Attention - This Space doesn't work in this shared UI
-For it to work, you can access the original or duplicate this Space and run it on your own profile using a GPU.
Download Zip ⚙ https://imgfil.com/2uxXcd
Do you love playing board games? Do you enjoy challenging your mind and logic? Do you want to have fun with your friends or family? If you answered yes to any of these questions, then you should try playing draft checkers. Draft checkers, also known as draughts or checkers, is a classic strategy board game that you can play on your PC. In this article, we will tell you everything you need to know about draft checkers, including its history, rules, benefits, challenges, and how to download it for your PC.
-Draft checkers is a group of strategy board games for two players that involve diagonal moves of uniform game pieces and mandatory captures by jumping over opponent pieces. The most common version of draft checkers is played on an 8x8 board with 12 pieces per player, but there are many other variations with different board sizes, number of pieces, and rules. The objective of draft checkers is to capture all of your opponent's pieces or block them from making any legal moves.
-DOWNLOAD — https://jinyurl.com/2uNMy0
Draft checkers is one of the oldest board games in the world, dating back to ancient times. The earliest evidence of draft checkers was found in Mesopotamia, around 3000 BC. The game was also played by the ancient Egyptians, Greeks, Romans, Persians, Chinese, Indians, and Arabs. The modern version of draft checkers was developed in France in the 12th century, and spread to other parts of Europe and America. Draft checkers became popular among people of all ages and social classes, and was considered a game of skill and intelligence.
-The basic rules of draft checkers are simple: each player has 12 pieces (usually black or white) that are placed on the dark squares of an 8x8 board. The players take turns moving one piece diagonally forward to an adjacent empty square. If a player can jump over an opponent's piece to an empty square behind it, they must do so and capture that piece. A piece that reaches the opposite end of the board becomes a king or queen, which can move and jump in any direction. The game ends when one player has no more pieces or legal moves.
-However, there are many variations of draft checkers that have different rules and features. Some of the most common variations are:
-You can choose the variation that suits your preference and skill level, or try them all to challenge yourself and have more fun.
-Draft checkers is not only a fun and entertaining game, but also a beneficial and challenging one. Some of the benefits of playing draft checkers are:
-Some of the challenges of playing draft checkers are:
-Draft checkers is a game that can help you grow as a person and have fun at the same time.
-If you want to play draft checkers on your PC, you will need to download it from a reliable source or website. There are many options available online, but not all of them are safe and secure. Some of them may contain viruses, malware, or spyware that can harm your PC or steal your personal information. Therefore, you should be careful and choose wisely when downloading draft checkers for your PC.
-Download draughts checkers game app
-How to download checkers by Dalmax for Windows 10
-Download free checkers game for Android
-Best checkers game to download for kids
-Download checkers 10x10 board game
-Download checkers with custom rules option
-Download checkers with different rule sets
-Download checkers with multiplayer mode
-Download checkers with AI difficulty levels
-Download checkers with undo function
-Download draughts HD for Windows 10
-Download draughts checkerboard game
-Download draughts with international rules
-Download draughts with Brazilian rules
-Download draughts with pool rules
-Download draughts with Spanish rules
-Download draughts with Russian rules
-Download draughts with Portuguese rules
-Download draughts with Czech rules
-Download draughts with Turkish rules
-Download draughts with Thai rules
-Download checkers | Draughts game by MGGAMES
-Download classic checkers game for PC
-Download checkers game with realistic graphics
-Download checkers game with offline mode
-Download checkers game with data encryption
-Download checkers game with data deletion option
-Download checkers game with trailer video
-Download checkers game with ratings and reviews
-Download checkers game with in-app purchases
-Download checkers game with ads-free option
-Download checkers game with strategy tips
-Download checkers game with analysis tool
-Download checkers game with board flipping feature
-Download checkers game with net energy gain feature
-Download checkers game with holy grail fusion experiment feature
-Download checkers game with mini sun feature
-Download checkers game with 100 million°C feature
-Download checkers game with 30 seconds feature
-Download checkers game with Korea Institute of Fusion Energy feature
To help you find the best sources and websites to download draft checkers for your PC, we have compiled a list of some of the most popular and trusted ones. Here they are:
-This is one of the best apps to play draft checkers on your PC. It has over 10 million downloads and 4.5 stars rating on Google Play. It offers different modes, levels, themes, boards, pieces, rules, and languages. You can play against the computer or online with other players. You can also customize the game according to your preferences. To download this app, you will need an Android emulator such as BlueStacks or NoxPlayer on your PC.
-This is another great option to play draft checkers on your PC. It is a free software that you can download from FileHippo, a reputable website that provides safe and secure downloads. It has over 100 thousand downloads and 3.9 stars rating on FileHippo. It features different variations of draft checkers such as American, International, Russian, Brazilian, Pool, Turkish, etc. You can play against the computer or with another player on the same PC. You can also adjust the difficulty level and speed of the game.
-This is a simple and easy way to play draft checkers on your PC. It is a free online game that you can access from any browser without downloading anything. It has over 50 thousand plays and 4 stars rating on ONLINESOLN, a website that provides free online games for everyone. It features a classic version of draft checkers with an 8x8 board and 12 pieces per player. You can play against the computer or with another player online. You can also undo or redo your moves if you make a mistake.
-Now that you know some of the best sources and websites to download draft checkers for your PC, you may wonder how to install and play it on your PC. Don't worry, we have got you covered. Here are the steps and tips to install and play draft checkers on your PC:
-The first step is to choose your preferred source and website to download draft checkers for your PC. You can use any of the sources and websites we mentioned above, or you can search for other options online. However, make sure that the source and website you choose is reliable, safe, and secure. You can check the reviews, ratings, comments, and feedback of other users to verify the quality and credibility of the source and website.
-The next step is to download the draft checkers file or app to your PC. Depending on the source and website you choose, you may need to create an account, sign in, or register before downloading. You may also need to agree to the terms and conditions, privacy policy, or license agreement of the source and website. After that, you can click on the download button or link and save the draft checkers file or app to your PC. You may need to choose a location or folder where you want to save the file or app.
-The third step is to install the draft checkers file or app on your PC. To do this, you need to locate the draft checkers file or app on your PC and double-click on it. You may need to grant permission or access to run the file or app on your PC. You may also need to follow the instructions or steps on the screen to complete the installation process. You may need to choose a destination or location where you want to install the file or app.
-The final step is to launch the draft checkers file or app and start playing. To do this, you need to find the draft checkers icon or shortcut on your PC and click on it. You may need to wait for a few seconds for the file or app to load and open. After that, you can choose the mode, level, theme, board, piece, rule, and language of the game. You can also invite or join other players online if you want to play with them. Then, you can start playing draft checkers on your PC and have fun.
-One tip that can help you enjoy playing draft checkers on your PC is to adjust the settings and preferences of the draft checkers file or app according to your needs. You can access the settings and preferences menu from the main screen or menu of the file or app. You can change various aspects of the game such as sound, music, graphics, speed, difficulty, etc. You can also enable or disable notifications, updates, ads, etc. You can also reset or restore the default settings and preferences if you want.
-Another tip that can help you enjoy playing draft checkers on your PC is to learn the basic strategies and tactics of draft checkers to improve your skills. You can find various resources online that can teach you how to play draft checkers better such as tutorials, guides, videos, blogs, forums, etc. You can also practice playing draft checkers regularly with different opponents and challenges. You can also learn from your mistakes and feedback from other players. By doing this, you can become a better player of draft checkers.
-Draft checkers is a classic strategy board game that you can play on your PC. It has a rich history, diverse rules, multiple benefits, and exciting challenges. It is easy to download, install, and play draft checkers on your PC. You just need to choose a reliable source or website to download draft checkers, follow the steps and tips to install and play draft checkers, and adjust the settings and preferences of the game according to your needs. You can also learn the basic strategies and tactics of draft checkers to improve your skills and have more fun. Draft checkers is a game that can keep you entertained, challenged, and satisfied for hours. So, what are you waiting for? Download draft checkers for your PC today and enjoy playing this classic strategy board game.
-Here are some of the frequently asked questions about draft checkers:
-- Apply Instruct Pix2Pix Diffusion to a video -
-You may also like:
-For it to work, you can access the original or duplicate this Space and run it on your own profile using a GPU.
- work with longer videos / skip the queue:
- """, elem_id="duplicate-container")
- submit_btn = gr.Button("Generate Pix2Pix video")
-
- with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
- community_icon = gr.HTML(community_icon_html)
- loading_icon = gr.HTML(loading_icon_html)
- share_button = gr.Button("Share to community", elem_id="share-btn")
-
- inputs = [prompt,video_inp,seed_inp, trim_in]
- outputs = [video_out, share_group]
-
- ex = gr.Examples(
- [
- ["Make it a marble sculpture", "./examples/pexels-jill-burrow-7665249_512x512.mp4", 422112651, 4],
- ["Make it molten lava", "./examples/Ocean_Pexels_ 8953474_512x512.mp4", 43571876, 4]
- ],
- inputs=inputs,
- outputs=outputs,
- fn=infer,
- cache_examples=False,
- )
-
- gr.HTML(article)
-
- submit_btn.click(infer, inputs, outputs)
- share_button.click(None, [], [], _js=share_js)
-
-
-
-demo.launch().queue(max_size=12)
diff --git a/spaces/AIZerotoHero-Health4All/03-Datasets/README.md b/spaces/AIZerotoHero-Health4All/03-Datasets/README.md
deleted file mode 100644
index 9983c25ef86c3b1d2b050948196fada07e34b270..0000000000000000000000000000000000000000
--- a/spaces/AIZerotoHero-Health4All/03-Datasets/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: 03 Datasets
-emoji: 📊
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/__init__.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/r/[id]/proxy+page.server.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/r/[id]/proxy+page.server.ts
deleted file mode 100644
index 2ad292a6e6ed3e1626067b8b162d6583ed575b7f..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/r/[id]/proxy+page.server.ts
+++ /dev/null
@@ -1,35 +0,0 @@
-// @ts-nocheck
-import type { PageServerLoad } from "./$types";
-import { collections } from "$lib/server/database";
-import { error } from "@sveltejs/kit";
-import type { WebSearchMessageResult } from "$lib/types/WebSearch";
-
-export const load = async ({ params }: Parameters
-  View on TensorFlow.org
- |
-
-  Run in Google Colab
- |
-
-  View on GitHub
- |
-
-  Download notebook
- |
-
-  See TF Hub model
- |
-
You may also like:
-- Sends an image in to CLIP Interrogator - to generate a text prompt which is then run through - Mubert text-to-music to generate music from the input image! -
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- 
-
- -
- -### GraphQL - -* [domain](../graphql/queries.md#domain) -* [listDomains](../graphql/queries.md#listdomains) -* [createDomains](../graphql/mutations.md#createdomain) -* [setDomain](../graphql/mutations.md#setdomain) -* [unsetDomain](../graphql/mutations.md#unsetdomain) - -#### Examples - -**Creating a Domain** - -```graphql -mutation createDomain { - createDomain(input: { name: "My New Domain", description: "An optional description" }) -} -``` - -This query will return an `urn` which you can use to fetch the Domain details. - -**Fetching a Domain by Urn** - -```graphql -query getDomain { - domain(urn: "urn:li:domain:engineering") { - urn - properties { - name - description - } - entities { - total - } - } -} -``` - -**Adding a Dataset to a Domain** - -```graphql -mutation setDomain { - setDomain(entityUrn: "urn:li:dataset:(urn:li:dataPlatform:hdfs,SampleHdfsDataset,PROD)", domainUrn: "urn:li:domain:engineering") -} -``` - -> Pro Tip! You can try out the sample queries by visiting `Runtime: {rt} minutes on CPU
" - if msg is not None: - html += f"Output will appear below:
") - gr.Markdown("### Summary Output") - summary_text = gr.Textbox( - label="Summary", placeholder="The generated summary will appear here" - ) - gr.Markdown( - "The summary scores can be thought of as representing the quality of the summary. less-negative numbers (closer to 0) are better:" - ) - summary_scores = gr.Textbox( - label="Summary Scores", placeholder="Summary scores will appear here" - ) - - gr.Markdown("---") - - with gr.Column(): - gr.Markdown("## About the Model") - gr.Markdown( - "- [This model](https://huggingface.co/pszemraj/led-large-book-summary) is a fine-tuned checkpoint of [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384) on the [BookSum dataset](https://arxiv.org/abs/2105.08209).The goal was to create a model that can generalize well and is useful in summarizing lots of text in academic and daily usage." - ) - gr.Markdown( - "- The two most important parameters-empirically-are the `num_beams` and `token_batch_length`. However, increasing these will also increase the amount of time it takes to generate a summary. The `length_penalty` and `repetition_penalty` parameters are also important for the model to generate good summaries." - ) - gr.Markdown( - "- The model can be used with tag [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). See the model card for details on usage & a notebook for a tutorial." - ) - gr.Markdown("---") - - load_examples_button.click( - fn=load_single_example_text, inputs=[example_name], outputs=[input_text] - ) - - load_file_button.click( - fn=load_uploaded_file, inputs=uploaded_file, outputs=[input_text] - ) - - summarize_button.click( - fn=proc_submission, - inputs=[ - input_text, - model_size, - num_beams, - token_batch_length, - length_penalty, - repetition_penalty, - no_repeat_ngram_size, - ], - outputs=[output_text, summary_text, summary_scores], - ) - - demo.launch(enable_queue=True, share=True) \ No newline at end of file diff --git a/spaces/awacke1/SaveAndReloadDataset/README.md b/spaces/awacke1/SaveAndReloadDataset/README.md deleted file mode 100644 index be02ef93b3af986d552e4f180fe4495492692b10..0000000000000000000000000000000000000000 --- a/spaces/awacke1/SaveAndReloadDataset/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: 💪 NLP SaveAndReloadDataset 💽 -emoji: 💪📚💽 -colorFrom: red -colorTo: indigo -sdk: streamlit -sdk_version: 1.9.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/awacke1/Video-View-Download/app.py b/spaces/awacke1/Video-View-Download/app.py deleted file mode 100644 index 67b136a2a692d43e72e5720c260269f727398898..0000000000000000000000000000000000000000 --- a/spaces/awacke1/Video-View-Download/app.py +++ /dev/null @@ -1,60 +0,0 @@ -import streamlit as st -from pytube import YouTube - -class YouTubeDownloader: - @staticmethod - def run(): - st.header("Video View and Download") - - option = st.selectbox('How would you like to be contacted for the download list?',('Email', 'Mobile phone')) - st.write('You selected:', option) - option2 = st.selectbox('Would you try one of these one minute AI generated science fiction short videos?',('https://youtu.be/v-qlGRxdm6c', 'https://youtu.be/5yToL7ymfNo')) - - url = st.text_input("YouTube Video URL:") - if option2: - url=option2 - - if url: - YouTubeDownloader.validate_url(url) - with st.expander("View"): - st.video(url) - if st.button("Download"): - YouTubeDownloader.cleanup() - file_ = YouTubeDownloader.download_video(url) - st.video(file_) - YouTubeDownloader.helper_message() - - @staticmethod - def download_video(url): - with st.spinner("Downloading..."): - local_file = ( - YouTube(url) - .streams.filter(progressive=True, file_extension="mp4") - .first() - .download() - ) - st.success("Downloaded") - return local_file - - @staticmethod - def validate_url(url): - import validators - if not validators.url(url): - st.error("Video URL invalid") - st.stop() - - @classmethod - def cleanup(cls): - import pathlib - import glob - - junks = glob.glob("*.mp4") - for junk in junks: - pathlib.Path(junk).unlink() - - @classmethod - def helper_message(cls): - st.write("To save click elipses ... and choose download") - -if __name__ == "__main__": - YouTubeDownloader.run() diff --git a/spaces/ayaanzaveri/whisper-webui/docs/colab.md b/spaces/ayaanzaveri/whisper-webui/docs/colab.md deleted file mode 100644 index 3fcdb835327238764fb643b9bbd2e27b6e14f58c..0000000000000000000000000000000000000000 --- a/spaces/ayaanzaveri/whisper-webui/docs/colab.md +++ /dev/null @@ -1,20 +0,0 @@ -# Running Whisper on Google Colab - -If you don't have a decent GPU or any experience in running command-line applications, you might want to try this Google Colab instead: - -* [Google Colab - Whisper WebUI GPU](https://colab.research.google.com/drive/1qeTSvi7Bt_5RMm88ipW4fkcsMOKlDDss?usp=sharing) -* [Screenshots](https://imgur.com/a/ZfY6uBO) - -The runtime (Runtime -> Change runtime type -> Hardware accelerator) should already be set top GPU. But if not, change it to GPU. - -Then, sign in to Google if you haven't already. Next, click on "Connect" at the top right. - -Under "Checking out WebUI from Git", click on the [play icon](https://imgur.com/a/81gOLyD) that appears in "[ ]" at the left. If you get a warning, click "Run anyway". - -After this step has completed, it should be get a green check mark. Then move on to the next section under "Installing dependencies", and click in "[ ]" again. This might take approximately 30 seconds. - -Once this has completed, scroll down to the "Run WebUI" section, and click on "[ ]". This will launch the WebUI in a shared link (expires in 72 hours). To open the UI, click on the link next to "Running on public URL", which will be something like https://12xxx.gradio.app/ - -The audio length in this version is not restricted, and it will run much faster as it is backed by a GPU. You can also run it using the "Large" model. Also note that it might take some time to start the model the first time, as it may need to download a 2.8 GB file on Google's servers. - -Once you're done, you can close the WebUI session by clicking the animated close button under "Run WebUI". You can also do this if you encounter any errors and need to restart the UI. You should also go to "Manage Sessions" and terminate the session, otherwise you may end up using all your free compute credits. \ No newline at end of file diff --git a/spaces/azizalto/us_patent_kaggle/README.md b/spaces/azizalto/us_patent_kaggle/README.md deleted file mode 100644 index 8ce4030fe6f491b0fd66587a940b06e72389b7f7..0000000000000000000000000000000000000000 --- a/spaces/azizalto/us_patent_kaggle/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Us_patent_kaggle -emoji: 🚀 -colorFrom: indigo -colorTo: red -sdk: streamlit -sdk_version: 1.2.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/banana-projects/web3d/node_modules/three/src/core/Uniform.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/core/Uniform.d.ts deleted file mode 100644 index 823ecc517a8625972e483cc7715a950334979f9e..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/src/core/Uniform.d.ts +++ /dev/null @@ -1,22 +0,0 @@ -export class Uniform { - constructor(value: any); - /** - * @deprecated - */ - constructor(type: string, value: any); - /** - * @deprecated - */ - type: string; - value: any; - /** - * @deprecated Use {@link Object3D#onBeforeRender object.onBeforeRender()} instead. - */ - dynamic: boolean; - onUpdateCallback: Function; - - /** - * @deprecated Use {@link Object3D#onBeforeRender object.onBeforeRender()} instead. - */ - onUpdate(callback: Function): Uniform; -} diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/models/base_model.py b/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/models/base_model.py deleted file mode 100644 index f06f9ca2ca213f1a7c400355e9c66eaa12b1b1c4..0000000000000000000000000000000000000000 --- a/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/models/base_model.py +++ /dev/null @@ -1,380 +0,0 @@ -import os -import time -import torch -from collections import OrderedDict -from copy import deepcopy -from torch.nn.parallel import DataParallel, DistributedDataParallel - -from basicsr.models import lr_scheduler as lr_scheduler -from basicsr.utils import get_root_logger -from basicsr.utils.dist_util import master_only - - -class BaseModel(): - """Base model.""" - - def __init__(self, opt): - self.opt = opt - self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu') - self.is_train = opt['is_train'] - self.schedulers = [] - self.optimizers = [] - - def feed_data(self, data): - pass - - def optimize_parameters(self): - pass - - def get_current_visuals(self): - pass - - def save(self, epoch, current_iter): - """Save networks and training state.""" - pass - - def validation(self, dataloader, current_iter, tb_logger, save_img=False): - """Validation function. - - Args: - dataloader (torch.utils.data.DataLoader): Validation dataloader. - current_iter (int): Current iteration. - tb_logger (tensorboard logger): Tensorboard logger. - save_img (bool): Whether to save images. Default: False. - """ - if self.opt['dist']: - self.dist_validation(dataloader, current_iter, tb_logger, save_img) - else: - self.nondist_validation(dataloader, current_iter, tb_logger, save_img) - - def _initialize_best_metric_results(self, dataset_name): - """Initialize the best metric results dict for recording the best metric value and iteration.""" - if hasattr(self, 'best_metric_results') and dataset_name in self.best_metric_results: - return - elif not hasattr(self, 'best_metric_results'): - self.best_metric_results = dict() - - # add a dataset record - record = dict() - for metric, content in self.opt['val']['metrics'].items(): - better = content.get('better', 'higher') - init_val = float('-inf') if better == 'higher' else float('inf') - record[metric] = dict(better=better, val=init_val, iter=-1) - self.best_metric_results[dataset_name] = record - - def _update_best_metric_result(self, dataset_name, metric, val, current_iter): - if self.best_metric_results[dataset_name][metric]['better'] == 'higher': - if val >= self.best_metric_results[dataset_name][metric]['val']: - self.best_metric_results[dataset_name][metric]['val'] = val - self.best_metric_results[dataset_name][metric]['iter'] = current_iter - else: - if val <= self.best_metric_results[dataset_name][metric]['val']: - self.best_metric_results[dataset_name][metric]['val'] = val - self.best_metric_results[dataset_name][metric]['iter'] = current_iter - - def model_ema(self, decay=0.999): - net_g = self.get_bare_model(self.net_g) - - net_g_params = dict(net_g.named_parameters()) - net_g_ema_params = dict(self.net_g_ema.named_parameters()) - - for k in net_g_ema_params.keys(): - net_g_ema_params[k].data.mul_(decay).add_(net_g_params[k].data, alpha=1 - decay) - - def get_current_log(self): - return self.log_dict - - def model_to_device(self, net): - """Model to device. It also warps models with DistributedDataParallel - or DataParallel. - - Args: - net (nn.Module) - """ - net = net.to(self.device) - if self.opt['dist']: - find_unused_parameters = self.opt.get('find_unused_parameters', False) - net = DistributedDataParallel( - net, device_ids=[torch.cuda.current_device()], find_unused_parameters=find_unused_parameters) - elif self.opt['num_gpu'] > 1: - net = DataParallel(net) - return net - - def get_optimizer(self, optim_type, params, lr, **kwargs): - if optim_type == 'Adam': - optimizer = torch.optim.Adam(params, lr, **kwargs) - else: - raise NotImplementedError(f'optimizer {optim_type} is not supperted yet.') - return optimizer - - def setup_schedulers(self): - """Set up schedulers.""" - train_opt = self.opt['train'] - scheduler_type = train_opt['scheduler'].pop('type') - if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']: - for optimizer in self.optimizers: - self.schedulers.append(lr_scheduler.MultiStepRestartLR(optimizer, **train_opt['scheduler'])) - elif scheduler_type == 'CosineAnnealingRestartLR': - for optimizer in self.optimizers: - self.schedulers.append(lr_scheduler.CosineAnnealingRestartLR(optimizer, **train_opt['scheduler'])) - else: - raise NotImplementedError(f'Scheduler {scheduler_type} is not implemented yet.') - - def get_bare_model(self, net): - """Get bare model, especially under wrapping with - DistributedDataParallel or DataParallel. - """ - if isinstance(net, (DataParallel, DistributedDataParallel)): - net = net.module - return net - - @master_only - def print_network(self, net): - """Print the str and parameter number of a network. - - Args: - net (nn.Module) - """ - if isinstance(net, (DataParallel, DistributedDataParallel)): - net_cls_str = f'{net.__class__.__name__} - {net.module.__class__.__name__}' - else: - net_cls_str = f'{net.__class__.__name__}' - - net = self.get_bare_model(net) - net_str = str(net) - net_params = sum(map(lambda x: x.numel(), net.parameters())) - - logger = get_root_logger() - logger.info(f'Network: {net_cls_str}, with parameters: {net_params:,d}') - logger.info(net_str) - - def _set_lr(self, lr_groups_l): - """Set learning rate for warmup. - - Args: - lr_groups_l (list): List for lr_groups, each for an optimizer. - """ - for optimizer, lr_groups in zip(self.optimizers, lr_groups_l): - for param_group, lr in zip(optimizer.param_groups, lr_groups): - param_group['lr'] = lr - - def _get_init_lr(self): - """Get the initial lr, which is set by the scheduler. - """ - init_lr_groups_l = [] - for optimizer in self.optimizers: - init_lr_groups_l.append([v['initial_lr'] for v in optimizer.param_groups]) - return init_lr_groups_l - - def update_learning_rate(self, current_iter, warmup_iter=-1): - """Update learning rate. - - Args: - current_iter (int): Current iteration. - warmup_iter (int)： Warmup iter numbers. -1 for no warmup. - Default： -1. - """ - if current_iter > 1: - for scheduler in self.schedulers: - scheduler.step() - # set up warm-up learning rate - if current_iter < warmup_iter: - # get initial lr for each group - init_lr_g_l = self._get_init_lr() - # modify warming-up learning rates - # currently only support linearly warm up - warm_up_lr_l = [] - for init_lr_g in init_lr_g_l: - warm_up_lr_l.append([v / warmup_iter * current_iter for v in init_lr_g]) - # set learning rate - self._set_lr(warm_up_lr_l) - - def get_current_learning_rate(self): - return [param_group['lr'] for param_group in self.optimizers[0].param_groups] - - @master_only - def save_network(self, net, net_label, current_iter, param_key='params'): - """Save networks. - - Args: - net (nn.Module | list[nn.Module]): Network(s) to be saved. - net_label (str): Network label. - current_iter (int): Current iter number. - param_key (str | list[str]): The parameter key(s) to save network. - Default: 'params'. - """ - if current_iter == -1: - current_iter = 'latest' - save_filename = f'{net_label}_{current_iter}.pth' - save_path = os.path.join(self.opt['path']['models'], save_filename) - - net = net if isinstance(net, list) else [net] - param_key = param_key if isinstance(param_key, list) else [param_key] - assert len(net) == len(param_key), 'The lengths of net and param_key should be the same.' - - save_dict = {} - for net_, param_key_ in zip(net, param_key): - net_ = self.get_bare_model(net_) - state_dict = net_.state_dict() - for key, param in state_dict.items(): - if key.startswith('module.'): # remove unnecessary 'module.' - key = key[7:] - state_dict[key] = param.cpu() - save_dict[param_key_] = state_dict - - # avoid occasional writing errors - retry = 3 - while retry > 0: - try: - torch.save(save_dict, save_path) - except Exception as e: - logger = get_root_logger() - logger.warning(f'Save model error: {e}, remaining retry times: {retry - 1}') - time.sleep(1) - else: - break - finally: - retry -= 1 - if retry == 0: - logger.warning(f'Still cannot save {save_path}. Just ignore it.') - # raise IOError(f'Cannot save {save_path}.') - - def _print_different_keys_loading(self, crt_net, load_net, strict=True): - """Print keys with different name or different size when loading models. - - 1. Print keys with different names. - 2. If strict=False, print the same key but with different tensor size. - It also ignore these keys with different sizes (not load). - - Args: - crt_net (torch model): Current network. - load_net (dict): Loaded network. - strict (bool): Whether strictly loaded. Default: True. - """ - crt_net = self.get_bare_model(crt_net) - crt_net = crt_net.state_dict() - crt_net_keys = set(crt_net.keys()) - load_net_keys = set(load_net.keys()) - - logger = get_root_logger() - if crt_net_keys != load_net_keys: - logger.warning('Current net - loaded net:') - for v in sorted(list(crt_net_keys - load_net_keys)): - logger.warning(f' {v}') - logger.warning('Loaded net - current net:') - for v in sorted(list(load_net_keys - crt_net_keys)): - logger.warning(f' {v}') - - # check the size for the same keys - if not strict: - common_keys = crt_net_keys & load_net_keys - for k in common_keys: - if crt_net[k].size() != load_net[k].size(): - logger.warning(f'Size different, ignore [{k}]: crt_net: ' - f'{crt_net[k].shape}; load_net: {load_net[k].shape}') - load_net[k + '.ignore'] = load_net.pop(k) - - def load_network(self, net, load_path, strict=True, param_key='params'): - """Load network. - - Args: - load_path (str): The path of networks to be loaded. - net (nn.Module): Network. - strict (bool): Whether strictly loaded. - param_key (str): The parameter key of loaded network. If set to - None, use the root 'path'. - Default: 'params'. - """ - logger = get_root_logger() - net = self.get_bare_model(net) - load_net = torch.load(load_path, map_location=lambda storage, loc: storage) - if param_key is not None: - if param_key not in load_net and 'params' in load_net: - param_key = 'params' - logger.info('Loading: params_ema does not exist, use params.') - load_net = load_net[param_key] - logger.info(f'Loading {net.__class__.__name__} model from {load_path}, with param key: [{param_key}].') - # remove unnecessary 'module.' - for k, v in deepcopy(load_net).items(): - if k.startswith('module.'): - load_net[k[7:]] = v - load_net.pop(k) - self._print_different_keys_loading(net, load_net, strict) - net.load_state_dict(load_net, strict=strict) - - @master_only - def save_training_state(self, epoch, current_iter): - """Save training states during training, which will be used for - resuming. - - Args: - epoch (int): Current epoch. - current_iter (int): Current iteration. - """ - if current_iter != -1: - state = {'epoch': epoch, 'iter': current_iter, 'optimizers': [], 'schedulers': []} - for o in self.optimizers: - state['optimizers'].append(o.state_dict()) - for s in self.schedulers: - state['schedulers'].append(s.state_dict()) - save_filename = f'{current_iter}.state' - save_path = os.path.join(self.opt['path']['training_states'], save_filename) - - # avoid occasional writing errors - retry = 3 - while retry > 0: - try: - torch.save(state, save_path) - except Exception as e: - logger = get_root_logger() - logger.warning(f'Save training state error: {e}, remaining retry times: {retry - 1}') - time.sleep(1) - else: - break - finally: - retry -= 1 - if retry == 0: - logger.warning(f'Still cannot save {save_path}. Just ignore it.') - # raise IOError(f'Cannot save {save_path}.') - - def resume_training(self, resume_state): - """Reload the optimizers and schedulers for resumed training. - - Args: - resume_state (dict): Resume state. - """ - resume_optimizers = resume_state['optimizers'] - resume_schedulers = resume_state['schedulers'] - assert len(resume_optimizers) == len(self.optimizers), 'Wrong lengths of optimizers' - assert len(resume_schedulers) == len(self.schedulers), 'Wrong lengths of schedulers' - for i, o in enumerate(resume_optimizers): - self.optimizers[i].load_state_dict(o) - for i, s in enumerate(resume_schedulers): - self.schedulers[i].load_state_dict(s) - - def reduce_loss_dict(self, loss_dict): - """reduce loss dict. - - In distributed training, it averages the losses among different GPUs . - - Args: - loss_dict (OrderedDict): Loss dict. - """ - with torch.no_grad(): - if self.opt['dist']: - keys = [] - losses = [] - for name, value in loss_dict.items(): - keys.append(name) - losses.append(value) - losses = torch.stack(losses, 0) - torch.distributed.reduce(losses, dst=0) - if self.opt['rank'] == 0: - losses /= self.opt['world_size'] - loss_dict = {key: loss for key, loss in zip(keys, losses)} - - log_dict = OrderedDict() - for name, value in loss_dict.items(): - log_dict[name] = value.mean().item() - - return log_dict diff --git a/spaces/bejaeger/filled-stacks-search/app.py b/spaces/bejaeger/filled-stacks-search/app.py deleted file mode 100644 index 683d383d73e368b88c5818535d8902ce52ae4fc1..0000000000000000000000000000000000000000 --- a/spaces/bejaeger/filled-stacks-search/app.py +++ /dev/null @@ -1,196 +0,0 @@ -import streamlit as st -import pinecone -from sentence_transformers import SentenceTransformer -import logging -import openai -import gradio as gr - -PINECONE_KEY = st.secrets["PINECONE_KEY"] # app.pinecone.io -OPENAI_KEY = None -# st.secrets["OPENAI_KEY"] -INDEX_ID = 'filled-stacks-search' - -@st.experimental_singleton -def init_openai(): - openai.api_key = OPENAI_KEY - -@st.experimental_singleton -def init_pinecone(): - pinecone.init(api_key=PINECONE_KEY, environment="us-west1-gcp") - return pinecone.Index(INDEX_ID) - -@st.experimental_singleton -def init_retriever(): - return SentenceTransformer("multi-qa-mpnet-base-dot-v1") - -def make_query(query, retriever, top_k=3, include_values=True, include_metadata=True, filter=None): - xq = retriever.encode([query]).tolist() - logging.info(f"Query: {query}") - attempt = 0 - while attempt < 3: - try: - xc = st.session_state.index.query( - xq, - top_k=top_k, - include_values=include_values, - include_metadata=include_metadata, - filter=filter - ) - matches = xc['matches'] - break - except: - # force reload - pinecone.init(api_key=PINECONE_KEY, environment="us-west1-gcp") - st.session_state.index = pinecone.Index(INDEX_ID) - attempt += 1 - matches = [] - if len(matches) == 0: - logging.error(f"Query failed") - return matches - -def get_prompt(matches): - contexts = [ - x['metadata']['text'] for x in matches - ] - prompt_start = ( - "Answer the question based on the context below.\n\n"+ - "Context:\n" - ) - prompt_end = ( - f"\n\nQuestion: {query}\nAnswer:" - ) - limit = 3750 - - for i in range(1, len(contexts)): - if len("\n\n--\n\n".join(contexts[:i])) >= limit: - prompt = ( - prompt_start + - "\n\n--\n\n".join(contexts[:i-1]) + - prompt_end - ) - break - elif i == len(contexts) - 1: - prompt = ( - prompt_start + - "\n\n--\n\n".join(contexts) + - prompt_end - ) - return prompt - -st.session_state.index = init_pinecone() -retriever = init_retriever() - -def card(thumbnail: str, title: str, urls: list, contexts: list, starts: list, ends: list): - meta = [(e, s, u, c) for e, s, u, c in zip(ends, starts, urls, contexts)] - meta.sort(reverse=False) - text_content = [] - current_start = 0 - current_end = 0 - for end, start, url, context in meta: - # reformat seconds to timestamp - time = start / 60 - mins = f"0{int(time)}"[-2:] - secs = f"0{int(round((time - int(mins))*60, 0))}"[-2:] - timestamp = f"{mins}:{secs}" - if start < current_end and start > current_start: - # this means it is a continuation of the previous sentence - text_content[-1][0] = text_content[-1][0].split(context[:10])[0] - text_content.append([f"[{timestamp}] {context.capitalize()}", url]) - else: - text_content.append(["xxLINEBREAKxx", ""]) - text_content.append([f"[{timestamp}] {context}", url]) - current_start = start - current_end = end - html_text = "" - for text, url in text_content: - if text == "xxLINEBREAKxx": - html_text += "Download ○○○ https://tinurli.com/2uwhVA
Download File ⭐ https://tinurli.com/2uwk14
{tb}"
- message = (
- ""
- "| Event | -Date | -
|---|---|
| Online application form release date | -January 10, 2023 | -
| Last date to submit online application form | -January 31, 2023 | -
| Dummy admit card release date | -February 15, 2023 | -
| Last date to request correction on dummy admit card | -February 20, 2023 | -
| Final admit card release date | -March 1, 2023 | -
| D.El.Ed entrance exam date | -March 15, 2023 | -
| D.El.Ed entrance exam result date | -April 15, 2023 | -
| D.El.Ed merit list release date | -April 30, 2023 | -
| D.El.Ed counselling and seat allotment date | -May 15, 2023 | -
| D.El.Ed admission confirmation date | -May 31, 2023 | -
The D.El.Ed entrance exam 2023 is a competitive exam that tests the candidates' aptitude, general knowledge, language skills, and teaching ability. The exam consists of 150 multiple choice questions that carry one mark each. The exam duration is two and a half hours and there is no negative marking. The exam syllabus covers the following topics:
-To prepare for the D.El.Ed entrance exam 2023, candidates should follow these tips:
-The D.El.Ed course opens up various career opportunities for the candidates who complete it successfully. Some of the possible career options are:
-D.El.Ed is a diploma course that prepares candidates for teaching at the elementary level. It is a mandatory qualification for applying for government teaching jobs in various states. The admission process for D.El.Ed involves filling up the online application form, downloading the dummy and final admit card, appearing for the entrance exam, checking the result and merit list, and participating in the counselling and seat allotment process. Candidates should check their dummy admit card carefully and report any errors to the board before the final admit card is issued. They should also prepare well for the entrance exam by following the tips given above. After completing the D.El.Ed course, candidates can pursue various career options such as elementary school teacher, educational counsellor, educational content developer, or educational researcher. D.El.Ed course is a rewarding and fulfilling programme that can help candidates to make a positive difference in the lives of young learners.
-Here are some frequently asked questions and answers related to the topic of D.El.Ed dummy admit card download:
-Facebook is one of the most popular social media platforms in the world, with over 2.95 billion active users as of January 2023. However, sometimes you may encounter problems with the Facebook app, such as not being able to download it, update it, or use some of its features.
-In this article, we will show you some common Facebook app issues and how to fix them. Whether you are using an Android, iOS, or Windows device, you will find some helpful tips and tricks to get your Facebook app working again.
-Download · https://urlca.com/2uOat2
Some of the common issues that users face with the Facebook app include:
-These issues may be caused by various factors, such as cache or temporary data, internet connection, browser settings, device storage, system updates, or Facebook server problems. To fix them, you need to troubleshoot the app and find out what is causing the problem.
-Here are some ways to troubleshoot and fix common Facebook app problems:
-Sometimes, the problem is not with your device or app, but with Facebook itself. If the social network is experiencing a site-wide issue, such as a server outage or a bug, you may not be able to download or use the app properly. You can check if Facebook is down by using another browser or device, or by visiting a website like Down Detector, which will let you know if there are any reported problems with Facebook. If there is, you can do nothing but wait until it is resolved.
-If Facebook is not down, but you still have problems with the app, you can try closing and reopening it. Sometimes, apps can malfunction or get stuck due to various reasons, and restarting them can fix the issue. You can also try logging out of Facebook and logging back in, which may help resync your data and settings.
-Another possible reason why your Facebook app is not working is that you are using an outdated version of the app. Updating your app can help fix bugs and errors, as well as provide new features and improvements. You can check for updates by going to the Google Play Store (or any other app store you use) and looking for the Facebook app. If there is an available update, you can download and install it. Alternatively, you can also download the latest version of the app directly from Facebook.
- -If updating your app doesn't solve the problem, you can try restarting your device. This can help clear any temporary data or cache that may be interfering with your app's performance. It can also refresh your system and resolve any minor glitches or errors. To restart your device, simply turn it off and then turn it back on after a few seconds.
-One of the most common reasons why you can't download or use Facebook is that you have a poor or unstable internet connection. Without a reliable network, your app may not be able to communicate with the Facebook servers or load the content properly. You can check your internet connection by opening another app or website that requires internet access, such as Google or YouTube. If they work fine, then your connection is not the problem. If they don't, then you need to fix your connection.
-Some ways to fix your internet connection are:
-Another possible reason why you can't download or use Facebook is that you have disabled or restricted the app's network permissions. This means that you have prevented the app from accessing the internet or using data in the background. This can affect the app's functionality and performance, as well as prevent it from downloading updates or content. You can check and change the app's network permissions by going to your device's settings and looking for the app manager or application list. Then, find the Facebook app and tap on it. You will see a list of permissions that the app has, such as camera, microphone, storage, etc. Make sure that the network permissions are enabled and not restricted.
-The app cache is a collection of temporary data that the app stores on your device to improve its speed and performance. However, sometimes the cache can get corrupted or outdated, which can cause problems with the app. Clearing the app cache can help fix these problems by deleting any unnecessary or invalid data and freeing up some space on your device. You can clear the app cache by going to your device's settings and looking for the app manager or application list. Then, find the Facebook app and tap on it. You will see an option to clear cache or data. Tap on it and confirm your action.
-If none of the above methods work, you can try deleting and reinstalling the app. This can help fix any major issues or errors that may be affecting your app, as well as give you a fresh start with the latest version of the app. To delete and reinstall the app, simply go to your device's settings and look for the app manager or application list. Then, find the Facebook app and tap on it. You will see an option to uninstall or delete the app. Tap on it and confirm your action. Then, go to the Google Play Store (or any other app store you use) and search for Facebook. Download and install it again.
-Besides downloading and using Facebook, you may also have problems with viewing or uploading videos and photos on Facebook. Here are some ways to fix them:
-If you can't play Facebook videos, you may see an error message such as "This video is unavailable" or "Sorry, something went wrong". This may be caused by various factors, such as browser settings, device compatibility, internet connection, or Facebook server issues. To fix this problem, you can try these steps:
-If you are not able to upload photos or videos on Facebook, you may see an error message such as "There was a problem uploading your photo" or "Your video could not be processed". This may be caused by various factors, such as file size, format, quality, internet connection, device storage, or Facebook server issues. To fix this problem, you can try these steps:
-In this article, we have shown you some common Facebook app issues and how to fix them. We have also shown you how to fix Facebook video and photo issues. We hope that these tips and tricks will help you download and use Facebook without any problems.
-Here are some frequently asked questions about Facebook app issues:
-If you are looking for a challenging and entertaining parking game for your Android device, you should try Car Parking APK Indir 4.7. 4. This is a popular game that simulates real-life parking situations with realistic physics and graphics. You can choose from a variety of cars, customize them to your liking, and test your driving skills in different parking scenarios. You can also play with other players online and compete for the best parking spot.
-Car Parking APK Indir 4.7. 4 is the latest version of the Car Parking Multiplayer game developed by olzhass. It is an offline and online parking simulation game that lets you drive and park various cars in different environments. You can enjoy the realistic driving experience with manual transmission, steering wheel, gas pedal, brake, clutch, and gear shift. You can also interact with other players in the multiplayer mode, chat with them, exchange cars, and join races.
-Download ✔✔✔ https://urlca.com/2uO9QD
One of the most exciting features of Car Parking APK Indir 4.7. 4 is the multiplayer mode, where you can play with other players from around the world. You can join or create a room, invite your friends, or join a random room. You can chat with other players, exchange cars, race with them, or cooperate to complete parking tasks.
-Another feature that makes Car Parking APK Indir 4.7. 4 stand out is the ability to customize your cars according to your preferences. You can choose from more than 100 cars, ranging from sedans, SUVs, sports cars, trucks, buses, and more. You can also modify your cars by changing their color, wheels, suspension, engine, turbo, exhaust, spoiler, and more. You can even create your own car with the car editor feature.
-car parking multiplayer apk download 4.7.4
-car parking simulator android 4.7.4 indir
-car parking game apk 4.7.4 free download
-car parking mod apk 4.7.4 unlimited money
-car parking multiplayer android oyun club 4.7.4
-car parking 3d apk indir 4.7.4
-car parking multiplayer hack apk 4.7.4
-car parking pro apk 4.7.4 latest version
-car parking multiplayer pc indir 4.7.4
-car parking online apk 4.7.4 for android
-car parking multiplayer 4.7.4 apk pure
-car parking simulator 2023 apk indir 4.7.4
-car parking game mod apk 4.7.4 all cars unlocked
-car parking multiplayer apk indir cepde 4.7.4
-car parking offline apk 4.7.4 no ads
-car parking multiplayer cheat apk 4.7.4
-car parking pro mod apk 4.7.4 premium unlocked
-car parking multiplayer indir android oyun club 4.7.4
-car parking online multiplayer apk 4.7.4
-car parking multiplayer 2023 apk indir 4.7.4
-car parking game hack apk 4.7.4 unlimited coins
-car parking multiplayer apk indir son sürüm 4.7.4
-car parking offline mod apk 4.7.4 unlimited money and gold
-car parking multiplayer cracked apk 4.7.4
-car parking pro premium apk 4.7.4 free download
-car parking multiplayer indir pc windows 10 4.7.4
-car parking online mod apk 4.7.4 all cars unlocked and unlimited money
-car parking multiplayer filehippo apk download for android devices 5 and above version [^1^]
-car parking simulator mod apk indir 2023 version of the game with realistic graphics and physics engine [^1^]
-car parking game online apk 2023 latest update with new features and challenges [^1^]
-car parking multiplayer olzhass apk indir official website of the developer with support and feedback [^2^]
-car parking pro hack apk indir unlimited money and gold coins for tuning and customizing your cars [^2^]
-car parking multiplayer android emulator for pc download and play the game on your computer with keyboard and mouse controls [^2^]
-car parking online hack apk indir unlimited money and gems for buying new cars and skins [^2^]
-car parking multiplayer filehippo.com android games simulation category where you can find other similar games to download and play [^1^]
-car parking simulator apk indir latest version with improved graphics and performance [^1^]
-car parking game offline apk indir no internet connection required to play the game anytime anywhere [^1^]
-car parking multiplayer police mode apk indir turn on the police mode and lead the cops on a thrilling highway chase [^1^]
-car parking pro premium mod apk indir unlock all the premium features and cars for free [^2^]
-car parking multiplayer android oyun club hileli modu indir cheat mode with unlimited money and all cars unlocked [^2^]
-car parking online multiplayer mode apk indir play with your friends or other players around the world in real time [^1^]
-car parking multiplayer filehippo.com user rating and reviews see what other users think about the game and share your feedback [^1^]
-car parking simulator open world mode apk indir explore the open world map with various gas stations and services [^1^]
-car parking game mod menu apk indir access the mod menu to activate different cheats and hacks in the game [^1^]
-car parking multiplayer olzhass privacy policy read the privacy policy of the developer before downloading and playing the game [^2^]
Car Parking APK Indir 4.7. 4 also boasts of realistic physics and graphics that make the game more immersive and enjoyable. The game uses a realistic car damage system that shows the effects of collisions and scratches on your car. The game also has realistic sound effects that match the engine type and car model. The game also has stunning graphics that show the details of the cars, the environment, the weather, and the lighting.
-The main challenge of Car Parking APK Indir 4.7. 4 is to park your car in different parking scenarios without hitting any obstacles or other cars. The game has more than 100 levels that vary in difficulty and complexity. You can park in parallel parking spots, diagonal parking spots, perpendicular parking spots, garage parking spots, and more. You can also park in different environments such as city streets, highways, airports, deserts, forests, and more.
-The first step to download and install Car Parking APK Indir 4.7. 4 is to find a reliable source that offers the APK file for free. You can search for the APK file on Google or use a reputable website that provides APK downloads such as APKPure or APKMirror. Make sure to check the file size, version, and reviews before downloading it.
-The next step is to enable unknown sources in your device settings. This will allow you to install apps that are not from the Google Play Store. To do this, go to your device settings, tap on security, and toggle on the unknown sources option. You may also need to grant permission to your browser or file manager to install apps from unknown sources.
-The final step is to install the APK file and launch the game. To do this, locate the downloaded APK file in your device storage, tap on it, and follow the installation instructions. Once the installation is complete, you can open the game and enjoy playing Car Parking APK Indir 4.7. 4.
-One of the tips for playing Car Parking APK Indir 4.7. 4 is to use the camera angles to your advantage. The game offers different camera angles that you can switch between by tapping on the camera icon on the screen. You can use the top-down view, the rear view, the front view, or the cockpit view to see your surroundings better and park more accurately.
-Another tip for playing Car Parking APK Indir 4.7. 4 is to follow the traffic rules and avoid collisions. The game has realistic traffic rules that you need to obey, such as stopping at red lights, yielding to pedestrians, and following speed limits. You also need to avoid hitting any obstacles or other cars, as this will damage your car and reduce your score. You can use the indicators, the horn, and the headlights to communicate with other drivers and avoid accidents.
-The last tip for playing Car Parking APK Indir 4.7. 4 is to earn money and upgrade your cars. You can earn money by completing parking tasks, winning races, or selling cars. You can use the money to buy new cars or upgrade your existing ones with better parts and accessories. You can also earn coins by watching ads or completing daily missions. You can use the coins to unlock premium cars or buy special items.
-Car Parking APK Indir 4.7. 4 is a fun and realistic parking game for Android devices that you can download and install for free. You can enjoy driving and parking various cars in different environments with realistic physics and graphics. You can also customize your cars, play with other players online, and earn money and coins to buy new cars or upgrade your old ones. If you are looking for a challenging and entertaining parking game, you should try Car Parking APK Indir 4.7. 4.
- FAQs - Q: What are the system requirements for Car Parking APK Indir 4.7. 4? - A: The game requires Android 5.0 or higher and at least 300 MB of free storage space. - Q: How can I play Car Parking APK Indir 4.7. 4 offline? - A: You can play the game offline by turning off your internet connection before launching the game. - Q: How can I contact the developer of Car Parking APK Indir 4.7. 4? - A: You can contact the developer by sending an email to olzhass@gmail.com or visiting their website at https://olzhass.com/. - Q: How can I report a bug or a problem with Car Parking APK Indir 4.7. 4? - A: You can report a bug or a problem by leaving a comment on the Google Play Store page of the game or sending an email to olzhass@gmail.com. - Q: How can I share my feedback or suggestions for Car Parking APK Indir 4.7. 4? - A: You can share your feedback or suggestions by leaving a review on the Google Play Store page of the game or sending an email to olzhass@gmail.com. 401be4b1e0Do you love watching live streams of talented hosts who can dance, sing, cook, joke, or do anything else that entertains you? Do you want to show your own talent and earn money from gifts? If yes, then you should try Boom Live, one of the best live streaming apps that offers a variety of short videos featuring talented hosts with positive and optimistic lives.
-Download 🗸 https://urlca.com/2uO8QI
However, there is a catch. To enjoy all the features of Boom Live, you need to spend some money. You need coins to enter rooms, send gifts, or unlock premium content. You also have to deal with annoying ads that interrupt your viewing experience. But don't worry, there is a solution. You can download Boom Live Mod APK 2022, a modified version of the app that gives you unlimited coins, unlocks all rooms, and removes all ads for free!
-In this article, we will tell you everything you need to know about Boom Live Mod APK 2022, including its features, how to download and install it, its benefits, and its risks. So, without further ado, let's get started!
-Boom Live Mod APK 2022 is a hacked version of the original app that gives you access to all the premium features without paying anything. Here are some of the features that you can enjoy with this mod:
-download boom live mod apk 2022 unlock all room
-download boom live mod apk 2022 free coin
-download boom live mod apk 2022 unlimited coins
-download boom live mod apk 2022 no ads
-download boom live mod apk 2022 latest version
-download boom live mod apk 2022 terbaru
-download boom live mod apk 2022 for android
-download boom live mod apk 2022 full version
-download boom live mod apk 2022 update
-download boom live mod apk 2022 gratis
-download boom live mod apk 2022 tanpa iklan
-download boom live mod apk 2022 premium
-download boom live mod apk 2022 pro
-download boom live mod apk 2022 vip
-download boom live mod apk 2022 hack
-download boom live mod apk 2022 cheat
-download boom live mod apk 2022 cracked
-download boom live mod apk 2022 patched
-download boom live mod apk 2022 offline
-download boom live mod apk 2022 online
-download boom live mod apk 2022 anti banned
-download boom live mod apk 2022 new version
-download boom live mod apk 2022 original
-download boom live mod apk 2022 official
-download boom live mod apk 2022 jalan tikus
-download boom live mod apk 2022 signal news id
-download boom live mod apk 2022 jokowinomics id
-download boom live mod apk 2022 koin tak terbatas
-download boom live mod apk 2022 semua room terbuka
-download boom live mod apk 2022 video call gratis
-download boom live mod apk 2022 filter menarik
-download boom live mod apk 2022 host berbakat
-download boom live mod apk 2022 komunitas positif
-download boom live mod apk 2022 streaming berkualitas
-download boom live mod apk 2022 hadiah menarik
-download boom live mod apk 2022 aplikasi hiburan
-download boom live mod apk 2022 cara install
-download boom live mod apk 2022 cara menggunakan
-download boom live mod apk 2022 fitur unggulan
-download boom live mod apk 2022 perbedaan original dan mod
Coins are the currency of Boom Live that you can use to enter rooms, send gifts, or unlock premium content. Normally, you have to buy coins with real money or earn them by watching ads or completing tasks. But with Boom Live Mod APK 2022, you can get unlimited coins for free! You can use them as much as you want without worrying about running out.
-Rooms are where the hosts stream their videos and interact with their viewers. There are different types of rooms, such as music, dance, comedy, food, beauty, etc. Some rooms are free to enter, while others require coins or invitations. With Boom Live Mod APK 2022, you can unlock all rooms and watch any host that you like without any restrictions.
-Ads are annoying and distracting, especially when they pop up in the middle of a live stream. They ruin your mood and waste your time. With Boom Live Mod APK 2022, you can get rid of all ads and enjoy a smooth and uninterrupted viewing experience.
-If you are interested in downloading and installing Boom Live Mod APK 2022 on your device, here are the steps that you need to follow:
-The first thing that you need to do is to find a reliable and safe source that provides the APK file of Boom Live Mod APK 2022. You can search for it on Google or use the link that we have provided below. Make sure that you download the latest version of the mod that is compatible with your device.
-Download Boom Live Mod APK 2022 here
-Before you can install the APK file, you need to enable unknown sources on your device. This is because the mod is not available on the official app store and you need to allow your device to install apps from other sources. To do this, go to your device settings, then security, then unknown sources, and toggle it on.
-Now that you have downloaded the APK file and enabled unknown sources, you can proceed to install the app. Locate the APK file in your device storage and tap on it. Follow the instructions on the screen and wait for the installation to complete. Once done, you can launch the app and enjoy unlimited live streaming with talented hosts!
-Boom Live Mod APK 2022 is not just a simple hack that gives you free coins and unlocks all rooms. It also has some amazing benefits that make it worth using. Here are some of them:
-One of the best things about Boom Live is that it has a diverse and talented pool of hosts who can entertain you with their skills and personalities. You can find hosts who can sing, dance, cook, joke, or do anything else that you like. You can also chat with them, send them gifts, or join their fan clubs. You can even become a host yourself and show your own talent to the world!
-If you have a talent that you want to share with others, you can become a host on Boom Live and stream your videos to millions of viewers. You can also earn money from gifts that your fans send you. Gifts are converted into diamonds, which can be exchanged for real cash. You can also participate in events and competitions that reward you with prizes and bonuses.
-Boom Live is not just an app, but a community of people who love live streaming and have positive and optimistic lives. You can meet new friends, join groups, follow topics, or create your own fan base. You can also support other hosts, join their live streams, or collaborate with them. You can also enjoy various activities and games that make live streaming more fun and interactive.
-While Boom Live Mod APK 2022 has many benefits, it also has some risks and dangers that you should be aware of before using it. Here are some of them:
-Boom Live Mod APK 2022 is an unauthorized modification of the original app that violates its terms of service. By using it, you are breaking the law and infringing on the rights of the developers and owners of Boom Live. You may face legal consequences or penalties if you are caught using it.
-Since Boom Live Mod APK 2022 is not available on the official app store, there is no guarantee that it is safe and secure to use. It may contain viruses or malware that can infect your device and compromise your data and privacy. It may also cause your device to malfunction or crash.
-Boom Live has a strict policy against cheating and hacking. If you use Boom Live Mod APK 2022, you may be detected by their system and flagged as a cheater or hacker. This may result in your account being banned or suspended by Boom Live. You may lose all your coins, diamonds, gifts, fans, and progress.
-Boom Live is a great app for live streaming lovers who want to watch talented hosts or show their own talent. However, it requires money to enjoy all its features. That's why some people use Boom Live Mod APK 2022, a hacked version of the app that gives them unlimited coins, unlocks all rooms, and removes all ads for free.
-However, using Boom Live Mod APK 2022 is not a good idea, as it has many risks and dangers. It is illegal, unsafe, and unethical to use. It may harm your device, compromise your data, and cause your account to be banned or suspended by Boom Live. Therefore, we do not recommend using it and advise you to use the original app instead.
-If you want to enjoy live streaming with talented hosts, you should support them by buying coins or earning them legitimately. You should also respect the terms of service of Boom Live and avoid cheating or hacking. This way, you can have a positive and fun live streaming experience without any problems.
-We hope that this article has helped you understand what Boom Live Mod APK 2022 is, how to download and install it, its benefits, and its risks. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
-Here are some frequently asked questions about Boom Live Mod APK 2022:
-Boom Live is a live streaming app that offers a variety of short videos featuring talented hosts with positive and optimistic lives. You can watch them, chat with them, send them gifts, or join their fan clubs. You can also become a host yourself and show your own talent to the world.
-Boom Live Mod APK 2022 is a modified version of the original app that gives you unlimited coins, unlocks all rooms, and removes all ads for free. You can use it to enjoy all the features of Boom Live without spending any money.
-No, Boom Live Mod APK 2022 is not safe to use. It is an unauthorized modification of the original app that violates its terms of service. It may contain viruses or malware that can harm your device and compromise your data and privacy. It may also cause your account to be banned or suspended by Boom Live.
-You can get coins on Boom Live by buying them with real money or earning them by watching ads or completing tasks. You can also get coins from gifts that other users send you.
-You can become a host on Boom Live by applying on the app and passing the audition. You need to have a talent that you can show to the viewers and a positive and optimistic attitude. You also need to follow the rules and guidelines of Boom Live and respect your fans and other hosts.
401be4b1e0Game of Thrones is one of the most popular and acclaimed TV shows of all time. Based on the fantasy novels by George R.R. Martin, it tells the epic story of the struggle for power and survival in the fictional land of Westeros. The show has won numerous awards, attracted millions of fans, and spawned a prequel series called House of the Dragon.
-One of the most recognizable aspects of Game of Thrones is its theme song, which plays during the opening credits. The theme song is composed by Ramin Djawadi, who also created the music for other shows like Westworld and The Mandalorian. The theme song is a powerful and catchy tune that sets the mood for the show and introduces its main locations with a stunning map animation.
-Download ✸ https://urlca.com/2uOdcV
MP3 is a common audio format that compresses sound data without losing much quality. The bitrate of an MP3 file indicates how much data is used to encode each second of audio. The higher the bitrate, the better the sound quality, but also the larger the file size. The highest quality MP3 has a bitrate of 320kbps, which means it uses 320 kilobits per second to encode the sound.
-If you love the Game of Thrones theme song and want to listen to it anytime, anywhere, you might want to download it in MP3 320kbps quality for free. This way, you can enjoy the best sound quality without paying anything or taking up too much space on your device. However, you should be careful about where and how you download the theme song, as not all sources are safe, legal, or reliable.
-In this article, we will show you two ways to download the Game of Thrones theme song in MP3 320kbps quality for free: using a YouTube to MP3 converter or using a free download site. We will also explain the pros and cons of each method, provide step-by-step instructions, and recommend some trustworthy websites. Finally, we will give you some tips and warnings for downloading the theme song safely and legally.
-One way to download the Game of Thrones theme song in MP3 320kbps quality for free is to use a YouTube to MP3 converter. This is a tool that allows you to convert any YouTube video into an MP3 file that you can download and save on your device.
-game of thrones opening music mp3 free download high quality
-download got theme song 320 kbps mp3 for free
-game of thrones intro song free mp3 download 320kbps
-got theme music mp3 320kbps free download
-game of thrones main title mp3 download free 320 kbps
-how to download game of thrones theme song in 320kbps mp3
-game of thrones soundtrack mp3 free download 320kbps
-game of thrones theme tune mp3 320kbps free download
-game of thrones opening credits song mp3 free download 320 kbps
-got main theme mp3 download free 320kbps
-game of thrones title track mp3 free download 320kbps
-game of thrones theme song ringtone mp3 free download 320kbps
-game of thrones intro music mp3 320kbps free download
-game of thrones theme song remix mp3 free download 320kbps
-game of thrones original theme song mp3 free download 320 kbps
-game of thrones theme song instrumental mp3 free download 320kbps
-game of thrones opening theme mp3 download 320kbps free
-game of thrones official theme song mp3 free download 320 kbps
-game of thrones theme song full version mp3 free download 320kbps
-game of thrones season 8 theme song mp3 free download 320kbps
-game of thrones best theme song mp3 free download 320 kbps
-game of thrones theme song violin mp3 free download 320kbps
-game of thrones epic theme song mp3 free download 320 kbps
-game of thrones season 1 theme song mp3 free download 320kbps
-game of thrones finale theme song mp3 free download 320 kbps
-game of thrones theme song guitar mp3 free download 320kbps
-game of thrones awesome theme song mp3 free download 320 kbps
-game of thrones season 7 theme song mp3 free download 320kbps
-game of thrones extended theme song mp3 free download 320 kbps
-game of thrones theme song piano mp3 free download 320kbps
-game of thrones rock theme song mp3 free download 320 kbps
-game of thrones season 6 theme song mp3 free download 320kbps
-game of thrones orchestral theme song mp3 free download 320 kbps
-game of thrones season 2 theme song mp3 free download 320kbps
-game of thrones metal theme song mp3 free download 320 kbps
-game of thrones season 5 theme song mp3 free download 320kbps
-game of thrones cello theme song mp3 free download 320 kbps
-game of thrones season 4 theme song mp3 free download 320kbps
-game of thrones dubstep theme song mp3 free download 320 kbps
-game of thrones season 9 theme song mp3 free download 320kbps (if it exists)
The main advantage of using a YouTube to MP3 converter is that it is easy and fast. You just need to copy and paste the URL of the YouTube video that contains the theme song, choose your desired output format and quality, and click on convert. Within seconds, you will have an MP3 file ready for download.
-Another advantage is that you can choose from many different versions of the theme song on YouTube. For example , you can find the original theme song, the extended version, the orchestral version, the metal version, the piano version, and many more. You can also find videos that combine the theme song with other sound effects, such as rain, fire, or dragon roars.
-The main disadvantage of using a YouTube to MP3 converter is that it may not be legal or ethical. YouTube's terms of service prohibit downloading any content from its platform without permission from the owner or a license from YouTube. The theme song of Game of Thrones is protected by copyright laws and belongs to the composer and the producers of the show. Therefore, downloading it without their consent may violate their rights and expose you to legal risks.
-Another disadvantage is that some YouTube to MP3 converters may not be safe or reliable. Some of them may contain malware, viruses, or spyware that can harm your device or steal your personal information. Some of them may also have low-quality conversions, broken links, or annoying ads and pop-ups. Therefore, you should be careful about which YouTube to MP3 converter you use and scan your device regularly for any threats.
-Here are the steps to use a YouTube to MP3 converter to download the Game of Thrones theme song in MP3 320kbps quality for free:
-There are many YouTube to MP3 converters available online, but not all of them are trustworthy. Here are some of the best ones that we have tested and verified for their safety, reliability, and quality:
-| Name | Website | Features |
|---|---|---|
| 4K Video Downloader | [2](https://www.4kdownload.com/products/product-videodownloader) | - Supports downloading videos and playlists from YouTube and other sites - Allows choosing output format, quality, and speed - Has a smart mode that applies preferred settings automatically - Has a desktop app and a browser extension |
| YTMP3 | [3](https://ytmp3.cc/en13/) | - Supports converting videos from YouTube to MP3 or MP4 - Allows choosing output quality up to 320kbps - Has a simple and user-friendly interface - Does not require registration or installation |
| MP3FY | [4](https://mp3fy.com/en1/) | - Supports converting videos from YouTube and over 1000 other sites to MP3 - Allows choosing output quality up to 320kbps - Has a fast and efficient conversion process - Does not have any ads or pop-ups |
| Y2mate | [5](https://y2mate.com/) | - Supports converting videos from YouTube to MP3, MP4, M4A, and more - Allows choosing output quality up to 320kbps - Has a search function that lets you find videos by keywords - Has multiple languages and options |
| Online Video Converter | [6](https://www.onlinevideoconverter.com/mp3-converter) | - Supports converting videos from YouTube and other sites to MP3, AAC, OGG, M4A, WMA, FLAC, and more - Allows choosing output quality up to 320kbps - Has a high compatibility with browsers and devices - Does not have any limitations or registrations |
These are just some of the YouTube to MP3 converters that you can use to download the Game of Thrones theme song in MP3 320kbps quality for free. However, you should always check the terms and conditions of each website before using it and respect the rights of the content owners.
-Another way to download the Game of Thrones theme song in MP3 320kbps quality for free is to use a free download site. This is a website that hosts various audio files that you can download and save on your device.
-The main advantage of using a free download site is that it is convenient and direct. You don't need to convert any video into an MP3 file, as the file is already available for download. You just need to search for the theme song, click on the download button, and save the file on your device.
-Another advantage is that you can find different versions and remixes of the theme song on a free download site. For example, you can find the original theme song, the trap remix, the violin cover, the acoustic guitar cover, and many more. You can also find mashups that mix the theme song with other songs or genres.
-The main disadvantage of using a free download site is that it may not be legal or ethical. As with YouTube, downloading any content from a free download site without permission from the owner or a license from the site may violate their rights and expose you to legal risks. The theme song of Game of Thrones is protected by copyright laws and belongs to the composer and the producers of the show. Therefore, downloading it without their consent may infringe their rights and harm their interests.
-Another disadvantage is that some free download sites may not be safe or reliable. Some of them may contain malware, viruses, or spyware that can harm your device or steal your personal information. Some of them may also have low-quality downloads, broken links, or annoying ads and pop-ups. Therefore, you should be careful about which free download site you use and scan your device regularly for any threats.
-Here are the steps to use a free download site to download the Game of Thrones theme song in MP3 320kbps quality for free:
-There are many free download sites available online, but not all of them are trustworthy. Here are some of the best ones that we have tested and verified for their safety, reliability, and quality:
-| Name | Website | Features |
|---|---|---|
| eMP3 Downloads | [7](https://www.emp3dl.com/) | - Supports downloading MP3 files from various sources - Allows choosing output quality up to 320kbps - Has a large and diverse collection of songs - Has a simple and user-friendly interface |
| Mp3Juices | [8](https://www.mp3juices.cc/) | - Supports downloading MP3 files from YouTube and other sites - Allows choosing output quality up to 320kbps - Has a fast and efficient search engine - Does not have any ads or pop-ups |
| Mp3Skull | [9](https://mp3skulls.to/) | - Supports downloading MP3 files from various sources - Allows choosing output quality up to 320kbps - Has a huge and updated database of songs - Has multiple languages and options |
| BeeMP3 | [10](https://beemp3s.net/) | - Supports downloading MP3 files from various sources - Has a simple and user-friendly interface - Does not require registration or installation |
| MP3Clan | [11](https://mp3clan.de/) | - Supports downloading MP3 files from various sources - Allows choosing output quality up to 320kbps - Has a large and diverse collection of songs - Has a search function that lets you find songs by keywords, artists, genres, or albums |
These are just some of the free download sites that you can use to download the Game of Thrones theme song in MP3 320kbps quality for free. However, you should always check the terms and conditions of each website before using it and respect the rights of the content owners.
-The Game of Thrones theme song is one of the most iconic and memorable TV show themes ever. It is a masterpiece of music that captures the essence and the atmosphere of the show. If you want to download it in MP3 320kbps quality for free, you have two options: using a YouTube to MP3 converter or using a free download site.
-Both methods have their advantages and disadvantages, so you should weigh them carefully before choosing one. You should also be aware of the legal and ethical implications of downloading the theme song without permission from the owner or a license from the site. You should also be careful about the safety and reliability of the websites that you use and scan your device regularly for any threats.
-We hope that this article has helped you learn how to download the Game of Thrones theme song in MP3 320kbps quality for free. We also hope that you enjoy listening to it and reliving the epic moments of the show. If you have any questions, comments, or feedback, please feel free to share them with us. We would love to hear from you!
-Here are some frequently asked questions about the Game of Thrones theme song and how to download it in MP3 320kbps quality for free:
-The composer of the Game of Thrones theme song is Ramin Djawadi, a German-Iranian composer who is known for his work on film and TV scores. He has also composed the music for other shows like Westworld, The Mandalorian, Prison Break, and Person of Interest.
-The original version of the Game of Thrones theme song is about 1 minute and 50 seconds long. However, there are also extended versions that are longer, such as the 10-minute version or the one-hour version. There are also shorter versions that are used for trailers or teasers.
-The Game of Thrones soundtrack has many other songs that are worth listening to, as they reflect the mood, the character, and the events of each scene. Some of the most popular and memorable songs are The Rains of Castamere, Light of the Seven, The Night King, Mhysa, The Winds of Winter, Jenny of Oldstones, and The Iron Throne.
-If you want to stream or buy the Game of Thrones soundtrack legally, you have several options. You can stream it on platforms like Spotify, Apple Music, YouTube Music, Amazon Music, or Deezer. You can also buy it on platforms like iTunes, Google Play Music, Amazon Music, or CD Baby.
-The theme song of House of the Dragon, the prequel series to Game of Thrones, has not been released yet. However, it is likely that it will have some similarities and differences with the theme song of Game of Thrones. It is possible that it will use some elements or motifs from the original theme song, but also introduce some new ones that reflect the history, the culture, and the characters of the prequel. It is also possible that it will have a different composer or a different style of music that suits the tone and the setting of the prequel.
197e85843dSomething Broke on the hugging face servers...temp solution
- """) - block.launch(server_name="0.0.0.0", server_port=7860) - -if __name__ == "__main__": - run() \ No newline at end of file diff --git a/spaces/davertor/colorizing_images/deoldify/__init__.py b/spaces/davertor/colorizing_images/deoldify/__init__.py deleted file mode 100644 index 6f1f292454f669f05a9cece885811af2accf4e39..0000000000000000000000000000000000000000 --- a/spaces/davertor/colorizing_images/deoldify/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from deoldify._device import _Device - -device = _Device() \ No newline at end of file diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/utils/general.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/utils/general.py deleted file mode 100644 index 1c8e14f56a107ec3a4269c382cfc5168ad780ffc..0000000000000000000000000000000000000000 --- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/yolov5face/utils/general.py +++ /dev/null @@ -1,271 +0,0 @@ -import math -import time - -import numpy as np -import torch -import torchvision - - -def check_img_size(img_size, s=32): - # Verify img_size is a multiple of stride s - new_size = make_divisible(img_size, int(s)) # ceil gs-multiple - # if new_size != img_size: - # print(f"WARNING: --img-size {img_size:g} must be multiple of max stride {s:g}, updating to {new_size:g}") - return new_size - - -def make_divisible(x, divisor): - # Returns x evenly divisible by divisor - return math.ceil(x / divisor) * divisor - - -def xyxy2xywh(x): - # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center - y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center - y[:, 2] = x[:, 2] - x[:, 0] # width - y[:, 3] = x[:, 3] - x[:, 1] # height - return y - - -def xywh2xyxy(x): - # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x - y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y - y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x - y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y - return y - - -def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): - # Rescale coords (xyxy) from img1_shape to img0_shape - if ratio_pad is None: # calculate from img0_shape - gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new - pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding - else: - gain = ratio_pad[0][0] - pad = ratio_pad[1] - - coords[:, [0, 2]] -= pad[0] # x padding - coords[:, [1, 3]] -= pad[1] # y padding - coords[:, :4] /= gain - clip_coords(coords, img0_shape) - return coords - - -def clip_coords(boxes, img_shape): - # Clip bounding xyxy bounding boxes to image shape (height, width) - boxes[:, 0].clamp_(0, img_shape[1]) # x1 - boxes[:, 1].clamp_(0, img_shape[0]) # y1 - boxes[:, 2].clamp_(0, img_shape[1]) # x2 - boxes[:, 3].clamp_(0, img_shape[0]) # y2 - - -def box_iou(box1, box2): - # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py - """ - Return intersection-over-union (Jaccard index) of boxes. - Both sets of boxes are expected to be in (x1, y1, x2, y2) format. - Arguments: - box1 (Tensor[N, 4]) - box2 (Tensor[M, 4]) - Returns: - iou (Tensor[N, M]): the NxM matrix containing the pairwise - IoU values for every element in boxes1 and boxes2 - """ - - def box_area(box): - return (box[2] - box[0]) * (box[3] - box[1]) - - area1 = box_area(box1.T) - area2 = box_area(box2.T) - - inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) - return inter / (area1[:, None] + area2 - inter) - - -def non_max_suppression_face(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()): - """Performs Non-Maximum Suppression (NMS) on inference results - Returns: - detections with shape: nx6 (x1, y1, x2, y2, conf, cls) - """ - - nc = prediction.shape[2] - 15 # number of classes - xc = prediction[..., 4] > conf_thres # candidates - - # Settings - # (pixels) maximum box width and height - max_wh = 4096 - time_limit = 10.0 # seconds to quit after - redundant = True # require redundant detections - multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) - merge = False # use merge-NMS - - t = time.time() - output = [torch.zeros((0, 16), device=prediction.device)] * prediction.shape[0] - for xi, x in enumerate(prediction): # image index, image inference - # Apply constraints - x = x[xc[xi]] # confidence - - # Cat apriori labels if autolabelling - if labels and len(labels[xi]): - label = labels[xi] - v = torch.zeros((len(label), nc + 15), device=x.device) - v[:, :4] = label[:, 1:5] # box - v[:, 4] = 1.0 # conf - v[range(len(label)), label[:, 0].long() + 15] = 1.0 # cls - x = torch.cat((x, v), 0) - - # If none remain process next image - if not x.shape[0]: - continue - - # Compute conf - x[:, 15:] *= x[:, 4:5] # conf = obj_conf * cls_conf - - # Box (center x, center y, width, height) to (x1, y1, x2, y2) - box = xywh2xyxy(x[:, :4]) - - # Detections matrix nx6 (xyxy, conf, landmarks, cls) - if multi_label: - i, j = (x[:, 15:] > conf_thres).nonzero(as_tuple=False).T - x = torch.cat((box[i], x[i, j + 15, None], x[:, 5:15], j[:, None].float()), 1) - else: # best class only - conf, j = x[:, 15:].max(1, keepdim=True) - x = torch.cat((box, conf, x[:, 5:15], j.float()), 1)[conf.view(-1) > conf_thres] - - # Filter by class - if classes is not None: - x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] - - # If none remain process next image - n = x.shape[0] # number of boxes - if not n: - continue - - # Batched NMS - c = x[:, 15:16] * (0 if agnostic else max_wh) # classes - boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores - i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS - - if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean) - # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) - iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix - weights = iou * scores[None] # box weights - x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes - if redundant: - i = i[iou.sum(1) > 1] # require redundancy - - output[xi] = x[i] - if (time.time() - t) > time_limit: - break # time limit exceeded - - return output - - -def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()): - """Performs Non-Maximum Suppression (NMS) on inference results - - Returns: - detections with shape: nx6 (x1, y1, x2, y2, conf, cls) - """ - - nc = prediction.shape[2] - 5 # number of classes - xc = prediction[..., 4] > conf_thres # candidates - - # Settings - # (pixels) maximum box width and height - max_wh = 4096 - time_limit = 10.0 # seconds to quit after - redundant = True # require redundant detections - multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) - merge = False # use merge-NMS - - t = time.time() - output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0] - for xi, x in enumerate(prediction): # image index, image inference - x = x[xc[xi]] # confidence - - # Cat apriori labels if autolabelling - if labels and len(labels[xi]): - label_id = labels[xi] - v = torch.zeros((len(label_id), nc + 5), device=x.device) - v[:, :4] = label_id[:, 1:5] # box - v[:, 4] = 1.0 # conf - v[range(len(label_id)), label_id[:, 0].long() + 5] = 1.0 # cls - x = torch.cat((x, v), 0) - - # If none remain process next image - if not x.shape[0]: - continue - - # Compute conf - x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf - - # Box (center x, center y, width, height) to (x1, y1, x2, y2) - box = xywh2xyxy(x[:, :4]) - - # Detections matrix nx6 (xyxy, conf, cls) - if multi_label: - i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T - x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) - else: # best class only - conf, j = x[:, 5:].max(1, keepdim=True) - x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] - - # Filter by class - if classes is not None: - x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] - - # Check shape - n = x.shape[0] # number of boxes - if not n: # no boxes - continue - - x = x[x[:, 4].argsort(descending=True)] # sort by confidence - - # Batched NMS - c = x[:, 5:6] * (0 if agnostic else max_wh) # classes - boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores - i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS - if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean) - # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) - iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix - weights = iou * scores[None] # box weights - x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes - if redundant: - i = i[iou.sum(1) > 1] # require redundancy - - output[xi] = x[i] - if (time.time() - t) > time_limit: - print(f"WARNING: NMS time limit {time_limit}s exceeded") - break # time limit exceeded - - return output - - -def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None): - # Rescale coords (xyxy) from img1_shape to img0_shape - if ratio_pad is None: # calculate from img0_shape - gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new - pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding - else: - gain = ratio_pad[0][0] - pad = ratio_pad[1] - - coords[:, [0, 2, 4, 6, 8]] -= pad[0] # x padding - coords[:, [1, 3, 5, 7, 9]] -= pad[1] # y padding - coords[:, :10] /= gain - coords[:, 0].clamp_(0, img0_shape[1]) # x1 - coords[:, 1].clamp_(0, img0_shape[0]) # y1 - coords[:, 2].clamp_(0, img0_shape[1]) # x2 - coords[:, 3].clamp_(0, img0_shape[0]) # y2 - coords[:, 4].clamp_(0, img0_shape[1]) # x3 - coords[:, 5].clamp_(0, img0_shape[0]) # y3 - coords[:, 6].clamp_(0, img0_shape[1]) # x4 - coords[:, 7].clamp_(0, img0_shape[0]) # y4 - coords[:, 8].clamp_(0, img0_shape[1]) # x5 - coords[:, 9].clamp_(0, img0_shape[0]) # y5 - return coords diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/_yaml/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/_yaml/__init__.py deleted file mode 100644 index 7baa8c4b68127d5cdf0be9a799429e61347c2694..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/_yaml/__init__.py +++ /dev/null @@ -1,33 +0,0 @@ -# This is a stub package designed to roughly emulate the _yaml -# extension module, which previously existed as a standalone module -# and has been moved into the `yaml` package namespace. -# It does not perfectly mimic its old counterpart, but should get -# close enough for anyone who's relying on it even when they shouldn't. -import yaml - -# in some circumstances, the yaml module we imoprted may be from a different version, so we need -# to tread carefully when poking at it here (it may not have the attributes we expect) -if not getattr(yaml, '__with_libyaml__', False): - from sys import version_info - - exc = ModuleNotFoundError if version_info >= (3, 6) else ImportError - raise exc("No module named '_yaml'") -else: - from yaml._yaml import * - import warnings - warnings.warn( - 'The _yaml extension module is now located at yaml._yaml' - ' and its location is subject to change. To use the' - ' LibYAML-based parser and emitter, import from `yaml`:' - ' `from yaml import CLoader as Loader, CDumper as Dumper`.', - DeprecationWarning - ) - del warnings - # Don't `del yaml` here because yaml is actually an existing - # namespace member of _yaml. - -__name__ = '_yaml' -# If the module is top-level (i.e. not a part of any specific package) -# then the attribute should be set to ''. -# https://docs.python.org/3.8/library/types.html -__package__ = '' diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/charset_normalizer/version.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/charset_normalizer/version.py deleted file mode 100644 index 5eed49a42ab22c53962c27e750f24ca0b63153d4..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/charset_normalizer/version.py +++ /dev/null @@ -1,6 +0,0 @@ -""" -Expose version -""" - -__version__ = "3.2.0" -VERSION = __version__.split(".") diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/queueing.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/queueing.py deleted file mode 100644 index 55257b746c4faa9d9e297af87cfd430b4550d8e3..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/queueing.py +++ /dev/null @@ -1,509 +0,0 @@ -from __future__ import annotations - -import asyncio -import copy -import time -from asyncio import TimeoutError as AsyncTimeOutError -from collections import deque -from typing import Any - -import fastapi -import httpx -from typing_extensions import Literal - -from gradio.data_classes import ( - Estimation, - LogMessage, - PredictBody, - Progress, - ProgressUnit, -) -from gradio.helpers import TrackedIterable -from gradio.utils import AsyncRequest, run_coro_in_background, set_task_name - - -class Event: - def __init__( - self, - websocket: fastapi.WebSocket, - session_hash: str, - fn_index: int, - ): - self.websocket = websocket - self.session_hash: str = session_hash - self.fn_index: int = fn_index - self._id = f"{self.session_hash}_{self.fn_index}" - self.data: PredictBody | None = None - self.lost_connection_time: float | None = None - self.token: str | None = None - self.progress: Progress | None = None - self.progress_pending: bool = False - self.log_messages: deque[LogMessage] = deque() - - async def disconnect(self, code: int = 1000): - await self.websocket.close(code=code) - - -class Queue: - def __init__( - self, - live_updates: bool, - concurrency_count: int, - update_intervals: float, - max_size: int | None, - blocks_dependencies: list, - ): - self.event_queue: deque[Event] = deque() - self.events_pending_reconnection = [] - self.stopped = False - self.max_thread_count = concurrency_count - self.update_intervals = update_intervals - self.active_jobs: list[None | list[Event]] = [None] * concurrency_count - self.delete_lock = asyncio.Lock() - self.server_path = None - self.duration_history_total = 0 - self.duration_history_count = 0 - self.avg_process_time = 0 - self.avg_concurrent_process_time = None - self.queue_duration = 1 - self.live_updates = live_updates - self.sleep_when_free = 0.05 - self.progress_update_sleep_when_free = 0.1 - self.max_size = max_size - self.blocks_dependencies = blocks_dependencies - self.access_token = "" - self.queue_client = None - self.continuous_tasks: list[Event] = [] - - async def start(self, ssl_verify=True): - # So that the client is attached to the running event loop - self.queue_client = httpx.AsyncClient(verify=ssl_verify) - - run_coro_in_background(self.start_processing) - run_coro_in_background(self.start_log_and_progress_updates) - if not self.live_updates: - run_coro_in_background(self.notify_clients) - - def close(self): - self.stopped = True - - def resume(self): - self.stopped = False - - def set_url(self, url: str): - self.server_path = url - - def set_access_token(self, token: str): - self.access_token = token - - def get_active_worker_count(self) -> int: - count = 0 - for worker in self.active_jobs: - if worker is not None: - count += 1 - return count - - def get_events_in_batch(self) -> tuple[list[Event] | None, bool]: - if not (self.event_queue): - return None, False - - first_event = self.event_queue.popleft() - events = [first_event] - - event_fn_index = first_event.fn_index - batch = self.blocks_dependencies[event_fn_index]["batch"] - - if batch: - batch_size = self.blocks_dependencies[event_fn_index]["max_batch_size"] - rest_of_batch = [ - event for event in self.event_queue if event.fn_index == event_fn_index - ][: batch_size - 1] - events.extend(rest_of_batch) - [self.event_queue.remove(event) for event in rest_of_batch] - - return events, batch - - async def start_processing(self) -> None: - while not self.stopped: - if not self.event_queue: - await asyncio.sleep(self.sleep_when_free) - continue - - if None not in self.active_jobs: - await asyncio.sleep(self.sleep_when_free) - continue - # Using mutex to avoid editing a list in use - async with self.delete_lock: - events, batch = self.get_events_in_batch() - - if events: - self.active_jobs[self.active_jobs.index(None)] = events - task = run_coro_in_background(self.process_events, events, batch) - run_coro_in_background(self.broadcast_live_estimations) - set_task_name(task, events[0].session_hash, events[0].fn_index, batch) - - async def start_log_and_progress_updates(self) -> None: - while not self.stopped: - events = [ - evt for job in self.active_jobs if job is not None for evt in job - ] + self.continuous_tasks - - if len(events) == 0: - await asyncio.sleep(self.progress_update_sleep_when_free) - continue - - for event in events: - if event.progress_pending and event.progress: - event.progress_pending = False - client_awake = await self.send_message(event, event.progress.dict()) - if not client_awake: - await self.clean_event(event) - await self.send_log_updates_for_event(event) - - await asyncio.sleep(self.progress_update_sleep_when_free) - - async def send_log_updates_for_event(self, event: Event) -> None: - while True: - try: - message = event.log_messages.popleft() - except IndexError: - break - client_awake = await self.send_message(event, message.dict()) - if not client_awake: - await self.clean_event(event) - - def set_progress( - self, - event_id: str, - iterables: list[TrackedIterable] | None, - ): - if iterables is None: - return - for job in self.active_jobs: - if job is None: - continue - for evt in job: - if evt._id == event_id: - progress_data: list[ProgressUnit] = [] - for iterable in iterables: - progress_unit = ProgressUnit( - index=iterable.index, - length=iterable.length, - unit=iterable.unit, - progress=iterable.progress, - desc=iterable.desc, - ) - progress_data.append(progress_unit) - evt.progress = Progress(progress_data=progress_data) - evt.progress_pending = True - - def log_message( - self, - event_id: str, - log: str, - level: Literal["info", "warning"], - ): - events = [ - evt for job in self.active_jobs if job is not None for evt in job - ] + self.continuous_tasks - for event in events: - if event._id == event_id: - log_message = LogMessage( - log=log, - level=level, - ) - event.log_messages.append(log_message) - - def push(self, event: Event) -> int | None: - """ - Add event to queue, or return None if Queue is full - Parameters: - event: Event to add to Queue - Returns: - rank of submitted Event - """ - queue_len = len(self.event_queue) - if self.max_size is not None and queue_len >= self.max_size: - return None - self.event_queue.append(event) - return queue_len - - async def clean_event(self, event: Event) -> None: - if event in self.event_queue: - async with self.delete_lock: - self.event_queue.remove(event) - - async def broadcast_live_estimations(self) -> None: - """ - Runs 2 functions sequentially instead of concurrently. Otherwise dced clients are tried to get deleted twice. - """ - if self.live_updates: - await self.broadcast_estimations() - - async def gather_event_data(self, event: Event, receive_timeout=60) -> bool: - """ - Gather data for the event - Parameters: - event: the Event to gather data for - receive_timeout: how long to wait for data to be received from frontend - """ - if not event.data: - client_awake = await self.send_message(event, {"msg": "send_data"}) - if not client_awake: - return False - data, client_awake = await self.get_message(event, timeout=receive_timeout) - if not client_awake: - # In the event, we timeout due to large data size - # Let the client know, otherwise will hang - await self.send_message( - event, - { - "msg": "process_completed", - "output": {"error": "Time out uploading data to server"}, - "success": False, - }, - ) - return False - event.data = data - return True - - async def notify_clients(self) -> None: - """ - Notify clients about events statuses in the queue periodically. - """ - while not self.stopped: - await asyncio.sleep(self.update_intervals) - if self.event_queue: - await self.broadcast_estimations() - - async def broadcast_estimations(self) -> None: - estimation = self.get_estimation() - # Send all messages concurrently - await asyncio.gather( - *[ - self.send_estimation(event, estimation, rank) - for rank, event in enumerate(self.event_queue) - ] - ) - - async def send_estimation( - self, event: Event, estimation: Estimation, rank: int - ) -> Estimation: - """ - Send estimation about ETA to the client. - - Parameters: - event: - estimation: - rank: - """ - estimation.rank = rank - - if self.avg_concurrent_process_time is not None: - estimation.rank_eta = ( - estimation.rank * self.avg_concurrent_process_time - + self.avg_process_time - ) - if None not in self.active_jobs: - # Add estimated amount of time for a thread to get empty - estimation.rank_eta += self.avg_concurrent_process_time - client_awake = await self.send_message(event, estimation.dict()) - if not client_awake: - await self.clean_event(event) - return estimation - - def update_estimation(self, duration: float) -> None: - """ - Update estimation by last x element's average duration. - - Parameters: - duration: - """ - self.duration_history_total += duration - self.duration_history_count += 1 - self.avg_process_time = ( - self.duration_history_total / self.duration_history_count - ) - self.avg_concurrent_process_time = self.avg_process_time / min( - self.max_thread_count, self.duration_history_count - ) - self.queue_duration = self.avg_concurrent_process_time * len(self.event_queue) - - def get_estimation(self) -> Estimation: - return Estimation( - queue_size=len(self.event_queue), - avg_event_process_time=self.avg_process_time, - avg_event_concurrent_process_time=self.avg_concurrent_process_time, - queue_eta=self.queue_duration, - ) - - def get_request_params(self, websocket: fastapi.WebSocket) -> dict[str, Any]: - params = { - "url": str(websocket.url), - "headers": dict(websocket.headers), - "query_params": dict(websocket.query_params), - "path_params": dict(websocket.path_params), - "client": {"host": websocket.client.host, "port": websocket.client.port}, # type: ignore - } - try: - params[ - "session" - ] = websocket.session # forward OAuth information if available - except Exception: - pass - return params - - async def call_prediction(self, events: list[Event], batch: bool): - data = events[0].data - assert data is not None, "No event data" - token = events[0].token - data.event_id = events[0]._id if not batch else None - try: - data.request = self.get_request_params(events[0].websocket) - except ValueError: - pass - - if batch: - data.data = list(zip(*[event.data.data for event in events if event.data])) - data.request = [ - self.get_request_params(event.websocket) - for event in events - if event.data - ] - data.batched = True - response = await AsyncRequest( - method=AsyncRequest.Method.POST, - url=f"{self.server_path}api/predict", - json=dict(data), - headers={"Authorization": f"Bearer {self.access_token}"}, - cookies={"access-token": token} if token is not None else None, - client=self.queue_client, - ) - return response - - async def process_events(self, events: list[Event], batch: bool) -> None: - awake_events: list[Event] = [] - try: - for event in events: - client_awake = await self.gather_event_data(event) - if client_awake: - client_awake = await self.send_message( - event, {"msg": "process_starts"} - ) - if client_awake: - awake_events.append(event) - if not awake_events: - return - begin_time = time.time() - response = await self.call_prediction(awake_events, batch) - if response.has_exception: - for event in awake_events: - await self.send_message( - event, - { - "msg": "process_completed", - "output": {"error": str(response.exception)}, - "success": False, - }, - ) - elif response.json.get("is_generating", False): - old_response = response - while response.json.get("is_generating", False): - old_response = response - open_ws = [] - for event in awake_events: - open = await self.send_message( - event, - { - "msg": "process_generating", - "output": old_response.json, - "success": old_response.status == 200, - }, - ) - open_ws.append(open) - awake_events = [ - e for e, is_open in zip(awake_events, open_ws) if is_open - ] - if not awake_events: - return - response = await self.call_prediction(awake_events, batch) - for event in awake_events: - if response.status != 200: - relevant_response = response - else: - relevant_response = old_response - await self.send_log_updates_for_event(event) - await self.send_message( - event, - { - "msg": "process_completed", - "output": relevant_response.json, - "success": relevant_response.status == 200, - }, - ) - else: - output = copy.deepcopy(response.json) - for e, event in enumerate(awake_events): - if batch and "data" in output: - output["data"] = list(zip(*response.json.get("data")))[e] - await self.send_log_updates_for_event( - event - ) # clean out pending log updates first - await self.send_message( - event, - { - "msg": "process_completed", - "output": output, - "success": response.status == 200, - }, - ) - end_time = time.time() - if response.status == 200: - self.update_estimation(end_time - begin_time) - except Exception as e: - print(e) - finally: - for event in awake_events: - try: - await event.disconnect() - except Exception: - pass - self.active_jobs[self.active_jobs.index(events)] = None - for event in events: - await self.clean_event(event) - # Always reset the state of the iterator - # If the job finished successfully, this has no effect - # If the job is cancelled, this will enable future runs - # to start "from scratch" - await self.reset_iterators(event.session_hash, event.fn_index) - - async def send_message(self, event, data: dict, timeout: float | int = 1) -> bool: - try: - await asyncio.wait_for( - event.websocket.send_json(data=data), timeout=timeout - ) - return True - except Exception: - await self.clean_event(event) - return False - - async def get_message(self, event, timeout=5) -> tuple[PredictBody | None, bool]: - try: - data = await asyncio.wait_for( - event.websocket.receive_json(), timeout=timeout - ) - return PredictBody(**data), True - except AsyncTimeOutError: - await self.clean_event(event) - return None, False - - async def reset_iterators(self, session_hash: str, fn_index: int): - await AsyncRequest( - method=AsyncRequest.Method.POST, - url=f"{self.server_path}reset", - json={ - "session_hash": session_hash, - "fn_index": fn_index, - }, - client=self.queue_client, - ) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-106fe5c7.js b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-106fe5c7.js deleted file mode 100644 index 33771ee7c535210d5b9bd2789f1671a9eb69cc4f..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-106fe5c7.js +++ /dev/null @@ -1,3727 +0,0 @@ -import{S as Hv,e as Gv,s as Wv,f as Gp,g as ga,h as ah,j as Nh,n as Nu,k as oh,al as Uo,a6 as Yv,m as O0,C as HW,ai as EI,N as ZT,O as GW,F as Wd,G as Yd,T as WW,w as Jf,u as Kf,H as Xd,E as IA,P as YW,r as XW,v as ZW,am as JW,av as u4,X as t7,o as FA,t as KW,x as QW,V as eY,ae as tY,Q as nY,R as rY}from"./index-9e76ffee.js";import{g as iY}from"./color-5a2b6a59.js";import{a as hv,n as aY,b as oY,c as vw,t as P0,f as RA,p as sY,d as lY,e as uY,g as SI,h as cY,i as fY,j as ad,R as xw,r as CI,k as JT,l as KT,C as QT,m as n7,o as r7,q as bw,s as a0,u as zA,v as ql,w as Xv,x as hY,y as dY,z as pY,A as gY,B as mY,D as yY,E as vY,F as xY,G as bY,H as _w,I as _Y,J as i7,K as a1,L as e6,M as ww,N as a7,O as Rd,P as Aw,Q as wY,S as bp,T as AY,U as NA,V as kY,W as F2,X as TY}from"./linear-bcbcf466.js";import{d as MY}from"./dsv-576afacd.js";import{B as EY}from"./Button-30a08c0b.js";import{E as SY}from"./Empty-8e3485c0.js";import{B as CY}from"./BlockLabel-9545c6da.js";function LY(e){let n,t,o,f,r,a,l;return{c(){n=Gp("svg"),t=Gp("circle"),o=Gp("circle"),f=Gp("circle"),r=Gp("circle"),a=Gp("circle"),l=Gp("path"),ga(t,"cx","20"),ga(t,"cy","4"),ga(t,"r","2"),ga(t,"fill","currentColor"),ga(o,"cx","8"),ga(o,"cy","16"),ga(o,"r","2"),ga(o,"fill","currentColor"),ga(f,"cx","28"),ga(f,"cy","12"),ga(f,"r","2"),ga(f,"fill","currentColor"),ga(r,"cx","11"),ga(r,"cy","7"),ga(r,"r","2"),ga(r,"fill","currentColor"),ga(a,"cx","16"),ga(a,"cy","24"),ga(a,"r","2"),ga(a,"fill","currentColor"),ga(l,"fill","currentColor"),ga(l,"d","M30 3.413L28.586 2L4 26.585V2H2v26a2 2 0 0 0 2 2h26v-2H5.413Z"),ga(n,"xmlns","http://www.w3.org/2000/svg"),ga(n,"xmlns:xlink","http://www.w3.org/1999/xlink"),ga(n,"aria-hidden","true"),ga(n,"role","img"),ga(n,"class","iconify iconify--carbon"),ga(n,"width","100%"),ga(n,"height","100%"),ga(n,"preserveAspectRatio","xMidYMid meet"),ga(n,"viewBox","0 0 32 32")},m(c,i){ah(c,n,i),Nh(n,t),Nh(n,o),Nh(n,f),Nh(n,r),Nh(n,a),Nh(n,l)},p:Nu,i:Nu,o:Nu,d(c){c&&oh(n)}}}let LI=class extends Hv{constructor(n){super(),Gv(this,n,null,LY,Wv,{})}};function Ab(e){throw new Error('Could not dynamically require "'+e+'". Please configure the dynamicRequireTargets or/and ignoreDynamicRequires option of @rollup/plugin-commonjs appropriately for this require call to work.')}var DI={exports:{}};(function(e,n){(function(t){e.exports=t()})(function(){return function t(o,f,r){function a(i,s){if(!f[i]){if(!o[i]){var u=typeof Ab=="function"&&Ab;if(!s&&u)return u(i,!0);if(l)return l(i,!0);var h=new Error("Cannot find module '"+i+"'");throw h.code="MODULE_NOT_FOUND",h}var d=f[i]={exports:{}};o[i][0].call(d.exports,function(m){return a(o[i][1][m]||m)},d,d.exports,t,o,f,r)}return f[i].exports}for(var l=typeof Ab=="function"&&Ab,c=0;c
-
-
-