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spaces/1gistliPinn/ChatGPT4/Examples/Aquachem Software Crack Free Download.md

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spaces/1gistliPinn/ChatGPT4/Examples/Edius Free VERIFIED Download Full Version For Windows 7 32-bit Software.md

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-<p>EDIUS Pro is a powerful video editing software that supports various formats and resolutions. It allows you to create professional-looking projects with ease and speed. EDIUS Pro is compatible with Windows 7 32-bit, but you need to have an EDIUS ID and QuickTime installed on your computer before you can use it. In this article, we will show you how to download and install EDIUS Pro for Windows 7 32-bit in a few simple steps.</p>
-<h2>Step 1: Download EDIUS Pro from the official website</h2>
-<p>The first thing you need to do is to download EDIUS Pro from the official website. You can choose between the latest version (EDIUS X) or the previous versions (EDIUS 9 or EDIUS 8). The latest version has more features and a redesigned core engine, but it also requires more system resources. The previous versions are still supported and updated, but they have less functionality. You can compare the different versions here: <a href="https://www.edius.net/compare.html">https://www.edius.net/compare.html</a></p>
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-<h2>Step 2: Extract the zip file and run the setup</h2>
-<p>After you have downloaded the zip file, you need to extract it to a folder on your computer. You can use any file compression software, such as WinRAR or 7-Zip, to do this. Right-click on the zip file and choose "Extract here" or "Extract to" and select a destination folder.</p>
-<p>Once you have extracted the zip file, you will see a folder with several files and subfolders. Double-click on the "Setup.exe" file to start the installation process. You will see a welcome screen with the End-User License Agreement. Read it carefully and click on "Accept" if you agree with the terms. Then click on "Install" to begin the installation.</p>
-<h2>Step 3: Follow the instructions and complete the installation</h2>
-<p>The installation process is quite simple and straightforward. You don't need to choose any options or settings, as everything is done automatically for you. The installer will copy the necessary files and create a shortcut icon on your desktop. The installation may take several minutes, depending on your system speed and performance.</p>
-<p>When the installation is finished, you will see a message saying "Installation completed successfully". Click on "Finish" to exit the installer. You can now launch EDIUS Pro from your desktop or start menu.</p>
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spaces/1gistliPinn/ChatGPT4/Examples/Eriyum Panikadu Book Free 16 !!TOP!!.md

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-<p>Eriyum Panikadu is a Tamil novel by P.H. Daniel, translated by Ra. Murugavel. It was first published in 1969 and is considered a classic of Tamil literature. The novel depicts the life and struggles of the tea plantation workers in the Nilgiris during the colonial era. The novel is based on the author's personal experience as a doctor and a trade union leader among the workers.</p>
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spaces/1line/AutoGPT/data_ingestion.py

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-import argparse
-import logging
-
-from autogpt.commands.file_operations import ingest_file, search_files
-from autogpt.config import Config
-from autogpt.memory import get_memory
-
-cfg = Config()
-
-
-def configure_logging():
-    logging.basicConfig(
-        filename="log-ingestion.txt",
-        filemode="a",
-        format="%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s",
-        datefmt="%H:%M:%S",
-        level=logging.DEBUG,
-    )
-    return logging.getLogger("AutoGPT-Ingestion")
-
-
-def ingest_directory(directory, memory, args):
-    """
-    Ingest all files in a directory by calling the ingest_file function for each file.
-
-    :param directory: The directory containing the files to ingest
-    :param memory: An object with an add() method to store the chunks in memory
-    """
-    try:
-        files = search_files(directory)
-        for file in files:
-            ingest_file(file, memory, args.max_length, args.overlap)
-    except Exception as e:
-        print(f"Error while ingesting directory '{directory}': {str(e)}")
-
-
-def main() -> None:
-    logger = configure_logging()
-
-    parser = argparse.ArgumentParser(
-        description="Ingest a file or a directory with multiple files into memory. "
-        "Make sure to set your .env before running this script."
-    )
-    group = parser.add_mutually_exclusive_group(required=True)
-    group.add_argument("--file", type=str, help="The file to ingest.")
-    group.add_argument(
-        "--dir", type=str, help="The directory containing the files to ingest."
-    )
-    parser.add_argument(
-        "--init",
-        action="store_true",
-        help="Init the memory and wipe its content (default: False)",
-        default=False,
-    )
-    parser.add_argument(
-        "--overlap",
-        type=int,
-        help="The overlap size between chunks when ingesting files (default: 200)",
-        default=200,
-    )
-    parser.add_argument(
-        "--max_length",
-        type=int,
-        help="The max_length of each chunk when ingesting files (default: 4000)",
-        default=4000,
-    )
-
-    args = parser.parse_args()
-
-    # Initialize memory
-    memory = get_memory(cfg, init=args.init)
-    print("Using memory of type: " + memory.__class__.__name__)
-
-    if args.file:
-        try:
-            ingest_file(args.file, memory, args.max_length, args.overlap)
-            print(f"File '{args.file}' ingested successfully.")
-        except Exception as e:
-            logger.error(f"Error while ingesting file '{args.file}': {str(e)}")
-            print(f"Error while ingesting file '{args.file}': {str(e)}")
-    elif args.dir:
-        try:
-            ingest_directory(args.dir, memory, args)
-            print(f"Directory '{args.dir}' ingested successfully.")
-        except Exception as e:
-            logger.error(f"Error while ingesting directory '{args.dir}': {str(e)}")
-            print(f"Error while ingesting directory '{args.dir}': {str(e)}")
-    else:
-        print(
-            "Please provide either a file path (--file) or a directory name (--dir)"
-            " inside the auto_gpt_workspace directory as input."
-        )
-
-
-if __name__ == "__main__":
-    main()

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/8 Ball Pool APK 5.9.0 - The Best Way to Experience the Worlds 1 Pool Game.md

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-<h1>8 Ball Pool 5.9.0 APK Download: Everything You Need to Know</h1>
-<p>If you are a fan of pool games, you have probably heard of 8 Ball Pool, the most popular and addictive pool game for Android devices. In this article, we will tell you everything you need to know about the latest version of this game, 8 Ball Pool 5.9.0, and how to download and install it on your device.</p>
- <h2>What is 8 Ball Pool?</h2>
-<p>8 Ball Pool is a pool game developed by Miniclip.com, a leading online gaming company. It allows you to play online with millions of players from around the world, or challenge your friends in one-on-one matches. You can also participate in tournaments, win trophies, and collect coins and cues.</p>
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- <h3>Features of 8 Ball Pool</h3>
-<p>Some of the features that make 8 Ball Pool stand out from other pool games are:</p>
-<ul>
-<li>Realistic physics and graphics that simulate the real pool experience.</li>
-<li>A variety of game modes, such as Classic, No Guidelines, 9 Ball, and more.</li>
-<li>A huge collection of cues, each with different stats and powers.</li>
-<li>A leveling system that matches you with players of similar skill level.</li>
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-<li>A club feature that lets you join or create a club and compete with other clubs.</li>
-<li>A shop feature that lets you buy coins, cues, chat packs, and more.</li>
-</ul>
- <h3>How to play 8 Ball Pool</h3>
-<p>The rules of 8 Ball Pool are simple and similar to the real pool game. You have to pot all your balls (solid or striped) before your opponent does, and then pot the black ball (the 8 ball) to win the game. You can use the cue stick to aim and adjust the power of your shot, and use the spin button to add spin to the cue ball. You have to be careful not to pot the cue ball or the wrong balls, or you will lose your turn or the game.</p>
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-<p>The latest version of 8 Ball Pool, released on June 14, 2023, brings some exciting new features and improvements to the game. Here are some of them:</p>
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-<p>Power Pots is a new game mode that challenges you to pot as many balls as possible in a limited time. The more balls you pot, the more points you get. You can also use power-ups to boost your score and get extra time. Power Pots is available for a limited time only, so don't miss it!</p>
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-</ul>
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-<ol>
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-<li>Search for 8 Ball Pool and tap on the game icon.</li>
-<li>Tap on the Update button and wait for the download to finish.</li>
-<li>Tap on the Open button and enjoy the game.</li>
-</ol>
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- <ol>
-<li><b>Is 8 Ball Pool 5.9.0 free?</b></li>
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-<ul>
-<li>Practice regularly and learn from your mistakes.</li>
-<li>Watch videos and tutorials from expert players and learn their strategies and techniques.</li>
-<li>Use the practice mode to hone your skills and try different shots and angles.</li>
-<li>Upgrade your cues and use the ones that suit your play style and preferences.</li>
-<li>Use the spin button to control the cue ball and create better positions for your next shots.</li>
-</ul>
-</ol></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Defense Zone 3 APK The Best Tower Defense Game for Android.md

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-<h1>Defense Zone 3 APK: A Review of the Popular Action/Strategy Game</h1>
- <p>If you are a fan of action and strategy games, you might have heard of Defense Zone 3, a long-awaited sequel to the popular game series. But did you know that you can also play this game on your Android device with the Defense Zone 3 APK? In this article, we will review the features, benefits, and gameplay of Defense Zone 3 APK, as well as show you how to download and install it on your device. We will also share some tips and tricks to help you win the game, and answer some frequently asked questions. So, let's get started!</p>
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- <p>Defense Zone 3 APK is an Android application package that allows you to play the game Defense Zone 3 on your Android device. Defense Zone 3 is an action/strategy game developed by ARTEM KOTOV, a Russian game developer. It is the third installment in the Defense Zone series, which has been praised for its stunning graphics, realistic sound effects, and challenging gameplay.</p>
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-<li>Once the download is complete, go to your device's settings and enable the installation of apps from unknown sources.</li>
-<li>Locate the downloaded APK file and tap on it to install it.</li>
-<li>Once the installation is complete, tap on the Open button and enjoy the game.</li>
-</ol>
- <h3>The requirements and compatibility of Defense Zone 3 APK</h3>
- <p>Before you download and install Defense Zone 3 APK, you should check if your device meets the minimum requirements and is compatible with the game. Here are the requirements and compatibility of Defense Zone 3 APK:</p>
- <ul>
-<li>Your device should have Android version 4.1 or higher.</li>
-<li>Your device should have at least 100 MB of free storage space.</li>
-<li>Your device should have at least 1 GB of RAM.</li>
-<li>Your device should have a stable internet connection.</li>
-<li>Your device should support OpenGL ES 2.0 or higher.</li>
-</ul>
- <h2>How to play Defense Zone 3 APK?</h2>
- <p>Now that you have downloaded and installed Defense Zone 3 APK, you are ready to play the game. Here are some basic instructions on how to play Defense Zone 3 APK:</p>
- <h3>The gameplay and controls of Defense Zone 3 APK</h3>
- <p>The gameplay of Defense Zone 3 APK is simple and intuitive. Your goal is to defend your base from the waves of enemies that will attack you from different directions. You will have to build and upgrade your turrets along the path that the enemies will take, and use your special abilities wisely to stop them from reaching your base.</p>
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- <p>If you want to master Defense Zone 3 APK and win every level, you will need some tips and tricks to help you out. Here are some of them:</p>
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-<li>Plan ahead and strategize. Before you start a level, study the map and see where the enemies will come from and where you can place your turrets. Try to cover all possible angles and choke points with your turrets, and leave some space for future upgrades.</li>
-<li>Balanced your turrets. Don't rely on one type of turret only, as different enemies have different strengths and weaknesses. Try to mix different types of turrets, such as machine guns, flamethrowers, lasers, rockets, etc., to deal with different types of enemies.</li>
-<li>Upgrade your turrets. Don't forget to upgrade your turrets as soon as you can afford it, as they will become more powerful and effective. You can upgrade your turrets up to four times, each time increasing their damage, range, speed, and special effects.</li>
-<li>Use your abilities wisely. You have four abilities that you can use in each level: air strike, nuclear bomb, freeze ray, and repair kit. Each ability has a cooldown time before you can use it again, so use them sparingly and strategically. For example, use air strike or nuclear bomb when there are many enemies clustered together, use freeze ray when there are fast or flying enemies, and use repair kit when your base or t urrets are damaged.</li>
-<li>Watch the tutorials and tips. If you are new to the game or need some guidance, you can watch the tutorials and tips that are available in the game. They will teach you the basics of the game, such as how to build, upgrade, and sell turrets, how to use abilities, and how to deal with different enemies.</li>
-</ul>
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- <p>You might be wondering why you should play Defense Zone 3 APK, when there are so many other games available on the market. Well, here are some reasons why you should give Defense Zone 3 APK a try:</p>
- <h3>The pros and cons of Defense Zone 3 APK</h3>
- <p>Like any other game, Defense Zone 3 APK has its pros and cons. Here are some of them:</p>
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-<tr>
-<th>Pros</th>
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-<td>It can be addictive and time-consuming.</td>
-</tr>
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-<td>It can drain your battery and data quickly.</td>
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- <h3>The ratings and reviews of Defense Zone 3 APK</h3>
- <p>If you are still not convinced, you can check out the ratings and reviews of Defense Zone 3 APK from other players. Here are some of them:</p>
- <ul>
-<li>"This is one of the best tower defense games I have ever played. The graphics are amazing, the gameplay is challenging, and the variety is awesome. I love the different weapons, landscapes, and enemies. I highly recommend this game to anyone who likes strategy games." - John Smith, 5 stars</li>
-<li>"I really enjoy playing this game, but I have some issues with it. The game crashes sometimes, the ads are annoying, and the levels are too hard. I wish there was a way to skip levels or get more coins. I hope the developers will fix these problems soon." - Jane Doe, 3 stars</li>
-<li>"This game is terrible. The graphics are poor, the gameplay is boring, and the variety is lacking. The weapons are weak, the landscapes are dull, and the enemies are stupid. I hate this game and I regret downloading it. Don't waste your time or money on this game." - Bob Jones, 1 star</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, Defense Zone 3 APK is a popular action/strategy game that you can play on your Android device. It has many features, benefits, and gameplay that make it an exciting and enjoyable game to play. It also has some drawbacks and challenges that make it a demanding and rewarding game to play. If you are looking for a free, fun, and challenging game to play on your device, you should give Defense Zone 3 APK a try.</p>
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- <p>Here are some frequently asked questions about Defense Zone 3 APK:</p>
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-<p>Yes, Defense Zone 3 APK is safe to download and install, as long as you download it from a trusted source, such as Google Play Store or a reputable website. You should also scan the APK file with an antivirus software before installing it.</p>
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-<p>There are 21 levels in Defense Zone 3 APK, each with different landscapes, enemies, and difficulties. You can also play in endless mode or custom mode for more variety and challenge.</p>
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-<p>You can contact the developers of Defense Zone 3 APK by sending an email to [email protected] or visiting their website at [Defense Zone 3 HD]. You can also follow them on Facebook, Twitter, and YouTube for more updates and news.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Final Cut Pro for Windows - Is It Possible? Heres the Answer.md

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-<p>Final Cut Pro is one of the most popular and powerful video editing software for Mac users. It offers a range of features and tools that can help you create stunning videos with ease. But what if you are a Windows user and want to use Final Cut Pro on your PC? Is it possible to download Final Cut Pro for Windows? And if so, how can you do it?</p>
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-<p>Running Final Cut Pro on Windows is not easy or advisable. There are several challenges and risks involved in trying to do so. Some of them are:</p>
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-<p>A virtual machine is a software that simulates a different operating system within your current operating system. For example, you can use a virtual machine to run macOS on your Windows PC. A hackintosh is a computer that runs macOS on non-Apple hardware. For example, you can install macOS on your custom-built PC.</p>
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-<p>Using either method to run Final Cut Pro on Windows has some risks and disadvantages that you should be aware of before trying them. Some of them are:</p>
-<ul>
-<li>You may violate Apple's terms and conditions and face legal consequences.</li>
-<li>You may damage your computer or lose your data due to errors or crashes.</li>
-<li>You may compromise your security or privacy due to viruses or malware.</li>
-<li>You may experience poor performance or quality due to compatibility issues or lack of optimization.</li>
-<li>You may not be able to access some features or functions due to limitations or restrictions.</li>
-<li>You may not receive any updates or support from Apple or any other official source.</li>
-</ul>
- <h2>What are the best alternatives to Final Cut Pro for Windows?</ <h2>What are the best alternatives to Final Cut Pro for Windows?</h2>
-<p>If you are looking for a video editing software that can match or surpass Final Cut Pro in terms of features, performance, and quality, but also works on Windows, you have plenty of options to choose from. There are many video editing software for Windows that cater to different levels of skills, budgets, and needs. Here are some of the criteria for choosing a good video editing software:</p>
-<ul>
-<li>It should have a user-friendly and customizable interface that suits your workflow and preferences.</li>
-<li>It should support a wide range of video formats, resolutions, frame rates, and codecs, as well as 4K video, HDR video, and VR video.</li>
-<li>It should offer a variety of video transitions and filters, as well as advanced tools such as multi-camera editing, motion tracking, color grading, and audio editing.</li>
-<li>It should have a fast and stable performance that can handle large or complex projects without lagging or crashing.</li>
-<li>It should have a reasonable price that fits your budget and offers good value for money.</li>
-</ul>
- <h3>The top three alternatives to Final Cut Pro for Windows</h3>
-<p>Based on the criteria above, we have selected the top three alternatives to Final Cut Pro for Windows that we think are worth considering. They are:</p>
- <h4>Adobe Premiere Pro</h4>
-<p>Adobe Premiere Pro is our top pick for the best video editing software for Windows. It is the industry-standard tool that is used by many professional videographers and editors around the world. It has all the features and tools you need to create stunning videos with ease. It is compatible with other Adobe products such as Photoshop and After Effects, which makes it ideal for cross-platform collaboration and integration. It also has a cloud-based service called Premiere Rush that lets you edit videos on your mobile devices and sync them with your desktop. It costs $20.99 per month or $239.88 per year as a standalone app, or $52.99 per month or $599.88 per year as part of the Adobe Creative Cloud suite.</p>
- <h4>CyberLink PowerDirector</h4>
-<p>CyberLink PowerDirector is another excellent video editing software for Windows that offers tons of tools and features for both beginners and experts. It has a user-friendly interface that is intuitive and customizable. It has a powerful media browser that lets you organize, preview, and import your media files easily. It has a smart conform feature that automatically crops your videos to fit different aspect ratios and social media platforms. It has an object tracker that uses machine learning to detect faces and objects and match their movement with titles and effects. It has a cinematic mode that lets you adjust focus points and depth of field on clips captured in cinematic mode on iPhone 13 or later. It has a duplicate detection feature that shows you any audio or video that appears more than once in your project. It has a proxy workflow that lets you generate proxy media in custom frame sizes from 12.5% to 100% of the original in ProRes Proxy or H.264. It has a range check feature that shows you which areas of an image are out of color gamut. It has a camera LUT feature that automatically applies look up tables (LUTs) to footage from select ARRI, Sony, Panasonic, and Canon cameras. It has an external monitoring feature that lets you monitor full-quality video up to 6K with Pro Display XDR or via HDMI on select Mac computers. It costs $69.99 for the lifetime license or $51.99 per year for the subscription.</p>
- <h4>Davinci Resolve</h4>
-<p>Davinci Resolve is a free video editing software for Windows that offers pro-level tools and features for advanced users. It has a modular interface that consists of different pages for different tasks, such as media management, editing, color grading, audio mixing, and delivery. It supports multiple video formats, resolutions, frame rates, and codecs, as well as 4K video, HDR video, and VR video. It offers a variety of video transitions and filters, as well as advanced tools such as multi-camera editing, motion tracking, keying, stabilization, noise reduction, and 3D editing. It also has a powerful color grading system that lets you adjust color balance, contrast, saturation, hue, luminance, and more with precision and control. It also has a professional audio mixing system that lets you edit soundtracks, add effects, mix channels, automate levels, and more with high-quality sound processing. The free version of Davinci Resolve has most of the features you need to create amazing videos, but if you want more advanced features such as collaboration tools, neural engine effects, facial recognition tracking, lens distortion correction, HDR grading tools and more, you can upgrade to the Studio version for $299.</p>
- <h2>Conclusion</h2>
-<p>Final Cut Pro is a great video editing software for Mac users, but it is not available for Windows users. If you want to use Final Cut Pro on Windows, you can try using a virtual machine or a hackintosh, but you will face many challenges and risks. A better option is to use one of the best alternatives to Final Cut Pro for Windows, such as Adobe Premiere Pro, CyberLink PowerDirector, or Davinci Resolve. These video editing software offer similar or better features, performance, and quality than Final Cut Pro, and they work seamlessly on Windows. They also have different price points and plans that suit different budgets and needs. You can download and try them for free and see which one works best for you.</p>
- <h3>Call to action and recommendations</h3>
-<p>If you are ready to start editing videos on your Windows PC, we recommend you to check out the following links:</p>
-<ul>
-<li>[Download Adobe Premiere Pro]</li>
-<li>[Download CyberLink PowerDirector]</li>
-<li>[Download Davinci Resolve]</li>
-</ul>
-<p>We hope this article has helped you learn how to download Final Cut Pro for Windows and what are the best alternatives to Final Cut Pro for Windows. If you have any questions or feedback, please leave a comment below. And if you liked this article, please share it with your friends and colleagues who might find it useful. Thank you for reading!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Final Cut Pro and Windows:</p>
- <h4>Can I get Final Cut Pro for free?</h4>
-<p>No, Final Cut Pro is not free. It costs $299.99 to buy from the App Store. However, you can get a free 90-day trial of Final Cut Pro from Apple's website. You can use this trial to test the software and see if it meets your needs.</p>
- <h4>Is Final Cut Pro better than Adobe Premiere Pro?</h4>
-<p>Final Cut Pro and Adobe Premiere Pro are both excellent video editing software that have their own strengths and weaknesses. Some of the factors that may influence your choice are:</p>
-<ul>
-<li>Your operating system. Final Cut Pro only works on Mac, while Adobe Premiere Pro works on both Mac and Windows.</li>
-<li>Your budget. Final Cut Pro has a one-time payment of $299.99, while Adobe Premiere Pro has a monthly or yearly subscription of $20.99 or $239.88 respectively.</li>
-<li>Your workflow. Final Cut Pro has a magnetic timeline that allows non-destructive editing of clips without collisions or syncing problems, while Adobe Premiere Pro has a traditional timeline that requires more manual adjustments and trimming of clips.</li>
-<li>Your integration. Final Cut Pro is optimized for Mac computers with Apple silicon and can tap into superfast unified memory and the Apple Neural Engine, while Adobe Premiere Pro is compatible with other Adobe products such as Photoshop and After Effects and can offer cross-platform collaboration and integration.</li>
-</ul>
-<p>The best way to decide which one is better for you is to try them both and compare their features, performance, and quality.</p>
- <h4>How long does it take to learn Final Cut Pro?</h4>
-<p>The time it takes to learn Final Cut Pro depends on your previous experience with video editing software, your learning style, and your goals. Generally speaking, Final Cut Pro has a user-friendly interface that is intuitive and customizable, which makes it easy to learn for beginners. However, it also has many advanced features and tools that require more practice and skill to master. A good way to learn Final Cut Pro is to follow online tutorials, courses, or books that teach you the basics and the best practices of video editing with Final Cut Pro. You can also join online communities or forums where you can ask questions, get feedback, and share tips with other users.</p>
- <h4>What are the system requirements for Final Cut Pro?</h4>
-<p>The minimum system requirements for running Final Cut Pro are:</p>
-<ul>
-<li>A Mac computer with macOS 10.15 or later.</li>
-<li>A 64-bit processor (Intel Core i5 or later, or Apple silicon).</li>
-<li>4 GB of RAM (8 GB recommended for 4K editing, 3D titles, and 360-degree video editing).</li>
-<li>Metal-capable graphics card (1 GB VRAM recommended for 4K editing, 3D titles, and 360-degree video editing).</li>
-<li>3.8 GB of disk space for the application and 38 GB of disk space for optional content.</li>
-<li>An internet connection for downloading additional content and updates.</li>
-</ul>
- <h4>Can I use Final Cut Pro on my iPad or iPhone?</h4>
-<p>No, Final Cut Pro is not available for iPad or iPhone. However, you can use iMovie, which is a free video editing app that is similar to Final Cut Pro but simpler and more streamlined. iMovie lets you edit videos on your iPad or iPhone with ease and fun. You can add titles, transitions, filters, music, sound effects, and more to your videos. You can also use the green-screen effect, the picture-in-picture effect, the split-screen effect, and the cinematic mode to create amazing videos. You can also export your videos to different formats and resolutions, and share them with your friends and family via social media, email, or AirDrop. You can download iMovie from the App Store.</p> 401be4b1e0<br />
-<br />
-<br />

spaces/A00001/bingothoo/tailwind.config.js

@@ -1,48 +0,0 @@
-/** @type {import('tailwindcss').Config} */
-module.exports = {
-  content: [
-    './src/pages/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/components/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/app/**/*.{js,ts,jsx,tsx,mdx}',
-    './src/ui/**/*.{js,ts,jsx,tsx,mdx}',
-  ],
-  "darkMode": "class",
-  theme: {
-    extend: {
-      colors: {
-        'primary-blue': 'rgb(var(--color-primary-blue) / <alpha-value>)',
-        secondary: 'rgb(var(--color-secondary) / <alpha-value>)',
-        'primary-background': 'rgb(var(--primary-background) / <alpha-value>)',
-        'primary-text': 'rgb(var(--primary-text) / <alpha-value>)',
-        'secondary-text': 'rgb(var(--secondary-text) / <alpha-value>)',
-        'light-text': 'rgb(var(--light-text) / <alpha-value>)',
-        'primary-border': 'rgb(var(--primary-border) / <alpha-value>)',
-      },
-      keyframes: {
-        slideDownAndFade: {
-          from: { opacity: 0, transform: 'translateY(-2px)' },
-          to: { opacity: 1, transform: 'translateY(0)' },
-        },
-        slideLeftAndFade: {
-          from: { opacity: 0, transform: 'translateX(2px)' },
-          to: { opacity: 1, transform: 'translateX(0)' },
-        },
-        slideUpAndFade: {
-          from: { opacity: 0, transform: 'translateY(2px)' },
-          to: { opacity: 1, transform: 'translateY(0)' },
-        },
-        slideRightAndFade: {
-          from: { opacity: 0, transform: 'translateX(2px)' },
-          to: { opacity: 1, transform: 'translateX(0)' },
-        },
-      },
-      animation: {
-        slideDownAndFade: 'slideDownAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideLeftAndFade: 'slideLeftAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideUpAndFade: 'slideUpAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-        slideRightAndFade: 'slideRightAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)',
-      },
-    },
-  },
-  plugins: [require('@headlessui/tailwindcss'), require('tailwind-scrollbar')],
-}

spaces/AI-Dashboards/Graph.NLP.Sentence.Similarity.Heatmap.KMeansCluster/app.py

@@ -1,77 +0,0 @@
-import streamlit as st
-import nltk
-from transformers import pipeline
-from sentence_transformers import SentenceTransformer
-from scipy.spatial.distance import cosine
-import numpy as np
-import seaborn as sns
-import matplotlib.pyplot as plt
-from sklearn.cluster import KMeans
-import tensorflow as tf
-import tensorflow_hub as hub
-
-
-def cluster_examples(messages, embed, nc=3):
-    km = KMeans(
-    n_clusters=nc, init='random',
-    n_init=10, max_iter=300, 
-    tol=1e-04, random_state=0
-    )
-    km = km.fit_predict(embed)
-    for n in range(nc):
-        idxs = [i for i in range(len(km)) if km[i] == n]
-        ms = [messages[i] for i in idxs]
-        st.markdown ("CLUSTER : %d"%n)
-        for m in ms:
-            st.markdown (m)
-
-
-def plot_heatmap(labels, heatmap, rotation=90):
-  sns.set(font_scale=1.2)
-  fig, ax = plt.subplots()
-  g = sns.heatmap(
-      heatmap,
-      xticklabels=labels,
-      yticklabels=labels,
-      vmin=-1,
-      vmax=1,
-      cmap="coolwarm")
-  g.set_xticklabels(labels, rotation=rotation)
-  g.set_title("Textual Similarity")
-
-  st.pyplot(fig)
-  #plt.show()
-
-#st.header("Sentence Similarity Demo")
-
-# Streamlit text boxes
-text = st.text_area('Enter sentences:', value="Self confidence in outcomes helps us win and to make us successful.\nShe has a seriously impressive intellect and mind.\nStimulating and deep conversation helps us develop and grow.\nFrom basic quantum particles we get aerodynamics, friction, surface tension, weather, electromagnetism.\nIf she actively engages and comments positively, her anger disappears adapting into win-win's favor.\nI love interesting topics of conversation and the understanding and exploration of thoughts.\nThere is the ability to manipulate things the way you want in your mind to go how you want when you are self confident, that we don’t understand yet.")
-
-nc = st.slider('Select a number of clusters:', min_value=1, max_value=15, value=3)
-
-model_type = st.radio("Choose model:", ('Sentence Transformer', 'Universal Sentence Encoder'), index=0)
-
-# Model setup
-if model_type == "Sentence Transformer":
-    model = SentenceTransformer('paraphrase-distilroberta-base-v1')
-elif model_type == "Universal Sentence Encoder":
-    model_url = "https://tfhub.dev/google/universal-sentence-encoder-large/5"
-    model = hub.load(model_url)
-
-nltk.download('punkt')
-
-# Run model
-if text:
-    sentences = nltk.tokenize.sent_tokenize(text)
-    if model_type == "Sentence Transformer":
-        embed = model.encode(sentences)
-    elif model_type == "Universal Sentence Encoder":
-        embed = model(sentences).numpy()
-    sim = np.zeros([len(embed), len(embed)])
-    for i,em in enumerate(embed):
-        for j,ea in enumerate(embed):
-            sim[i][j] = 1.0-cosine(em,ea)
-    st.subheader("Similarity Heatmap")
-    plot_heatmap(sentences, sim)
-    st.subheader("Results from K-Means Clustering")
-    cluster_examples(sentences, embed, nc)

spaces/AIGC-Audio/AudioGPT/NeuralSeq/utils/text_norm.py

@@ -1,790 +0,0 @@
-# coding=utf-8
-# Authors:
-#   2019.5 Zhiyang Zhou (https://github.com/Joee1995/chn_text_norm.git)
-#   2019.9 Jiayu DU
-#
-# requirements:
-#   - python 3.X
-# notes: python 2.X WILL fail or produce misleading results
-
-import sys, os, argparse, codecs, string, re
-
-# ================================================================================ #
-#                                    basic constant
-# ================================================================================ #
-CHINESE_DIGIS = u'零一二三四五六七八九'
-BIG_CHINESE_DIGIS_SIMPLIFIED = u'零壹贰叁肆伍陆柒捌玖'
-BIG_CHINESE_DIGIS_TRADITIONAL = u'零壹貳參肆伍陸柒捌玖'
-SMALLER_BIG_CHINESE_UNITS_SIMPLIFIED = u'十百千万'
-SMALLER_BIG_CHINESE_UNITS_TRADITIONAL = u'拾佰仟萬'
-LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'亿兆京垓秭穰沟涧正载'
-LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'億兆京垓秭穰溝澗正載'
-SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED = u'十百千万'
-SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL = u'拾佰仟萬'
-
-ZERO_ALT = u'〇'
-ONE_ALT = u'幺'
-TWO_ALTS = [u'两', u'兩']
-
-POSITIVE = [u'正', u'正']
-NEGATIVE = [u'负', u'負']
-POINT = [u'点', u'點']
-# PLUS = [u'加', u'加']
-# SIL = [u'杠', u'槓']
-
-# 中文数字系统类型
-NUMBERING_TYPES = ['low', 'mid', 'high']
-
-CURRENCY_NAMES = '(人民币|美元|日元|英镑|欧元|马克|法郎|加拿大元|澳元|港币|先令|芬兰马克|爱尔兰镑|' \
-                 '里拉|荷兰盾|埃斯库多|比塞塔|印尼盾|林吉特|新西兰元|比索|卢布|新加坡元|韩元|泰铢)'
-CURRENCY_UNITS = '((亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)'
-COM_QUANTIFIERS = '(匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|' \
-                  '砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|' \
-                  '针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|' \
-                  '毫|厘|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|' \
-                  '盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|旬|' \
-                  '纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块)'
-
-# punctuation information are based on Zhon project (https://github.com/tsroten/zhon.git)
-CHINESE_PUNC_STOP = '！？｡。'
-CHINESE_PUNC_NON_STOP = '＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃《》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏'
-CHINESE_PUNC_LIST = CHINESE_PUNC_STOP + CHINESE_PUNC_NON_STOP
-
-
-# ================================================================================ #
-#                                    basic class
-# ================================================================================ #
-class ChineseChar(object):
-    """
-    中文字符
-    每个字符对应简体和繁体,
-    e.g. 简体 = '负', 繁体 = '負'
-    转换时可转换为简体或繁体
-    """
-
-    def __init__(self, simplified, traditional):
-        self.simplified = simplified
-        self.traditional = traditional
-        # self.__repr__ = self.__str__
-
-    def __str__(self):
-        return self.simplified or self.traditional or None
-
-    def __repr__(self):
-        return self.__str__()
-
-
-class ChineseNumberUnit(ChineseChar):
-    """
-    中文数字/数位字符
-    每个字符除繁简体外还有一个额外的大写字符
-    e.g. '陆' 和 '陸'
-    """
-
-    def __init__(self, power, simplified, traditional, big_s, big_t):
-        super(ChineseNumberUnit, self).__init__(simplified, traditional)
-        self.power = power
-        self.big_s = big_s
-        self.big_t = big_t
-
-    def __str__(self):
-        return '10^{}'.format(self.power)
-
-    @classmethod
-    def create(cls, index, value, numbering_type=NUMBERING_TYPES[1], small_unit=False):
-
-        if small_unit:
-            return ChineseNumberUnit(power=index + 1,
-                                     simplified=value[0], traditional=value[1], big_s=value[1], big_t=value[1])
-        elif numbering_type == NUMBERING_TYPES[0]:
-            return ChineseNumberUnit(power=index + 8,
-                                     simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
-        elif numbering_type == NUMBERING_TYPES[1]:
-            return ChineseNumberUnit(power=(index + 2) * 4,
-                                     simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
-        elif numbering_type == NUMBERING_TYPES[2]:
-            return ChineseNumberUnit(power=pow(2, index + 3),
-                                     simplified=value[0], traditional=value[1], big_s=value[0], big_t=value[1])
-        else:
-            raise ValueError(
-                'Counting type should be in {0} ({1} provided).'.format(NUMBERING_TYPES, numbering_type))
-
-
-class ChineseNumberDigit(ChineseChar):
-    """
-    中文数字字符
-    """
-
-    def __init__(self, value, simplified, traditional, big_s, big_t, alt_s=None, alt_t=None):
-        super(ChineseNumberDigit, self).__init__(simplified, traditional)
-        self.value = value
-        self.big_s = big_s
-        self.big_t = big_t
-        self.alt_s = alt_s
-        self.alt_t = alt_t
-
-    def __str__(self):
-        return str(self.value)
-
-    @classmethod
-    def create(cls, i, v):
-        return ChineseNumberDigit(i, v[0], v[1], v[2], v[3])
-
-
-class ChineseMath(ChineseChar):
-    """
-    中文数位字符
-    """
-
-    def __init__(self, simplified, traditional, symbol, expression=None):
-        super(ChineseMath, self).__init__(simplified, traditional)
-        self.symbol = symbol
-        self.expression = expression
-        self.big_s = simplified
-        self.big_t = traditional
-
-
-CC, CNU, CND, CM = ChineseChar, ChineseNumberUnit, ChineseNumberDigit, ChineseMath
-
-
-class NumberSystem(object):
-    """
-    中文数字系统
-    """
-    pass
-
-
-class MathSymbol(object):
-    """
-    用于中文数字系统的数学符号 (繁/简体), e.g.
-    positive = ['正', '正']
-    negative = ['负', '負']
-    point = ['点', '點']
-    """
-
-    def __init__(self, positive, negative, point):
-        self.positive = positive
-        self.negative = negative
-        self.point = point
-
-    def __iter__(self):
-        for v in self.__dict__.values():
-            yield v
-
-
-# class OtherSymbol(object):
-#     """
-#     其他符号
-#     """
-#
-#     def __init__(self, sil):
-#         self.sil = sil
-#
-#     def __iter__(self):
-#         for v in self.__dict__.values():
-#             yield v
-
-
-# ================================================================================ #
-#                                    basic utils
-# ================================================================================ #
-def create_system(numbering_type=NUMBERING_TYPES[1]):
-    """
-    根据数字系统类型返回创建相应的数字系统，默认为 mid
-    NUMBERING_TYPES = ['low', 'mid', 'high']: 中文数字系统类型
-        low:  '兆' = '亿' * '十' = $10^{9}$,  '京' = '兆' * '十', etc.
-        mid:  '兆' = '亿' * '万' = $10^{12}$, '京' = '兆' * '万', etc.
-        high: '兆' = '亿' * '亿' = $10^{16}$, '京' = '兆' * '兆', etc.
-    返回对应的数字系统
-    """
-
-    # chinese number units of '亿' and larger
-    all_larger_units = zip(
-        LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED, LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL)
-    larger_units = [CNU.create(i, v, numbering_type, False)
-                    for i, v in enumerate(all_larger_units)]
-    # chinese number units of '十, 百, 千, 万'
-    all_smaller_units = zip(
-        SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED, SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL)
-    smaller_units = [CNU.create(i, v, small_unit=True)
-                     for i, v in enumerate(all_smaller_units)]
-    # digis
-    chinese_digis = zip(CHINESE_DIGIS, CHINESE_DIGIS,
-                        BIG_CHINESE_DIGIS_SIMPLIFIED, BIG_CHINESE_DIGIS_TRADITIONAL)
-    digits = [CND.create(i, v) for i, v in enumerate(chinese_digis)]
-    digits[0].alt_s, digits[0].alt_t = ZERO_ALT, ZERO_ALT
-    digits[1].alt_s, digits[1].alt_t = ONE_ALT, ONE_ALT
-    digits[2].alt_s, digits[2].alt_t = TWO_ALTS[0], TWO_ALTS[1]
-
-    # symbols
-    positive_cn = CM(POSITIVE[0], POSITIVE[1], '+', lambda x: x)
-    negative_cn = CM(NEGATIVE[0], NEGATIVE[1], '-', lambda x: -x)
-    point_cn = CM(POINT[0], POINT[1], '.', lambda x,
-                                                  y: float(str(x) + '.' + str(y)))
-    # sil_cn = CM(SIL[0], SIL[1], '-', lambda x, y: float(str(x) + '-' + str(y)))
-    system = NumberSystem()
-    system.units = smaller_units + larger_units
-    system.digits = digits
-    system.math = MathSymbol(positive_cn, negative_cn, point_cn)
-    # system.symbols = OtherSymbol(sil_cn)
-    return system
-
-
-def chn2num(chinese_string, numbering_type=NUMBERING_TYPES[1]):
-    def get_symbol(char, system):
-        for u in system.units:
-            if char in [u.traditional, u.simplified, u.big_s, u.big_t]:
-                return u
-        for d in system.digits:
-            if char in [d.traditional, d.simplified, d.big_s, d.big_t, d.alt_s, d.alt_t]:
-                return d
-        for m in system.math:
-            if char in [m.traditional, m.simplified]:
-                return m
-
-    def string2symbols(chinese_string, system):
-        int_string, dec_string = chinese_string, ''
-        for p in [system.math.point.simplified, system.math.point.traditional]:
-            if p in chinese_string:
-                int_string, dec_string = chinese_string.split(p)
-                break
-        return [get_symbol(c, system) for c in int_string], \
-               [get_symbol(c, system) for c in dec_string]
-
-    def correct_symbols(integer_symbols, system):
-        """
-        一百八 to 一百八十
-        一亿一千三百万 to 一亿 一千万 三百万
-        """
-
-        if integer_symbols and isinstance(integer_symbols[0], CNU):
-            if integer_symbols[0].power == 1:
-                integer_symbols = [system.digits[1]] + integer_symbols
-
-        if len(integer_symbols) > 1:
-            if isinstance(integer_symbols[-1], CND) and isinstance(integer_symbols[-2], CNU):
-                integer_symbols.append(
-                    CNU(integer_symbols[-2].power - 1, None, None, None, None))
-
-        result = []
-        unit_count = 0
-        for s in integer_symbols:
-            if isinstance(s, CND):
-                result.append(s)
-                unit_count = 0
-            elif isinstance(s, CNU):
-                current_unit = CNU(s.power, None, None, None, None)
-                unit_count += 1
-
-            if unit_count == 1:
-                result.append(current_unit)
-            elif unit_count > 1:
-                for i in range(len(result)):
-                    if isinstance(result[-i - 1], CNU) and result[-i - 1].power < current_unit.power:
-                        result[-i - 1] = CNU(result[-i - 1].power +
-                                             current_unit.power, None, None, None, None)
-        return result
-
-    def compute_value(integer_symbols):
-        """
-        Compute the value.
-        When current unit is larger than previous unit, current unit * all previous units will be used as all previous units.
-        e.g. '两千万' = 2000 * 10000 not 2000 + 10000
-        """
-        value = [0]
-        last_power = 0
-        for s in integer_symbols:
-            if isinstance(s, CND):
-                value[-1] = s.value
-            elif isinstance(s, CNU):
-                value[-1] *= pow(10, s.power)
-                if s.power > last_power:
-                    value[:-1] = list(map(lambda v: v *
-                                                    pow(10, s.power), value[:-1]))
-                    last_power = s.power
-                value.append(0)
-        return sum(value)
-
-    system = create_system(numbering_type)
-    int_part, dec_part = string2symbols(chinese_string, system)
-    int_part = correct_symbols(int_part, system)
-    int_str = str(compute_value(int_part))
-    dec_str = ''.join([str(d.value) for d in dec_part])
-    if dec_part:
-        return '{0}.{1}'.format(int_str, dec_str)
-    else:
-        return int_str
-
-
-def num2chn(number_string, numbering_type=NUMBERING_TYPES[1], big=False,
-            traditional=False, alt_zero=False, alt_one=False, alt_two=True,
-            use_zeros=True, use_units=True):
-    def get_value(value_string, use_zeros=True):
-
-        striped_string = value_string.lstrip('0')
-
-        # record nothing if all zeros
-        if not striped_string:
-            return []
-
-        # record one digits
-        elif len(striped_string) == 1:
-            if use_zeros and len(value_string) != len(striped_string):
-                return [system.digits[0], system.digits[int(striped_string)]]
-            else:
-                return [system.digits[int(striped_string)]]
-
-        # recursively record multiple digits
-        else:
-            result_unit = next(u for u in reversed(
-                system.units) if u.power < len(striped_string))
-            result_string = value_string[:-result_unit.power]
-            return get_value(result_string) + [result_unit] + get_value(striped_string[-result_unit.power:])
-
-    system = create_system(numbering_type)
-
-    int_dec = number_string.split('.')
-    if len(int_dec) == 1:
-        int_string = int_dec[0]
-        dec_string = ""
-    elif len(int_dec) == 2:
-        int_string = int_dec[0]
-        dec_string = int_dec[1]
-    else:
-        raise ValueError(
-            "invalid input num string with more than one dot: {}".format(number_string))
-
-    if use_units and len(int_string) > 1:
-        result_symbols = get_value(int_string)
-    else:
-        result_symbols = [system.digits[int(c)] for c in int_string]
-    dec_symbols = [system.digits[int(c)] for c in dec_string]
-    if dec_string:
-        result_symbols += [system.math.point] + dec_symbols
-
-    if alt_two:
-        liang = CND(2, system.digits[2].alt_s, system.digits[2].alt_t,
-                    system.digits[2].big_s, system.digits[2].big_t)
-        for i, v in enumerate(result_symbols):
-            if isinstance(v, CND) and v.value == 2:
-                next_symbol = result_symbols[i +
-                                             1] if i < len(result_symbols) - 1 else None
-                previous_symbol = result_symbols[i - 1] if i > 0 else None
-                if isinstance(next_symbol, CNU) and isinstance(previous_symbol, (CNU, type(None))):
-                    if next_symbol.power != 1 and ((previous_symbol is None) or (previous_symbol.power != 1)):
-                        result_symbols[i] = liang
-
-    # if big is True, '两' will not be used and `alt_two` has no impact on output
-    if big:
-        attr_name = 'big_'
-        if traditional:
-            attr_name += 't'
-        else:
-            attr_name += 's'
-    else:
-        if traditional:
-            attr_name = 'traditional'
-        else:
-            attr_name = 'simplified'
-
-    result = ''.join([getattr(s, attr_name) for s in result_symbols])
-
-    # if not use_zeros:
-    #     result = result.strip(getattr(system.digits[0], attr_name))
-
-    if alt_zero:
-        result = result.replace(
-            getattr(system.digits[0], attr_name), system.digits[0].alt_s)
-
-    if alt_one:
-        result = result.replace(
-            getattr(system.digits[1], attr_name), system.digits[1].alt_s)
-
-    for i, p in enumerate(POINT):
-        if result.startswith(p):
-            return CHINESE_DIGIS[0] + result
-
-    # ^10, 11, .., 19
-    if len(result) >= 2 and result[1] in [SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED[0],
-                                          SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL[0]] and \
-            result[0] in [CHINESE_DIGIS[1], BIG_CHINESE_DIGIS_SIMPLIFIED[1], BIG_CHINESE_DIGIS_TRADITIONAL[1]]:
-        result = result[1:]
-
-    return result
-
-
-# ================================================================================ #
-#                          different types of rewriters
-# ================================================================================ #
-class Cardinal:
-    """
-    CARDINAL类
-    """
-
-    def __init__(self, cardinal=None, chntext=None):
-        self.cardinal = cardinal
-        self.chntext = chntext
-
-    def chntext2cardinal(self):
-        return chn2num(self.chntext)
-
-    def cardinal2chntext(self):
-        return num2chn(self.cardinal)
-
-
-class Digit:
-    """
-    DIGIT类
-    """
-
-    def __init__(self, digit=None, chntext=None):
-        self.digit = digit
-        self.chntext = chntext
-
-    # def chntext2digit(self):
-    #     return chn2num(self.chntext)
-
-    def digit2chntext(self):
-        return num2chn(self.digit, alt_two=False, use_units=False)
-
-
-class TelePhone:
-    """
-    TELEPHONE类
-    """
-
-    def __init__(self, telephone=None, raw_chntext=None, chntext=None):
-        self.telephone = telephone
-        self.raw_chntext = raw_chntext
-        self.chntext = chntext
-
-    # def chntext2telephone(self):
-    #     sil_parts = self.raw_chntext.split('<SIL>')
-    #     self.telephone = '-'.join([
-    #         str(chn2num(p)) for p in sil_parts
-    #     ])
-    #     return self.telephone
-
-    def telephone2chntext(self, fixed=False):
-
-        if fixed:
-            sil_parts = self.telephone.split('-')
-            self.raw_chntext = '<SIL>'.join([
-                num2chn(part, alt_two=False, use_units=False) for part in sil_parts
-            ])
-            self.chntext = self.raw_chntext.replace('<SIL>', '')
-        else:
-            sp_parts = self.telephone.strip('+').split()
-            self.raw_chntext = '<SP>'.join([
-                num2chn(part, alt_two=False, use_units=False) for part in sp_parts
-            ])
-            self.chntext = self.raw_chntext.replace('<SP>', '')
-        return self.chntext
-
-
-class Fraction:
-    """
-    FRACTION类
-    """
-
-    def __init__(self, fraction=None, chntext=None):
-        self.fraction = fraction
-        self.chntext = chntext
-
-    def chntext2fraction(self):
-        denominator, numerator = self.chntext.split('分之')
-        return chn2num(numerator) + '/' + chn2num(denominator)
-
-    def fraction2chntext(self):
-        numerator, denominator = self.fraction.split('/')
-        return num2chn(denominator) + '分之' + num2chn(numerator)
-
-
-class Date:
-    """
-    DATE类
-    """
-
-    def __init__(self, date=None, chntext=None):
-        self.date = date
-        self.chntext = chntext
-
-    # def chntext2date(self):
-    #     chntext = self.chntext
-    #     try:
-    #         year, other = chntext.strip().split('年', maxsplit=1)
-    #         year = Digit(chntext=year).digit2chntext() + '年'
-    #     except ValueError:
-    #         other = chntext
-    #         year = ''
-    #     if other:
-    #         try:
-    #             month, day = other.strip().split('月', maxsplit=1)
-    #             month = Cardinal(chntext=month).chntext2cardinal() + '月'
-    #         except ValueError:
-    #             day = chntext
-    #             month = ''
-    #         if day:
-    #             day = Cardinal(chntext=day[:-1]).chntext2cardinal() + day[-1]
-    #     else:
-    #         month = ''
-    #         day = ''
-    #     date = year + month + day
-    #     self.date = date
-    #     return self.date
-
-    def date2chntext(self):
-        date = self.date
-        try:
-            year, other = date.strip().split('年', 1)
-            year = Digit(digit=year).digit2chntext() + '年'
-        except ValueError:
-            other = date
-            year = ''
-        if other:
-            try:
-                month, day = other.strip().split('月', 1)
-                month = Cardinal(cardinal=month).cardinal2chntext() + '月'
-            except ValueError:
-                day = date
-                month = ''
-            if day:
-                day = Cardinal(cardinal=day[:-1]).cardinal2chntext() + day[-1]
-        else:
-            month = ''
-            day = ''
-        chntext = year + month + day
-        self.chntext = chntext
-        return self.chntext
-
-
-class Money:
-    """
-    MONEY类
-    """
-
-    def __init__(self, money=None, chntext=None):
-        self.money = money
-        self.chntext = chntext
-
-    # def chntext2money(self):
-    #     return self.money
-
-    def money2chntext(self):
-        money = self.money
-        pattern = re.compile(r'(\d+(\.\d+)?)')
-        matchers = pattern.findall(money)
-        if matchers:
-            for matcher in matchers:
-                money = money.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext())
-        self.chntext = money
-        return self.chntext
-
-
-class Percentage:
-    """
-    PERCENTAGE类
-    """
-
-    def __init__(self, percentage=None, chntext=None):
-        self.percentage = percentage
-        self.chntext = chntext
-
-    def chntext2percentage(self):
-        return chn2num(self.chntext.strip().strip('百分之')) + '%'
-
-    def percentage2chntext(self):
-        return '百分之' + num2chn(self.percentage.strip().strip('%'))
-
-
-# ================================================================================ #
-#                            NSW Normalizer
-# ================================================================================ #
-class NSWNormalizer:
-    def __init__(self, raw_text):
-        self.raw_text = '^' + raw_text + '$'
-        self.norm_text = ''
-
-    def _particular(self):
-        text = self.norm_text
-        pattern = re.compile(r"(([a-zA-Z]+)二([a-zA-Z]+))")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('particular')
-            for matcher in matchers:
-                text = text.replace(matcher[0], matcher[1] + '2' + matcher[2], 1)
-        self.norm_text = text
-        return self.norm_text
-
-    def normalize(self, remove_punc=True):
-        text = self.raw_text
-
-        # 规范化日期
-        pattern = re.compile(r"\D+((([089]\d|(19|20)\d{2})年)?(\d{1,2}月(\d{1,2}[日号])?)?)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('date')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Date(date=matcher[0]).date2chntext(), 1)
-
-        # 规范化金钱
-        pattern = re.compile(r"\D+((\d+(\.\d+)?)[多余几]?" + CURRENCY_UNITS + r"(\d" + CURRENCY_UNITS + r"?)?)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('money')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Money(money=matcher[0]).money2chntext(), 1)
-
-        # 规范化固话/手机号码
-        # 手机
-        # http://www.jihaoba.com/news/show/13680
-        # 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198
-        # 联通：130、131、132、156、155、186、185、176
-        # 电信：133、153、189、180、181、177
-        pattern = re.compile(r"\D((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})\D")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('telephone')
-            for matcher in matchers:
-                text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(), 1)
-        # 固话
-        pattern = re.compile(r"\D((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})\D")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('fixed telephone')
-            for matcher in matchers:
-                text = text.replace(matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(fixed=True), 1)
-
-        # 规范化分数
-        pattern = re.compile(r"(\d+/\d+)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('fraction')
-            for matcher in matchers:
-                text = text.replace(matcher, Fraction(fraction=matcher).fraction2chntext(), 1)
-
-        # 规范化百分数
-        text = text.replace('％', '%')
-        pattern = re.compile(r"(\d+(\.\d+)?%)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('percentage')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Percentage(percentage=matcher[0]).percentage2chntext(), 1)
-
-        # 规范化纯数+量词
-        pattern = re.compile(r"(\d+(\.\d+)?)[多余几]?" + COM_QUANTIFIERS)
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('cardinal+quantifier')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1)
-
-        # 规范化数字编号
-        pattern = re.compile(r"(\d{4,32})")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('digit')
-            for matcher in matchers:
-                text = text.replace(matcher, Digit(digit=matcher).digit2chntext(), 1)
-
-        # 规范化纯数
-        pattern = re.compile(r"(\d+(\.\d+)?)")
-        matchers = pattern.findall(text)
-        if matchers:
-            # print('cardinal')
-            for matcher in matchers:
-                text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1)
-
-        self.norm_text = text
-        self._particular()
-
-        text = self.norm_text.lstrip('^').rstrip('$')
-        if remove_punc:
-            # Punctuations removal
-            old_chars = CHINESE_PUNC_LIST + string.punctuation  # includes all CN and EN punctuations
-            new_chars = ' ' * len(old_chars)
-            del_chars = ''
-            text = text.translate(str.maketrans(old_chars, new_chars, del_chars))
-        return text
-
-
-def nsw_test_case(raw_text):
-    print('I:' + raw_text)
-    print('O:' + NSWNormalizer(raw_text).normalize())
-    print('')
-
-
-def nsw_test():
-    nsw_test_case('固话：0595-23865596或23880880。')
-    nsw_test_case('固话：0595-23865596或23880880。')
-    nsw_test_case('手机：+86 19859213959或15659451527。')
-    nsw_test_case('分数：32477/76391。')
-    nsw_test_case('百分数：80.03%。')
-    nsw_test_case('编号：31520181154418。')
-    nsw_test_case('纯数：2983.07克或12345.60米。')
-    nsw_test_case('日期：1999年2月20日或09年3月15号。')
-    nsw_test_case('金钱：12块5，34.5元，20.1万')
-    nsw_test_case('特殊：O2O或B2C。')
-    nsw_test_case('3456万吨')
-    nsw_test_case('2938个')
-    nsw_test_case('938')
-    nsw_test_case('今天吃了115个小笼包231个馒头')
-    nsw_test_case('有62％的概率')
-
-
-if __name__ == '__main__':
-    # nsw_test()
-
-    p = argparse.ArgumentParser()
-    p.add_argument('ifile', help='input filename, assume utf-8 encoding')
-    p.add_argument('ofile', help='output filename')
-    p.add_argument('--to_upper', action='store_true', help='convert to upper case')
-    p.add_argument('--to_lower', action='store_true', help='convert to lower case')
-    p.add_argument('--has_key', action='store_true', help="input text has Kaldi's key as first field.")
-    p.add_argument('--log_interval', type=int, default=10000, help='log interval in number of processed lines')
-    args = p.parse_args()
-
-    ifile = codecs.open(args.ifile, 'r', 'utf8')
-    ofile = codecs.open(args.ofile, 'w+', 'utf8')
-
-    n = 0
-    for l in ifile:
-        key = ''
-        text = ''
-        if args.has_key:
-            cols = l.split(maxsplit=1)
-            key = cols[0]
-            if len(cols) == 2:
-                text = cols[1]
-            else:
-                text = ''
-        else:
-            text = l
-
-        # cases
-        if args.to_upper and args.to_lower:
-            sys.stderr.write('text norm: to_upper OR to_lower?')
-            exit(1)
-        if args.to_upper:
-            text = text.upper()
-        if args.to_lower:
-            text = text.lower()
-
-        # NSW(Non-Standard-Word) normalization
-        text = NSWNormalizer(text).normalize()
-
-        #
-        if args.has_key:
-            ofile.write(key + '\t' + text)
-        else:
-            ofile.write(text)
-
-        n += 1
-        if n % args.log_interval == 0:
-            sys.stderr.write("text norm: {} lines done.\n".format(n))
-
-    sys.stderr.write("text norm: {} lines done in total.\n".format(n))
-
-    ifile.close()
-    ofile.close()

spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/models/diffusion/dpm_solver/dpm_solver.py

@@ -1,1154 +0,0 @@
-import torch
-import torch.nn.functional as F
-import math
-from tqdm import tqdm
-
-
-class NoiseScheduleVP:
-    def __init__(
-            self,
-            schedule='discrete',
-            betas=None,
-            alphas_cumprod=None,
-            continuous_beta_0=0.1,
-            continuous_beta_1=20.,
-    ):
-        """Create a wrapper class for the forward SDE (VP type).
-        ***
-        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
-                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
-        ***
-        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
-        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
-        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
-            log_alpha_t = self.marginal_log_mean_coeff(t)
-            sigma_t = self.marginal_std(t)
-            lambda_t = self.marginal_lambda(t)
-        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
-            t = self.inverse_lambda(lambda_t)
-        ===============================================================
-        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
-        1. For discrete-time DPMs:
-            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
-                t_i = (i + 1) / N
-            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
-            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
-            Args:
-                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
-                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
-            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
-            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
-                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
-                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
-                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
-                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
-                and
-                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
-        2. For continuous-time DPMs:
-            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
-            schedule are the default settings in DDPM and improved-DDPM:
-            Args:
-                beta_min: A `float` number. The smallest beta for the linear schedule.
-                beta_max: A `float` number. The largest beta for the linear schedule.
-                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
-                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
-                T: A `float` number. The ending time of the forward process.
-        ===============================================================
-        Args:
-            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
-                    'linear' or 'cosine' for continuous-time DPMs.
-        Returns:
-            A wrapper object of the forward SDE (VP type).
-
-        ===============================================================
-        Example:
-        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', betas=betas)
-        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
-        # For continuous-time DPMs (VPSDE), linear schedule:
-        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
-        """
-
-        if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError(
-                "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(
-                    schedule))
-
-        self.schedule = schedule
-        if schedule == 'discrete':
-            if betas is not None:
-                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
-            else:
-                assert alphas_cumprod is not None
-                log_alphas = 0.5 * torch.log(alphas_cumprod)
-            self.total_N = len(log_alphas)
-            self.T = 1.
-            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
-            self.log_alpha_array = log_alphas.reshape((1, -1,))
-        else:
-            self.total_N = 1000
-            self.beta_0 = continuous_beta_0
-            self.beta_1 = continuous_beta_1
-            self.cosine_s = 0.008
-            self.cosine_beta_max = 999.
-            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (
-                        1. + self.cosine_s) / math.pi - self.cosine_s
-            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
-            self.schedule = schedule
-            if schedule == 'cosine':
-                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
-                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
-                self.T = 0.9946
-            else:
-                self.T = 1.
-
-    def marginal_log_mean_coeff(self, t):
-        """
-        Compute log(alpha_t) of a given continuous-time label t in [0, T].
-        """
-        if self.schedule == 'discrete':
-            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device),
-                                  self.log_alpha_array.to(t.device)).reshape((-1))
-        elif self.schedule == 'linear':
-            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
-        elif self.schedule == 'cosine':
-            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
-            log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
-            return log_alpha_t
-
-    def marginal_alpha(self, t):
-        """
-        Compute alpha_t of a given continuous-time label t in [0, T].
-        """
-        return torch.exp(self.marginal_log_mean_coeff(t))
-
-    def marginal_std(self, t):
-        """
-        Compute sigma_t of a given continuous-time label t in [0, T].
-        """
-        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
-
-    def marginal_lambda(self, t):
-        """
-        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
-        """
-        log_mean_coeff = self.marginal_log_mean_coeff(t)
-        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
-        return log_mean_coeff - log_std
-
-    def inverse_lambda(self, lamb):
-        """
-        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
-        """
-        if self.schedule == 'linear':
-            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            Delta = self.beta_0 ** 2 + tmp
-            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
-        elif self.schedule == 'discrete':
-            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
-            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]),
-                               torch.flip(self.t_array.to(lamb.device), [1]))
-            return t.reshape((-1,))
-        else:
-            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (
-                        1. + self.cosine_s) / math.pi - self.cosine_s
-            t = t_fn(log_alpha)
-            return t
-
-
-def model_wrapper(
-        model,
-        noise_schedule,
-        model_type="noise",
-        model_kwargs={},
-        guidance_type="uncond",
-        condition=None,
-        unconditional_condition=None,
-        guidance_scale=1.,
-        classifier_fn=None,
-        classifier_kwargs={},
-):
-    """Create a wrapper function for the noise prediction model.
-    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
-    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
-    We support four types of the diffusion model by setting `model_type`:
-        1. "noise": noise prediction model. (Trained by predicting noise).
-        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
-        3. "v": velocity prediction model. (Trained by predicting the velocity).
-            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
-            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
-                arXiv preprint arXiv:2202.00512 (2022).
-            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
-                arXiv preprint arXiv:2210.02303 (2022).
-
-        4. "score": marginal score function. (Trained by denoising score matching).
-            Note that the score function and the noise prediction model follows a simple relationship:
-            ```
-                noise(x_t, t) = -sigma_t * score(x_t, t)
-            ```
-    We support three types of guided sampling by DPMs by setting `guidance_type`:
-        1. "uncond": unconditional sampling by DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-            The input `classifier_fn` has the following format:
-            ``
-                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
-            ``
-            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
-                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
-        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
-            ``
-            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
-            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
-                arXiv preprint arXiv:2207.12598 (2022).
-
-    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
-    or continuous-time labels (i.e. epsilon to T).
-    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
-    ``
-        def model_fn(x, t_continuous) -> noise:
-            t_input = get_model_input_time(t_continuous)
-            return noise_pred(model, x, t_input, **model_kwargs)
-    ``
-    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
-    ===============================================================
-    Args:
-        model: A diffusion model with the corresponding format described above.
-        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-        model_type: A `str`. The parameterization type of the diffusion model.
-                    "noise" or "x_start" or "v" or "score".
-        model_kwargs: A `dict`. A dict for the other inputs of the model function.
-        guidance_type: A `str`. The type of the guidance for sampling.
-                    "uncond" or "classifier" or "classifier-free".
-        condition: A pytorch tensor. The condition for the guided sampling.
-                    Only used for "classifier" or "classifier-free" guidance type.
-        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
-                    Only used for "classifier-free" guidance type.
-        guidance_scale: A `float`. The scale for the guided sampling.
-        classifier_fn: A classifier function. Only used for the classifier guidance.
-        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
-    Returns:
-        A noise prediction model that accepts the noised data and the continuous time as the inputs.
-    """
-
-    def get_model_input_time(t_continuous):
-        """
-        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
-        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
-        For continuous-time DPMs, we just use `t_continuous`.
-        """
-        if noise_schedule.schedule == 'discrete':
-            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
-        else:
-            return t_continuous
-
-    def noise_pred_fn(x, t_continuous, cond=None):
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        t_input = get_model_input_time(t_continuous)
-        if cond is None:
-            output = model(x, t_input, **model_kwargs)
-        else:
-            output = model(x, t_input, cond, **model_kwargs)
-        if model_type == "noise":
-            return output
-        elif model_type == "x_start":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
-        elif model_type == "v":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
-        elif model_type == "score":
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return -expand_dims(sigma_t, dims) * output
-
-    def cond_grad_fn(x, t_input):
-        """
-        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
-        """
-        with torch.enable_grad():
-            x_in = x.detach().requires_grad_(True)
-            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
-            return torch.autograd.grad(log_prob.sum(), x_in)[0]
-
-    def model_fn(x, t_continuous):
-        """
-        The noise predicition model function that is used for DPM-Solver.
-        """
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        if guidance_type == "uncond":
-            return noise_pred_fn(x, t_continuous)
-        elif guidance_type == "classifier":
-            assert classifier_fn is not None
-            t_input = get_model_input_time(t_continuous)
-            cond_grad = cond_grad_fn(x, t_input)
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            noise = noise_pred_fn(x, t_continuous)
-            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
-        elif guidance_type == "classifier-free":
-            if guidance_scale == 1. or unconditional_condition is None:
-                return noise_pred_fn(x, t_continuous, cond=condition)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t_continuous] * 2)
-                c_in = torch.cat([unconditional_condition, condition])
-                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
-                return noise_uncond + guidance_scale * (noise - noise_uncond)
-
-    assert model_type in ["noise", "x_start", "v"]
-    assert guidance_type in ["uncond", "classifier", "classifier-free"]
-    return model_fn
-
-
-class DPM_Solver:
-    def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
-        """Construct a DPM-Solver.
-        We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
-        If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
-        If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
-            In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
-            The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
-        Args:
-            model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
-                ``
-                def model_fn(x, t_continuous):
-                    return noise
-                ``
-            noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-            predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
-            thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
-            max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
-
-        [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
-        """
-        self.model = model_fn
-        self.noise_schedule = noise_schedule
-        self.predict_x0 = predict_x0
-        self.thresholding = thresholding
-        self.max_val = max_val
-
-    def noise_prediction_fn(self, x, t):
-        """
-        Return the noise prediction model.
-        """
-        return self.model(x, t)
-
-    def data_prediction_fn(self, x, t):
-        """
-        Return the data prediction model (with thresholding).
-        """
-        noise = self.noise_prediction_fn(x, t)
-        dims = x.dim()
-        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
-        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
-        if self.thresholding:
-            p = 0.995  # A hyperparameter in the paper of "Imagen" [1].
-            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
-            x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model_fn(self, x, t):
-        """
-        Convert the model to the noise prediction model or the data prediction model.
-        """
-        if self.predict_x0:
-            return self.data_prediction_fn(x, t)
-        else:
-            return self.noise_prediction_fn(x, t)
-
-    def get_time_steps(self, skip_type, t_T, t_0, N, device):
-        """Compute the intermediate time steps for sampling.
-        Args:
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            N: A `int`. The total number of the spacing of the time steps.
-            device: A torch device.
-        Returns:
-            A pytorch tensor of the time steps, with the shape (N + 1,).
-        """
-        if skip_type == 'logSNR':
-            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
-            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
-            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
-            return self.noise_schedule.inverse_lambda(logSNR_steps)
-        elif skip_type == 'time_uniform':
-            return torch.linspace(t_T, t_0, N + 1).to(device)
-        elif skip_type == 'time_quadratic':
-            t_order = 2
-            t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device)
-            return t
-        else:
-            raise ValueError(
-                "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
-
-    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
-        """
-        Get the order of each step for sampling by the singlestep DPM-Solver.
-        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
-        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
-            - If order == 1:
-                We take `steps` of DPM-Solver-1 (i.e. DDIM).
-            - If order == 2:
-                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
-                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
-                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
-            - If order == 3:
-                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
-        ============================================
-        Args:
-            order: A `int`. The max order for the solver (2 or 3).
-            steps: A `int`. The total number of function evaluations (NFE).
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            device: A torch device.
-        Returns:
-            orders: A list of the solver order of each step.
-        """
-        if order == 3:
-            K = steps // 3 + 1
-            if steps % 3 == 0:
-                orders = [3, ] * (K - 2) + [2, 1]
-            elif steps % 3 == 1:
-                orders = [3, ] * (K - 1) + [1]
-            else:
-                orders = [3, ] * (K - 1) + [2]
-        elif order == 2:
-            if steps % 2 == 0:
-                K = steps // 2
-                orders = [2, ] * K
-            else:
-                K = steps // 2 + 1
-                orders = [2, ] * (K - 1) + [1]
-        elif order == 1:
-            K = 1
-            orders = [1, ] * steps
-        else:
-            raise ValueError("'order' must be '1' or '2' or '3'.")
-        if skip_type == 'logSNR':
-            # To reproduce the results in DPM-Solver paper
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
-        else:
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
-                torch.cumsum(torch.tensor([0, ] + orders)).to(device)]
-        return timesteps_outer, orders
-
-    def denoise_to_zero_fn(self, x, s):
-        """
-        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
-        """
-        return self.data_prediction_fn(x, s)
-
-    def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
-        """
-        DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_1 = torch.expm1(-h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-        else:
-            phi_1 = torch.expm1(h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-
-    def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False,
-                                            solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the second-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 0.5
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(
-            s1), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
-        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_1 = torch.expm1(-h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                    expand_dims(sigma_s1 / sigma_s, dims) * x
-                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (
-                                    model_s1 - model_s)
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_1 = torch.expm1(h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
-                )
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1}
-        else:
-            return x_t
-
-    def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None,
-                                           return_intermediate=False, solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
-                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 1. / 3.
-        if r2 is None:
-            r2 = 2. / 3.
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        lambda_s2 = lambda_s + r2 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        s2 = ns.inverse_lambda(lambda_s2)
-        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(
-            s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(
-            s2), ns.marginal_std(t)
-        alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_12 = torch.expm1(-r2 * h)
-            phi_1 = torch.expm1(-h)
-            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
-            phi_2 = phi_1 / h + 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                        expand_dims(sigma_s1 / sigma_s, dims) * x
-                        - expand_dims(alpha_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                    expand_dims(sigma_s2 / sigma_s, dims) * x
-                    - expand_dims(alpha_s2 * phi_12, dims) * model_s
-                    + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + expand_dims(alpha_t * phi_2, dims) * D1
-                        - expand_dims(alpha_t * phi_3, dims) * D2
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_12 = torch.expm1(r2 * h)
-            phi_1 = torch.expm1(h)
-            phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
-            phi_2 = phi_1 / h - 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                        expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                        - expand_dims(sigma_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                    expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s2 * phi_12, dims) * model_s
-                    - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - expand_dims(sigma_t * phi_2, dims) * D1
-                        - expand_dims(sigma_t * phi_3, dims) * D2
-                )
-
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
-        else:
-            return x_t
-
-    def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
-        """
-        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_1, model_prev_0 = model_prev_list
-        t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
-            t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0 = h_0 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        if self.predict_x0:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_prev_0, dims) * x
-                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                        - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(sigma_t / sigma_prev_0, dims) * x
-                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                        + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
-                )
-        else:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                        - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                        - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
-                )
-        return x_t
-
-    def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
-        """
-        Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_2, model_prev_1, model_prev_0 = model_prev_list
-        t_prev_2, t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(
-            t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_1 = lambda_prev_1 - lambda_prev_2
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0, r1 = h_0 / h, h_1 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
-        D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
-        D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
-        if self.predict_x0:
-            x_t = (
-                    expand_dims(sigma_t / sigma_prev_0, dims) * x
-                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                    + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
-                    - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2
-            )
-        else:
-            x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2
-            )
-        return x_t
-
-    def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None,
-                                     r2=None):
-        """
-        Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-            r1: A `float`. The hyperparameter of the second-order or third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
-        elif order == 2:
-            return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate,
-                                                            solver_type=solver_type, r1=r1)
-        elif order == 3:
-            return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate,
-                                                           solver_type=solver_type, r1=r1, r2=r2)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
-        """
-        Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
-        elif order == 2:
-            return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        elif order == 3:
-            return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5,
-                            solver_type='dpm_solver'):
-        """
-        The adaptive step size solver based on singlestep DPM-Solver.
-        Args:
-            x: A pytorch tensor. The initial value at time `t_T`.
-            order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            h_init: A `float`. The initial step size (for logSNR).
-            atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
-            rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
-            theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
-            t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the
-                current time and `t_0` is less than `t_err`. The default setting is 1e-5.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_0: A pytorch tensor. The approximated solution at time `t_0`.
-        [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
-        """
-        ns = self.noise_schedule
-        s = t_T * torch.ones((x.shape[0],)).to(x)
-        lambda_s = ns.marginal_lambda(s)
-        lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
-        h = h_init * torch.ones_like(s).to(x)
-        x_prev = x
-        nfe = 0
-        if order == 2:
-            r1 = 0.5
-            lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
-                                                                                               solver_type=solver_type,
-                                                                                               **kwargs)
-        elif order == 3:
-            r1, r2 = 1. / 3., 2. / 3.
-            lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
-                                                                                    return_intermediate=True,
-                                                                                    solver_type=solver_type)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2,
-                                                                                              solver_type=solver_type,
-                                                                                              **kwargs)
-        else:
-            raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
-        while torch.abs((s - t_0)).mean() > t_err:
-            t = ns.inverse_lambda(lambda_s + h)
-            x_lower, lower_noise_kwargs = lower_update(x, s, t)
-            x_higher = higher_update(x, s, t, **lower_noise_kwargs)
-            delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
-            norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
-            E = norm_fn((x_higher - x_lower) / delta).max()
-            if torch.all(E <= 1.):
-                x = x_higher
-                s = t
-                x_prev = x_lower
-                lambda_s = ns.marginal_lambda(s)
-            h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
-            nfe += order
-        print('adaptive solver nfe', nfe)
-        return x
-
-    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
-               method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
-               atol=0.0078, rtol=0.05,
-               ):
-        """
-        Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
-        =====================================================
-        We support the following algorithms for both noise prediction model and data prediction model:
-            - 'singlestep':
-                Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver.
-                We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
-                The total number of function evaluations (NFE) == `steps`.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    - If `order` == 1:
-                        - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
-                        - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
-                        - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                    - If `order` == 3:
-                        - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                        - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
-            - 'multistep':
-                Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
-                We initialize the first `order` values by lower order multistep solvers.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    Denote K = steps.
-                    - If `order` == 1:
-                        - We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
-                    - If `order` == 3:
-                        - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
-            - 'singlestep_fixed':
-                Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
-                We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
-            - 'adaptive':
-                Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
-                We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
-                You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
-                (NFE) and the sample quality.
-                    - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
-                    - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
-        =====================================================
-        Some advices for choosing the algorithm:
-            - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
-                Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
-                            skip_type='time_uniform', method='singlestep')
-            - For **guided sampling with large guidance scale** by DPMs:
-                Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
-                            skip_type='time_uniform', method='multistep')
-        We support three types of `skip_type`:
-            - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
-            - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
-            - 'time_quadratic': quadratic time for the time steps.
-        =====================================================
-        Args:
-            x: A pytorch tensor. The initial value at time `t_start`
-                e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
-            steps: A `int`. The total number of function evaluations (NFE).
-            t_start: A `float`. The starting time of the sampling.
-                If `T` is None, we use self.noise_schedule.T (default is 1.0).
-            t_end: A `float`. The ending time of the sampling.
-                If `t_end` is None, we use 1. / self.noise_schedule.total_N.
-                e.g. if total_N == 1000, we have `t_end` == 1e-3.
-                For discrete-time DPMs:
-                    - We recommend `t_end` == 1. / self.noise_schedule.total_N.
-                For continuous-time DPMs:
-                    - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
-            order: A `int`. The order of DPM-Solver.
-            skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
-            method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
-            denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
-                Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
-                This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
-                score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
-                for diffusion models sampling by diffusion SDEs for low-resolutional images
-                (such as CIFAR-10). However, we observed that such trick does not matter for
-                high-resolutional images. As it needs an additional NFE, we do not recommend
-                it for high-resolutional images.
-            lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
-                Only valid for `method=multistep` and `steps < 15`. We empirically find that
-                this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
-                (especially for steps <= 10). So we recommend to set it to be `True`.
-            solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
-            atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-            rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-        Returns:
-            x_end: A pytorch tensor. The approximated solution at time `t_end`.
-        """
-        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
-        t_T = self.noise_schedule.T if t_start is None else t_start
-        device = x.device
-        if method == 'adaptive':
-            with torch.no_grad():
-                x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol,
-                                             solver_type=solver_type)
-        elif method == 'multistep':
-            assert steps >= order
-            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
-            assert timesteps.shape[0] - 1 == steps
-            with torch.no_grad():
-                vec_t = timesteps[0].expand((x.shape[0]))
-                model_prev_list = [self.model_fn(x, vec_t)]
-                t_prev_list = [vec_t]
-                # Init the first `order` values by lower order multistep DPM-Solver.
-                for init_order in tqdm(range(1, order), desc="DPM init order"):
-                    vec_t = timesteps[init_order].expand(x.shape[0])
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order,
-                                                         solver_type=solver_type)
-                    model_prev_list.append(self.model_fn(x, vec_t))
-                    t_prev_list.append(vec_t)
-                # Compute the remaining values by `order`-th order multistep DPM-Solver.
-                for step in tqdm(range(order, steps + 1), desc="DPM multistep"):
-                    vec_t = timesteps[step].expand(x.shape[0])
-                    if lower_order_final and steps < 15:
-                        step_order = min(order, steps + 1 - step)
-                    else:
-                        step_order = order
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order,
-                                                         solver_type=solver_type)
-                    for i in range(order - 1):
-                        t_prev_list[i] = t_prev_list[i + 1]
-                        model_prev_list[i] = model_prev_list[i + 1]
-                    t_prev_list[-1] = vec_t
-                    # We do not need to evaluate the final model value.
-                    if step < steps:
-                        model_prev_list[-1] = self.model_fn(x, vec_t)
-        elif method in ['singlestep', 'singlestep_fixed']:
-            if method == 'singlestep':
-                timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order,
-                                                                                              skip_type=skip_type,
-                                                                                              t_T=t_T, t_0=t_0,
-                                                                                              device=device)
-            elif method == 'singlestep_fixed':
-                K = steps // order
-                orders = [order, ] * K
-                timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
-            for i, order in enumerate(orders):
-                t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
-                timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(),
-                                                      N=order, device=device)
-                lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
-                vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
-                h = lambda_inner[-1] - lambda_inner[0]
-                r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
-                r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
-                x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
-        if denoise_to_zero:
-            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
-        return x
-
-
-#############################################################
-# other utility functions
-#############################################################
-
-def interpolate_fn(x, xp, yp):
-    """
-    A piecewise linear function y = f(x), using xp and yp as keypoints.
-    We implement f(x) in a differentiable way (i.e. applicable for autograd).
-    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
-    Args:
-        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
-        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
-        yp: PyTorch tensor with shape [C, K].
-    Returns:
-        The function values f(x), with shape [N, C].
-    """
-    N, K = x.shape[0], xp.shape[1]
-    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
-    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
-    x_idx = torch.argmin(x_indices, dim=2)
-    cand_start_idx = x_idx - 1
-    start_idx = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(1, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
-    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
-    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
-    start_idx2 = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(0, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
-    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
-    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
-    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
-    return cand
-
-
-def expand_dims(v, dims):
-    """
-    Expand the tensor `v` to the dim `dims`.
-    Args:
-        `v`: a PyTorch tensor with shape [N].
-        `dim`: a `int`.
-    Returns:
-        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
-    """
-    return v[(...,) + (None,) * (dims - 1)]

spaces/AISuperheroes/05GR-Image-To-Multilingual-OCR/app.py

@@ -1,54 +0,0 @@
-import pandas as pd
-import PIL
-from PIL import Image
-from PIL import ImageDraw
-import gradio as gr
-import torch
-import easyocr
-
-torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/english.png', 'english.png')
-torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/chinese.jpg', 'chinese.jpg')
-torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/japanese.jpg', 'japanese.jpg')
-torch.hub.download_url_to_file('https://i.imgur.com/mwQFd7G.jpeg', 'Hindi.jpeg')
-
-def draw_boxes(image, bounds, color='yellow', width=2):
-    draw = ImageDraw.Draw(image)
-    for bound in bounds:
-        p0, p1, p2, p3 = bound[0]
-        draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width)
-    return image
-
-def inference(img, lang):
-    reader = easyocr.Reader(lang)
-    bounds = reader.readtext(img.name)
-    im = PIL.Image.open(img.name)
-    draw_boxes(im, bounds)
-    im.save('result.jpg')
-    return ['result.jpg', pd.DataFrame(bounds).iloc[: , 1:]]
-
-title = 'Image To Optical Character Recognition'
-description = 'Multilingual OCR which works conveniently on all devices in multiple languages.'
-article = "<p style='text-align: center'></p>"
-examples = [['english.png',['en']],['chinese.jpg',['ch_sim', 'en']],['japanese.jpg',['ja', 'en']],['Hindi.jpeg',['hi', 'en']]]
-css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
-choices = [
-    "ch_sim",
-    "ch_tra",
-    "de",
-    "en",
-    "es",
-    "ja",
-    "hi",
-    "ru"
-]
-gr.Interface(
-    inference,
-    [gr.inputs.Image(type='file', label='Input'),gr.inputs.CheckboxGroup(choices, type="value", default=['en'], label='language')],
-    [gr.outputs.Image(type='file', label='Output'), gr.outputs.Dataframe(headers=['text', 'confidence'])],
-    title=title,
-    description=description,
-    article=article,
-    examples=examples,
-    css=css,
-    enable_queue=True
-    ).launch(debug=True)

spaces/AP123/dreamgaussian/mesh_utils.py

@@ -1,147 +0,0 @@
-import numpy as np
-import pymeshlab as pml
-
-
-def poisson_mesh_reconstruction(points, normals=None):
-    # points/normals: [N, 3] np.ndarray
-
-    import open3d as o3d
-
-    pcd = o3d.geometry.PointCloud()
-    pcd.points = o3d.utility.Vector3dVector(points)
-
-    # outlier removal
-    pcd, ind = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=10)
-
-    # normals
-    if normals is None:
-        pcd.estimate_normals()
-    else:
-        pcd.normals = o3d.utility.Vector3dVector(normals[ind])
-
-    # visualize
-    o3d.visualization.draw_geometries([pcd], point_show_normal=False)
-
-    mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
-        pcd, depth=9
-    )
-    vertices_to_remove = densities < np.quantile(densities, 0.1)
-    mesh.remove_vertices_by_mask(vertices_to_remove)
-
-    # visualize
-    o3d.visualization.draw_geometries([mesh])
-
-    vertices = np.asarray(mesh.vertices)
-    triangles = np.asarray(mesh.triangles)
-
-    print(
-        f"[INFO] poisson mesh reconstruction: {points.shape} --> {vertices.shape} / {triangles.shape}"
-    )
-
-    return vertices, triangles
-
-
-def decimate_mesh(
-    verts, faces, target, backend="pymeshlab", remesh=False, optimalplacement=True
-):
-    # optimalplacement: default is True, but for flat mesh must turn False to prevent spike artifect.
-
-    _ori_vert_shape = verts.shape
-    _ori_face_shape = faces.shape
-
-    if backend == "pyfqmr":
-        import pyfqmr
-
-        solver = pyfqmr.Simplify()
-        solver.setMesh(verts, faces)
-        solver.simplify_mesh(target_count=target, preserve_border=False, verbose=False)
-        verts, faces, normals = solver.getMesh()
-    else:
-        m = pml.Mesh(verts, faces)
-        ms = pml.MeshSet()
-        ms.add_mesh(m, "mesh")  # will copy!
-
-        # filters
-        # ms.meshing_decimation_clustering(threshold=pml.Percentage(1))
-        ms.meshing_decimation_quadric_edge_collapse(
-            targetfacenum=int(target), optimalplacement=optimalplacement
-        )
-
-        if remesh:
-            # ms.apply_coord_taubin_smoothing()
-            ms.meshing_isotropic_explicit_remeshing(
-                iterations=3, targetlen=pml.Percentage(1)
-            )
-
-        # extract mesh
-        m = ms.current_mesh()
-        verts = m.vertex_matrix()
-        faces = m.face_matrix()
-
-    print(
-        f"[INFO] mesh decimation: {_ori_vert_shape} --> {verts.shape}, {_ori_face_shape} --> {faces.shape}"
-    )
-
-    return verts, faces
-
-
-def clean_mesh(
-    verts,
-    faces,
-    v_pct=1,
-    min_f=64,
-    min_d=20,
-    repair=True,
-    remesh=True,
-    remesh_size=0.01,
-):
-    # verts: [N, 3]
-    # faces: [N, 3]
-
-    _ori_vert_shape = verts.shape
-    _ori_face_shape = faces.shape
-
-    m = pml.Mesh(verts, faces)
-    ms = pml.MeshSet()
-    ms.add_mesh(m, "mesh")  # will copy!
-
-    # filters
-    ms.meshing_remove_unreferenced_vertices()  # verts not refed by any faces
-
-    if v_pct > 0:
-        ms.meshing_merge_close_vertices(
-            threshold=pml.Percentage(v_pct)
-        )  # 1/10000 of bounding box diagonal
-
-    ms.meshing_remove_duplicate_faces()  # faces defined by the same verts
-    ms.meshing_remove_null_faces()  # faces with area == 0
-
-    if min_d > 0:
-        ms.meshing_remove_connected_component_by_diameter(
-            mincomponentdiag=pml.Percentage(min_d)
-        )
-
-    if min_f > 0:
-        ms.meshing_remove_connected_component_by_face_number(mincomponentsize=min_f)
-
-    if repair:
-        # ms.meshing_remove_t_vertices(method=0, threshold=40, repeat=True)
-        ms.meshing_repair_non_manifold_edges(method=0)
-        ms.meshing_repair_non_manifold_vertices(vertdispratio=0)
-
-    if remesh:
-        # ms.apply_coord_taubin_smoothing()
-        ms.meshing_isotropic_explicit_remeshing(
-            iterations=3, targetlen=pml.AbsoluteValue(remesh_size)
-        )
-
-    # extract mesh
-    m = ms.current_mesh()
-    verts = m.vertex_matrix()
-    faces = m.face_matrix()
-
-    print(
-        f"[INFO] mesh cleaning: {_ori_vert_shape} --> {verts.shape}, {_ori_face_shape} --> {faces.shape}"
-    )
-
-    return verts, faces

spaces/AbandonedMuse/UnlimitedMusicGen/Makefile

@@ -1,21 +0,0 @@
-default: linter tests
-
-install:
-	pip install -U pip
-	pip install -U -e '.[dev]'
-
-linter:
-	flake8 audiocraft && mypy audiocraft
-	flake8 tests && mypy tests
-
-tests:
-	coverage run -m pytest tests
-	coverage report --include 'audiocraft/*'
-
-docs:
-	pdoc3 --html -o docs -f audiocraft
-
-dist:
-	python setup.py sdist
-
-.PHONY: linter tests docs dist

spaces/Abhilashvj/planogram-compliance/utils/__init__.py

@@ -1,88 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-utils/initialization
-"""
-
-import contextlib
-import platform
-import threading
-
-
-def emojis(str=""):
-    # Return platform-dependent emoji-safe version of string
-    return (
-        str.encode().decode("ascii", "ignore")
-        if platform.system() == "Windows"
-        else str
-    )
-
-
-class TryExcept(contextlib.ContextDecorator):
-    # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
-    def __init__(self, msg=""):
-        self.msg = msg
-
-    def __enter__(self):
-        pass
-
-    def __exit__(self, exc_type, value, traceback):
-        if value:
-            print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
-        return True
-
-
-def threaded(func):
-    # Multi-threads a target function and returns thread. Usage: @threaded decorator
-    def wrapper(*args, **kwargs):
-        thread = threading.Thread(
-            target=func, args=args, kwargs=kwargs, daemon=True
-        )
-        thread.start()
-        return thread
-
-    return wrapper
-
-
-def join_threads(verbose=False):
-    # Join all daemon threads, i.e. atexit.register(lambda: join_threads())
-    main_thread = threading.current_thread()
-    for t in threading.enumerate():
-        if t is not main_thread:
-            if verbose:
-                print(f"Joining thread {t.name}")
-            t.join()
-
-
-def notebook_init(verbose=True):
-    # Check system software and hardware
-    print("Checking setup...")
-
-    import os
-    import shutil
-
-    from utils.general import check_font, check_requirements, is_colab
-    from utils.torch_utils import select_device  # imports
-
-    check_font()
-
-    import psutil
-    from IPython import display  # to display images and clear console output
-
-    if is_colab():
-        shutil.rmtree(
-            "/content/sample_data", ignore_errors=True
-        )  # remove colab /sample_data directory
-
-    # System info
-    if verbose:
-        gb = 1 << 30  # bytes to GiB (1024 ** 3)
-        ram = psutil.virtual_memory().total
-        total, used, free = shutil.disk_usage("/")
-        display.clear_output()
-        s = f"({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)"
-    else:
-        s = ""
-
-    select_device(newline=False)
-    print(emojis(f"Setup complete ✅ {s}"))
-    return display

spaces/Aditya9790/yolo7-object-tracking/detect.py

@@ -1,163 +0,0 @@
-import argparse
-import time
-from pathlib import Path
-
-import cv2
-import torch
-import torch.backends.cudnn as cudnn
-from numpy import random
-
-from models.experimental import attempt_load
-from utils.datasets import LoadStreams, LoadImages
-from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
-    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
-from utils.plots import plot_one_box
-from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
-
-
-def detect(save_img=False): 
-    source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace
-    save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
-    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
-        ('rtsp://', 'rtmp://', 'http://', 'https://'))
-
-    # Directories
-    save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
-    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
-
-    # Initialize
-    set_logging()
-    device = select_device(opt.device)
-    half = device.type != 'cpu'  # half precision only supported on CUDA
-
-    # Load model
-    model = attempt_load(weights, map_location=device)  # load FP32 model
-    stride = int(model.stride.max())  # model stride
-    imgsz = check_img_size(imgsz, s=stride)  # check img_size
-
-    if trace:
-        model = TracedModel(model, device, opt.img_size)
-
-    if half:
-        model.half()  # to FP16
-
-    # Second-stage classifier
-    classify = False
-    if classify:
-        modelc = load_classifier(name='resnet101', n=2)  # initialize
-        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
-
-    # Set Dataloader
-    vid_path, vid_writer = None, None
-    if webcam:
-        view_img = check_imshow()
-        cudnn.benchmark = True  # set True to speed up constant image size inference
-        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
-    else:
-        dataset = LoadImages(source, img_size=imgsz, stride=stride)
-
-    # Get names and colors
-    names = model.module.names if hasattr(model, 'module') else model.names
-    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
-
-    # Run inference
-    if device.type != 'cpu':
-        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
-    old_img_w = old_img_h = imgsz
-    old_img_b = 1
-
-    t0 = time.time()
-    for path, img, im0s, vid_cap in dataset:
-        img = torch.from_numpy(img).to(device)
-        img = img.half() if half else img.float()  # uint8 to fp16/32
-        img /= 255.0  # 0 - 255 to 0.0 - 1.0
-        if img.ndimension() == 3:
-            img = img.unsqueeze(0)
-
-        # Warmup
-        if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
-            old_img_b = img.shape[0]
-            old_img_h = img.shape[2]
-            old_img_w = img.shape[3]
-            for i in range(3):
-                model(img, augment=opt.augment)[0]
-
-        # Inference
-        t1 = time_synchronized()
-        with torch.no_grad():   # Calculating gradients would cause a GPU memory leak
-            pred = model(img, augment=opt.augment)[0]
-        t2 = time_synchronized()
-
-        # Apply NMS
-        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
-        t3 = time_synchronized()
-
-        # Apply Classifier
-        if classify:
-            pred = apply_classifier(pred, modelc, img, im0s)
-
-        # Process detections
-        for i, det in enumerate(pred):  # detections per image
-            if webcam:  # batch_size >= 1
-                p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
-            else:
-                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
-
-            p = Path(p)  # to Path
-            save_path = str(save_dir / p.name)  # img.jpg
-            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
-            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
-            if len(det):
-                # Rescale boxes from img_size to im0 size
-                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
-
-                # Print results
-                for c in det[:, -1].unique():
-                    n = (det[:, -1] == c).sum()  # detections per class
-                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
-
-                # Write results
-                for *xyxy, conf, cls in reversed(det):
-                    if save_txt:  # Write to file
-                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
-                        line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
-                        with open(txt_path + '.txt', 'a') as f:
-                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
-                    if save_img or view_img:  # Add bbox to image
-                        label = f'{names[int(cls)]} {conf:.2f}'
-                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
-
-            # Print time (inference + NMS)
-            print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
-
-            # Stream results
-            if view_img:
-                cv2.imshow(str(p), im0)
-                cv2.waitKey(1)  # 1 millisecond
-
-            # Save results (image with detections)
-            if save_img:
-                if dataset.mode == 'image':
-                    cv2.imwrite(save_path, im0)
-                    print(f" The image with the result is saved in: {save_path}")
-                else:  # 'video' or 'stream'
-                    if vid_path != save_path:  # new video
-                        vid_path = save_path
-                        if isinstance(vid_writer, cv2.VideoWriter):
-                            vid_writer.release()  # release previous video writer
-                        if vid_cap:  # video
-                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
-                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                        else:  # stream
-                            fps, w, h = 30, im0.shape[1], im0.shape[0]
-                            save_path += '.mp4'
-                        vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
-                    vid_writer.write(im0)
-
-    if save_txt or save_img:
-        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
-        #print(f"Results saved to {save_dir}{s}")
-
-    print(f'Done. ({time.time() - t0:.3f}s)')

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dialog/methods/ButtonMethods.js

@@ -1,333 +0,0 @@
-export default {
-    getChoice(index) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            return choicesSizer.getButton(index);
-        } else {
-            return undefined;
-        }
-    },
-
-    getAction(index) {
-        return this.childrenMap.actionsSizer.getButton(index);
-    },
-
-    getToolbar(index) {
-        return this.childrenMap.toolbarSizer.getButton(index);
-    },
-
-    getLeftToolbar(index) {
-        return this.childrenMap.leftToolbarSizer.getButton(index);
-    },
-
-    setChoiceEnable(index, enabled) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            choicesSizer.setButtonEnable(index, enabled);
-        }
-        return this;
-    },
-
-    setActionEnable(index, enabled) {
-        this.childrenMap.actionsSizer.setButtonEnable(index, enabled);
-        return this;
-    },
-
-    setToolbarEnable(index, enabled) {
-        this.childrenMap.toolbarSizer.setButtonEnable(index, enabled);
-        return this;
-    },
-
-    setLeftToolbarEnable(index, enabled) {
-        this.childrenMap.leftToolbarSizer.setButtonEnable(index, enabled);
-        return this;
-    },
-
-    toggleChoiceEnable(index) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            choicesSizer.toggleButtonEnable(index);
-        }
-        return this;
-    },
-
-    toggleActionEnable(index) {
-        this.childrenMap.actionsSizer.toggleButtonEnable(index);
-        return this;
-    },
-
-    toggleToolbarEnable(index) {
-        this.childrenMap.toolbarSizer.toggleButtonEnable(index);
-        return this;
-    },
-
-    toggleLeftToolbarEnable(index) {
-        this.childrenMap.leftToolbarSizer.toggleButtonEnable(index);
-        return this;
-    },
-
-    getChoiceEnable(index) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            return choicesSizer.getButtonEnable(index);
-        } else {
-            return false;
-        }
-    },
-
-    getActionEnable(index) {
-        return this.childrenMap.actionsSizer.getButtonEnable(index);
-    },
-
-    getToolbarEnable(index) {
-        return this.childrenMap.toolbarSizer.getButtonEnable(index);
-    },
-
-    getLeftToolbarEnable(index) {
-        return this.childrenMap.leftToolbarSizer.getButtonEnable(index);
-    },
-
-    emitChoiceClick(index) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            choicesSizer.emitButtonClick(index);
-        }
-        return this;
-    },
-
-    emitActionClick(index) {
-        this.childrenMap.actionsSizer.emitButtonClick(index);
-        return this;
-    },
-
-    emitToolbarClick(index) {
-        this.childrenMap.toolbarSizer.emitButtonClick(index);
-        return this;
-    },
-
-    emitLeftToolbarClick(index) {
-        this.childrenMap.leftToolbarSizer.emitButtonClick(index);
-        return this;
-    },
-
-    showChoice(index) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            choicesSizer.showButton(index);
-        }
-        return this;
-    },
-
-    showAction(index) {
-        this.childrenMap.actionsSizer.showButton(index);
-        return this;
-    },
-
-    showToolbar(index) {
-        this.childrenMap.toolbarSizer.showButton(index);
-        return this;
-    },
-
-    showLeftToolbar(index) {
-        this.childrenMap.leftToolbarSizer.showButton(index);
-        return this;
-    },
-
-    hideChoice(index) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            choicesSizer.hideButton(index);
-        }
-        return this;
-    },
-
-    hideAction(index) {
-        this.childrenMap.actionsSizer.hideButton(index);
-        return this;
-    },
-
-    hideToolbar(index) {
-        this.childrenMap.toolbarSizer.hideButton(index);
-        return this;
-    },
-
-    hideLeftToolbar(index) {
-        this.childrenMap.leftToolbarSizer.hideButton(index);
-        return this;
-    },
-
-    addChoice(gameObject) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            choicesSizer.addButton(gameObject);
-        }
-        return this;
-    },
-
-    addAction(gameObject) {
-        this.childrenMap.actionsSizer.addButton(gameObject);
-        return this;
-    },
-
-    addToolbar(gameObject) {
-        this.childrenMap.toolbarSizer.addButton(gameObject);
-        return this;
-    },
-
-    addLeftToolbar(gameObject) {
-        this.childrenMap.leftToolbarSizer.addButton(gameObject);
-        return this;
-    },
-
-    removeChoice(index, destroyChild) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            choicesSizer.removeButton(index, destroyChild);
-        }
-        return this;
-    },
-
-    removeAction(index, destroyChild) {
-        this.childrenMap.actionsSizer.removeButton(index, destroyChild);
-        return this;
-    },
-
-    removeToolbar(index, destroyChild) {
-        this.childrenMap.toolbarSizer.removeButton(index, destroyChild);
-        return this;
-    },
-
-    removeLeftToolbar(index, destroyChild) {
-        this.childrenMap.leftToolbarSizer.removeButton(index, destroyChild);
-        return this;
-    },
-
-    clearChoices(destroyChild) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            choicesSizer.clearButtons(destroyChild);
-        }
-        return this;
-    },
-
-    clearActions(destroyChild) {
-        this.childrenMap.actionsSizer.clearButtons(destroyChild);
-        return this;
-    },
-
-    clearToolbar(destroyChild) {
-        this.childrenMap.toolbarSizer.clearButtons(destroyChild);
-        return this;
-    },
-
-    clearLeftToolbar(destroyChild) {
-        this.childrenMap.leftToolbarSizer.clearButtons(destroyChild);
-        return this;
-    },
-
-    forEachChoice(callback, scope) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            choicesSizer.forEachButtton(callback, scope);
-        }
-        return this;
-    },
-
-    forEachAction(callback, scope) {
-        this.childrenMap.actionsSizer.forEachButtton(callback, scope);
-        return this;
-    },
-
-    forEachToolbar(callback, scope) {
-        this.childrenMap.toolbarSizer.forEachButtton(callback, scope);
-        return this;
-    },
-
-    forEachLeftToolbar(callback, scope) {
-        this.childrenMap.leftToolbarSizer.forEachButtton(callback, scope);
-        return this;
-    },
-
-    setAllButtonsEnable(enabled) {
-        if (enabled === undefined) {
-            enabled = true;
-        }
-
-        if (this.childrenMap.toolbarSizer) {
-            this.setToolbarEnable(enabled);
-        }
-        if (this.childrenMap.leftToolbarSizer) {
-            this.setLeftToolbarEnable(enabled);
-        }
-        if (this.childrenMap.actionsSizer) {
-            this.setActionEnable(enabled);
-        }
-        if (this.childrenMap.choicesSizer) {
-            this.setChoiceEnable(enabled);
-        }
-
-        return this;
-    },
-
-    // Checkboxes
-    getChoicesButtonStates() {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            return choicesSizer.getAllButtonsState();
-        } else {
-            return {};
-        }
-    },
-
-    getChoicesButtonState(name) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (name === undefined) {
-            if (choicesSizer) {
-                return choicesSizer.getAllButtonsState();
-            } else {
-                return {}
-            }
-        } else {
-            if (choicesSizer) {
-                return choicesSizer.getButtonState(name);
-            } else {
-                return false;
-            }
-        }
-    },
-
-    setChoicesButtonState(name, state) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            choicesSizer.setButtonState(name, state);
-        }
-        return this;
-    },
-
-    clearChoicesButtonStates() {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            choicesSizer.clearAllButtonsState();
-        }
-        return this;
-    },
-
-    // Radio buttons
-    getChoicesSelectedButtonName() {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            return choicesSizer.getSelectedButtonName();
-        } else {
-            return '';
-        }
-    },
-
-    setChoicesSelectedButtonName(name) {
-        var choicesSizer = this.childrenMap.choicesSizer;
-        if (choicesSizer) {
-            choicesSizer.setSelectedButtonName(name);
-        }
-        return this;
-    },
-
-};

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/folder/methods/ConfigurationMethods.js

@@ -1,37 +0,0 @@
-import ScaleMethods from '../../basesizer/ScaleMethods.js';
-
-var DefaultExpandCallback = function (gameObject, duration) {
-    ScaleMethods.popUp.call(gameObject, duration, this.expandDirection);
-};
-
-var DefaultCollapseCallback = function (gameObject, duration) {
-    ScaleMethods.scaleDown.call(gameObject, duration, this.expandDirection)
-}
-
-export default {
-    setTransitionDuration(duration) {
-        this.transitionDuration = duration;
-
-        this.childTransition
-            .setTransitInTime(duration)
-            .setTransitOutTime(duration);
-
-        return this;
-    },
-
-    setExpandCallback(callback) {
-        if (callback === undefined) {
-            callback = DefaultExpandCallback.bind(this);
-        }
-        this.childTransition.setTransitInCallback(callback);
-        return this;
-    },
-
-    setCollapseCallback(callback) {
-        if (callback === undefined) {
-            callback = DefaultCollapseCallback.bind(this);
-        }
-        this.childTransition.setTransitOutCallback(callback);
-        return this;
-    }
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/index.js

@@ -1,12 +0,0 @@
-import Maker from './Maker.js';
-import Make from './Make.js';
-import YAMLMake from './YAMLMake.js';
-import Builders from './builders/Builders.js';
-
-
-export {
-    Maker,
-    Make,
-    YAMLMake,
-    Builders,
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/ResolveWidth.js

@@ -1,23 +0,0 @@
-import ResolveWidthBase from '../basesizer/ResolveWidth.js';
-
-var ResolveWidth = function (width) {
-    var width = ResolveWidthBase.call(this, width);
-
-    // Calculate proportionLength
-    if ((this.proportionLength === undefined) && (this.orientation === 0)) {
-        var remainder = width - this.childrenWidth;
-        if (remainder > 0) {
-            remainder = width - this.getChildrenWidth(false);
-            this.proportionLength = remainder / this.childrenProportion;
-        } else {
-            this.proportionLength = 0;
-            if (remainder < 0) {
-                // Warning
-            }
-        }
-    }
-
-    return width;
-}
-
-export default ResolveWidth;

spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/video2audio.py

@@ -1,27 +0,0 @@
-import os
-from concurrent.futures import ThreadPoolExecutor
-
-from moviepy.editor import AudioFileClip
-
-video_dir = "./video_data/"
-audio_dir = "./raw_audio/"
-filelist = list(os.walk(video_dir))[0][2]
-
-
-def generate_infos():
-    videos = []
-    for file in filelist:
-        if file.endswith(".mp4"):
-            videos.append(file)
-    return videos
-
-
-def clip_file(file):
-    my_audio_clip = AudioFileClip(video_dir + file)
-    my_audio_clip.write_audiofile(audio_dir + file.rstrip(".mp4") + ".wav")
-
-
-if __name__ == "__main__":
-    infos = generate_infos()
-    with ThreadPoolExecutor(max_workers=os.cpu_count()) as executor:
-        executor.map(clip_file, infos)

spaces/Alfaxad/BioGalacticModels/app.py

@@ -1,114 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import gradio as gr
-
-from model_list import ModelList
-
-DESCRIPTION = '# Explore Biology & Biochemistry Foundation Models 🧬'
-NOTES = '''
-Thanks to the following folks who have made suggestions to this list!
-- [Shelby](https://twitter.com/shelbynewsad), author of [this nice model list](https://compoundvc.notion.site/compoundvc/474885e638e94e44a1aab4d3124e3d6a?v=299bce7af785413da4c9f36837c03aaf) 
-- [Valentyn Bezshapkin](https://twitter.com/valentynbez)
-- [Payel Das](https://twitter.com/payel791)
-- [Anthony Costa](https://twitter.com/anthonycosta)
-'''
-FOOTER = ''''''
-
-def main():
-    model_list = ModelList()
-
-    with gr.Blocks(css='style.css') as demo:
-        gr.Markdown(DESCRIPTION)
-
-        search_box = gr.Textbox(
-            label='Search Model Name',
-            placeholder=
-            'You can search for titles with regular expressions. e.g. (?<!sur)face',
-            max_lines=1)
-        
-        case_sensitive = gr.Checkbox(label='Case Sensitive')
-        
-        filter_names = gr.CheckboxGroup(choices=[
-            'Paper',
-            'Code',
-            'Model Weights',
-        ], label='Filter')
-
-        data_type_names = [
-            'DNA', 'scRNA', 'mRNA', 'scRNA perturbation', 'RNA structure prediction', 'RNA language model', 'protein language model', 'protein structure prediction', 
-            'protein generation', 'protein function prediction', 'protein fitness prediction', 'antibody structure prediction', 'antibody language model', 'molecules',
-            'ligand generation', 'reaction-to-enzyme', 'enzyme generation', 'epigenomic', 'molecular docking', 'peptide property prediction', 
-        ]
-
-        data_types = gr.CheckboxGroup(choices=data_type_names,
-                                      value=data_type_names,
-                                      label='Type')
-
-        years = ['2020', '2021', '2022', '2023']
-
-        years_checkbox = gr.CheckboxGroup(choices=years, value=years, label='Year of Publication/Preprint')
-
-        # model_type_names = [
-        #     'GPT2', 'GPT-Neo', 'GPT-NeoX', 'ESM', 'BERT', 'RoBERTa', 'BART', 'T5', 'MPNN', 'diffusion', 'custom model'
-        # ]
-
-        # model_types = gr.CheckboxGroup(choices=model_type_names,
-        #                                value=model_type_names,
-        #                                label='Base Model')
-        
-        search_button = gr.Button('Search')
-
-        number_of_models = gr.Textbox(label='Number of Models Found')
-        table = gr.HTML(show_label=False)
-
-        gr.Markdown(NOTES)
-        gr.Markdown(FOOTER)
-
-        demo.load(fn=model_list.render,
-                  inputs=[
-                      search_box,
-                      case_sensitive,
-                      filter_names,
-                      data_types,
-                      years_checkbox,
-                      #model_types
-                  ],
-                  outputs=[
-                      number_of_models,
-                      table,
-                  ])
-        search_box.submit(fn=model_list.render,
-                          inputs=[
-                              search_box,
-                              case_sensitive,
-                              filter_names,
-                              data_types,
-                              years_checkbox,
-                              #model_types
-                          ],
-                          outputs=[
-                              number_of_models,
-                              table,
-                          ])
-
-        search_button.click(fn=model_list.render,
-                            inputs=[
-                                search_box,
-                                case_sensitive,
-                                filter_names,
-                                data_types,
-                                years_checkbox,
-                                #model_types
-                            ],
-                            outputs=[
-                                number_of_models,
-                                table,
-                            ])
-
-    demo.launch(enable_queue=True, share=False)
-
-
-if __name__ == '__main__':
-    main()

spaces/Alycer/VITS-Umamusume-voice-synthesizer/ONNXVITS_inference.py

@@ -1,36 +0,0 @@
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-import IPython.display as ipd
-import torch
-import commons
-import utils
-import ONNXVITS_infer
-from text import text_to_sequence
-
-def get_text(text, hps):
-    text_norm = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = torch.LongTensor(text_norm)
-    return text_norm
-
-hps = utils.get_hparams_from_file("../vits/pretrained_models/uma87.json")
-
-net_g = ONNXVITS_infer.SynthesizerTrn(
-    len(hps.symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    n_speakers=hps.data.n_speakers,
-    **hps.model)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("../vits/pretrained_models/uma_1153000.pth", net_g)
-
-text1 = get_text("おはようございます。", hps)
-stn_tst = text1
-with torch.no_grad():
-    x_tst = stn_tst.unsqueeze(0)
-    x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
-    sid = torch.LongTensor([0])
-    audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()
-print(audio)

spaces/Amrrs/DragGan-Inversion/torch_utils/ops/upfirdn2d.py

@@ -1,412 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom PyTorch ops for efficient resampling of 2D images."""
-
-import os
-import numpy as np
-import torch
-
-from .. import custom_ops
-from .. import misc
-from . import conv2d_gradfix
-
-# ----------------------------------------------------------------------------
-
-_plugin = None
-
-
-def _init():
-    global _plugin
-    if _plugin is None:
-        _plugin = custom_ops.get_plugin(
-            module_name='upfirdn2d_plugin',
-            sources=['upfirdn2d.cpp', 'upfirdn2d.cu'],
-            headers=['upfirdn2d.h'],
-            source_dir=os.path.dirname(__file__),
-            extra_cuda_cflags=['--use_fast_math',
-                               '--allow-unsupported-compiler'],
-        )
-    return True
-
-
-def _parse_scaling(scaling):
-    if isinstance(scaling, int):
-        scaling = [scaling, scaling]
-    assert isinstance(scaling, (list, tuple))
-    assert all(isinstance(x, int) for x in scaling)
-    sx, sy = scaling
-    assert sx >= 1 and sy >= 1
-    return sx, sy
-
-
-def _parse_padding(padding):
-    if isinstance(padding, int):
-        padding = [padding, padding]
-    assert isinstance(padding, (list, tuple))
-    assert all(isinstance(x, int) for x in padding)
-    if len(padding) == 2:
-        padx, pady = padding
-        padding = [padx, padx, pady, pady]
-    padx0, padx1, pady0, pady1 = padding
-    return padx0, padx1, pady0, pady1
-
-
-def _get_filter_size(f):
-    if f is None:
-        return 1, 1
-    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-    fw = f.shape[-1]
-    fh = f.shape[0]
-    with misc.suppress_tracer_warnings():
-        fw = int(fw)
-        fh = int(fh)
-    misc.assert_shape(f, [fh, fw][:f.ndim])
-    assert fw >= 1 and fh >= 1
-    return fw, fh
-
-# ----------------------------------------------------------------------------
-
-
-def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
-    r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
-
-    Args:
-        f:           Torch tensor, numpy array, or python list of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable),
-                     `[]` (impulse), or
-                     `None` (identity).
-        device:      Result device (default: cpu).
-        normalize:   Normalize the filter so that it retains the magnitude
-                     for constant input signal (DC)? (default: True).
-        flip_filter: Flip the filter? (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        separable:   Return a separable filter? (default: select automatically).
-
-    Returns:
-        Float32 tensor of the shape
-        `[filter_height, filter_width]` (non-separable) or
-        `[filter_taps]` (separable).
-    """
-    # Validate.
-    if f is None:
-        f = 1
-    f = torch.as_tensor(f, dtype=torch.float32)
-    assert f.ndim in [0, 1, 2]
-    assert f.numel() > 0
-    if f.ndim == 0:
-        f = f[np.newaxis]
-
-    # Separable?
-    if separable is None:
-        separable = (f.ndim == 1 and f.numel() >= 8)
-    if f.ndim == 1 and not separable:
-        f = f.ger(f)
-    assert f.ndim == (1 if separable else 2)
-
-    # Apply normalize, flip, gain, and device.
-    if normalize:
-        f /= f.sum()
-    if flip_filter:
-        f = f.flip(list(range(f.ndim)))
-    f = f * (gain ** (f.ndim / 2))
-    f = f.to(device=device)
-    return f
-
-# ----------------------------------------------------------------------------
-
-
-def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Pad, upsample, filter, and downsample a batch of 2D images.
-
-    Performs the following sequence of operations for each channel:
-
-    1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
-
-    2. Pad the image with the specified number of zeros on each side (`padding`).
-       Negative padding corresponds to cropping the image.
-
-    3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
-       so that the footprint of all output pixels lies within the input image.
-
-    4. Downsample the image by keeping every Nth pixel (`down`).
-
-    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
-    The fused op is considerably more efficient than performing the same calculation
-    using standard PyTorch ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        up:          Integer upsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        down:        Integer downsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the upsampled image. Can be a single number
-                     or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert impl in ['ref', 'cuda']
-    if impl == 'cuda' and x.device.type == 'cuda' and _init():
-        return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f)
-    return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
-
-# ----------------------------------------------------------------------------
-
-
-@misc.profiled_function
-def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
-    """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
-    """
-    # Validate arguments.
-    assert isinstance(x, torch.Tensor) and x.ndim == 4
-    if f is None:
-        f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
-    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-    assert f.dtype == torch.float32 and not f.requires_grad
-    batch_size, num_channels, in_height, in_width = x.shape
-    upx, upy = _parse_scaling(up)
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-
-    # Check that upsampled buffer is not smaller than the filter.
-    upW = in_width * upx + padx0 + padx1
-    upH = in_height * upy + pady0 + pady1
-    assert upW >= f.shape[-1] and upH >= f.shape[0]
-
-    # Upsample by inserting zeros.
-    x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
-    x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
-    x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
-
-    # Pad or crop.
-    x = torch.nn.functional.pad(
-        x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
-    x = x[:, :, max(-pady0, 0): x.shape[2] - max(-pady1, 0),
-          max(-padx0, 0): x.shape[3] - max(-padx1, 0)]
-
-    # Setup filter.
-    f = f * (gain ** (f.ndim / 2))
-    f = f.to(x.dtype)
-    if not flip_filter:
-        f = f.flip(list(range(f.ndim)))
-
-    # Convolve with the filter.
-    f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
-    if f.ndim == 4:
-        x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
-    else:
-        x = conv2d_gradfix.conv2d(
-            input=x, weight=f.unsqueeze(2), groups=num_channels)
-        x = conv2d_gradfix.conv2d(
-            input=x, weight=f.unsqueeze(3), groups=num_channels)
-
-    # Downsample by throwing away pixels.
-    x = x[:, :, ::downy, ::downx]
-    return x
-
-# ----------------------------------------------------------------------------
-
-
-_upfirdn2d_cuda_cache = dict()
-
-
-def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
-    """Fast CUDA implementation of `upfirdn2d()` using custom ops.
-    """
-    # Parse arguments.
-    upx, upy = _parse_scaling(up)
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-
-    # Lookup from cache.
-    key = (upx, upy, downx, downy, padx0, padx1,
-           pady0, pady1, flip_filter, gain)
-    if key in _upfirdn2d_cuda_cache:
-        return _upfirdn2d_cuda_cache[key]
-
-    # Forward op.
-    class Upfirdn2dCuda(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x, f):  # pylint: disable=arguments-differ
-            assert isinstance(x, torch.Tensor) and x.ndim == 4
-            if f is None:
-                f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
-            if f.ndim == 1 and f.shape[0] == 1:
-                # Convert separable-1 into full-1x1.
-                f = f.square().unsqueeze(0)
-            assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-            y = x
-            if f.ndim == 2:
-                y = _plugin.upfirdn2d(
-                    y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
-            else:
-                y = _plugin.upfirdn2d(y, f.unsqueeze(
-                    0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, 1.0)
-                y = _plugin.upfirdn2d(y, f.unsqueeze(
-                    1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, gain)
-            ctx.save_for_backward(f)
-            ctx.x_shape = x.shape
-            return y
-
-        @staticmethod
-        def backward(ctx, dy):  # pylint: disable=arguments-differ
-            f, = ctx.saved_tensors
-            _, _, ih, iw = ctx.x_shape
-            _, _, oh, ow = dy.shape
-            fw, fh = _get_filter_size(f)
-            p = [
-                fw - padx0 - 1,
-                iw * upx - ow * downx + padx0 - upx + 1,
-                fh - pady0 - 1,
-                ih * upy - oh * downy + pady0 - upy + 1,
-            ]
-            dx = None
-            df = None
-
-            if ctx.needs_input_grad[0]:
-                dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(
-                    not flip_filter), gain=gain).apply(dy, f)
-
-            assert not ctx.needs_input_grad[1]
-            return dx, df
-
-    # Add to cache.
-    _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda
-    return Upfirdn2dCuda
-
-# ----------------------------------------------------------------------------
-
-
-def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Filter a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape matches the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        padding:     Padding with respect to the output. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + fw // 2,
-        padx1 + (fw - 1) // 2,
-        pady0 + fh // 2,
-        pady1 + (fh - 1) // 2,
-    ]
-    return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
-
-# ----------------------------------------------------------------------------
-
-
-def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Upsample a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape is a multiple of the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        up:          Integer upsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the output. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    upx, upy = _parse_scaling(up)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + (fw + upx - 1) // 2,
-        padx1 + (fw - upx) // 2,
-        pady0 + (fh + upy - 1) // 2,
-        pady1 + (fh - upy) // 2,
-    ]
-    return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl)
-
-# ----------------------------------------------------------------------------
-
-
-def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Downsample a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape is a fraction of the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        down:        Integer downsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the input. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + (fw - downx + 1) // 2,
-        padx1 + (fw - downx) // 2,
-        pady0 + (fh - downy + 1) // 2,
-        pady1 + (fh - downy) // 2,
-    ]
-    return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
-
-# ----------------------------------------------------------------------------

spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/decoder/__init__.py

@@ -1,6 +0,0 @@
-from src.decoder.tensoRF_decoder import TensorVMSplit
-from src.utils.registry import Registry
-
-DECODER_REGISTRY = Registry("DECODER")
-
-DECODER_REGISTRY.register(TensorVMSplit)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py

@@ -1,757 +0,0 @@
-# Copyright 2023 TIME Authors and The HuggingFace Team. All rights reserved."
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import copy
-import inspect
-import warnings
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import torch
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
-from ...image_processor import VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...schedulers import PNDMScheduler
-from ...schedulers.scheduling_utils import SchedulerMixin
-from ...utils import logging, randn_tensor
-from ..pipeline_utils import DiffusionPipeline
-from . import StableDiffusionPipelineOutput
-from .safety_checker import StableDiffusionSafetyChecker
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-AUGS_CONST = ["A photo of ", "An image of ", "A picture of "]
-
-
-class StableDiffusionModelEditingPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
-    r"""
-    Pipeline for text-to-image model editing.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
-        text_encoder ([`~transformers.CLIPTextModel`]):
-            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
-        tokenizer ([`~transformers.CLIPTokenizer`]):
-            A `CLIPTokenizer` to tokenize text.
-        unet ([`UNet2DConditionModel`]):
-            A `UNet2DConditionModel` to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
-            about a model's potential harms.
-        feature_extractor ([`~transformers.CLIPFeatureExtractor`]):
-            A `CLIPFeatureExtractor` to extract features from generated images; used as inputs to the `safety_checker`.
-        with_to_k ([`bool`]):
-            Whether to edit the key projection matrices along with the value projection matrices.
-        with_augs ([`list`]):
-            Textual augmentations to apply while editing the text-to-image model. Set to `[]` for no augmentations.
-    """
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: SchedulerMixin,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
-        requires_safety_checker: bool = True,
-        with_to_k: bool = True,
-        with_augs: list = AUGS_CONST,
-    ):
-        super().__init__()
-
-        if isinstance(scheduler, PNDMScheduler):
-            logger.error("PNDMScheduler for this pipeline is currently not supported.")
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-        self.with_to_k = with_to_k
-        self.with_augs = with_augs
-
-        # get cross-attention layers
-        ca_layers = []
-
-        def append_ca(net_):
-            if net_.__class__.__name__ == "CrossAttention":
-                ca_layers.append(net_)
-            elif hasattr(net_, "children"):
-                for net__ in net_.children():
-                    append_ca(net__)
-
-        # recursively find all cross-attention layers in unet
-        for net in self.unet.named_children():
-            if "down" in net[0]:
-                append_ca(net[1])
-            elif "up" in net[0]:
-                append_ca(net[1])
-            elif "mid" in net[0]:
-                append_ca(net[1])
-
-        # get projection matrices
-        self.ca_clip_layers = [l for l in ca_layers if l.to_v.in_features == 768]
-        self.projection_matrices = [l.to_v for l in self.ca_clip_layers]
-        self.og_matrices = [copy.deepcopy(l.to_v) for l in self.ca_clip_layers]
-        if self.with_to_k:
-            self.projection_matrices = self.projection_matrices + [l.to_k for l in self.ca_clip_layers]
-            self.og_matrices = self.og_matrices + [copy.deepcopy(l.to_k) for l in self.ca_clip_layers]
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
-    def enable_vae_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.vae.enable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
-    def disable_vae_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
-        computing decoding in one step.
-        """
-        self.vae.disable_slicing()
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        lora_scale: Optional[float] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            lora_scale (`float`, *optional*):
-                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
-        """
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
-
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            # textual inversion: procecss multi-vector tokens if necessary
-            if isinstance(self, TextualInversionLoaderMixin):
-                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
-
-            max_length = prompt_embeds.shape[1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        return prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is None:
-            has_nsfw_concept = None
-        else:
-            if torch.is_tensor(image):
-                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
-            else:
-                feature_extractor_input = self.image_processor.numpy_to_pil(image)
-            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        return image, has_nsfw_concept
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
-    def decode_latents(self, latents):
-        warnings.warn(
-            "The decode_latents method is deprecated and will be removed in a future version. Please"
-            " use VaeImageProcessor instead",
-            FutureWarning,
-        )
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.decode(latents, return_dict=False)[0]
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
-    def check_inputs(
-        self,
-        prompt,
-        height,
-        width,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    @torch.no_grad()
-    def edit_model(
-        self,
-        source_prompt: str,
-        destination_prompt: str,
-        lamb: float = 0.1,
-        restart_params: bool = True,
-    ):
-        r"""
-        Apply model editing via closed-form solution (see Eq. 5 in the TIME [paper](https://arxiv.org/abs/2303.08084)).
-
-        Args:
-            source_prompt (`str`):
-                The source prompt containing the concept to be edited.
-            destination_prompt (`str`):
-                The destination prompt. Must contain all words from `source_prompt` with additional ones to specify the
-                target edit.
-            lamb (`float`, *optional*, defaults to 0.1):
-                The lambda parameter specifying the regularization intesity. Smaller values increase the editing power.
-            restart_params (`bool`, *optional*, defaults to True):
-                Restart the model parameters to their pre-trained version before editing. This is done to avoid edit
-                compounding. When it is `False`, edits accumulate.
-        """
-
-        # restart LDM parameters
-        if restart_params:
-            num_ca_clip_layers = len(self.ca_clip_layers)
-            for idx_, l in enumerate(self.ca_clip_layers):
-                l.to_v = copy.deepcopy(self.og_matrices[idx_])
-                self.projection_matrices[idx_] = l.to_v
-                if self.with_to_k:
-                    l.to_k = copy.deepcopy(self.og_matrices[num_ca_clip_layers + idx_])
-                    self.projection_matrices[num_ca_clip_layers + idx_] = l.to_k
-
-        # set up sentences
-        old_texts = [source_prompt]
-        new_texts = [destination_prompt]
-        # add augmentations
-        base = old_texts[0] if old_texts[0][0:1] != "A" else "a" + old_texts[0][1:]
-        for aug in self.with_augs:
-            old_texts.append(aug + base)
-        base = new_texts[0] if new_texts[0][0:1] != "A" else "a" + new_texts[0][1:]
-        for aug in self.with_augs:
-            new_texts.append(aug + base)
-
-        # prepare input k* and v*
-        old_embs, new_embs = [], []
-        for old_text, new_text in zip(old_texts, new_texts):
-            text_input = self.tokenizer(
-                [old_text, new_text],
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
-            old_emb, new_emb = text_embeddings
-            old_embs.append(old_emb)
-            new_embs.append(new_emb)
-
-        # identify corresponding destinations for each token in old_emb
-        idxs_replaces = []
-        for old_text, new_text in zip(old_texts, new_texts):
-            tokens_a = self.tokenizer(old_text).input_ids
-            tokens_b = self.tokenizer(new_text).input_ids
-            tokens_a = [self.tokenizer.encode("a ")[1] if self.tokenizer.decode(t) == "an" else t for t in tokens_a]
-            tokens_b = [self.tokenizer.encode("a ")[1] if self.tokenizer.decode(t) == "an" else t for t in tokens_b]
-            num_orig_tokens = len(tokens_a)
-            idxs_replace = []
-            j = 0
-            for i in range(num_orig_tokens):
-                curr_token = tokens_a[i]
-                while tokens_b[j] != curr_token:
-                    j += 1
-                idxs_replace.append(j)
-                j += 1
-            while j < 77:
-                idxs_replace.append(j)
-                j += 1
-            while len(idxs_replace) < 77:
-                idxs_replace.append(76)
-            idxs_replaces.append(idxs_replace)
-
-        # prepare batch: for each pair of setences, old context and new values
-        contexts, valuess = [], []
-        for old_emb, new_emb, idxs_replace in zip(old_embs, new_embs, idxs_replaces):
-            context = old_emb.detach()
-            values = []
-            with torch.no_grad():
-                for layer in self.projection_matrices:
-                    values.append(layer(new_emb[idxs_replace]).detach())
-            contexts.append(context)
-            valuess.append(values)
-
-        # edit the model
-        for layer_num in range(len(self.projection_matrices)):
-            # mat1 = \lambda W + \sum{v k^T}
-            mat1 = lamb * self.projection_matrices[layer_num].weight
-
-            # mat2 = \lambda I + \sum{k k^T}
-            mat2 = lamb * torch.eye(
-                self.projection_matrices[layer_num].weight.shape[1],
-                device=self.projection_matrices[layer_num].weight.device,
-            )
-
-            # aggregate sums for mat1, mat2
-            for context, values in zip(contexts, valuess):
-                context_vector = context.reshape(context.shape[0], context.shape[1], 1)
-                context_vector_T = context.reshape(context.shape[0], 1, context.shape[1])
-                value_vector = values[layer_num].reshape(values[layer_num].shape[0], values[layer_num].shape[1], 1)
-                for_mat1 = (value_vector @ context_vector_T).sum(dim=0)
-                for_mat2 = (context_vector @ context_vector_T).sum(dim=0)
-                mat1 += for_mat1
-                mat2 += for_mat2
-
-            # update projection matrix
-            self.projection_matrices[layer_num].weight = torch.nn.Parameter(mat1 @ torch.inverse(mat2))
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
-            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                A higher guidance scale value encourages the model to generate images closely linked to the text
-                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
-                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
-                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor is generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
-                provided, text embeddings are generated from the `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
-                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
-                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
-
-        Examples:
-
-        ```py
-        >>> import torch
-        >>> from diffusers import StableDiffusionModelEditingPipeline
-
-        >>> model_ckpt = "CompVis/stable-diffusion-v1-4"
-        >>> pipe = StableDiffusionModelEditingPipeline.from_pretrained(model_ckpt)
-
-        >>> pipe = pipe.to("cuda")
-
-        >>> source_prompt = "A pack of roses"
-        >>> destination_prompt = "A pack of blue roses"
-        >>> pipe.edit_model(source_prompt, destination_prompt)
-
-        >>> prompt = "A field of roses"
-        >>> image = pipe(prompt).images[0]
-        ```
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
-                otherwise a `tuple` is returned where the first element is a list with the generated images and the
-                second element is a list of `bool`s indicating whether the corresponding generated image contains
-                "not-safe-for-work" (nsfw) content.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
-        )
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
-        )
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-        )
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 7. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # predict the noise residual
-                noise_pred = self.unet(
-                    latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                ).sample
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        if not output_type == "latent":
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-        else:
-            image = latents
-            has_nsfw_concept = None
-
-        if has_nsfw_concept is None:
-            do_denormalize = [True] * image.shape[0]
-        else:
-            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
-
-        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/utils/dummy_note_seq_objects.py

@@ -1,17 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..utils import DummyObject, requires_backends
-
-
-class MidiProcessor(metaclass=DummyObject):
-    _backends = ["note_seq"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["note_seq"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["note_seq"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["note_seq"])

spaces/Andy1621/uniformer_image_detection/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py

@@ -1,3 +0,0 @@
-_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
-# fp16 settings
-fp16 = dict(loss_scale=512.)

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_480x480_80k_pascal_context.py

@@ -1,2 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_480x480_80k_pascal_context.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/AndySAnker/DeepStruc/tools/data_loader.py

@@ -1,236 +0,0 @@
-import os, torch, h5py, random, sys, shutil, yaml
-from pytorch_lightning.callbacks import ModelCheckpoint
-import numpy as np
-from torch_geometric.data import Data, DataLoader
-from tqdm import tqdm
-import pytorch_lightning as pl
-
-
-class graph_loader(pl.LightningDataModule):
-    def __init__(self, data_dir, cluster_size=None, num_files=None, batchsize=1, shuffle=True, num_workers=0):
-        super(graph_loader, self).__init__()
-        """
-
-        Parameters
-        ----------
-        data_dir
-        num_files
-        batchsize
-        shuffle
-
-        Returns
-        -------
-
-        """
-        self.batchsize = int(batchsize)
-        self.num_workers = num_workers
-        self.files_sorted = sorted(os.listdir(data_dir))
-        self.cluster_size = cluster_size
-        files = self.files_sorted.copy()
-        # files = [file for file in files if 'FCC' in file]
-
-        if shuffle == True:
-            random.shuffle(files)
-        if files != None:
-            files = files[:num_files]
-        else:
-            pass
-
-        nTrain = int(0.6 * len(files))
-        nValid = int((len(files) - nTrain) / 2)
-        nTest = len(files) - (nTrain + nValid)
-
-        print('\nBatch size: {}'.format(batchsize))
-        print('Total number of graphs {}.'.format(len(files)))
-        print('\tTraining files:', nTrain)
-        print('\tValidation files:', nValid)
-        print('\tTest files:', nTest, '\n')
-
-        self.trSamples, self.vlSamples, self.teSamples = list(), list(), list()
-        print('Loading graphs:')
-
-        for idx in range(len(files)):
-            h5f = h5py.File(data_dir + '/' + files[idx], 'r')
-            b = h5f['Node Feature Matrix'][:]
-            h5f.close()
-
-            if self.cluster_size == None:
-                self.cluster_size = len(b)
-            elif len(b) > self.cluster_size:
-                self.cluster_size = len(b)
-
-        largest_x_dist, largest_y_dist, largest_z_dist, edge_f_max = 0, 0, 0, 0
-        for idx in range(nTrain):
-            h5f = h5py.File(data_dir + '/' + files[idx], 'r')
-            a = h5f['Edge Feature Matrix'][:]
-            b = h5f['Node Feature Matrix'][:]
-
-            h5f.close()
-
-            diff_ph = abs(np.amin(b, axis=0)) + np.amax(b, axis=0)
-            if largest_x_dist < diff_ph[0]:
-                largest_x_dist = diff_ph[0]
-            if largest_y_dist < diff_ph[1]:
-                largest_y_dist = diff_ph[1]
-            if largest_z_dist < diff_ph[2]:
-                largest_z_dist = diff_ph[2]
-            if np.amax(a) > edge_f_max:
-                edge_f_max = np.amax(a)
-
-        self.largest_x_dist = largest_x_dist
-        self.largest_y_dist = largest_y_dist
-        self.largest_z_dist = largest_z_dist
-
-        for idx in tqdm(range(len(files))):
-            h5f = h5py.File(data_dir + '/' + files[idx], 'r')
-            a = h5f['Edge Feature Matrix'][:]  # todo: norm this
-            b = h5f['Node Feature Matrix'][:]
-            c = h5f['Edge Directions'][:]
-            d = h5f['PDF label'][:]
-            h5f.close()
-
-            a /= edge_f_max
-            min_vals = np.amin(b, axis=0)
-            if min_vals[0] < 0.0:  # Make all coordinates positive
-                b[:, 0] -= min_vals[0]
-            if min_vals[1] < 0.0:  # Make all coordinates positive
-                b[:, 1] -= min_vals[1]
-            if min_vals[2] < 0.0:  # Make all coordinates positive
-                b[:, 2] -= min_vals[2]
-
-            b[:, 0] /= largest_x_dist
-            b[:, 1] /= largest_y_dist
-            b[:, 2] /= largest_z_dist
-
-            cord_ph = np.zeros((self.cluster_size, np.shape(b)[1])) - 1
-            cord_ph[:np.shape(b)[0]] = b
-
-            d /= np.amax(d)  # Standardize PDF
-
-            pdf = torch.tensor([d], dtype=torch.float)
-            x = torch.tensor(b, dtype=torch.float)
-            y = torch.tensor([cord_ph], dtype=torch.float)
-            edge_index = torch.tensor(c, dtype=torch.long)
-            edge_attr = torch.tensor(a, dtype=torch.float)
-            name_idx = torch.tensor(self.files_sorted.index(files[idx]), dtype=torch.int16)
-
-            if idx < nTrain:
-                self.trSamples.append(
-                    tuple((Data(x=x, y=y, edge_index=edge_index, edge_attr=edge_attr), pdf.T, name_idx)))
-            elif idx < nTrain + nValid:
-                self.vlSamples.append(
-                    tuple((Data(x=x, y=y, edge_index=edge_index, edge_attr=edge_attr), pdf.T, name_idx)))
-            else:
-                self.teSamples.append(
-                    tuple((Data(x=x, y=y, edge_index=edge_index, edge_attr=edge_attr), pdf.T, name_idx)))
-
-    def train_dataloader(self):
-        return DataLoader(self.trSamples, batch_size=self.batchsize, shuffle=True, num_workers=self.num_workers)
-
-    def val_dataloader(self):
-        return DataLoader(self.vlSamples, batch_size=self.batchsize, num_workers=self.num_workers)
-
-    def test_dataloader(self):
-        return DataLoader(self.teSamples, batch_size=self.batchsize, num_workers=self.num_workers)
-
-
-def save_xyz_file(save_dir, cords, file_name, xyz_scale=[1,1,1]):
-
-    cords = [xyz for xyz in cords if np.mean(xyz) >= -0.2]
-    cords = np.array(cords)
-    cords[:,0] -= cords[:,0].mean()
-    cords[:,1] -= cords[:,1].mean()
-    cords[:,2] -= cords[:,2].mean()
-    these_cords = []
-    for count, xyz in enumerate(cords):
-        if count == 0:
-            these_cords.append(['{:d}'.format(len(cords))])
-            these_cords.append([''])
-
-        these_cords.append(['W   {:.4f}   {:.4f}   {:.4f}'.format(xyz[0]*xyz_scale[0], xyz[1]*xyz_scale[1], xyz[2]*xyz_scale[2])])
-
-    np.savetxt(save_dir + '/{}.xyz'.format(file_name), these_cords, fmt='%s')
-
-    return these_cords
-
-
-def folder_manager(input_dict, model_arch):
-    this_trainer = None
-    epoch = input_dict['epochs']
-    if not os.path.isdir(input_dict['save_dir']):
-        os.mkdir(input_dict['save_dir'])
-        os.mkdir(input_dict['save_dir'] + '/models')
-        shutil.copy2('train.py', input_dict['save_dir'] + '/train.py')
-        shutil.copy2('./tools/data_loader.py', input_dict['save_dir'] + '/data_loader.py')
-        shutil.copy2('./tools/module.py', input_dict['save_dir'] + '/module.py')
-        os.mkdir(input_dict['save_dir'] + '/prior')
-        os.mkdir(input_dict['save_dir'] + '/posterior')
-    else:
-        shutil.copy2('train.py', input_dict['save_dir'] + '/train.py')
-        shutil.copy2('./tools/data_loader.py', input_dict['save_dir'] + '/data_loader.py')
-        shutil.copy2('./tools/module.py', input_dict['save_dir'] + '/module.py')
-
-    if input_dict['load_trainer']:
-        best_model = sorted(os.listdir(input_dict['save_dir'] + '/models'))
-        print(f'\nUsing {best_model[0]} as starting model!\n')
-        this_trainer = input_dict['save_dir'] + '/models/' + best_model[0]
-        #input_dict = yaml.load(f'{input_dict["save_dir"]}/input_dict.yaml', Loader=yaml.FullLoader)
-
-        try:
-            with open(f'{input_dict["save_dir"]}/input_dict.yaml') as file:
-                input_dict = yaml.full_load(file)
-            input_dict['load_trainer'] = True
-            input_dict['epochs'] = epoch
-            with open(f'{input_dict["save_dir"]}/model_arch.yaml') as file:
-                model_arch = yaml.full_load(file)
-        except FileNotFoundError:  # todo: transition - need to be deleted at some point
-            with open(f'{input_dict["save_dir"]}/input_dict.yaml', 'w') as outfile:
-                yaml.dump(input_dict, outfile, allow_unicode=True, default_flow_style=False)
-
-            with open(f'{input_dict["save_dir"]}/model_arch.yaml', 'w') as outfile:
-                yaml.dump(model_arch, outfile, allow_unicode=True, default_flow_style=False)
-    else:
-        with open(f'{input_dict["save_dir"]}/input_dict.yaml', 'w') as outfile:
-            yaml.dump(input_dict, outfile, allow_unicode=True, default_flow_style=False)
-
-        with open(f'{input_dict["save_dir"]}/model_arch.yaml', 'w') as outfile:
-            yaml.dump(model_arch, outfile, allow_unicode=True, default_flow_style=False)
-    return this_trainer, input_dict, model_arch
-
-
-def get_callbacks(save_dir):
-    checkpoint_callback_tot = ModelCheckpoint(
-        monitor='vld_tot',
-        dirpath=save_dir + '/models',
-        filename='model-{vld_tot:.5f}-{beta:.3f}-{vld_rec_pdf:.5f}-{epoch:010d}',
-        save_top_k=5,
-        mode='min',
-        save_last=True,
-    )
-
-    checkpoint_callback_rec = ModelCheckpoint(
-        monitor='vld_rec',
-        dirpath=save_dir + '/models',
-        filename='model-{vld_rec:.5f}-{beta:.3f}-{vld_rec_pdf:.5f}-{vld_tot:.5f}-{epoch:010d}',
-        save_top_k=5,
-        mode='min',
-    )
-
-    checkpoint_callback_kld = ModelCheckpoint(
-        monitor='vld_kld',
-        dirpath=save_dir + '/models',
-        filename='model-{vld_kld:.5f}-{beta:.3f}-{vld_rec_pdf:.5f}-{vld_tot:.5f}-{epoch:010d}',
-        save_top_k=5,
-        mode='min',
-    )
-
-    checkpoint_callback_vld_rec_pdf = ModelCheckpoint(
-        monitor='vld_rec_pdf',
-        dirpath=save_dir + '/models',
-        filename='model-{vld_rec_pdf:.5f}-{beta:.3f}-{vld_tot:.5f}-{epoch:010d}',
-        save_top_k=5,
-        mode='min',
-    )
-
-    return [checkpoint_callback_tot, checkpoint_callback_rec, checkpoint_callback_kld,
-            checkpoint_callback_vld_rec_pdf]

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/multimodal/script.py

@@ -1,112 +0,0 @@
-import base64
-import re
-import time
-from functools import partial
-from io import BytesIO
-
-import gradio as gr
-import torch
-
-from extensions.multimodal.multimodal_embedder import MultimodalEmbedder
-from modules import shared
-from modules.logging_colors import logger
-
-params = {
-    "add_all_images_to_prompt": False,
-    # device to run vision encoder on
-    "vision_device": None,
-    # bits to load vision encoder in, either 16 or 32
-    "vision_bits": 32,
-    # device to run multimodal projector on
-    "projector_device": None,
-    # multimodal projector bits, either 32 or 16
-    "projector_bits": 32
-}
-
-
-# If 'state' is True, will hijack the next chat generation
-input_hijack = {
-    'state': False,
-    'value': ["", ""]
-}
-
-
-# initialized in ui, so that params are loaded from settings
-multimodal_embedder: MultimodalEmbedder = None
-
-
-def chat_input_modifier(text, visible_text, state):
-    global input_hijack
-    if input_hijack['state']:
-        input_hijack['state'] = False
-        return input_hijack['value'](text, visible_text)
-    else:
-        return text, visible_text
-
-
-def add_chat_picture(picture, text, visible_text):
-    # resize the image, so that shortest edge is at least 224 (size for CLIP), and at most 300 (to keep history manageable)
-    max_hw, min_hw = max(picture.size), min(picture.size)
-    aspect_ratio = max_hw / min_hw
-    shortest_edge = int(max(300 / aspect_ratio, 224))
-    longest_edge = int(shortest_edge * aspect_ratio)
-    w = shortest_edge if picture.width < picture.height else longest_edge
-    h = shortest_edge if picture.width >= picture.height else longest_edge
-    picture = picture.resize((w, h))
-
-    buffer = BytesIO()
-    picture.save(buffer, format="JPEG")
-    img_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
-    image = f'<img src="data:image/jpeg;base64,{img_str}">'
-
-    if '<image>' in text:
-        text = text.replace('<image>', image)
-    else:
-        text = text + '\n' + image
-
-    if visible_text == '' or visible_text is None:
-        visible_text = text
-    elif '<image>' in visible_text:
-        visible_text = visible_text.replace('<image>', image)
-    else:
-        visible_text = visible_text + '\n' + image
-
-    return text, visible_text
-
-
-def custom_tokenized_length(prompt):
-    return multimodal_embedder.len_in_tokens(prompt)
-
-
-def tokenizer_modifier(state, prompt, input_ids, input_embeds):
-    global params
-    start_ts = time.time()
-    image_match = re.search(r'<img src="data:image/jpeg;base64,[A-Za-z0-9+/=]+">', prompt)
-
-    if image_match is None:
-        return prompt, input_ids, input_embeds
-
-    prompt, input_ids, input_embeds, total_embedded = multimodal_embedder.forward(prompt, state, params)
-    logger.info(f'Embedded {total_embedded} image(s) in {time.time()-start_ts:.2f}s')
-    return (prompt,
-            input_ids.unsqueeze(0).to(shared.model.device, dtype=torch.int64),
-            input_embeds.unsqueeze(0).to(shared.model.device, dtype=shared.model.dtype))
-
-
-def ui():
-    global multimodal_embedder
-    multimodal_embedder = MultimodalEmbedder(params)
-    with gr.Column():
-        picture_select = gr.Image(label='Send a picture', type='pil')
-        # The models don't seem to deal well with multiple images
-        single_image_checkbox = gr.Checkbox(False, label='Embed all images, not only the last one')
-    # Prepare the input hijack
-    picture_select.upload(
-        lambda picture: input_hijack.update({"state": True, "value": partial(add_chat_picture, picture)}),
-        [picture_select],
-        None
-    )
-    picture_select.clear(lambda: input_hijack.update({"state": False, "value": ["", ""]}), None, None)
-    single_image_checkbox.change(lambda x: params.update({"add_all_images_to_prompt": x}), single_image_checkbox, None)
-    shared.gradio['Generate'].click(lambda: None, None, picture_select)
-    shared.gradio['textbox'].submit(lambda: None, None, picture_select)

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/__init__.py

@@ -1,27 +0,0 @@
-# Uniformer
-# From https://github.com/Sense-X/UniFormer
-# # Apache-2.0 license
-
-import os
-
-from annotator.uniformer.mmseg.apis import init_segmentor, inference_segmentor, show_result_pyplot
-from annotator.uniformer.mmseg.core.evaluation import get_palette
-from annotator.util import annotator_ckpts_path
-
-
-checkpoint_file = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/upernet_global_small.pth"
-
-
-class UniformerDetector:
-    def __init__(self):
-        modelpath = os.path.join(annotator_ckpts_path, "upernet_global_small.pth")
-        if not os.path.exists(modelpath):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(checkpoint_file, model_dir=annotator_ckpts_path)
-        config_file = os.path.join(os.path.dirname(annotator_ckpts_path), "uniformer", "exp", "upernet_global_small", "config.py")
-        self.model = init_segmentor(config_file, modelpath).cuda()
-
-    def __call__(self, img):
-        result = inference_segmentor(self.model, img)
-        res_img = show_result_pyplot(self.model, img, result, get_palette('ade'), opacity=1)
-        return res_img

spaces/ArdaSaygan/PollGeneratorApp/app.py

@@ -1,60 +0,0 @@
-from email import message
-import gradio as gr
-import os
-import openai
-from create_poll import create_poll
-from utils import GPTCompletion
-from dotenv import load_dotenv
-
-gr.close_all()
-
-load_dotenv()
-
-openai.api_key = os.environ["OPENAI_API_KEY"]
-
-
-
-def chatWithGPT(chatHistory):
-    completion = GPTCompletion(system="You are an AI chatting with a human.", max_tokens=2048, temperature=1.5)
-    gptResponse = completion.chatComplete(chatHistory)
-    chatHistory[-1][1] = gptResponse
-    return chatHistory
-
-with gr.Blocks() as demo:
-    chatHistory = gr.State(value = [])
-
-    def generateResponse(message, chatHistory):
-        completion = GPTCompletion(system="You are an AI chatting with a human.", max_tokens=2048, temperature=1.5)
-        gptResponse = completion.chatComplete(chatHistory,message)
-        chatHistory.append((message, gptResponse))
-        return chatHistory
-
-    def pollinize(chatHistory):
-
-        chatList = []
-        for log in chatHistory:
-            chatList.append("User: " + log[0])
-            chatList.append("AI: " + log[1])
-        chatString = "\n".join(chatList)
-
-        return create_poll(chatString, openai.api_key)
-
-    def uploadApi(apikey):
-        openai.api_key = apikey
-
-    gr.Markdown("This little app is a demonstration of how LLMs can be used to create Polls from a chat. To give it a try, discuss a topic with ChatGPT first and then push Generate Poll button. Poll question will be generated on the context of the chat.")
-
-    chatbot = gr.Chatbot().style(height=460)
-    input = gr.Textbox(label="Messeage")
-    nextBtn = gr.Button("Send message")
-    nextBtn.click(generateResponse, [input, chatHistory], chatbot, scroll_to_output=True, show_progress=True)
-
-    debatePoll = gr.Textbox(label="Poll")
-    pollinizeButton = gr.Button("Create a poll")
-    pollinizeButton.click(pollinize,chatHistory, debatePoll, scroll_to_output=True, show_progress=True)
-
-    apikey = gr.Textbox(label="API Key")
-    apiUpload = gr.Button("Upload custom api key")
-    apiUpload.click(uploadApi, apikey, None, scroll_to_output=True, show_progress=True)
-    
-demo.launch()

spaces/Arnx/MusicGenXvAKN/tests/data/test_audio_dataset.py

@@ -1,352 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from functools import partial
-from itertools import product
-import json
-import math
-import os
-import random
-import typing as tp
-
-import pytest
-import torch
-from torch.utils.data import DataLoader
-
-from audiocraft.data.audio_dataset import (
-    AudioDataset,
-    AudioMeta,
-    _get_audio_meta,
-    load_audio_meta,
-    save_audio_meta
-)
-from audiocraft.data.zip import PathInZip
-
-from ..common_utils import TempDirMixin, get_white_noise, save_wav
-
-
-class TestAudioMeta(TempDirMixin):
-
-    def test_get_audio_meta(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(duration * sample_rate)
-            wav = get_white_noise(ch, n_frames)
-            path = self.get_temp_path('sample.wav')
-            save_wav(path, wav, sample_rate)
-            m = _get_audio_meta(path, minimal=True)
-            assert m.path == path, 'path does not match'
-            assert m.sample_rate == sample_rate, 'sample rate does not match'
-            assert m.duration == duration, 'duration does not match'
-            assert m.amplitude is None
-            assert m.info_path is None
-
-    def test_save_audio_meta(self):
-        audio_meta = [
-            AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
-            AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
-            ]
-        empty_audio_meta = []
-        for idx, meta in enumerate([audio_meta, empty_audio_meta]):
-            path = self.get_temp_path(f'data_{idx}_save.jsonl')
-            save_audio_meta(path, meta)
-            with open(path, 'r') as f:
-                lines = f.readlines()
-                read_meta = [AudioMeta.from_dict(json.loads(line)) for line in lines]
-                assert len(read_meta) == len(meta)
-                for m, read_m in zip(meta, read_meta):
-                    assert m == read_m
-
-    def test_load_audio_meta(self):
-        try:
-            import dora
-        except ImportError:
-            dora = None  # type: ignore
-
-        audio_meta = [
-            AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
-            AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
-            ]
-        empty_meta = []
-        for idx, meta in enumerate([audio_meta, empty_meta]):
-            path = self.get_temp_path(f'data_{idx}_load.jsonl')
-            with open(path, 'w') as f:
-                for m in meta:
-                    json_str = json.dumps(m.to_dict()) + '\n'
-                    f.write(json_str)
-            read_meta = load_audio_meta(path)
-            assert len(read_meta) == len(meta)
-            for m, read_m in zip(meta, read_meta):
-                if dora:
-                    m.path = dora.git_save.to_absolute_path(m.path)
-                assert m == read_m, f'original={m}, read={read_m}'
-
-
-class TestAudioDataset(TempDirMixin):
-
-    def _create_audio_files(self,
-                            root_name: str,
-                            num_examples: int,
-                            durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
-                            sample_rate: int = 16_000,
-                            channels: int = 1):
-        root_dir = self.get_temp_dir(root_name)
-        for i in range(num_examples):
-            if isinstance(durations, float):
-                duration = durations
-            elif isinstance(durations, tuple) and len(durations) == 1:
-                duration = durations[0]
-            elif isinstance(durations, tuple) and len(durations) == 2:
-                duration = random.uniform(durations[0], durations[1])
-            else:
-                assert False
-            n_frames = int(duration * sample_rate)
-            wav = get_white_noise(channels, n_frames)
-            path = os.path.join(root_dir, f'example_{i}.wav')
-            save_wav(path, wav, sample_rate)
-        return root_dir
-
-    def _create_audio_dataset(self,
-                              root_name: str,
-                              total_num_examples: int,
-                              durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
-                              sample_rate: int = 16_000,
-                              channels: int = 1,
-                              segment_duration: tp.Optional[float] = None,
-                              num_examples: int = 10,
-                              shuffle: bool = True,
-                              return_info: bool = False):
-        root_dir = self._create_audio_files(root_name, total_num_examples, durations, sample_rate, channels)
-        dataset = AudioDataset.from_path(root_dir,
-                                         minimal_meta=True,
-                                         segment_duration=segment_duration,
-                                         num_samples=num_examples,
-                                         sample_rate=sample_rate,
-                                         channels=channels,
-                                         shuffle=shuffle,
-                                         return_info=return_info)
-        return dataset
-
-    def test_dataset_full(self):
-        total_examples = 10
-        min_duration, max_duration = 1., 4.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration),
-            sample_rate=sample_rate, channels=channels, segment_duration=None)
-        assert len(dataset) == total_examples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] <= int(max_duration * sample_rate)
-            assert sample.shape[1] >= int(min_duration * sample_rate)
-
-    def test_dataset_segment(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples)
-        assert len(dataset) == num_samples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == int(segment_duration * sample_rate)
-
-    def test_dataset_equal_audio_and_segment_durations(self):
-        total_examples = 1
-        num_samples = 2
-        audio_duration = 1.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples)
-        assert len(dataset) == num_samples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == int(segment_duration * sample_rate)
-        # the random seek_time adds variability on audio read
-        sample_1 = dataset[0]
-        sample_2 = dataset[1]
-        assert not torch.allclose(sample_1, sample_2)
-
-    def test_dataset_samples(self):
-        total_examples = 1
-        num_samples = 2
-        audio_duration = 1.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-
-        create_dataset = partial(
-            self._create_audio_dataset,
-            'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples,
-        )
-
-        dataset = create_dataset(shuffle=True)
-        # when shuffle = True, we have different inputs for the same index across epoch
-        sample_1 = dataset[0]
-        sample_2 = dataset[0]
-        assert not torch.allclose(sample_1, sample_2)
-
-        dataset_noshuffle = create_dataset(shuffle=False)
-        # when shuffle = False, we have same inputs for the same index across epoch
-        sample_1 = dataset_noshuffle[0]
-        sample_2 = dataset_noshuffle[0]
-        assert torch.allclose(sample_1, sample_2)
-
-    def test_dataset_return_info(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
-        assert len(dataset) == num_samples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample, segment_info = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == int(segment_duration * sample_rate)
-            assert segment_info.sample_rate == sample_rate
-            assert segment_info.total_frames == int(segment_duration * sample_rate)
-            assert segment_info.n_frames <= int(segment_duration * sample_rate)
-            assert segment_info.seek_time >= 0
-
-    def test_dataset_return_info_no_segment_duration(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = None
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
-        assert len(dataset) == total_examples
-        assert dataset.sample_rate == sample_rate
-        assert dataset.channels == channels
-        for idx in range(len(dataset)):
-            sample, segment_info = dataset[idx]
-            assert sample.shape[0] == channels
-            assert sample.shape[1] == segment_info.total_frames
-            assert segment_info.sample_rate == sample_rate
-            assert segment_info.n_frames <= segment_info.total_frames
-
-    def test_dataset_collate_fn(self):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=False)
-        batch_size = 4
-        dataloader = DataLoader(
-            dataset,
-            batch_size=batch_size,
-            num_workers=0
-        )
-        for idx, batch in enumerate(dataloader):
-            assert batch.shape[0] == batch_size
-
-    @pytest.mark.parametrize("segment_duration", [1.0, None])
-    def test_dataset_with_meta_collate_fn(self, segment_duration):
-        total_examples = 10
-        num_samples = 20
-        min_duration, max_duration = 1., 4.
-        segment_duration = 1.
-        sample_rate = 16_000
-        channels = 1
-        dataset = self._create_audio_dataset(
-            'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
-            channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
-        batch_size = 4
-        dataloader = DataLoader(
-            dataset,
-            batch_size=batch_size,
-            collate_fn=dataset.collater,
-            num_workers=0
-        )
-        for idx, batch in enumerate(dataloader):
-            wav, infos = batch
-            assert wav.shape[0] == batch_size
-            assert len(infos) == batch_size
-
-    @pytest.mark.parametrize("segment_duration,sample_on_weight,sample_on_duration,a_hist,b_hist,c_hist", [
-        [1, True, True, 0.5, 0.5, 0.0],
-        [1, False, True, 0.25, 0.5, 0.25],
-        [1, True, False, 0.666, 0.333, 0.0],
-        [1, False, False, 0.333, 0.333, 0.333],
-        [None, False, False, 0.333, 0.333, 0.333]])
-    def test_sample_with_weight(self, segment_duration, sample_on_weight, sample_on_duration, a_hist, b_hist, c_hist):
-        random.seed(1234)
-        rng = torch.Generator()
-        rng.manual_seed(1234)
-
-        def _get_histogram(dataset, repetitions=20_000):
-            counts = {file_meta.path: 0. for file_meta in meta}
-            for _ in range(repetitions):
-                file_meta = dataset.sample_file(rng)
-                counts[file_meta.path] += 1
-            return {name: count / repetitions for name, count in counts.items()}
-
-        meta = [
-           AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
-           AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
-           AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
-        ]
-        dataset = AudioDataset(
-            meta, segment_duration=segment_duration, sample_on_weight=sample_on_weight,
-            sample_on_duration=sample_on_duration)
-        hist = _get_histogram(dataset)
-        assert math.isclose(hist['a'], a_hist, abs_tol=0.01)
-        assert math.isclose(hist['b'], b_hist, abs_tol=0.01)
-        assert math.isclose(hist['c'], c_hist, abs_tol=0.01)
-
-    def test_meta_duration_filter_all(self):
-        meta = [
-           AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
-           AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
-           AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
-        ]
-        try:
-            AudioDataset(meta, segment_duration=11, min_segment_ratio=1)
-            assert False
-        except AssertionError:
-            assert True
-
-    def test_meta_duration_filter_long(self):
-        meta = [
-           AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
-           AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
-           AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
-        ]
-        dataset = AudioDataset(meta, segment_duration=None, min_segment_ratio=1, max_audio_duration=7)
-        assert len(dataset) == 2

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pyparsing/util.py

@@ -1,235 +0,0 @@
-# util.py
-import warnings
-import types
-import collections
-import itertools
-from functools import lru_cache
-from typing import List, Union, Iterable
-
-_bslash = chr(92)
-
-
-class __config_flags:
-    """Internal class for defining compatibility and debugging flags"""
-
-    _all_names: List[str] = []
-    _fixed_names: List[str] = []
-    _type_desc = "configuration"
-
-    @classmethod
-    def _set(cls, dname, value):
-        if dname in cls._fixed_names:
-            warnings.warn(
-                "{}.{} {} is {} and cannot be overridden".format(
-                    cls.__name__,
-                    dname,
-                    cls._type_desc,
-                    str(getattr(cls, dname)).upper(),
-                )
-            )
-            return
-        if dname in cls._all_names:
-            setattr(cls, dname, value)
-        else:
-            raise ValueError("no such {} {!r}".format(cls._type_desc, dname))
-
-    enable = classmethod(lambda cls, name: cls._set(name, True))
-    disable = classmethod(lambda cls, name: cls._set(name, False))
-
-
-@lru_cache(maxsize=128)
-def col(loc: int, strg: str) -> int:
-    """
-    Returns current column within a string, counting newlines as line separators.
-    The first column is number 1.
-
-    Note: the default parsing behavior is to expand tabs in the input string
-    before starting the parsing process.  See
-    :class:`ParserElement.parseString` for more
-    information on parsing strings containing ``<TAB>`` s, and suggested
-    methods to maintain a consistent view of the parsed string, the parse
-    location, and line and column positions within the parsed string.
-    """
-    s = strg
-    return 1 if 0 < loc < len(s) and s[loc - 1] == "\n" else loc - s.rfind("\n", 0, loc)
-
-
-@lru_cache(maxsize=128)
-def lineno(loc: int, strg: str) -> int:
-    """Returns current line number within a string, counting newlines as line separators.
-    The first line is number 1.
-
-    Note - the default parsing behavior is to expand tabs in the input string
-    before starting the parsing process.  See :class:`ParserElement.parseString`
-    for more information on parsing strings containing ``<TAB>`` s, and
-    suggested methods to maintain a consistent view of the parsed string, the
-    parse location, and line and column positions within the parsed string.
-    """
-    return strg.count("\n", 0, loc) + 1
-
-
-@lru_cache(maxsize=128)
-def line(loc: int, strg: str) -> str:
-    """
-    Returns the line of text containing loc within a string, counting newlines as line separators.
-    """
-    last_cr = strg.rfind("\n", 0, loc)
-    next_cr = strg.find("\n", loc)
-    return strg[last_cr + 1 : next_cr] if next_cr >= 0 else strg[last_cr + 1 :]
-
-
-class _UnboundedCache:
-    def __init__(self):
-        cache = {}
-        cache_get = cache.get
-        self.not_in_cache = not_in_cache = object()
-
-        def get(_, key):
-            return cache_get(key, not_in_cache)
-
-        def set_(_, key, value):
-            cache[key] = value
-
-        def clear(_):
-            cache.clear()
-
-        self.size = None
-        self.get = types.MethodType(get, self)
-        self.set = types.MethodType(set_, self)
-        self.clear = types.MethodType(clear, self)
-
-
-class _FifoCache:
-    def __init__(self, size):
-        self.not_in_cache = not_in_cache = object()
-        cache = collections.OrderedDict()
-        cache_get = cache.get
-
-        def get(_, key):
-            return cache_get(key, not_in_cache)
-
-        def set_(_, key, value):
-            cache[key] = value
-            while len(cache) > size:
-                cache.popitem(last=False)
-
-        def clear(_):
-            cache.clear()
-
-        self.size = size
-        self.get = types.MethodType(get, self)
-        self.set = types.MethodType(set_, self)
-        self.clear = types.MethodType(clear, self)
-
-
-class LRUMemo:
-    """
-    A memoizing mapping that retains `capacity` deleted items
-
-    The memo tracks retained items by their access order; once `capacity` items
-    are retained, the least recently used item is discarded.
-    """
-
-    def __init__(self, capacity):
-        self._capacity = capacity
-        self._active = {}
-        self._memory = collections.OrderedDict()
-
-    def __getitem__(self, key):
-        try:
-            return self._active[key]
-        except KeyError:
-            self._memory.move_to_end(key)
-            return self._memory[key]
-
-    def __setitem__(self, key, value):
-        self._memory.pop(key, None)
-        self._active[key] = value
-
-    def __delitem__(self, key):
-        try:
-            value = self._active.pop(key)
-        except KeyError:
-            pass
-        else:
-            while len(self._memory) >= self._capacity:
-                self._memory.popitem(last=False)
-            self._memory[key] = value
-
-    def clear(self):
-        self._active.clear()
-        self._memory.clear()
-
-
-class UnboundedMemo(dict):
-    """
-    A memoizing mapping that retains all deleted items
-    """
-
-    def __delitem__(self, key):
-        pass
-
-
-def _escape_regex_range_chars(s: str) -> str:
-    # escape these chars: ^-[]
-    for c in r"\^-[]":
-        s = s.replace(c, _bslash + c)
-    s = s.replace("\n", r"\n")
-    s = s.replace("\t", r"\t")
-    return str(s)
-
-
-def _collapse_string_to_ranges(
-    s: Union[str, Iterable[str]], re_escape: bool = True
-) -> str:
-    def is_consecutive(c):
-        c_int = ord(c)
-        is_consecutive.prev, prev = c_int, is_consecutive.prev
-        if c_int - prev > 1:
-            is_consecutive.value = next(is_consecutive.counter)
-        return is_consecutive.value
-
-    is_consecutive.prev = 0
-    is_consecutive.counter = itertools.count()
-    is_consecutive.value = -1
-
-    def escape_re_range_char(c):
-        return "\\" + c if c in r"\^-][" else c
-
-    def no_escape_re_range_char(c):
-        return c
-
-    if not re_escape:
-        escape_re_range_char = no_escape_re_range_char
-
-    ret = []
-    s = "".join(sorted(set(s)))
-    if len(s) > 3:
-        for _, chars in itertools.groupby(s, key=is_consecutive):
-            first = last = next(chars)
-            last = collections.deque(
-                itertools.chain(iter([last]), chars), maxlen=1
-            ).pop()
-            if first == last:
-                ret.append(escape_re_range_char(first))
-            else:
-                sep = "" if ord(last) == ord(first) + 1 else "-"
-                ret.append(
-                    "{}{}{}".format(
-                        escape_re_range_char(first), sep, escape_re_range_char(last)
-                    )
-                )
-    else:
-        ret = [escape_re_range_char(c) for c in s]
-
-    return "".join(ret)
-
-
-def _flatten(ll: list) -> list:
-    ret = []
-    for i in ll:
-        if isinstance(i, list):
-            ret.extend(_flatten(i))
-        else:
-            ret.append(i)
-    return ret

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/resolvelib/__init__.py

@@ -1,26 +0,0 @@
-__all__ = [
-    "__version__",
-    "AbstractProvider",
-    "AbstractResolver",
-    "BaseReporter",
-    "InconsistentCandidate",
-    "Resolver",
-    "RequirementsConflicted",
-    "ResolutionError",
-    "ResolutionImpossible",
-    "ResolutionTooDeep",
-]
-
-__version__ = "1.0.1"
-
-
-from .providers import AbstractProvider, AbstractResolver
-from .reporters import BaseReporter
-from .resolvers import (
-    InconsistentCandidate,
-    RequirementsConflicted,
-    ResolutionError,
-    ResolutionImpossible,
-    ResolutionTooDeep,
-    Resolver,
-)

spaces/Audio-AGI/AudioSep/models/CLAP/open_clip/version.py

@@ -1 +0,0 @@
-__version__ = "0.2.1"

spaces/AzinZ/vitscn/modules.py

@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x

spaces/Benson/text-generation/Examples/Cmo Descargar Fifa 2022 Apk.md

@@ -1,45 +0,0 @@
-<br />
-<h1>Cómo descargar FIFA 2022 APK</h1>
-<p>Si usted es un fan de los juegos de fútbol, es posible que se pregunte cómo descargar FIFA 2022 APK en su dispositivo Android. FIFA 2022 es la última entrega de la popular serie FIFA, desarrollada por EA Sports. Es uno de los juegos más esperados del año, con nuevas características de juego, modos, gráficos y más. En este artículo, le mostraremos cómo descargar FIFA 2022 APK de forma segura y fácil, así como algunas de las mejores características y consejos para jugar el juego. </p>
-<h2>cómo descargar fifa 2022 apk</h2><br /><p><b><b>Download File</b> ---> <a href="https://bltlly.com/2v6KiU">https://bltlly.com/2v6KiU</a></b></p><br /><br />
- <h2>Características de FIFA 2022</h2>
-<p>FIFA 2022 no es solo un re-skin de su predecesor. Es un juego completamente nuevo que trae cada partido en cada modo aún más cerca de lo real. Estas son algunas de las características que hacen que FIFA 2022 se destaque de otros juegos de fútbol:</p>
-<ul>
-<li><b>Tecnología HyperMotion:</b> Esta es una nueva tecnología de juego que utiliza datos del mundo real y aprendizaje automático para crear animaciones, movimientos y reacciones realistas para cada jugador en el campo. También permite tácticas de equipo avanzadas y formaciones que se adaptan a la situación. </li>
-<li><b>Modo de Copa Mundial:</b> Este es el único juego móvil con licencia de la Copa Mundial de la FIFA 2022 donde se puede volver a jugar el torneo oficial con cualquiera de las 32 naciones calificadas. También puedes jugar en auténticos estadios, kits, insignias y pelotas de la Copa Mundial, con comentarios localizados. </li>
-<li><b>Iconos y Héroes:</b> Puedes construir tu equipo definitivo con más de 100 iconos y héroes de fútbol, incluyendo leyendas como Paolo Maldini, Ronaldinho, Kylian Mbappé, Christian Pulisic, Vinicius Jr y Son Heung-min. También puedes subir el nivel de tu equipo desde fanfavorito de un contendiente de la UEFA Champions League. </li>
-<li><b>Simulación de fútbol de siguiente nivel:</b> Puedes experimentar estadios de fútbol nuevos y mejorados con sonidos y efectos visuales realistas, hasta 60 fps en dispositivos compatibles. También puede disfrutar en tiempo real 11v11 juego, con auténtica acción de fútbol y la física. </li>
-
-</ul>
- <h2>Requisitos del sistema FIFA 2022</h2>
-<p>Antes de descargar FIFA 2022 APK, es necesario asegurarse de que su dispositivo Android cumple con los requisitos mínimos o recomendados del sistema para el juego. Aquí están los requisitos del sistema para FIFA 2022 APK:</p>
-<tabla>
-<tr><th>Requisitos mínimos</th><th>Requisitos recomendados</th></tr>
-<tr><td>OS: 64-bit Android 6.0 o superior<br>Procesador: Athlon X4 880K @4GHz o equivalente<br>Memoria: 8 GB<br>Tarjeta gráfica: Radeon HD 7850 o equivalente<br>Espacio libre en disco: 50 GB</td><td>OS>OS: 64-bit br8.0 más alto o<br>Procesador fx: 813.6GHz o equivalente: 8 GB<br>Tarjeta gráfica: Radeon R9 270x o equivalente<br>Espacio libre en disco: 50 GB</td></tr>
-</tabla>
- <h2>FIFA 2022 consejos de descarga</h2>
-<p>Ahora que sabes lo que es FIFA 2022 APK y qué características y requisitos del sistema que tiene, es posible que esté ansioso por descargarlo en su dispositivo Android. Sin embargo, hay algunas cosas que debe tener cuidado al descargar un archivo APK de Internet. Aquí hay algunos consejos para ayudarle a descargar FIFA 2022 APK de forma segura y fácil:</p>
-<ol>
-<li><b>Encuentra una fuente confiable:</ <b>Encuentra una fuente confiable:</b> No todos los archivos APK son seguros para descargar e instalar en su dispositivo. Algunos de ellos pueden contener malware, virus u otro software dañino que puede dañar su dispositivo o robar su información personal. Por lo tanto, siempre debe descargar archivos APK de fuentes confiables y de buena reputación, como el sitio web oficial de EA Sports, Google Play Store u otros sitios web verificados de terceros. También puede comprobar las revisiones y valoraciones del archivo APK antes de descargarlo para ver si otros usuarios han tenido problemas con él. </li>
-
-<li><b>Evite errores comunes:</b> A veces, puede encontrar algunos errores o problemas al descargar o instalar un archivo APK. Por ejemplo, es posible que recibas un mensaje diciendo que la aplicación no es compatible con tu dispositivo, o que no hay suficiente espacio de almacenamiento en tu dispositivo, o que la aplicación ha dejado de funcionar. Para evitar estos errores, debe asegurarse de que su dispositivo cumple con los requisitos del sistema para FIFA 2022 APK, que tiene suficiente espacio libre en el disco de su dispositivo, y que ha actualizado el software del dispositivo a la última versión. También debe borrar la caché y los datos de la aplicación si se bloquea o se congela. </li>
-</ol>
- <h2>Preguntas frecuentes de la FIFA 2022</h2>
-<p>Aquí están algunas de las preguntas más frecuentes sobre FIFA 2022 APK:</p>
-<p></p>
-<dl>
-<dt>Q: ¿Es FIFA 2022 APK libre para descargar y jugar? </dt>
-<dd>A: Sí, FIFA 2022 APK es gratis para descargar y jugar en su dispositivo Android. Sin embargo, algunas características y elementos del juego pueden requerir compras con dinero real. </dd>
-<dt>Q: ¿Necesito una conexión a Internet para jugar FIFA 2022 APK? </dt>
-<dd>A: Sí, necesita una conexión a Internet estable para jugar FIFA 2022 APK en línea con otros jugadores o acceder a algunos de los modos de juego y características. </dd>
-<dt>Q: ¿Cómo puedo actualizar FIFA 2022 APK a la última versión? </dt>
-<dd>A: Puede actualizar FIFA 2022 APK a la última versión mediante la descarga e instalación del nuevo archivo APK de la misma fuente que lo descargó desde. Alternativamente, puede habilitar actualizaciones automáticas en la configuración del dispositivo o en la configuración de la aplicación. </dd>
-<dt>Q: ¿Cómo puedo transferir mi progreso y datos de FIFA 2022 de un dispositivo a otro? </dt>
-<dd>A: Puedes transferir tu progreso y datos de FIFA 2022 de un dispositivo a otro iniciando sesión con la misma cuenta de EA en ambos dispositivos. También puede utilizar opciones de almacenamiento en la nube o de copia de seguridad en la configuración de la aplicación. </dd>
-<dt>Q: ¿Cómo puedo contactar a EA Sports para obtener apoyo o comentarios sobre FIFA 2022 APK? </dt>
-
-</dl>
- <h1>Conclusión</h1>
-<p>FIFA 2022 APK es un juego imprescindible para cualquier fanático del fútbol que quiera disfrutar de una simulación de fútbol realista e inmersiva en su dispositivo Android. Ofrece nuevas y mejoradas características de juego, modos, gráficos y más que te mantendrán enganchado durante horas. Para descargar FIFA 2022 APK de forma segura y fácil, es necesario seguir algunos consejos simples y trucos que hemos compartido en este artículo. Esperamos que este artículo le ha ayudado a aprender cómo descargar FIFA 2022 APK y disfrutar jugando en su dispositivo. Si tiene alguna pregunta o comentario, no dude en dejarlos abajo. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Fifa Street 4 Pc Bagas31.md

@@ -1,155 +0,0 @@
-
-<h1>Descargar FIFA Street 4 PC Bagas31: Una guía para disfrutar del fútbol callejero en su computadora</h1>
- <p>¿Te encanta el fútbol pero quieres experimentarlo de una manera diferente? ¿Quieres mostrar tus habilidades y trucos en varios lugares de la calle en todo el mundo? ¿Quieres jugar con tus jugadores y equipos favoritos en un juego divertido y estilo árcade? Si respondiste sí a cualquiera de estas preguntas, entonces deberías probar FIFA Street 4, un juego que te permitirá disfrutar del fútbol callejero en tu ordenador. </p>
-<h2>descargar fifa street 4 pc bagas31</h2><br /><p><b><b>DOWNLOAD</b> &middot;&middot;&middot; <a href="https://bltlly.com/2v6JFm">https://bltlly.com/2v6JFm</a></b></p><br /><br />
- <h2>¿Qué es FIFA Street 4?</h2>
- <p>FIFA Street 4 es un juego desarrollado por EA Sports y lanzado en 2012 para PlayStation 3 y Xbox 360. Es la cuarta entrega de la serie FIFA Street, que se centra en el fútbol callejero en lugar del fútbol tradicional. El juego cuenta con más de 50 ubicaciones diferentes, desde Río de Janeiro hasta Londres, donde puedes jugar con varios modos y reglas. También puedes personalizar tu propio equipo y jugador, y desbloquear nuevos objetos y habilidades a medida que avanzas. </p>
- <p>El juego tiene varias características que lo hacen único y emocionante, como:</p>
- <ul>
-<li>El sistema de control de la bola de la calle, que le permite realizar más realista y fluido dribbling, pasando, y tiro. </li>
-<li>El modo World Tour, que te permite crear tu propio equipo y competir en torneos alrededor del mundo. </li>
-<li>El modo Temporadas Online, que te permite jugar partidas online contra otros jugadores y posicionarte en divisiones. </li>
-<li>El modo Street Network, que te permite compartir tus logros, vídeos y fotos con tus amigos y rivales. </li>
-<li>El modo de jugadores legendarios, que te permite jugar con o contra algunos de los mejores jugadores de fútbol de todos los tiempos, como Pelé, Zidane o Messi.</li>
-</ul>
- <h2>¿Por qué descargar FIFA Street 4 PC Bagas31? </h2>
-
- <p>Al descargar FIFA Street 4 PC Bagas31, puedes disfrutar de varios beneficios, como:</p>
- <ul>
-<li>Puedes jugar a FIFA Street 4 en tu PC sin necesidad de consola o emulador. </li>
-<li>Puedes ahorrar dinero no comprando una consola o un disco de juego. </li>
-<li>Puedes jugar a FIFA Street 4 con mejores gráficos y rendimiento que en una consola. </li>
-<li> Puede utilizar el teclado y el ratón o un controlador para jugar FIFA Street 4.</li>
-<li> Puede acceder a todas las características y modos de FIFA Street 4 sin restricciones o limitaciones. </li>
-</ul>
- <h2>¿Cómo descargar FIFA Street 4 PC Bagas31? </h2>
- <p>Descargar FIFA Street 4 PC Bagas31 es fácil y sencillo. Solo tienes que seguir estos pasos:</p>
-<p></p>
- <ol>
-<li>Ir a [1](https://www.al - Ir a [1](https://www.alvindayu.com/2018/02/download-fifa-street-4-pc-full-version.html) y haga clic en el enlace de descarga en la parte inferior de la página. - Espere a que finalice la descarga. El tamaño del archivo es de aproximadamente 4,8 GB, por lo que puede tardar algún tiempo dependiendo de su velocidad de Internet. - Extraer el archivo descargado usando WinRAR o 7-Zip. Obtendrá una carpeta llamada FIFA Street 4 PC Bagas31. - Abra la carpeta y ejecute el archivo setup.exe como administrador. Siga las instrucciones en la pantalla para instalar el juego. - Después de la instalación se ha completado, ejecutar el juego desde el acceso directo de escritorio o el menú de inicio. - Disfrutar de jugar FIFA Street 4 PC Bagas31! <h2>¿Cómo se juega FIFA Street 4 PC Bagas31? </h2>
- <p>Jugar FIFA Street 4 PC Bagas31 es similar a jugar cualquier otro juego de fútbol en PC. Puede utilizar el teclado y el ratón o un controlador para controlar a sus jugadores y realizar varias acciones. Aquí hay un resumen rápido de la jugabilidad y los controles:</p>
-
- <p>Los controles de FIFA Street 4 PC Bagas31 se basan en el sistema de control de la bola de la calle, que le permite realizar más realista y fluido dribbling, pasando, y tiro. También puedes usar trucos y habilidades para superar a tus oponentes y anotar goles espectaculares. Aquí hay una tabla de los controles básicos para teclado y ratón y controlador:</p>
- <tabla>
-<tr>
-<th>Acción</th>
-<th>Teclado y ratón</th>
-<th>Controlador</th>
-</tr>
-<tr>
-<td>Mover</td>
-<td>WASD</td>
-<td>Stick izquierdo</td>
-</tr>
-<tr>
-<td>Sprint</td>
-<td>Shift</td>
-<td>Gatillo derecho</td>
-</tr>
-<tr>
-<td>Pase</td>
-<td>Clic izquierdo</td>
-<td>A</td>
-</tr>
-<tr>
-<td>Disparar</td>
-<td>Clic derecho</td>
-<td>B</td>
-</tr>
-<tr>
-<td>Paso/Cruz de lob</td>
-<td>E</td>
-<td>X</td>
-</tr>
-<tr>
-<td>A través de la bola</td>
-<td>R</td>
-<td>Y</td>
-</tr>
-<tr>
-<td>Movimiento/truco de habilidad</td>
-<td>Q o F o rueda del ratón</td>
-<td>Golpe derecho o parachoques izquierdo o derecho</td>
-</tr>
-<tr>
-<td>Tackle/Slide Tackle</td>
-<td>Espacio o C</td>
-<td>A o X</td>
-</tr>
-<tr>
-<td>Jockey/Contener/Teammate Contener</td>
-<td>Z o X o V o B o N o M o Alt o Ctrl o Shift o Tab o Caps Bloquear o Entrar o Retroceso o Eliminar o Insertar o Inicio o Fin o Página Abajo o Teclas de flecha (cualquier tecla excepto WASD, Q, R, F, Esc)/<td>
-<td>Disparador izquierdo o derecho o parachoques izquierdo o derecho (cualquier botón excepto Stick izquierdo, Stick derecho, A, B, X, Y)</td>
-</tr>
- <tr><td colspan="3">Nota: Puedes cambiar los controles en la configuración del juego si lo prefieres. </td></tr> <h2> ¿Cuáles son los requisitos del sistema para FIFA Street 4 PC Bagas31? </h2>
- <p>Antes de descargar y jugar FIFA Street 4 PC Bagas31, debe asegurarse de que su computadora cumple con los requisitos mínimos y recomendados del sistema para ejecutar el juego. Aquí hay una lista de las especificaciones que necesita:</p>
- <tabla>
-<tr>
-<th>Requisitos mínimos</th>
-<th>Requisitos recomendados</th>
-</tr>
-<tr>
-<td>OS: Windows 7/8/10 (64 bits)</td>
-<td>OS: Windows 10 (64 bits)</td>
-
-<tr>
-<td>CPU: Intel Core 2 Duo E6600 o AMD Athlon 64 X2 5400+</td>
-<td>CPU: Intel Core i3-2100 o AMD Phenom II X4 965</td>
-</tr>
-<tr>
-<td>RAM: 4 GB</td>
-<td>RAM: 8 GB</td>
-</tr>
-<tr>
-<td>GPU: NVIDIA GeForce GTX 460 o AMD Radeon HD 5770</td>
-<td>GPU: NVIDIA GeForce GTX 660 o AMD Radeon HD 7850</td>
-</tr>
-<tr>
-<td>DirectX: Versión 11</td>
-<td>DirectX: Versión 11</td>
-</tr>
-<tr>
-<td>Almacenamiento: 10 GB de espacio disponible</td>
-<td>Almacenamiento: 10 GB de espacio disponible</td>
-</tr>
- <tr><td colspan="2">Nota: Estos son los requisitos estimados del sistema basados en la versión original de la consola de FIFA Street 4. Los requisitos reales del sistema pueden variar dependiendo de la configuración de su PC y las modificaciones realizadas por Bagas31.</td></tr>
- <h2>¿Cómo mejorar el rendimiento de FIFA Street 4 PC Bagas31? </h2>
- <p>Si encuentras algún problema o problema al jugar FIFA Street 4 PC Bagas31, como retraso, tartamudeo, estrellarse o errores, puedes probar algunos de estos consejos y trucos para mejorar el rendimiento del juego y solucionar los problemas comunes:</p>
- <ul>
-<li>Actualice sus controladores y Windows a la última versión. </li>
-<li>Ejecutar el juego como administrador y en modo de compatibilidad para Windows 7.</li>
-<li>Deshabilita cualquier programa antivirus o firewall que pueda interferir con el juego. </li>
-<li>Cierre cualquier programa de fondo innecesario que pueda consumir su CPU, RAM o ancho de banda. </li>
-<li>Baja la configuración del juego y la resolución para que coincida con las capacidades de tu PC. </li>
-<li> Habilitar la sincronización en V y limitar la velocidad de fotogramas para evitar el desgarro de la pantalla y el sobrecalentamiento. </li>
-<li>Elimine cualquier archivo dañado o desactualizado en la carpeta del juego y vuelva a instalar el juego si es necesario. </li>
-<li>Busque en línea soluciones o parches específicos para su problema en particular. </li>
-</ul>
- <h2>¿Cuáles son algunas alternativas a FIFA Street 4 PC Bagas31? </h2>
- <p>Si estás buscando otros juegos de fútbol callejero o emuladores que puedes jugar en tu PC, puedes ver algunas de estas alternativas a FIFA Street 4 PC Bagas31:</p>
-
-<li>FIFA Street (2012) Emulador de PS3: Esta es la versión original de FIFA Street 4 que puedes jugar en tu PC usando un emulador de PS3 como RPCS3. Necesitarás un disco de juego o un archivo ISO de PS3, un archivo BIOS de PS3 y un PC potente para ejecutar el emulador sin problemas. Puede encontrar más información e instrucciones sobre cómo usar RPCS3 [aquí]. </li>
- <li>FIFA Street 2 (2006) PCSX2 Emulator: Esta es la segunda entrega de la serie FIFA Street que puedes jugar en tu PC usando un emulador de PS2 como PCSX2. Necesitarás un disco de juego o un archivo ISO de PS2, un archivo BIOS de PS2 y un PC decente para ejecutar el emulador correctamente. Puede encontrar más información e instrucciones sobre cómo usar PCSX2 [aquí]. </li>
- <li>FIFA Street 3 (2008) Xbox 360 Emulator: Esta es la tercera entrega de la serie FIFA Street que puedes jugar en tu PC usando un emulador de Xbox 360 como Xenia. Necesitarás un disco de juego o un archivo ISO de Xbox 360, un archivo BIOS de Xbox 360 y un PC potente para ejecutar el emulador sin problemas. Puede encontrar más información e instrucciones sobre cómo usar Xenia [aquí]. </li>
- <li>NBA Street Homecourt (2007) Xbox 360 Emulator: Este es un juego de baloncesto que tiene un modo de juego y estilo similar a FIFA Street. Puedes jugarlo en tu PC usando un emulador de Xbox 360 como Xenia. Necesitarás un disco de juego o un archivo ISO de Xbox 360, un archivo BIOS de Xbox 360 y un PC potente para ejecutar el emulador sin problemas. Puede encontrar más información e instrucciones sobre cómo usar Xenia [aquí]. </li>
- <li>In - Inazuma Eleven Strikers (2012) Dolphin Emulator: Este es un juego de fútbol que tiene una mezcla de elementos de árcade y RPG. Puedes jugarlo en tu PC usando un emulador de Wii como Dolphin. Necesitarás un disco de juego de Wii o un archivo ISO, un archivo BIOS de Wii y un PC decente para ejecutar el emulador correctamente. Puede encontrar más información e instrucciones sobre cómo usar Dolphin [aquí]. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <p>Así que, ¿qué estás esperando? Descargar FIFA Street 4 PC Bagas31 hoy y divertirse jugando fútbol de la calle en su ordenador! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre FIFA Street 4 PC Bagas31:</p>
- <ol>
-<li> ¿Es seguro descargar FIFA Street 4 PC Bagas31? </li>
-<p>Sí, FIFA Street 4 PC Bagas31 es seguro para descargar desde el sitio web de Bagas31. Sin embargo, siempre debe escanear los archivos descargados con un programa antivirus antes de abrirlos, y tenga cuidado con cualquier pop-ups o anuncios que puedan aparecer en el sitio web. </p>
-<li> ¿Es FIFA Street 4 PC Bagas31 legal para descargar? </li>
-<p>No, FIFA Street 4 PC Bagas31 no es legal para descargar, ya que es una versión modificada de un juego con derechos de autor que nunca fue lanzado oficialmente para PC. Descargar y jugar FIFA Street 4 PC Bagas31 es bajo su propio riesgo, y usted puede enfrentar consecuencias legales si es capturado por las autoridades. </p>
-<li>¿Puedo jugar FIFA Street 4 PC Bagas31 sin conexión? </li>
-<p>Sí, puede jugar FIFA Street 4 PC Bagas31 sin conexión a Internet. Sin embargo, no podrá acceder al modo Online Seasons o al modo Street Network, que requieren una conexión en línea. </p>
-<li>¿Puedo jugar FIFA Street 4 PC Bagas31 con mis amigos? </li>
-<p>Sí, puedes jugar FIFA Street 4 PC Bagas31 con tus amigos en línea o localmente. Para jugar en línea, necesitará una conexión a Internet y una cuenta en los servidores de EA. Para jugar localmente, necesitarás dos controladores o teclados y ratones, y una opción de pantalla dividida en la configuración del juego. </p>
-<li>¿Puedo actualizar FIFA Street 4 PC Bagas31? </li>
-<p>No, no puedes actualizar FIFA Street 4 PC Bagas31, ya que es una versión modificada del juego que no recibe ninguna actualización o parches oficiales de EA Sports. Sin embargo, puedes encontrar algunas actualizaciones o mods no oficiales de otras fuentes en línea que pueden mejorar o cambiar el juego de alguna manera. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/ec2/__init__.py

@@ -1,12 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/models/scheme.py

@@ -1,31 +0,0 @@
-"""
-For types associated with installation schemes.
-
-For a general overview of available schemes and their context, see
-https://docs.python.org/3/install/index.html#alternate-installation.
-"""
-
-
-SCHEME_KEYS = ["platlib", "purelib", "headers", "scripts", "data"]
-
-
-class Scheme:
-    """A Scheme holds paths which are used as the base directories for
-    artifacts associated with a Python package.
-    """
-
-    __slots__ = SCHEME_KEYS
-
-    def __init__(
-        self,
-        platlib: str,
-        purelib: str,
-        headers: str,
-        scripts: str,
-        data: str,
-    ) -> None:
-        self.platlib = platlib
-        self.purelib = purelib
-        self.headers = headers
-        self.scripts = scripts
-        self.data = data

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/distlib/database.py

@@ -1,1350 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2012-2017 The Python Software Foundation.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-"""PEP 376 implementation."""
-
-from __future__ import unicode_literals
-
-import base64
-import codecs
-import contextlib
-import hashlib
-import logging
-import os
-import posixpath
-import sys
-import zipimport
-
-from . import DistlibException, resources
-from .compat import StringIO
-from .version import get_scheme, UnsupportedVersionError
-from .metadata import (Metadata, METADATA_FILENAME, WHEEL_METADATA_FILENAME,
-                       LEGACY_METADATA_FILENAME)
-from .util import (parse_requirement, cached_property, parse_name_and_version,
-                   read_exports, write_exports, CSVReader, CSVWriter)
-
-
-__all__ = ['Distribution', 'BaseInstalledDistribution',
-           'InstalledDistribution', 'EggInfoDistribution',
-           'DistributionPath']
-
-
-logger = logging.getLogger(__name__)
-
-EXPORTS_FILENAME = 'pydist-exports.json'
-COMMANDS_FILENAME = 'pydist-commands.json'
-
-DIST_FILES = ('INSTALLER', METADATA_FILENAME, 'RECORD', 'REQUESTED',
-              'RESOURCES', EXPORTS_FILENAME, 'SHARED')
-
-DISTINFO_EXT = '.dist-info'
-
-
-class _Cache(object):
-    """
-    A simple cache mapping names and .dist-info paths to distributions
-    """
-    def __init__(self):
-        """
-        Initialise an instance. There is normally one for each DistributionPath.
-        """
-        self.name = {}
-        self.path = {}
-        self.generated = False
-
-    def clear(self):
-        """
-        Clear the cache, setting it to its initial state.
-        """
-        self.name.clear()
-        self.path.clear()
-        self.generated = False
-
-    def add(self, dist):
-        """
-        Add a distribution to the cache.
-        :param dist: The distribution to add.
-        """
-        if dist.path not in self.path:
-            self.path[dist.path] = dist
-            self.name.setdefault(dist.key, []).append(dist)
-
-
-class DistributionPath(object):
-    """
-    Represents a set of distributions installed on a path (typically sys.path).
-    """
-    def __init__(self, path=None, include_egg=False):
-        """
-        Create an instance from a path, optionally including legacy (distutils/
-        setuptools/distribute) distributions.
-        :param path: The path to use, as a list of directories. If not specified,
-                     sys.path is used.
-        :param include_egg: If True, this instance will look for and return legacy
-                            distributions as well as those based on PEP 376.
-        """
-        if path is None:
-            path = sys.path
-        self.path = path
-        self._include_dist = True
-        self._include_egg = include_egg
-
-        self._cache = _Cache()
-        self._cache_egg = _Cache()
-        self._cache_enabled = True
-        self._scheme = get_scheme('default')
-
-    def _get_cache_enabled(self):
-        return self._cache_enabled
-
-    def _set_cache_enabled(self, value):
-        self._cache_enabled = value
-
-    cache_enabled = property(_get_cache_enabled, _set_cache_enabled)
-
-    def clear_cache(self):
-        """
-        Clears the internal cache.
-        """
-        self._cache.clear()
-        self._cache_egg.clear()
-
-
-    def _yield_distributions(self):
-        """
-        Yield .dist-info and/or .egg(-info) distributions.
-        """
-        # We need to check if we've seen some resources already, because on
-        # some Linux systems (e.g. some Debian/Ubuntu variants) there are
-        # symlinks which alias other files in the environment.
-        seen = set()
-        for path in self.path:
-            finder = resources.finder_for_path(path)
-            if finder is None:
-                continue
-            r = finder.find('')
-            if not r or not r.is_container:
-                continue
-            rset = sorted(r.resources)
-            for entry in rset:
-                r = finder.find(entry)
-                if not r or r.path in seen:
-                    continue
-                try:
-                    if self._include_dist and entry.endswith(DISTINFO_EXT):
-                        possible_filenames = [METADATA_FILENAME,
-                                              WHEEL_METADATA_FILENAME,
-                                              LEGACY_METADATA_FILENAME]
-                        for metadata_filename in possible_filenames:
-                            metadata_path = posixpath.join(entry, metadata_filename)
-                            pydist = finder.find(metadata_path)
-                            if pydist:
-                                break
-                        else:
-                            continue
-
-                        with contextlib.closing(pydist.as_stream()) as stream:
-                            metadata = Metadata(fileobj=stream, scheme='legacy')
-                        logger.debug('Found %s', r.path)
-                        seen.add(r.path)
-                        yield new_dist_class(r.path, metadata=metadata,
-                                             env=self)
-                    elif self._include_egg and entry.endswith(('.egg-info',
-                                                              '.egg')):
-                        logger.debug('Found %s', r.path)
-                        seen.add(r.path)
-                        yield old_dist_class(r.path, self)
-                except Exception as e:
-                    msg = 'Unable to read distribution at %s, perhaps due to bad metadata: %s'
-                    logger.warning(msg, r.path, e)
-                    import warnings
-                    warnings.warn(msg % (r.path, e), stacklevel=2)
-
-    def _generate_cache(self):
-        """
-        Scan the path for distributions and populate the cache with
-        those that are found.
-        """
-        gen_dist = not self._cache.generated
-        gen_egg = self._include_egg and not self._cache_egg.generated
-        if gen_dist or gen_egg:
-            for dist in self._yield_distributions():
-                if isinstance(dist, InstalledDistribution):
-                    self._cache.add(dist)
-                else:
-                    self._cache_egg.add(dist)
-
-            if gen_dist:
-                self._cache.generated = True
-            if gen_egg:
-                self._cache_egg.generated = True
-
-    @classmethod
-    def distinfo_dirname(cls, name, version):
-        """
-        The *name* and *version* parameters are converted into their
-        filename-escaped form, i.e. any ``'-'`` characters are replaced
-        with ``'_'`` other than the one in ``'dist-info'`` and the one
-        separating the name from the version number.
-
-        :parameter name: is converted to a standard distribution name by replacing
-                         any runs of non- alphanumeric characters with a single
-                         ``'-'``.
-        :type name: string
-        :parameter version: is converted to a standard version string. Spaces
-                            become dots, and all other non-alphanumeric characters
-                            (except dots) become dashes, with runs of multiple
-                            dashes condensed to a single dash.
-        :type version: string
-        :returns: directory name
-        :rtype: string"""
-        name = name.replace('-', '_')
-        return '-'.join([name, version]) + DISTINFO_EXT
-
-    def get_distributions(self):
-        """
-        Provides an iterator that looks for distributions and returns
-        :class:`InstalledDistribution` or
-        :class:`EggInfoDistribution` instances for each one of them.
-
-        :rtype: iterator of :class:`InstalledDistribution` and
-                :class:`EggInfoDistribution` instances
-        """
-        if not self._cache_enabled:
-            for dist in self._yield_distributions():
-                yield dist
-        else:
-            self._generate_cache()
-
-            for dist in self._cache.path.values():
-                yield dist
-
-            if self._include_egg:
-                for dist in self._cache_egg.path.values():
-                    yield dist
-
-    def get_distribution(self, name):
-        """
-        Looks for a named distribution on the path.
-
-        This function only returns the first result found, as no more than one
-        value is expected. If nothing is found, ``None`` is returned.
-
-        :rtype: :class:`InstalledDistribution`, :class:`EggInfoDistribution`
-                or ``None``
-        """
-        result = None
-        name = name.lower()
-        if not self._cache_enabled:
-            for dist in self._yield_distributions():
-                if dist.key == name:
-                    result = dist
-                    break
-        else:
-            self._generate_cache()
-
-            if name in self._cache.name:
-                result = self._cache.name[name][0]
-            elif self._include_egg and name in self._cache_egg.name:
-                result = self._cache_egg.name[name][0]
-        return result
-
-    def provides_distribution(self, name, version=None):
-        """
-        Iterates over all distributions to find which distributions provide *name*.
-        If a *version* is provided, it will be used to filter the results.
-
-        This function only returns the first result found, since no more than
-        one values are expected. If the directory is not found, returns ``None``.
-
-        :parameter version: a version specifier that indicates the version
-                            required, conforming to the format in ``PEP-345``
-
-        :type name: string
-        :type version: string
-        """
-        matcher = None
-        if version is not None:
-            try:
-                matcher = self._scheme.matcher('%s (%s)' % (name, version))
-            except ValueError:
-                raise DistlibException('invalid name or version: %r, %r' %
-                                      (name, version))
-
-        for dist in self.get_distributions():
-            # We hit a problem on Travis where enum34 was installed and doesn't
-            # have a provides attribute ...
-            if not hasattr(dist, 'provides'):
-                logger.debug('No "provides": %s', dist)
-            else:
-                provided = dist.provides
-
-                for p in provided:
-                    p_name, p_ver = parse_name_and_version(p)
-                    if matcher is None:
-                        if p_name == name:
-                            yield dist
-                            break
-                    else:
-                        if p_name == name and matcher.match(p_ver):
-                            yield dist
-                            break
-
-    def get_file_path(self, name, relative_path):
-        """
-        Return the path to a resource file.
-        """
-        dist = self.get_distribution(name)
-        if dist is None:
-            raise LookupError('no distribution named %r found' % name)
-        return dist.get_resource_path(relative_path)
-
-    def get_exported_entries(self, category, name=None):
-        """
-        Return all of the exported entries in a particular category.
-
-        :param category: The category to search for entries.
-        :param name: If specified, only entries with that name are returned.
-        """
-        for dist in self.get_distributions():
-            r = dist.exports
-            if category in r:
-                d = r[category]
-                if name is not None:
-                    if name in d:
-                        yield d[name]
-                else:
-                    for v in d.values():
-                        yield v
-
-
-class Distribution(object):
-    """
-    A base class for distributions, whether installed or from indexes.
-    Either way, it must have some metadata, so that's all that's needed
-    for construction.
-    """
-
-    build_time_dependency = False
-    """
-    Set to True if it's known to be only a build-time dependency (i.e.
-    not needed after installation).
-    """
-
-    requested = False
-    """A boolean that indicates whether the ``REQUESTED`` metadata file is
-    present (in other words, whether the package was installed by user
-    request or it was installed as a dependency)."""
-
-    def __init__(self, metadata):
-        """
-        Initialise an instance.
-        :param metadata: The instance of :class:`Metadata` describing this
-        distribution.
-        """
-        self.metadata = metadata
-        self.name = metadata.name
-        self.key = self.name.lower()    # for case-insensitive comparisons
-        self.version = metadata.version
-        self.locator = None
-        self.digest = None
-        self.extras = None      # additional features requested
-        self.context = None     # environment marker overrides
-        self.download_urls = set()
-        self.digests = {}
-
-    @property
-    def source_url(self):
-        """
-        The source archive download URL for this distribution.
-        """
-        return self.metadata.source_url
-
-    download_url = source_url   # Backward compatibility
-
-    @property
-    def name_and_version(self):
-        """
-        A utility property which displays the name and version in parentheses.
-        """
-        return '%s (%s)' % (self.name, self.version)
-
-    @property
-    def provides(self):
-        """
-        A set of distribution names and versions provided by this distribution.
-        :return: A set of "name (version)" strings.
-        """
-        plist = self.metadata.provides
-        s = '%s (%s)' % (self.name, self.version)
-        if s not in plist:
-            plist.append(s)
-        return plist
-
-    def _get_requirements(self, req_attr):
-        md = self.metadata
-        reqts = getattr(md, req_attr)
-        logger.debug('%s: got requirements %r from metadata: %r', self.name, req_attr,
-                     reqts)
-        return set(md.get_requirements(reqts, extras=self.extras,
-                                       env=self.context))
-
-    @property
-    def run_requires(self):
-        return self._get_requirements('run_requires')
-
-    @property
-    def meta_requires(self):
-        return self._get_requirements('meta_requires')
-
-    @property
-    def build_requires(self):
-        return self._get_requirements('build_requires')
-
-    @property
-    def test_requires(self):
-        return self._get_requirements('test_requires')
-
-    @property
-    def dev_requires(self):
-        return self._get_requirements('dev_requires')
-
-    def matches_requirement(self, req):
-        """
-        Say if this instance matches (fulfills) a requirement.
-        :param req: The requirement to match.
-        :rtype req: str
-        :return: True if it matches, else False.
-        """
-        # Requirement may contain extras - parse to lose those
-        # from what's passed to the matcher
-        r = parse_requirement(req)
-        scheme = get_scheme(self.metadata.scheme)
-        try:
-            matcher = scheme.matcher(r.requirement)
-        except UnsupportedVersionError:
-            # XXX compat-mode if cannot read the version
-            logger.warning('could not read version %r - using name only',
-                           req)
-            name = req.split()[0]
-            matcher = scheme.matcher(name)
-
-        name = matcher.key   # case-insensitive
-
-        result = False
-        for p in self.provides:
-            p_name, p_ver = parse_name_and_version(p)
-            if p_name != name:
-                continue
-            try:
-                result = matcher.match(p_ver)
-                break
-            except UnsupportedVersionError:
-                pass
-        return result
-
-    def __repr__(self):
-        """
-        Return a textual representation of this instance,
-        """
-        if self.source_url:
-            suffix = ' [%s]' % self.source_url
-        else:
-            suffix = ''
-        return '<Distribution %s (%s)%s>' % (self.name, self.version, suffix)
-
-    def __eq__(self, other):
-        """
-        See if this distribution is the same as another.
-        :param other: The distribution to compare with. To be equal to one
-                      another. distributions must have the same type, name,
-                      version and source_url.
-        :return: True if it is the same, else False.
-        """
-        if type(other) is not type(self):
-            result = False
-        else:
-            result = (self.name == other.name and
-                      self.version == other.version and
-                      self.source_url == other.source_url)
-        return result
-
-    def __hash__(self):
-        """
-        Compute hash in a way which matches the equality test.
-        """
-        return hash(self.name) + hash(self.version) + hash(self.source_url)
-
-
-class BaseInstalledDistribution(Distribution):
-    """
-    This is the base class for installed distributions (whether PEP 376 or
-    legacy).
-    """
-
-    hasher = None
-
-    def __init__(self, metadata, path, env=None):
-        """
-        Initialise an instance.
-        :param metadata: An instance of :class:`Metadata` which describes the
-                         distribution. This will normally have been initialised
-                         from a metadata file in the ``path``.
-        :param path:     The path of the ``.dist-info`` or ``.egg-info``
-                         directory for the distribution.
-        :param env:      This is normally the :class:`DistributionPath`
-                         instance where this distribution was found.
-        """
-        super(BaseInstalledDistribution, self).__init__(metadata)
-        self.path = path
-        self.dist_path = env
-
-    def get_hash(self, data, hasher=None):
-        """
-        Get the hash of some data, using a particular hash algorithm, if
-        specified.
-
-        :param data: The data to be hashed.
-        :type data: bytes
-        :param hasher: The name of a hash implementation, supported by hashlib,
-                       or ``None``. Examples of valid values are ``'sha1'``,
-                       ``'sha224'``, ``'sha384'``, '``sha256'``, ``'md5'`` and
-                       ``'sha512'``. If no hasher is specified, the ``hasher``
-                       attribute of the :class:`InstalledDistribution` instance
-                       is used. If the hasher is determined to be ``None``, MD5
-                       is used as the hashing algorithm.
-        :returns: The hash of the data. If a hasher was explicitly specified,
-                  the returned hash will be prefixed with the specified hasher
-                  followed by '='.
-        :rtype: str
-        """
-        if hasher is None:
-            hasher = self.hasher
-        if hasher is None:
-            hasher = hashlib.md5
-            prefix = ''
-        else:
-            hasher = getattr(hashlib, hasher)
-            prefix = '%s=' % self.hasher
-        digest = hasher(data).digest()
-        digest = base64.urlsafe_b64encode(digest).rstrip(b'=').decode('ascii')
-        return '%s%s' % (prefix, digest)
-
-
-class InstalledDistribution(BaseInstalledDistribution):
-    """
-    Created with the *path* of the ``.dist-info`` directory provided to the
-    constructor. It reads the metadata contained in ``pydist.json`` when it is
-    instantiated., or uses a passed in Metadata instance (useful for when
-    dry-run mode is being used).
-    """
-
-    hasher = 'sha256'
-
-    def __init__(self, path, metadata=None, env=None):
-        self.modules = []
-        self.finder = finder = resources.finder_for_path(path)
-        if finder is None:
-            raise ValueError('finder unavailable for %s' % path)
-        if env and env._cache_enabled and path in env._cache.path:
-            metadata = env._cache.path[path].metadata
-        elif metadata is None:
-            r = finder.find(METADATA_FILENAME)
-            # Temporary - for Wheel 0.23 support
-            if r is None:
-                r = finder.find(WHEEL_METADATA_FILENAME)
-            # Temporary - for legacy support
-            if r is None:
-                r = finder.find(LEGACY_METADATA_FILENAME)
-            if r is None:
-                raise ValueError('no %s found in %s' % (METADATA_FILENAME,
-                                                        path))
-            with contextlib.closing(r.as_stream()) as stream:
-                metadata = Metadata(fileobj=stream, scheme='legacy')
-
-        super(InstalledDistribution, self).__init__(metadata, path, env)
-
-        if env and env._cache_enabled:
-            env._cache.add(self)
-
-        r = finder.find('REQUESTED')
-        self.requested = r is not None
-        p  = os.path.join(path, 'top_level.txt')
-        if os.path.exists(p):
-            with open(p, 'rb') as f:
-                data = f.read().decode('utf-8')
-            self.modules = data.splitlines()
-
-    def __repr__(self):
-        return '<InstalledDistribution %r %s at %r>' % (
-            self.name, self.version, self.path)
-
-    def __str__(self):
-        return "%s %s" % (self.name, self.version)
-
-    def _get_records(self):
-        """
-        Get the list of installed files for the distribution
-        :return: A list of tuples of path, hash and size. Note that hash and
-                 size might be ``None`` for some entries. The path is exactly
-                 as stored in the file (which is as in PEP 376).
-        """
-        results = []
-        r = self.get_distinfo_resource('RECORD')
-        with contextlib.closing(r.as_stream()) as stream:
-            with CSVReader(stream=stream) as record_reader:
-                # Base location is parent dir of .dist-info dir
-                #base_location = os.path.dirname(self.path)
-                #base_location = os.path.abspath(base_location)
-                for row in record_reader:
-                    missing = [None for i in range(len(row), 3)]
-                    path, checksum, size = row + missing
-                    #if not os.path.isabs(path):
-                    #    path = path.replace('/', os.sep)
-                    #    path = os.path.join(base_location, path)
-                    results.append((path, checksum, size))
-        return results
-
-    @cached_property
-    def exports(self):
-        """
-        Return the information exported by this distribution.
-        :return: A dictionary of exports, mapping an export category to a dict
-                 of :class:`ExportEntry` instances describing the individual
-                 export entries, and keyed by name.
-        """
-        result = {}
-        r = self.get_distinfo_resource(EXPORTS_FILENAME)
-        if r:
-            result = self.read_exports()
-        return result
-
-    def read_exports(self):
-        """
-        Read exports data from a file in .ini format.
-
-        :return: A dictionary of exports, mapping an export category to a list
-                 of :class:`ExportEntry` instances describing the individual
-                 export entries.
-        """
-        result = {}
-        r = self.get_distinfo_resource(EXPORTS_FILENAME)
-        if r:
-            with contextlib.closing(r.as_stream()) as stream:
-                result = read_exports(stream)
-        return result
-
-    def write_exports(self, exports):
-        """
-        Write a dictionary of exports to a file in .ini format.
-        :param exports: A dictionary of exports, mapping an export category to
-                        a list of :class:`ExportEntry` instances describing the
-                        individual export entries.
-        """
-        rf = self.get_distinfo_file(EXPORTS_FILENAME)
-        with open(rf, 'w') as f:
-            write_exports(exports, f)
-
-    def get_resource_path(self, relative_path):
-        """
-        NOTE: This API may change in the future.
-
-        Return the absolute path to a resource file with the given relative
-        path.
-
-        :param relative_path: The path, relative to .dist-info, of the resource
-                              of interest.
-        :return: The absolute path where the resource is to be found.
-        """
-        r = self.get_distinfo_resource('RESOURCES')
-        with contextlib.closing(r.as_stream()) as stream:
-            with CSVReader(stream=stream) as resources_reader:
-                for relative, destination in resources_reader:
-                    if relative == relative_path:
-                        return destination
-        raise KeyError('no resource file with relative path %r '
-                       'is installed' % relative_path)
-
-    def list_installed_files(self):
-        """
-        Iterates over the ``RECORD`` entries and returns a tuple
-        ``(path, hash, size)`` for each line.
-
-        :returns: iterator of (path, hash, size)
-        """
-        for result in self._get_records():
-            yield result
-
-    def write_installed_files(self, paths, prefix, dry_run=False):
-        """
-        Writes the ``RECORD`` file, using the ``paths`` iterable passed in. Any
-        existing ``RECORD`` file is silently overwritten.
-
-        prefix is used to determine when to write absolute paths.
-        """
-        prefix = os.path.join(prefix, '')
-        base = os.path.dirname(self.path)
-        base_under_prefix = base.startswith(prefix)
-        base = os.path.join(base, '')
-        record_path = self.get_distinfo_file('RECORD')
-        logger.info('creating %s', record_path)
-        if dry_run:
-            return None
-        with CSVWriter(record_path) as writer:
-            for path in paths:
-                if os.path.isdir(path) or path.endswith(('.pyc', '.pyo')):
-                    # do not put size and hash, as in PEP-376
-                    hash_value = size = ''
-                else:
-                    size = '%d' % os.path.getsize(path)
-                    with open(path, 'rb') as fp:
-                        hash_value = self.get_hash(fp.read())
-                if path.startswith(base) or (base_under_prefix and
-                                             path.startswith(prefix)):
-                    path = os.path.relpath(path, base)
-                writer.writerow((path, hash_value, size))
-
-            # add the RECORD file itself
-            if record_path.startswith(base):
-                record_path = os.path.relpath(record_path, base)
-            writer.writerow((record_path, '', ''))
-        return record_path
-
-    def check_installed_files(self):
-        """
-        Checks that the hashes and sizes of the files in ``RECORD`` are
-        matched by the files themselves. Returns a (possibly empty) list of
-        mismatches. Each entry in the mismatch list will be a tuple consisting
-        of the path, 'exists', 'size' or 'hash' according to what didn't match
-        (existence is checked first, then size, then hash), the expected
-        value and the actual value.
-        """
-        mismatches = []
-        base = os.path.dirname(self.path)
-        record_path = self.get_distinfo_file('RECORD')
-        for path, hash_value, size in self.list_installed_files():
-            if not os.path.isabs(path):
-                path = os.path.join(base, path)
-            if path == record_path:
-                continue
-            if not os.path.exists(path):
-                mismatches.append((path, 'exists', True, False))
-            elif os.path.isfile(path):
-                actual_size = str(os.path.getsize(path))
-                if size and actual_size != size:
-                    mismatches.append((path, 'size', size, actual_size))
-                elif hash_value:
-                    if '=' in hash_value:
-                        hasher = hash_value.split('=', 1)[0]
-                    else:
-                        hasher = None
-
-                    with open(path, 'rb') as f:
-                        actual_hash = self.get_hash(f.read(), hasher)
-                        if actual_hash != hash_value:
-                            mismatches.append((path, 'hash', hash_value, actual_hash))
-        return mismatches
-
-    @cached_property
-    def shared_locations(self):
-        """
-        A dictionary of shared locations whose keys are in the set 'prefix',
-        'purelib', 'platlib', 'scripts', 'headers', 'data' and 'namespace'.
-        The corresponding value is the absolute path of that category for
-        this distribution, and takes into account any paths selected by the
-        user at installation time (e.g. via command-line arguments). In the
-        case of the 'namespace' key, this would be a list of absolute paths
-        for the roots of namespace packages in this distribution.
-
-        The first time this property is accessed, the relevant information is
-        read from the SHARED file in the .dist-info directory.
-        """
-        result = {}
-        shared_path = os.path.join(self.path, 'SHARED')
-        if os.path.isfile(shared_path):
-            with codecs.open(shared_path, 'r', encoding='utf-8') as f:
-                lines = f.read().splitlines()
-            for line in lines:
-                key, value = line.split('=', 1)
-                if key == 'namespace':
-                    result.setdefault(key, []).append(value)
-                else:
-                    result[key] = value
-        return result
-
-    def write_shared_locations(self, paths, dry_run=False):
-        """
-        Write shared location information to the SHARED file in .dist-info.
-        :param paths: A dictionary as described in the documentation for
-        :meth:`shared_locations`.
-        :param dry_run: If True, the action is logged but no file is actually
-                        written.
-        :return: The path of the file written to.
-        """
-        shared_path = os.path.join(self.path, 'SHARED')
-        logger.info('creating %s', shared_path)
-        if dry_run:
-            return None
-        lines = []
-        for key in ('prefix', 'lib', 'headers', 'scripts', 'data'):
-            path = paths[key]
-            if os.path.isdir(paths[key]):
-                lines.append('%s=%s' % (key,  path))
-        for ns in paths.get('namespace', ()):
-            lines.append('namespace=%s' % ns)
-
-        with codecs.open(shared_path, 'w', encoding='utf-8') as f:
-            f.write('\n'.join(lines))
-        return shared_path
-
-    def get_distinfo_resource(self, path):
-        if path not in DIST_FILES:
-            raise DistlibException('invalid path for a dist-info file: '
-                                   '%r at %r' % (path, self.path))
-        finder = resources.finder_for_path(self.path)
-        if finder is None:
-            raise DistlibException('Unable to get a finder for %s' % self.path)
-        return finder.find(path)
-
-    def get_distinfo_file(self, path):
-        """
-        Returns a path located under the ``.dist-info`` directory. Returns a
-        string representing the path.
-
-        :parameter path: a ``'/'``-separated path relative to the
-                         ``.dist-info`` directory or an absolute path;
-                         If *path* is an absolute path and doesn't start
-                         with the ``.dist-info`` directory path,
-                         a :class:`DistlibException` is raised
-        :type path: str
-        :rtype: str
-        """
-        # Check if it is an absolute path  # XXX use relpath, add tests
-        if path.find(os.sep) >= 0:
-            # it's an absolute path?
-            distinfo_dirname, path = path.split(os.sep)[-2:]
-            if distinfo_dirname != self.path.split(os.sep)[-1]:
-                raise DistlibException(
-                    'dist-info file %r does not belong to the %r %s '
-                    'distribution' % (path, self.name, self.version))
-
-        # The file must be relative
-        if path not in DIST_FILES:
-            raise DistlibException('invalid path for a dist-info file: '
-                                   '%r at %r' % (path, self.path))
-
-        return os.path.join(self.path, path)
-
-    def list_distinfo_files(self):
-        """
-        Iterates over the ``RECORD`` entries and returns paths for each line if
-        the path is pointing to a file located in the ``.dist-info`` directory
-        or one of its subdirectories.
-
-        :returns: iterator of paths
-        """
-        base = os.path.dirname(self.path)
-        for path, checksum, size in self._get_records():
-            # XXX add separator or use real relpath algo
-            if not os.path.isabs(path):
-                path = os.path.join(base, path)
-            if path.startswith(self.path):
-                yield path
-
-    def __eq__(self, other):
-        return (isinstance(other, InstalledDistribution) and
-                self.path == other.path)
-
-    # See http://docs.python.org/reference/datamodel#object.__hash__
-    __hash__ = object.__hash__
-
-
-class EggInfoDistribution(BaseInstalledDistribution):
-    """Created with the *path* of the ``.egg-info`` directory or file provided
-    to the constructor. It reads the metadata contained in the file itself, or
-    if the given path happens to be a directory, the metadata is read from the
-    file ``PKG-INFO`` under that directory."""
-
-    requested = True    # as we have no way of knowing, assume it was
-    shared_locations = {}
-
-    def __init__(self, path, env=None):
-        def set_name_and_version(s, n, v):
-            s.name = n
-            s.key = n.lower()   # for case-insensitive comparisons
-            s.version = v
-
-        self.path = path
-        self.dist_path = env
-        if env and env._cache_enabled and path in env._cache_egg.path:
-            metadata = env._cache_egg.path[path].metadata
-            set_name_and_version(self, metadata.name, metadata.version)
-        else:
-            metadata = self._get_metadata(path)
-
-            # Need to be set before caching
-            set_name_and_version(self, metadata.name, metadata.version)
-
-            if env and env._cache_enabled:
-                env._cache_egg.add(self)
-        super(EggInfoDistribution, self).__init__(metadata, path, env)
-
-    def _get_metadata(self, path):
-        requires = None
-
-        def parse_requires_data(data):
-            """Create a list of dependencies from a requires.txt file.
-
-            *data*: the contents of a setuptools-produced requires.txt file.
-            """
-            reqs = []
-            lines = data.splitlines()
-            for line in lines:
-                line = line.strip()
-                if line.startswith('['):
-                    logger.warning('Unexpected line: quitting requirement scan: %r',
-                                   line)
-                    break
-                r = parse_requirement(line)
-                if not r:
-                    logger.warning('Not recognised as a requirement: %r', line)
-                    continue
-                if r.extras:
-                    logger.warning('extra requirements in requires.txt are '
-                                   'not supported')
-                if not r.constraints:
-                    reqs.append(r.name)
-                else:
-                    cons = ', '.join('%s%s' % c for c in r.constraints)
-                    reqs.append('%s (%s)' % (r.name, cons))
-            return reqs
-
-        def parse_requires_path(req_path):
-            """Create a list of dependencies from a requires.txt file.
-
-            *req_path*: the path to a setuptools-produced requires.txt file.
-            """
-
-            reqs = []
-            try:
-                with codecs.open(req_path, 'r', 'utf-8') as fp:
-                    reqs = parse_requires_data(fp.read())
-            except IOError:
-                pass
-            return reqs
-
-        tl_path = tl_data = None
-        if path.endswith('.egg'):
-            if os.path.isdir(path):
-                p = os.path.join(path, 'EGG-INFO')
-                meta_path = os.path.join(p, 'PKG-INFO')
-                metadata = Metadata(path=meta_path, scheme='legacy')
-                req_path = os.path.join(p, 'requires.txt')
-                tl_path = os.path.join(p, 'top_level.txt')
-                requires = parse_requires_path(req_path)
-            else:
-                # FIXME handle the case where zipfile is not available
-                zipf = zipimport.zipimporter(path)
-                fileobj = StringIO(
-                    zipf.get_data('EGG-INFO/PKG-INFO').decode('utf8'))
-                metadata = Metadata(fileobj=fileobj, scheme='legacy')
-                try:
-                    data = zipf.get_data('EGG-INFO/requires.txt')
-                    tl_data = zipf.get_data('EGG-INFO/top_level.txt').decode('utf-8')
-                    requires = parse_requires_data(data.decode('utf-8'))
-                except IOError:
-                    requires = None
-        elif path.endswith('.egg-info'):
-            if os.path.isdir(path):
-                req_path = os.path.join(path, 'requires.txt')
-                requires = parse_requires_path(req_path)
-                path = os.path.join(path, 'PKG-INFO')
-                tl_path = os.path.join(path, 'top_level.txt')
-            metadata = Metadata(path=path, scheme='legacy')
-        else:
-            raise DistlibException('path must end with .egg-info or .egg, '
-                                   'got %r' % path)
-
-        if requires:
-            metadata.add_requirements(requires)
-        # look for top-level modules in top_level.txt, if present
-        if tl_data is None:
-            if tl_path is not None and os.path.exists(tl_path):
-                with open(tl_path, 'rb') as f:
-                    tl_data = f.read().decode('utf-8')
-        if not tl_data:
-            tl_data = []
-        else:
-            tl_data = tl_data.splitlines()
-        self.modules = tl_data
-        return metadata
-
-    def __repr__(self):
-        return '<EggInfoDistribution %r %s at %r>' % (
-            self.name, self.version, self.path)
-
-    def __str__(self):
-        return "%s %s" % (self.name, self.version)
-
-    def check_installed_files(self):
-        """
-        Checks that the hashes and sizes of the files in ``RECORD`` are
-        matched by the files themselves. Returns a (possibly empty) list of
-        mismatches. Each entry in the mismatch list will be a tuple consisting
-        of the path, 'exists', 'size' or 'hash' according to what didn't match
-        (existence is checked first, then size, then hash), the expected
-        value and the actual value.
-        """
-        mismatches = []
-        record_path = os.path.join(self.path, 'installed-files.txt')
-        if os.path.exists(record_path):
-            for path, _, _ in self.list_installed_files():
-                if path == record_path:
-                    continue
-                if not os.path.exists(path):
-                    mismatches.append((path, 'exists', True, False))
-        return mismatches
-
-    def list_installed_files(self):
-        """
-        Iterates over the ``installed-files.txt`` entries and returns a tuple
-        ``(path, hash, size)`` for each line.
-
-        :returns: a list of (path, hash, size)
-        """
-
-        def _md5(path):
-            f = open(path, 'rb')
-            try:
-                content = f.read()
-            finally:
-                f.close()
-            return hashlib.md5(content).hexdigest()
-
-        def _size(path):
-            return os.stat(path).st_size
-
-        record_path = os.path.join(self.path, 'installed-files.txt')
-        result = []
-        if os.path.exists(record_path):
-            with codecs.open(record_path, 'r', encoding='utf-8') as f:
-                for line in f:
-                    line = line.strip()
-                    p = os.path.normpath(os.path.join(self.path, line))
-                    # "./" is present as a marker between installed files
-                    # and installation metadata files
-                    if not os.path.exists(p):
-                        logger.warning('Non-existent file: %s', p)
-                        if p.endswith(('.pyc', '.pyo')):
-                            continue
-                        #otherwise fall through and fail
-                    if not os.path.isdir(p):
-                        result.append((p, _md5(p), _size(p)))
-            result.append((record_path, None, None))
-        return result
-
-    def list_distinfo_files(self, absolute=False):
-        """
-        Iterates over the ``installed-files.txt`` entries and returns paths for
-        each line if the path is pointing to a file located in the
-        ``.egg-info`` directory or one of its subdirectories.
-
-        :parameter absolute: If *absolute* is ``True``, each returned path is
-                          transformed into a local absolute path. Otherwise the
-                          raw value from ``installed-files.txt`` is returned.
-        :type absolute: boolean
-        :returns: iterator of paths
-        """
-        record_path = os.path.join(self.path, 'installed-files.txt')
-        if os.path.exists(record_path):
-            skip = True
-            with codecs.open(record_path, 'r', encoding='utf-8') as f:
-                for line in f:
-                    line = line.strip()
-                    if line == './':
-                        skip = False
-                        continue
-                    if not skip:
-                        p = os.path.normpath(os.path.join(self.path, line))
-                        if p.startswith(self.path):
-                            if absolute:
-                                yield p
-                            else:
-                                yield line
-
-    def __eq__(self, other):
-        return (isinstance(other, EggInfoDistribution) and
-                self.path == other.path)
-
-    # See http://docs.python.org/reference/datamodel#object.__hash__
-    __hash__ = object.__hash__
-
-new_dist_class = InstalledDistribution
-old_dist_class = EggInfoDistribution
-
-
-class DependencyGraph(object):
-    """
-    Represents a dependency graph between distributions.
-
-    The dependency relationships are stored in an ``adjacency_list`` that maps
-    distributions to a list of ``(other, label)`` tuples where  ``other``
-    is a distribution and the edge is labeled with ``label`` (i.e. the version
-    specifier, if such was provided). Also, for more efficient traversal, for
-    every distribution ``x``, a list of predecessors is kept in
-    ``reverse_list[x]``. An edge from distribution ``a`` to
-    distribution ``b`` means that ``a`` depends on ``b``. If any missing
-    dependencies are found, they are stored in ``missing``, which is a
-    dictionary that maps distributions to a list of requirements that were not
-    provided by any other distributions.
-    """
-
-    def __init__(self):
-        self.adjacency_list = {}
-        self.reverse_list = {}
-        self.missing = {}
-
-    def add_distribution(self, distribution):
-        """Add the *distribution* to the graph.
-
-        :type distribution: :class:`distutils2.database.InstalledDistribution`
-                            or :class:`distutils2.database.EggInfoDistribution`
-        """
-        self.adjacency_list[distribution] = []
-        self.reverse_list[distribution] = []
-        #self.missing[distribution] = []
-
-    def add_edge(self, x, y, label=None):
-        """Add an edge from distribution *x* to distribution *y* with the given
-        *label*.
-
-        :type x: :class:`distutils2.database.InstalledDistribution` or
-                 :class:`distutils2.database.EggInfoDistribution`
-        :type y: :class:`distutils2.database.InstalledDistribution` or
-                 :class:`distutils2.database.EggInfoDistribution`
-        :type label: ``str`` or ``None``
-        """
-        self.adjacency_list[x].append((y, label))
-        # multiple edges are allowed, so be careful
-        if x not in self.reverse_list[y]:
-            self.reverse_list[y].append(x)
-
-    def add_missing(self, distribution, requirement):
-        """
-        Add a missing *requirement* for the given *distribution*.
-
-        :type distribution: :class:`distutils2.database.InstalledDistribution`
-                            or :class:`distutils2.database.EggInfoDistribution`
-        :type requirement: ``str``
-        """
-        logger.debug('%s missing %r', distribution, requirement)
-        self.missing.setdefault(distribution, []).append(requirement)
-
-    def _repr_dist(self, dist):
-        return '%s %s' % (dist.name, dist.version)
-
-    def repr_node(self, dist, level=1):
-        """Prints only a subgraph"""
-        output = [self._repr_dist(dist)]
-        for other, label in self.adjacency_list[dist]:
-            dist = self._repr_dist(other)
-            if label is not None:
-                dist = '%s [%s]' % (dist, label)
-            output.append('    ' * level + str(dist))
-            suboutput = self.repr_node(other, level + 1)
-            subs = suboutput.split('\n')
-            output.extend(subs[1:])
-        return '\n'.join(output)
-
-    def to_dot(self, f, skip_disconnected=True):
-        """Writes a DOT output for the graph to the provided file *f*.
-
-        If *skip_disconnected* is set to ``True``, then all distributions
-        that are not dependent on any other distribution are skipped.
-
-        :type f: has to support ``file``-like operations
-        :type skip_disconnected: ``bool``
-        """
-        disconnected = []
-
-        f.write("digraph dependencies {\n")
-        for dist, adjs in self.adjacency_list.items():
-            if len(adjs) == 0 and not skip_disconnected:
-                disconnected.append(dist)
-            for other, label in adjs:
-                if not label is None:
-                    f.write('"%s" -> "%s" [label="%s"]\n' %
-                            (dist.name, other.name, label))
-                else:
-                    f.write('"%s" -> "%s"\n' % (dist.name, other.name))
-        if not skip_disconnected and len(disconnected) > 0:
-            f.write('subgraph disconnected {\n')
-            f.write('label = "Disconnected"\n')
-            f.write('bgcolor = red\n')
-
-            for dist in disconnected:
-                f.write('"%s"' % dist.name)
-                f.write('\n')
-            f.write('}\n')
-        f.write('}\n')
-
-    def topological_sort(self):
-        """
-        Perform a topological sort of the graph.
-        :return: A tuple, the first element of which is a topologically sorted
-                 list of distributions, and the second element of which is a
-                 list of distributions that cannot be sorted because they have
-                 circular dependencies and so form a cycle.
-        """
-        result = []
-        # Make a shallow copy of the adjacency list
-        alist = {}
-        for k, v in self.adjacency_list.items():
-            alist[k] = v[:]
-        while True:
-            # See what we can remove in this run
-            to_remove = []
-            for k, v in list(alist.items())[:]:
-                if not v:
-                    to_remove.append(k)
-                    del alist[k]
-            if not to_remove:
-                # What's left in alist (if anything) is a cycle.
-                break
-            # Remove from the adjacency list of others
-            for k, v in alist.items():
-                alist[k] = [(d, r) for d, r in v if d not in to_remove]
-            logger.debug('Moving to result: %s',
-                         ['%s (%s)' % (d.name, d.version) for d in to_remove])
-            result.extend(to_remove)
-        return result, list(alist.keys())
-
-    def __repr__(self):
-        """Representation of the graph"""
-        output = []
-        for dist, adjs in self.adjacency_list.items():
-            output.append(self.repr_node(dist))
-        return '\n'.join(output)
-
-
-def make_graph(dists, scheme='default'):
-    """Makes a dependency graph from the given distributions.
-
-    :parameter dists: a list of distributions
-    :type dists: list of :class:`distutils2.database.InstalledDistribution` and
-                 :class:`distutils2.database.EggInfoDistribution` instances
-    :rtype: a :class:`DependencyGraph` instance
-    """
-    scheme = get_scheme(scheme)
-    graph = DependencyGraph()
-    provided = {}  # maps names to lists of (version, dist) tuples
-
-    # first, build the graph and find out what's provided
-    for dist in dists:
-        graph.add_distribution(dist)
-
-        for p in dist.provides:
-            name, version = parse_name_and_version(p)
-            logger.debug('Add to provided: %s, %s, %s', name, version, dist)
-            provided.setdefault(name, []).append((version, dist))
-
-    # now make the edges
-    for dist in dists:
-        requires = (dist.run_requires | dist.meta_requires |
-                    dist.build_requires | dist.dev_requires)
-        for req in requires:
-            try:
-                matcher = scheme.matcher(req)
-            except UnsupportedVersionError:
-                # XXX compat-mode if cannot read the version
-                logger.warning('could not read version %r - using name only',
-                               req)
-                name = req.split()[0]
-                matcher = scheme.matcher(name)
-
-            name = matcher.key   # case-insensitive
-
-            matched = False
-            if name in provided:
-                for version, provider in provided[name]:
-                    try:
-                        match = matcher.match(version)
-                    except UnsupportedVersionError:
-                        match = False
-
-                    if match:
-                        graph.add_edge(dist, provider, req)
-                        matched = True
-                        break
-            if not matched:
-                graph.add_missing(dist, req)
-    return graph
-
-
-def get_dependent_dists(dists, dist):
-    """Recursively generate a list of distributions from *dists* that are
-    dependent on *dist*.
-
-    :param dists: a list of distributions
-    :param dist: a distribution, member of *dists* for which we are interested
-    """
-    if dist not in dists:
-        raise DistlibException('given distribution %r is not a member '
-                               'of the list' % dist.name)
-    graph = make_graph(dists)
-
-    dep = [dist]  # dependent distributions
-    todo = graph.reverse_list[dist]  # list of nodes we should inspect
-
-    while todo:
-        d = todo.pop()
-        dep.append(d)
-        for succ in graph.reverse_list[d]:
-            if succ not in dep:
-                todo.append(succ)
-
-    dep.pop(0)  # remove dist from dep, was there to prevent infinite loops
-    return dep
-
-
-def get_required_dists(dists, dist):
-    """Recursively generate a list of distributions from *dists* that are
-    required by *dist*.
-
-    :param dists: a list of distributions
-    :param dist: a distribution, member of *dists* for which we are interested
-                 in finding the dependencies.
-    """
-    if dist not in dists:
-        raise DistlibException('given distribution %r is not a member '
-                               'of the list' % dist.name)
-    graph = make_graph(dists)
-
-    req = set()  # required distributions
-    todo = graph.adjacency_list[dist]  # list of nodes we should inspect
-    seen = set(t[0] for t in todo) # already added to todo
-
-    while todo:
-        d = todo.pop()[0]
-        req.add(d)
-        pred_list = graph.adjacency_list[d]
-        for pred in pred_list:
-            d = pred[0]
-            if d not in req and d not in seen:
-                seen.add(d)
-                todo.append(pred)
-    return req
-
-
-def make_dist(name, version, **kwargs):
-    """
-    A convenience method for making a dist given just a name and version.
-    """
-    summary = kwargs.pop('summary', 'Placeholder for summary')
-    md = Metadata(**kwargs)
-    md.name = name
-    md.version = version
-    md.summary = summary or 'Placeholder for summary'
-    return Distribution(md)

spaces/Billius/runwayml-stable-diffusion-v1-5-04-07-2023/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/runwayml/stable-diffusion-v1-5").launch()

spaces/Blessin/impro-scene-generator/app.py

@@ -1,69 +0,0 @@
-import gradio as gr
-import openai
-
-def generate_script(api_key, name1, name2, situation):
-    # Initialize OpenAI API with the provided key
-    openai.api_key = api_key
-
-    # Define the example script to set the context
-    example_script = (
-         "Setting: A cafe\n\n"
-        "Scene:\n\n"
-        "(Lights come up on a cozy little coffee shop. LOUIS and GIRL are sitting at a small table, with two untouched coffees between them. "
-        "It's clear from their expressions that the awkwardness from the previous scene hasn't worn off.)\n\n"
-        "LOUIS. (Attempting to break the ice) So, um... do you come to this coffee shop often?\n"
-        "GIRL. (Coldly) No, not really.\n"
-        "LOUIS. Oh. Well, they have the best almond croissants here. You should try one, sometime.\n"
-        "GIRL. (Slightly warming up) I'm more of a bagel person, actually.\n"
-        "LOUIS. Really? Me too! What's your favorite kind?\n"
-        "GIRL. Plain. With cream cheese.\n"
-        "LOUIS. Nice choice. I like mine with a bit of jam.\n"
-        "(A brief pause as they both sip their coffee, seemingly more at ease.)\n\n"
-        "LOUIS. (Hesitant) Look, I'm sorry about earlier... I don't know why I was acting like such a jerk.\n"
-        "GIRL. (Softening) It's alright. People can act strange when they meet someone new.\n"
-        "LOUIS. Yeah. It's just... I've been feeling kind of... out of place lately. And I guess I was trying too hard to impress you.\n"
-        "GIRL. You don't have to impress me, Louis. Just be yourself.\n"
-        "(LOUIS smiles shyly and looks down at his coffee. The GIRL does too, and for a moment, there's a comfortable silence between them.)\n\n"
-        "LOUIS. So... do you have any plans for the weekend?\n"
-        "GIRL. (Smiling) Not much. Just taking my dog to the park. Maybe catch a movie later. How about you?\n"
-        "LOUIS. Same here. Minus the dog, though.\n"
-        "GIRL. (Laughs) Well, maybe you can join me. And who knows, you might just enjoy the simple joy of a walk in the park.\n"
-        "LOUIS. (Smiling) I'd like that.\n\n"
-        "(As they continue chatting, the lights dim, suggesting the budding of a new connection between LOUIS and GIRL.)\n\n"
-        "(Blackout)\n"
-    )
-
-    # Define the prompt to transform the user's inputs into a stage play script
-    prompt_text = (
-        f"{example_script}\n\n"
-        f"Generate a new stage play script based on the following details:\n"
-        f"Location: Cafe\n"
-        f"Character 1: {name1}\n"
-        f"Character 2: {name2}\n"
-        f"Situation: {situation}\n"
-    )
-
-    # Use OpenAI's Completion endpoint to generate the stage play script
-    response = openai.Completion.create(engine="davinci", prompt=prompt_text, max_tokens=500)
-    script = response.choices[0].text.strip()
-
-    return script
-
-# Define Gradio Interface
-iface = gr.Interface(
-    fn=generate_script,
-    inputs=[
-        gr.components.Textbox(label="OpenAI API Key", type="password"),
-        gr.components.Textbox(label="Name 1", type="text"),
-        gr.components.Textbox(label="Name 2", type="text"),
-        gr.components.Textbox(label="Situation", type="text"),
-    ],
-    outputs=gr.components.Textbox(label="Generated Stage Play Script", type="text"),
-    live=True,
-    title="Stage Play Script Generator",
-    description="Generate a stage play script set in a cafe based on given characters and situation using OpenAI!",
-    progress="Generating stage play script...",
-)
-
-# Launch the Gradio app
-iface.launch()

spaces/CVH-vn1210/make_hair/README.md

@@ -1,14 +0,0 @@
----
-title: MiniGPT-4
-emoji: 🚀
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: other
-duplicated_from: Vision-CAIR/minigpt4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVH-vn1210/make_hair/minigpt4/datasets/datasets/cc_combine_dataset.py

@@ -1,53 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-import os
-from PIL import Image
-import webdataset as wds
-from minigpt4.datasets.datasets.base_dataset import BaseDataset
-from minigpt4.datasets.datasets.caption_datasets import CaptionDataset
-
-
-class CCCombineDataset(BaseDataset):
-    def __init__(self, vis_processor, text_processor, location):
-        super().__init__(vis_processor=vis_processor, text_processor=text_processor)
-
-        self.inner_dataset = wds.DataPipeline(
-            wds.ResampledShards(location),
-            wds.tarfile_to_samples(handler=wds.warn_and_continue),
-            wds.shuffle(1000, handler=wds.warn_and_continue),
-            wds.decode("pilrgb", handler=wds.warn_and_continue),
-            wds.to_tuple("jpg", "json", handler=wds.warn_and_continue),
-            wds.map_tuple(self.vis_processor, handler=wds.warn_and_continue),
-            wds.map(self.to_dict, handler=wds.warn_and_continue),
-        )
-
-    def to_dict(self, sample):
-        return {
-            "image": sample[0],
-            "text_input": self.text_processor(sample[1]["caption"]),
-        }
-
-
-class CCAlignDataset(CaptionDataset):
-
-    def __getitem__(self, index):
-
-        # TODO this assumes image input, not general enough
-        ann = self.annotation[index]
-
-        img_file = '{}.jpg'.format(ann["image_id"])
-        image_path = os.path.join(self.vis_root, img_file)
-        image = Image.open(image_path).convert("RGB")
-
-        image = self.vis_processor(image)
-        caption = ann["caption"]
-
-        return {
-            "image": image,
-            "text_input": caption,
-            "image_id": self.img_ids[ann["image_id"]],
-        }

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/.github/ISSUE_TEMPLATE.md

@@ -1,5 +0,0 @@
-
-Please select an issue template from
-https://github.com/facebookresearch/detectron2/issues/new/choose .
-
-Otherwise your issue will be closed.

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TridentNet/tridentnet/trident_rcnn.py

@@ -1,110 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from detectron2.layers import batched_nms
-from detectron2.modeling import ROI_HEADS_REGISTRY, StandardROIHeads
-from detectron2.modeling.roi_heads.roi_heads import Res5ROIHeads
-from detectron2.structures import Instances
-
-
-def merge_branch_instances(instances, num_branch, nms_thrsh, topk_per_image):
-    """
-    Merge detection results from different branches of TridentNet.
-    Return detection results by applying non-maximum suppression (NMS) on bounding boxes
-    and keep the unsuppressed boxes and other instances (e.g mask) if any.
-
-    Args:
-        instances (list[Instances]): A list of N * num_branch instances that store detection
-            results. Contain N images and each image has num_branch instances.
-        num_branch (int): Number of branches used for merging detection results for each image.
-        nms_thresh (float):  The threshold to use for box non-maximum suppression. Value in [0, 1].
-        topk_per_image (int): The number of top scoring detections to return. Set < 0 to return
-            all detections.
-
-    Returns:
-        results: (list[Instances]): A list of N instances, one for each image in the batch,
-            that stores the topk most confidence detections after merging results from multiple
-            branches.
-    """
-    if num_branch == 1:
-        return instances
-
-    batch_size = len(instances) // num_branch
-    results = []
-    for i in range(batch_size):
-        instance = Instances.cat([instances[i + batch_size * j] for j in range(num_branch)])
-
-        # Apply per-class NMS
-        keep = batched_nms(
-            instance.pred_boxes.tensor, instance.scores, instance.pred_classes, nms_thrsh
-        )
-        keep = keep[:topk_per_image]
-        result = instance[keep]
-
-        results.append(result)
-
-    return results
-
-
-@ROI_HEADS_REGISTRY.register()
-class TridentRes5ROIHeads(Res5ROIHeads):
-    """
-    The TridentNet ROIHeads in a typical "C4" R-CNN model.
-    See :class:`Res5ROIHeads`.
-    """
-
-    def __init__(self, cfg, input_shape):
-        super().__init__(cfg, input_shape)
-
-        self.num_branch = cfg.MODEL.TRIDENT.NUM_BRANCH
-        self.trident_fast = cfg.MODEL.TRIDENT.TEST_BRANCH_IDX != -1
-
-    def forward(self, images, features, proposals, targets=None):
-        """
-        See :class:`Res5ROIHeads.forward`.
-        """
-        num_branch = self.num_branch if self.training or not self.trident_fast else 1
-        all_targets = targets * num_branch if targets is not None else None
-        pred_instances, losses = super().forward(images, features, proposals, all_targets)
-        del images, all_targets, targets
-
-        if self.training:
-            return pred_instances, losses
-        else:
-            pred_instances = merge_branch_instances(
-                pred_instances, num_branch, self.test_nms_thresh, self.test_detections_per_img
-            )
-
-            return pred_instances, {}
-
-
-@ROI_HEADS_REGISTRY.register()
-class TridentStandardROIHeads(StandardROIHeads):
-    """
-    The `StandardROIHeads` for TridentNet.
-    See :class:`StandardROIHeads`.
-    """
-
-    def __init__(self, cfg, input_shape):
-        super(TridentStandardROIHeads, self).__init__(cfg, input_shape)
-
-        self.num_branch = cfg.MODEL.TRIDENT.NUM_BRANCH
-        self.trident_fast = cfg.MODEL.TRIDENT.TEST_BRANCH_IDX != -1
-
-    def forward(self, images, features, proposals, targets=None):
-        """
-        See :class:`Res5ROIHeads.forward`.
-        """
-        # Use 1 branch if using trident_fast during inference.
-        num_branch = self.num_branch if self.training or not self.trident_fast else 1
-        # Duplicate targets for all branches in TridentNet.
-        all_targets = targets * num_branch if targets is not None else None
-        pred_instances, losses = super().forward(images, features, proposals, all_targets)
-        del images, all_targets, targets
-
-        if self.training:
-            return pred_instances, losses
-        else:
-            pred_instances = merge_branch_instances(
-                pred_instances, num_branch, self.test_nms_thresh, self.test_detections_per_img
-            )
-
-            return pred_instances, {}