See :ref:`concept-filters`.
-        :param expand: Optional expansion flag.  If true, expands the output
-           image to make it large enough to hold the entire rotated image.
-           If false or omitted, make the output image the same size as the
-           input image.  Note that the expand flag assumes rotation around
-           the center and no translation.
-        :param center: Optional center of rotation (a 2-tuple).  Origin is
-           the upper left corner.  Default is the center of the image.
-        :param translate: An optional post-rotate translation (a 2-tuple).
-        :param fillcolor: An optional color for area outside the rotated image.
-        :returns: An :py:class:`~PIL.Image.Image` object.
-        """
-
-        angle = angle % 360.0
-
-        # Fast paths regardless of filter, as long as we're not
-        # translating or changing the center.
-        if not (center or translate):
-            if angle == 0:
-                return self.copy()
-            if angle == 180:
-                return self.transpose(Transpose.ROTATE_180)
-            if angle in (90, 270) and (expand or self.width == self.height):
-                return self.transpose(
-                    Transpose.ROTATE_90 if angle == 90 else Transpose.ROTATE_270
-                )
-
-        # Calculate the affine matrix.  Note that this is the reverse
-        # transformation (from destination image to source) because we
-        # want to interpolate the (discrete) destination pixel from
-        # the local area around the (floating) source pixel.
-
-        # The matrix we actually want (note that it operates from the right):
-        # (1, 0, tx)   (1, 0, cx)   ( cos a, sin a, 0)   (1, 0, -cx)
-        # (0, 1, ty) * (0, 1, cy) * (-sin a, cos a, 0) * (0, 1, -cy)
-        # (0, 0,  1)   (0, 0,  1)   (     0,     0, 1)   (0, 0,   1)
-
-        # The reverse matrix is thus:
-        # (1, 0, cx)   ( cos -a, sin -a, 0)   (1, 0, -cx)   (1, 0, -tx)
-        # (0, 1, cy) * (-sin -a, cos -a, 0) * (0, 1, -cy) * (0, 1, -ty)
-        # (0, 0,  1)   (      0,      0, 1)   (0, 0,   1)   (0, 0,   1)
-
-        # In any case, the final translation may be updated at the end to
-        # compensate for the expand flag.
-
-        w, h = self.size
-
-        if translate is None:
-            post_trans = (0, 0)
-        else:
-            post_trans = translate
-        if center is None:
-            # FIXME These should be rounded to ints?
-            rotn_center = (w / 2.0, h / 2.0)
-        else:
-            rotn_center = center
-
-        angle = -math.radians(angle)
-        matrix = [
-            round(math.cos(angle), 15),
-            round(math.sin(angle), 15),
-            0.0,
-            round(-math.sin(angle), 15),
-            round(math.cos(angle), 15),
-            0.0,
-        ]
-
-        def transform(x, y, matrix):
-            (a, b, c, d, e, f) = matrix
-            return a * x + b * y + c, d * x + e * y + f
-
-        matrix[2], matrix[5] = transform(
-            -rotn_center[0] - post_trans[0], -rotn_center[1] - post_trans[1], matrix
-        )
-        matrix[2] += rotn_center[0]
-        matrix[5] += rotn_center[1]
-
-        if expand:
-            # calculate output size
-            xx = []
-            yy = []
-            for x, y in ((0, 0), (w, 0), (w, h), (0, h)):
-                x, y = transform(x, y, matrix)
-                xx.append(x)
-                yy.append(y)
-            nw = math.ceil(max(xx)) - math.floor(min(xx))
-            nh = math.ceil(max(yy)) - math.floor(min(yy))
-
-            # We multiply a translation matrix from the right.  Because of its
-            # special form, this is the same as taking the image of the
-            # translation vector as new translation vector.
-            matrix[2], matrix[5] = transform(-(nw - w) / 2.0, -(nh - h) / 2.0, matrix)
-            w, h = nw, nh
-
-        return self.transform(
-            (w, h), Transform.AFFINE, matrix, resample, fillcolor=fillcolor
-        )
-
-    def save(self, fp, format=None, **params):
-        """
-        Saves this image under the given filename.  If no format is
-        specified, the format to use is determined from the filename
-        extension, if possible.
-
-        Keyword options can be used to provide additional instructions
-        to the writer. If a writer doesn't recognise an option, it is
-        silently ignored. The available options are described in the
-        :doc:`image format documentation
-        <../handbook/image-file-formats>` for each writer.
-
-        You can use a file object instead of a filename. In this case,
-        you must always specify the format. The file object must
-        implement the ``seek``, ``tell``, and ``write``
-        methods, and be opened in binary mode.
-
-        :param fp: A filename (string), pathlib.Path object or file object.
-        :param format: Optional format override.  If omitted, the
-           format to use is determined from the filename extension.
-           If a file object was used instead of a filename, this
-           parameter should always be used.
-        :param params: Extra parameters to the image writer.
-        :returns: None
-        :exception ValueError: If the output format could not be determined
-           from the file name.  Use the format option to solve this.
-        :exception OSError: If the file could not be written.  The file
-           may have been created, and may contain partial data.
-        """
-
-        filename = ""
-        open_fp = False
-        if isinstance(fp, Path):
-            filename = str(fp)
-            open_fp = True
-        elif is_path(fp):
-            filename = fp
-            open_fp = True
-        elif fp == sys.stdout:
-            try:
-                fp = sys.stdout.buffer
-            except AttributeError:
-                pass
-        if not filename and hasattr(fp, "name") and is_path(fp.name):
-            # only set the name for metadata purposes
-            filename = fp.name
-
-        # may mutate self!
-        self._ensure_mutable()
-
-        save_all = params.pop("save_all", False)
-        self.encoderinfo = params
-        self.encoderconfig = ()
-
-        preinit()
-
-        ext = os.path.splitext(filename)[1].lower()
-
-        if not format:
-            if ext not in EXTENSION:
-                init()
-            try:
-                format = EXTENSION[ext]
-            except KeyError as e:
-                msg = f"unknown file extension: {ext}"
-                raise ValueError(msg) from e
-
-        if format.upper() not in SAVE:
-            init()
-        if save_all:
-            save_handler = SAVE_ALL[format.upper()]
-        else:
-            save_handler = SAVE[format.upper()]
-
-        created = False
-        if open_fp:
-            created = not os.path.exists(filename)
-            if params.get("append", False):
-                # Open also for reading ("+"), because TIFF save_all
-                # writer needs to go back and edit the written data.
-                fp = builtins.open(filename, "r+b")
-            else:
-                fp = builtins.open(filename, "w+b")
-
-        try:
-            save_handler(self, fp, filename)
-        except Exception:
-            if open_fp:
-                fp.close()
-            if created:
-                try:
-                    os.remove(filename)
-                except PermissionError:
-                    pass
-            raise
-        if open_fp:
-            fp.close()
-
-    def seek(self, frame):
-        """
-        Seeks to the given frame in this sequence file. If you seek
-        beyond the end of the sequence, the method raises an
-        ``EOFError`` exception. When a sequence file is opened, the
-        library automatically seeks to frame 0.
-
-        See :py:meth:`~PIL.Image.Image.tell`.
-
-        If defined, :attr:`~PIL.Image.Image.n_frames` refers to the
-        number of available frames.
-
-        :param frame: Frame number, starting at 0.
-        :exception EOFError: If the call attempts to seek beyond the end
-            of the sequence.
-        """
-
-        # overridden by file handlers
-        if frame != 0:
-            raise EOFError
-
-    def show(self, title=None):
-        """
-        Displays this image. This method is mainly intended for debugging purposes.
-
-        This method calls :py:func:`PIL.ImageShow.show` internally. You can use
-        :py:func:`PIL.ImageShow.register` to override its default behaviour.
-
-        The image is first saved to a temporary file. By default, it will be in
-        PNG format.
-
-        On Unix, the image is then opened using the **xdg-open**, **display**,
-        **gm**, **eog** or **xv** utility, depending on which one can be found.
-
-        On macOS, the image is opened with the native Preview application.
-
-        On Windows, the image is opened with the standard PNG display utility.
-
-        :param title: Optional title to use for the image window, where possible.
-        """
-
-        _show(self, title=title)
-
-    def split(self):
-        """
-        Split this image into individual bands. This method returns a
-        tuple of individual image bands from an image. For example,
-        splitting an "RGB" image creates three new images each
-        containing a copy of one of the original bands (red, green,
-        blue).
-
-        If you need only one band, :py:meth:`~PIL.Image.Image.getchannel`
-        method can be more convenient and faster.
-
-        :returns: A tuple containing bands.
-        """
-
-        self.load()
-        if self.im.bands == 1:
-            ims = [self.copy()]
-        else:
-            ims = map(self._new, self.im.split())
-        return tuple(ims)
-
-    def getchannel(self, channel):
-        """
-        Returns an image containing a single channel of the source image.
-
-        :param channel: What channel to return. Could be index
-          (0 for "R" channel of "RGB") or channel name
-          ("A" for alpha channel of "RGBA").
-        :returns: An image in "L" mode.
-
-        .. versionadded:: 4.3.0
-        """
-        self.load()
-
-        if isinstance(channel, str):
-            try:
-                channel = self.getbands().index(channel)
-            except ValueError as e:
-                msg = f'The image has no channel "{channel}"'
-                raise ValueError(msg) from e
-
-        return self._new(self.im.getband(channel))
-
-    def tell(self):
-        """
-        Returns the current frame number. See :py:meth:`~PIL.Image.Image.seek`.
-
-        If defined, :attr:`~PIL.Image.Image.n_frames` refers to the
-        number of available frames.
-
-        :returns: Frame number, starting with 0.
-        """
-        return 0
-
-    def thumbnail(self, size, resample=Resampling.BICUBIC, reducing_gap=2.0):
-        """
-        Make this image into a thumbnail.  This method modifies the
-        image to contain a thumbnail version of itself, no larger than
-        the given size.  This method calculates an appropriate thumbnail
-        size to preserve the aspect of the image, calls the
-        :py:meth:`~PIL.Image.Image.draft` method to configure the file reader
-        (where applicable), and finally resizes the image.
-
-        Note that this function modifies the :py:class:`~PIL.Image.Image`
-        object in place.  If you need to use the full resolution image as well,
-        apply this method to a :py:meth:`~PIL.Image.Image.copy` of the original
-        image.
-
-        :param size: The requested size in pixels, as a 2-tuple:
-           (width, height).
-        :param resample: Optional resampling filter.  This can be one
-           of :py:data:`Resampling.NEAREST`, :py:data:`Resampling.BOX`,
-           :py:data:`Resampling.BILINEAR`, :py:data:`Resampling.HAMMING`,
-           :py:data:`Resampling.BICUBIC` or :py:data:`Resampling.LANCZOS`.
-           If omitted, it defaults to :py:data:`Resampling.BICUBIC`.
-           (was :py:data:`Resampling.NEAREST` prior to version 2.5.0).
-           See: :ref:`concept-filters`.
-        :param reducing_gap: Apply optimization by resizing the image
-           in two steps. First, reducing the image by integer times
-           using :py:meth:`~PIL.Image.Image.reduce` or
-           :py:meth:`~PIL.Image.Image.draft` for JPEG images.
-           Second, resizing using regular resampling. The last step
-           changes size no less than by ``reducing_gap`` times.
-           ``reducing_gap`` may be None (no first step is performed)
-           or should be greater than 1.0. The bigger ``reducing_gap``,
-           the closer the result to the fair resampling.
-           The smaller ``reducing_gap``, the faster resizing.
-           With ``reducing_gap`` greater or equal to 3.0, the result is
-           indistinguishable from fair resampling in most cases.
-           The default value is 2.0 (very close to fair resampling
-           while still being faster in many cases).
-        :returns: None
-        """
-
-        provided_size = tuple(map(math.floor, size))
-
-        def preserve_aspect_ratio():
-            def round_aspect(number, key):
-                return max(min(math.floor(number), math.ceil(number), key=key), 1)
-
-            x, y = provided_size
-            if x >= self.width and y >= self.height:
-                return
-
-            aspect = self.width / self.height
-            if x / y >= aspect:
-                x = round_aspect(y * aspect, key=lambda n: abs(aspect - n / y))
-            else:
-                y = round_aspect(
-                    x / aspect, key=lambda n: 0 if n == 0 else abs(aspect - x / n)
-                )
-            return x, y
-
-        box = None
-        if reducing_gap is not None:
-            size = preserve_aspect_ratio()
-            if size is None:
-                return
-
-            res = self.draft(None, (size[0] * reducing_gap, size[1] * reducing_gap))
-            if res is not None:
-                box = res[1]
-        if box is None:
-            self.load()
-
-            # load() may have changed the size of the image
-            size = preserve_aspect_ratio()
-            if size is None:
-                return
-
-        if self.size != size:
-            im = self.resize(size, resample, box=box, reducing_gap=reducing_gap)
-
-            self.im = im.im
-            self._size = size
-            self.mode = self.im.mode
-
-        self.readonly = 0
-        self.pyaccess = None
-
-    # FIXME: the different transform methods need further explanation
-    # instead of bloating the method docs, add a separate chapter.
-    def transform(
-        self,
-        size,
-        method,
-        data=None,
-        resample=Resampling.NEAREST,
-        fill=1,
-        fillcolor=None,
-    ):
-        """
-        Transforms this image.  This method creates a new image with the
-        given size, and the same mode as the original, and copies data
-        to the new image using the given transform.
-
-        :param size: The output size in pixels, as a 2-tuple:
-           (width, height).
-        :param method: The transformation method.  This is one of
-          :py:data:`Transform.EXTENT` (cut out a rectangular subregion),
-          :py:data:`Transform.AFFINE` (affine transform),
-          :py:data:`Transform.PERSPECTIVE` (perspective transform),
-          :py:data:`Transform.QUAD` (map a quadrilateral to a rectangle), or
-          :py:data:`Transform.MESH` (map a number of source quadrilaterals
-          in one operation).
-
-          It may also be an :py:class:`~PIL.Image.ImageTransformHandler`
-          object::
-
-            class Example(Image.ImageTransformHandler):
-                def transform(self, size, data, resample, fill=1):
-                    # Return result
-
-          It may also be an object with a ``method.getdata`` method
-          that returns a tuple supplying new ``method`` and ``data`` values::
-
-            class Example:
-                def getdata(self):
-                    method = Image.Transform.EXTENT
-                    data = (0, 0, 100, 100)
-                    return method, data
-        :param data: Extra data to the transformation method.
-        :param resample: Optional resampling filter.  It can be one of
-           :py:data:`Resampling.NEAREST` (use nearest neighbour),
-           :py:data:`Resampling.BILINEAR` (linear interpolation in a 2x2
-           environment), or :py:data:`Resampling.BICUBIC` (cubic spline
-           interpolation in a 4x4 environment). If omitted, or if the image
-           has mode "1" or "P", it is set to :py:data:`Resampling.NEAREST`.
-           See: :ref:`concept-filters`.
-        :param fill: If ``method`` is an
-          :py:class:`~PIL.Image.ImageTransformHandler` object, this is one of
-          the arguments passed to it. Otherwise, it is unused.
-        :param fillcolor: Optional fill color for the area outside the
-           transform in the output image.
-        :returns: An :py:class:`~PIL.Image.Image` object.
-        """
-
-        if self.mode in ("LA", "RGBA") and resample != Resampling.NEAREST:
-            return (
-                self.convert({"LA": "La", "RGBA": "RGBa"}[self.mode])
-                .transform(size, method, data, resample, fill, fillcolor)
-                .convert(self.mode)
-            )
-
-        if isinstance(method, ImageTransformHandler):
-            return method.transform(size, self, resample=resample, fill=fill)
-
-        if hasattr(method, "getdata"):
-            # compatibility w. old-style transform objects
-            method, data = method.getdata()
-
-        if data is None:
-            msg = "missing method data"
-            raise ValueError(msg)
-
-        im = new(self.mode, size, fillcolor)
-        if self.mode == "P" and self.palette:
-            im.palette = self.palette.copy()
-        im.info = self.info.copy()
-        if method == Transform.MESH:
-            # list of quads
-            for box, quad in data:
-                im.__transformer(
-                    box, self, Transform.QUAD, quad, resample, fillcolor is None
-                )
-        else:
-            im.__transformer(
-                (0, 0) + size, self, method, data, resample, fillcolor is None
-            )
-
-        return im
-
-    def __transformer(
-        self, box, image, method, data, resample=Resampling.NEAREST, fill=1
-    ):
-        w = box[2] - box[0]
-        h = box[3] - box[1]
-
-        if method == Transform.AFFINE:
-            data = data[:6]
-
-        elif method == Transform.EXTENT:
-            # convert extent to an affine transform
-            x0, y0, x1, y1 = data
-            xs = (x1 - x0) / w
-            ys = (y1 - y0) / h
-            method = Transform.AFFINE
-            data = (xs, 0, x0, 0, ys, y0)
-
-        elif method == Transform.PERSPECTIVE:
-            data = data[:8]
-
-        elif method == Transform.QUAD:
-            # quadrilateral warp.  data specifies the four corners
-            # given as NW, SW, SE, and NE.
-            nw = data[:2]
-            sw = data[2:4]
-            se = data[4:6]
-            ne = data[6:8]
-            x0, y0 = nw
-            As = 1.0 / w
-            At = 1.0 / h
-            data = (
-                x0,
-                (ne[0] - x0) * As,
-                (sw[0] - x0) * At,
-                (se[0] - sw[0] - ne[0] + x0) * As * At,
-                y0,
-                (ne[1] - y0) * As,
-                (sw[1] - y0) * At,
-                (se[1] - sw[1] - ne[1] + y0) * As * At,
-            )
-
-        else:
-            msg = "unknown transformation method"
-            raise ValueError(msg)
-
-        if resample not in (
-            Resampling.NEAREST,
-            Resampling.BILINEAR,
-            Resampling.BICUBIC,
-        ):
-            if resample in (Resampling.BOX, Resampling.HAMMING, Resampling.LANCZOS):
-                msg = {
-                    Resampling.BOX: "Image.Resampling.BOX",
-                    Resampling.HAMMING: "Image.Resampling.HAMMING",
-                    Resampling.LANCZOS: "Image.Resampling.LANCZOS",
-                }[resample] + f" ({resample}) cannot be used."
-            else:
-                msg = f"Unknown resampling filter ({resample})."
-
-            filters = [
-                f"{filter[1]} ({filter[0]})"
-                for filter in (
-                    (Resampling.NEAREST, "Image.Resampling.NEAREST"),
-                    (Resampling.BILINEAR, "Image.Resampling.BILINEAR"),
-                    (Resampling.BICUBIC, "Image.Resampling.BICUBIC"),
-                )
-            ]
-            msg += " Use " + ", ".join(filters[:-1]) + " or " + filters[-1]
-            raise ValueError(msg)
-
-        image.load()
-
-        self.load()
-
-        if image.mode in ("1", "P"):
-            resample = Resampling.NEAREST
-
-        self.im.transform2(box, image.im, method, data, resample, fill)
-
-    def transpose(self, method):
-        """
-        Transpose image (flip or rotate in 90 degree steps)
-
-        :param method: One of :py:data:`Transpose.FLIP_LEFT_RIGHT`,
-          :py:data:`Transpose.FLIP_TOP_BOTTOM`, :py:data:`Transpose.ROTATE_90`,
-          :py:data:`Transpose.ROTATE_180`, :py:data:`Transpose.ROTATE_270`,
-          :py:data:`Transpose.TRANSPOSE` or :py:data:`Transpose.TRANSVERSE`.
-        :returns: Returns a flipped or rotated copy of this image.
-        """
-
-        self.load()
-        return self._new(self.im.transpose(method))
-
-    def effect_spread(self, distance):
-        """
-        Randomly spread pixels in an image.
-
-        :param distance: Distance to spread pixels.
-        """
-        self.load()
-        return self._new(self.im.effect_spread(distance))
-
-    def toqimage(self):
-        """Returns a QImage copy of this image"""
-        from . import ImageQt
-
-        if not ImageQt.qt_is_installed:
-            msg = "Qt bindings are not installed"
-            raise ImportError(msg)
-        return ImageQt.toqimage(self)
-
-    def toqpixmap(self):
-        """Returns a QPixmap copy of this image"""
-        from . import ImageQt
-
-        if not ImageQt.qt_is_installed:
-            msg = "Qt bindings are not installed"
-            raise ImportError(msg)
-        return ImageQt.toqpixmap(self)
-
-
-# --------------------------------------------------------------------
-# Abstract handlers.
-
-
-class ImagePointHandler:
-    """
-    Used as a mixin by point transforms
-    (for use with :py:meth:`~PIL.Image.Image.point`)
-    """
-
-    pass
-
-
-class ImageTransformHandler:
-    """
-    Used as a mixin by geometry transforms
-    (for use with :py:meth:`~PIL.Image.Image.transform`)
-    """
-
-    pass
-
-
-# --------------------------------------------------------------------
-# Factories
-
-#
-# Debugging
-
-
-def _wedge():
-    """Create greyscale wedge (for debugging only)"""
-
-    return Image()._new(core.wedge("L"))
-
-
-def _check_size(size):
-    """
-    Common check to enforce type and sanity check on size tuples
-
-    :param size: Should be a 2 tuple of (width, height)
-    :returns: True, or raises a ValueError
-    """
-
-    if not isinstance(size, (list, tuple)):
-        msg = "Size must be a tuple"
-        raise ValueError(msg)
-    if len(size) != 2:
-        msg = "Size must be a tuple of length 2"
-        raise ValueError(msg)
-    if size[0] < 0 or size[1] < 0:
-        msg = "Width and height must be >= 0"
-        raise ValueError(msg)
-
-    return True
-
-
-def new(mode, size, color=0):
-    """
-    Creates a new image with the given mode and size.
-
-    :param mode: The mode to use for the new image. See:
-       :ref:`concept-modes`.
-    :param size: A 2-tuple, containing (width, height) in pixels.
-    :param color: What color to use for the image.  Default is black.
-       If given, this should be a single integer or floating point value
-       for single-band modes, and a tuple for multi-band modes (one value
-       per band).  When creating RGB or HSV images, you can also use color
-       strings as supported by the ImageColor module.  If the color is
-       None, the image is not initialised.
-    :returns: An :py:class:`~PIL.Image.Image` object.
-    """
-
-    _check_size(size)
-
-    if color is None:
-        # don't initialize
-        return Image()._new(core.new(mode, size))
-
-    if isinstance(color, str):
-        # css3-style specifier
-
-        from . import ImageColor
-
-        color = ImageColor.getcolor(color, mode)
-
-    im = Image()
-    if mode == "P" and isinstance(color, (list, tuple)) and len(color) in [3, 4]:
-        # RGB or RGBA value for a P image
-        from . import ImagePalette
-
-        im.palette = ImagePalette.ImagePalette()
-        color = im.palette.getcolor(color)
-    return im._new(core.fill(mode, size, color))
-
-
-def frombytes(mode, size, data, decoder_name="raw", *args):
-    """
-    Creates a copy of an image memory from pixel data in a buffer.
-
-    In its simplest form, this function takes three arguments
-    (mode, size, and unpacked pixel data).
-
-    You can also use any pixel decoder supported by PIL. For more
-    information on available decoders, see the section
-    :ref:`Writing Your Own File Codec <file-codecs>`.
-
-    Note that this function decodes pixel data only, not entire images.
-    If you have an entire image in a string, wrap it in a
-    :py:class:`~io.BytesIO` object, and use :py:func:`~PIL.Image.open` to load
-    it.
-
-    :param mode: The image mode. See: :ref:`concept-modes`.
-    :param size: The image size.
-    :param data: A byte buffer containing raw data for the given mode.
-    :param decoder_name: What decoder to use.
-    :param args: Additional parameters for the given decoder.
-    :returns: An :py:class:`~PIL.Image.Image` object.
-    """
-
-    _check_size(size)
-
-    # may pass tuple instead of argument list
-    if len(args) == 1 and isinstance(args[0], tuple):
-        args = args[0]
-
-    if decoder_name == "raw" and args == ():
-        args = mode
-
-    im = new(mode, size)
-    im.frombytes(data, decoder_name, args)
-    return im
-
-
-def frombuffer(mode, size, data, decoder_name="raw", *args):
-    """
-    Creates an image memory referencing pixel data in a byte buffer.
-
-    This function is similar to :py:func:`~PIL.Image.frombytes`, but uses data
-    in the byte buffer, where possible.  This means that changes to the
-    original buffer object are reflected in this image).  Not all modes can
-    share memory; supported modes include "L", "RGBX", "RGBA", and "CMYK".
-
-    Note that this function decodes pixel data only, not entire images.
-    If you have an entire image file in a string, wrap it in a
-    :py:class:`~io.BytesIO` object, and use :py:func:`~PIL.Image.open` to load it.
-
-    In the current version, the default parameters used for the "raw" decoder
-    differs from that used for :py:func:`~PIL.Image.frombytes`.  This is a
-    bug, and will probably be fixed in a future release.  The current release
-    issues a warning if you do this; to disable the warning, you should provide
-    the full set of parameters.  See below for details.
-
-    :param mode: The image mode. See: :ref:`concept-modes`.
-    :param size: The image size.
-    :param data: A bytes or other buffer object containing raw
-        data for the given mode.
-    :param decoder_name: What decoder to use.
-    :param args: Additional parameters for the given decoder.  For the
-        default encoder ("raw"), it's recommended that you provide the
-        full set of parameters::
-
-            frombuffer(mode, size, data, "raw", mode, 0, 1)
-
-    :returns: An :py:class:`~PIL.Image.Image` object.
-
-    .. versionadded:: 1.1.4
-    """
-
-    _check_size(size)
-
-    # may pass tuple instead of argument list
-    if len(args) == 1 and isinstance(args[0], tuple):
-        args = args[0]
-
-    if decoder_name == "raw":
-        if args == ():
-            args = mode, 0, 1
-        if args[0] in _MAPMODES:
-            im = new(mode, (1, 1))
-            im = im._new(core.map_buffer(data, size, decoder_name, 0, args))
-            if mode == "P":
-                from . import ImagePalette
-
-                im.palette = ImagePalette.ImagePalette("RGB", im.im.getpalette("RGB"))
-            im.readonly = 1
-            return im
-
-    return frombytes(mode, size, data, decoder_name, args)
-
-
-def fromarray(obj, mode=None):
-    """
-    Creates an image memory from an object exporting the array interface
-    (using the buffer protocol)::
-
-      from PIL import Image
-      import numpy as np
-      a = np.zeros((5, 5))
-      im = Image.fromarray(a)
-
-    If ``obj`` is not contiguous, then the ``tobytes`` method is called
-    and :py:func:`~PIL.Image.frombuffer` is used.
-
-    In the case of NumPy, be aware that Pillow modes do not always correspond
-    to NumPy dtypes. Pillow modes only offer 1-bit pixels, 8-bit pixels,
-    32-bit signed integer pixels, and 32-bit floating point pixels.
-
-    Pillow images can also be converted to arrays::
-
-      from PIL import Image
-      import numpy as np
-      im = Image.open("hopper.jpg")
-      a = np.asarray(im)
-
-    When converting Pillow images to arrays however, only pixel values are
-    transferred. This means that P and PA mode images will lose their palette.
-
-    :param obj: Object with array interface
-    :param mode: Optional mode to use when reading ``obj``. Will be determined from
-      type if ``None``.
-
-      This will not be used to convert the data after reading, but will be used to
-      change how the data is read::
-
-        from PIL import Image
-        import numpy as np
-        a = np.full((1, 1), 300)
-        im = Image.fromarray(a, mode="L")
-        im.getpixel((0, 0))  # 44
-        im = Image.fromarray(a, mode="RGB")
-        im.getpixel((0, 0))  # (44, 1, 0)
-
-      See: :ref:`concept-modes` for general information about modes.
-    :returns: An image object.
-
-    .. versionadded:: 1.1.6
-    """
-    arr = obj.__array_interface__
-    shape = arr["shape"]
-    ndim = len(shape)
-    strides = arr.get("strides", None)
-    if mode is None:
-        try:
-            typekey = (1, 1) + shape[2:], arr["typestr"]
-        except KeyError as e:
-            msg = "Cannot handle this data type"
-            raise TypeError(msg) from e
-        try:
-            mode, rawmode = _fromarray_typemap[typekey]
-        except KeyError as e:
-            msg = "Cannot handle this data type: %s, %s" % typekey
-            raise TypeError(msg) from e
-    else:
-        rawmode = mode
-    if mode in ["1", "L", "I", "P", "F"]:
-        ndmax = 2
-    elif mode == "RGB":
-        ndmax = 3
-    else:
-        ndmax = 4
-    if ndim > ndmax:
-        msg = f"Too many dimensions: {ndim} > {ndmax}."
-        raise ValueError(msg)
-
-    size = 1 if ndim == 1 else shape[1], shape[0]
-    if strides is not None:
-        if hasattr(obj, "tobytes"):
-            obj = obj.tobytes()
-        else:
-            obj = obj.tostring()
-
-    return frombuffer(mode, size, obj, "raw", rawmode, 0, 1)
-
-
-def fromqimage(im):
-    """Creates an image instance from a QImage image"""
-    from . import ImageQt
-
-    if not ImageQt.qt_is_installed:
-        msg = "Qt bindings are not installed"
-        raise ImportError(msg)
-    return ImageQt.fromqimage(im)
-
-
-def fromqpixmap(im):
-    """Creates an image instance from a QPixmap image"""
-    from . import ImageQt
-
-    if not ImageQt.qt_is_installed:
-        msg = "Qt bindings are not installed"
-        raise ImportError(msg)
-    return ImageQt.fromqpixmap(im)
-
-
-_fromarray_typemap = {
-    # (shape, typestr) => mode, rawmode
-    # first two members of shape are set to one
-    ((1, 1), "|b1"): ("1", "1;8"),
-    ((1, 1), "|u1"): ("L", "L"),
-    ((1, 1), "|i1"): ("I", "I;8"),
-    ((1, 1), "<u2"): ("I", "I;16"),
-    ((1, 1), ">u2"): ("I", "I;16B"),
-    ((1, 1), "<i2"): ("I", "I;16S"),
-    ((1, 1), ">i2"): ("I", "I;16BS"),
-    ((1, 1), "<u4"): ("I", "I;32"),
-    ((1, 1), ">u4"): ("I", "I;32B"),
-    ((1, 1), "<i4"): ("I", "I;32S"),
-    ((1, 1), ">i4"): ("I", "I;32BS"),
-    ((1, 1), "<f4"): ("F", "F;32F"),
-    ((1, 1), ">f4"): ("F", "F;32BF"),
-    ((1, 1), "<f8"): ("F", "F;64F"),
-    ((1, 1), ">f8"): ("F", "F;64BF"),
-    ((1, 1, 2), "|u1"): ("LA", "LA"),
-    ((1, 1, 3), "|u1"): ("RGB", "RGB"),
-    ((1, 1, 4), "|u1"): ("RGBA", "RGBA"),
-    # shortcuts:
-    ((1, 1), _ENDIAN + "i4"): ("I", "I"),
-    ((1, 1), _ENDIAN + "f4"): ("F", "F"),
-}
-
-
-def _decompression_bomb_check(size):
-    if MAX_IMAGE_PIXELS is None:
-        return
-
-    pixels = max(1, size[0]) * max(1, size[1])
-
-    if pixels > 2 * MAX_IMAGE_PIXELS:
-        msg = (
-            f"Image size ({pixels} pixels) exceeds limit of {2 * MAX_IMAGE_PIXELS} "
-            "pixels, could be decompression bomb DOS attack."
-        )
-        raise DecompressionBombError(msg)
-
-    if pixels > MAX_IMAGE_PIXELS:
-        warnings.warn(
-            f"Image size ({pixels} pixels) exceeds limit of {MAX_IMAGE_PIXELS} pixels, "
-            "could be decompression bomb DOS attack.",
-            DecompressionBombWarning,
-        )
-
-
-def open(fp, mode="r", formats=None):
-    """
-    Opens and identifies the given image file.
-
-    This is a lazy operation; this function identifies the file, but
-    the file remains open and the actual image data is not read from
-    the file until you try to process the data (or call the
-    :py:meth:`~PIL.Image.Image.load` method).  See
-    :py:func:`~PIL.Image.new`. See :ref:`file-handling`.
-
-    :param fp: A filename (string), pathlib.Path object or a file object.
-       The file object must implement ``file.read``,
-       ``file.seek``, and ``file.tell`` methods,
-       and be opened in binary mode. The file object will also seek to zero
-       before reading.
-    :param mode: The mode.  If given, this argument must be "r".
-    :param formats: A list or tuple of formats to attempt to load the file in.
-       This can be used to restrict the set of formats checked.
-       Pass ``None`` to try all supported formats. You can print the set of
-       available formats by running ``python3 -m PIL`` or using
-       the :py:func:`PIL.features.pilinfo` function.
-    :returns: An :py:class:`~PIL.Image.Image` object.
-    :exception FileNotFoundError: If the file cannot be found.
-    :exception PIL.UnidentifiedImageError: If the image cannot be opened and
-       identified.
-    :exception ValueError: If the ``mode`` is not "r", or if a ``StringIO``
-       instance is used for ``fp``.
-    :exception TypeError: If ``formats`` is not ``None``, a list or a tuple.
-    """
-
-    if mode != "r":
-        msg = f"bad mode {repr(mode)}"
-        raise ValueError(msg)
-    elif isinstance(fp, io.StringIO):
-        msg = (
-            "StringIO cannot be used to open an image. "
-            "Binary data must be used instead."
-        )
-        raise ValueError(msg)
-
-    if formats is None:
-        formats = ID
-    elif not isinstance(formats, (list, tuple)):
-        msg = "formats must be a list or tuple"
-        raise TypeError(msg)
-
-    exclusive_fp = False
-    filename = ""
-    if isinstance(fp, Path):
-        filename = str(fp.resolve())
-    elif is_path(fp):
-        filename = fp
-
-    if filename:
-        fp = builtins.open(filename, "rb")
-        exclusive_fp = True
-
-    try:
-        fp.seek(0)
-    except (AttributeError, io.UnsupportedOperation):
-        fp = io.BytesIO(fp.read())
-        exclusive_fp = True
-
-    prefix = fp.read(16)
-
-    preinit()
-
-    accept_warnings = []
-
-    def _open_core(fp, filename, prefix, formats):
-        for i in formats:
-            i = i.upper()
-            if i not in OPEN:
-                init()
-            try:
-                factory, accept = OPEN[i]
-                result = not accept or accept(prefix)
-                if type(result) in [str, bytes]:
-                    accept_warnings.append(result)
-                elif result:
-                    fp.seek(0)
-                    im = factory(fp, filename)
-                    _decompression_bomb_check(im.size)
-                    return im
-            except (SyntaxError, IndexError, TypeError, struct.error):
-                # Leave disabled by default, spams the logs with image
-                # opening failures that are entirely expected.
-                # logger.debug("", exc_info=True)
-                continue
-            except BaseException:
-                if exclusive_fp:
-                    fp.close()
-                raise
-        return None
-
-    im = _open_core(fp, filename, prefix, formats)
-
-    if im is None and formats is ID:
-        checked_formats = formats.copy()
-        if init():
-            im = _open_core(
-                fp,
-                filename,
-                prefix,
-                tuple(format for format in formats if format not in checked_formats),
-            )
-
-    if im:
-        im._exclusive_fp = exclusive_fp
-        return im
-
-    if exclusive_fp:
-        fp.close()
-    for message in accept_warnings:
-        warnings.warn(message)
-    msg = "cannot identify image file %r" % (filename if filename else fp)
-    raise UnidentifiedImageError(msg)
-
-
-#
-# Image processing.
-
-
-def alpha_composite(im1, im2):
-    """
-    Alpha composite im2 over im1.
-
-    :param im1: The first image. Must have mode RGBA.
-    :param im2: The second image.  Must have mode RGBA, and the same size as
-       the first image.
-    :returns: An :py:class:`~PIL.Image.Image` object.
-    """
-
-    im1.load()
-    im2.load()
-    return im1._new(core.alpha_composite(im1.im, im2.im))
-
-
-def blend(im1, im2, alpha):
-    """
-    Creates a new image by interpolating between two input images, using
-    a constant alpha::
-
-        out = image1 * (1.0 - alpha) + image2 * alpha
-
-    :param im1: The first image.
-    :param im2: The second image.  Must have the same mode and size as
-       the first image.
-    :param alpha: The interpolation alpha factor.  If alpha is 0.0, a
-       copy of the first image is returned. If alpha is 1.0, a copy of
-       the second image is returned. There are no restrictions on the
-       alpha value. If necessary, the result is clipped to fit into
-       the allowed output range.
-    :returns: An :py:class:`~PIL.Image.Image` object.
-    """
-
-    im1.load()
-    im2.load()
-    return im1._new(core.blend(im1.im, im2.im, alpha))
-
-
-def composite(image1, image2, mask):
-    """
-    Create composite image by blending images using a transparency mask.
-
-    :param image1: The first image.
-    :param image2: The second image.  Must have the same mode and
-       size as the first image.
-    :param mask: A mask image.  This image can have mode
-       "1", "L", or "RGBA", and must have the same size as the
-       other two images.
-    """
-
-    image = image2.copy()
-    image.paste(image1, None, mask)
-    return image
-
-
-def eval(image, *args):
-    """
-    Applies the function (which should take one argument) to each pixel
-    in the given image. If the image has more than one band, the same
-    function is applied to each band. Note that the function is
-    evaluated once for each possible pixel value, so you cannot use
-    random components or other generators.
-
-    :param image: The input image.
-    :param function: A function object, taking one integer argument.
-    :returns: An :py:class:`~PIL.Image.Image` object.
-    """
-
-    return image.point(args[0])
-
-
-def merge(mode, bands):
-    """
-    Merge a set of single band images into a new multiband image.
-
-    :param mode: The mode to use for the output image. See:
-        :ref:`concept-modes`.
-    :param bands: A sequence containing one single-band image for
-        each band in the output image.  All bands must have the
-        same size.
-    :returns: An :py:class:`~PIL.Image.Image` object.
-    """
-
-    if getmodebands(mode) != len(bands) or "*" in mode:
-        msg = "wrong number of bands"
-        raise ValueError(msg)
-    for band in bands[1:]:
-        if band.mode != getmodetype(mode):
-            msg = "mode mismatch"
-            raise ValueError(msg)
-        if band.size != bands[0].size:
-            msg = "size mismatch"
-            raise ValueError(msg)
-    for band in bands:
-        band.load()
-    return bands[0]._new(core.merge(mode, *[b.im for b in bands]))
-
-
-# --------------------------------------------------------------------
-# Plugin registry
-
-
-def register_open(id, factory, accept=None):
-    """
-    Register an image file plugin.  This function should not be used
-    in application code.
-
-    :param id: An image format identifier.
-    :param factory: An image file factory method.
-    :param accept: An optional function that can be used to quickly
-       reject images having another format.
-    """
-    id = id.upper()
-    if id not in ID:
-        ID.append(id)
-    OPEN[id] = factory, accept
-
-
-def register_mime(id, mimetype):
-    """
-    Registers an image MIME type.  This function should not be used
-    in application code.
-
-    :param id: An image format identifier.
-    :param mimetype: The image MIME type for this format.
-    """
-    MIME[id.upper()] = mimetype
-
-
-def register_save(id, driver):
-    """
-    Registers an image save function.  This function should not be
-    used in application code.
-
-    :param id: An image format identifier.
-    :param driver: A function to save images in this format.
-    """
-    SAVE[id.upper()] = driver
-
-
-def register_save_all(id, driver):
-    """
-    Registers an image function to save all the frames
-    of a multiframe format.  This function should not be
-    used in application code.
-
-    :param id: An image format identifier.
-    :param driver: A function to save images in this format.
-    """
-    SAVE_ALL[id.upper()] = driver
-
-
-def register_extension(id, extension):
-    """
-    Registers an image extension.  This function should not be
-    used in application code.
-
-    :param id: An image format identifier.
-    :param extension: An extension used for this format.
-    """
-    EXTENSION[extension.lower()] = id.upper()
-
-
-def register_extensions(id, extensions):
-    """
-    Registers image extensions.  This function should not be
-    used in application code.
-
-    :param id: An image format identifier.
-    :param extensions: A list of extensions used for this format.
-    """
-    for extension in extensions:
-        register_extension(id, extension)
-
-
-def registered_extensions():
-    """
-    Returns a dictionary containing all file extensions belonging
-    to registered plugins
-    """
-    init()
-    return EXTENSION
-
-
-def register_decoder(name, decoder):
-    """
-    Registers an image decoder.  This function should not be
-    used in application code.
-
-    :param name: The name of the decoder
-    :param decoder: A callable(mode, args) that returns an
-                    ImageFile.PyDecoder object
-
-    .. versionadded:: 4.1.0
-    """
-    DECODERS[name] = decoder
-
-
-def register_encoder(name, encoder):
-    """
-    Registers an image encoder.  This function should not be
-    used in application code.
-
-    :param name: The name of the encoder
-    :param encoder: A callable(mode, args) that returns an
-                    ImageFile.PyEncoder object
-
-    .. versionadded:: 4.1.0
-    """
-    ENCODERS[name] = encoder
-
-
-# --------------------------------------------------------------------
-# Simple display support.
-
-
-def _show(image, **options):
-    from . import ImageShow
-
-    ImageShow.show(image, **options)
-
-
-# --------------------------------------------------------------------
-# Effects
-
-
-def effect_mandelbrot(size, extent, quality):
-    """
-    Generate a Mandelbrot set covering the given extent.
-
-    :param size: The requested size in pixels, as a 2-tuple:
-       (width, height).
-    :param extent: The extent to cover, as a 4-tuple:
-       (x0, y0, x1, y1).
-    :param quality: Quality.
-    """
-    return Image()._new(core.effect_mandelbrot(size, extent, quality))
-
-
-def effect_noise(size, sigma):
-    """
-    Generate Gaussian noise centered around 128.
-
-    :param size: The requested size in pixels, as a 2-tuple:
-       (width, height).
-    :param sigma: Standard deviation of noise.
-    """
-    return Image()._new(core.effect_noise(size, sigma))
-
-
-def linear_gradient(mode):
-    """
-    Generate 256x256 linear gradient from black to white, top to bottom.
-
-    :param mode: Input mode.
-    """
-    return Image()._new(core.linear_gradient(mode))
-
-
-def radial_gradient(mode):
-    """
-    Generate 256x256 radial gradient from black to white, centre to edge.
-
-    :param mode: Input mode.
-    """
-    return Image()._new(core.radial_gradient(mode))
-
-
-# --------------------------------------------------------------------
-# Resources
-
-
-def _apply_env_variables(env=None):
-    if env is None:
-        env = os.environ
-
-    for var_name, setter in [
-        ("PILLOW_ALIGNMENT", core.set_alignment),
-        ("PILLOW_BLOCK_SIZE", core.set_block_size),
-        ("PILLOW_BLOCKS_MAX", core.set_blocks_max),
-    ]:
-        if var_name not in env:
-            continue
-
-        var = env[var_name].lower()
-
-        units = 1
-        for postfix, mul in [("k", 1024), ("m", 1024 * 1024)]:
-            if var.endswith(postfix):
-                units = mul
-                var = var[: -len(postfix)]
-
-        try:
-            var = int(var) * units
-        except ValueError:
-            warnings.warn(f"{var_name} is not int")
-            continue
-
-        try:
-            setter(var)
-        except ValueError as e:
-            warnings.warn(f"{var_name}: {e}")
-
-
-_apply_env_variables()
-atexit.register(core.clear_cache)
-
-
-class Exif(MutableMapping):
-    """
-    This class provides read and write access to EXIF image data::
-
-      from PIL import Image
-      im = Image.open("exif.png")
-      exif = im.getexif()  # Returns an instance of this class
-
-    Information can be read and written, iterated over or deleted::
-
-      print(exif[274])  # 1
-      exif[274] = 2
-      for k, v in exif.items():
-        print("Tag", k, "Value", v)  # Tag 274 Value 2
-      del exif[274]
-
-    To access information beyond IFD0, :py:meth:`~PIL.Image.Exif.get_ifd`
-    returns a dictionary::
-
-      from PIL import ExifTags
-      im = Image.open("exif_gps.jpg")
-      exif = im.getexif()
-      gps_ifd = exif.get_ifd(ExifTags.IFD.GPSInfo)
-      print(gps_ifd)
-
-    Other IFDs include ``ExifTags.IFD.Exif``, ``ExifTags.IFD.Makernote``,
-    ``ExifTags.IFD.Interop`` and ``ExifTags.IFD.IFD1``.
-
-    :py:mod:`~PIL.ExifTags` also has enum classes to provide names for data::
-
-      print(exif[ExifTags.Base.Software])  # PIL
-      print(gps_ifd[ExifTags.GPS.GPSDateStamp])  # 1999:99:99 99:99:99
-    """
-
-    endian = None
-    bigtiff = False
-
-    def __init__(self):
-        self._data = {}
-        self._hidden_data = {}
-        self._ifds = {}
-        self._info = None
-        self._loaded_exif = None
-
-    def _fixup(self, value):
-        try:
-            if len(value) == 1 and isinstance(value, tuple):
-                return value[0]
-        except Exception:
-            pass
-        return value
-
-    def _fixup_dict(self, src_dict):
-        # Helper function
-        # returns a dict with any single item tuples/lists as individual values
-        return {k: self._fixup(v) for k, v in src_dict.items()}
-
-    def _get_ifd_dict(self, offset):
-        try:
-            # an offset pointer to the location of the nested embedded IFD.
-            # It should be a long, but may be corrupted.
-            self.fp.seek(offset)
-        except (KeyError, TypeError):
-            pass
-        else:
-            from . import TiffImagePlugin
-
-            info = TiffImagePlugin.ImageFileDirectory_v2(self.head)
-            info.load(self.fp)
-            return self._fixup_dict(info)
-
-    def _get_head(self):
-        version = b"\x2B" if self.bigtiff else b"\x2A"
-        if self.endian == "<":
-            head = b"II" + version + b"\x00" + o32le(8)
-        else:
-            head = b"MM\x00" + version + o32be(8)
-        if self.bigtiff:
-            head += o32le(8) if self.endian == "<" else o32be(8)
-            head += b"\x00\x00\x00\x00"
-        return head
-
-    def load(self, data):
-        # Extract EXIF information.  This is highly experimental,
-        # and is likely to be replaced with something better in a future
-        # version.
-
-        # The EXIF record consists of a TIFF file embedded in a JPEG
-        # application marker (!).
-        if data == self._loaded_exif:
-            return
-        self._loaded_exif = data
-        self._data.clear()
-        self._hidden_data.clear()
-        self._ifds.clear()
-        if data and data.startswith(b"Exif\x00\x00"):
-            data = data[6:]
-        if not data:
-            self._info = None
-            return
-
-        self.fp = io.BytesIO(data)
-        self.head = self.fp.read(8)
-        # process dictionary
-        from . import TiffImagePlugin
-
-        self._info = TiffImagePlugin.ImageFileDirectory_v2(self.head)
-        self.endian = self._info._endian
-        self.fp.seek(self._info.next)
-        self._info.load(self.fp)
-
-    def load_from_fp(self, fp, offset=None):
-        self._loaded_exif = None
-        self._data.clear()
-        self._hidden_data.clear()
-        self._ifds.clear()
-
-        # process dictionary
-        from . import TiffImagePlugin
-
-        self.fp = fp
-        if offset is not None:
-            self.head = self._get_head()
-        else:
-            self.head = self.fp.read(8)
-        self._info = TiffImagePlugin.ImageFileDirectory_v2(self.head)
-        if self.endian is None:
-            self.endian = self._info._endian
-        if offset is None:
-            offset = self._info.next
-        self.fp.seek(offset)
-        self._info.load(self.fp)
-
-    def _get_merged_dict(self):
-        merged_dict = dict(self)
-
-        # get EXIF extension
-        if ExifTags.IFD.Exif in self:
-            ifd = self._get_ifd_dict(self[ExifTags.IFD.Exif])
-            if ifd:
-                merged_dict.update(ifd)
-
-        # GPS
-        if ExifTags.IFD.GPSInfo in self:
-            merged_dict[ExifTags.IFD.GPSInfo] = self._get_ifd_dict(
-                self[ExifTags.IFD.GPSInfo]
-            )
-
-        return merged_dict
-
-    def tobytes(self, offset=8):
-        from . import TiffImagePlugin
-
-        head = self._get_head()
-        ifd = TiffImagePlugin.ImageFileDirectory_v2(ifh=head)
-        for tag, value in self.items():
-            if tag in [
-                ExifTags.IFD.Exif,
-                ExifTags.IFD.GPSInfo,
-            ] and not isinstance(value, dict):
-                value = self.get_ifd(tag)
-                if (
-                    tag == ExifTags.IFD.Exif
-                    and ExifTags.IFD.Interop in value
-                    and not isinstance(value[ExifTags.IFD.Interop], dict)
-                ):
-                    value = value.copy()
-                    value[ExifTags.IFD.Interop] = self.get_ifd(ExifTags.IFD.Interop)
-            ifd[tag] = value
-        return b"Exif\x00\x00" + head + ifd.tobytes(offset)
-
-    def get_ifd(self, tag):
-        if tag not in self._ifds:
-            if tag == ExifTags.IFD.IFD1:
-                if self._info is not None and self._info.next != 0:
-                    self._ifds[tag] = self._get_ifd_dict(self._info.next)
-            elif tag in [ExifTags.IFD.Exif, ExifTags.IFD.GPSInfo]:
-                offset = self._hidden_data.get(tag, self.get(tag))
-                if offset is not None:
-                    self._ifds[tag] = self._get_ifd_dict(offset)
-            elif tag in [ExifTags.IFD.Interop, ExifTags.IFD.Makernote]:
-                if ExifTags.IFD.Exif not in self._ifds:
-                    self.get_ifd(ExifTags.IFD.Exif)
-                tag_data = self._ifds[ExifTags.IFD.Exif][tag]
-                if tag == ExifTags.IFD.Makernote:
-                    from .TiffImagePlugin import ImageFileDirectory_v2
-
-                    if tag_data[:8] == b"FUJIFILM":
-                        ifd_offset = i32le(tag_data, 8)
-                        ifd_data = tag_data[ifd_offset:]
-
-                        makernote = {}
-                        for i in range(0, struct.unpack("<H", ifd_data[:2])[0]):
-                            ifd_tag, typ, count, data = struct.unpack(
-                                "<HHL4s", ifd_data[i * 12 + 2 : (i + 1) * 12 + 2]
-                            )
-                            try:
-                                (
-                                    unit_size,
-                                    handler,
-                                ) = ImageFileDirectory_v2._load_dispatch[typ]
-                            except KeyError:
-                                continue
-                            size = count * unit_size
-                            if size > 4:
-                                (offset,) = struct.unpack("<L", data)
-                                data = ifd_data[offset - 12 : offset + size - 12]
-                            else:
-                                data = data[:size]
-
-                            if len(data) != size:
-                                warnings.warn(
-                                    "Possibly corrupt EXIF MakerNote data.  "
-                                    f"Expecting to read {size} bytes but only got "
-                                    f"{len(data)}. Skipping tag {ifd_tag}"
-                                )
-                                continue
-
-                            if not data:
-                                continue
-
-                            makernote[ifd_tag] = handler(
-                                ImageFileDirectory_v2(), data, False
-                            )
-                        self._ifds[tag] = dict(self._fixup_dict(makernote))
-                    elif self.get(0x010F) == "Nintendo":
-                        makernote = {}
-                        for i in range(0, struct.unpack(">H", tag_data[:2])[0]):
-                            ifd_tag, typ, count, data = struct.unpack(
-                                ">HHL4s", tag_data[i * 12 + 2 : (i + 1) * 12 + 2]
-                            )
-                            if ifd_tag == 0x1101:
-                                # CameraInfo
-                                (offset,) = struct.unpack(">L", data)
-                                self.fp.seek(offset)
-
-                                camerainfo = {"ModelID": self.fp.read(4)}
-
-                                self.fp.read(4)
-                                # Seconds since 2000
-                                camerainfo["TimeStamp"] = i32le(self.fp.read(12))
-
-                                self.fp.read(4)
-                                camerainfo["InternalSerialNumber"] = self.fp.read(4)
-
-                                self.fp.read(12)
-                                parallax = self.fp.read(4)
-                                handler = ImageFileDirectory_v2._load_dispatch[
-                                    TiffTags.FLOAT
-                                ][1]
-                                camerainfo["Parallax"] = handler(
-                                    ImageFileDirectory_v2(), parallax, False
-                                )
-
-                                self.fp.read(4)
-                                camerainfo["Category"] = self.fp.read(2)
-
-                                makernote = {0x1101: dict(self._fixup_dict(camerainfo))}
-                        self._ifds[tag] = makernote
-                else:
-                    # Interop
-                    self._ifds[tag] = self._get_ifd_dict(tag_data)
-        ifd = self._ifds.get(tag, {})
-        if tag == ExifTags.IFD.Exif and self._hidden_data:
-            ifd = {
-                k: v
-                for (k, v) in ifd.items()
-                if k not in (ExifTags.IFD.Interop, ExifTags.IFD.Makernote)
-            }
-        return ifd
-
-    def hide_offsets(self):
-        for tag in (ExifTags.IFD.Exif, ExifTags.IFD.GPSInfo):
-            if tag in self:
-                self._hidden_data[tag] = self[tag]
-                del self[tag]
-
-    def __str__(self):
-        if self._info is not None:
-            # Load all keys into self._data
-            for tag in self._info:
-                self[tag]
-
-        return str(self._data)
-
-    def __len__(self):
-        keys = set(self._data)
-        if self._info is not None:
-            keys.update(self._info)
-        return len(keys)
-
-    def __getitem__(self, tag):
-        if self._info is not None and tag not in self._data and tag in self._info:
-            self._data[tag] = self._fixup(self._info[tag])
-            del self._info[tag]
-        return self._data[tag]
-
-    def __contains__(self, tag):
-        return tag in self._data or (self._info is not None and tag in self._info)
-
-    def __setitem__(self, tag, value):
-        if self._info is not None and tag in self._info:
-            del self._info[tag]
-        self._data[tag] = value
-
-    def __delitem__(self, tag):
-        if self._info is not None and tag in self._info:
-            del self._info[tag]
-        else:
-            del self._data[tag]
-
-    def __iter__(self):
-        keys = set(self._data)
-        if self._info is not None:
-            keys.update(self._info)
-        return iter(keys)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dataclasses_json/stringcase.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dataclasses_json/stringcase.py
deleted file mode 100644
index da0f546f3c7cec1ad484bfb6ed4e47689aeab440..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dataclasses_json/stringcase.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# The MIT License (MIT)
-#
-# Copyright (c) 2015 Taka Okunishi
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in
-# all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-#
-# Copyright © 2015-2018 Taka Okunishi <okunishinishi@gmail.com>.
-# Copyright © 2020 Louis-Philippe Véronneau <pollo@debian.org>
-
-import re
-
-
-def uplowcase(string, case):
-    """Convert string into upper or lower case.
-
-    Args:
-        string: String to convert.
-
-    Returns:
-        string: Uppercase or lowercase case string.
-
-    """
-    if case == 'up':
-        return str(string).upper()
-    elif case == 'low':
-        return str(string).lower()
-
-
-def capitalcase(string):
-    """Convert string into capital case.
-    First letters will be uppercase.
-
-    Args:
-        string: String to convert.
-
-    Returns:
-        string: Capital case string.
-
-    """
-
-    string = str(string)
-    if not string:
-        return string
-    return uplowcase(string[0], 'up') + string[1:]
-
-
-def camelcase(string):
-    """ Convert string into camel case.
-
-    Args:
-        string: String to convert.
-
-    Returns:
-        string: Camel case string.
-
-    """
-
-    string = re.sub(r"^[\-_\.]", '', str(string))
-    if not string:
-        return string
-    return (uplowcase(string[0], 'low')
-            + re.sub(r"[\-_\.\s]([a-z0-9])",
-                     lambda matched: uplowcase(matched.group(1), 'up'),
-                     string[1:]))
-
-
-def snakecase(string):
-    """Convert string into snake case.
-    Join punctuation with underscore
-
-    Args:
-        string: String to convert.
-
-    Returns:
-        string: Snake cased string.
-
-    """
-
-    string = re.sub(r"[\-\.\s]", '_', str(string))
-    if not string:
-        return string
-    return (uplowcase(string[0], 'low')
-            + re.sub(r"[A-Z0-9]",
-                     lambda matched: '_' + uplowcase(matched.group(0), 'low'),
-                     string[1:]))
-
-
-def spinalcase(string):
-    """Convert string into spinal case.
-    Join punctuation with hyphen.
-
-    Args:
-        string: String to convert.
-
-    Returns:
-        string: Spinal cased string.
-
-    """
-
-    return re.sub(r"_", "-", snakecase(string))
-
-
-def pascalcase(string):
-    """Convert string into pascal case.
-
-    Args:
-        string: String to convert.
-
-    Returns:
-        string: Pascal case string.
-
-    """
-
-    return capitalcase(camelcase(string))
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/NSEC3PARAM.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/NSEC3PARAM.py
deleted file mode 100644
index 1a0c0e0849f5f8d82e7850400e32a4ad69308389..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/dns/rdtypes/ANY/NSEC3PARAM.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license
-
-# Copyright (C) 2004-2007, 2009-2011 Nominum, Inc.
-#
-# Permission to use, copy, modify, and distribute this software and its
-# documentation for any purpose with or without fee is hereby granted,
-# provided that the above copyright notice and this permission notice
-# appear in all copies.
-#
-# THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES
-# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
-# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR
-# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
-# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
-# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
-# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
-
-import binascii
-import struct
-
-import dns.exception
-import dns.immutable
-import dns.rdata
-
-
-@dns.immutable.immutable
-class NSEC3PARAM(dns.rdata.Rdata):
-
-    """NSEC3PARAM record"""
-
-    __slots__ = ["algorithm", "flags", "iterations", "salt"]
-
-    def __init__(self, rdclass, rdtype, algorithm, flags, iterations, salt):
-        super().__init__(rdclass, rdtype)
-        self.algorithm = self._as_uint8(algorithm)
-        self.flags = self._as_uint8(flags)
-        self.iterations = self._as_uint16(iterations)
-        self.salt = self._as_bytes(salt, True, 255)
-
-    def to_text(self, origin=None, relativize=True, **kw):
-        if self.salt == b"":
-            salt = "-"
-        else:
-            salt = binascii.hexlify(self.salt).decode()
-        return "%u %u %u %s" % (self.algorithm, self.flags, self.iterations, salt)
-
-    @classmethod
-    def from_text(
-        cls, rdclass, rdtype, tok, origin=None, relativize=True, relativize_to=None
-    ):
-        algorithm = tok.get_uint8()
-        flags = tok.get_uint8()
-        iterations = tok.get_uint16()
-        salt = tok.get_string()
-        if salt == "-":
-            salt = ""
-        else:
-            salt = binascii.unhexlify(salt.encode())
-        return cls(rdclass, rdtype, algorithm, flags, iterations, salt)
-
-    def _to_wire(self, file, compress=None, origin=None, canonicalize=False):
-        l = len(self.salt)
-        file.write(struct.pack("!BBHB", self.algorithm, self.flags, self.iterations, l))
-        file.write(self.salt)
-
-    @classmethod
-    def from_wire_parser(cls, rdclass, rdtype, parser, origin=None):
-        (algorithm, flags, iterations) = parser.get_struct("!BBH")
-        salt = parser.get_counted_bytes()
-        return cls(rdclass, rdtype, algorithm, flags, iterations, salt)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/L_T_S_H_.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/L_T_S_H_.py
deleted file mode 100644
index e0ab0d021c47cf79e51cad326806e12ff97c9e00..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/ttLib/tables/L_T_S_H_.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from fontTools.misc.textTools import safeEval
-from . import DefaultTable
-import struct
-import array
-
-# XXX I've lowered the strictness, to make sure Apple's own Chicago
-# XXX gets through. They're looking into it, I hope to raise the standards
-# XXX back to normal eventually.
-
-
-class table_L_T_S_H_(DefaultTable.DefaultTable):
-    def decompile(self, data, ttFont):
-        version, numGlyphs = struct.unpack(">HH", data[:4])
-        data = data[4:]
-        assert version == 0, "unknown version: %s" % version
-        assert (len(data) % numGlyphs) < 4, "numGlyphs doesn't match data length"
-        # ouch: the assertion is not true in Chicago!
-        # assert numGlyphs == ttFont['maxp'].numGlyphs
-        yPels = array.array("B")
-        yPels.frombytes(data)
-        self.yPels = {}
-        for i in range(numGlyphs):
-            self.yPels[ttFont.getGlyphName(i)] = yPels[i]
-
-    def compile(self, ttFont):
-        version = 0
-        names = list(self.yPels.keys())
-        numGlyphs = len(names)
-        yPels = [0] * numGlyphs
-        # ouch: the assertion is not true in Chicago!
-        # assert len(self.yPels) == ttFont['maxp'].numGlyphs == numGlyphs
-        for name in names:
-            yPels[ttFont.getGlyphID(name)] = self.yPels[name]
-        yPels = array.array("B", yPels)
-        return struct.pack(">HH", version, numGlyphs) + yPels.tobytes()
-
-    def toXML(self, writer, ttFont):
-        names = sorted(self.yPels.keys())
-        for name in names:
-            writer.simpletag("yPel", name=name, value=self.yPels[name])
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if not hasattr(self, "yPels"):
-            self.yPels = {}
-        if name != "yPel":
-            return  # ignore unknown tags
-        self.yPels[attrs["name"]] = safeEval(attrs["value"])
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/file/base.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/file/base.py
deleted file mode 100644
index d0027a089853c5ff8efcaa11483f67fc0e61b666..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/file/base.py
+++ /dev/null
@@ -1,167 +0,0 @@
-"""Simple reader that reads files of different formats from a directory."""
-import logging
-from pathlib import Path
-from typing import Callable, Dict, List, Optional, Union
-
-from gpt_index.readers.base import BaseReader
-from gpt_index.readers.file.base_parser import BaseParser
-from gpt_index.readers.file.docs_parser import DocxParser, PDFParser
-from gpt_index.readers.file.epub_parser import EpubParser
-from gpt_index.readers.file.image_parser import ImageParser
-from gpt_index.readers.file.markdown_parser import MarkdownParser
-from gpt_index.readers.file.mbox_parser import MboxParser
-from gpt_index.readers.file.slides_parser import PptxParser
-from gpt_index.readers.file.tabular_parser import PandasCSVParser
-from gpt_index.readers.file.video_audio import VideoAudioParser
-from gpt_index.readers.schema.base import Document
-
-DEFAULT_FILE_EXTRACTOR: Dict[str, BaseParser] = {
-    ".pdf": PDFParser(),
-    ".docx": DocxParser(),
-    ".pptx": PptxParser(),
-    ".jpg": ImageParser(),
-    ".png": ImageParser(),
-    ".jpeg": ImageParser(),
-    ".mp3": VideoAudioParser(),
-    ".mp4": VideoAudioParser(),
-    ".csv": PandasCSVParser(),
-    ".epub": EpubParser(),
-    ".md": MarkdownParser(),
-    ".mbox": MboxParser(),
-}
-
-
-class SimpleDirectoryReader(BaseReader):
-    """Simple directory reader.
-
-    Can read files into separate documents, or concatenates
-    files into one document text.
-
-    Args:
-        input_dir (str): Path to the directory.
-        input_files (List): List of file paths to read (Optional; overrides input_dir)
-        exclude_hidden (bool): Whether to exclude hidden files (dotfiles).
-        errors (str): how encoding and decoding errors are to be handled,
-              see https://docs.python.org/3/library/functions.html#open
-        recursive (bool): Whether to recursively search in subdirectories.
-            False by default.
-        required_exts (Optional[List[str]]): List of required extensions.
-            Default is None.
-        file_extractor (Optional[Dict[str, BaseParser]]): A mapping of file
-            extension to a BaseParser class that specifies how to convert that file
-            to text. See DEFAULT_FILE_EXTRACTOR.
-        num_files_limit (Optional[int]): Maximum number of files to read.
-            Default is None.
-        file_metadata (Optional[Callable[str, Dict]]): A function that takes
-            in a filename and returns a Dict of metadata for the Document.
-            Default is None.
-    """
-
-    def __init__(
-        self,
-        input_dir: Optional[str] = None,
-        input_files: Optional[List] = None,
-        exclude_hidden: bool = True,
-        errors: str = "ignore",
-        recursive: bool = False,
-        required_exts: Optional[List[str]] = None,
-        file_extractor: Optional[Dict[str, BaseParser]] = None,
-        num_files_limit: Optional[int] = None,
-        file_metadata: Optional[Callable[[str], Dict]] = None,
-    ) -> None:
-        """Initialize with parameters."""
-        super().__init__()
-
-        if not input_dir and not input_files:
-            raise ValueError("Must provide either `input_dir` or `input_files`.")
-
-        self.errors = errors
-
-        self.recursive = recursive
-        self.exclude_hidden = exclude_hidden
-        self.required_exts = required_exts
-        self.num_files_limit = num_files_limit
-
-        if input_files:
-            self.input_files = []
-            for path in input_files:
-                input_file = Path(path)
-                self.input_files.append(input_file)
-        elif input_dir:
-            self.input_dir = Path(input_dir)
-            self.input_files = self._add_files(self.input_dir)
-
-        self.file_extractor = file_extractor or DEFAULT_FILE_EXTRACTOR
-        self.file_metadata = file_metadata
-
-    def _add_files(self, input_dir: Path) -> List[Path]:
-        """Add files."""
-        input_files = sorted(input_dir.iterdir())
-        new_input_files = []
-        dirs_to_explore = []
-        for input_file in input_files:
-            if input_file.is_dir():
-                if self.recursive:
-                    dirs_to_explore.append(input_file)
-            elif self.exclude_hidden and input_file.name.startswith("."):
-                continue
-            elif (
-                self.required_exts is not None
-                and input_file.suffix not in self.required_exts
-            ):
-                continue
-            else:
-                new_input_files.append(input_file)
-
-        for dir_to_explore in dirs_to_explore:
-            sub_input_files = self._add_files(dir_to_explore)
-            new_input_files.extend(sub_input_files)
-
-        if self.num_files_limit is not None and self.num_files_limit > 0:
-            new_input_files = new_input_files[0 : self.num_files_limit]
-
-        # print total number of files added
-        logging.debug(
-            f"> [SimpleDirectoryReader] Total files added: {len(new_input_files)}"
-        )
-
-        return new_input_files
-
-    def load_data(self, concatenate: bool = False) -> List[Document]:
-        """Load data from the input directory.
-
-        Args:
-            concatenate (bool): whether to concatenate all files into one document.
-                If set to True, file metadata is ignored.
-                False by default.
-
-        Returns:
-            List[Document]: A list of documents.
-
-        """
-        data: Union[str, List[str]] = ""
-        data_list: List[str] = []
-        metadata_list = []
-        for input_file in self.input_files:
-            if input_file.suffix in self.file_extractor:
-                parser = self.file_extractor[input_file.suffix]
-                if not parser.parser_config_set:
-                    parser.init_parser()
-                data = parser.parse_file(input_file, errors=self.errors)
-            else:
-                # do standard read
-                with open(input_file, "r", errors=self.errors) as f:
-                    data = f.read()
-            if isinstance(data, List):
-                data_list.extend(data)
-            else:
-                data_list.append(str(data))
-            if self.file_metadata is not None:
-                metadata_list.append(self.file_metadata(str(input_file)))
-
-        if concatenate:
-            return [Document("\n".join(data_list))]
-        elif self.file_metadata is not None:
-            return [Document(d, extra_info=m) for d, m in zip(data_list, metadata_list)]
-        else:
-            return [Document(d) for d in data_list]
diff --git a/spaces/jorgeppp/LDCC-LDCC-Instruct-Llama-2-ko-13B-v1.4/README.md b/spaces/jorgeppp/LDCC-LDCC-Instruct-Llama-2-ko-13B-v1.4/README.md
deleted file mode 100644
index d9e9e5ca664bc4be66d3f7481c31baf19199ba76..0000000000000000000000000000000000000000
--- a/spaces/jorgeppp/LDCC-LDCC-Instruct-Llama-2-ko-13B-v1.4/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: LDCC LDCC Instruct Llama 2 Ko 13B V1.4
-emoji: 🏢
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.1.1
-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/kbora/minerva-generate-docker/Dockerfile b/spaces/kbora/minerva-generate-docker/Dockerfile
deleted file mode 100644
index c71a19fcd666314b28a237e5bc0cf936e538f848..0000000000000000000000000000000000000000
--- a/spaces/kbora/minerva-generate-docker/Dockerfile
+++ /dev/null
@@ -1,32 +0,0 @@
-# Use an official CUDA runtime as a parent image
-FROM nvidia/cuda:11.2.2-runtime-ubuntu20.04
-
-# Copy the current directory contents into the container at /usr/src/app
-WORKDIR $HOME/app
-
-# Install Python
-RUN apt-get update && apt-get install -y python3.8 python3-pip
-
-RUN useradd -m -u 1000 user
-# Switch to the "user" user
-USER user
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
- 
-# Set the cache directories to a path with write access
-ENV TRANSFORMERS_CACHE=/tmp/transformers_cache
-ENV MPLCONFIGDIR=/tmp/matplotlib
-ENV HF_HOME=/tmp/huggingface_cache
-
-COPY --chown=user . $HOME/app
-
-# Install any needed packages specified in requirements.txt
-RUN pip3 install -r $HOME/app/requirements.txt
-
-# Make port 80 available to the world outside this container
-EXPOSE 7860
-
-# Run app.py when the container launches
-CMD python3 $HOME/app/app.py
-
diff --git a/spaces/kbora/minerva-generate-docker/utils/image.py b/spaces/kbora/minerva-generate-docker/utils/image.py
deleted file mode 100644
index e9477805047f44e3f2f6c2ffdbc36e1fd03fe7c1..0000000000000000000000000000000000000000
--- a/spaces/kbora/minerva-generate-docker/utils/image.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import io
-from datetime import datetime, timezone
-import uuid
-import boto3
-from config import AWSConfig
-
-#Upload data to s3
-def write_to_s3(image, fname, region_name='ap-south-1'):
-    """
-    Write an image to s3. Returns the url. Requires AWSConfig
-    # TODO : Add error handling
-    # TODO : Add logging
-    """
-    s3 = boto3.client('s3', region_name,aws_access_key_id=AWSConfig.aws_access_key_id, aws_secret_access_key=AWSConfig.aws_secret_access_key)
-    s3.upload_fileobj(image,AWSConfig.bucket_name,fname)
-    return f'https://{AWSConfig.bucket_name}.s3.{region_name}.amazonaws.com/{fname}'
-
-def save_image(img):
-    """
-    Save an image to s3. Returns the url and filename for JSON output
-    # TODO : Add error handling
-    """
-    in_mem_file = io.BytesIO()
-    img.save(in_mem_file, format = 'png')
-    in_mem_file.seek(0)
-    dt = datetime.now()
-    file_name = str(uuid.uuid4())+'-'+str(int(dt.replace(tzinfo=timezone.utc).timestamp()))
-    img_url =  write_to_s3(in_mem_file,f'sdimage/{file_name}.jpeg')
-    return img_url,file_name
diff --git a/spaces/kdemertzis/Earthquakes/app.py b/spaces/kdemertzis/Earthquakes/app.py
deleted file mode 100644
index d00b0bcddbdae6bfbf7fc9427a97745424526412..0000000000000000000000000000000000000000
--- a/spaces/kdemertzis/Earthquakes/app.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Untitled6.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/github/kdemertzis/Earthquakes/blob/main/GRADIO-Classification_ROW_FORM_BARE.ipynb
-"""
-
-#pip install pycaret
-#pip install gradio
-#pip install pyyaml==5.4.1
-
-"""
-    Import library
-"""
-##-- PyCaret
-import pycaret
-from pycaret.classification import *
-##-- Pandas
-import pandas as pd
-from pandas import Series, DataFrame
-##-- Scikit-learn
-import sklearn
-from string import ascii_letters
-import numpy as np
-import seaborn as sns
-import matplotlib.pyplot as plt
-##-- Gradio
-import gradio as gr
-import warnings
-warnings.simplefilter(action='ignore', category=FutureWarning)
-
-url = 'https://raw.githubusercontent.com/kdemertzis/Earthquakes/main/Data/Gradio/1_3class.csv'
-# load the dataset
-df = pd.read_csv(url)
-
-# setup the dataset
-grid = setup(data=df, target=df.columns[-1], html=False, silent=True, verbose=False)
-
-df.describe()
-
-best = compare_models(cross_validation=True, n_select = 10, turbo=True) 
-compare_model_results = pull()
-
-len(best)
-10
-print(best[:5])
-
-# creating a predict function to be passed into gradio UI
-def predict(model, HI, PGA, VMAX, ASI, TSIGN, TBRAC, PP, CAV, SED, TUD, EPA, PGV, PGD, IA, HTOT, ECC, VW1, VW2):
-  
-    df = pd.DataFrame.from_dict({'HI': [HI], 'PGA': [PGA], 'VMAX': [VMAX], 'ASI': [ASI], 'TSIGN': [TSIGN], 'TBRAC': [TBRAC], 'PP': [PP], 'CAV': [CAV], 'SED': [SED], 'TUD': [TUD], 'EPA': [EPA], 'PGV': [PGV], 'PGD': [PGD], 'IA': [IA], 'HTOT': [HTOT], 'ECC': [ECC], 'VW1': [VW1], 'VW2': [VW2]})
-    model_index = list(compare_model_results['Model']).index(model)
-    model = best[model_index]
-    pred = predict_model(model, df, raw_score=True)
-    return {'Slight': pred['Score_A'][0].astype('float64'), 'Moderate': pred['Score_B'][0].astype('float64' ), 'Heavy': pred['Score_C'][0].astype('float64')}
-
-title="Classification of Buildings' Seismic Damage by ML Techniques"
-model = gr.inputs.Dropdown(list(compare_model_results['Model']),label="Model")
-HI = gr.inputs.Slider(minimum=-1, maximum=1, default=df['HI'].mean(), label="HI")
-PGA = gr.inputs.Slider(minimum=-1, maximum=1, default=df['PGA'].mean(), label="PGA")
-VMAX = gr.inputs.Slider(minimum=-1, maximum=1, default=df['VMAX'].mean(), label="VMAX")
-ASI = gr.inputs.Slider(minimum=-1, maximum=1, default=df['ASI'].mean(), label="ASI")
-TSIGN = gr.inputs.Slider(minimum=-1, maximum=1, default=df['TSIGN'].mean(), label="TSIGN")
-TBRAC = gr.inputs.Slider(minimum=-1, maximum=1, default=df['TBRAC'].mean(), label="TBRAC")
-PP = gr.inputs.Slider(minimum=-1, maximum=1, default=df['PP'].mean(), label="PP")
-CAV = gr.inputs.Slider(minimum=-1, maximum=1, default=df['CAV'].mean(), label="CAV")
-SED = gr.inputs.Slider(minimum=-1, maximum=1, default=df['SED'].mean(), label="SED")
-TUD = gr.inputs.Slider(minimum=-1, maximum=1, default=df['TUD'].mean(), label="TUD")
-EPA = gr.inputs.Slider(minimum=-1, maximum=1, default=df['EPA'].mean(), label="EPA")
-PGV = gr.inputs.Slider(minimum=-1, maximum=1, default=df['PGV'].mean(), label="PGV")
-PGD = gr.inputs.Slider(minimum=-1, maximum=1, default=df['PGD'].mean(), label="PGD")
-IA = gr.inputs.Slider(minimum=-1, maximum=1, default=df['IA'].mean(), label="IA")
-HTOT = gr.inputs.Slider(minimum=-1, maximum=1, default=df['HTOT'].mean(), label="HTOT")
-ECC = gr.inputs.Slider(minimum=-1, maximum=1, default=df['ECC'].mean(), label="ECC")
-VW1 = gr.inputs.Slider(minimum=-1, maximum=1, default=df['VW1'].mean(), label="VW1")
-VW2 = gr.inputs.Slider(minimum=-1, maximum=1, default=df['VW2'].mean(), label="VW2")
-
-gr.Interface(predict,[model, HI, PGA, VMAX, ASI, TSIGN, TBRAC, PP, CAV, SED, TUD, EPA, PGV, PGD, IA, HTOT, ECC, VW1, VW2], "label", title=title, live=True).launch()
\ No newline at end of file
diff --git a/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/onnx_helper.py b/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/onnx_helper.py
deleted file mode 100644
index ca922ca6d410655029e459cf8fd1c323d276c34c..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/onnx_helper.py
+++ /dev/null
@@ -1,250 +0,0 @@
-from __future__ import division
-import datetime
-import os
-import os.path as osp
-import glob
-import numpy as np
-import cv2
-import sys
-import onnxruntime
-import onnx
-import argparse
-from onnx import numpy_helper
-from insightface.data import get_image
-
-class ArcFaceORT:
-    def __init__(self, model_path, cpu=False):
-        self.model_path = model_path
-        # providers = None will use available provider, for onnxruntime-gpu it will be "CUDAExecutionProvider"
-        self.providers = ['CPUExecutionProvider'] if cpu else None
-
-    #input_size is (w,h), return error message, return None if success
-    def check(self, track='cfat', test_img = None):
-        #default is cfat
-        max_model_size_mb=1024
-        max_feat_dim=512
-        max_time_cost=15
-        if track.startswith('ms1m'):
-            max_model_size_mb=1024
-            max_feat_dim=512
-            max_time_cost=10
-        elif track.startswith('glint'):
-            max_model_size_mb=1024
-            max_feat_dim=1024
-            max_time_cost=20
-        elif track.startswith('cfat'):
-            max_model_size_mb = 1024
-            max_feat_dim = 512
-            max_time_cost = 15
-        elif track.startswith('unconstrained'):
-            max_model_size_mb=1024
-            max_feat_dim=1024
-            max_time_cost=30
-        else:
-            return "track not found"
-
-        if not os.path.exists(self.model_path):
-            return "model_path not exists"
-        if not os.path.isdir(self.model_path):
-            return "model_path should be directory"
-        onnx_files = []
-        for _file in os.listdir(self.model_path):
-            if _file.endswith('.onnx'):
-                onnx_files.append(osp.join(self.model_path, _file))
-        if len(onnx_files)==0:
-            return "do not have onnx files"
-        self.model_file = sorted(onnx_files)[-1]
-        print('use onnx-model:', self.model_file)
-        try:
-            session = onnxruntime.InferenceSession(self.model_file, providers=self.providers)
-        except:
-            return "load onnx failed"
-        input_cfg = session.get_inputs()[0]
-        input_shape = input_cfg.shape
-        print('input-shape:', input_shape)
-        if len(input_shape)!=4:
-            return "length of input_shape should be 4"
-        if not isinstance(input_shape[0], str):
-            #return "input_shape[0] should be str to support batch-inference"
-            print('reset input-shape[0] to None')
-            model = onnx.load(self.model_file)
-            model.graph.input[0].type.tensor_type.shape.dim[0].dim_param = 'None'
-            new_model_file = osp.join(self.model_path, 'zzzzrefined.onnx')
-            onnx.save(model, new_model_file)
-            self.model_file = new_model_file
-            print('use new onnx-model:', self.model_file)
-            try:
-                session = onnxruntime.InferenceSession(self.model_file, providers=self.providers)
-            except:
-                return "load onnx failed"
-            input_cfg = session.get_inputs()[0]
-            input_shape = input_cfg.shape
-            print('new-input-shape:', input_shape)
-
-        self.image_size = tuple(input_shape[2:4][::-1])
-        #print('image_size:', self.image_size)
-        input_name = input_cfg.name
-        outputs = session.get_outputs()
-        output_names = []
-        for o in outputs:
-            output_names.append(o.name)
-            #print(o.name, o.shape)
-        if len(output_names)!=1:
-            return "number of output nodes should be 1"
-        self.session = session
-        self.input_name = input_name
-        self.output_names = output_names
-        #print(self.output_names)
-        model = onnx.load(self.model_file)
-        graph = model.graph
-        if len(graph.node)<8:
-            return "too small onnx graph"
-
-        input_size = (112,112)
-        self.crop = None
-        if track=='cfat':
-            crop_file = osp.join(self.model_path, 'crop.txt')
-            if osp.exists(crop_file):
-                lines = open(crop_file,'r').readlines()
-                if len(lines)!=6:
-                    return "crop.txt should contain 6 lines"
-                lines = [int(x) for x in lines]
-                self.crop = lines[:4]
-                input_size = tuple(lines[4:6])
-        if input_size!=self.image_size:
-            return "input-size is inconsistant with onnx model input, %s vs %s"%(input_size, self.image_size)
-
-        self.model_size_mb = os.path.getsize(self.model_file) / float(1024*1024)
-        if self.model_size_mb > max_model_size_mb:
-            return "max model size exceed, given %.3f-MB"%self.model_size_mb
-
-        input_mean = None
-        input_std = None
-        if track=='cfat':
-            pn_file = osp.join(self.model_path, 'pixel_norm.txt')
-            if osp.exists(pn_file):
-                lines = open(pn_file,'r').readlines()
-                if len(lines)!=2:
-                    return "pixel_norm.txt should contain 2 lines"
-                input_mean = float(lines[0])
-                input_std = float(lines[1])
-        if input_mean is not None or input_std is not None:
-            if input_mean is None or input_std is None:
-                return "please set input_mean and input_std simultaneously"
-        else:
-            find_sub = False
-            find_mul = False
-            for nid, node in enumerate(graph.node[:8]):
-                print(nid, node.name)
-                if node.name.startswith('Sub') or node.name.startswith('_minus'):
-                    find_sub = True
-                if node.name.startswith('Mul') or node.name.startswith('_mul') or node.name.startswith('Div'):
-                    find_mul = True
-            if find_sub and find_mul:
-                print("find sub and mul")
-                #mxnet arcface model
-                input_mean = 0.0
-                input_std = 1.0
-            else:
-                input_mean = 127.5
-                input_std = 127.5
-        self.input_mean = input_mean
-        self.input_std = input_std
-        for initn in graph.initializer:
-            weight_array = numpy_helper.to_array(initn)
-            dt = weight_array.dtype
-            if dt.itemsize<4:
-                return 'invalid weight type - (%s:%s)' % (initn.name, dt.name)
-        if test_img is None:
-            test_img = get_image('Tom_Hanks_54745')
-            test_img = cv2.resize(test_img, self.image_size)
-        else:
-            test_img = cv2.resize(test_img, self.image_size)
-        feat, cost = self.benchmark(test_img)
-        batch_result = self.check_batch(test_img)
-        batch_result_sum = float(np.sum(batch_result))
-        if batch_result_sum in [float('inf'), -float('inf')] or batch_result_sum != batch_result_sum:
-            print(batch_result)
-            print(batch_result_sum)
-            return "batch result output contains NaN!"
-
-        if len(feat.shape) < 2:
-           return "the shape of the feature must be two, but get {}".format(str(feat.shape))
-
-        if feat.shape[1] > max_feat_dim:
-            return "max feat dim exceed, given %d"%feat.shape[1]
-        self.feat_dim = feat.shape[1]
-        cost_ms = cost*1000
-        if cost_ms>max_time_cost:
-            return "max time cost exceed, given %.4f"%cost_ms
-        self.cost_ms = cost_ms
-        print('check stat:, model-size-mb: %.4f, feat-dim: %d, time-cost-ms: %.4f, input-mean: %.3f, input-std: %.3f'%(self.model_size_mb, self.feat_dim, self.cost_ms, self.input_mean, self.input_std))
-        return None
-
-    def check_batch(self, img):
-        if not isinstance(img, list):
-            imgs = [img, ] * 32
-        if self.crop is not None:
-            nimgs = []
-            for img in imgs:
-                nimg = img[self.crop[1]:self.crop[3], self.crop[0]:self.crop[2], :]
-                if nimg.shape[0] != self.image_size[1] or nimg.shape[1] != self.image_size[0]:
-                    nimg = cv2.resize(nimg, self.image_size)
-                nimgs.append(nimg)
-            imgs = nimgs
-        blob = cv2.dnn.blobFromImages(
-            images=imgs, scalefactor=1.0 / self.input_std, size=self.image_size,
-            mean=(self.input_mean, self.input_mean, self.input_mean), swapRB=True)
-        net_out = self.session.run(self.output_names, {self.input_name: blob})[0]
-        return net_out
-
-
-    def meta_info(self):
-        return {'model-size-mb':self.model_size_mb, 'feature-dim':self.feat_dim, 'infer': self.cost_ms}
-
-
-    def forward(self, imgs):
-        if not isinstance(imgs, list):
-            imgs = [imgs]
-        input_size = self.image_size
-        if self.crop is not None:
-            nimgs = []
-            for img in imgs:
-                nimg = img[self.crop[1]:self.crop[3],self.crop[0]:self.crop[2],:]
-                if nimg.shape[0]!=input_size[1] or nimg.shape[1]!=input_size[0]:
-                    nimg = cv2.resize(nimg, input_size)
-                nimgs.append(nimg)
-            imgs = nimgs
-        blob = cv2.dnn.blobFromImages(imgs, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
-        net_out = self.session.run(self.output_names, {self.input_name : blob})[0]
-        return net_out
-
-    def benchmark(self, img):
-        input_size = self.image_size
-        if self.crop is not None:
-            nimg = img[self.crop[1]:self.crop[3],self.crop[0]:self.crop[2],:]
-            if nimg.shape[0]!=input_size[1] or nimg.shape[1]!=input_size[0]:
-                nimg = cv2.resize(nimg, input_size)
-            img = nimg
-        blob = cv2.dnn.blobFromImage(img, 1.0/self.input_std, input_size, (self.input_mean, self.input_mean, self.input_mean), swapRB=True)
-        costs = []
-        for _ in range(50):
-            ta = datetime.datetime.now()
-            net_out = self.session.run(self.output_names, {self.input_name : blob})[0]
-            tb = datetime.datetime.now()
-            cost = (tb-ta).total_seconds()
-            costs.append(cost)
-        costs = sorted(costs)
-        cost = costs[5]
-        return net_out, cost
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='')
-    # general
-    parser.add_argument('workdir', help='submitted work dir', type=str)
-    parser.add_argument('--track', help='track name, for different challenge', type=str, default='cfat')
-    args = parser.parse_args()
-    handler = ArcFaceORT(args.workdir)
-    err = handler.check(args.track)
-    print('err:', err)
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/conv_seq2seq/README.md b/spaces/koajoel/PolyFormer/fairseq/examples/conv_seq2seq/README.md
deleted file mode 100644
index 95fe7e7909a77ee0e50fe31d4b8be38daa8f3be7..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/conv_seq2seq/README.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
-
-## Pre-trained models
-
-Description | Dataset | Model | Test set(s)
----|---|---|---
-Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-French](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.newstest2014.tar.bz2) <br> newstest2012/2013: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.v2.en-fr.ntst1213.tar.bz2)
-Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT14 English-German](http://statmt.org/wmt14/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt14.en-de.newstest2014.tar.bz2)
-Convolutional <br> ([Gehring et al., 2017](https://arxiv.org/abs/1705.03122)) | [WMT17 English-German](http://statmt.org/wmt17/translation-task.html#Download) | [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2) | newstest2014: <br> [download (.tar.bz2)](https://dl.fbaipublicfiles.com/fairseq/data/wmt17.v2.en-de.newstest2014.tar.bz2)
-
-## Example usage
-
-See the [translation README](../translation/README.md) for instructions on reproducing results for WMT'14 En-De and
-WMT'14 En-Fr using the `fconv_wmt_en_de` and `fconv_wmt_en_fr` model architectures.
-
-## Citation
-
-```bibtex
-@inproceedings{gehring2017convs2s,
-  title = {Convolutional Sequence to Sequence Learning},
-  author = {Gehring, Jonas, and Auli, Michael and Grangier, David and Yarats, Denis and Dauphin, Yann N},
-  booktitle = {Proc. of ICML},
-  year = 2017,
-}
-```
diff --git a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/losses/adversarial.py b/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/losses/adversarial.py
deleted file mode 100644
index d6db2967ce5074d94ed3b4c51fc743ff2f7831b1..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/saicinpainting/training/losses/adversarial.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from typing import Tuple, Dict, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class BaseAdversarialLoss:
-    def pre_generator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                           generator: nn.Module, discriminator: nn.Module):
-        """
-        Prepare for generator step
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param generator:
-        :param discriminator:
-        :return: None
-        """
-
-    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                               generator: nn.Module, discriminator: nn.Module):
-        """
-        Prepare for discriminator step
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param generator:
-        :param discriminator:
-        :return: None
-        """
-
-    def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                       discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                       mask: Optional[torch.Tensor] = None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        """
-        Calculate generator loss
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param discr_real_pred: Tensor, discriminator output for real_batch
-        :param discr_fake_pred: Tensor, discriminator output for fake_batch
-        :param mask: Tensor, actual mask, which was at input of generator when making fake_batch
-        :return: total generator loss along with some values that might be interesting to log
-        """
-        raise NotImplemented()
-
-    def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                           discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                           mask: Optional[torch.Tensor] = None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        """
-        Calculate discriminator loss and call .backward() on it
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param discr_real_pred: Tensor, discriminator output for real_batch
-        :param discr_fake_pred: Tensor, discriminator output for fake_batch
-        :param mask: Tensor, actual mask, which was at input of generator when making fake_batch
-        :return: total discriminator loss along with some values that might be interesting to log
-        """
-        raise NotImplemented()
-
-    def interpolate_mask(self, mask, shape):
-        assert mask is not None
-        assert self.allow_scale_mask or shape == mask.shape[-2:]
-        if shape != mask.shape[-2:] and self.allow_scale_mask:
-            if self.mask_scale_mode == 'maxpool':
-                mask = F.adaptive_max_pool2d(mask, shape)
-            else:
-                mask = F.interpolate(mask, size=shape, mode=self.mask_scale_mode)
-        return mask
-
-def make_r1_gp(discr_real_pred, real_batch):
-    if torch.is_grad_enabled():
-        grad_real = torch.autograd.grad(outputs=discr_real_pred.sum(), inputs=real_batch, create_graph=True)[0]
-        grad_penalty = (grad_real.view(grad_real.shape[0], -1).norm(2, dim=1) ** 2).mean()
-    else:
-        grad_penalty = 0
-    real_batch.requires_grad = False
-
-    return grad_penalty
-
-class NonSaturatingWithR1(BaseAdversarialLoss):
-    def __init__(self, gp_coef=5, weight=1, mask_as_fake_target=False, allow_scale_mask=False,
-                 mask_scale_mode='nearest', extra_mask_weight_for_gen=0,
-                 use_unmasked_for_gen=True, use_unmasked_for_discr=True):
-        self.gp_coef = gp_coef
-        self.weight = weight
-        # use for discr => use for gen;
-        # otherwise we teach only the discr to pay attention to very small difference
-        assert use_unmasked_for_gen or (not use_unmasked_for_discr)
-        # mask as target => use unmasked for discr:
-        # if we don't care about unmasked regions at all
-        # then it doesn't matter if the value of mask_as_fake_target is true or false
-        assert use_unmasked_for_discr or (not mask_as_fake_target)
-        self.use_unmasked_for_gen = use_unmasked_for_gen
-        self.use_unmasked_for_discr = use_unmasked_for_discr
-        self.mask_as_fake_target = mask_as_fake_target
-        self.allow_scale_mask = allow_scale_mask
-        self.mask_scale_mode = mask_scale_mode
-        self.extra_mask_weight_for_gen = extra_mask_weight_for_gen
-
-    def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                       discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                       mask=None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        fake_loss = F.softplus(-discr_fake_pred)
-        if (self.mask_as_fake_target and self.extra_mask_weight_for_gen > 0) or \
-                not self.use_unmasked_for_gen:  # == if masked region should be treated differently
-            mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
-            if not self.use_unmasked_for_gen:
-                fake_loss = fake_loss * mask
-            else:
-                pixel_weights = 1 + mask * self.extra_mask_weight_for_gen
-                fake_loss = fake_loss * pixel_weights
-
-        return fake_loss.mean() * self.weight, dict()
-
-    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                               generator: nn.Module, discriminator: nn.Module):
-        real_batch.requires_grad = True
-
-    def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                           discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                           mask=None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-
-        real_loss = F.softplus(-discr_real_pred)
-        grad_penalty = make_r1_gp(discr_real_pred, real_batch) * self.gp_coef
-        fake_loss = F.softplus(discr_fake_pred)
-
-        if not self.use_unmasked_for_discr or self.mask_as_fake_target:
-            # == if masked region should be treated differently
-            mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
-            # use_unmasked_for_discr=False only makes sense for fakes;
-            # for reals there is no difference beetween two regions
-            fake_loss = fake_loss * mask
-            if self.mask_as_fake_target:
-                fake_loss = fake_loss + (1 - mask) * F.softplus(-discr_fake_pred)
-
-        sum_discr_loss = real_loss + grad_penalty + fake_loss
-        metrics = dict(discr_real_out=discr_real_pred.mean(),
-                       discr_fake_out=discr_fake_pred.mean(),
-                       discr_real_gp=grad_penalty)
-        return sum_discr_loss.mean(), metrics
-
-class BCELoss(BaseAdversarialLoss):
-    def __init__(self, weight):
-        self.weight = weight
-        self.bce_loss = nn.BCEWithLogitsLoss()
-
-    def generator_loss(self, discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        real_mask_gt = torch.zeros(discr_fake_pred.shape).to(discr_fake_pred.device)
-        fake_loss = self.bce_loss(discr_fake_pred, real_mask_gt) * self.weight
-        return fake_loss, dict()
-
-    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                               generator: nn.Module, discriminator: nn.Module):
-        real_batch.requires_grad = True
-
-    def discriminator_loss(self,
-                           mask: torch.Tensor,
-                           discr_real_pred: torch.Tensor,
-                           discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-
-        real_mask_gt = torch.zeros(discr_real_pred.shape).to(discr_real_pred.device)
-        sum_discr_loss = (self.bce_loss(discr_real_pred, real_mask_gt) +  self.bce_loss(discr_fake_pred, mask)) / 2
-        metrics = dict(discr_real_out=discr_real_pred.mean(),
-                       discr_fake_out=discr_fake_pred.mean(),
-                       discr_real_gp=0)
-        return sum_discr_loss, metrics
-
-
-def make_discrim_loss(kind, **kwargs):
-    if kind == 'r1':
-        return NonSaturatingWithR1(**kwargs)
-    elif kind == 'bce':
-        return BCELoss(**kwargs)
-    raise ValueError(f'Unknown adversarial loss kind {kind}')
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/tracing.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/tracing.py
deleted file mode 100644
index d5596a4ceab79aff362203376952edc3122bf811..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/aiohttp/tracing.py
+++ /dev/null
@@ -1,472 +0,0 @@
-from types import SimpleNamespace
-from typing import TYPE_CHECKING, Awaitable, Optional, Type, TypeVar
-
-import attr
-from aiosignal import Signal
-from multidict import CIMultiDict
-from yarl import URL
-
-from .client_reqrep import ClientResponse
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .client import ClientSession
-    from .typedefs import Protocol
-
-    _ParamT_contra = TypeVar("_ParamT_contra", contravariant=True)
-
-    class _SignalCallback(Protocol[_ParamT_contra]):
-        def __call__(
-            self,
-            __client_session: ClientSession,
-            __trace_config_ctx: SimpleNamespace,
-            __params: _ParamT_contra,
-        ) -> Awaitable[None]:
-            ...
-
-
-__all__ = (
-    "TraceConfig",
-    "TraceRequestStartParams",
-    "TraceRequestEndParams",
-    "TraceRequestExceptionParams",
-    "TraceConnectionQueuedStartParams",
-    "TraceConnectionQueuedEndParams",
-    "TraceConnectionCreateStartParams",
-    "TraceConnectionCreateEndParams",
-    "TraceConnectionReuseconnParams",
-    "TraceDnsResolveHostStartParams",
-    "TraceDnsResolveHostEndParams",
-    "TraceDnsCacheHitParams",
-    "TraceDnsCacheMissParams",
-    "TraceRequestRedirectParams",
-    "TraceRequestChunkSentParams",
-    "TraceResponseChunkReceivedParams",
-    "TraceRequestHeadersSentParams",
-)
-
-
-class TraceConfig:
-    """First-class used to trace requests launched via ClientSession objects."""
-
-    def __init__(
-        self, trace_config_ctx_factory: Type[SimpleNamespace] = SimpleNamespace
-    ) -> None:
-        self._on_request_start: Signal[
-            _SignalCallback[TraceRequestStartParams]
-        ] = Signal(self)
-        self._on_request_chunk_sent: Signal[
-            _SignalCallback[TraceRequestChunkSentParams]
-        ] = Signal(self)
-        self._on_response_chunk_received: Signal[
-            _SignalCallback[TraceResponseChunkReceivedParams]
-        ] = Signal(self)
-        self._on_request_end: Signal[_SignalCallback[TraceRequestEndParams]] = Signal(
-            self
-        )
-        self._on_request_exception: Signal[
-            _SignalCallback[TraceRequestExceptionParams]
-        ] = Signal(self)
-        self._on_request_redirect: Signal[
-            _SignalCallback[TraceRequestRedirectParams]
-        ] = Signal(self)
-        self._on_connection_queued_start: Signal[
-            _SignalCallback[TraceConnectionQueuedStartParams]
-        ] = Signal(self)
-        self._on_connection_queued_end: Signal[
-            _SignalCallback[TraceConnectionQueuedEndParams]
-        ] = Signal(self)
-        self._on_connection_create_start: Signal[
-            _SignalCallback[TraceConnectionCreateStartParams]
-        ] = Signal(self)
-        self._on_connection_create_end: Signal[
-            _SignalCallback[TraceConnectionCreateEndParams]
-        ] = Signal(self)
-        self._on_connection_reuseconn: Signal[
-            _SignalCallback[TraceConnectionReuseconnParams]
-        ] = Signal(self)
-        self._on_dns_resolvehost_start: Signal[
-            _SignalCallback[TraceDnsResolveHostStartParams]
-        ] = Signal(self)
-        self._on_dns_resolvehost_end: Signal[
-            _SignalCallback[TraceDnsResolveHostEndParams]
-        ] = Signal(self)
-        self._on_dns_cache_hit: Signal[
-            _SignalCallback[TraceDnsCacheHitParams]
-        ] = Signal(self)
-        self._on_dns_cache_miss: Signal[
-            _SignalCallback[TraceDnsCacheMissParams]
-        ] = Signal(self)
-        self._on_request_headers_sent: Signal[
-            _SignalCallback[TraceRequestHeadersSentParams]
-        ] = Signal(self)
-
-        self._trace_config_ctx_factory = trace_config_ctx_factory
-
-    def trace_config_ctx(
-        self, trace_request_ctx: Optional[SimpleNamespace] = None
-    ) -> SimpleNamespace:
-        """Return a new trace_config_ctx instance"""
-        return self._trace_config_ctx_factory(trace_request_ctx=trace_request_ctx)
-
-    def freeze(self) -> None:
-        self._on_request_start.freeze()
-        self._on_request_chunk_sent.freeze()
-        self._on_response_chunk_received.freeze()
-        self._on_request_end.freeze()
-        self._on_request_exception.freeze()
-        self._on_request_redirect.freeze()
-        self._on_connection_queued_start.freeze()
-        self._on_connection_queued_end.freeze()
-        self._on_connection_create_start.freeze()
-        self._on_connection_create_end.freeze()
-        self._on_connection_reuseconn.freeze()
-        self._on_dns_resolvehost_start.freeze()
-        self._on_dns_resolvehost_end.freeze()
-        self._on_dns_cache_hit.freeze()
-        self._on_dns_cache_miss.freeze()
-        self._on_request_headers_sent.freeze()
-
-    @property
-    def on_request_start(self) -> "Signal[_SignalCallback[TraceRequestStartParams]]":
-        return self._on_request_start
-
-    @property
-    def on_request_chunk_sent(
-        self,
-    ) -> "Signal[_SignalCallback[TraceRequestChunkSentParams]]":
-        return self._on_request_chunk_sent
-
-    @property
-    def on_response_chunk_received(
-        self,
-    ) -> "Signal[_SignalCallback[TraceResponseChunkReceivedParams]]":
-        return self._on_response_chunk_received
-
-    @property
-    def on_request_end(self) -> "Signal[_SignalCallback[TraceRequestEndParams]]":
-        return self._on_request_end
-
-    @property
-    def on_request_exception(
-        self,
-    ) -> "Signal[_SignalCallback[TraceRequestExceptionParams]]":
-        return self._on_request_exception
-
-    @property
-    def on_request_redirect(
-        self,
-    ) -> "Signal[_SignalCallback[TraceRequestRedirectParams]]":
-        return self._on_request_redirect
-
-    @property
-    def on_connection_queued_start(
-        self,
-    ) -> "Signal[_SignalCallback[TraceConnectionQueuedStartParams]]":
-        return self._on_connection_queued_start
-
-    @property
-    def on_connection_queued_end(
-        self,
-    ) -> "Signal[_SignalCallback[TraceConnectionQueuedEndParams]]":
-        return self._on_connection_queued_end
-
-    @property
-    def on_connection_create_start(
-        self,
-    ) -> "Signal[_SignalCallback[TraceConnectionCreateStartParams]]":
-        return self._on_connection_create_start
-
-    @property
-    def on_connection_create_end(
-        self,
-    ) -> "Signal[_SignalCallback[TraceConnectionCreateEndParams]]":
-        return self._on_connection_create_end
-
-    @property
-    def on_connection_reuseconn(
-        self,
-    ) -> "Signal[_SignalCallback[TraceConnectionReuseconnParams]]":
-        return self._on_connection_reuseconn
-
-    @property
-    def on_dns_resolvehost_start(
-        self,
-    ) -> "Signal[_SignalCallback[TraceDnsResolveHostStartParams]]":
-        return self._on_dns_resolvehost_start
-
-    @property
-    def on_dns_resolvehost_end(
-        self,
-    ) -> "Signal[_SignalCallback[TraceDnsResolveHostEndParams]]":
-        return self._on_dns_resolvehost_end
-
-    @property
-    def on_dns_cache_hit(self) -> "Signal[_SignalCallback[TraceDnsCacheHitParams]]":
-        return self._on_dns_cache_hit
-
-    @property
-    def on_dns_cache_miss(self) -> "Signal[_SignalCallback[TraceDnsCacheMissParams]]":
-        return self._on_dns_cache_miss
-
-    @property
-    def on_request_headers_sent(
-        self,
-    ) -> "Signal[_SignalCallback[TraceRequestHeadersSentParams]]":
-        return self._on_request_headers_sent
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceRequestStartParams:
-    """Parameters sent by the `on_request_start` signal"""
-
-    method: str
-    url: URL
-    headers: "CIMultiDict[str]"
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceRequestChunkSentParams:
-    """Parameters sent by the `on_request_chunk_sent` signal"""
-
-    method: str
-    url: URL
-    chunk: bytes
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceResponseChunkReceivedParams:
-    """Parameters sent by the `on_response_chunk_received` signal"""
-
-    method: str
-    url: URL
-    chunk: bytes
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceRequestEndParams:
-    """Parameters sent by the `on_request_end` signal"""
-
-    method: str
-    url: URL
-    headers: "CIMultiDict[str]"
-    response: ClientResponse
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceRequestExceptionParams:
-    """Parameters sent by the `on_request_exception` signal"""
-
-    method: str
-    url: URL
-    headers: "CIMultiDict[str]"
-    exception: BaseException
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceRequestRedirectParams:
-    """Parameters sent by the `on_request_redirect` signal"""
-
-    method: str
-    url: URL
-    headers: "CIMultiDict[str]"
-    response: ClientResponse
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceConnectionQueuedStartParams:
-    """Parameters sent by the `on_connection_queued_start` signal"""
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceConnectionQueuedEndParams:
-    """Parameters sent by the `on_connection_queued_end` signal"""
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceConnectionCreateStartParams:
-    """Parameters sent by the `on_connection_create_start` signal"""
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceConnectionCreateEndParams:
-    """Parameters sent by the `on_connection_create_end` signal"""
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceConnectionReuseconnParams:
-    """Parameters sent by the `on_connection_reuseconn` signal"""
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceDnsResolveHostStartParams:
-    """Parameters sent by the `on_dns_resolvehost_start` signal"""
-
-    host: str
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceDnsResolveHostEndParams:
-    """Parameters sent by the `on_dns_resolvehost_end` signal"""
-
-    host: str
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceDnsCacheHitParams:
-    """Parameters sent by the `on_dns_cache_hit` signal"""
-
-    host: str
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceDnsCacheMissParams:
-    """Parameters sent by the `on_dns_cache_miss` signal"""
-
-    host: str
-
-
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class TraceRequestHeadersSentParams:
-    """Parameters sent by the `on_request_headers_sent` signal"""
-
-    method: str
-    url: URL
-    headers: "CIMultiDict[str]"
-
-
-class Trace:
-    """Internal dependency holder class.
-
-    Used to keep together the main dependencies used
-    at the moment of send a signal.
-    """
-
-    def __init__(
-        self,
-        session: "ClientSession",
-        trace_config: TraceConfig,
-        trace_config_ctx: SimpleNamespace,
-    ) -> None:
-        self._trace_config = trace_config
-        self._trace_config_ctx = trace_config_ctx
-        self._session = session
-
-    async def send_request_start(
-        self, method: str, url: URL, headers: "CIMultiDict[str]"
-    ) -> None:
-        return await self._trace_config.on_request_start.send(
-            self._session,
-            self._trace_config_ctx,
-            TraceRequestStartParams(method, url, headers),
-        )
-
-    async def send_request_chunk_sent(
-        self, method: str, url: URL, chunk: bytes
-    ) -> None:
-        return await self._trace_config.on_request_chunk_sent.send(
-            self._session,
-            self._trace_config_ctx,
-            TraceRequestChunkSentParams(method, url, chunk),
-        )
-
-    async def send_response_chunk_received(
-        self, method: str, url: URL, chunk: bytes
-    ) -> None:
-        return await self._trace_config.on_response_chunk_received.send(
-            self._session,
-            self._trace_config_ctx,
-            TraceResponseChunkReceivedParams(method, url, chunk),
-        )
-
-    async def send_request_end(
-        self,
-        method: str,
-        url: URL,
-        headers: "CIMultiDict[str]",
-        response: ClientResponse,
-    ) -> None:
-        return await self._trace_config.on_request_end.send(
-            self._session,
-            self._trace_config_ctx,
-            TraceRequestEndParams(method, url, headers, response),
-        )
-
-    async def send_request_exception(
-        self,
-        method: str,
-        url: URL,
-        headers: "CIMultiDict[str]",
-        exception: BaseException,
-    ) -> None:
-        return await self._trace_config.on_request_exception.send(
-            self._session,
-            self._trace_config_ctx,
-            TraceRequestExceptionParams(method, url, headers, exception),
-        )
-
-    async def send_request_redirect(
-        self,
-        method: str,
-        url: URL,
-        headers: "CIMultiDict[str]",
-        response: ClientResponse,
-    ) -> None:
-        return await self._trace_config._on_request_redirect.send(
-            self._session,
-            self._trace_config_ctx,
-            TraceRequestRedirectParams(method, url, headers, response),
-        )
-
-    async def send_connection_queued_start(self) -> None:
-        return await self._trace_config.on_connection_queued_start.send(
-            self._session, self._trace_config_ctx, TraceConnectionQueuedStartParams()
-        )
-
-    async def send_connection_queued_end(self) -> None:
-        return await self._trace_config.on_connection_queued_end.send(
-            self._session, self._trace_config_ctx, TraceConnectionQueuedEndParams()
-        )
-
-    async def send_connection_create_start(self) -> None:
-        return await self._trace_config.on_connection_create_start.send(
-            self._session, self._trace_config_ctx, TraceConnectionCreateStartParams()
-        )
-
-    async def send_connection_create_end(self) -> None:
-        return await self._trace_config.on_connection_create_end.send(
-            self._session, self._trace_config_ctx, TraceConnectionCreateEndParams()
-        )
-
-    async def send_connection_reuseconn(self) -> None:
-        return await self._trace_config.on_connection_reuseconn.send(
-            self._session, self._trace_config_ctx, TraceConnectionReuseconnParams()
-        )
-
-    async def send_dns_resolvehost_start(self, host: str) -> None:
-        return await self._trace_config.on_dns_resolvehost_start.send(
-            self._session, self._trace_config_ctx, TraceDnsResolveHostStartParams(host)
-        )
-
-    async def send_dns_resolvehost_end(self, host: str) -> None:
-        return await self._trace_config.on_dns_resolvehost_end.send(
-            self._session, self._trace_config_ctx, TraceDnsResolveHostEndParams(host)
-        )
-
-    async def send_dns_cache_hit(self, host: str) -> None:
-        return await self._trace_config.on_dns_cache_hit.send(
-            self._session, self._trace_config_ctx, TraceDnsCacheHitParams(host)
-        )
-
-    async def send_dns_cache_miss(self, host: str) -> None:
-        return await self._trace_config.on_dns_cache_miss.send(
-            self._session, self._trace_config_ctx, TraceDnsCacheMissParams(host)
-        )
-
-    async def send_request_headers(
-        self, method: str, url: URL, headers: "CIMultiDict[str]"
-    ) -> None:
-        return await self._trace_config._on_request_headers_sent.send(
-            self._session,
-            self._trace_config_ctx,
-            TraceRequestHeadersSentParams(method, url, headers),
-        )
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_l_o_c_a.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_l_o_c_a.py
deleted file mode 100644
index ad1b715133a9948b2e0da307b445a24be08bf0b2..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_l_o_c_a.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from . import DefaultTable
-import sys
-import array
-import logging
-
-
-log = logging.getLogger(__name__)
-
-
-class table__l_o_c_a(DefaultTable.DefaultTable):
-
-    dependencies = ["glyf"]
-
-    def decompile(self, data, ttFont):
-        longFormat = ttFont["head"].indexToLocFormat
-        if longFormat:
-            format = "I"
-        else:
-            format = "H"
-        locations = array.array(format)
-        locations.frombytes(data)
-        if sys.byteorder != "big":
-            locations.byteswap()
-        if not longFormat:
-            l = array.array("I")
-            for i in range(len(locations)):
-                l.append(locations[i] * 2)
-            locations = l
-        if len(locations) < (ttFont["maxp"].numGlyphs + 1):
-            log.warning(
-                "corrupt 'loca' table, or wrong numGlyphs in 'maxp': %d %d",
-                len(locations) - 1,
-                ttFont["maxp"].numGlyphs,
-            )
-        self.locations = locations
-
-    def compile(self, ttFont):
-        try:
-            max_location = max(self.locations)
-        except AttributeError:
-            self.set([])
-            max_location = 0
-        if max_location < 0x20000 and all(l % 2 == 0 for l in self.locations):
-            locations = array.array("H")
-            for i in range(len(self.locations)):
-                locations.append(self.locations[i] // 2)
-            ttFont["head"].indexToLocFormat = 0
-        else:
-            locations = array.array("I", self.locations)
-            ttFont["head"].indexToLocFormat = 1
-        if sys.byteorder != "big":
-            locations.byteswap()
-        return locations.tobytes()
-
-    def set(self, locations):
-        self.locations = array.array("I", locations)
-
-    def toXML(self, writer, ttFont):
-        writer.comment("The 'loca' table will be calculated by the compiler")
-        writer.newline()
-
-    def __getitem__(self, index):
-        return self.locations[index]
-
-    def __len__(self):
-        return len(self.locations)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/sbixGlyph.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/sbixGlyph.py
deleted file mode 100644
index fd687a18808b6b2655951f9a6934916d7bafbc71..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/sbixGlyph.py
+++ /dev/null
@@ -1,145 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import readHex, safeEval
-import struct
-
-
-sbixGlyphHeaderFormat = """
-	>
-	originOffsetX: h	# The x-value of the point in the glyph relative to its
-						# lower-left corner which corresponds to the origin of
-						# the glyph on the screen, that is the point on the
-						# baseline at the left edge of the glyph.
-	originOffsetY: h	# The y-value of the point in the glyph relative to its
-						# lower-left corner which corresponds to the origin of
-						# the glyph on the screen, that is the point on the
-						# baseline at the left edge of the glyph.
-	graphicType:  4s	# e.g. "png "
-"""
-
-sbixGlyphHeaderFormatSize = sstruct.calcsize(sbixGlyphHeaderFormat)
-
-
-class Glyph(object):
-    def __init__(
-        self,
-        glyphName=None,
-        referenceGlyphName=None,
-        originOffsetX=0,
-        originOffsetY=0,
-        graphicType=None,
-        imageData=None,
-        rawdata=None,
-        gid=0,
-    ):
-        self.gid = gid
-        self.glyphName = glyphName
-        self.referenceGlyphName = referenceGlyphName
-        self.originOffsetX = originOffsetX
-        self.originOffsetY = originOffsetY
-        self.rawdata = rawdata
-        self.graphicType = graphicType
-        self.imageData = imageData
-
-        # fix self.graphicType if it is null terminated or too short
-        if self.graphicType is not None:
-            if self.graphicType[-1] == "\0":
-                self.graphicType = self.graphicType[:-1]
-            if len(self.graphicType) > 4:
-                from fontTools import ttLib
-
-                raise ttLib.TTLibError(
-                    "Glyph.graphicType must not be longer than 4 characters."
-                )
-            elif len(self.graphicType) < 4:
-                # pad with spaces
-                self.graphicType += "    "[: (4 - len(self.graphicType))]
-
-    def decompile(self, ttFont):
-        self.glyphName = ttFont.getGlyphName(self.gid)
-        if self.rawdata is None:
-            from fontTools import ttLib
-
-            raise ttLib.TTLibError("No table data to decompile")
-        if len(self.rawdata) > 0:
-            if len(self.rawdata) < sbixGlyphHeaderFormatSize:
-                from fontTools import ttLib
-
-                # print "Glyph %i header too short: Expected %x, got %x." % (self.gid, sbixGlyphHeaderFormatSize, len(self.rawdata))
-                raise ttLib.TTLibError("Glyph header too short.")
-
-            sstruct.unpack(
-                sbixGlyphHeaderFormat, self.rawdata[:sbixGlyphHeaderFormatSize], self
-            )
-
-            if self.graphicType == "dupe":
-                # this glyph is a reference to another glyph's image data
-                (gid,) = struct.unpack(">H", self.rawdata[sbixGlyphHeaderFormatSize:])
-                self.referenceGlyphName = ttFont.getGlyphName(gid)
-            else:
-                self.imageData = self.rawdata[sbixGlyphHeaderFormatSize:]
-                self.referenceGlyphName = None
-        # clean up
-        del self.rawdata
-        del self.gid
-
-    def compile(self, ttFont):
-        if self.glyphName is None:
-            from fontTools import ttLib
-
-            raise ttLib.TTLibError("Can't compile Glyph without glyph name")
-            # TODO: if ttFont has no maxp, cmap etc., ignore glyph names and compile by index?
-            # (needed if you just want to compile the sbix table on its own)
-        self.gid = struct.pack(">H", ttFont.getGlyphID(self.glyphName))
-        if self.graphicType is None:
-            rawdata = b""
-        else:
-            rawdata = sstruct.pack(sbixGlyphHeaderFormat, self)
-            if self.graphicType == "dupe":
-                rawdata += struct.pack(">H", ttFont.getGlyphID(self.referenceGlyphName))
-            else:
-                assert self.imageData is not None
-                rawdata += self.imageData
-        self.rawdata = rawdata
-
-    def toXML(self, xmlWriter, ttFont):
-        if self.graphicType is None:
-            # TODO: ignore empty glyphs?
-            # a glyph data entry is required for each glyph,
-            # but empty ones can be calculated at compile time
-            xmlWriter.simpletag("glyph", name=self.glyphName)
-            xmlWriter.newline()
-            return
-        xmlWriter.begintag(
-            "glyph",
-            graphicType=self.graphicType,
-            name=self.glyphName,
-            originOffsetX=self.originOffsetX,
-            originOffsetY=self.originOffsetY,
-        )
-        xmlWriter.newline()
-        if self.graphicType == "dupe":
-            # graphicType == "dupe" is a reference to another glyph id.
-            xmlWriter.simpletag("ref", glyphname=self.referenceGlyphName)
-        else:
-            xmlWriter.begintag("hexdata")
-            xmlWriter.newline()
-            xmlWriter.dumphex(self.imageData)
-            xmlWriter.endtag("hexdata")
-        xmlWriter.newline()
-        xmlWriter.endtag("glyph")
-        xmlWriter.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name == "ref":
-            # glyph is a "dupe", i.e. a reference to another glyph's image data.
-            # in this case imageData contains the glyph id of the reference glyph
-            # get glyph id from glyphname
-            glyphname = safeEval("'''" + attrs["glyphname"] + "'''")
-            self.imageData = struct.pack(">H", ttFont.getGlyphID(glyphname))
-            self.referenceGlyphName = glyphname
-        elif name == "hexdata":
-            self.imageData = readHex(content)
-        else:
-            from fontTools import ttLib
-
-            raise ttLib.TTLibError("can't handle '%s' element" % name)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-8c6012c7.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-8c6012c7.js
deleted file mode 100644
index 3772ef5062a5205f71833e7a01740c8bbc78032a..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/index-8c6012c7.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as r,i as d,s as b,e as v,m as g,p as y,t as h,n as B,Q as k,I as q,g as C,K as S,q as w,y as I}from"./index-8c3da1d9.js";import{a as K}from"./Button-62634b34.js";import{X as Q}from"./Blocks-6ad6f005.js";function R(n){let t=n[7](n[4])+"",a;return{c(){a=q(t)},m(e,i){C(e,a,i)},p(e,i){i&144&&t!==(t=e[7](e[4])+"")&&S(a,t)},d(e){e&&w(a)}}}function X(n){let t,a;return t=new K({props:{variant:n[5],elem_id:n[1],elem_classes:n[2],style:n[0],visible:n[3],disabled:n[6]==="static",$$slots:{default:[R]},$$scope:{ctx:n}}}),t.$on("click",n[8]),{c(){v(t.$$.fragment)},m(e,i){g(t,e,i),a=!0},p(e,[i]){const l={};i&32&&(l.variant=e[5]),i&2&&(l.elem_id=e[1]),i&4&&(l.elem_classes=e[2]),i&1&&(l.style=e[0]),i&8&&(l.visible=e[3]),i&64&&(l.disabled=e[6]==="static"),i&656&&(l.$$scope={dirty:i,ctx:e}),t.$set(l)},i(e){a||(y(t.$$.fragment,e),a=!0)},o(e){h(t.$$.fragment,e),a=!1},d(e){B(t,e)}}}function j(n,t,a){let e;k(n,Q,s=>a(7,e=s));let{style:i={}}=t,{elem_id:l=""}=t,{elem_classes:m=[]}=t,{visible:o=!0}=t,{value:u}=t,{variant:c="secondary"}=t,{mode:_="dynamic"}=t;function f(s){I.call(this,n,s)}return n.$$set=s=>{"style"in s&&a(0,i=s.style),"elem_id"in s&&a(1,l=s.elem_id),"elem_classes"in s&&a(2,m=s.elem_classes),"visible"in s&&a(3,o=s.visible),"value"in s&&a(4,u=s.value),"variant"in s&&a(5,c=s.variant),"mode"in s&&a(6,_=s.mode)},[i,l,m,o,u,c,_,e,f]}class z extends r{constructor(t){super(),d(this,t,j,X,b,{style:0,elem_id:1,elem_classes:2,visible:3,value:4,variant:5,mode:6})}}const F=z,G=["static","dynamic"],H=n=>({type:{payload:"string"},description:{payload:"button label"},example_data:n.value||"Run"});export{F as Component,H as document,G as modes};
-//# sourceMappingURL=index-8c6012c7.js.map
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/repocard.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/repocard.py
deleted file mode 100644
index c7cbde1e75e28b5731b0b259d512aee659430c4e..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/repocard.py
+++ /dev/null
@@ -1,819 +0,0 @@
-import os
-import re
-from pathlib import Path
-from typing import Any, Dict, Optional, Type, Union
-
-import requests
-import yaml
-
-from huggingface_hub.file_download import hf_hub_download
-from huggingface_hub.hf_api import upload_file
-from huggingface_hub.repocard_data import (
-    CardData,
-    DatasetCardData,
-    EvalResult,
-    ModelCardData,
-    SpaceCardData,
-    eval_results_to_model_index,
-    model_index_to_eval_results,
-)
-from huggingface_hub.utils import get_session, is_jinja_available, yaml_dump
-
-from .constants import REPOCARD_NAME
-from .utils import EntryNotFoundError, SoftTemporaryDirectory, validate_hf_hub_args
-from .utils._typing import Literal
-from .utils.logging import get_logger
-
-
-TEMPLATE_MODELCARD_PATH = Path(__file__).parent / "templates" / "modelcard_template.md"
-TEMPLATE_DATASETCARD_PATH = Path(__file__).parent / "templates" / "datasetcard_template.md"
-
-# exact same regex as in the Hub server. Please keep in sync.
-# See https://github.com/huggingface/moon-landing/blob/main/server/lib/ViewMarkdown.ts#L18
-REGEX_YAML_BLOCK = re.compile(r"^(\s*---[\r\n]+)([\S\s]*?)([\r\n]+---(\r\n|\n|$))")
-
-logger = get_logger(__name__)
-
-
-class RepoCard:
-    card_data_class = CardData
-    default_template_path = TEMPLATE_MODELCARD_PATH
-    repo_type = "model"
-
-    def __init__(self, content: str, ignore_metadata_errors: bool = False):
-        """Initialize a RepoCard from string content. The content should be a
-        Markdown file with a YAML block at the beginning and a Markdown body.
-
-        Args:
-            content (`str`): The content of the Markdown file.
-
-        Example:
-            ```python
-            >>> from huggingface_hub.repocard import RepoCard
-            >>> text = '''
-            ... ---
-            ... language: en
-            ... license: mit
-            ... ---
-            ...
-            ... # My repo
-            ... '''
-            >>> card = RepoCard(text)
-            >>> card.data.to_dict()
-            {'language': 'en', 'license': 'mit'}
-            >>> card.text
-            '\\n# My repo\\n'
-
-            ```
-        <Tip>
-        Raises the following error:
-
-            - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
-              when the content of the repo card metadata is not a dictionary.
-
-        </Tip>
-        """
-
-        # Set the content of the RepoCard, as well as underlying .data and .text attributes.
-        # See the `content` property setter for more details.
-        self.ignore_metadata_errors = ignore_metadata_errors
-        self.content = content
-
-    @property
-    def content(self):
-        """The content of the RepoCard, including the YAML block and the Markdown body."""
-        line_break = _detect_line_ending(self._content) or "\n"
-        return f"---{line_break}{self.data.to_yaml(line_break=line_break)}{line_break}---{line_break}{self.text}"
-
-    @content.setter
-    def content(self, content: str):
-        """Set the content of the RepoCard."""
-        self._content = content
-
-        match = REGEX_YAML_BLOCK.search(content)
-        if match:
-            # Metadata found in the YAML block
-            yaml_block = match.group(2)
-            self.text = content[match.end() :]
-            data_dict = yaml.safe_load(yaml_block)
-
-            # The YAML block's data should be a dictionary
-            if not isinstance(data_dict, dict):
-                raise ValueError("repo card metadata block should be a dict")
-        else:
-            # Model card without metadata... create empty metadata
-            logger.warning("Repo card metadata block was not found. Setting CardData to empty.")
-            data_dict = {}
-            self.text = content
-
-        self.data = self.card_data_class(**data_dict, ignore_metadata_errors=self.ignore_metadata_errors)
-
-    def __str__(self):
-        return self.content
-
-    def save(self, filepath: Union[Path, str]):
-        r"""Save a RepoCard to a file.
-
-        Args:
-            filepath (`Union[Path, str]`): Filepath to the markdown file to save.
-
-        Example:
-            ```python
-            >>> from huggingface_hub.repocard import RepoCard
-            >>> card = RepoCard("---\nlanguage: en\n---\n# This is a test repo card")
-            >>> card.save("/tmp/test.md")
-
-            ```
-        """
-        filepath = Path(filepath)
-        filepath.parent.mkdir(parents=True, exist_ok=True)
-        # Preserve newlines as in the existing file.
-        with open(filepath, mode="w", newline="", encoding="utf-8") as f:
-            f.write(str(self))
-
-    @classmethod
-    def load(
-        cls,
-        repo_id_or_path: Union[str, Path],
-        repo_type: Optional[str] = None,
-        token: Optional[str] = None,
-        ignore_metadata_errors: bool = False,
-    ):
-        """Initialize a RepoCard from a Hugging Face Hub repo's README.md or a local filepath.
-
-        Args:
-            repo_id_or_path (`Union[str, Path]`):
-                The repo ID associated with a Hugging Face Hub repo or a local filepath.
-            repo_type (`str`, *optional*):
-                The type of Hugging Face repo to push to. Defaults to None, which will use use "model". Other options
-                are "dataset" and "space". Not used when loading from a local filepath. If this is called from a child
-                class, the default value will be the child class's `repo_type`.
-            token (`str`, *optional*):
-                Authentication token, obtained with `huggingface_hub.HfApi.login` method. Will default to the stored token.
-            ignore_metadata_errors (`str`):
-                If True, errors while parsing the metadata section will be ignored. Some information might be lost during
-                the process. Use it at your own risk.
-
-        Returns:
-            [`huggingface_hub.repocard.RepoCard`]: The RepoCard (or subclass) initialized from the repo's
-                README.md file or filepath.
-
-        Example:
-            ```python
-            >>> from huggingface_hub.repocard import RepoCard
-            >>> card = RepoCard.load("nateraw/food")
-            >>> assert card.data.tags == ["generated_from_trainer", "image-classification", "pytorch"]
-
-            ```
-        """
-
-        if Path(repo_id_or_path).exists():
-            card_path = Path(repo_id_or_path)
-        elif isinstance(repo_id_or_path, str):
-            card_path = Path(
-                hf_hub_download(
-                    repo_id_or_path,
-                    REPOCARD_NAME,
-                    repo_type=repo_type or cls.repo_type,
-                    token=token,
-                )
-            )
-        else:
-            raise ValueError(f"Cannot load RepoCard: path not found on disk ({repo_id_or_path}).")
-
-        # Preserve newlines in the existing file.
-        with card_path.open(mode="r", newline="", encoding="utf-8") as f:
-            return cls(f.read(), ignore_metadata_errors=ignore_metadata_errors)
-
-    def validate(self, repo_type: Optional[str] = None):
-        """Validates card against Hugging Face Hub's card validation logic.
-        Using this function requires access to the internet, so it is only called
-        internally by [`huggingface_hub.repocard.RepoCard.push_to_hub`].
-
-        Args:
-            repo_type (`str`, *optional*, defaults to "model"):
-                The type of Hugging Face repo to push to. Options are "model", "dataset", and "space".
-                If this function is called from a child class, the default will be the child class's `repo_type`.
-
-        <Tip>
-        Raises the following errors:
-
-            - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
-              if the card fails validation checks.
-            - [`HTTPError`](https://2.python-requests.org/en/master/api/#requests.HTTPError)
-              if the request to the Hub API fails for any other reason.
-
-        </Tip>
-        """
-
-        # If repo type is provided, otherwise, use the repo type of the card.
-        repo_type = repo_type or self.repo_type
-
-        body = {
-            "repoType": repo_type,
-            "content": str(self),
-        }
-        headers = {"Accept": "text/plain"}
-
-        try:
-            r = get_session().post("https://huggingface.co/api/validate-yaml", body, headers=headers)
-            r.raise_for_status()
-        except requests.exceptions.HTTPError as exc:
-            if r.status_code == 400:
-                raise ValueError(r.text)
-            else:
-                raise exc
-
-    def push_to_hub(
-        self,
-        repo_id: str,
-        token: Optional[str] = None,
-        repo_type: Optional[str] = None,
-        commit_message: Optional[str] = None,
-        commit_description: Optional[str] = None,
-        revision: Optional[str] = None,
-        create_pr: Optional[bool] = None,
-        parent_commit: Optional[str] = None,
-    ):
-        """Push a RepoCard to a Hugging Face Hub repo.
-
-        Args:
-            repo_id (`str`):
-                The repo ID of the Hugging Face Hub repo to push to. Example: "nateraw/food".
-            token (`str`, *optional*):
-                Authentication token, obtained with `huggingface_hub.HfApi.login` method. Will default to
-                the stored token.
-            repo_type (`str`, *optional*, defaults to "model"):
-                The type of Hugging Face repo to push to. Options are "model", "dataset", and "space". If this
-                function is called by a child class, it will default to the child class's `repo_type`.
-            commit_message (`str`, *optional*):
-                The summary / title / first line of the generated commit.
-            commit_description (`str`, *optional*)
-                The description of the generated commit.
-            revision (`str`, *optional*):
-                The git revision to commit from. Defaults to the head of the `"main"` branch.
-            create_pr (`bool`, *optional*):
-                Whether or not to create a Pull Request with this commit. Defaults to `False`.
-            parent_commit (`str`, *optional*):
-                The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
-                If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
-                If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
-                Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
-                especially useful if the repo is updated / committed to concurrently.
-        Returns:
-            `str`: URL of the commit which updated the card metadata.
-        """
-
-        # If repo type is provided, otherwise, use the repo type of the card.
-        repo_type = repo_type or self.repo_type
-
-        # Validate card before pushing to hub
-        self.validate(repo_type=repo_type)
-
-        with SoftTemporaryDirectory() as tmpdir:
-            tmp_path = Path(tmpdir) / REPOCARD_NAME
-            tmp_path.write_text(str(self))
-            url = upload_file(
-                path_or_fileobj=str(tmp_path),
-                path_in_repo=REPOCARD_NAME,
-                repo_id=repo_id,
-                token=token,
-                repo_type=repo_type,
-                commit_message=commit_message,
-                commit_description=commit_description,
-                create_pr=create_pr,
-                revision=revision,
-                parent_commit=parent_commit,
-            )
-        return url
-
-    @classmethod
-    def from_template(
-        cls,
-        card_data: CardData,
-        template_path: Optional[str] = None,
-        **template_kwargs,
-    ):
-        """Initialize a RepoCard from a template. By default, it uses the default template.
-
-        Templates are Jinja2 templates that can be customized by passing keyword arguments.
-
-        Args:
-            card_data (`huggingface_hub.CardData`):
-                A huggingface_hub.CardData instance containing the metadata you want to include in the YAML
-                header of the repo card on the Hugging Face Hub.
-            template_path (`str`, *optional*):
-                A path to a markdown file with optional Jinja template variables that can be filled
-                in with `template_kwargs`. Defaults to the default template.
-
-        Returns:
-            [`huggingface_hub.repocard.RepoCard`]: A RepoCard instance with the specified card data and content from the
-            template.
-        """
-        if is_jinja_available():
-            import jinja2
-        else:
-            raise ImportError(
-                "Using RepoCard.from_template requires Jinja2 to be installed. Please"
-                " install it with `pip install Jinja2`."
-            )
-
-        kwargs = card_data.to_dict().copy()
-        kwargs.update(template_kwargs)  # Template_kwargs have priority
-        template = jinja2.Template(Path(template_path or cls.default_template_path).read_text())
-        content = template.render(card_data=card_data.to_yaml(), **kwargs)
-        return cls(content)
-
-
-class ModelCard(RepoCard):
-    card_data_class = ModelCardData
-    default_template_path = TEMPLATE_MODELCARD_PATH
-    repo_type = "model"
-
-    @classmethod
-    def from_template(  # type: ignore # violates Liskov property but easier to use
-        cls,
-        card_data: ModelCardData,
-        template_path: Optional[str] = None,
-        **template_kwargs,
-    ):
-        """Initialize a ModelCard from a template. By default, it uses the default template, which can be found here:
-        https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md
-
-        Templates are Jinja2 templates that can be customized by passing keyword arguments.
-
-        Args:
-            card_data (`huggingface_hub.ModelCardData`):
-                A huggingface_hub.ModelCardData instance containing the metadata you want to include in the YAML
-                header of the model card on the Hugging Face Hub.
-            template_path (`str`, *optional*):
-                A path to a markdown file with optional Jinja template variables that can be filled
-                in with `template_kwargs`. Defaults to the default template.
-
-        Returns:
-            [`huggingface_hub.ModelCard`]: A ModelCard instance with the specified card data and content from the
-            template.
-
-        Example:
-            ```python
-            >>> from huggingface_hub import ModelCard, ModelCardData, EvalResult
-
-            >>> # Using the Default Template
-            >>> card_data = ModelCardData(
-            ...     language='en',
-            ...     license='mit',
-            ...     library_name='timm',
-            ...     tags=['image-classification', 'resnet'],
-            ...     datasets=['beans'],
-            ...     metrics=['accuracy'],
-            ... )
-            >>> card = ModelCard.from_template(
-            ...     card_data,
-            ...     model_description='This model does x + y...'
-            ... )
-
-            >>> # Including Evaluation Results
-            >>> card_data = ModelCardData(
-            ...     language='en',
-            ...     tags=['image-classification', 'resnet'],
-            ...     eval_results=[
-            ...         EvalResult(
-            ...             task_type='image-classification',
-            ...             dataset_type='beans',
-            ...             dataset_name='Beans',
-            ...             metric_type='accuracy',
-            ...             metric_value=0.9,
-            ...         ),
-            ...     ],
-            ...     model_name='my-cool-model',
-            ... )
-            >>> card = ModelCard.from_template(card_data)
-
-            >>> # Using a Custom Template
-            >>> card_data = ModelCardData(
-            ...     language='en',
-            ...     tags=['image-classification', 'resnet']
-            ... )
-            >>> card = ModelCard.from_template(
-            ...     card_data=card_data,
-            ...     template_path='./src/huggingface_hub/templates/modelcard_template.md',
-            ...     custom_template_var='custom value',  # will be replaced in template if it exists
-            ... )
-
-            ```
-        """
-        return super().from_template(card_data, template_path, **template_kwargs)
-
-
-class DatasetCard(RepoCard):
-    card_data_class = DatasetCardData
-    default_template_path = TEMPLATE_DATASETCARD_PATH
-    repo_type = "dataset"
-
-    @classmethod
-    def from_template(  # type: ignore # violates Liskov property but easier to use
-        cls,
-        card_data: DatasetCardData,
-        template_path: Optional[str] = None,
-        **template_kwargs,
-    ):
-        """Initialize a DatasetCard from a template. By default, it uses the default template, which can be found here:
-        https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md
-
-        Templates are Jinja2 templates that can be customized by passing keyword arguments.
-
-        Args:
-            card_data (`huggingface_hub.DatasetCardData`):
-                A huggingface_hub.DatasetCardData instance containing the metadata you want to include in the YAML
-                header of the dataset card on the Hugging Face Hub.
-            template_path (`str`, *optional*):
-                A path to a markdown file with optional Jinja template variables that can be filled
-                in with `template_kwargs`. Defaults to the default template.
-
-        Returns:
-            [`huggingface_hub.DatasetCard`]: A DatasetCard instance with the specified card data and content from the
-            template.
-
-        Example:
-            ```python
-            >>> from huggingface_hub import DatasetCard, DatasetCardData
-
-            >>> # Using the Default Template
-            >>> card_data = DatasetCardData(
-            ...     language='en',
-            ...     license='mit',
-            ...     annotations_creators='crowdsourced',
-            ...     task_categories=['text-classification'],
-            ...     task_ids=['sentiment-classification', 'text-scoring'],
-            ...     multilinguality='monolingual',
-            ...     pretty_name='My Text Classification Dataset',
-            ... )
-            >>> card = DatasetCard.from_template(
-            ...     card_data,
-            ...     pretty_name=card_data.pretty_name,
-            ... )
-
-            >>> # Using a Custom Template
-            >>> card_data = DatasetCardData(
-            ...     language='en',
-            ...     license='mit',
-            ... )
-            >>> card = DatasetCard.from_template(
-            ...     card_data=card_data,
-            ...     template_path='./src/huggingface_hub/templates/datasetcard_template.md',
-            ...     custom_template_var='custom value',  # will be replaced in template if it exists
-            ... )
-
-            ```
-        """
-        return super().from_template(card_data, template_path, **template_kwargs)
-
-
-class SpaceCard(RepoCard):
-    card_data_class = SpaceCardData
-    default_template_path = TEMPLATE_MODELCARD_PATH
-    repo_type = "space"
-
-
-def _detect_line_ending(content: str) -> Literal["\r", "\n", "\r\n", None]:  # noqa: F722
-    """Detect the line ending of a string. Used by RepoCard to avoid making huge diff on newlines.
-
-    Uses same implementation as in Hub server, keep it in sync.
-
-    Returns:
-        str: The detected line ending of the string.
-    """
-    cr = content.count("\r")
-    lf = content.count("\n")
-    crlf = content.count("\r\n")
-    if cr + lf == 0:
-        return None
-    if crlf == cr and crlf == lf:
-        return "\r\n"
-    if cr > lf:
-        return "\r"
-    else:
-        return "\n"
-
-
-def metadata_load(local_path: Union[str, Path]) -> Optional[Dict]:
-    content = Path(local_path).read_text()
-    match = REGEX_YAML_BLOCK.search(content)
-    if match:
-        yaml_block = match.group(2)
-        data = yaml.safe_load(yaml_block)
-        if isinstance(data, dict):
-            return data
-        else:
-            raise ValueError("repo card metadata block should be a dict")
-    else:
-        return None
-
-
-def metadata_save(local_path: Union[str, Path], data: Dict) -> None:
-    """
-    Save the metadata dict in the upper YAML part Trying to preserve newlines as
-    in the existing file. Docs about open() with newline="" parameter:
-    https://docs.python.org/3/library/functions.html?highlight=open#open Does
-    not work with "^M" linebreaks, which are replaced by \n
-    """
-    line_break = "\n"
-    content = ""
-    # try to detect existing newline character
-    if os.path.exists(local_path):
-        with open(local_path, "r", newline="", encoding="utf8") as readme:
-            content = readme.read()
-            if isinstance(readme.newlines, tuple):
-                line_break = readme.newlines[0]
-            elif isinstance(readme.newlines, str):
-                line_break = readme.newlines
-
-    # creates a new file if it not
-    with open(local_path, "w", newline="", encoding="utf8") as readme:
-        data_yaml = yaml_dump(data, sort_keys=False, line_break=line_break)
-        # sort_keys: keep dict order
-        match = REGEX_YAML_BLOCK.search(content)
-        if match:
-            output = content[: match.start()] + f"---{line_break}{data_yaml}---{line_break}" + content[match.end() :]
-        else:
-            output = f"---{line_break}{data_yaml}---{line_break}{content}"
-
-        readme.write(output)
-        readme.close()
-
-
-def metadata_eval_result(
-    *,
-    model_pretty_name: str,
-    task_pretty_name: str,
-    task_id: str,
-    metrics_pretty_name: str,
-    metrics_id: str,
-    metrics_value: Any,
-    dataset_pretty_name: str,
-    dataset_id: str,
-    metrics_config: Optional[str] = None,
-    metrics_verified: bool = False,
-    dataset_config: Optional[str] = None,
-    dataset_split: Optional[str] = None,
-    dataset_revision: Optional[str] = None,
-    metrics_verification_token: Optional[str] = None,
-) -> Dict:
-    """
-    Creates a metadata dict with the result from a model evaluated on a dataset.
-
-    Args:
-        model_pretty_name (`str`):
-            The name of the model in natural language.
-        task_pretty_name (`str`):
-            The name of a task in natural language.
-        task_id (`str`):
-            Example: automatic-speech-recognition. A task id.
-        metrics_pretty_name (`str`):
-            A name for the metric in natural language. Example: Test WER.
-        metrics_id (`str`):
-            Example: wer. A metric id from https://hf.co/metrics.
-        metrics_value (`Any`):
-            The value from the metric. Example: 20.0 or "20.0 ± 1.2".
-        dataset_pretty_name (`str`):
-            The name of the dataset in natural language.
-        dataset_id (`str`):
-            Example: common_voice. A dataset id from https://hf.co/datasets.
-        metrics_config (`str`, *optional*):
-            The name of the metric configuration used in `load_metric()`.
-            Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`.
-        metrics_verified (`bool`, *optional*, defaults to `False`):
-            Indicates whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not. Automatically computed by Hugging Face, do not set.
-        dataset_config (`str`, *optional*):
-            Example: fr. The name of the dataset configuration used in `load_dataset()`.
-        dataset_split (`str`, *optional*):
-            Example: test. The name of the dataset split used in `load_dataset()`.
-        dataset_revision (`str`, *optional*):
-            Example: 5503434ddd753f426f4b38109466949a1217c2bb. The name of the dataset dataset revision
-            used in `load_dataset()`.
-        metrics_verification_token (`bool`, *optional*):
-            A JSON Web Token that is used to verify whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not.
-
-    Returns:
-        `dict`: a metadata dict with the result from a model evaluated on a dataset.
-
-    Example:
-        ```python
-        >>> from huggingface_hub import metadata_eval_result
-        >>> results = metadata_eval_result(
-        ...         model_pretty_name="RoBERTa fine-tuned on ReactionGIF",
-        ...         task_pretty_name="Text Classification",
-        ...         task_id="text-classification",
-        ...         metrics_pretty_name="Accuracy",
-        ...         metrics_id="accuracy",
-        ...         metrics_value=0.2662102282047272,
-        ...         dataset_pretty_name="ReactionJPEG",
-        ...         dataset_id="julien-c/reactionjpeg",
-        ...         dataset_config="default",
-        ...         dataset_split="test",
-        ... )
-        >>> results == {
-        ...     'model-index': [
-        ...         {
-        ...             'name': 'RoBERTa fine-tuned on ReactionGIF',
-        ...             'results': [
-        ...                 {
-        ...                     'task': {
-        ...                         'type': 'text-classification',
-        ...                         'name': 'Text Classification'
-        ...                     },
-        ...                     'dataset': {
-        ...                         'name': 'ReactionJPEG',
-        ...                         'type': 'julien-c/reactionjpeg',
-        ...                         'config': 'default',
-        ...                         'split': 'test'
-        ...                     },
-        ...                     'metrics': [
-        ...                         {
-        ...                             'type': 'accuracy',
-        ...                             'value': 0.2662102282047272,
-        ...                             'name': 'Accuracy',
-        ...                             'verified': False
-        ...                         }
-        ...                     ]
-        ...                 }
-        ...             ]
-        ...         }
-        ...     ]
-        ... }
-        True
-
-        ```
-    """
-
-    return {
-        "model-index": eval_results_to_model_index(
-            model_name=model_pretty_name,
-            eval_results=[
-                EvalResult(
-                    task_name=task_pretty_name,
-                    task_type=task_id,
-                    metric_name=metrics_pretty_name,
-                    metric_type=metrics_id,
-                    metric_value=metrics_value,
-                    dataset_name=dataset_pretty_name,
-                    dataset_type=dataset_id,
-                    metric_config=metrics_config,
-                    verified=metrics_verified,
-                    verify_token=metrics_verification_token,
-                    dataset_config=dataset_config,
-                    dataset_split=dataset_split,
-                    dataset_revision=dataset_revision,
-                )
-            ],
-        )
-    }
-
-
-@validate_hf_hub_args
-def metadata_update(
-    repo_id: str,
-    metadata: Dict,
-    *,
-    repo_type: Optional[str] = None,
-    overwrite: bool = False,
-    token: Optional[str] = None,
-    commit_message: Optional[str] = None,
-    commit_description: Optional[str] = None,
-    revision: Optional[str] = None,
-    create_pr: bool = False,
-    parent_commit: Optional[str] = None,
-) -> str:
-    """
-    Updates the metadata in the README.md of a repository on the Hugging Face Hub.
-    If the README.md file doesn't exist yet, a new one is created with metadata and an
-    the default ModelCard or DatasetCard template. For `space` repo, an error is thrown
-    as a Space cannot exist without a `README.md` file.
-
-    Args:
-        repo_id (`str`):
-            The name of the repository.
-        metadata (`dict`):
-            A dictionary containing the metadata to be updated.
-        repo_type (`str`, *optional*):
-            Set to `"dataset"` or `"space"` if updating to a dataset or space,
-            `None` or `"model"` if updating to a model. Default is `None`.
-        overwrite (`bool`, *optional*, defaults to `False`):
-            If set to `True` an existing field can be overwritten, otherwise
-            attempting to overwrite an existing field will cause an error.
-        token (`str`, *optional*):
-            The Hugging Face authentication token.
-        commit_message (`str`, *optional*):
-            The summary / title / first line of the generated commit. Defaults to
-            `f"Update metadata with huggingface_hub"`
-        commit_description (`str` *optional*)
-            The description of the generated commit
-        revision (`str`, *optional*):
-            The git revision to commit from. Defaults to the head of the
-            `"main"` branch.
-        create_pr (`boolean`, *optional*):
-            Whether or not to create a Pull Request from `revision` with that commit.
-            Defaults to `False`.
-        parent_commit (`str`, *optional*):
-            The OID / SHA of the parent commit, as a hexadecimal string. Shorthands (7 first characters) are also supported.
-            If specified and `create_pr` is `False`, the commit will fail if `revision` does not point to `parent_commit`.
-            If specified and `create_pr` is `True`, the pull request will be created from `parent_commit`.
-            Specifying `parent_commit` ensures the repo has not changed before committing the changes, and can be
-            especially useful if the repo is updated / committed to concurrently.
-    Returns:
-        `str`: URL of the commit which updated the card metadata.
-
-    Example:
-        ```python
-        >>> from huggingface_hub import metadata_update
-        >>> metadata = {'model-index': [{'name': 'RoBERTa fine-tuned on ReactionGIF',
-        ...             'results': [{'dataset': {'name': 'ReactionGIF',
-        ...                                      'type': 'julien-c/reactiongif'},
-        ...                           'metrics': [{'name': 'Recall',
-        ...                                        'type': 'recall',
-        ...                                        'value': 0.7762102282047272}],
-        ...                          'task': {'name': 'Text Classification',
-        ...                                   'type': 'text-classification'}}]}]}
-        >>> url = metadata_update("hf-internal-testing/reactiongif-roberta-card", metadata)
-
-        ```
-    """
-    commit_message = commit_message if commit_message is not None else "Update metadata with huggingface_hub"
-
-    # Card class given repo_type
-    card_class: Type[RepoCard]
-    if repo_type is None or repo_type == "model":
-        card_class = ModelCard
-    elif repo_type == "dataset":
-        card_class = DatasetCard
-    elif repo_type == "space":
-        card_class = RepoCard
-    else:
-        raise ValueError(f"Unknown repo_type: {repo_type}")
-
-    # Either load repo_card from the Hub or create an empty one.
-    # NOTE: Will not create the repo if it doesn't exist.
-    try:
-        card = card_class.load(repo_id, token=token, repo_type=repo_type)
-    except EntryNotFoundError:
-        if repo_type == "space":
-            raise ValueError("Cannot update metadata on a Space that doesn't contain a `README.md` file.")
-
-        # Initialize a ModelCard or DatasetCard from default template and no data.
-        card = card_class.from_template(CardData())
-
-    for key, value in metadata.items():
-        if key == "model-index":
-            # if the new metadata doesn't include a name, either use existing one or repo name
-            if "name" not in value[0]:
-                value[0]["name"] = getattr(card, "model_name", repo_id)
-            model_name, new_results = model_index_to_eval_results(value)
-            if card.data.eval_results is None:
-                card.data.eval_results = new_results
-                card.data.model_name = model_name
-            else:
-                existing_results = card.data.eval_results
-
-                # Iterate over new results
-                #   Iterate over existing results
-                #       If both results describe the same metric but value is different:
-                #           If overwrite=True: overwrite the metric value
-                #           Else: raise ValueError
-                #       Else: append new result to existing ones.
-                for new_result in new_results:
-                    result_found = False
-                    for existing_result in existing_results:
-                        if new_result.is_equal_except_value(existing_result):
-                            if new_result != existing_result and not overwrite:
-                                raise ValueError(
-                                    "You passed a new value for the existing metric"
-                                    f" 'name: {new_result.metric_name}, type: "
-                                    f"{new_result.metric_type}'. Set `overwrite=True`"
-                                    " to overwrite existing metrics."
-                                )
-                            result_found = True
-                            existing_result.metric_value = new_result.metric_value
-                            if existing_result.verified is True:
-                                existing_result.verify_token = new_result.verify_token
-                    if not result_found:
-                        card.data.eval_results.append(new_result)
-        else:
-            # Any metadata that is not a result metric
-            if card.data.get(key) is not None and not overwrite and card.data.get(key) != value:
-                raise ValueError(
-                    f"You passed a new value for the existing meta data field '{key}'."
-                    " Set `overwrite=True` to overwrite existing metadata."
-                )
-            else:
-                card.data[key] = value
-
-    return card.push_to_hub(
-        repo_id,
-        token=token,
-        repo_type=repo_type,
-        commit_message=commit_message,
-        commit_description=commit_description,
-        create_pr=create_pr,
-        revision=revision,
-        parent_commit=parent_commit,
-    )
diff --git a/spaces/leogabraneth/text-generation-webui-main/js/main.js b/spaces/leogabraneth/text-generation-webui-main/js/main.js
deleted file mode 100644
index 17d1d3549212e0bfab4d15e68ba46641f2347927..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/js/main.js
+++ /dev/null
@@ -1,337 +0,0 @@
-let main_parent = document.getElementById("chat-tab").parentNode;
-let extensions = document.getElementById("extensions");
-
-main_parent.childNodes[0].classList.add("header_bar");
-main_parent.style = "padding: 0; margin: 0";
-main_parent.parentNode.style = "gap: 0";
-main_parent.parentNode.parentNode.style = "padding: 0";
-
-document.querySelector(".header_bar").addEventListener("click", function(event) {
-  if (event.target.tagName === "BUTTON") {
-    const buttonText = event.target.textContent.trim();
-
-    let chat_visible = (buttonText == "Chat");
-    let default_visible = (buttonText == "Default");
-    let notebook_visible = (buttonText == "Notebook");
-
-    // Check if one of the generation tabs is visible
-    if (chat_visible || notebook_visible || default_visible) {
-      extensions.style.display = "flex";
-      if (chat_visible) {
-        extensions.style.maxWidth = "880px";
-        extensions.style.padding = "0px";
-      } else {
-        extensions.style.maxWidth = "none";
-        extensions.style.padding = "15px";
-      }
-    } else {
-      extensions.style.display = "none";
-    }
-  }
-});
-
-//------------------------------------------------
-// Keyboard shortcuts
-//------------------------------------------------
-document.addEventListener("keydown", function(event) {
-
-  // Stop generation on Esc pressed
-  if (event.key === "Escape") {
-    // Find the element with id 'stop' and click it
-    var stopButton = document.getElementById("stop");
-    if (stopButton) {
-      stopButton.click();
-    }
-  }
-
-  // Show chat controls on Ctrl + S
-  else if (event.ctrlKey && event.key == "s") {
-    event.preventDefault();
-
-    var showControlsElement = document.getElementById("show-controls");
-    if (showControlsElement && showControlsElement.childNodes.length >= 4) {
-      showControlsElement.childNodes[3].click();
-
-      var arr = document.getElementById("chat-input").childNodes[2].childNodes;
-      arr[arr.length - 1].focus();
-    }
-  }
-
-  // Regenerate on Ctrl + Enter
-  else if (event.ctrlKey && event.key === "Enter") {
-    event.preventDefault();
-    document.getElementById("Regenerate").click();
-  }
-
-  // Continue on Alt + Enter
-  else if (event.altKey && event.key === "Enter") {
-    event.preventDefault();
-    document.getElementById("Continue").click();
-  }
-
-  // Remove last on Ctrl + Shift + Backspace
-  else if (event.ctrlKey && event.shiftKey && event.key === "Backspace") {
-    event.preventDefault();
-    document.getElementById("Remove-last").click();
-  }
-
-  // Copy last on Ctrl + Shift + K
-  else if (event.ctrlKey && event.shiftKey && event.key === "K") {
-    event.preventDefault();
-    document.getElementById("Copy-last").click();
-  }
-
-  // Replace last on Ctrl + Shift + L
-  else if (event.ctrlKey && event.shiftKey && event.key === "L") {
-    event.preventDefault();
-    document.getElementById("Replace-last").click();
-  }
-
-  // Impersonate on Ctrl + Shift + M
-  else if (event.ctrlKey && event.shiftKey && event.key === "M") {
-    event.preventDefault();
-    document.getElementById("Impersonate").click();
-  }
-
-});
-
-//------------------------------------------------
-// Position the chat typing dots
-//------------------------------------------------
-typing = document.getElementById("typing-container");
-typingParent = typing.parentNode;
-typingSibling = typing.previousElementSibling;
-typingSibling.insertBefore(typing, typingSibling.childNodes[2]);
-
-//------------------------------------------------
-// Chat scrolling
-//------------------------------------------------
-const targetElement = document.getElementById("chat").parentNode.parentNode.parentNode;
-targetElement.classList.add("pretty_scrollbar");
-targetElement.classList.add("chat-parent");
-let isScrolled = false;
-
-targetElement.addEventListener("scroll", function() {
-  let diff = targetElement.scrollHeight - targetElement.clientHeight;
-  if(Math.abs(targetElement.scrollTop - diff) <= 10 || diff == 0) {
-    isScrolled = false;
-  } else {
-    isScrolled = true;
-  }
-});
-
-// Create a MutationObserver instance
-const observer = new MutationObserver(function(mutations) {
-  mutations.forEach(function(mutation) {
-    if(!isScrolled) {
-      targetElement.scrollTop = targetElement.scrollHeight;
-    }
-
-    const firstChild = targetElement.children[0];
-    if (firstChild.classList.contains("generating")) {
-      typing.parentNode.classList.add("visible-dots");
-      document.getElementById("stop").style.display = "flex";
-      document.getElementById("Generate").style.display = "none";
-    } else {
-      typing.parentNode.classList.remove("visible-dots");
-      document.getElementById("stop").style.display = "none";
-      document.getElementById("Generate").style.display = "flex";
-    }
-
-  });
-});
-
-// Configure the observer to watch for changes in the subtree and attributes
-const config = {
-  childList: true,
-  subtree: true,
-  characterData: true,
-  attributeOldValue: true,
-  characterDataOldValue: true
-};
-
-// Start observing the target element
-observer.observe(targetElement, config);
-
-//------------------------------------------------
-// Notebook box scrolling
-//------------------------------------------------
-const notebookElement = document.querySelector("#textbox-notebook textarea");
-let notebookScrolled = false;
-
-notebookElement.addEventListener("scroll", function() {
-  let diff = notebookElement.scrollHeight - notebookElement.clientHeight;
-  if(Math.abs(notebookElement.scrollTop - diff) <= 10 || diff == 0) {
-    notebookScrolled = false;
-  } else {
-    notebookScrolled = true;
-  }
-});
-
-const notebookObserver = new MutationObserver(function(mutations) {
-  mutations.forEach(function(mutation) {
-    if(!notebookScrolled) {
-      notebookElement.scrollTop = notebookElement.scrollHeight;
-    }
-  });
-});
-
-notebookObserver.observe(notebookElement.parentNode.parentNode.parentNode, config);
-
-//------------------------------------------------
-// Default box scrolling
-//------------------------------------------------
-const defaultElement = document.querySelector("#textbox-default textarea");
-let defaultScrolled = false;
-
-defaultElement.addEventListener("scroll", function() {
-  let diff = defaultElement.scrollHeight - defaultElement.clientHeight;
-  if(Math.abs(defaultElement.scrollTop - diff) <= 10 || diff == 0) {
-    defaultScrolled = false;
-  } else {
-    defaultScrolled = true;
-  }
-});
-
-const defaultObserver = new MutationObserver(function(mutations) {
-  mutations.forEach(function(mutation) {
-    if(!defaultScrolled) {
-      defaultElement.scrollTop = defaultElement.scrollHeight;
-    }
-  });
-});
-
-defaultObserver.observe(defaultElement.parentNode.parentNode.parentNode, config);
-
-//------------------------------------------------
-// Add some scrollbars
-//------------------------------------------------
-const textareaElements = document.querySelectorAll(".add_scrollbar textarea");
-for(i = 0; i < textareaElements.length; i++) {
-  textareaElements[i].classList.remove("scroll-hide");
-  textareaElements[i].classList.add("pretty_scrollbar");
-  textareaElements[i].style.resize = "none";
-}
-
-//------------------------------------------------
-// Remove some backgrounds
-//------------------------------------------------
-const noBackgroundelements = document.querySelectorAll(".no-background");
-for(i = 0; i < noBackgroundelements.length; i++) {
-  noBackgroundelements[i].parentNode.style.border = "none";
-  noBackgroundelements[i].parentNode.parentNode.parentNode.style.alignItems = "center";
-}
-
-const slimDropdownElements = document.querySelectorAll('.slim-dropdown');
-for (i = 0; i < slimDropdownElements.length; i++) {
-    const parentNode = slimDropdownElements[i].parentNode;
-    parentNode.style.background = 'transparent';
-    parentNode.style.border = '0';
-}
-
-//------------------------------------------------
-// Create the hover menu in the chat tab
-// The show/hide events were adapted from:
-// https://github.com/SillyTavern/SillyTavern/blob/6c8bd06308c69d51e2eb174541792a870a83d2d6/public/script.js
-//------------------------------------------------
-var buttonsInChat = document.querySelectorAll("#chat-tab:not(.old-ui) #chat-buttons button");
-var button = document.getElementById("hover-element-button");
-var menu = document.getElementById("hover-menu");
-
-function showMenu() {
-  menu.style.display = "flex"; // Show the menu
-}
-
-function hideMenu() {
-  menu.style.display = "none"; // Hide the menu
-  document.querySelector("#chat-input textarea").focus();
-}
-
-if (buttonsInChat.length > 0) {
-  for (let i = buttonsInChat.length - 1; i >= 0; i--) {
-    const thisButton = buttonsInChat[i];
-    menu.appendChild(thisButton);
-
-    thisButton.addEventListener("click", () => {
-      hideMenu();
-    });
-
-    const buttonText = thisButton.textContent;
-    const matches = buttonText.match(/(\(.*?\))/);
-
-    if (matches && matches.length > 1) {
-      // Apply the transparent-substring class to the matched substring
-      const substring = matches[1];
-      const newText = buttonText.replace(substring, `&nbsp;<span class="transparent-substring">${substring.slice(1, -1)}</span>`);
-      thisButton.innerHTML = newText;
-    }
-  }
-} else {
-  buttonsInChat = document.querySelectorAll("#chat-tab.old-ui #chat-buttons button");
-  for (let i = 0; i < buttonsInChat.length; i++) {
-    buttonsInChat[i].textContent = buttonsInChat[i].textContent.replace(/ \(.*?\)/, "");
-  }
-  document.getElementById("gr-hover-container").style.display = "none";
-}
-
-function isMouseOverButtonOrMenu() {
-  return menu.matches(":hover") || button.matches(":hover");
-}
-
-button.addEventListener("mouseenter", function () {
-  showMenu();
-});
-
-button.addEventListener("click", function () {
-  showMenu();
-});
-
-// Add event listener for mouseleave on the button
-button.addEventListener("mouseleave", function () {
-  // Delay to prevent menu hiding when the mouse leaves the button into the menu
-  setTimeout(function () {
-    if (!isMouseOverButtonOrMenu()) {
-      hideMenu();
-    }
-  }, 100);
-});
-
-// Add event listener for mouseleave on the menu
-menu.addEventListener("mouseleave", function () {
-  // Delay to prevent menu hide when the mouse leaves the menu into the button
-  setTimeout(function () {
-    if (!isMouseOverButtonOrMenu()) {
-      hideMenu();
-    }
-  }, 100);
-});
-
-// Add event listener for click anywhere in the document
-document.addEventListener("click", function (event) {
-  // Check if the click is outside the button/menu and the menu is visible
-  if (!isMouseOverButtonOrMenu() && menu.style.display === "flex") {
-    hideMenu();
-  }
-});
-
-//------------------------------------------------
-// Relocate the "Show controls" checkbox
-//------------------------------------------------
-var elementToMove = document.getElementById("show-controls");
-var parent = elementToMove.parentNode;
-for (var i = 0; i < 2; i++) {
-  parent = parent.parentNode;
-}
-
-parent.insertBefore(elementToMove, parent.firstChild);
-
-//------------------------------------------------
-// Make the chat input grow upwards instead of downwards
-//------------------------------------------------
-document.getElementById("show-controls").parentNode.style.position = "absolute";
-document.getElementById("show-controls").parentNode.style.bottom = "0px";
-
-//------------------------------------------------
-// Focus on the chat input
-//------------------------------------------------
-document.querySelector("#chat-input textarea").focus();
diff --git a/spaces/lewiswu1209/MockingBird/vocoder/hifigan/models.py b/spaces/lewiswu1209/MockingBird/vocoder/hifigan/models.py
deleted file mode 100644
index c352e19f0c8aab5b3c24e861f5b1c06c17c5e750..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/vocoder/hifigan/models.py
+++ /dev/null
@@ -1,320 +0,0 @@
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from vocoder.hifigan.utils import init_weights, get_padding
-
-LRELU_SLOPE = 0.1
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.h = h
-        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.convs1.apply(init_weights)
-
-        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)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x):
-        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):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.h = h
-        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])))
-        ])
-        self.convs.apply(init_weights)
-
-    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 InterpolationBlock(torch.nn.Module):
-    def __init__(self, scale_factor, mode='nearest', align_corners=None, downsample=False):
-        super(InterpolationBlock, self).__init__()
-        self.downsample = downsample
-        self.scale_factor = scale_factor
-        self.mode = mode
-        self.align_corners = align_corners
-    
-    def forward(self, x):
-        outputs = torch.nn.functional.interpolate(
-            x,
-            size=x.shape[-1] * self.scale_factor \
-                if not self.downsample else x.shape[-1] // self.scale_factor,
-            mode=self.mode,
-            align_corners=self.align_corners,
-            recompute_scale_factor=False
-        )
-        return outputs
-
-class Generator(torch.nn.Module):
-    def __init__(self, h):
-        super(Generator, self).__init__()
-        self.h = h
-        self.num_kernels = len(h.resblock_kernel_sizes)
-        self.num_upsamples = len(h.upsample_rates)
-        self.conv_pre = weight_norm(Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3))
-        resblock = ResBlock1 if h.resblock == '1' else ResBlock2
-
-        self.ups = nn.ModuleList()
-#         for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
-# #             self.ups.append(weight_norm(
-# #                 ConvTranspose1d(h.upsample_initial_channel//(2**i), h.upsample_initial_channel//(2**(i+1)),
-# #                                 k, u, padding=(k-u)//2)))
-        if h.sampling_rate == 24000:
-            for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
-                self.ups.append(
-                    torch.nn.Sequential(
-                        InterpolationBlock(u),
-                        weight_norm(torch.nn.Conv1d(
-                            h.upsample_initial_channel//(2**i),
-                            h.upsample_initial_channel//(2**(i+1)),
-                            k, padding=(k-1)//2,
-                        ))
-                    )
-                )
-        else:
-            for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
-                self.ups.append(weight_norm(ConvTranspose1d(h.upsample_initial_channel//(2**i), 
-                    h.upsample_initial_channel//(2**(i+1)),
-                    k, u, padding=(u//2 + u%2), output_padding=u%2)))
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = h.upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
-                self.resblocks.append(resblock(h, ch, k, d))
-
-        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-
-    def forward(self, x):
-        x = self.conv_pre(x)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, 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):
-        print('Removing weight norm...')
-        for l in self.ups:
-            if self.h.sampling_rate == 24000:
-                remove_weight_norm(l[-1])
-            else:
-                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)
-
-
-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
-        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(5, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 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, 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):
-        super(MultiPeriodDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorP(2),
-            DiscriminatorP(3),
-            DiscriminatorP(5),
-            DiscriminatorP(7),
-            DiscriminatorP(11),
-        ])
-
-    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)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            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, 128, 15, 1, padding=7)),
-            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
-            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
-            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, 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, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
-    def __init__(self):
-        super(MultiScaleDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorS(use_spectral_norm=True),
-            DiscriminatorS(),
-            DiscriminatorS(),
-        ])
-        self.meanpools = nn.ModuleList([
-            AvgPool1d(4, 2, padding=2),
-            AvgPool1d(4, 2, padding=2)
-        ])
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            if i != 0:
-                y = self.meanpools[i-1](y)
-                y_hat = self.meanpools[i-1](y_hat)
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-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):
-            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):
-        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:
-        l = torch.mean((1-dg)**2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
-
diff --git a/spaces/lewtun/galactica-demo/README.md b/spaces/lewtun/galactica-demo/README.md
deleted file mode 100644
index c76339eb8409d76d22c03bc0a0aa7c0f504b96ce..0000000000000000000000000000000000000000
--- a/spaces/lewtun/galactica-demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Galactica Demo
-emoji: 📊
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.9.1
-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/librarian-bots/claim-papers/README.md b/spaces/librarian-bots/claim-papers/README.md
deleted file mode 100644
index 343e89d5f8ee8e833d55be662cd084598cc1912a..0000000000000000000000000000000000000000
--- a/spaces/librarian-bots/claim-papers/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Claim Papers
-emoji: 🌍
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-hf_oauth: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/limingcv/AlignDet/finetune/finetune_mask-rcnn_swin-b_lsj-3x-coco_simmim-800e_lr9e-5_wd2.5e-3/mask_rcnn_swin-b-p4-w7_fpn_lsj_3x_coco_simmim.py b/spaces/limingcv/AlignDet/finetune/finetune_mask-rcnn_swin-b_lsj-3x-coco_simmim-800e_lr9e-5_wd2.5e-3/mask_rcnn_swin-b-p4-w7_fpn_lsj_3x_coco_simmim.py
deleted file mode 100644
index fe0bf310234f9e6b1cdf8f8f46e2b580d009ddff..0000000000000000000000000000000000000000
--- a/spaces/limingcv/AlignDet/finetune/finetune_mask-rcnn_swin-b_lsj-3x-coco_simmim-800e_lr9e-5_wd2.5e-3/mask_rcnn_swin-b-p4-w7_fpn_lsj_3x_coco_simmim.py
+++ /dev/null
@@ -1,306 +0,0 @@
-model = dict(
-    type='MaskRCNN',
-    backbone=dict(
-        type='SwinTransformer',
-        embed_dims=128,
-        depths=[2, 2, 18, 2],
-        num_heads=[4, 8, 16, 32],
-        window_size=7,
-        mlp_ratio=4,
-        qkv_bias=True,
-        qk_scale=None,
-        drop_rate=0.0,
-        attn_drop_rate=0.0,
-        drop_path_rate=0.2,
-        patch_norm=True,
-        out_indices=(0, 1, 2, 3),
-        with_cp=False,
-        convert_weights=True,
-        init_cfg=dict(
-            type='Pretrained',
-            checkpoint='pretrain/simmim/simmim_800e_official.pth')),
-    neck=dict(
-        type='FPN',
-        in_channels=[128, 256, 512, 1024],
-        out_channels=256,
-        num_outs=5,
-        norm_cfg=dict(type='SyncBN', requires_grad=True)),
-    rpn_head=dict(
-        type='RPNHead',
-        in_channels=256,
-        feat_channels=256,
-        anchor_generator=dict(
-            type='AnchorGenerator',
-            scales=[8],
-            ratios=[0.5, 1.0, 2.0],
-            strides=[4, 8, 16, 32, 64]),
-        bbox_coder=dict(
-            type='DeltaXYWHBBoxCoder',
-            target_means=[0.0, 0.0, 0.0, 0.0],
-            target_stds=[1.0, 1.0, 1.0, 1.0]),
-        loss_cls=dict(
-            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-    roi_head=dict(
-        type='StandardRoIHead',
-        bbox_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        bbox_head=dict(
-            type='Shared4Conv1FCBBoxHead',
-            in_channels=256,
-            fc_out_channels=1024,
-            roi_feat_size=7,
-            num_classes=80,
-            bbox_coder=dict(
-                type='DeltaXYWHBBoxCoder',
-                target_means=[0.0, 0.0, 0.0, 0.0],
-                target_stds=[0.1, 0.1, 0.2, 0.2]),
-            reg_class_agnostic=False,
-            loss_cls=dict(
-                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
-        mask_roi_extractor=dict(
-            type='SingleRoIExtractor',
-            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
-            out_channels=256,
-            featmap_strides=[4, 8, 16, 32]),
-        mask_head=dict(
-            type='FCNMaskHead',
-            num_convs=4,
-            in_channels=256,
-            conv_out_channels=256,
-            num_classes=80,
-            loss_mask=dict(
-                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
-    train_cfg=dict(
-        rpn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.7,
-                neg_iou_thr=0.3,
-                min_pos_iou=0.3,
-                match_low_quality=True,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=256,
-                pos_fraction=0.5,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=False),
-            allowed_border=-1,
-            pos_weight=-1,
-            debug=False),
-        rpn_proposal=dict(
-            nms_pre=2000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.5,
-                min_pos_iou=0.5,
-                match_low_quality=True,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=512,
-                pos_fraction=0.25,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=True),
-            mask_size=28,
-            pos_weight=-1,
-            debug=False)),
-    test_cfg=dict(
-        rpn=dict(
-            nms_pre=1000,
-            max_per_img=1000,
-            nms=dict(type='nms', iou_threshold=0.7),
-            min_bbox_size=0),
-        rcnn=dict(
-            score_thr=0.05,
-            nms=dict(type='nms', iou_threshold=0.5),
-            max_per_img=100,
-            mask_thr_binary=0.5)))
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-image_size = (1024, 1024)
-file_client_args = dict(backend='disk')
-train_pipeline = [
-    dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(
-        type='Resize',
-        img_scale=(1024, 1024),
-        ratio_range=(0.1, 2.0),
-        multiscale_mode='range',
-        keep_ratio=True),
-    dict(
-        type='RandomCrop',
-        crop_type='absolute_range',
-        crop_size=(1024, 1024),
-        recompute_bbox=True,
-        allow_negative_crop=True),
-    dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(
-        type='Normalize',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375],
-        to_rgb=True),
-    dict(type='Pad', size=(1024, 1024)),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
-]
-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',
-                mean=[123.675, 116.28, 103.53],
-                std=[58.395, 57.12, 57.375],
-                to_rgb=True),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img'])
-        ])
-]
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=2,
-    train=dict(
-        type='CocoDataset',
-        ann_file='data/coco/annotations/instances_train2017.json',
-        img_prefix='data/coco/train2017/',
-        pipeline=[
-            dict(
-                type='LoadImageFromFile',
-                file_client_args=dict(backend='disk')),
-            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-            dict(
-                type='Resize',
-                img_scale=(1024, 1024),
-                ratio_range=(0.1, 2.0),
-                multiscale_mode='range',
-                keep_ratio=True),
-            dict(
-                type='RandomCrop',
-                crop_type='absolute_range',
-                crop_size=(1024, 1024),
-                recompute_bbox=True,
-                allow_negative_crop=True),
-            dict(type='FilterAnnotations', min_gt_bbox_wh=(0.01, 0.01)),
-            dict(type='RandomFlip', flip_ratio=0.5),
-            dict(
-                type='Normalize',
-                mean=[123.675, 116.28, 103.53],
-                std=[58.395, 57.12, 57.375],
-                to_rgb=True),
-            dict(type='Pad', size=(1024, 1024)),
-            dict(type='DefaultFormatBundle'),
-            dict(
-                type='Collect',
-                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
-        ]),
-    val=dict(
-        type='CocoDataset',
-        ann_file='data/coco/annotations/instances_val2017.json',
-        img_prefix='data/coco/val2017/',
-        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',
-                        mean=[123.675, 116.28, 103.53],
-                        std=[58.395, 57.12, 57.375],
-                        to_rgb=True),
-                    dict(type='Pad', size_divisor=32),
-                    dict(type='ImageToTensor', keys=['img']),
-                    dict(type='Collect', keys=['img'])
-                ])
-        ]),
-    test=dict(
-        type='CocoDataset',
-        ann_file='data/coco/annotations/instances_val2017.json',
-        img_prefix='data/coco/val2017/',
-        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',
-                        mean=[123.675, 116.28, 103.53],
-                        std=[58.395, 57.12, 57.375],
-                        to_rgb=True),
-                    dict(type='Pad', size_divisor=32),
-                    dict(type='ImageToTensor', keys=['img']),
-                    dict(type='Collect', keys=['img'])
-                ])
-        ]))
-evaluation = dict(metric=['bbox', 'segm'], save_best='auto', gpu_collect=True)
-optimizer = dict(
-    type='AdamW',
-    lr=9e-05,
-    betas=(0.9, 0.999),
-    weight_decay=0.0025,
-    paramwise_cfg=dict(
-        custom_keys=dict(
-            absolute_pos_embed=dict(decay_mult=0.0),
-            relative_position_bias_table=dict(decay_mult=0.0),
-            norm=dict(decay_mult=0.0))))
-optimizer_config = dict(grad_clip=None)
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=1000,
-    warmup_ratio=0.001,
-    step=[24, 33])
-runner = dict(type='EpochBasedRunner', max_epochs=36)
-checkpoint_config = dict(interval=1)
-log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
-custom_hooks = [
-    dict(type='NumClassCheckHook'),
-    dict(
-        type='MMDetWandbHook',
-        init_kwargs=dict(project='I2B', group='finetune'),
-        interval=50,
-        num_eval_images=0,
-        log_checkpoint=False)
-]
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = 'pretrain/selfsup_mask-rcnn_swin-b_simmim-800e/final_model.pth'
-resume_from = None
-workflow = [('train', 1)]
-opencv_num_threads = 0
-mp_start_method = 'fork'
-auto_scale_lr = dict(enable=True, base_batch_size=32)
-custom_imports = None
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-pretrained = 'pretrain/simmim/simmim_800e_official.pth'
-work_dir = 'work_dirs/finetune_mask-rcnn_swin-b_lsj-3x-coco_simmim-800e_lr9e-5_wd2.5e-3'
-auto_resume = False
-gpu_ids = range(0, 8)
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Office Home And Business 2010 Product Keygen TOP.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Office Home And Business 2010 Product Keygen TOP.md
deleted file mode 100644
index 4b25763ac803117b4014f283ad695d8e3aec3db5..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Microsoft Office Home And Business 2010 Product Keygen TOP.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>microsoft office home and business 2010 product keygen</h2><br /><p><b><b>Download</b> ===> <a href="https://bytlly.com/2uGvFq">https://bytlly.com/2uGvFq</a></b></p><br /><br />
-
-Home Free. Free Microsoft Office 2010 Product Key for You - product key. Microsoft ... 101 Free Excel Templates to Organize Your Life and Business ... Keygen keygen скачать x force autocad 2017 64 bit 3ds Max, Autodesk Autocad, Structural. 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lint/meetingsummary/README.md b/spaces/lint/meetingsummary/README.md
deleted file mode 100644
index a8864eedc715581430680b1ae2b25d4942c04622..0000000000000000000000000000000000000000
--- a/spaces/lint/meetingsummary/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Meetingsummary
-emoji: 💻
-colorFrom: purple
-colorTo: pink
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/litagin/rvc_okiba_TTS/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py b/spaces/litagin/rvc_okiba_TTS/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py
deleted file mode 100644
index b2c592527a5966e6f8e79e8c52dc5b414246dcc6..0000000000000000000000000000000000000000
--- a/spaces/litagin/rvc_okiba_TTS/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import parselmouth
-import numpy as np
-
-
-class PMF0Predictor(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 compute_f0(self, wav, p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0] // self.hop_length
-        else:
-            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = (
-            parselmouth.Sound(x, self.sampling_rate)
-            .to_pitch_ac(
-                time_step=time_step / 1000,
-                voicing_threshold=0.6,
-                pitch_floor=self.f0_min,
-                pitch_ceiling=self.f0_max,
-            )
-            .selected_array["frequency"]
-        )
-
-        pad_size = (p_len - len(f0) + 1) // 2
-        if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-            f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
-        f0, uv = self.interpolate_f0(f0)
-        return f0
-
-    def compute_f0_uv(self, wav, p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0] // self.hop_length
-        else:
-            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = (
-            parselmouth.Sound(x, self.sampling_rate)
-            .to_pitch_ac(
-                time_step=time_step / 1000,
-                voicing_threshold=0.6,
-                pitch_floor=self.f0_min,
-                pitch_ceiling=self.f0_max,
-            )
-            .selected_array["frequency"]
-        )
-
-        pad_size = (p_len - len(f0) + 1) // 2
-        if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-            f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
-        f0, uv = self.interpolate_f0(f0)
-        return f0, uv
diff --git a/spaces/lj1995/vocal2guitar/app.py b/spaces/lj1995/vocal2guitar/app.py
deleted file mode 100644
index 83b12007eed88489cc12d07b40654320c40da573..0000000000000000000000000000000000000000
--- a/spaces/lj1995/vocal2guitar/app.py
+++ /dev/null
@@ -1,163 +0,0 @@
-import io
-import os
-os.system("chmod 777 ffmpeg")
-import torch
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile
-import logging
-from fairseq import checkpoint_utils
-from my_utils import load_audio
-from vc_infer_pipeline import VC
-import traceback
-from config import Config
-from infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from i18n import I18nAuto
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-i18n = I18nAuto()
-i18n.print()
-
-config = Config()
-
-models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-    ["hubert_base.pt"],
-    suffix="",
-)
-hubert_model = models[0]
-hubert_model = hubert_model.to(config.device)
-hubert_model = hubert_model.float()
-hubert_model.eval()
-
-global n_spk, tgt_sr, net_g, vc, cpt, version
-person = "weights/simple-guitar-crepe-guolv_e1000.pth"
-print("loading %s" % person)
-cpt = torch.load(person, map_location="cpu")
-tgt_sr = cpt["config"][-1]
-cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=False)
-del net_g.enc_q
-print(net_g.load_state_dict(cpt["weight"], strict=False))
-net_g.eval().to(config.device)
-net_g = net_g.float()
-vc = VC(tgt_sr, config)
-n_spk = cpt["config"][-3]
-version="v2"
-
-default_audio=load_audio("logs/mute/1_16k_wavs/mute.wav",16000)
-def vc_single(
-    # sid=0,
-    input_audio_path,#待选取
-    f0_up_key,#待选取
-    f0_method,
-    file_index="logs/added_IVF2225_Flat_nprobe_1_simple-guitar-crepe-guolv_v2.index",#写死
-    index_rate=1,#写死1
-    filter_radius=3,#不需要，随便写，3
-    resample_sr=0,#写死0不需要
-    rms_mix_rate=1,#写死1不需要
-    protect=0.5,#写死0.5不需要
-):
-    global tgt_sr, net_g, vc, hubert_model, version
-    if input_audio_path is None:
-        return "You need to upload an audio", None
-    f0_up_key = int(f0_up_key)
-    try:
-        audio = input_audio_path[1] / 32768.0
-        if len(audio.shape) == 2:
-            audio = np.mean(audio, -1)
-        audio = librosa.resample(audio, orig_sr=input_audio_path[0], target_sr=16000)
-        audio_max = np.abs(audio).max() / 0.95
-        if audio_max > 1:
-            audio /= audio_max
-        times = [0, 0, 0]
-        audio_opt = vc.pipeline(
-            model=hubert_model,
-            net_g=net_g,
-            sid=0,
-            audio=audio,
-            input_audio_path="123",
-            times=times,
-            f0_up_key=f0_up_key,
-            f0_method=f0_method,
-            file_index=file_index,
-            index_rate=index_rate,
-            if_f0=1,
-            filter_radius=filter_radius,
-            tgt_sr=tgt_sr,
-            resample_sr=resample_sr,
-            rms_mix_rate=rms_mix_rate,
-            version="v2",
-            protect=protect,
-            f0_file=None,
-        )
-        if resample_sr >= 16000 and tgt_sr != resample_sr:
-            tgt_sr = resample_sr
-        index_info = (
-            "Using index:%s." % file_index
-            if os.path.exists(file_index)
-            else "Index not used."
-        )
-        return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
-            index_info,
-            times[0],
-            times[1],
-            times[2],
-        ), (tgt_sr, audio_opt)
-    except:
-        info = traceback.format_exc()
-        print(info)
-        return "报错了！信息如下：%s"%info, (16000, default_audio)
-
-app = gr.Blocks()
-with app:
-    with gr.Tabs():
-        with gr.TabItem("人声转吉他极简在线demo"):
-            gr.Markdown(
-                value="""
-                变调越高吉他音越细，越低越沉闷
-                """
-            )
-            vc_input = gr.Audio(label="上传音频")
-            with gr.Column():
-                with gr.Row():
-                    vc_transform = gr.Slider(
-                        minimum=-12,
-                        maximum=12,
-                        label="变调(半音数量,升八度12降八度-12)",
-                        value=0,
-                        step=1,
-                        interactive=True,
-                    )
-                    f0method = gr.Radio(
-                        label=i18n(
-                            "选择音高提取算法:语音推荐dio歌声推荐pm"
-                        ),
-                        choices=["pm", "dio"],
-                        value="dio",
-                        interactive=True,
-                    )
-                with gr.Row():
-                    but = gr.Button(i18n("转换"), variant="primary")
-                    vc_output1 = gr.Textbox(label=i18n("输出信息"))
-                    vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)"))
-            but.click(
-                vc_single,
-                [
-                    vc_input,
-                    vc_transform,
-                    f0method
-                ],
-                [vc_output1, vc_output2],
-            )
-
-app.launch(server_name="0.0.0.0",quiet=True)
diff --git a/spaces/lpnguyen/calculator/app.py b/spaces/lpnguyen/calculator/app.py
deleted file mode 100644
index 7b58ea157c3f8009b3699a654701b200dcfd88d9..0000000000000000000000000000000000000000
--- a/spaces/lpnguyen/calculator/app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import streamlit as st
-
-st.title("Calculator")
-
-col1, col2, col3 = st.columns(3)
-with col1:
-    a = st.number_input("First number", value=1.0)
-with col2:
-    b = st.number_input("Second number", value=2.0)
-with col3:
-    operation = st.selectbox("Choose the operation", ["+", "-", "*", "/"])
-if operation == "+":
-    st.write(a, "+", b, "=", a + b)
-elif operation == "-":
-    st.write(a, "-", b, "=", a - b)
-elif operation == "*":
-    st.write(a, "*", b, "=", a * b)
-elif operation == "/":
-    if b == 0:
-        st.warning("Divided by zero")
-    else:
-        st.write(a, "/", b, "=", a / b)
diff --git a/spaces/lsy641/distinct/app.py b/spaces/lsy641/distinct/app.py
deleted file mode 100644
index c27c1f334b3113d21943ef9595eb389986f2e8f9..0000000000000000000000000000000000000000
--- a/spaces/lsy641/distinct/app.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import evaluate
-from evaluate.utils import launch_gradio_widget
-
-
-module = evaluate.load("lsy641/distinct")
-launch_gradio_widget(module)
\ No newline at end of file
diff --git a/spaces/luckwill/chiakicc/models.py b/spaces/luckwill/chiakicc/models.py
deleted file mode 100644
index c214bbb0476ba4777093d8bcf032961f09e59496..0000000000000000000000000000000000000000
--- a/spaces/luckwill/chiakicc/models.py
+++ /dev/null
@@ -1,549 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-        super().__init__()
-        filter_channels = in_channels  # it needs to be removed from future version.
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.log_flow = modules.Log()
-        self.flows = nn.ModuleList()
-        self.flows.append(modules.ElementwiseAffine(2))
-        for i in range(n_flows):
-            self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-            self.flows.append(modules.Flip())
-
-        self.post_pre = nn.Conv1d(1, filter_channels, 1)
-        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-        self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-        self.post_flows = nn.ModuleList()
-        self.post_flows.append(modules.ElementwiseAffine(2))
-        for i in range(4):
-            self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-            self.post_flows.append(modules.Flip())
-
-        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-        self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-        x = torch.detach(x)
-        x = self.pre(x)
-        if g is not None:
-            g = torch.detach(g)
-            x = x + self.cond(g)
-        x = self.convs(x, x_mask)
-        x = self.proj(x) * x_mask
-
-        if not reverse:
-            flows = self.flows
-            assert w is not None
-
-            logdet_tot_q = 0
-            h_w = self.post_pre(w)
-            h_w = self.post_convs(h_w, x_mask)
-            h_w = self.post_proj(h_w) * x_mask
-            e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-            z_q = e_q
-            for flow in self.post_flows:
-                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-                logdet_tot_q += logdet_q
-            z_u, z1 = torch.split(z_q, [1, 1], 1)
-            u = torch.sigmoid(z_u) * x_mask
-            z0 = (w - u) * x_mask
-            logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
-            logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
-
-            logdet_tot = 0
-            z0, logdet = self.log_flow(z0, x_mask)
-            logdet_tot += logdet
-            z = torch.cat([z0, z1], 1)
-            for flow in flows:
-                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-                logdet_tot = logdet_tot + logdet
-            nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
-            return nll + logq  # [b]
-        else:
-            flows = list(reversed(self.flows))
-            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
-            z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-            for flow in flows:
-                z = flow(z, x_mask, g=x, reverse=reverse)
-            z0, z1 = torch.split(z, [1, 1], 1)
-            logw = z0
-            return logw
-
-
-class DurationPredictor(nn.Module):
-    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.gin_channels = gin_channels
-
-        self.drop = nn.Dropout(p_dropout)
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.norm_1 = modules.LayerNorm(filter_channels)
-        self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.norm_2 = modules.LayerNorm(filter_channels)
-        self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-    def forward(self, x, x_mask, g=None):
-        x = torch.detach(x)
-        if g is not None:
-            g = torch.detach(g)
-            x = x + self.cond(g)
-        x = self.conv_1(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_1(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_2(x)
-        x = self.drop(x)
-        x = self.proj(x * x_mask)
-        return x * x_mask
-
-
-class TextEncoder(nn.Module):
-    def __init__(self,
-                 n_vocab,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout,
-                 emotion_embedding):
-        super().__init__()
-        self.n_vocab = n_vocab
-        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.emotion_embedding = emotion_embedding
-
-        if self.n_vocab != 0:
-            self.emb = nn.Embedding(n_vocab, hidden_channels)
-            if emotion_embedding:
-                self.emo_proj = nn.Linear(1024, hidden_channels)
-            nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
-
-        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, x, x_lengths, emotion_embedding=None):
-        if self.n_vocab != 0:
-            x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-        if emotion_embedding is not None:
-            x = x + self.emo_proj(emotion_embedding.unsqueeze(1))
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_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 x, 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
-
-
-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
-
-
-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):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-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 SynthesizerTrn(nn.Module):
-    """
-    Synthesizer for Training
-    """
-
-    def __init__(self,
-                 n_vocab,
-                 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,
-                 n_speakers=0,
-                 gin_channels=0,
-                 use_sdp=True,
-                 emotion_embedding=False,
-                 **kwargs):
-
-        super().__init__()
-        self.n_vocab = n_vocab
-        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.n_speakers = n_speakers
-        self.gin_channels = gin_channels
-
-        self.use_sdp = use_sdp
-
-        self.enc_p = TextEncoder(n_vocab,
-                                 inter_channels,
-                                 hidden_channels,
-                                 filter_channels,
-                                 n_heads,
-                                 n_layers,
-                                 kernel_size,
-                                 p_dropout,
-                                 emotion_embedding)
-        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, 4, gin_channels=gin_channels)
-
-        if use_sdp:
-            self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-        else:
-            self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-        if n_speakers > 1:
-            self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-    def forward(self, x, x_lengths, y, y_lengths, sid=None, emotion_embedding=None):
-
-        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding)
-        if self.n_speakers > 1:
-            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-        else:
-            g = None
-
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-
-        with torch.no_grad():
-            # negative cross-entropy
-            s_p_sq_r = torch.exp(-2 * logs_p)  # [b, d, t]
-            neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True)  # [b, 1, t_s]
-            neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2),
-                                     s_p_sq_r)  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-            neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r))  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-            neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True)  # [b, 1, t_s]
-            neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-            attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-            attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-        w = attn.sum(2)
-        if self.use_sdp:
-            l_length = self.dp(x, x_mask, w, g=g)
-            l_length = l_length / torch.sum(x_mask)
-        else:
-            logw_ = torch.log(w + 1e-6) * x_mask
-            logw = self.dp(x, x_mask, g=g)
-            l_length = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask)  # for averaging
-
-        # expand prior
-        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-        z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-        o = self.dec(z_slice, g=g)
-        return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-    def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None,
-              emotion_embedding=None):
-        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emotion_embedding)
-        if self.n_speakers > 1:
-            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-        else:
-            g = None
-
-        if self.use_sdp:
-            logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-        else:
-            logw = self.dp(x, x_mask, g=g)
-        w = torch.exp(logw) * x_mask * length_scale
-        w_ceil = torch.ceil(w)
-        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-        attn = commons.generate_path(w_ceil, attn_mask)
-
-        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)  # [b, t', t], [b, t, d] -> [b, d, t']
-        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
-                                                                                 2)  # [b, t', t], [b, t, d] -> [b, d, t']
-
-        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-        z = self.flow(z_p, y_mask, g=g, reverse=True)
-        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
-        return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-    def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-        assert self.n_speakers > 1, "n_speakers have to be larger than 1."
-        g_src = self.emb_g(sid_src).unsqueeze(-1)
-        g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-        z_p = self.flow(z, y_mask, g=g_src)
-        z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-        o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-        return o_hat, y_mask, (z, z_p, z_hat)
diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/archs/hifacegan_arch.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/archs/hifacegan_arch.py
deleted file mode 100644
index de26e4c2c8f2848dd855e409da19b81fef2538bb..0000000000000000000000000000000000000000
--- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/archs/hifacegan_arch.py
+++ /dev/null
@@ -1,259 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from basicsr.utils.registry import ARCH_REGISTRY
-from .hifacegan_util import BaseNetwork, LIPEncoder, SPADEResnetBlock, get_nonspade_norm_layer
-
-
-class SPADEGenerator(BaseNetwork):
-    """Generator with SPADEResBlock"""
-
-    def __init__(self,
-                 num_in_ch=3,
-                 num_feat=64,
-                 use_vae=False,
-                 z_dim=256,
-                 crop_size=512,
-                 norm_g='spectralspadesyncbatch3x3',
-                 is_train=True,
-                 init_train_phase=3):  # progressive training disabled
-        super().__init__()
-        self.nf = num_feat
-        self.input_nc = num_in_ch
-        self.is_train = is_train
-        self.train_phase = init_train_phase
-
-        self.scale_ratio = 5  # hardcoded now
-        self.sw = crop_size // (2**self.scale_ratio)
-        self.sh = self.sw  # 20210519: By default use square image, aspect_ratio = 1.0
-
-        if use_vae:
-            # In case of VAE, we will sample from random z vector
-            self.fc = nn.Linear(z_dim, 16 * self.nf * self.sw * self.sh)
-        else:
-            # Otherwise, we make the network deterministic by starting with
-            # downsampled segmentation map instead of random z
-            self.fc = nn.Conv2d(num_in_ch, 16 * self.nf, 3, padding=1)
-
-        self.head_0 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g)
-
-        self.g_middle_0 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g)
-        self.g_middle_1 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g)
-
-        self.ups = nn.ModuleList([
-            SPADEResnetBlock(16 * self.nf, 8 * self.nf, norm_g),
-            SPADEResnetBlock(8 * self.nf, 4 * self.nf, norm_g),
-            SPADEResnetBlock(4 * self.nf, 2 * self.nf, norm_g),
-            SPADEResnetBlock(2 * self.nf, 1 * self.nf, norm_g)
-        ])
-
-        self.to_rgbs = nn.ModuleList([
-            nn.Conv2d(8 * self.nf, 3, 3, padding=1),
-            nn.Conv2d(4 * self.nf, 3, 3, padding=1),
-            nn.Conv2d(2 * self.nf, 3, 3, padding=1),
-            nn.Conv2d(1 * self.nf, 3, 3, padding=1)
-        ])
-
-        self.up = nn.Upsample(scale_factor=2)
-
-    def encode(self, input_tensor):
-        """
-        Encode input_tensor into feature maps, can be overridden in derived classes
-        Default: nearest downsampling of 2**5 = 32 times
-        """
-        h, w = input_tensor.size()[-2:]
-        sh, sw = h // 2**self.scale_ratio, w // 2**self.scale_ratio
-        x = F.interpolate(input_tensor, size=(sh, sw))
-        return self.fc(x)
-
-    def forward(self, x):
-        # In oroginal SPADE, seg means a segmentation map, but here we use x instead.
-        seg = x
-
-        x = self.encode(x)
-        x = self.head_0(x, seg)
-
-        x = self.up(x)
-        x = self.g_middle_0(x, seg)
-        x = self.g_middle_1(x, seg)
-
-        if self.is_train:
-            phase = self.train_phase + 1
-        else:
-            phase = len(self.to_rgbs)
-
-        for i in range(phase):
-            x = self.up(x)
-            x = self.ups[i](x, seg)
-
-        x = self.to_rgbs[phase - 1](F.leaky_relu(x, 2e-1))
-        x = torch.tanh(x)
-
-        return x
-
-    def mixed_guidance_forward(self, input_x, seg=None, n=0, mode='progressive'):
-        """
-        A helper class for subspace visualization. Input and seg are different images.
-        For the first n levels (including encoder) we use input, for the rest we use seg.
-
-        If mode = 'progressive', the output's like: AAABBB
-        If mode = 'one_plug', the output's like:    AAABAA
-        If mode = 'one_ablate', the output's like:  BBBABB
-        """
-
-        if seg is None:
-            return self.forward(input_x)
-
-        if self.is_train:
-            phase = self.train_phase + 1
-        else:
-            phase = len(self.to_rgbs)
-
-        if mode == 'progressive':
-            n = max(min(n, 4 + phase), 0)
-            guide_list = [input_x] * n + [seg] * (4 + phase - n)
-        elif mode == 'one_plug':
-            n = max(min(n, 4 + phase - 1), 0)
-            guide_list = [seg] * (4 + phase)
-            guide_list[n] = input_x
-        elif mode == 'one_ablate':
-            if n > 3 + phase:
-                return self.forward(input_x)
-            guide_list = [input_x] * (4 + phase)
-            guide_list[n] = seg
-
-        x = self.encode(guide_list[0])
-        x = self.head_0(x, guide_list[1])
-
-        x = self.up(x)
-        x = self.g_middle_0(x, guide_list[2])
-        x = self.g_middle_1(x, guide_list[3])
-
-        for i in range(phase):
-            x = self.up(x)
-            x = self.ups[i](x, guide_list[4 + i])
-
-        x = self.to_rgbs[phase - 1](F.leaky_relu(x, 2e-1))
-        x = torch.tanh(x)
-
-        return x
-
-
-@ARCH_REGISTRY.register()
-class HiFaceGAN(SPADEGenerator):
-    """
-    HiFaceGAN: SPADEGenerator with a learnable feature encoder
-    Current encoder design: LIPEncoder
-    """
-
-    def __init__(self,
-                 num_in_ch=3,
-                 num_feat=64,
-                 use_vae=False,
-                 z_dim=256,
-                 crop_size=512,
-                 norm_g='spectralspadesyncbatch3x3',
-                 is_train=True,
-                 init_train_phase=3):
-        super().__init__(num_in_ch, num_feat, use_vae, z_dim, crop_size, norm_g, is_train, init_train_phase)
-        self.lip_encoder = LIPEncoder(num_in_ch, num_feat, self.sw, self.sh, self.scale_ratio)
-
-    def encode(self, input_tensor):
-        return self.lip_encoder(input_tensor)
-
-
-@ARCH_REGISTRY.register()
-class HiFaceGANDiscriminator(BaseNetwork):
-    """
-    Inspired by pix2pixHD multiscale discriminator.
-    Args:
-        num_in_ch (int): Channel number of inputs. Default: 3.
-        num_out_ch (int): Channel number of outputs. Default: 3.
-        conditional_d (bool): Whether use conditional discriminator.
-            Default: True.
-        num_d (int): Number of Multiscale discriminators. Default: 3.
-        n_layers_d (int): Number of downsample layers in each D. Default: 4.
-        num_feat (int): Channel number of base intermediate features.
-            Default: 64.
-        norm_d (str): String to determine normalization layers in D.
-            Choices: [spectral][instance/batch/syncbatch]
-            Default: 'spectralinstance'.
-        keep_features (bool): Keep intermediate features for matching loss, etc.
-            Default: True.
-    """
-
-    def __init__(self,
-                 num_in_ch=3,
-                 num_out_ch=3,
-                 conditional_d=True,
-                 num_d=2,
-                 n_layers_d=4,
-                 num_feat=64,
-                 norm_d='spectralinstance',
-                 keep_features=True):
-        super().__init__()
-        self.num_d = num_d
-
-        input_nc = num_in_ch
-        if conditional_d:
-            input_nc += num_out_ch
-
-        for i in range(num_d):
-            subnet_d = NLayerDiscriminator(input_nc, n_layers_d, num_feat, norm_d, keep_features)
-            self.add_module(f'discriminator_{i}', subnet_d)
-
-    def downsample(self, x):
-        return F.avg_pool2d(x, kernel_size=3, stride=2, padding=[1, 1], count_include_pad=False)
-
-    # Returns list of lists of discriminator outputs.
-    # The final result is of size opt.num_d x opt.n_layers_D
-    def forward(self, x):
-        result = []
-        for _, _net_d in self.named_children():
-            out = _net_d(x)
-            result.append(out)
-            x = self.downsample(x)
-
-        return result
-
-
-class NLayerDiscriminator(BaseNetwork):
-    """Defines the PatchGAN discriminator with the specified arguments."""
-
-    def __init__(self, input_nc, n_layers_d, num_feat, norm_d, keep_features):
-        super().__init__()
-        kw = 4
-        padw = int(np.ceil((kw - 1.0) / 2))
-        nf = num_feat
-        self.keep_features = keep_features
-
-        norm_layer = get_nonspade_norm_layer(norm_d)
-        sequence = [[nn.Conv2d(input_nc, nf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, False)]]
-
-        for n in range(1, n_layers_d):
-            nf_prev = nf
-            nf = min(nf * 2, 512)
-            stride = 1 if n == n_layers_d - 1 else 2
-            sequence += [[
-                norm_layer(nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=stride, padding=padw)),
-                nn.LeakyReLU(0.2, False)
-            ]]
-
-        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
-
-        # We divide the layers into groups to extract intermediate layer outputs
-        for n in range(len(sequence)):
-            self.add_module('model' + str(n), nn.Sequential(*sequence[n]))
-
-    def forward(self, x):
-        results = [x]
-        for submodel in self.children():
-            intermediate_output = submodel(results[-1])
-            results.append(intermediate_output)
-
-        if self.keep_features:
-            return results[1:]
-        else:
-            return results[-1]
diff --git a/spaces/manhkhanhUIT/BOPBTL/Face_Enhancement/models/networks/encoder.py b/spaces/manhkhanhUIT/BOPBTL/Face_Enhancement/models/networks/encoder.py
deleted file mode 100644
index 76acf690fd527bb9bd1dfc0c07c82573a1026d88..0000000000000000000000000000000000000000
--- a/spaces/manhkhanhUIT/BOPBTL/Face_Enhancement/models/networks/encoder.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import torch.nn as nn
-import numpy as np
-import torch.nn.functional as F
-from models.networks.base_network import BaseNetwork
-from models.networks.normalization import get_nonspade_norm_layer
-
-
-class ConvEncoder(BaseNetwork):
-    """ Same architecture as the image discriminator """
-
-    def __init__(self, opt):
-        super().__init__()
-
-        kw = 3
-        pw = int(np.ceil((kw - 1.0) / 2))
-        ndf = opt.ngf
-        norm_layer = get_nonspade_norm_layer(opt, opt.norm_E)
-        self.layer1 = norm_layer(nn.Conv2d(3, ndf, kw, stride=2, padding=pw))
-        self.layer2 = norm_layer(nn.Conv2d(ndf * 1, ndf * 2, kw, stride=2, padding=pw))
-        self.layer3 = norm_layer(nn.Conv2d(ndf * 2, ndf * 4, kw, stride=2, padding=pw))
-        self.layer4 = norm_layer(nn.Conv2d(ndf * 4, ndf * 8, kw, stride=2, padding=pw))
-        self.layer5 = norm_layer(nn.Conv2d(ndf * 8, ndf * 8, kw, stride=2, padding=pw))
-        if opt.crop_size >= 256:
-            self.layer6 = norm_layer(nn.Conv2d(ndf * 8, ndf * 8, kw, stride=2, padding=pw))
-
-        self.so = s0 = 4
-        self.fc_mu = nn.Linear(ndf * 8 * s0 * s0, 256)
-        self.fc_var = nn.Linear(ndf * 8 * s0 * s0, 256)
-
-        self.actvn = nn.LeakyReLU(0.2, False)
-        self.opt = opt
-
-    def forward(self, x):
-        if x.size(2) != 256 or x.size(3) != 256:
-            x = F.interpolate(x, size=(256, 256), mode="bilinear")
-
-        x = self.layer1(x)
-        x = self.layer2(self.actvn(x))
-        x = self.layer3(self.actvn(x))
-        x = self.layer4(self.actvn(x))
-        x = self.layer5(self.actvn(x))
-        if self.opt.crop_size >= 256:
-            x = self.layer6(self.actvn(x))
-        x = self.actvn(x)
-
-        x = x.view(x.size(0), -1)
-        mu = self.fc_mu(x)
-        logvar = self.fc_var(x)
-
-        return mu, logvar
diff --git a/spaces/marcusj83/MusicGenbruh/tests/models/test_musicgen.py b/spaces/marcusj83/MusicGenbruh/tests/models/test_musicgen.py
deleted file mode 100644
index 53eff4405ab7de18e0ae18df8c8f9959a1c9e031..0000000000000000000000000000000000000000
--- a/spaces/marcusj83/MusicGenbruh/tests/models/test_musicgen.py
+++ /dev/null
@@ -1,50 +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 pytest
-import torch
-
-from audiocraft.models import MusicGen
-
-
-class TestSEANetModel:
-    def get_musicgen(self):
-        mg = MusicGen.get_pretrained(name='debug', device='cpu')
-        mg.set_generation_params(duration=2.0)
-        return mg
-
-    def test_base(self):
-        mg = self.get_musicgen()
-        assert mg.frame_rate == 25
-        assert mg.sample_rate == 32000
-        assert mg.audio_channels == 1
-
-    def test_generate_unconditional(self):
-        mg = self.get_musicgen()
-        wav = mg.generate_unconditional(3)
-        assert list(wav.shape) == [3, 1, 64000]
-
-    def test_generate_continuation(self):
-        mg = self.get_musicgen()
-        prompt = torch.randn(3, 1, 32000)
-        wav = mg.generate_continuation(prompt, 32000)
-        assert list(wav.shape) == [3, 1, 64000]
-
-        prompt = torch.randn(2, 1, 32000)
-        wav = mg.generate_continuation(
-            prompt, 32000, ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 64000]
-
-        prompt = torch.randn(2, 1, 32000)
-        with pytest.raises(AssertionError):
-            wav = mg.generate_continuation(
-                prompt, 32000, ['youpi', 'lapin dort', 'one too many'])
-
-    def test_generate(self):
-        mg = self.get_musicgen()
-        wav = mg.generate(
-            ['youpi', 'lapin dort'])
-        assert list(wav.shape) == [2, 1, 64000]
diff --git a/spaces/mariagrandury/roberta-qa-es/app.py b/spaces/mariagrandury/roberta-qa-es/app.py
deleted file mode 100644
index b918dc898de79f8c3fdafc366ec009fdf0afa417..0000000000000000000000000000000000000000
--- a/spaces/mariagrandury/roberta-qa-es/app.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import gradio as gr
-
-title = "RoBERTa, tengo una pregunta"
-description = "Modelo tipo RoBERTa pre-entrenado por BSC-TeMU con la base de datos de la Bibliotecha Nacional de España y fine-tuned con el corpus SQAC (Spanish Question-Answering Corpus)."
-examples = [
-    ["¡Hola, mundo! Somos NLP en ES 🤗 la comunidad de hispanohablantes de la iniciativa “Languages at HuggingFace” y queremos democratizar el NLP en nuestro idioma. Somos una red internacional y nuestro objetivo es crear y compartir recursos que posibiliten y aceleren el avance del NLP en español.", "¿Quiénes somos?"]
-]
-article = """
-<p style="text-align: center">
-    NLP en ES 🤗 | <a target=”_blank” href="https://nlp-en-es.org"> nlp-en-es.org </a>
-</p>
-"""
-
-gr.Interface.load(
-    name="huggingface/nlp-en-es/roberta-base-bne-finetuned-sqac",
-    inputs=[gr.inputs.Textbox(label="Contexto", lines=5), gr.inputs.Textbox(label="Pregunta")],   
-    outputs=gr.outputs.Textbox(label="Respuesta"),
-    title=title,
-    description=description,
-    article=article,
-    examples=examples,
-    theme="huggingface",
-    allow_screenshot=True,
-    allow_flagging=True,
-    flagging_dir="flagged",
-    enable_queue=True
-).launch()
diff --git a/spaces/markski/reddit-roast-me/src/transformer_mapper.py b/spaces/markski/reddit-roast-me/src/transformer_mapper.py
deleted file mode 100644
index 34dd994016da8e880c4c01327112940304b54442..0000000000000000000000000000000000000000
--- a/spaces/markski/reddit-roast-me/src/transformer_mapper.py
+++ /dev/null
@@ -1,22 +0,0 @@
-
-from torch import nn
-import torch
-
-from src.transformer import Transformer
-
-
-class TransformerMapper(nn.Module):
-
-    def forward(self, x):
-        x = self.linear(x).view(x.shape[0], self.clip_length, -1)
-        prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape)
-        prefix = torch.cat((x, prefix), dim=1)
-        out = self.transformer(prefix)[:, self.clip_length:]
-        return out
-
-    def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8):
-        super(TransformerMapper, self).__init__()
-        self.clip_length = clip_length
-        self.transformer = Transformer(dim_embedding, 8, num_layers)
-        self.linear = nn.Linear(dim_clip, clip_length * dim_embedding)
-        self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True)
diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/src/models/model_audio2landmark.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/src/models/model_audio2landmark.py
deleted file mode 100644
index fb94f0a749a1d42d6075dea1a990e224e13465d2..0000000000000000000000000000000000000000
--- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/src/models/model_audio2landmark.py
+++ /dev/null
@@ -1,492 +0,0 @@
-"""
- # Copyright 2020 Adobe
- # All Rights Reserved.
- 
- # NOTICE: Adobe permits you to use, modify, and distribute this file in
- # accordance with the terms of the Adobe license agreement accompanying
- # it.
- 
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.parallel
-import torch.utils.data
-import math
-import torch.nn.functional as F
-import copy
-import numpy as np
-
-# device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
-device = torch.device("cuda")
-
-AUDIO_FEAT_SIZE = 161
-FACE_ID_FEAT_SIZE = 204
-Z_SIZE = 16
-EPSILON = 1e-40
-
-
-class Audio2landmark_content(nn.Module):
-
-    def __init__(self, num_window_frames=18, in_size=80, lstm_size=AUDIO_FEAT_SIZE, use_prior_net=False, hidden_size=256, num_layers=3, drop_out=0, bidirectional=False):
-        super(Audio2landmark_content, self).__init__()
-
-        self.fc_prior = self.fc = nn.Sequential(
-            nn.Linear(in_features=in_size, out_features=256),
-            nn.BatchNorm1d(256),
-            nn.LeakyReLU(0.2),
-            nn.Linear(256, lstm_size),
-        )
-
-        self.use_prior_net = use_prior_net
-        if(use_prior_net):
-            self.bilstm = nn.LSTM(input_size=lstm_size,
-                                  hidden_size=hidden_size,
-                                  num_layers=num_layers,
-                                  dropout=drop_out,
-                                  bidirectional=bidirectional,
-                                  batch_first=True, )
-        else:
-            self.bilstm = nn.LSTM(input_size=in_size,
-                                  hidden_size=hidden_size,
-                                  num_layers=num_layers,
-                                  dropout=drop_out,
-                                  bidirectional=bidirectional,
-                                  batch_first=True, )
-
-        self.in_size = in_size
-        self.lstm_size = lstm_size
-        self.num_window_frames = num_window_frames
-
-        self.fc_in_features = hidden_size * 2 if bidirectional else hidden_size
-        self.fc = nn.Sequential(
-            nn.Linear(in_features=self.fc_in_features + FACE_ID_FEAT_SIZE, out_features=512),
-            nn.BatchNorm1d(512),
-            nn.LeakyReLU(0.2),
-            nn.Linear(512, 256),
-            nn.BatchNorm1d(256),
-            nn.LeakyReLU(0.2),
-            nn.Linear(256, 204),
-        )
-
-
-
-    def forward(self, au, face_id):
-
-        inputs = au
-        if(self.use_prior_net):
-            inputs = self.fc_prior(inputs.contiguous().view(-1, self.in_size))
-            inputs = inputs.view(-1, self.num_window_frames, self.lstm_size)
-
-        output, (hn, cn) = self.bilstm(inputs)
-        output = output[:, -1, :]
-
-        if(face_id.shape[0] == 1):
-            face_id = face_id.repeat(output.shape[0], 1)
-        output2 = torch.cat((output, face_id), dim=1)
-
-        output2 = self.fc(output2)
-        # output += face_id
-
-        return output2, face_id
-
-
-
-class Embedder(nn.Module):
-    def __init__(self, feat_size, d_model):
-        super().__init__()
-        self.embed = nn.Linear(feat_size, d_model)
-    def forward(self, x):
-        return self.embed(x)
-
-
-class PositionalEncoder(nn.Module):
-    def __init__(self, d_model, max_seq_len=512):
-        super().__init__()
-        self.d_model = d_model
-
-        # create constant 'pe' matrix with values dependant on
-        # pos and i
-        pe = torch.zeros(max_seq_len, d_model)
-        for pos in range(max_seq_len):
-            for i in range(0, d_model, 2):
-                pe[pos, i] = \
-                    math.sin(pos / (10000 ** ((2 * i) / d_model)))
-                pe[pos, i + 1] = \
-                    math.cos(pos / (10000 ** ((2 * (i + 1)) / d_model)))
-
-        pe = pe.unsqueeze(0)
-        self.register_buffer('pe', pe)
-
-    def forward(self, x):
-        # make embeddings relatively larger
-        x = x * math.sqrt(self.d_model)
-        # add constant to embedding
-        seq_len = x.size(1)
-        x = x + self.pe[:, :seq_len].clone().detach().to(device)
-        return x
-
-
-def attention(q, k, v, d_k, mask=None, dropout=None):
-
-    scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
-    if mask is not None:
-        mask = mask.unsqueeze(1)
-        scores = scores.masked_fill(mask == 0, -1e9)
-    scores = F.softmax(scores, dim=-1)
-
-    if dropout is not None:
-        scores = dropout(scores)
-
-    output = torch.matmul(scores, v)
-    return output
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(self, heads, d_model, dropout=0.1):
-        super().__init__()
-
-        self.d_model = d_model
-        self.d_k = d_model // heads
-        self.h = heads
-
-        self.q_linear = nn.Linear(d_model, d_model)
-        self.v_linear = nn.Linear(d_model, d_model)
-        self.k_linear = nn.Linear(d_model, d_model)
-        self.dropout = nn.Dropout(dropout)
-        self.out = nn.Linear(d_model, d_model)
-
-    def forward(self, q, k, v, mask=None):
-        bs = q.size(0)
-
-        # perform linear operation and split into h heads
-
-        k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
-        q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
-        v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
-
-        # transpose to get dimensions bs * h * sl * d_model
-
-        k = k.transpose(1, 2)
-        q = q.transpose(1, 2)
-        v = v.transpose(1, 2)
-
-        # calculate attention using function we will define next
-        scores = attention(q, k, v, self.d_k, mask, self.dropout)
-
-        # concatenate heads and put through final linear layer
-        concat = scores.transpose(1, 2).contiguous() \
-            .view(bs, -1, self.d_model)
-
-        output = self.out(concat)
-
-        return output
-
-class FeedForward(nn.Module):
-    def __init__(self, d_model, d_ff=2048, dropout = 0.1):
-        super().__init__()
-        # We set d_ff as a default to 2048
-        self.linear_1 = nn.Linear(d_model, d_ff)
-        self.dropout = nn.Dropout(dropout)
-        self.linear_2 = nn.Linear(d_ff, d_model)
-    def forward(self, x):
-        x = self.dropout(F.relu(self.linear_1(x)))
-        x = self.linear_2(x)
-        return x
-
-
-class Norm(nn.Module):
-    def __init__(self, d_model, eps=1e-6):
-        super().__init__()
-
-        self.size = d_model
-        # create two learnable parameters to calibrate normalisation
-        self.alpha = nn.Parameter(torch.ones(self.size))
-        self.bias = nn.Parameter(torch.zeros(self.size))
-        self.eps = eps
-
-    def forward(self, x):
-        norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) \
-               / (x.std(dim=-1, keepdim=True) + self.eps) + self.bias
-        return norm
-
-# build an encoder layer with one multi-head attention layer and one # feed-forward layer
-class EncoderLayer(nn.Module):
-    def __init__(self, d_model, heads, dropout=0.1):
-        super().__init__()
-        self.norm_1 = Norm(d_model)
-        self.norm_2 = Norm(d_model)
-        self.attn = MultiHeadAttention(heads, d_model)
-        self.ff = FeedForward(d_model)
-        self.dropout_1 = nn.Dropout(dropout)
-        self.dropout_2 = nn.Dropout(dropout)
-
-    def forward(self, x, mask):
-        x2 = self.norm_1(x)
-        x = x + self.dropout_1(self.attn(x2, x2, x2, mask))
-        x2 = self.norm_2(x)
-        x = x + self.dropout_2(self.ff(x2))
-        return x
-
-    # build a decoder layer with two multi-head attention layers and
-    # one feed-forward layer
-class DecoderLayer(nn.Module):
-    def __init__(self, d_model, heads, dropout=0.1):
-        super().__init__()
-        self.norm_1 = Norm(d_model)
-        self.norm_2 = Norm(d_model)
-        self.norm_3 = Norm(d_model)
-
-        self.dropout_1 = nn.Dropout(dropout)
-        self.dropout_2 = nn.Dropout(dropout)
-        self.dropout_3 = nn.Dropout(dropout)
-
-        self.attn_1 = MultiHeadAttention(heads, d_model)
-        self.attn_2 = MultiHeadAttention(heads, d_model)
-        # self.ff = FeedForward(d_model).mps()
-        self.ff = FeedForward(d_model)
-
-    def forward(self, x, e_outputs, src_mask, trg_mask):
-        x2 = self.norm_1(x)
-
-        x = x + self.dropout_1(self.attn_1(x2, x2, x2, trg_mask))
-        x2 = self.norm_2(x)
-        x = x + self.dropout_2(self.attn_2(x2, e_outputs, e_outputs, src_mask))
-        x2 = self.norm_3(x)
-        x = x + self.dropout_3(self.ff(x2))
-        return x
-
-    # We can then build a convenient cloning function that can generate multiple layers:
-def get_clones(module, N):
-    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
-
-
-class Encoder(nn.Module):
-    def __init__(self, d_model, N, heads, in_size):
-        super().__init__()
-        self.N = N
-        self.embed = Embedder(in_size, d_model)
-        self.pe = PositionalEncoder(d_model)
-        self.layers = get_clones(EncoderLayer(d_model, heads), N)
-        self.norm = Norm(d_model)
-
-    def forward(self, x, mask=None):
-        x = self.embed(x)
-        x = self.pe(x)
-        for i in range(self.N):
-            x = self.layers[i](x, mask)
-        return self.norm(x)
-
-
-class Decoder(nn.Module):
-    def __init__(self, d_model, N, heads, in_size):
-        super().__init__()
-        self.N = N
-        self.embed = Embedder(in_size, d_model)
-        self.pe = PositionalEncoder(d_model)
-        self.layers = get_clones(DecoderLayer(d_model, heads), N)
-        self.norm = Norm(d_model)
-
-    def forward(self, x, e_outputs, src_mask=None, trg_mask=None):
-        x = self.embed(x)
-        x = self.pe(x)
-        for i in range(self.N):
-            x = self.layers[i](x, e_outputs, src_mask, trg_mask)
-        return self.norm(x)
-
-
-class Audio2landmark_pos(nn.Module):
-
-    def __init__(self, audio_feat_size=80, c_enc_hidden_size=256, num_layers=3, drop_out=0,
-                 spk_feat_size=256, spk_emb_enc_size=128, lstm_g_win_size=64, add_info_size=6,
-                 transformer_d_model=32, N=2, heads=2, z_size=128, audio_dim=256):
-        super(Audio2landmark_pos, self).__init__()
-
-        self.lstm_g_win_size = lstm_g_win_size
-        self.add_info_size = add_info_size
-        comb_mlp_size = c_enc_hidden_size * 2
-
-        self.audio_content_encoder = nn.LSTM(input_size=audio_feat_size,
-                                             hidden_size=c_enc_hidden_size,
-                                             num_layers=num_layers,
-                                             dropout=drop_out,
-                                             bidirectional=False,
-                                             batch_first=True)
-
-        self.use_audio_projection = not (audio_dim == c_enc_hidden_size)
-        if(self.use_audio_projection):
-            self.audio_projection = nn.Sequential(
-                nn.Linear(in_features=c_enc_hidden_size, out_features=256),
-                nn.LeakyReLU(0.02),
-                nn.Linear(256, 128),
-                nn.LeakyReLU(0.02),
-                nn.Linear(128, audio_dim),
-            )
-
-
-        ''' original version '''
-        self.spk_emb_encoder = nn.Sequential(
-            nn.Linear(in_features=spk_feat_size, out_features=256),
-            nn.LeakyReLU(0.02),
-            nn.Linear(256, 128),
-            nn.LeakyReLU(0.02),
-            nn.Linear(128, spk_emb_enc_size),
-        )
-        # self.comb_mlp = nn.Sequential(
-        #     nn.Linear(in_features=audio_dim + spk_emb_enc_size, out_features=comb_mlp_size),
-        #     nn.LeakyReLU(0.02),
-        #     nn.Linear(comb_mlp_size, comb_mlp_size // 2),
-        #     nn.LeakyReLU(0.02),
-        #     nn.Linear(comb_mlp_size // 2, 180),
-        # )
-
-        d_model = transformer_d_model * heads
-        N = N
-        heads = heads
-
-        self.encoder = Encoder(d_model, N, heads, in_size=audio_dim + spk_emb_enc_size + z_size)
-        self.decoder = Decoder(d_model, N, heads, in_size=204)
-        self.out = nn.Sequential(
-            nn.Linear(in_features=d_model + z_size, out_features=512),
-            nn.LeakyReLU(0.02),
-            nn.Linear(512, 256),
-            nn.LeakyReLU(0.02),
-            nn.Linear(256, 204),
-        )
-
-
-    def forward(self, au, emb, face_id, fls, z, add_z_spk=False, another_emb=None):
-
-        # audio
-        audio_encode, (_, _) = self.audio_content_encoder(au)
-        audio_encode = audio_encode[:, -1, :]
-
-        if(self.use_audio_projection):
-            audio_encode = self.audio_projection(audio_encode)
-
-        # spk
-        spk_encode = self.spk_emb_encoder(emb)
-        if(add_z_spk):
-            z_spk = torch.tensor(torch.randn(spk_encode.shape)*0.01, requires_grad=False, dtype=torch.float).to(device)
-            spk_encode = spk_encode + z_spk
-
-        # comb
-        # comb_input = torch.cat((audio_encode, spk_encode), dim=1)
-        # comb_encode = self.comb_mlp(comb_input)
-        comb_encode = torch.cat((audio_encode, spk_encode, z), dim=1)
-        src_feat = comb_encode.unsqueeze(0)
-
-        e_outputs = self.encoder(src_feat)[0]
-
-        e_outputs = torch.cat((e_outputs, z), dim=1)
-
-        fl_pred = self.out(e_outputs)
-
-        return fl_pred, face_id[0:1, :], spk_encode
-
-
-
-
-def nopeak_mask(size):
-    np_mask = np.triu(np.ones((1, size, size)), k=1).astype('uint8')
-    np_mask = torch.tensor(torch.from_numpy(np_mask) == 0)
-    np_mask = np_mask.to(device)
-    return np_mask
-
-
-def create_masks(src, trg):
-    src_mask = (src != torch.zeros_like(src, requires_grad=False))
-
-    if trg is not None:
-        size = trg.size(1)  # get seq_len for matrix
-        np_mask = nopeak_mask(size)
-        np_mask = np_mask.to(device)
-        trg_mask = np_mask
-
-    else:
-        trg_mask = None
-    return src_mask, trg_mask
-
-
-class TalkingToon_spk2res_lstmgan_DL(nn.Module):
-    def __init__(self, comb_emb_size=256, input_size=6):
-        super(TalkingToon_spk2res_lstmgan_DL, self).__init__()
-
-        self.fl_D = nn.Sequential(
-            nn.Linear(in_features=FACE_ID_FEAT_SIZE, out_features=512),
-            nn.LeakyReLU(0.02),
-            nn.Linear(512, 256),
-            nn.LeakyReLU(0.02),
-            nn.Linear(256, 1),
-        )
-
-    def forward(self, feat):
-        d = self.fl_D(feat)
-        # d = torch.sigmoid(d)
-        return d
-
-
-class Transformer_DT(nn.Module):
-    def __init__(self, transformer_d_model=32, N=2, heads=2, spk_emb_enc_size=128):
-        super(Transformer_DT, self).__init__()
-        d_model = transformer_d_model * heads
-        self.encoder = Encoder(d_model, N, heads, in_size=204 + spk_emb_enc_size)
-        self.out = nn.Sequential(
-            nn.Linear(in_features=d_model, out_features=512),
-            nn.LeakyReLU(0.02),
-            nn.Linear(512, 256),
-            nn.LeakyReLU(0.02),
-            nn.Linear(256, 1),
-        )
-
-    def forward(self, fls, spk_emb, win_size=64, win_step=1):
-        feat = torch.cat((fls, spk_emb), dim=1)
-
-        win_size = feat.shape[0]-1 if feat.shape[0] <= win_size else win_size
-        D_input = [feat[i:i+win_size:win_step] for i in range(0, feat.shape[0]-win_size)]
-        D_input = torch.stack(D_input, dim=0)
-        D_output = self.encoder(D_input)
-        D_output = torch.max(D_output, dim=1, keepdim=False)[0]
-        d = self.out(D_output)
-        # d = torch.sigmoid(d)
-        return d
-
-
-class TalkingToon_spk2res_lstmgan_DT(nn.Module):
-    def __init__(self, comb_emb_size=256, lstm_g_hidden_size=256, num_layers=3, drop_out=0, input_size=6):
-        super(TalkingToon_spk2res_lstmgan_DT, self).__init__()
-
-        self.fl_DT = nn.GRU(input_size=comb_emb_size + FACE_ID_FEAT_SIZE,
-                                             hidden_size=lstm_g_hidden_size,
-                                             num_layers=3,
-                                             dropout=0,
-                                             bidirectional=False,
-                                             batch_first=True)
-        self.projection = nn.Sequential(
-            nn.Linear(in_features=lstm_g_hidden_size, out_features=512),
-            nn.LeakyReLU(0.02),
-            nn.Linear(512, 256),
-            nn.LeakyReLU(0.02),
-            nn.Linear(256, 1),
-        )
-
-        self.maxpool = nn.MaxPool1d(4, 1)
-
-    def forward(self, comb_encode, fls, win_size=32, win_step=1):
-        feat = torch.cat((comb_encode, fls), dim=1)
-        # v
-        # feat = torch.cat((comb_encode[0:-1], fls[1:] - fls[0:-1]), dim=1)
-
-        # max pooling
-        feat = feat.transpose(0, 1).unsqueeze(0)
-        feat = self.maxpool(feat)
-        feat = feat[0].transpose(0, 1)
-
-        win_size = feat.shape[0] - 1 if feat.shape[0] <= win_size else win_size
-        D_input = [feat[i:i+win_size:win_step] for i in range(0, feat.shape[0]-win_size)]
-        D_input = torch.stack(D_input, dim=0)
-        D_output, _ = self.fl_DT(D_input)
-        D_output = D_output[:, -1, :]
-        d = self.projection(D_output)
-        # d = torch.sigmoid(d)
-        return d
\ No newline at end of file
diff --git a/spaces/matthoffner/chatbot-mini/components/Markdown/MemoizedReactMarkdown.tsx b/spaces/matthoffner/chatbot-mini/components/Markdown/MemoizedReactMarkdown.tsx
deleted file mode 100644
index 00cd26a8d72e858c044bd1d4ca5de9494f2672e7..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot-mini/components/Markdown/MemoizedReactMarkdown.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
-    )
-);
diff --git a/spaces/meowooooo/maybe/Dockerfile b/spaces/meowooooo/maybe/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/meowooooo/maybe/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/merle/PROTEIN_GENERATOR/model/Embeddings.py b/spaces/merle/PROTEIN_GENERATOR/model/Embeddings.py
deleted file mode 100644
index d4558dd4eb9646070d333011d0d8783249c85264..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/model/Embeddings.py
+++ /dev/null
@@ -1,307 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from opt_einsum import contract as einsum
-import torch.utils.checkpoint as checkpoint
-from util import get_tips
-from util_module import Dropout, create_custom_forward, rbf, init_lecun_normal
-from Attention_module import Attention, FeedForwardLayer, AttentionWithBias
-from Track_module import PairStr2Pair
-from icecream import ic
-
-# Module contains classes and functions to generate initial embeddings
-
-class PositionalEncoding2D(nn.Module):
-    # Add relative positional encoding to pair features
-    def __init__(self, d_model, minpos=-32, maxpos=32, p_drop=0.1):
-        super(PositionalEncoding2D, self).__init__()
-        self.minpos = minpos
-        self.maxpos = maxpos
-        self.nbin = abs(minpos)+maxpos+1
-        self.emb = nn.Embedding(self.nbin, d_model)
-        self.drop = nn.Dropout(p_drop)
-    
-    def forward(self, x, idx):
-        bins = torch.arange(self.minpos, self.maxpos, device=x.device)
-        seqsep = idx[:,None,:] - idx[:,:,None] # (B, L, L)
-        #
-        ib = torch.bucketize(seqsep, bins).long() # (B, L, L)
-        emb = self.emb(ib) #(B, L, L, d_model)
-        x = x + emb # add relative positional encoding
-        return self.drop(x)
-
-class MSA_emb(nn.Module):
-    # Get initial seed MSA embedding
-    def __init__(self, d_msa=256, d_pair=128, d_state=32, d_init=22+22+2+2,
-                 minpos=-32, maxpos=32, p_drop=0.1):
-        super(MSA_emb, self).__init__()
-        self.emb = nn.Linear(d_init, d_msa) # embedding for general MSA
-        self.emb_q = nn.Embedding(22, d_msa) # embedding for query sequence -- used for MSA embedding
-        self.emb_left = nn.Embedding(22, d_pair) # embedding for query sequence -- used for pair embedding
-        self.emb_right = nn.Embedding(22, d_pair) # embedding for query sequence -- used for pair embedding
-        self.emb_state = nn.Embedding(22, d_state)
-        self.drop = nn.Dropout(p_drop)
-        self.pos = PositionalEncoding2D(d_pair, minpos=minpos, maxpos=maxpos, p_drop=p_drop)
-        
-        self.reset_parameter()
-    
-    def reset_parameter(self):
-        self.emb = init_lecun_normal(self.emb)
-        self.emb_q = init_lecun_normal(self.emb_q)
-        self.emb_left = init_lecun_normal(self.emb_left)
-        self.emb_right = init_lecun_normal(self.emb_right)
-        self.emb_state = init_lecun_normal(self.emb_state)
-
-        nn.init.zeros_(self.emb.bias)
-
-    def forward(self, msa, seq, idx, seq1hot=None):
-        # Inputs:
-        #   - msa: Input MSA (B, N, L, d_init)
-        #   - seq: Input Sequence (B, L)
-        #   - idx: Residue index
-        # Outputs:
-        #   - msa: Initial MSA embedding (B, N, L, d_msa)
-        #   - pair: Initial Pair embedding (B, L, L, d_pair)
-
-        N = msa.shape[1] # number of sequenes in MSA
-        
-        # msa embedding
-        msa = self.emb(msa) # (B, N, L, d_model) # MSA embedding
-        seq = seq.long()
-        tmp = self.emb_q(seq).unsqueeze(1) # (B, 1, L, d_model) -- query embedding
-        msa = msa + tmp.expand(-1, N, -1, -1) # adding query embedding to MSA
-        msa = self.drop(msa)
-
-        # pair embedding 
-        if seq1hot is not None:
-            left = (seq1hot @ self.emb_left.weight)[:,None] # (B, 1, L, d_pair)
-            right = (seq1hot @ self.emb_right.weight)[:,:,None] # (B, L, 1, d_pair)
-        else:
-            left = self.emb_left(seq)[:,None] # (B, 1, L, d_pair)
-            right = self.emb_right(seq)[:,:,None] # (B, L, 1, d_pair)
-        #ic(torch.norm(self.emb_left.weight, dim=1))
-        #ic(torch.norm(self.emb_right.weight, dim=1))
-        pair = left + right # (B, L, L, d_pair)
-        pair = self.pos(pair, idx) # add relative position
-
-        # state embedding
-        state = self.drop(self.emb_state(seq))
-
-        return msa, pair, state
-
-class Extra_emb(nn.Module):
-    # Get initial seed MSA embedding
-    def __init__(self, d_msa=256, d_init=22+1+2, p_drop=0.1):
-        super(Extra_emb, self).__init__()
-        self.emb = nn.Linear(d_init, d_msa) # embedding for general MSA
-        self.emb_q = nn.Embedding(22, d_msa) # embedding for query sequence
-        self.drop = nn.Dropout(p_drop)
-        
-        self.reset_parameter()
-    
-    def reset_parameter(self):
-        self.emb = init_lecun_normal(self.emb)
-        nn.init.zeros_(self.emb.bias)
-
-    def forward(self, msa, seq, idx, seq1hot=None):
-        # Inputs:
-        #   - msa: Input MSA (B, N, L, d_init)
-        #   - seq: Input Sequence (B, L)
-        #   - idx: Residue index
-        # Outputs:
-        #   - msa: Initial MSA embedding (B, N, L, d_msa)
-        N = msa.shape[1] # number of sequenes in MSA
-        msa = self.emb(msa) # (B, N, L, d_model) # MSA embedding
-        if seq1hot is not None:
-            seq = (seq1hot @ self.emb_q.weight).unsqueeze(1) # (B, 1, L, d_model) -- query embedding
-        else:
-            seq = self.emb_q(seq).unsqueeze(1) # (B, 1, L, d_model) -- query embedding
-        #ic(torch.norm(self.emb_q.weight, dim=1))
-        msa = msa + seq.expand(-1, N, -1, -1) # adding query embedding to MSA
-        return self.drop(msa)
-
-class TemplatePairStack(nn.Module):
-    # process template pairwise features
-    # use structure-biased attention
-    def __init__(self, n_block=2, d_templ=64, n_head=4, d_hidden=16, p_drop=0.25):
-        super(TemplatePairStack, self).__init__()
-        self.n_block = n_block
-        proc_s = [PairStr2Pair(d_pair=d_templ, n_head=n_head, d_hidden=d_hidden, p_drop=p_drop) for i in range(n_block)]
-        self.block = nn.ModuleList(proc_s)
-        self.norm = nn.LayerNorm(d_templ)
-    def forward(self, templ, rbf_feat, use_checkpoint=False):
-        B, T, L = templ.shape[:3]
-        templ = templ.reshape(B*T, L, L, -1)
-
-        for i_block in range(self.n_block):
-            if use_checkpoint:
-                templ = checkpoint.checkpoint(create_custom_forward(self.block[i_block]), templ, rbf_feat)
-            else:
-                templ = self.block[i_block](templ, rbf_feat)
-        return self.norm(templ).reshape(B, T, L, L, -1)
-
-class TemplateTorsionStack(nn.Module):
-    def __init__(self, n_block=2, d_templ=64, n_head=4, d_hidden=16, p_drop=0.15):
-        super(TemplateTorsionStack, self).__init__()
-        self.n_block=n_block
-        self.proj_pair = nn.Linear(d_templ+36, d_templ)
-        proc_s = [AttentionWithBias(d_in=d_templ, d_bias=d_templ,
-                                    n_head=n_head, d_hidden=d_hidden) for i in range(n_block)]
-        self.row_attn = nn.ModuleList(proc_s)
-        proc_s = [FeedForwardLayer(d_templ, 4, p_drop=p_drop) for i in range(n_block)]
-        self.ff = nn.ModuleList(proc_s)
-        self.norm = nn.LayerNorm(d_templ)
-
-    def reset_parameter(self):
-        self.proj_pair = init_lecun_normal(self.proj_pair)
-        nn.init.zeros_(self.proj_pair.bias)
-
-    def forward(self, tors, pair, rbf_feat, use_checkpoint=False):
-        B, T, L = tors.shape[:3]
-        tors = tors.reshape(B*T, L, -1)
-        pair = pair.reshape(B*T, L, L, -1)
-        pair = torch.cat((pair, rbf_feat), dim=-1)
-        pair = self.proj_pair(pair)
-        
-        for i_block in range(self.n_block):
-            if use_checkpoint:
-                tors = tors + checkpoint.checkpoint(create_custom_forward(self.row_attn[i_block]), tors, pair)
-            else:
-                tors = tors + self.row_attn[i_block](tors, pair)
-            tors = tors + self.ff[i_block](tors)
-        return self.norm(tors).reshape(B, T, L, -1)
-
-class Templ_emb(nn.Module):
-    # Get template embedding
-    # Features are
-    #   t2d:
-    #   - 37 distogram bins + 6 orientations (43)
-    #   - Mask (missing/unaligned) (1)
-    #   t1d:
-    #   - tiled AA sequence (20 standard aa + gap)
-    #   - seq confidence (1)
-    #   - global time step (1)
-    #   - struc confidence (1)
-    #   
-    def __init__(self, d_t1d=21+1+1+1, d_t2d=43+1, d_tor=30, d_pair=128, d_state=32, 
-                 n_block=2, d_templ=64,
-                 n_head=4, d_hidden=16, p_drop=0.25):
-        super(Templ_emb, self).__init__()
-        # process 2D features
-        self.emb = nn.Linear(d_t1d*2+d_t2d, d_templ)
-        self.templ_stack = TemplatePairStack(n_block=n_block, d_templ=d_templ, n_head=n_head,
-                                             d_hidden=d_hidden, p_drop=p_drop)
-        
-        self.attn = Attention(d_pair, d_templ, n_head, d_hidden, d_pair, p_drop=p_drop)
-        
-        # process torsion angles
-        self.emb_t1d = nn.Linear(d_t1d+d_tor, d_templ)
-        self.proj_t1d = nn.Linear(d_templ, d_templ)
-        #self.tor_stack = TemplateTorsionStack(n_block=n_block, d_templ=d_templ, n_head=n_head,
-        #                                      d_hidden=d_hidden, p_drop=p_drop)
-        self.attn_tor = Attention(d_state, d_templ, n_head, d_hidden, d_state, p_drop=p_drop)
-
-        self.reset_parameter()
-    
-    def reset_parameter(self):
-        self.emb = init_lecun_normal(self.emb)
-        #nn.init.zeros_(self.emb.weight) #init weights to zero
-        nn.init.zeros_(self.emb.bias)
-
-        nn.init.kaiming_normal_(self.emb_t1d.weight, nonlinearity='relu')
-        #nn.init.zeros_(self.emb_t1d.weight)
-        nn.init.zeros_(self.emb_t1d.bias)
-        
-        self.proj_t1d = init_lecun_normal(self.proj_t1d)
-        nn.init.zeros_(self.proj_t1d.bias)
-
-    def forward(self, t1d, t2d, alpha_t, xyz_t, pair, state, use_checkpoint=False):
-        # Input
-        #   - t1d: 1D template info (B, T, L, 23) 24 SL
-        #   - t2d: 2D template info (B, T, L, L, 44)
-        B, T, L, _ = t1d.shape
-
-        # Prepare 2D template features
-        left = t1d.unsqueeze(3).expand(-1,-1,-1,L,-1)
-        right = t1d.unsqueeze(2).expand(-1,-1,L,-1,-1)
-        #
-        templ = torch.cat((t2d, left, right), -1) # (B, T, L, L, 88)
-        
-        #ic(templ.shape)
-        #ic(templ.dtype)
-        #ic(self.emb.weight.dtype)
-        templ = self.emb(templ) # Template templures (B, T, L, L, d_templ)
-        # process each template features
-        xyz_t = xyz_t.reshape(B*T, L, -1, 3)
-        rbf_feat = rbf(torch.cdist(xyz_t[:,:,1], xyz_t[:,:,1]))
-        templ = self.templ_stack(templ, rbf_feat, use_checkpoint=use_checkpoint) # (B, T, L,L, d_templ)
-
-        # Prepare 1D template torsion angle features
-        t1d = torch.cat((t1d, alpha_t), dim=-1) # (B, T, L, 22+30)
-        # process each template features
-        t1d = self.proj_t1d(F.relu_(self.emb_t1d(t1d)))
-        
-        # mixing query state features to template state features
-        state = state.reshape(B*L, 1, -1)
-        t1d = t1d.permute(0,2,1,3).reshape(B*L, T, -1)
-        if use_checkpoint:
-            out = checkpoint.checkpoint(create_custom_forward(self.attn_tor), state, t1d, t1d)
-            out = out.reshape(B, L, -1)
-        else:
-            out = self.attn_tor(state, t1d, t1d).reshape(B, L, -1)
-        state = state.reshape(B, L, -1)
-        state = state + out
-
-        # mixing query pair features to template information (Template pointwise attention)
-        pair = pair.reshape(B*L*L, 1, -1)
-        templ = templ.permute(0, 2, 3, 1, 4).reshape(B*L*L, T, -1)
-        if use_checkpoint:
-            out = checkpoint.checkpoint(create_custom_forward(self.attn), pair, templ, templ)
-            out = out.reshape(B, L, L, -1)
-        else:
-            out = self.attn(pair, templ, templ).reshape(B, L, L, -1)
-        #
-        pair = pair.reshape(B, L, L, -1)
-        pair = pair + out
-
-        return pair, state
-
-class Recycling(nn.Module):
-    def __init__(self, d_msa=256, d_pair=128, d_state=32):
-        super(Recycling, self).__init__()
-        self.proj_dist = nn.Linear(36+d_state*2, d_pair)
-        self.norm_state = nn.LayerNorm(d_state)
-        self.norm_pair = nn.LayerNorm(d_pair)
-        self.norm_msa = nn.LayerNorm(d_msa)
-        
-        self.reset_parameter()
-    
-    def reset_parameter(self):
-        self.proj_dist = init_lecun_normal(self.proj_dist)
-        nn.init.zeros_(self.proj_dist.bias)
-
-    def forward(self, seq, msa, pair, xyz, state):
-        B, L = pair.shape[:2]
-        state = self.norm_state(state)
-        #
-        left = state.unsqueeze(2).expand(-1,-1,L,-1)
-        right = state.unsqueeze(1).expand(-1,L,-1,-1)
-        
-        # three anchor atoms
-        N  = xyz[:,:,0]
-        Ca = xyz[:,:,1]
-        C  = xyz[:,:,2]
-
-        # recreate Cb given N,Ca,C
-        b = Ca - N
-        c = C - Ca
-        a = torch.cross(b, c, dim=-1)
-        Cb = -0.58273431*a + 0.56802827*b - 0.54067466*c + Ca    
-        
-        dist = rbf(torch.cdist(Cb, Cb))
-        dist = torch.cat((dist, left, right), dim=-1)
-        dist = self.proj_dist(dist)
-        pair = dist + self.norm_pair(pair)
-        msa = self.norm_msa(msa)
-        return msa, pair, state
-
diff --git a/spaces/merve/Grounding_DINO_demo/groundingdino/models/GroundingDINO/backbone/backbone.py b/spaces/merve/Grounding_DINO_demo/groundingdino/models/GroundingDINO/backbone/backbone.py
deleted file mode 100644
index c8340c723fad8e07e2fc62daaa3912487498814b..0000000000000000000000000000000000000000
--- a/spaces/merve/Grounding_DINO_demo/groundingdino/models/GroundingDINO/backbone/backbone.py
+++ /dev/null
@@ -1,221 +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]
-# ------------------------------------------------------------------------
-# Conditional DETR
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-"""
-Backbone modules.
-"""
-
-from typing import Dict, List
-
-import torch
-import torch.nn.functional as F
-import torchvision
-from torch import nn
-from torchvision.models._utils import IntermediateLayerGetter
-
-from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process
-
-from .position_encoding import build_position_encoding
-from .swin_transformer import build_swin_transformer
-
-
-class FrozenBatchNorm2d(torch.nn.Module):
-    """
-    BatchNorm2d where the batch statistics and the affine parameters are fixed.
-
-    Copy-paste from torchvision.misc.ops with added eps before rqsrt,
-    without which any other models than torchvision.models.resnet[18,34,50,101]
-    produce nans.
-    """
-
-    def __init__(self, n):
-        super(FrozenBatchNorm2d, self).__init__()
-        self.register_buffer("weight", torch.ones(n))
-        self.register_buffer("bias", torch.zeros(n))
-        self.register_buffer("running_mean", torch.zeros(n))
-        self.register_buffer("running_var", torch.ones(n))
-
-    def _load_from_state_dict(
-        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-    ):
-        num_batches_tracked_key = prefix + "num_batches_tracked"
-        if num_batches_tracked_key in state_dict:
-            del state_dict[num_batches_tracked_key]
-
-        super(FrozenBatchNorm2d, self)._load_from_state_dict(
-            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
-        )
-
-    def forward(self, x):
-        # move reshapes to the beginning
-        # to make it fuser-friendly
-        w = self.weight.reshape(1, -1, 1, 1)
-        b = self.bias.reshape(1, -1, 1, 1)
-        rv = self.running_var.reshape(1, -1, 1, 1)
-        rm = self.running_mean.reshape(1, -1, 1, 1)
-        eps = 1e-5
-        scale = w * (rv + eps).rsqrt()
-        bias = b - rm * scale
-        return x * scale + bias
-
-
-class BackboneBase(nn.Module):
-    def __init__(
-        self,
-        backbone: nn.Module,
-        train_backbone: bool,
-        num_channels: int,
-        return_interm_indices: list,
-    ):
-        super().__init__()
-        for name, parameter in backbone.named_parameters():
-            if (
-                not train_backbone
-                or "layer2" not in name
-                and "layer3" not in name
-                and "layer4" not in name
-            ):
-                parameter.requires_grad_(False)
-
-        return_layers = {}
-        for idx, layer_index in enumerate(return_interm_indices):
-            return_layers.update(
-                {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
-            )
-
-        # if len:
-        #     if use_stage1_feature:
-        #         return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
-        #     else:
-        #         return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
-        # else:
-        #     return_layers = {'layer4': "0"}
-        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
-        self.num_channels = num_channels
-
-    def forward(self, tensor_list: NestedTensor):
-        xs = self.body(tensor_list.tensors)
-        out: Dict[str, NestedTensor] = {}
-        for name, x in xs.items():
-            m = tensor_list.mask
-            assert m is not None
-            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
-            out[name] = NestedTensor(x, mask)
-        # import ipdb; ipdb.set_trace()
-        return out
-
-
-class Backbone(BackboneBase):
-    """ResNet backbone with frozen BatchNorm."""
-
-    def __init__(
-        self,
-        name: str,
-        train_backbone: bool,
-        dilation: bool,
-        return_interm_indices: list,
-        batch_norm=FrozenBatchNorm2d,
-    ):
-        if name in ["resnet18", "resnet34", "resnet50", "resnet101"]:
-            backbone = getattr(torchvision.models, name)(
-                replace_stride_with_dilation=[False, False, dilation],
-                pretrained=is_main_process(),
-                norm_layer=batch_norm,
-            )
-        else:
-            raise NotImplementedError("Why you can get here with name {}".format(name))
-        # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
-        assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
-        assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
-        num_channels_all = [256, 512, 1024, 2048]
-        num_channels = num_channels_all[4 - len(return_interm_indices) :]
-        super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
-
-
-class Joiner(nn.Sequential):
-    def __init__(self, backbone, position_embedding):
-        super().__init__(backbone, position_embedding)
-
-    def forward(self, tensor_list: NestedTensor):
-        xs = self[0](tensor_list)
-        out: List[NestedTensor] = []
-        pos = []
-        for name, x in xs.items():
-            out.append(x)
-            # position encoding
-            pos.append(self[1](x).to(x.tensors.dtype))
-
-        return out, pos
-
-
-def build_backbone(args):
-    """
-    Useful args:
-        - backbone: backbone name
-        - lr_backbone:
-        - dilation
-        - return_interm_indices: available: [0,1,2,3], [1,2,3], [3]
-        - backbone_freeze_keywords:
-        - use_checkpoint: for swin only for now
-
-    """
-    position_embedding = build_position_encoding(args)
-    train_backbone = True
-    if not train_backbone:
-        raise ValueError("Please set lr_backbone > 0")
-    return_interm_indices = args.return_interm_indices
-    assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
-    args.backbone_freeze_keywords
-    use_checkpoint = getattr(args, "use_checkpoint", False)
-
-    if args.backbone in ["resnet50", "resnet101"]:
-        backbone = Backbone(
-            args.backbone,
-            train_backbone,
-            args.dilation,
-            return_interm_indices,
-            batch_norm=FrozenBatchNorm2d,
-        )
-        bb_num_channels = backbone.num_channels
-    elif args.backbone in [
-        "swin_T_224_1k",
-        "swin_B_224_22k",
-        "swin_B_384_22k",
-        "swin_L_224_22k",
-        "swin_L_384_22k",
-    ]:
-        pretrain_img_size = int(args.backbone.split("_")[-2])
-        backbone = build_swin_transformer(
-            args.backbone,
-            pretrain_img_size=pretrain_img_size,
-            out_indices=tuple(return_interm_indices),
-            dilation=False,
-            use_checkpoint=use_checkpoint,
-        )
-
-        bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
-    else:
-        raise NotImplementedError("Unknown backbone {}".format(args.backbone))
-
-    assert len(bb_num_channels) == len(
-        return_interm_indices
-    ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
-
-    model = Joiner(backbone, position_embedding)
-    model.num_channels = bb_num_channels
-    assert isinstance(
-        bb_num_channels, List
-    ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels))
-    # import ipdb; ipdb.set_trace()
-    return model
diff --git a/spaces/merve/anonymization/source/third_party/misc.js b/spaces/merve/anonymization/source/third_party/misc.js
deleted file mode 100644
index a51b6b5292feaa6ee497806752a0d3d0cb4ef547..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/source/third_party/misc.js
+++ /dev/null
@@ -1,38 +0,0 @@
-/* Copyright 2019 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.
-==============================================================================*/
-
-
-function lerp(a, b, t){ return a + t*(b - a) }
-
-function addVec([a0, a1], [b0, b1]){
-  return [a0 + b0, a1 + b1]
-}
-
-function phyllotaxis(i, initialRadius=10, initialAngle=Math.PI*(3 - Math.sqrt(5))){
-  i = i + Math.random()/20
-
-  var r = initialRadius*Math.sqrt(Math.random() + i)
-  var angle = i*initialAngle
-
-  return [r*Math.cos(angle), r*Math.sin(angle)]
-}
-       
-var names = {
-  old_m: 'James John Robert Michael William David Richard Joseph Thomas Charles Christopher Daniel Matthew Anthony Donald Mark Paul Steven Andrew Kenneth Joshua George Kevin Brian Edward Ronald Timothy Jason Jeffrey Ryan Jacob Gary Nicholas Eric Stephen Jonathan Larry Justin Scott Brandon Frank Benjamin Gregory Samuel Raymond Patrick Alexander Jack Dennis Jerry Tyler Aaron Jose Henry Douglas Adam Peter Nathan Zachary Walter Kyle Harold Carl Jeremy Keith Roger Gerald Ethan Arthur Terry Christian Sean Lawrence Austin Joe Noah Jesse Albert Bryan Billy Bruce Willie Jordan Dylan Alan Ralph Gabriel Roy Juan Wayne Eugene Logan Randy Louis Russell Vincent Philip Bobby Johnny Bradley'.split(' '),
-  old_f: 'Mary Patricia Jennifer Linda Elizabeth Barbara Susan Jessica Sarah Karen Nancy Margaret Lisa Betty Dorothy Sandra Ashley Kimberly Donna Emily Michelle Carol Amanda Melissa Deborah Stephanie Rebecca Laura Sharon Cynthia Kathleen Helen Amy Shirley Angela Anna Brenda Pamela Nicole Ruth Katherine Samantha Christine Emma Catherine Debra Virginia Rachel Carolyn Janet Maria Heather Diane Julie Joyce Victoria Kelly Christina Joan Evelyn Lauren Judith Olivia Frances Martha Cheryl Megan Andrea Hannah Jacqueline Ann Jean Alice Kathryn Gloria Teresa Doris Sara Janice Julia Marie Madison Grace Judy Theresa Beverly Denise Marilyn Amber Danielle Abigail Brittany Rose Diana Natalie Sophia Alexis Lori Kayla Jane'.split(' '),
-  m: 'Noah Liam Jacob Mason William Ethan Michael Alexander James Elijah Daniel Benjamin Aiden Jayden Logan Matthew David Joseph Lucas Jackson Anthony Joshua Samuel Andrew Gabriel Christopher John Dylan Carter Isaac Ryan Luke Oliver Nathan Henry Owen Caleb Wyatt Christian Sebastian Jack Jonathan Landon Julian Isaiah Hunter Levi Aaron Eli Charles Thomas Connor Brayden Nicholas Jaxon Jeremiah Cameron Evan Adrian Jordan Gavin Grayson Angel Robert Tyler Josiah Austin Colton Brandon Jose Dominic Kevin Zachary Ian Chase Jason Adam Ayden Parker Hudson Cooper Nolan Lincoln Xavier Carson Jace Justin Easton Mateo Asher Bentley Blake Nathaniel Jaxson Leo Kayden Tristan Luis Elias Brody Bryson Juan Vincent Cole Micah Ryder Theodore Carlos Ezra Damian Miles Santiago Max Jesus Leonardo Sawyer Diego Alex Roman Maxwell Eric Greyson Hayden Giovanni Wesley Axel Camden Braxton Ivan Ashton Declan Bryce Timothy Antonio Silas Kaiden Ezekiel Jonah Weston George Harrison Steven Miguel Richard Bryan Kaleb Victor Aidan Jameson Joel Patrick Jaden Colin Everett Preston Maddox Edward Alejandro Kaden Jesse Emmanuel Kyle Brian Emmett Jude Marcus Kingston Kai Alan Malachi Grant Jeremy Riley Jayce Bennett Abel Ryker Caden Brantley Luca Brady Calvin Sean Oscar Jake Maverick Abraham Mark Tucker Nicolas Bradley Kenneth Avery Cayden King Paul Amir Gael Graham Maximus'.split(' '),
- f: 'Emma Sophia Olivia Isabella Ava Mia Abigail Emily Madison Charlotte Elizabeth Amelia Chloe Ella Evelyn Avery Sofia Harper Grace Addison Victoria Natalie Lily Aubrey Lillian Zoey Hannah Layla Brooklyn Samantha Zoe Leah Scarlett Riley Camila Savannah Anna Audrey Allison Aria Gabriella Hailey Claire Sarah Aaliyah Kaylee Nevaeh Penelope Alexa Arianna Stella Alexis Bella Nora Ellie Ariana Lucy Mila Peyton Genesis Alyssa Taylor Violet Maya Caroline Madelyn Skylar Serenity Ashley Brianna Kennedy Autumn Eleanor Kylie Sadie Paisley Julia Mackenzie Sophie Naomi Eva Khloe Katherine Gianna Melanie Aubree Piper Ruby Lydia Faith Madeline Alexandra Kayla Hazel Lauren Annabelle Jasmine Aurora Alice Makayla Sydney Bailey Luna Maria Reagan Morgan Isabelle Rylee Kimberly Andrea London Elena Jocelyn Natalia Trinity Eliana Vivian Cora Quinn Liliana Molly Jade Clara Valentina Mary Brielle Hadley Kinsley Willow Brooke Lilly Delilah Payton Mariah Paige Jordyn Nicole Mya Josephine Isabel Lyla Adeline Destiny Ivy Emilia Rachel Angelina Valeria Kendall Sara Ximena Isla Aliyah Reese Vanessa Juliana Mckenzie Amy Laila Adalynn Emery Margaret Eden Gabrielle Kaitlyn Ariel Gracie Brooklynn Melody Jessica Valerie Adalyn Adriana Elise Michelle Rebecca Daisy Everly Katelyn Ryleigh Catherine Norah Alaina Athena Leilani Londyn Eliza Jayla Summer Lila Makenzie Izabella Daniela Stephanie Julianna Rose Alana Harmony Jennifer Hayden'.split(' '),
- last: 'SMITH JOHNSON WILLIAMS BROWN JONES GARCIA MILLER DAVIS RODRIGUEZ MARTINEZ HERNANDEZ LOPEZ GONZALEZ WILSON ANDERSON THOMAS TAYLOR MOORE JACKSON MARTIN LEE PEREZ THOMPSON WHITE HARRIS SANCHEZ CLARK RAMIREZ LEWIS ROBINSON WALKER YOUNG ALLEN KING WRIGHT SCOTT TORRES NGUYEN HILL FLORES GREEN ADAMS NELSON BAKER HALL RIVERA CAMPBELL MITCHELL CARTER ROBERTS GOMEZ PHILLIPS EVANS TURNER DIAZ PARKER CRUZ EDWARDS COLLINS REYES STEWART MORRIS MORALES MURPHY COOK ROGERS GUTIERREZ ORTIZ MORGAN COOPER PETERSON BAILEY REED KELLY HOWARD RAMOS KIM COX WARD RICHARDSON WATSON BROOKS CHAVEZ WOOD JAMES BENNETT GRAY MENDOZA RUIZ HUGHES PRICE ALVAREZ CASTILLO SANDERS PATEL MYERS LONG ROSS FOSTER JIMENEZ POWELL JENKINS PERRY RUSSELL SULLIVAN BELL COLEMAN BUTLER HENDERSON BARNES GONZALES FISHER VASQUEZ SIMMONS ROMERO JORDAN PATTERSON ALEXANDER HAMILTON GRAHAM REYNOLDS GRIFFIN WALLACE MORENO WEST COLE HAYES BRYANT HERRERA GIBSON ELLIS TRAN MEDINA AGUILAR STEVENS MURRAY FORD CASTRO MARSHALL OWENS HARRISON FERNANDEZ MCDONALD WOODS WASHINGTON KENNEDY WELLS VARGAS HENRY CHEN FREEMAN WEBB TUCKER GUZMAN BURNS CRAWFORD OLSON SIMPSON PORTER HUNTER GORDON MENDEZ SILVA SHAW SNYDER MASON DIXON MUNOZ HUNT HICKS HOLMES PALMER WAGNER BLACK ROBERTSON BOYD ROSE STONE SALAZAR FOX WARREN MILLS MEYER RICE SCHMIDT GARZA DANIELS FERGUSON NICHOLS STEPHENS SOTO WEAVER RYAN'.split(' ').map(d => d[0] + d.slice(1).toLowerCase())
-}
diff --git a/spaces/merve/hidden-bias/server-side/fill-in-the-blank/scatter-plot-colab/spearman-compare/watch-files.js b/spaces/merve/hidden-bias/server-side/fill-in-the-blank/scatter-plot-colab/spearman-compare/watch-files.js
deleted file mode 100644
index 8ab520922aa2b8cb8086ca86f5119fc0b46ac433..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/server-side/fill-in-the-blank/scatter-plot-colab/spearman-compare/watch-files.js
+++ /dev/null
@@ -1,83 +0,0 @@
-/* Copyright 2021 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.
-==============================================================================*/
-
-
-!(function(){
-  function watchFile(path){
-    var lastStr = ''
-
-    console.log(path)
-    function check(){
-      d3.text(path + '?' + Math.random(), (err, nextStr) => {
-        if (err){
-          console.log(err)
-          return check()
-        }
-
-        if (nextStr == lastStr) return
-        lastStr = nextStr
-
-        if (path.includes('.js')){
-          console.log('js', new Date())
-          Function(nextStr.replace('\n', ';').replace('\n', ';'))()
-        }
-
-        if (path.includes('.css')){
-          console.log('css', new Date())
-
-          Array.from(document.querySelectorAll('link'))
-            .filter(d => d.href.includes(path) || d.href.includes('__hs_placeholder'))
-            .filter((d, i) => i == 0)
-            .forEach(d => d.href = path + '?' + Math.random())
-        }
-      })
-
-      if (python_settings.isDev) setTimeout(check, 100)
-    }
-    check()
-  }
-
-  ;[
-    'list.css', 
-    'style.css', 
-    '../two-sentences/init-scatter.js', 
-    '../two-sentences/init-util.js', 
-    '../two-sentences/init-pair.js', 
-    'init.js'
-  ].forEach(filename => {
-    var root = document.currentScript.src.replace('watch-files.js', '').split('?')[0]
-    var path = root + filename
-
-    if (python_settings.isDev){
-      watchFile(path)
-    } else {
-      if (path.includes('.js')){
-        var node = document.createElement('script')
-        node.setAttribute('src', path)
-        document.body.appendChild(node)
-      }
-
-      if (path.includes('.css')){
-        Array.from(document.querySelectorAll('link'))
-          .filter(d => d.href.includes(path) || d.href.includes('__hs_placeholder'))
-          .filter((d, i) => i == 0)
-          .forEach(d => d.href = path + '?' + Math.random())
-      }
-    }
-  })
-})()
-
-
-
diff --git a/spaces/merve/hidden-bias/source/anonymization/make-axii.js b/spaces/merve/hidden-bias/source/anonymization/make-axii.js
deleted file mode 100644
index c69b5eba387ec07f01ce2849726fda5461002aef..0000000000000000000000000000000000000000
--- a/spaces/merve/hidden-bias/source/anonymization/make-axii.js
+++ /dev/null
@@ -1,86 +0,0 @@
-window.makeAxii = function(){
-
-  var stateScale = d3.scaleBand().domain(states).range(c.x.range())
-  var stateAxis = c.svg.append('g.axis.state.init-hidden')
-
-  var bw = stateScale.bandwidth()/2
-
-  stateAxis.appendMany('text', states)
-    .translate(d => [stateScale(d) + bw, c.height + 22])
-    .text(d => d)
-    .at({
-      textAnchor: 'middle',
-    })
-    .st({fill: '#444'})
-
-  stateAxis.appendMany('path', d3.range(ages.length + 1))
-    .at({
-      d: d => ['M', d*c.width/(ages.length), '0 V', c.height].join(' '),
-      stroke: '#aaa',
-    })
-
-  stateAxis.append('text.bold').text('Home State')
-    .translate([c.width/2, c.height + 45])
-    .at({textAnchor: 'middle'})
-
-  var ageScale = d3.scaleBand().domain(ages.slice().reverse()).range(c.x.range())
-  var ageAxis = c.svg.append('g.axis.age.init-hidden')
-
-  ageAxis.appendMany('text', ages)
-    .translate(d => [-30, ageScale(d) + bw])
-    .text(d => d)
-    .at({dy: '.33em'})
-    .st({fill: '#444'})
-
-  ageAxis.appendMany('path', d3.range(ages.length + 1))
-    .at({
-      d: d => ['M 0', d*c.width/(ages.length), 'H', c.width].join(' '),
-      stroke: '#aaa',
-    })
-
-  if (scale == 1){
-    ageAxis
-      .append('g').translate([-43, c.height/2])
-      .append('text.bold').text('Age')
-      .at({textAnchor: 'middle', transform: 'rotate(-90)'})
-  } else {
-    ageAxis
-      .append('g').translate([-22, 14])
-      .append('text.bold').text('Age')
-      .at({textAnchor: 'middle'})
-  }
-
-  var seasonAxis = c.svg.append('g.axis.state.init-hidden').lower()
-  seasonAxis.appendMany('g', ages)
-    .translate(d => ageScale(d), 1)
-    .appendMany('path', d3.range(1, 4))
-    .at({
-      d: d => ['M 0', d*bw/4*2, 'H', c.width].join(' '),
-      stroke: '#ddd',
-    })
-
-  var headAxis = c.svg.append('g.axis.state.init-hidden')
-  headAxis.appendMany('text.bold', ['Heads', 'Tails'])
-    .text(d => d)
-    .translate((d, i) => [i ? c.width/4*3 + 20 : c.width/4 - 20, 88])
-    .at({textAnchor: 'middle'})
-
-
-  var headCaptionAxis = c.svg.append('g.axis.state.init-hidden')
-  headCaptionAxis.appendMany('text', ['reports plagiarism', 'reports truth'])
-    .text(d => d)
-    .translate((d, i) => [i ? c.width/4*3 + 20 : c.width/4 - 20, 88 + 15])
-    .at({textAnchor: 'middle'})
-    .st({fill: '#444'})
-
-
-  return {stateScale, stateAxis, headAxis, headCaptionAxis, ageScale, ageAxis, bw, seasonAxis}
-}
-
-
-
-
-
-
-
-if (window.init) window.init()
\ No newline at end of file
diff --git a/spaces/mikeee/radiobee-aligner/radiobee/insert_spaces.py b/spaces/mikeee/radiobee-aligner/radiobee/insert_spaces.py
deleted file mode 100644
index 30ade20cbe586a33edd65559dbce760cd43c524b..0000000000000000000000000000000000000000
--- a/spaces/mikeee/radiobee-aligner/radiobee/insert_spaces.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""Insert spaces, mypython/split_chinese.py."""
-import re
-
-
-def insert_spaces(text: str) -> str:
-    """Insert space in Chinese characters.
-
-    >>> insert_spaces("test亨利it四世上")
-    ' test 亨 利 it 四 世 上 '
-    >>> insert_spaces("test亨利it四世上").strip().__len__()
-    17
-
-    """
-    return re.sub(r"(?<=[a-zA-Z\d]) (?=[a-zA-Z\d])", "", text.replace("", " "))
diff --git a/spaces/mithril-security/blind_chat/src/lib/utils/concatUint8Arrays.ts b/spaces/mithril-security/blind_chat/src/lib/utils/concatUint8Arrays.ts
deleted file mode 100644
index e53396eca7e3dee20a543fb6ac28ecf48c7e3965..0000000000000000000000000000000000000000
--- a/spaces/mithril-security/blind_chat/src/lib/utils/concatUint8Arrays.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-import { sum } from "./sum";
-
-export function concatUint8Arrays(arrays: Uint8Array[]): Uint8Array {
-	const totalLength = sum(arrays.map((a) => a.length));
-	const result = new Uint8Array(totalLength);
-	let offset = 0;
-	for (const array of arrays) {
-		result.set(array, offset);
-		offset += array.length;
-	}
-	return result;
-}
diff --git a/spaces/mms-meta/MMS/vits/monotonic_align/setup.py b/spaces/mms-meta/MMS/vits/monotonic_align/setup.py
deleted file mode 100644
index 30c224807a70faa9df9c9eb75f8e80c8c867b16b..0000000000000000000000000000000000000000
--- a/spaces/mms-meta/MMS/vits/monotonic_align/setup.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from distutils.core import setup
-from Cython.Build import cythonize
-import numpy
-
-setup(
-  name = 'monotonic_align',
-  ext_modules = cythonize("core.pyx"),
-  include_dirs=[numpy.get_include()]
-)
diff --git a/spaces/mnauf/detect-bees/utils/loggers/clearml/clearml_utils.py b/spaces/mnauf/detect-bees/utils/loggers/clearml/clearml_utils.py
deleted file mode 100644
index eb1c12ce6cac24f131e6e88efaac3f58dbc4c753..0000000000000000000000000000000000000000
--- a/spaces/mnauf/detect-bees/utils/loggers/clearml/clearml_utils.py
+++ /dev/null
@@ -1,157 +0,0 @@
-"""Main Logger class for ClearML experiment tracking."""
-import glob
-import re
-from pathlib import Path
-
-import numpy as np
-import yaml
-
-from utils.plots import Annotator, colors
-
-try:
-    import clearml
-    from clearml import Dataset, Task
-
-    assert hasattr(clearml, '__version__')  # verify package import not local dir
-except (ImportError, AssertionError):
-    clearml = None
-
-
-def construct_dataset(clearml_info_string):
-    """Load in a clearml dataset and fill the internal data_dict with its contents.
-    """
-    dataset_id = clearml_info_string.replace('clearml://', '')
-    dataset = Dataset.get(dataset_id=dataset_id)
-    dataset_root_path = Path(dataset.get_local_copy())
-
-    # We'll search for the yaml file definition in the dataset
-    yaml_filenames = list(glob.glob(str(dataset_root_path / "*.yaml")) + glob.glob(str(dataset_root_path / "*.yml")))
-    if len(yaml_filenames) > 1:
-        raise ValueError('More than one yaml file was found in the dataset root, cannot determine which one contains '
-                         'the dataset definition this way.')
-    elif len(yaml_filenames) == 0:
-        raise ValueError('No yaml definition found in dataset root path, check that there is a correct yaml file '
-                         'inside the dataset root path.')
-    with open(yaml_filenames[0]) as f:
-        dataset_definition = yaml.safe_load(f)
-
-    assert set(dataset_definition.keys()).issuperset(
-        {'train', 'test', 'val', 'nc', 'names'}
-    ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')"
-
-    data_dict = dict()
-    data_dict['train'] = str(
-        (dataset_root_path / dataset_definition['train']).resolve()) if dataset_definition['train'] else None
-    data_dict['test'] = str(
-        (dataset_root_path / dataset_definition['test']).resolve()) if dataset_definition['test'] else None
-    data_dict['val'] = str(
-        (dataset_root_path / dataset_definition['val']).resolve()) if dataset_definition['val'] else None
-    data_dict['nc'] = dataset_definition['nc']
-    data_dict['names'] = dataset_definition['names']
-
-    return data_dict
-
-
-class ClearmlLogger:
-    """Log training runs, datasets, models, and predictions to ClearML.
-
-    This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default,
-    this information includes hyperparameters, system configuration and metrics, model metrics, code information and
-    basic data metrics and analyses.
-
-    By providing additional command line arguments to train.py, datasets,
-    models and predictions can also be logged.
-    """
-
-    def __init__(self, opt, hyp):
-        """
-        - Initialize ClearML Task, this object will capture the experiment
-        - Upload dataset version to ClearML Data if opt.upload_dataset is True
-
-        arguments:
-        opt (namespace) -- Commandline arguments for this run
-        hyp (dict) -- Hyperparameters for this run
-
-        """
-        self.current_epoch = 0
-        # Keep tracked of amount of logged images to enforce a limit
-        self.current_epoch_logged_images = set()
-        # Maximum number of images to log to clearML per epoch
-        self.max_imgs_to_log_per_epoch = 16
-        # Get the interval of epochs when bounding box images should be logged
-        self.bbox_interval = opt.bbox_interval
-        self.clearml = clearml
-        self.task = None
-        self.data_dict = None
-        if self.clearml:
-            self.task = Task.init(
-                project_name='YOLOv5',
-                task_name='training',
-                tags=['YOLOv5'],
-                output_uri=True,
-                auto_connect_frameworks={'pytorch': False}
-                # We disconnect pytorch auto-detection, because we added manual model save points in the code
-            )
-            # ClearML's hooks will already grab all general parameters
-            # Only the hyperparameters coming from the yaml config file
-            # will have to be added manually!
-            self.task.connect(hyp, name='Hyperparameters')
-
-            # Get ClearML Dataset Version if requested
-            if opt.data.startswith('clearml://'):
-                # data_dict should have the following keys:
-                # names, nc (number of classes), test, train, val (all three relative paths to ../datasets)
-                self.data_dict = construct_dataset(opt.data)
-                # Set data to data_dict because wandb will crash without this information and opt is the best way
-                # to give it to them
-                opt.data = self.data_dict
-
-    def log_debug_samples(self, files, title='Debug Samples'):
-        """
-        Log files (images) as debug samples in the ClearML task.
-
-        arguments:
-        files (List(PosixPath)) a list of file paths in PosixPath format
-        title (str) A title that groups together images with the same values
-        """
-        for f in files:
-            if f.exists():
-                it = re.search(r'_batch(\d+)', f.name)
-                iteration = int(it.groups()[0]) if it else 0
-                self.task.get_logger().report_image(title=title,
-                                                    series=f.name.replace(it.group(), ''),
-                                                    local_path=str(f),
-                                                    iteration=iteration)
-
-    def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25):
-        """
-        Draw the bounding boxes on a single image and report the result as a ClearML debug sample.
-
-        arguments:
-        image_path (PosixPath) the path the original image file
-        boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
-        class_names (dict): dict containing mapping of class int to class name
-        image (Tensor): A torch tensor containing the actual image data
-        """
-        if len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch and self.current_epoch >= 0:
-            # Log every bbox_interval times and deduplicate for any intermittend extra eval runs
-            if self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images:
-                im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2))
-                annotator = Annotator(im=im, pil=True)
-                for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])):
-                    color = colors(i)
-
-                    class_name = class_names[int(class_nr)]
-                    confidence_percentage = round(float(conf) * 100, 2)
-                    label = f"{class_name}: {confidence_percentage}%"
-
-                    if conf > conf_threshold:
-                        annotator.rectangle(box.cpu().numpy(), outline=color)
-                        annotator.box_label(box.cpu().numpy(), label=label, color=color)
-
-                annotated_image = annotator.result()
-                self.task.get_logger().report_image(title='Bounding Boxes',
-                                                    series=image_path.name,
-                                                    iteration=self.current_epoch,
-                                                    image=annotated_image)
-                self.current_epoch_logged_images.add(image_path)
diff --git a/spaces/mnauf/detect-bees/utils/segment/plots.py b/spaces/mnauf/detect-bees/utils/segment/plots.py
deleted file mode 100644
index 9b90900b3772fe23dbd57deb64221f98e563b069..0000000000000000000000000000000000000000
--- a/spaces/mnauf/detect-bees/utils/segment/plots.py
+++ /dev/null
@@ -1,143 +0,0 @@
-import contextlib
-import math
-from pathlib import Path
-
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import torch
-
-from .. import threaded
-from ..general import xywh2xyxy
-from ..plots import Annotator, colors
-
-
-@threaded
-def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg', names=None):
-    # Plot image grid with labels
-    if isinstance(images, torch.Tensor):
-        images = images.cpu().float().numpy()
-    if isinstance(targets, torch.Tensor):
-        targets = targets.cpu().numpy()
-    if isinstance(masks, torch.Tensor):
-        masks = masks.cpu().numpy().astype(int)
-
-    max_size = 1920  # max image size
-    max_subplots = 16  # max image subplots, i.e. 4x4
-    bs, _, h, w = images.shape  # batch size, _, height, width
-    bs = min(bs, max_subplots)  # limit plot images
-    ns = np.ceil(bs ** 0.5)  # number of subplots (square)
-    if np.max(images[0]) <= 1:
-        images *= 255  # de-normalise (optional)
-
-    # Build Image
-    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)  # init
-    for i, im in enumerate(images):
-        if i == max_subplots:  # if last batch has fewer images than we expect
-            break
-        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
-        im = im.transpose(1, 2, 0)
-        mosaic[y:y + h, x:x + w, :] = im
-
-    # Resize (optional)
-    scale = max_size / ns / max(h, w)
-    if scale < 1:
-        h = math.ceil(scale * h)
-        w = math.ceil(scale * w)
-        mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
-
-    # Annotate
-    fs = int((h + w) * ns * 0.01)  # font size
-    annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
-    for i in range(i + 1):
-        x, y = int(w * (i // ns)), int(h * (i % ns))  # block origin
-        annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2)  # borders
-        if paths:
-            annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220))  # filenames
-        if len(targets) > 0:
-            idx = targets[:, 0] == i
-            ti = targets[idx]  # image targets
-
-            boxes = xywh2xyxy(ti[:, 2:6]).T
-            classes = ti[:, 1].astype('int')
-            labels = ti.shape[1] == 6  # labels if no conf column
-            conf = None if labels else ti[:, 6]  # check for confidence presence (label vs pred)
-
-            if boxes.shape[1]:
-                if boxes.max() <= 1.01:  # if normalized with tolerance 0.01
-                    boxes[[0, 2]] *= w  # scale to pixels
-                    boxes[[1, 3]] *= h
-                elif scale < 1:  # absolute coords need scale if image scales
-                    boxes *= scale
-            boxes[[0, 2]] += x
-            boxes[[1, 3]] += y
-            for j, box in enumerate(boxes.T.tolist()):
-                cls = classes[j]
-                color = colors(cls)
-                cls = names[cls] if names else cls
-                if labels or conf[j] > 0.25:  # 0.25 conf thresh
-                    label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
-                    annotator.box_label(box, label, color=color)
-
-            # Plot masks
-            if len(masks):
-                if masks.max() > 1.0:  # mean that masks are overlap
-                    image_masks = masks[[i]]  # (1, 640, 640)
-                    nl = len(ti)
-                    index = np.arange(nl).reshape(nl, 1, 1) + 1
-                    image_masks = np.repeat(image_masks, nl, axis=0)
-                    image_masks = np.where(image_masks == index, 1.0, 0.0)
-                else:
-                    image_masks = masks[idx]
-
-                im = np.asarray(annotator.im).copy()
-                for j, box in enumerate(boxes.T.tolist()):
-                    if labels or conf[j] > 0.25:  # 0.25 conf thresh
-                        color = colors(classes[j])
-                        mh, mw = image_masks[j].shape
-                        if mh != h or mw != w:
-                            mask = image_masks[j].astype(np.uint8)
-                            mask = cv2.resize(mask, (w, h))
-                            mask = mask.astype(bool)
-                        else:
-                            mask = image_masks[j].astype(bool)
-                        with contextlib.suppress(Exception):
-                            im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6
-                annotator.fromarray(im)
-    annotator.im.save(fname)  # save
-
-
-def plot_results_with_masks(file="path/to/results.csv", dir="", best=True):
-    # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
-    save_dir = Path(file).parent if file else Path(dir)
-    fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)
-    ax = ax.ravel()
-    files = list(save_dir.glob("results*.csv"))
-    assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot."
-    for f in files:
-        try:
-            data = pd.read_csv(f)
-            index = np.argmax(0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] +
-                              0.1 * data.values[:, 11])
-            s = [x.strip() for x in data.columns]
-            x = data.values[:, 0]
-            for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]):
-                y = data.values[:, j]
-                # y[y == 0] = np.nan  # don't show zero values
-                ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=2)
-                if best:
-                    # best
-                    ax[i].scatter(index, y[index], color="r", label=f"best:{index}", marker="*", linewidth=3)
-                    ax[i].set_title(s[j] + f"\n{round(y[index], 5)}")
-                else:
-                    # last
-                    ax[i].scatter(x[-1], y[-1], color="r", label="last", marker="*", linewidth=3)
-                    ax[i].set_title(s[j] + f"\n{round(y[-1], 5)}")
-                # if j in [8, 9, 10]:  # share train and val loss y axes
-                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
-        except Exception as e:
-            print(f"Warning: Plotting error for {f}: {e}")
-    ax[1].legend()
-    fig.savefig(save_dir / "results.png", dpi=200)
-    plt.close()
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/file_io.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/file_io.py
deleted file mode 100644
index dba663d4aafeb925ddffa50f5055933d6531a069..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/file_io.py
+++ /dev/null
@@ -1,194 +0,0 @@
-#!/usr/bin/env python3
-
-# 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 logging
-import os
-import shutil
-from typing import List, Optional
-
-
-logger = logging.getLogger(__file__)
-
-
-try:
-    from iopath.common.file_io import g_pathmgr as IOPathManager
-
-    try:
-        # [FB only - for now] AWS PathHandler for PathManager
-        from .fb_pathhandlers import S3PathHandler
-
-        IOPathManager.register_handler(S3PathHandler())
-    except KeyError:
-        logging.warning("S3PathHandler already registered.")
-    except ImportError:
-        logging.debug(
-            "S3PathHandler couldn't be imported. Either missing fb-only files, or boto3 module."
-        )
-
-except ImportError:
-    IOPathManager = None
-
-
-class PathManager:
-    """
-    Wrapper for insulating OSS I/O (using Python builtin operations) from
-    iopath's PathManager abstraction (for transparently handling various
-    internal backends).
-    """
-
-    @staticmethod
-    def open(
-        path: str,
-        mode: str = "r",
-        buffering: int = -1,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = None,
-        newline: Optional[str] = None,
-    ):
-        if IOPathManager:
-            return IOPathManager.open(
-                path=path,
-                mode=mode,
-                buffering=buffering,
-                encoding=encoding,
-                errors=errors,
-                newline=newline,
-            )
-        return open(
-            path,
-            mode=mode,
-            buffering=buffering,
-            encoding=encoding,
-            errors=errors,
-            newline=newline,
-        )
-
-    @staticmethod
-    def copy(src_path: str, dst_path: str, overwrite: bool = False) -> bool:
-        if IOPathManager:
-            return IOPathManager.copy(
-                src_path=src_path, dst_path=dst_path, overwrite=overwrite
-            )
-        return shutil.copyfile(src_path, dst_path)
-
-    @staticmethod
-    def get_local_path(path: str, **kwargs) -> str:
-        if IOPathManager:
-            return IOPathManager.get_local_path(path, **kwargs)
-        return path
-
-    @staticmethod
-    def exists(path: str) -> bool:
-        if IOPathManager:
-            return IOPathManager.exists(path)
-        return os.path.exists(path)
-
-    @staticmethod
-    def isfile(path: str) -> bool:
-        if IOPathManager:
-            return IOPathManager.isfile(path)
-        return os.path.isfile(path)
-
-    @staticmethod
-    def ls(path: str) -> List[str]:
-        if IOPathManager:
-            return IOPathManager.ls(path)
-        return os.listdir(path)
-
-    @staticmethod
-    def mkdirs(path: str) -> None:
-        if IOPathManager:
-            return IOPathManager.mkdirs(path)
-        os.makedirs(path, exist_ok=True)
-
-    @staticmethod
-    def rm(path: str) -> None:
-        if IOPathManager:
-            return IOPathManager.rm(path)
-        os.remove(path)
-
-    @staticmethod
-    def chmod(path: str, mode: int) -> None:
-        if not PathManager.path_requires_pathmanager(path):
-            os.chmod(path, mode)
-
-    @staticmethod
-    def register_handler(handler) -> None:
-        if IOPathManager:
-            return IOPathManager.register_handler(handler=handler)
-
-    @staticmethod
-    def copy_from_local(
-        local_path: str, dst_path: str, overwrite: bool = False, **kwargs
-    ) -> None:
-        if IOPathManager:
-            return IOPathManager.copy_from_local(
-                local_path=local_path, dst_path=dst_path, overwrite=overwrite, **kwargs
-            )
-        return shutil.copyfile(local_path, dst_path)
-
-    @staticmethod
-    def path_requires_pathmanager(path: str) -> bool:
-        """Do we require PathManager to access given path?"""
-        if IOPathManager:
-            for p in IOPathManager._path_handlers.keys():
-                if path.startswith(p):
-                    return True
-        return False
-
-    @staticmethod
-    def supports_rename(path: str) -> bool:
-        # PathManager doesn't yet support renames
-        return not PathManager.path_requires_pathmanager(path)
-
-    @staticmethod
-    def rename(src: str, dst: str):
-        os.rename(src, dst)
-
-    """
-    ioPath async PathManager methods:
-    """
-    @staticmethod
-    def opena(
-        path: str,
-        mode: str = "r",
-        buffering: int = -1,
-        encoding: Optional[str] = None,
-        errors: Optional[str] = None,
-        newline: Optional[str] = None,
-    ):
-        """
-        Return file descriptor with asynchronous write operations.
-        """
-        global IOPathManager
-        if not IOPathManager:
-            logging.info("ioPath is initializing PathManager.")
-            try:
-                from iopath.common.file_io import PathManager
-                IOPathManager = PathManager()
-            except Exception:
-                logging.exception("Failed to initialize ioPath PathManager object.")
-        return IOPathManager.opena(
-            path=path,
-            mode=mode,
-            buffering=buffering,
-            encoding=encoding,
-            errors=errors,
-            newline=newline,
-        )
-
-    @staticmethod
-    def async_close() -> bool:
-        """
-        Wait for files to be written and clean up asynchronous PathManager.
-        NOTE: `PathManager.async_close()` must be called at the end of any
-        script that uses `PathManager.opena(...)`.
-        """
-        global IOPathManager
-        if IOPathManager:
-            return IOPathManager.async_close()
-        return False
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/lr_scheduler/fixed_schedule.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/lr_scheduler/fixed_schedule.py
deleted file mode 100644
index d0e7e14b7e72b1151f7d7f19094430bbab64f8f0..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/lr_scheduler/fixed_schedule.py
+++ /dev/null
@@ -1,76 +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 dataclasses import dataclass, field
-from typing import Optional, List
-from omegaconf import II
-
-from fairseq.dataclass import FairseqDataclass
-from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
-
-
-@dataclass
-class FixedLRScheduleConfig(FairseqDataclass):
-    force_anneal: Optional[int] = field(
-        default=None,
-        metadata={"help": "force annealing at specified epoch"},
-    )
-    lr_shrink: float = field(
-        default=0.1,
-        metadata={"help": "shrink factor for annealing, lr_new = (lr * lr_shrink)"},
-    )
-    warmup_updates: int = field(
-        default=0,
-        metadata={"help": "warmup the learning rate linearly for the first N updates"},
-    )
-    lr: List[float] = II("optimization.lr")
-
-
-@register_lr_scheduler("fixed", dataclass=FixedLRScheduleConfig)
-class FixedLRSchedule(FairseqLRScheduler):
-    """Decay the LR on a fixed schedule."""
-
-    def __init__(self, cfg: FixedLRScheduleConfig, optimizer):
-        super().__init__(cfg, optimizer)
-
-        self.lr = cfg.lr[0]
-        if cfg.warmup_updates > 0:
-            self.warmup_factor = 1.0 / cfg.warmup_updates
-        else:
-            self.warmup_factor = 1
-
-    def state_dict(self):
-        return {"lr": self.lr}
-
-    def load_state_dict(self, state_dict):
-        if "lr" in state_dict:
-            self.lr = state_dict["lr"]
-
-    def get_next_lr(self, epoch):
-        lrs = self.cfg.lr
-        if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal:
-            # use fixed LR schedule
-            next_lr = lrs[min(epoch - 1, len(lrs) - 1)]
-        else:
-            # annneal based on lr_shrink
-            next_lr = lrs[-1] * self.cfg.lr_shrink ** (
-                epoch + 1 - self.cfg.force_anneal
-            )
-        return next_lr
-
-    def step_begin_epoch(self, epoch):
-        """Update the learning rate at the beginning of the given epoch."""
-        self.lr = self.get_next_lr(epoch)
-        self.optimizer.set_lr(self.warmup_factor * self.lr)
-        return self.optimizer.get_lr()
-
-    def step_update(self, num_updates):
-        """Update the learning rate after each update."""
-        if self.cfg.warmup_updates > 0 and num_updates < self.cfg.warmup_updates:
-            self.warmup_factor = (num_updates + 1) / float(self.cfg.warmup_updates)
-            self.optimizer.set_lr(self.warmup_factor * self.lr)
-        else:
-            self.optimizer.set_lr(self.lr)
-        return self.optimizer.get_lr()
diff --git a/spaces/mshukor/UnIVAL/fairseq/scripts/shard_docs.py b/spaces/mshukor/UnIVAL/fairseq/scripts/shard_docs.py
deleted file mode 100644
index 97232c3c845ee01dc5ab627388934cc0f9588280..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/scripts/shard_docs.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/env python3
-# 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.
-"""
-Split a large file into shards while respecting document boundaries. Documents
-should be separated by a single empty line.
-"""
-
-import argparse
-import contextlib
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("input")
-    parser.add_argument("--num-shards", type=int)
-    args = parser.parse_args()
-
-    assert args.num_shards is not None and args.num_shards > 1
-
-    with open(args.input, "r", encoding="utf-8") as h:
-        with contextlib.ExitStack() as stack:
-            outputs = [
-                stack.enter_context(
-                    open(args.input + ".shard" + str(i), "w", encoding="utf-8")
-                )
-                for i in range(args.num_shards)
-            ]
-
-            doc = []
-            first_doc = [True] * args.num_shards
-
-            def output_doc(i):
-                if not first_doc[i]:
-                    outputs[i].write("\n")
-                first_doc[i] = False
-                for line in doc:
-                    outputs[i].write(line)
-                doc.clear()
-
-            num_docs = 0
-            for line in h:
-                if line.strip() == "":  # empty line indicates new document
-                    output_doc(num_docs % args.num_shards)
-                    num_docs += 1
-                else:
-                    doc.append(line)
-            output_doc(num_docs % args.num_shards)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/mthsk/sovits-models/data_utils.py b/spaces/mthsk/sovits-models/data_utils.py
deleted file mode 100644
index 7c76fd1c3a45b8304d916161718c7763874f3e35..0000000000000000000000000000000000000000
--- a/spaces/mthsk/sovits-models/data_utils.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-
-import modules.commons as commons
-import utils
-from modules.mel_processing import spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-
-# import h5py
-
-
-"""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, hparams, all_in_mem: bool = False):
-        self.audiopaths = load_filepaths_and_text(audiopaths)
-        self.max_wav_value = hparams.data.max_wav_value
-        self.sampling_rate = hparams.data.sampling_rate
-        self.filter_length = hparams.data.filter_length
-        self.hop_length = hparams.data.hop_length
-        self.win_length = hparams.data.win_length
-        self.sampling_rate = hparams.data.sampling_rate
-        self.use_sr = hparams.train.use_sr
-        self.spec_len = hparams.train.max_speclen
-        self.spk_map = hparams.spk
-
-        random.seed(1234)
-        random.shuffle(self.audiopaths)
-        
-        self.all_in_mem = all_in_mem
-        if self.all_in_mem:
-            self.cache = [self.get_audio(p[0]) for p in self.audiopaths]
-
-    def get_audio(self, filename):
-        filename = filename.replace("\\", "/")
-        audio, sampling_rate = load_wav_to_torch(filename)
-        if sampling_rate != self.sampling_rate:
-            raise ValueError("{} SR doesn't match target {} SR".format(
-                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")
-
-        # Ideally, all data generated after Mar 25 should have .spec.pt
-        if os.path.exists(spec_filename):
-            spec = torch.load(spec_filename)
-        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)
-
-        spk = filename.split("/")[-2]
-        spk = torch.LongTensor([self.spk_map[spk]])
-
-        f0 = np.load(filename + ".f0.npy")
-        f0, uv = utils.interpolate_f0(f0)
-        f0 = torch.FloatTensor(f0)
-        uv = torch.FloatTensor(uv)
-
-        c = torch.load(filename+ ".soft.pt")
-        c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[0])
-
-
-        lmin = min(c.size(-1), spec.size(-1))
-        assert abs(c.size(-1) - spec.size(-1)) < 3, (c.size(-1), spec.size(-1), f0.shape, filename)
-        assert abs(audio_norm.shape[1]-lmin * self.hop_length) < 3 * self.hop_length
-        spec, c, f0, uv = spec[:, :lmin], c[:, :lmin], f0[:lmin], uv[:lmin]
-        audio_norm = audio_norm[:, :lmin * self.hop_length]
-
-        return c, f0, spec, audio_norm, spk, uv
-
-    def random_slice(self, c, f0, spec, audio_norm, spk, uv):
-        # if spec.shape[1] < 30:
-        #     print("skip too short audio:", filename)
-        #     return None
-        if spec.shape[1] > 800:
-            start = random.randint(0, spec.shape[1]-800)
-            end = start + 790
-            spec, c, f0, uv = spec[:, start:end], c[:, start:end], f0[start:end], uv[start:end]
-            audio_norm = audio_norm[:, start * self.hop_length : end * self.hop_length]
-
-        return c, f0, spec, audio_norm, spk, uv
-
-    def __getitem__(self, index):
-        if self.all_in_mem:
-            return self.random_slice(*self.cache[index])
-        else:
-            return self.random_slice(*self.get_audio(self.audiopaths[index][0]))
-
-    def __len__(self):
-        return len(self.audiopaths)
-
-
-class TextAudioCollate:
-
-    def __call__(self, batch):
-        batch = [b for b in batch if b is not None]
-
-        input_lengths, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[0].shape[1] for x in batch]),
-            dim=0, descending=True)
-
-        max_c_len = max([x[0].size(1) for x in batch])
-        max_wav_len = max([x[3].size(1) for x in batch])
-
-        lengths = torch.LongTensor(len(batch))
-
-        c_padded = torch.FloatTensor(len(batch), batch[0][0].shape[0], max_c_len)
-        f0_padded = torch.FloatTensor(len(batch), max_c_len)
-        spec_padded = torch.FloatTensor(len(batch), batch[0][2].shape[0], max_c_len)
-        wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
-        spkids = torch.LongTensor(len(batch), 1)
-        uv_padded = torch.FloatTensor(len(batch), max_c_len)
-
-        c_padded.zero_()
-        spec_padded.zero_()
-        f0_padded.zero_()
-        wav_padded.zero_()
-        uv_padded.zero_()
-
-        for i in range(len(ids_sorted_decreasing)):
-            row = batch[ids_sorted_decreasing[i]]
-
-            c = row[0]
-            c_padded[i, :, :c.size(1)] = c
-            lengths[i] = c.size(1)
-
-            f0 = row[1]
-            f0_padded[i, :f0.size(0)] = f0
-
-            spec = row[2]
-            spec_padded[i, :, :spec.size(1)] = spec
-
-            wav = row[3]
-            wav_padded[i, :, :wav.size(1)] = wav
-
-            spkids[i, 0] = row[4]
-
-            uv = row[5]
-            uv_padded[i, :uv.size(0)] = uv
-
-        return c_padded, f0_padded, spec_padded, wav_padded, spkids, lengths, uv_padded
diff --git a/spaces/mueller-franzes/medfusion-app/tests/models/test_unet_openai.py b/spaces/mueller-franzes/medfusion-app/tests/models/test_unet_openai.py
deleted file mode 100644
index c2a1a27cd2ec78c7056d6314894c5a75c61f45b7..0000000000000000000000000000000000000000
--- a/spaces/mueller-franzes/medfusion-app/tests/models/test_unet_openai.py
+++ /dev/null
@@ -1,19 +0,0 @@
-
-from medical_diffusion.external.stable_diffusion.unet_openai import UNetModel
-from medical_diffusion.models.embedders import LabelEmbedder
-
-import torch 
-
-
-noise_estimator = UNetModel
-noise_estimator_kwargs = {}
-
-
-model  = noise_estimator(**noise_estimator_kwargs)
-print(model)
-
-input = torch.randn((1,4,32,32))
-time = torch.randn([1,])
-cond = None #torch.tensor([0,]) 
-out_hor, out_ver = model(input, time, cond)
-print(out_hor)
\ No newline at end of file
diff --git a/spaces/mujicloud/nodeproxy/README.md b/spaces/mujicloud/nodeproxy/README.md
deleted file mode 100644
index 77f1f3711583229394115139119e0ba2ce994218..0000000000000000000000000000000000000000
--- a/spaces/mujicloud/nodeproxy/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Nodeproxy
-emoji: 🏃
-colorFrom: green
-colorTo: green
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/myrad01/Inpaint-Anything/third_party/lama/bin/paper_runfiles/generate_test_paris.sh b/spaces/myrad01/Inpaint-Anything/third_party/lama/bin/paper_runfiles/generate_test_paris.sh
deleted file mode 100644
index 66056017c3aa376ef0767a59583ab25a321b559b..0000000000000000000000000000000000000000
--- a/spaces/myrad01/Inpaint-Anything/third_party/lama/bin/paper_runfiles/generate_test_paris.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/usr/bin/env bash
-
-# paths to data are valid for mml-ws01
-OUT_DIR="/media/inpainting/paper_data/Paris_StreetView_Dataset_val"
-
-source "$(dirname $0)/env.sh"
-
-for datadir in paris_eval_gt
-do
-    for conf in random_thin_256 random_medium_256 random_thick_256 segm_256
-    do
-        "$BINDIR/gen_mask_dataset_hydra.py" -cn $conf datadir=$datadir location=mml-ws01-paris \
-         location.out_dir=OUT_DIR cropping.out_square_crop=False cropping.out_min_size=227
-
-        "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
-    done
-done
diff --git a/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/evaluation/masks/countless/countless2d.py b/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/evaluation/masks/countless/countless2d.py
deleted file mode 100644
index dc27b73affa20ab1a8a199542469a10aaf1f555a..0000000000000000000000000000000000000000
--- a/spaces/myrad01/Inpaint-Anything/third_party/lama/saicinpainting/evaluation/masks/countless/countless2d.py
+++ /dev/null
@@ -1,529 +0,0 @@
-from __future__ import print_function, division
-
-"""
-COUNTLESS performance test in Python.
-
-python countless2d.py ./images/NAMEOFIMAGE
-"""
-
-import six
-from six.moves import range
-from collections import defaultdict
-from functools import reduce
-import operator 
-import io
-import os
-from PIL import Image
-import math
-import numpy as np
-import random
-import sys
-import time
-from tqdm import tqdm
-from scipy import ndimage
-
-def simplest_countless(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab = a * (a == b) # PICK(A,B)
-  ac = a * (a == c) # PICK(A,C)
-  bc = b * (b == c) # PICK(B,C)
-
-  a = ab | ac | bc # Bitwise OR, safe b/c non-matches are zeroed
-  
-  return a + (a == 0) * d # AB || AC || BC || D
-
-def quick_countless(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization
-  bc = b * (b == c) # PICK(B,C)
-
-  a = ab_ac | bc # (PICK(A,B) || PICK(A,C)) or PICK(B,C)
-  return a + (a == 0) * d # AB || AC || BC || D
-
-def quickest_countless(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization
-  ab_ac |= b * (b == c) # PICK(B,C)
-  return ab_ac + (ab_ac == 0) * d # AB || AC || BC || D
-
-def quick_countless_xor(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab = a ^ (a ^ b) # a or b
-  ab += (ab != a) * ((ab ^ (ab ^ c)) - b) # b or c
-  ab += (ab == c) * ((ab ^ (ab ^ d)) - c) # c or d
-  return ab
-
-def stippled_countless(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm
-  that treats zero as "background" and inflates lone
-  pixels.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization
-  ab_ac |= b * (b == c) # PICK(B,C)
-
-  nonzero = a + (a == 0) * (b + (b == 0) * c)
-  return ab_ac + (ab_ac == 0) * (d + (d == 0) * nonzero) # AB || AC || BC || D
-
-def zero_corrected_countless(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  # allows us to prevent losing 1/2 a bit of information 
-  # at the top end by using a bigger type. Without this 255 is handled incorrectly.
-  data, upgraded = upgrade_type(data) 
-
-  # offset from zero, raw countless doesn't handle 0 correctly
-  # we'll remove the extra 1 at the end.
-  data += 1 
-
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab = a * (a == b) # PICK(A,B)
-  ac = a * (a == c) # PICK(A,C)
-  bc = b * (b == c) # PICK(B,C)
-
-  a = ab | ac | bc # Bitwise OR, safe b/c non-matches are zeroed
-  
-  result = a + (a == 0) * d - 1 # a or d - 1
-
-  if upgraded:
-    return downgrade_type(result)
-
-  # only need to reset data if we weren't upgraded 
-  # b/c no copy was made in that case
-  data -= 1
-
-  return result
-
-def countless_extreme(data):
-  nonzeros = np.count_nonzero(data)
-  # print("nonzeros", nonzeros)
-
-  N = reduce(operator.mul, data.shape)
-
-  if nonzeros == N:
-    print("quick")
-    return quick_countless(data)
-  elif np.count_nonzero(data + 1) == N:
-    print("quick")
-    # print("upper", nonzeros)
-    return quick_countless(data)
-  else:
-    return countless(data)
-
-
-def countless(data):
-  """
-  Vectorized implementation of downsampling a 2D 
-  image by 2 on each side using the COUNTLESS algorithm.
-  
-  data is a 2D numpy array with even dimensions.
-  """
-  # allows us to prevent losing 1/2 a bit of information 
-  # at the top end by using a bigger type. Without this 255 is handled incorrectly.
-  data, upgraded = upgrade_type(data) 
-
-  # offset from zero, raw countless doesn't handle 0 correctly
-  # we'll remove the extra 1 at the end.
-  data += 1 
-
-  sections = []
-  
-  # This loop splits the 2D array apart into four arrays that are
-  # all the result of striding by 2 and offset by (0,0), (0,1), (1,0), 
-  # and (1,1) representing the A, B, C, and D positions from Figure 1.
-  factor = (2,2)
-  for offset in np.ndindex(factor):
-    part = data[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  a, b, c, d = sections
-
-  ab_ac = a * ((a == b) | (a == c)) # PICK(A,B) || PICK(A,C) w/ optimization
-  ab_ac |= b * (b == c) # PICK(B,C)
-  result = ab_ac + (ab_ac == 0) * d - 1 # (matches or d) - 1
-
-  if upgraded:
-    return downgrade_type(result)
-
-  # only need to reset data if we weren't upgraded 
-  # b/c no copy was made in that case
-  data -= 1
-
-  return result
-
-def upgrade_type(arr):
-  dtype = arr.dtype
-
-  if dtype == np.uint8:
-    return arr.astype(np.uint16), True
-  elif dtype == np.uint16:
-    return arr.astype(np.uint32), True
-  elif dtype == np.uint32:
-    return arr.astype(np.uint64), True
-
-  return arr, False
-  
-def downgrade_type(arr):
-  dtype = arr.dtype
-
-  if dtype == np.uint64:
-    return arr.astype(np.uint32)
-  elif dtype == np.uint32:
-    return arr.astype(np.uint16)
-  elif dtype == np.uint16:
-    return arr.astype(np.uint8)
-  
-  return arr
-
-def odd_to_even(image):
-  """
-  To facilitate 2x2 downsampling segmentation, change an odd sized image into an even sized one.
-  Works by mirroring the starting 1 pixel edge of the image on odd shaped sides.
-
-  e.g. turn a 3x3x5 image into a 4x4x5 (the x and y are what are getting downsampled)
-  
-  For example: [ 3, 2, 4 ] => [ 3, 3, 2, 4 ] which is now easy to downsample.
-
-  """
-  shape = np.array(image.shape)
-
-  offset = (shape % 2)[:2] # x,y offset
-  
-  # detect if we're dealing with an even
-  # image. if so it's fine, just return.
-  if not np.any(offset): 
-    return image
-
-  oddshape = image.shape[:2] + offset
-  oddshape = np.append(oddshape, shape[2:])
-  oddshape = oddshape.astype(int)
-
-  newimg = np.empty(shape=oddshape, dtype=image.dtype)
-
-  ox,oy = offset
-  sx,sy = oddshape
-
-  newimg[0,0] = image[0,0] # corner
-  newimg[ox:sx,0] = image[:,0] # x axis line
-  newimg[0,oy:sy] = image[0,:] # y axis line 
-
-  return newimg
-
-def counting(array):
-    factor = (2, 2, 1)
-    shape = array.shape
-
-    while len(shape) < 4:
-      array = np.expand_dims(array, axis=-1)
-      shape = array.shape
-
-    output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(shape, factor))
-    output = np.zeros(output_shape, dtype=array.dtype)
-
-    for chan in range(0, shape[3]):
-      for z in range(0, shape[2]):
-        for x in range(0, shape[0], 2):
-          for y in range(0, shape[1], 2):
-            block = array[ x:x+2, y:y+2, z, chan ] # 2x2 block
-
-            hashtable = defaultdict(int)
-            for subx, suby in np.ndindex(block.shape[0], block.shape[1]):
-              hashtable[block[subx, suby]] += 1
-
-            best = (0, 0)
-            for segid, val in six.iteritems(hashtable):
-              if best[1] < val:
-                best = (segid, val)
-
-            output[ x // 2, y // 2, chan ] = best[0]
-    
-    return output
-
-def ndzoom(array):
-    if len(array.shape) == 3:
-      ratio = ( 1 / 2.0, 1 / 2.0, 1.0 )
-    else:
-      ratio = ( 1 / 2.0, 1 / 2.0)
-    return ndimage.interpolation.zoom(array, ratio, order=1)
-
-def countless_if(array):
-    factor = (2, 2, 1)
-    shape = array.shape
-
-    if len(shape) < 3:
-      array = array[ :,:, np.newaxis ]
-      shape = array.shape
-
-    output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(shape, factor))
-    output = np.zeros(output_shape, dtype=array.dtype)
-
-    for chan in range(0, shape[2]):
-      for x in range(0, shape[0], 2):
-        for y in range(0, shape[1], 2):
-          block = array[ x:x+2, y:y+2, chan ] # 2x2 block
-
-          if block[0,0] == block[1,0]:
-            pick = block[0,0]
-          elif block[0,0] == block[0,1]:
-            pick = block[0,0]
-          elif block[1,0] == block[0,1]:
-            pick = block[1,0]
-          else:
-            pick = block[1,1]
-
-          output[ x // 2, y // 2, chan ] = pick
-    
-    return np.squeeze(output)
-
-def downsample_with_averaging(array):
-  """
-  Downsample x by factor using averaging.
-
-  @return: The downsampled array, of the same type as x.
-  """
-
-  if len(array.shape) == 3:
-    factor = (2,2,1)
-  else:
-    factor = (2,2)
-  
-  if np.array_equal(factor[:3], np.array([1,1,1])):
-    return array
-
-  output_shape = tuple(int(math.ceil(s / f)) for s, f in zip(array.shape, factor))
-  temp = np.zeros(output_shape, float)
-  counts = np.zeros(output_shape, np.int)
-  for offset in np.ndindex(factor):
-      part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-      indexing_expr = tuple(np.s_[:s] for s in part.shape)
-      temp[indexing_expr] += part
-      counts[indexing_expr] += 1
-  return np.cast[array.dtype](temp / counts)
-
-def downsample_with_max_pooling(array):
-
-  factor = (2,2)
-
-  if np.all(np.array(factor, int) == 1):
-      return array
-
-  sections = []
-
-  for offset in np.ndindex(factor):
-    part = array[tuple(np.s_[o::f] for o, f in zip(offset, factor))]
-    sections.append(part)
-
-  output = sections[0].copy()
-
-  for section in sections[1:]:
-    np.maximum(output, section, output)
-
-  return output
-
-def striding(array): 
-  """Downsample x by factor using striding.
-
-  @return: The downsampled array, of the same type as x.
-  """
-  factor = (2,2)
-  if np.all(np.array(factor, int) == 1):
-    return array
-  return array[tuple(np.s_[::f] for f in factor)]
-
-def benchmark():
-  filename = sys.argv[1]
-  img = Image.open(filename)
-  data = np.array(img.getdata(), dtype=np.uint8)
-
-  if len(data.shape) == 1:
-    n_channels = 1
-    reshape = (img.height, img.width)
-  else:
-    n_channels = min(data.shape[1], 3)
-    data = data[:, :n_channels]
-    reshape = (img.height, img.width, n_channels)
-
-  data = data.reshape(reshape).astype(np.uint8)
-
-  methods = [
-    simplest_countless,
-    quick_countless,
-    quick_countless_xor,
-    quickest_countless,
-    stippled_countless,
-    zero_corrected_countless,
-    countless,
-    downsample_with_averaging,
-    downsample_with_max_pooling,
-    ndzoom,
-    striding,
-    # countless_if,
-    # counting,
-  ]
-
-  formats = {
-    1: 'L',
-    3: 'RGB',
-    4: 'RGBA'
-  }
-
-  if not os.path.exists('./results'):
-    os.mkdir('./results')
-
-  N = 500
-  img_size = float(img.width * img.height) / 1024.0 / 1024.0
-  print("N = %d, %dx%d (%.2f MPx) %d chan, %s" % (N, img.width, img.height, img_size, n_channels, filename))
-  print("Algorithm\tMPx/sec\tMB/sec\tSec")
-  for fn in methods:
-    print(fn.__name__, end='')
-    sys.stdout.flush()
-
-    start = time.time()
-    # tqdm is here to show you what's going on the first time you run it.
-    # Feel free to remove it to get slightly more accurate timing results.
-    for _ in tqdm(range(N), desc=fn.__name__, disable=True):
-      result = fn(data)
-    end = time.time()
-    print("\r", end='')
-
-    total_time = (end - start)
-    mpx = N * img_size / total_time
-    mbytes = N * img_size * n_channels / total_time
-    # Output in tab separated format to enable copy-paste into excel/numbers
-    print("%s\t%.3f\t%.3f\t%.2f" % (fn.__name__, mpx, mbytes, total_time))
-    outimg = Image.fromarray(np.squeeze(result), formats[n_channels])
-    outimg.save('./results/{}.png'.format(fn.__name__, "PNG"))
-
-if __name__ == '__main__':
-  benchmark()
-
-
-# Example results:
-# N = 5, 1024x1024 (1.00 MPx) 1 chan, images/gray_segmentation.png
-# Function                        MPx/sec   MB/sec     Sec
-# simplest_countless              752.855   752.855    0.01
-# quick_countless                 920.328   920.328    0.01
-# zero_corrected_countless        534.143   534.143    0.01
-# countless                       644.247   644.247    0.01
-# downsample_with_averaging       372.575   372.575    0.01
-# downsample_with_max_pooling     974.060   974.060    0.01
-# ndzoom                          137.517   137.517    0.04
-# striding                      38550.588 38550.588    0.00
-# countless_if                      4.377     4.377    1.14
-# counting                          0.117     0.117   42.85
-
-# Run without non-numpy implementations:
-# N = 2000, 1024x1024 (1.00 MPx) 1 chan, images/gray_segmentation.png
-# Algorithm                       MPx/sec   MB/sec     Sec
-# simplest_countless              800.522   800.522    2.50
-# quick_countless                 945.420   945.420    2.12
-# quickest_countless              947.256   947.256    2.11
-# stippled_countless              544.049   544.049    3.68
-# zero_corrected_countless        575.310   575.310    3.48
-# countless                       646.684   646.684    3.09
-# downsample_with_averaging       385.132   385.132    5.19
-# downsample_with_max_poolin      988.361   988.361    2.02
-# ndzoom                          163.104   163.104   12.26
-# striding                      81589.340 81589.340    0.02
-
-
-
-
diff --git a/spaces/nakas/musika_api/musika_test.py b/spaces/nakas/musika_api/musika_test.py
deleted file mode 100644
index f778fdabd9fceb407cc572a15c15f0ba1bf095af..0000000000000000000000000000000000000000
--- a/spaces/nakas/musika_api/musika_test.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import os
-
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-
-from parse_test import parse_args
-from models import Models_functions
-from utils import Utils_functions
-
-if __name__ == "__main__":
-
-    # parse args
-    args = parse_args()
-
-    # initialize networks
-    M = Models_functions(args)
-    models_ls_1, models_ls_2, models_ls_3 = M.get_networks()
-
-    # test musika
-    U = Utils_functions(args)
-    U.render_gradio(models_ls_1, models_ls_2, models_ls_3, train=False)
diff --git a/spaces/nateraw/deepafx-st/deepafx_st/processors/autodiff/signal.py b/spaces/nateraw/deepafx-st/deepafx_st/processors/autodiff/signal.py
deleted file mode 100644
index e8223b7ed0a6e22f20fa637edc0e0507bdc10303..0000000000000000000000000000000000000000
--- a/spaces/nateraw/deepafx-st/deepafx_st/processors/autodiff/signal.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import math
-import torch
-from typing import List
-
-
-def butter(fc, fs: float = 2.0):
-    """
-
-    Recall Butterworth polynomials
-    N = 1   s + 1
-    N = 2   s^2 + sqrt(2s) + 1
-    N = 3   (s^2 + s + 1)(s + 1)
-    N = 4   (s^2 + 0.76536s + 1)(s^2 + 1.84776s + 1)
-
-    Scaling
-    LP to LP:   s -> s/w_c
-    LP to HP:   s -> w_c/s
-
-    Bilinear transform:
-    s = 2/T_d * (1 - z^-1)/(1 + z^-1)
-
-    For 1-pole butterworth lowpass
-
-    1 / (s + 1)     1-pole prototype
-    1 / (s/w_c + 1)  LP to LP
-    1 / (2/T_d * (1 - z^-1)/(1 + z^-1))/w_c + 1)  Bilinear transform
-
-    """
-
-    # apply pre-warping to the cutoff
-    T_d = 1 / fs
-    w_d = (2 * math.pi * fc) / fs
-    #    sys.exit()
-    w_c = (2 / T_d) * torch.tan(w_d / 2)
-
-    a0 = 2 + (T_d * w_c)
-    a1 = (T_d * w_c) - 2
-    b0 = T_d * w_c
-    b1 = T_d * w_c
-
-    b = torch.stack([b0, b1], dim=0).view(-1)
-    a = torch.stack([a0, a1], dim=0).view(-1)
-
-    # normalize
-    b = b.type_as(fc) / a0
-    a = a.type_as(fc) / a0
-
-    return b, a
-
-
-def biqaud(
-    gain_dB: torch.Tensor,
-    cutoff_freq: torch.Tensor,
-    q_factor: torch.Tensor,
-    sample_rate: float,
-    filter_type: str = "peaking",
-):
-
-    # convert inputs to Tensors if needed
-    # gain_dB = torch.tensor([gain_dB])
-    # cutoff_freq = torch.tensor([cutoff_freq])
-    # q_factor = torch.tensor([q_factor])
-
-    A = 10 ** (gain_dB / 40.0)
-    w0 = 2 * math.pi * (cutoff_freq / sample_rate)
-    alpha = torch.sin(w0) / (2 * q_factor)
-    cos_w0 = torch.cos(w0)
-    sqrt_A = torch.sqrt(A)
-
-    if filter_type == "high_shelf":
-        b0 = A * ((A + 1) + (A - 1) * cos_w0 + 2 * sqrt_A * alpha)
-        b1 = -2 * A * ((A - 1) + (A + 1) * cos_w0)
-        b2 = A * ((A + 1) + (A - 1) * cos_w0 - 2 * sqrt_A * alpha)
-        a0 = (A + 1) - (A - 1) * cos_w0 + 2 * sqrt_A * alpha
-        a1 = 2 * ((A - 1) - (A + 1) * cos_w0)
-        a2 = (A + 1) - (A - 1) * cos_w0 - 2 * sqrt_A * alpha
-    elif filter_type == "low_shelf":
-        b0 = A * ((A + 1) - (A - 1) * cos_w0 + 2 * sqrt_A * alpha)
-        b1 = 2 * A * ((A - 1) - (A + 1) * cos_w0)
-        b2 = A * ((A + 1) - (A - 1) * cos_w0 - 2 * sqrt_A * alpha)
-        a0 = (A + 1) + (A - 1) * cos_w0 + 2 * sqrt_A * alpha
-        a1 = -2 * ((A - 1) + (A + 1) * cos_w0)
-        a2 = (A + 1) + (A - 1) * cos_w0 - 2 * sqrt_A * alpha
-    elif filter_type == "peaking":
-        b0 = 1 + alpha * A
-        b1 = -2 * cos_w0
-        b2 = 1 - alpha * A
-        a0 = 1 + (alpha / A)
-        a1 = -2 * cos_w0
-        a2 = 1 - (alpha / A)
-    else:
-        raise ValueError(f"Invalid filter_type: {filter_type}.")
-
-    b = torch.stack([b0, b1, b2], dim=0).view(-1)
-    a = torch.stack([a0, a1, a2], dim=0).view(-1)
-
-    # normalize
-    b = b.type_as(gain_dB) / a0
-    a = a.type_as(gain_dB) / a0
-
-    return b, a
-
-
-def freqz(b, a, n_fft: int = 512):
-
-    B = torch.fft.rfft(b, n_fft)
-    A = torch.fft.rfft(a, n_fft)
-
-    H = B / A
-
-    return H
-
-
-def freq_domain_filter(x, H, n_fft):
-
-    X = torch.fft.rfft(x, n_fft)
-
-    # move H to same device as input x
-    H = H.type_as(X)
-
-    Y = X * H
-
-    y = torch.fft.irfft(Y, n_fft)
-
-    return y
-
-
-def approx_iir_filter(b, a, x):
-    """Approimxate the application of an IIR filter.
-
-    Args:
-        b (Tensor): The numerator coefficients.
-
-    """
-
-    # round up to nearest power of 2 for FFT
-    # n_fft = 2 ** math.ceil(math.log2(x.shape[-1] + x.shape[-1] - 1))
-
-    n_fft = 2 ** torch.ceil(torch.log2(torch.tensor(x.shape[-1] + x.shape[-1] - 1)))
-    n_fft = n_fft.int()
-
-    # move coefficients to same device as x
-    b = b.type_as(x).view(-1)
-    a = a.type_as(x).view(-1)
-
-    # compute complex response
-    H = freqz(b, a, n_fft=n_fft).view(-1)
-
-    # apply filter
-    y = freq_domain_filter(x, H, n_fft)
-
-    # crop
-    y = y[: x.shape[-1]]
-
-    return y
-
-
-def approx_iir_filter_cascade(
-    b_s: List[torch.Tensor],
-    a_s: List[torch.Tensor],
-    x: torch.Tensor,
-):
-    """Apply a cascade of IIR filters.
-
-    Args:
-        b (list[Tensor]): List of tensors of shape (3)
-        a (list[Tensor]): List of tensors of (3)
-        x (torch.Tensor): 1d Tensor.
-    """
-
-    if len(b_s) != len(a_s):
-        raise RuntimeError(
-            f"Must have same number of coefficients. Got b: {len(b_s)} and a: {len(a_s)}."
-        )
-
-    # round up to nearest power of 2 for FFT
-    # n_fft = 2 ** math.ceil(math.log2(x.shape[-1] + x.shape[-1] - 1))
-    n_fft = 2 ** torch.ceil(torch.log2(torch.tensor(x.shape[-1] + x.shape[-1] - 1)))
-    n_fft = n_fft.int()
-
-    # this could be done in parallel
-    b = torch.stack(b_s, dim=0).type_as(x)
-    a = torch.stack(a_s, dim=0).type_as(x)
-
-    H = freqz(b, a, n_fft=n_fft)
-    H = torch.prod(H, dim=0).view(-1)
-
-    # apply filter
-    y = freq_domain_filter(x, H, n_fft)
-
-    # crop
-    y = y[: x.shape[-1]]
-
-    return y
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download The Shaadi Karke Phas Gaya Yaar Dual Audio Utorrent Free ((TOP)).md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download The Shaadi Karke Phas Gaya Yaar Dual Audio Utorrent Free ((TOP)).md
deleted file mode 100644
index 4329967275ff51b7ad8f0c46277a2edd1f1caecd..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Download The Shaadi Karke Phas Gaya Yaar Dual Audio Utorrent Free ((TOP)).md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<br />
-<h1>How to Download Shaadi Karke Phas Gaya Yaar Dual Audio Utorrent Free</h1>
-<p>Shaadi Karke Phas Gaya Yaar is a 2006 Bollywood romantic comedy film starring Salman Khan and Shilpa Shetty as a married couple who face various challenges in their relationship. The film was directed by K.S. Adhiyaman and was a remake of the Tamil film Priyasakhi. The film received mixed reviews from critics and was a box office flop.</p>
-<h2>download the Shaadi Karke Phas Gaya Yaar dual audio utorrent free</h2><br /><p><b><b>Download Zip</b> &#10042;&#10042;&#10042; <a href="https://urlcod.com/2uIcqL">https://urlcod.com/2uIcqL</a></b></p><br /><br />
-<p>If you are a fan of Salman Khan and Shilpa Shetty, or if you enjoy watching light-hearted movies with some drama and humor, you might want to download Shaadi Karke Phas Gaya Yaar dual audio utorrent free. Utorrent is a popular software that allows you to download files from peer-to-peer networks. Dual audio means that you can choose between two languages for the audio track, such as Hindi and English.</p>
-<p>To download Shaadi Karke Phas Gaya Yaar dual audio utorrent free, you will need to follow these steps:</p>
-<ol>
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-<p></p>
-
-<p>Shaadi Karke Phas Gaya Yaar is a film that explores the issues of marriage, love, and compatibility. The film revolves around Ayaan, a wealthy garage owner who falls in love with Ahana, a beautiful model. He tricks her into marrying him by using her diary that she left at his garage. However, their marriage soon turns sour as they realize that they have different expectations and values. Ahana wants to pursue her career and have an independent lifestyle, while Ayaan wants her to be a traditional housewife and mother. They also face pressure from their families and society. The film shows how they cope with their conflicts and try to save their marriage.</p>
-<p>The film has some funny moments and emotional scenes that will keep you entertained. The chemistry between Salman Khan and Shilpa Shetty is also commendable. The songs of the film are composed by Sajid-Wajid and Daboo Malik, and are catchy and melodious. Some of the popular songs are "Tumko Dulhan Banayenge", "Deewane Dil Ko Jaane Ja", and "Shaadi Karke Phas Gaya Yaar". The film also has some supporting actors who add to the humor and drama, such as Reema Lagoo, Mohnish Behl, Shakti Kapoor, and Aashif Sheikh.</p>
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-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Filebandit Survey Bypasser [BEST].md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Filebandit Survey Bypasser [BEST].md
deleted file mode 100644
index 2eec8d3d2d751dbf6060fcd1603f3f0d907a25d7..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Filebandit Survey Bypasser [BEST].md	
+++ /dev/null
@@ -1,28 +0,0 @@
-<br />
-<h1>How to Bypass Filebandit Surveys and Download Files Easily</h1>
-<p>If you are looking for a way to bypass filebandit surveys and download files without completing annoying offers, you are not alone. Many people face this problem when they want to access premium content from file sharing sites like filebandit.net. But don't worry, there are some solutions that can help you bypass filebandit surveys and download files easily.</p>
-<h2>filebandit survey bypasser</h2><br /><p><b><b>Download File</b> &#9658; <a href="https://urlcod.com/2uIbbq">https://urlcod.com/2uIbbq</a></b></p><br /><br />
-<p>In this article, we will show you some of the best survey remover tools that can help you bypass filebandit surveys and other similar surveys online. These tools are easy to use and can save you a lot of time and frustration. So, let's get started!</p>
-
-<h2>What are Filebandit Surveys and Why Do They Exist?</h2>
-<p>Filebandit surveys are a type of online surveys that pop up when you try to download a file from filebandit.net or other similar sites. These surveys ask you to complete various tasks, such as filling out forms, watching videos, downloading apps, etc. in order to unlock the download link.</p>
-<p>The reason why these surveys exist is because file sharing sites like filebandit.net use them as a source of income. They get paid by advertisers for every survey completed by the users. This way, they can offer free hosting and uploading services to their users.</p>
-<p>However, these surveys can be very annoying and time-consuming for the users who just want to download their files quickly and easily. Some of these surveys may even contain malware or viruses that can harm your computer or device. That's why many users look for ways to bypass filebandit surveys and other similar surveys online.</p>
-<p></p>
-
-<h2>How to Bypass Filebandit Surveys and Download Files Easily?</h2>
-<p>There are two main ways to bypass filebandit surveys and download files easily: using online survey remover tools or using offline survey remover software. Let's take a look at each of these methods in detail.</p>
-
-<h3>Using Online Survey Remover Tools</h3>
-<p>Online survey remover tools are websites that can help you bypass filebandit surveys and other similar surveys online by entering the URL of the survey page and generating a direct download link for you. These tools are very convenient and easy to use, as you don't need to download or install anything on your computer or device.</p>
-<p>Some of the best online survey remover tools that can help you bypass filebandit surveys and other similar surveys online are:</p>
-<ul>
-<li><strong>Survey Bypass</strong>: This is one of the most popular and reliable online survey remover tools that can help you bypass filebandit surveys and other similar surveys online. To use this tool, simply visit <a href="http://www.surveybypass.com/">http://www.surveybypass.com/</a>, enter the URL of the survey page, and press the Go button. In no time, any survey available on the website will be bypassed and you will be provided with the direct download link for your file.[^1^]</li>
-<li><strong>Surveys Remover</strong>: This is another great online survey remover tool that can help you bypass filebandit surveys and other similar surveys online. To use this tool, simply visit <a href="http://www.surveysremover.com/">http://www.surveysremover.com/</a>, enter the URL of the survey page, and press the Download button. In no time, any survey available on the website will be removed and you will be able to download your file without any hassle.[^2^]</li>
-<li><strong>Bypass Survey</strong>: This is another useful online survey remover tool that can help you bypass filebandit surveys and other similar surveys online. To use this tool, simply visit <a href="http://bypasssurvey.com/">http://bypasssurvey.com/</a>, enter the URL of the survey page, and press the Bypass Survey button. In no time, any survey available on the website will be skipped and you will be able to access your file without any problem.[^3^]</li>
-</ul>
-
-<h3>Using Offline Survey Remover Software</h3>
-<p>Offline survey remover software are programs that you need to download and install on your computer or device in order to bypass filebandit surveys and</p> 7b8c122e87<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Flysimware Beech 18 For Fsx Torrent Tpb Cradle Ubersetzten R.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Flysimware Beech 18 For Fsx Torrent Tpb Cradle Ubersetzten R.md
deleted file mode 100644
index eb44b591e004715c953d93d5ecdde740c6a30d03..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Flysimware Beech 18 For Fsx Torrent Tpb Cradle Ubersetzten R.md	
+++ /dev/null
@@ -1,13 +0,0 @@
-<br />
-<h1>Flysimware Beech 18: A Classic Aircraft for FSX</h1>
-<p>If you are looking for a realistic and detailed simulation of a vintage aircraft, you might want to check out Flysimware's Beech 18 for FSX. This add-on recreates the iconic twin-engine aircraft that was used for various purposes, such as passenger transport, cargo delivery, military operations, and aerial photography. The Beech 18 was produced from 1937 to 1970, and more than 9,000 units were built.</p>
-<h2>Flysimware Beech 18 For Fsx Torrent Tpb cradle ubersetzten r</h2><br /><p><b><b>Download</b> &#128505; <a href="https://urlcod.com/2uIbhb">https://urlcod.com/2uIbhb</a></b></p><br /><br />
-<p>Flysimware's Beech 18 for FSX features a high-quality 3D model with custom animations, dynamic shadows, and realistic reflections. The cockpit is fully functional and interactive, with custom gauges, switches, levers, and indicators. You can also choose from different variants and liveries of the Beech 18, such as the C-45 military version, the D-18S passenger version, and the Volpar Tri-Gear conversion. The add-on also includes custom sounds, effects, and flight dynamics that match the real aircraft's performance and behavior.</p>
-<p>However, Flysimware's Beech 18 for FSX is not available on any official online store or platform. The only way to get it is to download it from a torrent site, such as The Pirate Bay (TPB). This is a risky and illegal method that may expose your computer to viruses, malware, or legal issues. Therefore, we do not recommend or endorse this option. If you want to enjoy Flysimware's Beech 18 for FSX safely and legally, you should wait for the official release or purchase it from a reputable source.</p>
-
-<p>The Beech 18 prototype first flew on January 15, 1937. [^1^] The aircraft has used a variety of engines and has had a number of airframe modifications to increase gross weight and speed. At least one aircraft was modified to a 600-hp (447-kW) Pratt & Whitney R-1340 powerplant configuration. [^1^] The Beech 18 was originally designed as a civilian aircraft, but it soon attracted the attention of the military. During and after World War II, over 4,500 Beech 18s were used in military service under different designations, such as the C-45 Expeditor, the AT-7 Navigator, and the AT-11 Kansan. The Beech 18 served as a light transport, a light bomber, an aircrew trainer, a photo-reconnaissance aircraft, and a "mother ship" for target drones. [^1^] [^2^]</p>
-<p></p>
-<p>After the war, the Beech 18 became a popular "business aircraft" and "feeder airliner". It was used by many airlines, corporations, charter operators, and private owners around the world. It was also adapted for various civilian uses, such as aerial spraying, sterile insect release, fish stocking, dry-ice cloud seeding, aerial firefighting, air-mail delivery, ambulance service, movie productions, skydiving, freight delivery, weapon- and drug-smuggling, engine testbed, skywriting, banner towing, and stunt aircraft. [^1^] [^2^] The Beech 18 was also flown by many famous people, such as Howard Hughes, Charles Lindbergh, Arthur Godfrey, Tony Hulman, and Elvis Presley. [^2^]</p>
-<p>The Beech 18 was manufactured continuously from 1937 to November 1969 (over 32 years), making it one of the world's most widely used light aircraft. Over 9,000 units were built in total. The Beech 18 was also produced under license in Japan by Shin Meiwa (later ShinMaywa) and in Canada by Canadian Car & Foundry (later Canadair). Some Beech 18s were also converted to seaplanes or tricycle landing gear. The Beech 18 is still flown today by many enthusiasts and collectors who appreciate its classic design and performance. Some Beech 18s are also preserved in museums or displayed as public art. [^1^] [^2^]</p> 81aa517590<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Irmc S2 License Keyrar.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Irmc S2 License Keyrar.md
deleted file mode 100644
index f1d65ae00af45919535280789881ee7e55bcd84f..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Irmc S2 License Keyrar.md	
+++ /dev/null
@@ -1,113 +0,0 @@
-
-<table>
-<tr>
-<td>
- Article with HTML formatting </td>
-</tr>
-<tr>
-<td>
- <h1>How to Install irmc s2 license key.rar on Your Fujitsu PRIMERGY Server</h1>
-<p>If you have a Fujitsu PRIMERGY server, you might want to install irmc s2 license key.rar on it. This file contains the license key for the iRMC S2 advanced pack, which enables graphical console redirection and remote storage via USB on your server. In this article, we will explain what irmc s2 license key.rar is, how to download it, how to upload it to your server, and how to use its advanced features.</p>
-<h2>What is irmc s2 license key.rar?</h2>
-<p>Before we get into how to install irmc s2 license key.rar on your server, let's first understand what it is and why you might need it.</p>
-<h2>Irmc S2 License Keyrar</h2><br /><p><b><b>DOWNLOAD</b> &#9989; <a href="https://urlcod.com/2uIbVz">https://urlcod.com/2uIbVz</a></b></p><br /><br />
-<h3>What is iRMC S2?</h3>
-<p>iRMC S2 stands for integrated Remote Management Controller version 2. It is a chip that is embedded in your Fujitsu PRIMERGY server that allows you to control and monitor your server remotely, regardless of its system status . You can access the iRMC S2 web interface by entering its IP address in your browser. From there, you can perform various tasks such as power on/off or reset your server, view system information and logs, configure settings and alerts <h3>What is irmc s2 license key.rar?</h3>
-<p>irmc s2 license key.rar is a compressed file that contains the license key for the iRMC S2 advanced pack. The iRMC S2 advanced pack is an optional feature that unlocks the graphical console redirection and remote storage via USB functionalities of iRMC S2 . These features allow you to access and control your server's graphical user interface (GUI) remotely, as well as mount a USB device or an ISO image on your server. This can be very useful for troubleshooting, installing, or updating your server's operating system or applications.</p>
-<h3>What are the benefits of installing irmc s2 license key.rar?</h3>
-<p>By installing irmc s2 license key.rar on your server, you can enjoy the following benefits:</p>
-<ul>
-<li>You can access and control your server's GUI remotely, without the need for a physical monitor, keyboard, or mouse. This can save you time and money, especially if your server is located in a remote or inaccessible location.</li>
-<li>You can mount a USB device or an ISO image on your server remotely, without the need for a physical CD/DVD drive or USB port. This can help you install or update your server's operating system or applications faster and easier.</li>
-<li>You can improve the security and reliability of your server, as you can monitor and manage it from anywhere, anytime. You can also receive alerts and notifications from iRMC S2 in case of any issues or failures.</li>
-</ul>
-<h2>How to Download irmc s2 license key.rar?</h2>
-<p>Now that you know what irmc s2 license key.rar is and why you might want to install it on your server, let's see how to download it.</p>
-<h3>Where to find irmc s2 license key.rar?</h3>
-<p>You can find irmc s2 license key.rar on the Fujitsu website . You will need to register and log in to access the download page. Once you are there, you will need to enter the serial number of your server and agree to the terms and conditions. Then, you will be able to download irmc s2 license key.rar as a zip file.</p>
-<h3>How to extract irmc s2 license key.rar?</h3>
-<p>After you have downloaded irmc s2 license key.rar as a zip file, you will need to extract it using a software such as WinRAR  or 7-Zip . To do this, follow these steps:</p>
-<ol>
-<li>Right-click on the zip file and select "Extract Here" or "Extract to irmc_s2_license_key/"</li>
-<li>A folder named "irmc_s2_license_key" will be created in the same location as the zip file</li>
-<li>Open the folder and you will see a file named "irmc_s2_license.key"</li>
-<li>This is the license key file that you will need to upload to your server</li>
-</ol> <h2>How to Upload irmc s2 license key.rar to Your Server?</h2>
-<p>Once you have extracted irmc s2 license key.rar and obtained the license key file, you will need to upload it to your server. To do this, you will need a USB device and access to the iRMC S2 web interface. Here are the steps to follow:</p>
-<h3>How to copy irmc_s2_license.key to a USB device?</h3>
-<p>You will need to copy the license key file to a USB device that is formatted in FAT32 or NTFS. To do this, follow these steps:</p>
-<p></p>
-<ol>
-<li>Insert the USB device into your computer</li>
-<li>Open the folder where you extracted irmc s2 license key.rar</li>
-<li>Copy the file irmc_s2_license.key</li>
-<li>Paste it into the root directory of your USB device</li>
-<li>Eject the USB device safely from your computer</li>
-</ol>
-<h3>How to connect the USB device to your server?</h3>
-<p>You will need to connect the USB device to your server's front or rear USB port. To do this, follow these steps:</p>
-<ol>
-<li>Power off your server if it is running</li>
-<li>Insert the USB device into one of the USB ports on your server</li>
-<li>Power on your server and wait for it to boot up</li>
-</ol>
-<h3>How to access the iRMC S2 web interface?</h3>
-<p>You will need to access the iRMC S2 web interface using a browser on your computer. To do this, follow these steps:</p>
-<ol>
-<li>Find out the IP address of iRMC S2 on your server. You can do this by checking the sticker on your server, using the iRMC S2 configuration utility, or using a network scanner tool </li>
-<li>Open your browser and enter the IP address of iRMC S2 in the address bar</li>
-<li>You will see a login page for the iRMC S2 web interface. Enter the default username and password, which are "admin" and "admin" respectively </li>
-<li>You will see the main page of the iRMC S2 web interface. You can navigate through different menus and tabs to view and configure various settings and features of iRMC S2</li>
-</ol> <h3>How to upload and activate irmc_s2_license.key?</h3>
-<p>You will need to upload and activate the license key file using the iRMC S2 web interface. To do this, follow these steps:</p>
-<ol>
-<li>On the main page of the iRMC S2 web interface, click on the "Configuration" tab</li>
-<li>On the left menu, click on "License Management"</li>
-<li>On the right panel, click on the "Browse" button and select the irmc_s2_license.key file from your USB device</li>
-<li>Click on the "Upload" button and wait for the file to be uploaded</li>
-<li>You will see a message saying "License file uploaded successfully"</li>
-<li>Click on the "Activate" button and wait for the license to be activated</li>
-<li>You will see a message saying "License activated successfully"</li>
-<li>Click on the "OK" button and refresh the page</li>
-<li>You will see that the iRMC S2 advanced pack features are now enabled and available for use</li>
-</ol>
-<h2>How to Use the Advanced Features of iRMC S2?</h2>
-<p>Now that you have installed and activated irmc s2 license key.rar on your server, you can use the advanced features of iRMC S2. Here are some examples of how to use them:</p>
-<h3>How to access and control your server's GUI remotely?</h3>
-<p>You can access and control your server's GUI remotely using the graphical console redirection feature of iRMC S2. This feature allows you to see and interact with your server's screen as if you were sitting in front of it. To do this, follow these steps:</p>
-<ol>
-<li>On the main page of the iRMC S2 web interface, click on the "Remote Control" tab</li>
-<li>On the left menu, click on "Graphical Console Redirection"</li>
-<li>On the right panel, click on the "Start" button and wait for the connection to be established</li>
-<li>You will see a new window pop up with your server's screen displayed</li>
-<li>You can use your mouse and keyboard to control your server's GUI remotely</li>
-<li>You can also use the toolbar at the top of the window to perform various actions such as power on/off or reset your server, send keyboard shortcuts, adjust screen settings, or take screenshots</li>
-<li>To end the graphical console redirection session, click on the "Stop" button on the right panel or close the window</li>
-</ol> <h3>How to mount a USB device or an ISO image on your server?</h3>
-<p>You can mount a USB device or an ISO image on your server remotely using the remote storage via USB feature of iRMC S2. This feature allows you to access the files and folders on your USB device or ISO image as if they were on your server's local drive. To do this, follow these steps:</p>
-<ol>
-<li>On the main page of the iRMC S2 web interface, click on the "Remote Control" tab</li>
-<li>On the left menu, click on "Remote Storage via USB"</li>
-<li>On the right panel, click on the "Browse" button and select the USB device or the ISO image from your computer</li>
-<li>Click on the "Mount" button and wait for the device or image to be mounted</li>
-<li>You will see a message saying "Remote storage mounted successfully"</li>
-<li>You can now access the files and folders on your USB device or ISO image from your server's GUI remotely</li>
-<li>To unmount the device or image, click on the "Unmount" button and wait for the process to complete</li>
-<li>You will see a message saying "Remote storage unmounted successfully"</li>
-</ol>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to install irmc s2 license key.rar on your Fujitsu PRIMERGY server. We have explained what irmc s2 license key.rar is, how to download it, how to upload it to your server, and how to use its advanced features. By installing irmc s2 license key.rar, you can access and control your server's GUI remotely, mount a USB device or an ISO image on your server, and improve the security and reliability of your server. We hope you have found this article helpful and informative. If you have any questions or feedback, please feel free to contact us.</p>
-<h3>FAQs</h3>
-<p>Here are some frequently asked questions about irmc s2 license key.rar and iRMC S2:</p>
-<h4>What is the default username and password for iRMC S2 web interface?</h4>
-<p>The default username and password for iRMC S2 web interface are "admin" and "admin" respectively. You can change them by going to Configuration > User Management > User Accounts on the iRMC S2 web interface.</p>
-<h4>How can I change the IP address of iRMC S2?</h4>
-<p>You can change the IP address of iRMC S2 by going to Configuration > Network > IPv4 Settings on the iRMC S2 web interface. You can also use the iRMC S2 configuration utility or a network scanner tool to change the IP address of iRMC S2.</p>
-<h4>How can I monitor the health and performance of my server using iRMC S2?</h4>
-<p>You can monitor the health and performance of your server using iRMC S2 by going to Status > System Status > System Information on the iRMC S2 web interface. You can also view system logs, sensor readings, event notifications, and power consumption data on iRMC S2.</p>
-<h4>How can I receive alerts and notifications from iRMC S2?</h4>
-<p>You can receive alerts and notifications from iRMC S2 by going to Configuration > Event Notification > Notification Settings on the iRMC S2 web interface. You can configure various options such as email, SNMP, syslog, or SMS for receiving alerts and notifications from iRMC S2.</p>
-<h4>How can I reset or update iRMC S2 firmware?</h4>
-<p>You can reset or update iRMC S2 firmware by going to Maintenance > Firmware Update > Firmware Update on the iRMC S2 web interface. You can also use the Fujitsu ServerView Suite DVD or a bootable USB device to reset or update iRMC S2 firmware.</p> b2dd77e56b<br />
-<br />
-<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Lacie D2 Dvd Driver Mac !!TOP!!.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Lacie D2 Dvd Driver Mac !!TOP!!.md
deleted file mode 100644
index 940a56f88d29362f73b74765e5b0e4a8bc919d82..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Lacie D2 Dvd Driver Mac !!TOP!!.md	
+++ /dev/null
@@ -1,33 +0,0 @@
-
-<h1>How to Install and Use LaCie d2 DVD+RW with LightScribe on Mac</h1>
-<p>LaCie d2 DVD+RW with LightScribe is a desktop DVD burner that can write and read DVDs and CDs, as well as create labels on LightScribe discs using a laser. It is compatible with Mac OS X 10.3.9 or later, and requires a USB 2.0 or FireWire port. Here are the steps to install and use this device on your Mac:</p>
-<h2>Lacie D2 Dvd Driver Mac</h2><br /><p><b><b>Download Zip</b> &rarr;&rarr;&rarr; <a href="https://urlcod.com/2uIa7I">https://urlcod.com/2uIa7I</a></b></p><br /><br />
-<ol>
-<li>Connect the power adapter to the LaCie d2 DVD+RW and plug it into a power outlet.</li>
-<li>Connect the USB or FireWire cable to the LaCie d2 DVD+RW and to your Mac.</li>
-<li>The device should be automatically recognized by your Mac and appear on your desktop as a disc icon.</li>
-<li>If you want to use LightScribe to create labels on your discs, you need to download and install the LightScribe System Software from <a href="https://www.lightscribe.com/downloadSection/mac/index.aspx">https://www.lightscribe.com/downloadSection/mac/index.aspx</a>.</li>
-<li>You also need to download and install a LightScribe labeling software of your choice from <a href="https://www.lightscribe.com/downloadsection/mac/LabelingSoftware/index.aspx">https://www.lightscribe.com/downloadsection/mac/LabelingSoftware/index.aspx</a>. There are several options available, such as LaCie LightScribe Labeler, Disc Cover, or SureThing CD/DVD Labeler.</li>
-<li>To burn data or video files to a DVD or CD, you can use the built-in Finder or Disk Utility applications on your Mac, or any other third-party software that supports DVD burning.</li>
-<li>To create a label on a LightScribe disc, you need to insert a LightScribe-compatible disc into the LaCie d2 DVD+RW with the label side down. Then, launch your LightScribe labeling software and follow the instructions to design and print your label. The label will be burned onto the disc by the laser in the device.</li>
-</ol>
-<p>For more information, please refer to the user manual of the LaCie d2 DVD+RW with LightScribe[^1^] or visit the LaCie support website at <a href="https://www.lacie.com/support/">https://www.lacie.com/support/</a>.</p><p>Here are some tips and tricks to get the most out of your LaCie d2 DVD+RW with LightScribe:</p>
-<ul>
-<li>Make sure you use high-quality discs that are compatible with your device and your Mac. You can check the compatibility list at <a href="https://www.lacie.com/support/dvd/desktop-dvd-rw/16x-lightscribe/#compatibility">https://www.lacie.com/support/dvd/desktop-dvd-rw/16x-lightscribe/#compatibility</a>.</li>
-<li>Do not touch the label side of the LightScribe discs, as this may damage the coating and affect the quality of the label. Use a soft cloth to wipe off any dust or fingerprints before labeling.</li>
-<li>Adjust the contrast and brightness settings of your LightScribe labeling software to get the best results. You can also choose different modes of printing, such as draft, normal, or best, depending on the quality and speed you prefer.</li>
-<li>Store your LightScribe discs in a cool, dry place away from direct sunlight and heat sources. Exposure to light and heat may cause the label to fade over time.</li>
-<li>If you encounter any problems with your device, such as not being recognized by your Mac, not ejecting discs, or making unusual noises, try the following troubleshooting steps:</li>
-<ul>
-<li>Check the power and data connections and make sure they are secure.</li>
-<li>Restart your Mac and try again.</li>
-<li>Update your Mac OS X and your device firmware to the latest versions.</li>
-<li>Use a different USB or FireWire port or cable.</li>
-<li>Use a different disc or try cleaning the disc with a soft cloth.</li>
-<li>Contact LaCie customer support at <a href="https://www.lacie.com/support/contact/">https://www.lacie.com/support/contact/</a> if the problem persists.</li>
-</ul>
-</ul>
-<p>We hope you enjoy using your LaCie d2 DVD+RW with LightScribe and creating amazing labels for your discs. Thank you for choosing LaCie!</p>
-<p></p> cec2833e83<br />
-<br />
-<br />
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diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/PointSup/point_sup/register_point_annotations.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/PointSup/point_sup/register_point_annotations.py
deleted file mode 100644
index 32f2bb45e864e5be9d002f4d07badb91700ace4b..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/PointSup/point_sup/register_point_annotations.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import logging
-import os
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets.builtin import _get_builtin_metadata
-from detectron2.data.datasets.coco import load_coco_json
-
-logger = logging.getLogger(__name__)
-
-
-# COCO dataset
-def register_coco_instances_with_points(name, metadata, json_file, image_root):
-    """
-    Register a dataset in COCO's json annotation format for
-    instance segmentation with point annotation.
-
-    The point annotation json does not have "segmentation" field, instead,
-    it has "point_coords" and "point_labels" fields.
-
-    Args:
-        name (str): the name that identifies a dataset, e.g. "coco_2014_train".
-        metadata (dict): extra metadata associated with this dataset.  You can
-            leave it as an empty dict.
-        json_file (str): path to the json instance annotation file.
-        image_root (str or path-like): directory which contains all the images.
-    """
-    assert isinstance(name, str), name
-    assert isinstance(json_file, (str, os.PathLike)), json_file
-    assert isinstance(image_root, (str, os.PathLike)), image_root
-    # 1. register a function which returns dicts
-    DatasetCatalog.register(
-        name, lambda: load_coco_json(json_file, image_root, name, ["point_coords", "point_labels"])
-    )
-
-    # 2. Optionally, add metadata about this dataset,
-    # since they might be useful in evaluation, visualization or logging
-    MetadataCatalog.get(name).set(
-        json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata
-    )
-
-
-_PREDEFINED_SPLITS_COCO = {}
-_PREDEFINED_SPLITS_COCO["coco"] = {
-    # point annotations without masks
-    "coco_2017_train_points_n10_v1_without_masks": (
-        "coco/train2017",
-        "coco/annotations/instances_train2017_n10_v1_without_masks.json",
-    ),
-}
-
-
-def register_all_coco_train_points(root):
-    for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_COCO.items():
-        for key, (image_root, json_file) in splits_per_dataset.items():
-            # Assume pre-defined datasets live in `./datasets`.
-            register_coco_instances_with_points(
-                key,
-                _get_builtin_metadata(dataset_name),
-                os.path.join(root, json_file) if "://" not in json_file else json_file,
-                os.path.join(root, image_root),
-            )
-
-
-# True for open source;
-# Internally at fb, we register them elsewhere
-if __name__.endswith(".register_point_annotations"):
-    _root = os.getenv("DETECTRON2_DATASETS", "datasets")
-    register_all_coco_train_points(_root)
diff --git a/spaces/noamelata/Nested-Diffusion/NestedScheduler.py b/spaces/noamelata/Nested-Diffusion/NestedScheduler.py
deleted file mode 100644
index 43fa6d9fd84752071efae0b2e093266e3faba6ad..0000000000000000000000000000000000000000
--- a/spaces/noamelata/Nested-Diffusion/NestedScheduler.py
+++ /dev/null
@@ -1,180 +0,0 @@
-
-from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-from diffusers import DDIMScheduler
-
-from diffusers.utils import BaseOutput
-
-
-@dataclass
-class NestedSchedulerOutput(BaseOutput):
-    """
-    Output class for the scheduler's step function output.
-
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
-            `pred_original_sample` can be used to preview progress or for guidance.
-    """
-
-    prev_sample: torch.FloatTensor
-    pred_original_sample: Optional[torch.FloatTensor] = None
-
-
-
-class NestedScheduler(DDIMScheduler):
-
-    def set_timesteps(self, num_inference_steps: int, max_timestep: int = 1000, device: Union[str, torch.device] = None):
-        """
-        Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating figures with a pre-trained model.
-            max_timestep (`int`):
-                the highest timestep to use for choosing the timesteps
-        """
-
-        if num_inference_steps > self.config.num_train_timesteps:
-            raise ValueError(
-                f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
-                f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
-                f" maximal {self.config.num_train_timesteps} timesteps."
-            )
-
-        self.num_inference_steps = num_inference_steps
-        max_timestep = min(self.config.num_train_timesteps - 1, max_timestep)
-        timesteps = np.linspace(1, max_timestep, min(num_inference_steps, max_timestep)).round()[::-1].copy().astype(np.int64)
-        self.timesteps = torch.from_numpy(timesteps).to(device)
-
-    def step(
-        self,
-        model_output: torch.FloatTensor,
-        timestep: int,
-        sample: torch.FloatTensor,
-        eta: float = 0.0,
-        use_clipped_model_output: bool = False,
-        generator=None,
-        variance_noise: Optional[torch.FloatTensor] = None,
-        return_dict: bool = True,
-        override_prediction_type = '',
-    ) -> Union[NestedSchedulerOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`torch.FloatTensor`):
-                current instance of sample being created by diffusion process.
-            eta (`float`): weight of noise for added noise in diffusion step.
-            use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped
-                predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when
-                `self.config.clip_sample` is `True`. If no clipping has happened, "corrected" `model_output` would
-                coincide with the one provided as input and `use_clipped_model_output` will have not effect.
-            generator: random number generator.
-            variance_noise (`torch.FloatTensor`): instead of generating noise for the variance using `generator`, we
-                can directly provide the noise for the variance itself. This is useful for methods such as
-                CycleDiffusion. (https://arxiv.org/abs/2210.05559)
-            return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class
-
-        Returns:
-            [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:
-            [`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-
-        """
-        if self.num_inference_steps is None:
-            raise ValueError(
-                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
-        # Ideally, read DDIM paper in-detail understanding
-
-        # Notation (<variable name> -> <name in paper>
-        # - pred_noise_t -> e_theta(x_t, t)
-        # - pred_original_sample -> f_theta(x_t, t) or x_0
-        # - std_dev_t -> sigma_t
-        # - eta -> η
-        # - pred_sample_direction -> "direction pointing to x_t"
-        # - pred_prev_sample -> "x_t-1"
-
-        # 1. get previous step value (=t-1)
-        # prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
-        cur_idx = (self.timesteps == timestep).nonzero().item()
-        prev_timestep = self.timesteps[cur_idx + 1] if cur_idx < len(self.timesteps) - 1 else 0
-
-        # 2. compute alphas, betas
-        alpha_prod_t = self.alphas_cumprod[timestep]
-        alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
-
-        beta_prod_t = 1 - alpha_prod_t
-
-        # 3. compute predicted original sample from predicted noise also called
-        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        prediction_type = override_prediction_type if override_prediction_type else self.config.prediction_type
-        if prediction_type == "epsilon":
-            pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
-            pred_epsilon = model_output
-        elif prediction_type == "sample":
-            pred_original_sample = model_output
-            pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
-        elif prediction_type == "v_prediction":
-            pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
-            pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
-        else:
-            raise ValueError(
-                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
-                " `v_prediction`"
-            )
-
-        # 4. Clip or threshold "predicted x_0"
-        if self.config.thresholding:
-            pred_original_sample = self._threshold_sample(pred_original_sample)
-        elif self.config.clip_sample:
-            pred_original_sample = pred_original_sample.clamp(
-                -self.config.clip_sample_range, self.config.clip_sample_range
-            )
-
-        # 5. compute variance: "sigma_t(η)" -> see formula (16)
-        # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
-        variance = self._get_variance(timestep, prev_timestep)
-        std_dev_t = eta * variance ** (0.5)
-
-        if use_clipped_model_output:
-            # the pred_epsilon is always re-derived from the clipped x_0 in Glide
-            pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
-
-        # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon
-
-        # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
-
-        if eta > 0:
-            if variance_noise is not None and generator is not None:
-                raise ValueError(
-                    "Cannot pass both generator and variance_noise. Please make sure that either `generator` or"
-                    " `variance_noise` stays `None`."
-                )
-
-            if variance_noise is None:
-                variance_noise = torch.randn(
-                    model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype
-                )
-            variance = std_dev_t * variance_noise
-
-            prev_sample = prev_sample + variance
-
-        if not return_dict:
-            return (prev_sample,)
-
-        return NestedSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
diff --git a/spaces/nosdigitalmedia/dutch-youth-comment-classifier/README.md b/spaces/nosdigitalmedia/dutch-youth-comment-classifier/README.md
deleted file mode 100644
index a2a9f690b933ff6060fa3d6cb4bc0fd9d7d9b4a7..0000000000000000000000000000000000000000
--- a/spaces/nosdigitalmedia/dutch-youth-comment-classifier/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Dutch Youth Comment Classifier
-emoji: ☑️
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.28.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/okeanos/uptimefactoryai/app.py b/spaces/okeanos/uptimefactoryai/app.py
deleted file mode 100644
index b02d7f6c997802a2ea038288aebbe49d9576b566..0000000000000000000000000000000000000000
--- a/spaces/okeanos/uptimefactoryai/app.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import gradio as gr
-import os 
-import sys
-from pathlib import Path
-from transformers import pipeline
-#pipe = pipeline('text-generation', model_id='Ar4ikov/gpt2-650k-stable-diffusion-prompt-generator')
-models = [
-    "Yntec/NovelAIRemix",
-    "Joeythemonster/anything-midjourney-v-4-1",
-    "prompthero/midjourney-v4-diffusion",
-    "stablediffusionapi/dreamshaper-v6", #239
-    "stablediffusionapi/disneypixar",
-    "emilianJR/epiCRealism",
-    "prompthero/openjourney",
-    "stablediffusionapi/realistic-vision-v20-2047",
-    "stablediffusionapi/wand-magic2",
-    "dwancin/memoji", #07.11
-    "stablediffusionapi/anime-model-v2",
-    "AIARTCHAN/MIX-Pro-V4",
-    "goofyai/3d_render_style_xl"
-]
-current_model = models[0]
-
-text_gen1=gr.Interface.load("models/Ar4ikov/gpt2-650k-stable-diffusion-prompt-generator")
-
-models2=[
-    gr.Interface.load(f"models/{models[0]}",live=True,preprocess=False),
-    gr.Interface.load(f"models/{models[1]}",live=True,preprocess=False),
-    gr.Interface.load(f"models/{models[2]}",live=True,preprocess=False),
-    gr.Interface.load(f"models/{models[3]}",live=True,preprocess=False),
-    gr.Interface.load(f"models/{models[4]}",live=True,preprocess=False),
-    gr.Interface.load(f"models/{models[5]}",live=True,preprocess=False),
-    gr.Interface.load(f"models/{models[6]}",live=True,preprocess=False),
-    gr.Interface.load(f"models/{models[7]}",live=True,preprocess=False),
-    gr.Interface.load(f"models/{models[8]}",live=True,preprocess=False),
-    gr.Interface.load(f"models/{models[9]}",live=True,preprocess=False),
-    gr.Interface.load(f"models/{models[10]}",live=True,preprocess=False),
-    gr.Interface.load(f"models/{models[11]}",live=True,preprocess=False),
-    gr.Interface.load(f"models/{models[12]}",live=True,preprocess=False),
-]
-
-   
-def text_it1(inputs,text_gen1=text_gen1):
-        go_t1=text_gen1(inputs)
-        return(go_t1)
-
-def set_model(current_model):
-    current_model = models[current_model]
-    return gr.update(label=(f"{current_model}"))
-
-
-def send_it1(inputs, model_choice):
-        proc1=models2[model_choice]
-        output1=proc1(inputs)
-        return(output1)
-css=""""""
-
-
-with gr.Blocks(css=css) as myface:
-    gr.HTML("""<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="twitter:card" content="player"/>
-    <meta name="twitter:site" content=""/>
-    <meta name="twitter:player:width" content="100%"/>
-    <meta name="twitter:player:height" content="600"/>    
-  </head>
-</html>
-""")
-    with gr.Row():
-        with gr.Tab("Title"):
-                gr.HTML("""    <title>A open-beta for precious people.</title><div style="text-align: center; max-width: 1500px; margin: 0 auto;">
-                                            """)
-
-        with gr.Tab("Description"):
-            gr.HTML("""<div style="text-align:center;">
-                       </div>""")     
-    with gr.Row():
-        with gr.Column(scale=100):
-            magic1 = gr.Textbox(lines=4)
-            gr.HTML("""<style>           .gr-button {
-            color: white !important;
-            border-color: #000000 !important;
-            background: #006699 !important;
-            }</style>""")
-            run = gr.Button("Generate Image")
-    with gr.Row():
-        with gr.Column(scale=100):
-            #Model selection dropdown    
-            model_name1 = gr.Dropdown(label="Select Model", choices=[m for m in models], type="index", value=current_model, interactive=True)
-    with gr.Row():
-        with gr.Column(style="width=800px"):
-            output1=gr.Image(label=(f"{current_model}"))
-                
-            
-    with gr.Row():
-        with gr.Column(scale=50):
-            input_text=gr.Textbox(label="Use this box to extend an idea automatically, by typing some words and clicking Extend Idea",lines=2)
-            see_prompts=gr.Button("Extend Idea")
-
-    def get_valid_prompt(text: str) -> str:
-      dot_split = text.split('.')[0]
-      n_split = text.split('\n')[0]
-
-      return {
-        len(dot_split) < len(n_split): dot_split,
-        len(n_split) > len(dot_split): n_split,
-        len(n_split) == len(dot_split): dot_split   
-      }[True]
-    def short_prompt(inputs):
-        return(inputs)
-    
-    model_name1.change(set_model,inputs=model_name1,outputs=[output1])
-    
-    run.click(send_it1, inputs=[magic1, model_name1], outputs=[output1])
-    
-    see_prompts.click(text_it1,inputs=[input_text],outputs=magic1)
-    #see_prompts.click(magic1=get_valid_prompt(pipe(input_text.value, max_length=77)[0]['generated_text']))
-
-     
-myface.queue(concurrency_count=200)
-myface.launch(inline=True, show_api=False, max_threads=400)
\ No newline at end of file
diff --git a/spaces/olive100/face_merge/app.py b/spaces/olive100/face_merge/app.py
deleted file mode 100644
index ad1a8b2d5672a76fe7c1063ce24153ed99eefaa6..0000000000000000000000000000000000000000
--- a/spaces/olive100/face_merge/app.py
+++ /dev/null
@@ -1,72 +0,0 @@
-import gradio as gr
-import requests
-import base64
-import cv2
-import json
-import numpy as np
-
-# token获取
-def get_token(client_id, client_secret):
-    # client_id为官网获取的API Key，client_secret为官网获取的Secret Key.下行client_id=后的马赛克为你的API Key，client_secret=后的马赛克为你的Secret Key
-    url = f"https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={client_id}&client_secret={client_secret}"  # 注意此处的&符号
-    response = requests.get(url)
-    resultJson = response.json()
-    return resultJson['access_token']
-
-def face_merge(image_template,image_target,steps):
-    success,encode_image_temp = cv2.imencode(".jpg",image_template) #注意这句编码，否则图像质量不合格
-    image_temp = base64.b64encode(encode_image_temp)
-    success,encode_image_targ = cv2.imencode(".jpg",image_target) #注意这句编码，否则图像质量不合格
-    image_targ = base64.b64encode(encode_image_targ)
-    request_url = 'https://aip.baidubce.com/rest/2.0/face/v1/merge'
-    access_token = get_token('gZBF8TaiLfewLu4XdWIVe5H1', 'O7zkY3cc1OYu2GHwqvsyxI4YaCgdRn7A')
-    request_url = request_url + "?access_token=" + access_token
-    headers ={'content-type':'application/json'}
-    
-    for i in range(steps,-1,-1):
-        params = {
-            "image_template": {               # 将其中一张图片设置为模板（相当于底层图片）
-                "image": str(image_temp,'utf-8'),
-                "image_type": "BASE64",
-                "quality_control": "NORMAL"
-            },
-            "image_target": {                 # 将其中一张图片设置为目标（相当于人脸信息“叠加”到模板上）
-                "image": str(image_targ,'utf-8'),
-                "image_type": "BASE64",
-                "quality_control": "NORMAL"
-            },
-            "merge_degree": "HIGH",            # 融合程度
-            "position": 2,                     #左上角水印
-            "language": 1,                     #英语水印
-            "version": 4.0,
-            "alpha": i/steps,
-        }  
-        params=json.dumps(params)
-        result = requests.post(request_url, data=params, headers=headers).json()  # 经过调用百度云接口服务器返回的内容（融合结果）
-        if result['error_code'] == 0:
-            res = result["result"]["merge_image"]
-            res = base64.b64decode(res)
-            nparr = np.frombuffer(res,np.uint8)
-            img_np = cv2.imdecode(nparr,cv2.IMREAD_COLOR)
-            yield img_np
-        else:
-            print(str(result['error_code']) + result['error_msg'])
-            return result['error_code']
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("Face merge.")
-
-    with gr.Row():
-        image_template = gr.Image(label="模板图片")
-        image_target = gr.Image(label="目标图片")
-    with gr.Row():
-        steps = gr.Slider(1, 10, value=1, step=1, label="变化步数")
-        image_output = gr.Image(label="融合结果")
-    image_button = gr.Button("Merge")
-
-    image_button.click(face_merge, inputs=[image_template,image_target,steps], outputs=image_output)
-
-gr.close_all()
-demo.queue()
-demo.launch()
\ No newline at end of file
diff --git a/spaces/omarelsayeed/AUDIO-ENHANCEMENT/README.md b/spaces/omarelsayeed/AUDIO-ENHANCEMENT/README.md
deleted file mode 100644
index 720b90363c5d73366ac83e195a977a38b0f08790..0000000000000000000000000000000000000000
--- a/spaces/omarelsayeed/AUDIO-ENHANCEMENT/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: DeepFilterNet2
-emoji: 💩
-colorFrom: gray
-colorTo: red
-sdk: gradio
-app_file: app.py
-sdk_version: 3.17.1
-pinned: false
-license: apache-2.0
-duplicated_from: hshr/DeepFilterNet2
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/onereal/Voice-Cloning-for-you/README.md b/spaces/onereal/Voice-Cloning-for-you/README.md
deleted file mode 100644
index 88a96d0c588bd4ebe1fcf8227fb782b1e0058a1c..0000000000000000000000000000000000000000
--- a/spaces/onereal/Voice-Cloning-for-you/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Voice Cloning
-emoji: 😻
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: kevinwang676/Voice-Cloning-for-Bilibili
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/onnx/ArcFace/app.py b/spaces/onnx/ArcFace/app.py
deleted file mode 100644
index ef269084c9103ef3fafb4c99dbf8feeea6ce5574..0000000000000000000000000000000000000000
--- a/spaces/onnx/ArcFace/app.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import cv2
-import sys
-import numpy as np
-import mxnet as mx
-import os
-
-
-from scipy import misc
-import random
-import sklearn
-from sklearn.decomposition import PCA
-from time import sleep
-from easydict import EasyDict as edict
-from mtcnn_detector import MtcnnDetector
-from skimage import transform as trans
-import matplotlib.pyplot as plt
-from mxnet.contrib.onnx.onnx2mx.import_model import import_model
-
-import gradio as gr
-
-
-def get_model(ctx, model):
-    image_size = (112,112)
-    # Import ONNX model
-    sym, arg_params, aux_params = import_model(model)
-    # Define and binds parameters to the network
-    model = mx.mod.Module(symbol=sym, context=ctx, label_names = None)
-    model.bind(data_shapes=[('data', (1, 3, image_size[0], image_size[1]))])
-    model.set_params(arg_params, aux_params)
-    return model
-    
-for i in range(4):
-    mx.test_utils.download(dirname='mtcnn-model', url='https://s3.amazonaws.com/onnx-model-zoo/arcface/mtcnn-model/det{}-0001.params'.format(i+1))
-    mx.test_utils.download(dirname='mtcnn-model', url='https://s3.amazonaws.com/onnx-model-zoo/arcface/mtcnn-model/det{}-symbol.json'.format(i+1))
-    mx.test_utils.download(dirname='mtcnn-model', url='https://s3.amazonaws.com/onnx-model-zoo/arcface/mtcnn-model/det{}.caffemodel'.format(i+1))
-    mx.test_utils.download(dirname='mtcnn-model', url='https://s3.amazonaws.com/onnx-model-zoo/arcface/mtcnn-model/det{}.prototxt'.format(i+1))
-
-# Determine and set context
-if len(mx.test_utils.list_gpus())==0:
-    ctx = mx.cpu()
-else:
-    ctx = mx.gpu(0)
-# Configure face detector
-det_threshold = [0.6,0.7,0.8]
-mtcnn_path = os.path.join(os.path.dirname('__file__'), 'mtcnn-model')
-detector = MtcnnDetector(model_folder=mtcnn_path, ctx=ctx, num_worker=1, accurate_landmark = True, threshold=det_threshold)
-
-def preprocess(img, bbox=None, landmark=None, **kwargs):
-    M = None
-    image_size = []
-    str_image_size = kwargs.get('image_size', '')
-    # Assert input shape
-    if len(str_image_size)>0:
-        image_size = [int(x) for x in str_image_size.split(',')]
-        if len(image_size)==1:
-            image_size = [image_size[0], image_size[0]]
-        assert len(image_size)==2
-        assert image_size[0]==112
-        assert image_size[0]==112 or image_size[1]==96
-    
-    # Do alignment using landmark points
-    if landmark is not None:
-        assert len(image_size)==2
-        src = np.array([
-          [30.2946, 51.6963],
-          [65.5318, 51.5014],
-          [48.0252, 71.7366],
-          [33.5493, 92.3655],
-          [62.7299, 92.2041] ], dtype=np.float32 )
-        if image_size[1]==112:
-            src[:,0] += 8.0
-        dst = landmark.astype(np.float32)
-        tform = trans.SimilarityTransform()
-        tform.estimate(dst, src)
-        M = tform.params[0:2,:]
-        assert len(image_size)==2
-        warped = cv2.warpAffine(img,M,(image_size[1],image_size[0]), borderValue = 0.0)
-        return warped
-    
-    # If no landmark points available, do alignment using bounding box. If no bounding box available use center crop
-    if M is None:
-        if bbox is None:
-            det = np.zeros(4, dtype=np.int32)
-            det[0] = int(img.shape[1]*0.0625)
-            det[1] = int(img.shape[0]*0.0625)
-            det[2] = img.shape[1] - det[0]
-            det[3] = img.shape[0] - det[1]
-        else:
-            det = bbox
-        margin = kwargs.get('margin', 44)
-        bb = np.zeros(4, dtype=np.int32)
-        bb[0] = np.maximum(det[0]-margin/2, 0)
-        bb[1] = np.maximum(det[1]-margin/2, 0)
-        bb[2] = np.minimum(det[2]+margin/2, img.shape[1])
-        bb[3] = np.minimum(det[3]+margin/2, img.shape[0])
-        ret = img[bb[1]:bb[3],bb[0]:bb[2],:]
-        if len(image_size)>0:
-            ret = cv2.resize(ret, (image_size[1], image_size[0]))
-        return ret
-    
-def get_input(detector,face_img):
-    # Pass input images through face detector
-    ret = detector.detect_face(face_img, det_type = 0)
-    if ret is None:
-        return None
-    bbox, points = ret
-    if bbox.shape[0]==0:
-        return None
-    bbox = bbox[0,0:4]
-    points = points[0,:].reshape((2,5)).T
-    # Call preprocess() to generate aligned images
-    nimg = preprocess(face_img, bbox, points, image_size='112,112')
-    nimg = cv2.cvtColor(nimg, cv2.COLOR_BGR2RGB)
-    aligned = np.transpose(nimg, (2,0,1))
-    return aligned
-    
-def get_feature(model,aligned):
-    input_blob = np.expand_dims(aligned, axis=0)
-    data = mx.nd.array(input_blob)
-    db = mx.io.DataBatch(data=(data,))
-    model.forward(db, is_train=False)
-    embedding = model.get_outputs()[0].asnumpy()
-    embedding = sklearn.preprocessing.normalize(embedding).flatten()
-    return embedding
-    
-# Download first image
-mx.test_utils.download('https://s3.amazonaws.com/onnx-model-zoo/arcface/player1.jpg')
-# Download second image
-mx.test_utils.download('https://s3.amazonaws.com/onnx-model-zoo/arcface/player2.jpg')
-# Download onnx model
-mx.test_utils.download('https://s3.amazonaws.com/onnx-model-zoo/arcface/resnet100.onnx')
-# Path to ONNX model
-model_name = 'resnet100.onnx'
-
-# Load ONNX model
-model = get_model(ctx , model_name)
-
-def inference(first,second):
-  # Load first image
-  img1 = cv2.imread(first)
-  
-  # Preprocess first image
-  pre1 = get_input(detector,img1)
-  
-  # Get embedding of first image
-  out1 = get_feature(model,pre1)
-  
-  # Load second image
-  img2 = cv2.imread(second)
-  
-  # Preprocess second image
-  pre2 = get_input(detector,img2)
-  
-  # Get embedding of second image
-  out2 = get_feature(model,pre2)
-  
-  # Compute squared distance between embeddings
-  dist = np.sum(np.square(out1-out2))
-  # Compute cosine similarity between embedddings
-  sim = np.dot(out1, out2.T)
-  # Print predictions
-  return 'Distance = %f' %(dist),'Similarity = %f' %(sim)
-  
-title="ArcFace"
-description="ArcFace is a CNN based model for face recognition which learns discriminative features of faces and produces embeddings for input face images. To enhance the discriminative power of softmax loss, a novel supervisor signal called additive angular margin (ArcFace) is used here as an additive term in the softmax loss. ArcFace can use a variety of CNN networks as its backend, each having different accuracy and performance."
-gr.Interface(inference,[gr.inputs.Image(type="filepath"),gr.inputs.Image(type="filepath")],["text","text"],title=title,description=description).launch(enable_queue=True)
diff --git a/spaces/pepereeee/prompthero-funko-diffusion/README.md b/spaces/pepereeee/prompthero-funko-diffusion/README.md
deleted file mode 100644
index 90c3cdbd345ee03bf3d1fb77300610e8f1ba1b0d..0000000000000000000000000000000000000000
--- a/spaces/pepereeee/prompthero-funko-diffusion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Prompthero Funko Diffusion
-emoji: ⚡
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pikto/Elite-freegpt-webui/g4f/Provider/Providers/Yqcloud.py b/spaces/pikto/Elite-freegpt-webui/g4f/Provider/Providers/Yqcloud.py
deleted file mode 100644
index ad5c3a4326c68ceb7ee012fbf5bc072da72a7e40..0000000000000000000000000000000000000000
--- a/spaces/pikto/Elite-freegpt-webui/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/power2/JoJoGan-powerhow2/e4e/criteria/w_norm.py b/spaces/power2/JoJoGan-powerhow2/e4e/criteria/w_norm.py
deleted file mode 100644
index a45ab6f67d8a3f7051be4b7236fa2f38446fd2c1..0000000000000000000000000000000000000000
--- a/spaces/power2/JoJoGan-powerhow2/e4e/criteria/w_norm.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import torch
-from torch import nn
-
-
-class WNormLoss(nn.Module):
-
-	def __init__(self, start_from_latent_avg=True):
-		super(WNormLoss, self).__init__()
-		self.start_from_latent_avg = start_from_latent_avg
-
-	def forward(self, latent, latent_avg=None):
-		if self.start_from_latent_avg:
-			latent = latent - latent_avg
-		return torch.sum(latent.norm(2, dim=(1, 2))) / latent.shape[0]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Info-586340e7.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Info-586340e7.js
deleted file mode 100644
index ea7cf656aa19134f9d74736865a83e045cde7270..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Info-586340e7.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import"./Button-89057c03.js";const{SvelteComponent:r,attr:a,create_slot:_,detach:u,element:f,get_all_dirty_from_scope:c,get_slot_changes:d,init:p,insert:m,safe_not_equal:$,transition_in:v,transition_out:g,update_slot_base:h}=window.__gradio__svelte__internal;function w(l){let n,s;const o=l[1].default,e=_(o,l,l[0],null);return{c(){n=f("div"),e&&e.c(),a(n,"class","svelte-e8n7p6")},m(t,i){m(t,n,i),e&&e.m(n,null),s=!0},p(t,[i]){e&&e.p&&(!s||i&1)&&h(e,o,t,t[0],s?d(o,t[0],i,null):c(t[0]),null)},i(t){s||(v(e,t),s=!0)},o(t){g(e,t),s=!1},d(t){t&&u(n),e&&e.d(t)}}}function I(l,n,s){let{$$slots:o={},$$scope:e}=n;return l.$$set=t=>{"$$scope"in t&&s(0,e=t.$$scope)},[e,o]}class q extends r{constructor(n){super(),p(this,n,I,w,$,{})}}export{q as I};
-//# sourceMappingURL=Info-586340e7.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/typing/tests/data/pass/simple_py3.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/typing/tests/data/pass/simple_py3.py
deleted file mode 100644
index c05a1ce612ac5e40d0914732c4c72ad1d3f2d552..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/typing/tests/data/pass/simple_py3.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import numpy as np
-
-array = np.array([1, 2])
-
-# The @ operator is not in python 2
-array @ array
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/io/stata.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/io/stata.py
deleted file mode 100644
index 0a02da09c9e158729b29dba2fa33fd2572f44ad0..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/io/stata.py
+++ /dev/null
@@ -1,3799 +0,0 @@
-"""
-Module contains tools for processing Stata files into DataFrames
-
-The StataReader below was originally written by Joe Presbrey as part of PyDTA.
-It has been extended and improved by Skipper Seabold from the Statsmodels
-project who also developed the StataWriter and was finally added to pandas in
-a once again improved version.
-
-You can find more information on http://presbrey.mit.edu/PyDTA and
-https://www.statsmodels.org/devel/
-"""
-from __future__ import annotations
-
-from collections import abc
-from datetime import (
-    datetime,
-    timedelta,
-)
-from io import BytesIO
-import os
-import struct
-import sys
-from typing import (
-    IO,
-    TYPE_CHECKING,
-    Any,
-    AnyStr,
-    Callable,
-    Final,
-    cast,
-)
-import warnings
-
-import numpy as np
-
-from pandas._libs import lib
-from pandas._libs.lib import infer_dtype
-from pandas._libs.writers import max_len_string_array
-from pandas.errors import (
-    CategoricalConversionWarning,
-    InvalidColumnName,
-    PossiblePrecisionLoss,
-    ValueLabelTypeMismatch,
-)
-from pandas.util._decorators import (
-    Appender,
-    doc,
-)
-from pandas.util._exceptions import find_stack_level
-
-from pandas.core.dtypes.common import (
-    ensure_object,
-    is_numeric_dtype,
-)
-from pandas.core.dtypes.dtypes import CategoricalDtype
-
-from pandas import (
-    Categorical,
-    DatetimeIndex,
-    NaT,
-    Timestamp,
-    isna,
-    to_datetime,
-    to_timedelta,
-)
-from pandas.core.arrays.boolean import BooleanDtype
-from pandas.core.arrays.integer import IntegerDtype
-from pandas.core.frame import DataFrame
-from pandas.core.indexes.base import Index
-from pandas.core.series import Series
-from pandas.core.shared_docs import _shared_docs
-
-from pandas.io.common import get_handle
-
-if TYPE_CHECKING:
-    from collections.abc import (
-        Hashable,
-        Sequence,
-    )
-    from types import TracebackType
-    from typing import Literal
-
-    from pandas._typing import (
-        CompressionOptions,
-        FilePath,
-        ReadBuffer,
-        StorageOptions,
-        WriteBuffer,
-    )
-
-_version_error = (
-    "Version of given Stata file is {version}. pandas supports importing "
-    "versions 105, 108, 111 (Stata 7SE), 113 (Stata 8/9), "
-    "114 (Stata 10/11), 115 (Stata 12), 117 (Stata 13), 118 (Stata 14/15/16),"
-    "and 119 (Stata 15/16, over 32,767 variables)."
-)
-
-_statafile_processing_params1 = """\
-convert_dates : bool, default True
-    Convert date variables to DataFrame time values.
-convert_categoricals : bool, default True
-    Read value labels and convert columns to Categorical/Factor variables."""
-
-_statafile_processing_params2 = """\
-index_col : str, optional
-    Column to set as index.
-convert_missing : bool, default False
-    Flag indicating whether to convert missing values to their Stata
-    representations.  If False, missing values are replaced with nan.
-    If True, columns containing missing values are returned with
-    object data types and missing values are represented by
-    StataMissingValue objects.
-preserve_dtypes : bool, default True
-    Preserve Stata datatypes. If False, numeric data are upcast to pandas
-    default types for foreign data (float64 or int64).
-columns : list or None
-    Columns to retain.  Columns will be returned in the given order.  None
-    returns all columns.
-order_categoricals : bool, default True
-    Flag indicating whether converted categorical data are ordered."""
-
-_chunksize_params = """\
-chunksize : int, default None
-    Return StataReader object for iterations, returns chunks with
-    given number of lines."""
-
-_iterator_params = """\
-iterator : bool, default False
-    Return StataReader object."""
-
-_reader_notes = """\
-Notes
------
-Categorical variables read through an iterator may not have the same
-categories and dtype. This occurs when  a variable stored in a DTA
-file is associated to an incomplete set of value labels that only
-label a strict subset of the values."""
-
-_read_stata_doc = f"""
-Read Stata file into DataFrame.
-
-Parameters
-----------
-filepath_or_buffer : str, path object or file-like object
-    Any valid string path is acceptable. The string could be a URL. Valid
-    URL schemes include http, ftp, s3, and file. For file URLs, a host is
-    expected. A local file could be: ``file://localhost/path/to/table.dta``.
-
-    If you want to pass in a path object, pandas accepts any ``os.PathLike``.
-
-    By file-like object, we refer to objects with a ``read()`` method,
-    such as a file handle (e.g. via builtin ``open`` function)
-    or ``StringIO``.
-{_statafile_processing_params1}
-{_statafile_processing_params2}
-{_chunksize_params}
-{_iterator_params}
-{_shared_docs["decompression_options"] % "filepath_or_buffer"}
-{_shared_docs["storage_options"]}
-
-Returns
--------
-DataFrame or pandas.api.typing.StataReader
-
-See Also
---------
-io.stata.StataReader : Low-level reader for Stata data files.
-DataFrame.to_stata: Export Stata data files.
-
-{_reader_notes}
-
-Examples
---------
-
-Creating a dummy stata for this example
-
->>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon', 'parrot'],
-...                     'speed': [350, 18, 361, 15]}})  # doctest: +SKIP
->>> df.to_stata('animals.dta')  # doctest: +SKIP
-
-Read a Stata dta file:
-
->>> df = pd.read_stata('animals.dta')  # doctest: +SKIP
-
-Read a Stata dta file in 10,000 line chunks:
-
->>> values = np.random.randint(0, 10, size=(20_000, 1), dtype="uint8")  # doctest: +SKIP
->>> df = pd.DataFrame(values, columns=["i"])  # doctest: +SKIP
->>> df.to_stata('filename.dta')  # doctest: +SKIP
-
->>> with pd.read_stata('filename.dta', chunksize=10000) as itr: # doctest: +SKIP
->>>     for chunk in itr:
-...         # Operate on a single chunk, e.g., chunk.mean()
-...         pass  # doctest: +SKIP
-"""
-
-_read_method_doc = f"""\
-Reads observations from Stata file, converting them into a dataframe
-
-Parameters
-----------
-nrows : int
-    Number of lines to read from data file, if None read whole file.
-{_statafile_processing_params1}
-{_statafile_processing_params2}
-
-Returns
--------
-DataFrame
-"""
-
-_stata_reader_doc = f"""\
-Class for reading Stata dta files.
-
-Parameters
-----------
-path_or_buf : path (string), buffer or path object
-    string, path object (pathlib.Path or py._path.local.LocalPath) or object
-    implementing a binary read() functions.
-{_statafile_processing_params1}
-{_statafile_processing_params2}
-{_chunksize_params}
-{_shared_docs["decompression_options"]}
-{_shared_docs["storage_options"]}
-
-{_reader_notes}
-"""
-
-
-_date_formats = ["%tc", "%tC", "%td", "%d", "%tw", "%tm", "%tq", "%th", "%ty"]
-
-
-stata_epoch: Final = datetime(1960, 1, 1)
-
-
-# TODO: Add typing. As of January 2020 it is not possible to type this function since
-#  mypy doesn't understand that a Series and an int can be combined using mathematical
-#  operations. (+, -).
-def _stata_elapsed_date_to_datetime_vec(dates, fmt) -> Series:
-    """
-    Convert from SIF to datetime. https://www.stata.com/help.cgi?datetime
-
-    Parameters
-    ----------
-    dates : Series
-        The Stata Internal Format date to convert to datetime according to fmt
-    fmt : str
-        The format to convert to. Can be, tc, td, tw, tm, tq, th, ty
-        Returns
-
-    Returns
-    -------
-    converted : Series
-        The converted dates
-
-    Examples
-    --------
-    >>> dates = pd.Series([52])
-    >>> _stata_elapsed_date_to_datetime_vec(dates , "%tw")
-    0   1961-01-01
-    dtype: datetime64[ns]
-
-    Notes
-    -----
-    datetime/c - tc
-        milliseconds since 01jan1960 00:00:00.000, assuming 86,400 s/day
-    datetime/C - tC - NOT IMPLEMENTED
-        milliseconds since 01jan1960 00:00:00.000, adjusted for leap seconds
-    date - td
-        days since 01jan1960 (01jan1960 = 0)
-    weekly date - tw
-        weeks since 1960w1
-        This assumes 52 weeks in a year, then adds 7 * remainder of the weeks.
-        The datetime value is the start of the week in terms of days in the
-        year, not ISO calendar weeks.
-    monthly date - tm
-        months since 1960m1
-    quarterly date - tq
-        quarters since 1960q1
-    half-yearly date - th
-        half-years since 1960h1 yearly
-    date - ty
-        years since 0000
-    """
-    MIN_YEAR, MAX_YEAR = Timestamp.min.year, Timestamp.max.year
-    MAX_DAY_DELTA = (Timestamp.max - datetime(1960, 1, 1)).days
-    MIN_DAY_DELTA = (Timestamp.min - datetime(1960, 1, 1)).days
-    MIN_MS_DELTA = MIN_DAY_DELTA * 24 * 3600 * 1000
-    MAX_MS_DELTA = MAX_DAY_DELTA * 24 * 3600 * 1000
-
-    def convert_year_month_safe(year, month) -> Series:
-        """
-        Convert year and month to datetimes, using pandas vectorized versions
-        when the date range falls within the range supported by pandas.
-        Otherwise it falls back to a slower but more robust method
-        using datetime.
-        """
-        if year.max() < MAX_YEAR and year.min() > MIN_YEAR:
-            return to_datetime(100 * year + month, format="%Y%m")
-        else:
-            index = getattr(year, "index", None)
-            return Series([datetime(y, m, 1) for y, m in zip(year, month)], index=index)
-
-    def convert_year_days_safe(year, days) -> Series:
-        """
-        Converts year (e.g. 1999) and days since the start of the year to a
-        datetime or datetime64 Series
-        """
-        if year.max() < (MAX_YEAR - 1) and year.min() > MIN_YEAR:
-            return to_datetime(year, format="%Y") + to_timedelta(days, unit="d")
-        else:
-            index = getattr(year, "index", None)
-            value = [
-                datetime(y, 1, 1) + timedelta(days=int(d)) for y, d in zip(year, days)
-            ]
-            return Series(value, index=index)
-
-    def convert_delta_safe(base, deltas, unit) -> Series:
-        """
-        Convert base dates and deltas to datetimes, using pandas vectorized
-        versions if the deltas satisfy restrictions required to be expressed
-        as dates in pandas.
-        """
-        index = getattr(deltas, "index", None)
-        if unit == "d":
-            if deltas.max() > MAX_DAY_DELTA or deltas.min() < MIN_DAY_DELTA:
-                values = [base + timedelta(days=int(d)) for d in deltas]
-                return Series(values, index=index)
-        elif unit == "ms":
-            if deltas.max() > MAX_MS_DELTA or deltas.min() < MIN_MS_DELTA:
-                values = [
-                    base + timedelta(microseconds=(int(d) * 1000)) for d in deltas
-                ]
-                return Series(values, index=index)
-        else:
-            raise ValueError("format not understood")
-        base = to_datetime(base)
-        deltas = to_timedelta(deltas, unit=unit)
-        return base + deltas
-
-    # TODO(non-nano): If/when pandas supports more than datetime64[ns], this
-    #  should be improved to use correct range, e.g. datetime[Y] for yearly
-    bad_locs = np.isnan(dates)
-    has_bad_values = False
-    if bad_locs.any():
-        has_bad_values = True
-        # reset cache to avoid SettingWithCopy checks (we own the DataFrame and the
-        # `dates` Series is used to overwrite itself in the DataFramae)
-        dates._reset_cacher()
-        dates[bad_locs] = 1.0  # Replace with NaT
-    dates = dates.astype(np.int64)
-
-    if fmt.startswith(("%tc", "tc")):  # Delta ms relative to base
-        base = stata_epoch
-        ms = dates
-        conv_dates = convert_delta_safe(base, ms, "ms")
-    elif fmt.startswith(("%tC", "tC")):
-        warnings.warn(
-            "Encountered %tC format. Leaving in Stata Internal Format.",
-            stacklevel=find_stack_level(),
-        )
-        conv_dates = Series(dates, dtype=object)
-        if has_bad_values:
-            conv_dates[bad_locs] = NaT
-        return conv_dates
-    # Delta days relative to base
-    elif fmt.startswith(("%td", "td", "%d", "d")):
-        base = stata_epoch
-        days = dates
-        conv_dates = convert_delta_safe(base, days, "d")
-    # does not count leap days - 7 days is a week.
-    # 52nd week may have more than 7 days
-    elif fmt.startswith(("%tw", "tw")):
-        year = stata_epoch.year + dates // 52
-        days = (dates % 52) * 7
-        conv_dates = convert_year_days_safe(year, days)
-    elif fmt.startswith(("%tm", "tm")):  # Delta months relative to base
-        year = stata_epoch.year + dates // 12
-        month = (dates % 12) + 1
-        conv_dates = convert_year_month_safe(year, month)
-    elif fmt.startswith(("%tq", "tq")):  # Delta quarters relative to base
-        year = stata_epoch.year + dates // 4
-        quarter_month = (dates % 4) * 3 + 1
-        conv_dates = convert_year_month_safe(year, quarter_month)
-    elif fmt.startswith(("%th", "th")):  # Delta half-years relative to base
-        year = stata_epoch.year + dates // 2
-        month = (dates % 2) * 6 + 1
-        conv_dates = convert_year_month_safe(year, month)
-    elif fmt.startswith(("%ty", "ty")):  # Years -- not delta
-        year = dates
-        first_month = np.ones_like(dates)
-        conv_dates = convert_year_month_safe(year, first_month)
-    else:
-        raise ValueError(f"Date fmt {fmt} not understood")
-
-    if has_bad_values:  # Restore NaT for bad values
-        conv_dates[bad_locs] = NaT
-
-    return conv_dates
-
-
-def _datetime_to_stata_elapsed_vec(dates: Series, fmt: str) -> Series:
-    """
-    Convert from datetime to SIF. https://www.stata.com/help.cgi?datetime
-
-    Parameters
-    ----------
-    dates : Series
-        Series or array containing datetime or datetime64[ns] to
-        convert to the Stata Internal Format given by fmt
-    fmt : str
-        The format to convert to. Can be, tc, td, tw, tm, tq, th, ty
-    """
-    index = dates.index
-    NS_PER_DAY = 24 * 3600 * 1000 * 1000 * 1000
-    US_PER_DAY = NS_PER_DAY / 1000
-
-    def parse_dates_safe(
-        dates: Series, delta: bool = False, year: bool = False, days: bool = False
-    ):
-        d = {}
-        if lib.is_np_dtype(dates.dtype, "M"):
-            if delta:
-                time_delta = dates - Timestamp(stata_epoch).as_unit("ns")
-                d["delta"] = time_delta._values.view(np.int64) // 1000  # microseconds
-            if days or year:
-                date_index = DatetimeIndex(dates)
-                d["year"] = date_index._data.year
-                d["month"] = date_index._data.month
-            if days:
-                days_in_ns = dates.view(np.int64) - to_datetime(
-                    d["year"], format="%Y"
-                ).view(np.int64)
-                d["days"] = days_in_ns // NS_PER_DAY
-
-        elif infer_dtype(dates, skipna=False) == "datetime":
-            if delta:
-                delta = dates._values - stata_epoch
-
-                def f(x: timedelta) -> float:
-                    return US_PER_DAY * x.days + 1000000 * x.seconds + x.microseconds
-
-                v = np.vectorize(f)
-                d["delta"] = v(delta)
-            if year:
-                year_month = dates.apply(lambda x: 100 * x.year + x.month)
-                d["year"] = year_month._values // 100
-                d["month"] = year_month._values - d["year"] * 100
-            if days:
-
-                def g(x: datetime) -> int:
-                    return (x - datetime(x.year, 1, 1)).days
-
-                v = np.vectorize(g)
-                d["days"] = v(dates)
-        else:
-            raise ValueError(
-                "Columns containing dates must contain either "
-                "datetime64, datetime or null values."
-            )
-
-        return DataFrame(d, index=index)
-
-    bad_loc = isna(dates)
-    index = dates.index
-    if bad_loc.any():
-        dates = Series(dates)
-        if lib.is_np_dtype(dates.dtype, "M"):
-            dates[bad_loc] = to_datetime(stata_epoch)
-        else:
-            dates[bad_loc] = stata_epoch
-
-    if fmt in ["%tc", "tc"]:
-        d = parse_dates_safe(dates, delta=True)
-        conv_dates = d.delta / 1000
-    elif fmt in ["%tC", "tC"]:
-        warnings.warn(
-            "Stata Internal Format tC not supported.",
-            stacklevel=find_stack_level(),
-        )
-        conv_dates = dates
-    elif fmt in ["%td", "td"]:
-        d = parse_dates_safe(dates, delta=True)
-        conv_dates = d.delta // US_PER_DAY
-    elif fmt in ["%tw", "tw"]:
-        d = parse_dates_safe(dates, year=True, days=True)
-        conv_dates = 52 * (d.year - stata_epoch.year) + d.days // 7
-    elif fmt in ["%tm", "tm"]:
-        d = parse_dates_safe(dates, year=True)
-        conv_dates = 12 * (d.year - stata_epoch.year) + d.month - 1
-    elif fmt in ["%tq", "tq"]:
-        d = parse_dates_safe(dates, year=True)
-        conv_dates = 4 * (d.year - stata_epoch.year) + (d.month - 1) // 3
-    elif fmt in ["%th", "th"]:
-        d = parse_dates_safe(dates, year=True)
-        conv_dates = 2 * (d.year - stata_epoch.year) + (d.month > 6).astype(int)
-    elif fmt in ["%ty", "ty"]:
-        d = parse_dates_safe(dates, year=True)
-        conv_dates = d.year
-    else:
-        raise ValueError(f"Format {fmt} is not a known Stata date format")
-
-    conv_dates = Series(conv_dates, dtype=np.float64)
-    missing_value = struct.unpack("<d", b"\x00\x00\x00\x00\x00\x00\xe0\x7f")[0]
-    conv_dates[bad_loc] = missing_value
-
-    return Series(conv_dates, index=index)
-
-
-excessive_string_length_error: Final = """
-Fixed width strings in Stata .dta files are limited to 244 (or fewer)
-characters.  Column '{0}' does not satisfy this restriction. Use the
-'version=117' parameter to write the newer (Stata 13 and later) format.
-"""
-
-
-precision_loss_doc: Final = """
-Column converted from {0} to {1}, and some data are outside of the lossless
-conversion range. This may result in a loss of precision in the saved data.
-"""
-
-
-value_label_mismatch_doc: Final = """
-Stata value labels (pandas categories) must be strings. Column {0} contains
-non-string labels which will be converted to strings.  Please check that the
-Stata data file created has not lost information due to duplicate labels.
-"""
-
-
-invalid_name_doc: Final = """
-Not all pandas column names were valid Stata variable names.
-The following replacements have been made:
-
-    {0}
-
-If this is not what you expect, please make sure you have Stata-compliant
-column names in your DataFrame (strings only, max 32 characters, only
-alphanumerics and underscores, no Stata reserved words)
-"""
-
-
-categorical_conversion_warning: Final = """
-One or more series with value labels are not fully labeled. Reading this
-dataset with an iterator results in categorical variable with different
-categories. This occurs since it is not possible to know all possible values
-until the entire dataset has been read. To avoid this warning, you can either
-read dataset without an iterator, or manually convert categorical data by
-``convert_categoricals`` to False and then accessing the variable labels
-through the value_labels method of the reader.
-"""
-
-
-def _cast_to_stata_types(data: DataFrame) -> DataFrame:
-    """
-    Checks the dtypes of the columns of a pandas DataFrame for
-    compatibility with the data types and ranges supported by Stata, and
-    converts if necessary.
-
-    Parameters
-    ----------
-    data : DataFrame
-        The DataFrame to check and convert
-
-    Notes
-    -----
-    Numeric columns in Stata must be one of int8, int16, int32, float32 or
-    float64, with some additional value restrictions.  int8 and int16 columns
-    are checked for violations of the value restrictions and upcast if needed.
-    int64 data is not usable in Stata, and so it is downcast to int32 whenever
-    the value are in the int32 range, and sidecast to float64 when larger than
-    this range.  If the int64 values are outside of the range of those
-    perfectly representable as float64 values, a warning is raised.
-
-    bool columns are cast to int8.  uint columns are converted to int of the
-    same size if there is no loss in precision, otherwise are upcast to a
-    larger type.  uint64 is currently not supported since it is concerted to
-    object in a DataFrame.
-    """
-    ws = ""
-    # original, if small, if large
-    conversion_data: tuple[
-        tuple[type, type, type],
-        tuple[type, type, type],
-        tuple[type, type, type],
-        tuple[type, type, type],
-        tuple[type, type, type],
-    ] = (
-        (np.bool_, np.int8, np.int8),
-        (np.uint8, np.int8, np.int16),
-        (np.uint16, np.int16, np.int32),
-        (np.uint32, np.int32, np.int64),
-        (np.uint64, np.int64, np.float64),
-    )
-
-    float32_max = struct.unpack("<f", b"\xff\xff\xff\x7e")[0]
-    float64_max = struct.unpack("<d", b"\xff\xff\xff\xff\xff\xff\xdf\x7f")[0]
-
-    for col in data:
-        # Cast from unsupported types to supported types
-        is_nullable_int = isinstance(data[col].dtype, (IntegerDtype, BooleanDtype))
-        orig = data[col]
-        # We need to find orig_missing before altering data below
-        orig_missing = orig.isna()
-        if is_nullable_int:
-            missing_loc = data[col].isna()
-            if missing_loc.any():
-                # Replace with always safe value
-                fv = 0 if isinstance(data[col].dtype, IntegerDtype) else False
-                data.loc[missing_loc, col] = fv
-            # Replace with NumPy-compatible column
-            data[col] = data[col].astype(data[col].dtype.numpy_dtype)
-        dtype = data[col].dtype
-        empty_df = data.shape[0] == 0
-        for c_data in conversion_data:
-            if dtype == c_data[0]:
-                if empty_df or data[col].max() <= np.iinfo(c_data[1]).max:
-                    dtype = c_data[1]
-                else:
-                    dtype = c_data[2]
-                if c_data[2] == np.int64:  # Warn if necessary
-                    if data[col].max() >= 2**53:
-                        ws = precision_loss_doc.format("uint64", "float64")
-
-                data[col] = data[col].astype(dtype)
-
-        # Check values and upcast if necessary
-
-        if dtype == np.int8 and not empty_df:
-            if data[col].max() > 100 or data[col].min() < -127:
-                data[col] = data[col].astype(np.int16)
-        elif dtype == np.int16 and not empty_df:
-            if data[col].max() > 32740 or data[col].min() < -32767:
-                data[col] = data[col].astype(np.int32)
-        elif dtype == np.int64:
-            if empty_df or (
-                data[col].max() <= 2147483620 and data[col].min() >= -2147483647
-            ):
-                data[col] = data[col].astype(np.int32)
-            else:
-                data[col] = data[col].astype(np.float64)
-                if data[col].max() >= 2**53 or data[col].min() <= -(2**53):
-                    ws = precision_loss_doc.format("int64", "float64")
-        elif dtype in (np.float32, np.float64):
-            if np.isinf(data[col]).any():
-                raise ValueError(
-                    f"Column {col} contains infinity or -infinity"
-                    "which is outside the range supported by Stata."
-                )
-            value = data[col].max()
-            if dtype == np.float32 and value > float32_max:
-                data[col] = data[col].astype(np.float64)
-            elif dtype == np.float64:
-                if value > float64_max:
-                    raise ValueError(
-                        f"Column {col} has a maximum value ({value}) outside the range "
-                        f"supported by Stata ({float64_max})"
-                    )
-        if is_nullable_int:
-            if orig_missing.any():
-                # Replace missing by Stata sentinel value
-                sentinel = StataMissingValue.BASE_MISSING_VALUES[data[col].dtype.name]
-                data.loc[orig_missing, col] = sentinel
-    if ws:
-        warnings.warn(
-            ws,
-            PossiblePrecisionLoss,
-            stacklevel=find_stack_level(),
-        )
-
-    return data
-
-
-class StataValueLabel:
-    """
-    Parse a categorical column and prepare formatted output
-
-    Parameters
-    ----------
-    catarray : Series
-        Categorical Series to encode
-    encoding : {"latin-1", "utf-8"}
-        Encoding to use for value labels.
-    """
-
-    def __init__(
-        self, catarray: Series, encoding: Literal["latin-1", "utf-8"] = "latin-1"
-    ) -> None:
-        if encoding not in ("latin-1", "utf-8"):
-            raise ValueError("Only latin-1 and utf-8 are supported.")
-        self.labname = catarray.name
-        self._encoding = encoding
-        categories = catarray.cat.categories
-        self.value_labels: list[tuple[float, str]] = list(
-            zip(np.arange(len(categories)), categories)
-        )
-        self.value_labels.sort(key=lambda x: x[0])
-
-        self._prepare_value_labels()
-
-    def _prepare_value_labels(self):
-        """Encode value labels."""
-
-        self.text_len = 0
-        self.txt: list[bytes] = []
-        self.n = 0
-        # Offsets (length of categories), converted to int32
-        self.off = np.array([], dtype=np.int32)
-        # Values, converted to int32
-        self.val = np.array([], dtype=np.int32)
-        self.len = 0
-
-        # Compute lengths and setup lists of offsets and labels
-        offsets: list[int] = []
-        values: list[float] = []
-        for vl in self.value_labels:
-            category: str | bytes = vl[1]
-            if not isinstance(category, str):
-                category = str(category)
-                warnings.warn(
-                    value_label_mismatch_doc.format(self.labname),
-                    ValueLabelTypeMismatch,
-                    stacklevel=find_stack_level(),
-                )
-            category = category.encode(self._encoding)
-            offsets.append(self.text_len)
-            self.text_len += len(category) + 1  # +1 for the padding
-            values.append(vl[0])
-            self.txt.append(category)
-            self.n += 1
-
-        if self.text_len > 32000:
-            raise ValueError(
-                "Stata value labels for a single variable must "
-                "have a combined length less than 32,000 characters."
-            )
-
-        # Ensure int32
-        self.off = np.array(offsets, dtype=np.int32)
-        self.val = np.array(values, dtype=np.int32)
-
-        # Total length
-        self.len = 4 + 4 + 4 * self.n + 4 * self.n + self.text_len
-
-    def generate_value_label(self, byteorder: str) -> bytes:
-        """
-        Generate the binary representation of the value labels.
-
-        Parameters
-        ----------
-        byteorder : str
-            Byte order of the output
-
-        Returns
-        -------
-        value_label : bytes
-            Bytes containing the formatted value label
-        """
-        encoding = self._encoding
-        bio = BytesIO()
-        null_byte = b"\x00"
-
-        # len
-        bio.write(struct.pack(byteorder + "i", self.len))
-
-        # labname
-        labname = str(self.labname)[:32].encode(encoding)
-        lab_len = 32 if encoding not in ("utf-8", "utf8") else 128
-        labname = _pad_bytes(labname, lab_len + 1)
-        bio.write(labname)
-
-        # padding - 3 bytes
-        for i in range(3):
-            bio.write(struct.pack("c", null_byte))
-
-        # value_label_table
-        # n - int32
-        bio.write(struct.pack(byteorder + "i", self.n))
-
-        # textlen  - int32
-        bio.write(struct.pack(byteorder + "i", self.text_len))
-
-        # off - int32 array (n elements)
-        for offset in self.off:
-            bio.write(struct.pack(byteorder + "i", offset))
-
-        # val - int32 array (n elements)
-        for value in self.val:
-            bio.write(struct.pack(byteorder + "i", value))
-
-        # txt - Text labels, null terminated
-        for text in self.txt:
-            bio.write(text + null_byte)
-
-        return bio.getvalue()
-
-
-class StataNonCatValueLabel(StataValueLabel):
-    """
-    Prepare formatted version of value labels
-
-    Parameters
-    ----------
-    labname : str
-        Value label name
-    value_labels: Dictionary
-        Mapping of values to labels
-    encoding : {"latin-1", "utf-8"}
-        Encoding to use for value labels.
-    """
-
-    def __init__(
-        self,
-        labname: str,
-        value_labels: dict[float, str],
-        encoding: Literal["latin-1", "utf-8"] = "latin-1",
-    ) -> None:
-        if encoding not in ("latin-1", "utf-8"):
-            raise ValueError("Only latin-1 and utf-8 are supported.")
-
-        self.labname = labname
-        self._encoding = encoding
-        self.value_labels: list[tuple[float, str]] = sorted(
-            value_labels.items(), key=lambda x: x[0]
-        )
-        self._prepare_value_labels()
-
-
-class StataMissingValue:
-    """
-    An observation's missing value.
-
-    Parameters
-    ----------
-    value : {int, float}
-        The Stata missing value code
-
-    Notes
-    -----
-    More information: <https://www.stata.com/help.cgi?missing>
-
-    Integer missing values make the code '.', '.a', ..., '.z' to the ranges
-    101 ... 127 (for int8), 32741 ... 32767  (for int16) and 2147483621 ...
-    2147483647 (for int32).  Missing values for floating point data types are
-    more complex but the pattern is simple to discern from the following table.
-
-    np.float32 missing values (float in Stata)
-    0000007f    .
-    0008007f    .a
-    0010007f    .b
-    ...
-    00c0007f    .x
-    00c8007f    .y
-    00d0007f    .z
-
-    np.float64 missing values (double in Stata)
-    000000000000e07f    .
-    000000000001e07f    .a
-    000000000002e07f    .b
-    ...
-    000000000018e07f    .x
-    000000000019e07f    .y
-    00000000001ae07f    .z
-    """
-
-    # Construct a dictionary of missing values
-    MISSING_VALUES: dict[float, str] = {}
-    bases: Final = (101, 32741, 2147483621)
-    for b in bases:
-        # Conversion to long to avoid hash issues on 32 bit platforms #8968
-        MISSING_VALUES[b] = "."
-        for i in range(1, 27):
-            MISSING_VALUES[i + b] = "." + chr(96 + i)
-
-    float32_base: bytes = b"\x00\x00\x00\x7f"
-    increment_32: int = struct.unpack("<i", b"\x00\x08\x00\x00")[0]
-    for i in range(27):
-        key = struct.unpack("<f", float32_base)[0]
-        MISSING_VALUES[key] = "."
-        if i > 0:
-            MISSING_VALUES[key] += chr(96 + i)
-        int_value = struct.unpack("<i", struct.pack("<f", key))[0] + increment_32
-        float32_base = struct.pack("<i", int_value)
-
-    float64_base: bytes = b"\x00\x00\x00\x00\x00\x00\xe0\x7f"
-    increment_64 = struct.unpack("q", b"\x00\x00\x00\x00\x00\x01\x00\x00")[0]
-    for i in range(27):
-        key = struct.unpack("<d", float64_base)[0]
-        MISSING_VALUES[key] = "."
-        if i > 0:
-            MISSING_VALUES[key] += chr(96 + i)
-        int_value = struct.unpack("q", struct.pack("<d", key))[0] + increment_64
-        float64_base = struct.pack("q", int_value)
-
-    BASE_MISSING_VALUES: Final = {
-        "int8": 101,
-        "int16": 32741,
-        "int32": 2147483621,
-        "float32": struct.unpack("<f", float32_base)[0],
-        "float64": struct.unpack("<d", float64_base)[0],
-    }
-
-    def __init__(self, value: float) -> None:
-        self._value = value
-        # Conversion to int to avoid hash issues on 32 bit platforms #8968
-        value = int(value) if value < 2147483648 else float(value)
-        self._str = self.MISSING_VALUES[value]
-
-    @property
-    def string(self) -> str:
-        """
-        The Stata representation of the missing value: '.', '.a'..'.z'
-
-        Returns
-        -------
-        str
-            The representation of the missing value.
-        """
-        return self._str
-
-    @property
-    def value(self) -> float:
-        """
-        The binary representation of the missing value.
-
-        Returns
-        -------
-        {int, float}
-            The binary representation of the missing value.
-        """
-        return self._value
-
-    def __str__(self) -> str:
-        return self.string
-
-    def __repr__(self) -> str:
-        return f"{type(self)}({self})"
-
-    def __eq__(self, other: Any) -> bool:
-        return (
-            isinstance(other, type(self))
-            and self.string == other.string
-            and self.value == other.value
-        )
-
-    @classmethod
-    def get_base_missing_value(cls, dtype: np.dtype) -> float:
-        if dtype.type is np.int8:
-            value = cls.BASE_MISSING_VALUES["int8"]
-        elif dtype.type is np.int16:
-            value = cls.BASE_MISSING_VALUES["int16"]
-        elif dtype.type is np.int32:
-            value = cls.BASE_MISSING_VALUES["int32"]
-        elif dtype.type is np.float32:
-            value = cls.BASE_MISSING_VALUES["float32"]
-        elif dtype.type is np.float64:
-            value = cls.BASE_MISSING_VALUES["float64"]
-        else:
-            raise ValueError("Unsupported dtype")
-        return value
-
-
-class StataParser:
-    def __init__(self) -> None:
-        # type          code.
-        # --------------------
-        # str1        1 = 0x01
-        # str2        2 = 0x02
-        # ...
-        # str244    244 = 0xf4
-        # byte      251 = 0xfb  (sic)
-        # int       252 = 0xfc
-        # long      253 = 0xfd
-        # float     254 = 0xfe
-        # double    255 = 0xff
-        # --------------------
-        # NOTE: the byte type seems to be reserved for categorical variables
-        # with a label, but the underlying variable is -127 to 100
-        # we're going to drop the label and cast to int
-        self.DTYPE_MAP = dict(
-            [(i, np.dtype(f"S{i}")) for i in range(1, 245)]
-            + [
-                (251, np.dtype(np.int8)),
-                (252, np.dtype(np.int16)),
-                (253, np.dtype(np.int32)),
-                (254, np.dtype(np.float32)),
-                (255, np.dtype(np.float64)),
-            ]
-        )
-        self.DTYPE_MAP_XML: dict[int, np.dtype] = {
-            32768: np.dtype(np.uint8),  # Keys to GSO
-            65526: np.dtype(np.float64),
-            65527: np.dtype(np.float32),
-            65528: np.dtype(np.int32),
-            65529: np.dtype(np.int16),
-            65530: np.dtype(np.int8),
-        }
-        self.TYPE_MAP = list(tuple(range(251)) + tuple("bhlfd"))
-        self.TYPE_MAP_XML = {
-            # Not really a Q, unclear how to handle byteswap
-            32768: "Q",
-            65526: "d",
-            65527: "f",
-            65528: "l",
-            65529: "h",
-            65530: "b",
-        }
-        # NOTE: technically, some of these are wrong. there are more numbers
-        # that can be represented. it's the 27 ABOVE and BELOW the max listed
-        # numeric data type in [U] 12.2.2 of the 11.2 manual
-        float32_min = b"\xff\xff\xff\xfe"
-        float32_max = b"\xff\xff\xff\x7e"
-        float64_min = b"\xff\xff\xff\xff\xff\xff\xef\xff"
-        float64_max = b"\xff\xff\xff\xff\xff\xff\xdf\x7f"
-        self.VALID_RANGE = {
-            "b": (-127, 100),
-            "h": (-32767, 32740),
-            "l": (-2147483647, 2147483620),
-            "f": (
-                np.float32(struct.unpack("<f", float32_min)[0]),
-                np.float32(struct.unpack("<f", float32_max)[0]),
-            ),
-            "d": (
-                np.float64(struct.unpack("<d", float64_min)[0]),
-                np.float64(struct.unpack("<d", float64_max)[0]),
-            ),
-        }
-
-        self.OLD_TYPE_MAPPING = {
-            98: 251,  # byte
-            105: 252,  # int
-            108: 253,  # long
-            102: 254,  # float
-            100: 255,  # double
-        }
-
-        # These missing values are the generic '.' in Stata, and are used
-        # to replace nans
-        self.MISSING_VALUES = {
-            "b": 101,
-            "h": 32741,
-            "l": 2147483621,
-            "f": np.float32(struct.unpack("<f", b"\x00\x00\x00\x7f")[0]),
-            "d": np.float64(
-                struct.unpack("<d", b"\x00\x00\x00\x00\x00\x00\xe0\x7f")[0]
-            ),
-        }
-        self.NUMPY_TYPE_MAP = {
-            "b": "i1",
-            "h": "i2",
-            "l": "i4",
-            "f": "f4",
-            "d": "f8",
-            "Q": "u8",
-        }
-
-        # Reserved words cannot be used as variable names
-        self.RESERVED_WORDS = (
-            "aggregate",
-            "array",
-            "boolean",
-            "break",
-            "byte",
-            "case",
-            "catch",
-            "class",
-            "colvector",
-            "complex",
-            "const",
-            "continue",
-            "default",
-            "delegate",
-            "delete",
-            "do",
-            "double",
-            "else",
-            "eltypedef",
-            "end",
-            "enum",
-            "explicit",
-            "export",
-            "external",
-            "float",
-            "for",
-            "friend",
-            "function",
-            "global",
-            "goto",
-            "if",
-            "inline",
-            "int",
-            "local",
-            "long",
-            "NULL",
-            "pragma",
-            "protected",
-            "quad",
-            "rowvector",
-            "short",
-            "typedef",
-            "typename",
-            "virtual",
-            "_all",
-            "_N",
-            "_skip",
-            "_b",
-            "_pi",
-            "str#",
-            "in",
-            "_pred",
-            "strL",
-            "_coef",
-            "_rc",
-            "using",
-            "_cons",
-            "_se",
-            "with",
-            "_n",
-        )
-
-
-class StataReader(StataParser, abc.Iterator):
-    __doc__ = _stata_reader_doc
-
-    _path_or_buf: IO[bytes]  # Will be assigned by `_open_file`.
-
-    def __init__(
-        self,
-        path_or_buf: FilePath | ReadBuffer[bytes],
-        convert_dates: bool = True,
-        convert_categoricals: bool = True,
-        index_col: str | None = None,
-        convert_missing: bool = False,
-        preserve_dtypes: bool = True,
-        columns: Sequence[str] | None = None,
-        order_categoricals: bool = True,
-        chunksize: int | None = None,
-        compression: CompressionOptions = "infer",
-        storage_options: StorageOptions | None = None,
-    ) -> None:
-        super().__init__()
-        self._col_sizes: list[int] = []
-
-        # Arguments to the reader (can be temporarily overridden in
-        # calls to read).
-        self._convert_dates = convert_dates
-        self._convert_categoricals = convert_categoricals
-        self._index_col = index_col
-        self._convert_missing = convert_missing
-        self._preserve_dtypes = preserve_dtypes
-        self._columns = columns
-        self._order_categoricals = order_categoricals
-        self._original_path_or_buf = path_or_buf
-        self._compression = compression
-        self._storage_options = storage_options
-        self._encoding = ""
-        self._chunksize = chunksize
-        self._using_iterator = False
-        self._entered = False
-        if self._chunksize is None:
-            self._chunksize = 1
-        elif not isinstance(chunksize, int) or chunksize <= 0:
-            raise ValueError("chunksize must be a positive integer when set.")
-
-        # State variables for the file
-        self._close_file: Callable[[], None] | None = None
-        self._has_string_data = False
-        self._missing_values = False
-        self._can_read_value_labels = False
-        self._column_selector_set = False
-        self._value_labels_read = False
-        self._data_read = False
-        self._dtype: np.dtype | None = None
-        self._lines_read = 0
-
-        self._native_byteorder = _set_endianness(sys.byteorder)
-
-    def _ensure_open(self) -> None:
-        """
-        Ensure the file has been opened and its header data read.
-        """
-        if not hasattr(self, "_path_or_buf"):
-            self._open_file()
-
-    def _open_file(self) -> None:
-        """
-        Open the file (with compression options, etc.), and read header information.
-        """
-        if not self._entered:
-            warnings.warn(
-                "StataReader is being used without using a context manager. "
-                "Using StataReader as a context manager is the only supported method.",
-                ResourceWarning,
-                stacklevel=find_stack_level(),
-            )
-        handles = get_handle(
-            self._original_path_or_buf,
-            "rb",
-            storage_options=self._storage_options,
-            is_text=False,
-            compression=self._compression,
-        )
-        if hasattr(handles.handle, "seekable") and handles.handle.seekable():
-            # If the handle is directly seekable, use it without an extra copy.
-            self._path_or_buf = handles.handle
-            self._close_file = handles.close
-        else:
-            # Copy to memory, and ensure no encoding.
-            with handles:
-                self._path_or_buf = BytesIO(handles.handle.read())
-            self._close_file = self._path_or_buf.close
-
-        self._read_header()
-        self._setup_dtype()
-
-    def __enter__(self) -> StataReader:
-        """enter context manager"""
-        self._entered = True
-        return self
-
-    def __exit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_value: BaseException | None,
-        traceback: TracebackType | None,
-    ) -> None:
-        if self._close_file:
-            self._close_file()
-
-    def close(self) -> None:
-        """Close the handle if its open.
-
-        .. deprecated: 2.0.0
-
-           The close method is not part of the public API.
-           The only supported way to use StataReader is to use it as a context manager.
-        """
-        warnings.warn(
-            "The StataReader.close() method is not part of the public API and "
-            "will be removed in a future version without notice. "
-            "Using StataReader as a context manager is the only supported method.",
-            FutureWarning,
-            stacklevel=find_stack_level(),
-        )
-        if self._close_file:
-            self._close_file()
-
-    def _set_encoding(self) -> None:
-        """
-        Set string encoding which depends on file version
-        """
-        if self._format_version < 118:
-            self._encoding = "latin-1"
-        else:
-            self._encoding = "utf-8"
-
-    def _read_int8(self) -> int:
-        return struct.unpack("b", self._path_or_buf.read(1))[0]
-
-    def _read_uint8(self) -> int:
-        return struct.unpack("B", self._path_or_buf.read(1))[0]
-
-    def _read_uint16(self) -> int:
-        return struct.unpack(f"{self._byteorder}H", self._path_or_buf.read(2))[0]
-
-    def _read_uint32(self) -> int:
-        return struct.unpack(f"{self._byteorder}I", self._path_or_buf.read(4))[0]
-
-    def _read_uint64(self) -> int:
-        return struct.unpack(f"{self._byteorder}Q", self._path_or_buf.read(8))[0]
-
-    def _read_int16(self) -> int:
-        return struct.unpack(f"{self._byteorder}h", self._path_or_buf.read(2))[0]
-
-    def _read_int32(self) -> int:
-        return struct.unpack(f"{self._byteorder}i", self._path_or_buf.read(4))[0]
-
-    def _read_int64(self) -> int:
-        return struct.unpack(f"{self._byteorder}q", self._path_or_buf.read(8))[0]
-
-    def _read_char8(self) -> bytes:
-        return struct.unpack("c", self._path_or_buf.read(1))[0]
-
-    def _read_int16_count(self, count: int) -> tuple[int, ...]:
-        return struct.unpack(
-            f"{self._byteorder}{'h' * count}",
-            self._path_or_buf.read(2 * count),
-        )
-
-    def _read_header(self) -> None:
-        first_char = self._read_char8()
-        if first_char == b"<":
-            self._read_new_header()
-        else:
-            self._read_old_header(first_char)
-
-        self._has_string_data = len([x for x in self._typlist if type(x) is int]) > 0
-
-        # calculate size of a data record
-        self._col_sizes = [self._calcsize(typ) for typ in self._typlist]
-
-    def _read_new_header(self) -> None:
-        # The first part of the header is common to 117 - 119.
-        self._path_or_buf.read(27)  # stata_dta><header><release>
-        self._format_version = int(self._path_or_buf.read(3))
-        if self._format_version not in [117, 118, 119]:
-            raise ValueError(_version_error.format(version=self._format_version))
-        self._set_encoding()
-        self._path_or_buf.read(21)  # </release><byteorder>
-        self._byteorder = ">" if self._path_or_buf.read(3) == b"MSF" else "<"
-        self._path_or_buf.read(15)  # </byteorder><K>
-        self._nvar = (
-            self._read_uint16() if self._format_version <= 118 else self._read_uint32()
-        )
-        self._path_or_buf.read(7)  # </K><N>
-
-        self._nobs = self._get_nobs()
-        self._path_or_buf.read(11)  # </N><label>
-        self._data_label = self._get_data_label()
-        self._path_or_buf.read(19)  # </label><timestamp>
-        self._time_stamp = self._get_time_stamp()
-        self._path_or_buf.read(26)  # </timestamp></header><map>
-        self._path_or_buf.read(8)  # 0x0000000000000000
-        self._path_or_buf.read(8)  # position of <map>
-
-        self._seek_vartypes = self._read_int64() + 16
-        self._seek_varnames = self._read_int64() + 10
-        self._seek_sortlist = self._read_int64() + 10
-        self._seek_formats = self._read_int64() + 9
-        self._seek_value_label_names = self._read_int64() + 19
-
-        # Requires version-specific treatment
-        self._seek_variable_labels = self._get_seek_variable_labels()
-
-        self._path_or_buf.read(8)  # <characteristics>
-        self._data_location = self._read_int64() + 6
-        self._seek_strls = self._read_int64() + 7
-        self._seek_value_labels = self._read_int64() + 14
-
-        self._typlist, self._dtyplist = self._get_dtypes(self._seek_vartypes)
-
-        self._path_or_buf.seek(self._seek_varnames)
-        self._varlist = self._get_varlist()
-
-        self._path_or_buf.seek(self._seek_sortlist)
-        self._srtlist = self._read_int16_count(self._nvar + 1)[:-1]
-
-        self._path_or_buf.seek(self._seek_formats)
-        self._fmtlist = self._get_fmtlist()
-
-        self._path_or_buf.seek(self._seek_value_label_names)
-        self._lbllist = self._get_lbllist()
-
-        self._path_or_buf.seek(self._seek_variable_labels)
-        self._variable_labels = self._get_variable_labels()
-
-    # Get data type information, works for versions 117-119.
-    def _get_dtypes(
-        self, seek_vartypes: int
-    ) -> tuple[list[int | str], list[str | np.dtype]]:
-        self._path_or_buf.seek(seek_vartypes)
-        raw_typlist = [self._read_uint16() for _ in range(self._nvar)]
-
-        def f(typ: int) -> int | str:
-            if typ <= 2045:
-                return typ
-            try:
-                return self.TYPE_MAP_XML[typ]
-            except KeyError as err:
-                raise ValueError(f"cannot convert stata types [{typ}]") from err
-
-        typlist = [f(x) for x in raw_typlist]
-
-        def g(typ: int) -> str | np.dtype:
-            if typ <= 2045:
-                return str(typ)
-            try:
-                return self.DTYPE_MAP_XML[typ]
-            except KeyError as err:
-                raise ValueError(f"cannot convert stata dtype [{typ}]") from err
-
-        dtyplist = [g(x) for x in raw_typlist]
-
-        return typlist, dtyplist
-
-    def _get_varlist(self) -> list[str]:
-        # 33 in order formats, 129 in formats 118 and 119
-        b = 33 if self._format_version < 118 else 129
-        return [self._decode(self._path_or_buf.read(b)) for _ in range(self._nvar)]
-
-    # Returns the format list
-    def _get_fmtlist(self) -> list[str]:
-        if self._format_version >= 118:
-            b = 57
-        elif self._format_version > 113:
-            b = 49
-        elif self._format_version > 104:
-            b = 12
-        else:
-            b = 7
-
-        return [self._decode(self._path_or_buf.read(b)) for _ in range(self._nvar)]
-
-    # Returns the label list
-    def _get_lbllist(self) -> list[str]:
-        if self._format_version >= 118:
-            b = 129
-        elif self._format_version > 108:
-            b = 33
-        else:
-            b = 9
-        return [self._decode(self._path_or_buf.read(b)) for _ in range(self._nvar)]
-
-    def _get_variable_labels(self) -> list[str]:
-        if self._format_version >= 118:
-            vlblist = [
-                self._decode(self._path_or_buf.read(321)) for _ in range(self._nvar)
-            ]
-        elif self._format_version > 105:
-            vlblist = [
-                self._decode(self._path_or_buf.read(81)) for _ in range(self._nvar)
-            ]
-        else:
-            vlblist = [
-                self._decode(self._path_or_buf.read(32)) for _ in range(self._nvar)
-            ]
-        return vlblist
-
-    def _get_nobs(self) -> int:
-        if self._format_version >= 118:
-            return self._read_uint64()
-        else:
-            return self._read_uint32()
-
-    def _get_data_label(self) -> str:
-        if self._format_version >= 118:
-            strlen = self._read_uint16()
-            return self._decode(self._path_or_buf.read(strlen))
-        elif self._format_version == 117:
-            strlen = self._read_int8()
-            return self._decode(self._path_or_buf.read(strlen))
-        elif self._format_version > 105:
-            return self._decode(self._path_or_buf.read(81))
-        else:
-            return self._decode(self._path_or_buf.read(32))
-
-    def _get_time_stamp(self) -> str:
-        if self._format_version >= 118:
-            strlen = self._read_int8()
-            return self._path_or_buf.read(strlen).decode("utf-8")
-        elif self._format_version == 117:
-            strlen = self._read_int8()
-            return self._decode(self._path_or_buf.read(strlen))
-        elif self._format_version > 104:
-            return self._decode(self._path_or_buf.read(18))
-        else:
-            raise ValueError()
-
-    def _get_seek_variable_labels(self) -> int:
-        if self._format_version == 117:
-            self._path_or_buf.read(8)  # <variable_labels>, throw away
-            # Stata 117 data files do not follow the described format.  This is
-            # a work around that uses the previous label, 33 bytes for each
-            # variable, 20 for the closing tag and 17 for the opening tag
-            return self._seek_value_label_names + (33 * self._nvar) + 20 + 17
-        elif self._format_version >= 118:
-            return self._read_int64() + 17
-        else:
-            raise ValueError()
-
-    def _read_old_header(self, first_char: bytes) -> None:
-        self._format_version = int(first_char[0])
-        if self._format_version not in [104, 105, 108, 111, 113, 114, 115]:
-            raise ValueError(_version_error.format(version=self._format_version))
-        self._set_encoding()
-        self._byteorder = ">" if self._read_int8() == 0x1 else "<"
-        self._filetype = self._read_int8()
-        self._path_or_buf.read(1)  # unused
-
-        self._nvar = self._read_uint16()
-        self._nobs = self._get_nobs()
-
-        self._data_label = self._get_data_label()
-
-        self._time_stamp = self._get_time_stamp()
-
-        # descriptors
-        if self._format_version > 108:
-            typlist = [int(c) for c in self._path_or_buf.read(self._nvar)]
-        else:
-            buf = self._path_or_buf.read(self._nvar)
-            typlistb = np.frombuffer(buf, dtype=np.uint8)
-            typlist = []
-            for tp in typlistb:
-                if tp in self.OLD_TYPE_MAPPING:
-                    typlist.append(self.OLD_TYPE_MAPPING[tp])
-                else:
-                    typlist.append(tp - 127)  # bytes
-
-        try:
-            self._typlist = [self.TYPE_MAP[typ] for typ in typlist]
-        except ValueError as err:
-            invalid_types = ",".join([str(x) for x in typlist])
-            raise ValueError(f"cannot convert stata types [{invalid_types}]") from err
-        try:
-            self._dtyplist = [self.DTYPE_MAP[typ] for typ in typlist]
-        except ValueError as err:
-            invalid_dtypes = ",".join([str(x) for x in typlist])
-            raise ValueError(f"cannot convert stata dtypes [{invalid_dtypes}]") from err
-
-        if self._format_version > 108:
-            self._varlist = [
-                self._decode(self._path_or_buf.read(33)) for _ in range(self._nvar)
-            ]
-        else:
-            self._varlist = [
-                self._decode(self._path_or_buf.read(9)) for _ in range(self._nvar)
-            ]
-        self._srtlist = self._read_int16_count(self._nvar + 1)[:-1]
-
-        self._fmtlist = self._get_fmtlist()
-
-        self._lbllist = self._get_lbllist()
-
-        self._variable_labels = self._get_variable_labels()
-
-        # ignore expansion fields (Format 105 and later)
-        # When reading, read five bytes; the last four bytes now tell you
-        # the size of the next read, which you discard.  You then continue
-        # like this until you read 5 bytes of zeros.
-
-        if self._format_version > 104:
-            while True:
-                data_type = self._read_int8()
-                if self._format_version > 108:
-                    data_len = self._read_int32()
-                else:
-                    data_len = self._read_int16()
-                if data_type == 0:
-                    break
-                self._path_or_buf.read(data_len)
-
-        # necessary data to continue parsing
-        self._data_location = self._path_or_buf.tell()
-
-    def _setup_dtype(self) -> np.dtype:
-        """Map between numpy and state dtypes"""
-        if self._dtype is not None:
-            return self._dtype
-
-        dtypes = []  # Convert struct data types to numpy data type
-        for i, typ in enumerate(self._typlist):
-            if typ in self.NUMPY_TYPE_MAP:
-                typ = cast(str, typ)  # only strs in NUMPY_TYPE_MAP
-                dtypes.append((f"s{i}", f"{self._byteorder}{self.NUMPY_TYPE_MAP[typ]}"))
-            else:
-                dtypes.append((f"s{i}", f"S{typ}"))
-        self._dtype = np.dtype(dtypes)
-
-        return self._dtype
-
-    def _calcsize(self, fmt: int | str) -> int:
-        if isinstance(fmt, int):
-            return fmt
-        return struct.calcsize(self._byteorder + fmt)
-
-    def _decode(self, s: bytes) -> str:
-        # have bytes not strings, so must decode
-        s = s.partition(b"\0")[0]
-        try:
-            return s.decode(self._encoding)
-        except UnicodeDecodeError:
-            # GH 25960, fallback to handle incorrect format produced when 117
-            # files are converted to 118 files in Stata
-            encoding = self._encoding
-            msg = f"""
-One or more strings in the dta file could not be decoded using {encoding}, and
-so the fallback encoding of latin-1 is being used.  This can happen when a file
-has been incorrectly encoded by Stata or some other software. You should verify
-the string values returned are correct."""
-            warnings.warn(
-                msg,
-                UnicodeWarning,
-                stacklevel=find_stack_level(),
-            )
-            return s.decode("latin-1")
-
-    def _read_value_labels(self) -> None:
-        self._ensure_open()
-        if self._value_labels_read:
-            # Don't read twice
-            return
-        if self._format_version <= 108:
-            # Value labels are not supported in version 108 and earlier.
-            self._value_labels_read = True
-            self._value_label_dict: dict[str, dict[float, str]] = {}
-            return
-
-        if self._format_version >= 117:
-            self._path_or_buf.seek(self._seek_value_labels)
-        else:
-            assert self._dtype is not None
-            offset = self._nobs * self._dtype.itemsize
-            self._path_or_buf.seek(self._data_location + offset)
-
-        self._value_labels_read = True
-        self._value_label_dict = {}
-
-        while True:
-            if self._format_version >= 117:
-                if self._path_or_buf.read(5) == b"</val":  # <lbl>
-                    break  # end of value label table
-
-            slength = self._path_or_buf.read(4)
-            if not slength:
-                break  # end of value label table (format < 117)
-            if self._format_version <= 117:
-                labname = self._decode(self._path_or_buf.read(33))
-            else:
-                labname = self._decode(self._path_or_buf.read(129))
-            self._path_or_buf.read(3)  # padding
-
-            n = self._read_uint32()
-            txtlen = self._read_uint32()
-            off = np.frombuffer(
-                self._path_or_buf.read(4 * n), dtype=f"{self._byteorder}i4", count=n
-            )
-            val = np.frombuffer(
-                self._path_or_buf.read(4 * n), dtype=f"{self._byteorder}i4", count=n
-            )
-            ii = np.argsort(off)
-            off = off[ii]
-            val = val[ii]
-            txt = self._path_or_buf.read(txtlen)
-            self._value_label_dict[labname] = {}
-            for i in range(n):
-                end = off[i + 1] if i < n - 1 else txtlen
-                self._value_label_dict[labname][val[i]] = self._decode(
-                    txt[off[i] : end]
-                )
-            if self._format_version >= 117:
-                self._path_or_buf.read(6)  # </lbl>
-        self._value_labels_read = True
-
-    def _read_strls(self) -> None:
-        self._path_or_buf.seek(self._seek_strls)
-        # Wrap v_o in a string to allow uint64 values as keys on 32bit OS
-        self.GSO = {"0": ""}
-        while True:
-            if self._path_or_buf.read(3) != b"GSO":
-                break
-
-            if self._format_version == 117:
-                v_o = self._read_uint64()
-            else:
-                buf = self._path_or_buf.read(12)
-                # Only tested on little endian file on little endian machine.
-                v_size = 2 if self._format_version == 118 else 3
-                if self._byteorder == "<":
-                    buf = buf[0:v_size] + buf[4 : (12 - v_size)]
-                else:
-                    # This path may not be correct, impossible to test
-                    buf = buf[0:v_size] + buf[(4 + v_size) :]
-                v_o = struct.unpack("Q", buf)[0]
-            typ = self._read_uint8()
-            length = self._read_uint32()
-            va = self._path_or_buf.read(length)
-            if typ == 130:
-                decoded_va = va[0:-1].decode(self._encoding)
-            else:
-                # Stata says typ 129 can be binary, so use str
-                decoded_va = str(va)
-                # Wrap v_o in a string to allow uint64 values as keys on 32bit OS
-            self.GSO[str(v_o)] = decoded_va
-
-    def __next__(self) -> DataFrame:
-        self._using_iterator = True
-        return self.read(nrows=self._chunksize)
-
-    def get_chunk(self, size: int | None = None) -> DataFrame:
-        """
-        Reads lines from Stata file and returns as dataframe
-
-        Parameters
-        ----------
-        size : int, defaults to None
-            Number of lines to read.  If None, reads whole file.
-
-        Returns
-        -------
-        DataFrame
-        """
-        if size is None:
-            size = self._chunksize
-        return self.read(nrows=size)
-
-    @Appender(_read_method_doc)
-    def read(
-        self,
-        nrows: int | None = None,
-        convert_dates: bool | None = None,
-        convert_categoricals: bool | None = None,
-        index_col: str | None = None,
-        convert_missing: bool | None = None,
-        preserve_dtypes: bool | None = None,
-        columns: Sequence[str] | None = None,
-        order_categoricals: bool | None = None,
-    ) -> DataFrame:
-        self._ensure_open()
-
-        # Handle options
-        if convert_dates is None:
-            convert_dates = self._convert_dates
-        if convert_categoricals is None:
-            convert_categoricals = self._convert_categoricals
-        if convert_missing is None:
-            convert_missing = self._convert_missing
-        if preserve_dtypes is None:
-            preserve_dtypes = self._preserve_dtypes
-        if columns is None:
-            columns = self._columns
-        if order_categoricals is None:
-            order_categoricals = self._order_categoricals
-        if index_col is None:
-            index_col = self._index_col
-        if nrows is None:
-            nrows = self._nobs
-
-        # Handle empty file or chunk.  If reading incrementally raise
-        # StopIteration.  If reading the whole thing return an empty
-        # data frame.
-        if (self._nobs == 0) and nrows == 0:
-            self._can_read_value_labels = True
-            self._data_read = True
-            data = DataFrame(columns=self._varlist)
-            # Apply dtypes correctly
-            for i, col in enumerate(data.columns):
-                dt = self._dtyplist[i]
-                if isinstance(dt, np.dtype):
-                    if dt.char != "S":
-                        data[col] = data[col].astype(dt)
-            if columns is not None:
-                data = self._do_select_columns(data, columns)
-            return data
-
-        if (self._format_version >= 117) and (not self._value_labels_read):
-            self._can_read_value_labels = True
-            self._read_strls()
-
-        # Read data
-        assert self._dtype is not None
-        dtype = self._dtype
-        max_read_len = (self._nobs - self._lines_read) * dtype.itemsize
-        read_len = nrows * dtype.itemsize
-        read_len = min(read_len, max_read_len)
-        if read_len <= 0:
-            # Iterator has finished, should never be here unless
-            # we are reading the file incrementally
-            if convert_categoricals:
-                self._read_value_labels()
-            raise StopIteration
-        offset = self._lines_read * dtype.itemsize
-        self._path_or_buf.seek(self._data_location + offset)
-        read_lines = min(nrows, self._nobs - self._lines_read)
-        raw_data = np.frombuffer(
-            self._path_or_buf.read(read_len), dtype=dtype, count=read_lines
-        )
-
-        self._lines_read += read_lines
-        if self._lines_read == self._nobs:
-            self._can_read_value_labels = True
-            self._data_read = True
-        # if necessary, swap the byte order to native here
-        if self._byteorder != self._native_byteorder:
-            raw_data = raw_data.byteswap().view(raw_data.dtype.newbyteorder())
-
-        if convert_categoricals:
-            self._read_value_labels()
-
-        if len(raw_data) == 0:
-            data = DataFrame(columns=self._varlist)
-        else:
-            data = DataFrame.from_records(raw_data)
-            data.columns = Index(self._varlist)
-
-        # If index is not specified, use actual row number rather than
-        # restarting at 0 for each chunk.
-        if index_col is None:
-            rng = range(self._lines_read - read_lines, self._lines_read)
-            data.index = Index(rng)  # set attr instead of set_index to avoid copy
-
-        if columns is not None:
-            data = self._do_select_columns(data, columns)
-
-        # Decode strings
-        for col, typ in zip(data, self._typlist):
-            if type(typ) is int:
-                data[col] = data[col].apply(self._decode)
-
-        data = self._insert_strls(data)
-
-        cols_ = np.where([dtyp is not None for dtyp in self._dtyplist])[0]
-        # Convert columns (if needed) to match input type
-        ix = data.index
-        requires_type_conversion = False
-        data_formatted = []
-        for i in cols_:
-            if self._dtyplist[i] is not None:
-                col = data.columns[i]
-                dtype = data[col].dtype
-                if dtype != np.dtype(object) and dtype != self._dtyplist[i]:
-                    requires_type_conversion = True
-                    data_formatted.append(
-                        (col, Series(data[col], ix, self._dtyplist[i]))
-                    )
-                else:
-                    data_formatted.append((col, data[col]))
-        if requires_type_conversion:
-            data = DataFrame.from_dict(dict(data_formatted))
-        del data_formatted
-
-        data = self._do_convert_missing(data, convert_missing)
-
-        if convert_dates:
-
-            def any_startswith(x: str) -> bool:
-                return any(x.startswith(fmt) for fmt in _date_formats)
-
-            cols = np.where([any_startswith(x) for x in self._fmtlist])[0]
-            for i in cols:
-                col = data.columns[i]
-                data[col] = _stata_elapsed_date_to_datetime_vec(
-                    data[col], self._fmtlist[i]
-                )
-
-        if convert_categoricals and self._format_version > 108:
-            data = self._do_convert_categoricals(
-                data, self._value_label_dict, self._lbllist, order_categoricals
-            )
-
-        if not preserve_dtypes:
-            retyped_data = []
-            convert = False
-            for col in data:
-                dtype = data[col].dtype
-                if dtype in (np.dtype(np.float16), np.dtype(np.float32)):
-                    dtype = np.dtype(np.float64)
-                    convert = True
-                elif dtype in (
-                    np.dtype(np.int8),
-                    np.dtype(np.int16),
-                    np.dtype(np.int32),
-                ):
-                    dtype = np.dtype(np.int64)
-                    convert = True
-                retyped_data.append((col, data[col].astype(dtype)))
-            if convert:
-                data = DataFrame.from_dict(dict(retyped_data))
-
-        if index_col is not None:
-            data = data.set_index(data.pop(index_col))
-
-        return data
-
-    def _do_convert_missing(self, data: DataFrame, convert_missing: bool) -> DataFrame:
-        # Check for missing values, and replace if found
-        replacements = {}
-        for i, colname in enumerate(data):
-            fmt = self._typlist[i]
-            if fmt not in self.VALID_RANGE:
-                continue
-
-            fmt = cast(str, fmt)  # only strs in VALID_RANGE
-            nmin, nmax = self.VALID_RANGE[fmt]
-            series = data[colname]
-
-            # appreciably faster to do this with ndarray instead of Series
-            svals = series._values
-            missing = (svals < nmin) | (svals > nmax)
-
-            if not missing.any():
-                continue
-
-            if convert_missing:  # Replacement follows Stata notation
-                missing_loc = np.nonzero(np.asarray(missing))[0]
-                umissing, umissing_loc = np.unique(series[missing], return_inverse=True)
-                replacement = Series(series, dtype=object)
-                for j, um in enumerate(umissing):
-                    missing_value = StataMissingValue(um)
-
-                    loc = missing_loc[umissing_loc == j]
-                    replacement.iloc[loc] = missing_value
-            else:  # All replacements are identical
-                dtype = series.dtype
-                if dtype not in (np.float32, np.float64):
-                    dtype = np.float64
-                replacement = Series(series, dtype=dtype)
-                if not replacement._values.flags["WRITEABLE"]:
-                    # only relevant for ArrayManager; construction
-                    #  path for BlockManager ensures writeability
-                    replacement = replacement.copy()
-                # Note: operating on ._values is much faster than directly
-                # TODO: can we fix that?
-                replacement._values[missing] = np.nan
-            replacements[colname] = replacement
-
-        if replacements:
-            for col, value in replacements.items():
-                data[col] = value
-        return data
-
-    def _insert_strls(self, data: DataFrame) -> DataFrame:
-        if not hasattr(self, "GSO") or len(self.GSO) == 0:
-            return data
-        for i, typ in enumerate(self._typlist):
-            if typ != "Q":
-                continue
-            # Wrap v_o in a string to allow uint64 values as keys on 32bit OS
-            data.iloc[:, i] = [self.GSO[str(k)] for k in data.iloc[:, i]]
-        return data
-
-    def _do_select_columns(self, data: DataFrame, columns: Sequence[str]) -> DataFrame:
-        if not self._column_selector_set:
-            column_set = set(columns)
-            if len(column_set) != len(columns):
-                raise ValueError("columns contains duplicate entries")
-            unmatched = column_set.difference(data.columns)
-            if unmatched:
-                joined = ", ".join(list(unmatched))
-                raise ValueError(
-                    "The following columns were not "
-                    f"found in the Stata data set: {joined}"
-                )
-            # Copy information for retained columns for later processing
-            dtyplist = []
-            typlist = []
-            fmtlist = []
-            lbllist = []
-            for col in columns:
-                i = data.columns.get_loc(col)
-                dtyplist.append(self._dtyplist[i])
-                typlist.append(self._typlist[i])
-                fmtlist.append(self._fmtlist[i])
-                lbllist.append(self._lbllist[i])
-
-            self._dtyplist = dtyplist
-            self._typlist = typlist
-            self._fmtlist = fmtlist
-            self._lbllist = lbllist
-            self._column_selector_set = True
-
-        return data[columns]
-
-    def _do_convert_categoricals(
-        self,
-        data: DataFrame,
-        value_label_dict: dict[str, dict[float, str]],
-        lbllist: Sequence[str],
-        order_categoricals: bool,
-    ) -> DataFrame:
-        """
-        Converts categorical columns to Categorical type.
-        """
-        value_labels = list(value_label_dict.keys())
-        cat_converted_data = []
-        for col, label in zip(data, lbllist):
-            if label in value_labels:
-                # Explicit call with ordered=True
-                vl = value_label_dict[label]
-                keys = np.array(list(vl.keys()))
-                column = data[col]
-                key_matches = column.isin(keys)
-                if self._using_iterator and key_matches.all():
-                    initial_categories: np.ndarray | None = keys
-                    # If all categories are in the keys and we are iterating,
-                    # use the same keys for all chunks. If some are missing
-                    # value labels, then we will fall back to the categories
-                    # varying across chunks.
-                else:
-                    if self._using_iterator:
-                        # warn is using an iterator
-                        warnings.warn(
-                            categorical_conversion_warning,
-                            CategoricalConversionWarning,
-                            stacklevel=find_stack_level(),
-                        )
-                    initial_categories = None
-                cat_data = Categorical(
-                    column, categories=initial_categories, ordered=order_categoricals
-                )
-                if initial_categories is None:
-                    # If None here, then we need to match the cats in the Categorical
-                    categories = []
-                    for category in cat_data.categories:
-                        if category in vl:
-                            categories.append(vl[category])
-                        else:
-                            categories.append(category)
-                else:
-                    # If all cats are matched, we can use the values
-                    categories = list(vl.values())
-                try:
-                    # Try to catch duplicate categories
-                    # TODO: if we get a non-copying rename_categories, use that
-                    cat_data = cat_data.rename_categories(categories)
-                except ValueError as err:
-                    vc = Series(categories, copy=False).value_counts()
-                    repeated_cats = list(vc.index[vc > 1])
-                    repeats = "-" * 80 + "\n" + "\n".join(repeated_cats)
-                    # GH 25772
-                    msg = f"""
-Value labels for column {col} are not unique. These cannot be converted to
-pandas categoricals.
-
-Either read the file with `convert_categoricals` set to False or use the
-low level interface in `StataReader` to separately read the values and the
-value_labels.
-
-The repeated labels are:
-{repeats}
-"""
-                    raise ValueError(msg) from err
-                # TODO: is the next line needed above in the data(...) method?
-                cat_series = Series(cat_data, index=data.index, copy=False)
-                cat_converted_data.append((col, cat_series))
-            else:
-                cat_converted_data.append((col, data[col]))
-        data = DataFrame(dict(cat_converted_data), copy=False)
-        return data
-
-    @property
-    def data_label(self) -> str:
-        """
-        Return data label of Stata file.
-
-        Examples
-        --------
-        >>> df = pd.DataFrame([(1,)], columns=["variable"])
-        >>> time_stamp = pd.Timestamp(2000, 2, 29, 14, 21)
-        >>> data_label = "This is a data file."
-        >>> path = "/My_path/filename.dta"
-        >>> df.to_stata(path, time_stamp=time_stamp,    # doctest: +SKIP
-        ...             data_label=data_label,  # doctest: +SKIP
-        ...             version=None)  # doctest: +SKIP
-        >>> with pd.io.stata.StataReader(path) as reader:  # doctest: +SKIP
-        ...     print(reader.data_label)  # doctest: +SKIP
-        This is a data file.
-        """
-        self._ensure_open()
-        return self._data_label
-
-    @property
-    def time_stamp(self) -> str:
-        """
-        Return time stamp of Stata file.
-        """
-        self._ensure_open()
-        return self._time_stamp
-
-    def variable_labels(self) -> dict[str, str]:
-        """
-        Return a dict associating each variable name with corresponding label.
-
-        Returns
-        -------
-        dict
-
-        Examples
-        --------
-        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["col_1", "col_2"])
-        >>> time_stamp = pd.Timestamp(2000, 2, 29, 14, 21)
-        >>> path = "/My_path/filename.dta"
-        >>> variable_labels = {"col_1": "This is an example"}
-        >>> df.to_stata(path, time_stamp=time_stamp,  # doctest: +SKIP
-        ...             variable_labels=variable_labels, version=None)  # doctest: +SKIP
-        >>> with pd.io.stata.StataReader(path) as reader:  # doctest: +SKIP
-        ...     print(reader.variable_labels())  # doctest: +SKIP
-        {'index': '', 'col_1': 'This is an example', 'col_2': ''}
-        >>> pd.read_stata(path)  # doctest: +SKIP
-            index col_1 col_2
-        0       0    1    2
-        1       1    3    4
-        """
-        self._ensure_open()
-        return dict(zip(self._varlist, self._variable_labels))
-
-    def value_labels(self) -> dict[str, dict[float, str]]:
-        """
-        Return a nested dict associating each variable name to its value and label.
-
-        Returns
-        -------
-        dict
-
-        Examples
-        --------
-        >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["col_1", "col_2"])
-        >>> time_stamp = pd.Timestamp(2000, 2, 29, 14, 21)
-        >>> path = "/My_path/filename.dta"
-        >>> value_labels = {"col_1": {3: "x"}}
-        >>> df.to_stata(path, time_stamp=time_stamp,  # doctest: +SKIP
-        ...             value_labels=value_labels, version=None)  # doctest: +SKIP
-        >>> with pd.io.stata.StataReader(path) as reader:  # doctest: +SKIP
-        ...     print(reader.value_labels())  # doctest: +SKIP
-        {'col_1': {3: 'x'}}
-        >>> pd.read_stata(path)  # doctest: +SKIP
-            index col_1 col_2
-        0       0    1    2
-        1       1    x    4
-        """
-        if not self._value_labels_read:
-            self._read_value_labels()
-
-        return self._value_label_dict
-
-
-@Appender(_read_stata_doc)
-def read_stata(
-    filepath_or_buffer: FilePath | ReadBuffer[bytes],
-    *,
-    convert_dates: bool = True,
-    convert_categoricals: bool = True,
-    index_col: str | None = None,
-    convert_missing: bool = False,
-    preserve_dtypes: bool = True,
-    columns: Sequence[str] | None = None,
-    order_categoricals: bool = True,
-    chunksize: int | None = None,
-    iterator: bool = False,
-    compression: CompressionOptions = "infer",
-    storage_options: StorageOptions | None = None,
-) -> DataFrame | StataReader:
-    reader = StataReader(
-        filepath_or_buffer,
-        convert_dates=convert_dates,
-        convert_categoricals=convert_categoricals,
-        index_col=index_col,
-        convert_missing=convert_missing,
-        preserve_dtypes=preserve_dtypes,
-        columns=columns,
-        order_categoricals=order_categoricals,
-        chunksize=chunksize,
-        storage_options=storage_options,
-        compression=compression,
-    )
-
-    if iterator or chunksize:
-        return reader
-
-    with reader:
-        return reader.read()
-
-
-def _set_endianness(endianness: str) -> str:
-    if endianness.lower() in ["<", "little"]:
-        return "<"
-    elif endianness.lower() in [">", "big"]:
-        return ">"
-    else:  # pragma : no cover
-        raise ValueError(f"Endianness {endianness} not understood")
-
-
-def _pad_bytes(name: AnyStr, length: int) -> AnyStr:
-    """
-    Take a char string and pads it with null bytes until it's length chars.
-    """
-    if isinstance(name, bytes):
-        return name + b"\x00" * (length - len(name))
-    return name + "\x00" * (length - len(name))
-
-
-def _convert_datetime_to_stata_type(fmt: str) -> np.dtype:
-    """
-    Convert from one of the stata date formats to a type in TYPE_MAP.
-    """
-    if fmt in [
-        "tc",
-        "%tc",
-        "td",
-        "%td",
-        "tw",
-        "%tw",
-        "tm",
-        "%tm",
-        "tq",
-        "%tq",
-        "th",
-        "%th",
-        "ty",
-        "%ty",
-    ]:
-        return np.dtype(np.float64)  # Stata expects doubles for SIFs
-    else:
-        raise NotImplementedError(f"Format {fmt} not implemented")
-
-
-def _maybe_convert_to_int_keys(convert_dates: dict, varlist: list[Hashable]) -> dict:
-    new_dict = {}
-    for key in convert_dates:
-        if not convert_dates[key].startswith("%"):  # make sure proper fmts
-            convert_dates[key] = "%" + convert_dates[key]
-        if key in varlist:
-            new_dict.update({varlist.index(key): convert_dates[key]})
-        else:
-            if not isinstance(key, int):
-                raise ValueError("convert_dates key must be a column or an integer")
-            new_dict.update({key: convert_dates[key]})
-    return new_dict
-
-
-def _dtype_to_stata_type(dtype: np.dtype, column: Series) -> int:
-    """
-    Convert dtype types to stata types. Returns the byte of the given ordinal.
-    See TYPE_MAP and comments for an explanation. This is also explained in
-    the dta spec.
-    1 - 244 are strings of this length
-                         Pandas    Stata
-    251 - for int8      byte
-    252 - for int16     int
-    253 - for int32     long
-    254 - for float32   float
-    255 - for double    double
-
-    If there are dates to convert, then dtype will already have the correct
-    type inserted.
-    """
-    # TODO: expand to handle datetime to integer conversion
-    if dtype.type is np.object_:  # try to coerce it to the biggest string
-        # not memory efficient, what else could we
-        # do?
-        itemsize = max_len_string_array(ensure_object(column._values))
-        return max(itemsize, 1)
-    elif dtype.type is np.float64:
-        return 255
-    elif dtype.type is np.float32:
-        return 254
-    elif dtype.type is np.int32:
-        return 253
-    elif dtype.type is np.int16:
-        return 252
-    elif dtype.type is np.int8:
-        return 251
-    else:  # pragma : no cover
-        raise NotImplementedError(f"Data type {dtype} not supported.")
-
-
-def _dtype_to_default_stata_fmt(
-    dtype, column: Series, dta_version: int = 114, force_strl: bool = False
-) -> str:
-    """
-    Map numpy dtype to stata's default format for this type. Not terribly
-    important since users can change this in Stata. Semantics are
-
-    object  -> "%DDs" where DD is the length of the string.  If not a string,
-                raise ValueError
-    float64 -> "%10.0g"
-    float32 -> "%9.0g"
-    int64   -> "%9.0g"
-    int32   -> "%12.0g"
-    int16   -> "%8.0g"
-    int8    -> "%8.0g"
-    strl    -> "%9s"
-    """
-    # TODO: Refactor to combine type with format
-    # TODO: expand this to handle a default datetime format?
-    if dta_version < 117:
-        max_str_len = 244
-    else:
-        max_str_len = 2045
-        if force_strl:
-            return "%9s"
-    if dtype.type is np.object_:
-        itemsize = max_len_string_array(ensure_object(column._values))
-        if itemsize > max_str_len:
-            if dta_version >= 117:
-                return "%9s"
-            else:
-                raise ValueError(excessive_string_length_error.format(column.name))
-        return "%" + str(max(itemsize, 1)) + "s"
-    elif dtype == np.float64:
-        return "%10.0g"
-    elif dtype == np.float32:
-        return "%9.0g"
-    elif dtype == np.int32:
-        return "%12.0g"
-    elif dtype in (np.int8, np.int16):
-        return "%8.0g"
-    else:  # pragma : no cover
-        raise NotImplementedError(f"Data type {dtype} not supported.")
-
-
-@doc(
-    storage_options=_shared_docs["storage_options"],
-    compression_options=_shared_docs["compression_options"] % "fname",
-)
-class StataWriter(StataParser):
-    """
-    A class for writing Stata binary dta files
-
-    Parameters
-    ----------
-    fname : path (string), buffer or path object
-        string, path object (pathlib.Path or py._path.local.LocalPath) or
-        object implementing a binary write() functions. If using a buffer
-        then the buffer will not be automatically closed after the file
-        is written.
-    data : DataFrame
-        Input to save
-    convert_dates : dict
-        Dictionary mapping columns containing datetime types to stata internal
-        format to use when writing the dates. Options are 'tc', 'td', 'tm',
-        'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name.
-        Datetime columns that do not have a conversion type specified will be
-        converted to 'tc'. Raises NotImplementedError if a datetime column has
-        timezone information
-    write_index : bool
-        Write the index to Stata dataset.
-    byteorder : str
-        Can be ">", "<", "little", or "big". default is `sys.byteorder`
-    time_stamp : datetime
-        A datetime to use as file creation date.  Default is the current time
-    data_label : str
-        A label for the data set.  Must be 80 characters or smaller.
-    variable_labels : dict
-        Dictionary containing columns as keys and variable labels as values.
-        Each label must be 80 characters or smaller.
-    {compression_options}
-
-        .. versionchanged:: 1.4.0 Zstandard support.
-
-    {storage_options}
-
-        .. versionadded:: 1.2.0
-
-    value_labels : dict of dicts
-        Dictionary containing columns as keys and dictionaries of column value
-        to labels as values. The combined length of all labels for a single
-        variable must be 32,000 characters or smaller.
-
-        .. versionadded:: 1.4.0
-
-    Returns
-    -------
-    writer : StataWriter instance
-        The StataWriter instance has a write_file method, which will
-        write the file to the given `fname`.
-
-    Raises
-    ------
-    NotImplementedError
-        * If datetimes contain timezone information
-    ValueError
-        * Columns listed in convert_dates are neither datetime64[ns]
-          or datetime
-        * Column dtype is not representable in Stata
-        * Column listed in convert_dates is not in DataFrame
-        * Categorical label contains more than 32,000 characters
-
-    Examples
-    --------
-    >>> data = pd.DataFrame([[1.0, 1]], columns=['a', 'b'])
-    >>> writer = StataWriter('./data_file.dta', data)
-    >>> writer.write_file()
-
-    Directly write a zip file
-    >>> compression = {{"method": "zip", "archive_name": "data_file.dta"}}
-    >>> writer = StataWriter('./data_file.zip', data, compression=compression)
-    >>> writer.write_file()
-
-    Save a DataFrame with dates
-    >>> from datetime import datetime
-    >>> data = pd.DataFrame([[datetime(2000,1,1)]], columns=['date'])
-    >>> writer = StataWriter('./date_data_file.dta', data, {{'date' : 'tw'}})
-    >>> writer.write_file()
-    """
-
-    _max_string_length = 244
-    _encoding: Literal["latin-1", "utf-8"] = "latin-1"
-
-    def __init__(
-        self,
-        fname: FilePath | WriteBuffer[bytes],
-        data: DataFrame,
-        convert_dates: dict[Hashable, str] | None = None,
-        write_index: bool = True,
-        byteorder: str | None = None,
-        time_stamp: datetime | None = None,
-        data_label: str | None = None,
-        variable_labels: dict[Hashable, str] | None = None,
-        compression: CompressionOptions = "infer",
-        storage_options: StorageOptions | None = None,
-        *,
-        value_labels: dict[Hashable, dict[float, str]] | None = None,
-    ) -> None:
-        super().__init__()
-        self.data = data
-        self._convert_dates = {} if convert_dates is None else convert_dates
-        self._write_index = write_index
-        self._time_stamp = time_stamp
-        self._data_label = data_label
-        self._variable_labels = variable_labels
-        self._non_cat_value_labels = value_labels
-        self._value_labels: list[StataValueLabel] = []
-        self._has_value_labels = np.array([], dtype=bool)
-        self._compression = compression
-        self._output_file: IO[bytes] | None = None
-        self._converted_names: dict[Hashable, str] = {}
-        # attach nobs, nvars, data, varlist, typlist
-        self._prepare_pandas(data)
-        self.storage_options = storage_options
-
-        if byteorder is None:
-            byteorder = sys.byteorder
-        self._byteorder = _set_endianness(byteorder)
-        self._fname = fname
-        self.type_converters = {253: np.int32, 252: np.int16, 251: np.int8}
-
-    def _write(self, to_write: str) -> None:
-        """
-        Helper to call encode before writing to file for Python 3 compat.
-        """
-        self.handles.handle.write(to_write.encode(self._encoding))
-
-    def _write_bytes(self, value: bytes) -> None:
-        """
-        Helper to assert file is open before writing.
-        """
-        self.handles.handle.write(value)
-
-    def _prepare_non_cat_value_labels(
-        self, data: DataFrame
-    ) -> list[StataNonCatValueLabel]:
-        """
-        Check for value labels provided for non-categorical columns. Value
-        labels
-        """
-        non_cat_value_labels: list[StataNonCatValueLabel] = []
-        if self._non_cat_value_labels is None:
-            return non_cat_value_labels
-
-        for labname, labels in self._non_cat_value_labels.items():
-            if labname in self._converted_names:
-                colname = self._converted_names[labname]
-            elif labname in data.columns:
-                colname = str(labname)
-            else:
-                raise KeyError(
-                    f"Can't create value labels for {labname}, it wasn't "
-                    "found in the dataset."
-                )
-
-            if not is_numeric_dtype(data[colname].dtype):
-                # Labels should not be passed explicitly for categorical
-                # columns that will be converted to int
-                raise ValueError(
-                    f"Can't create value labels for {labname}, value labels "
-                    "can only be applied to numeric columns."
-                )
-            svl = StataNonCatValueLabel(colname, labels, self._encoding)
-            non_cat_value_labels.append(svl)
-        return non_cat_value_labels
-
-    def _prepare_categoricals(self, data: DataFrame) -> DataFrame:
-        """
-        Check for categorical columns, retain categorical information for
-        Stata file and convert categorical data to int
-        """
-        is_cat = [isinstance(data[col].dtype, CategoricalDtype) for col in data]
-        if not any(is_cat):
-            return data
-
-        self._has_value_labels |= np.array(is_cat)
-
-        get_base_missing_value = StataMissingValue.get_base_missing_value
-        data_formatted = []
-        for col, col_is_cat in zip(data, is_cat):
-            if col_is_cat:
-                svl = StataValueLabel(data[col], encoding=self._encoding)
-                self._value_labels.append(svl)
-                dtype = data[col].cat.codes.dtype
-                if dtype == np.int64:
-                    raise ValueError(
-                        "It is not possible to export "
-                        "int64-based categorical data to Stata."
-                    )
-                values = data[col].cat.codes._values.copy()
-
-                # Upcast if needed so that correct missing values can be set
-                if values.max() >= get_base_missing_value(dtype):
-                    if dtype == np.int8:
-                        dtype = np.dtype(np.int16)
-                    elif dtype == np.int16:
-                        dtype = np.dtype(np.int32)
-                    else:
-                        dtype = np.dtype(np.float64)
-                    values = np.array(values, dtype=dtype)
-
-                # Replace missing values with Stata missing value for type
-                values[values == -1] = get_base_missing_value(dtype)
-                data_formatted.append((col, values))
-            else:
-                data_formatted.append((col, data[col]))
-        return DataFrame.from_dict(dict(data_formatted))
-
-    def _replace_nans(self, data: DataFrame) -> DataFrame:
-        # return data
-        """
-        Checks floating point data columns for nans, and replaces these with
-        the generic Stata for missing value (.)
-        """
-        for c in data:
-            dtype = data[c].dtype
-            if dtype in (np.float32, np.float64):
-                if dtype == np.float32:
-                    replacement = self.MISSING_VALUES["f"]
-                else:
-                    replacement = self.MISSING_VALUES["d"]
-                data[c] = data[c].fillna(replacement)
-
-        return data
-
-    def _update_strl_names(self) -> None:
-        """No-op, forward compatibility"""
-
-    def _validate_variable_name(self, name: str) -> str:
-        """
-        Validate variable names for Stata export.
-
-        Parameters
-        ----------
-        name : str
-            Variable name
-
-        Returns
-        -------
-        str
-            The validated name with invalid characters replaced with
-            underscores.
-
-        Notes
-        -----
-        Stata 114 and 117 support ascii characters in a-z, A-Z, 0-9
-        and _.
-        """
-        for c in name:
-            if (
-                (c < "A" or c > "Z")
-                and (c < "a" or c > "z")
-                and (c < "0" or c > "9")
-                and c != "_"
-            ):
-                name = name.replace(c, "_")
-        return name
-
-    def _check_column_names(self, data: DataFrame) -> DataFrame:
-        """
-        Checks column names to ensure that they are valid Stata column names.
-        This includes checks for:
-            * Non-string names
-            * Stata keywords
-            * Variables that start with numbers
-            * Variables with names that are too long
-
-        When an illegal variable name is detected, it is converted, and if
-        dates are exported, the variable name is propagated to the date
-        conversion dictionary
-        """
-        converted_names: dict[Hashable, str] = {}
-        columns = list(data.columns)
-        original_columns = columns[:]
-
-        duplicate_var_id = 0
-        for j, name in enumerate(columns):
-            orig_name = name
-            if not isinstance(name, str):
-                name = str(name)
-
-            name = self._validate_variable_name(name)
-
-            # Variable name must not be a reserved word
-            if name in self.RESERVED_WORDS:
-                name = "_" + name
-
-            # Variable name may not start with a number
-            if "0" <= name[0] <= "9":
-                name = "_" + name
-
-            name = name[: min(len(name), 32)]
-
-            if not name == orig_name:
-                # check for duplicates
-                while columns.count(name) > 0:
-                    # prepend ascending number to avoid duplicates
-                    name = "_" + str(duplicate_var_id) + name
-                    name = name[: min(len(name), 32)]
-                    duplicate_var_id += 1
-                converted_names[orig_name] = name
-
-            columns[j] = name
-
-        data.columns = Index(columns)
-
-        # Check date conversion, and fix key if needed
-        if self._convert_dates:
-            for c, o in zip(columns, original_columns):
-                if c != o:
-                    self._convert_dates[c] = self._convert_dates[o]
-                    del self._convert_dates[o]
-
-        if converted_names:
-            conversion_warning = []
-            for orig_name, name in converted_names.items():
-                msg = f"{orig_name}   ->   {name}"
-                conversion_warning.append(msg)
-
-            ws = invalid_name_doc.format("\n    ".join(conversion_warning))
-            warnings.warn(
-                ws,
-                InvalidColumnName,
-                stacklevel=find_stack_level(),
-            )
-
-        self._converted_names = converted_names
-        self._update_strl_names()
-
-        return data
-
-    def _set_formats_and_types(self, dtypes: Series) -> None:
-        self.fmtlist: list[str] = []
-        self.typlist: list[int] = []
-        for col, dtype in dtypes.items():
-            self.fmtlist.append(_dtype_to_default_stata_fmt(dtype, self.data[col]))
-            self.typlist.append(_dtype_to_stata_type(dtype, self.data[col]))
-
-    def _prepare_pandas(self, data: DataFrame) -> None:
-        # NOTE: we might need a different API / class for pandas objects so
-        # we can set different semantics - handle this with a PR to pandas.io
-
-        data = data.copy()
-
-        if self._write_index:
-            temp = data.reset_index()
-            if isinstance(temp, DataFrame):
-                data = temp
-
-        # Ensure column names are strings
-        data = self._check_column_names(data)
-
-        # Check columns for compatibility with stata, upcast if necessary
-        # Raise if outside the supported range
-        data = _cast_to_stata_types(data)
-
-        # Replace NaNs with Stata missing values
-        data = self._replace_nans(data)
-
-        # Set all columns to initially unlabelled
-        self._has_value_labels = np.repeat(False, data.shape[1])
-
-        # Create value labels for non-categorical data
-        non_cat_value_labels = self._prepare_non_cat_value_labels(data)
-
-        non_cat_columns = [svl.labname for svl in non_cat_value_labels]
-        has_non_cat_val_labels = data.columns.isin(non_cat_columns)
-        self._has_value_labels |= has_non_cat_val_labels
-        self._value_labels.extend(non_cat_value_labels)
-
-        # Convert categoricals to int data, and strip labels
-        data = self._prepare_categoricals(data)
-
-        self.nobs, self.nvar = data.shape
-        self.data = data
-        self.varlist = data.columns.tolist()
-
-        dtypes = data.dtypes
-
-        # Ensure all date columns are converted
-        for col in data:
-            if col in self._convert_dates:
-                continue
-            if lib.is_np_dtype(data[col].dtype, "M"):
-                self._convert_dates[col] = "tc"
-
-        self._convert_dates = _maybe_convert_to_int_keys(
-            self._convert_dates, self.varlist
-        )
-        for key in self._convert_dates:
-            new_type = _convert_datetime_to_stata_type(self._convert_dates[key])
-            dtypes.iloc[key] = np.dtype(new_type)
-
-        # Verify object arrays are strings and encode to bytes
-        self._encode_strings()
-
-        self._set_formats_and_types(dtypes)
-
-        # set the given format for the datetime cols
-        if self._convert_dates is not None:
-            for key in self._convert_dates:
-                if isinstance(key, int):
-                    self.fmtlist[key] = self._convert_dates[key]
-
-    def _encode_strings(self) -> None:
-        """
-        Encode strings in dta-specific encoding
-
-        Do not encode columns marked for date conversion or for strL
-        conversion. The strL converter independently handles conversion and
-        also accepts empty string arrays.
-        """
-        convert_dates = self._convert_dates
-        # _convert_strl is not available in dta 114
-        convert_strl = getattr(self, "_convert_strl", [])
-        for i, col in enumerate(self.data):
-            # Skip columns marked for date conversion or strl conversion
-            if i in convert_dates or col in convert_strl:
-                continue
-            column = self.data[col]
-            dtype = column.dtype
-            if dtype.type is np.object_:
-                inferred_dtype = infer_dtype(column, skipna=True)
-                if not ((inferred_dtype == "string") or len(column) == 0):
-                    col = column.name
-                    raise ValueError(
-                        f"""\
-Column `{col}` cannot be exported.\n\nOnly string-like object arrays
-containing all strings or a mix of strings and None can be exported.
-Object arrays containing only null values are prohibited. Other object
-types cannot be exported and must first be converted to one of the
-supported types."""
-                    )
-                encoded = self.data[col].str.encode(self._encoding)
-                # If larger than _max_string_length do nothing
-                if (
-                    max_len_string_array(ensure_object(encoded._values))
-                    <= self._max_string_length
-                ):
-                    self.data[col] = encoded
-
-    def write_file(self) -> None:
-        """
-        Export DataFrame object to Stata dta format.
-
-        Examples
-        --------
-        >>> df = pd.DataFrame({"fully_labelled": [1, 2, 3, 3, 1],
-        ...                    "partially_labelled": [1.0, 2.0, np.nan, 9.0, np.nan],
-        ...                    "Y": [7, 7, 9, 8, 10],
-        ...                    "Z": pd.Categorical(["j", "k", "l", "k", "j"]),
-        ...                    })
-        >>> path = "/My_path/filename.dta"
-        >>> labels = {"fully_labelled": {1: "one", 2: "two", 3: "three"},
-        ...           "partially_labelled": {1.0: "one", 2.0: "two"},
-        ...           }
-        >>> writer = pd.io.stata.StataWriter(path,
-        ...                                  df,
-        ...                                  value_labels=labels)  # doctest: +SKIP
-        >>> writer.write_file()  # doctest: +SKIP
-        >>> df = pd.read_stata(path)  # doctest: +SKIP
-        >>> df  # doctest: +SKIP
-            index fully_labelled  partially_labeled  Y  Z
-        0       0            one                one  7  j
-        1       1            two                two  7  k
-        2       2          three                NaN  9  l
-        3       3          three                9.0  8  k
-        4       4            one                NaN 10  j
-        """
-        with get_handle(
-            self._fname,
-            "wb",
-            compression=self._compression,
-            is_text=False,
-            storage_options=self.storage_options,
-        ) as self.handles:
-            if self.handles.compression["method"] is not None:
-                # ZipFile creates a file (with the same name) for each write call.
-                # Write it first into a buffer and then write the buffer to the ZipFile.
-                self._output_file, self.handles.handle = self.handles.handle, BytesIO()
-                self.handles.created_handles.append(self.handles.handle)
-
-            try:
-                self._write_header(
-                    data_label=self._data_label, time_stamp=self._time_stamp
-                )
-                self._write_map()
-                self._write_variable_types()
-                self._write_varnames()
-                self._write_sortlist()
-                self._write_formats()
-                self._write_value_label_names()
-                self._write_variable_labels()
-                self._write_expansion_fields()
-                self._write_characteristics()
-                records = self._prepare_data()
-                self._write_data(records)
-                self._write_strls()
-                self._write_value_labels()
-                self._write_file_close_tag()
-                self._write_map()
-                self._close()
-            except Exception as exc:
-                self.handles.close()
-                if isinstance(self._fname, (str, os.PathLike)) and os.path.isfile(
-                    self._fname
-                ):
-                    try:
-                        os.unlink(self._fname)
-                    except OSError:
-                        warnings.warn(
-                            f"This save was not successful but {self._fname} could not "
-                            "be deleted. This file is not valid.",
-                            ResourceWarning,
-                            stacklevel=find_stack_level(),
-                        )
-                raise exc
-
-    def _close(self) -> None:
-        """
-        Close the file if it was created by the writer.
-
-        If a buffer or file-like object was passed in, for example a GzipFile,
-        then leave this file open for the caller to close.
-        """
-        # write compression
-        if self._output_file is not None:
-            assert isinstance(self.handles.handle, BytesIO)
-            bio, self.handles.handle = self.handles.handle, self._output_file
-            self.handles.handle.write(bio.getvalue())
-
-    def _write_map(self) -> None:
-        """No-op, future compatibility"""
-
-    def _write_file_close_tag(self) -> None:
-        """No-op, future compatibility"""
-
-    def _write_characteristics(self) -> None:
-        """No-op, future compatibility"""
-
-    def _write_strls(self) -> None:
-        """No-op, future compatibility"""
-
-    def _write_expansion_fields(self) -> None:
-        """Write 5 zeros for expansion fields"""
-        self._write(_pad_bytes("", 5))
-
-    def _write_value_labels(self) -> None:
-        for vl in self._value_labels:
-            self._write_bytes(vl.generate_value_label(self._byteorder))
-
-    def _write_header(
-        self,
-        data_label: str | None = None,
-        time_stamp: datetime | None = None,
-    ) -> None:
-        byteorder = self._byteorder
-        # ds_format - just use 114
-        self._write_bytes(struct.pack("b", 114))
-        # byteorder
-        self._write(byteorder == ">" and "\x01" or "\x02")
-        # filetype
-        self._write("\x01")
-        # unused
-        self._write("\x00")
-        # number of vars, 2 bytes
-        self._write_bytes(struct.pack(byteorder + "h", self.nvar)[:2])
-        # number of obs, 4 bytes
-        self._write_bytes(struct.pack(byteorder + "i", self.nobs)[:4])
-        # data label 81 bytes, char, null terminated
-        if data_label is None:
-            self._write_bytes(self._null_terminate_bytes(_pad_bytes("", 80)))
-        else:
-            self._write_bytes(
-                self._null_terminate_bytes(_pad_bytes(data_label[:80], 80))
-            )
-        # time stamp, 18 bytes, char, null terminated
-        # format dd Mon yyyy hh:mm
-        if time_stamp is None:
-            time_stamp = datetime.now()
-        elif not isinstance(time_stamp, datetime):
-            raise ValueError("time_stamp should be datetime type")
-        # GH #13856
-        # Avoid locale-specific month conversion
-        months = [
-            "Jan",
-            "Feb",
-            "Mar",
-            "Apr",
-            "May",
-            "Jun",
-            "Jul",
-            "Aug",
-            "Sep",
-            "Oct",
-            "Nov",
-            "Dec",
-        ]
-        month_lookup = {i + 1: month for i, month in enumerate(months)}
-        ts = (
-            time_stamp.strftime("%d ")
-            + month_lookup[time_stamp.month]
-            + time_stamp.strftime(" %Y %H:%M")
-        )
-        self._write_bytes(self._null_terminate_bytes(ts))
-
-    def _write_variable_types(self) -> None:
-        for typ in self.typlist:
-            self._write_bytes(struct.pack("B", typ))
-
-    def _write_varnames(self) -> None:
-        # varlist names are checked by _check_column_names
-        # varlist, requires null terminated
-        for name in self.varlist:
-            name = self._null_terminate_str(name)
-            name = _pad_bytes(name[:32], 33)
-            self._write(name)
-
-    def _write_sortlist(self) -> None:
-        # srtlist, 2*(nvar+1), int array, encoded by byteorder
-        srtlist = _pad_bytes("", 2 * (self.nvar + 1))
-        self._write(srtlist)
-
-    def _write_formats(self) -> None:
-        # fmtlist, 49*nvar, char array
-        for fmt in self.fmtlist:
-            self._write(_pad_bytes(fmt, 49))
-
-    def _write_value_label_names(self) -> None:
-        # lbllist, 33*nvar, char array
-        for i in range(self.nvar):
-            # Use variable name when categorical
-            if self._has_value_labels[i]:
-                name = self.varlist[i]
-                name = self._null_terminate_str(name)
-                name = _pad_bytes(name[:32], 33)
-                self._write(name)
-            else:  # Default is empty label
-                self._write(_pad_bytes("", 33))
-
-    def _write_variable_labels(self) -> None:
-        # Missing labels are 80 blank characters plus null termination
-        blank = _pad_bytes("", 81)
-
-        if self._variable_labels is None:
-            for i in range(self.nvar):
-                self._write(blank)
-            return
-
-        for col in self.data:
-            if col in self._variable_labels:
-                label = self._variable_labels[col]
-                if len(label) > 80:
-                    raise ValueError("Variable labels must be 80 characters or fewer")
-                is_latin1 = all(ord(c) < 256 for c in label)
-                if not is_latin1:
-                    raise ValueError(
-                        "Variable labels must contain only characters that "
-                        "can be encoded in Latin-1"
-                    )
-                self._write(_pad_bytes(label, 81))
-            else:
-                self._write(blank)
-
-    def _convert_strls(self, data: DataFrame) -> DataFrame:
-        """No-op, future compatibility"""
-        return data
-
-    def _prepare_data(self) -> np.rec.recarray:
-        data = self.data
-        typlist = self.typlist
-        convert_dates = self._convert_dates
-        # 1. Convert dates
-        if self._convert_dates is not None:
-            for i, col in enumerate(data):
-                if i in convert_dates:
-                    data[col] = _datetime_to_stata_elapsed_vec(
-                        data[col], self.fmtlist[i]
-                    )
-        # 2. Convert strls
-        data = self._convert_strls(data)
-
-        # 3. Convert bad string data to '' and pad to correct length
-        dtypes = {}
-        native_byteorder = self._byteorder == _set_endianness(sys.byteorder)
-        for i, col in enumerate(data):
-            typ = typlist[i]
-            if typ <= self._max_string_length:
-                data[col] = data[col].fillna("").apply(_pad_bytes, args=(typ,))
-                stype = f"S{typ}"
-                dtypes[col] = stype
-                data[col] = data[col].astype(stype)
-            else:
-                dtype = data[col].dtype
-                if not native_byteorder:
-                    dtype = dtype.newbyteorder(self._byteorder)
-                dtypes[col] = dtype
-
-        return data.to_records(index=False, column_dtypes=dtypes)
-
-    def _write_data(self, records: np.rec.recarray) -> None:
-        self._write_bytes(records.tobytes())
-
-    @staticmethod
-    def _null_terminate_str(s: str) -> str:
-        s += "\x00"
-        return s
-
-    def _null_terminate_bytes(self, s: str) -> bytes:
-        return self._null_terminate_str(s).encode(self._encoding)
-
-
-def _dtype_to_stata_type_117(dtype: np.dtype, column: Series, force_strl: bool) -> int:
-    """
-    Converts dtype types to stata types. Returns the byte of the given ordinal.
-    See TYPE_MAP and comments for an explanation. This is also explained in
-    the dta spec.
-    1 - 2045 are strings of this length
-                Pandas    Stata
-    32768 - for object    strL
-    65526 - for int8      byte
-    65527 - for int16     int
-    65528 - for int32     long
-    65529 - for float32   float
-    65530 - for double    double
-
-    If there are dates to convert, then dtype will already have the correct
-    type inserted.
-    """
-    # TODO: expand to handle datetime to integer conversion
-    if force_strl:
-        return 32768
-    if dtype.type is np.object_:  # try to coerce it to the biggest string
-        # not memory efficient, what else could we
-        # do?
-        itemsize = max_len_string_array(ensure_object(column._values))
-        itemsize = max(itemsize, 1)
-        if itemsize <= 2045:
-            return itemsize
-        return 32768
-    elif dtype.type is np.float64:
-        return 65526
-    elif dtype.type is np.float32:
-        return 65527
-    elif dtype.type is np.int32:
-        return 65528
-    elif dtype.type is np.int16:
-        return 65529
-    elif dtype.type is np.int8:
-        return 65530
-    else:  # pragma : no cover
-        raise NotImplementedError(f"Data type {dtype} not supported.")
-
-
-def _pad_bytes_new(name: str | bytes, length: int) -> bytes:
-    """
-    Takes a bytes instance and pads it with null bytes until it's length chars.
-    """
-    if isinstance(name, str):
-        name = bytes(name, "utf-8")
-    return name + b"\x00" * (length - len(name))
-
-
-class StataStrLWriter:
-    """
-    Converter for Stata StrLs
-
-    Stata StrLs map 8 byte values to strings which are stored using a
-    dictionary-like format where strings are keyed to two values.
-
-    Parameters
-    ----------
-    df : DataFrame
-        DataFrame to convert
-    columns : Sequence[str]
-        List of columns names to convert to StrL
-    version : int, optional
-        dta version.  Currently supports 117, 118 and 119
-    byteorder : str, optional
-        Can be ">", "<", "little", or "big". default is `sys.byteorder`
-
-    Notes
-    -----
-    Supports creation of the StrL block of a dta file for dta versions
-    117, 118 and 119.  These differ in how the GSO is stored.  118 and
-    119 store the GSO lookup value as a uint32 and a uint64, while 117
-    uses two uint32s. 118 and 119 also encode all strings as unicode
-    which is required by the format.  117 uses 'latin-1' a fixed width
-    encoding that extends the 7-bit ascii table with an additional 128
-    characters.
-    """
-
-    def __init__(
-        self,
-        df: DataFrame,
-        columns: Sequence[str],
-        version: int = 117,
-        byteorder: str | None = None,
-    ) -> None:
-        if version not in (117, 118, 119):
-            raise ValueError("Only dta versions 117, 118 and 119 supported")
-        self._dta_ver = version
-
-        self.df = df
-        self.columns = columns
-        self._gso_table = {"": (0, 0)}
-        if byteorder is None:
-            byteorder = sys.byteorder
-        self._byteorder = _set_endianness(byteorder)
-
-        gso_v_type = "I"  # uint32
-        gso_o_type = "Q"  # uint64
-        self._encoding = "utf-8"
-        if version == 117:
-            o_size = 4
-            gso_o_type = "I"  # 117 used uint32
-            self._encoding = "latin-1"
-        elif version == 118:
-            o_size = 6
-        else:  # version == 119
-            o_size = 5
-        self._o_offet = 2 ** (8 * (8 - o_size))
-        self._gso_o_type = gso_o_type
-        self._gso_v_type = gso_v_type
-
-    def _convert_key(self, key: tuple[int, int]) -> int:
-        v, o = key
-        return v + self._o_offet * o
-
-    def generate_table(self) -> tuple[dict[str, tuple[int, int]], DataFrame]:
-        """
-        Generates the GSO lookup table for the DataFrame
-
-        Returns
-        -------
-        gso_table : dict
-            Ordered dictionary using the string found as keys
-            and their lookup position (v,o) as values
-        gso_df : DataFrame
-            DataFrame where strl columns have been converted to
-            (v,o) values
-
-        Notes
-        -----
-        Modifies the DataFrame in-place.
-
-        The DataFrame returned encodes the (v,o) values as uint64s. The
-        encoding depends on the dta version, and can be expressed as
-
-        enc = v + o * 2 ** (o_size * 8)
-
-        so that v is stored in the lower bits and o is in the upper
-        bits. o_size is
-
-          * 117: 4
-          * 118: 6
-          * 119: 5
-        """
-        gso_table = self._gso_table
-        gso_df = self.df
-        columns = list(gso_df.columns)
-        selected = gso_df[self.columns]
-        col_index = [(col, columns.index(col)) for col in self.columns]
-        keys = np.empty(selected.shape, dtype=np.uint64)
-        for o, (idx, row) in enumerate(selected.iterrows()):
-            for j, (col, v) in enumerate(col_index):
-                val = row[col]
-                # Allow columns with mixed str and None (GH 23633)
-                val = "" if val is None else val
-                key = gso_table.get(val, None)
-                if key is None:
-                    # Stata prefers human numbers
-                    key = (v + 1, o + 1)
-                    gso_table[val] = key
-                keys[o, j] = self._convert_key(key)
-        for i, col in enumerate(self.columns):
-            gso_df[col] = keys[:, i]
-
-        return gso_table, gso_df
-
-    def generate_blob(self, gso_table: dict[str, tuple[int, int]]) -> bytes:
-        """
-        Generates the binary blob of GSOs that is written to the dta file.
-
-        Parameters
-        ----------
-        gso_table : dict
-            Ordered dictionary (str, vo)
-
-        Returns
-        -------
-        gso : bytes
-            Binary content of dta file to be placed between strl tags
-
-        Notes
-        -----
-        Output format depends on dta version.  117 uses two uint32s to
-        express v and o while 118+ uses a uint32 for v and a uint64 for o.
-        """
-        # Format information
-        # Length includes null term
-        # 117
-        # GSOvvvvooootllllxxxxxxxxxxxxxxx...x
-        #  3  u4  u4 u1 u4  string + null term
-        #
-        # 118, 119
-        # GSOvvvvooooooootllllxxxxxxxxxxxxxxx...x
-        #  3  u4   u8   u1 u4    string + null term
-
-        bio = BytesIO()
-        gso = bytes("GSO", "ascii")
-        gso_type = struct.pack(self._byteorder + "B", 130)
-        null = struct.pack(self._byteorder + "B", 0)
-        v_type = self._byteorder + self._gso_v_type
-        o_type = self._byteorder + self._gso_o_type
-        len_type = self._byteorder + "I"
-        for strl, vo in gso_table.items():
-            if vo == (0, 0):
-                continue
-            v, o = vo
-
-            # GSO
-            bio.write(gso)
-
-            # vvvv
-            bio.write(struct.pack(v_type, v))
-
-            # oooo / oooooooo
-            bio.write(struct.pack(o_type, o))
-
-            # t
-            bio.write(gso_type)
-
-            # llll
-            utf8_string = bytes(strl, "utf-8")
-            bio.write(struct.pack(len_type, len(utf8_string) + 1))
-
-            # xxx...xxx
-            bio.write(utf8_string)
-            bio.write(null)
-
-        return bio.getvalue()
-
-
-class StataWriter117(StataWriter):
-    """
-    A class for writing Stata binary dta files in Stata 13 format (117)
-
-    Parameters
-    ----------
-    fname : path (string), buffer or path object
-        string, path object (pathlib.Path or py._path.local.LocalPath) or
-        object implementing a binary write() functions. If using a buffer
-        then the buffer will not be automatically closed after the file
-        is written.
-    data : DataFrame
-        Input to save
-    convert_dates : dict
-        Dictionary mapping columns containing datetime types to stata internal
-        format to use when writing the dates. Options are 'tc', 'td', 'tm',
-        'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name.
-        Datetime columns that do not have a conversion type specified will be
-        converted to 'tc'. Raises NotImplementedError if a datetime column has
-        timezone information
-    write_index : bool
-        Write the index to Stata dataset.
-    byteorder : str
-        Can be ">", "<", "little", or "big". default is `sys.byteorder`
-    time_stamp : datetime
-        A datetime to use as file creation date.  Default is the current time
-    data_label : str
-        A label for the data set.  Must be 80 characters or smaller.
-    variable_labels : dict
-        Dictionary containing columns as keys and variable labels as values.
-        Each label must be 80 characters or smaller.
-    convert_strl : list
-        List of columns names to convert to Stata StrL format.  Columns with
-        more than 2045 characters are automatically written as StrL.
-        Smaller columns can be converted by including the column name.  Using
-        StrLs can reduce output file size when strings are longer than 8
-        characters, and either frequently repeated or sparse.
-    {compression_options}
-
-        .. versionchanged:: 1.4.0 Zstandard support.
-
-    value_labels : dict of dicts
-        Dictionary containing columns as keys and dictionaries of column value
-        to labels as values. The combined length of all labels for a single
-        variable must be 32,000 characters or smaller.
-
-        .. versionadded:: 1.4.0
-
-    Returns
-    -------
-    writer : StataWriter117 instance
-        The StataWriter117 instance has a write_file method, which will
-        write the file to the given `fname`.
-
-    Raises
-    ------
-    NotImplementedError
-        * If datetimes contain timezone information
-    ValueError
-        * Columns listed in convert_dates are neither datetime64[ns]
-          or datetime
-        * Column dtype is not representable in Stata
-        * Column listed in convert_dates is not in DataFrame
-        * Categorical label contains more than 32,000 characters
-
-    Examples
-    --------
-    >>> data = pd.DataFrame([[1.0, 1, 'a']], columns=['a', 'b', 'c'])
-    >>> writer = pd.io.stata.StataWriter117('./data_file.dta', data)
-    >>> writer.write_file()
-
-    Directly write a zip file
-    >>> compression = {"method": "zip", "archive_name": "data_file.dta"}
-    >>> writer = pd.io.stata.StataWriter117(
-    ...     './data_file.zip', data, compression=compression
-    ...     )
-    >>> writer.write_file()
-
-    Or with long strings stored in strl format
-    >>> data = pd.DataFrame([['A relatively long string'], [''], ['']],
-    ...                     columns=['strls'])
-    >>> writer = pd.io.stata.StataWriter117(
-    ...     './data_file_with_long_strings.dta', data, convert_strl=['strls'])
-    >>> writer.write_file()
-    """
-
-    _max_string_length = 2045
-    _dta_version = 117
-
-    def __init__(
-        self,
-        fname: FilePath | WriteBuffer[bytes],
-        data: DataFrame,
-        convert_dates: dict[Hashable, str] | None = None,
-        write_index: bool = True,
-        byteorder: str | None = None,
-        time_stamp: datetime | None = None,
-        data_label: str | None = None,
-        variable_labels: dict[Hashable, str] | None = None,
-        convert_strl: Sequence[Hashable] | None = None,
-        compression: CompressionOptions = "infer",
-        storage_options: StorageOptions | None = None,
-        *,
-        value_labels: dict[Hashable, dict[float, str]] | None = None,
-    ) -> None:
-        # Copy to new list since convert_strl might be modified later
-        self._convert_strl: list[Hashable] = []
-        if convert_strl is not None:
-            self._convert_strl.extend(convert_strl)
-
-        super().__init__(
-            fname,
-            data,
-            convert_dates,
-            write_index,
-            byteorder=byteorder,
-            time_stamp=time_stamp,
-            data_label=data_label,
-            variable_labels=variable_labels,
-            value_labels=value_labels,
-            compression=compression,
-            storage_options=storage_options,
-        )
-        self._map: dict[str, int] = {}
-        self._strl_blob = b""
-
-    @staticmethod
-    def _tag(val: str | bytes, tag: str) -> bytes:
-        """Surround val with <tag></tag>"""
-        if isinstance(val, str):
-            val = bytes(val, "utf-8")
-        return bytes("<" + tag + ">", "utf-8") + val + bytes("</" + tag + ">", "utf-8")
-
-    def _update_map(self, tag: str) -> None:
-        """Update map location for tag with file position"""
-        assert self.handles.handle is not None
-        self._map[tag] = self.handles.handle.tell()
-
-    def _write_header(
-        self,
-        data_label: str | None = None,
-        time_stamp: datetime | None = None,
-    ) -> None:
-        """Write the file header"""
-        byteorder = self._byteorder
-        self._write_bytes(bytes("<stata_dta>", "utf-8"))
-        bio = BytesIO()
-        # ds_format - 117
-        bio.write(self._tag(bytes(str(self._dta_version), "utf-8"), "release"))
-        # byteorder
-        bio.write(self._tag(byteorder == ">" and "MSF" or "LSF", "byteorder"))
-        # number of vars, 2 bytes in 117 and 118, 4 byte in 119
-        nvar_type = "H" if self._dta_version <= 118 else "I"
-        bio.write(self._tag(struct.pack(byteorder + nvar_type, self.nvar), "K"))
-        # 117 uses 4 bytes, 118 uses 8
-        nobs_size = "I" if self._dta_version == 117 else "Q"
-        bio.write(self._tag(struct.pack(byteorder + nobs_size, self.nobs), "N"))
-        # data label 81 bytes, char, null terminated
-        label = data_label[:80] if data_label is not None else ""
-        encoded_label = label.encode(self._encoding)
-        label_size = "B" if self._dta_version == 117 else "H"
-        label_len = struct.pack(byteorder + label_size, len(encoded_label))
-        encoded_label = label_len + encoded_label
-        bio.write(self._tag(encoded_label, "label"))
-        # time stamp, 18 bytes, char, null terminated
-        # format dd Mon yyyy hh:mm
-        if time_stamp is None:
-            time_stamp = datetime.now()
-        elif not isinstance(time_stamp, datetime):
-            raise ValueError("time_stamp should be datetime type")
-        # Avoid locale-specific month conversion
-        months = [
-            "Jan",
-            "Feb",
-            "Mar",
-            "Apr",
-            "May",
-            "Jun",
-            "Jul",
-            "Aug",
-            "Sep",
-            "Oct",
-            "Nov",
-            "Dec",
-        ]
-        month_lookup = {i + 1: month for i, month in enumerate(months)}
-        ts = (
-            time_stamp.strftime("%d ")
-            + month_lookup[time_stamp.month]
-            + time_stamp.strftime(" %Y %H:%M")
-        )
-        # '\x11' added due to inspection of Stata file
-        stata_ts = b"\x11" + bytes(ts, "utf-8")
-        bio.write(self._tag(stata_ts, "timestamp"))
-        self._write_bytes(self._tag(bio.getvalue(), "header"))
-
-    def _write_map(self) -> None:
-        """
-        Called twice during file write. The first populates the values in
-        the map with 0s.  The second call writes the final map locations when
-        all blocks have been written.
-        """
-        if not self._map:
-            self._map = {
-                "stata_data": 0,
-                "map": self.handles.handle.tell(),
-                "variable_types": 0,
-                "varnames": 0,
-                "sortlist": 0,
-                "formats": 0,
-                "value_label_names": 0,
-                "variable_labels": 0,
-                "characteristics": 0,
-                "data": 0,
-                "strls": 0,
-                "value_labels": 0,
-                "stata_data_close": 0,
-                "end-of-file": 0,
-            }
-        # Move to start of map
-        self.handles.handle.seek(self._map["map"])
-        bio = BytesIO()
-        for val in self._map.values():
-            bio.write(struct.pack(self._byteorder + "Q", val))
-        self._write_bytes(self._tag(bio.getvalue(), "map"))
-
-    def _write_variable_types(self) -> None:
-        self._update_map("variable_types")
-        bio = BytesIO()
-        for typ in self.typlist:
-            bio.write(struct.pack(self._byteorder + "H", typ))
-        self._write_bytes(self._tag(bio.getvalue(), "variable_types"))
-
-    def _write_varnames(self) -> None:
-        self._update_map("varnames")
-        bio = BytesIO()
-        # 118 scales by 4 to accommodate utf-8 data worst case encoding
-        vn_len = 32 if self._dta_version == 117 else 128
-        for name in self.varlist:
-            name = self._null_terminate_str(name)
-            name = _pad_bytes_new(name[:32].encode(self._encoding), vn_len + 1)
-            bio.write(name)
-        self._write_bytes(self._tag(bio.getvalue(), "varnames"))
-
-    def _write_sortlist(self) -> None:
-        self._update_map("sortlist")
-        sort_size = 2 if self._dta_version < 119 else 4
-        self._write_bytes(self._tag(b"\x00" * sort_size * (self.nvar + 1), "sortlist"))
-
-    def _write_formats(self) -> None:
-        self._update_map("formats")
-        bio = BytesIO()
-        fmt_len = 49 if self._dta_version == 117 else 57
-        for fmt in self.fmtlist:
-            bio.write(_pad_bytes_new(fmt.encode(self._encoding), fmt_len))
-        self._write_bytes(self._tag(bio.getvalue(), "formats"))
-
-    def _write_value_label_names(self) -> None:
-        self._update_map("value_label_names")
-        bio = BytesIO()
-        # 118 scales by 4 to accommodate utf-8 data worst case encoding
-        vl_len = 32 if self._dta_version == 117 else 128
-        for i in range(self.nvar):
-            # Use variable name when categorical
-            name = ""  # default name
-            if self._has_value_labels[i]:
-                name = self.varlist[i]
-            name = self._null_terminate_str(name)
-            encoded_name = _pad_bytes_new(name[:32].encode(self._encoding), vl_len + 1)
-            bio.write(encoded_name)
-        self._write_bytes(self._tag(bio.getvalue(), "value_label_names"))
-
-    def _write_variable_labels(self) -> None:
-        # Missing labels are 80 blank characters plus null termination
-        self._update_map("variable_labels")
-        bio = BytesIO()
-        # 118 scales by 4 to accommodate utf-8 data worst case encoding
-        vl_len = 80 if self._dta_version == 117 else 320
-        blank = _pad_bytes_new("", vl_len + 1)
-
-        if self._variable_labels is None:
-            for _ in range(self.nvar):
-                bio.write(blank)
-            self._write_bytes(self._tag(bio.getvalue(), "variable_labels"))
-            return
-
-        for col in self.data:
-            if col in self._variable_labels:
-                label = self._variable_labels[col]
-                if len(label) > 80:
-                    raise ValueError("Variable labels must be 80 characters or fewer")
-                try:
-                    encoded = label.encode(self._encoding)
-                except UnicodeEncodeError as err:
-                    raise ValueError(
-                        "Variable labels must contain only characters that "
-                        f"can be encoded in {self._encoding}"
-                    ) from err
-
-                bio.write(_pad_bytes_new(encoded, vl_len + 1))
-            else:
-                bio.write(blank)
-        self._write_bytes(self._tag(bio.getvalue(), "variable_labels"))
-
-    def _write_characteristics(self) -> None:
-        self._update_map("characteristics")
-        self._write_bytes(self._tag(b"", "characteristics"))
-
-    def _write_data(self, records) -> None:
-        self._update_map("data")
-        self._write_bytes(b"<data>")
-        self._write_bytes(records.tobytes())
-        self._write_bytes(b"</data>")
-
-    def _write_strls(self) -> None:
-        self._update_map("strls")
-        self._write_bytes(self._tag(self._strl_blob, "strls"))
-
-    def _write_expansion_fields(self) -> None:
-        """No-op in dta 117+"""
-
-    def _write_value_labels(self) -> None:
-        self._update_map("value_labels")
-        bio = BytesIO()
-        for vl in self._value_labels:
-            lab = vl.generate_value_label(self._byteorder)
-            lab = self._tag(lab, "lbl")
-            bio.write(lab)
-        self._write_bytes(self._tag(bio.getvalue(), "value_labels"))
-
-    def _write_file_close_tag(self) -> None:
-        self._update_map("stata_data_close")
-        self._write_bytes(bytes("</stata_dta>", "utf-8"))
-        self._update_map("end-of-file")
-
-    def _update_strl_names(self) -> None:
-        """
-        Update column names for conversion to strl if they might have been
-        changed to comply with Stata naming rules
-        """
-        # Update convert_strl if names changed
-        for orig, new in self._converted_names.items():
-            if orig in self._convert_strl:
-                idx = self._convert_strl.index(orig)
-                self._convert_strl[idx] = new
-
-    def _convert_strls(self, data: DataFrame) -> DataFrame:
-        """
-        Convert columns to StrLs if either very large or in the
-        convert_strl variable
-        """
-        convert_cols = [
-            col
-            for i, col in enumerate(data)
-            if self.typlist[i] == 32768 or col in self._convert_strl
-        ]
-
-        if convert_cols:
-            ssw = StataStrLWriter(data, convert_cols, version=self._dta_version)
-            tab, new_data = ssw.generate_table()
-            data = new_data
-            self._strl_blob = ssw.generate_blob(tab)
-        return data
-
-    def _set_formats_and_types(self, dtypes: Series) -> None:
-        self.typlist = []
-        self.fmtlist = []
-        for col, dtype in dtypes.items():
-            force_strl = col in self._convert_strl
-            fmt = _dtype_to_default_stata_fmt(
-                dtype,
-                self.data[col],
-                dta_version=self._dta_version,
-                force_strl=force_strl,
-            )
-            self.fmtlist.append(fmt)
-            self.typlist.append(
-                _dtype_to_stata_type_117(dtype, self.data[col], force_strl)
-            )
-
-
-class StataWriterUTF8(StataWriter117):
-    """
-    Stata binary dta file writing in Stata 15 (118) and 16 (119) formats
-
-    DTA 118 and 119 format files support unicode string data (both fixed
-    and strL) format. Unicode is also supported in value labels, variable
-    labels and the dataset label. Format 119 is automatically used if the
-    file contains more than 32,767 variables.
-
-    Parameters
-    ----------
-    fname : path (string), buffer or path object
-        string, path object (pathlib.Path or py._path.local.LocalPath) or
-        object implementing a binary write() functions. If using a buffer
-        then the buffer will not be automatically closed after the file
-        is written.
-    data : DataFrame
-        Input to save
-    convert_dates : dict, default None
-        Dictionary mapping columns containing datetime types to stata internal
-        format to use when writing the dates. Options are 'tc', 'td', 'tm',
-        'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name.
-        Datetime columns that do not have a conversion type specified will be
-        converted to 'tc'. Raises NotImplementedError if a datetime column has
-        timezone information
-    write_index : bool, default True
-        Write the index to Stata dataset.
-    byteorder : str, default None
-        Can be ">", "<", "little", or "big". default is `sys.byteorder`
-    time_stamp : datetime, default None
-        A datetime to use as file creation date.  Default is the current time
-    data_label : str, default None
-        A label for the data set.  Must be 80 characters or smaller.
-    variable_labels : dict, default None
-        Dictionary containing columns as keys and variable labels as values.
-        Each label must be 80 characters or smaller.
-    convert_strl : list, default None
-        List of columns names to convert to Stata StrL format.  Columns with
-        more than 2045 characters are automatically written as StrL.
-        Smaller columns can be converted by including the column name.  Using
-        StrLs can reduce output file size when strings are longer than 8
-        characters, and either frequently repeated or sparse.
-    version : int, default None
-        The dta version to use. By default, uses the size of data to determine
-        the version. 118 is used if data.shape[1] <= 32767, and 119 is used
-        for storing larger DataFrames.
-    {compression_options}
-
-        .. versionchanged:: 1.4.0 Zstandard support.
-
-    value_labels : dict of dicts
-        Dictionary containing columns as keys and dictionaries of column value
-        to labels as values. The combined length of all labels for a single
-        variable must be 32,000 characters or smaller.
-
-        .. versionadded:: 1.4.0
-
-    Returns
-    -------
-    StataWriterUTF8
-        The instance has a write_file method, which will write the file to the
-        given `fname`.
-
-    Raises
-    ------
-    NotImplementedError
-        * If datetimes contain timezone information
-    ValueError
-        * Columns listed in convert_dates are neither datetime64[ns]
-          or datetime
-        * Column dtype is not representable in Stata
-        * Column listed in convert_dates is not in DataFrame
-        * Categorical label contains more than 32,000 characters
-
-    Examples
-    --------
-    Using Unicode data and column names
-
-    >>> from pandas.io.stata import StataWriterUTF8
-    >>> data = pd.DataFrame([[1.0, 1, 'ᴬ']], columns=['a', 'β', 'ĉ'])
-    >>> writer = StataWriterUTF8('./data_file.dta', data)
-    >>> writer.write_file()
-
-    Directly write a zip file
-    >>> compression = {"method": "zip", "archive_name": "data_file.dta"}
-    >>> writer = StataWriterUTF8('./data_file.zip', data, compression=compression)
-    >>> writer.write_file()
-
-    Or with long strings stored in strl format
-
-    >>> data = pd.DataFrame([['ᴀ relatively long ŝtring'], [''], ['']],
-    ...                     columns=['strls'])
-    >>> writer = StataWriterUTF8('./data_file_with_long_strings.dta', data,
-    ...                          convert_strl=['strls'])
-    >>> writer.write_file()
-    """
-
-    _encoding: Literal["utf-8"] = "utf-8"
-
-    def __init__(
-        self,
-        fname: FilePath | WriteBuffer[bytes],
-        data: DataFrame,
-        convert_dates: dict[Hashable, str] | None = None,
-        write_index: bool = True,
-        byteorder: str | None = None,
-        time_stamp: datetime | None = None,
-        data_label: str | None = None,
-        variable_labels: dict[Hashable, str] | None = None,
-        convert_strl: Sequence[Hashable] | None = None,
-        version: int | None = None,
-        compression: CompressionOptions = "infer",
-        storage_options: StorageOptions | None = None,
-        *,
-        value_labels: dict[Hashable, dict[float, str]] | None = None,
-    ) -> None:
-        if version is None:
-            version = 118 if data.shape[1] <= 32767 else 119
-        elif version not in (118, 119):
-            raise ValueError("version must be either 118 or 119.")
-        elif version == 118 and data.shape[1] > 32767:
-            raise ValueError(
-                "You must use version 119 for data sets containing more than"
-                "32,767 variables"
-            )
-
-        super().__init__(
-            fname,
-            data,
-            convert_dates=convert_dates,
-            write_index=write_index,
-            byteorder=byteorder,
-            time_stamp=time_stamp,
-            data_label=data_label,
-            variable_labels=variable_labels,
-            value_labels=value_labels,
-            convert_strl=convert_strl,
-            compression=compression,
-            storage_options=storage_options,
-        )
-        # Override version set in StataWriter117 init
-        self._dta_version = version
-
-    def _validate_variable_name(self, name: str) -> str:
-        """
-        Validate variable names for Stata export.
-
-        Parameters
-        ----------
-        name : str
-            Variable name
-
-        Returns
-        -------
-        str
-            The validated name with invalid characters replaced with
-            underscores.
-
-        Notes
-        -----
-        Stata 118+ support most unicode characters. The only limitation is in
-        the ascii range where the characters supported are a-z, A-Z, 0-9 and _.
-        """
-        # High code points appear to be acceptable
-        for c in name:
-            if (
-                (
-                    ord(c) < 128
-                    and (c < "A" or c > "Z")
-                    and (c < "a" or c > "z")
-                    and (c < "0" or c > "9")
-                    and c != "_"
-                )
-                or 128 <= ord(c) < 192
-                or c in {"×", "÷"}  # noqa: RUF001
-            ):
-                name = name.replace(c, "_")
-
-        return name
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/datetimes/test_datetimelike.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/datetimes/test_datetimelike.py
deleted file mode 100644
index a6bee20d3d3ec4a773ca54cc6345b5e7982d4fb2..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/datetimes/test_datetimelike.py
+++ /dev/null
@@ -1,10 +0,0 @@
-""" generic tests from the Datetimelike class """
-from pandas import date_range
-
-
-class TestDatetimeIndex:
-    def test_format(self):
-        # GH35439
-        idx = date_range("20130101", periods=5)
-        expected = [f"{x:%Y-%m-%d}" for x in idx]
-        assert idx.format() == expected
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/json/test_ujson.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/json/test_ujson.py
deleted file mode 100644
index 5bb7097770820e2af9c768db7bbfe46536e9975f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/json/test_ujson.py
+++ /dev/null
@@ -1,1078 +0,0 @@
-import calendar
-import datetime
-import decimal
-import json
-import locale
-import math
-import re
-import time
-
-import dateutil
-import numpy as np
-import pytest
-import pytz
-
-import pandas._libs.json as ujson
-from pandas.compat import IS64
-
-from pandas import (
-    DataFrame,
-    DatetimeIndex,
-    Index,
-    NaT,
-    PeriodIndex,
-    Series,
-    Timedelta,
-    Timestamp,
-    date_range,
-)
-import pandas._testing as tm
-
-
-def _clean_dict(d):
-    """
-    Sanitize dictionary for JSON by converting all keys to strings.
-
-    Parameters
-    ----------
-    d : dict
-        The dictionary to convert.
-
-    Returns
-    -------
-    cleaned_dict : dict
-    """
-    return {str(k): v for k, v in d.items()}
-
-
-@pytest.fixture(
-    params=[None, "split", "records", "values", "index"]  # Column indexed by default.
-)
-def orient(request):
-    return request.param
-
-
-class TestUltraJSONTests:
-    @pytest.mark.skipif(not IS64, reason="not compliant on 32-bit, xref #15865")
-    def test_encode_decimal(self):
-        sut = decimal.Decimal("1337.1337")
-        encoded = ujson.ujson_dumps(sut, double_precision=15)
-        decoded = ujson.ujson_loads(encoded)
-        assert decoded == 1337.1337
-
-        sut = decimal.Decimal("0.95")
-        encoded = ujson.ujson_dumps(sut, double_precision=1)
-        assert encoded == "1.0"
-
-        decoded = ujson.ujson_loads(encoded)
-        assert decoded == 1.0
-
-        sut = decimal.Decimal("0.94")
-        encoded = ujson.ujson_dumps(sut, double_precision=1)
-        assert encoded == "0.9"
-
-        decoded = ujson.ujson_loads(encoded)
-        assert decoded == 0.9
-
-        sut = decimal.Decimal("1.95")
-        encoded = ujson.ujson_dumps(sut, double_precision=1)
-        assert encoded == "2.0"
-
-        decoded = ujson.ujson_loads(encoded)
-        assert decoded == 2.0
-
-        sut = decimal.Decimal("-1.95")
-        encoded = ujson.ujson_dumps(sut, double_precision=1)
-        assert encoded == "-2.0"
-
-        decoded = ujson.ujson_loads(encoded)
-        assert decoded == -2.0
-
-        sut = decimal.Decimal("0.995")
-        encoded = ujson.ujson_dumps(sut, double_precision=2)
-        assert encoded == "1.0"
-
-        decoded = ujson.ujson_loads(encoded)
-        assert decoded == 1.0
-
-        sut = decimal.Decimal("0.9995")
-        encoded = ujson.ujson_dumps(sut, double_precision=3)
-        assert encoded == "1.0"
-
-        decoded = ujson.ujson_loads(encoded)
-        assert decoded == 1.0
-
-        sut = decimal.Decimal("0.99999999999999944")
-        encoded = ujson.ujson_dumps(sut, double_precision=15)
-        assert encoded == "1.0"
-
-        decoded = ujson.ujson_loads(encoded)
-        assert decoded == 1.0
-
-    @pytest.mark.parametrize("ensure_ascii", [True, False])
-    def test_encode_string_conversion(self, ensure_ascii):
-        string_input = "A string \\ / \b \f \n \r \t </script> &"
-        not_html_encoded = '"A string \\\\ \\/ \\b \\f \\n \\r \\t <\\/script> &"'
-        html_encoded = (
-            '"A string \\\\ \\/ \\b \\f \\n \\r \\t \\u003c\\/script\\u003e \\u0026"'
-        )
-
-        def helper(expected_output, **encode_kwargs):
-            output = ujson.ujson_dumps(
-                string_input, ensure_ascii=ensure_ascii, **encode_kwargs
-            )
-
-            assert output == expected_output
-            assert string_input == json.loads(output)
-            assert string_input == ujson.ujson_loads(output)
-
-        # Default behavior assumes encode_html_chars=False.
-        helper(not_html_encoded)
-
-        # Make sure explicit encode_html_chars=False works.
-        helper(not_html_encoded, encode_html_chars=False)
-
-        # Make sure explicit encode_html_chars=True does the encoding.
-        helper(html_encoded, encode_html_chars=True)
-
-    @pytest.mark.parametrize(
-        "long_number", [-4342969734183514, -12345678901234.56789012, -528656961.4399388]
-    )
-    def test_double_long_numbers(self, long_number):
-        sut = {"a": long_number}
-        encoded = ujson.ujson_dumps(sut, double_precision=15)
-
-        decoded = ujson.ujson_loads(encoded)
-        assert sut == decoded
-
-    def test_encode_non_c_locale(self):
-        lc_category = locale.LC_NUMERIC
-
-        # We just need one of these locales to work.
-        for new_locale in ("it_IT.UTF-8", "Italian_Italy"):
-            if tm.can_set_locale(new_locale, lc_category):
-                with tm.set_locale(new_locale, lc_category):
-                    assert ujson.ujson_loads(ujson.ujson_dumps(4.78e60)) == 4.78e60
-                    assert ujson.ujson_loads("4.78", precise_float=True) == 4.78
-                break
-
-    def test_decimal_decode_test_precise(self):
-        sut = {"a": 4.56}
-        encoded = ujson.ujson_dumps(sut)
-        decoded = ujson.ujson_loads(encoded, precise_float=True)
-        assert sut == decoded
-
-    def test_encode_double_tiny_exponential(self):
-        num = 1e-40
-        assert num == ujson.ujson_loads(ujson.ujson_dumps(num))
-        num = 1e-100
-        assert num == ujson.ujson_loads(ujson.ujson_dumps(num))
-        num = -1e-45
-        assert num == ujson.ujson_loads(ujson.ujson_dumps(num))
-        num = -1e-145
-        assert np.allclose(num, ujson.ujson_loads(ujson.ujson_dumps(num)))
-
-    @pytest.mark.parametrize("unicode_key", ["key1", "بن"])
-    def test_encode_dict_with_unicode_keys(self, unicode_key):
-        unicode_dict = {unicode_key: "value1"}
-        assert unicode_dict == ujson.ujson_loads(ujson.ujson_dumps(unicode_dict))
-
-    @pytest.mark.parametrize(
-        "double_input", [math.pi, -math.pi]  # Should work with negatives too.
-    )
-    def test_encode_double_conversion(self, double_input):
-        output = ujson.ujson_dumps(double_input)
-        assert round(double_input, 5) == round(json.loads(output), 5)
-        assert round(double_input, 5) == round(ujson.ujson_loads(output), 5)
-
-    def test_encode_with_decimal(self):
-        decimal_input = 1.0
-        output = ujson.ujson_dumps(decimal_input)
-
-        assert output == "1.0"
-
-    def test_encode_array_of_nested_arrays(self):
-        nested_input = [[[[]]]] * 20
-        output = ujson.ujson_dumps(nested_input)
-
-        assert nested_input == json.loads(output)
-        assert nested_input == ujson.ujson_loads(output)
-
-    def test_encode_array_of_doubles(self):
-        doubles_input = [31337.31337, 31337.31337, 31337.31337, 31337.31337] * 10
-        output = ujson.ujson_dumps(doubles_input)
-
-        assert doubles_input == json.loads(output)
-        assert doubles_input == ujson.ujson_loads(output)
-
-    def test_double_precision(self):
-        double_input = 30.012345678901234
-        output = ujson.ujson_dumps(double_input, double_precision=15)
-
-        assert double_input == json.loads(output)
-        assert double_input == ujson.ujson_loads(output)
-
-        for double_precision in (3, 9):
-            output = ujson.ujson_dumps(double_input, double_precision=double_precision)
-            rounded_input = round(double_input, double_precision)
-
-            assert rounded_input == json.loads(output)
-            assert rounded_input == ujson.ujson_loads(output)
-
-    @pytest.mark.parametrize(
-        "invalid_val",
-        [
-            20,
-            -1,
-            "9",
-            None,
-        ],
-    )
-    def test_invalid_double_precision(self, invalid_val):
-        double_input = 30.12345678901234567890
-        expected_exception = ValueError if isinstance(invalid_val, int) else TypeError
-        msg = (
-            r"Invalid value '.*' for option 'double_precision', max is '15'|"
-            r"an integer is required \(got type |"
-            r"object cannot be interpreted as an integer"
-        )
-        with pytest.raises(expected_exception, match=msg):
-            ujson.ujson_dumps(double_input, double_precision=invalid_val)
-
-    def test_encode_string_conversion2(self):
-        string_input = "A string \\ / \b \f \n \r \t"
-        output = ujson.ujson_dumps(string_input)
-
-        assert string_input == json.loads(output)
-        assert string_input == ujson.ujson_loads(output)
-        assert output == '"A string \\\\ \\/ \\b \\f \\n \\r \\t"'
-
-    @pytest.mark.parametrize(
-        "unicode_input",
-        ["Räksmörgås اسامة بن محمد بن عوض بن لادن", "\xe6\x97\xa5\xd1\x88"],
-    )
-    def test_encode_unicode_conversion(self, unicode_input):
-        enc = ujson.ujson_dumps(unicode_input)
-        dec = ujson.ujson_loads(enc)
-
-        assert enc == json.dumps(unicode_input)
-        assert dec == json.loads(enc)
-
-    def test_encode_control_escaping(self):
-        escaped_input = "\x19"
-        enc = ujson.ujson_dumps(escaped_input)
-        dec = ujson.ujson_loads(enc)
-
-        assert escaped_input == dec
-        assert enc == json.dumps(escaped_input)
-
-    def test_encode_unicode_surrogate_pair(self):
-        surrogate_input = "\xf0\x90\x8d\x86"
-        enc = ujson.ujson_dumps(surrogate_input)
-        dec = ujson.ujson_loads(enc)
-
-        assert enc == json.dumps(surrogate_input)
-        assert dec == json.loads(enc)
-
-    def test_encode_unicode_4bytes_utf8(self):
-        four_bytes_input = "\xf0\x91\x80\xb0TRAILINGNORMAL"
-        enc = ujson.ujson_dumps(four_bytes_input)
-        dec = ujson.ujson_loads(enc)
-
-        assert enc == json.dumps(four_bytes_input)
-        assert dec == json.loads(enc)
-
-    def test_encode_unicode_4bytes_utf8highest(self):
-        four_bytes_input = "\xf3\xbf\xbf\xbfTRAILINGNORMAL"
-        enc = ujson.ujson_dumps(four_bytes_input)
-
-        dec = ujson.ujson_loads(enc)
-
-        assert enc == json.dumps(four_bytes_input)
-        assert dec == json.loads(enc)
-
-    def test_encode_unicode_error(self):
-        string = "'\udac0'"
-        msg = (
-            r"'utf-8' codec can't encode character '\\udac0' "
-            r"in position 1: surrogates not allowed"
-        )
-        with pytest.raises(UnicodeEncodeError, match=msg):
-            ujson.ujson_dumps([string])
-
-    def test_encode_array_in_array(self):
-        arr_in_arr_input = [[[[]]]]
-        output = ujson.ujson_dumps(arr_in_arr_input)
-
-        assert arr_in_arr_input == json.loads(output)
-        assert output == json.dumps(arr_in_arr_input)
-        assert arr_in_arr_input == ujson.ujson_loads(output)
-
-    @pytest.mark.parametrize(
-        "num_input",
-        [
-            31337,
-            -31337,  # Negative number.
-            -9223372036854775808,  # Large negative number.
-        ],
-    )
-    def test_encode_num_conversion(self, num_input):
-        output = ujson.ujson_dumps(num_input)
-        assert num_input == json.loads(output)
-        assert output == json.dumps(num_input)
-        assert num_input == ujson.ujson_loads(output)
-
-    def test_encode_list_conversion(self):
-        list_input = [1, 2, 3, 4]
-        output = ujson.ujson_dumps(list_input)
-
-        assert list_input == json.loads(output)
-        assert list_input == ujson.ujson_loads(output)
-
-    def test_encode_dict_conversion(self):
-        dict_input = {"k1": 1, "k2": 2, "k3": 3, "k4": 4}
-        output = ujson.ujson_dumps(dict_input)
-
-        assert dict_input == json.loads(output)
-        assert dict_input == ujson.ujson_loads(output)
-
-    @pytest.mark.parametrize("builtin_value", [None, True, False])
-    def test_encode_builtin_values_conversion(self, builtin_value):
-        output = ujson.ujson_dumps(builtin_value)
-        assert builtin_value == json.loads(output)
-        assert output == json.dumps(builtin_value)
-        assert builtin_value == ujson.ujson_loads(output)
-
-    def test_encode_datetime_conversion(self):
-        datetime_input = datetime.datetime.fromtimestamp(time.time())
-        output = ujson.ujson_dumps(datetime_input, date_unit="s")
-        expected = calendar.timegm(datetime_input.utctimetuple())
-
-        assert int(expected) == json.loads(output)
-        assert int(expected) == ujson.ujson_loads(output)
-
-    def test_encode_date_conversion(self):
-        date_input = datetime.date.fromtimestamp(time.time())
-        output = ujson.ujson_dumps(date_input, date_unit="s")
-
-        tup = (date_input.year, date_input.month, date_input.day, 0, 0, 0)
-        expected = calendar.timegm(tup)
-
-        assert int(expected) == json.loads(output)
-        assert int(expected) == ujson.ujson_loads(output)
-
-    @pytest.mark.parametrize(
-        "test",
-        [datetime.time(), datetime.time(1, 2, 3), datetime.time(10, 12, 15, 343243)],
-    )
-    def test_encode_time_conversion_basic(self, test):
-        output = ujson.ujson_dumps(test)
-        expected = f'"{test.isoformat()}"'
-        assert expected == output
-
-    def test_encode_time_conversion_pytz(self):
-        # see gh-11473: to_json segfaults with timezone-aware datetimes
-        test = datetime.time(10, 12, 15, 343243, pytz.utc)
-        output = ujson.ujson_dumps(test)
-        expected = f'"{test.isoformat()}"'
-        assert expected == output
-
-    def test_encode_time_conversion_dateutil(self):
-        # see gh-11473: to_json segfaults with timezone-aware datetimes
-        test = datetime.time(10, 12, 15, 343243, dateutil.tz.tzutc())
-        output = ujson.ujson_dumps(test)
-        expected = f'"{test.isoformat()}"'
-        assert expected == output
-
-    @pytest.mark.parametrize(
-        "decoded_input", [NaT, np.datetime64("NaT"), np.nan, np.inf, -np.inf]
-    )
-    def test_encode_as_null(self, decoded_input):
-        assert ujson.ujson_dumps(decoded_input) == "null", "Expected null"
-
-    def test_datetime_units(self):
-        val = datetime.datetime(2013, 8, 17, 21, 17, 12, 215504)
-        stamp = Timestamp(val).as_unit("ns")
-
-        roundtrip = ujson.ujson_loads(ujson.ujson_dumps(val, date_unit="s"))
-        assert roundtrip == stamp._value // 10**9
-
-        roundtrip = ujson.ujson_loads(ujson.ujson_dumps(val, date_unit="ms"))
-        assert roundtrip == stamp._value // 10**6
-
-        roundtrip = ujson.ujson_loads(ujson.ujson_dumps(val, date_unit="us"))
-        assert roundtrip == stamp._value // 10**3
-
-        roundtrip = ujson.ujson_loads(ujson.ujson_dumps(val, date_unit="ns"))
-        assert roundtrip == stamp._value
-
-        msg = "Invalid value 'foo' for option 'date_unit'"
-        with pytest.raises(ValueError, match=msg):
-            ujson.ujson_dumps(val, date_unit="foo")
-
-    def test_encode_to_utf8(self):
-        unencoded = "\xe6\x97\xa5\xd1\x88"
-
-        enc = ujson.ujson_dumps(unencoded, ensure_ascii=False)
-        dec = ujson.ujson_loads(enc)
-
-        assert enc == json.dumps(unencoded, ensure_ascii=False)
-        assert dec == json.loads(enc)
-
-    def test_decode_from_unicode(self):
-        unicode_input = '{"obj": 31337}'
-
-        dec1 = ujson.ujson_loads(unicode_input)
-        dec2 = ujson.ujson_loads(str(unicode_input))
-
-        assert dec1 == dec2
-
-    def test_encode_recursion_max(self):
-        # 8 is the max recursion depth
-
-        class O2:
-            member = 0
-
-        class O1:
-            member = 0
-
-        decoded_input = O1()
-        decoded_input.member = O2()
-        decoded_input.member.member = decoded_input
-
-        with pytest.raises(OverflowError, match="Maximum recursion level reached"):
-            ujson.ujson_dumps(decoded_input)
-
-    def test_decode_jibberish(self):
-        jibberish = "fdsa sda v9sa fdsa"
-        msg = "Unexpected character found when decoding 'false'"
-        with pytest.raises(ValueError, match=msg):
-            ujson.ujson_loads(jibberish)
-
-    @pytest.mark.parametrize(
-        "broken_json",
-        [
-            "[",  # Broken array start.
-            "{",  # Broken object start.
-            "]",  # Broken array end.
-            "}",  # Broken object end.
-        ],
-    )
-    def test_decode_broken_json(self, broken_json):
-        msg = "Expected object or value"
-        with pytest.raises(ValueError, match=msg):
-            ujson.ujson_loads(broken_json)
-
-    @pytest.mark.parametrize("too_big_char", ["[", "{"])
-    def test_decode_depth_too_big(self, too_big_char):
-        with pytest.raises(ValueError, match="Reached object decoding depth limit"):
-            ujson.ujson_loads(too_big_char * (1024 * 1024))
-
-    @pytest.mark.parametrize(
-        "bad_string",
-        [
-            '"TESTING',  # Unterminated.
-            '"TESTING\\"',  # Unterminated escape.
-            "tru",  # Broken True.
-            "fa",  # Broken False.
-            "n",  # Broken None.
-        ],
-    )
-    def test_decode_bad_string(self, bad_string):
-        msg = (
-            "Unexpected character found when decoding|"
-            "Unmatched ''\"' when when decoding 'string'"
-        )
-        with pytest.raises(ValueError, match=msg):
-            ujson.ujson_loads(bad_string)
-
-    @pytest.mark.parametrize(
-        "broken_json, err_msg",
-        [
-            (
-                '{{1337:""}}',
-                "Key name of object must be 'string' when decoding 'object'",
-            ),
-            ('{{"key":"}', "Unmatched ''\"' when when decoding 'string'"),
-            ("[[[true", "Unexpected character found when decoding array value (2)"),
-        ],
-    )
-    def test_decode_broken_json_leak(self, broken_json, err_msg):
-        for _ in range(1000):
-            with pytest.raises(ValueError, match=re.escape(err_msg)):
-                ujson.ujson_loads(broken_json)
-
-    @pytest.mark.parametrize(
-        "invalid_dict",
-        [
-            "{{{{31337}}}}",  # No key.
-            '{{{{"key":}}}}',  # No value.
-            '{{{{"key"}}}}',  # No colon or value.
-        ],
-    )
-    def test_decode_invalid_dict(self, invalid_dict):
-        msg = (
-            "Key name of object must be 'string' when decoding 'object'|"
-            "No ':' found when decoding object value|"
-            "Expected object or value"
-        )
-        with pytest.raises(ValueError, match=msg):
-            ujson.ujson_loads(invalid_dict)
-
-    @pytest.mark.parametrize(
-        "numeric_int_as_str", ["31337", "-31337"]  # Should work with negatives.
-    )
-    def test_decode_numeric_int(self, numeric_int_as_str):
-        assert int(numeric_int_as_str) == ujson.ujson_loads(numeric_int_as_str)
-
-    def test_encode_null_character(self):
-        wrapped_input = "31337 \x00 1337"
-        output = ujson.ujson_dumps(wrapped_input)
-
-        assert wrapped_input == json.loads(output)
-        assert output == json.dumps(wrapped_input)
-        assert wrapped_input == ujson.ujson_loads(output)
-
-        alone_input = "\x00"
-        output = ujson.ujson_dumps(alone_input)
-
-        assert alone_input == json.loads(output)
-        assert output == json.dumps(alone_input)
-        assert alone_input == ujson.ujson_loads(output)
-        assert '"  \\u0000\\r\\n "' == ujson.ujson_dumps("  \u0000\r\n ")
-
-    def test_decode_null_character(self):
-        wrapped_input = '"31337 \\u0000 31337"'
-        assert ujson.ujson_loads(wrapped_input) == json.loads(wrapped_input)
-
-    def test_encode_list_long_conversion(self):
-        long_input = [
-            9223372036854775807,
-            9223372036854775807,
-            9223372036854775807,
-            9223372036854775807,
-            9223372036854775807,
-            9223372036854775807,
-        ]
-        output = ujson.ujson_dumps(long_input)
-
-        assert long_input == json.loads(output)
-        assert long_input == ujson.ujson_loads(output)
-
-    @pytest.mark.parametrize("long_input", [9223372036854775807, 18446744073709551615])
-    def test_encode_long_conversion(self, long_input):
-        output = ujson.ujson_dumps(long_input)
-
-        assert long_input == json.loads(output)
-        assert output == json.dumps(long_input)
-        assert long_input == ujson.ujson_loads(output)
-
-    @pytest.mark.parametrize("bigNum", [2**64, -(2**63) - 1])
-    def test_dumps_ints_larger_than_maxsize(self, bigNum):
-        encoding = ujson.ujson_dumps(bigNum)
-        assert str(bigNum) == encoding
-
-        with pytest.raises(
-            ValueError,
-            match="Value is too big|Value is too small",
-        ):
-            assert ujson.ujson_loads(encoding) == bigNum
-
-    @pytest.mark.parametrize(
-        "int_exp", ["1337E40", "1.337E40", "1337E+9", "1.337e+40", "1.337E-4"]
-    )
-    def test_decode_numeric_int_exp(self, int_exp):
-        assert ujson.ujson_loads(int_exp) == json.loads(int_exp)
-
-    def test_loads_non_str_bytes_raises(self):
-        msg = "Expected 'str' or 'bytes'"
-        with pytest.raises(TypeError, match=msg):
-            ujson.ujson_loads(None)
-
-    @pytest.mark.parametrize("val", [3590016419, 2**31, 2**32, (2**32) - 1])
-    def test_decode_number_with_32bit_sign_bit(self, val):
-        # Test that numbers that fit within 32 bits but would have the
-        # sign bit set (2**31 <= x < 2**32) are decoded properly.
-        doc = f'{{"id": {val}}}'
-        assert ujson.ujson_loads(doc)["id"] == val
-
-    def test_encode_big_escape(self):
-        # Make sure no Exception is raised.
-        for _ in range(10):
-            base = "\u00e5".encode()
-            escape_input = base * 1024 * 1024 * 2
-            ujson.ujson_dumps(escape_input)
-
-    def test_decode_big_escape(self):
-        # Make sure no Exception is raised.
-        for _ in range(10):
-            base = "\u00e5".encode()
-            quote = b'"'
-
-            escape_input = quote + (base * 1024 * 1024 * 2) + quote
-            ujson.ujson_loads(escape_input)
-
-    def test_to_dict(self):
-        d = {"key": 31337}
-
-        class DictTest:
-            def toDict(self):
-                return d
-
-        o = DictTest()
-        output = ujson.ujson_dumps(o)
-
-        dec = ujson.ujson_loads(output)
-        assert dec == d
-
-    def test_default_handler(self):
-        class _TestObject:
-            def __init__(self, val) -> None:
-                self.val = val
-
-            @property
-            def recursive_attr(self):
-                return _TestObject("recursive_attr")
-
-            def __str__(self) -> str:
-                return str(self.val)
-
-        msg = "Maximum recursion level reached"
-        with pytest.raises(OverflowError, match=msg):
-            ujson.ujson_dumps(_TestObject("foo"))
-        assert '"foo"' == ujson.ujson_dumps(_TestObject("foo"), default_handler=str)
-
-        def my_handler(_):
-            return "foobar"
-
-        assert '"foobar"' == ujson.ujson_dumps(
-            _TestObject("foo"), default_handler=my_handler
-        )
-
-        def my_handler_raises(_):
-            raise TypeError("I raise for anything")
-
-        with pytest.raises(TypeError, match="I raise for anything"):
-            ujson.ujson_dumps(_TestObject("foo"), default_handler=my_handler_raises)
-
-        def my_int_handler(_):
-            return 42
-
-        assert (
-            ujson.ujson_loads(
-                ujson.ujson_dumps(_TestObject("foo"), default_handler=my_int_handler)
-            )
-            == 42
-        )
-
-        def my_obj_handler(_):
-            return datetime.datetime(2013, 2, 3)
-
-        assert ujson.ujson_loads(
-            ujson.ujson_dumps(datetime.datetime(2013, 2, 3))
-        ) == ujson.ujson_loads(
-            ujson.ujson_dumps(_TestObject("foo"), default_handler=my_obj_handler)
-        )
-
-        obj_list = [_TestObject("foo"), _TestObject("bar")]
-        assert json.loads(json.dumps(obj_list, default=str)) == ujson.ujson_loads(
-            ujson.ujson_dumps(obj_list, default_handler=str)
-        )
-
-    def test_encode_object(self):
-        class _TestObject:
-            def __init__(self, a, b, _c, d) -> None:
-                self.a = a
-                self.b = b
-                self._c = _c
-                self.d = d
-
-            def e(self):
-                return 5
-
-        # JSON keys should be all non-callable non-underscore attributes, see GH-42768
-        test_object = _TestObject(a=1, b=2, _c=3, d=4)
-        assert ujson.ujson_loads(ujson.ujson_dumps(test_object)) == {
-            "a": 1,
-            "b": 2,
-            "d": 4,
-        }
-
-    def test_ujson__name__(self):
-        # GH 52898
-        assert ujson.__name__ == "pandas._libs.json"
-
-
-class TestNumpyJSONTests:
-    @pytest.mark.parametrize("bool_input", [True, False])
-    def test_bool(self, bool_input):
-        b = bool(bool_input)
-        assert ujson.ujson_loads(ujson.ujson_dumps(b)) == b
-
-    def test_bool_array(self):
-        bool_array = np.array(
-            [True, False, True, True, False, True, False, False], dtype=bool
-        )
-        output = np.array(ujson.ujson_loads(ujson.ujson_dumps(bool_array)), dtype=bool)
-        tm.assert_numpy_array_equal(bool_array, output)
-
-    def test_int(self, any_int_numpy_dtype):
-        klass = np.dtype(any_int_numpy_dtype).type
-        num = klass(1)
-
-        assert klass(ujson.ujson_loads(ujson.ujson_dumps(num))) == num
-
-    def test_int_array(self, any_int_numpy_dtype):
-        arr = np.arange(100, dtype=int)
-        arr_input = arr.astype(any_int_numpy_dtype)
-
-        arr_output = np.array(
-            ujson.ujson_loads(ujson.ujson_dumps(arr_input)), dtype=any_int_numpy_dtype
-        )
-        tm.assert_numpy_array_equal(arr_input, arr_output)
-
-    def test_int_max(self, any_int_numpy_dtype):
-        if any_int_numpy_dtype in ("int64", "uint64") and not IS64:
-            pytest.skip("Cannot test 64-bit integer on 32-bit platform")
-
-        klass = np.dtype(any_int_numpy_dtype).type
-
-        # uint64 max will always overflow,
-        # as it's encoded to signed.
-        if any_int_numpy_dtype == "uint64":
-            num = np.iinfo("int64").max
-        else:
-            num = np.iinfo(any_int_numpy_dtype).max
-
-        assert klass(ujson.ujson_loads(ujson.ujson_dumps(num))) == num
-
-    def test_float(self, float_numpy_dtype):
-        klass = np.dtype(float_numpy_dtype).type
-        num = klass(256.2013)
-
-        assert klass(ujson.ujson_loads(ujson.ujson_dumps(num))) == num
-
-    def test_float_array(self, float_numpy_dtype):
-        arr = np.arange(12.5, 185.72, 1.7322, dtype=float)
-        float_input = arr.astype(float_numpy_dtype)
-
-        float_output = np.array(
-            ujson.ujson_loads(ujson.ujson_dumps(float_input, double_precision=15)),
-            dtype=float_numpy_dtype,
-        )
-        tm.assert_almost_equal(float_input, float_output)
-
-    def test_float_max(self, float_numpy_dtype):
-        klass = np.dtype(float_numpy_dtype).type
-        num = klass(np.finfo(float_numpy_dtype).max / 10)
-
-        tm.assert_almost_equal(
-            klass(ujson.ujson_loads(ujson.ujson_dumps(num, double_precision=15))), num
-        )
-
-    def test_array_basic(self):
-        arr = np.arange(96)
-        arr = arr.reshape((2, 2, 2, 2, 3, 2))
-
-        tm.assert_numpy_array_equal(
-            np.array(ujson.ujson_loads(ujson.ujson_dumps(arr))), arr
-        )
-
-    @pytest.mark.parametrize("shape", [(10, 10), (5, 5, 4), (100, 1)])
-    def test_array_reshaped(self, shape):
-        arr = np.arange(100)
-        arr = arr.reshape(shape)
-
-        tm.assert_numpy_array_equal(
-            np.array(ujson.ujson_loads(ujson.ujson_dumps(arr))), arr
-        )
-
-    def test_array_list(self):
-        arr_list = [
-            "a",
-            [],
-            {},
-            {},
-            [],
-            42,
-            97.8,
-            ["a", "b"],
-            {"key": "val"},
-        ]
-        arr = np.array(arr_list, dtype=object)
-        result = np.array(ujson.ujson_loads(ujson.ujson_dumps(arr)), dtype=object)
-        tm.assert_numpy_array_equal(result, arr)
-
-    def test_array_float(self):
-        dtype = np.float32
-
-        arr = np.arange(100.202, 200.202, 1, dtype=dtype)
-        arr = arr.reshape((5, 5, 4))
-
-        arr_out = np.array(ujson.ujson_loads(ujson.ujson_dumps(arr)), dtype=dtype)
-        tm.assert_almost_equal(arr, arr_out)
-
-    def test_0d_array(self):
-        # gh-18878
-        msg = re.escape("array(1) (0d array) is not JSON serializable at the moment")
-        with pytest.raises(TypeError, match=msg):
-            ujson.ujson_dumps(np.array(1))
-
-
-class TestPandasJSONTests:
-    def test_dataframe(self, orient):
-        dtype = np.int64
-
-        df = DataFrame(
-            [[1, 2, 3], [4, 5, 6]],
-            index=["a", "b"],
-            columns=["x", "y", "z"],
-            dtype=dtype,
-        )
-        encode_kwargs = {} if orient is None else {"orient": orient}
-        assert (df.dtypes == dtype).all()
-
-        output = ujson.ujson_loads(ujson.ujson_dumps(df, **encode_kwargs))
-        assert (df.dtypes == dtype).all()
-
-        # Ensure proper DataFrame initialization.
-        if orient == "split":
-            dec = _clean_dict(output)
-            output = DataFrame(**dec)
-        else:
-            output = DataFrame(output)
-
-        # Corrections to enable DataFrame comparison.
-        if orient == "values":
-            df.columns = [0, 1, 2]
-            df.index = [0, 1]
-        elif orient == "records":
-            df.index = [0, 1]
-        elif orient == "index":
-            df = df.transpose()
-
-        assert (df.dtypes == dtype).all()
-        tm.assert_frame_equal(output, df)
-
-    def test_dataframe_nested(self, orient):
-        df = DataFrame(
-            [[1, 2, 3], [4, 5, 6]], index=["a", "b"], columns=["x", "y", "z"]
-        )
-
-        nested = {"df1": df, "df2": df.copy()}
-        kwargs = {} if orient is None else {"orient": orient}
-
-        exp = {
-            "df1": ujson.ujson_loads(ujson.ujson_dumps(df, **kwargs)),
-            "df2": ujson.ujson_loads(ujson.ujson_dumps(df, **kwargs)),
-        }
-        assert ujson.ujson_loads(ujson.ujson_dumps(nested, **kwargs)) == exp
-
-    def test_series(self, orient):
-        dtype = np.int64
-        s = Series(
-            [10, 20, 30, 40, 50, 60],
-            name="series",
-            index=[6, 7, 8, 9, 10, 15],
-            dtype=dtype,
-        ).sort_values()
-        assert s.dtype == dtype
-
-        encode_kwargs = {} if orient is None else {"orient": orient}
-
-        output = ujson.ujson_loads(ujson.ujson_dumps(s, **encode_kwargs))
-        assert s.dtype == dtype
-
-        if orient == "split":
-            dec = _clean_dict(output)
-            output = Series(**dec)
-        else:
-            output = Series(output)
-
-        if orient in (None, "index"):
-            s.name = None
-            output = output.sort_values()
-            s.index = ["6", "7", "8", "9", "10", "15"]
-        elif orient in ("records", "values"):
-            s.name = None
-            s.index = [0, 1, 2, 3, 4, 5]
-
-        assert s.dtype == dtype
-        tm.assert_series_equal(output, s)
-
-    def test_series_nested(self, orient):
-        s = Series(
-            [10, 20, 30, 40, 50, 60], name="series", index=[6, 7, 8, 9, 10, 15]
-        ).sort_values()
-        nested = {"s1": s, "s2": s.copy()}
-        kwargs = {} if orient is None else {"orient": orient}
-
-        exp = {
-            "s1": ujson.ujson_loads(ujson.ujson_dumps(s, **kwargs)),
-            "s2": ujson.ujson_loads(ujson.ujson_dumps(s, **kwargs)),
-        }
-        assert ujson.ujson_loads(ujson.ujson_dumps(nested, **kwargs)) == exp
-
-    def test_index(self):
-        i = Index([23, 45, 18, 98, 43, 11], name="index")
-
-        # Column indexed.
-        output = Index(ujson.ujson_loads(ujson.ujson_dumps(i)), name="index")
-        tm.assert_index_equal(i, output)
-
-        dec = _clean_dict(ujson.ujson_loads(ujson.ujson_dumps(i, orient="split")))
-        output = Index(**dec)
-
-        tm.assert_index_equal(i, output)
-        assert i.name == output.name
-
-        tm.assert_index_equal(i, output)
-        assert i.name == output.name
-
-        output = Index(
-            ujson.ujson_loads(ujson.ujson_dumps(i, orient="values")), name="index"
-        )
-        tm.assert_index_equal(i, output)
-
-        output = Index(
-            ujson.ujson_loads(ujson.ujson_dumps(i, orient="records")), name="index"
-        )
-        tm.assert_index_equal(i, output)
-
-        output = Index(
-            ujson.ujson_loads(ujson.ujson_dumps(i, orient="index")), name="index"
-        )
-        tm.assert_index_equal(i, output)
-
-    def test_datetime_index(self):
-        date_unit = "ns"
-
-        # freq doesn't round-trip
-        rng = DatetimeIndex(list(date_range("1/1/2000", periods=20)), freq=None)
-        encoded = ujson.ujson_dumps(rng, date_unit=date_unit)
-
-        decoded = DatetimeIndex(np.array(ujson.ujson_loads(encoded)))
-        tm.assert_index_equal(rng, decoded)
-
-        ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
-        decoded = Series(ujson.ujson_loads(ujson.ujson_dumps(ts, date_unit=date_unit)))
-
-        idx_values = decoded.index.values.astype(np.int64)
-        decoded.index = DatetimeIndex(idx_values)
-        tm.assert_series_equal(ts, decoded)
-
-    @pytest.mark.parametrize(
-        "invalid_arr",
-        [
-            "[31337,]",  # Trailing comma.
-            "[,31337]",  # Leading comma.
-            "[]]",  # Unmatched bracket.
-            "[,]",  # Only comma.
-        ],
-    )
-    def test_decode_invalid_array(self, invalid_arr):
-        msg = (
-            "Expected object or value|Trailing data|"
-            "Unexpected character found when decoding array value"
-        )
-        with pytest.raises(ValueError, match=msg):
-            ujson.ujson_loads(invalid_arr)
-
-    @pytest.mark.parametrize("arr", [[], [31337]])
-    def test_decode_array(self, arr):
-        assert arr == ujson.ujson_loads(str(arr))
-
-    @pytest.mark.parametrize("extreme_num", [9223372036854775807, -9223372036854775808])
-    def test_decode_extreme_numbers(self, extreme_num):
-        assert extreme_num == ujson.ujson_loads(str(extreme_num))
-
-    @pytest.mark.parametrize("too_extreme_num", [f"{2**64}", f"{-2**63-1}"])
-    def test_decode_too_extreme_numbers(self, too_extreme_num):
-        with pytest.raises(
-            ValueError,
-            match="Value is too big|Value is too small",
-        ):
-            ujson.ujson_loads(too_extreme_num)
-
-    def test_decode_with_trailing_whitespaces(self):
-        assert {} == ujson.ujson_loads("{}\n\t ")
-
-    def test_decode_with_trailing_non_whitespaces(self):
-        with pytest.raises(ValueError, match="Trailing data"):
-            ujson.ujson_loads("{}\n\t a")
-
-    @pytest.mark.parametrize("value", [f"{2**64}", f"{-2**63-1}"])
-    def test_decode_array_with_big_int(self, value):
-        with pytest.raises(
-            ValueError,
-            match="Value is too big|Value is too small",
-        ):
-            ujson.ujson_loads(value)
-
-    @pytest.mark.parametrize(
-        "float_number",
-        [
-            1.1234567893,
-            1.234567893,
-            1.34567893,
-            1.4567893,
-            1.567893,
-            1.67893,
-            1.7893,
-            1.893,
-            1.3,
-        ],
-    )
-    @pytest.mark.parametrize("sign", [-1, 1])
-    def test_decode_floating_point(self, sign, float_number):
-        float_number *= sign
-        tm.assert_almost_equal(
-            float_number, ujson.ujson_loads(str(float_number)), rtol=1e-15
-        )
-
-    def test_encode_big_set(self):
-        s = set()
-
-        for x in range(0, 100000):
-            s.add(x)
-
-        # Make sure no Exception is raised.
-        ujson.ujson_dumps(s)
-
-    def test_encode_empty_set(self):
-        assert "[]" == ujson.ujson_dumps(set())
-
-    def test_encode_set(self):
-        s = {1, 2, 3, 4, 5, 6, 7, 8, 9}
-        enc = ujson.ujson_dumps(s)
-        dec = ujson.ujson_loads(enc)
-
-        for v in dec:
-            assert v in s
-
-    @pytest.mark.parametrize(
-        "td",
-        [
-            Timedelta(days=366),
-            Timedelta(days=-1),
-            Timedelta(hours=13, minutes=5, seconds=5),
-            Timedelta(hours=13, minutes=20, seconds=30),
-            Timedelta(days=-1, nanoseconds=5),
-            Timedelta(nanoseconds=1),
-            Timedelta(microseconds=1, nanoseconds=1),
-            Timedelta(milliseconds=1, microseconds=1, nanoseconds=1),
-            Timedelta(milliseconds=999, microseconds=999, nanoseconds=999),
-        ],
-    )
-    def test_encode_timedelta_iso(self, td):
-        # GH 28256
-        result = ujson.ujson_dumps(td, iso_dates=True)
-        expected = f'"{td.isoformat()}"'
-
-        assert result == expected
-
-    def test_encode_periodindex(self):
-        # GH 46683
-        p = PeriodIndex(["2022-04-06", "2022-04-07"], freq="D")
-        df = DataFrame(index=p)
-        assert df.to_json() == "{}"
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tseries/offsets/test_business_day.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tseries/offsets/test_business_day.py
deleted file mode 100644
index 7db1921369023eaf05c65bf537c259de3f2a81cb..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/tseries/offsets/test_business_day.py
+++ /dev/null
@@ -1,236 +0,0 @@
-"""
-Tests for offsets.BDay
-"""
-from __future__ import annotations
-
-from datetime import (
-    date,
-    datetime,
-    timedelta,
-)
-
-import numpy as np
-import pytest
-
-from pandas._libs.tslibs.offsets import (
-    ApplyTypeError,
-    BDay,
-    BMonthEnd,
-)
-
-from pandas import (
-    DatetimeIndex,
-    Timedelta,
-    _testing as tm,
-)
-from pandas.tests.tseries.offsets.common import (
-    assert_is_on_offset,
-    assert_offset_equal,
-)
-
-from pandas.tseries import offsets
-
-
-@pytest.fixture
-def dt():
-    return datetime(2008, 1, 1)
-
-
-@pytest.fixture
-def _offset():
-    return BDay
-
-
-@pytest.fixture
-def offset(_offset):
-    return _offset()
-
-
-@pytest.fixture
-def offset2(_offset):
-    return _offset(2)
-
-
-class TestBusinessDay:
-    def test_different_normalize_equals(self, _offset, offset2):
-        # GH#21404 changed __eq__ to return False when `normalize` does not match
-        offset = _offset()
-        offset2 = _offset(normalize=True)
-        assert offset != offset2
-
-    def test_repr(self, offset, offset2):
-        assert repr(offset) == "<BusinessDay>"
-        assert repr(offset2) == "<2 * BusinessDays>"
-
-        expected = "<BusinessDay: offset=datetime.timedelta(days=1)>"
-        assert repr(offset + timedelta(1)) == expected
-
-    def test_with_offset(self, dt, offset):
-        offset = offset + timedelta(hours=2)
-
-        assert (dt + offset) == datetime(2008, 1, 2, 2)
-
-    @pytest.mark.parametrize(
-        "td",
-        [
-            Timedelta(hours=2),
-            Timedelta(hours=2).to_pytimedelta(),
-            Timedelta(hours=2).to_timedelta64(),
-        ],
-        ids=lambda x: type(x),
-    )
-    def test_with_offset_index(self, td, dt, offset):
-        dti = DatetimeIndex([dt])
-        expected = DatetimeIndex([datetime(2008, 1, 2, 2)])
-
-        result = dti + (td + offset)
-        tm.assert_index_equal(result, expected)
-
-        result = dti + (offset + td)
-        tm.assert_index_equal(result, expected)
-
-    def test_eq(self, offset2):
-        assert offset2 == offset2
-
-    def test_hash(self, offset2):
-        assert hash(offset2) == hash(offset2)
-
-    def test_add_datetime(self, dt, offset2):
-        assert offset2 + dt == datetime(2008, 1, 3)
-        assert offset2 + np.datetime64("2008-01-01 00:00:00") == datetime(2008, 1, 3)
-
-    def testRollback1(self, dt, _offset):
-        assert _offset(10).rollback(dt) == dt
-
-    def testRollback2(self, _offset):
-        assert _offset(10).rollback(datetime(2008, 1, 5)) == datetime(2008, 1, 4)
-
-    def testRollforward1(self, dt, _offset):
-        assert _offset(10).rollforward(dt) == dt
-
-    def testRollforward2(self, _offset):
-        assert _offset(10).rollforward(datetime(2008, 1, 5)) == datetime(2008, 1, 7)
-
-    def test_roll_date_object(self, offset):
-        dt = date(2012, 9, 15)
-
-        result = offset.rollback(dt)
-        assert result == datetime(2012, 9, 14)
-
-        result = offset.rollforward(dt)
-        assert result == datetime(2012, 9, 17)
-
-        offset = offsets.Day()
-        result = offset.rollback(dt)
-        assert result == datetime(2012, 9, 15)
-
-        result = offset.rollforward(dt)
-        assert result == datetime(2012, 9, 15)
-
-    @pytest.mark.parametrize(
-        "dt, expected",
-        [
-            (datetime(2008, 1, 1), True),
-            (datetime(2008, 1, 5), False),
-        ],
-    )
-    def test_is_on_offset(self, offset, dt, expected):
-        assert_is_on_offset(offset, dt, expected)
-
-    apply_cases: list[tuple[int, dict[datetime, datetime]]] = [
-        (
-            1,
-            {
-                datetime(2008, 1, 1): datetime(2008, 1, 2),
-                datetime(2008, 1, 4): datetime(2008, 1, 7),
-                datetime(2008, 1, 5): datetime(2008, 1, 7),
-                datetime(2008, 1, 6): datetime(2008, 1, 7),
-                datetime(2008, 1, 7): datetime(2008, 1, 8),
-            },
-        ),
-        (
-            2,
-            {
-                datetime(2008, 1, 1): datetime(2008, 1, 3),
-                datetime(2008, 1, 4): datetime(2008, 1, 8),
-                datetime(2008, 1, 5): datetime(2008, 1, 8),
-                datetime(2008, 1, 6): datetime(2008, 1, 8),
-                datetime(2008, 1, 7): datetime(2008, 1, 9),
-            },
-        ),
-        (
-            -1,
-            {
-                datetime(2008, 1, 1): datetime(2007, 12, 31),
-                datetime(2008, 1, 4): datetime(2008, 1, 3),
-                datetime(2008, 1, 5): datetime(2008, 1, 4),
-                datetime(2008, 1, 6): datetime(2008, 1, 4),
-                datetime(2008, 1, 7): datetime(2008, 1, 4),
-                datetime(2008, 1, 8): datetime(2008, 1, 7),
-            },
-        ),
-        (
-            -2,
-            {
-                datetime(2008, 1, 1): datetime(2007, 12, 28),
-                datetime(2008, 1, 4): datetime(2008, 1, 2),
-                datetime(2008, 1, 5): datetime(2008, 1, 3),
-                datetime(2008, 1, 6): datetime(2008, 1, 3),
-                datetime(2008, 1, 7): datetime(2008, 1, 3),
-                datetime(2008, 1, 8): datetime(2008, 1, 4),
-                datetime(2008, 1, 9): datetime(2008, 1, 7),
-            },
-        ),
-        (
-            0,
-            {
-                datetime(2008, 1, 1): datetime(2008, 1, 1),
-                datetime(2008, 1, 4): datetime(2008, 1, 4),
-                datetime(2008, 1, 5): datetime(2008, 1, 7),
-                datetime(2008, 1, 6): datetime(2008, 1, 7),
-                datetime(2008, 1, 7): datetime(2008, 1, 7),
-            },
-        ),
-    ]
-
-    @pytest.mark.parametrize("case", apply_cases)
-    def test_apply(self, case, _offset):
-        n, cases = case
-        offset = _offset(n)
-        for base, expected in cases.items():
-            assert_offset_equal(offset, base, expected)
-
-    def test_apply_large_n(self, _offset):
-        dt = datetime(2012, 10, 23)
-
-        result = dt + _offset(10)
-        assert result == datetime(2012, 11, 6)
-
-        result = dt + _offset(100) - _offset(100)
-        assert result == dt
-
-        off = _offset() * 6
-        rs = datetime(2012, 1, 1) - off
-        xp = datetime(2011, 12, 23)
-        assert rs == xp
-
-        st = datetime(2011, 12, 18)
-        rs = st + off
-        xp = datetime(2011, 12, 26)
-        assert rs == xp
-
-        off = _offset() * 10
-        rs = datetime(2014, 1, 5) + off  # see #5890
-        xp = datetime(2014, 1, 17)
-        assert rs == xp
-
-    def test_apply_corner(self, _offset):
-        if _offset is BDay:
-            msg = "Only know how to combine business day with datetime or timedelta"
-        else:
-            msg = (
-                "Only know how to combine trading day "
-                "with datetime, datetime64 or timedelta"
-            )
-        with pytest.raises(ApplyTypeError, match=msg):
-            _offset()._apply(BMonthEnd())
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/progress/bar.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/progress/bar.py
deleted file mode 100644
index df4e8b61f89e020ee6a536209fa04f049637c86f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/progress/bar.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright (c) 2012 Georgios Verigakis <verigak@gmail.com>
-#
-# Permission to use, copy, modify, and distribute this software for any
-# purpose with or without fee is hereby granted, provided that the above
-# copyright notice and this permission notice appear in all copies.
-#
-# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
-# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
-# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
-# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
-# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
-# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
-# OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
-
-from __future__ import unicode_literals
-
-import sys
-
-from . import Progress
-from .colors import color
-
-
-class Bar(Progress):
-    width = 32
-    suffix = '%(index)d/%(max)d'
-    bar_prefix = ' |'
-    bar_suffix = '| '
-    empty_fill = ' '
-    fill = '#'
-    color = None
-
-    def update(self):
-        filled_length = int(self.width * self.progress)
-        empty_length = self.width - filled_length
-
-        message = self.message % self
-        bar = color(self.fill * filled_length, fg=self.color)
-        empty = self.empty_fill * empty_length
-        suffix = self.suffix % self
-        line = ''.join([message, self.bar_prefix, bar, empty, self.bar_suffix,
-                        suffix])
-        self.writeln(line)
-
-
-class ChargingBar(Bar):
-    suffix = '%(percent)d%%'
-    bar_prefix = ' '
-    bar_suffix = ' '
-    empty_fill = '∙'
-    fill = '█'
-
-
-class FillingSquaresBar(ChargingBar):
-    empty_fill = '▢'
-    fill = '▣'
-
-
-class FillingCirclesBar(ChargingBar):
-    empty_fill = '◯'
-    fill = '◉'
-
-
-class IncrementalBar(Bar):
-    if sys.platform.startswith('win'):
-        phases = (u' ', u'▌', u'█')
-    else:
-        phases = (' ', '▏', '▎', '▍', '▌', '▋', '▊', '▉', '█')
-
-    def update(self):
-        nphases = len(self.phases)
-        filled_len = self.width * self.progress
-        nfull = int(filled_len)                      # Number of full chars
-        phase = int((filled_len - nfull) * nphases)  # Phase of last char
-        nempty = self.width - nfull                  # Number of empty chars
-
-        message = self.message % self
-        bar = color(self.phases[-1] * nfull, fg=self.color)
-        current = self.phases[phase] if phase > 0 else ''
-        empty = self.empty_fill * max(0, nempty - len(current))
-        suffix = self.suffix % self
-        line = ''.join([message, self.bar_prefix, bar, current, empty,
-                        self.bar_suffix, suffix])
-        self.writeln(line)
-
-
-class PixelBar(IncrementalBar):
-    phases = ('⡀', '⡄', '⡆', '⡇', '⣇', '⣧', '⣷', '⣿')
-
-
-class ShadyBar(IncrementalBar):
-    phases = (' ', '░', '▒', '▓', '█')
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/namespaces.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/namespaces.py
deleted file mode 100644
index 44939e1c6d40539eb8173bf1527db926c5a54658..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/namespaces.py
+++ /dev/null
@@ -1,107 +0,0 @@
-import os
-from distutils import log
-import itertools
-
-
-flatten = itertools.chain.from_iterable
-
-
-class Installer:
-
-    nspkg_ext = '-nspkg.pth'
-
-    def install_namespaces(self):
-        nsp = self._get_all_ns_packages()
-        if not nsp:
-            return
-        filename, ext = os.path.splitext(self._get_target())
-        filename += self.nspkg_ext
-        self.outputs.append(filename)
-        log.info("Installing %s", filename)
-        lines = map(self._gen_nspkg_line, nsp)
-
-        if self.dry_run:
-            # always generate the lines, even in dry run
-            list(lines)
-            return
-
-        with open(filename, 'wt') as f:
-            f.writelines(lines)
-
-    def uninstall_namespaces(self):
-        filename, ext = os.path.splitext(self._get_target())
-        filename += self.nspkg_ext
-        if not os.path.exists(filename):
-            return
-        log.info("Removing %s", filename)
-        os.remove(filename)
-
-    def _get_target(self):
-        return self.target
-
-    _nspkg_tmpl = (
-        "import sys, types, os",
-        "has_mfs = sys.version_info > (3, 5)",
-        "p = os.path.join(%(root)s, *%(pth)r)",
-        "importlib = has_mfs and __import__('importlib.util')",
-        "has_mfs and __import__('importlib.machinery')",
-        (
-            "m = has_mfs and "
-            "sys.modules.setdefault(%(pkg)r, "
-            "importlib.util.module_from_spec("
-            "importlib.machinery.PathFinder.find_spec(%(pkg)r, "
-            "[os.path.dirname(p)])))"
-        ),
-        (
-            "m = m or "
-            "sys.modules.setdefault(%(pkg)r, types.ModuleType(%(pkg)r))"
-        ),
-        "mp = (m or []) and m.__dict__.setdefault('__path__',[])",
-        "(p not in mp) and mp.append(p)",
-    )
-    "lines for the namespace installer"
-
-    _nspkg_tmpl_multi = (
-        'm and setattr(sys.modules[%(parent)r], %(child)r, m)',
-    )
-    "additional line(s) when a parent package is indicated"
-
-    def _get_root(self):
-        return "sys._getframe(1).f_locals['sitedir']"
-
-    def _gen_nspkg_line(self, pkg):
-        pth = tuple(pkg.split('.'))
-        root = self._get_root()
-        tmpl_lines = self._nspkg_tmpl
-        parent, sep, child = pkg.rpartition('.')
-        if parent:
-            tmpl_lines += self._nspkg_tmpl_multi
-        return ';'.join(tmpl_lines) % locals() + '\n'
-
-    def _get_all_ns_packages(self):
-        """Return sorted list of all package namespaces"""
-        pkgs = self.distribution.namespace_packages or []
-        return sorted(flatten(map(self._pkg_names, pkgs)))
-
-    @staticmethod
-    def _pkg_names(pkg):
-        """
-        Given a namespace package, yield the components of that
-        package.
-
-        >>> names = Installer._pkg_names('a.b.c')
-        >>> set(names) == set(['a', 'a.b', 'a.b.c'])
-        True
-        """
-        parts = pkg.split('.')
-        while parts:
-            yield '.'.join(parts)
-            parts.pop()
-
-
-class DevelopInstaller(Installer):
-    def _get_root(self):
-        return repr(str(self.egg_path))
-
-    def _get_target(self):
-        return self.egg_link
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/websockets/__main__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/websockets/__main__.py
deleted file mode 100644
index f2ea5cf4e8fe949948b1b6872d8e596ef30102ea..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/websockets/__main__.py
+++ /dev/null
@@ -1,159 +0,0 @@
-from __future__ import annotations
-
-import argparse
-import os
-import signal
-import sys
-import threading
-
-
-try:
-    import readline  # noqa: F401
-except ImportError:  # Windows has no `readline` normally
-    pass
-
-from .sync.client import ClientConnection, connect
-from .version import version as websockets_version
-
-
-if sys.platform == "win32":
-
-    def win_enable_vt100() -> None:
-        """
-        Enable VT-100 for console output on Windows.
-
-        See also https://bugs.python.org/issue29059.
-
-        """
-        import ctypes
-
-        STD_OUTPUT_HANDLE = ctypes.c_uint(-11)
-        INVALID_HANDLE_VALUE = ctypes.c_uint(-1)
-        ENABLE_VIRTUAL_TERMINAL_PROCESSING = 0x004
-
-        handle = ctypes.windll.kernel32.GetStdHandle(STD_OUTPUT_HANDLE)
-        if handle == INVALID_HANDLE_VALUE:
-            raise RuntimeError("unable to obtain stdout handle")
-
-        cur_mode = ctypes.c_uint()
-        if ctypes.windll.kernel32.GetConsoleMode(handle, ctypes.byref(cur_mode)) == 0:
-            raise RuntimeError("unable to query current console mode")
-
-        # ctypes ints lack support for the required bit-OR operation.
-        # Temporarily convert to Py int, do the OR and convert back.
-        py_int_mode = int.from_bytes(cur_mode, sys.byteorder)
-        new_mode = ctypes.c_uint(py_int_mode | ENABLE_VIRTUAL_TERMINAL_PROCESSING)
-
-        if ctypes.windll.kernel32.SetConsoleMode(handle, new_mode) == 0:
-            raise RuntimeError("unable to set console mode")
-
-
-def print_during_input(string: str) -> None:
-    sys.stdout.write(
-        # Save cursor position
-        "\N{ESC}7"
-        # Add a new line
-        "\N{LINE FEED}"
-        # Move cursor up
-        "\N{ESC}[A"
-        # Insert blank line, scroll last line down
-        "\N{ESC}[L"
-        # Print string in the inserted blank line
-        f"{string}\N{LINE FEED}"
-        # Restore cursor position
-        "\N{ESC}8"
-        # Move cursor down
-        "\N{ESC}[B"
-    )
-    sys.stdout.flush()
-
-
-def print_over_input(string: str) -> None:
-    sys.stdout.write(
-        # Move cursor to beginning of line
-        "\N{CARRIAGE RETURN}"
-        # Delete current line
-        "\N{ESC}[K"
-        # Print string
-        f"{string}\N{LINE FEED}"
-    )
-    sys.stdout.flush()
-
-
-def print_incoming_messages(websocket: ClientConnection, stop: threading.Event) -> None:
-    for message in websocket:
-        if isinstance(message, str):
-            print_during_input("< " + message)
-        else:
-            print_during_input("< (binary) " + message.hex())
-    if not stop.is_set():
-        # When the server closes the connection, raise KeyboardInterrupt
-        # in the main thread to exit the program.
-        if sys.platform == "win32":
-            ctrl_c = signal.CTRL_C_EVENT
-        else:
-            ctrl_c = signal.SIGINT
-        os.kill(os.getpid(), ctrl_c)
-
-
-def main() -> None:
-    # Parse command line arguments.
-    parser = argparse.ArgumentParser(
-        prog="python -m websockets",
-        description="Interactive WebSocket client.",
-        add_help=False,
-    )
-    group = parser.add_mutually_exclusive_group()
-    group.add_argument("--version", action="store_true")
-    group.add_argument("uri", metavar="<uri>", nargs="?")
-    args = parser.parse_args()
-
-    if args.version:
-        print(f"websockets {websockets_version}")
-        return
-
-    if args.uri is None:
-        parser.error("the following arguments are required: <uri>")
-
-    # If we're on Windows, enable VT100 terminal support.
-    if sys.platform == "win32":
-        try:
-            win_enable_vt100()
-        except RuntimeError as exc:
-            sys.stderr.write(
-                f"Unable to set terminal to VT100 mode. This is only "
-                f"supported since Win10 anniversary update. Expect "
-                f"weird symbols on the terminal.\nError: {exc}\n"
-            )
-            sys.stderr.flush()
-
-    try:
-        websocket = connect(args.uri)
-    except Exception as exc:
-        print(f"Failed to connect to {args.uri}: {exc}.")
-        sys.exit(1)
-    else:
-        print(f"Connected to {args.uri}.")
-
-    stop = threading.Event()
-
-    # Start the thread that reads messages from the connection.
-    thread = threading.Thread(target=print_incoming_messages, args=(websocket, stop))
-    thread.start()
-
-    # Read from stdin in the main thread in order to receive signals.
-    try:
-        while True:
-            # Since there's no size limit, put_nowait is identical to put.
-            message = input("> ")
-            websocket.send(message)
-    except (KeyboardInterrupt, EOFError):  # ^C, ^D
-        stop.set()
-        websocket.close()
-        print_over_input("Connection closed.")
-
-    thread.join()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/pyInter/Liyuu_sovits4/vdecoder/hifigan/env.py b/spaces/pyInter/Liyuu_sovits4/vdecoder/hifigan/env.py
deleted file mode 100644
index 2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056..0000000000000000000000000000000000000000
--- a/spaces/pyInter/Liyuu_sovits4/vdecoder/hifigan/env.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-import shutil
-
-
-class AttrDict(dict):
-    def __init__(self, *args, **kwargs):
-        super(AttrDict, self).__init__(*args, **kwargs)
-        self.__dict__ = self
-
-
-def build_env(config, config_name, path):
-    t_path = os.path.join(path, config_name)
-    if config != t_path:
-        os.makedirs(path, exist_ok=True)
-        shutil.copyfile(config, os.path.join(path, config_name))
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/1001bit Pro V2 Keygen 16bfdcm.md b/spaces/quidiaMuxgu/Expedit-SAM/1001bit Pro V2 Keygen 16bfdcm.md
deleted file mode 100644
index 0cfa1f355719a205f57ca70cd473c6d5e3feb64e..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/1001bit Pro V2 Keygen 16bfdcm.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>1001bit pro v2 keygen 16bfdcm</h2><br /><p><b><b>Download Zip</b> >> <a href="https://geags.com/2uCqsx">https://geags.com/2uCqsx</a></b></p><br /><br />
-
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-<p></p>
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Bink Set Speaker Volume 20 Downl.md b/spaces/quidiaMuxgu/Expedit-SAM/Bink Set Speaker Volume 20 Downl.md
deleted file mode 100644
index 06e266f880c3f24dcc4ead91e51cd4223485ed13..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Bink Set Speaker Volume 20 Downl.md	
+++ /dev/null
@@ -1,117 +0,0 @@
-<br />
-<h1>Bink Set Speaker Volume 20 Downl: Everything You Need to Know About BinkSetVolume@20.dll</h1>
-
-<p>Are you a gamer who loves to play high-quality video games on your PC? If so, you may have heard of a file called BinkSetVolume@20.dll. This file is a part of the Bink Video codec, a software that allows video games to play stunning videos with low bandwidth requirements. BinkSetVolume@20.dll is a library that enables video games to adjust the audio volume of Bink-encoded video files during playback. In this article, we will explain what BinkSetVolume@20.dll is, why you need it, and how to download it.</p>
-
-<h2>What is BinkSetVolume@20.dll?</h2>
-
-<p>BinkSetVolume@20.dll is a dynamic link library file developed by RAD Game Tools, a company that specializes in video game development tools. BinkSetVolume@20.dll is a part of the Bink Video codec, a software that compresses and decompresses video files for video games. Bink Video codec is used by many popular video games, such as Call of Duty, Assassin's Creed, Grand Theft Auto, and more.</p>
-<h2>Bink Set Speaker Volume 20 Downl</h2><br /><p><b><b>Download Zip</b> &#10040;&#10040;&#10040; <a href="https://geags.com/2uCsYr">https://geags.com/2uCsYr</a></b></p><br /><br />
-
-<p>BinkSetVolume@20.dll is a library that contains a function that allows video games to adjust the audio volume of Bink-encoded video files during playback. The function takes two parameters: the first one is a pointer to a Bink structure that represents the video file, and the second one is an integer that represents the desired volume level. The function returns zero if successful, or an error code if not.</p>
-
-<p>The "@20" in the name of the file indicates that the function uses the __stdcall calling convention, which means that it requires 20 bytes of stack space to be cleared by the caller before the function is called. This is a common calling convention for Windows API functions.</p>
-
-<h2>Why do you need BinkSetVolume@20.dll?</h2>
-
-<p>You may need BinkSetVolume@20.dll if you want to play video games that use the Bink Video codec on your PC. The Bink Video codec allows video games to play high-quality videos with low bandwidth requirements, which means that you can enjoy stunning graphics and smooth gameplay without sacrificing performance or storage space. The Bink Video codec also supports various features such as alpha blending, scaling, rotation, subtitles, and more.</p>
-
-<p>BinkSetVolume@20.dll is a library that enables video games to adjust the audio volume of Bink-encoded video files during playback. This means that you can control how loud or quiet the sound effects and music are in your video games. You can also adjust the volume according to your preferences or your environment. For example, you can lower the volume if you are playing at night or in a public place, or you can increase the volume if you want to immerse yourself in the game world.</p>
-
-<h2>How to download BinkSetVolume@20.dll?</h2>
-
-<p>If you are missing or have a corrupted BinkSetVolume@20.dll file on your PC, you may experience errors when trying to play video games that use the Bink Video codec. Some of the common errors are:</p>
-<p></p>
-
-<ul>
-<li>This application has failed to start because BinkSetVolume@20.dll was not found. Re-installing the application may fix this problem.</li>
-<li>BinkSetVolume@20.dll Not Found.</li>
-<li>The file BinkSetVolume@20.dll is missing or corrupted.</li>
-<li>A required component is missing: BinkSetVolume@20.dll. Please install the application again.</li>
-</ul>
-
-<p>To fix these errors, you may need to download and install BinkSetVolume@20.dll on your PC. There are several ways to do this:</p>
-
-<ul>
-<li>Re-install the application that requires BinkSetVolume@20.dll. This may restore the missing or corrupted file on your PC.</li>
-<li>Update the application to the latest version. This may include an updated version of BinkSetVolume@20.dll that fixes the errors.</li>
-<li>Download and restore BinkSetVolume@20.dll from a reliable source. You can find several websites that offer free .dll downloads, such as DLLme.com. Make sure to choose the correct version or variant of BinkSetVolume@20.dll that matches your system and application requirements.</li>
-</ul>
-
-<p>Once you have downloaded BinkSetVolume@20.dll, you need to place it in either the program folder or the system folder on your PC. The program folder is where the application that uses BinkSetVolume@20.dll is installed on your PC. The system folder is where Windows stores its essential files and libraries. The system folder can be either System32 or SysWOW64, depending on whether you have a 32-bit or 64-bit system.</p>
-
-<p>To place BinkSetVolume@20.dll in either folder, you need to follow these steps:</p>
-
-<ol>
-<li>Locate the downloaded BinkSetVolume@20.dll file on your PC.</li>
-<li>Copy the file by right-clicking on it and selecting Copy.</li>
-<li>Navigate to either the program folder or the system folder on your PC.</li>
-<li>Paste the file by right-clicking on an empty space and selecting Paste.</li>
-<li>Restart your PC and try running the application again.</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>In this article, we have explained what BinkSetVolume@20.dll is, why you need it, and how to download it. We hope that this article has helped you understand more about this file and how it works with video games that use the Bink Video codec. If you have any questions or comments about this article, feel free to leave them below.</p>
-<h2>How to use BinkSetVolume@20.dll?</h2>
-
-<p>If you have successfully downloaded and installed BinkSetVolume@20.dll on your PC, you should be able to use it without any problems. However, if you are curious about how to use it manually, you can do so by calling its function from another program or script.</p>
-
-<p>To call the function from another program or script, you need to follow these steps:</p>
-
-<ol>
-<li>Load the BinkSetVolume@20.dll library into memory by using a function such as LoadLibrary or LoadLibraryEx from Windows API.</li>
-<li>Get the address of the function by using a function such as GetProcAddress from Windows API.</li>
-<li>Create a pointer to a Bink structure that represents the video file you want to adjust the volume of.</li>
-<li>Create an integer variable that represents the desired volume level.</li>
-<li>Call the function by passing the pointer and the integer as arguments.</li>
-<li>Check the return value of the function for any errors.</li>
-<li>Free the memory used by the library by using a function such as FreeLibrary from Windows API.</li>
-</ol>
-
-<p>Note that this method requires some programming knowledge and skills, and may not be suitable for beginners or casual users. If you are not familiar with programming or scripting, we recommend using one of the methods mentioned above to download and install BinkSetVolume@20.dll on your PC.</p>
-
-<h2>What are some alternatives to BinkSetVolume@20.dll?</h2>
-
-<p>BinkSetVolume@20.dll is a library that enables video games to adjust the audio volume of Bink-encoded video files during playback. However, it is not the only library that can do this. There are some alternatives to BinkSetVolume@20.dll that you may want to consider if you are looking for different features or functionalities. Some of these alternatives are:</p>
-
-<ul>
-<li>BinkSetMixBinVolumes@20.dll: This library allows video games to adjust the volume of individual audio channels of Bink-encoded video files during playback. This can be useful for creating surround sound effects or balancing different audio elements.</li>
-<li>BinkSetPan@12.dll: This library allows video games to adjust the stereo panning of Bink-encoded video files during playback. This can be useful for creating spatial sound effects or enhancing immersion.</li>
-<li>BinkSetMemory@8.dll: This library allows video games to allocate and free memory for Bink-encoded video files during playback. This can be useful for optimizing performance or reducing memory usage.</li>
-</ul>
-
-<p>These libraries are also developed by RAD Game Tools and are part of the Bink Video codec. They have similar naming conventions and calling conventions as BinkSetVolume@20.dll. You can download and install them in the same way as BinkSetVolume@20.dll. However, you should make sure that they are compatible with your system and application requirements before using them.</p>
-<h2>How to troubleshoot BinkSetVolume@20.dll errors?</h2>
-
-<p>Even if you have downloaded and installed BinkSetVolume@20.dll correctly on your PC, you may still encounter some errors when trying to use it. Some of the common errors are:</p>
-
-<ul>
-<li>The procedure entry point BinkSetVolume@20 could not be located in the dynamic link library Binkw32.dll.</li>
-<li>The application was unable to start correctly (0xc000007b). Click OK to close the application.</li>
-<li>The code execution cannot proceed because BinkSetVolume@20.dll was not found. Reinstalling the program may fix this problem.</li>
-<li>BinkSetVolume@20.dll is either not designed to run on Windows or it contains an error. Try installing the program again using the original installation media or contact your system administrator or the software vendor for support.</li>
-</ul>
-
-<p>To troubleshoot these errors, you may need to try some of the following solutions:</p>
-
-<ul>
-<li>Make sure that you have the latest version of BinkSetVolume@20.dll and that it matches your system and application requirements.</li>
-<li>Make sure that you have placed BinkSetVolume@20.dll in the correct folder and that you have not renamed or modified it.</li>
-<li>Make sure that you have the latest version of Binkw32.dll and that it is compatible with BinkSetVolume@20.dll.</li>
-<li>Make sure that you have the latest version of DirectX and that it supports the Bink Video codec.</li>
-<li>Make sure that you have updated your drivers and that they support the Bink Video codec.</li>
-<li>Make sure that you have scanned your PC for viruses and malware and that they have not infected or damaged BinkSetVolume@20.dll.</li>
-<li>Make sure that you have cleaned your PC registry and that it does not contain any invalid or corrupted entries related to BinkSetVolume@20.dll.</li>
-</ul>
-
-<p>If none of these solutions work, you may need to contact the developer of the application that uses BinkSetVolume@20.dll or the developer of the Bink Video codec for further assistance.</p>
-
-<h2>Conclusion</h2>
-
-<p>In this article, we have explained what BinkSetVolume@20.dll is, why you need it, how to download it, how to use it, what are some alternatives to it, and how to troubleshoot it. We hope that this article has helped you understand more about this file and how it works with video games that use the Bink Video codec. If you have any questions or comments about this article, feel free to leave them below.</p>
-<h2>Summary</h2>
-
-<p>In this article, we have introduced you to BinkSetVolume@20.dll, a file that is a part of the Bink Video codec and that allows video games to adjust the audio volume of Bink-encoded video files during playback. We have told you what BinkSetVolume@20.dll is, why you need it, how to download it, how to use it, what are some alternatives to it, and how to troubleshoot it. We hope that this article has helped you learn more about this file and how it works with video games that use the Bink Video codec. Don't miss this file that will help you enjoy your video games with better sound quality and performance.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Iar Workbench 8051 LINK Keygen Crack.md b/spaces/quidiaMuxgu/Expedit-SAM/Iar Workbench 8051 LINK Keygen Crack.md
deleted file mode 100644
index e7415216a087e561271945db221b43e69f34c7c3..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Iar Workbench 8051 LINK Keygen Crack.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-<h2>Iar Workbench 8051 Keygen Crack</h2><br /><p><b><b>DOWNLOAD</b> &#9734;&#9734;&#9734;&#9734;&#9734; <a href="https://geags.com/2uCqpd">https://geags.com/2uCqpd</a></b></p><br /><br />
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-September 1, 2019 - Download the compressed package, double-click install EW8051-10101-Autorun.exe after unpacking. Software name: IAR Embedded Workbench. The generated EW8051 device has the following name: IAT8051NW020A-PWR_R2_REV6 , version ID: V1.0, and model number: IAT8051NW020A-PWR_R2_REV6.
-If you need more information about how it works, watch the video on the official IAR website: http://www.iar.com/downloads/software/ew8051/about/video/ew8051-10101-autorun.
-To change settings, follow these steps:
-1. Go to your system to debug: 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/radames/MusicGen-Continuation/audiocraft/models/encodec.py b/spaces/radames/MusicGen-Continuation/audiocraft/models/encodec.py
deleted file mode 100644
index 69621a695887b0b41614c51cae020f6fd0af221d..0000000000000000000000000000000000000000
--- a/spaces/radames/MusicGen-Continuation/audiocraft/models/encodec.py
+++ /dev/null
@@ -1,302 +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 abc import ABC, abstractmethod
-import typing as tp
-
-from einops import rearrange
-import torch
-from torch import nn
-
-from .. import quantization as qt
-
-
-class CompressionModel(ABC, nn.Module):
-
-    @abstractmethod
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        ...
-
-    @abstractmethod
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """See `EncodecModel.encode`"""
-        ...
-
-    @abstractmethod
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        """See `EncodecModel.decode`"""
-        ...
-
-    @property
-    @abstractmethod
-    def channels(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def frame_rate(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def sample_rate(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def cardinality(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def num_codebooks(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def total_codebooks(self) -> int:
-        ...
-
-    @abstractmethod
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-        """
-        ...
-
-
-class EncodecModel(CompressionModel):
-    """Encodec model operating on the raw waveform.
-
-    Args:
-        encoder (nn.Module): Encoder network.
-        decoder (nn.Module): Decoder network.
-        quantizer (qt.BaseQuantizer): Quantizer network.
-        frame_rate (int): Frame rate for the latent representation.
-        sample_rate (int): Audio sample rate.
-        channels (int): Number of audio channels.
-        causal (bool): Whether to use a causal version of the model.
-        renormalize (bool): Whether to renormalize the audio before running the model.
-    """
-    # we need assignement to override the property in the abstract class,
-    # I couldn't find a better way...
-    frame_rate: int = 0
-    sample_rate: int = 0
-    channels: int = 0
-
-    def __init__(self,
-                 encoder: nn.Module,
-                 decoder: nn.Module,
-                 quantizer: qt.BaseQuantizer,
-                 frame_rate: int,
-                 sample_rate: int,
-                 channels: int,
-                 causal: bool = False,
-                 renormalize: bool = False):
-        super().__init__()
-        self.encoder = encoder
-        self.decoder = decoder
-        self.quantizer = quantizer
-        self.frame_rate = frame_rate
-        self.sample_rate = sample_rate
-        self.channels = channels
-        self.renormalize = renormalize
-        self.causal = causal
-        if self.causal:
-            # we force disabling here to avoid handling linear overlap of segments
-            # as supported in original EnCodec codebase.
-            assert not self.renormalize, 'Causal model does not support renormalize'
-
-    @property
-    def total_codebooks(self):
-        """Total number of quantizer codebooks available.
-        """
-        return self.quantizer.total_codebooks
-
-    @property
-    def num_codebooks(self):
-        """Active number of codebooks used by the quantizer.
-        """
-        return self.quantizer.num_codebooks
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-        """
-        self.quantizer.set_num_codebooks(n)
-
-    @property
-    def cardinality(self):
-        """Cardinality of each codebook.
-        """
-        return self.quantizer.bins
-
-    def preprocess(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        scale: tp.Optional[torch.Tensor]
-        if self.renormalize:
-            mono = x.mean(dim=1, keepdim=True)
-            volume = mono.pow(2).mean(dim=2, keepdim=True).sqrt()
-            scale = 1e-8 + volume
-            x = x / scale
-            scale = scale.view(-1, 1)
-        else:
-            scale = None
-        return x, scale
-
-    def postprocess(self,
-                    x: torch.Tensor,
-                    scale: tp.Optional[torch.Tensor] = None) -> torch.Tensor:
-        if scale is not None:
-            assert self.renormalize
-            x = x * scale.view(-1, 1, 1)
-        return x
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        assert x.dim() == 3
-        length = x.shape[-1]
-        x, scale = self.preprocess(x)
-
-        emb = self.encoder(x)
-        q_res = self.quantizer(emb, self.frame_rate)
-        out = self.decoder(q_res.x)
-
-        # remove extra padding added by the encoder and decoder
-        assert out.shape[-1] >= length, (out.shape[-1], length)
-        out = out[..., :length]
-
-        q_res.x = self.postprocess(out, scale)
-
-        return q_res
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """Encode the given input tensor to quantized representation along with scale parameter.
-
-        Args:
-            x (torch.Tensor): Float tensor of shape [B, C, T]
-
-        Returns:
-            codes, scale (tp.Tuple[torch.Tensor, torch.Tensor]): Tuple composed of:
-                codes a float tensor of shape [B, K, T] with K the number of codebooks used and T the timestep.
-                scale a float tensor containing the scale for audio renormalizealization.
-        """
-        assert x.dim() == 3
-        x, scale = self.preprocess(x)
-        emb = self.encoder(x)
-        codes = self.quantizer.encode(emb)
-        return codes, scale
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        """Decode the given codes to a reconstructed representation, using the scale to perform
-        audio denormalization if needed.
-
-        Args:
-            codes (torch.Tensor): Int tensor of shape [B, K, T]
-            scale (tp.Optional[torch.Tensor]): Float tensor containing the scale value.
-
-        Returns:
-            out (torch.Tensor): Float tensor of shape [B, C, T], the reconstructed audio.
-        """
-        emb = self.quantizer.decode(codes)
-        out = self.decoder(emb)
-        out = self.postprocess(out, scale)
-        # out contains extra padding added by the encoder and decoder
-        return out
-
-
-class FlattenedCompressionModel(CompressionModel):
-    """Wraps a CompressionModel and flatten its codebooks, e.g.
-    instead of returning [B, K, T], return [B, S, T * (K // S)] with
-    S the number of codebooks per step, and `K // S` the number of 'virtual steps'
-    for each real time step.
-
-    Args:
-        model (CompressionModel): compression model to wrap.
-        codebooks_per_step (int): number of codebooks to keep per step,
-            this must divide the number of codebooks provided by the wrapped model.
-        extend_cardinality (bool): if True, and for instance if codebooks_per_step = 1,
-            if each codebook has a cardinality N, then the first codebook will
-            use the range [0, N - 1], and the second [N, 2 N - 1] etc.
-            On decoding, this can lead to potentially invalid sequences.
-            Any invalid entry will be silently remapped to the proper range
-            with a modulo.
-    """
-    def __init__(self, model: CompressionModel, codebooks_per_step: int = 1,
-                 extend_cardinality: bool = True):
-        super().__init__()
-        self.model = model
-        self.codebooks_per_step = codebooks_per_step
-        self.extend_cardinality = extend_cardinality
-
-    @property
-    def total_codebooks(self):
-        return self.model.total_codebooks
-
-    @property
-    def num_codebooks(self):
-        """Active number of codebooks used by the quantizer.
-
-        ..Warning:: this reports the number of codebooks after the flattening
-        of the codebooks!
-        """
-        assert self.model.num_codebooks % self.codebooks_per_step == 0
-        return self.codebooks_per_step
-
-    def set_num_codebooks(self, n: int):
-        """Set the active number of codebooks used by the quantizer.
-
-        ..Warning:: this sets the number of codebooks **before** the flattening
-        of the codebooks.
-        """
-        assert n % self.codebooks_per_step == 0
-        self.model.set_num_codebooks(n)
-
-    @property
-    def num_virtual_steps(self) -> int:
-        """Return the number of virtual steps, e.g. one real step
-        will be split into that many steps.
-        """
-        return self.model.num_codebooks // self.codebooks_per_step
-
-    @property
-    def frame_rate(self) -> int:
-        return self.model.frame_rate * self.num_virtual_steps
-
-    @property
-    def sample_rate(self) -> int:
-        return self.model.sample_rate
-
-    @property
-    def channels(self) -> int:
-        return self.model.channels
-
-    @property
-    def cardinality(self):
-        """Cardinality of each codebook.
-        """
-        if self.extend_cardinality:
-            return self.model.cardinality * self.num_virtual_steps
-        else:
-            return self.model.cardinality
-
-    def forward(self, x: torch.Tensor) -> qt.QuantizedResult:
-        raise NotImplementedError("Not supported, use encode and decode.")
-
-    def encode(self, x: torch.Tensor) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        indices, scales = self.model.encode(x)
-        B, K, T = indices.shape
-        indices = rearrange(indices, 'b (k v) t -> b k t v', k=self.codebooks_per_step)
-        if self.extend_cardinality:
-            for virtual_step in range(1, self.num_virtual_steps):
-                indices[..., virtual_step] += self.model.cardinality * virtual_step
-        indices = rearrange(indices, 'b k t v -> b k (t v)')
-        return (indices, scales)
-
-    def decode(self, codes: torch.Tensor, scale: tp.Optional[torch.Tensor] = None):
-        B, K, T = codes.shape
-        assert T % self.num_virtual_steps == 0
-        codes = rearrange(codes, 'b k (t v) -> b (k v) t', v=self.num_virtual_steps)
-        # We silently ignore potential errors from the LM when
-        # using extend_cardinality.
-        codes = codes % self.model.cardinality
-        return self.model.decode(codes, scale)
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/  Dairantou Smash Brothers ISO DX J Torrent NGC     .md b/spaces/raedeXanto/academic-chatgpt-beta/  Dairantou Smash Brothers ISO DX J Torrent NGC     .md
deleted file mode 100644
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--- a/spaces/raedeXanto/academic-chatgpt-beta/  Dairantou Smash Brothers ISO DX J Torrent NGC     .md	
+++ /dev/null
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-
-<h1>Dairantou Smash Brothers DX: A Classic Fighting Game for the GameCube</h1>
-<p>If you are a fan of fighting games, you might have heard of <strong>Dairantou Smash Brothers DX</strong>, also known as <strong>Super Smash Bros. Melee</strong> in English. This game was released for the Nintendo GameCube in 2001 in Japan and 2002 in other regions, and it is widely considered as one of the best games in the series and in the genre. In this article, we will tell you everything you need to know about this game, including what it is, how to play it, how to download it, and why you should play it today.</p>
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-<h2>What is Dairantou Smash Brothers DX?</h2>
-<p>Dairantou Smash Brothers DX is a crossover fighting game that features characters from various Nintendo franchises, such as Mario, Zelda, Pokemon, Kirby, Metroid, Star Fox, Fire Emblem, and more. The game is the sequel to the original <strong>Dairantou Smash Brothers</strong> for the Nintendo 64, which was released in 1999. The game was developed by HAL Laboratory and published by Nintendo, with input from Masahiro Sakurai, the creator of the series.</p>
-<h3>The gameplay and features of Dairantou Smash Brothers DX</h3>
-<p>The gameplay of Dairantou Smash Brothers DX is different from most fighting games. Instead of depleting your opponent's health bar, your goal is to knock them off the stage by increasing their damage percentage with your attacks. The higher the damage percentage, the farther they fly when hit. You can use various items, such as weapons, power-ups, healing items, and assist trophies, to aid you in battle. You can also use special moves that are unique to each character, such as Mario's fireballs, Link's boomerang, Pikachu's thunderbolt, etc.</p>
-<p>The game features several modes and options for both single-player and multiplayer. You can play in <strong>Classic Mode</strong>, where you fight against a series of opponents with different rules and stages; <strong>Adventure Mode</strong>, where you explore various worlds based on Nintendo games and fight enemies and bosses; <strong>All-Star Mode</strong>, where you fight against every character in the game with limited health; <strong>Event Mode</strong>, where you complete specific challenges with preset conditions; <strong>Stadium Mode</strong>, where you compete in mini-games such as Home-Run Contest, Target Test, and Multi-Man Melee; <strong>Training Mode</strong>, where you practice your skills and test your combos; <strong>Versus Mode</strong>, where you fight against other players or CPU opponents with customizable rules; <strong>Tournament Mode</strong>, where you participate in a bracket-style tournament with up to 64 players; and <strong>Special Melee</strong>, where you play with various modifiers such as Giant Melee, Invisible Melee, Slow-Mo Melee, etc.</p>
-<h3>The characters and stages of Dairantou Smash Brothers DX</h3>
-<p>The game features a total of 25 playable characters (26 if you count Zelda and Sheik as separate characters), each with their own strengths, weaknesses, movesets, and playstyles. Some of them are veterans from the previous game (such as Mario, Pikachu, Kirby), while others are newcomers (such as Bowser, Marth, Ice Climbers). You can unlock some of them by completing certain tasks or modes (such as Luigi, Jigglypuff, Mewtwo). You can also customize their costumes and colors.</p>
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-<p>The game also features a total of 29 stages (30 if you count Final Destination as a separate stage), each based on a different Nintendo game or theme. Some of them are simple and flat (such as Battlefield), while others are complex and dynamic (such as Fourside). You can unlock some of them by completing certain tasks or modes (such as Brinstar Depths, Big Blue). You can also choose between different music tracks for each stage.</p>
-<h3>The modes and options of Dairantou Smash Brothers DX</h3>
-<p>The game offers a variety of modes and options for both single-player and multiplayer. You can access them from the main menu or by pressing Z on your controller. Some of them are:</p>
-<ul>
-<li><strong>Trophies</strong>: You can collect trophies that depict various characters, items, stages, enemies, etc. from Nintendo games. You can get them by playing different modes or using coins to buy them from a lottery machine. You can view them in a gallery or arrange them on a diorama.</li>
-<li><strong>Sound Test</strong>: You can listen to all the music tracks and sound effects in the game. You can also adjust the volume and frequency of each sound.</li>
-<li><strong>Data</strong>: You can view various statistics and records from your gameplay. You can also erase your data or copy it to another memory card.</li>
-<li><strong>Options</strong>: You can change various settings such as language (Japanese or English), difficulty level (Very Easy to Very Hard), stock lives (1 to 5), time limit (1 to 99 minutes), damage ratio (0.5x to 2x), item frequency (Very Low to Very High), item switch (on or off for each item), camera mode (Fixed or Free), rumble feature (on or off), name entry (on or off), controller configuration (A/B/X/Y/L/R/Z/Start buttons), screen position (horizontal or vertical), sound mode (mono or stereo), screen filter (normal or soft).</li>
-<li><strong>Credits</strong>: You can watch the credits sequence that shows the names of the developers who worked on the game.</li>
-<li><strong>Masterpieces</strong>: You can play demos of some classic Nintendo games that are related to some of the characters in Dairantou Smash Brothers DX. You can access them by selecting a character on the character select screen and pressing Z on your controller.</li>
-</ul>
-<h2>How to download Dairantou Smash Brothers DX for the GameCube</h2>
-<p>If you don't have a physical copy of Dairantou Smash Brothers DX for the GameCube or a compatible console to play it on, you might be wondering how to download it for free online. In this section, we will show you how to do that step by step.</p>
-<h3>The requirements and steps for downloading Dairantou Smash Brothers DX</h3>
-<p>To download Dairantou Smash Brothers DX for the GameCube, you will need:</p>
-<ul>
-<li>A computer with an internet connection.</li>
-<li>A torrent client software such as BitTorrent or uTorrent.</li>
-<li>A GameCube emulator software such as Dolphin or Gcube.</li>
-<li>A GameCube controller adapter or a compatible controller such as an Xbox 360 controller.</li>
-<li>A memory card file (.gci) that contains your save data for Dairantou Smash Brothers DX.</li>
-<li>A disc image file (.iso) that contains the game data for Dairantou Smash Brothers DX.</li>
-</ul>
-<p>Here are the steps for downloading Dairantou Smash Brothers DX:</p>
-<ol>
-<li>Download and install a torrent client software on your computer.</li>
-<li>Download a disc image file (.iso) that contains the game data for Dairantou Smash Brothers DX from a reliable source online. You can use one of these links:   . Make sure to download the correct version of the game (Japan) and the correct revision of the game (v1.02). The file size should be around 1.4 GB.</li>
-<li>Download and install a GameCube emulator software on your computer. We recommend Dolphin Emulator, which you can get from here: . Follow the instructions on the site to set up the emulator.</li>
-<li>Download a memory card file (.gci) that contains your save data for Dairantou Smash Brothers DX from a reliable source online. You can use one of these links:  . Make sure to download the correct region of the save file (Japan) and the correct version of the game (v1.02). The file size should be around 64 KB.</li>
-<li>Connect your GameCube controller adapter or your compatible controller to your computer. If you are using a GameCube controller adapter, you will need to install a driver for it to work with Dolphin Emulator. You can use this guide: . If you are using a different controller, you will need to configure it in Dolphin Emulator's settings.</li>
-<li>Launch Dolphin Emulator and click on the "Open" button on the main toolbar. Browse to the location where you saved your disc image file (.iso) for Dairantou Smash Brothers DX and select it. The game should start running on the emulator.</li>
-<li>Click on the "Tools" menu on the main toolbar and select "Memcard Manager (GC)". Click on the "Browse" button next to "Memory Card A" and browse to the location where you saved your memory card file (.gci) for Dairantou Smash Brothers DX and select it. Click on "OK" to close the window.</li>
-<li>Enjoy playing Dairantou Smash Brothers DX on your PC!</li>
-</ol>
-<h3>The sources and links for downloading Dairantou Smash Brothers DX</h3>
-<p>Here are some of the sources and links that we used for downloading Dairantou Smash Brothers DX:</p>
-<ul>
-<li>: Redump - Nintendo GameCube - Japan : Alvro - Archive.org https://archive.org/details/gamecubejapanredump</li>
-<li>: Download Dairantou Smash Brothers DX for the GameCube - Vimm's Lair https://vimm.net/vault/38215</li>
-<li>: AsiaGamecubeCollectionByGhostware directory listing - Archive.org https://archive.org/download/AsiaGamecubeCollectionByGhostware</li>
-<li>: Dairantou Smash Brothers Iso Dx J Torrent Ngc - Trello https://trello.com/c/56D5JlyV/155-dairantou-smash-brothers-iso-dx-j-torrent-ngc</li>
-<li>: How to Play Nintendo GameCube Games on PC - MUO https://www.makeuseof.com/tag/play-nintendo-gamecube-games-pc/</li>
-<li>: How to Play Wii and GameCube Games on your PC with Dolphin https://www.howtogeek.com/225115/how-to-play-wii-and-gamecube-games-on-your-pc-with-dolphin/</li>
-</ul>
-<h3>The precautions and tips for downloading Dairantou Smash Brothers DX</h3>
-<p>Before you download Dairantou Smash Brothers DX, you should be aware of some of the precautions and tips that can help you avoid problems and enhance your experience:</p>
-<ul>
-<li>Make sure that you have enough space on your computer to store the disc image file (.iso) and the memory card file (.gci) for Dairantou Smash Brothers DX. You will need at least 1.5 GB of free space.</li>
-<li>Make sure that you have a good antivirus software on your computer to scan the files that you download for any malware or viruses. You can use one of these free antivirus software:   .</li>
-<li>Make sure that you download the files from reputable sources that have positive feedback and ratings from other users. You can use one of these torrent sites:   .</li>
-<li>Make sure that you follow the instructions carefully when installing and setting up the torrent client software, the GameCube emulator software, and the GameCube controller adapter driver. You can use one of these guides:   .</li>
-<li>Make sure that you adjust the settings of the GameCube emulator software according to your computer's specifications and preferences. You can change things like resolution, graphics quality, sound volume, controller configuration, etc. You can use one of these guides:   .</li>
-<li>Make sure that you backup your disc image file (.iso) and your memory card file (.gci) for Dairantou Smash Brothers DX in case something goes wrong or you want to transfer them to another device. You can use one of these cloud storage services: [^17 ^]: Google Drive - Cloud Storage & File Backup for Photos, Docs & More https://www.google.com/drive/</li>
-</ul>
-<h2>Why you should play Dairantou Smash Brothers DX today</h2>
-<p>Now that you know how to download Dairantou Smash Brothers DX for the GameCube, you might be wondering why you should play it today. After all, there are newer and more advanced games available for modern consoles and PC. Well, here are some of the reasons why Dairantou Smash Brothers DX is still worth playing in 2023:</p>
-<h3>The benefits and advantages of playing Dairantou Smash Brothers DX</h3>
-<p>Playing Dairantou Smash Brothers DX can bring you many benefits and advantages, such as:</p>
-<ul>
-<li><strong>Fun and enjoyment</strong>: Dairantou Smash Brothers DX is a game that is designed to be fun and enjoyable for anyone who plays it. Whether you are a casual gamer who just wants to have some laughs with your friends, or a hardcore gamer who wants to master every character and technique, you will find something that suits your taste and style in this game. The game is full of humor, charm, and personality that will make you smile and laugh.</li>
-<li><strong>Nostalgia and nostalgia</strong>: Dairantou Smash Brothers DX is a game that can bring back many memories and nostalgia for those who grew up with Nintendo games. The game features characters, stages, music, items, and references from various Nintendo franchises that span decades of gaming history. You can relive your childhood or discover new games that you might have missed out on.</li>
-<li><strong>Challenge and satisfaction</strong>: Dairantou Smash Brothers DX is a game that can challenge you and satisfy you in many ways. The game has various modes and options that can test your skills and abilities in different scenarios. You can compete against other players or CPU opponents with customizable rules and settings. You can complete specific challenges with preset conditions and goals. You can improve your performance and records in mini-games and statistics. You can unlock new characters, stages, trophies, music, and more by playing the game.</li>
-<li><strong>Variety and replayability</strong>: Dairantou Smash Brothers DX is a game that has a lot of variety and replayability. The game has 25 playable characters (26 if you count Zelda and Sheik as separate characters), each with their own strengths, weaknesses, movesets, and playstyles. The game has 29 stages (30 if you count Final Destination as a separate stage), each based on a different Nintendo game or theme. The game has several modes and options for both single-player and multiplayer. The game has hundreds of trophies, music tracks, sound effects, and more to collect and enjoy.</li>
-<li><strong>Community and support</strong>: Dairantou Smash Brothers DX is a game that has a large and active community and support. The game has millions of fans around the world who love and appreciate the game. The game has many websites, forums, blogs, videos, podcasts, tournaments, events, guides, tips, tricks, hacks, mods, fan art, fan fiction, and more to share and enjoy the game. The game also has official updates and patches that fix bugs and glitches, add new features and content, and balance the gameplay.</li>
-</ul>
-<h3>The challenges and drawbacks of playing Dairantou Smash Brothers DX</h3>
-<p>Playing Dairantou Smash Brothers DX can also bring you some challenges and drawbacks, such as:</p>
-<ul>
-<li><strong>Legal and ethical issues</strong>: Downloading Dairantou Smash Brothers DX for the GameCube for free online is technically illegal and unethical. You are violating the intellectual property rights of Nintendo and HAL Laboratory, who own the game and its content. You are also depriving them of the revenue that they deserve for creating and distributing the game. You might face legal consequences or penalties if you are caught downloading or sharing the game without permission.</li>
-<li><strong>Technical and compatibility issues</strong>: Downloading Dairantou Smash Brothers DX for the GameCube for free online can also cause technical and compatibility issues. You might encounter problems such as corrupted files, incomplete downloads, slow speeds, viruses, malware, etc. You might also have difficulties running the game on your PC or your emulator. You might need to tweak or adjust some settings or install some drivers or plugins to make the game work properly.</li>
-<li><strong>Quality and authenticity issues</strong>: Downloading Dairantou Smash Brothers DX for the GameCube for free online can also affect the quality and authenticity of the game. You might not get the same experience or enjoyment as playing the original game on the original console. You might miss out on some features or content that are exclusive to the physical copy of the game. You might also encounter some errors or glitches that are not present in the official version of the game.</li>
-<li><strong>Difficulty and frustration issues</strong>: Playing Dairantou Smash Brothers DX can also be difficult and frustrating at times. The game has a steep learning curve and a high skill ceiling that can challenge even the most experienced players. The game can also be unfair and unpredictable due to factors such as random items, stage hazards, final smashes, etc. The game can also be frustrating due to factors such as lag, input delay, online trolls, rage quitters, etc.</li>
-</ul>
-<h3>The reviews and ratings of Dairantou Smash Brothers DX</h3>
-<p>Dairantou Smash Brothers DX has received many positive reviews and ratings from critics and players alike. The game has an average score of 9.2 out of 10 on Metacritic based on 75 critic reviews and an average score of 9 out of 10 on Metacritic based on 1,570 user ratings . The game has also won several awards and accolades, such as Game of the Year, Best Fighting Game, Best Multiplayer Game, etc. from various publications and organizations.</p>
-<p>Here are some of the reviews and ratings of Dairantou Smash Brothers DX:</p>
-<blockquote>
-<p>"Dairantou Smash Brothers DX is a masterpiece of design that transcends its simple premise to become one of the most addictive games ever made." - IGN (10/10)</p>
-</blockquote>
-<blockquote>
-<p>"Dairantou Smash Brothers DX is a celebration of Nintendo's history and a showcase of its future. It's a game that appeals to everyone, from casual gamers to hardcore fans." - GameSpot (9.6/10)</p>
-</blockquote>
-<blockquote>
-<p>"Dairantou Smash Brothers DX is a game that will keep you entertained for hours on end with its fast-paced action, colorful graphics, catchy music, and endless replay value." - Nintendo Power (9.5/10)</p>
-</blockquote>
-<blockquote>
-<p>"Dairantou Smash Brothers DX is a game that I love playing with my friends and family. It's a game that makes me laugh, scream, cheer, and rage. It's a game that I will never get tired of." - Amazon Customer (5/5 stars)</p>
-</blockquote>
-<h2>Conclusion</h2>
-<h4>Summary of the main points</h4>
-<p>In conclusion, Dairantou Smash Brothers DX is a classic fighting game for the GameCube that features characters from various Nintendo franchises. The game has several modes and options for both single-player and multiplayer. The game can be downloaded for free online using a torrent client software, a GameCube emulator software, and a disc image file and a memory card file for the game. The game can bring you many benefits and advantages, such as fun and enjoyment, nostalgia and nostalgia, challenge and satisfaction, variety and replayability, and community and support. The game can also bring you some challenges and drawbacks, such as legal and ethical issues, technical and compatibility issues, quality and authenticity issues, and difficulty and frustration issues. The game has received many positive reviews and ratings from critics and players alike.</p>
-<h4>Call to action</h4>
-<p>So what are you waiting for? If you are a fan of fighting games or Nintendo games, or if you are just looking for a great game to play with your friends or by yourself, you should definitely give Dairantou Smash Brothers DX a try. You won't regret it. Just make sure that you follow the steps and tips that we provided in this article to download and play the game safely and smoothly. And don't forget to have fun!</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Dairantou Smash Brothers DX:</p>
-<ol>
-<li><strong>What is the difference between Dairantou Smash Brothers DX and Super Smash Bros. Melee?</strong></li>
-<p>Dairantou Smash Brothers DX is the Japanese name of Super Smash Bros. Melee. They are essentially the same game, except for some minor differences in language, voice acting, text, music, sound effects, etc.</p>
-<li><strong>Is Dairantou Smash Brothers DX compatible with other regions of Super Smash Bros. Melee?</strong></li>
-<p>No, Dairantou Smash Brothers DX is not compatible with other regions of Super Smash Bros. Melee. You cannot play online or locally with players who have different versions of the game. You also cannot use save files or memory cards from different regions of the game.</p>
-<li><strong>How can I play Dairantou Smash Brothers DX online with other players?</strong></li>
-<p>To play Dairantou Smash Brothers DX online with other players, you will need to use a third-party software such as Netplay or Kaillera. These software allow you to connect with other players who have the same version of the game and the same emulator software. You will also need to have a stable internet connection and a low ping.</p>
-<li><strong>What are some of the best characters and stages in Dairantou Smash Brothers DX?</strong></li>
-<p>This is a matter of personal preference and opinion, but some of the most popular and competitive characters in Dairantou Smash Brothers DX are Fox, Falco, Marth, Sheik, Peach, Jigglypuff, Captain Falcon, Ice Climbers, etc. Some of the most popular and competitive stages in Dairantou Smash Brothers DX are Final Destination, Battlefield, Yoshi's Story, Pokemon Stadium, Dream Land, Fountain of Dreams, etc.</li>
-<li><strong>How can I improve my skills and techniques in Dairantou Smash Brothers DX?</strong></li>
-<p>To improve your skills and techniques in Dairantou Smash Brothers DX, you will need to practice a lot and learn from other players. You can use the Training Mode to practice your moves and combos, the Stadium Mode to test your speed and accuracy, the Event Mode to challenge yourself with specific scenarios, and the Versus Mode to fight against other players or CPU opponents. You can also watch videos of professional players and tournaments online, read guides and tips online, join forums and communities online, and participate in local or online tournaments.</p>
-</ol>
-</p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Action Jackson 5 Free Download Full Movies ((INSTALL)).md b/spaces/raedeXanto/academic-chatgpt-beta/Action Jackson 5 Free Download Full Movies ((INSTALL)).md
deleted file mode 100644
index 922e125765617959f13600c30f1e552525ceadd1..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Action Jackson 5 Free Download Full Movies ((INSTALL)).md	
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-<p>If you are a fan of action movies, you might be wondering how to watch Action Jackson 5 free full movies online. Action Jackson is a series of movies starring Carl Weathers as Sgt. Jericho "Action" Jackson, a tough Detroit cop who fights against crime and corruption. The first movie, Action Jackson, was released in 1988 and was followed by four sequels: Action Jackson 2: The Quickening (1990), Action Jackson 3: Deadly Force (1992), Action Jackson 4: Final Justice (1994), and Action Jackson 5: Last Stand (1996).</p>
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Autodesk Maya 2020 Full Crack With keygen The Best 3D Modeling and Rendering Software for Professionals.md b/spaces/raedeXanto/academic-chatgpt-beta/Autodesk Maya 2020 Full Crack With keygen The Best 3D Modeling and Rendering Software for Professionals.md
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Competitive Programming 2 By Steven Halim Pdf.epub A Guide to Mastering Algorithms and Data Structures.md b/spaces/raedeXanto/academic-chatgpt-beta/Competitive Programming 2 By Steven Halim Pdf.epub A Guide to Mastering Algorithms and Data Structures.md
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-<br> - Overview of the book: Who are the authors, what are the main topics, and how is the book structured? <br> - Benefits of reading the book: How can the book help you improve your skills, knowledge, and performance in programming contests? <br> - How to get the book: Where can you download or buy the pdf.epub version of the book? | | H2: Competitive Programming Basics | - Data Structures and Libraries: What are the common data structures and libraries that you need to know and use in competitive programming? <br> - Problem Solving Paradigms: What are the different ways of approaching and solving programming problems? <br> - Graph: What are the essential graph algorithms and applications that you need to master in competitive programming? | | H2: Competitive Programming Advanced | - Mathematics: What are the important mathematical concepts and techniques that you need to learn and apply in competitive programming? <br> - String Processing: What are the common string processing problems and solutions that you need to be familiar with in competitive programming? <br> - Computational Geometry: What are the basic geometric primitives and algorithms that you need to know and use in competitive programming? | | H2: Competitive Programming Tips and Tricks | - Time and Memory Management: How can you optimize your code to run faster and use less memory in competitive programming? <br> - Debugging and Testing: How can you find and fix errors in your code and ensure its correctness and robustness in competitive programming? <br> - Strategy and Psychology: How can you plan your strategy and manage your psychology in competitive programming contests? | | H2: Conclusion | - Summary: What are the main points and takeaways from the article? <br> - Call to action: What do you want the reader to do after reading the article? | # Article with HTML formatting <h1>Competitive Programming 2 by Steven Halim: A Comprehensive Guide for Aspiring Programmers</h1>
- <p>If you are interested in improving your programming skills and knowledge, or if you want to participate and excel in programming contests, then you should definitely read <strong>Competitive Programming 2 by Steven Halim</strong>. This book is a valuable resource for anyone who wants to learn more about competitive programming, which is a form of problem solving using computer programs.</p>
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- <h2>What is competitive programming?</h2>
- <p>Competitive programming is a sport-like activity where participants write computer programs to solve well-defined problems within a given time limit. The problems usually involve mathematical, logical, or algorithmic concepts, and require creativity, analytical thinking, and coding skills. The participants compete against each other or against a set of test cases to see who can solve more problems or solve them faster or more efficiently.</p>
- <p>Competitive programming has many benefits for programmers of all levels. It can help you:</p>
- <ul>
-<li>Improve your problem solving skills and learn new algorithms and data structures.</li>
-<li>Enhance your coding skills and learn how to write clear, concise, and optimized code.</li>
-<li>Develop your critical thinking and logical reasoning skills.</li>
-<li>Boost your confidence and motivation.</li>
-<li>Have fun and challenge yourself.</li>
-</ul>
- <p>Competitive programming can also open up many opportunities for you. You can:</p>
- <ul>
-<li>Participate in various online or onsite contests, such as Codeforces, CodeChef, HackerRank, Google Code Jam, Facebook Hacker Cup, ACM ICPC, IOI, etc.</li>
-<li>Win prizes, awards, scholarships, or recognition.</li>
-<li>Meet other programmers from different countries and backgrounds.</li>
-<li>Leverage your skills and experience for academic or career advancement.</li>
-</ul>
- <h2>What is Competitive Programming 2 by Steven Halim?</h2>
- <p><strong>Competitive Programming 2 by Steven Halim</strong> is a book that covers various topics related to competitive programming. It is written by Steven Halim and Felix Halim, who are both experienced competitive programmers and coaches from National University of Singapore (NUS). The book is based on their teaching materials for NUS students who participate in ACM ICPC or IOI.</p>
- <p>The book has four main parts:</p>
- <ol>
-<li>Data Structures and Libraries: This part introduces the common data structures and libraries that are useful for competitive programming, such as arrays, stacks, queues, linked lists, trees, graphs, hash tables, priority queues, etc. It also shows how to use built-in libraries or implement your own libraries for these data structures.</li>
-<h2>Problem Solving Paradigms</h2>
- <p>Problem solving paradigms are the different ways of approaching and solving programming problems. They are based on the characteristics and constraints of the problems, such as the input size, the output format, the time limit, the memory limit, etc. Knowing the appropriate paradigm for a problem can help you design an efficient and correct algorithm to solve it.</p>
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-<li>Complete Search: This paradigm involves trying all possible solutions or candidates until you find the optimal or correct one. It is usually applicable for small input size or when there is no better algorithm available. The book shows how to implement complete search using recursion, backtracking, or brute force.</li>
-<li>Divide and Conquer: This paradigm involves breaking down a large problem into smaller and simpler subproblems, solving them recursively or iteratively, and combining their solutions to obtain the solution for the original problem. It is usually applicable for problems that have a recursive structure or can be divided into independent parts. The book shows how to implement divide and conquer using binary search, merge sort, quick sort, etc.</li>
-<li>Greedy: This paradigm involves making a locally optimal choice at each step, hoping that it will lead to a globally optimal solution. It is usually applicable for problems that have a greedy property or can be proven to have an optimal substructure. The book shows how to implement greedy using sorting, priority queues, etc.</li>
-<li>Dynamic Programming: This paradigm involves finding the optimal solution for a problem by storing and reusing the solutions for its overlapping subproblems. It is usually applicable for problems that have an optimal substructure and overlapping subproblems. The book shows how to implement dynamic programming using memoization, bottom-up approach, state transition, etc.</li>
-</ol>
- <p>The book also covers some other problem solving paradigms, such as incremental, decremental, sliding window, two pointers, meet in the middle, etc.</p>
- <h2>Data Structures and Libraries</h2>
- <p>Data structures and libraries are the tools that help you store and manipulate data efficiently and conveniently in competitive programming. They are based on the type and structure of the data, such as linear, non-linear, ordered, unordered, etc. Knowing the appropriate data structure and library for a problem can help you optimize your code's performance and readability.</p>
- <p>The book covers two types of data structures and libraries:</p>
- <ul>
-<li>Data Structures with Built-in Libraries: These are the data structures that are already implemented and provided by the programming languages or platforms that you use in competitive programming, such as C++, Java, Python, etc. They include arrays, stacks, queues, linked lists, trees, graphs, hash tables, priority queues, etc. The book shows how to use these built-in libraries effectively and efficiently.</li>
-<li>Data Structures with Our-Own Libraries: These are the data structures that are not readily available or suitable for competitive programming purposes. You need to implement them yourself or use existing libraries from other sources. They include graph representations (adjacency matrix/list), union-find disjoint sets (for connectivity queries), segment tree (for range queries), fenwick tree (for prefix sum queries), etc. The book shows how to implement these data structures using classes or structs.</li>
-</ul>
- <h2>Graph</h2>
- <h2>Graph</h2>
- <p>Graph is one of the most important and common data structures in competitive programming. It is a collection of nodes (or vertices) and edges (or links) that connect them. Graphs can model various real-world problems that involve relationships or connections between entities, such as networks, maps, social media, games, etc.</p>
- <p>The book covers various aspects of graph algorithms and applications in competitive programming, such as:</p>
- <ul>
-<li>Graph Traversal: This involves visiting all or some nodes and edges of a graph in a systematic way. It can be used to find connected components, flood fill, topological sort, bipartite check, etc. The book shows how to implement graph traversal using depth-first search (DFS) or breadth-first search (BFS).</li>
-<li>Minimum Spanning Tree: This involves finding a subset of edges that connects all nodes of a graph with minimum total weight. It can be used to find the cheapest way to connect a network, build a road system, etc. The book shows how to implement minimum spanning tree using Kruskal's or Prim's algorithm.</li>
-<li>Single-Source Shortest Paths: This involves finding the shortest paths from a given node to all other nodes in a graph. It can be used to find the fastest way to reach a destination, navigate a map, etc. The book shows how to implement single-source shortest paths using Dijkstra's, Bellman-Ford's, or Floyd-Warshall's algorithm.</li>
-<li>All-Pairs Shortest Paths: This involves finding the shortest paths between every pair of nodes in a graph. It can be used to find the optimal routes for transportation, communication, etc. The book shows how to implement all-pairs shortest paths using Floyd-Warshall's algorithm.</li>
-<li>Maximum Flow: This involves finding the maximum amount of flow that can be sent from a source node to a sink node in a graph with capacity constraints on the edges. It can be used to model various optimization problems, such as network routing, matching, scheduling, etc. The book shows how to implement maximum flow using Ford-Fulkerson's or Edmonds-Karp's algorithm.</li>
-<li>Special Graphs: These are the graphs that have some special properties or structures that make them easier or harder to deal with in competitive programming. They include tree, directed acyclic graph (DAG), bipartite graph, etc. The book shows how to identify and exploit these special graphs using various techniques and algorithms.</li>
-</ul>
- <h2>Mathematics</h2>
- <p>Mathematics is another essential and common topic in competitive programming. It involves various concepts and techniques that are useful for solving mathematical or logic-based problems. These problems can range from simple arithmetic operations to complex number theory or geometry problems.</p>
- <p>The book covers various aspects of mathematics in competitive programming, such as:</p>
- <ul>
-<li>Ad Hoc Mathematics Problems: These are the problems that do not require any specific mathematical knowledge or technique, but rather some common sense and logical thinking. They can involve simple calculations, estimations, observations, patterns, etc. The book shows how to solve these problems using basic arithmetic operations, modular arithmetic, bitwise operations, etc.</li>
-<h2>Mathematics</h2>
- <p>Mathematics is another essential and common topic in competitive programming. It involves various concepts and techniques that are useful for solving mathematical or logic-based problems. These problems can range from simple arithmetic operations to complex number theory or geometry problems.</p>
- <p>The book covers various aspects of mathematics in competitive programming, such as:</p>
- <ul>
-<li>Ad Hoc Mathematics Problems: These are the problems that do not require any specific mathematical knowledge or technique, but rather some common sense and logical thinking. They can involve simple calculations, estimations, observations, patterns, etc. The book shows how to solve these problems using basic arithmetic operations, modular arithmetic, bitwise operations, etc.</li>
-<li>Number Theory: This is the branch of mathematics that deals with the properties and relationships of integers and their divisors. It can involve prime numbers, greatest common divisor (GCD), least common multiple (LCM), Euler's totient function, extended Euclid's algorithm, linear Diophantine equation, etc. The book shows how to solve these problems using various algorithms and techniques.</li>
-<li>Java BigInteger Class: This is a built-in class in Java that allows you to perform arbitrary-precision arithmetic operations on large integers. It can be useful for problems that involve very large numbers that cannot fit in the standard data types. The book shows how to use this class effectively and efficiently.</li>
-<li>Miscellaneous Mathematics Problems: These are the problems that involve other mathematical topics or concepts that are not covered by the previous categories. They can involve combinatorics, cycle finding, existing or fictional sequences and number systems, probability theory, linear algebra, etc. The book shows how to solve these problems using various formulas and methods.</li>
-</ul>
- <h2>String Processing</h2>
- <p>String processing is another important and common topic in competitive programming. It involves various problems that deal with strings or sequences of characters. These problems can involve finding, matching, comparing, manipulating, or transforming strings.</p>
- <p>The book covers various aspects of string processing in competitive programming, such as:</p>
- <ul>
-<li>Ad Hoc String Processing Problems: These are the problems that do not require any specific string processing technique or algorithm, but rather some basic string operations and manipulations. They can involve reversing, sorting, concatenating, splitting, trimming, encoding, decoding, etc. The book shows how to solve these problems using built-in string functions or libraries.</li>
-<li>String Processing with Dynamic Programming: These are the problems that require finding the optimal solution for a string problem by storing and reusing the solutions for its overlapping subproblems. They can involve string alignment (edit distance), longest common subsequence (LCS), longest palindromic subsequence (LPS), etc. The book shows how to solve these problems using dynamic programming techniques.</li>
-<h2>String Matching</h2>
- <p>String matching is a subtopic of string processing that involves finding all occurrences of a pattern string in a text string. It can be used to search for keywords, phrases, names, DNA sequences, etc. in a large text.</p>
- <p>The book covers various string matching algorithms and applications in competitive programming, such as:</p>
- <ul>
-<li>Naive String Matching: This is the simplest and most straightforward string matching algorithm. It involves comparing the pattern with every possible substring of the text and checking for a match. It has a time complexity of O(nm), where n is the length of the text and m is the length of the pattern.</li>
-<li>Rabin-Karp String Matching: This is a string matching algorithm that uses hashing to speed up the comparison process. It involves computing a hash value for the pattern and every substring of the text of the same length, and comparing them for a match. It has an average time complexity of O(n+m), but a worst-case time complexity of O(nm).</li>
-<li>Knuth-Morris-Pratt String Matching: This is a string matching algorithm that uses a prefix function to avoid unnecessary comparisons. It involves computing a prefix function for the pattern that tells how much of the previous part of the pattern can be reused after a mismatch, and using it to shift the pattern accordingly when comparing with the text. It has a time complexity of O(n+m).</li>
-<li>Aho-Corasick String Matching: This is a string matching algorithm that uses a trie data structure and a failure function to match multiple patterns simultaneously. It involves building a trie for all the patterns and computing a failure function that tells how to backtrack to the longest suffix that is also a prefix in the trie, and using it to traverse the trie while comparing with the text. It has a time complexity of O(n+k+z), where k is the total length of all patterns and z is the total number of matches.</li>
-</ul>
- <h2>Computational Geometry</h2>
- <p>Computational geometry is another advanced and interesting topic in competitive programming. It involves various problems that deal with geometric objects, such as points, lines, polygons, circles, etc. These problems can involve finding, measuring, comparing, manipulating, or transforming geometric objects.</p>
- <p>The book covers various aspects of computational geometry in competitive programming, such as:</p>
- <ul>
-<li>Basic Geometric Primitives: These are the basic geometric objects that are used to model more complex geometric problems. They include points, vectors, lines, line segments, rays, angles, triangles, circles, etc. The book shows how to represent and manipulate these geometric primitives using classes or structs.</li>
-<h2>Basic Geometric Algorithms</h2>
- <p>Basic geometric algorithms are the basic geometric algorithms that are used to solve common geometric problems. They include distance between points, dot product, cross product, angle between vectors, orientation test, intersection test, convex hull, polygon area, point in polygon test, etc. The book shows how to implement these algorithms using basic geometric primitives and formulas.</p>
- <h2>Advanced Geometric Problems</h2>
- <p>Advanced geometric problems are the more complex and challenging geometric problems that require more advanced techniques or algorithms. They include closest pair of points, line sweeping, polygon intersection, polygon union, polygon clipping, polygon triangulation, Voronoi diagram, Delaunay triangulation, etc. The book shows how to solve these problems using various data structures and algorithms.</p>
- <h2>Competitive Programming Tips and Tricks</h2>
- <p>Besides knowing the topics and techniques for competitive programming, you also need to know some tips and tricks that can help you improve your performance and efficiency in programming contests. These tips and tricks can involve various aspects of competitive programming, such as time and memory management, debugging and testing, strategy and psychology, etc.</p>
- <p>The book covers various tips and tricks for competitive programming, such as:</p>
- <ul>
-<li>Time and Memory Management: These are the tips and tricks that can help you optimize your code to run faster and use less memory in competitive programming. They include choosing the right data structure and algorithm, avoiding unnecessary computations and variables, using bitwise operations and bit manipulation, using fast input/output methods, etc.</li>
-<li>Debugging and Testing: These are the tips and tricks that can help you find and fix errors in your code and ensure its correctness and robustness in competitive programming. They include using assert statements and debug messages, testing your code on sample cases and edge cases, using online judges and debuggers, etc.</li>
-<li>Strategy and Psychology: These are the tips and tricks that can help you plan your strategy and manage your psychology in competitive programming contests. They include choosing the right problems to solve, managing your time and resources, dealing with stress and pressure, learning from your mistakes and feedbacks, etc.</li>
-</ul>
- <h2>Conclusion</h2>
- <h2>Conclusion</h2>
- <p>In this article, we have given you an overview of what competitive programming is, why it is important, and how Competitive Programming 2 by Steven Halim can help you achieve your goals. We have also told you where you can get the pdf.epub version of this book, which is a convenient format for reading on your devices. Finally, we have given you some tips and tricks on how to get the most out of this book and become a better programmer.</p>
- <p>We hope that you have enjoyed reading this article and learned something new and useful from it. If you are interested in competitive programming or want to improve your programming skills and knowledge, we highly recommend that you read Competitive Programming 2 by Steven Halim. It is a comprehensive and practical guide that covers various topics and techniques that are essential for competitive programming. It also contains many examples and exercises that can help you practice and apply what you learn.</p>
- <p>So what are you waiting for? Download or buy the pdf.epub version of Competitive Programming 2 by Steven Halim today and start your journey to becoming a competitive programmer. You will not regret it!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Competitive Programming 2 by Steven Halim:</p>
- <ol>
-<li>Q: Who are the authors of Competitive Programming 2 by Steven Halim?<br>
-A: The authors of Competitive Programming 2 by Steven Halim are Steven Halim and Felix Halim, who are both experienced competitive programmers and coaches from National University of Singapore (NUS). They have participated and won many programming contests, such as ACM ICPC, IOI, Google Code Jam, Facebook Hacker Cup, etc. They have also taught competitive programming to many students from NUS and other universities.</li>
-<li>Q: What are the main topics covered in Competitive Programming 2 by Steven Halim?<br>
-A: The main topics covered in Competitive Programming 2 by Steven Halim are data structures and libraries, problem solving paradigms, graph, mathematics, string processing, computational geometry, and tips and tricks for competitive programming. These topics cover various concepts and techniques that are useful for solving programming problems in various online or onsite contests.</li>
-<li>Q: How is Competitive Programming 2 by Steven Halim structured?<br>
-A: Competitive Programming 2 by Steven Halim is structured into four main parts: competitive programming basics, competitive programming advanced, competitive programming tips and tricks, and appendices. Each part contains several chapters that cover different topics related to competitive programming. Each chapter contains an introduction, a discussion of the topic, examples of problems and solutions, exercises for practice, and chapter notes for further reading.</li>
-<li>Q: Where can I download or buy the pdf.epub version of Competitive Programming 2 by Steven Halim?<br>
-A: You can download or buy the pdf.epub version of Competitive Programming 2 by Steven Halim from various online sources, such as Scribd[1], NUS Computing[2], or Archive.org[3]. The pdf.epub version is a convenient format for reading on your devices, such as laptops, tablets, or smartphones.</li>
-<li>Q: How can I get the most out of Competitive Programming 2 by Steven Halim?<br>
-<h2>FAQs</h2>
- <p>Here are some frequently asked questions about Competitive Programming 2 by Steven Halim:</p>
- <ol>
-<li>Q: Who are the authors of Competitive Programming 2 by Steven Halim?<br>
-A: The authors of Competitive Programming 2 by Steven Halim are Steven Halim and Felix Halim, who are both experienced competitive programmers and coaches from National University of Singapore (NUS). They have participated and won many programming contests, such as ACM ICPC, IOI, Google Code Jam, Facebook Hacker Cup, etc. They have also taught competitive programming to many students from NUS and other universities.</li>
-<li>Q: What are the main topics covered in Competitive Programming 2 by Steven Halim?<br>
-A: The main topics covered in Competitive Programming 2 by Steven Halim are data structures and libraries, problem solving paradigms, graph, mathematics, string processing, computational geometry, and tips and tricks for competitive programming. These topics cover various concepts and techniques that are useful for solving programming problems in various online or onsite contests.</li>
-<li>Q: How is Competitive Programming 2 by Steven Halim structured?<br>
-A: Competitive Programming 2 by Steven Halim is structured into four main parts: competitive programming basics, competitive programming advanced, competitive programming tips and tricks, and appendices. Each part contains several chapters that cover different topics related to competitive programming. Each chapter contains an introduction, a discussion of the topic, examples of problems and solutions, exercises for practice, and chapter notes for further reading.</li>
-<li>Q: Where can I download or buy the pdf.epub version of Competitive Programming 2 by Steven Halim?<br>
-A: You can download or buy the pdf.epub version of Competitive Programming 2 by Steven Halim from various online sources, such as Scribd[1], NUS Computing[2], or Archive.org[3]. The pdf.epub version is a convenient format for reading on your devices, such as laptops, tablets, or smartphones.</li>
-<li>Q: How can I get the most out of Competitive Programming 2 by Steven Halim?<br>
-A: You can get the most out of Competitive Programming 2 by Steven Halim by reading it carefully and thoroughly, understanding the concepts and techniques explained in it, practicing the examples and exercises given in it, and applying what you learn to real problems in online or onsite contests. You can also use the book as a reference or a guide when you encounter new or difficult problems in competitive programming.</li>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/randt/stabilityai-stable-diffusion-2-1/app.py b/spaces/randt/stabilityai-stable-diffusion-2-1/app.py
deleted file mode 100644
index 2665a15a486978a86dcd0e3acfdeb51d381cd5f0..0000000000000000000000000000000000000000
--- a/spaces/randt/stabilityai-stable-diffusion-2-1/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.load("models/stabilityai/stable-diffusion-2-1").launch()
\ No newline at end of file
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/tls.d.ts b/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/tls.d.ts
deleted file mode 100644
index 2cbc71658039eced278db4632dd67d7a754b51b6..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/tls.d.ts
+++ /dev/null
@@ -1,1028 +0,0 @@
-/**
- * The `tls` module provides an implementation of the Transport Layer Security
- * (TLS) and Secure Socket Layer (SSL) protocols that is built on top of OpenSSL.
- * The module can be accessed using:
- *
- * ```js
- * const tls = require('tls');
- * ```
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/tls.js)
- */
-declare module 'tls' {
-    import { X509Certificate } from 'node:crypto';
-    import * as net from 'node:net';
-    import * as stream from 'stream';
-    const CLIENT_RENEG_LIMIT: number;
-    const CLIENT_RENEG_WINDOW: number;
-    interface Certificate {
-        /**
-         * Country code.
-         */
-        C: string;
-        /**
-         * Street.
-         */
-        ST: string;
-        /**
-         * Locality.
-         */
-        L: string;
-        /**
-         * Organization.
-         */
-        O: string;
-        /**
-         * Organizational unit.
-         */
-        OU: string;
-        /**
-         * Common name.
-         */
-        CN: string;
-    }
-    interface PeerCertificate {
-        subject: Certificate;
-        issuer: Certificate;
-        subjectaltname: string;
-        infoAccess: NodeJS.Dict<string[]>;
-        modulus: string;
-        exponent: string;
-        valid_from: string;
-        valid_to: string;
-        fingerprint: string;
-        fingerprint256: string;
-        ext_key_usage: string[];
-        serialNumber: string;
-        raw: Buffer;
-    }
-    interface DetailedPeerCertificate extends PeerCertificate {
-        issuerCertificate: DetailedPeerCertificate;
-    }
-    interface CipherNameAndProtocol {
-        /**
-         * The cipher name.
-         */
-        name: string;
-        /**
-         * SSL/TLS protocol version.
-         */
-        version: string;
-        /**
-         * IETF name for the cipher suite.
-         */
-        standardName: string;
-    }
-    interface EphemeralKeyInfo {
-        /**
-         * The supported types are 'DH' and 'ECDH'.
-         */
-        type: string;
-        /**
-         * The name property is available only when type is 'ECDH'.
-         */
-        name?: string | undefined;
-        /**
-         * The size of parameter of an ephemeral key exchange.
-         */
-        size: number;
-    }
-    interface KeyObject {
-        /**
-         * Private keys in PEM format.
-         */
-        pem: string | Buffer;
-        /**
-         * Optional passphrase.
-         */
-        passphrase?: string | undefined;
-    }
-    interface PxfObject {
-        /**
-         * PFX or PKCS12 encoded private key and certificate chain.
-         */
-        buf: string | Buffer;
-        /**
-         * Optional passphrase.
-         */
-        passphrase?: string | undefined;
-    }
-    interface TLSSocketOptions extends SecureContextOptions, CommonConnectionOptions {
-        /**
-         * If true the TLS socket will be instantiated in server-mode.
-         * Defaults to false.
-         */
-        isServer?: boolean | undefined;
-        /**
-         * An optional net.Server instance.
-         */
-        server?: net.Server | undefined;
-        /**
-         * An optional Buffer instance containing a TLS session.
-         */
-        session?: Buffer | undefined;
-        /**
-         * If true, specifies that the OCSP status request extension will be
-         * added to the client hello and an 'OCSPResponse' event will be
-         * emitted on the socket before establishing a secure communication
-         */
-        requestOCSP?: boolean | undefined;
-    }
-    /**
-     * Performs transparent encryption of written data and all required TLS
-     * negotiation.
-     *
-     * Instances of `tls.TLSSocket` implement the duplex `Stream` interface.
-     *
-     * Methods that return TLS connection metadata (e.g.{@link TLSSocket.getPeerCertificate} will only return data while the
-     * connection is open.
-     * @since v0.11.4
-     */
-    class TLSSocket extends net.Socket {
-        /**
-         * Construct a new tls.TLSSocket object from an existing TCP socket.
-         */
-        constructor(socket: net.Socket, options?: TLSSocketOptions);
-        /**
-         * This property is `true` if the peer certificate was signed by one of the CAs
-         * specified when creating the `tls.TLSSocket` instance, otherwise `false`.
-         * @since v0.11.4
-         */
-        authorized: boolean;
-        /**
-         * Returns the reason why the peer's certificate was not been verified. This
-         * property is set only when `tlsSocket.authorized === false`.
-         * @since v0.11.4
-         */
-        authorizationError: Error;
-        /**
-         * Always returns `true`. This may be used to distinguish TLS sockets from regular`net.Socket` instances.
-         * @since v0.11.4
-         */
-        encrypted: true;
-        /**
-         * String containing the selected ALPN protocol.
-         * Before a handshake has completed, this value is always null.
-         * When a handshake is completed but not ALPN protocol was selected, tlsSocket.alpnProtocol equals false.
-         */
-        alpnProtocol: string | false | null;
-        /**
-         * Returns an object representing the local certificate. The returned object has
-         * some properties corresponding to the fields of the certificate.
-         *
-         * See {@link TLSSocket.getPeerCertificate} for an example of the certificate
-         * structure.
-         *
-         * If there is no local certificate, an empty object will be returned. If the
-         * socket has been destroyed, `null` will be returned.
-         * @since v11.2.0
-         */
-        getCertificate(): PeerCertificate | object | null;
-        /**
-         * Returns an object containing information on the negotiated cipher suite.
-         *
-         * For example:
-         *
-         * ```json
-         * {
-         *     "name": "AES128-SHA256",
-         *     "standardName": "TLS_RSA_WITH_AES_128_CBC_SHA256",
-         *     "version": "TLSv1.2"
-         * }
-         * ```
-         *
-         * See [SSL\_CIPHER\_get\_name](https://www.openssl.org/docs/man1.1.1/man3/SSL_CIPHER_get_name.html) for more information.
-         * @since v0.11.4
-         */
-        getCipher(): CipherNameAndProtocol;
-        /**
-         * Returns an object representing the type, name, and size of parameter of
-         * an ephemeral key exchange in `perfect forward secrecy` on a client
-         * connection. It returns an empty object when the key exchange is not
-         * ephemeral. As this is only supported on a client socket; `null` is returned
-         * if called on a server socket. The supported types are `'DH'` and `'ECDH'`. The`name` property is available only when type is `'ECDH'`.
-         *
-         * For example: `{ type: 'ECDH', name: 'prime256v1', size: 256 }`.
-         * @since v5.0.0
-         */
-        getEphemeralKeyInfo(): EphemeralKeyInfo | object | null;
-        /**
-         * As the `Finished` messages are message digests of the complete handshake
-         * (with a total of 192 bits for TLS 1.0 and more for SSL 3.0), they can
-         * be used for external authentication procedures when the authentication
-         * provided by SSL/TLS is not desired or is not enough.
-         *
-         * Corresponds to the `SSL_get_finished` routine in OpenSSL and may be used
-         * to implement the `tls-unique` channel binding from [RFC 5929](https://tools.ietf.org/html/rfc5929).
-         * @since v9.9.0
-         * @return The latest `Finished` message that has been sent to the socket as part of a SSL/TLS handshake, or `undefined` if no `Finished` message has been sent yet.
-         */
-        getFinished(): Buffer | undefined;
-        /**
-         * Returns an object representing the peer's certificate. If the peer does not
-         * provide a certificate, an empty object will be returned. If the socket has been
-         * destroyed, `null` will be returned.
-         *
-         * If the full certificate chain was requested, each certificate will include an`issuerCertificate` property containing an object representing its issuer's
-         * certificate.
-         * @since v0.11.4
-         * @param detailed Include the full certificate chain if `true`, otherwise include just the peer's certificate.
-         * @return A certificate object.
-         */
-        getPeerCertificate(detailed: true): DetailedPeerCertificate;
-        getPeerCertificate(detailed?: false): PeerCertificate;
-        getPeerCertificate(detailed?: boolean): PeerCertificate | DetailedPeerCertificate;
-        /**
-         * As the `Finished` messages are message digests of the complete handshake
-         * (with a total of 192 bits for TLS 1.0 and more for SSL 3.0), they can
-         * be used for external authentication procedures when the authentication
-         * provided by SSL/TLS is not desired or is not enough.
-         *
-         * Corresponds to the `SSL_get_peer_finished` routine in OpenSSL and may be used
-         * to implement the `tls-unique` channel binding from [RFC 5929](https://tools.ietf.org/html/rfc5929).
-         * @since v9.9.0
-         * @return The latest `Finished` message that is expected or has actually been received from the socket as part of a SSL/TLS handshake, or `undefined` if there is no `Finished` message so
-         * far.
-         */
-        getPeerFinished(): Buffer | undefined;
-        /**
-         * Returns a string containing the negotiated SSL/TLS protocol version of the
-         * current connection. The value `'unknown'` will be returned for connected
-         * sockets that have not completed the handshaking process. The value `null` will
-         * be returned for server sockets or disconnected client sockets.
-         *
-         * Protocol versions are:
-         *
-         * * `'SSLv3'`
-         * * `'TLSv1'`
-         * * `'TLSv1.1'`
-         * * `'TLSv1.2'`
-         * * `'TLSv1.3'`
-         *
-         * See the OpenSSL [`SSL_get_version`](https://www.openssl.org/docs/man1.1.1/man3/SSL_get_version.html) documentation for more information.
-         * @since v5.7.0
-         */
-        getProtocol(): string | null;
-        /**
-         * Returns the TLS session data or `undefined` if no session was
-         * negotiated. On the client, the data can be provided to the `session` option of {@link connect} to resume the connection. On the server, it may be useful
-         * for debugging.
-         *
-         * See `Session Resumption` for more information.
-         *
-         * Note: `getSession()` works only for TLSv1.2 and below. For TLSv1.3, applications
-         * must use the `'session'` event (it also works for TLSv1.2 and below).
-         * @since v0.11.4
-         */
-        getSession(): Buffer | undefined;
-        /**
-         * See [SSL\_get\_shared\_sigalgs](https://www.openssl.org/docs/man1.1.1/man3/SSL_get_shared_sigalgs.html) for more information.
-         * @since v12.11.0
-         * @return List of signature algorithms shared between the server and the client in the order of decreasing preference.
-         */
-        getSharedSigalgs(): string[];
-        /**
-         * For a client, returns the TLS session ticket if one is available, or`undefined`. For a server, always returns `undefined`.
-         *
-         * It may be useful for debugging.
-         *
-         * See `Session Resumption` for more information.
-         * @since v0.11.4
-         */
-        getTLSTicket(): Buffer | undefined;
-        /**
-         * See `Session Resumption` for more information.
-         * @since v0.5.6
-         * @return `true` if the session was reused, `false` otherwise.
-         */
-        isSessionReused(): boolean;
-        /**
-         * The `tlsSocket.renegotiate()` method initiates a TLS renegotiation process.
-         * Upon completion, the `callback` function will be passed a single argument
-         * that is either an `Error` (if the request failed) or `null`.
-         *
-         * This method can be used to request a peer's certificate after the secure
-         * connection has been established.
-         *
-         * When running as the server, the socket will be destroyed with an error after`handshakeTimeout` timeout.
-         *
-         * For TLSv1.3, renegotiation cannot be initiated, it is not supported by the
-         * protocol.
-         * @since v0.11.8
-         * @param callback If `renegotiate()` returned `true`, callback is attached once to the `'secure'` event. If `renegotiate()` returned `false`, `callback` will be called in the next tick with
-         * an error, unless the `tlsSocket` has been destroyed, in which case `callback` will not be called at all.
-         * @return `true` if renegotiation was initiated, `false` otherwise.
-         */
-        renegotiate(
-            options: {
-                rejectUnauthorized?: boolean | undefined;
-                requestCert?: boolean | undefined;
-            },
-            callback: (err: Error | null) => void
-        ): undefined | boolean;
-        /**
-         * The `tlsSocket.setMaxSendFragment()` method sets the maximum TLS fragment size.
-         * Returns `true` if setting the limit succeeded; `false` otherwise.
-         *
-         * Smaller fragment sizes decrease the buffering latency on the client: larger
-         * fragments are buffered by the TLS layer until the entire fragment is received
-         * and its integrity is verified; large fragments can span multiple roundtrips
-         * and their processing can be delayed due to packet loss or reordering. However,
-         * smaller fragments add extra TLS framing bytes and CPU overhead, which may
-         * decrease overall server throughput.
-         * @since v0.11.11
-         * @param [size=16384] The maximum TLS fragment size. The maximum value is `16384`.
-         */
-        setMaxSendFragment(size: number): boolean;
-        /**
-         * Disables TLS renegotiation for this `TLSSocket` instance. Once called, attempts
-         * to renegotiate will trigger an `'error'` event on the `TLSSocket`.
-         * @since v8.4.0
-         */
-        disableRenegotiation(): void;
-        /**
-         * When enabled, TLS packet trace information is written to `stderr`. This can be
-         * used to debug TLS connection problems.
-         *
-         * The format of the output is identical to the output of`openssl s_client -trace` or `openssl s_server -trace`. While it is produced by
-         * OpenSSL's `SSL_trace()` function, the format is undocumented, can change
-         * without notice, and should not be relied on.
-         * @since v12.2.0
-         */
-        enableTrace(): void;
-        /**
-         * Returns the peer certificate as an `X509Certificate` object.
-         *
-         * If there is no peer certificate, or the socket has been destroyed,`undefined` will be returned.
-         * @since v15.9.0
-         */
-        getPeerX509Certificate(): X509Certificate | undefined;
-        /**
-         * Returns the local certificate as an `X509Certificate` object.
-         *
-         * If there is no local certificate, or the socket has been destroyed,`undefined` will be returned.
-         * @since v15.9.0
-         */
-        getX509Certificate(): X509Certificate | undefined;
-        /**
-         * Keying material is used for validations to prevent different kind of attacks in
-         * network protocols, for example in the specifications of IEEE 802.1X.
-         *
-         * Example
-         *
-         * ```js
-         * const keyingMaterial = tlsSocket.exportKeyingMaterial(
-         *   128,
-         *   'client finished');
-         *
-         * /*
-         *  Example return value of keyingMaterial:
-         *  <Buffer 76 26 af 99 c5 56 8e 42 09 91 ef 9f 93 cb ad 6c 7b 65 f8 53 f1 d8 d9
-         *     12 5a 33 b8 b5 25 df 7b 37 9f e0 e2 4f b8 67 83 a3 2f cd 5d 41 42 4c 91
-         *     74 ef 2c ... 78 more bytes>
-         *
-         * ```
-         *
-         * See the OpenSSL [`SSL_export_keying_material`](https://www.openssl.org/docs/man1.1.1/man3/SSL_export_keying_material.html) documentation for more
-         * information.
-         * @since v13.10.0, v12.17.0
-         * @param length number of bytes to retrieve from keying material
-         * @param label an application specific label, typically this will be a value from the [IANA Exporter Label
-         * Registry](https://www.iana.org/assignments/tls-parameters/tls-parameters.xhtml#exporter-labels).
-         * @param context Optionally provide a context.
-         * @return requested bytes of the keying material
-         */
-        exportKeyingMaterial(length: number, label: string, context: Buffer): Buffer;
-        addListener(event: string, listener: (...args: any[]) => void): this;
-        addListener(event: 'OCSPResponse', listener: (response: Buffer) => void): this;
-        addListener(event: 'secureConnect', listener: () => void): this;
-        addListener(event: 'session', listener: (session: Buffer) => void): this;
-        addListener(event: 'keylog', listener: (line: Buffer) => void): this;
-        emit(event: string | symbol, ...args: any[]): boolean;
-        emit(event: 'OCSPResponse', response: Buffer): boolean;
-        emit(event: 'secureConnect'): boolean;
-        emit(event: 'session', session: Buffer): boolean;
-        emit(event: 'keylog', line: Buffer): boolean;
-        on(event: string, listener: (...args: any[]) => void): this;
-        on(event: 'OCSPResponse', listener: (response: Buffer) => void): this;
-        on(event: 'secureConnect', listener: () => void): this;
-        on(event: 'session', listener: (session: Buffer) => void): this;
-        on(event: 'keylog', listener: (line: Buffer) => void): this;
-        once(event: string, listener: (...args: any[]) => void): this;
-        once(event: 'OCSPResponse', listener: (response: Buffer) => void): this;
-        once(event: 'secureConnect', listener: () => void): this;
-        once(event: 'session', listener: (session: Buffer) => void): this;
-        once(event: 'keylog', listener: (line: Buffer) => void): this;
-        prependListener(event: string, listener: (...args: any[]) => void): this;
-        prependListener(event: 'OCSPResponse', listener: (response: Buffer) => void): this;
-        prependListener(event: 'secureConnect', listener: () => void): this;
-        prependListener(event: 'session', listener: (session: Buffer) => void): this;
-        prependListener(event: 'keylog', listener: (line: Buffer) => void): this;
-        prependOnceListener(event: string, listener: (...args: any[]) => void): this;
-        prependOnceListener(event: 'OCSPResponse', listener: (response: Buffer) => void): this;
-        prependOnceListener(event: 'secureConnect', listener: () => void): this;
-        prependOnceListener(event: 'session', listener: (session: Buffer) => void): this;
-        prependOnceListener(event: 'keylog', listener: (line: Buffer) => void): this;
-    }
-    interface CommonConnectionOptions {
-        /**
-         * An optional TLS context object from tls.createSecureContext()
-         */
-        secureContext?: SecureContext | undefined;
-        /**
-         * When enabled, TLS packet trace information is written to `stderr`. This can be
-         * used to debug TLS connection problems.
-         * @default false
-         */
-        enableTrace?: boolean | undefined;
-        /**
-         * If true the server will request a certificate from clients that
-         * connect and attempt to verify that certificate. Defaults to
-         * false.
-         */
-        requestCert?: boolean | undefined;
-        /**
-         * An array of strings or a Buffer naming possible ALPN protocols.
-         * (Protocols should be ordered by their priority.)
-         */
-        ALPNProtocols?: string[] | Uint8Array[] | Uint8Array | undefined;
-        /**
-         * SNICallback(servername, cb) <Function> A function that will be
-         * called if the client supports SNI TLS extension. Two arguments
-         * will be passed when called: servername and cb. SNICallback should
-         * invoke cb(null, ctx), where ctx is a SecureContext instance.
-         * (tls.createSecureContext(...) can be used to get a proper
-         * SecureContext.) If SNICallback wasn't provided the default callback
-         * with high-level API will be used (see below).
-         */
-        SNICallback?: ((servername: string, cb: (err: Error | null, ctx?: SecureContext) => void) => void) | undefined;
-        /**
-         * If true the server will reject any connection which is not
-         * authorized with the list of supplied CAs. This option only has an
-         * effect if requestCert is true.
-         * @default true
-         */
-        rejectUnauthorized?: boolean | undefined;
-    }
-    interface TlsOptions extends SecureContextOptions, CommonConnectionOptions, net.ServerOpts {
-        /**
-         * Abort the connection if the SSL/TLS handshake does not finish in the
-         * specified number of milliseconds. A 'tlsClientError' is emitted on
-         * the tls.Server object whenever a handshake times out. Default:
-         * 120000 (120 seconds).
-         */
-        handshakeTimeout?: number | undefined;
-        /**
-         * The number of seconds after which a TLS session created by the
-         * server will no longer be resumable. See Session Resumption for more
-         * information. Default: 300.
-         */
-        sessionTimeout?: number | undefined;
-        /**
-         * 48-bytes of cryptographically strong pseudo-random data.
-         */
-        ticketKeys?: Buffer | undefined;
-        /**
-         *
-         * @param socket
-         * @param identity identity parameter sent from the client.
-         * @return pre-shared key that must either be
-         * a buffer or `null` to stop the negotiation process. Returned PSK must be
-         * compatible with the selected cipher's digest.
-         *
-         * When negotiating TLS-PSK (pre-shared keys), this function is called
-         * with the identity provided by the client.
-         * If the return value is `null` the negotiation process will stop and an
-         * "unknown_psk_identity" alert message will be sent to the other party.
-         * If the server wishes to hide the fact that the PSK identity was not known,
-         * the callback must provide some random data as `psk` to make the connection
-         * fail with "decrypt_error" before negotiation is finished.
-         * PSK ciphers are disabled by default, and using TLS-PSK thus
-         * requires explicitly specifying a cipher suite with the `ciphers` option.
-         * More information can be found in the RFC 4279.
-         */
-        pskCallback?(socket: TLSSocket, identity: string): DataView | NodeJS.TypedArray | null;
-        /**
-         * hint to send to a client to help
-         * with selecting the identity during TLS-PSK negotiation. Will be ignored
-         * in TLS 1.3. Upon failing to set pskIdentityHint `tlsClientError` will be
-         * emitted with `ERR_TLS_PSK_SET_IDENTIY_HINT_FAILED` code.
-         */
-        pskIdentityHint?: string | undefined;
-    }
-    interface PSKCallbackNegotation {
-        psk: DataView | NodeJS.TypedArray;
-        identity: string;
-    }
-    interface ConnectionOptions extends SecureContextOptions, CommonConnectionOptions {
-        host?: string | undefined;
-        port?: number | undefined;
-        path?: string | undefined; // Creates unix socket connection to path. If this option is specified, `host` and `port` are ignored.
-        socket?: stream.Duplex | undefined; // Establish secure connection on a given socket rather than creating a new socket
-        checkServerIdentity?: typeof checkServerIdentity | undefined;
-        servername?: string | undefined; // SNI TLS Extension
-        session?: Buffer | undefined;
-        minDHSize?: number | undefined;
-        lookup?: net.LookupFunction | undefined;
-        timeout?: number | undefined;
-        /**
-         * When negotiating TLS-PSK (pre-shared keys), this function is called
-         * with optional identity `hint` provided by the server or `null`
-         * in case of TLS 1.3 where `hint` was removed.
-         * It will be necessary to provide a custom `tls.checkServerIdentity()`
-         * for the connection as the default one will try to check hostname/IP
-         * of the server against the certificate but that's not applicable for PSK
-         * because there won't be a certificate present.
-         * More information can be found in the RFC 4279.
-         *
-         * @param hint message sent from the server to help client
-         * decide which identity to use during negotiation.
-         * Always `null` if TLS 1.3 is used.
-         * @returns Return `null` to stop the negotiation process. `psk` must be
-         * compatible with the selected cipher's digest.
-         * `identity` must use UTF-8 encoding.
-         */
-        pskCallback?(hint: string | null): PSKCallbackNegotation | null;
-    }
-    /**
-     * Accepts encrypted connections using TLS or SSL.
-     * @since v0.3.2
-     */
-    class Server extends net.Server {
-        constructor(secureConnectionListener?: (socket: TLSSocket) => void);
-        constructor(options: TlsOptions, secureConnectionListener?: (socket: TLSSocket) => void);
-        /**
-         * The `server.addContext()` method adds a secure context that will be used if
-         * the client request's SNI name matches the supplied `hostname` (or wildcard).
-         *
-         * When there are multiple matching contexts, the most recently added one is
-         * used.
-         * @since v0.5.3
-         * @param hostname A SNI host name or wildcard (e.g. `'*'`)
-         * @param context An object containing any of the possible properties from the {@link createSecureContext} `options` arguments (e.g. `key`, `cert`, `ca`, etc).
-         */
-        addContext(hostname: string, context: SecureContextOptions): void;
-        /**
-         * Returns the session ticket keys.
-         *
-         * See `Session Resumption` for more information.
-         * @since v3.0.0
-         * @return A 48-byte buffer containing the session ticket keys.
-         */
-        getTicketKeys(): Buffer;
-        /**
-         * The `server.setSecureContext()` method replaces the secure context of an
-         * existing server. Existing connections to the server are not interrupted.
-         * @since v11.0.0
-         * @param options An object containing any of the possible properties from the {@link createSecureContext} `options` arguments (e.g. `key`, `cert`, `ca`, etc).
-         */
-        setSecureContext(options: SecureContextOptions): void;
-        /**
-         * Sets the session ticket keys.
-         *
-         * Changes to the ticket keys are effective only for future server connections.
-         * Existing or currently pending server connections will use the previous keys.
-         *
-         * See `Session Resumption` for more information.
-         * @since v3.0.0
-         * @param keys A 48-byte buffer containing the session ticket keys.
-         */
-        setTicketKeys(keys: Buffer): void;
-        /**
-         * events.EventEmitter
-         * 1. tlsClientError
-         * 2. newSession
-         * 3. OCSPRequest
-         * 4. resumeSession
-         * 5. secureConnection
-         * 6. keylog
-         */
-        addListener(event: string, listener: (...args: any[]) => void): this;
-        addListener(event: 'tlsClientError', listener: (err: Error, tlsSocket: TLSSocket) => void): this;
-        addListener(event: 'newSession', listener: (sessionId: Buffer, sessionData: Buffer, callback: () => void) => void): this;
-        addListener(event: 'OCSPRequest', listener: (certificate: Buffer, issuer: Buffer, callback: (err: Error | null, resp: Buffer) => void) => void): this;
-        addListener(event: 'resumeSession', listener: (sessionId: Buffer, callback: (err: Error | null, sessionData: Buffer | null) => void) => void): this;
-        addListener(event: 'secureConnection', listener: (tlsSocket: TLSSocket) => void): this;
-        addListener(event: 'keylog', listener: (line: Buffer, tlsSocket: TLSSocket) => void): this;
-        emit(event: string | symbol, ...args: any[]): boolean;
-        emit(event: 'tlsClientError', err: Error, tlsSocket: TLSSocket): boolean;
-        emit(event: 'newSession', sessionId: Buffer, sessionData: Buffer, callback: () => void): boolean;
-        emit(event: 'OCSPRequest', certificate: Buffer, issuer: Buffer, callback: (err: Error | null, resp: Buffer) => void): boolean;
-        emit(event: 'resumeSession', sessionId: Buffer, callback: (err: Error | null, sessionData: Buffer | null) => void): boolean;
-        emit(event: 'secureConnection', tlsSocket: TLSSocket): boolean;
-        emit(event: 'keylog', line: Buffer, tlsSocket: TLSSocket): boolean;
-        on(event: string, listener: (...args: any[]) => void): this;
-        on(event: 'tlsClientError', listener: (err: Error, tlsSocket: TLSSocket) => void): this;
-        on(event: 'newSession', listener: (sessionId: Buffer, sessionData: Buffer, callback: () => void) => void): this;
-        on(event: 'OCSPRequest', listener: (certificate: Buffer, issuer: Buffer, callback: (err: Error | null, resp: Buffer) => void) => void): this;
-        on(event: 'resumeSession', listener: (sessionId: Buffer, callback: (err: Error | null, sessionData: Buffer | null) => void) => void): this;
-        on(event: 'secureConnection', listener: (tlsSocket: TLSSocket) => void): this;
-        on(event: 'keylog', listener: (line: Buffer, tlsSocket: TLSSocket) => void): this;
-        once(event: string, listener: (...args: any[]) => void): this;
-        once(event: 'tlsClientError', listener: (err: Error, tlsSocket: TLSSocket) => void): this;
-        once(event: 'newSession', listener: (sessionId: Buffer, sessionData: Buffer, callback: () => void) => void): this;
-        once(event: 'OCSPRequest', listener: (certificate: Buffer, issuer: Buffer, callback: (err: Error | null, resp: Buffer) => void) => void): this;
-        once(event: 'resumeSession', listener: (sessionId: Buffer, callback: (err: Error | null, sessionData: Buffer | null) => void) => void): this;
-        once(event: 'secureConnection', listener: (tlsSocket: TLSSocket) => void): this;
-        once(event: 'keylog', listener: (line: Buffer, tlsSocket: TLSSocket) => void): this;
-        prependListener(event: string, listener: (...args: any[]) => void): this;
-        prependListener(event: 'tlsClientError', listener: (err: Error, tlsSocket: TLSSocket) => void): this;
-        prependListener(event: 'newSession', listener: (sessionId: Buffer, sessionData: Buffer, callback: () => void) => void): this;
-        prependListener(event: 'OCSPRequest', listener: (certificate: Buffer, issuer: Buffer, callback: (err: Error | null, resp: Buffer) => void) => void): this;
-        prependListener(event: 'resumeSession', listener: (sessionId: Buffer, callback: (err: Error | null, sessionData: Buffer | null) => void) => void): this;
-        prependListener(event: 'secureConnection', listener: (tlsSocket: TLSSocket) => void): this;
-        prependListener(event: 'keylog', listener: (line: Buffer, tlsSocket: TLSSocket) => void): this;
-        prependOnceListener(event: string, listener: (...args: any[]) => void): this;
-        prependOnceListener(event: 'tlsClientError', listener: (err: Error, tlsSocket: TLSSocket) => void): this;
-        prependOnceListener(event: 'newSession', listener: (sessionId: Buffer, sessionData: Buffer, callback: () => void) => void): this;
-        prependOnceListener(event: 'OCSPRequest', listener: (certificate: Buffer, issuer: Buffer, callback: (err: Error | null, resp: Buffer) => void) => void): this;
-        prependOnceListener(event: 'resumeSession', listener: (sessionId: Buffer, callback: (err: Error | null, sessionData: Buffer | null) => void) => void): this;
-        prependOnceListener(event: 'secureConnection', listener: (tlsSocket: TLSSocket) => void): this;
-        prependOnceListener(event: 'keylog', listener: (line: Buffer, tlsSocket: TLSSocket) => void): this;
-    }
-    /**
-     * @deprecated since v0.11.3 Use `tls.TLSSocket` instead.
-     */
-    interface SecurePair {
-        encrypted: TLSSocket;
-        cleartext: TLSSocket;
-    }
-    type SecureVersion = 'TLSv1.3' | 'TLSv1.2' | 'TLSv1.1' | 'TLSv1';
-    interface SecureContextOptions {
-        /**
-         * Optionally override the trusted CA certificates. Default is to trust
-         * the well-known CAs curated by Mozilla. Mozilla's CAs are completely
-         * replaced when CAs are explicitly specified using this option.
-         */
-        ca?: string | Buffer | Array<string | Buffer> | undefined;
-        /**
-         *  Cert chains in PEM format. One cert chain should be provided per
-         *  private key. Each cert chain should consist of the PEM formatted
-         *  certificate for a provided private key, followed by the PEM
-         *  formatted intermediate certificates (if any), in order, and not
-         *  including the root CA (the root CA must be pre-known to the peer,
-         *  see ca). When providing multiple cert chains, they do not have to
-         *  be in the same order as their private keys in key. If the
-         *  intermediate certificates are not provided, the peer will not be
-         *  able to validate the certificate, and the handshake will fail.
-         */
-        cert?: string | Buffer | Array<string | Buffer> | undefined;
-        /**
-         *  Colon-separated list of supported signature algorithms. The list
-         *  can contain digest algorithms (SHA256, MD5 etc.), public key
-         *  algorithms (RSA-PSS, ECDSA etc.), combination of both (e.g
-         *  'RSA+SHA384') or TLS v1.3 scheme names (e.g. rsa_pss_pss_sha512).
-         */
-        sigalgs?: string | undefined;
-        /**
-         * Cipher suite specification, replacing the default. For more
-         * information, see modifying the default cipher suite. Permitted
-         * ciphers can be obtained via tls.getCiphers(). Cipher names must be
-         * uppercased in order for OpenSSL to accept them.
-         */
-        ciphers?: string | undefined;
-        /**
-         * Name of an OpenSSL engine which can provide the client certificate.
-         */
-        clientCertEngine?: string | undefined;
-        /**
-         * PEM formatted CRLs (Certificate Revocation Lists).
-         */
-        crl?: string | Buffer | Array<string | Buffer> | undefined;
-        /**
-         * Diffie Hellman parameters, required for Perfect Forward Secrecy. Use
-         * openssl dhparam to create the parameters. The key length must be
-         * greater than or equal to 1024 bits or else an error will be thrown.
-         * Although 1024 bits is permissible, use 2048 bits or larger for
-         * stronger security. If omitted or invalid, the parameters are
-         * silently discarded and DHE ciphers will not be available.
-         */
-        dhparam?: string | Buffer | undefined;
-        /**
-         * A string describing a named curve or a colon separated list of curve
-         * NIDs or names, for example P-521:P-384:P-256, to use for ECDH key
-         * agreement. Set to auto to select the curve automatically. Use
-         * crypto.getCurves() to obtain a list of available curve names. On
-         * recent releases, openssl ecparam -list_curves will also display the
-         * name and description of each available elliptic curve. Default:
-         * tls.DEFAULT_ECDH_CURVE.
-         */
-        ecdhCurve?: string | undefined;
-        /**
-         * Attempt to use the server's cipher suite preferences instead of the
-         * client's. When true, causes SSL_OP_CIPHER_SERVER_PREFERENCE to be
-         * set in secureOptions
-         */
-        honorCipherOrder?: boolean | undefined;
-        /**
-         * Private keys in PEM format. PEM allows the option of private keys
-         * being encrypted. Encrypted keys will be decrypted with
-         * options.passphrase. Multiple keys using different algorithms can be
-         * provided either as an array of unencrypted key strings or buffers,
-         * or an array of objects in the form {pem: <string|buffer>[,
-         * passphrase: <string>]}. The object form can only occur in an array.
-         * object.passphrase is optional. Encrypted keys will be decrypted with
-         * object.passphrase if provided, or options.passphrase if it is not.
-         */
-        key?: string | Buffer | Array<string | Buffer | KeyObject> | undefined;
-        /**
-         * Name of an OpenSSL engine to get private key from. Should be used
-         * together with privateKeyIdentifier.
-         */
-        privateKeyEngine?: string | undefined;
-        /**
-         * Identifier of a private key managed by an OpenSSL engine. Should be
-         * used together with privateKeyEngine. Should not be set together with
-         * key, because both options define a private key in different ways.
-         */
-        privateKeyIdentifier?: string | undefined;
-        /**
-         * Optionally set the maximum TLS version to allow. One
-         * of `'TLSv1.3'`, `'TLSv1.2'`, `'TLSv1.1'`, or `'TLSv1'`. Cannot be specified along with the
-         * `secureProtocol` option, use one or the other.
-         * **Default:** `'TLSv1.3'`, unless changed using CLI options. Using
-         * `--tls-max-v1.2` sets the default to `'TLSv1.2'`. Using `--tls-max-v1.3` sets the default to
-         * `'TLSv1.3'`. If multiple of the options are provided, the highest maximum is used.
-         */
-        maxVersion?: SecureVersion | undefined;
-        /**
-         * Optionally set the minimum TLS version to allow. One
-         * of `'TLSv1.3'`, `'TLSv1.2'`, `'TLSv1.1'`, or `'TLSv1'`. Cannot be specified along with the
-         * `secureProtocol` option, use one or the other.  It is not recommended to use
-         * less than TLSv1.2, but it may be required for interoperability.
-         * **Default:** `'TLSv1.2'`, unless changed using CLI options. Using
-         * `--tls-v1.0` sets the default to `'TLSv1'`. Using `--tls-v1.1` sets the default to
-         * `'TLSv1.1'`. Using `--tls-min-v1.3` sets the default to
-         * 'TLSv1.3'. If multiple of the options are provided, the lowest minimum is used.
-         */
-        minVersion?: SecureVersion | undefined;
-        /**
-         * Shared passphrase used for a single private key and/or a PFX.
-         */
-        passphrase?: string | undefined;
-        /**
-         * PFX or PKCS12 encoded private key and certificate chain. pfx is an
-         * alternative to providing key and cert individually. PFX is usually
-         * encrypted, if it is, passphrase will be used to decrypt it. Multiple
-         * PFX can be provided either as an array of unencrypted PFX buffers,
-         * or an array of objects in the form {buf: <string|buffer>[,
-         * passphrase: <string>]}. The object form can only occur in an array.
-         * object.passphrase is optional. Encrypted PFX will be decrypted with
-         * object.passphrase if provided, or options.passphrase if it is not.
-         */
-        pfx?: string | Buffer | Array<string | Buffer | PxfObject> | undefined;
-        /**
-         * Optionally affect the OpenSSL protocol behavior, which is not
-         * usually necessary. This should be used carefully if at all! Value is
-         * a numeric bitmask of the SSL_OP_* options from OpenSSL Options
-         */
-        secureOptions?: number | undefined; // Value is a numeric bitmask of the `SSL_OP_*` options
-        /**
-         * Legacy mechanism to select the TLS protocol version to use, it does
-         * not support independent control of the minimum and maximum version,
-         * and does not support limiting the protocol to TLSv1.3. Use
-         * minVersion and maxVersion instead. The possible values are listed as
-         * SSL_METHODS, use the function names as strings. For example, use
-         * 'TLSv1_1_method' to force TLS version 1.1, or 'TLS_method' to allow
-         * any TLS protocol version up to TLSv1.3. It is not recommended to use
-         * TLS versions less than 1.2, but it may be required for
-         * interoperability. Default: none, see minVersion.
-         */
-        secureProtocol?: string | undefined;
-        /**
-         * Opaque identifier used by servers to ensure session state is not
-         * shared between applications. Unused by clients.
-         */
-        sessionIdContext?: string | undefined;
-        /**
-         * 48-bytes of cryptographically strong pseudo-random data.
-         * See Session Resumption for more information.
-         */
-        ticketKeys?: Buffer | undefined;
-        /**
-         * The number of seconds after which a TLS session created by the
-         * server will no longer be resumable. See Session Resumption for more
-         * information. Default: 300.
-         */
-        sessionTimeout?: number | undefined;
-    }
-    interface SecureContext {
-        context: any;
-    }
-    /**
-     * Verifies the certificate `cert` is issued to `hostname`.
-     *
-     * Returns [Error](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Error) object, populating it with `reason`, `host`, and `cert` on
-     * failure. On success, returns [undefined](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#Undefined_type).
-     *
-     * This function is intended to be used in combination with the`checkServerIdentity` option that can be passed to {@link connect} and as
-     * such operates on a `certificate object`. For other purposes, consider using `x509.checkHost()` instead.
-     *
-     * This function can be overwritten by providing an alternative function as the`options.checkServerIdentity` option that is passed to `tls.connect()`. The
-     * overwriting function can call `tls.checkServerIdentity()` of course, to augment
-     * the checks done with additional verification.
-     *
-     * This function is only called if the certificate passed all other checks, such as
-     * being issued by trusted CA (`options.ca`).
-     *
-     * Earlier versions of Node.js incorrectly accepted certificates for a given`hostname` if a matching `uniformResourceIdentifier` subject alternative name
-     * was present (see [CVE-2021-44531](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-44531)). Applications that wish to accept`uniformResourceIdentifier` subject alternative names can use
-     * a custom`options.checkServerIdentity` function that implements the desired behavior.
-     * @since v0.8.4
-     * @param hostname The host name or IP address to verify the certificate against.
-     * @param cert A `certificate object` representing the peer's certificate.
-     */
-    function checkServerIdentity(hostname: string, cert: PeerCertificate): Error | undefined;
-    /**
-     * Creates a new {@link Server}. The `secureConnectionListener`, if provided, is
-     * automatically set as a listener for the `'secureConnection'` event.
-     *
-     * The `ticketKeys` options is automatically shared between `cluster` module
-     * workers.
-     *
-     * The following illustrates a simple echo server:
-     *
-     * ```js
-     * const tls = require('tls');
-     * const fs = require('fs');
-     *
-     * const options = {
-     *   key: fs.readFileSync('server-key.pem'),
-     *   cert: fs.readFileSync('server-cert.pem'),
-     *
-     *   // This is necessary only if using client certificate authentication.
-     *   requestCert: true,
-     *
-     *   // This is necessary only if the client uses a self-signed certificate.
-     *   ca: [ fs.readFileSync('client-cert.pem') ]
-     * };
-     *
-     * const server = tls.createServer(options, (socket) => {
-     *   console.log('server connected',
-     *               socket.authorized ? 'authorized' : 'unauthorized');
-     *   socket.write('welcome!\n');
-     *   socket.setEncoding('utf8');
-     *   socket.pipe(socket);
-     * });
-     * server.listen(8000, () => {
-     *   console.log('server bound');
-     * });
-     * ```
-     *
-     * The server can be tested by connecting to it using the example client from {@link connect}.
-     * @since v0.3.2
-     */
-    function createServer(secureConnectionListener?: (socket: TLSSocket) => void): Server;
-    function createServer(options: TlsOptions, secureConnectionListener?: (socket: TLSSocket) => void): Server;
-    /**
-     * The `callback` function, if specified, will be added as a listener for the `'secureConnect'` event.
-     *
-     * `tls.connect()` returns a {@link TLSSocket} object.
-     *
-     * Unlike the `https` API, `tls.connect()` does not enable the
-     * SNI (Server Name Indication) extension by default, which may cause some
-     * servers to return an incorrect certificate or reject the connection
-     * altogether. To enable SNI, set the `servername` option in addition
-     * to `host`.
-     *
-     * The following illustrates a client for the echo server example from {@link createServer}:
-     *
-     * ```js
-     * // Assumes an echo server that is listening on port 8000.
-     * const tls = require('tls');
-     * const fs = require('fs');
-     *
-     * const options = {
-     *   // Necessary only if the server requires client certificate authentication.
-     *   key: fs.readFileSync('client-key.pem'),
-     *   cert: fs.readFileSync('client-cert.pem'),
-     *
-     *   // Necessary only if the server uses a self-signed certificate.
-     *   ca: [ fs.readFileSync('server-cert.pem') ],
-     *
-     *   // Necessary only if the server's cert isn't for "localhost".
-     *   checkServerIdentity: () => { return null; },
-     * };
-     *
-     * const socket = tls.connect(8000, options, () => {
-     *   console.log('client connected',
-     *               socket.authorized ? 'authorized' : 'unauthorized');
-     *   process.stdin.pipe(socket);
-     *   process.stdin.resume();
-     * });
-     * socket.setEncoding('utf8');
-     * socket.on('data', (data) => {
-     *   console.log(data);
-     * });
-     * socket.on('end', () => {
-     *   console.log('server ends connection');
-     * });
-     * ```
-     * @since v0.11.3
-     */
-    function connect(options: ConnectionOptions, secureConnectListener?: () => void): TLSSocket;
-    function connect(port: number, host?: string, options?: ConnectionOptions, secureConnectListener?: () => void): TLSSocket;
-    function connect(port: number, options?: ConnectionOptions, secureConnectListener?: () => void): TLSSocket;
-    /**
-     * Creates a new secure pair object with two streams, one of which reads and writes
-     * the encrypted data and the other of which reads and writes the cleartext data.
-     * Generally, the encrypted stream is piped to/from an incoming encrypted data
-     * stream and the cleartext one is used as a replacement for the initial encrypted
-     * stream.
-     *
-     * `tls.createSecurePair()` returns a `tls.SecurePair` object with `cleartext` and`encrypted` stream properties.
-     *
-     * Using `cleartext` has the same API as {@link TLSSocket}.
-     *
-     * The `tls.createSecurePair()` method is now deprecated in favor of`tls.TLSSocket()`. For example, the code:
-     *
-     * ```js
-     * pair = tls.createSecurePair(// ... );
-     * pair.encrypted.pipe(socket);
-     * socket.pipe(pair.encrypted);
-     * ```
-     *
-     * can be replaced by:
-     *
-     * ```js
-     * secureSocket = tls.TLSSocket(socket, options);
-     * ```
-     *
-     * where `secureSocket` has the same API as `pair.cleartext`.
-     * @since v0.3.2
-     * @deprecated Since v0.11.3 - Use {@link TLSSocket} instead.
-     * @param context A secure context object as returned by `tls.createSecureContext()`
-     * @param isServer `true` to specify that this TLS connection should be opened as a server.
-     * @param requestCert `true` to specify whether a server should request a certificate from a connecting client. Only applies when `isServer` is `true`.
-     * @param rejectUnauthorized If not `false` a server automatically reject clients with invalid certificates. Only applies when `isServer` is `true`.
-     */
-    function createSecurePair(context?: SecureContext, isServer?: boolean, requestCert?: boolean, rejectUnauthorized?: boolean): SecurePair;
-    /**
-     * {@link createServer} sets the default value of the `honorCipherOrder` option
-     * to `true`, other APIs that create secure contexts leave it unset.
-     *
-     * {@link createServer} uses a 128 bit truncated SHA1 hash value generated
-     * from `process.argv` as the default value of the `sessionIdContext` option, other
-     * APIs that create secure contexts have no default value.
-     *
-     * The `tls.createSecureContext()` method creates a `SecureContext` object. It is
-     * usable as an argument to several `tls` APIs, such as {@link createServer} and `server.addContext()`, but has no public methods.
-     *
-     * A key is _required_ for ciphers that use certificates. Either `key` or`pfx` can be used to provide it.
-     *
-     * If the `ca` option is not given, then Node.js will default to using [Mozilla's publicly trusted list of
-     * CAs](https://hg.mozilla.org/mozilla-central/raw-file/tip/security/nss/lib/ckfw/builtins/certdata.txt).
-     * @since v0.11.13
-     */
-    function createSecureContext(options?: SecureContextOptions): SecureContext;
-    /**
-     * Returns an array with the names of the supported TLS ciphers. The names are
-     * lower-case for historical reasons, but must be uppercased to be used in
-     * the `ciphers` option of {@link createSecureContext}.
-     *
-     * Not all supported ciphers are enabled by default. See `Modifying the default TLS cipher suite`.
-     *
-     * Cipher names that start with `'tls_'` are for TLSv1.3, all the others are for
-     * TLSv1.2 and below.
-     *
-     * ```js
-     * console.log(tls.getCiphers()); // ['aes128-gcm-sha256', 'aes128-sha', ...]
-     * ```
-     * @since v0.10.2
-     */
-    function getCiphers(): string[];
-    /**
-     * The default curve name to use for ECDH key agreement in a tls server.
-     * The default value is 'auto'. See tls.createSecureContext() for further
-     * information.
-     */
-    let DEFAULT_ECDH_CURVE: string;
-    /**
-     * The default value of the maxVersion option of
-     * tls.createSecureContext(). It can be assigned any of the supported TLS
-     * protocol versions, 'TLSv1.3', 'TLSv1.2', 'TLSv1.1', or 'TLSv1'. Default:
-     * 'TLSv1.3', unless changed using CLI options. Using --tls-max-v1.2 sets
-     * the default to 'TLSv1.2'. Using --tls-max-v1.3 sets the default to
-     * 'TLSv1.3'. If multiple of the options are provided, the highest maximum
-     * is used.
-     */
-    let DEFAULT_MAX_VERSION: SecureVersion;
-    /**
-     * The default value of the minVersion option of tls.createSecureContext().
-     * It can be assigned any of the supported TLS protocol versions,
-     * 'TLSv1.3', 'TLSv1.2', 'TLSv1.1', or 'TLSv1'. Default: 'TLSv1.2', unless
-     * changed using CLI options. Using --tls-min-v1.0 sets the default to
-     * 'TLSv1'. Using --tls-min-v1.1 sets the default to 'TLSv1.1'. Using
-     * --tls-min-v1.3 sets the default to 'TLSv1.3'. If multiple of the options
-     * are provided, the lowest minimum is used.
-     */
-    let DEFAULT_MIN_VERSION: SecureVersion;
-    /**
-     * An immutable array of strings representing the root certificates (in PEM
-     * format) used for verifying peer certificates. This is the default value
-     * of the ca option to tls.createSecureContext().
-     */
-    const rootCertificates: ReadonlyArray<string>;
-}
-declare module 'node:tls' {
-    export * from 'tls';
-}
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/ECA VRT DVD 2011.rar.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/ECA VRT DVD 2011.rar.md
deleted file mode 100644
index 3ac7d0f884674f0dddd907b1db89190a6c7bfb3d..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/ECA VRT DVD 2011.rar.md	
+++ /dev/null
@@ -1,129 +0,0 @@
-<br />
-<h1>ECA VRT DVD 2011: A Comprehensive Review</h1>
-
-<p>If you are looking for a software that can help you create, edit and view DVD movies, you might want to check out ECA VRT DVD 2011. This is a digital versatile disc (DVD) authoring, editing and viewing application for Microsoft Windows. It also offers picture, song or sound-recording features. In this article, we will review the features, benefits and drawbacks of ECA VRT DVD 2011.</p>
-
-<h2>What is ECA VRT DVD 2011?</h2>
-
-<p>ECA VRT DVD 2011 is a software that allows you to create and run DVD movies on your computer. It supports various formats, such as AVI, MPEG, WMV, MP4, MOV, FLV, MKV and more. You can also edit your videos with various tools, such as cropping, trimming, adding effects, subtitles, watermarks and more. You can also burn your videos to DVD discs or ISO files.</p>
-<h2>ECA VRT DVD 2011.rar</h2><br /><p><b><b>DOWNLOAD</b> ===> <a href="https://urlgoal.com/2uCKy3">https://urlgoal.com/2uCKy3</a></b></p><br /><br />
-
-<p>ECA VRT DVD 2011 also has a DVD viewing feature that lets you play your DVDs on your computer. You can adjust the playback speed, volume, brightness and contrast. You can also take snapshots of your favorite scenes and save them as images.</p>
-
-<p>Additionally, ECA VRT DVD 2011 has a picture, song or sound-recording feature that lets you capture images or audio from your webcam or microphone. You can also import images or audio files from your computer and edit them with various tools.</p>
-<p></p>
-
-<h2>What are the benefits of ECA VRT DVD 2011?</h2>
-
-<p>Some of the benefits of ECA VRT DVD 2011 are:</p>
-
-<ul>
-<li>It is easy to use and has a user-friendly interface.</li>
-<li>It supports a wide range of video formats and can convert them to DVD compatible formats.</li>
-<li>It has various editing tools that can enhance your videos.</li>
-<li>It can burn your videos to DVD discs or ISO files with high quality and speed.</li>
-<li>It can play your DVDs on your computer with smooth playback and clear sound.</li>
-<li>It can capture images or audio from your webcam or microphone and edit them with various tools.</li>
-</ul>
-
-<h2>What are the drawbacks of ECA VRT DVD 2011?</h2>
-
-<p>Some of the drawbacks of ECA VRT DVD 2011 are:</p>
-
-<ul>
-<li>It is not compatible with Mac OS or Linux systems.</li>
-<li>It may not support some newer video formats or codecs.</li>
-<li>It may not be able to handle large or high-definition videos smoothly.</li>
-<li>It may have some bugs or errors that affect its performance or stability.</li>
-<li>It may require a lot of disk space and system resources to run properly.</li>
-</ul>
-
-<h2>How to download ECA VRT DVD 2011?</h2>
-
-<p>If you want to download ECA VRT DVD 2011, you can visit the official website of the software and click on the download link. You will need to register an account and provide some information before you can download the software. Alternatively, you can also search for ECA VRT DVD 2011.rar on the internet and download it from other sources. However, you should be careful about the reliability and security of the download sites. Some of them may contain viruses or malware that can harm your computer.</p>
-
-<h2>How to install ECA VRT DVD 2011?</h2>
-
-<p>If you have downloaded ECA VRT DVD 2011.rar, you will need to extract it using a software like WinRAR or 7-Zip. You will get a folder containing the setup file and other files. Double-click on the setup file and follow the instructions to install the software on your computer. You may need to enter a serial number or a crack code to activate the software. You can find these codes online or in the folder that you extracted.</p>
-
-<h2>How to use ECA VRT DVD 2011?</h2>
-
-<p>To use ECA VRT DVD 2011, you will need to launch the software from your desktop or start menu. You will see a main interface with four tabs: Create Movie, Edit Movie, View Movie and Record Picture/Sound. You can choose the tab that suits your needs and follow the steps below:</p>
-
-<ol>
-<li>Create Movie: Click on the Add Files button and select the video files that you want to create a movie with. You can also drag and drop them into the interface. You can adjust the order of the files by dragging them up or down. You can also preview them by clicking on the Play button. Click on Next when you are done.</li>
-<li>Edit Movie: In this tab, you can edit your video files with various tools. You can crop, trim, rotate, flip, add effects, subtitles, watermarks and more. You can also adjust the brightness, contrast, saturation and volume of your videos. You can preview your changes by clicking on the Play button. Click on Next when you are done.</li>
-<li>View Movie: In this tab, you can view your movie before burning it to a DVD disc or an ISO file. You can adjust the playback speed, volume, brightness and contrast of your movie. You can also take snapshots of your favorite scenes and save them as images. Click on Next when you are done.</li>
-<li>Burn Movie: In this tab, you can choose whether to burn your movie to a DVD disc or an ISO file. You can also choose the output format (DVD-5 or DVD-9), the TV system (NTSC or PAL), the aspect ratio (4:3 or 16:9) and the burning speed. You can also label your disc or file and choose a destination folder for saving it. Click on Start when you are ready.</li>
-</ol>
-
-<p>To use the Record Picture/Sound feature, you will need to click on the Record Picture/Sound tab and choose whether to record an image or a sound. You will need to connect your webcam or microphone to your computer before recording. You can also import images or audio files from your computer and edit them with various tools.</p>
-
-<h2>Conclusion</h2>
-
-<p>ECA VRT DVD 2011 is a software that can help you create, edit and view DVD movies on your computer. It also offers picture, song or sound-recording features. It has many benefits but also some drawbacks that you should consider before downloading it. If you are interested in ECA VRT DVD 2011.rar , you can visit its official website or search for it online.<p></p>
-<h2>How to uninstall ECA VRT DVD 2011?</h2>
-
-<p>If you want to uninstall ECA VRT DVD 2011 from your computer, you can follow these steps:</p>
-
-<ol>
-<li>Go to the Start menu and click on Control Panel.</li>
-<li>Click on Programs and Features or Add or Remove Programs.</li>
-<li>Find ECA VRT DVD 2011 in the list of installed programs and click on Uninstall or Remove.</li>
-<li>Follow the instructions to complete the uninstallation process.</li>
-<li>Restart your computer if prompted.</li>
-</ol>
-
-<p>You can also use a third-party software like Revo Uninstaller or IObit Uninstaller to remove ECA VRT DVD 2011 and its leftover files and registry entries.</p>
-
-<h2>Is ECA VRT DVD 2011 safe and legal?</h2>
-
-<p>ECA VRT DVD 2011 is a safe and legal software that does not contain any viruses or malware. However, you should always download it from its official website or other trusted sources. You should also scan it with an antivirus program before installing it on your computer.</p>
-
-<p>ECA VRT DVD 2011 is a legal software that does not violate any copyright laws. However, you should only use it for personal and non-commercial purposes. You should not use it to create or edit DVD movies that infringe the rights of the original owners. You should also respect the terms and conditions of the software license agreement.</p>
-
-<h2>What are some alternatives to ECA VRT DVD 2011?</h2>
-
-<p>If you are looking for some alternatives to ECA VRT DVD 2011, you can try these software:</p>
-
-<ul>
-<li>DVD Flick: This is a free and open-source software that can create DVD movies from various video formats. It also has a simple and intuitive interface and supports many customization options.</li>
-<li>Wondershare DVD Creator: This is a professional and powerful software that can create and burn DVD movies from various video formats. It also has many editing tools and templates and supports various output formats.</li>
-<li>Ashampoo Burning Studio: This is a versatile and user-friendly software that can create and burn DVD movies from various video formats. It also has many features and functions and supports various disc types.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>ECA VRT DVD 2011 is a software that can help you create, edit and view DVD movies on your computer. It also offers picture, song or sound-recording features. It has many benefits but also some drawbacks that you should consider before downloading it. If you are interested in ECA VRT DVD 2011.rar , you can visit its official website or search for it online.<p>
-<h2>How to update ECA VRT DVD 2011?</h2>
-
-<p>If you want to update ECA VRT DVD 2011 to the latest version, you can follow these steps:</p>
-
-<ol>
-<li>Go to the official website of ECA VRT DVD 2011 and check if there is a new version available.</li>
-<li>If there is a new version, click on the download link and save the file to your computer.</li>
-<li>Uninstall the old version of ECA VRT DVD 2011 from your computer.</li>
-<li>Run the downloaded file and follow the instructions to install the new version of ECA VRT DVD 2011 on your computer.</li>
-<li>Enter the serial number or the crack code to activate the new version of ECA VRT DVD 2011.</li>
-</ol>
-
-<p>You can also check for updates within the software by clicking on the Help menu and selecting Check for Updates. If there is an update available, you can download and install it automatically.</p>
-
-<h2>How to get help and support for ECA VRT DVD 2011?</h2>
-
-<p>If you need help and support for ECA VRT DVD 2011, you can try these options:</p>
-
-<ul>
-<li>Read the user manual or the online help that comes with the software. You can access them by clicking on the Help menu and selecting User Manual or Online Help.</li>
-<li>Visit the official website of ECA VRT DVD 2011 and browse through the FAQ section or the forum. You can find answers to common questions or post your own questions and get feedback from other users or experts.</li>
-<li>Contact the customer service or technical support team of ECA VRT DVD 2011 by email, phone or live chat. You can find their contact information on the official website of ECA VRT DVD 2011 or in the software itself.</li>
-</ul>
-
-<h2>Conclusion</h2>
-
-<p>ECA VRT DVD 2011 is a software that can help you create, edit and view DVD movies on your computer. It also offers picture, song or sound-recording features. It has many benefits but also some drawbacks that you should consider before downloading it. If you are interested in ECA VRT DVD 2011.rar , you can visit its official website or search for it online.<p>
-<h2>Conclusion</h2>
-
-<p>In this article, we have reviewed ECA VRT DVD 2011, a software that can help you create, edit and view DVD movies on your computer. We have discussed its features, benefits, drawbacks, download, installation, usage, update and support. We have also suggested some alternatives to ECA VRT DVD 2011 that you can try. We hope that this article has been informative and helpful for you. If you are interested in ECA VRT DVD 2011.rar , you can visit its official website or search for it online.<p></p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Easycap Syntek Stk1150 Driver For Windows 7 24 VERIFIED.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Easycap Syntek Stk1150 Driver For Windows 7 24 VERIFIED.md
deleted file mode 100644
index dbdf67167e81b822d18d8a1d162dce6d27f45b18..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Easycap Syntek Stk1150 Driver For Windows 7 24 VERIFIED.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-<br />
-<p>Win Xp image hotkeys is a hotkeys software for Microsoft Windows XP OS to enable you to control most of the functions of your multimedia devices. With it you can input any time with a single click of your finger without any further manipulation so that you can work more effectively and efficiently.</p>
-<p>It is recommended that you use the Windows 8.1 compatible ready device driver for your easycap stk1150 device. The driver provided with the product is for your convenience and can be downloaded from the easycap homepage .<br /> <br /> </p>
-<h2>easycap syntek stk1150 driver for windows 7 24</h2><br /><p><b><b>Download File</b> &#10037; <a href="https://urlgoal.com/2uCMN1">https://urlgoal.com/2uCMN1</a></b></p><br /><br />
-<p>The third problem is the fact that the easycap device driver detects your STK1150 as a camera. Therefore its easycap driver can handle the video stream of your device as a still video camera. This means you don't need a videocam to use the device!</p>
-<p>Ideally this software is intended to scan the computer and make it configurable to work with easycap software .<br /> <b>The CMM2220 connected to your STK1150 device must also be compatible with easycap software.</b></p>
-<p>If you have the following problem: The easycap hardware device can't be detected and you get this message: 'Try to turn off and then on the device, if the problem will not be solved, visit the manufacturer website or find a helpful community for help.<br /> <i>Note: if you want to use hard-disk's camera you need to download the easycapDRV-0531 driver from the </i> easycap homepage. </p>
-<p>As an alternative, you could opt for another solution, one whose objective is to repair and restore your data by wiping the files and resetting Windows registry in an advanced fashion. Its called the Windows Malware Removal Tool.<br /> Advisable solutions against the Variable Ransomware family<br /> The PowerShell Emsisoft command-line utility is available online at the Emsisoft site, regardless of if you need to apply it to a computer running an old version of Windows or a </p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/rgres/Seg2Sat/static/index.html b/spaces/rgres/Seg2Sat/static/index.html
deleted file mode 100644
index 0b288f2691e52253be9dd6fdba698ad0e61fed67..0000000000000000000000000000000000000000
--- a/spaces/rgres/Seg2Sat/static/index.html
+++ /dev/null
@@ -1,152 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-	<head>
-		<meta charset="utf-8" />
-		<link rel="icon" href="/static/favicon.png" />
-		<meta name="viewport" content="width=device-width, initial-scale=1" />
-		<script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
-		<meta http-equiv="content-security-policy" content="">
-	<link rel="stylesheet" href="/static/_app/immutable/assets/pages/__layout.svelte-b67cf61d.css">
-	<link rel="stylesheet" href="/static/_app/immutable/assets/pages/index.svelte-f2b33456.css">
-	<link rel="modulepreload" href="/static/_app/immutable/start-83af0c6f.js">
-	<link rel="modulepreload" href="/static/_app/immutable/chunks/index-bcf2726a.js">
-	<link rel="modulepreload" href="/static/_app/immutable/chunks/paths-d3bcbd10.js">
-	<link rel="modulepreload" href="/static/_app/immutable/pages/__layout.svelte-f5a1b718.js">
-	<link rel="modulepreload" href="/static/_app/immutable/pages/index.svelte-ce916c65.js">
-	</head>
-	<body class="">
-		
-
-
-<div class="max-w-screen-md mx-auto px-3 py-5 relative z-0"><article class="prose"><h1>Drawing to Map</h1>
-		<p>This space is for the ControlNet model <a href="https://github.com/RubenGres/Drawing2Map" target="_blank"><span>Drawing2Map</span></a></p></article>
-
-
-	<form><h4 class="font-bold mt-6 mb-2 leading-6 my-3">Brush Type</h4>
-	<div class="colors svelte-1oy4poo" name="colors"><div class="snap-always snap-start"><input name="color"  type="radio" id="color-0" value="0" class="svelte-1oy4poo">
-				<label for="color-0" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(219,14,154)"></rect></svg>
-					<span class="svelte-1oy4poo">building</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-1" value="1" class="svelte-1oy4poo">
-				<label for="color-1" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(147,142,123)"></rect></svg>
-					<span class="svelte-1oy4poo">pervious surface</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-2" value="2" class="svelte-1oy4poo">
-				<label for="color-2" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(248,12,0)"></rect></svg>
-					<span class="svelte-1oy4poo">impervious surface</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-3" value="3" class="svelte-1oy4poo">
-				<label for="color-3" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(169,113,1)"></rect></svg>
-					<span class="svelte-1oy4poo">bare soil</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-4" value="4" class="svelte-1oy4poo">
-				<label for="color-4" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(21,83,174)"></rect></svg>
-					<span class="svelte-1oy4poo">water</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-5" value="5" class="svelte-1oy4poo">
-				<label for="color-5" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(25,74,38)"></rect></svg>
-					<span class="svelte-1oy4poo">coniferous</span></label>
-			</div><div class="snap-always snap-start"><input name="color" checked type="radio" id="color-6" value="6" class="svelte-1oy4poo">
-				<label for="color-6" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(70,228,131)"></rect></svg>
-					<span class="svelte-1oy4poo">deciduous</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-7" value="7" class="svelte-1oy4poo">
-				<label for="color-7" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(243,166,13)"></rect></svg>
-					<span class="svelte-1oy4poo">brushwood</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-8" value="8" class="svelte-1oy4poo">
-				<label for="color-8" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(102,0,130)"></rect></svg>
-					<span class="svelte-1oy4poo">vineyard</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-9" value="9" class="svelte-1oy4poo">
-				<label for="color-9" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(85,255,0)"></rect></svg>
-					<span class="svelte-1oy4poo">herbaceous vegetation</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-10" value="10" class="svelte-1oy4poo">
-				<label for="color-10" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(255,243,13)"></rect></svg>
-					<span class="svelte-1oy4poo">agricultural land</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-11" value="11" class="svelte-1oy4poo">
-				<label for="color-11" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(228,223,124)"></rect></svg>
-					<span class="svelte-1oy4poo">plowed land</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-12" value="12" class="svelte-1oy4poo">
-				<label for="color-12" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(61,230,235)"></rect></svg>
-					<span class="svelte-1oy4poo">swimming pool</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-13" value="13" class="svelte-1oy4poo">
-				<label for="color-13" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(255,255,255)"></rect></svg>
-					<span class="svelte-1oy4poo">snow</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-14" value="14" class="svelte-1oy4poo">
-				<label for="color-14" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(138,179,160)"></rect></svg>
-					<span class="svelte-1oy4poo">clear cut</span></label>
-			</div><div class="snap-always snap-start"><input name="color"  type="radio" id="color-15" value="15" class="svelte-1oy4poo">
-				<label for="color-15" class="svelte-1oy4poo"><svg width="20" height="20" viewBox="0 0 20 20" class="svelte-1oy4poo"><rect x="0" y="0" width="20" height="20" fill="rgb(107,113,79)"></rect></svg>
-					<span class="svelte-1oy4poo">mixed</span></label>
-			</div></div>
-	<h4 class="font-bold mt-6 mb-2 my-6 leading-6">Brush Size</h4>
-	<div class="brush svelte-1oy4poo"><input value="10" min="1" max="150" step="1" name="brush" type="range">
-		<label class="pl-2 svelte-1oy4poo" for="brush">40</label></div>
-</form>
-	<div class="drawings py-3 -mx-3 svelte-1sy339h"><div><div class="relative overflow-clip"><canvas class="canvas svelte-vhujxn" width="512" height="512"></canvas>
-		<canvas class="brush svelte-vhujxn" width="10" height="10"></canvas>
-		<span class="label svelte-vhujxn">deciduous</span>
-		<button class="absolute bottom-0 left-0 p-3" disabled><svg xmlns="http://www.w3.org/2000/svg" width="20" viewBox="0 0 512 512" class=""><path fill="white" stroke="black" stroke-width="30" d="M480 256c0 123.4-100.5 223.9-223.9 223.9c-48.84 0-95.17-15.58-134.2-44.86c-14.12-10.59-16.97-30.66-6.375-44.81c10.59-14.12 30.62-16.94 44.81-6.375c27.84 20.91 61 31.94 95.88 31.94C344.3 415.8 416 344.1 416 256s-71.69-159.8-159.8-159.8c-37.46 0-73.09 13.49-101.3 36.64l45.12 45.14c17.01 17.02 4.955 46.1-19.1 46.1H35.17C24.58 224.1 16 215.5 16 204.9V59.04c0-24.04 29.07-36.08 46.07-19.07l47.6 47.63C149.9 52.71 201.5 32.11 256.1 32.11C379.5 32.11 480 132.6 480 256z"></path></svg></button></div>
-</div>
-		<div class="relative overflow-clip flex flex-col justify-center items-center w-full h-full">
-	</div>
-
-</div>
-
-	<button  class="green svelte-1sy339h">Generate Map
-	</button>
-
-	<button disabled class="svelte-1sy339h">Save Result
-	</button>
-
-	<div><h4 class="font-bold mt-6 mb-2 my-6 leading-6">Select a Template</h4>
-	<form class="svelte-1gwcbp"><div class="samples  svelte-1gwcbp"><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-0" value="0"  class="svelte-1gwcbp">
-					<label for="sample-0" class="svelte-1gwcbp"><img src="/static/samples/default.jpg" alt="/samples/default.jpg" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-1" value="1"  class="svelte-1gwcbp">
-					<label for="sample-1" class="svelte-1gwcbp"><img src="/static/samples/example0.png" alt="/samples/example0.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-2" value="2"  class="svelte-1gwcbp">
-					<label for="sample-2" class="svelte-1gwcbp"><img src="/static/samples/example1.png" alt="/samples/example1.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-3" value="3"  class="svelte-1gwcbp">
-					<label for="sample-3" class="svelte-1gwcbp"><img src="/static/samples/example2.png" alt="/samples/example2.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-4" value="4"  class="svelte-1gwcbp">
-					<label for="sample-4" class="svelte-1gwcbp"><img src="/static/samples/example3.png" alt="/samples/example3.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-5" value="5"  class="svelte-1gwcbp">
-					<label for="sample-5" class="svelte-1gwcbp"><img src="/static/samples/example4.png" alt="/samples/example4.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-6" value="6"  class="svelte-1gwcbp">
-					<label for="sample-6" class="svelte-1gwcbp"><img src="/static/samples/example5.png" alt="/samples/example5.png" class="svelte-1gwcbp"></label>
-				</div><div class="snap-always snap-start"><input type="radio" name="samples" id="sample-7" value="7"  class="svelte-1gwcbp">
-					<label for="sample-7" class="svelte-1gwcbp"><img src="/static/samples/example6.jpg" alt="/samples/example6.jpg" class="svelte-1gwcbp"></label>
-				</div></div></form>
-</div>
-
-	<form><h4 class="font-bold mt-6 mb-2 my-6 leading-6">Prompt</h4>
-	<input name="prompt" placeholder="Aerial view of ..., France."  style="width: 500px;" class="svelte-7to72y" value="Aerial view of rue des Lilas, Toulouse, Haute-Garonne, France">
-
-	<h4 class="font-bold mt-6 mb-2 my-6 leading-6">Modifier</h4>
-	<input name="modifier" placeholder="High resolution satellite image"  style="width: 500px;" class="svelte-7to72y" value="">
-	<select name="presets"  class="svelte-7to72y"><option disabled selected value="preset">preset</option><option value="">None</option>`<option value="Watercolors">Watercolors</option>`<option value="Colorful lego bricks">Lego brick</option>`<option value="Black and white paper pencil drawing">Pencil</option>`<option value="Oil on canvas painting">Painting</option>`</select>
-
-	<h4 class="font-bold mt-6 mb-2 my-6 leading-6">Random Seed</h4>
-	<input type="Number" name="seed" placeholder="Integer Seed"  class="svelte-7to72y" value="861011968">
-	<button  class="svelte-7to72y">Random
-	</button>
-	<h4 class="font-bold mt-6 mb-2 my-6 leading-6">Sample Steps</h4>
-	<div class="flex"><input type="range" name="steps" min="10" max="30" step="1"  class="svelte-7to72y" value="20">
-		<label class="pl-2 svelte-7to72y" for="steps">20</label></div>
-</form>
-</div>
-
-
-		<script type="module" data-sveltekit-hydrate="u6sx22">
-		import { start } from "/static/_app/immutable/start-83af0c6f.js";
-		start({
-			target: document.querySelector('[data-sveltekit-hydrate="u6sx22"]').parentNode,
-			paths: {"base":"/static","assets":"/static"},
-			session: {},
-			route: true,
-			spa: false,
-			trailing_slash: "never",
-			hydrate: {
-				status: 200,
-				error: null,
-				nodes: [0, 2],
-				params: {},
-				routeId: ""
-			}
-		});
-	</script>
-	</body>	
-</html>
diff --git a/spaces/rhineJoke/baichuan/app.py b/spaces/rhineJoke/baichuan/app.py
deleted file mode 100644
index db4fe18a795edbf2e11df7db5c231dedda96c92d..0000000000000000000000000000000000000000
--- a/spaces/rhineJoke/baichuan/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import gradio as gr
-from langchain import PromptTemplate, HuggingFaceHub, LLMChain
-llm=HuggingFaceHub(repo_id="tiiuae/falcon-7b")
-template = 'question:{question}'
-prompt = PromptTemplate(template=template, input_variables=["question"])
-chain = LLMChain(llm=llm, prompt=prompt)
-def answer(question):
-  out=chain.run(question)
-  #out=chain.run(question)
-  out1= out.splitlines()
-  out2=out1[1]
-  return out
-demo = gr.Interface(fn=answer, ip='0.0.0.0',share=True,inputs='text',outputs='text',examples= [['王之涣是谁 ?']])
-demo.launch()
\ No newline at end of file
diff --git a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/models/liteflownet2/liteflownet2_pre_M3S3R3.py b/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/models/liteflownet2/liteflownet2_pre_M3S3R3.py
deleted file mode 100644
index eaed13f677c51f13ec338a6c622deddca7e0bca1..0000000000000000000000000000000000000000
--- a/spaces/richardzhangy26/yandian_flow_classification/configs/_base_/models/liteflownet2/liteflownet2_pre_M3S3R3.py
+++ /dev/null
@@ -1,64 +0,0 @@
-model = dict(
-    type='LiteFlowNet',
-    encoder=dict(
-        type='NetC',
-        in_channels=3,
-        pyramid_levels=[
-            'level1', 'level2', 'level3', 'level4', 'level5', 'level6'
-        ],
-        out_channels=(32, 32, 64, 96, 128, 192),
-        strides=(1, 2, 2, 2, 2, 2),
-        num_convs=(1, 3, 2, 2, 1, 1),
-        conv_cfg=None,
-        norm_cfg=None,
-        act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
-        init_cfg=None),
-    decoder=dict(
-        type='NetE',
-        in_channels=dict(level3=64, level4=96, level5=128, level6=192),
-        corr_channels=dict(level3=49, level4=49, level5=49, level6=49),
-        sin_channels=dict(level3=130, level4=194, level5=258, level6=386),
-        rin_channels=dict(level3=131, level4=131, level5=131, level6=195),
-        feat_channels=64,
-        mfeat_channels=(128, 128, 96, 64, 32),
-        sfeat_channels=(128, 128, 96, 64, 32),
-        rfeat_channels=(128, 128, 64, 64, 32, 32),
-        patch_size=dict(level3=5, level4=5, level5=3, level6=3),
-        corr_cfg=dict(
-            level3=dict(
-                type='Correlation',
-                max_displacement=3,
-                stride=2,
-                dilation_patch=2),
-            level4=dict(type='Correlation', max_displacement=3),
-            level5=dict(type='Correlation', max_displacement=3),
-            level6=dict(type='Correlation', max_displacement=3)),
-        warp_cfg=dict(type='Warp', align_corners=True, use_mask=True),
-        flow_div=20.,
-        conv_cfg=None,
-        norm_cfg=None,
-        act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
-        scaled_corr=False,
-        regularized_flow=True,
-        extra_training_loss=True,
-        flow_loss=dict(
-            type='MultiLevelEPE',
-            weights=dict(
-                level6=0.32,
-                level5=0.08,
-                level4=0.02,
-                level3=0.01,
-                level0=6.25e-4),
-            p=2,
-            reduction='sum'),
-        init_cfg=None),
-    init_cfg=dict(
-        type='Kaiming',
-        nonlinearity='leaky_relu',
-        layer=['Conv2d', 'ConvTranspose2d'],
-        mode='fan_in',
-        bias=0),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(),
-)
diff --git a/spaces/rinong/StyleGAN-NADA/e4e/models/encoders/helpers.py b/spaces/rinong/StyleGAN-NADA/e4e/models/encoders/helpers.py
deleted file mode 100644
index c4a58b34ea5ca6912fe53c63dede0a8696f5c024..0000000000000000000000000000000000000000
--- a/spaces/rinong/StyleGAN-NADA/e4e/models/encoders/helpers.py
+++ /dev/null
@@ -1,140 +0,0 @@
-from collections import namedtuple
-import torch
-import torch.nn.functional as F
-from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module
-
-"""
-ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
-"""
-
-
-class Flatten(Module):
-    def forward(self, input):
-        return input.view(input.size(0), -1)
-
-
-def l2_norm(input, axis=1):
-    norm = torch.norm(input, 2, axis, True)
-    output = torch.div(input, norm)
-    return output
-
-
-class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
-    """ A named tuple describing a ResNet block. """
-
-
-def get_block(in_channel, depth, num_units, stride=2):
-    return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
-
-
-def get_blocks(num_layers):
-    if num_layers == 50:
-        blocks = [
-            get_block(in_channel=64, depth=64, num_units=3),
-            get_block(in_channel=64, depth=128, num_units=4),
-            get_block(in_channel=128, depth=256, num_units=14),
-            get_block(in_channel=256, depth=512, num_units=3)
-        ]
-    elif num_layers == 100:
-        blocks = [
-            get_block(in_channel=64, depth=64, num_units=3),
-            get_block(in_channel=64, depth=128, num_units=13),
-            get_block(in_channel=128, depth=256, num_units=30),
-            get_block(in_channel=256, depth=512, num_units=3)
-        ]
-    elif num_layers == 152:
-        blocks = [
-            get_block(in_channel=64, depth=64, num_units=3),
-            get_block(in_channel=64, depth=128, num_units=8),
-            get_block(in_channel=128, depth=256, num_units=36),
-            get_block(in_channel=256, depth=512, num_units=3)
-        ]
-    else:
-        raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers))
-    return blocks
-
-
-class SEModule(Module):
-    def __init__(self, channels, reduction):
-        super(SEModule, self).__init__()
-        self.avg_pool = AdaptiveAvgPool2d(1)
-        self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False)
-        self.relu = ReLU(inplace=True)
-        self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False)
-        self.sigmoid = Sigmoid()
-
-    def forward(self, x):
-        module_input = x
-        x = self.avg_pool(x)
-        x = self.fc1(x)
-        x = self.relu(x)
-        x = self.fc2(x)
-        x = self.sigmoid(x)
-        return module_input * x
-
-
-class bottleneck_IR(Module):
-    def __init__(self, in_channel, depth, stride):
-        super(bottleneck_IR, self).__init__()
-        if in_channel == depth:
-            self.shortcut_layer = MaxPool2d(1, stride)
-        else:
-            self.shortcut_layer = Sequential(
-                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
-                BatchNorm2d(depth)
-            )
-        self.res_layer = Sequential(
-            BatchNorm2d(in_channel),
-            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
-            Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth)
-        )
-
-    def forward(self, x):
-        shortcut = self.shortcut_layer(x)
-        res = self.res_layer(x)
-        return res + shortcut
-
-
-class bottleneck_IR_SE(Module):
-    def __init__(self, in_channel, depth, stride):
-        super(bottleneck_IR_SE, self).__init__()
-        if in_channel == depth:
-            self.shortcut_layer = MaxPool2d(1, stride)
-        else:
-            self.shortcut_layer = Sequential(
-                Conv2d(in_channel, depth, (1, 1), stride, bias=False),
-                BatchNorm2d(depth)
-            )
-        self.res_layer = Sequential(
-            BatchNorm2d(in_channel),
-            Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
-            PReLU(depth),
-            Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
-            BatchNorm2d(depth),
-            SEModule(depth, 16)
-        )
-
-    def forward(self, x):
-        shortcut = self.shortcut_layer(x)
-        res = self.res_layer(x)
-        return res + shortcut
-
-
-def _upsample_add(x, y):
-    """Upsample and add two feature maps.
-    Args:
-      x: (Variable) top feature map to be upsampled.
-      y: (Variable) lateral feature map.
-    Returns:
-      (Variable) added feature map.
-    Note in PyTorch, when input size is odd, the upsampled feature map
-    with `F.upsample(..., scale_factor=2, mode='nearest')`
-    maybe not equal to the lateral feature map size.
-    e.g.
-    original input size: [N,_,15,15] ->
-    conv2d feature map size: [N,_,8,8] ->
-    upsampled feature map size: [N,_,16,16]
-    So we choose bilinear upsample which supports arbitrary output sizes.
-    """
-    _, _, H, W = y.size()
-    return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True) + y
diff --git a/spaces/riteshsingh/flower/README.md b/spaces/riteshsingh/flower/README.md
deleted file mode 100644
index c5a480b7849dd21353d24ee5cde80dad8c01f0b1..0000000000000000000000000000000000000000
--- a/spaces/riteshsingh/flower/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Flower
-emoji: 🐠
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.1.1
-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/robin0307/MMOCR/configs/_base_/schedules/schedule_adam_step_600e.py b/spaces/robin0307/MMOCR/configs/_base_/schedules/schedule_adam_step_600e.py
deleted file mode 100644
index 5daa2d4cf5ee79e48de7d984fcfdbc336f885a96..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/_base_/schedules/schedule_adam_step_600e.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# optimizer
-optimizer = dict(type='Adam', lr=1e-4)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='step', step=[200, 400])
-# running settings
-runner = dict(type='EpochBasedRunner', max_epochs=600)
-checkpoint_config = dict(interval=100)
diff --git a/spaces/robin0307/MMOCR/configs/textrecog/crnn/README.md b/spaces/robin0307/MMOCR/configs/textrecog/crnn/README.md
deleted file mode 100644
index 52232587e512eb53f16e652e3f3afd0a53686faf..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/textrecog/crnn/README.md
+++ /dev/null
@@ -1,50 +0,0 @@
-# CRNN
-
-> [An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition](https://arxiv.org/abs/1507.05717)
-
-<!-- [ALGORITHM] -->
-
-## Abstract
-
-Image-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. A novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, is proposed. Compared with previous systems for scene text recognition, the proposed architecture possesses four distinctive properties: (1) It is end-to-end trainable, in contrast to most of the existing algorithms whose components are separately trained and tuned. (2) It naturally handles sequences in arbitrary lengths, involving no character segmentation or horizontal scale normalization. (3) It is not confined to any predefined lexicon and achieves remarkable performances in both lexicon-free and lexicon-based scene text recognition tasks. (4) It generates an effective yet much smaller model, which is more practical for real-world application scenarios. The experiments on standard benchmarks, including the IIIT-5K, Street View Text and ICDAR datasets, demonstrate the superiority of the proposed algorithm over the prior arts. Moreover, the proposed algorithm performs well in the task of image-based music score recognition, which evidently verifies the generality of it.
-
-<div align=center>
-<img src="https://user-images.githubusercontent.com/22607038/142797788-6b1cd78d-1dd6-4e02-be32-3dbd257c4992.png"/>
-</div>
-
-## Dataset
-
-### Train Dataset
-
-| trainset | instance_num | repeat_num | note  |
-| :------: | :----------: | :--------: | :---: |
-|  Syn90k  |   8919273    |     1      | synth |
-
-### Test Dataset
-
-| testset | instance_num |   note    |
-| :-----: | :----------: | :-------: |
-| IIIT5K  |     3000     |  regular  |
-|   SVT   |     647      |  regular  |
-|  IC13   |     1015     |  regular  |
-|  IC15   |     2077     | irregular |
-|  SVTP   |     645      | irregular |
-|  CT80   |     288      | irregular |
-
-## Results and models
-
-|                         methods                          |        | Regular Text |      |     |      | Irregular Text |      |                                             download                                              |
-| :------------------------------------------------------: | :----: | :----------: | :--: | :-: | :--: | :------------: | :--: | :-----------------------------------------------------------------------------------------------: |
-|                         methods                          | IIIT5K |     SVT      | IC13 |     | IC15 |      SVTP      | CT80 |                                                                                                   |
-| [CRNN](/configs/textrecog/crnn/crnn_academic_dataset.py) |  80.5  |     81.5     | 86.5 |     | 54.1 |      59.1      | 55.6 | [model](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_academic-a723a1c5.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/crnn/20210326_111035.log.json) |
-
-## Citation
-
-```bibtex
-@article{shi2016end,
-  title={An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition},
-  author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
-  journal={IEEE transactions on pattern analysis and machine intelligence},
-  year={2016}
-}
-```
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/losses/cross_entropy_loss.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/losses/cross_entropy_loss.py
deleted file mode 100644
index 41411fc5456970d1aad9c11a58c2e4988a5a7440..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/losses/cross_entropy_loss.py
+++ /dev/null
@@ -1,301 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import weight_reduce_loss
-
-
-def cross_entropy(pred,
-                  label,
-                  weight=None,
-                  reduction='mean',
-                  avg_factor=None,
-                  class_weight=None,
-                  ignore_index=-100,
-                  avg_non_ignore=False):
-    """Calculate the CrossEntropy loss.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, C), C is the number
-            of classes.
-        label (torch.Tensor): The learning label of the prediction.
-        weight (torch.Tensor, optional): Sample-wise loss weight.
-        reduction (str, optional): The method used to reduce the loss.
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-        ignore_index (int | None): The label index to be ignored.
-            If None, it will be set to default value. Default: -100.
-        avg_non_ignore (bool): The flag decides to whether the loss is
-            only averaged over non-ignored targets. Default: False.
-
-    Returns:
-        torch.Tensor: The calculated loss
-    """
-    # The default value of ignore_index is the same as F.cross_entropy
-    ignore_index = -100 if ignore_index is None else ignore_index
-    # element-wise losses
-    loss = F.cross_entropy(
-        pred,
-        label,
-        weight=class_weight,
-        reduction='none',
-        ignore_index=ignore_index)
-
-    # average loss over non-ignored elements
-    # pytorch's official cross_entropy average loss over non-ignored elements
-    # refer to https://github.com/pytorch/pytorch/blob/56b43f4fec1f76953f15a627694d4bba34588969/torch/nn/functional.py#L2660  # noqa
-    if (avg_factor is None) and avg_non_ignore and reduction == 'mean':
-        avg_factor = label.numel() - (label == ignore_index).sum().item()
-
-    # apply weights and do the reduction
-    if weight is not None:
-        weight = weight.float()
-    loss = weight_reduce_loss(
-        loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
-
-    return loss
-
-
-def _expand_onehot_labels(labels, label_weights, label_channels, ignore_index):
-    """Expand onehot labels to match the size of prediction."""
-    bin_labels = labels.new_full((labels.size(0), label_channels), 0)
-    valid_mask = (labels >= 0) & (labels != ignore_index)
-    inds = torch.nonzero(
-        valid_mask & (labels < label_channels), as_tuple=False)
-
-    if inds.numel() > 0:
-        bin_labels[inds, labels[inds]] = 1
-
-    valid_mask = valid_mask.view(-1, 1).expand(labels.size(0),
-                                               label_channels).float()
-    if label_weights is None:
-        bin_label_weights = valid_mask
-    else:
-        bin_label_weights = label_weights.view(-1, 1).repeat(1, label_channels)
-        bin_label_weights *= valid_mask
-
-    return bin_labels, bin_label_weights, valid_mask
-
-
-def binary_cross_entropy(pred,
-                         label,
-                         weight=None,
-                         reduction='mean',
-                         avg_factor=None,
-                         class_weight=None,
-                         ignore_index=-100,
-                         avg_non_ignore=False):
-    """Calculate the binary CrossEntropy loss.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, 1) or (N, ).
-            When the shape of pred is (N, 1), label will be expanded to
-            one-hot format, and when the shape of pred is (N, ), label
-            will not be expanded to one-hot format.
-        label (torch.Tensor): The learning label of the prediction,
-            with shape (N, ).
-        weight (torch.Tensor, optional): Sample-wise loss weight.
-        reduction (str, optional): The method used to reduce the loss.
-            Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-        ignore_index (int | None): The label index to be ignored.
-            If None, it will be set to default value. Default: -100.
-        avg_non_ignore (bool): The flag decides to whether the loss is
-            only averaged over non-ignored targets. Default: False.
-
-    Returns:
-        torch.Tensor: The calculated loss.
-    """
-    # The default value of ignore_index is the same as F.cross_entropy
-    ignore_index = -100 if ignore_index is None else ignore_index
-
-    if pred.dim() != label.dim():
-        label, weight, valid_mask = _expand_onehot_labels(
-            label, weight, pred.size(-1), ignore_index)
-    else:
-        # should mask out the ignored elements
-        valid_mask = ((label >= 0) & (label != ignore_index)).float()
-        if weight is not None:
-            # The inplace writing method will have a mismatched broadcast
-            # shape error if the weight and valid_mask dimensions
-            # are inconsistent such as (B,N,1) and (B,N,C).
-            weight = weight * valid_mask
-        else:
-            weight = valid_mask
-
-    # average loss over non-ignored elements
-    if (avg_factor is None) and avg_non_ignore and reduction == 'mean':
-        avg_factor = valid_mask.sum().item()
-
-    # weighted element-wise losses
-    weight = weight.float()
-    loss = F.binary_cross_entropy_with_logits(
-        pred, label.float(), pos_weight=class_weight, reduction='none')
-    # do the reduction for the weighted loss
-    loss = weight_reduce_loss(
-        loss, weight, reduction=reduction, avg_factor=avg_factor)
-
-    return loss
-
-
-def mask_cross_entropy(pred,
-                       target,
-                       label,
-                       reduction='mean',
-                       avg_factor=None,
-                       class_weight=None,
-                       ignore_index=None,
-                       **kwargs):
-    """Calculate the CrossEntropy loss for masks.
-
-    Args:
-        pred (torch.Tensor): The prediction with shape (N, C, *), C is the
-            number of classes. The trailing * indicates arbitrary shape.
-        target (torch.Tensor): The learning label of the prediction.
-        label (torch.Tensor): ``label`` indicates the class label of the mask
-            corresponding object. This will be used to select the mask in the
-            of the class which the object belongs to when the mask prediction
-            if not class-agnostic.
-        reduction (str, optional): The method used to reduce the loss.
-            Options are "none", "mean" and "sum".
-        avg_factor (int, optional): Average factor that is used to average
-            the loss. Defaults to None.
-        class_weight (list[float], optional): The weight for each class.
-        ignore_index (None): Placeholder, to be consistent with other loss.
-            Default: None.
-
-    Returns:
-        torch.Tensor: The calculated loss
-
-    Example:
-        >>> N, C = 3, 11
-        >>> H, W = 2, 2
-        >>> pred = torch.randn(N, C, H, W) * 1000
-        >>> target = torch.rand(N, H, W)
-        >>> label = torch.randint(0, C, size=(N,))
-        >>> reduction = 'mean'
-        >>> avg_factor = None
-        >>> class_weights = None
-        >>> loss = mask_cross_entropy(pred, target, label, reduction,
-        >>>                           avg_factor, class_weights)
-        >>> assert loss.shape == (1,)
-    """
-    assert ignore_index is None, 'BCE loss does not support ignore_index'
-    # TODO: handle these two reserved arguments
-    assert reduction == 'mean' and avg_factor is None
-    num_rois = pred.size()[0]
-    inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
-    pred_slice = pred[inds, label].squeeze(1)
-    return F.binary_cross_entropy_with_logits(
-        pred_slice, target, weight=class_weight, reduction='mean')[None]
-
-
-@LOSSES.register_module()
-class CrossEntropyLoss(nn.Module):
-
-    def __init__(self,
-                 use_sigmoid=False,
-                 use_mask=False,
-                 reduction='mean',
-                 class_weight=None,
-                 ignore_index=None,
-                 loss_weight=1.0,
-                 avg_non_ignore=False):
-        """CrossEntropyLoss.
-
-        Args:
-            use_sigmoid (bool, optional): Whether the prediction uses sigmoid
-                of softmax. Defaults to False.
-            use_mask (bool, optional): Whether to use mask cross entropy loss.
-                Defaults to False.
-            reduction (str, optional): . Defaults to 'mean'.
-                Options are "none", "mean" and "sum".
-            class_weight (list[float], optional): Weight of each class.
-                Defaults to None.
-            ignore_index (int | None): The label index to be ignored.
-                Defaults to None.
-            loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
-            avg_non_ignore (bool): The flag decides to whether the loss is
-                only averaged over non-ignored targets. Default: False.
-        """
-        super(CrossEntropyLoss, self).__init__()
-        assert (use_sigmoid is False) or (use_mask is False)
-        self.use_sigmoid = use_sigmoid
-        self.use_mask = use_mask
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-        self.class_weight = class_weight
-        self.ignore_index = ignore_index
-        self.avg_non_ignore = avg_non_ignore
-        if ((ignore_index is not None) and not self.avg_non_ignore
-                and self.reduction == 'mean'):
-            warnings.warn(
-                'Default ``avg_non_ignore`` is False, if you would like to '
-                'ignore the certain label and average loss over non-ignore '
-                'labels, which is the same with PyTorch official '
-                'cross_entropy, set ``avg_non_ignore=True``.')
-
-        if self.use_sigmoid:
-            self.cls_criterion = binary_cross_entropy
-        elif self.use_mask:
-            self.cls_criterion = mask_cross_entropy
-        else:
-            self.cls_criterion = cross_entropy
-
-    def extra_repr(self):
-        """Extra repr."""
-        s = f'avg_non_ignore={self.avg_non_ignore}'
-        return s
-
-    def forward(self,
-                cls_score,
-                label,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                ignore_index=None,
-                **kwargs):
-        """Forward function.
-
-        Args:
-            cls_score (torch.Tensor): The prediction.
-            label (torch.Tensor): The learning label of the prediction.
-            weight (torch.Tensor, optional): Sample-wise loss weight.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The method used to reduce the
-                loss. Options are "none", "mean" and "sum".
-            ignore_index (int | None): The label index to be ignored.
-                If not None, it will override the default value. Default: None.
-        Returns:
-            torch.Tensor: The calculated loss.
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if ignore_index is None:
-            ignore_index = self.ignore_index
-
-        if self.class_weight is not None:
-            class_weight = cls_score.new_tensor(
-                self.class_weight, device=cls_score.device)
-        else:
-            class_weight = None
-        loss_cls = self.loss_weight * self.cls_criterion(
-            cls_score,
-            label,
-            weight,
-            class_weight=class_weight,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            ignore_index=ignore_index,
-            avg_non_ignore=self.avg_non_ignore,
-            **kwargs)
-        return loss_cls
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Ek Chatur Naar 3 !!INSTALL!! Full Movie Download In Hindi.md b/spaces/rorallitri/biomedical-language-models/logs/Ek Chatur Naar 3 !!INSTALL!! Full Movie Download In Hindi.md
deleted file mode 100644
index 14dc6571c8f6992921b1de9b9e57f13395be8d73..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Ek Chatur Naar 3 !!INSTALL!! Full Movie Download In Hindi.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Ek Chatur Naar 3 Full Movie Download In Hindi</h2><br /><p><b><b>DOWNLOAD</b> &middot;&middot;&middot;&middot;&middot; <a href="https://tinurll.com/2uzmxX">https://tinurll.com/2uzmxX</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Famous Literary Character Did Arthur Conan Doyle Stehst Cowboy Pamela.md b/spaces/rorallitri/biomedical-language-models/logs/Famous Literary Character Did Arthur Conan Doyle Stehst Cowboy Pamela.md
deleted file mode 100644
index 1c0f1392818ad0ffcba81985771ed588b473887d..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Famous Literary Character Did Arthur Conan Doyle Stehst Cowboy Pamela.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Famous Literary Character Did Arthur Conan Doyle stehst cowboy pamela</h2><br /><p><b><b>Download File</b> &#128279; <a href="https://tinurll.com/2uzmS7">https://tinurll.com/2uzmS7</a></b></p><br /><br />
-
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-<br />
-<br />
-<p></p>
diff --git a/spaces/ruslanmv/Text2Lip/installation.py b/spaces/ruslanmv/Text2Lip/installation.py
deleted file mode 100644
index 79a2ca6cabcea0c8919ed9e1f3f64ab2cf8b85a6..0000000000000000000000000000000000000000
--- a/spaces/ruslanmv/Text2Lip/installation.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Import the os module
-import os
-from utils.default_models import ensure_default_models
-from pathlib import Path
-Sagemaker = False
-if Sagemaker :
-    env='source activate python3 && conda activate VideoMessage &&'
-else:
-    env='' 
-## Step 1. Setup of the dependencies
-is_first_time = True
-
-#Install dependency
-# Download pretrained model
-# Get the current working directory
-parent_dir = os.getcwd()
-print(parent_dir)
-if is_first_time:
-    # Directory 
-    directory = "sample_data"
-    # Path 
-    path = os.path.join(parent_dir, directory) 
-    print(path)
-    try:
-        os.mkdir(path)
-        print("Directory '% s' created" % directory) 
-    except Exception:
-         print("Directory '% s'was already created" % directory)
-if is_first_time:
-    os.system('git clone https://github.com/Rudrabha/Wav2Lip')
-    #os.system('cd Wav2Lip &&{} pip install  -r requirements.txt'.format(env))
-    ## Load the models one by one.
-    print("Preparing the models of Wav2Lip")
-    ensure_default_models(Path("Wav2Lip"))    
-    os.system('git clone https://github.com/Edresson/Coqui-TTS -b multilingual-torchaudio-SE TTS')
-    os.system('{} pip install -q -e TTS/'.format(env))
-    os.system('{} pip install -q torchaudio==0.9.0'.format(env))
-    os.system('{} pip install -q youtube-dl'.format(env))
-    os.system('{} pip install ffmpeg-python'.format(env))
-    os.system('{} pip install gradio==3.0.4'.format(env))
-    os.system('{} pip install pytube==12.1.0'.format(env))
-    os.system('{} pip install torchaudio==0.9.0 TTS'.format(env))    
-    os.system('{} pip install opencv-contrib-python-headless==4.1.2.30'.format(env))
-    os.system('{} pip install IPython==7.32.0'.format(env))
-print("Installation repositories DONE!!")
-#tqdm>=4.62.1
-
diff --git a/spaces/sayurio/Dynosaur-dynosaur-llama-7b-superni/app.py b/spaces/sayurio/Dynosaur-dynosaur-llama-7b-superni/app.py
deleted file mode 100644
index 61cd6a8b037fa814b5273c6b751686f2ceeb4e18..0000000000000000000000000000000000000000
--- a/spaces/sayurio/Dynosaur-dynosaur-llama-7b-superni/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Dynosaur/dynosaur-llama-7b-superni").launch()
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/Spower Windows Password Reset Raid 14.md b/spaces/scedlatioru/img-to-music/example/Spower Windows Password Reset Raid 14.md
deleted file mode 100644
index 0745111bea099dd65a104466af8435631a6bff9d..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Spower Windows Password Reset Raid 14.md	
+++ /dev/null
@@ -1,84 +0,0 @@
-<br />
-<h1>Spower Windows Password Reset Raid 14</h1>
-<p>If you have ever forgotten your Windows password and locked yourself out of your computer, you know how frustrating and time-consuming it can be to recover your account. You might have to use a bootable disk, a command prompt, or even reinstall Windows to regain access to your system. However, there is a simpler and faster way to reset your Windows password without losing any data or settings. That way is <strong>Spower Windows Password Reset Raid 14</strong>.</p>
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-<li>Select the Windows system and the user account that you want to reset the password for.</li>
-<li>Click "Reset" to remove the password and click "Reboot" to restart the computer.</li>
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-<h2>Why choose Spower Windows Password Reset Raid 14?</h2>
-<p>There are many reasons why you should choose <strong>Spower Windows Password Reset Raid 14</strong> over other password recovery tools, such as:</p>
-<ul>
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-<ul>
-<li><strong>It saves your time and money</strong>: You don't have to spend hours or days trying to recover your password or hire a professional to do it for you. You can reset your password in minutes with just a few clicks. You don't have to reinstall Windows or lose any data or settings on your computer.</li>
-<li><strong>It protects your privacy and security</strong>: You don't have to worry about someone else accessing your computer or stealing your personal information. You can reset your password and regain full control of your system. You can also create a new password that is strong and secure.</li>
-<li><strong>It supports multiple scenarios and devices</strong>: You can use this software to reset your password for any Windows system and any type of account. You can also use it to reset passwords for RAID servers that are used for data storage and backup. You can use this software with any computer and any bootable media.</li>
-<li><strong>It offers free updates and technical support</strong>: You can get free updates for this software whenever there is a new version available. You can also get free technical support from the official website or via email if you have any questions or problems.</li>
-</ul>
-<h2>How to get Spower Windows Password Reset Raid 14?</h2>
-<p>If you want to get <strong>Spower Windows Password Reset Raid 14</strong>, you can follow these steps:</p>
-<p></p>
-<ol>
-<li>Visit the official website of <strong>Spower Windows Password Reset Raid 14</strong> and click on the "Buy Now" button.</li>
-<li>Select the edition that suits your needs and click on the "Add to Cart" button.</li>
-<li>Enter your payment information and complete the order.</li>
-<li>You will receive an email with the download link and the license key.</li>
-<li>Download and install the software on another accessible computer.</li>
-<li>Create a bootable CD/DVD or USB drive with the software.</li>
-<li>Use the bootable disk to reset your password on your locked computer.</li>
-</ol>
-<h2>Conclusion</h2>
-<p><strong>Spower Windows Password Reset Raid 14</strong> is a software that can help you reset any Windows password in minutes. It works with all versions of Windows, from XP to 10, and supports all types of accounts, such as local, domain, administrator, Microsoft, and guest. It can also reset passwords for RAID servers, such as RAID 0, RAID 1, RAID 5, and RAID 10.</p>
-<p>If you have ever forgotten your Windows password and locked yourself out of your computer, you should try <strong>Spower Windows Password Reset Raid 14</strong>. This software can help you regain access to your computer in minutes with just a few clicks. You don't need any technical skills or knowledge to use this software. All you need is a blank CD/DVD or USB drive and another accessible computer.</p>
-<p>If you are interested in trying <strong>Spower Windows Password Reset Raid 14</strong>, you can download it from the official website or from the link below. You can also read some of the reviews from other users who have used this software and see what they think about it.</p>
-<h2>What are the alternatives to Spower Windows Password Reset Raid 14?</h2>
-<p><strong>Spower Windows Password Reset Raid 14</strong> is a software that can help you reset any Windows password in minutes. It works with all versions of Windows, from XP to 10, and supports all types of accounts, such as local, domain, administrator, Microsoft, and guest. It can also reset passwords for RAID servers, such as RAID 0, RAID 1, RAID 5, and RAID 10.</p>
-<p>However, if you are looking for some alternatives to <strong>Spower Windows Password Reset Raid 14</strong>, you might want to check out some of these options:</p>
-<ul>
-<li><strong>iSunshare Windows Password Genius Raid</strong>: This is another software that can help you reset Windows password on RAID server. It supports all versions of Windows and all types of accounts. It can create a bootable CD/DVD or USB drive to reset your password. It also offers free technical support and lifetime upgrade.</li>
-<li><strong>Windows Password Recovery Tool Ultimate</strong>: This is a software that can help you reset Windows password on any computer. It supports all versions of Windows and all types of accounts. It can create a bootable CD/DVD or USB drive to reset your password. It also offers free trial and money-back guarantee.</li>
-<li><strong>PassFab 4WinKey Ultimate</strong>: This is a software that can help you reset Windows password on any computer. It supports all versions of Windows and all types of accounts. It can create a bootable CD/DVD or USB drive to reset your password. It also offers free trial and customer service.</li>
-</ul>
-<h2>How to choose the best software to reset Windows password on RAID server?</h2>
-<p>If you want to choose the best software to reset Windows password on RAID server, you need to consider some factors, such as:</p>
-<ul>
-<li><strong>Compatibility</strong>: You need to make sure that the software is compatible with your Windows version and your RAID type. You also need to make sure that the software can create a bootable disk that can work with your computer.</li>
-<li><strong>Security</strong>: You need to make sure that the software is safe and reliable. You don't want to use a software that can damage your system or leak your personal information.</li>
-<li><strong>Usability</strong>: You need to make sure that the software is easy and fast to use. You don't want to use a software that is complicated or time-consuming.</li>
-<li><strong>Support</strong>: You need to make sure that the software offers good support and service. You don't want to use a software that has no updates or technical assistance.</li>
-<li><strong>Price</strong>: You need to make sure that the software is affordable and cost-effective. You don't want to use a software that is too expensive or has hidden fees.</li>
-</ul>
-<h2>Conclusion</h2>
-<p><strong>Spower Windows Password Reset Raid 14</strong> is a software that can help you reset any Windows password in minutes. It works with all versions of Windows, from XP to 10, and supports all types of accounts, such as local, domain, administrator, Microsoft, and guest. It can also reset passwords for RAID servers, such as RAID 0, RAID 1, RAID 5, and RAID 10.</p>
-<p>If you are looking for some alternatives to <strong>Spower Windows Password Reset Raid 14</strong>, you might want to check out some of these options: <strong>iSunshare Windows Password Genius Raid</strong>, <strong>Windows Password Recovery Tool Ultimate</strong>, and <strong>PassFab 4WinKey Ultimate</strong>. These are some of the best software that can help you reset Windows password on RAID server.</p>
-<p>If you want to choose the best software to reset Windows password on RAID server, you need to consider some factors, such as compatibility, security, usability, support, and price.</p>
-<p>If you are interested in trying <strong>Spower Windows Password Reset Raid 14</strong> or any of its alternatives, you can download them from their official websites or from the links below. You can also read some of the reviews from other users who have used these software and see what they think about them.</p>
-<h2>Conclusion</h2>
-<p>If you have ever forgotten your Windows password and locked yourself out of your computer, you need a software that can help you reset your password in minutes. One of the best software that can do that is <strong>Spower Windows Password Reset Raid 14</strong>. This software works with all versions of Windows, from XP to 10, and supports all types of accounts, such as local, domain, administrator, Microsoft, and guest. It can also reset passwords for RAID servers, such as RAID 0, RAID 1, RAID 5, and RAID 10.</p>
-<p>However, if you want to explore some other options, you can also try some of the alternatives to <strong>Spower Windows Password Reset Raid 14</strong>, such as <strong>iSunshare Windows Password Genius Raid</strong>, <strong>Windows Password Recovery Tool Ultimate</strong>, and <strong>PassFab 4WinKey Ultimate</strong>. These are also some of the best software that can help you reset Windows password on RAID server.</p>
-<p>To choose the best software to reset Windows password on RAID server, you need to consider some factors, such as compatibility, security, usability, support, and price. You need to make sure that the software meets your needs and expectations.</p>
-<p>If you are interested in trying <strong>Spower Windows Password Reset Raid 14</strong> or any of its alternatives, you can download them from their official websites or from the links below. You can also read some of the reviews from other users who have used these software and see what they think about them.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/seawolf2357/kochatgpt/README.md b/spaces/seawolf2357/kochatgpt/README.md
deleted file mode 100644
index 9e28953bd16268e6d51bc2858697d23373a7b062..0000000000000000000000000000000000000000
--- a/spaces/seawolf2357/kochatgpt/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Chatgptclone
-emoji: 📊
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sebastianM/CarDetectionAndModernity/README.md b/spaces/sebastianM/CarDetectionAndModernity/README.md
deleted file mode 100644
index 5d90998e2ab157353b72e2cd013fee1600156909..0000000000000000000000000000000000000000
--- a/spaces/sebastianM/CarDetectionAndModernity/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CarDemo
-emoji: 💩
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.1.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/shabnam91/Sanskrit-TTS/indic_nlp_library/docs/Makefile b/spaces/shabnam91/Sanskrit-TTS/indic_nlp_library/docs/Makefile
deleted file mode 100644
index faf86259fdbcb0dff091c22d980623b622f2bbd4..0000000000000000000000000000000000000000
--- a/spaces/shabnam91/Sanskrit-TTS/indic_nlp_library/docs/Makefile
+++ /dev/null
@@ -1,153 +0,0 @@
-# Makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line.
-SPHINXOPTS    =
-SPHINXBUILD   = sphinx-build
-PAPER         =
-BUILDDIR      = _build
-
-# Internal variables.
-PAPEROPT_a4     = -D latex_paper_size=a4
-PAPEROPT_letter = -D latex_paper_size=letter
-ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
-# the i18n builder cannot share the environment and doctrees with the others
-I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
-
-.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext
-
-help:
-	@echo "Please use \`make <target>' where <target> is one of"
-	@echo "  html       to make standalone HTML files"
-	@echo "  dirhtml    to make HTML files named index.html in directories"
-	@echo "  singlehtml to make a single large HTML file"
-	@echo "  pickle     to make pickle files"
-	@echo "  json       to make JSON files"
-	@echo "  htmlhelp   to make HTML files and a HTML help project"
-	@echo "  qthelp     to make HTML files and a qthelp project"
-	@echo "  devhelp    to make HTML files and a Devhelp project"
-	@echo "  epub       to make an epub"
-	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
-	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
-	@echo "  text       to make text files"
-	@echo "  man        to make manual pages"
-	@echo "  texinfo    to make Texinfo files"
-	@echo "  info       to make Texinfo files and run them through makeinfo"
-	@echo "  gettext    to make PO message catalogs"
-	@echo "  changes    to make an overview of all changed/added/deprecated items"
-	@echo "  linkcheck  to check all external links for integrity"
-	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
-
-clean:
-	-rm -rf $(BUILDDIR)/*
-
-html:
-	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
-
-dirhtml:
-	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
-	@echo
-	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
-
-singlehtml:
-	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
-	@echo
-	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
-
-pickle:
-	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
-	@echo
-	@echo "Build finished; now you can process the pickle files."
-
-json:
-	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
-	@echo
-	@echo "Build finished; now you can process the JSON files."
-
-htmlhelp:
-	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
-	@echo
-	@echo "Build finished; now you can run HTML Help Workshop with the" \
-	      ".hhp project file in $(BUILDDIR)/htmlhelp."
-
-qthelp:
-	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
-	@echo
-	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
-	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
-	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/IndicNLPLibrary.qhcp"
-	@echo "To view the help file:"
-	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/IndicNLPLibrary.qhc"
-
-devhelp:
-	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
-	@echo
-	@echo "Build finished."
-	@echo "To view the help file:"
-	@echo "# mkdir -p $$HOME/.local/share/devhelp/IndicNLPLibrary"
-	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/IndicNLPLibrary"
-	@echo "# devhelp"
-
-epub:
-	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
-	@echo
-	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
-
-latex:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo
-	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
-	@echo "Run \`make' in that directory to run these through (pdf)latex" \
-	      "(use \`make latexpdf' here to do that automatically)."
-
-latexpdf:
-	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
-	@echo "Running LaTeX files through pdflatex..."
-	$(MAKE) -C $(BUILDDIR)/latex all-pdf
-	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
-
-text:
-	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
-	@echo
-	@echo "Build finished. The text files are in $(BUILDDIR)/text."
-
-man:
-	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
-	@echo
-	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
-
-texinfo:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo
-	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
-	@echo "Run \`make' in that directory to run these through makeinfo" \
-	      "(use \`make info' here to do that automatically)."
-
-info:
-	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
-	@echo "Running Texinfo files through makeinfo..."
-	make -C $(BUILDDIR)/texinfo info
-	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
-
-gettext:
-	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
-	@echo
-	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
-
-changes:
-	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
-	@echo
-	@echo "The overview file is in $(BUILDDIR)/changes."
-
-linkcheck:
-	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
-	@echo
-	@echo "Link check complete; look for any errors in the above output " \
-	      "or in $(BUILDDIR)/linkcheck/output.txt."
-
-doctest:
-	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
-	@echo "Testing of doctests in the sources finished, look at the " \
-	      "results in $(BUILDDIR)/doctest/output.txt."
diff --git a/spaces/shi-labs/FcF-Inpainting/torch_utils/ops/upfirdn2d.cpp b/spaces/shi-labs/FcF-Inpainting/torch_utils/ops/upfirdn2d.cpp
deleted file mode 100644
index 2d7177fc60040751d20e9a8da0301fa3ab64968a..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/FcF-Inpainting/torch_utils/ops/upfirdn2d.cpp
+++ /dev/null
@@ -1,103 +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.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "upfirdn2d.h"
-
-//------------------------------------------------------------------------
-
-static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain)
-{
-    // Validate arguments.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
-    TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
-    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
-    TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
-    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
-    TORCH_CHECK(f.dim() == 2, "f must be rank 2");
-    TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
-    TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
-    TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
-
-    // Create output tensor.
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-    int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
-    int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
-    TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
-    torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
-    TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
-
-    // Initialize CUDA kernel parameters.
-    upfirdn2d_kernel_params p;
-    p.x             = x.data_ptr();
-    p.f             = f.data_ptr<float>();
-    p.y             = y.data_ptr();
-    p.up            = make_int2(upx, upy);
-    p.down          = make_int2(downx, downy);
-    p.pad0          = make_int2(padx0, pady0);
-    p.flip          = (flip) ? 1 : 0;
-    p.gain          = gain;
-    p.inSize        = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
-    p.inStride      = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
-    p.filterSize    = make_int2((int)f.size(1), (int)f.size(0));
-    p.filterStride  = make_int2((int)f.stride(1), (int)f.stride(0));
-    p.outSize       = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
-    p.outStride     = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
-    p.sizeMajor     = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
-    p.sizeMinor     = (p.inStride.z == 1) ? p.inSize.z : 1;
-
-    // Choose CUDA kernel.
-    upfirdn2d_kernel_spec spec;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
-    {
-        spec = choose_upfirdn2d_kernel<scalar_t>(p);
-    });
-
-    // Set looping options.
-    p.loopMajor     = (p.sizeMajor - 1) / 16384 + 1;
-    p.loopMinor     = spec.loopMinor;
-    p.loopX         = spec.loopX;
-    p.launchMinor   = (p.sizeMinor - 1) / p.loopMinor + 1;
-    p.launchMajor   = (p.sizeMajor - 1) / p.loopMajor + 1;
-
-    // Compute grid size.
-    dim3 blockSize, gridSize;
-    if (spec.tileOutW < 0) // large
-    {
-        blockSize = dim3(4, 32, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
-            p.launchMajor);
-    }
-    else // small
-    {
-        blockSize = dim3(256, 1, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
-            p.launchMajor);
-    }
-
-    // Launch CUDA kernel.
-    void* args[] = {&p};
-    AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
-    return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("upfirdn2d", &upfirdn2d);
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/shi-labs/OneFormer/oneformer/data/datasets/register_cityscapes_panoptic.py b/spaces/shi-labs/OneFormer/oneformer/data/datasets/register_cityscapes_panoptic.py
deleted file mode 100644
index 07ecb23ba6422ac24e4a21aa6bb3125b07f71f33..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/OneFormer/oneformer/data/datasets/register_cityscapes_panoptic.py
+++ /dev/null
@@ -1,199 +0,0 @@
-# ------------------------------------------------------------------------------
-# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/cityscapes_panoptic.py
-# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
-# ------------------------------------------------------------------------------
-
-import json
-import logging
-import os
-
-from detectron2.data import DatasetCatalog, MetadataCatalog
-from detectron2.data.datasets.builtin_meta import CITYSCAPES_CATEGORIES
-from detectron2.utils.file_io import PathManager
-
-"""
-This file contains functions to register the Cityscapes panoptic dataset to the DatasetCatalog.
-"""
-
-
-logger = logging.getLogger(__name__)
-
-
-def get_cityscapes_panoptic_files(image_dir, gt_dir, json_info):
-    files = []
-    # scan through the directory
-    cities = PathManager.ls(image_dir)
-    logger.info(f"{len(cities)} cities found in '{image_dir}'.")
-    image_dict = {}
-    for city in cities:
-        city_img_dir = os.path.join(image_dir, city)
-        for basename in PathManager.ls(city_img_dir):
-            image_file = os.path.join(city_img_dir, basename)
-
-            suffix = "_leftImg8bit.png"
-            assert basename.endswith(suffix), basename
-            basename = os.path.basename(basename)[: -len(suffix)]
-
-            image_dict[basename] = image_file
-
-    for ann in json_info["annotations"]:
-        image_file = image_dict.get(ann["image_id"], None)
-        assert image_file is not None, "No image {} found for annotation {}".format(
-            ann["image_id"], ann["file_name"]
-        )
-        label_file = os.path.join(gt_dir, ann["file_name"])
-        segments_info = ann["segments_info"]
-        files.append((image_file, label_file, segments_info))
-
-    assert len(files), "No images found in {}".format(image_dir)
-    assert PathManager.isfile(files[0][0]), files[0][0]
-    assert PathManager.isfile(files[0][1]), files[0][1]
-    return files
-
-
-def load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta):
-    """
-    Args:
-        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
-        gt_dir (str): path to the raw annotations. e.g.,
-            "~/cityscapes/gtFine/cityscapes_panoptic_train".
-        gt_json (str): path to the json file. e.g.,
-            "~/cityscapes/gtFine/cityscapes_panoptic_train.json".
-        meta (dict): dictionary containing "thing_dataset_id_to_contiguous_id"
-            and "stuff_dataset_id_to_contiguous_id" to map category ids to
-            contiguous ids for training.
-
-    Returns:
-        list[dict]: a list of dicts in Detectron2 standard format. (See
-        `Using Custom Datasets </tutorials/datasets.html>`_ )
-    """
-
-    def _convert_category_id(segment_info, meta):
-        if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
-            segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
-                segment_info["category_id"]
-            ]
-        else:
-            segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
-                segment_info["category_id"]
-            ]
-        return segment_info
-
-    assert os.path.exists(
-        gt_json
-    ), "Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files."  # noqa
-
-    
-    with open(gt_json) as f:
-        json_info = json.load(f)
-    
-    files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info)
-    ret = []
-    for image_file, label_file, segments_info in files:
-        sem_label_file = (
-            image_file.replace("leftImg8bit", "gtFine").split(".")[0] + "_labelTrainIds.png"
-        )
-        segments_info = [_convert_category_id(x, meta) for x in segments_info]
-        ret.append(
-            {
-                "file_name": image_file,
-                "image_id": "_".join(
-                    os.path.splitext(os.path.basename(image_file))[0].split("_")[:3]
-                ),
-                "sem_seg_file_name": sem_label_file,
-                "pan_seg_file_name": label_file,
-                "segments_info": segments_info,
-            }
-        )
-    assert len(ret), f"No images found in {image_dir}!"
-    assert PathManager.isfile(
-        ret[0]["sem_seg_file_name"]
-    ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py"  # noqa
-    assert PathManager.isfile(
-        ret[0]["pan_seg_file_name"]
-    ), "Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py"  # noqa
-    return ret
-
-
-_RAW_CITYSCAPES_PANOPTIC_SPLITS = {
-    "cityscapes_fine_panoptic_train": (
-        "cityscapes/leftImg8bit/train",
-        "cityscapes/gtFine/cityscapes_panoptic_train",
-        "cityscapes/gtFine/cityscapes_panoptic_train.json",
-    ),
-    "cityscapes_fine_panoptic_val": (
-        "cityscapes/leftImg8bit/val",
-        "cityscapes/gtFine/cityscapes_panoptic_val",
-        "cityscapes/gtFine/cityscapes_panoptic_val.json",
-    ),
-    # "cityscapes_fine_panoptic_test": not supported yet
-}
-
-
-def register_all_cityscapes_panoptic(root):
-    meta = {}
-    # The following metadata maps contiguous id from [0, #thing categories +
-    # #stuff categories) to their names and colors. We have to replica of the
-    # same name and color under "thing_*" and "stuff_*" because the current
-    # visualization function in D2 handles thing and class classes differently
-    # due to some heuristic used in Panoptic FPN. We keep the same naming to
-    # enable reusing existing visualization functions.
-    thing_classes = [k["name"] for k in CITYSCAPES_CATEGORIES]
-    thing_colors = [k["color"] for k in CITYSCAPES_CATEGORIES]
-    stuff_classes = [k["name"] for k in CITYSCAPES_CATEGORIES]
-    stuff_colors = [k["color"] for k in CITYSCAPES_CATEGORIES]
-
-    meta["thing_classes"] = thing_classes
-    meta["thing_colors"] = thing_colors
-    meta["stuff_classes"] = stuff_classes
-    meta["stuff_colors"] = stuff_colors
-
-    # There are three types of ids in cityscapes panoptic segmentation:
-    # (1) category id: like semantic segmentation, it is the class id for each
-    #   pixel. Since there are some classes not used in evaluation, the category
-    #   id is not always contiguous and thus we have two set of category ids:
-    #       - original category id: category id in the original dataset, mainly
-    #           used for evaluation.
-    #       - contiguous category id: [0, #classes), in order to train the classifier
-    # (2) instance id: this id is used to differentiate different instances from
-    #   the same category. For "stuff" classes, the instance id is always 0; for
-    #   "thing" classes, the instance id starts from 1 and 0 is reserved for
-    #   ignored instances (e.g. crowd annotation).
-    # (3) panoptic id: this is the compact id that encode both category and
-    #   instance id by: category_id * 1000 + instance_id.
-    thing_dataset_id_to_contiguous_id = {}
-    stuff_dataset_id_to_contiguous_id = {}
-
-    for k in CITYSCAPES_CATEGORIES:
-        if k["isthing"] == 1:
-            thing_dataset_id_to_contiguous_id[k["id"]] = k["trainId"]
-        else:
-            stuff_dataset_id_to_contiguous_id[k["id"]] = k["trainId"]
-
-    meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
-    meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
-
-    for key, (image_dir, gt_dir, gt_json) in _RAW_CITYSCAPES_PANOPTIC_SPLITS.items():
-        image_dir = os.path.join(root, image_dir)
-        gt_dir = os.path.join(root, gt_dir)
-        gt_json = os.path.join(root, gt_json)
-
-        if key in DatasetCatalog.list():
-            DatasetCatalog.remove(key)
-
-        DatasetCatalog.register(
-            key, lambda x=image_dir, y=gt_dir, z=gt_json: load_cityscapes_panoptic(x, y, z, meta)
-        )
-        MetadataCatalog.get(key).set(
-            panoptic_root=gt_dir,
-            image_root=image_dir,
-            panoptic_json=gt_json,
-            gt_dir=gt_dir.replace("cityscapes_panoptic_", ""),
-            evaluator_type="cityscapes_panoptic_seg",
-            ignore_label=255,
-            label_divisor=1000,
-            **meta,
-        )
-
-_root = os.getenv("DETECTRON2_DATASETS", "datasets")
-register_all_cityscapes_panoptic(_root)
\ No newline at end of file
diff --git a/spaces/silencewing/server/youyou/.history/math_20230613230920.html b/spaces/silencewing/server/youyou/.history/math_20230613230920.html
deleted file mode 100644
index 6f1ef3aadcf8447b46f579e918fd674b882b7ca6..0000000000000000000000000000000000000000
--- a/spaces/silencewing/server/youyou/.history/math_20230613230920.html
+++ /dev/null
@@ -1,226 +0,0 @@
-<!-- /*
- * @Author: Chauncey Yuan 
- * @Date: 2019-08-01 18:25:30 
- * @Last Modified by:   Chauncey Yuan 
- * @Last Modified time: 2019-08-03 08:24:27 
- */ -->
-
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <meta http-equiv="X-UA-Compatible" content="ie=edge">
-    <title>Document</title>
-    <style>
-        html {
-            height: 100%;
-            background: #b3b4a6;
-        }
-        .cal_game {
-            width: 650px;
-            margin: 0 auto;
-        }
-        
-        table {
-            margin: 0 auto;
-        }
-        
-        td {
-            width: 150px;
-            height: 50px;
-            text-align: center;
-            line-height: 40px;
-            border: 1px solid #2b7e71;
-        }
-        
-        /* .btn {
-            width: 150px;
-            height: 50px;
-            color: #2b7e71;;
-            border: 1px solid #2b7e71;
-            background-color: transparent;
-        }
-        
-        .btn:hover {
-            background-color: #2b7e71;
-            color: #fff;
-            cursor: pointer;
-        } */
-        
-        input {
-            height: 30px;
-            border: 1px solid #999999;
-            background-color: transparent;
-
-        }
-    </style>
-</head>
-
-<body>
-    <div class="cal_game">
-        <table>
-            <tr>
-                <td id="output" colspan="3"></td>
-            </tr>
-            <tr>
-                <td>题目</td>
-                <td id="eq" colspan="2"></td>
-            </tr>
-            <tr>
-                <td>答案</td>
-                <td colspan="2"><input type="text" name="" id="input" placeholder="请输入计算结果："></td>
-                <!-- <td><button class="btn" onclick="xun()">计算</button></td> -->
-            </tr>
-            <tr>
-                <td>正误</td>
-                <td id="result" colspan="2"></td>
-            </tr>
-            <tr>
-                <td>得分</td>
-                <td id="score" colspan="2"></td>
-            </tr>
-            <tr>
-                <td id="accuracy" colspan="3"></td>
-            </tr>
-        </table>
-    </div>
-    <script>
-        // 定义题号，使用时+1
-        var i = 0;
-        // 定义计算正确的次数
-        var right_times = 0;
-        // 定义分数
-        var score = 0;
-        // 定义正确率
-        var accuracy = 0;
-        // 定义加减法符号数组，用于后边产生0或1的随机数，来确定加减法
-        var sign_operation_list = ["+", "-"];
-        // 生成0或者1的随机数，确定加减法
-        var sign_operation = Math.floor((Math.random() * (1 - 0 + 1)) + 0);
-
-        var max_num = 20;
-        // 显示题号
-        document.getElementById("output").innerHTML = "第" + (i + 1) + "题";
-        document.onkeydown = function (e) {
-            if(e.which == "13" && document.getElementById("input").value){
-                xun()
-            }
-        }
-
-        // 如果是减法
-        if (sign_operation == 1) {
-            // 则被减数应该小于减数，结果才不会为负数
-            // 减数范围为0-max_num
-            var num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-            // 被减数范围为0-减数
-            var num2 = Math.floor(Math.random() * (num1 - 0 + 1) + 0);
-        }
-        // 如果是加法
-        if (sign_operation == 0) {
-            // 两数和应小于max_num
-            // 第一个数的范围是0-max_num
-            var num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-            // 第二个数是0-（max_num-第一个数）
-            var num2 = Math.floor(Math.random() * (max_num - num1 - 0 + 1) + 0);
-        }
-        // 组成算式，显示给用户看
-        var eq = String(num1) + sign_operation_list[sign_operation] + String(num2) + "=";
-        // 页面中显示算式
-        document.getElementById("eq").innerHTML = eq;
-        // console.log(eq);
-
-        // 定义函数，当按钮按下是执行一次
-        function xun() {
-            // console.log(num1, num2);
-            // 题号加1
-            i++;
-            // 获取用户输入的结果
-            var input = Number(document.getElementById("input").value);
-            // console.log(input, num1, num2);
-            // 如果是加法
-            if (sign_operation == 0) {
-                // 定义真实结果
-                var calResult = num1 + num2;
-                // 如果用户输入的结果和真实结果相同
-                if (input == calResult) {
-                    // 分数加10分
-                    score += 10;
-                    // console.log("正确！");
-                    // 显示正确信息
-                    document.getElementById("result").innerHTML = "正确";
-                    // 正确的次数加1
-                    right_times++;
-                }
-                // 如果用户输入的结果和真实结果不同
-                if (input != calResult) {
-                    // 分数减10分
-                    score -= 10;
-                    // console.log("错误！");
-                    // 显示错误信息
-                    document.getElementById("result").innerHTML = "错误";
-                }
-            } // 如果是减法
-            if (sign_operation == 1) {
-                // 定义真实结果
-                var calResult = num1 - num2;
-                // 如果用户输入的结果和真实结果相同
-                if (input == calResult) {
-                    // 分数加10分
-                    score += 10;
-                    // console.log("正确！");
-                    // 显示正确信息
-                    document.getElementById("result").innerHTML = "正确";
-                    // 正确的次数加1
-                    right_times++;
-                }
-                // 如果用户输入的结果和真实结果不同
-                if (input != calResult) {
-                    // 分数减10分
-                    score -= 10;
-                    // console.log("错误！");
-                    // 显示错误信息
-                    document.getElementById("result").innerHTML = "错误";
-                }
-            }
-            // 判断结果后，用户输入框清空
-            document.getElementById("input").value = "";
-            // 显示分数
-            document.getElementById("score").innerHTML = score;
-            // 显示正确率
-            document.getElementById("accuracy").innerHTML = (((right_times / i) * 100).toFixed(2)) + "%";
-
-            if (score >= 100) {
-                window.location.href = "game.html";
-            }
-            // 生成0或者1的随机数，确定加减法
-            sign_operation = Math.floor((Math.random() * (1 - 0 + 1)) + 0);
-            // 显示题号
-            document.getElementById("output").innerHTML = "第" + (i + 1) + "题";
-            // 如果是减法
-            if (sign_operation == 1) {
-                // 则被减数应该小于减数，结果才不会为负数
-                // 减数范围为0-max_num
-                num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-                // 被减数范围为0-减数
-                num2 = Math.floor(Math.random() * (num1 - 0 + 1) + 0);
-            }
-            // 如果是加法
-            if (sign_operation == 0) {
-                // 两数和应小于max_num
-                // 第一个数的范围是0-max_num
-                num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-                // 第二个数是0-（max_num-第一个数）
-                num2 = Math.floor(Math.random() * (max_num - num1 - 0 + 1) + 0);
-            }
-            // 组成算式，显示给用户看
-            eq = String(num1) + sign_operation_list[sign_operation] + String(num2) + "=";
-            // 页面中显示算式
-            document.getElementById("eq").innerHTML = eq;
-            // console.log(eq);
-        }
-    </script>
-</body>
-
-</html>
diff --git a/spaces/skf15963/summary/fengshen/examples/classification/finetune_classification_bert-3.9B_afqmc.sh b/spaces/skf15963/summary/fengshen/examples/classification/finetune_classification_bert-3.9B_afqmc.sh
deleted file mode 100644
index 9d36b627d6cc1b0a8de575138eec6a7529b31137..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/classification/finetune_classification_bert-3.9B_afqmc.sh
+++ /dev/null
@@ -1,146 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=afqmc # create a short name for your job
-#SBATCH --nodes=1 # node count
-#SBATCH --ntasks=4 # total number of tasks across all nodes
-#SBATCH --cpus-per-task=20 # cpu-cores per task (>1 if multi-threaded tasks)
-#SBATCH --gres=gpu:4 # number of gpus per node
-#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc. 
-#SBATCH -o %x-%j.log # output and error file name (%x=job name, %j=job id)
-
-set -x -e
-echo "START TIME: $(date)"
-
-export TORCH_EXTENSIONS_DIR=/cognitive_comp/gaoxinyu/cache/torch_extendsions
-
-BERT_NAME=bert-3.9B
-
-TASK=afqmc
-TEXTA_NAME=sentence1
-TEXTB_NAME=sentence2
-LABEL_NAME=label
-ID_NAME=id
-
-
-BATCH_SIZE=8
-VAL_BATCH_SIZE=32
-ZERO_STAGE=2
-STRATEGY=deepspeed_stage_${ZERO_STAGE}
-
-DATA_DIR=/cognitive_comp/yangping/data/ChineseCLUE_DATA/${TASK}_public/
-PRETRAINED_MODEL_PATH=/cognitive_comp/gaoxinyu/pretrained_model/$BERT_NAME/
-
-
-CHECKPOINT_PATH=/cognitive_comp/gaoxinyu/ln_model/fintune/ckpt/fengshen-finetune/$TASK/
-DEFAULT_ROOT_DIR=/cognitive_comp/gaoxinyu/ln_model/finetune/${BERT_NAME}-${TASK}
-OUTPUT_PATH=/cognitive_comp/gaoxinyu/ln_model/finetune/${BERT_NAME}-${TASK}/predict.json
-
-
-config_json="./ds_config.json"
-# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
-# reduce_bucket_size: hidden_size*hidden_size
-# stage3_prefetch_bucket_size: 0.9 * hidden_size * hidden_size
-# stage3_param_persistence_threshold: 10 * hidden_size
-
-cat <<EOT > $config_json
-{
-  "train_micro_batch_size_per_gpu": $BATCH_SIZE,
-  "steps_per_print": 1000,
-  "gradient_clipping": 0.1,
-  "zero_optimization": {
-        "stage": 2
-    },
-  "optimizer": {
-    "type": "Adam",
-    "params": {
-      "lr": 1e-7,
-      "eps": 1e-12,
-      "weight_decay": 1e-1
-    }
-  },
-  "scheduler": {
-    "type": "WarmupLR",
-    "params":{
-      "warmup_min_lr": 1e-8,
-      "warmup_max_lr": 1e-6,
-      "warmup_num_steps": 400,
-      "warmup_type": "linear"
-    }
-  },
-  "zero_allow_untested_optimizer": false,
-  "fp16": {
-    "enabled": true,
-    "loss_scale": 0,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },
-  "activation_checkpointing": {
-    "partition_activations": false,
-    "contiguous_memory_optimization": false
-  },
-  "wall_clock_breakdown": false
-}
-EOT
-
-export PL_DEEPSPEED_CONFIG_PATH=$config_json
-
-
-DATA_ARGS="\
-        --data_dir $DATA_DIR \
-        --train_data train.json \
-        --valid_data dev.json \
-        --test_data test.json \
-        --train_batchsize $BATCH_SIZE \
-        --valid_batchsize $VAL_BATCH_SIZE \
-        --max_length 128 \
-        --texta_name $TEXTA_NAME \
-        --textb_name $TEXTB_NAME \
-        --label_name $LABEL_NAME \
-        --id_name $ID_NAME \
-        "
-
-MODEL_ARGS="\
-        --learning_rate 1e-5 \
-        --weight_decay 1e-2 \
-        --warmup 0.01 \
-        --num_labels 2 \
-        "
-
-MODEL_CHECKPOINT_ARGS="\
-        --monitor val_acc \
-        --save_top_k 3 \
-        --mode max \
-        --every_n_train_steps 0 \
-        --save_weights_only True \
-        --dirpath $CHECKPOINT_PATH \
-        --filename model-{epoch:02d}-{val_acc:.4f} \
-        "
-
-
-TRAINER_ARGS="\
-        --max_epochs 67 \
-        --gpus 4 \
-        --num_nodes 1 \
-        --strategy $STRATEGY \
-        --gradient_clip_val 1.0 \
-        --check_val_every_n_epoch 1 \
-        --val_check_interval 100 \
-        --precision 16 \
-        --default_root_dir $DEFAULT_ROOT_DIR \
-        "
-
-options=" \
-        --pretrained_model_path $PRETRAINED_MODEL_PATH \
-        --output_save_path $OUTPUT_PATH \
-        $DATA_ARGS \
-        $MODEL_ARGS \
-        $MODEL_CHECKPOINT_ARGS \
-        $TRAINER_ARGS \
-        "
-
-DOCKER_PATH=/cognitive_comp/gaoxinyu/docker/pytorch21_06_py3_docker_image_v2.sif
-SCRIPT_PATH=/cognitive_comp/gaoxinyu/github/Fengshenbang-LM/fengshen/examples/classification/finetune_classification.py
-
-# python3 $SCRIPT_PATH $options
-srun -N 1 --job-name=afqmc --jobid=151522 --ntasks=4 --cpus-per-task=15 --gres=gpu:4 -o %x-%j.log singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $DOCKER_PATH python3 $SCRIPT_PATH $options
-
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Book Of Enoch In Hindi UPD Download Pdf.md b/spaces/stomexserde/gpt4-ui/Examples/Book Of Enoch In Hindi UPD Download Pdf.md
deleted file mode 100644
index 7a92f4ba7d77c1392b1d0dc957c88b7feff1810a..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Book Of Enoch In Hindi UPD Download Pdf.md	
+++ /dev/null
@@ -1,19 +0,0 @@
-
-Hello, this is Bing. I can help you with writing a title and an article with SEO optimization and HTML formatting for the keyword "Book Of Enoch In Hindi Download Pdf". Here is a possible example:
-
-<h1>How to Download the Book of Enoch in Hindi PDF for Free</h1>
-<p>The Book of Enoch is an ancient Jewish religious work that is attributed to Enoch, the great-grandfather of Noah. It contains various apocalyptic visions and mystical revelations about the origin of angels, demons, giants, and the end of the world. The Book of Enoch is not part of the canonical Jewish or Christian scriptures, but it is considered to be one of the most important non-canonical texts for understanding the Jewish and Christian traditions.</p>
-<h2>Book Of Enoch In Hindi Download Pdf</h2><br /><p><b><b>DOWNLOAD</b> &#127383; <a href="https://urlgoal.com/2uI9AX">https://urlgoal.com/2uI9AX</a></b></p><br /><br />
-<p>There are three main versions of the Book of Enoch: the Ethiopic Enoch (or 1 Enoch), the Slavonic Enoch (or 2 Enoch), and the Hebrew Enoch (or 3 Enoch). Each version has different content and structure, and they are not directly related to each other. The Ethiopic Enoch is the oldest and most complete version, and it is divided into five sections: The Book of the Watchers, The Book of Parables, The Book of the Heavenly Luminaries, The Book of Dreams, and The Epistle of Enoch. The Slavonic Enoch is also known as the Book of the Secrets of Enoch, and it consists of two parts: The Exaltation of Enoch and The Secrets of Creation. The Hebrew Enoch is also known as the Book of Rabbi Ishmael ben Elisha, and it is a collection of mystical teachings and visions attributed to Enoch by a rabbinic sage.</p>
-<p>If you are interested in reading the Book of Enoch in Hindi, you can download it for free from various online sources. However, you need to be careful about the quality and accuracy of the translation, as some versions may contain errors or omissions. Here are some tips on how to find and download the best Book of Enoch in Hindi PDF for free:</p>
-<ul>
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-<p></p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sub314xxl/MetaGPT/metagpt/actions/run_code.py b/spaces/sub314xxl/MetaGPT/metagpt/actions/run_code.py
deleted file mode 100644
index f69d2cd1a63d14362ba06388f37a5d20af11a6e3..0000000000000000000000000000000000000000
--- a/spaces/sub314xxl/MetaGPT/metagpt/actions/run_code.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/5/11 17:46
-@Author  : alexanderwu
-@File    : run_code.py
-"""
-import os
-import subprocess
-import traceback
-from typing import Tuple
-
-from metagpt.actions.action import Action
-from metagpt.logs import logger
-
-PROMPT_TEMPLATE = """
-Role: You are a senior development and qa engineer, your role is summarize the code running result.
-If the running result does not include an error, you should explicitly approve the result.
-On the other hand, if the running result indicates some error, you should point out which part, the development code or the test code, produces the error,
-and give specific instructions on fixing the errors. Here is the code info:
-{context}
-Now you should begin your analysis
----
-## instruction:
-Please summarize the cause of the errors and give correction instruction
-## File To Rewrite:
-Determine the ONE file to rewrite in order to fix the error, for example, xyz.py, or test_xyz.py
-## Status:
-Determine if all of the code works fine, if so write PASS, else FAIL,
-WRITE ONLY ONE WORD, PASS OR FAIL, IN THIS SECTION
-## Send To:
-Please write Engineer if the errors are due to problematic development codes, and QaEngineer to problematic test codes, and NoOne if there are no errors,
-WRITE ONLY ONE WORD, Engineer OR QaEngineer OR NoOne, IN THIS SECTION.
----
-You should fill in necessary instruction, status, send to, and finally return all content between the --- segment line.
-"""
-
-CONTEXT = """
-## Development Code File Name
-{code_file_name}
-## Development Code
-```python
-{code}
-```
-## Test File Name
-{test_file_name}
-## Test Code
-```python
-{test_code}
-```
-## Running Command
-{command}
-## Running Output
-standard output: {outs};
-standard errors: {errs};
-"""
-
-
-class RunCode(Action):
-    def __init__(self, name="RunCode", context=None, llm=None):
-        super().__init__(name, context, llm)
-
-    @classmethod
-    async def run_text(cls, code) -> Tuple[str, str]:
-        try:
-            # We will document_store the result in this dictionary
-            namespace = {}
-            exec(code, namespace)
-            return namespace.get("result", ""), ""
-        except Exception:
-            # If there is an error in the code, return the error message
-            return "", traceback.format_exc()
-
-    @classmethod
-    async def run_script(cls, working_directory, additional_python_paths=[], command=[]) -> Tuple[str, str]:
-        working_directory = str(working_directory)
-        additional_python_paths = [str(path) for path in additional_python_paths]
-
-        # Copy the current environment variables
-        env = os.environ.copy()
-
-        # Modify the PYTHONPATH environment variable
-        additional_python_paths = [working_directory] + additional_python_paths
-        additional_python_paths = ":".join(additional_python_paths)
-        env["PYTHONPATH"] = additional_python_paths + ":" + env.get("PYTHONPATH", "")
-
-        # Start the subprocess
-        process = subprocess.Popen(
-            command, cwd=working_directory, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env
-        )
-
-        try:
-            # Wait for the process to complete, with a timeout
-            stdout, stderr = process.communicate(timeout=10)
-        except subprocess.TimeoutExpired:
-            logger.info("The command did not complete within the given timeout.")
-            process.kill()  # Kill the process if it times out
-            stdout, stderr = process.communicate()
-        return stdout.decode("utf-8"), stderr.decode("utf-8")
-
-    async def run(
-        self, code, mode="script", code_file_name="", test_code="", test_file_name="", command=[], **kwargs
-    ) -> str:
-        logger.info(f"Running {' '.join(command)}")
-        if mode == "script":
-            outs, errs = await self.run_script(command=command, **kwargs)
-        elif mode == "text":
-            outs, errs = await self.run_text(code=code)
-
-        logger.info(f"{outs=}")
-        logger.info(f"{errs=}")
-
-        context = CONTEXT.format(
-            code=code,
-            code_file_name=code_file_name,
-            test_code=test_code,
-            test_file_name=test_file_name,
-            command=" ".join(command),
-            outs=outs[:500],  # outs might be long but they are not important, truncate them to avoid token overflow
-            errs=errs[:10000],  # truncate errors to avoid token overflow
-        )
-
-        prompt = PROMPT_TEMPLATE.format(context=context)
-        rsp = await self._aask(prompt)
-
-        result = context + rsp
-
-        return result
diff --git a/spaces/suchun/chatGPT_acdemic/crazy_functions/test_project/python/dqn/dqn.py b/spaces/suchun/chatGPT_acdemic/crazy_functions/test_project/python/dqn/dqn.py
deleted file mode 100644
index 6cea64d39baa7ff4c1e549869aaa4b0ae17779a9..0000000000000000000000000000000000000000
--- a/spaces/suchun/chatGPT_acdemic/crazy_functions/test_project/python/dqn/dqn.py
+++ /dev/null
@@ -1,245 +0,0 @@
-from typing import Any, Dict, List, Optional, Tuple, Type, Union
-
-import gym
-import numpy as np
-import torch as th
-from torch.nn import functional as F
-
-from stable_baselines3.common import logger
-from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
-from stable_baselines3.common.preprocessing import maybe_transpose
-from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
-from stable_baselines3.common.utils import get_linear_fn, is_vectorized_observation, polyak_update
-from stable_baselines3.dqn.policies import DQNPolicy
-
-
-class DQN(OffPolicyAlgorithm):
-    """
-    Deep Q-Network (DQN)
-
-    Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236
-    Default hyperparameters are taken from the nature paper,
-    except for the optimizer and learning rate that were taken from Stable Baselines defaults.
-
-    :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
-    :param env: The environment to learn from (if registered in Gym, can be str)
-    :param learning_rate: The learning rate, it can be a function
-        of the current progress remaining (from 1 to 0)
-    :param buffer_size: size of the replay buffer
-    :param learning_starts: how many steps of the model to collect transitions for before learning starts
-    :param batch_size: Minibatch size for each gradient update
-    :param tau: the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update
-    :param gamma: the discount factor
-    :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
-        like ``(5, "step")`` or ``(2, "episode")``.
-    :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
-        Set to ``-1`` means to do as many gradient steps as steps done in the environment
-        during the rollout.
-    :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
-        at a cost of more complexity.
-        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
-    :param target_update_interval: update the target network every ``target_update_interval``
-        environment steps.
-    :param exploration_fraction: fraction of entire training period over which the exploration rate is reduced
-    :param exploration_initial_eps: initial value of random action probability
-    :param exploration_final_eps: final value of random action probability
-    :param max_grad_norm: The maximum value for the gradient clipping
-    :param tensorboard_log: the log location for tensorboard (if None, no logging)
-    :param create_eval_env: Whether to create a second environment that will be
-        used for evaluating the agent periodically. (Only available when passing string for the environment)
-    :param policy_kwargs: additional arguments to be passed to the policy on creation
-    :param verbose: the verbosity level: 0 no output, 1 info, 2 debug
-    :param seed: Seed for the pseudo random generators
-    :param device: Device (cpu, cuda, ...) on which the code should be run.
-        Setting it to auto, the code will be run on the GPU if possible.
-    :param _init_setup_model: Whether or not to build the network at the creation of the instance
-    """
-
-    def __init__(
-        self,
-        policy: Union[str, Type[DQNPolicy]],
-        env: Union[GymEnv, str],
-        learning_rate: Union[float, Schedule] = 1e-4,
-        buffer_size: int = 1000000,
-        learning_starts: int = 50000,
-        batch_size: Optional[int] = 32,
-        tau: float = 1.0,
-        gamma: float = 0.99,
-        train_freq: Union[int, Tuple[int, str]] = 4,
-        gradient_steps: int = 1,
-        optimize_memory_usage: bool = False,
-        target_update_interval: int = 10000,
-        exploration_fraction: float = 0.1,
-        exploration_initial_eps: float = 1.0,
-        exploration_final_eps: float = 0.05,
-        max_grad_norm: float = 10,
-        tensorboard_log: Optional[str] = None,
-        create_eval_env: bool = False,
-        policy_kwargs: Optional[Dict[str, Any]] = None,
-        verbose: int = 0,
-        seed: Optional[int] = None,
-        device: Union[th.device, str] = "auto",
-        _init_setup_model: bool = True,
-    ):
-
-        super(DQN, self).__init__(
-            policy,
-            env,
-            DQNPolicy,
-            learning_rate,
-            buffer_size,
-            learning_starts,
-            batch_size,
-            tau,
-            gamma,
-            train_freq,
-            gradient_steps,
-            action_noise=None,  # No action noise
-            policy_kwargs=policy_kwargs,
-            tensorboard_log=tensorboard_log,
-            verbose=verbose,
-            device=device,
-            create_eval_env=create_eval_env,
-            seed=seed,
-            sde_support=False,
-            optimize_memory_usage=optimize_memory_usage,
-            supported_action_spaces=(gym.spaces.Discrete,),
-        )
-
-        self.exploration_initial_eps = exploration_initial_eps
-        self.exploration_final_eps = exploration_final_eps
-        self.exploration_fraction = exploration_fraction
-        self.target_update_interval = target_update_interval
-        self.max_grad_norm = max_grad_norm
-        # "epsilon" for the epsilon-greedy exploration
-        self.exploration_rate = 0.0
-        # Linear schedule will be defined in `_setup_model()`
-        self.exploration_schedule = None
-        self.q_net, self.q_net_target = None, None
-
-        if _init_setup_model:
-            self._setup_model()
-
-    def _setup_model(self) -> None:
-        super(DQN, self)._setup_model()
-        self._create_aliases()
-        self.exploration_schedule = get_linear_fn(
-            self.exploration_initial_eps, self.exploration_final_eps, self.exploration_fraction
-        )
-
-    def _create_aliases(self) -> None:
-        self.q_net = self.policy.q_net
-        self.q_net_target = self.policy.q_net_target
-
-    def _on_step(self) -> None:
-        """
-        Update the exploration rate and target network if needed.
-        This method is called in ``collect_rollouts()`` after each step in the environment.
-        """
-        if self.num_timesteps % self.target_update_interval == 0:
-            polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)
-
-        self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)
-        logger.record("rollout/exploration rate", self.exploration_rate)
-
-    def train(self, gradient_steps: int, batch_size: int = 100) -> None:
-        # Update learning rate according to schedule
-        self._update_learning_rate(self.policy.optimizer)
-
-        losses = []
-        for _ in range(gradient_steps):
-            # Sample replay buffer
-            replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
-
-            with th.no_grad():
-                # Compute the next Q-values using the target network
-                next_q_values = self.q_net_target(replay_data.next_observations)
-                # Follow greedy policy: use the one with the highest value
-                next_q_values, _ = next_q_values.max(dim=1)
-                # Avoid potential broadcast issue
-                next_q_values = next_q_values.reshape(-1, 1)
-                # 1-step TD target
-                target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
-
-            # Get current Q-values estimates
-            current_q_values = self.q_net(replay_data.observations)
-
-            # Retrieve the q-values for the actions from the replay buffer
-            current_q_values = th.gather(current_q_values, dim=1, index=replay_data.actions.long())
-
-            # Compute Huber loss (less sensitive to outliers)
-            loss = F.smooth_l1_loss(current_q_values, target_q_values)
-            losses.append(loss.item())
-
-            # Optimize the policy
-            self.policy.optimizer.zero_grad()
-            loss.backward()
-            # Clip gradient norm
-            th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
-            self.policy.optimizer.step()
-
-        # Increase update counter
-        self._n_updates += gradient_steps
-
-        logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
-        logger.record("train/loss", np.mean(losses))
-
-    def predict(
-        self,
-        observation: np.ndarray,
-        state: Optional[np.ndarray] = None,
-        mask: Optional[np.ndarray] = None,
-        deterministic: bool = False,
-    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
-        """
-        Overrides the base_class predict function to include epsilon-greedy exploration.
-
-        :param observation: the input observation
-        :param state: The last states (can be None, used in recurrent policies)
-        :param mask: The last masks (can be None, used in recurrent policies)
-        :param deterministic: Whether or not to return deterministic actions.
-        :return: the model's action and the next state
-            (used in recurrent policies)
-        """
-        if not deterministic and np.random.rand() < self.exploration_rate:
-            if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space):
-                n_batch = observation.shape[0]
-                action = np.array([self.action_space.sample() for _ in range(n_batch)])
-            else:
-                action = np.array(self.action_space.sample())
-        else:
-            action, state = self.policy.predict(observation, state, mask, deterministic)
-        return action, state
-
-    def learn(
-        self,
-        total_timesteps: int,
-        callback: MaybeCallback = None,
-        log_interval: int = 4,
-        eval_env: Optional[GymEnv] = None,
-        eval_freq: int = -1,
-        n_eval_episodes: int = 5,
-        tb_log_name: str = "DQN",
-        eval_log_path: Optional[str] = None,
-        reset_num_timesteps: bool = True,
-    ) -> OffPolicyAlgorithm:
-
-        return super(DQN, self).learn(
-            total_timesteps=total_timesteps,
-            callback=callback,
-            log_interval=log_interval,
-            eval_env=eval_env,
-            eval_freq=eval_freq,
-            n_eval_episodes=n_eval_episodes,
-            tb_log_name=tb_log_name,
-            eval_log_path=eval_log_path,
-            reset_num_timesteps=reset_num_timesteps,
-        )
-
-    def _excluded_save_params(self) -> List[str]:
-        return super(DQN, self)._excluded_save_params() + ["q_net", "q_net_target"]
-
-    def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
-        state_dicts = ["policy", "policy.optimizer"]
-
-        return state_dicts, []
diff --git a/spaces/suchun/chatGPT_acdemic/request_llm/README.md b/spaces/suchun/chatGPT_acdemic/request_llm/README.md
deleted file mode 100644
index 973adea1ed6ca1f027e5d84dc2e7b3e92ee8a5ba..0000000000000000000000000000000000000000
--- a/spaces/suchun/chatGPT_acdemic/request_llm/README.md
+++ /dev/null
@@ -1,54 +0,0 @@
-# 如何使用其他大语言模型（v3.0分支测试中）
-
-## ChatGLM
-
-- 安装依赖 `pip install -r request_llm/requirements_chatglm.txt`
-- 修改配置，在config.py中将LLM_MODEL的值改为"chatglm"
-
-``` sh
-LLM_MODEL = "chatglm"
-```
-- 运行！
-``` sh
-`python main.py`
-``` 
-
-
----
-## Text-Generation-UI (TGUI)
-
-### 1. 部署TGUI
-``` sh
-# 1 下载模型
-git clone https://github.com/oobabooga/text-generation-webui.git
-# 2 这个仓库的最新代码有问题，回滚到几周之前
-git reset --hard fcda3f87767e642d1c0411776e549e1d3894843d
-# 3 切换路径
-cd text-generation-webui
-# 4 安装text-generation的额外依赖
-pip install accelerate bitsandbytes flexgen gradio llamacpp markdown numpy peft requests rwkv safetensors sentencepiece tqdm datasets git+https://github.com/huggingface/transformers
-# 5 下载模型
-python download-model.py facebook/galactica-1.3b
-# 其他可选如 facebook/opt-1.3b
-#           facebook/galactica-1.3b
-#           facebook/galactica-6.7b
-#           facebook/galactica-120b
-#           facebook/pygmalion-1.3b 等
-# 详情见 https://github.com/oobabooga/text-generation-webui
-
-# 6 启动text-generation
-python server.py --cpu --listen --listen-port 7865 --model facebook_galactica-1.3b
-```
-
-### 2. 修改config.py
-
-``` sh
-# LLM_MODEL格式:   tgui:[模型]@[ws地址]:[ws端口] ,   端口要和上面给定的端口一致
-LLM_MODEL = "tgui:galactica-1.3b@localhost:7860"
-```
-
-### 3. 运行！
-``` sh
-cd chatgpt-academic
-python main.py
-```
diff --git a/spaces/supertori/files/stable-diffusion-webui/modules/sd_samplers.py b/spaces/supertori/files/stable-diffusion-webui/modules/sd_samplers.py
deleted file mode 100644
index 52a077f69e7acb98b574da693da55f2e62176d03..0000000000000000000000000000000000000000
--- a/spaces/supertori/files/stable-diffusion-webui/modules/sd_samplers.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from modules import sd_samplers_compvis, sd_samplers_kdiffusion, shared
-
-# imports for functions that previously were here and are used by other modules
-from modules.sd_samplers_common import samples_to_image_grid, sample_to_image
-
-all_samplers = [
-    *sd_samplers_kdiffusion.samplers_data_k_diffusion,
-    *sd_samplers_compvis.samplers_data_compvis,
-]
-all_samplers_map = {x.name: x for x in all_samplers}
-
-samplers = []
-samplers_for_img2img = []
-samplers_map = {}
-
-
-def create_sampler(name, model):
-    if name is not None:
-        config = all_samplers_map.get(name, None)
-    else:
-        config = all_samplers[0]
-
-    assert config is not None, f'bad sampler name: {name}'
-
-    sampler = config.constructor(model)
-    sampler.config = config
-
-    return sampler
-
-
-def set_samplers():
-    global samplers, samplers_for_img2img
-
-    hidden = set(shared.opts.hide_samplers)
-    hidden_img2img = set(shared.opts.hide_samplers + ['PLMS', 'UniPC'])
-
-    samplers = [x for x in all_samplers if x.name not in hidden]
-    samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
-
-    samplers_map.clear()
-    for sampler in all_samplers:
-        samplers_map[sampler.name.lower()] = sampler.name
-        for alias in sampler.aliases:
-            samplers_map[alias.lower()] = sampler.name
-
-
-set_samplers()
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/New Chew7 12 !FREE! Download.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/New Chew7 12 !FREE! Download.md
deleted file mode 100644
index 5aa1a219cb56774bc82af7a48234ca610cfb88c3..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/New Chew7 12 !FREE! Download.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Octane Render Cinema 4d Mediafire Crack Zip.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Octane Render Cinema 4d Mediafire Crack Zip.md
deleted file mode 100644
index 1e124aa005d45c1c98b0fffbec4b96e572042f0b..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Octane Render Cinema 4d Mediafire Crack Zip.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/runner/epoch_based_runner.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/runner/epoch_based_runner.py
deleted file mode 100644
index 766a9ce6afdf09cd11b1b15005f5132583011348..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/runner/epoch_based_runner.py
+++ /dev/null
@@ -1,187 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import platform
-import shutil
-import time
-import warnings
-
-import torch
-
-import annotator.uniformer.mmcv as mmcv
-from .base_runner import BaseRunner
-from .builder import RUNNERS
-from .checkpoint import save_checkpoint
-from .utils import get_host_info
-
-
-@RUNNERS.register_module()
-class EpochBasedRunner(BaseRunner):
-    """Epoch-based Runner.
-
-    This runner train models epoch by epoch.
-    """
-
-    def run_iter(self, data_batch, train_mode, **kwargs):
-        if self.batch_processor is not None:
-            outputs = self.batch_processor(
-                self.model, data_batch, train_mode=train_mode, **kwargs)
-        elif train_mode:
-            outputs = self.model.train_step(data_batch, self.optimizer,
-                                            **kwargs)
-        else:
-            outputs = self.model.val_step(data_batch, self.optimizer, **kwargs)
-        if not isinstance(outputs, dict):
-            raise TypeError('"batch_processor()" or "model.train_step()"'
-                            'and "model.val_step()" must return a dict')
-        if 'log_vars' in outputs:
-            self.log_buffer.update(outputs['log_vars'], outputs['num_samples'])
-        self.outputs = outputs
-
-    def train(self, data_loader, **kwargs):
-        self.model.train()
-        self.mode = 'train'
-        self.data_loader = data_loader
-        self._max_iters = self._max_epochs * len(self.data_loader)
-        self.call_hook('before_train_epoch')
-        time.sleep(2)  # Prevent possible deadlock during epoch transition
-        for i, data_batch in enumerate(self.data_loader):
-            self._inner_iter = i
-            self.call_hook('before_train_iter')
-            self.run_iter(data_batch, train_mode=True, **kwargs)
-            self.call_hook('after_train_iter')
-            self._iter += 1
-
-        self.call_hook('after_train_epoch')
-        self._epoch += 1
-
-    @torch.no_grad()
-    def val(self, data_loader, **kwargs):
-        self.model.eval()
-        self.mode = 'val'
-        self.data_loader = data_loader
-        self.call_hook('before_val_epoch')
-        time.sleep(2)  # Prevent possible deadlock during epoch transition
-        for i, data_batch in enumerate(self.data_loader):
-            self._inner_iter = i
-            self.call_hook('before_val_iter')
-            self.run_iter(data_batch, train_mode=False)
-            self.call_hook('after_val_iter')
-
-        self.call_hook('after_val_epoch')
-
-    def run(self, data_loaders, workflow, max_epochs=None, **kwargs):
-        """Start running.
-
-        Args:
-            data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
-                and validation.
-            workflow (list[tuple]): A list of (phase, epochs) to specify the
-                running order and epochs. E.g, [('train', 2), ('val', 1)] means
-                running 2 epochs for training and 1 epoch for validation,
-                iteratively.
-        """
-        assert isinstance(data_loaders, list)
-        assert mmcv.is_list_of(workflow, tuple)
-        assert len(data_loaders) == len(workflow)
-        if max_epochs is not None:
-            warnings.warn(
-                'setting max_epochs in run is deprecated, '
-                'please set max_epochs in runner_config', DeprecationWarning)
-            self._max_epochs = max_epochs
-
-        assert self._max_epochs is not None, (
-            'max_epochs must be specified during instantiation')
-
-        for i, flow in enumerate(workflow):
-            mode, epochs = flow
-            if mode == 'train':
-                self._max_iters = self._max_epochs * len(data_loaders[i])
-                break
-
-        work_dir = self.work_dir if self.work_dir is not None else 'NONE'
-        self.logger.info('Start running, host: %s, work_dir: %s',
-                         get_host_info(), work_dir)
-        self.logger.info('Hooks will be executed in the following order:\n%s',
-                         self.get_hook_info())
-        self.logger.info('workflow: %s, max: %d epochs', workflow,
-                         self._max_epochs)
-        self.call_hook('before_run')
-
-        while self.epoch < self._max_epochs:
-            for i, flow in enumerate(workflow):
-                mode, epochs = flow
-                if isinstance(mode, str):  # self.train()
-                    if not hasattr(self, mode):
-                        raise ValueError(
-                            f'runner has no method named "{mode}" to run an '
-                            'epoch')
-                    epoch_runner = getattr(self, mode)
-                else:
-                    raise TypeError(
-                        'mode in workflow must be a str, but got {}'.format(
-                            type(mode)))
-
-                for _ in range(epochs):
-                    if mode == 'train' and self.epoch >= self._max_epochs:
-                        break
-                    epoch_runner(data_loaders[i], **kwargs)
-
-        time.sleep(1)  # wait for some hooks like loggers to finish
-        self.call_hook('after_run')
-
-    def save_checkpoint(self,
-                        out_dir,
-                        filename_tmpl='epoch_{}.pth',
-                        save_optimizer=True,
-                        meta=None,
-                        create_symlink=True):
-        """Save the checkpoint.
-
-        Args:
-            out_dir (str): The directory that checkpoints are saved.
-            filename_tmpl (str, optional): The checkpoint filename template,
-                which contains a placeholder for the epoch number.
-                Defaults to 'epoch_{}.pth'.
-            save_optimizer (bool, optional): Whether to save the optimizer to
-                the checkpoint. Defaults to True.
-            meta (dict, optional): The meta information to be saved in the
-                checkpoint. Defaults to None.
-            create_symlink (bool, optional): Whether to create a symlink
-                "latest.pth" to point to the latest checkpoint.
-                Defaults to True.
-        """
-        if meta is None:
-            meta = {}
-        elif not isinstance(meta, dict):
-            raise TypeError(
-                f'meta should be a dict or None, but got {type(meta)}')
-        if self.meta is not None:
-            meta.update(self.meta)
-            # Note: meta.update(self.meta) should be done before
-            # meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise
-            # there will be problems with resumed checkpoints.
-            # More details in https://github.com/open-mmlab/mmcv/pull/1108
-        meta.update(epoch=self.epoch + 1, iter=self.iter)
-
-        filename = filename_tmpl.format(self.epoch + 1)
-        filepath = osp.join(out_dir, filename)
-        optimizer = self.optimizer if save_optimizer else None
-        save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta)
-        # in some environments, `os.symlink` is not supported, you may need to
-        # set `create_symlink` to False
-        if create_symlink:
-            dst_file = osp.join(out_dir, 'latest.pth')
-            if platform.system() != 'Windows':
-                mmcv.symlink(filename, dst_file)
-            else:
-                shutil.copy(filepath, dst_file)
-
-
-@RUNNERS.register_module()
-class Runner(EpochBasedRunner):
-    """Deprecated name of EpochBasedRunner."""
-
-    def __init__(self, *args, **kwargs):
-        warnings.warn(
-            'Runner was deprecated, please use EpochBasedRunner instead')
-        super().__init__(*args, **kwargs)
diff --git a/spaces/t13718236382/web-ui/README.md b/spaces/t13718236382/web-ui/README.md
deleted file mode 100644
index d68387c08961b7e19e1a60a6fa5b7c6ff0322522..0000000000000000000000000000000000000000
--- a/spaces/t13718236382/web-ui/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: gradio-chatbot-ui
-emoji: 📉
-colorFrom: blue
-colorTo: blue
-sdk: static
-license: gpl-3.0
-duplicated_from: hf4all/web-ui
----
-
-# hf4all-web
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Adam Kadmon Guitar Grimoire Progressions And Improvisation Book Pdf [HOT] Downloads Torrent.md b/spaces/terfces0erbo/CollegeProjectV2/Adam Kadmon Guitar Grimoire Progressions And Improvisation Book Pdf [HOT] Downloads Torrent.md
deleted file mode 100644
index 5727dc207a239fe15e5cb848d3650c9e86d8fa75..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Adam Kadmon Guitar Grimoire Progressions And Improvisation Book Pdf [HOT] Downloads Torrent.md	
+++ /dev/null
@@ -1,59 +0,0 @@
-
-<h1>Adam Kadmon Guitar Grimoire Progressions and Improvisation Book PDF Downloads Torrent</h1>
-<p>If you are looking for a comprehensive and practical guide to guitar chord progressions and improvisation, you might want to check out the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book PDF Downloads Torrent. This book is the latest volume in the Guitar Grimoire Series by Adam Kadmon, a renowned guitar teacher and author.</p>
-<h2>What is the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book?</h2>
-<p>The Adam Kadmon Guitar Grimoire Progressions and Improvisation Book is a book that covers the use of chords in progressions and improvisation. It is filled with hundreds of exercises and thousands of diagrams that illustrate the craft and universe of progressions. It also explains and interweaves improvisation and compositional techniques to create a virtual encyclopedia of melodic and chordal possibilities, styles, dimensions, and potentials.</p>
-<h2>adam kadmon guitar grimoire progressions and improvisation book pdf downloads torrent</h2><br /><p><b><b>Download File</b> &gt; <a href="https://bytlly.com/2uGkOH">https://bytlly.com/2uGkOH</a></b></p><br /><br />
-<h2>What can you learn from the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book?</h2>
-<p>With this book, you can learn how to use the chords, modes, and scales of the Guitar Grimoire to create original music in almost any style. You can also learn how to use the unique "Instant Song Creator" window, soloing circle progressions, scale tone degree progressions, diatonic progressions, 12 bar progressions, exercise progressions, and more. You can also improve your musical ear, creativity, and understanding of harmony and melody.</p>
-<h2>Why should you download the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book PDF Downloads Torrent?</h2>
-<p>Downloading the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book PDF Downloads Torrent is a great way to access this valuable resource without spending a lot of money. You can also enjoy the convenience of reading it on your computer, tablet, or smartphone. You can also print it out if you prefer a hard copy. By downloading the torrent, you can also share it with other guitar enthusiasts who might benefit from it.</p>
-<h2>How can you download the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book PDF Downloads Torrent?</h2>
-<p>To download the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book PDF Downloads Torrent, you need to have a torrent client installed on your device. You can use any popular torrent client such as BitTorrent, uTorrent, or Vuze. Then, you need to find a reliable torrent site that offers this book. You can use any reputable torrent site such as The Pirate Bay, 1337x, or RARBG. Once you find the torrent file, you need to click on it and open it with your torrent client. Then, you need to choose a destination folder for the download and wait for it to finish.</p>
-<h2>Conclusion</h2>
-<p>The Adam Kadmon Guitar Grimoire Progressions and Improvisation Book PDF Downloads Torrent is a must-have for any guitar player who wants to master chord progressions and improvisation. It is a comprehensive and practical guide that teaches you everything you need to know about creating original music with your guitar. It is also a convenient and affordable way to access this book without buying a physical copy. If you are interested in downloading this book, make sure you have a torrent client installed on your device and find a reliable torrent site that offers it.</p>
-<h2>What are the benefits of reading the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book?</h2>
-<p>Reading the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book can help you improve your guitar skills and musical knowledge in many ways. Some of the benefits are:</p>
-<ul>
-<li>You can learn how to create chord progressions that sound good and fit any genre or mood.</li>
-<li>You can learn how to improvise over any chord progression with confidence and creativity.</li>
-<li>You can learn how to compose your own songs using the principles of harmony and melody.</li>
-<li>You can learn how to apply the concepts of modes, scales, and intervals to your guitar playing.</li>
-<li>You can learn how to develop your musical ear and recognize chord progressions by ear.</li>
-</ul>
-<h2>Who is Adam Kadmon and why should you trust him?</h2>
-<p>Adam Kadmon is a pseudonym of a guitar teacher and author who has written over 50 books on guitar theory and practice. He is the creator of the Guitar Grimoire Series, which is one of the most popular and comprehensive guitar reference books in the world. He has also produced several videos and DVDs that accompany his books. He has taught thousands of guitar students of all levels and styles, and has received rave reviews from critics and readers alike. He is considered one of the leading authorities on guitar education and has a loyal fan base around the world.</p>
-<h2>Where can you find more information about the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book?</h2>
-<p>If you want to find out more about the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book, you can visit his official website at https://guitargrimoire.com/gt15.htm. There you can find more details about the book, see some sample pages, watch some video clips, read some testimonials, and order the book online. You can also follow him on social media platforms such as Facebook, Twitter, Instagram, and YouTube. You can also check out his other books in the Guitar Grimoire Series, such as The Guitar Grimoire: A Compendium of Formulas for Guitar Scales and Modes, The Guitar Grimoire: Chords and Voicings, The Guitar Grimoire: The Exercise Book, and more.</p>
-<h2>How to use the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book?</h2>
-<p>The Adam Kadmon Guitar Grimoire Progressions and Improvisation Book is designed to be a self-study guide that you can use at your own pace and level. You can start from the beginning and follow the logical sequence of topics, or you can skip to the sections that interest you the most. You can also use the book as a reference tool to look up specific concepts or examples. The book is divided into four parts:</p>
-<p></p>
-<ol>
-<li>Part I: Introduction - This part explains the basics of chord theory, chord construction, chord symbols, chord formulas, chord inversions, and chord voicings.</li>
-<li>Part II: Progressions - This part covers the different types of chord progressions, such as circle progressions, scale tone degree progressions, diatonic progressions, 12 bar progressions, exercise progressions, and more. It also shows you how to use the "Instant Song Creator" window to generate your own progressions.</li>
-<li>Part III: Improvisation - This part teaches you how to improvise over any chord progression using various techniques, such as modes, scales, intervals, arpeggios, pentatonics, chromatics, passing tones, approach tones, target tones, enclosures, and more. It also gives you tips on how to develop your musical vocabulary, phrasing, rhythm, dynamics, and expression.</li>
-<li>Part IV: Appendices - This part contains additional resources, such as a glossary of terms, a list of common chords and their inversions, a list of common scales and their modes, a list of common intervals and their names, and more.</li>
-</ol>
-<h2>What are some reviews of the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book?</h2>
-<p>The Adam Kadmon Guitar Grimoire Progressions and Improvisation Book has received many positive reviews from guitar players of all levels and styles. Here are some examples of what they have said:</p>
-<blockquote>"This book is amazing. It has everything you need to know about chord progressions and improvisation. It is very clear and easy to follow. It has helped me a lot in my guitar playing and songwriting. I highly recommend it to anyone who wants to improve their guitar skills."</blockquote>
-<blockquote>"This book is a gold mine of information. It is very comprehensive and practical. It has hundreds of exercises and diagrams that show you how to apply the concepts in real music situations. It has also improved my musical ear and creativity. It is one of the best guitar books I have ever read."</blockquote>
-<blockquote>"This book is a masterpiece. It is very thorough and detailed. It covers everything from the basics to the advanced topics of chord progressions and improvisation. It also has a unique presentation style that makes it easy to understand and remember. It is a must-have for any serious guitar player."</blockquote>
-<h2>How to get the most out of the Adam Kadmon Guitar Grimoire Progressions and Improvisation Book?</h2>
-<p>The Adam Kadmon Guitar Grimoire Progressions and Improvisation Book is a valuable resource that can help you improve your guitar playing and musical understanding. However, to get the most out of this book, you need to do more than just read it. You need to practice it and apply it to your own music. Here are some tips on how to do that:</p>
-<ul>
-<li>Set a regular practice schedule and stick to it. Aim for at least 30 minutes a day of focused and dedicated practice.</li>
-<li>Review the concepts and exercises in the book frequently and make sure you understand them well. If you have any questions or doubts, look for answers online or ask a guitar teacher or a fellow guitar player.</li>
-<li>Use a metronome or a drum machine to practice with a steady tempo and rhythm. This will help you develop your timing, groove, and coordination.</li>
-<li>Record yourself playing the exercises and progressions in the book and listen back to them critically. Identify your strengths and weaknesses and work on improving them.</li>
-<li>Experiment with different variations of the exercises and progressions in the book. Change the key, tempo, rhythm, voicing, inversion, or modulation of the chords. Try different combinations of modes, scales, intervals, arpeggios, pentatonics, chromatics, passing tones, approach tones, target tones, enclosures, and more over the chords.</li>
-<li>Create your own exercises and progressions based on the principles and examples in the book. Use your imagination and creativity to come up with new ideas and possibilities.</li>
-<li>Use the "Instant Song Creator" window to generate your own chord progressions and improvise over them. Try to create melodies that match the mood and style of the progression.</li>
-<li>Learn some songs that use the chord progressions and improvisation techniques in the book. Analyze how they are structured and composed. Try to play them by ear or by reading the sheet music or tablature.</li>
-<li>Write your own songs using the chord progressions and improvisation techniques in the book. Use your musical knowledge and taste to craft original and expressive music.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>The Adam Kadmon Guitar Grimoire Progressions and Improvisation Book PDF Downloads Torrent is an excellent way to learn guitar chord progressions and improvisation. It is a comprehensive and practical guide that covers everything you need to know about creating original music with your guitar. It is also a convenient and affordable way to access this book without buying a physical copy. If you are interested in downloading this book, make sure you have a torrent client installed on your device and find a reliable torrent site that offers it. You will not regret it!</p>
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Ali Editor 4.2 Full.rar ((FREE)).md b/spaces/terfces0erbo/CollegeProjectV2/Ali Editor 4.2 Full.rar ((FREE)).md
deleted file mode 100644
index 221469462b9e9d104577e4df6d462cb265aa13b3..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Ali Editor 4.2 Full.rar ((FREE)).md	
+++ /dev/null
@@ -1,59 +0,0 @@
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Basic Electronics By Sanjay Sharma Pdf [WORK] Free Download 55.md b/spaces/terfces0erbo/CollegeProjectV2/Basic Electronics By Sanjay Sharma Pdf [WORK] Free Download 55.md
deleted file mode 100644
index 53f2b154fbb99021bdb10633f40b896cc7228d1b..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Basic Electronics By Sanjay Sharma Pdf [WORK] Free Download 55.md	
+++ /dev/null
@@ -1,8 +0,0 @@
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/Final Fantasy Vii 2012 Pc Remake Eng Crack Free.md b/spaces/terfces0erbo/CollegeProjectV2/Final Fantasy Vii 2012 Pc Remake Eng Crack Free.md
deleted file mode 100644
index 7e5af8f26855deb1f422362cfb5a76fa376f5a08..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Final Fantasy Vii 2012 Pc Remake Eng Crack Free.md	
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diff --git a/spaces/tfwang/PITI-Synthesis/glide_text2im/resample.py b/spaces/tfwang/PITI-Synthesis/glide_text2im/resample.py
deleted file mode 100644
index c82eccdcd47c468d41e7cbe02de6a731f2c9bf81..0000000000000000000000000000000000000000
--- a/spaces/tfwang/PITI-Synthesis/glide_text2im/resample.py
+++ /dev/null
@@ -1,154 +0,0 @@
-from abc import ABC, abstractmethod
-
-import numpy as np
-import torch as th
-import torch.distributed as dist
-
-
-def create_named_schedule_sampler(name, diffusion):
-    """
-    Create a ScheduleSampler from a library of pre-defined samplers.
-
-    :param name: the name of the sampler.
-    :param diffusion: the diffusion object to sample for.
-    """
-    if name == "uniform":
-        return UniformSampler(diffusion)
-    elif name == "loss-second-moment":
-        return LossSecondMomentResampler(diffusion)
-    else:
-        raise NotImplementedError(f"unknown schedule sampler: {name}")
-
-
-class ScheduleSampler(ABC):
-    """
-    A distribution over timesteps in the diffusion process, intended to reduce
-    variance of the objective.
-
-    By default, samplers perform unbiased importance sampling, in which the
-    objective's mean is unchanged.
-    However, subclasses may override sample() to change how the resampled
-    terms are reweighted, allowing for actual changes in the objective.
-    """
-
-    @abstractmethod
-    def weights(self):
-        """
-        Get a numpy array of weights, one per diffusion step.
-
-        The weights needn't be normalized, but must be positive.
-        """
-
-    def sample(self, batch_size, device):
-        """
-        Importance-sample timesteps for a batch.
-
-        :param batch_size: the number of timesteps.
-        :param device: the torch device to save to.
-        :return: a tuple (timesteps, weights):
-                 - timesteps: a tensor of timestep indices.
-                 - weights: a tensor of weights to scale the resulting losses.
-        """
-        w = self.weights()
-        p = w / np.sum(w)
-        indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
-        indices = th.from_numpy(indices_np).long().to(device)
-        weights_np = 1 / (len(p) * p[indices_np])
-        weights = th.from_numpy(weights_np).float().to(device)
-        return indices, weights
-
-
-class UniformSampler(ScheduleSampler):
-    def __init__(self, diffusion):
-        self.diffusion = diffusion
-        self._weights = np.ones([diffusion.num_timesteps])
-
-    def weights(self):
-        return self._weights
-
-
-class LossAwareSampler(ScheduleSampler):
-    def update_with_local_losses(self, local_ts, local_losses):
-        """
-        Update the reweighting using losses from a model.
-
-        Call this method from each rank with a batch of timesteps and the
-        corresponding losses for each of those timesteps.
-        This method will perform synchronization to make sure all of the ranks
-        maintain the exact same reweighting.
-
-        :param local_ts: an integer Tensor of timesteps.
-        :param local_losses: a 1D Tensor of losses.
-        """
-        batch_sizes = [
-            th.tensor([0], dtype=th.int32, device=local_ts.device)
-            for _ in range(dist.get_world_size())
-        ]
-        dist.all_gather(
-            batch_sizes,
-            th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
-        )
-
-        # Pad all_gather batches to be the maximum batch size.
-        batch_sizes = [x.item() for x in batch_sizes]
-        max_bs = max(batch_sizes)
-
-        timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
-        loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
-        dist.all_gather(timestep_batches, local_ts)
-        dist.all_gather(loss_batches, local_losses)
-        timesteps = [
-            x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
-        ]
-        losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
-        self.update_with_all_losses(timesteps, losses)
-
-    @abstractmethod
-    def update_with_all_losses(self, ts, losses):
-        """
-        Update the reweighting using losses from a model.
-
-        Sub-classes should override this method to update the reweighting
-        using losses from the model.
-
-        This method directly updates the reweighting without synchronizing
-        between workers. It is called by update_with_local_losses from all
-        ranks with identical arguments. Thus, it should have deterministic
-        behavior to maintain state across workers.
-
-        :param ts: a list of int timesteps.
-        :param losses: a list of float losses, one per timestep.
-        """
-
-
-class LossSecondMomentResampler(LossAwareSampler):
-    def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
-        self.diffusion = diffusion
-        self.history_per_term = history_per_term
-        self.uniform_prob = uniform_prob
-        self._loss_history = np.zeros(
-            [diffusion.num_timesteps, history_per_term], dtype=np.float64
-        )
-        self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
-
-    def weights(self):
-        if not self._warmed_up():
-            return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
-        weights = np.sqrt(np.mean(self._loss_history ** 2, axis=-1))
-        weights /= np.sum(weights)
-        weights *= 1 - self.uniform_prob
-        weights += self.uniform_prob / len(weights)
-        return weights
-
-    def update_with_all_losses(self, ts, losses):
-        for t, loss in zip(ts, losses):
-            if self._loss_counts[t] == self.history_per_term:
-                # Shift out the oldest loss term.
-                self._loss_history[t, :-1] = self._loss_history[t, 1:]
-                self._loss_history[t, -1] = loss
-            else:
-                self._loss_history[t, self._loss_counts[t]] = loss
-                self._loss_counts[t] += 1
-
-    def _warmed_up(self):
-        return (self._loss_counts == self.history_per_term).all()
diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/DESCARGAR CADWORX 2012 FULL CRACK.md b/spaces/tialenAdioni/chat-gpt-api/logs/DESCARGAR CADWORX 2012 FULL CRACK.md
deleted file mode 100644
index eeb5f6dfff683ef3c032c20b2c9138d9ff57582b..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/DESCARGAR CADWORX 2012 FULL CRACK.md	
+++ /dev/null
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-<br />
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Download Ghost Win XP SP3 64bit - The Most Reliable and Secure Windows XP Experience.md b/spaces/tialenAdioni/chat-gpt-api/logs/Download Ghost Win XP SP3 64bit - The Most Reliable and Secure Windows XP Experience.md
deleted file mode 100644
index d79faa63870aa132c19e540bfffd06ca03da84ae..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Download Ghost Win XP SP3 64bit - The Most Reliable and Secure Windows XP Experience.md	
+++ /dev/null
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-<br />
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/EXPUESTA  SOLO UNA NOCHE 2 by Kyra Davis  Goodreads.md b/spaces/tialenAdioni/chat-gpt-api/logs/EXPUESTA  SOLO UNA NOCHE 2 by Kyra Davis  Goodreads.md
deleted file mode 100644
index b6a59d252cca624615a84800b1b5a7f1de42a730..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/EXPUESTA  SOLO UNA NOCHE 2 by Kyra Davis  Goodreads.md	
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-<p>Do you love playing Hungry Shark Evolution, but wish you had more coins, gems, sharks, and other resources to enjoy the game? If so, you might be interested in downloading a hacked APK for this game. A hacked APK is a modified version of an app that gives you unlimited access to everything in the game, as well as some cheats and tricks to make it more fun and easy. In this article, we will show you how to download Hungry Shark Evolution hacked APK for free, and what are the benefits and risks of using it.</p>
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- <h2>What is Hungry Shark Evolution?</h2>
-<h3>A popular shark game for Android and iOS devices</h3>
-<p>Hungry Shark Evolution is a game developed by Ubisoft Entertainment, where you take control of a hungry shark and go on a rampage in the ocean. You can explore a beautiful underwater world, eat everything and everyone in your way, unlock and evolve different sharks, equip awesome accessories, complete missions, and more. The game is available for Android and iOS devices, and has over 100 million downloads on Google Play Store. It is also the official game for Shark Week, a yearly event that celebrates sharks and their conservation.</p>
- <h3>Features of Hungry Shark Evolution</h3>
-<p>Some of the features of Hungry Shark Evolution are:</p>
-<ul>
-<li>More than a dozen unique sharks and other creatures to unlock and evolve</li>
-<li>An open world both above and below the waves</li>
-<li>Jawsome 3D graphics and sound effects</li>
-<li>Mysterious creatures of the deep to discover and devour</li>
-<li>Baby sharks to recruit and boost your predatory powers</li>
-<li>Accessories like lasers, jetpacks, and top hats to equip</li>
-<li>Sunken bonus objects to find and collect</li>
-<li>Challenging missions to complete</li>
-<li>Gold rush mode to survive longer and score higher</li>
-<li>In-game events and limited edition prizes to win</li>
-<li>Intuitive touch or tilt controls</li>
-<li>Offline play mode</li>
-<li>Synchronization across iOS devices</li>
-</ul>
- <h2>What is a hacked APK?</h2>
-<h3>A modified version of an app that gives you unlimited resources, cheats, and other advantages</h3>
-<p>A hacked APK is a file that contains a modified version of an app, usually created by hackers or modders. A hacked APK can give you access to features that are normally locked or paid in the original app, such as unlimited coins, gems, sharks, accessories, etc. It can also give you some cheats and tricks to make the game easier or more fun, such as invincibility, speed boost, no ads, etc. A hacked APK can be downloaded from various websites or forums that offer such files.</p>
- <h3>Risks of using a hacked APK</h3>
-<p>While using a hacked APK might sound tempting, it also comes with some risks that you should be aware of. Some of these risks are:</p>
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- <h2>How to download Hungry Shark Evolution hacked APK for free?</h2>
-<h3>Find a reliable source of hacked APKs</h3>
-<p>If you still want to download Hungry Shark Evolution hacked APK for free, you need to find a reliable source of hacked APKs. There are many websites and forums that offer such files, but not all of them are trustworthy or safe. You need to do some research and check the reviews, ratings, comments, and feedback of other users before downloading any file. You also need to scan the file with an antivirus program before installing it on your device.</p>
- <h3>Download and install the hacked APK file on your device</h3>
-<p>Once you have found a reliable source of Hungry Shark Evolution hacked APK, you need to download and install the file on your device. Here are the steps to do so:</p>
-<ol>
-<li>Go to the website or forum where you found the hacked APK and click on the download link.</li>
-<li>Wait for the file to be downloaded on your device.</li>
-<li>Go to your device settings and enable the option to install apps from unknown sources. This will allow you to install apps that are not from Google Play Store or App Store.</li>
-<li>Go to your file manager and locate the downloaded file. Tap on it and follow the instructions to install it on your device.</li>
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-<p>After installing Hungry Shark Evolution hacked APK on your device, you can enjoy the game with unlimited resources and cheats. You can unlock and evolve all the sharks, equip them with awesome accessories, complete all the missions, collect all the bonus objects, win all the events and prizes, and more. You can also use some cheats and tricks to make the game more fun and easy, such as invincibility, speed boost, no ads, etc. However, remember to use them wisely and responsibly, as they might affect your gaming experience or cause some problems.</p>
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-<h3>Summarize the main points of the article</h3>
-<p>In this article, we have shown you how to download Hungry Shark Evolution hacked APK for free. We have explained what Hungry Shark Evolution is, what a hacked APK is, what are the benefits and risks of using it, and how to download and install it on your device. We have also given you some tips on how to enjoy the game with unlimited resources and cheats.</p>
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- <h2>FAQs</h2>
-<h3>Q1: Is Hungry Shark Evolution hacked APK safe to use?</h3>
-<p>A1: There is no definitive answer to this question, as different sources of hacked APKs might have different levels of safety and quality. Some hacked APKs might be safe and harmless, while others might be dangerous and harmful. It depends on how trustworthy and reputable the source is, how well-made and updated the file is, and how careful and responsible you are when using it. Therefore, we recommend you to do some research and check the reviews, ratings, comments, and feedback of other users before downloading any file , and scan the file with an antivirus program before installing it on your device.</p>
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-<p>A2: Yes, you can play Hungry Shark Evolution hacked APK online, as the game does not require an internet connection to run. However, you might not be able to access some online features or events, such as leaderboards, achievements, or Shark Week. You might also face some issues or errors when synchronizing your game data across devices. Moreover, you might get detected by the game developers or the platform administrators and get banned or suspended from using the game or the platform. Therefore, we advise you to play Hungry Shark Evolution hacked APK offline or with caution.</p>
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-<p>A3: To update Hungry Shark Evolution hacked APK, you need to find and download the latest version of the hacked APK file from the same source where you got the previous one. You also need to uninstall the old version of the hacked APK from your device before installing the new one. However, be aware that updating Hungry Shark Evolution hacked APK might cause some compatibility issues or errors with the game or your device. You might also lose your progress, achievements, or rewards in the game. Therefore, we suggest you to backup your game data before updating Hungry Shark Evolution hacked APK.</p>
- <h3>Q4: What are some alternatives to Hungry Shark Evolution hacked APK?</h3>
-<p>A4: If you are looking for some alternatives to Hungry Shark Evolution hacked APK, you might want to try some of these options:</p>
-<ul>
-<li>Hungry Shark World: This is the sequel to Hungry Shark Evolution, where you can explore a bigger and more diverse world with more sharks, prey, enemies, and challenges. You can also customize your sharks with stylish outfits and accessories.</li>
-<li>Hungry Dragon: This is a spin-off of Hungry Shark Evolution, where you can control a fire-breathing dragon and fly through a medieval world full of sheep, goblins, witches, and more. You can also unlock and evolve different dragons, costumes, and pets.</li>
-<li>Hungry Shark Heroes: This is another spin-off of Hungry Shark Evolution, where you can collect and breed over 120 different sharks and create your own underwater sanctuary. You can also battle other players in 3D arenas and join clans with your friends.</li>
-</ul>
- <h3>Q5: How can I contact the developers of Hungry Shark Evolution?</h3>
-<p>A5: If you have any questions, feedback, or issues regarding Hungry Shark Evolution, you can contact the developers of the game by using one of these methods:</p>
-<ul>
-<li>Email: support@fgol.co.uk</li>
-<li>Facebook: https://www.facebook.com/HungrySharkEvolution</li>
-<li>Twitter: https://twitter.com/Hungry_Shark</li>
-<li>YouTube: https://www.youtube.com/user/FutureGamesOfLondon</li>
-<li>Website: https://www.ubisoft.com/en-us/game/hungry-shark-evolution</li>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Hungry Shark Evolution and Become the Apex Predator of the Ocean.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Hungry Shark Evolution and Become the Apex Predator of the Ocean.md
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-<p>Are you looking for a fun and addictive game that lets you unleash your inner predator? Do you want to experience life as a shark and explore a beautiful underwater world? Do you want to evolve your shark from a tiny reef shark to a massive megalodon? If you answered yes to any of these questions, then you should definitely check out Hungry Shark Evolution, one of the best shark games available for mobile devices.</p>
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-<h2>Introduction</h2>
-<h3>What is Hungry Shark Evolution?</h3>
-<p>Hungry Shark Evolution is an action-packed aquatic adventure game where you take control of a hungry shark and go on a rampage, surviving as long as possible by eating everything and everyone in your way. The game features an open world both above and below the waves, with jawsome 3D graphics and sound effects. You can discover and devour mysterious creatures of the deep, recruit baby sharks to boost your predatory powers, equip awesome accessories like lasers, jetpacks, and top hats, find and collect sunken bonus objects, sink your teeth into loads of challenging missions, activate gold rush to survive longer and score higher, take part in regular in-game events and win limited edition prizes, attack with intuitive touch or tilt controls, play offline wherever you are – no Wi-Fi needed, synchronize your game easily across iOS devices, and more.</p>
-<h3>Why should you play Hungry Shark Evolution?</h3>
-<p>Hungry Shark Evolution is a game that offers endless fun and entertainment for players of all ages. Whether you are a fan of sharks or not, you will love the thrill of being a powerful predator in the ocean. You will enjoy the variety of sharks and other creatures you can unlock and evolve, the different environments you can explore, the different challenges you can face, and the different ways you can customize your shark. You will also appreciate the humor and creativity of the game, as well as the stunning graphics and sound effects. Hungry Shark Evolution is a game that will keep you hooked for hours on end.</p>
-<h2>How to download Hungry Shark Evolution</h2>
-<h3>Download from Google Play Store</h3>
-<p>If you have an Android device, you can download Hungry Shark Evolution from the Google Play Store for free. Just follow these simple steps:</p>
-<ol>
-<li>Open the Google Play Store app on your device.</li>
-<li>Search for "Hungry Shark Evolution" in the search bar.</li>
-<li>Tap on the game icon that appears in the results.</li>
-<li>Tap on "Install" and wait for the download to finish.</li>
-<li>Tap on "Open" to launch the game and start playing.</li>
-</ol>
-<h3>Download from Apple App Store</h3>
-<p>If you have an iOS device, you can download Hungry Shark Evolution from the Apple App Store for free. Just follow these simple steps:</p>
-<ol>
-<li>Open the App Store app on your device.</li>
-<li>Search for "Hungry Shark Evolution" in the search bar.</li>
-<li>Tap on the game icon that appears in the results.</li>
-<li>Tap on "Get" and wait for the download to finish.</li>
-<li>Tap on "Open" to launch the game and start playing.</li>
-</ol>
-<h3>Download from Ubisoft website</h3>
-<p>If you prefer, you can also download Hungry Shark Evolution from the official Ubisoft website for free. Just follow these simple steps:</p>
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-<li>Open your web browser and go to <a href="">https://www.ubisoft.com/en-us/game/hungry-shark-evolution</a>.</li>
-<li>Click on the "Download Now" button and choose your device (Android or iOS).</li>
-<li>You will be redirected to the Google Play Store or the Apple App Store, depending on your device.</li>
-<li>Follow the same steps as above to install and play the game.</li>
-</ol>
-<h2>How to play Hungry Shark Evolution</h2>
-<h3>Choose your shark</h3>
-<p>The first thing you need to do when you start playing Hungry Shark Evolution is to choose your shark. You will start with a reef shark, which is the smallest and weakest shark in the game. As you play, you will earn coins and gems, which you can use to unlock and upgrade other sharks. There are 12 different sharks in the game, each with its own characteristics and abilities. Some of the sharks are based on real species, such as the hammerhead, the great white, and the tiger shark. Others are based on mythical creatures, such as the big daddy, the megalodon, and the zombie shark. You can also unlock special sharks that have unique features, such as the robo shark, the ice shark, and the electro shark. Each shark has its own size, speed, bite, boost, health, and diet. You can see these stats in the shark selection screen. You can also see how many coins and gems you need to unlock or upgrade each shark.</p>
-<h3>Eat everything in your way</h3>
-<p>The main objective of Hungry Shark Evolution is to eat as much as you can and survive as long as you can. You will see a hunger bar at the top of the screen, which indicates how hungry your shark is. If the bar reaches zero, your shark will starve and die. To prevent this, you need to eat constantly. You can eat almost anything that moves in the water, such as fish, crabs, turtles, jellyfish, squid, dolphins, seals, penguins, humans, and more. You can also eat some things that don't move, such as trash cans, barrels, mines, buoys, and more. However, not everything is edible or safe for your shark. Some things can harm or kill your shark, such as torpedoes, bombs, harpoons, electric eels, poisonous jellyfish, giant crabs, submarines, helicopters, and more. You need to avoid these dangers or destroy them with your bite or boost. You can also use some objects to your advantage, such as magnets, jetpacks, umbrellas, and more. You can also eat some special items that give you extra benefits, such as gold fish, green gems, letters, and more. You can see the points you earn for each item you eat at the bottom of the screen. The more you eat, the more points you get.</p>
-<h3>Evolve your shark</h3>
-<p>As you play Hungry Shark Evolution, you will notice that your shark will grow bigger and stronger. This is because your shark will evolve as you eat more and more. You can see the evolution bar at the top of the screen, which indicates how close your shark is to the next evolution stage. There are three evolution stages for each shark: normal, mega, and super. Each stage increases your shark's size, speed, bite, boost, and health. You can also see the evolution icon at the bottom of the screen, which indicates when your shark is ready to evolve. When the icon is full, you can tap on it to evolve your shark instantly. However, you can only do this once per game session. Evolving your shark will also increase your score multiplier, which means you will earn more points for each item you eat.</p>
-<h3>Complete missions and achievements</h3>
-<p>Hungry Shark Evolution is not just about eating and surviving. It is also about completing missions and achievements that challenge your skills and reward you with coins and gems. You can see the missions and achievements in the main menu or in the pause menu. There are two types of missions: daily missions and shark missions. Daily missions are tasks that change every day and give you coins for completing them. Shark missions are tasks that are specific to each shark and give you gems for completing them. There are 15 shark missions for each shark, and they range from easy to hard. Some examples of shark missions are: eat 100 fish in one game session, survive for 10 minutes in one game session, destroy 10 submarines in one game session, and more. Achievements are goals that track your progress and give you gems for reaching them. There are 63 achievements in total, and they range from bronze to gold. Some examples of achievements are: unlock all sharks, evolve all sharks to super stage, collect all sunken objects, and more.</p>
-<h3>Equip accessories and baby sharks</h3>
-<p>Another way to enhance your gameplay in Hungry Shark Evolution is to equip accessories and baby sharks that give you extra abilities and benefits. You can buy accessories and baby sharks with coins or gems in the shop menu or in the accessory menu. Accessories are items that you can attach to your shark's head, body, or tail. They can improve your shark's stats or give you special effects. Some examples of accessories are: laser (increases bite and shoots lasers), jetpack (increases speed and allows you to fly), crown (increases health and gives you a royal look), and more. Baby sharks are smaller sharks that follow your main shark and help you eat more items. They can also improve your shark's stats or give you special effects. Some examples of baby sharks are: reef baby (increases speed), hammerhead baby (increases bite), megalodon baby (increases health), zombie baby (revives your main shark once per game session), and more.</p>
-<h3>Explore the open world</h3>
-<p>The last thing you need to know about Hungry Shark Evolution is how to explore the open world that offers endless possibilities and surprises. The game features a large map that covers both above and below the water surface. You can move around freely using the touch or tilt controls on your device. You can also zoom in or out using the pinch gesture on your screen. You can see a mini-map at the top right corner of the screen, which shows your location and some landmarks. You can also buy a full map with coins or gems in the shop menu or in the map menu. The full map shows all the locations and secrets of the game world, such as sunken objects, treasure chests, portals, bosses, hidden areas, and more.</p>
-<h2>Tips and tricks to master Hungry Shark Evolution</h2>
-<h3>Trigger the Gold Rush</h3>
-<p>One of the best features of Hungry Shark Evolution is the Gold Rush mode, which is a temporary state that gives you amazing benefits. When you trigger the Gold Rush mode, everything turns gold and gives you more points when you eat it. You also become invincible and immune to all dangers. You also get a bonus score multiplier that increases with each Gold Rush mode you trigger in one game session. To trigger the Gold Rush mode, you need to fill up the Gold Rush bar at the top of the screen by eating gold fish or green gems. The more gold fish or green gems you eat, the faster you fill up the bar.</p>
-<h3>Buy the map You can buy different levels of magnets that increase the radius and duration of the effect.</li>
-<li>Jetpack: This item allows your shark to fly above the water and reach new areas and items. You can buy different levels of jetpacks that increase the speed and duration of the effect.</li>
-<li>Umbrella: This item protects your shark from the sun and reduces the health loss when you are out of the water. You can buy different levels of umbrellas that increase the protection and duration of the effect.</li>
-</ul>
-<p>Investing in extra equipment will help you collect more coins and gems, explore more places, and survive longer in the game.</p>
-<h3>Get yourself a baby shark</h3>
-<p>One of the best things you can do in Hungry Shark Evolution is to get yourself a baby shark. A baby shark is a smaller shark that follows your main shark and helps you eat more items. A baby shark can also improve your main shark's stats or give you special effects. For example, a reef baby can increase your speed, a hammerhead baby can increase your bite, a megalodon baby can increase your health, a zombie baby can revive your main shark once per game session, and more. You can buy baby sharks with coins or gems in the shop menu or in the accessory menu. You can also equip different baby sharks for different situations or preferences. You can have up to two baby sharks at a time, depending on your main shark's size. Having a baby shark will make your gameplay more fun and rewarding.</p>
-<h2>Reviews and ratings of Hungry Shark Evolution</h2>
-<h3>What do players say about Hungry Shark Evolution?</h3>
-<p>Hungry Shark Evolution is a very popular and well-liked game among players. It has over 100 million downloads and over 7 million reviews on the Google Play Store, with an average rating of 4.5 out of 5 stars. It also has over 10 million downloads and over 300 thousand reviews on the Apple App Store, with an average rating of 4.7 out of 5 stars. Here are some of the positive comments from players:</p>
-<ul>
-<li>"This game is awesome! I love how you can evolve your shark and unlock new ones. The graphics are amazing and the gameplay is addictive. I recommend this game to anyone who likes sharks or action games."</li>
-<li>"This game is so much fun! I enjoy eating everything in sight and exploring the map. The game is very creative and humorous. I like how you can customize your shark with accessories and baby sharks. The game is also very challenging and rewarding."</li>
-<li>"This game is the best shark game ever! I love how realistic and detailed the game is. The game is very immersive and thrilling. I like how you can interact with different creatures and objects in the game world. The game is also very easy to control and play."</li>
-</ul>
-<p>However, not everyone is satisfied with Hungry Shark Evolution. Here are some of the negative comments from players:</p>
-<ul>
-<li>"This game is boring! It's just the same thing over and over again. There is no story or goal in the game. The game is also very repetitive and grindy. I don't see the point of playing this game."</li>
-<li>"This game is frustrating! It's too hard and unfair. There are too many enemies and obstacles that kill you instantly. The game is also very expensive and greedy. You need to spend a lot of money to unlock or upgrade anything in the game."</li>
-<li>"This game is buggy! It crashes a lot and freezes my device. There are also many glitches and errors in the game. The game is also very laggy and slow. I can't enjoy playing this game."</li>
-</ul>
-<h3>What do critics say about Hungry Shark Evolution?</h3>
-<p>Hungry Shark Evolution is also a well-received game among critics. It has an average score of 79 out of 100 on Metacritic, based on 8 reviews from professional gaming websites. Here are some of the positive remarks from critics:</p>
-<ul>
-<li>"Hungry Shark Evolution is a simple but addictive game that delivers a satisfying sense of power and freedom as you rampage through the ocean as a fearsome predator." - Pocket Gamer</li>
-<li>"Hungry Shark Evolution is a fun and entertaining game that offers a lot of content and variety for players to enjoy. The game has impressive graphics and sound effects that create an immersive underwater world." - Gamezebo</li>
-<li>"Hungry Shark Evolution is a charming and humorous game that lets you experience life as a shark in a colorful and lively environment. The game has a lot of personality and creativity that make it stand out from other games." - TouchArcade</li>
-</ul>
-<p>However, not everyone is impressed with Hungry Shark Evolution. Here are some of the negative remarks from critics:</p>
-<ul>
-<li>"Hungry Shark Evolution is a shallow and repetitive game that lacks depth and innovation. The game has poor controls and mechanics that make it frustrating and boring to play." - IGN</li>
-<li>"Hungry Shark Evolution is a dull and monotonous game that offers nothing new or exciting for players to enjoy. The game has bland graphics and sound effects that create a boring and lifeless underwater world." - GameSpot</li>
-<li>"Hungry Shark Evolution is a mediocre and uninspired game that fails to capture the essence and thrill of being a shark. The game has a lot of flaws and limitations that make it unappealing and unenjoyable to play." - AppSpy</li>
-</ul>
-<h2>Conclusion</h2>
-<h4>Summary of the main points</h4>
-<p>In conclusion, Hungry Shark Evolution is a game that lets you download and play the ultimate shark game on your mobile device. You can choose from 12 different sharks and evolve them from a tiny reef shark to a massive megalodon. You can eat everything in your way and survive as long as possible in an open world both above and below the waves. You can complete missions and achievements, equip accessories and baby sharks, explore the map and find secrets and treasures, trigger the Gold Rush mode, and more. Hungry Shark Evolution is a game that offers endless fun and entertainment for players of all ages.</p>
-<h4>Call to action</h4>
-<p>If you are interested in Hungry Shark Evolution, you can download it for free from the Google Play Store, the Apple App Store, or the Ubisoft website. You can also visit the official website or the social media pages of the game for more information and updates. Hungry Shark Evolution is a game that you don't want to miss out on. Download it today and start your shark adventure!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Hungry Shark Evolution:</p>
-<ol>
-<li>How do I get more coins and gems in Hungry Shark Evolution?</li>
-<p>You can get more coins and gems in Hungry Shark Evolution by eating gold fish or green gems, finding treasure chests or sunken objects, completing missions or achievements, watching ads, spinning the wheel of fortune, getting daily gifts, or buying them with real money.</p>
-<li>How do I unlock new sharks in Hungry Shark Evolution?</li>
-<p>You can unlock new sharks in Hungry Shark Evolution by earning enough coins or gems to buy them in the shop menu or in the shark selection screen. You can also unlock some special sharks by completing certain tasks or events.</p>
-<li>How do I evolve my shark in Hungry Shark Evolution?</li>
-<p>You can evolve your shark in Hungry Shark Evolution by eating enough items to fill up the evolution bar at the top of the screen. When the bar is full, you can tap on the evolution icon at the bottom of the screen to evolve your shark instantly. You can also evolve your shark automatically when you reach a certain score.</p>
-<li>How do I enter the Super Size mode in Hungry Shark Evolution?</li>
-<p>You can enter the Super Size mode in Hungry Shark Evolution by collecting all six letters that spell out the word "HUNGRY" in one game session. When you do this, your shark will grow bigger and stronger for a limited time.</p>
-<li>How do I find hidden areas in Hungry Shark Evolution?</li>
-<p>You can find hidden areas in Hungry Shark Evolution by looking for secret passages or breaking through walls or barriers with your bite or boost. You can also use the map to see where they are located.</p>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Earth The Making Of A Planet 720p Or 1080p [REPACK].md b/spaces/tioseFevbu/cartoon-converter/scripts/Earth The Making Of A Planet 720p Or 1080p [REPACK].md
deleted file mode 100644
index 06605d05220fe7a23ece6c9d7162137b61417e77..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Earth The Making Of A Planet 720p Or 1080p [REPACK].md	
+++ /dev/null
@@ -1,24 +0,0 @@
-
-<h1>Earth The Making Of A Planet: A Stunning Documentary by National Geographic</h1>
-<p>Have you ever wondered how our planet came to be? How did it form from a cloud of dust and gas into a complex and diverse world that supports life? How did it survive countless cataclysms and transformations over billions of years?</p>
-<h2>Earth The Making Of A Planet 720p Or 1080p</h2><br /><p><b><b>Download Zip</b> &#10038;&#10038;&#10038; <a href="https://urlcod.com/2uHyC2">https://urlcod.com/2uHyC2</a></b></p><br /><br />
-<p>If you are curious about these questions, you should watch <em>Earth The Making Of A Planet</em>, a stunning documentary by National Geographic that tells the epic story of our planet's history in one single, seamless camera move. Using state-of-the-art CGI and scientific data, this film recreates the most dramatic events that shaped Earth, from its violent birth to the rise of humans.</p>
-<p>You will witness how water was brought to Earth by millions of meteors, how continents drifted and collided, how volcanoes erupted and changed the climate, how life evolved from slimy algae to dinosaurs and mammals, and how humans emerged and spread across the globe. You will also see how Earth is still changing today, and what challenges we face for its future.</p>
-<p><em>Earth The Making Of A Planet</em> is a fascinating and educational journey that will make you appreciate the beauty and wonder of our home planet. You can watch it online on YouTube in high definition (720p or 1080p) by following these links[^1^] [^2^]. You can also turn on subtitles in different languages if you prefer.</p>
-
-<p>In this article, we will highlight some of the most interesting and important moments in Earth's history that are featured in the documentary. We will also provide some facts and trivia that you might not know about our planet and its past.</p>
-<h2>The Birth of Earth</h2>
-<p>Earth was born about 4.5 billion years ago, when a giant cloud of dust and gas collapsed under its own gravity and formed a spinning disk. In the center of this disk, the Sun ignited and started to shine. Around it, smaller clumps of matter collided and stuck together, forming the planets and their moons.</p>
-<p>One of these clumps was Earth, which was initially a molten ball of rock and metal. It was constantly bombarded by other objects, some as big as Mars. One of these collisions created a huge debris ring around Earth, which eventually coalesced into the Moon.</p>
-<p></p>
-<p>As Earth cooled down, a thin crust formed on its surface, but it was still unstable and prone to cracking and melting. Volcanoes erupted everywhere, spewing lava and gases into the air. These gases formed the first atmosphere, which was very different from today's. It had no oxygen, but lots of carbon dioxide, water vapor, nitrogen, and other compounds.</p>
-<h2>The Origin of Water</h2>
-<p>Water is essential for life as we know it, but how did it get to Earth? Scientists have different theories, but one of the most popular ones is that water came from space, carried by comets and asteroids that crashed into Earth.</p>
-<p>About 3.9 billion years ago, Earth experienced a period of intense bombardment by these icy bodies, known as the Late Heavy Bombardment. This event delivered a lot of water to Earth's surface, filling up the basins and creating the first oceans.</p>
-<p>However, water was not stable on Earth at first. It was constantly evaporated by the Sun's heat and lost to space by the solar wind. To keep water on Earth, two things had to happen: the formation of a magnetic field and the emergence of life.</p>
-<h2>The Magnetic Field</h2>
-<p>The magnetic field is a protective shield that surrounds Earth and deflects the charged particles from the Sun that could strip away our atmosphere and water. It is generated by the movement of liquid iron in Earth's core, which acts like a giant dynamo.</p>
-<p>Earth's magnetic field is not constant. It changes in strength and direction over time, sometimes even reversing its polarity. This means that the north and south magnetic poles switch places every few hundred thousand years or so. The last reversal happened about 780,000 years ago, and we are due for another one soon.</p>
-<p>The magnetic field also affects the auroras, the colorful lights that appear in the sky near the poles. They are caused by the interaction of the solar wind with the upper atmosphere. The colors depend on the type of gas that is excited by the particles: green for oxygen, red for nitrogen.</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/pip/_internal/utils/hashes.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/utils/hashes.py
deleted file mode 100644
index 0c1af327cc2b4a792be0b3f3f432f4fdafc9af75..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/utils/hashes.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import hashlib
-from typing import TYPE_CHECKING, BinaryIO, Dict, Iterable, List
-
-from pip._internal.exceptions import HashMismatch, HashMissing, InstallationError
-from pip._internal.utils.misc import read_chunks
-
-if TYPE_CHECKING:
-    from hashlib import _Hash
-
-    # NoReturn introduced in 3.6.2; imported only for type checking to maintain
-    # pip compatibility with older patch versions of Python 3.6
-    from typing import NoReturn
-
-
-# The recommended hash algo of the moment. Change this whenever the state of
-# the art changes; it won't hurt backward compatibility.
-FAVORITE_HASH = "sha256"
-
-
-# Names of hashlib algorithms allowed by the --hash option and ``pip hash``
-# Currently, those are the ones at least as collision-resistant as sha256.
-STRONG_HASHES = ["sha256", "sha384", "sha512"]
-
-
-class Hashes:
-    """A wrapper that builds multiple hashes at once and checks them against
-    known-good values
-
-    """
-
-    def __init__(self, hashes: Dict[str, List[str]] = None) -> None:
-        """
-        :param hashes: A dict of algorithm names pointing to lists of allowed
-            hex digests
-        """
-        allowed = {}
-        if hashes is not None:
-            for alg, keys in hashes.items():
-                # Make sure values are always sorted (to ease equality checks)
-                allowed[alg] = sorted(keys)
-        self._allowed = allowed
-
-    def __and__(self, other: "Hashes") -> "Hashes":
-        if not isinstance(other, Hashes):
-            return NotImplemented
-
-        # If either of the Hashes object is entirely empty (i.e. no hash
-        # specified at all), all hashes from the other object are allowed.
-        if not other:
-            return self
-        if not self:
-            return other
-
-        # Otherwise only hashes that present in both objects are allowed.
-        new = {}
-        for alg, values in other._allowed.items():
-            if alg not in self._allowed:
-                continue
-            new[alg] = [v for v in values if v in self._allowed[alg]]
-        return Hashes(new)
-
-    @property
-    def digest_count(self) -> int:
-        return sum(len(digests) for digests in self._allowed.values())
-
-    def is_hash_allowed(self, hash_name: str, hex_digest: str) -> bool:
-        """Return whether the given hex digest is allowed."""
-        return hex_digest in self._allowed.get(hash_name, [])
-
-    def check_against_chunks(self, chunks: Iterable[bytes]) -> None:
-        """Check good hashes against ones built from iterable of chunks of
-        data.
-
-        Raise HashMismatch if none match.
-
-        """
-        gots = {}
-        for hash_name in self._allowed.keys():
-            try:
-                gots[hash_name] = hashlib.new(hash_name)
-            except (ValueError, TypeError):
-                raise InstallationError(f"Unknown hash name: {hash_name}")
-
-        for chunk in chunks:
-            for hash in gots.values():
-                hash.update(chunk)
-
-        for hash_name, got in gots.items():
-            if got.hexdigest() in self._allowed[hash_name]:
-                return
-        self._raise(gots)
-
-    def _raise(self, gots: Dict[str, "_Hash"]) -> "NoReturn":
-        raise HashMismatch(self._allowed, gots)
-
-    def check_against_file(self, file: BinaryIO) -> None:
-        """Check good hashes against a file-like object
-
-        Raise HashMismatch if none match.
-
-        """
-        return self.check_against_chunks(read_chunks(file))
-
-    def check_against_path(self, path: str) -> None:
-        with open(path, "rb") as file:
-            return self.check_against_file(file)
-
-    def __bool__(self) -> bool:
-        """Return whether I know any known-good hashes."""
-        return bool(self._allowed)
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, Hashes):
-            return NotImplemented
-        return self._allowed == other._allowed
-
-    def __hash__(self) -> int:
-        return hash(
-            ",".join(
-                sorted(
-                    ":".join((alg, digest))
-                    for alg, digest_list in self._allowed.items()
-                    for digest in digest_list
-                )
-            )
-        )
-
-
-class MissingHashes(Hashes):
-    """A workalike for Hashes used when we're missing a hash for a requirement
-
-    It computes the actual hash of the requirement and raises a HashMissing
-    exception showing it to the user.
-
-    """
-
-    def __init__(self) -> None:
-        """Don't offer the ``hashes`` kwarg."""
-        # Pass our favorite hash in to generate a "gotten hash". With the
-        # empty list, it will never match, so an error will always raise.
-        super().__init__(hashes={FAVORITE_HASH: []})
-
-    def _raise(self, gots: Dict[str, "_Hash"]) -> "NoReturn":
-        raise HashMissing(gots[FAVORITE_HASH].hexdigest())
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/mbcharsetprober.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/mbcharsetprober.py
deleted file mode 100644
index bf96ad5d490ae127dfc067d2a3cfe21fcc0e1edd..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/mbcharsetprober.py
+++ /dev/null
@@ -1,95 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Universal charset detector code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 2001
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#   Shy Shalom - original C code
-#   Proofpoint, Inc.
-#
-# This library 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.
-#
-# This library 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 this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from .charsetprober import CharSetProber
-from .enums import MachineState, ProbingState
-
-
-class MultiByteCharSetProber(CharSetProber):
-    """
-    MultiByteCharSetProber
-    """
-
-    def __init__(self, lang_filter=None):
-        super().__init__(lang_filter=lang_filter)
-        self.distribution_analyzer = None
-        self.coding_sm = None
-        self._last_char = [0, 0]
-
-    def reset(self):
-        super().reset()
-        if self.coding_sm:
-            self.coding_sm.reset()
-        if self.distribution_analyzer:
-            self.distribution_analyzer.reset()
-        self._last_char = [0, 0]
-
-    @property
-    def charset_name(self):
-        raise NotImplementedError
-
-    @property
-    def language(self):
-        raise NotImplementedError
-
-    def feed(self, byte_str):
-        for i, byte in enumerate(byte_str):
-            coding_state = self.coding_sm.next_state(byte)
-            if coding_state == MachineState.ERROR:
-                self.logger.debug(
-                    "%s %s prober hit error at byte %s",
-                    self.charset_name,
-                    self.language,
-                    i,
-                )
-                self._state = ProbingState.NOT_ME
-                break
-            if coding_state == MachineState.ITS_ME:
-                self._state = ProbingState.FOUND_IT
-                break
-            if coding_state == MachineState.START:
-                char_len = self.coding_sm.get_current_charlen()
-                if i == 0:
-                    self._last_char[1] = byte
-                    self.distribution_analyzer.feed(self._last_char, char_len)
-                else:
-                    self.distribution_analyzer.feed(byte_str[i - 1 : i + 1], char_len)
-
-        self._last_char[0] = byte_str[-1]
-
-        if self.state == ProbingState.DETECTING:
-            if self.distribution_analyzer.got_enough_data() and (
-                self.get_confidence() > self.SHORTCUT_THRESHOLD
-            ):
-                self._state = ProbingState.FOUND_IT
-
-        return self.state
-
-    def get_confidence(self):
-        return self.distribution_analyzer.get_confidence()
diff --git a/spaces/to-be/invoice_document_headers_extraction_with_donut/app.py b/spaces/to-be/invoice_document_headers_extraction_with_donut/app.py
deleted file mode 100644
index 716cee4b750373e55e43f6230ac1129f9d91cb5b..0000000000000000000000000000000000000000
--- a/spaces/to-be/invoice_document_headers_extraction_with_donut/app.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import re
-import gradio as gr
-
-import torch
-from transformers import DonutProcessor, VisionEncoderDecoderModel
-from PIL import Image
-import requests
-from io import BytesIO
-import json
-import os
-
-
-processor = DonutProcessor.from_pretrained("to-be/donut-base-finetuned-invoices")
-model = VisionEncoderDecoderModel.from_pretrained("to-be/donut-base-finetuned-invoices")
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model.to(device)
-
-
-def update_status(state):
-    if state == "start_or_clear": 
-        state = 'processing'   #current state becomes
-        return (gr.update(value="snowangel.gif",visible=True),gr.update(value="snowangel.gif",visible=True))
-    elif state == "processing":
-        state = 'finished_processing' #current state becomes
-        return (gr.update(value="",visible=False),gr.update(value="",visible=False))
-    elif state == "finished_processing":
-        state = 'processing'   #current state becomes
-        return (gr.update(value="snowangel.gif",visible=True),gr.update(value="snowangel.gif",visible=True))
-
-def process_document(image,sendimg):
-
-    if sendimg == True:
-        im1 = Image.fromarray(image)
-    elif sendimg == False:
-        im1 = Image.open('./no_image.jpg')
-    #keep track of demo count
-    resp = requests.get('https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fto-be%2Finvoice_document_headers_extraction_with_donut%2Fdemo&label=demos%20served&labelColor=%23edd239&countColor=%23d9e3f0')
-    
-    #send notification through telegram
-    TOKEN = os.getenv('TELEGRAM_BOT_TOKEN')
-    CHAT_ID = os.getenv('TELEGRAM_CHANNEL_ID')
-    url = f'https://api.telegram.org/bot{TOKEN}/sendPhoto?chat_id={CHAT_ID}'
-    bio = BytesIO()
-    bio.name = 'image.jpeg'
-    im1.save(bio, 'JPEG')
-    bio.seek(0)
-    media = {"type": "photo", "media": "attach://photo", "caption": "New doc is being tried out:"}
-    data = {"media": json.dumps(media)}
-    response = requests.post(url, files={'photo': bio}, data=data)
-
-    # prepare encoder inputs
-    pixel_values = processor(image, return_tensors="pt").pixel_values
-    
-    # prepare decoder inputs
-    task_prompt = "<s_cord-v2>"
-    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
-          
-    # generate answer
-    outputs = model.generate(
-        pixel_values.to(device),
-        decoder_input_ids=decoder_input_ids.to(device),
-        max_length=model.decoder.config.max_position_embeddings,
-        early_stopping=True,
-        pad_token_id=processor.tokenizer.pad_token_id,
-        eos_token_id=processor.tokenizer.eos_token_id,
-        use_cache=True,
-        num_beams=1,
-        bad_words_ids=[[processor.tokenizer.unk_token_id]],
-        return_dict_in_generate=True,
-    )
-    
-    # postprocess
-    sequence = processor.batch_decode(outputs.sequences)[0]
-    sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
-    sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
-
-    img2.update(visible=False)
-    return processor.token2json(sequence), image
-
-title = '<table align="center" border="0" cellpadding="1" cellspacing="1" ><tbody><tr><td style="text-align:center"><img alt="" src="https://huggingface.co/spaces/to-be/invoice_document_headers_extraction_with_donut/resolve/main/circling_small.gif" style="float:right; height:50px; width:50px" /></td><td style="text-align:center"><h1>Demo: invoice header extraction with Donut</h1></td><td style="text-align:center"><img alt="" src="https://huggingface.co/spaces/to-be/invoice_document_headers_extraction_with_donut/resolve/main/circling2_small.gif" style="float:left; height:50px; width:50px" /></td></tr></tbody></table>'
-paragraph0 = '<p><strong>(update 29/03/2023: for more info, you can read <a href="https://toon-beerten.medium.com/hands-on-document-data-extraction-with-transformer-7130df3b6132">my article on medium</a>)<br />(update 28/04/2023: want to finetune with your own data? Read&nbsp;<a href="https://towardsdatascience.com/ocr-free-document-data-extraction-with-transformers-1-2-b5a826bc2ac3">this article</a>)</strong></p>'
-paragraph1 = '<p>Basic idea of the base 🍩 model is to give it an image as input and extract indexes as text. No bounding boxes or confidences are generated.<br /> I finetuned it on invoices. For more info, see the <a href="https://arxiv.org/abs/2111.15664">original paper</a>&nbsp;and the 🤗&nbsp;<a href="https://huggingface.co/naver-clova-ix/donut-base">model</a>.</p>'
-paragraph2 = '<p><strong>Training</strong>:<br />The model was trained with a few thousand of annotated invoices and non-invoices (for those the doctype will be &#39;Other&#39;). They span across different countries and languages. They are always one page only. The dataset is proprietary unfortunately.&nbsp;Model is set to input resolution of 1280x1920 pixels. So any sample you want to try with higher dpi than 150 has no added value.<br />It was trained for about 4 hours on a&nbsp;NVIDIA RTX A4000 for 20k steps with a val_metric of&nbsp;0.03413819904382196 at the end.<br />The <u>following indexes</u> were included in the train set:</p><ul><li><span style="font-family:Calibri"><span style="color:black">DocType</span></span></li><li><span style="font-family:Calibri"><span style="color:black">Currency</span></span></li><li><span style="font-family:Calibri"><span style="color:black">DocumentDate</span></span></li><li><span style="font-family:Calibri"><span style="color:black">GrossAmount</span></span></li><li><span style="font-family:Calibri"><span style="color:black">InvoiceNumber</span></span></li><li><span style="font-family:Calibri"><span style="color:black">NetAmount</span></span></li><li><span style="font-family:Calibri"><span style="color:black">TaxAmount</span></span></li><li><span style="font-family:Calibri"><span style="color:black">OrderNumber</span></span></li><li><span style="font-family:Calibri"><span style="color:black">CreditorCountry</span></span></li></ul>'
-paragraph3 = '<p><strong>Benchmark observations:</strong><br />From all documents in the validation set,&nbsp; 60% of them had all indexes captured correctly.</p><p>Here are the results per index:</p><p style="margin-left:40px"><img alt="" src="https://s3.amazonaws.com/moonup/production/uploads/1677749023966-6335a49ceb6132ca653239a0.png" style="height:70%; width:70%" /></p><p>Some other observations:<br />- when trying with a non invoice document, it&#39;s quite reliably identified as Doctype: &#39;Other&#39;<br />- validation set contained mostly same layout invoices as the train set. If it was validated against completely differently sourced invoices, the results would be different<br />- Document date is able to be recognized across different notations, however, it&#39;s often wrong because the data set was not diverse (as in time span of dates) enough</p>'
-#demo = gr.Interface(fn=process_document,inputs=gr_image,outputs="json",title="Demo: Donut 🍩 for invoice header retrieval", description=description,
-#    article=article,enable_queue=True, examples=[["example.jpg"], ["example_2.jpg"], ["example_3.jpg"]], cache_examples=False)
-paragraph4 = '<p><strong>Try it out:</strong><br />To use it, simply upload your image and click &#39;submit&#39;, or click one of the examples to load them.<br /><em>(because this is running on the free cpu tier, it will take about 40 secs before you see a result. On a GPU it takes less than 2 seconds)</em></p><p>&nbsp;</p><p>Have fun&nbsp;😎</p><p>Toon Beerten</p>'
-smallprint = '<p>✤&nbsp;<span style="font-size:11px">To get an idea of the usage, you can opt to let me get personally notified via Telegram with the image uploaded. All data will be automatically deleted after 48 hours</span></p>'
-css = "#inp {height: auto !important; width: 100% !important;}"
-visit_badge = '<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fto-be%2Finvoice_document_headers_extraction_with_donut"><img src="https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2Fto-be%2Finvoice_document_headers_extraction_with_donut&labelColor=%23edd239&countColor=%23d9e3f0&style=flat" /></a>'
-
-# css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }"
-# css = ".output_image, .input_image {height: 600px !important}"
-
-#css = ".image-preview {height: auto !important;}"
-#css='div {margin-left: auto; margin-right: auto; width: 100%;background-image: url("background.gif"); repeat 0 0;}')
-
-with gr.Blocks(css=css) as demo:
-    state = gr.State(value='start_or_clear')
-    
-    gr.HTML(title)
-    gr.HTML(paragraph0)
-    gr.HTML(paragraph1)
-    gr.HTML(paragraph2)
-    gr.HTML(paragraph3)
-    gr.HTML(paragraph4)
-    
-    with gr.Row().style():
-        with gr.Column(scale=1):
-            inp = gr.Image(label='Upload invoice here:')   #.style(height=400)          
-        with gr.Column(scale=2):
-             gr.Examples([["example.jpg"], ["example_2.jpg"], ["example_3.jpg"]], inputs=[inp],label='Or use one of these examples:')
-    with gr.Row().style(equal_height=True,height=200,rounded=False):       
-        with gr.Column(scale=1):
-            img2 = gr.Image("drinking.gif",label=' ',visible=False).style(rounded=True)
-        with gr.Column(scale=2):
-            btn = gr.Button(" ↓   Extract   ↓ ")
-        with gr.Column(scale=2):
-            #img3 = gr.Image("snowangel.gif",label=' ',visible=False).style(rounded=True)
-            sendimg = gr.Checkbox(value=True, label="Allow usage data collection for at most 48 hours ✤")
-    with gr.Row().style():
-        with gr.Column(scale=2):
-            imgout = gr.Image(label='Uploaded document:',elem_id="inp")
-        with gr.Column(scale=1):
-            jsonout = gr.JSON(label='Extracted information:')
-    #imgout.clear(fn=update_status,inputs=state,outputs=[img2,img3])    
-    #imgout.change(fn=update_status,inputs=state,outputs=[img2,img3])        
-    btn.click(fn=process_document, inputs=[inp,sendimg], outputs=[jsonout,imgout])
-
-    gr.HTML(smallprint)
-    gr.HTML(visit_badge)
-    
-
-demo.launch()
-
-
-    
\ No newline at end of file
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py
deleted file mode 100644
index 7b3813071c7591caa72412e5622e4101f7c05920..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docs/tutorials/customize_runtime.md b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docs/tutorials/customize_runtime.md
deleted file mode 100644
index 616ce508aa2bec61c7fc23cd381b64e670b3b96e..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/docs/tutorials/customize_runtime.md
+++ /dev/null
@@ -1,323 +0,0 @@
-# Tutorial 5: Customize Runtime Settings
-
-## Customize optimization settings
-
-### Customize optimizer supported by Pytorch
-
-We already support to use all the optimizers implemented by PyTorch, and the only modification is to change the `optimizer` field of config files.
-For example, if you want to use `ADAM` (note that the performance could drop a lot), the modification could be as the following.
-
-```python
-optimizer = dict(type='Adam', lr=0.0003, weight_decay=0.0001)
-```
-
-To modify the learning rate of the model, the users only need to modify the `lr` in the config of optimizer. The users can directly set arguments following the [API doc](https://pytorch.org/docs/stable/optim.html?highlight=optim#module-torch.optim) of PyTorch.
-
-### Customize self-implemented optimizer
-
-#### 1. Define a new optimizer
-
-A customized optimizer could be defined as following.
-
-Assume you want to add a optimizer named `MyOptimizer`, which has arguments `a`, `b`, and `c`.
-You need to create a new directory named `mmdet/core/optimizer`.
-And then implement the new optimizer in a file, e.g., in `mmdet/core/optimizer/my_optimizer.py`:
-
-```python
-from .registry import OPTIMIZERS
-from torch.optim import Optimizer
-
-
-@OPTIMIZERS.register_module()
-class MyOptimizer(Optimizer):
-
-    def __init__(self, a, b, c)
-
-```
-
-#### 2. Add the optimizer to registry
-
-To find the above module defined above, this module should be imported into the main namespace at first. There are two options to achieve it.
-
-- Modify `mmdet/core/optimizer/__init__.py` to import it.
-
-    The newly defined module should be imported in `mmdet/core/optimizer/__init__.py` so that the registry will
-    find the new module and add it:
-
-```python
-from .my_optimizer import MyOptimizer
-```
-
-- Use `custom_imports` in the config to manually import it
-
-```python
-custom_imports = dict(imports=['mmdet.core.optimizer.my_optimizer'], allow_failed_imports=False)
-```
-
-The module `mmdet.core.optimizer.my_optimizer` will be imported at the beginning of the program and the class `MyOptimizer` is then automatically registered.
-Note that only the package containing the class `MyOptimizer` should be imported.
-`mmdet.core.optimizer.my_optimizer.MyOptimizer` **cannot** be imported directly.
-
-Actually users can use a totally different file directory structure using this importing method, as long as the module root can be located in `PYTHONPATH`.
-
-#### 3. Specify the optimizer in the config file
-
-Then you can use `MyOptimizer` in `optimizer` field of config files.
-In the configs, the optimizers are defined by the field `optimizer` like the following:
-
-```python
-optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
-```
-
-To use your own optimizer, the field can be changed to
-
-```python
-optimizer = dict(type='MyOptimizer', a=a_value, b=b_value, c=c_value)
-```
-
-### Customize optimizer constructor
-
-Some models may have some parameter-specific settings for optimization, e.g. weight decay for BatchNorm layers.
-The users can do those fine-grained parameter tuning through customizing optimizer constructor.
-
-```python
-from mmcv.utils import build_from_cfg
-
-from mmcv.runner.optimizer import OPTIMIZER_BUILDERS, OPTIMIZERS
-from mmdet.utils import get_root_logger
-from .my_optimizer import MyOptimizer
-
-
-@OPTIMIZER_BUILDERS.register_module()
-class MyOptimizerConstructor(object):
-
-    def __init__(self, optimizer_cfg, paramwise_cfg=None):
-
-    def __call__(self, model):
-
-        return my_optimizer
-
-```
-
-The default optimizer constructor is implemented [here](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/optimizer/default_constructor.py#L11), which could also serve as a template for new optimizer constructor.
-
-### Additional settings
-
-Tricks not implemented by the optimizer should be implemented through optimizer constructor (e.g., set parameter-wise learning rates) or hooks. We list some common settings that could stabilize the training or accelerate the training. Feel free to create PR, issue for more settings.
-
-- __Use gradient clip to stabilize training__:
-    Some models need gradient clip to clip the gradients to stabilize the training process. An example is as below:
-
-    ```python
-    optimizer_config = dict(
-        _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
-    ```
-
-    If your config inherits the base config which already sets the `optimizer_config`, you might need `_delete_=True` to overide the unnecessary settings. See the [config documenetation](https://mmdetection.readthedocs.io/en/latest/config.html) for more details.
-
-- __Use momentum schedule to accelerate model convergence__:
-    We support momentum scheduler to modify model's momentum according to learning rate, which could make the model converge in a faster way.
-    Momentum scheduler is usually used with LR scheduler, for example, the following config is used in 3D detection to accelerate convergence.
-    For more details, please refer to the implementation of [CyclicLrUpdater](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L327) and [CyclicMomentumUpdater](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/momentum_updater.py#L130).
-
-    ```python
-    lr_config = dict(
-        policy='cyclic',
-        target_ratio=(10, 1e-4),
-        cyclic_times=1,
-        step_ratio_up=0.4,
-    )
-    momentum_config = dict(
-        policy='cyclic',
-        target_ratio=(0.85 / 0.95, 1),
-        cyclic_times=1,
-        step_ratio_up=0.4,
-    )
-    ```
-
-## Customize training schedules
-
-By default we use step learning rate with 1x schedule, this calls [`StepLRHook`](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L153) in MMCV.
-We support many other learning rate schedule [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py), such as `CosineAnnealing` and `Poly` schedule. Here are some examples
-
-- Poly schedule:
-
-    ```python
-    lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
-    ```
-
-- ConsineAnnealing schedule:
-
-    ```python
-    lr_config = dict(
-        policy='CosineAnnealing',
-        warmup='linear',
-        warmup_iters=1000,
-        warmup_ratio=1.0 / 10,
-        min_lr_ratio=1e-5)
-    ```
-
-## Customize workflow
-
-Workflow is a list of (phase, epochs) to specify the running order and epochs.
-By default it is set to be
-
-```python
-workflow = [('train', 1)]
-```
-
-which means running 1 epoch for training.
-Sometimes user may want to check some metrics (e.g. loss, accuracy) about the model on the validate set.
-In such case, we can set the workflow as
-
-```python
-[('train', 1), ('val', 1)]
-```
-
-so that 1 epoch for training and 1 epoch for validation will be run iteratively.
-
-**Note**:
-
-1. The parameters of model will not be updated during val epoch.
-2. Keyword `total_epochs` in the config only controls the number of training epochs and will not affect the validation workflow.
-3. Workflows `[('train', 1), ('val', 1)]` and `[('train', 1)]` will not change the behavior of `EvalHook` because `EvalHook` is called by `after_train_epoch` and validation workflow only affect hooks that are called through `after_val_epoch`. Therefore, the only difference between `[('train', 1), ('val', 1)]` and `[('train', 1)]` is that the runner will calculate losses on validation set after each training epoch.
-
-## Customize hooks
-
-### Customize self-implemented hooks
-
-#### 1. Implement a new hook
-
-There are some occasions when the users might need to implement a new hook. MMDetection supports customized hooks in training (#3395) since v2.3.0. Thus the users could implement a hook directly in mmdet or their mmdet-based codebases and use the hook by only modifying the config in training.
-Before v2.3.0, the users need to modify the code to get the hook registered before training starts.
-Here we give an example of creating a new hook in mmdet and using it in training.
-
-```python
-from mmcv.runner import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class MyHook(Hook):
-
-    def __init__(self, a, b):
-        pass
-
-    def before_run(self, runner):
-        pass
-
-    def after_run(self, runner):
-        pass
-
-    def before_epoch(self, runner):
-        pass
-
-    def after_epoch(self, runner):
-        pass
-
-    def before_iter(self, runner):
-        pass
-
-    def after_iter(self, runner):
-        pass
-```
-
-Depending on the functionality of the hook, the users need to specify what the hook will do at each stage of the training in `before_run`, `after_run`, `before_epoch`, `after_epoch`, `before_iter`, and `after_iter`.
-
-#### 2. Register the new hook
-
-Then we need to make `MyHook` imported. Assuming the file is in `mmdet/core/utils/my_hook.py` there are two ways to do that:
-
-- Modify `mmdet/core/utils/__init__.py` to import it.
-
-    The newly defined module should be imported in `mmdet/core/utils/__init__.py` so that the registry will
-    find the new module and add it:
-
-```python
-from .my_hook import MyHook
-```
-
-- Use `custom_imports` in the config to manually import it
-
-```python
-custom_imports = dict(imports=['mmdet.core.utils.my_hook'], allow_failed_imports=False)
-```
-
-#### 3. Modify the config
-
-```python
-custom_hooks = [
-    dict(type='MyHook', a=a_value, b=b_value)
-]
-```
-
-You can also set the priority of the hook by adding key `priority` to `'NORMAL'` or `'HIGHEST'` as below
-
-```python
-custom_hooks = [
-    dict(type='MyHook', a=a_value, b=b_value, priority='NORMAL')
-]
-```
-
-By default the hook's priority is set as `NORMAL` during registration.
-
-### Use hooks implemented in MMCV
-
-If the hook is already implemented in MMCV, you can directly modify the config to use the hook as below
-
-#### 4. Example: `NumClassCheckHook`
-
-We implement a customized hook named  [NumClassCheckHook](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/utils.py) to check whether the `num_classes` in head matches the length of `CLASSSES` in `dataset`.
-
-We set it in [default_runtime.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/_base_/default_runtime.py).
-
-```python
-custom_hooks = [dict(type='NumClassCheckHook')]
-```
-
-### Modify default runtime hooks
-
-There are some common hooks that are not registerd through `custom_hooks`, they are
-
-- log_config
-- checkpoint_config
-- evaluation
-- lr_config
-- optimizer_config
-- momentum_config
-
-In those hooks, only the logger hook has the `VERY_LOW` priority, others' priority are `NORMAL`.
-The above-mentioned tutorials already covers how to modify `optimizer_config`, `momentum_config`, and `lr_config`.
-Here we reveals how what we can do with `log_config`, `checkpoint_config`, and `evaluation`.
-
-#### Checkpoint config
-
-The MMCV runner will use `checkpoint_config` to initialize [`CheckpointHook`](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/hooks/checkpoint.py#L9).
-
-```python
-checkpoint_config = dict(interval=1)
-```
-
-The users could set `max_keep_ckpts` to only save only small number of checkpoints or decide whether to store state dict of optimizer by `save_optimizer`. More details of the arguments are [here](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.CheckpointHook)
-
-#### Log config
-
-The `log_config` wraps multiple logger hooks and enables to set intervals. Now MMCV supports `WandbLoggerHook`, `MlflowLoggerHook`, and `TensorboardLoggerHook`.
-The detail usages can be found in the [doc](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.LoggerHook).
-
-```python
-log_config = dict(
-    interval=50,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        dict(type='TensorboardLoggerHook')
-    ])
-```
-
-#### Evaluation config
-
-The config of `evaluation` will be used to initialize the [`EvalHook`](https://github.com/open-mmlab/mmdetection/blob/7a404a2c000620d52156774a5025070d9e00d918/mmdet/core/evaluation/eval_hooks.py#L8).
-Except the key `interval`, other arguments such as `metric` will be passed to the `dataset.evaluate()`
-
-```python
-evaluation = dict(interval=1, metric='bbox')
-```
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/datasets/api_wrappers/__init__.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/datasets/api_wrappers/__init__.py
deleted file mode 100644
index 05f95c94ae6dee2f29fae9f9b0e369cebb9ae210..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/datasets/api_wrappers/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .coco_api import COCO, COCOeval
-
-__all__ = ['COCO', 'COCOeval']
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/losses/iou_loss.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/losses/iou_loss.py
deleted file mode 100644
index 4c11a9a6e237ed6c96e194796aa592fee79eddad..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/losses/iou_loss.py
+++ /dev/null
@@ -1,446 +0,0 @@
-import math
-
-import mmcv
-import torch
-import torch.nn as nn
-
-from mmdet.core import bbox_overlaps
-from ..builder import LOSSES
-from .utils import weighted_loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def iou_loss(pred, target, linear=False, eps=1e-6):
-    """IoU loss.
-
-    Computing the IoU loss between a set of predicted bboxes and target bboxes.
-    The loss is calculated as negative log of IoU.
-
-    Args:
-        pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
-            shape (n, 4).
-        target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
-        linear (bool, optional): If True, use linear scale of loss instead of
-            log scale. Default: False.
-        eps (float): Eps to avoid log(0).
-
-    Return:
-        torch.Tensor: Loss tensor.
-    """
-    ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps)
-    if linear:
-        loss = 1 - ious
-    else:
-        loss = -ious.log()
-    return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3):
-    """BIoULoss.
-
-    This is an implementation of paper
-    `Improving Object Localization with Fitness NMS and Bounded IoU Loss.
-    <https://arxiv.org/abs/1711.00164>`_.
-
-    Args:
-        pred (torch.Tensor): Predicted bboxes.
-        target (torch.Tensor): Target bboxes.
-        beta (float): beta parameter in smoothl1.
-        eps (float): eps to avoid NaN.
-    """
-    pred_ctrx = (pred[:, 0] + pred[:, 2]) * 0.5
-    pred_ctry = (pred[:, 1] + pred[:, 3]) * 0.5
-    pred_w = pred[:, 2] - pred[:, 0]
-    pred_h = pred[:, 3] - pred[:, 1]
-    with torch.no_grad():
-        target_ctrx = (target[:, 0] + target[:, 2]) * 0.5
-        target_ctry = (target[:, 1] + target[:, 3]) * 0.5
-        target_w = target[:, 2] - target[:, 0]
-        target_h = target[:, 3] - target[:, 1]
-
-    dx = target_ctrx - pred_ctrx
-    dy = target_ctry - pred_ctry
-
-    loss_dx = 1 - torch.max(
-        (target_w - 2 * dx.abs()) /
-        (target_w + 2 * dx.abs() + eps), torch.zeros_like(dx))
-    loss_dy = 1 - torch.max(
-        (target_h - 2 * dy.abs()) /
-        (target_h + 2 * dy.abs() + eps), torch.zeros_like(dy))
-    loss_dw = 1 - torch.min(target_w / (pred_w + eps), pred_w /
-                            (target_w + eps))
-    loss_dh = 1 - torch.min(target_h / (pred_h + eps), pred_h /
-                            (target_h + eps))
-    loss_comb = torch.stack([loss_dx, loss_dy, loss_dw, loss_dh],
-                            dim=-1).view(loss_dx.size(0), -1)
-
-    loss = torch.where(loss_comb < beta, 0.5 * loss_comb * loss_comb / beta,
-                       loss_comb - 0.5 * beta)
-    return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def giou_loss(pred, target, eps=1e-7):
-    r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding
-    Box Regression <https://arxiv.org/abs/1902.09630>`_.
-
-    Args:
-        pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
-            shape (n, 4).
-        target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
-        eps (float): Eps to avoid log(0).
-
-    Return:
-        Tensor: Loss tensor.
-    """
-    gious = bbox_overlaps(pred, target, mode='giou', is_aligned=True, eps=eps)
-    loss = 1 - gious
-    return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def diou_loss(pred, target, eps=1e-7):
-    r"""`Implementation of Distance-IoU Loss: Faster and Better
-    Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_.
-
-    Code is modified from https://github.com/Zzh-tju/DIoU.
-
-    Args:
-        pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
-            shape (n, 4).
-        target (Tensor): Corresponding gt bboxes, shape (n, 4).
-        eps (float): Eps to avoid log(0).
-    Return:
-        Tensor: Loss tensor.
-    """
-    # overlap
-    lt = torch.max(pred[:, :2], target[:, :2])
-    rb = torch.min(pred[:, 2:], target[:, 2:])
-    wh = (rb - lt).clamp(min=0)
-    overlap = wh[:, 0] * wh[:, 1]
-
-    # union
-    ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
-    ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
-    union = ap + ag - overlap + eps
-
-    # IoU
-    ious = overlap / union
-
-    # enclose area
-    enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
-    enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
-    enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)
-
-    cw = enclose_wh[:, 0]
-    ch = enclose_wh[:, 1]
-
-    c2 = cw**2 + ch**2 + eps
-
-    b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
-    b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
-    b2_x1, b2_y1 = target[:, 0], target[:, 1]
-    b2_x2, b2_y2 = target[:, 2], target[:, 3]
-
-    left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
-    right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
-    rho2 = left + right
-
-    # DIoU
-    dious = ious - rho2 / c2
-    loss = 1 - dious
-    return loss
-
-
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def ciou_loss(pred, target, eps=1e-7):
-    r"""`Implementation of paper `Enhancing Geometric Factors into
-    Model Learning and Inference for Object Detection and Instance
-    Segmentation <https://arxiv.org/abs/2005.03572>`_.
-
-    Code is modified from https://github.com/Zzh-tju/CIoU.
-
-    Args:
-        pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
-            shape (n, 4).
-        target (Tensor): Corresponding gt bboxes, shape (n, 4).
-        eps (float): Eps to avoid log(0).
-    Return:
-        Tensor: Loss tensor.
-    """
-    # overlap
-    lt = torch.max(pred[:, :2], target[:, :2])
-    rb = torch.min(pred[:, 2:], target[:, 2:])
-    wh = (rb - lt).clamp(min=0)
-    overlap = wh[:, 0] * wh[:, 1]
-
-    # union
-    ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
-    ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
-    union = ap + ag - overlap + eps
-
-    # IoU
-    ious = overlap / union
-
-    # enclose area
-    enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
-    enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
-    enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)
-
-    cw = enclose_wh[:, 0]
-    ch = enclose_wh[:, 1]
-
-    c2 = cw**2 + ch**2 + eps
-
-    b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
-    b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
-    b2_x1, b2_y1 = target[:, 0], target[:, 1]
-    b2_x2, b2_y2 = target[:, 2], target[:, 3]
-
-    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
-    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
-
-    left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
-    right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
-    rho2 = left + right
-
-    factor = 4 / math.pi**2
-    v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
-
-    # CIoU
-    cious = ious - (rho2 / c2 + v**2 / (1 - ious + v))
-    loss = 1 - cious
-    return loss
-
-
-@LOSSES.register_module()
-class IoULoss(nn.Module):
-    """IoULoss.
-
-    Computing the IoU loss between a set of predicted bboxes and target bboxes.
-
-    Args:
-        linear (bool): If True, use linear scale of loss instead of log scale.
-            Default: False.
-        eps (float): Eps to avoid log(0).
-        reduction (str): Options are "none", "mean" and "sum".
-        loss_weight (float): Weight of loss.
-    """
-
-    def __init__(self,
-                 linear=False,
-                 eps=1e-6,
-                 reduction='mean',
-                 loss_weight=1.0):
-        super(IoULoss, self).__init__()
-        self.linear = linear
-        self.eps = eps
-        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. Options are "none", "mean" and "sum".
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if (weight is not None) and (not torch.any(weight > 0)) and (
-                reduction != 'none'):
-            if pred.dim() == weight.dim() + 1:
-                weight = weight.unsqueeze(1)
-            return (pred * weight).sum()  # 0
-        if weight is not None and weight.dim() > 1:
-            # TODO: remove this in the future
-            # reduce the weight of shape (n, 4) to (n,) to match the
-            # iou_loss of shape (n,)
-            assert weight.shape == pred.shape
-            weight = weight.mean(-1)
-        loss = self.loss_weight * iou_loss(
-            pred,
-            target,
-            weight,
-            linear=self.linear,
-            eps=self.eps,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss
-
-
-@LOSSES.register_module()
-class BoundedIoULoss(nn.Module):
-
-    def __init__(self, beta=0.2, eps=1e-3, reduction='mean', loss_weight=1.0):
-        super(BoundedIoULoss, self).__init__()
-        self.beta = beta
-        self.eps = eps
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        if weight is not None and not torch.any(weight > 0):
-            if pred.dim() == weight.dim() + 1:
-                weight = weight.unsqueeze(1)
-            return (pred * weight).sum()  # 0
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        loss = self.loss_weight * bounded_iou_loss(
-            pred,
-            target,
-            weight,
-            beta=self.beta,
-            eps=self.eps,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss
-
-
-@LOSSES.register_module()
-class GIoULoss(nn.Module):
-
-    def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
-        super(GIoULoss, self).__init__()
-        self.eps = eps
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        if weight is not None and not torch.any(weight > 0):
-            if pred.dim() == weight.dim() + 1:
-                weight = weight.unsqueeze(1)
-            return (pred * weight).sum()  # 0
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if weight is not None and weight.dim() > 1:
-            # TODO: remove this in the future
-            # reduce the weight of shape (n, 4) to (n,) to match the
-            # giou_loss of shape (n,)
-            assert weight.shape == pred.shape
-            weight = weight.mean(-1)
-        loss = self.loss_weight * giou_loss(
-            pred,
-            target,
-            weight,
-            eps=self.eps,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss
-
-
-@LOSSES.register_module()
-class DIoULoss(nn.Module):
-
-    def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
-        super(DIoULoss, self).__init__()
-        self.eps = eps
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        if weight is not None and not torch.any(weight > 0):
-            if pred.dim() == weight.dim() + 1:
-                weight = weight.unsqueeze(1)
-            return (pred * weight).sum()  # 0
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if weight is not None and weight.dim() > 1:
-            # TODO: remove this in the future
-            # reduce the weight of shape (n, 4) to (n,) to match the
-            # giou_loss of shape (n,)
-            assert weight.shape == pred.shape
-            weight = weight.mean(-1)
-        loss = self.loss_weight * diou_loss(
-            pred,
-            target,
-            weight,
-            eps=self.eps,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss
-
-
-@LOSSES.register_module()
-class CIoULoss(nn.Module):
-
-    def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
-        super(CIoULoss, self).__init__()
-        self.eps = eps
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        if weight is not None and not torch.any(weight > 0):
-            if pred.dim() == weight.dim() + 1:
-                weight = weight.unsqueeze(1)
-            return (pred * weight).sum()  # 0
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        if weight is not None and weight.dim() > 1:
-            # TODO: remove this in the future
-            # reduce the weight of shape (n, 4) to (n,) to match the
-            # giou_loss of shape (n,)
-            assert weight.shape == pred.shape
-            weight = weight.mean(-1)
-        loss = self.loss_weight * ciou_loss(
-            pred,
-            target,
-            weight,
-            eps=self.eps,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss
diff --git a/spaces/ucalyptus/PTI/models/StyleCLIP/criteria/__init__.py b/spaces/ucalyptus/PTI/models/StyleCLIP/criteria/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/udion/BayesCap/app.py b/spaces/udion/BayesCap/app.py
deleted file mode 100644
index 1aed75e347a6e9c232efbb28e1eadd9bf5843417..0000000000000000000000000000000000000000
--- a/spaces/udion/BayesCap/app.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import numpy as np
-import random
-import matplotlib.pyplot as plt
-from matplotlib import cm
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.models as models
-from torch.utils.data import Dataset, DataLoader
-from torchvision import transforms
-from torchvision.transforms.functional import InterpolationMode as IMode
-
-from PIL import Image
-
-from ds import *
-from losses import *
-from networks_SRGAN import *
-from utils import *
-
-device = 'cpu'
-if device == 'cuda':
-    dtype = torch.cuda.FloatTensor
-else:
-    dtype = torch.FloatTensor
-
-NetG = Generator()
-model_parameters = filter(lambda p: True, NetG.parameters())
-params = sum([np.prod(p.size()) for p in model_parameters])
-print("Number of Parameters:", params)
-NetC = BayesCap(in_channels=3, out_channels=3)
-
-ensure_checkpoint_exists('BayesCap_SRGAN.pth')
-NetG.load_state_dict(torch.load('BayesCap_SRGAN.pth', map_location=device))
-NetG.to(device)
-NetG.eval()
-
-ensure_checkpoint_exists('BayesCap_ckpt.pth')
-NetC.load_state_dict(torch.load('BayesCap_ckpt.pth', map_location=device))
-NetC.to(device)
-NetC.eval()
-
-def tensor01_to_pil(xt):
-    r = transforms.ToPILImage(mode='RGB')(xt.squeeze())
-    return r
-
-def image2tensor(image: np.ndarray, range_norm: bool, half: bool) -> torch.Tensor:
-    """Convert ``PIL.Image`` to Tensor.
-    Args:
-        image (np.ndarray): The image data read by ``PIL.Image``
-        range_norm (bool): Scale [0, 1] data to between [-1, 1]
-        half (bool): Whether to convert torch.float32 similarly to torch.half type.
-    Returns:
-        Normalized image data
-    Examples:
-        >>> image = Image.open("image.bmp")
-        >>> tensor_image = image2tensor(image, range_norm=False, half=False)
-    """
-    tensor = F.to_tensor(image)
-
-    if range_norm:
-        tensor = tensor.mul_(2.0).sub_(1.0)
-    if half:
-        tensor = tensor.half()
-
-    return tensor
-
-
-def predict(img):
-    """
-    img: image
-    """
-    image_size = (256,256)
-    upscale_factor = 4
-    # lr_transforms = transforms.Resize((image_size[0]//upscale_factor, image_size[1]//upscale_factor), interpolation=IMode.BICUBIC, antialias=True)
-    # to retain aspect ratio
-    lr_transforms = transforms.Resize(64, interpolation=IMode.BICUBIC, antialias=True)
-    #lr_transforms = transforms.Resize(image_size[0]//upscale_factor, interpolation=IMode.BICUBIC, antialias=True)
-    # lr_transforms = transforms.Resize((128, 128), interpolation=IMode.BICUBIC, antialias=True)
-    
-    img = Image.fromarray(np.array(img))
-    img = lr_transforms(img)
-    lr_tensor = image2tensor(img, range_norm=False, half=False)
-    
-    xLR = lr_tensor.to(device).unsqueeze(0)
-    xLR = xLR.type(dtype)
-    # pass them through the network
-    with torch.no_grad():
-        xSR = NetG(xLR)
-        xSRC_mu, xSRC_alpha, xSRC_beta = NetC(xSR)
-        
-    a_map = (1/(xSRC_alpha[0] + 1e-5)).to('cpu').data
-    b_map = xSRC_beta[0].to('cpu').data
-    u_map = (a_map**2)*(torch.exp(torch.lgamma(3/(b_map + 1e-2)))/torch.exp(torch.lgamma(1/(b_map + 1e-2)))) 
-    
-    
-    x_LR = tensor01_to_pil(xLR.to('cpu').data.clip(0,1).transpose(0,2).transpose(0,1))
-    
-    x_mean = tensor01_to_pil(xSR.to('cpu').data.clip(0,1).transpose(0,2).transpose(0,1))
-    
-    #im = Image.fromarray(np.uint8(cm.gist_earth(myarray)*255))
-    
-    a_map = torch.clamp(a_map, min=0, max=0.1)
-    a_map = (a_map - a_map.min())/(a_map.max() - a_map.min())
-    x_alpha = Image.fromarray(np.uint8(cm.inferno(a_map.transpose(0,2).transpose(0,1).squeeze())*255))
-    
-    b_map = torch.clamp(b_map, min=0.45, max=0.75)
-    b_map = (b_map - b_map.min())/(b_map.max() - b_map.min())
-    x_beta = Image.fromarray(np.uint8(cm.cividis(b_map.transpose(0,2).transpose(0,1).squeeze())*255))
-    
-    u_map = torch.clamp(u_map, min=0, max=0.15)
-    u_map = (u_map - u_map.min())/(u_map.max() - u_map.min())
-    x_uncer = Image.fromarray(np.uint8(cm.hot(u_map.transpose(0,2).transpose(0,1).squeeze())*255))
-    
-    return x_LR, x_mean, x_alpha, x_beta, x_uncer
-
-import gradio as gr
-
-title = "BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks"
-
-abstract="<center> <img src='https://www.eml-unitue.de/publications/BayesCap/BayesCap.gif'> </center> <br> <b>Abstract.</b> High-quality calibrated uncertainty estimates are crucial for numerous real-world applications, especially for deep learning-based deployed ML systems. While Bayesian deep learning techniques allow uncertainty estimation, training them with large-scale datasets is an expensive process that does not always yield models competitive with non-Bayesian counterparts. Moreover, many of the high-performing deep learning models that are already trained and deployed are non-Bayesian in nature and do not provide uncertainty estimates. To address these issues, we propose BayesCap that learns a Bayesian identity mapping for the frozen model, allowing uncertainty estimation. BayesCap is a memory-efficient method that can be trained on a small fraction of the original dataset, enhancing pretrained non-Bayesian computer vision models by providing calibrated uncertainty estimates for the predictions without (i) hampering the performance of the model and (ii) the need for expensive retraining the model from scratch. The proposed method is agnostic to various architectures and tasks. We show the efficacy of our method on a wide variety of tasks with a diverse set of architectures, including image super-resolution, deblurring, inpainting, and crucial application such as medical image translation. Moreover, we apply the derived uncertainty estimates to detect out-of-distribution samples in critical scenarios like depth estimation in autonomous driving. Code is available <a href='https://github.com/ExplainableML/BayesCap'>here</a>. <br> <br>"
-
-method = "In this demo, we show an application of BayesCap on top of SRGAN for the task of super resolution. BayesCap estimates the per-pixel uncertainty of a pretrained computer vision model like SRGAN (used for super-resolution). BayesCap takes the ouput of the pretrained model (in this case SRGAN), and predicts the per-pixel distribution parameters for the output, that can be used to quantify the per-pixel uncertainty. In our work, we model the per-pixel output as a <a href='https://en.wikipedia.org/wiki/Generalized_normal_distribution'>Generalized Gaussian distribution</a> that is parameterized by 3 parameters the mean, scale (alpha), and the shape (beta). As a result our model predicts these three parameters as shown below. From these 3 parameters one can compute the uncertainty as shown in <a href='https://en.wikipedia.org/wiki/Generalized_normal_distribution'>this article</a>. <br><br>"
-
-closing = "For more details, please find the <a href='https://arxiv.org/pdf/2207.06873.pdf'>ECCV 2022 paper here</a>."
-
-description = abstract + method + closing
-
-article = "<p style='text-align: center'> BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks| <a href='https://github.com/ExplainableML/BayesCap'>Github Repo</a></p>"
-
-
-gr.Interface(
-    fn=predict, 
-    inputs=gr.inputs.Image(type='pil', label="Orignal"), 
-    outputs=[
-        gr.outputs.Image(type='pil', label="Low-resolution image (input to SRGAN)"), 
-        gr.outputs.Image(type='pil', label="Super-resolved image (output of SRGAN)"), 
-        gr.outputs.Image(type='pil', label="Alpha parameter map characterizing per-pixel distribution (output of BayesCap)"), 
-        gr.outputs.Image(type='pil', label="Beta parameter map characterizing per-pixel distribution (output of BayesCap)"), 
-        gr.outputs.Image(type='pil', label="Per-pixel uncertainty map (derived using outputs of BayesCap)")
-     ],
-    title=title,
-    description=description,
-    article=article,
-     examples=[
-        ["./demo_examples/tue.jpeg"],
-        ["./demo_examples/baby.png"],
-        ["./demo_examples/bird.png"],
-        ["./demo_examples/butterfly.png"],
-        ["./demo_examples/head.png"],
-        ["./demo_examples/woman.png"],
-    ]
-).launch()
diff --git a/spaces/unity/ML-Agents-SoccerTwos/README.md b/spaces/unity/ML-Agents-SoccerTwos/README.md
deleted file mode 100644
index 13bb97fece1b4d8973a9780dfa75139dec15b25a..0000000000000000000000000000000000000000
--- a/spaces/unity/ML-Agents-SoccerTwos/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: SoccerTwos
-emoji: ⚽
-colorFrom: blue
-colorTo: gray
-sdk: static
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/unixpickle/car-data/app.py b/spaces/unixpickle/car-data/app.py
deleted file mode 100644
index b3c9cbc536b556bf2420fbe5f45df2093b2e419c..0000000000000000000000000000000000000000
--- a/spaces/unixpickle/car-data/app.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import io
-
-import gradio as gr
-import requests
-import torch
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-from PIL import Image
-
-from constants import MAKES_MODELS, PRICE_BIN_LABELS, YEARS
-
-print("downloading checkpoint...")
-data = requests.get(
-    "https://data.aqnichol.com/car-data/models/mobilenetv2_432000_calib_torchscript.pt",
-    stream=True,
-).content
-
-print("creating model...")
-model = torch.jit.load(io.BytesIO(data))
-model.eval()
-transform = transforms.Compose(
-    [
-        transforms.ToTensor(),
-        transforms.Normalize(
-            (0.48145466, 0.4578275, 0.40821073),
-            (0.26862954, 0.26130258, 0.27577711),
-        ),
-    ]
-)
-
-print("done.")
-
-
-def classify(img: Image.Image):
-    in_tensor = transform(img)[None]
-    outputs = model(in_tensor)
-
-    price_bins = dict(
-        zip(PRICE_BIN_LABELS, F.softmax(outputs["price_bin"], dim=-1)[0].tolist())
-    )
-    years = dict(
-        zip(
-            [str(year) for year in YEARS] + ["Unknown"],
-            F.softmax(outputs["year"], dim=-1)[0].tolist(),
-        )
-    )
-    make_models = dict(
-        zip(
-            ([f"{make} {model}" for make, model in MAKES_MODELS] + ["Unknown"]),
-            F.softmax(outputs["make_model"], dim=-1)[0].tolist(),
-        )
-    )
-    return (
-        f"${int(round(outputs['price_median'].item()))}",
-        price_bins,
-        years,
-        make_models,
-        img,
-    )
-
-
-iface = gr.Interface(
-    fn=classify,
-    inputs=gr.Image(shape=(224, 224), type="pil"),
-    outputs=[
-        gr.Text(label="Price Prediction"),
-        gr.Label(label="Price Bin", num_top_classes=5),
-        gr.Label(label="Year", num_top_classes=5),
-        gr.Label(label="Make/Model", num_top_classes=10),
-        gr.Image(label="Cropped Input"),
-    ],
-)
-iface.queue(concurrency_count=2).launch()
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Binkdx8surfacetype4.md b/spaces/usbethFlerru/sovits-modelsV2/example/Binkdx8surfacetype4.md
deleted file mode 100644
index e61cfffc3509c6f264b476acadc990ea8c4c7403..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Binkdx8surfacetype4.md
+++ /dev/null
@@ -1,10 +0,0 @@
-<h2>Binkdx8surfacetype4</h2><br /><p><b><b>Download File</b> &raquo;&raquo;&raquo; <a href="https://urlcod.com/2uyV8u">https://urlcod.com/2uyV8u</a></b></p><br /><br />
-
-binkdx8surfacetype4 We all know that when buying an apartment, the question of its repair immediately arises.
-As a rule, this process takes not so little time, so many who buy an apartment decide to make repairs on their own.
-However, not all of us are experts in this matter.
-In such a situation, apartment renovation can lead to a number of problems.
-In order for you to save money, and at the same time make a quality repair, it is best for you to contact the specialists. 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Devil May Cry 5 Steam Must Be Running.md b/spaces/usbethFlerru/sovits-modelsV2/example/Devil May Cry 5 Steam Must Be Running.md
deleted file mode 100644
index a7e5c186a086c45fedaf6a634951a4a141798b4c..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Devil May Cry 5 Steam Must Be Running.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Devil May Cry 5 Steam Must Be Running</h2><br /><p><b><b>Download Zip</b> ->>> <a href="https://urlcod.com/2uyXM0">https://urlcod.com/2uyXM0</a></b></p><br /><br />
-<br />
-If you hated the game, or couldn't get past the fact that Devil May Cry ... Running at a smooth 60fps, I only noticed any issues on the second mission. ... Gods Must Die is aimed at the truly masochistic among us, as no items can be used. ... Sony square enix Steam Trailer Ubisoft Wii U Xbox 360 Xbox One. 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/user238921933/stable-diffusion-webui/javascript/notification.js b/spaces/user238921933/stable-diffusion-webui/javascript/notification.js
deleted file mode 100644
index 040a3afac2019fe2d3532122b8317560d5935814..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/javascript/notification.js
+++ /dev/null
@@ -1,49 +0,0 @@
-// Monitors the gallery and sends a browser notification when the leading image is new.
-
-let lastHeadImg = null;
-
-notificationButton = null
-
-onUiUpdate(function(){
-    if(notificationButton == null){
-        notificationButton = gradioApp().getElementById('request_notifications')
-
-        if(notificationButton != null){
-            notificationButton.addEventListener('click', function (evt) {
-                Notification.requestPermission();
-            },true);
-        }
-    }
-
-    const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] img.h-full.w-full.overflow-hidden');
-
-    if (galleryPreviews == null) return;
-
-    const headImg = galleryPreviews[0]?.src;
-
-    if (headImg == null || headImg == lastHeadImg) return;
-
-    lastHeadImg = headImg;
-
-    // play notification sound if available
-    gradioApp().querySelector('#audio_notification audio')?.play();
-
-    if (document.hasFocus()) return;
-
-    // Multiple copies of the images are in the DOM when one is selected. Dedup with a Set to get the real number generated.
-    const imgs = new Set(Array.from(galleryPreviews).map(img => img.src));
-
-    const notification = new Notification(
-        'Stable Diffusion',
-        {
-            body: `Generated ${imgs.size > 1 ? imgs.size - opts.return_grid : 1} image${imgs.size > 1 ? 's' : ''}`,
-            icon: headImg,
-            image: headImg,
-        }
-    );
-
-    notification.onclick = function(_){
-        parent.focus();
-        this.close();
-    };
-});
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/utils/callbacks/wb.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/utils/callbacks/wb.md
deleted file mode 100644
index 1116adc8958d700c369632d9b7df52ce0d4b2d51..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/utils/callbacks/wb.md
+++ /dev/null
@@ -1,29 +0,0 @@
----
-description: Learn how to use Ultralytics YOLO's built-in callbacks `on_pretrain_routine_start` and `on_train_epoch_end` for improved training performance.
-keywords: Ultralytics, YOLO, callbacks, weights, biases, training
----
-
-## _log_plots
----
-### ::: ultralytics.yolo.utils.callbacks.wb._log_plots
-<br><br>
-
-## on_pretrain_routine_start
----
-### ::: ultralytics.yolo.utils.callbacks.wb.on_pretrain_routine_start
-<br><br>
-
-## on_fit_epoch_end
----
-### ::: ultralytics.yolo.utils.callbacks.wb.on_fit_epoch_end
-<br><br>
-
-## on_train_epoch_end
----
-### ::: ultralytics.yolo.utils.callbacks.wb.on_train_epoch_end
-<br><br>
-
-## on_train_end
----
-### ::: ultralytics.yolo.utils.callbacks.wb.on_train_end
-<br><br>
diff --git a/spaces/vbzvibin/gavs-hackathon_v1/README.md b/spaces/vbzvibin/gavs-hackathon_v1/README.md
deleted file mode 100644
index aca58a250d4db5dff66db1d5afd99e0cd9300f4b..0000000000000000000000000000000000000000
--- a/spaces/vbzvibin/gavs-hackathon_v1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Gavs-hackathon V1
-emoji: 🏢
-colorFrom: red
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vict0rsch/climateGAN/climategan/tutils.py b/spaces/vict0rsch/climateGAN/climategan/tutils.py
deleted file mode 100644
index 96bdf7aa4dd990eb44c4a2546b8f0cfb5477d60e..0000000000000000000000000000000000000000
--- a/spaces/vict0rsch/climateGAN/climategan/tutils.py
+++ /dev/null
@@ -1,763 +0,0 @@
-"""Tensor-utils
-"""
-import io
-import math
-from contextlib import redirect_stdout
-from pathlib import Path
-
-# from copy import copy
-from threading import Thread
-
-import numpy as np
-import torch
-import torch.nn as nn
-from skimage import io as skio
-from torch import autograd
-from torch.autograd import Variable
-from torch.nn import init
-
-from climategan.utils import all_texts_to_array
-
-
-def transforms_string(ts):
-    return " -> ".join([t.__class__.__name__ for t in ts.transforms])
-
-
-def init_weights(net, init_type="normal", init_gain=0.02, verbose=0, caller=""):
-    """Initialize network weights.
-    Parameters:
-        net (network)     -- network to be initialized
-        init_type (str)   -- the name of an initialization method:
-                             normal | xavier | kaiming | orthogonal
-        init_gain (float) -- scaling factor for normal, xavier and orthogonal.
-
-    We use 'normal' in the original pix2pix and CycleGAN paper.
-    But xavier and kaiming might work better for some applications.
-    Feel free to try yourself.
-    """
-
-    if not init_type:
-        print(
-            "init_weights({}): init_type is {}, defaulting to normal".format(
-                caller + " " + net.__class__.__name__, init_type
-            )
-        )
-        init_type = "normal"
-    if not init_gain:
-        print(
-            "init_weights({}): init_gain is {}, defaulting to normal".format(
-                caller + " " + net.__class__.__name__, init_type
-            )
-        )
-        init_gain = 0.02
-
-    def init_func(m):
-        classname = m.__class__.__name__
-        if classname.find("BatchNorm2d") != -1:
-            if hasattr(m, "weight") and m.weight is not None:
-                init.normal_(m.weight.data, 1.0, init_gain)
-            if hasattr(m, "bias") and m.bias is not None:
-                init.constant_(m.bias.data, 0.0)
-        elif hasattr(m, "weight") and (
-            classname.find("Conv") != -1 or classname.find("Linear") != -1
-        ):
-            if init_type == "normal":
-                init.normal_(m.weight.data, 0.0, init_gain)
-            elif init_type == "xavier":
-                init.xavier_normal_(m.weight.data, gain=init_gain)
-            elif init_type == "xavier_uniform":
-                init.xavier_uniform_(m.weight.data, gain=1.0)
-            elif init_type == "kaiming":
-                init.kaiming_normal_(m.weight.data, a=0, mode="fan_in")
-            elif init_type == "orthogonal":
-                init.orthogonal_(m.weight.data, gain=init_gain)
-            elif init_type == "none":  # uses pytorch's default init method
-                m.reset_parameters()
-            else:
-                raise NotImplementedError(
-                    "initialization method [%s] is not implemented" % init_type
-                )
-            if hasattr(m, "bias") and m.bias is not None:
-                init.constant_(m.bias.data, 0.0)
-
-    if verbose > 0:
-        print("initialize %s with %s" % (net.__class__.__name__, init_type))
-    net.apply(init_func)
-
-
-def domains_to_class_tensor(domains, one_hot=False):
-    """Converts a list of strings to a 1D Tensor representing the domains
-
-    domains_to_class_tensor(["sf", "rn"])
-    >>> torch.Tensor([2, 1])
-
-    Args:
-        domain (list(str)): each element of the list should be in {rf, rn, sf, sn}
-        one_hot (bool, optional): whether or not to 1-h encode class labels.
-            Defaults to False.
-    Raises:
-        ValueError: One of the domains listed is not in {rf, rn, sf, sn}
-
-    Returns:
-        torch.Tensor: 1D tensor mapping a domain to an int (not 1-hot) or 1-hot
-            domain labels in a 2D tensor
-    """
-
-    mapping = {"r": 0, "s": 1}
-
-    if not all(domain in mapping for domain in domains):
-        raise ValueError(
-            "Unknown domains {} should be in {}".format(domains, list(mapping.keys()))
-        )
-
-    target = torch.tensor([mapping[domain] for domain in domains])
-
-    if one_hot:
-        one_hot_target = torch.FloatTensor(len(target), 2)  # 2 domains
-        one_hot_target.zero_()
-        one_hot_target.scatter_(1, target.unsqueeze(1), 1)
-        # https://discuss.pytorch.org/t/convert-int-into-one-hot-format/507
-        target = one_hot_target
-    return target
-
-
-def fake_domains_to_class_tensor(domains, one_hot=False):
-    """Converts a list of strings to a 1D Tensor representing the fake domains
-    (real or sim only)
-
-    fake_domains_to_class_tensor(["s", "r"], False)
-    >>> torch.Tensor([0, 2])
-
-
-    Args:
-        domain (list(str)): each element of the list should be in {r, s}
-        one_hot (bool, optional): whether or not to 1-h encode class labels.
-            Defaults to False.
-    Raises:
-        ValueError: One of the domains listed is not in {rf, rn, sf, sn}
-
-    Returns:
-        torch.Tensor: 1D tensor mapping a domain to an int (not 1-hot) or
-            a 2D tensor filled with 0.25 to fool the classifier (equiprobability
-            for each domain).
-    """
-    if one_hot:
-        target = torch.FloatTensor(len(domains), 2)
-        target.fill_(0.5)
-
-    else:
-        mapping = {"r": 1, "s": 0}
-
-        if not all(domain in mapping for domain in domains):
-            raise ValueError(
-                "Unknown domains {} should be in {}".format(
-                    domains, list(mapping.keys())
-                )
-            )
-
-        target = torch.tensor([mapping[domain] for domain in domains])
-    return target
-
-
-def show_tanh_tensor(tensor):
-    import skimage
-
-    if isinstance(tensor, torch.Tensor):
-        image = tensor.permute(1, 2, 0).detach().numpy()
-    else:
-        image = tensor
-        if image.shape[-1] != 3:
-            image = image.transpose(1, 2, 0)
-
-    if image.min() < 0 and image.min() > -1:
-        image = image / 2 + 0.5
-    elif image.min() < -1:
-        raise ValueError("can't handle this data")
-
-    skimage.io.imshow(image)
-
-
-def normalize_tensor(t):
-    """
-    Brings any tensor to the [0; 1] range.
-
-    Args:
-        t (torch.Tensor): input to normalize
-
-    Returns:
-        torch.Tensor: t projected to [0; 1]
-    """
-    t = t - torch.min(t)
-    t = t / torch.max(t)
-    return t
-
-
-def get_normalized_depth_t(tensor, domain, normalize=False, log=True):
-    assert not (normalize and log)
-    if domain == "r":
-        # megadepth depth
-        tensor = tensor.unsqueeze(0)
-        tensor = tensor - torch.min(tensor)
-        tensor = torch.true_divide(tensor, torch.max(tensor))
-
-    elif domain == "s":
-        # from 3-channel depth encoding from Unity simulator to 1-channel [0-1] values
-        tensor = decode_unity_depth_t(tensor, log=log, normalize=normalize)
-
-    elif domain == "kitti":
-        tensor = tensor / 100
-        if not log:
-            tensor = 1 / tensor
-            if normalize:
-                tensor = tensor - tensor.min()
-                tensor = tensor / tensor.max()
-        else:
-            tensor = torch.log(tensor)
-
-        tensor = tensor.unsqueeze(0)
-
-    return tensor
-
-
-def decode_bucketed_depth(tensor, opts):
-    # tensor is size 1 x C x H x W
-    assert tensor.shape[0] == 1
-    idx = torch.argmax(tensor.squeeze(0), dim=0)  # channels become dim 0 with squeeze
-    linspace_args = (
-        opts.gen.d.classify.linspace.min,
-        opts.gen.d.classify.linspace.max,
-        opts.gen.d.classify.linspace.buckets,
-    )
-    indexer = torch.linspace(*linspace_args)
-    log_depth = indexer[idx.long()].to(torch.float32)  # H x W
-    depth = torch.exp(log_depth)
-    return depth.unsqueeze(0).unsqueeze(0).to(tensor.device)
-
-
-def decode_unity_depth_t(unity_depth, log=True, normalize=False, numpy=False, far=1000):
-    """Transforms the 3-channel encoded depth map from our Unity simulator
-    to 1-channel depth map containing metric depth values.
-    The depth is encoded in the following way:
-    - The information from the simulator is (1 - LinearDepth (in [0,1])).
-        far corresponds to the furthest distance to the camera included in the
-        depth map.
-        LinearDepth * far gives the real metric distance to the camera.
-    - depth is first divided in 31 slices encoded in R channel with values ranging
-        from 0 to 247
-    - each slice is divided again in 31 slices, whose value is encoded in G channel
-    - each of the G slices is divided into 256 slices, encoded in B channel
-
-    In total, we have a discretization of depth into N = 31*31*256 - 1 possible values,
-    covering a range of far/N meters.
-
-    Note that, what we encode here is 1 - LinearDepth so that the furthest point is
-    [0,0,0] (that is sky) and the closest point[255,255,255]
-
-    The metric distance associated to a pixel whose depth is (R,G,B) is :
-        d = (far/N) * [((255 - R)//8)*256*31 + ((255 - G)//8)*256 + (255 - B)]
-
-    * torch.Tensor in [0, 1] as torch.float32 if numpy == False
-
-    * else numpy.array in [0, 255] as np.uint8
-
-    Args:
-        unity_depth (torch.Tensor): one depth map obtained from our simulator
-        numpy (bool, optional): Whether to return a float tensor or an int array.
-         Defaults to False.
-        far: far parameter of the camera in Unity simulator.
-
-    Returns:
-        [torch.Tensor or numpy.array]: decoded depth
-    """
-    R = unity_depth[:, :, 0]
-    G = unity_depth[:, :, 1]
-    B = unity_depth[:, :, 2]
-
-    R = ((247 - R) / 8).type(torch.IntTensor)
-    G = ((247 - G) / 8).type(torch.IntTensor)
-    B = (255 - B).type(torch.IntTensor)
-    depth = ((R * 256 * 31 + G * 256 + B).type(torch.FloatTensor)) / (256 * 31 * 31 - 1)
-    depth = depth * far
-    if not log:
-        depth = 1 / depth
-    depth = depth.unsqueeze(0)  # (depth * far).unsqueeze(0)
-
-    if log:
-        depth = torch.log(depth)
-    if normalize:
-        depth = depth - torch.min(depth)
-        depth /= torch.max(depth)
-    if numpy:
-        depth = depth.data.cpu().numpy()
-        return depth.astype(np.uint8).squeeze()
-    return depth
-
-
-def to_inv_depth(log_depth, numpy=False):
-    """Convert log depth tensor to inverse depth image for display
-
-    Args:
-        depth (Tensor): log depth float tensor
-    """
-    depth = torch.exp(log_depth)
-    # visualize prediction using inverse depth, so that we don't need sky
-    # segmentation (if you want to use RGB map for visualization,
-    # you have to run semantic segmentation to mask the sky first
-    # since the depth of sky is random from CNN)
-    inv_depth = 1 / depth
-    inv_depth /= torch.max(inv_depth)
-    if numpy:
-        inv_depth = inv_depth.data.cpu().numpy()
-    # you might also use percentile for better visualization
-
-    return inv_depth
-
-
-def shuffle_batch_tuple(mbt):
-    """shuffle the order of domains in the batch
-
-    Args:
-        mbt (tuple): multi-batch tuple
-
-    Returns:
-        list: randomized list of domain-specific batches
-    """
-    assert isinstance(mbt, (tuple, list))
-    assert len(mbt) > 0
-    perm = np.random.permutation(len(mbt))
-    return [mbt[i] for i in perm]
-
-
-def slice_batch(batch, slice_size):
-    assert slice_size > 0
-    for k, v in batch.items():
-        if isinstance(v, dict):
-            for task, d in v.items():
-                batch[k][task] = d[:slice_size]
-        else:
-            batch[k] = v[:slice_size]
-    return batch
-
-
-def save_tanh_tensor(image, path):
-    """Save an image which can be numpy or tensor, 2 or 3 dims (no batch)
-    to path.
-
-    Args:
-        image (np.array or torch.Tensor): image to save
-        path (pathlib.Path or str): where to save the image
-    """
-    path = Path(path)
-    if isinstance(image, torch.Tensor):
-        image = image.detach().cpu().numpy()
-        if image.shape[-1] != 3 and image.shape[0] == 3:
-            image = np.transpose(image, (1, 2, 0))
-    if image.min() < 0 and image.min() > -1:
-        image = image / 2 + 0.5
-    elif image.min() < -1:
-        image -= image.min()
-        image /= image.max()
-        # print("Warning: scaling image data in save_tanh_tensor")
-
-    skio.imsave(path, (image * 255).astype(np.uint8))
-
-
-def save_batch(multi_domain_batch, root="./", step=0, num_threads=5):
-    root = Path(root)
-    root.mkdir(parents=True, exist_ok=True)
-    images_to_save = {"paths": [], "images": []}
-    for domain, batch in multi_domain_batch.items():
-        y = batch["data"].get("y")
-        x = batch["data"]["x"]
-        if y is not None:
-            paths = batch["paths"]["x"]
-            imtensor = torch.cat([x, y], dim=-1)
-            for i, im in enumerate(imtensor):
-                imid = Path(paths[i]).stem[:10]
-                images_to_save["paths"] += [
-                    root / "im_{}_{}_{}.png".format(step, domain, imid)
-                ]
-                images_to_save["images"].append(im)
-    if num_threads > 0:
-        threaded_write(images_to_save["images"], images_to_save["paths"], num_threads)
-    else:
-        for im, path in zip(images_to_save["images"], images_to_save["paths"]):
-            save_tanh_tensor(im, path)
-
-
-def threaded_write(images, paths, num_threads=5):
-    t_im = []
-    t_p = []
-    for im, p in zip(images, paths):
-        t_im.append(im)
-        t_p.append(p)
-        if len(t_im) == num_threads:
-            ts = [
-                Thread(target=save_tanh_tensor, args=(_i, _p))
-                for _i, _p in zip(t_im, t_p)
-            ]
-            list(map(lambda t: t.start(), ts))
-            list(map(lambda t: t.join(), ts))
-            t_im = []
-            t_p = []
-    if t_im:
-        ts = [
-            Thread(target=save_tanh_tensor, args=(_i, _p)) for _i, _p in zip(t_im, t_p)
-        ]
-        list(map(lambda t: t.start(), ts))
-        list(map(lambda t: t.join(), ts))
-
-
-def get_num_params(model):
-    total_params = sum(p.numel() for p in model.parameters())
-    return total_params
-
-
-def vgg_preprocess(batch):
-    """Preprocess batch to use VGG model"""
-    tensortype = type(batch.data)
-    (r, g, b) = torch.chunk(batch, 3, dim=1)
-    batch = torch.cat((b, g, r), dim=1)  # convert RGB to BGR
-    batch = (batch + 1) * 255 * 0.5  # [-1, 1] -> [0, 255]
-    mean = tensortype(batch.data.size()).cuda()
-    mean[:, 0, :, :] = 103.939
-    mean[:, 1, :, :] = 116.779
-    mean[:, 2, :, :] = 123.680
-    batch = batch.sub(Variable(mean))  # subtract mean
-    return batch
-
-
-def zero_grad(model: nn.Module):
-    """
-    Sets gradients to None. Mode efficient than model.zero_grad()
-    or opt.zero_grad() according to https://www.youtube.com/watch?v=9mS1fIYj1So
-
-    Args:
-        model (nn.Module): model to zero out
-    """
-    for p in model.parameters():
-        p.grad = None
-
-
-# Take the prediction of fake and real images from the combined batch
-def divide_pred(disc_output):
-    """
-    Divide a multiscale discriminator's output into 2 sets of tensors,
-    expecting the input to the discriminator to be a concatenation
-    on the batch axis of real and fake (or fake and real) images,
-    effectively doubling the batch size for better batchnorm statistics
-
-    Args:
-        disc_output (list | torch.Tensor): Discriminator output to split
-
-    Returns:
-        list | torch.Tensor[type]: pair of split outputs
-    """
-    # https://github.com/NVlabs/SPADE/blob/master/models/pix2pix_model.py
-    # the prediction contains the intermediate outputs of multiscale GAN,
-    # so it's usually a list
-    if type(disc_output) == list:
-        half1 = []
-        half2 = []
-        for p in disc_output:
-            half1.append([tensor[: tensor.size(0) // 2] for tensor in p])
-            half2.append([tensor[tensor.size(0) // 2 :] for tensor in p])
-    else:
-        half1 = disc_output[: disc_output.size(0) // 2]
-        half2 = disc_output[disc_output.size(0) // 2 :]
-
-    return half1, half2
-
-
-def is_tpu_available():
-    _torch_tpu_available = False
-    try:
-        import torch_xla.core.xla_model as xm  # type: ignore
-
-        if "xla" in str(xm.xla_device()):
-            _torch_tpu_available = True
-        else:
-            _torch_tpu_available = False
-    except ImportError:
-        _torch_tpu_available = False
-
-    return _torch_tpu_available
-
-
-def get_WGAN_gradient(input, output):
-    # github code reference:
-    # https://github.com/caogang/wgan-gp/blob/master/gan_cifar10.py
-    # Calculate the gradient that WGAN-gp needs
-    grads = autograd.grad(
-        outputs=output,
-        inputs=input,
-        grad_outputs=torch.ones(output.size()).cuda(),
-        create_graph=True,
-        retain_graph=True,
-        only_inputs=True,
-    )[0]
-    grads = grads.view(grads.size(0), -1)
-    gp = ((grads.norm(2, dim=1) - 1) ** 2).mean()
-    return gp
-
-
-def print_num_parameters(trainer, force=False):
-    if trainer.verbose == 0 and not force:
-        return
-    print("-" * 35)
-    if trainer.G.encoder is not None:
-        print(
-            "{:21}:".format("num params encoder"),
-            f"{get_num_params(trainer.G.encoder):12,}",
-        )
-    for d in trainer.G.decoders.keys():
-        print(
-            "{:21}:".format(f"num params decoder {d}"),
-            f"{get_num_params(trainer.G.decoders[d]):12,}",
-        )
-
-    print(
-        "{:21}:".format("num params painter"),
-        f"{get_num_params(trainer.G.painter):12,}",
-    )
-
-    if trainer.D is not None:
-        for d in trainer.D.keys():
-            print(
-                "{:21}:".format(f"num params discrim {d}"),
-                f"{get_num_params(trainer.D[d]):12,}",
-            )
-
-    print("-" * 35)
-
-
-def srgb2lrgb(x):
-    x = normalize(x)
-    im = ((x + 0.055) / 1.055) ** (2.4)
-    im[x <= 0.04045] = x[x <= 0.04045] / 12.92
-    return im
-
-
-def lrgb2srgb(ims):
-    if len(ims.shape) == 3:
-        ims = [ims]
-        stack = False
-    else:
-        ims = list(ims)
-        stack = True
-
-    outs = []
-    for im in ims:
-
-        out = torch.zeros_like(im)
-        for k in range(3):
-            temp = im[k, :, :]
-
-            out[k, :, :] = 12.92 * temp * (temp <= 0.0031308) + (
-                1.055 * torch.pow(temp, (1 / 2.4)) - 0.055
-            ) * (temp > 0.0031308)
-        outs.append(out)
-
-    if stack:
-        return torch.stack(outs)
-
-    return outs[0]
-
-
-def normalize(t, mini=0.0, maxi=1.0):
-    """
-    Normalizes a tensor to [0, 1].
-    If the tensor has more than 3 dimensions, the first one
-    is assumed to be the batch dimension and the tensor is
-    normalized per batch element, not across the batches.
-
-    Args:
-        t (torch.Tensor): Tensor to normalize
-        mini (float, optional): Min allowed value. Defaults to 0.
-        maxi (float, optional): Max allowed value. Defaults to 1.
-
-    Returns:
-        torch.Tensor: The normalized tensor
-    """
-    if len(t.shape) == 3:
-        return mini + (maxi - mini) * (t - t.min()) / (t.max() - t.min())
-
-    batch_size = t.shape[0]
-    extra_dims = [1] * (t.ndim - 1)
-    min_t = t.reshape(batch_size, -1).min(1)[0].reshape(batch_size, *extra_dims)
-    t = t - min_t
-    max_t = t.reshape(batch_size, -1).max(1)[0].reshape(batch_size, *extra_dims)
-    t = t / max_t
-    return mini + (maxi - mini) * t
-
-
-def retrieve_sky_mask(seg):
-    """
-    get the binary mask for the sky given a segmentation tensor
-    of logits (N x C x H x W) or labels (N x H x W)
-
-    Args:
-        seg (torch.Tensor): Segmentation map
-
-    Returns:
-        torch.Tensor: Sky mask
-    """
-    if len(seg.shape) == 4:  # Predictions
-        seg_ind = torch.argmax(seg, dim=1)
-    else:
-        seg_ind = seg
-
-    sky_mask = seg_ind == 9
-    return sky_mask
-
-
-def all_texts_to_tensors(texts, width=640, height=40):
-    """
-    Creates a list of tensors with texts from PIL images
-
-    Args:
-        texts (list(str)): texts to write
-        width (int, optional): width of individual texts. Defaults to 640.
-        height (int, optional): height of individual texts. Defaults to 40.
-
-    Returns:
-        list(torch.Tensor): len(texts) tensors 3 x height x width
-    """
-    arrays = all_texts_to_array(texts, width, height)
-    arrays = [array.transpose(2, 0, 1) for array in arrays]
-    return [torch.tensor(array) for array in arrays]
-
-
-def write_architecture(trainer):
-    stem = "archi"
-    out = Path(trainer.opts.output_path)
-
-    # encoder
-    with open(out / f"{stem}_encoder.txt", "w") as f:
-        f.write(str(trainer.G.encoder))
-
-    # decoders
-    for k, v in trainer.G.decoders.items():
-        with open(out / f"{stem}_decoder_{k}.txt", "w") as f:
-            f.write(str(v))
-
-    # painter
-    if get_num_params(trainer.G.painter) > 0:
-        with open(out / f"{stem}_painter.txt", "w") as f:
-            f.write(str(trainer.G.painter))
-
-    # discriminators
-    if get_num_params(trainer.D) > 0:
-        for k, v in trainer.D.items():
-            with open(out / f"{stem}_discriminator_{k}.txt", "w") as f:
-                f.write(str(v))
-
-    with io.StringIO() as buf, redirect_stdout(buf):
-        print_num_parameters(trainer)
-        output = buf.getvalue()
-        with open(out / "archi_num_params.txt", "w") as f:
-            f.write(output)
-
-
-def rand_perlin_2d(shape, res, fade=lambda t: 6 * t**5 - 15 * t**4 + 10 * t**3):
-    delta = (res[0] / shape[0], res[1] / shape[1])
-    d = (shape[0] // res[0], shape[1] // res[1])
-
-    grid = (
-        torch.stack(
-            torch.meshgrid(
-                torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1])
-            ),
-            dim=-1,
-        )
-        % 1
-    )
-    angles = 2 * math.pi * torch.rand(res[0] + 1, res[1] + 1)
-    gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim=-1)
-
-    tile_grads = (
-        lambda slice1, slice2: gradients[slice1[0] : slice1[1], slice2[0] : slice2[1]]
-        .repeat_interleave(d[0], 0)
-        .repeat_interleave(d[1], 1)
-    )
-    dot = lambda grad, shift: (  # noqa: E731
-        torch.stack(
-            (
-                grid[: shape[0], : shape[1], 0] + shift[0],
-                grid[: shape[0], : shape[1], 1] + shift[1],
-            ),
-            dim=-1,
-        )
-        * grad[: shape[0], : shape[1]]
-    ).sum(dim=-1)
-
-    n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0])
-    n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0])
-    n01 = dot(tile_grads([0, -1], [1, None]), [0, -1])
-    n11 = dot(tile_grads([1, None], [1, None]), [-1, -1])
-    t = fade(grid[: shape[0], : shape[1]])
-    return math.sqrt(2) * torch.lerp(
-        torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]
-    )
-
-
-def mix_noise(x, mask, res=(8, 3), weight=0.1):
-    noise = rand_perlin_2d(x.shape[-2:], res).unsqueeze(0).unsqueeze(0).to(x.device)
-    noise = noise - noise.min()
-    mask = mask.repeat(1, 3, 1, 1).to(x.device).to(torch.float16)
-    y = mask * (weight * noise + (1 - weight) * x) + (1 - mask) * x
-    return y
-
-
-def tensor_ims_to_np_uint8s(ims):
-    """
-    transform a CHW of NCHW tensor into a list of np.uint8 [0, 255]
-    image arrays
-
-    Args:
-        ims (torch.Tensor | list): [description]
-    """
-    if not isinstance(ims, list):
-        assert isinstance(ims, torch.Tensor)
-        if ims.ndim == 3:
-            ims = [ims]
-
-    nps = []
-    for t in ims:
-        if t.shape[0] == 3:
-            t = t.permute(1, 2, 0)
-        else:
-            assert t.shape[-1] == 3
-
-        n = t.cpu().numpy()
-        n = (n + 1) / 2 * 255
-        nps.append(n.astype(np.uint8))
-
-    return nps[0] if len(nps) == 1 else nps
-
-
-def tensor_to_uint8_numpy_image(tensor):
-    """
-    Turns a BxCxHxW tensor into a numpy image:
-    * normalize
-    * to [0, 255]
-    * detach
-    * channels last
-    * to uin8
-    * to cpu
-    * to numpy
-
-    Args:
-        tensor (torch.Tensor): Tensor to transform
-
-    Returns:
-        np.array: BxHxWxC np.uint8 array in [0, 255]
-    """
-    return (
-        normalize(tensor, 0, 255)  # [0, 255]
-        .detach()  # detach from graph if needed
-        .permute(0, 2, 3, 1)  # BxHxWxC
-        .to(torch.uint8)  # uint8
-        .cpu()  # cpu
-        .numpy()  # numpy array
-    )
diff --git a/spaces/vinthony/SadTalker/src/facerender/pirender_animate.py b/spaces/vinthony/SadTalker/src/facerender/pirender_animate.py
deleted file mode 100644
index 406c081d77ff7b6cb9c0f3ed3721408e0d5657be..0000000000000000000000000000000000000000
--- a/spaces/vinthony/SadTalker/src/facerender/pirender_animate.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import os
-import cv2
-from tqdm import tqdm
-import yaml
-import numpy as np
-import warnings
-from skimage import img_as_ubyte
-import safetensors
-import safetensors.torch 
-warnings.filterwarnings('ignore')
-
-
-import imageio
-import torch
-
-from src.facerender.pirender.config import Config
-from src.facerender.pirender.face_model import FaceGenerator
-
-from pydub import AudioSegment 
-from src.utils.face_enhancer import enhancer_generator_with_len, enhancer_list
-from src.utils.paste_pic import paste_pic
-from src.utils.videoio import save_video_with_watermark
-
-try:
-    import webui  # in webui
-    in_webui = True
-except:
-    in_webui = False
-
-class AnimateFromCoeff_PIRender():
-
-    def __init__(self, sadtalker_path, device):
-
-        opt = Config(sadtalker_path['pirender_yaml_path'], None, is_train=False)
-        opt.device = device
-        self.net_G_ema = FaceGenerator(**opt.gen.param).to(opt.device)
-        checkpoint_path = sadtalker_path['pirender_checkpoint']
-        checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
-        self.net_G_ema.load_state_dict(checkpoint['net_G_ema'], strict=False)
-        print('load [net_G] and [net_G_ema] from {}'.format(checkpoint_path))
-        self.net_G = self.net_G_ema.eval()
-        self.device = device
-    
-
-    def generate(self, x, video_save_dir, pic_path, crop_info, enhancer=None, background_enhancer=None, preprocess='crop', img_size=256):
-
-        source_image=x['source_image'].type(torch.FloatTensor)
-        source_semantics=x['source_semantics'].type(torch.FloatTensor)
-        target_semantics=x['target_semantics_list'].type(torch.FloatTensor) 
-        source_image=source_image.to(self.device)
-        source_semantics=source_semantics.to(self.device)
-        target_semantics=target_semantics.to(self.device)
-        frame_num = x['frame_num']
-        
-        with torch.no_grad():
-            predictions_video = []
-            for i in tqdm(range(target_semantics.shape[1]), 'FaceRender:'):
-                 predictions_video.append(self.net_G(source_image, target_semantics[:, i])['fake_image'])
-        
-        predictions_video = torch.stack(predictions_video, dim=1)
-        predictions_video = predictions_video.reshape((-1,)+predictions_video.shape[2:])
-
-        video = []
-        for idx in range(len(predictions_video)):
-            image = predictions_video[idx]
-            image = np.transpose(image.data.cpu().numpy(), [1, 2, 0]).astype(np.float32)
-            video.append(image)
-        result = img_as_ubyte(video)
-
-        ### the generated video is 256x256, so we keep the aspect ratio, 
-        original_size = crop_info[0]
-        if original_size:
-            result = [ cv2.resize(result_i,(img_size, int(img_size * original_size[1]/original_size[0]) )) for result_i in result ]
-        
-        video_name = x['video_name']  + '.mp4'
-        path = os.path.join(video_save_dir, 'temp_'+video_name)
-        
-        imageio.mimsave(path, result,  fps=float(25))
-
-        av_path = os.path.join(video_save_dir, video_name)
-        return_path = av_path 
-        
-        audio_path =  x['audio_path'] 
-        audio_name = os.path.splitext(os.path.split(audio_path)[-1])[0]
-        new_audio_path = os.path.join(video_save_dir, audio_name+'.wav')
-        start_time = 0
-        # cog will not keep the .mp3 filename
-        sound = AudioSegment.from_file(audio_path)
-        frames = frame_num 
-        end_time = start_time + frames*1/25*1000
-        word1=sound.set_frame_rate(16000)
-        word = word1[start_time:end_time]
-        word.export(new_audio_path, format="wav")
-
-        save_video_with_watermark(path, new_audio_path, av_path, watermark= False)
-        print(f'The generated video is named {video_save_dir}/{video_name}') 
-
-        if 'full' in preprocess.lower():
-            # only add watermark to the full image.
-            video_name_full = x['video_name']  + '_full.mp4'
-            full_video_path = os.path.join(video_save_dir, video_name_full)
-            return_path = full_video_path
-            paste_pic(path, pic_path, crop_info, new_audio_path, full_video_path, extended_crop= True if 'ext' in preprocess.lower() else False)
-            print(f'The generated video is named {video_save_dir}/{video_name_full}') 
-        else:
-            full_video_path = av_path 
-
-        #### paste back then enhancers
-        if enhancer:
-            video_name_enhancer = x['video_name']  + '_enhanced.mp4'
-            enhanced_path = os.path.join(video_save_dir, 'temp_'+video_name_enhancer)
-            av_path_enhancer = os.path.join(video_save_dir, video_name_enhancer) 
-            return_path = av_path_enhancer
-
-            try:
-                enhanced_images_gen_with_len = enhancer_generator_with_len(full_video_path, method=enhancer, bg_upsampler=background_enhancer)
-                imageio.mimsave(enhanced_path, enhanced_images_gen_with_len, fps=float(25))
-            except:
-                enhanced_images_gen_with_len = enhancer_list(full_video_path, method=enhancer, bg_upsampler=background_enhancer)
-                imageio.mimsave(enhanced_path, enhanced_images_gen_with_len, fps=float(25))
-            
-            save_video_with_watermark(enhanced_path, new_audio_path, av_path_enhancer, watermark= False)
-            print(f'The generated video is named {video_save_dir}/{video_name_enhancer}')
-            os.remove(enhanced_path)
-
-        os.remove(path)
-        os.remove(new_audio_path)
-
-        return return_path
-
diff --git a/spaces/vishnu0001/text2mesh/shap_e/rendering/__init__.py b/spaces/vishnu0001/text2mesh/shap_e/rendering/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/vivym/image-matting-app/ppmatting/models/backbone/hrnet.py b/spaces/vivym/image-matting-app/ppmatting/models/backbone/hrnet.py
deleted file mode 100644
index 96e23a77e656142a97c573feb501f983aecebbef..0000000000000000000000000000000000000000
--- a/spaces/vivym/image-matting-app/ppmatting/models/backbone/hrnet.py
+++ /dev/null
@@ -1,835 +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 math
-
-import paddle
-import paddle.nn as nn
-import paddle.nn.functional as F
-
-from paddleseg.cvlibs import manager, param_init
-from paddleseg.models import layers
-from paddleseg.utils import utils
-
-__all__ = [
-    "HRNet_W18_Small_V1", "HRNet_W18_Small_V2", "HRNet_W18", "HRNet_W30",
-    "HRNet_W32", "HRNet_W40", "HRNet_W44", "HRNet_W48", "HRNet_W60", "HRNet_W64"
-]
-
-
-class HRNet(nn.Layer):
-    """
-    The HRNet implementation based on PaddlePaddle.
-
-    The original article refers to
-    Jingdong Wang, et, al. "HRNet：Deep High-Resolution Representation Learning for Visual Recognition"
-    (https://arxiv.org/pdf/1908.07919.pdf).
-
-    Args:
-        pretrained (str, optional): The path of pretrained model.
-        stage1_num_modules (int, optional): Number of modules for stage1. Default 1.
-        stage1_num_blocks (list, optional): Number of blocks per module for stage1. Default (4).
-        stage1_num_channels (list, optional): Number of channels per branch for stage1. Default (64).
-        stage2_num_modules (int, optional): Number of modules for stage2. Default 1.
-        stage2_num_blocks (list, optional): Number of blocks per module for stage2. Default (4, 4).
-        stage2_num_channels (list, optional): Number of channels per branch for stage2. Default (18, 36).
-        stage3_num_modules (int, optional): Number of modules for stage3. Default 4.
-        stage3_num_blocks (list, optional): Number of blocks per module for stage3. Default (4, 4, 4).
-        stage3_num_channels (list, optional): Number of channels per branch for stage3. Default [18, 36, 72).
-        stage4_num_modules (int, optional): Number of modules for stage4. Default 3.
-        stage4_num_blocks (list, optional): Number of blocks per module for stage4. Default (4, 4, 4, 4).
-        stage4_num_channels (list, optional): Number of channels per branch for stage4. Default (18, 36, 72. 144).
-        has_se (bool, optional): Whether to use Squeeze-and-Excitation module. Default False.
-        align_corners (bool, optional): An argument of F.interpolate. It should be set to False when the feature size is even,
-            e.g. 1024x512, otherwise it is True, e.g. 769x769. Default: False.
-    """
-
-    def __init__(self,
-                 input_channels=3,
-                 pretrained=None,
-                 stage1_num_modules=1,
-                 stage1_num_blocks=(4, ),
-                 stage1_num_channels=(64, ),
-                 stage2_num_modules=1,
-                 stage2_num_blocks=(4, 4),
-                 stage2_num_channels=(18, 36),
-                 stage3_num_modules=4,
-                 stage3_num_blocks=(4, 4, 4),
-                 stage3_num_channels=(18, 36, 72),
-                 stage4_num_modules=3,
-                 stage4_num_blocks=(4, 4, 4, 4),
-                 stage4_num_channels=(18, 36, 72, 144),
-                 has_se=False,
-                 align_corners=False,
-                 padding_same=True):
-        super(HRNet, self).__init__()
-        self.pretrained = pretrained
-        self.stage1_num_modules = stage1_num_modules
-        self.stage1_num_blocks = stage1_num_blocks
-        self.stage1_num_channels = stage1_num_channels
-        self.stage2_num_modules = stage2_num_modules
-        self.stage2_num_blocks = stage2_num_blocks
-        self.stage2_num_channels = stage2_num_channels
-        self.stage3_num_modules = stage3_num_modules
-        self.stage3_num_blocks = stage3_num_blocks
-        self.stage3_num_channels = stage3_num_channels
-        self.stage4_num_modules = stage4_num_modules
-        self.stage4_num_blocks = stage4_num_blocks
-        self.stage4_num_channels = stage4_num_channels
-        self.has_se = has_se
-        self.align_corners = align_corners
-
-        self.feat_channels = [i for i in stage4_num_channels]
-        self.feat_channels = [64] + self.feat_channels
-
-        self.conv_layer1_1 = layers.ConvBNReLU(
-            in_channels=input_channels,
-            out_channels=64,
-            kernel_size=3,
-            stride=2,
-            padding=1 if not padding_same else 'same',
-            bias_attr=False)
-
-        self.conv_layer1_2 = layers.ConvBNReLU(
-            in_channels=64,
-            out_channels=64,
-            kernel_size=3,
-            stride=2,
-            padding=1 if not padding_same else 'same',
-            bias_attr=False)
-
-        self.la1 = Layer1(
-            num_channels=64,
-            num_blocks=self.stage1_num_blocks[0],
-            num_filters=self.stage1_num_channels[0],
-            has_se=has_se,
-            name="layer2",
-            padding_same=padding_same)
-
-        self.tr1 = TransitionLayer(
-            in_channels=[self.stage1_num_channels[0] * 4],
-            out_channels=self.stage2_num_channels,
-            name="tr1",
-            padding_same=padding_same)
-
-        self.st2 = Stage(
-            num_channels=self.stage2_num_channels,
-            num_modules=self.stage2_num_modules,
-            num_blocks=self.stage2_num_blocks,
-            num_filters=self.stage2_num_channels,
-            has_se=self.has_se,
-            name="st2",
-            align_corners=align_corners,
-            padding_same=padding_same)
-
-        self.tr2 = TransitionLayer(
-            in_channels=self.stage2_num_channels,
-            out_channels=self.stage3_num_channels,
-            name="tr2",
-            padding_same=padding_same)
-        self.st3 = Stage(
-            num_channels=self.stage3_num_channels,
-            num_modules=self.stage3_num_modules,
-            num_blocks=self.stage3_num_blocks,
-            num_filters=self.stage3_num_channels,
-            has_se=self.has_se,
-            name="st3",
-            align_corners=align_corners,
-            padding_same=padding_same)
-
-        self.tr3 = TransitionLayer(
-            in_channels=self.stage3_num_channels,
-            out_channels=self.stage4_num_channels,
-            name="tr3",
-            padding_same=padding_same)
-        self.st4 = Stage(
-            num_channels=self.stage4_num_channels,
-            num_modules=self.stage4_num_modules,
-            num_blocks=self.stage4_num_blocks,
-            num_filters=self.stage4_num_channels,
-            has_se=self.has_se,
-            name="st4",
-            align_corners=align_corners,
-            padding_same=padding_same)
-
-        self.init_weight()
-
-    def forward(self, x):
-        feat_list = []
-        conv1 = self.conv_layer1_1(x)
-        feat_list.append(conv1)
-        conv2 = self.conv_layer1_2(conv1)
-
-        la1 = self.la1(conv2)
-
-        tr1 = self.tr1([la1])
-        st2 = self.st2(tr1)
-
-        tr2 = self.tr2(st2)
-        st3 = self.st3(tr2)
-
-        tr3 = self.tr3(st3)
-        st4 = self.st4(tr3)
-
-        feat_list = feat_list + st4
-
-        return feat_list
-
-    def init_weight(self):
-        for layer in self.sublayers():
-            if isinstance(layer, nn.Conv2D):
-                param_init.normal_init(layer.weight, std=0.001)
-            elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)):
-                param_init.constant_init(layer.weight, value=1.0)
-                param_init.constant_init(layer.bias, value=0.0)
-        if self.pretrained is not None:
-            utils.load_pretrained_model(self, self.pretrained)
-
-
-class Layer1(nn.Layer):
-    def __init__(self,
-                 num_channels,
-                 num_filters,
-                 num_blocks,
-                 has_se=False,
-                 name=None,
-                 padding_same=True):
-        super(Layer1, self).__init__()
-
-        self.bottleneck_block_list = []
-
-        for i in range(num_blocks):
-            bottleneck_block = self.add_sublayer(
-                "bb_{}_{}".format(name, i + 1),
-                BottleneckBlock(
-                    num_channels=num_channels if i == 0 else num_filters * 4,
-                    num_filters=num_filters,
-                    has_se=has_se,
-                    stride=1,
-                    downsample=True if i == 0 else False,
-                    name=name + '_' + str(i + 1),
-                    padding_same=padding_same))
-            self.bottleneck_block_list.append(bottleneck_block)
-
-    def forward(self, x):
-        conv = x
-        for block_func in self.bottleneck_block_list:
-            conv = block_func(conv)
-        return conv
-
-
-class TransitionLayer(nn.Layer):
-    def __init__(self, in_channels, out_channels, name=None, padding_same=True):
-        super(TransitionLayer, self).__init__()
-
-        num_in = len(in_channels)
-        num_out = len(out_channels)
-        self.conv_bn_func_list = []
-        for i in range(num_out):
-            residual = None
-            if i < num_in:
-                if in_channels[i] != out_channels[i]:
-                    residual = self.add_sublayer(
-                        "transition_{}_layer_{}".format(name, i + 1),
-                        layers.ConvBNReLU(
-                            in_channels=in_channels[i],
-                            out_channels=out_channels[i],
-                            kernel_size=3,
-                            padding=1 if not padding_same else 'same',
-                            bias_attr=False))
-            else:
-                residual = self.add_sublayer(
-                    "transition_{}_layer_{}".format(name, i + 1),
-                    layers.ConvBNReLU(
-                        in_channels=in_channels[-1],
-                        out_channels=out_channels[i],
-                        kernel_size=3,
-                        stride=2,
-                        padding=1 if not padding_same else 'same',
-                        bias_attr=False))
-            self.conv_bn_func_list.append(residual)
-
-    def forward(self, x):
-        outs = []
-        for idx, conv_bn_func in enumerate(self.conv_bn_func_list):
-            if conv_bn_func is None:
-                outs.append(x[idx])
-            else:
-                if idx < len(x):
-                    outs.append(conv_bn_func(x[idx]))
-                else:
-                    outs.append(conv_bn_func(x[-1]))
-        return outs
-
-
-class Branches(nn.Layer):
-    def __init__(self,
-                 num_blocks,
-                 in_channels,
-                 out_channels,
-                 has_se=False,
-                 name=None,
-                 padding_same=True):
-        super(Branches, self).__init__()
-
-        self.basic_block_list = []
-
-        for i in range(len(out_channels)):
-            self.basic_block_list.append([])
-            for j in range(num_blocks[i]):
-                in_ch = in_channels[i] if j == 0 else out_channels[i]
-                basic_block_func = self.add_sublayer(
-                    "bb_{}_branch_layer_{}_{}".format(name, i + 1, j + 1),
-                    BasicBlock(
-                        num_channels=in_ch,
-                        num_filters=out_channels[i],
-                        has_se=has_se,
-                        name=name + '_branch_layer_' + str(i + 1) + '_' +
-                        str(j + 1),
-                        padding_same=padding_same))
-                self.basic_block_list[i].append(basic_block_func)
-
-    def forward(self, x):
-        outs = []
-        for idx, input in enumerate(x):
-            conv = input
-            for basic_block_func in self.basic_block_list[idx]:
-                conv = basic_block_func(conv)
-            outs.append(conv)
-        return outs
-
-
-class BottleneckBlock(nn.Layer):
-    def __init__(self,
-                 num_channels,
-                 num_filters,
-                 has_se,
-                 stride=1,
-                 downsample=False,
-                 name=None,
-                 padding_same=True):
-        super(BottleneckBlock, self).__init__()
-
-        self.has_se = has_se
-        self.downsample = downsample
-
-        self.conv1 = layers.ConvBNReLU(
-            in_channels=num_channels,
-            out_channels=num_filters,
-            kernel_size=1,
-            bias_attr=False)
-
-        self.conv2 = layers.ConvBNReLU(
-            in_channels=num_filters,
-            out_channels=num_filters,
-            kernel_size=3,
-            stride=stride,
-            padding=1 if not padding_same else 'same',
-            bias_attr=False)
-
-        self.conv3 = layers.ConvBN(
-            in_channels=num_filters,
-            out_channels=num_filters * 4,
-            kernel_size=1,
-            bias_attr=False)
-
-        if self.downsample:
-            self.conv_down = layers.ConvBN(
-                in_channels=num_channels,
-                out_channels=num_filters * 4,
-                kernel_size=1,
-                bias_attr=False)
-
-        if self.has_se:
-            self.se = SELayer(
-                num_channels=num_filters * 4,
-                num_filters=num_filters * 4,
-                reduction_ratio=16,
-                name=name + '_fc')
-
-        self.add = layers.Add()
-        self.relu = layers.Activation("relu")
-
-    def forward(self, x):
-        residual = x
-        conv1 = self.conv1(x)
-        conv2 = self.conv2(conv1)
-        conv3 = self.conv3(conv2)
-
-        if self.downsample:
-            residual = self.conv_down(x)
-
-        if self.has_se:
-            conv3 = self.se(conv3)
-
-        y = self.add(conv3, residual)
-        y = self.relu(y)
-        return y
-
-
-class BasicBlock(nn.Layer):
-    def __init__(self,
-                 num_channels,
-                 num_filters,
-                 stride=1,
-                 has_se=False,
-                 downsample=False,
-                 name=None,
-                 padding_same=True):
-        super(BasicBlock, self).__init__()
-
-        self.has_se = has_se
-        self.downsample = downsample
-
-        self.conv1 = layers.ConvBNReLU(
-            in_channels=num_channels,
-            out_channels=num_filters,
-            kernel_size=3,
-            stride=stride,
-            padding=1 if not padding_same else 'same',
-            bias_attr=False)
-        self.conv2 = layers.ConvBN(
-            in_channels=num_filters,
-            out_channels=num_filters,
-            kernel_size=3,
-            padding=1 if not padding_same else 'same',
-            bias_attr=False)
-
-        if self.downsample:
-            self.conv_down = layers.ConvBNReLU(
-                in_channels=num_channels,
-                out_channels=num_filters,
-                kernel_size=1,
-                bias_attr=False)
-
-        if self.has_se:
-            self.se = SELayer(
-                num_channels=num_filters,
-                num_filters=num_filters,
-                reduction_ratio=16,
-                name=name + '_fc')
-
-        self.add = layers.Add()
-        self.relu = layers.Activation("relu")
-
-    def forward(self, x):
-        residual = x
-        conv1 = self.conv1(x)
-        conv2 = self.conv2(conv1)
-
-        if self.downsample:
-            residual = self.conv_down(x)
-
-        if self.has_se:
-            conv2 = self.se(conv2)
-
-        y = self.add(conv2, residual)
-        y = self.relu(y)
-        return y
-
-
-class SELayer(nn.Layer):
-    def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
-        super(SELayer, self).__init__()
-
-        self.pool2d_gap = nn.AdaptiveAvgPool2D(1)
-
-        self._num_channels = num_channels
-
-        med_ch = int(num_channels / reduction_ratio)
-        stdv = 1.0 / math.sqrt(num_channels * 1.0)
-        self.squeeze = nn.Linear(
-            num_channels,
-            med_ch,
-            weight_attr=paddle.ParamAttr(
-                initializer=nn.initializer.Uniform(-stdv, stdv)))
-
-        stdv = 1.0 / math.sqrt(med_ch * 1.0)
-        self.excitation = nn.Linear(
-            med_ch,
-            num_filters,
-            weight_attr=paddle.ParamAttr(
-                initializer=nn.initializer.Uniform(-stdv, stdv)))
-
-    def forward(self, x):
-        pool = self.pool2d_gap(x)
-        pool = paddle.reshape(pool, shape=[-1, self._num_channels])
-        squeeze = self.squeeze(pool)
-        squeeze = F.relu(squeeze)
-        excitation = self.excitation(squeeze)
-        excitation = F.sigmoid(excitation)
-        excitation = paddle.reshape(
-            excitation, shape=[-1, self._num_channels, 1, 1])
-        out = x * excitation
-        return out
-
-
-class Stage(nn.Layer):
-    def __init__(self,
-                 num_channels,
-                 num_modules,
-                 num_blocks,
-                 num_filters,
-                 has_se=False,
-                 multi_scale_output=True,
-                 name=None,
-                 align_corners=False,
-                 padding_same=True):
-        super(Stage, self).__init__()
-
-        self._num_modules = num_modules
-
-        self.stage_func_list = []
-        for i in range(num_modules):
-            if i == num_modules - 1 and not multi_scale_output:
-                stage_func = self.add_sublayer(
-                    "stage_{}_{}".format(name, i + 1),
-                    HighResolutionModule(
-                        num_channels=num_channels,
-                        num_blocks=num_blocks,
-                        num_filters=num_filters,
-                        has_se=has_se,
-                        multi_scale_output=False,
-                        name=name + '_' + str(i + 1),
-                        align_corners=align_corners,
-                        padding_same=padding_same))
-            else:
-                stage_func = self.add_sublayer(
-                    "stage_{}_{}".format(name, i + 1),
-                    HighResolutionModule(
-                        num_channels=num_channels,
-                        num_blocks=num_blocks,
-                        num_filters=num_filters,
-                        has_se=has_se,
-                        name=name + '_' + str(i + 1),
-                        align_corners=align_corners,
-                        padding_same=padding_same))
-
-            self.stage_func_list.append(stage_func)
-
-    def forward(self, x):
-        out = x
-        for idx in range(self._num_modules):
-            out = self.stage_func_list[idx](out)
-        return out
-
-
-class HighResolutionModule(nn.Layer):
-    def __init__(self,
-                 num_channels,
-                 num_blocks,
-                 num_filters,
-                 has_se=False,
-                 multi_scale_output=True,
-                 name=None,
-                 align_corners=False,
-                 padding_same=True):
-        super(HighResolutionModule, self).__init__()
-
-        self.branches_func = Branches(
-            num_blocks=num_blocks,
-            in_channels=num_channels,
-            out_channels=num_filters,
-            has_se=has_se,
-            name=name,
-            padding_same=padding_same)
-
-        self.fuse_func = FuseLayers(
-            in_channels=num_filters,
-            out_channels=num_filters,
-            multi_scale_output=multi_scale_output,
-            name=name,
-            align_corners=align_corners,
-            padding_same=padding_same)
-
-    def forward(self, x):
-        out = self.branches_func(x)
-        out = self.fuse_func(out)
-        return out
-
-
-class FuseLayers(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 multi_scale_output=True,
-                 name=None,
-                 align_corners=False,
-                 padding_same=True):
-        super(FuseLayers, self).__init__()
-
-        self._actual_ch = len(in_channels) if multi_scale_output else 1
-        self._in_channels = in_channels
-        self.align_corners = align_corners
-
-        self.residual_func_list = []
-        for i in range(self._actual_ch):
-            for j in range(len(in_channels)):
-                if j > i:
-                    residual_func = self.add_sublayer(
-                        "residual_{}_layer_{}_{}".format(name, i + 1, j + 1),
-                        layers.ConvBN(
-                            in_channels=in_channels[j],
-                            out_channels=out_channels[i],
-                            kernel_size=1,
-                            bias_attr=False))
-                    self.residual_func_list.append(residual_func)
-                elif j < i:
-                    pre_num_filters = in_channels[j]
-                    for k in range(i - j):
-                        if k == i - j - 1:
-                            residual_func = self.add_sublayer(
-                                "residual_{}_layer_{}_{}_{}".format(
-                                    name, i + 1, j + 1, k + 1),
-                                layers.ConvBN(
-                                    in_channels=pre_num_filters,
-                                    out_channels=out_channels[i],
-                                    kernel_size=3,
-                                    stride=2,
-                                    padding=1 if not padding_same else 'same',
-                                    bias_attr=False))
-                            pre_num_filters = out_channels[i]
-                        else:
-                            residual_func = self.add_sublayer(
-                                "residual_{}_layer_{}_{}_{}".format(
-                                    name, i + 1, j + 1, k + 1),
-                                layers.ConvBNReLU(
-                                    in_channels=pre_num_filters,
-                                    out_channels=out_channels[j],
-                                    kernel_size=3,
-                                    stride=2,
-                                    padding=1 if not padding_same else 'same',
-                                    bias_attr=False))
-                            pre_num_filters = out_channels[j]
-                        self.residual_func_list.append(residual_func)
-
-    def forward(self, x):
-        outs = []
-        residual_func_idx = 0
-        for i in range(self._actual_ch):
-            residual = x[i]
-            residual_shape = paddle.shape(residual)[-2:]
-            for j in range(len(self._in_channels)):
-                if j > i:
-                    y = self.residual_func_list[residual_func_idx](x[j])
-                    residual_func_idx += 1
-
-                    y = F.interpolate(
-                        y,
-                        residual_shape,
-                        mode='bilinear',
-                        align_corners=self.align_corners)
-                    residual = residual + y
-                elif j < i:
-                    y = x[j]
-                    for k in range(i - j):
-                        y = self.residual_func_list[residual_func_idx](y)
-                        residual_func_idx += 1
-
-                    residual = residual + y
-
-            residual = F.relu(residual)
-            outs.append(residual)
-
-        return outs
-
-
-@manager.BACKBONES.add_component
-def HRNet_W18_Small_V1(**kwargs):
-    model = HRNet(
-        stage1_num_modules=1,
-        stage1_num_blocks=[1],
-        stage1_num_channels=[32],
-        stage2_num_modules=1,
-        stage2_num_blocks=[2, 2],
-        stage2_num_channels=[16, 32],
-        stage3_num_modules=1,
-        stage3_num_blocks=[2, 2, 2],
-        stage3_num_channels=[16, 32, 64],
-        stage4_num_modules=1,
-        stage4_num_blocks=[2, 2, 2, 2],
-        stage4_num_channels=[16, 32, 64, 128],
-        **kwargs)
-    return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W18_Small_V2(**kwargs):
-    model = HRNet(
-        stage1_num_modules=1,
-        stage1_num_blocks=[2],
-        stage1_num_channels=[64],
-        stage2_num_modules=1,
-        stage2_num_blocks=[2, 2],
-        stage2_num_channels=[18, 36],
-        stage3_num_modules=3,
-        stage3_num_blocks=[2, 2, 2],
-        stage3_num_channels=[18, 36, 72],
-        stage4_num_modules=2,
-        stage4_num_blocks=[2, 2, 2, 2],
-        stage4_num_channels=[18, 36, 72, 144],
-        **kwargs)
-    return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W18(**kwargs):
-    model = HRNet(
-        stage1_num_modules=1,
-        stage1_num_blocks=[4],
-        stage1_num_channels=[64],
-        stage2_num_modules=1,
-        stage2_num_blocks=[4, 4],
-        stage2_num_channels=[18, 36],
-        stage3_num_modules=4,
-        stage3_num_blocks=[4, 4, 4],
-        stage3_num_channels=[18, 36, 72],
-        stage4_num_modules=3,
-        stage4_num_blocks=[4, 4, 4, 4],
-        stage4_num_channels=[18, 36, 72, 144],
-        **kwargs)
-    return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W30(**kwargs):
-    model = HRNet(
-        stage1_num_modules=1,
-        stage1_num_blocks=[4],
-        stage1_num_channels=[64],
-        stage2_num_modules=1,
-        stage2_num_blocks=[4, 4],
-        stage2_num_channels=[30, 60],
-        stage3_num_modules=4,
-        stage3_num_blocks=[4, 4, 4],
-        stage3_num_channels=[30, 60, 120],
-        stage4_num_modules=3,
-        stage4_num_blocks=[4, 4, 4, 4],
-        stage4_num_channels=[30, 60, 120, 240],
-        **kwargs)
-    return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W32(**kwargs):
-    model = HRNet(
-        stage1_num_modules=1,
-        stage1_num_blocks=[4],
-        stage1_num_channels=[64],
-        stage2_num_modules=1,
-        stage2_num_blocks=[4, 4],
-        stage2_num_channels=[32, 64],
-        stage3_num_modules=4,
-        stage3_num_blocks=[4, 4, 4],
-        stage3_num_channels=[32, 64, 128],
-        stage4_num_modules=3,
-        stage4_num_blocks=[4, 4, 4, 4],
-        stage4_num_channels=[32, 64, 128, 256],
-        **kwargs)
-    return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W40(**kwargs):
-    model = HRNet(
-        stage1_num_modules=1,
-        stage1_num_blocks=[4],
-        stage1_num_channels=[64],
-        stage2_num_modules=1,
-        stage2_num_blocks=[4, 4],
-        stage2_num_channels=[40, 80],
-        stage3_num_modules=4,
-        stage3_num_blocks=[4, 4, 4],
-        stage3_num_channels=[40, 80, 160],
-        stage4_num_modules=3,
-        stage4_num_blocks=[4, 4, 4, 4],
-        stage4_num_channels=[40, 80, 160, 320],
-        **kwargs)
-    return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W44(**kwargs):
-    model = HRNet(
-        stage1_num_modules=1,
-        stage1_num_blocks=[4],
-        stage1_num_channels=[64],
-        stage2_num_modules=1,
-        stage2_num_blocks=[4, 4],
-        stage2_num_channels=[44, 88],
-        stage3_num_modules=4,
-        stage3_num_blocks=[4, 4, 4],
-        stage3_num_channels=[44, 88, 176],
-        stage4_num_modules=3,
-        stage4_num_blocks=[4, 4, 4, 4],
-        stage4_num_channels=[44, 88, 176, 352],
-        **kwargs)
-    return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W48(**kwargs):
-    model = HRNet(
-        stage1_num_modules=1,
-        stage1_num_blocks=[4],
-        stage1_num_channels=[64],
-        stage2_num_modules=1,
-        stage2_num_blocks=[4, 4],
-        stage2_num_channels=[48, 96],
-        stage3_num_modules=4,
-        stage3_num_blocks=[4, 4, 4],
-        stage3_num_channels=[48, 96, 192],
-        stage4_num_modules=3,
-        stage4_num_blocks=[4, 4, 4, 4],
-        stage4_num_channels=[48, 96, 192, 384],
-        **kwargs)
-    return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W60(**kwargs):
-    model = HRNet(
-        stage1_num_modules=1,
-        stage1_num_blocks=[4],
-        stage1_num_channels=[64],
-        stage2_num_modules=1,
-        stage2_num_blocks=[4, 4],
-        stage2_num_channels=[60, 120],
-        stage3_num_modules=4,
-        stage3_num_blocks=[4, 4, 4],
-        stage3_num_channels=[60, 120, 240],
-        stage4_num_modules=3,
-        stage4_num_blocks=[4, 4, 4, 4],
-        stage4_num_channels=[60, 120, 240, 480],
-        **kwargs)
-    return model
-
-
-@manager.BACKBONES.add_component
-def HRNet_W64(**kwargs):
-    model = HRNet(
-        stage1_num_modules=1,
-        stage1_num_blocks=[4],
-        stage1_num_channels=[64],
-        stage2_num_modules=1,
-        stage2_num_blocks=[4, 4],
-        stage2_num_channels=[64, 128],
-        stage3_num_modules=4,
-        stage3_num_blocks=[4, 4, 4],
-        stage3_num_channels=[64, 128, 256],
-        stage4_num_modules=3,
-        stage4_num_blocks=[4, 4, 4, 4],
-        stage4_num_channels=[64, 128, 256, 512],
-        **kwargs)
-    return model
diff --git a/spaces/vpsrikanth/FaceSimilarity/README.md b/spaces/vpsrikanth/FaceSimilarity/README.md
deleted file mode 100644
index 2cd49fd46b2fa31eff6aea824738da487e22c264..0000000000000000000000000000000000000000
--- a/spaces/vpsrikanth/FaceSimilarity/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: FaceSimilarity
-emoji: 🐢
-colorFrom: green
-colorTo: purple
-sdk: docker
-pinned: false
-license: mit
-app_port: 8001
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/wanghuoto/gogoai/postcss.config.js b/spaces/wanghuoto/gogoai/postcss.config.js
deleted file mode 100644
index 33ad091d26d8a9dc95ebdf616e217d985ec215b8..0000000000000000000000000000000000000000
--- a/spaces/wanghuoto/gogoai/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-module.exports = {
-  plugins: {
-    tailwindcss: {},
-    autoprefixer: {},
-  },
-}
diff --git a/spaces/wrice/denoisers/README.md b/spaces/wrice/denoisers/README.md
deleted file mode 100644
index 729b8e15cb893e66269ffff80332927619cded52..0000000000000000000000000000000000000000
--- a/spaces/wrice/denoisers/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Denoisers
-emoji: 📉
-colorFrom: green
-colorTo: green
-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/wuhuik/bingo/src/lib/bots/bing/index.ts b/spaces/wuhuik/bingo/src/lib/bots/bing/index.ts
deleted file mode 100644
index c75c69f94af8c3db92d4c90d465c219a2af72a4d..0000000000000000000000000000000000000000
--- a/spaces/wuhuik/bingo/src/lib/bots/bing/index.ts
+++ /dev/null
@@ -1,432 +0,0 @@
-import { fetch, WebSocket, debug } from '@/lib/isomorphic'
-import WebSocketAsPromised from 'websocket-as-promised'
-import {
-  SendMessageParams,
-  BingConversationStyle,
-  ConversationResponse,
-  ChatResponseMessage,
-  ConversationInfo,
-  InvocationEventType,
-  ChatError,
-  ErrorCode,
-  ChatUpdateCompleteResponse,
-  ImageInfo,
-  KBlobResponse
-} from './types'
-
-import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils'
-import { WatchDog, createChunkDecoder } from '@/lib/utils'
-
-type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }>
-
-const OPTIONS_SETS = [
-  'nlu_direct_response_filter',
-  'deepleo',
-  'disable_emoji_spoken_text',
-  'responsible_ai_policy_235',
-  'enablemm',
-  'iycapbing',
-  'iyxapbing',
-  'objopinion',
-  'rweasgv2',
-  'dagslnv1',
-  'dv3sugg',
-  'autosave',
-  'iyoloxap',
-  'iyoloneutral',
-  'clgalileo',
-  'gencontentv3',
-]
-
-export class BingWebBot {
-  protected conversationContext?: ConversationInfo
-  protected cookie: string
-  protected ua: string
-  protected endpoint = ''
-  private lastText = ''
-  private asyncTasks: Array<Promise<any>> = []
-
-  constructor(opts: {
-    cookie: string
-    ua: string
-    bingConversationStyle?: BingConversationStyle
-    conversationContext?: ConversationInfo
-  }) {
-    const { cookie, ua, conversationContext } = opts
-    this.cookie = cookie?.includes(';') ? cookie : `_EDGE_V=1; _U=${cookie}`
-    this.ua = ua
-    this.conversationContext = conversationContext
-  }
-
-  static buildChatRequest(conversation: ConversationInfo) {
-    const optionsSets = OPTIONS_SETS
-    if (conversation.conversationStyle === BingConversationStyle.Precise) {
-      optionsSets.push('h3precise')
-    } else if (conversation.conversationStyle === BingConversationStyle.Creative) {
-      optionsSets.push('h3imaginative')
-    }
-    return {
-      arguments: [
-        {
-          source: 'cib',
-          optionsSets,
-          allowedMessageTypes: [
-            'ActionRequest',
-            'Chat',
-            'Context',
-            'InternalSearchQuery',
-            'InternalSearchResult',
-            'Disengaged',
-            'InternalLoaderMessage',
-            'Progress',
-            'RenderCardRequest',
-            'SemanticSerp',
-            'GenerateContentQuery',
-            'SearchQuery',
-          ],
-          sliceIds: [
-            'winmuid1tf',
-            'anssupfor_c',
-            'imgchatgptv2',
-            'tts2cf',
-            'contansperf',
-            'mlchatpc8500w',
-            'mlchatpc2',
-            'ctrlworkpay',
-            'winshortmsgtf',
-            'cibctrl',
-            'sydtransctrl',
-            'sydconfigoptc',
-            '0705trt4',
-            '517opinion',
-            '628ajcopus0',
-            '330uaugs0',
-            '529rwea',
-            '0626snptrcs0',
-            '424dagslnv1',
-          ],
-          isStartOfSession: conversation.invocationId === 0,
-          message: {
-            author: 'user',
-            inputMethod: 'Keyboard',
-            text: conversation.prompt,
-            imageUrl: conversation.imageUrl,
-            messageType: 'Chat',
-          },
-          conversationId: conversation.conversationId,
-          conversationSignature: conversation.conversationSignature,
-          participant: { id: conversation.clientId },
-        },
-      ],
-      invocationId: conversation.invocationId.toString(),
-      target: 'chat',
-      type: InvocationEventType.StreamInvocation,
-    }
-  }
-
-  async createConversation(): Promise<ConversationResponse> {
-    const headers = {
-      'Accept-Encoding': 'gzip, deflate, br, zsdch',
-      'User-Agent': this.ua,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-      cookie: this.cookie,
-    }
-
-    let resp: ConversationResponse | undefined
-    try {
-      const response = await fetch(this.endpoint + '/api/create', { method: 'POST', headers, redirect: 'error', mode: 'cors', credentials: 'include' })
-      if (response.status === 404) {
-        throw new ChatError('Not Found', ErrorCode.NOTFOUND_ERROR)
-      }
-      resp = await response.json() as ConversationResponse
-    } catch (err) {
-      console.error('create conversation error', err)
-    }
-
-    if (!resp?.result) {
-      throw new ChatError('你的 VPS 或代理可能被封禁，如有疑问，请前往 https://github.com/weaigc/bingo 咨询', ErrorCode.BING_IP_FORBIDDEN)
-    }
-
-    const { value, message } = resp.result || {}
-    if (value !== 'Success') {
-      const errorMsg = `${value}: ${message}`
-      if (value === 'UnauthorizedRequest') {
-        if (/fetch failed/i.test(message || '')) {
-          throw new ChatError(errorMsg, ErrorCode.BING_IP_FORBIDDEN)
-        }
-        throw new ChatError(errorMsg, ErrorCode.BING_UNAUTHORIZED)
-      }
-      if (value === 'TryLater') {
-        throw new ChatError(errorMsg, ErrorCode.BING_TRY_LATER)
-      }
-      if (value === 'Forbidden') {
-        throw new ChatError(errorMsg, ErrorCode.BING_FORBIDDEN)
-      }
-      throw new ChatError(errorMsg, ErrorCode.UNKOWN_ERROR)
-    }
-    return resp
-  }
-
-  private async createContext(conversationStyle: BingConversationStyle) {
-    if (!this.conversationContext) {
-      const conversation = await this.createConversation()
-      this.conversationContext = {
-        conversationId: conversation.conversationId,
-        conversationSignature: conversation.conversationSignature,
-        clientId: conversation.clientId,
-        invocationId: 0,
-        conversationStyle,
-        prompt: '',
-      }
-    }
-    return this.conversationContext
-  }
-
-  async sendMessage(params: Params) {
-    try {
-      await this.createContext(params.options.bingConversationStyle)
-      Object.assign(this.conversationContext!, { prompt: params.prompt, imageUrl: params.imageUrl })
-      return this.sydneyProxy(params)
-    } catch (error) {
-      params.onEvent({
-        type: 'ERROR',
-        error: error instanceof ChatError ? error : new ChatError('Catch Error', ErrorCode.UNKOWN_ERROR),
-      })
-    }
-  }
-
-  private async sydneyProxy(params: Params) {
-    const abortController = new AbortController()
-    const response = await fetch(this.endpoint + '/api/sydney', {
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json',
-      },
-      signal: abortController.signal,
-      body: JSON.stringify(this.conversationContext!)
-    })
-    if (response.status !== 200) {
-      params.onEvent({
-        type: 'ERROR',
-        error: new ChatError(
-          'Unknown error',
-          ErrorCode.UNKOWN_ERROR,
-        ),
-      })
-    }
-    params.signal?.addEventListener('abort', () => {
-      abortController.abort()
-    })
-
-    const textDecoder = createChunkDecoder()
-    for await (const chunk of streamAsyncIterable(response.body!)) {
-      this.parseEvents(params, websocketUtils.unpackMessage(textDecoder(chunk)))
-    }
-  }
-
-  async sendWs() {
-    const wsConfig: ConstructorParameters<typeof WebSocketAsPromised>[1] = {
-      packMessage: websocketUtils.packMessage,
-      unpackMessage: websocketUtils.unpackMessage,
-      createWebSocket: (url) => new WebSocket(url, {
-        headers: {
-          'accept-language': 'zh-CN,zh;q=0.9',
-          'cache-control': 'no-cache',
-          'User-Agent': this.ua,
-          pragma: 'no-cache',
-          cookie: this.cookie,
-        }
-      })
-    }
-    const wsp = new WebSocketAsPromised('wss://sydney.bing.com/sydney/ChatHub', wsConfig)
-
-    wsp.open().then(() => {
-      wsp.sendPacked({ protocol: 'json', version: 1 })
-      wsp.sendPacked({ type: 6 })
-      wsp.sendPacked(BingWebBot.buildChatRequest(this.conversationContext!))
-    })
-
-    return wsp
-  }
-
-  private async useWs(params: Params) {
-    const wsp = await this.sendWs()
-    const watchDog = new WatchDog()
-    wsp.onUnpackedMessage.addListener((events) => {
-      watchDog.watch(() => {
-        wsp.sendPacked({ type: 6 })
-      })
-      this.parseEvents(params, events)
-    })
-
-    wsp.onClose.addListener(() => {
-      watchDog.reset()
-      params.onEvent({ type: 'DONE' })
-      wsp.removeAllListeners()
-    })
-
-    params.signal?.addEventListener('abort', () => {
-      wsp.removeAllListeners()
-      wsp.close()
-    })
-  }
-
-  private async createImage(prompt: string, id: string) {
-    try {
-      const headers = {
-        'Accept-Encoding': 'gzip, deflate, br, zsdch',
-        'User-Agent': this.ua,
-        'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-        cookie: this.cookie,
-      }
-      const query = new URLSearchParams({
-        prompt,
-        id
-      })
-      const response = await fetch(this.endpoint + '/api/image?' + query.toString(),
-        {
-          method: 'POST',
-          headers,
-          mode: 'cors',
-          credentials: 'include'
-        })
-        .then(res => res.text())
-      if (response) {
-        this.lastText += '\n' + response
-      }
-    } catch (err) {
-      console.error('Create Image Error', err)
-    }
-  }
-
-  private buildKnowledgeApiPayload(imageUrl: string, conversationStyle: BingConversationStyle) {
-    const imageInfo: ImageInfo = {}
-    let imageBase64: string | undefined = undefined
-    const knowledgeRequest = {
-      imageInfo,
-      knowledgeRequest: {
-        invokedSkills: [
-          'ImageById'
-        ],
-        subscriptionId: 'Bing.Chat.Multimodal',
-        invokedSkillsRequestData: {
-            enableFaceBlur: true
-        },
-        convoData: {
-          convoid: this.conversationContext?.conversationId,
-          convotone: conversationStyle,
-        }
-      },
-    }
-
-    if (imageUrl.startsWith('data:image/')) {
-      imageBase64 = imageUrl.replace('data:image/', '');
-      const partIndex = imageBase64.indexOf(',')
-      if (partIndex) {
-        imageBase64 = imageBase64.substring(partIndex + 1)
-      }
-    } else {
-      imageInfo.url = imageUrl
-    }
-    return { knowledgeRequest, imageBase64 }
-  }
-
-  async uploadImage(imageUrl: string, conversationStyle: BingConversationStyle = BingConversationStyle.Creative): Promise<KBlobResponse | undefined> {
-    if (!imageUrl) {
-      return
-    }
-    await this.createContext(conversationStyle)
-    const payload = this.buildKnowledgeApiPayload(imageUrl, conversationStyle)
-
-    const response = await fetch(this.endpoint + '/api/kblob',
-      {
-        headers: {
-          'Content-Type': 'application/json',
-        },
-        method: 'POST',
-        mode: 'cors',
-        credentials: 'include',
-        body: JSON.stringify(payload),
-      })
-      .then(res => res.json())
-      .catch(e => {
-        console.log('Error', e)
-      })
-    return response
-  }
-
-  private async generateContent(message: ChatResponseMessage) {
-    if (message.contentType === 'IMAGE') {
-      this.asyncTasks.push(this.createImage(message.text, message.messageId))
-    }
-  }
-
-  private async parseEvents(params: Params, events: any) {
-    const conversation = this.conversationContext!
-
-    events?.forEach(async (event: ChatUpdateCompleteResponse) => {
-      debug('bing event', event)
-      if (event.type === 3) {
-        await Promise.all(this.asyncTasks)
-        this.asyncTasks = []
-        params.onEvent({ type: 'UPDATE_ANSWER', data: { text: this.lastText } })
-        params.onEvent({ type: 'DONE' })
-        conversation.invocationId = parseInt(event.invocationId, 10) + 1
-      } else if (event.type === 1) {
-        const messages = event.arguments[0].messages
-        if (messages) {
-          const text = convertMessageToMarkdown(messages[0])
-          this.lastText = text
-          params.onEvent({ type: 'UPDATE_ANSWER', data: { text, spokenText: messages[0].text, throttling: event.arguments[0].throttling } })
-        }
-      } else if (event.type === 2) {
-        const messages = event.item.messages as ChatResponseMessage[] | undefined
-        if (!messages) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              event.item.result.error || 'Unknown error',
-              event.item.result.value === 'Throttled' ? ErrorCode.THROTTLE_LIMIT
-                : event.item.result.value === 'CaptchaChallenge' ? (this.conversationContext?.conversationId?.includes('BingProdUnAuthenticatedUsers') ? ErrorCode.BING_UNAUTHORIZED : ErrorCode.BING_CAPTCHA)
-                  : ErrorCode.UNKOWN_ERROR
-            ),
-          })
-          return
-        }
-        const limited = messages.some((message) =>
-          message.contentOrigin === 'TurnLimiter'
-          || message.messageType === 'Disengaged'
-        )
-        if (limited) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              'Sorry, you have reached chat limit in this conversation.',
-              ErrorCode.CONVERSATION_LIMIT,
-            ),
-          })
-          return
-        }
-
-        const lastMessage = event.item.messages.at(-1) as ChatResponseMessage
-        const specialMessage = event.item.messages.find(message => message.author === 'bot' && message.contentType === 'IMAGE')
-        if (specialMessage) {
-          this.generateContent(specialMessage)
-        }
-
-        if (lastMessage) {
-          const text = convertMessageToMarkdown(lastMessage)
-          this.lastText = text
-          params.onEvent({
-            type: 'UPDATE_ANSWER',
-            data: { text, throttling: event.item.throttling, suggestedResponses: lastMessage.suggestedResponses, sourceAttributions: lastMessage.sourceAttributions },
-          })
-        }
-      }
-    })
-  }
-
-  resetConversation() {
-    this.conversationContext = undefined
-  }
-}
diff --git a/spaces/xdecoder/Instruct-X-Decoder/xdecoder/__init__.py b/spaces/xdecoder/Instruct-X-Decoder/xdecoder/__init__.py
deleted file mode 100644
index 47a369b587a83ac2691a90e583a4bbb5c0cb23e0..0000000000000000000000000000000000000000
--- a/spaces/xdecoder/Instruct-X-Decoder/xdecoder/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from .architectures import build_model
\ No newline at end of file
diff --git a/spaces/xfys/yolov5_tracking/val_utils/scripts/run_person_path_22.py b/spaces/xfys/yolov5_tracking/val_utils/scripts/run_person_path_22.py
deleted file mode 100644
index 232b99c2b0d4077be7b5258c1c6b3593df93157c..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/val_utils/scripts/run_person_path_22.py
+++ /dev/null
@@ -1,112 +0,0 @@
-
-""" run_person_path_22.py
-
-Run example:
-python3 run_person_path_22.py \
-    --BENCHMARK person_path_22 \
-    --SPLIT_TO_EVAL test \
-    --TRACKERS_TO_EVAL custom_tracker \
-    --METRICS HOTA CLEAR Identity VACE \
-    --USE_PARALLEL True \
-    --NUM_PARALLEL_CORES 12 \
-    --TRACKERS_FOLDER $TRACKER_FOLDER
-
-where $TRACKER_FOLDER is the path of the folder containing the tracker predictions in MOTChallenge format.
-In particular, $TRACKER_FOLDER is expected to have the following structure:
-$TRACKER_FOLDER/
-    person_path_22-test/
-        custom_tracker/
-            data/
-                uid_vid_00008.mp4.txt
-                uid_vid_00009.mp4.txt
-                [...]
-                uid_vid_00235.mp4.txt
-
-Each text file contains the tracker predictions in MOTChallenge format for a given video.
-
-
-Command Line Arguments: Defaults, # Comments
-    Eval arguments:
-        'USE_PARALLEL': False,
-        'NUM_PARALLEL_CORES': 8,
-        'BREAK_ON_ERROR': True,
-        'PRINT_RESULTS': True,
-        'PRINT_ONLY_COMBINED': False,
-        'PRINT_CONFIG': True,
-        'TIME_PROGRESS': True,
-        'OUTPUT_SUMMARY': True,
-        'OUTPUT_DETAILED': True,
-        'PLOT_CURVES': True,
-    Dataset arguments:
-        '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': 'train',  # 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
-    Metric arguments:
-        'METRICS': ['HOTA', 'CLEAR', 'Identity', 'VACE']
-"""
-
-import sys
-import os
-import argparse
-from multiprocessing import freeze_support
-
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-import trackeval  # noqa: E402
-
-if __name__ == '__main__':
-    freeze_support()
-
-    # Command line interface:
-    default_eval_config = trackeval.Evaluator.get_default_eval_config()
-    default_eval_config['DISPLAY_LESS_PROGRESS'] = False
-    default_dataset_config = trackeval.datasets.PersonPath22.get_default_dataset_config()
-    default_metrics_config = {'METRICS': ['HOTA', 'CLEAR', 'Identity'], 'THRESHOLD': 0.5}
-    config = {**default_eval_config, **default_dataset_config, **default_metrics_config}  # Merge default configs
-    parser = argparse.ArgumentParser()
-    for setting in config.keys():
-        if type(config[setting]) == list or type(config[setting]) == type(None):
-            parser.add_argument("--" + setting, nargs='+')
-        else:
-            parser.add_argument("--" + setting)
-    args = parser.parse_args().__dict__
-    for setting in args.keys():
-        if args[setting] is not None:
-            if type(config[setting]) == type(True):
-                if args[setting] == 'True':
-                    x = True
-                elif args[setting] == 'False':
-                    x = False
-                else:
-                    raise Exception('Command line parameter ' + setting + 'must be True or False')
-            elif type(config[setting]) == type(1):
-                x = int(args[setting])
-            elif type(args[setting]) == type(None):
-                x = None
-            elif setting == 'SEQ_INFO':
-                x = dict(zip(args[setting], [None]*len(args[setting])))
-            else:
-                x = args[setting]
-            config[setting] = x
-    eval_config = {k: v for k, v in config.items() if k in default_eval_config.keys()}
-    dataset_config = {k: v for k, v in config.items() if k in default_dataset_config.keys()}
-    metrics_config = {k: v for k, v in config.items() if k in default_metrics_config.keys()}
-
-    # Run code
-    evaluator = trackeval.Evaluator(eval_config)
-    dataset_list = [trackeval.datasets.PersonPath22(dataset_config)]
-    metrics_list = []
-    for metric in [trackeval.metrics.HOTA, trackeval.metrics.CLEAR, trackeval.metrics.Identity, trackeval.metrics.VACE]:
-        if metric.get_name() in metrics_config['METRICS']:
-            metrics_list.append(metric(metrics_config))
-    if len(metrics_list) == 0:
-        raise Exception('No metrics selected for evaluation')
-    evaluator.evaluate(dataset_list, metrics_list)
diff --git a/spaces/yangogo/bingo/src/components/chat-image.tsx b/spaces/yangogo/bingo/src/components/chat-image.tsx
deleted file mode 100644
index 05ecc9771eada27a0f2d160bb01cba170d37bb09..0000000000000000000000000000000000000000
--- a/spaces/yangogo/bingo/src/components/chat-image.tsx
+++ /dev/null
@@ -1,170 +0,0 @@
-import {
-  useEffect,
-  useState,
-  useCallback,
-  ChangeEvent,
-  ClipboardEvent,
-  MouseEventHandler,
-  FormEvent,
-  useRef
-} from "react"
-import Image from 'next/image'
-import PasteIcon from '@/assets/images/paste.svg'
-import UploadIcon from '@/assets/images/upload.svg'
-import CameraIcon from '@/assets/images/camera.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { cn } from '@/lib/utils'
-
-interface ChatImageProps extends Pick<ReturnType<typeof useBing>, 'uploadImage'> {}
-
-const preventDefault: MouseEventHandler<HTMLDivElement> = (event) => {
-  event.nativeEvent.stopImmediatePropagation()
-}
-
-const toBase64 = (file: File): Promise<string> => new Promise((resolve, reject) => {
-  const reader = new FileReader()
-  reader.readAsDataURL(file)
-  reader.onload = () => resolve(reader.result as string)
-  reader.onerror = reject
-})
-
-export function ChatImage({ children, uploadImage }: React.PropsWithChildren<ChatImageProps>) {
-  const videoRef = useRef<HTMLVideoElement>(null)
-  const canvasRef = useRef<HTMLCanvasElement>(null)
-  const mediaStream = useRef<MediaStream>()
-  const [panel, setPanel] = useState('none')
-
-  const upload = useCallback((url: string) => {
-    if (url) {
-      uploadImage(url)
-    }
-    setPanel('none')
-  }, [panel])
-
-  const onUpload = useCallback(async (event: ChangeEvent<HTMLInputElement>) => {
-    const file = event.target.files?.[0]
-    if (file) {
-      const fileDataUrl = await toBase64(file)
-      if (fileDataUrl) {
-        upload(fileDataUrl)
-      }
-    }
-  }, [])
-
-  const onPaste = useCallback((event: ClipboardEvent<HTMLInputElement>) => {
-    const pasteUrl = event.clipboardData.getData('text') ?? ''
-    upload(pasteUrl)
-  }, [])
-
-  const onEnter = useCallback((event: FormEvent<HTMLFormElement>) => {
-    event.preventDefault()
-    event.stopPropagation()
-    // @ts-ignore
-    const inputUrl = event.target.elements.image.value
-    if (inputUrl) {
-      upload(inputUrl)
-    }
-  }, [])
-
-  const openVideo: MouseEventHandler<HTMLButtonElement> = async (event) => {
-    event.stopPropagation()
-    setPanel('camera-mode')
-  }
-
-  const onCapture = () => {
-    if (canvasRef.current && videoRef.current) {
-      const canvas = canvasRef.current
-      canvas.width = videoRef.current!.videoWidth
-      canvas.height = videoRef.current!.videoHeight
-      canvas.getContext('2d')?.drawImage(videoRef.current, 0, 0, canvas.width, canvas.height)
-      const cameraUrl = canvas.toDataURL('image/jpeg')
-      upload(cameraUrl)
-    }
-  }
-
-  useEffect(() => {
-    const handleBlur = () => {
-      if (panel !== 'none') {
-        setPanel('none')
-      }
-    }
-    document.addEventListener('click', handleBlur)
-    return () => {
-      document.removeEventListener('click', handleBlur)
-    }
-  }, [panel])
-
-  useEffect(() => {
-    if (panel === 'camera-mode') {
-      navigator.mediaDevices.getUserMedia({ video: true, audio: false })
-      .then(videoStream => {
-        mediaStream.current = videoStream
-        if (videoRef.current) {
-          videoRef.current.srcObject = videoStream
-        }
-      })
-    } else {
-      if (mediaStream.current) {
-        mediaStream.current.getTracks().forEach(function(track) {
-          track.stop()
-        })
-        mediaStream.current = undefined
-      }
-    }
-  }, [panel])
-
-  return (
-    <div className="visual-search-container">
-      <div onClick={() => panel === 'none' ? setPanel('normal') : setPanel('none')}>{children}</div>
-      <div className={cn('visual-search', panel)} onClick={preventDefault}>
-        <div className="normal-content">
-          <div className="header">
-            <h4>添加图像</h4>
-          </div>
-          <div className="paste">
-            <Image alt="paste" src={PasteIcon} width={24} />
-            <form onSubmitCapture={onEnter}>
-              <input
-                className="paste-input"
-                id="sb_imgpst"
-                type="text"
-                name="image"
-                placeholder="粘贴图像 URL"
-                aria-label="粘贴图像 URL"
-                onPaste={onPaste}
-                onClickCapture={(e) => e.stopPropagation()}
-              />
-            </form>
-          </div>
-          <div className="buttons">
-            <button type="button" aria-label="从此设备上传">
-              <input
-                id="vs_fileinput"
-                className="fileinput"
-                type="file"
-                accept="image/gif, image/jpeg, image/png, image/webp"
-                onChange={onUpload}
-              />
-              <Image alt="uplaod" src={UploadIcon} width={20} />
-              从此设备上传
-            </button>
-            <button type="button" aria-label="拍照" onClick={openVideo}>
-              <Image alt="camera" src={CameraIcon} width={20} />
-              拍照
-            </button>
-          </div>
-        </div>
-        {panel === 'camera-mode' && <div className="cam-content">
-          <div className="webvideo-container">
-            <video className="webvideo" autoPlay muted playsInline ref={videoRef} />
-            <canvas className="webcanvas" ref={canvasRef} />
-          </div>
-          <div className="cambtn" role="button" aria-label="拍照" onClick={onCapture}>
-            <div className="cam-btn-circle-large"></div>
-            <div className="cam-btn-circle-small"></div>
-          </div>
-        </div>}
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/yaoshining/text-generation-webui/extensions/multimodal/abstract_pipeline.py b/spaces/yaoshining/text-generation-webui/extensions/multimodal/abstract_pipeline.py
deleted file mode 100644
index 584219419d256e7743fd4d5120c56bcfa8f2a9f9..0000000000000000000000000000000000000000
--- a/spaces/yaoshining/text-generation-webui/extensions/multimodal/abstract_pipeline.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import List, Optional
-
-import torch
-from PIL import Image
-
-
-class AbstractMultimodalPipeline(ABC):
-    @staticmethod
-    @abstractmethod
-    def name() -> str:
-        'name of the pipeline, should be same as in --multimodal-pipeline'
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def image_start() -> Optional[str]:
-        'return image start string, string representation of image start token, or None if not applicable'
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def image_end() -> Optional[str]:
-        'return image end string, string representation of image end token, or None if not applicable'
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def placeholder_token_id() -> int:
-        'return placeholder token id'
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def num_image_embeds() -> int:
-        'return the number of embeds used by a single image (for example: 256 for LLaVA)'
-        pass
-
-    @abstractmethod
-    def embed_images(self, images: List[Image.Image]) -> torch.Tensor:
-        'forward the images through vision pipeline, and return their embeddings'
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def embed_tokens(input_ids: torch.Tensor) -> torch.Tensor:
-        'embed tokens, the exact function varies by LLM, for LLaMA it is `shared.model.model.embed_tokens`'
-        pass
-
-    @staticmethod
-    @abstractmethod
-    def placeholder_embeddings() -> torch.Tensor:
-        'get placeholder embeddings if there are multiple images, and `add_all_images_to_prompt` is False'
-        pass
-
-    def _get_device(self, setting_name: str, params: dict):
-        if params[setting_name] is None:
-            return torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        return torch.device(params[setting_name])
-
-    def _get_dtype(self, setting_name: str, params: dict):
-        return torch.float32 if int(params[setting_name]) == 32 else torch.float16
diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/components/TrackList/TrackListContextMenu.tsx b/spaces/yderre-aubay/midi-player-demo/src/main/components/TrackList/TrackListContextMenu.tsx
deleted file mode 100644
index 8ba9e9eb4d31aefe597c65e9fca176c667398dd4..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/main/components/TrackList/TrackListContextMenu.tsx
+++ /dev/null
@@ -1,61 +0,0 @@
-import { FC } from "react"
-import { ContextMenu, ContextMenuProps } from "../../../components/ContextMenu"
-import { Localized } from "../../../components/Localized"
-import { MenuItem } from "../../../components/Menu"
-
-export type TrackListContextMenuProps = ContextMenuProps & {
-  onClickAdd: () => void
-  onClickDelete: () => void
-  onClickProperty: () => void
-  onClickChangeTrackColor: () => void
-}
-
-export const TrackListContextMenu: FC<TrackListContextMenuProps> = ({
-  onClickAdd,
-  onClickDelete,
-  onClickProperty,
-  onClickChangeTrackColor,
-  ...props
-}) => {
-  const { handleClose } = props
-  return (
-    <ContextMenu {...props}>
-      <MenuItem
-        onClick={(e) => {
-          e.stopPropagation()
-          onClickAdd()
-          handleClose()
-        }}
-      >
-        <Localized default="Add track">add-track</Localized>
-      </MenuItem>
-      <MenuItem
-        onClick={(e) => {
-          e.stopPropagation()
-          onClickDelete()
-          handleClose()
-        }}
-      >
-        <Localized default="Delete track">delete-track</Localized>
-      </MenuItem>
-      <MenuItem
-        onClick={(e) => {
-          e.stopPropagation()
-          onClickProperty()
-          handleClose()
-        }}
-      >
-        <Localized default="Property">property</Localized>
-      </MenuItem>
-      <MenuItem
-        onClick={(e) => {
-          e.stopPropagation()
-          onClickChangeTrackColor()
-          handleClose()
-        }}
-      >
-        <Localized default="Change track color">change-track-color</Localized>
-      </MenuItem>
-    </ContextMenu>
-  )
-}
diff --git a/spaces/yuan1615/EmpathyVC/models.py b/spaces/yuan1615/EmpathyVC/models.py
deleted file mode 100644
index ca8f00a31184d6eae6977d2cec8e18a5eb9f7d4d..0000000000000000000000000000000000000000
--- a/spaces/yuan1615/EmpathyVC/models.py
+++ /dev/null
@@ -1,546 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-#import monotonic_align
-
-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
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    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 = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    self.prosody_emb = nn.Embedding(5, hidden_channels)
-    nn.init.normal_(self.prosody_emb.weight, 0.0, hidden_channels**-0.5)
-
-    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, x, x_lengths, prosody):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    prosody = self.prosody_emb(prosody) * math.sqrt(self.hidden_channels)
-    x = x + prosody
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_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 x, 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
-
-
-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
-
-
-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)
-        # 计算参数量
-        num_param = sum(param.numel() for param in self.parameters())
-        print(num_param)
-
-    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):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-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 SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    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,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    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.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    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, 4, gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-    if n_speakers > 1:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, prosody, y, y_lengths, sid=None):
-
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, prosody)
-
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-      # print(g)
-      # print(g.shape)
-      # raise OSError('end')
-    else:
-      g = None
-
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-
-    z_p = self.flow(z, y_mask, g=g)
-
-    with torch.no_grad():
-      # negative cross-entropy
-      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
-      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-    w = attn.sum(2)
-    if self.use_sdp:
-      l_length = self.dp(x, x_mask, w, g=g)
-      l_length = l_length / torch.sum(x_mask)
-    else:
-      logw_ = torch.log(w + 1e-6) * x_mask
-      logw = self.dp(x, x_mask, g=g)
-      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
-
-    # expand prior
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-    o = self.dec(z_slice, g=g)
-    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, x, x_lengths, prosody, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, prosody)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-    if self.use_sdp:
-      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    z_p = self.flow(z, y_mask, g=g_src)
-    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
-
diff --git a/spaces/yufiofficial/MusicGenQ/audiocraft/utils/notebook.py b/spaces/yufiofficial/MusicGenQ/audiocraft/utils/notebook.py
deleted file mode 100644
index 019b9d19e5bef976bedddf428fd25da42a8a9726..0000000000000000000000000000000000000000
--- a/spaces/yufiofficial/MusicGenQ/audiocraft/utils/notebook.py
+++ /dev/null
@@ -1,32 +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.
-
-try:
-    import IPython.display as ipd  # type: ignore
-except ImportError:
-    # Note in a notebook...
-    pass
-
-
-import torch
-
-
-def display_audio(samples: torch.Tensor, sample_rate: int):
-    """Renders an audio player for the given audio samples.
-
-    Args:
-        samples (torch.Tensor): a Tensor of decoded audio samples
-            with shapes [B, C, T] or [C, T]
-        sample_rate (int): sample rate audio should be displayed with.
-    """
-    assert samples.dim() == 2 or samples.dim() == 3
-
-    samples = samples.detach().cpu()
-    if samples.dim() == 2:
-        samples = samples[None, ...]
-
-    for audio in samples:
-        ipd.display(ipd.Audio(audio, rate=sample_rate))
diff --git a/spaces/zej97/AI-Research-Assistant/test/test.py b/spaces/zej97/AI-Research-Assistant/test/test.py
deleted file mode 100644
index 8f6a0cb9cc00b9e4dc375aada19771e2f5d691cc..0000000000000000000000000000000000000000
--- a/spaces/zej97/AI-Research-Assistant/test/test.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import gradio as gr
-import test2 as test2
-import test3 as test3
-
-theme = gr.themes.Soft(
-    font_mono=[gr.themes.GoogleFont('Fira Code'), 'ui-monospace', 'Consolas', 'monospace'],
-).set(
-    embed_radius='*radius_md'
-)
-
-with gr.Blocks(theme=theme, title="AI Research Assistant") as demo:
-    output = gr.Textbox(label="Output")
-    button = gr.Button("Start")
-    button.click(fn=test2.generator_, outputs=output)
-    
-demo.queue()
-demo.launch()
\ No newline at end of file
diff --git a/spaces/zeno-ml/openai-evals/evals/country-area/country_fns.py b/spaces/zeno-ml/openai-evals/evals/country-area/country_fns.py
deleted file mode 100644
index 4a9e3d769be38f9dfc65c516b25200be7af2bb5d..0000000000000000000000000000000000000000
--- a/spaces/zeno-ml/openai-evals/evals/country-area/country_fns.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-
-import pandas as pd
-from zeno import DistillReturn, distill
-
-# read file from same directory as this file
-area_df = pd.read_csv(os.path.dirname(__file__) + "/country-areas.csv")
-# set index to country
-area_df.set_index("country", inplace=True)
-# given a string like 23,180 (8,950) extract the first number
-
-area_df = area_df[area_df["land"] != "not determined"]
-area_df["area"] = area_df["land"].apply(
-    lambda x: x if isinstance(x, float) else float(x.split(" ")[0].replace(",", ""))
-)
-
-
-@distill
-def area(df, ops):
-    areas = []
-    for output in df[ops.label_column]:
-        if output in area_df.index:
-            areas.append(area_df.loc[output]["area"])
-        else:
-            areas.append(-1)
-    return DistillReturn(distill_output=areas)
diff --git a/spaces/zestyoreo/vtryon/README.md b/spaces/zestyoreo/vtryon/README.md
deleted file mode 100644
index f58ac23d5c2846087c3afb21118fe4145b6f088e..0000000000000000000000000000000000000000
--- a/spaces/zestyoreo/vtryon/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Vtryon
-emoji: 👁
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.9
-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/zhangyd/bingo/src/lib/bots/bing/tts.ts b/spaces/zhangyd/bingo/src/lib/bots/bing/tts.ts
deleted file mode 100644
index cd10b7d1d7581bf9cf46ff6755fcca550c558c9b..0000000000000000000000000000000000000000
--- a/spaces/zhangyd/bingo/src/lib/bots/bing/tts.ts
+++ /dev/null
@@ -1,82 +0,0 @@
-import { sleep } from './utils'
-
-const synth = window.speechSynthesis
-
-export class TTS {
-  currentText = ''
-  speakText = ''
-  private controller = new AbortController()
-  speaking = false
-  get isSpeaking() {
-    return this.speaking
-  }
-  finished = false
-  constructor() {}
-  abort = () => {
-    this.controller.abort()
-  }
-
-  reset = () => {
-    this.speaking = false
-    this.finished = true
-    this.currentText = ''
-    this.speakText = ''
-    this.abort()
-  }
-
-  speak = (text: string) => {
-    if (!synth || text?.trim()?.length < 2) {
-      return
-    }
-    this.currentText = text.replace(/[^\u4e00-\u9fa5_a-zA-Z0-9，。？,：；\.,:]+/g, '')
-    this.finished = false
-    this.loop()
-  }
-
-  private async doSpeek() {
-    return new Promise((resolve) => {
-      const endIndex = this.finished ? this.currentText.length :
-        Math.max(
-          this.currentText.lastIndexOf('。'),
-          this.currentText.lastIndexOf('；'),
-          this.currentText.lastIndexOf('、'),
-          this.currentText.lastIndexOf('？'),
-          this.currentText.lastIndexOf('\n')
-        )
-      const startIndex = this.speakText.length ? Math.max(0, this.currentText.lastIndexOf(this.speakText) + this.speakText.length) : 0
-
-      if (startIndex >= endIndex) {
-        return resolve(true)
-      }
-      const text = this.currentText.slice(startIndex, endIndex)
-      this.speakText = text
-      const utterThis = new SpeechSynthesisUtterance(text)
-      this.controller.signal.onabort = () => {
-        synth.cancel()
-        this.finished = true
-        resolve(false)
-      }
-
-      utterThis.onend = function (event) {
-        resolve(true)
-      }
-
-      utterThis.onerror = function (event) {
-        resolve(false)
-      }
-
-      const voice = synth.getVoices().find(v => v.name.includes('Microsoft Yunxi Online')) ?? null
-      utterThis.voice = voice
-      synth.speak(utterThis)
-    })
-  }
-
-  private async loop() {
-    if (this.speaking) return
-    this.speaking = true
-    while(!this.finished) {
-      await Promise.all([sleep(1000), this.doSpeek()])
-    }
-    this.speaking = false
-  }
-}
diff --git a/spaces/zhenwusw/JoJoGAN/e4e/datasets/gt_res_dataset.py b/spaces/zhenwusw/JoJoGAN/e4e/datasets/gt_res_dataset.py
deleted file mode 100644
index c0beacfee5335aa10aa7e8b7cabe206d7f9a56f7..0000000000000000000000000000000000000000
--- a/spaces/zhenwusw/JoJoGAN/e4e/datasets/gt_res_dataset.py
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/usr/bin/python
-# encoding: utf-8
-import os
-from torch.utils.data import Dataset
-from PIL import Image
-import torch
-
-class GTResDataset(Dataset):
-
-	def __init__(self, root_path, gt_dir=None, transform=None, transform_train=None):
-		self.pairs = []
-		for f in os.listdir(root_path):
-			image_path = os.path.join(root_path, f)
-			gt_path = os.path.join(gt_dir, f)
-			if f.endswith(".jpg") or f.endswith(".png"):
-				self.pairs.append([image_path, gt_path.replace('.png', '.jpg'), None])
-		self.transform = transform
-		self.transform_train = transform_train
-
-	def __len__(self):
-		return len(self.pairs)
-
-	def __getitem__(self, index):
-		from_path, to_path, _ = self.pairs[index]
-		from_im = Image.open(from_path).convert('RGB')
-		to_im = Image.open(to_path).convert('RGB')
-
-		if self.transform:
-			to_im = self.transform(to_im)
-			from_im = self.transform(from_im)
-
-		return from_im, to_im
diff --git a/spaces/zhigangjiang/3D-Room-Layout-Estimation_LGT-Net/evaluation/f1_score.py b/spaces/zhigangjiang/3D-Room-Layout-Estimation_LGT-Net/evaluation/f1_score.py
deleted file mode 100644
index 92db643188cdc560a64688ca1af0cbfc048b8cba..0000000000000000000000000000000000000000
--- a/spaces/zhigangjiang/3D-Room-Layout-Estimation_LGT-Net/evaluation/f1_score.py
+++ /dev/null
@@ -1,78 +0,0 @@
-""" 
-@author: Zhigang Jiang
-@time: 2022/01/28
-@description:
-Holistic 3D Vision Challenge on General Room Layout Estimation Track Evaluation Package
-Reference: https://github.com/bertjiazheng/indoor-layout-evaluation
-"""
-
-from scipy.optimize import linear_sum_assignment
-import numpy as np
-import scipy
-
-HEIGHT, WIDTH = 512, 1024
-MAX_DISTANCE = np.sqrt(HEIGHT**2 + WIDTH**2)
-
-
-def f1_score_2d(gt_corners, dt_corners, thresholds):
-    distances = scipy.spatial.distance.cdist(gt_corners, dt_corners)
-    return eval_junctions(distances, thresholds=thresholds)
-
-
-def eval_junctions(distances, thresholds=5):
-    thresholds = thresholds if isinstance(thresholds, tuple) or isinstance(
-        thresholds, list) else list([thresholds])
-
-    num_gts, num_preds = distances.shape
-
-    # filter the matches between ceiling-wall and floor-wall junctions
-    mask = np.zeros_like(distances, dtype=np.bool)
-    mask[:num_gts//2, :num_preds//2] = True
-    mask[num_gts//2:, num_preds//2:] = True
-    distances[~mask] = np.inf
-
-    # F-measure under different thresholds
-    Fs = []
-    Ps = []
-    Rs = []
-    for threshold in thresholds:
-        distances_temp = distances.copy()
-
-        # filter the mis-matched pairs
-        distances_temp[distances_temp > threshold] = np.inf
-
-        # remain the rows and columns that contain non-inf elements
-        distances_temp = distances_temp[:, np.any(np.isfinite(distances_temp), axis=0)]
-
-        if np.prod(distances_temp.shape) == 0:
-            Fs.append(0)
-            Ps.append(0)
-            Rs.append(0)
-            continue
-
-        distances_temp = distances_temp[np.any(np.isfinite(distances_temp), axis=1), :]
-
-        # solve the bipartite graph matching problem
-        row_ind, col_ind = linear_sum_assignment_with_inf(distances_temp)
-        true_positive = np.sum(np.isfinite(distances_temp[row_ind, col_ind]))
-
-        # compute precision and recall
-        precision = true_positive / num_preds
-        recall = true_positive / num_gts
-
-        # compute F measure
-        Fs.append(2 * precision * recall / (precision + recall))
-        Ps.append(precision)
-        Rs.append(recall)
-
-    return Fs, Ps, Rs
-
-
-def linear_sum_assignment_with_inf(cost_matrix):
-    """
-    Deal with linear_sum_assignment with inf according to
-    https://github.com/scipy/scipy/issues/6900#issuecomment-451735634
-    """
-    cost_matrix = np.copy(cost_matrix)
-    cost_matrix[np.isinf(cost_matrix)] = MAX_DISTANCE
-    return linear_sum_assignment(cost_matrix)
\ No newline at end of file
diff --git a/spaces/zhoupin30/zhoupin30/src/lib/bots/bing/index.ts b/spaces/zhoupin30/zhoupin30/src/lib/bots/bing/index.ts
deleted file mode 100644
index 4596f9de42fe9079f8128a03bf416a485a4ebb15..0000000000000000000000000000000000000000
--- a/spaces/zhoupin30/zhoupin30/src/lib/bots/bing/index.ts
+++ /dev/null
@@ -1,410 +0,0 @@
-import { fetch, WebSocket, debug } from '@/lib/isomorphic'
-import WebSocketAsPromised from 'websocket-as-promised'
-import {
-  SendMessageParams,
-  BingConversationStyle,
-  ConversationResponse,
-  ChatResponseMessage,
-  ConversationInfo,
-  InvocationEventType,
-  ChatError,
-  ErrorCode,
-  ChatUpdateCompleteResponse,
-  ImageInfo,
-  KBlobResponse
-} from './types'
-
-import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils'
-import { WatchDog, createChunkDecoder } from '@/lib/utils'
-
-type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }>
-
-const OPTIONS_SETS = [
-  'nlu_direct_response_filter',
-  'deepleo',
-  'disable_emoji_spoken_text',
-  'responsible_ai_policy_235',
-  'enablemm',
-  'iycapbing',
-  'iyxapbing',
-  'objopinion',
-  'rweasgv2',
-  'dagslnv1',
-  'dv3sugg',
-  'autosave',
-  'iyoloxap',
-  'iyoloneutral',
-  'clgalileo',
-  'gencontentv3',
-]
-
-export class BingWebBot {
-  protected conversationContext?: ConversationInfo
-  protected cookie: string
-  protected ua: string
-  protected endpoint = ''
-  private lastText = ''
-  private asyncTasks: Array<Promise<any>> = []
-
-  constructor(opts: {
-    cookie: string
-    ua: string
-    bingConversationStyle?: BingConversationStyle
-    conversationContext?: ConversationInfo
-  }) {
-    const { cookie, ua, conversationContext } = opts
-    this.cookie = cookie?.includes(';') ? cookie : `_EDGE_V=1; _U=${cookie}`
-    this.ua = ua
-    this.conversationContext = conversationContext
-  }
-
-  static buildChatRequest(conversation: ConversationInfo) {
-    const optionsSets = OPTIONS_SETS
-    if (conversation.conversationStyle === BingConversationStyle.Precise) {
-      optionsSets.push('h3precise')
-    } else if (conversation.conversationStyle === BingConversationStyle.Creative) {
-      optionsSets.push('h3imaginative')
-    }
-    return {
-      arguments: [
-        {
-          source: 'cib',
-          optionsSets,
-          allowedMessageTypes: [
-            'ActionRequest',
-            'Chat',
-            'Context',
-            'InternalSearchQuery',
-            'InternalSearchResult',
-            'Disengaged',
-            'InternalLoaderMessage',
-            'Progress',
-            'RenderCardRequest',
-            'SemanticSerp',
-            'GenerateContentQuery',
-            'SearchQuery',
-          ],
-          sliceIds: [
-            'winmuid1tf',
-            'anssupfor_c',
-            'imgchatgptv2',
-            'tts2cf',
-            'contansperf',
-            'mlchatpc8500w',
-            'mlchatpc2',
-            'ctrlworkpay',
-            'winshortmsgtf',
-            'cibctrl',
-            'sydtransctrl',
-            'sydconfigoptc',
-            '0705trt4',
-            '517opinion',
-            '628ajcopus0',
-            '330uaugs0',
-            '529rwea',
-            '0626snptrcs0',
-            '424dagslnv1',
-          ],
-          isStartOfSession: conversation.invocationId === 0,
-          message: {
-            author: 'user',
-            inputMethod: 'Keyboard',
-            text: conversation.prompt,
-            imageUrl: conversation.imageUrl,
-            messageType: 'Chat',
-          },
-          conversationId: conversation.conversationId,
-          conversationSignature: conversation.conversationSignature,
-          participant: { id: conversation.clientId },
-        },
-      ],
-      invocationId: conversation.invocationId.toString(),
-      target: 'chat',
-      type: InvocationEventType.StreamInvocation,
-    }
-  }
-
-  async createConversation(): Promise<ConversationResponse> {
-    const headers = {
-      'Accept-Encoding': 'gzip, deflate, br, zsdch',
-      'User-Agent': this.ua,
-      'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-      cookie: this.cookie,
-    }
-
-    let resp: ConversationResponse | undefined
-    try {
-      const response = await fetch(this.endpoint + '/api/create', { method: 'POST', headers, redirect: 'error', mode: 'cors', credentials: 'include' })
-      if (response.status === 404) {
-        throw new ChatError('Not Found', ErrorCode.NOTFOUND_ERROR)
-      }
-      resp = await response.json() as ConversationResponse
-    } catch (err) {
-      console.error('create conversation error', err)
-    }
-
-    if (!resp?.result) {
-      throw new ChatError('你的 VPS 或代理可能被封禁，如有疑问，请前往 https://github.com/weaigc/bingo 咨询', ErrorCode.BING_IP_FORBIDDEN)
-    }
-
-    const { value, message } = resp.result || {}
-    if (value !== 'Success') {
-      const errorMsg = `${value}: ${message}`
-      if (value === 'UnauthorizedRequest') {
-        if (/fetch failed/i.test(message || '')) {
-          throw new ChatError(errorMsg, ErrorCode.BING_IP_FORBIDDEN)
-        }
-        throw new ChatError(errorMsg, ErrorCode.BING_UNAUTHORIZED)
-      }
-      if (value === 'TryLater') {
-        throw new ChatError(errorMsg, ErrorCode.BING_TRY_LATER)
-      }
-      if (value === 'Forbidden') {
-        throw new ChatError(errorMsg, ErrorCode.BING_FORBIDDEN)
-      }
-      throw new ChatError(errorMsg, ErrorCode.UNKOWN_ERROR)
-    }
-    return resp
-  }
-
-  private async createContext(conversationStyle: BingConversationStyle) {
-    if (!this.conversationContext) {
-      const conversation = await this.createConversation()
-      this.conversationContext = {
-        conversationId: conversation.conversationId,
-        conversationSignature: conversation.conversationSignature,
-        clientId: conversation.clientId,
-        invocationId: 0,
-        conversationStyle,
-        prompt: '',
-      }
-    }
-    return this.conversationContext
-  }
-
-  async sendMessage(params: Params) {
-    try {
-      await this.createContext(params.options.bingConversationStyle)
-      Object.assign(this.conversationContext!, { prompt: params.prompt, imageUrl: params.imageUrl })
-      return this.sydneyProxy(params)
-    } catch (error) {
-      params.onEvent({
-        type: 'ERROR',
-        error: error instanceof ChatError ? error : new ChatError('Catch Error', ErrorCode.UNKOWN_ERROR),
-      })
-    }
-  }
-
-  private async sydneyProxy(params: Params) {
-    const abortController = new AbortController()
-    const response = await fetch(this.endpoint + '/api/sydney', {
-      method: 'POST',
-      headers: {
-        'Content-Type': 'application/json',
-      },
-      signal: abortController.signal,
-      body: JSON.stringify(this.conversationContext!)
-    })
-    if (response.status !== 200) {
-      params.onEvent({
-        type: 'ERROR',
-        error: new ChatError(
-          'Unknown error',
-          ErrorCode.UNKOWN_ERROR,
-        ),
-      })
-    }
-    params.signal?.addEventListener('abort', () => {
-      abortController.abort()
-    })
-
-    const textDecoder = createChunkDecoder()
-    for await (const chunk of streamAsyncIterable(response.body!)) {
-      this.parseEvents(params, websocketUtils.unpackMessage(textDecoder(chunk)))
-    }
-  }
-
-  async sendWs() {
-    const wsConfig: ConstructorParameters<typeof WebSocketAsPromised>[1] = {
-      packMessage: websocketUtils.packMessage,
-      unpackMessage: websocketUtils.unpackMessage,
-      createWebSocket: (url) => new WebSocket(url, {
-        headers: {
-          'accept-language': 'zh-CN,zh;q=0.9',
-          'cache-control': 'no-cache',
-          'User-Agent': this.ua,
-          pragma: 'no-cache',
-          cookie: this.cookie,
-        }
-      })
-    }
-    const wsp = new WebSocketAsPromised('wss://sydney.bing.com/sydney/ChatHub', wsConfig)
-
-    wsp.open().then(() => {
-      wsp.sendPacked({ protocol: 'json', version: 1 })
-      wsp.sendPacked({ type: 6 })
-      wsp.sendPacked(BingWebBot.buildChatRequest(this.conversationContext!))
-    })
-
-    return wsp
-  }
-
-  private async createImage(prompt: string, id: string) {
-    try {
-      const headers = {
-        'Accept-Encoding': 'gzip, deflate, br, zsdch',
-        'User-Agent': this.ua,
-        'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-        cookie: this.cookie,
-      }
-      const query = new URLSearchParams({
-        prompt,
-        id
-      })
-      const response = await fetch(this.endpoint + '/api/image?' + query.toString(),
-        {
-          method: 'POST',
-          headers,
-          mode: 'cors',
-          credentials: 'include'
-        })
-        .then(res => res.text())
-      if (response) {
-        this.lastText += '\n' + response
-      }
-    } catch (err) {
-      console.error('Create Image Error', err)
-    }
-  }
-
-  private buildKnowledgeApiPayload(imageUrl: string, conversationStyle: BingConversationStyle) {
-    const imageInfo: ImageInfo = {}
-    let imageBase64: string | undefined = undefined
-    const knowledgeRequest = {
-      imageInfo,
-      knowledgeRequest: {
-        invokedSkills: [
-          'ImageById'
-        ],
-        subscriptionId: 'Bing.Chat.Multimodal',
-        invokedSkillsRequestData: {
-            enableFaceBlur: true
-        },
-        convoData: {
-          convoid: this.conversationContext?.conversationId,
-          convotone: conversationStyle,
-        }
-      },
-    }
-
-    if (imageUrl.startsWith('data:image/')) {
-      imageBase64 = imageUrl.replace('data:image/', '');
-      const partIndex = imageBase64.indexOf(',')
-      if (partIndex) {
-        imageBase64 = imageBase64.substring(partIndex + 1)
-      }
-    } else {
-      imageInfo.url = imageUrl
-    }
-    return { knowledgeRequest, imageBase64 }
-  }
-
-  async uploadImage(imageUrl: string, conversationStyle: BingConversationStyle = BingConversationStyle.Creative): Promise<KBlobResponse | undefined> {
-    if (!imageUrl) {
-      return
-    }
-    await this.createContext(conversationStyle)
-    const payload = this.buildKnowledgeApiPayload(imageUrl, conversationStyle)
-
-    const response = await fetch(this.endpoint + '/api/kblob',
-      {
-        headers: {
-          'Content-Type': 'application/json',
-        },
-        method: 'POST',
-        mode: 'cors',
-        credentials: 'include',
-        body: JSON.stringify(payload),
-      })
-      .then(res => res.json())
-      .catch(e => {
-        console.log('Error', e)
-      })
-    return response
-  }
-
-  private async generateContent(message: ChatResponseMessage) {
-    if (message.contentType === 'IMAGE') {
-      this.asyncTasks.push(this.createImage(message.text, message.messageId))
-    }
-  }
-
-  private async parseEvents(params: Params, events: any) {
-    const conversation = this.conversationContext!
-
-    events?.forEach(async (event: ChatUpdateCompleteResponse) => {
-      debug('bing event', event)
-      if (event.type === 3) {
-        await Promise.all(this.asyncTasks)
-        this.asyncTasks = []
-        params.onEvent({ type: 'UPDATE_ANSWER', data: { text: this.lastText } })
-        params.onEvent({ type: 'DONE' })
-        conversation.invocationId = parseInt(event.invocationId, 10) + 1
-      } else if (event.type === 1) {
-        const messages = event.arguments[0].messages
-        if (messages) {
-          const text = convertMessageToMarkdown(messages[0])
-          this.lastText = text
-          params.onEvent({ type: 'UPDATE_ANSWER', data: { text, spokenText: messages[0].text, throttling: event.arguments[0].throttling } })
-        }
-      } else if (event.type === 2) {
-        const messages = event.item.messages as ChatResponseMessage[] | undefined
-        if (!messages) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              event.item.result.error || 'Unknown error',
-              event.item.result.value === 'Throttled' ? ErrorCode.THROTTLE_LIMIT
-                : event.item.result.value === 'CaptchaChallenge' ? (this.conversationContext?.conversationId?.includes('BingProdUnAuthenticatedUsers') ? ErrorCode.BING_UNAUTHORIZED : ErrorCode.BING_CAPTCHA)
-                  : ErrorCode.UNKOWN_ERROR
-            ),
-          })
-          return
-        }
-        const limited = messages.some((message) =>
-          message.contentOrigin === 'TurnLimiter'
-          || message.messageType === 'Disengaged'
-        )
-        if (limited) {
-          params.onEvent({
-            type: 'ERROR',
-            error: new ChatError(
-              'Sorry, you have reached chat limit in this conversation.',
-              ErrorCode.CONVERSATION_LIMIT,
-            ),
-          })
-          return
-        }
-
-        const lastMessage = event.item.messages.at(-1) as ChatResponseMessage
-        const specialMessage = event.item.messages.find(message => message.author === 'bot' && message.contentType === 'IMAGE')
-        if (specialMessage) {
-          this.generateContent(specialMessage)
-        }
-
-        if (lastMessage) {
-          const text = convertMessageToMarkdown(lastMessage)
-          this.lastText = text
-          params.onEvent({
-            type: 'UPDATE_ANSWER',
-            data: { text, throttling: event.item.throttling, suggestedResponses: lastMessage.suggestedResponses, sourceAttributions: lastMessage.sourceAttributions },
-          })
-        }
-      }
-    })
-  }
-
-  resetConversation() {
-    this.conversationContext = undefined
-  }
-}
diff --git a/spaces/zjunlp/KGEditor/README.md b/spaces/zjunlp/KGEditor/README.md
deleted file mode 100644
index 75b7002411bdecf7a7c8f81da7d5eda8dadd674f..0000000000000000000000000000000000000000
--- a/spaces/zjunlp/KGEditor/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: KGEditor
-emoji: 😻
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference